\"\n ],\n \"text/plain\": [\n \" Country 1980 1981 1982 1983 1984 1985\\n\",\n \"0 Afghanistan 16 39 39 47 71 340\\n\",\n \"1 Albania 1 0 0 0 0 0\\n\",\n \"2 Algeria 80 67 71 69 63 44\\n\",\n \"3 American Samoa 0 1 0 0 0 0\\n\",\n \"4 Andorra 0 0 0 0 0 0\\n\",\n \".. ... ... ... ... ... ... ...\\n\",\n \"190 Viet Nam 1191 1829 2162 3404 7583 5907\\n\",\n \"191 Western Sahara 0 0 0 0 0 0\\n\",\n \"192 Yemen 1 2 1 6 0 18\\n\",\n \"193 Zambia 11 17 11 7 16 9\\n\",\n \"194 Zimbabwe 72 114 102 44 32 29\\n\",\n \"\\n\",\n \"[195 rows x 7 columns]\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]] # returns a dataframe\\n\",\n \"# notice that 'Country' is string, and the years are integers. \\n\",\n \"# for the sake of consistency, we will convert all column names to string later on.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### Select Row\\n\",\n \"\\n\",\n \"There are main 3 ways to select rows:\\n\",\n \"\\n\",\n \"```python\\n\",\n \" df.loc[label] \\n\",\n \" #filters by the labels of the index/column\\n\",\n \" df.iloc[index] \\n\",\n \" #filters by the positions of the index/column\\n\",\n \"```\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Before we proceed, notice that the defaul index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\\n\",\n \"\\n\",\n \"This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_can.set_index('Country', inplace=True)\\n\",\n \"# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Continent
\\n\",\n \"
Region
\\n\",\n \"
DevName
\\n\",\n \"
1980
\\n\",\n \"
1981
\\n\",\n \"
1982
\\n\",\n \"
1983
\\n\",\n \"
1984
\\n\",\n \"
1985
\\n\",\n \"
1986
\\n\",\n \"
...
\\n\",\n \"
2005
\\n\",\n \"
2006
\\n\",\n \"
2007
\\n\",\n \"
2008
\\n\",\n \"
2009
\\n\",\n \"
2010
\\n\",\n \"
2011
\\n\",\n \"
2012
\\n\",\n \"
2013
\\n\",\n \"
Total
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Country
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
Afghanistan
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
16
\\n\",\n \"
39
\\n\",\n \"
39
\\n\",\n \"
47
\\n\",\n \"
71
\\n\",\n \"
340
\\n\",\n \"
496
\\n\",\n \"
...
\\n\",\n \"
3436
\\n\",\n \"
3009
\\n\",\n \"
2652
\\n\",\n \"
2111
\\n\",\n \"
1746
\\n\",\n \"
1758
\\n\",\n \"
2203
\\n\",\n \"
2635
\\n\",\n \"
2004
\\n\",\n \"
58639
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Albania
\\n\",\n \"
Europe
\\n\",\n \"
Southern Europe
\\n\",\n \"
Developed regions
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
...
\\n\",\n \"
1223
\\n\",\n \"
856
\\n\",\n \"
702
\\n\",\n \"
560
\\n\",\n \"
716
\\n\",\n \"
561
\\n\",\n \"
539
\\n\",\n \"
620
\\n\",\n \"
603
\\n\",\n \"
15699
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Algeria
\\n\",\n \"
Africa
\\n\",\n \"
Northern Africa
\\n\",\n \"
Developing regions
\\n\",\n \"
80
\\n\",\n \"
67
\\n\",\n \"
71
\\n\",\n \"
69
\\n\",\n \"
63
\\n\",\n \"
44
\\n\",\n \"
69
\\n\",\n \"
...
\\n\",\n \"
3626
\\n\",\n \"
4807
\\n\",\n \"
3623
\\n\",\n \"
4005
\\n\",\n \"
5393
\\n\",\n \"
4752
\\n\",\n \"
4325
\\n\",\n \"
3774
\\n\",\n \"
4331
\\n\",\n \"
69439
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
3 rows × 38 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Continent Region DevName 1980 1981 1982 \\\\\\n\",\n \"Country \\n\",\n \"Afghanistan Asia Southern Asia Developing regions 16 39 39 \\n\",\n \"Albania Europe Southern Europe Developed regions 1 0 0 \\n\",\n \"Algeria Africa Northern Africa Developing regions 80 67 71 \\n\",\n \"\\n\",\n \" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\\\\n\",\n \"Country ... \\n\",\n \"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \\n\",\n \"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \\n\",\n \"Algeria 69 63 44 69 ... 3626 4807 3623 4005 5393 4752 \\n\",\n \"\\n\",\n \" 2011 2012 2013 Total \\n\",\n \"Country \\n\",\n \"Afghanistan 2203 2635 2004 58639 \\n\",\n \"Albania 539 620 603 15699 \\n\",\n \"Algeria 4325 3774 4331 69439 \\n\",\n \"\\n\",\n \"[3 rows x 38 columns]\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_can.head(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# optional: to remove the name of the index\\n\",\n \"df_can.index.name = None\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\\n\",\n \"\\n\",\n \"```\\n\",\n \"1. The full row data (all columns)\\n\",\n \"2. For year 2013\\n\",\n \"3. For years 1980 to 1985\\n\",\n \"```\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Continent Asia\\n\",\n \"Region Eastern Asia\\n\",\n \"DevName Developed regions\\n\",\n \"1980 701\\n\",\n \"1981 756\\n\",\n \"1982 598\\n\",\n \"1983 309\\n\",\n \"1984 246\\n\",\n \"1985 198\\n\",\n \"1986 248\\n\",\n \"1987 422\\n\",\n \"1988 324\\n\",\n \"1989 494\\n\",\n \"1990 379\\n\",\n \"1991 506\\n\",\n \"1992 605\\n\",\n \"1993 907\\n\",\n \"1994 956\\n\",\n \"1995 826\\n\",\n \"1996 994\\n\",\n \"1997 924\\n\",\n \"1998 897\\n\",\n \"1999 1083\\n\",\n \"2000 1010\\n\",\n \"2001 1092\\n\",\n \"2002 806\\n\",\n \"2003 817\\n\",\n \"2004 973\\n\",\n \"2005 1067\\n\",\n \"2006 1212\\n\",\n \"2007 1250\\n\",\n \"2008 1284\\n\",\n \"2009 1194\\n\",\n \"2010 1168\\n\",\n \"2011 1265\\n\",\n \"2012 1214\\n\",\n \"2013 982\\n\",\n \"Total 27707\\n\",\n \"Name: Japan, dtype: object\\n\",\n \"Continent Asia\\n\",\n \"Region Eastern Asia\\n\",\n \"DevName Developed regions\\n\",\n \"1980 701\\n\",\n \"1981 756\\n\",\n \"1982 598\\n\",\n \"1983 309\\n\",\n \"1984 246\\n\",\n \"1985 198\\n\",\n \"1986 248\\n\",\n \"1987 422\\n\",\n \"1988 324\\n\",\n \"1989 494\\n\",\n \"1990 379\\n\",\n \"1991 506\\n\",\n \"1992 605\\n\",\n \"1993 907\\n\",\n \"1994 956\\n\",\n \"1995 826\\n\",\n \"1996 994\\n\",\n \"1997 924\\n\",\n \"1998 897\\n\",\n \"1999 1083\\n\",\n \"2000 1010\\n\",\n \"2001 1092\\n\",\n \"2002 806\\n\",\n \"2003 817\\n\",\n \"2004 973\\n\",\n \"2005 1067\\n\",\n \"2006 1212\\n\",\n \"2007 1250\\n\",\n \"2008 1284\\n\",\n \"2009 1194\\n\",\n \"2010 1168\\n\",\n \"2011 1265\\n\",\n \"2012 1214\\n\",\n \"2013 982\\n\",\n \"Total 27707\\n\",\n \"Name: Japan, dtype: object\\n\",\n \"Continent Asia\\n\",\n \"Region Eastern Asia\\n\",\n \"DevName Developed regions\\n\",\n \"1980 701\\n\",\n \"1981 756\\n\",\n \"1982 598\\n\",\n \"1983 309\\n\",\n \"1984 246\\n\",\n \"1985 198\\n\",\n \"1986 248\\n\",\n \"1987 422\\n\",\n \"1988 324\\n\",\n \"1989 494\\n\",\n \"1990 379\\n\",\n \"1991 506\\n\",\n \"1992 605\\n\",\n \"1993 907\\n\",\n \"1994 956\\n\",\n \"1995 826\\n\",\n \"1996 994\\n\",\n \"1997 924\\n\",\n \"1998 897\\n\",\n \"1999 1083\\n\",\n \"2000 1010\\n\",\n \"2001 1092\\n\",\n \"2002 806\\n\",\n \"2003 817\\n\",\n \"2004 973\\n\",\n \"2005 1067\\n\",\n \"2006 1212\\n\",\n \"2007 1250\\n\",\n \"2008 1284\\n\",\n \"2009 1194\\n\",\n \"2010 1168\\n\",\n \"2011 1265\\n\",\n \"2012 1214\\n\",\n \"2013 982\\n\",\n \"Total 27707\\n\",\n \"Name: Japan, dtype: object\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 1. the full row data (all columns)\\n\",\n \"print(df_can.loc['Japan'])\\n\",\n \"\\n\",\n \"# alternate methods\\n\",\n \"print(df_can.iloc[87])\\n\",\n \"print(df_can[df_can.index == 'Japan'].T.squeeze())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"982\\n\",\n \"982\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 2. for year 2013\\n\",\n \"print(df_can.loc['Japan', 2013])\\n\",\n \"\\n\",\n \"# alternate method\\n\",\n \"print(df_can.iloc[87, 36]) # year 2013 is the last column, with a positional index of 36\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1980 701\\n\",\n \"1981 756\\n\",\n \"1982 598\\n\",\n \"1983 309\\n\",\n \"1984 246\\n\",\n \"1984 246\\n\",\n \"Name: Japan, dtype: object\\n\",\n \"1980 701\\n\",\n \"1981 756\\n\",\n \"1982 598\\n\",\n \"1983 309\\n\",\n \"1984 246\\n\",\n \"1985 198\\n\",\n \"Name: Japan, dtype: object\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 3. for years 1980 to 1985\\n\",\n \"print(df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]])\\n\",\n \"print(df_can.iloc[87, [3, 4, 5, 6, 7, 8]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index. \\n\",\n \"\\n\",\n \"To avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_can.columns = list(map(str, df_can.columns))\\n\",\n \"# [print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['1980',\\n\",\n \" '1981',\\n\",\n \" '1982',\\n\",\n \" '1983',\\n\",\n \" '1984',\\n\",\n \" '1985',\\n\",\n \" '1986',\\n\",\n \" '1987',\\n\",\n \" '1988',\\n\",\n \" '1989',\\n\",\n \" '1990',\\n\",\n \" '1991',\\n\",\n \" '1992',\\n\",\n \" '1993',\\n\",\n \" '1994',\\n\",\n \" '1995',\\n\",\n \" '1996',\\n\",\n \" '1997',\\n\",\n \" '1998',\\n\",\n \" '1999',\\n\",\n \" '2000',\\n\",\n \" '2001',\\n\",\n \" '2002',\\n\",\n \" '2003',\\n\",\n \" '2004',\\n\",\n \" '2005',\\n\",\n \" '2006',\\n\",\n \" '2007',\\n\",\n \" '2008',\\n\",\n \" '2009',\\n\",\n \" '2010',\\n\",\n \" '2011',\\n\",\n \" '2012',\\n\",\n \" '2013']\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# useful for plotting later on\\n\",\n \"years = list(map(str, range(1980, 2014)))\\n\",\n \"years\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### Filtering based on a criteria\\n\",\n \"\\n\",\n \"To filter the dataframe based on a condition, we simply pass the condition as a boolean vector. \\n\",\n \"\\n\",\n \"For example, Let's filter the dataframe to show the data on Asian countries (AreaName = Asia).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Afghanistan True\\n\",\n \"Albania False\\n\",\n \"Algeria False\\n\",\n \"American Samoa False\\n\",\n \"Andorra False\\n\",\n \" ... \\n\",\n \"Viet Nam True\\n\",\n \"Western Sahara False\\n\",\n \"Yemen True\\n\",\n \"Zambia False\\n\",\n \"Zimbabwe False\\n\",\n \"Name: Continent, Length: 195, dtype: bool\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 1. create the condition boolean series\\n\",\n \"condition = df_can['Continent'] == 'Asia'\\n\",\n \"print(condition)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Before we proceed: let's review the changes we have made to our dataframe.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Continent
\\n\",\n \"
Region
\\n\",\n \"
DevName
\\n\",\n \"
1980
\\n\",\n \"
1981
\\n\",\n \"
1982
\\n\",\n \"
1983
\\n\",\n \"
1984
\\n\",\n \"
1985
\\n\",\n \"
1986
\\n\",\n \"
...
\\n\",\n \"
2005
\\n\",\n \"
2006
\\n\",\n \"
2007
\\n\",\n \"
2008
\\n\",\n \"
2009
\\n\",\n \"
2010
\\n\",\n \"
2011
\\n\",\n \"
2012
\\n\",\n \"
2013
\\n\",\n \"
Total
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
Afghanistan
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
16
\\n\",\n \"
39
\\n\",\n \"
39
\\n\",\n \"
47
\\n\",\n \"
71
\\n\",\n \"
340
\\n\",\n \"
496
\\n\",\n \"
...
\\n\",\n \"
3436
\\n\",\n \"
3009
\\n\",\n \"
2652
\\n\",\n \"
2111
\\n\",\n \"
1746
\\n\",\n \"
1758
\\n\",\n \"
2203
\\n\",\n \"
2635
\\n\",\n \"
2004
\\n\",\n \"
58639
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Armenia
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
224
\\n\",\n \"
218
\\n\",\n \"
198
\\n\",\n \"
205
\\n\",\n \"
267
\\n\",\n \"
252
\\n\",\n \"
236
\\n\",\n \"
258
\\n\",\n \"
207
\\n\",\n \"
3310
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Azerbaijan
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
359
\\n\",\n \"
236
\\n\",\n \"
203
\\n\",\n \"
125
\\n\",\n \"
165
\\n\",\n \"
209
\\n\",\n \"
138
\\n\",\n \"
161
\\n\",\n \"
57
\\n\",\n \"
2649
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Bahrain
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
3
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
12
\\n\",\n \"
12
\\n\",\n \"
22
\\n\",\n \"
9
\\n\",\n \"
35
\\n\",\n \"
28
\\n\",\n \"
21
\\n\",\n \"
39
\\n\",\n \"
32
\\n\",\n \"
475
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Bangladesh
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
83
\\n\",\n \"
84
\\n\",\n \"
86
\\n\",\n \"
81
\\n\",\n \"
98
\\n\",\n \"
92
\\n\",\n \"
486
\\n\",\n \"
...
\\n\",\n \"
4171
\\n\",\n \"
4014
\\n\",\n \"
2897
\\n\",\n \"
2939
\\n\",\n \"
2104
\\n\",\n \"
4721
\\n\",\n \"
2694
\\n\",\n \"
2640
\\n\",\n \"
3789
\\n\",\n \"
65568
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Bhutan
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
5
\\n\",\n \"
10
\\n\",\n \"
7
\\n\",\n \"
36
\\n\",\n \"
865
\\n\",\n \"
1464
\\n\",\n \"
1879
\\n\",\n \"
1075
\\n\",\n \"
487
\\n\",\n \"
5876
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Brunei Darussalam
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
79
\\n\",\n \"
6
\\n\",\n \"
8
\\n\",\n \"
2
\\n\",\n \"
2
\\n\",\n \"
4
\\n\",\n \"
12
\\n\",\n \"
...
\\n\",\n \"
4
\\n\",\n \"
5
\\n\",\n \"
11
\\n\",\n \"
10
\\n\",\n \"
5
\\n\",\n \"
12
\\n\",\n \"
6
\\n\",\n \"
3
\\n\",\n \"
6
\\n\",\n \"
600
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Cambodia
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
12
\\n\",\n \"
19
\\n\",\n \"
26
\\n\",\n \"
33
\\n\",\n \"
10
\\n\",\n \"
7
\\n\",\n \"
8
\\n\",\n \"
...
\\n\",\n \"
370
\\n\",\n \"
529
\\n\",\n \"
460
\\n\",\n \"
354
\\n\",\n \"
203
\\n\",\n \"
200
\\n\",\n \"
196
\\n\",\n \"
233
\\n\",\n \"
288
\\n\",\n \"
6538
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
China
\\n\",\n \"
Asia
\\n\",\n \"
Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
5123
\\n\",\n \"
6682
\\n\",\n \"
3308
\\n\",\n \"
1863
\\n\",\n \"
1527
\\n\",\n \"
1816
\\n\",\n \"
1960
\\n\",\n \"
...
\\n\",\n \"
42584
\\n\",\n \"
33518
\\n\",\n \"
27642
\\n\",\n \"
30037
\\n\",\n \"
29622
\\n\",\n \"
30391
\\n\",\n \"
28502
\\n\",\n \"
33024
\\n\",\n \"
34129
\\n\",\n \"
659962
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
China, Hong Kong Special Administrative Region
\\n\",\n \"
Asia
\\n\",\n \"
Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
729
\\n\",\n \"
712
\\n\",\n \"
674
\\n\",\n \"
897
\\n\",\n \"
657
\\n\",\n \"
623
\\n\",\n \"
591
\\n\",\n \"
728
\\n\",\n \"
774
\\n\",\n \"
9327
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
China, Macao Special Administrative Region
\\n\",\n \"
Asia
\\n\",\n \"
Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
21
\\n\",\n \"
32
\\n\",\n \"
16
\\n\",\n \"
12
\\n\",\n \"
21
\\n\",\n \"
21
\\n\",\n \"
13
\\n\",\n \"
33
\\n\",\n \"
29
\\n\",\n \"
284
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Cyprus
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
132
\\n\",\n \"
128
\\n\",\n \"
84
\\n\",\n \"
46
\\n\",\n \"
46
\\n\",\n \"
43
\\n\",\n \"
48
\\n\",\n \"
...
\\n\",\n \"
7
\\n\",\n \"
9
\\n\",\n \"
4
\\n\",\n \"
7
\\n\",\n \"
6
\\n\",\n \"
18
\\n\",\n \"
6
\\n\",\n \"
12
\\n\",\n \"
16
\\n\",\n \"
1126
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Democratic People's Republic of Korea
\\n\",\n \"
Asia
\\n\",\n \"
Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
3
\\n\",\n \"
1
\\n\",\n \"
4
\\n\",\n \"
3
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
14
\\n\",\n \"
10
\\n\",\n \"
7
\\n\",\n \"
19
\\n\",\n \"
11
\\n\",\n \"
45
\\n\",\n \"
97
\\n\",\n \"
66
\\n\",\n \"
17
\\n\",\n \"
388
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Georgia
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
114
\\n\",\n \"
125
\\n\",\n \"
132
\\n\",\n \"
112
\\n\",\n \"
128
\\n\",\n \"
126
\\n\",\n \"
139
\\n\",\n \"
147
\\n\",\n \"
125
\\n\",\n \"
2068
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
India
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
8880
\\n\",\n \"
8670
\\n\",\n \"
8147
\\n\",\n \"
7338
\\n\",\n \"
5704
\\n\",\n \"
4211
\\n\",\n \"
7150
\\n\",\n \"
...
\\n\",\n \"
36210
\\n\",\n \"
33848
\\n\",\n \"
28742
\\n\",\n \"
28261
\\n\",\n \"
29456
\\n\",\n \"
34235
\\n\",\n \"
27509
\\n\",\n \"
30933
\\n\",\n \"
33087
\\n\",\n \"
691904
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Indonesia
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
186
\\n\",\n \"
178
\\n\",\n \"
252
\\n\",\n \"
115
\\n\",\n \"
123
\\n\",\n \"
100
\\n\",\n \"
127
\\n\",\n \"
...
\\n\",\n \"
632
\\n\",\n \"
613
\\n\",\n \"
657
\\n\",\n \"
661
\\n\",\n \"
504
\\n\",\n \"
712
\\n\",\n \"
390
\\n\",\n \"
395
\\n\",\n \"
387
\\n\",\n \"
13150
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Iran (Islamic Republic of)
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1172
\\n\",\n \"
1429
\\n\",\n \"
1822
\\n\",\n \"
1592
\\n\",\n \"
1977
\\n\",\n \"
1648
\\n\",\n \"
1794
\\n\",\n \"
...
\\n\",\n \"
5837
\\n\",\n \"
7480
\\n\",\n \"
6974
\\n\",\n \"
6475
\\n\",\n \"
6580
\\n\",\n \"
7477
\\n\",\n \"
7479
\\n\",\n \"
7534
\\n\",\n \"
11291
\\n\",\n \"
175923
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Iraq
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
262
\\n\",\n \"
245
\\n\",\n \"
260
\\n\",\n \"
380
\\n\",\n \"
428
\\n\",\n \"
231
\\n\",\n \"
265
\\n\",\n \"
...
\\n\",\n \"
2226
\\n\",\n \"
1788
\\n\",\n \"
2406
\\n\",\n \"
3543
\\n\",\n \"
5450
\\n\",\n \"
5941
\\n\",\n \"
6196
\\n\",\n \"
4041
\\n\",\n \"
4918
\\n\",\n \"
69789
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Israel
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1403
\\n\",\n \"
1711
\\n\",\n \"
1334
\\n\",\n \"
541
\\n\",\n \"
446
\\n\",\n \"
680
\\n\",\n \"
1212
\\n\",\n \"
...
\\n\",\n \"
2446
\\n\",\n \"
2625
\\n\",\n \"
2401
\\n\",\n \"
2562
\\n\",\n \"
2316
\\n\",\n \"
2755
\\n\",\n \"
1970
\\n\",\n \"
2134
\\n\",\n \"
1945
\\n\",\n \"
66508
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Japan
\\n\",\n \"
Asia
\\n\",\n \"
Eastern Asia
\\n\",\n \"
Developed regions
\\n\",\n \"
701
\\n\",\n \"
756
\\n\",\n \"
598
\\n\",\n \"
309
\\n\",\n \"
246
\\n\",\n \"
198
\\n\",\n \"
248
\\n\",\n \"
...
\\n\",\n \"
1067
\\n\",\n \"
1212
\\n\",\n \"
1250
\\n\",\n \"
1284
\\n\",\n \"
1194
\\n\",\n \"
1168
\\n\",\n \"
1265
\\n\",\n \"
1214
\\n\",\n \"
982
\\n\",\n \"
27707
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Jordan
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
177
\\n\",\n \"
160
\\n\",\n \"
155
\\n\",\n \"
113
\\n\",\n \"
102
\\n\",\n \"
179
\\n\",\n \"
181
\\n\",\n \"
...
\\n\",\n \"
1940
\\n\",\n \"
1827
\\n\",\n \"
1421
\\n\",\n \"
1581
\\n\",\n \"
1235
\\n\",\n \"
1831
\\n\",\n \"
1635
\\n\",\n \"
1206
\\n\",\n \"
1255
\\n\",\n \"
35406
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Kazakhstan
\\n\",\n \"
Asia
\\n\",\n \"
Central Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
506
\\n\",\n \"
408
\\n\",\n \"
436
\\n\",\n \"
394
\\n\",\n \"
431
\\n\",\n \"
377
\\n\",\n \"
381
\\n\",\n \"
462
\\n\",\n \"
348
\\n\",\n \"
8490
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Kuwait
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
8
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
4
\\n\",\n \"
4
\\n\",\n \"
...
\\n\",\n \"
66
\\n\",\n \"
35
\\n\",\n \"
62
\\n\",\n \"
53
\\n\",\n \"
68
\\n\",\n \"
67
\\n\",\n \"
58
\\n\",\n \"
73
\\n\",\n \"
48
\\n\",\n \"
2025
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Kyrgyzstan
\\n\",\n \"
Asia
\\n\",\n \"
Central Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
173
\\n\",\n \"
161
\\n\",\n \"
135
\\n\",\n \"
168
\\n\",\n \"
173
\\n\",\n \"
157
\\n\",\n \"
159
\\n\",\n \"
278
\\n\",\n \"
123
\\n\",\n \"
2353
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Lao People's Democratic Republic
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
11
\\n\",\n \"
6
\\n\",\n \"
16
\\n\",\n \"
16
\\n\",\n \"
7
\\n\",\n \"
17
\\n\",\n \"
21
\\n\",\n \"
...
\\n\",\n \"
42
\\n\",\n \"
74
\\n\",\n \"
53
\\n\",\n \"
32
\\n\",\n \"
39
\\n\",\n \"
54
\\n\",\n \"
22
\\n\",\n \"
25
\\n\",\n \"
15
\\n\",\n \"
1089
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Lebanon
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1409
\\n\",\n \"
1119
\\n\",\n \"
1159
\\n\",\n \"
789
\\n\",\n \"
1253
\\n\",\n \"
1683
\\n\",\n \"
2576
\\n\",\n \"
...
\\n\",\n \"
3709
\\n\",\n \"
3802
\\n\",\n \"
3467
\\n\",\n \"
3566
\\n\",\n \"
3077
\\n\",\n \"
3432
\\n\",\n \"
3072
\\n\",\n \"
1614
\\n\",\n \"
2172
\\n\",\n \"
115359
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Malaysia
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
786
\\n\",\n \"
816
\\n\",\n \"
813
\\n\",\n \"
448
\\n\",\n \"
384
\\n\",\n \"
374
\\n\",\n \"
425
\\n\",\n \"
...
\\n\",\n \"
593
\\n\",\n \"
580
\\n\",\n \"
600
\\n\",\n \"
658
\\n\",\n \"
640
\\n\",\n \"
802
\\n\",\n \"
409
\\n\",\n \"
358
\\n\",\n \"
204
\\n\",\n \"
24417
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Maldives
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
7
\\n\",\n \"
4
\\n\",\n \"
3
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
30
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Mongolia
\\n\",\n \"
Asia
\\n\",\n \"
Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
59
\\n\",\n \"
64
\\n\",\n \"
82
\\n\",\n \"
59
\\n\",\n \"
118
\\n\",\n \"
169
\\n\",\n \"
103
\\n\",\n \"
68
\\n\",\n \"
99
\\n\",\n \"
952
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Myanmar
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
80
\\n\",\n \"
62
\\n\",\n \"
46
\\n\",\n \"
31
\\n\",\n \"
41
\\n\",\n \"
23
\\n\",\n \"
18
\\n\",\n \"
...
\\n\",\n \"
210
\\n\",\n \"
953
\\n\",\n \"
1887
\\n\",\n \"
975
\\n\",\n \"
1153
\\n\",\n \"
556
\\n\",\n \"
368
\\n\",\n \"
193
\\n\",\n \"
262
\\n\",\n \"
9245
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Nepal
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
6
\\n\",\n \"
1
\\n\",\n \"
2
\\n\",\n \"
4
\\n\",\n \"
13
\\n\",\n \"
...
\\n\",\n \"
607
\\n\",\n \"
540
\\n\",\n \"
511
\\n\",\n \"
581
\\n\",\n \"
561
\\n\",\n \"
1392
\\n\",\n \"
1129
\\n\",\n \"
1185
\\n\",\n \"
1308
\\n\",\n \"
10222
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Oman
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
8
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
14
\\n\",\n \"
18
\\n\",\n \"
16
\\n\",\n \"
10
\\n\",\n \"
7
\\n\",\n \"
14
\\n\",\n \"
10
\\n\",\n \"
13
\\n\",\n \"
11
\\n\",\n \"
224
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Pakistan
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
978
\\n\",\n \"
972
\\n\",\n \"
1201
\\n\",\n \"
900
\\n\",\n \"
668
\\n\",\n \"
514
\\n\",\n \"
691
\\n\",\n \"
...
\\n\",\n \"
14314
\\n\",\n \"
13127
\\n\",\n \"
10124
\\n\",\n \"
8994
\\n\",\n \"
7217
\\n\",\n \"
6811
\\n\",\n \"
7468
\\n\",\n \"
11227
\\n\",\n \"
12603
\\n\",\n \"
241600
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Philippines
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
6051
\\n\",\n \"
5921
\\n\",\n \"
5249
\\n\",\n \"
4562
\\n\",\n \"
3801
\\n\",\n \"
3150
\\n\",\n \"
4166
\\n\",\n \"
...
\\n\",\n \"
18139
\\n\",\n \"
18400
\\n\",\n \"
19837
\\n\",\n \"
24887
\\n\",\n \"
28573
\\n\",\n \"
38617
\\n\",\n \"
36765
\\n\",\n \"
34315
\\n\",\n \"
29544
\\n\",\n \"
511391
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Qatar
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
...
\\n\",\n \"
11
\\n\",\n \"
2
\\n\",\n \"
5
\\n\",\n \"
9
\\n\",\n \"
6
\\n\",\n \"
18
\\n\",\n \"
3
\\n\",\n \"
14
\\n\",\n \"
6
\\n\",\n \"
157
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Republic of Korea
\\n\",\n \"
Asia
\\n\",\n \"
Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1011
\\n\",\n \"
1456
\\n\",\n \"
1572
\\n\",\n \"
1081
\\n\",\n \"
847
\\n\",\n \"
962
\\n\",\n \"
1208
\\n\",\n \"
...
\\n\",\n \"
5832
\\n\",\n \"
6215
\\n\",\n \"
5920
\\n\",\n \"
7294
\\n\",\n \"
5874
\\n\",\n \"
5537
\\n\",\n \"
4588
\\n\",\n \"
5316
\\n\",\n \"
4509
\\n\",\n \"
142581
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Saudi Arabia
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
4
\\n\",\n \"
1
\\n\",\n \"
2
\\n\",\n \"
5
\\n\",\n \"
...
\\n\",\n \"
198
\\n\",\n \"
252
\\n\",\n \"
188
\\n\",\n \"
249
\\n\",\n \"
246
\\n\",\n \"
330
\\n\",\n \"
278
\\n\",\n \"
286
\\n\",\n \"
267
\\n\",\n \"
3425
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Singapore
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
241
\\n\",\n \"
301
\\n\",\n \"
337
\\n\",\n \"
169
\\n\",\n \"
128
\\n\",\n \"
139
\\n\",\n \"
205
\\n\",\n \"
...
\\n\",\n \"
392
\\n\",\n \"
298
\\n\",\n \"
690
\\n\",\n \"
734
\\n\",\n \"
366
\\n\",\n \"
805
\\n\",\n \"
219
\\n\",\n \"
146
\\n\",\n \"
141
\\n\",\n \"
14579
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Sri Lanka
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
185
\\n\",\n \"
371
\\n\",\n \"
290
\\n\",\n \"
197
\\n\",\n \"
1086
\\n\",\n \"
845
\\n\",\n \"
1838
\\n\",\n \"
...
\\n\",\n \"
4930
\\n\",\n \"
4714
\\n\",\n \"
4123
\\n\",\n \"
4756
\\n\",\n \"
4547
\\n\",\n \"
4422
\\n\",\n \"
3309
\\n\",\n \"
3338
\\n\",\n \"
2394
\\n\",\n \"
148358
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
State of Palestine
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
453
\\n\",\n \"
627
\\n\",\n \"
441
\\n\",\n \"
481
\\n\",\n \"
400
\\n\",\n \"
654
\\n\",\n \"
555
\\n\",\n \"
533
\\n\",\n \"
462
\\n\",\n \"
6512
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Syrian Arab Republic
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
315
\\n\",\n \"
419
\\n\",\n \"
409
\\n\",\n \"
269
\\n\",\n \"
264
\\n\",\n \"
385
\\n\",\n \"
493
\\n\",\n \"
...
\\n\",\n \"
1458
\\n\",\n \"
1145
\\n\",\n \"
1056
\\n\",\n \"
919
\\n\",\n \"
917
\\n\",\n \"
1039
\\n\",\n \"
1005
\\n\",\n \"
650
\\n\",\n \"
1009
\\n\",\n \"
31485
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Tajikistan
\\n\",\n \"
Asia
\\n\",\n \"
Central Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
85
\\n\",\n \"
46
\\n\",\n \"
44
\\n\",\n \"
15
\\n\",\n \"
50
\\n\",\n \"
52
\\n\",\n \"
47
\\n\",\n \"
34
\\n\",\n \"
39
\\n\",\n \"
503
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Thailand
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
56
\\n\",\n \"
53
\\n\",\n \"
113
\\n\",\n \"
65
\\n\",\n \"
82
\\n\",\n \"
66
\\n\",\n \"
78
\\n\",\n \"
...
\\n\",\n \"
575
\\n\",\n \"
500
\\n\",\n \"
487
\\n\",\n \"
519
\\n\",\n \"
512
\\n\",\n \"
499
\\n\",\n \"
396
\\n\",\n \"
296
\\n\",\n \"
400
\\n\",\n \"
9174
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Turkey
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
481
\\n\",\n \"
874
\\n\",\n \"
706
\\n\",\n \"
280
\\n\",\n \"
338
\\n\",\n \"
202
\\n\",\n \"
257
\\n\",\n \"
...
\\n\",\n \"
2065
\\n\",\n \"
1638
\\n\",\n \"
1463
\\n\",\n \"
1122
\\n\",\n \"
1238
\\n\",\n \"
1492
\\n\",\n \"
1257
\\n\",\n \"
1068
\\n\",\n \"
729
\\n\",\n \"
31781
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Turkmenistan
\\n\",\n \"
Asia
\\n\",\n \"
Central Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
40
\\n\",\n \"
26
\\n\",\n \"
37
\\n\",\n \"
13
\\n\",\n \"
20
\\n\",\n \"
30
\\n\",\n \"
20
\\n\",\n \"
20
\\n\",\n \"
14
\\n\",\n \"
310
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
United Arab Emirates
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
2
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
2
\\n\",\n \"
0
\\n\",\n \"
5
\\n\",\n \"
...
\\n\",\n \"
31
\\n\",\n \"
42
\\n\",\n \"
37
\\n\",\n \"
33
\\n\",\n \"
37
\\n\",\n \"
86
\\n\",\n \"
60
\\n\",\n \"
54
\\n\",\n \"
46
\\n\",\n \"
836
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Uzbekistan
\\n\",\n \"
Asia
\\n\",\n \"
Central Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
330
\\n\",\n \"
262
\\n\",\n \"
284
\\n\",\n \"
215
\\n\",\n \"
288
\\n\",\n \"
289
\\n\",\n \"
162
\\n\",\n \"
235
\\n\",\n \"
167
\\n\",\n \"
3368
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Viet Nam
\\n\",\n \"
Asia
\\n\",\n \"
South-Eastern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1191
\\n\",\n \"
1829
\\n\",\n \"
2162
\\n\",\n \"
3404
\\n\",\n \"
7583
\\n\",\n \"
5907
\\n\",\n \"
2741
\\n\",\n \"
...
\\n\",\n \"
1852
\\n\",\n \"
3153
\\n\",\n \"
2574
\\n\",\n \"
1784
\\n\",\n \"
2171
\\n\",\n \"
1942
\\n\",\n \"
1723
\\n\",\n \"
1731
\\n\",\n \"
2112
\\n\",\n \"
97146
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Yemen
\\n\",\n \"
Asia
\\n\",\n \"
Western Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
6
\\n\",\n \"
0
\\n\",\n \"
18
\\n\",\n \"
7
\\n\",\n \"
...
\\n\",\n \"
161
\\n\",\n \"
140
\\n\",\n \"
122
\\n\",\n \"
133
\\n\",\n \"
128
\\n\",\n \"
211
\\n\",\n \"
160
\\n\",\n \"
174
\\n\",\n \"
217
\\n\",\n \"
2985
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
49 rows × 38 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Continent Region \\\\\\n\",\n \"Afghanistan Asia Southern Asia \\n\",\n \"Armenia Asia Western Asia \\n\",\n \"Azerbaijan Asia Western Asia \\n\",\n \"Bahrain Asia Western Asia \\n\",\n \"Bangladesh Asia Southern Asia \\n\",\n \"Bhutan Asia Southern Asia \\n\",\n \"Brunei Darussalam Asia South-Eastern Asia \\n\",\n \"Cambodia Asia South-Eastern Asia \\n\",\n \"China Asia Eastern Asia \\n\",\n \"China, Hong Kong Special Administrative Region Asia Eastern Asia \\n\",\n \"China, Macao Special Administrative Region Asia Eastern Asia \\n\",\n \"Cyprus Asia Western Asia \\n\",\n \"Democratic People's Republic of Korea Asia Eastern Asia \\n\",\n \"Georgia Asia Western Asia \\n\",\n \"India Asia Southern Asia \\n\",\n \"Indonesia Asia South-Eastern Asia \\n\",\n \"Iran (Islamic Republic of) Asia Southern Asia \\n\",\n \"Iraq Asia Western Asia \\n\",\n \"Israel Asia Western Asia \\n\",\n \"Japan Asia Eastern Asia \\n\",\n \"Jordan Asia Western Asia \\n\",\n \"Kazakhstan Asia Central Asia \\n\",\n \"Kuwait Asia Western Asia \\n\",\n \"Kyrgyzstan Asia Central Asia \\n\",\n \"Lao People's Democratic Republic Asia South-Eastern Asia \\n\",\n \"Lebanon Asia Western Asia \\n\",\n \"Malaysia Asia South-Eastern Asia \\n\",\n \"Maldives Asia Southern Asia \\n\",\n \"Mongolia Asia Eastern Asia \\n\",\n \"Myanmar Asia South-Eastern Asia \\n\",\n \"Nepal Asia Southern Asia \\n\",\n \"Oman Asia Western Asia \\n\",\n \"Pakistan Asia Southern Asia \\n\",\n \"Philippines Asia South-Eastern Asia \\n\",\n \"Qatar Asia Western Asia \\n\",\n \"Republic of Korea Asia Eastern Asia \\n\",\n \"Saudi Arabia Asia Western Asia \\n\",\n \"Singapore Asia South-Eastern Asia \\n\",\n \"Sri Lanka Asia Southern Asia \\n\",\n \"State of Palestine Asia Western Asia \\n\",\n \"Syrian Arab Republic Asia Western Asia \\n\",\n \"Tajikistan Asia Central Asia \\n\",\n \"Thailand Asia South-Eastern Asia \\n\",\n \"Turkey Asia Western Asia \\n\",\n \"Turkmenistan Asia Central Asia \\n\",\n \"United Arab Emirates Asia Western Asia \\n\",\n \"Uzbekistan Asia Central Asia \\n\",\n \"Viet Nam Asia South-Eastern Asia \\n\",\n \"Yemen Asia Western Asia \\n\",\n \"\\n\",\n \" DevName 1980 \\\\\\n\",\n \"Afghanistan Developing regions 16 \\n\",\n \"Armenia Developing regions 0 \\n\",\n \"Azerbaijan Developing regions 0 \\n\",\n \"Bahrain Developing regions 0 \\n\",\n \"Bangladesh Developing regions 83 \\n\",\n \"Bhutan Developing regions 0 \\n\",\n \"Brunei Darussalam Developing regions 79 \\n\",\n \"Cambodia Developing regions 12 \\n\",\n \"China Developing regions 5123 \\n\",\n \"China, Hong Kong Special Administrative Region Developing regions 0 \\n\",\n \"China, Macao Special Administrative Region Developing regions 0 \\n\",\n \"Cyprus Developing regions 132 \\n\",\n \"Democratic People's Republic of Korea Developing regions 1 \\n\",\n \"Georgia Developing regions 0 \\n\",\n \"India Developing regions 8880 \\n\",\n \"Indonesia Developing regions 186 \\n\",\n \"Iran (Islamic Republic of) Developing regions 1172 \\n\",\n \"Iraq Developing regions 262 \\n\",\n \"Israel Developing regions 1403 \\n\",\n \"Japan Developed regions 701 \\n\",\n \"Jordan Developing regions 177 \\n\",\n \"Kazakhstan Developing regions 0 \\n\",\n \"Kuwait Developing regions 1 \\n\",\n \"Kyrgyzstan Developing regions 0 \\n\",\n \"Lao People's Democratic Republic Developing regions 11 \\n\",\n \"Lebanon Developing regions 1409 \\n\",\n \"Malaysia Developing regions 786 \\n\",\n \"Maldives Developing regions 0 \\n\",\n \"Mongolia Developing regions 0 \\n\",\n \"Myanmar Developing regions 80 \\n\",\n \"Nepal Developing regions 1 \\n\",\n \"Oman Developing regions 0 \\n\",\n \"Pakistan Developing regions 978 \\n\",\n \"Philippines Developing regions 6051 \\n\",\n \"Qatar Developing regions 0 \\n\",\n \"Republic of Korea Developing regions 1011 \\n\",\n \"Saudi Arabia Developing regions 0 \\n\",\n \"Singapore Developing regions 241 \\n\",\n \"Sri Lanka Developing regions 185 \\n\",\n \"State of Palestine Developing regions 0 \\n\",\n \"Syrian Arab Republic Developing regions 315 \\n\",\n \"Tajikistan Developing regions 0 \\n\",\n \"Thailand Developing regions 56 \\n\",\n \"Turkey Developing regions 481 \\n\",\n \"Turkmenistan Developing regions 0 \\n\",\n \"United Arab Emirates Developing regions 0 \\n\",\n \"Uzbekistan Developing regions 0 \\n\",\n \"Viet Nam Developing regions 1191 \\n\",\n \"Yemen Developing regions 1 \\n\",\n \"\\n\",\n \" 1981 1982 1983 1984 1985 \\\\\\n\",\n \"Afghanistan 39 39 47 71 340 \\n\",\n \"Armenia 0 0 0 0 0 \\n\",\n \"Azerbaijan 0 0 0 0 0 \\n\",\n \"Bahrain 2 1 1 1 3 \\n\",\n \"Bangladesh 84 86 81 98 92 \\n\",\n \"Bhutan 0 0 0 1 0 \\n\",\n \"Brunei Darussalam 6 8 2 2 4 \\n\",\n \"Cambodia 19 26 33 10 7 \\n\",\n \"China 6682 3308 1863 1527 1816 \\n\",\n \"China, Hong Kong Special Administrative Region 0 0 0 0 0 \\n\",\n \"China, Macao Special Administrative Region 0 0 0 0 0 \\n\",\n \"Cyprus 128 84 46 46 43 \\n\",\n \"Democratic People's Republic of Korea 1 3 1 4 3 \\n\",\n \"Georgia 0 0 0 0 0 \\n\",\n \"India 8670 8147 7338 5704 4211 \\n\",\n \"Indonesia 178 252 115 123 100 \\n\",\n \"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 \\n\",\n \"Iraq 245 260 380 428 231 \\n\",\n \"Israel 1711 1334 541 446 680 \\n\",\n \"Japan 756 598 309 246 198 \\n\",\n \"Jordan 160 155 113 102 179 \\n\",\n \"Kazakhstan 0 0 0 0 0 \\n\",\n \"Kuwait 0 8 2 1 4 \\n\",\n \"Kyrgyzstan 0 0 0 0 0 \\n\",\n \"Lao People's Democratic Republic 6 16 16 7 17 \\n\",\n \"Lebanon 1119 1159 789 1253 1683 \\n\",\n \"Malaysia 816 813 448 384 374 \\n\",\n \"Maldives 0 0 1 0 0 \\n\",\n \"Mongolia 0 0 0 0 0 \\n\",\n \"Myanmar 62 46 31 41 23 \\n\",\n \"Nepal 1 6 1 2 4 \\n\",\n \"Oman 0 0 8 0 0 \\n\",\n \"Pakistan 972 1201 900 668 514 \\n\",\n \"Philippines 5921 5249 4562 3801 3150 \\n\",\n \"Qatar 0 0 0 0 0 \\n\",\n \"Republic of Korea 1456 1572 1081 847 962 \\n\",\n \"Saudi Arabia 0 1 4 1 2 \\n\",\n \"Singapore 301 337 169 128 139 \\n\",\n \"Sri Lanka 371 290 197 1086 845 \\n\",\n \"State of Palestine 0 0 0 0 0 \\n\",\n \"Syrian Arab Republic 419 409 269 264 385 \\n\",\n \"Tajikistan 0 0 0 0 0 \\n\",\n \"Thailand 53 113 65 82 66 \\n\",\n \"Turkey 874 706 280 338 202 \\n\",\n \"Turkmenistan 0 0 0 0 0 \\n\",\n \"United Arab Emirates 2 2 1 2 0 \\n\",\n \"Uzbekistan 0 0 0 0 0 \\n\",\n \"Viet Nam 1829 2162 3404 7583 5907 \\n\",\n \"Yemen 2 1 6 0 18 \\n\",\n \"\\n\",\n \" 1986 ... 2005 2006 \\\\\\n\",\n \"Afghanistan 496 ... 3436 3009 \\n\",\n \"Armenia 0 ... 224 218 \\n\",\n \"Azerbaijan 0 ... 359 236 \\n\",\n \"Bahrain 0 ... 12 12 \\n\",\n \"Bangladesh 486 ... 4171 4014 \\n\",\n \"Bhutan 0 ... 5 10 \\n\",\n \"Brunei Darussalam 12 ... 4 5 \\n\",\n \"Cambodia 8 ... 370 529 \\n\",\n \"China 1960 ... 42584 33518 \\n\",\n \"China, Hong Kong Special Administrative Region 0 ... 729 712 \\n\",\n \"China, Macao Special Administrative Region 0 ... 21 32 \\n\",\n \"Cyprus 48 ... 7 9 \\n\",\n \"Democratic People's Republic of Korea 0 ... 14 10 \\n\",\n \"Georgia 0 ... 114 125 \\n\",\n \"India 7150 ... 36210 33848 \\n\",\n \"Indonesia 127 ... 632 613 \\n\",\n \"Iran (Islamic Republic of) 1794 ... 5837 7480 \\n\",\n \"Iraq 265 ... 2226 1788 \\n\",\n \"Israel 1212 ... 2446 2625 \\n\",\n \"Japan 248 ... 1067 1212 \\n\",\n \"Jordan 181 ... 1940 1827 \\n\",\n \"Kazakhstan 0 ... 506 408 \\n\",\n \"Kuwait 4 ... 66 35 \\n\",\n \"Kyrgyzstan 0 ... 173 161 \\n\",\n \"Lao People's Democratic Republic 21 ... 42 74 \\n\",\n \"Lebanon 2576 ... 3709 3802 \\n\",\n \"Malaysia 425 ... 593 580 \\n\",\n \"Maldives 0 ... 0 0 \\n\",\n \"Mongolia 0 ... 59 64 \\n\",\n \"Myanmar 18 ... 210 953 \\n\",\n \"Nepal 13 ... 607 540 \\n\",\n \"Oman 0 ... 14 18 \\n\",\n \"Pakistan 691 ... 14314 13127 \\n\",\n \"Philippines 4166 ... 18139 18400 \\n\",\n \"Qatar 1 ... 11 2 \\n\",\n \"Republic of Korea 1208 ... 5832 6215 \\n\",\n \"Saudi Arabia 5 ... 198 252 \\n\",\n \"Singapore 205 ... 392 298 \\n\",\n \"Sri Lanka 1838 ... 4930 4714 \\n\",\n \"State of Palestine 0 ... 453 627 \\n\",\n \"Syrian Arab Republic 493 ... 1458 1145 \\n\",\n \"Tajikistan 0 ... 85 46 \\n\",\n \"Thailand 78 ... 575 500 \\n\",\n \"Turkey 257 ... 2065 1638 \\n\",\n \"Turkmenistan 0 ... 40 26 \\n\",\n \"United Arab Emirates 5 ... 31 42 \\n\",\n \"Uzbekistan 0 ... 330 262 \\n\",\n \"Viet Nam 2741 ... 1852 3153 \\n\",\n \"Yemen 7 ... 161 140 \\n\",\n \"\\n\",\n \" 2007 2008 2009 2010 \\\\\\n\",\n \"Afghanistan 2652 2111 1746 1758 \\n\",\n \"Armenia 198 205 267 252 \\n\",\n \"Azerbaijan 203 125 165 209 \\n\",\n \"Bahrain 22 9 35 28 \\n\",\n \"Bangladesh 2897 2939 2104 4721 \\n\",\n \"Bhutan 7 36 865 1464 \\n\",\n \"Brunei Darussalam 11 10 5 12 \\n\",\n \"Cambodia 460 354 203 200 \\n\",\n \"China 27642 30037 29622 30391 \\n\",\n \"China, Hong Kong Special Administrative Region 674 897 657 623 \\n\",\n \"China, Macao Special Administrative Region 16 12 21 21 \\n\",\n \"Cyprus 4 7 6 18 \\n\",\n \"Democratic People's Republic of Korea 7 19 11 45 \\n\",\n \"Georgia 132 112 128 126 \\n\",\n \"India 28742 28261 29456 34235 \\n\",\n \"Indonesia 657 661 504 712 \\n\",\n \"Iran (Islamic Republic of) 6974 6475 6580 7477 \\n\",\n \"Iraq 2406 3543 5450 5941 \\n\",\n \"Israel 2401 2562 2316 2755 \\n\",\n \"Japan 1250 1284 1194 1168 \\n\",\n \"Jordan 1421 1581 1235 1831 \\n\",\n \"Kazakhstan 436 394 431 377 \\n\",\n \"Kuwait 62 53 68 67 \\n\",\n \"Kyrgyzstan 135 168 173 157 \\n\",\n \"Lao People's Democratic Republic 53 32 39 54 \\n\",\n \"Lebanon 3467 3566 3077 3432 \\n\",\n \"Malaysia 600 658 640 802 \\n\",\n \"Maldives 2 1 7 4 \\n\",\n \"Mongolia 82 59 118 169 \\n\",\n \"Myanmar 1887 975 1153 556 \\n\",\n \"Nepal 511 581 561 1392 \\n\",\n \"Oman 16 10 7 14 \\n\",\n \"Pakistan 10124 8994 7217 6811 \\n\",\n \"Philippines 19837 24887 28573 38617 \\n\",\n \"Qatar 5 9 6 18 \\n\",\n \"Republic of Korea 5920 7294 5874 5537 \\n\",\n \"Saudi Arabia 188 249 246 330 \\n\",\n \"Singapore 690 734 366 805 \\n\",\n \"Sri Lanka 4123 4756 4547 4422 \\n\",\n \"State of Palestine 441 481 400 654 \\n\",\n \"Syrian Arab Republic 1056 919 917 1039 \\n\",\n \"Tajikistan 44 15 50 52 \\n\",\n \"Thailand 487 519 512 499 \\n\",\n \"Turkey 1463 1122 1238 1492 \\n\",\n \"Turkmenistan 37 13 20 30 \\n\",\n \"United Arab Emirates 37 33 37 86 \\n\",\n \"Uzbekistan 284 215 288 289 \\n\",\n \"Viet Nam 2574 1784 2171 1942 \\n\",\n \"Yemen 122 133 128 211 \\n\",\n \"\\n\",\n \" 2011 2012 2013 Total \\n\",\n \"Afghanistan 2203 2635 2004 58639 \\n\",\n \"Armenia 236 258 207 3310 \\n\",\n \"Azerbaijan 138 161 57 2649 \\n\",\n \"Bahrain 21 39 32 475 \\n\",\n \"Bangladesh 2694 2640 3789 65568 \\n\",\n \"Bhutan 1879 1075 487 5876 \\n\",\n \"Brunei Darussalam 6 3 6 600 \\n\",\n \"Cambodia 196 233 288 6538 \\n\",\n \"China 28502 33024 34129 659962 \\n\",\n \"China, Hong Kong Special Administrative Region 591 728 774 9327 \\n\",\n \"China, Macao Special Administrative Region 13 33 29 284 \\n\",\n \"Cyprus 6 12 16 1126 \\n\",\n \"Democratic People's Republic of Korea 97 66 17 388 \\n\",\n \"Georgia 139 147 125 2068 \\n\",\n \"India 27509 30933 33087 691904 \\n\",\n \"Indonesia 390 395 387 13150 \\n\",\n \"Iran (Islamic Republic of) 7479 7534 11291 175923 \\n\",\n \"Iraq 6196 4041 4918 69789 \\n\",\n \"Israel 1970 2134 1945 66508 \\n\",\n \"Japan 1265 1214 982 27707 \\n\",\n \"Jordan 1635 1206 1255 35406 \\n\",\n \"Kazakhstan 381 462 348 8490 \\n\",\n \"Kuwait 58 73 48 2025 \\n\",\n \"Kyrgyzstan 159 278 123 2353 \\n\",\n \"Lao People's Democratic Republic 22 25 15 1089 \\n\",\n \"Lebanon 3072 1614 2172 115359 \\n\",\n \"Malaysia 409 358 204 24417 \\n\",\n \"Maldives 3 1 1 30 \\n\",\n \"Mongolia 103 68 99 952 \\n\",\n \"Myanmar 368 193 262 9245 \\n\",\n \"Nepal 1129 1185 1308 10222 \\n\",\n \"Oman 10 13 11 224 \\n\",\n \"Pakistan 7468 11227 12603 241600 \\n\",\n \"Philippines 36765 34315 29544 511391 \\n\",\n \"Qatar 3 14 6 157 \\n\",\n \"Republic of Korea 4588 5316 4509 142581 \\n\",\n \"Saudi Arabia 278 286 267 3425 \\n\",\n \"Singapore 219 146 141 14579 \\n\",\n \"Sri Lanka 3309 3338 2394 148358 \\n\",\n \"State of Palestine 555 533 462 6512 \\n\",\n \"Syrian Arab Republic 1005 650 1009 31485 \\n\",\n \"Tajikistan 47 34 39 503 \\n\",\n \"Thailand 396 296 400 9174 \\n\",\n \"Turkey 1257 1068 729 31781 \\n\",\n \"Turkmenistan 20 20 14 310 \\n\",\n \"United Arab Emirates 60 54 46 836 \\n\",\n \"Uzbekistan 162 235 167 3368 \\n\",\n \"Viet Nam 1723 1731 2112 97146 \\n\",\n \"Yemen 160 174 217 2985 \\n\",\n \"\\n\",\n \"[49 rows x 38 columns]\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 2. pass this condition into the dataFrame\\n\",\n \"df_can[condition]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Continent
\\n\",\n \"
Region
\\n\",\n \"
DevName
\\n\",\n \"
1980
\\n\",\n \"
1981
\\n\",\n \"
1982
\\n\",\n \"
1983
\\n\",\n \"
1984
\\n\",\n \"
1985
\\n\",\n \"
1986
\\n\",\n \"
...
\\n\",\n \"
2005
\\n\",\n \"
2006
\\n\",\n \"
2007
\\n\",\n \"
2008
\\n\",\n \"
2009
\\n\",\n \"
2010
\\n\",\n \"
2011
\\n\",\n \"
2012
\\n\",\n \"
2013
\\n\",\n \"
Total
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
Afghanistan
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
16
\\n\",\n \"
39
\\n\",\n \"
39
\\n\",\n \"
47
\\n\",\n \"
71
\\n\",\n \"
340
\\n\",\n \"
496
\\n\",\n \"
...
\\n\",\n \"
3436
\\n\",\n \"
3009
\\n\",\n \"
2652
\\n\",\n \"
2111
\\n\",\n \"
1746
\\n\",\n \"
1758
\\n\",\n \"
2203
\\n\",\n \"
2635
\\n\",\n \"
2004
\\n\",\n \"
58639
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Bangladesh
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
83
\\n\",\n \"
84
\\n\",\n \"
86
\\n\",\n \"
81
\\n\",\n \"
98
\\n\",\n \"
92
\\n\",\n \"
486
\\n\",\n \"
...
\\n\",\n \"
4171
\\n\",\n \"
4014
\\n\",\n \"
2897
\\n\",\n \"
2939
\\n\",\n \"
2104
\\n\",\n \"
4721
\\n\",\n \"
2694
\\n\",\n \"
2640
\\n\",\n \"
3789
\\n\",\n \"
65568
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Bhutan
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
5
\\n\",\n \"
10
\\n\",\n \"
7
\\n\",\n \"
36
\\n\",\n \"
865
\\n\",\n \"
1464
\\n\",\n \"
1879
\\n\",\n \"
1075
\\n\",\n \"
487
\\n\",\n \"
5876
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
India
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
8880
\\n\",\n \"
8670
\\n\",\n \"
8147
\\n\",\n \"
7338
\\n\",\n \"
5704
\\n\",\n \"
4211
\\n\",\n \"
7150
\\n\",\n \"
...
\\n\",\n \"
36210
\\n\",\n \"
33848
\\n\",\n \"
28742
\\n\",\n \"
28261
\\n\",\n \"
29456
\\n\",\n \"
34235
\\n\",\n \"
27509
\\n\",\n \"
30933
\\n\",\n \"
33087
\\n\",\n \"
691904
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Iran (Islamic Republic of)
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1172
\\n\",\n \"
1429
\\n\",\n \"
1822
\\n\",\n \"
1592
\\n\",\n \"
1977
\\n\",\n \"
1648
\\n\",\n \"
1794
\\n\",\n \"
...
\\n\",\n \"
5837
\\n\",\n \"
7480
\\n\",\n \"
6974
\\n\",\n \"
6475
\\n\",\n \"
6580
\\n\",\n \"
7477
\\n\",\n \"
7479
\\n\",\n \"
7534
\\n\",\n \"
11291
\\n\",\n \"
175923
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Maldives
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
...
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
7
\\n\",\n \"
4
\\n\",\n \"
3
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
30
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Nepal
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
6
\\n\",\n \"
1
\\n\",\n \"
2
\\n\",\n \"
4
\\n\",\n \"
13
\\n\",\n \"
...
\\n\",\n \"
607
\\n\",\n \"
540
\\n\",\n \"
511
\\n\",\n \"
581
\\n\",\n \"
561
\\n\",\n \"
1392
\\n\",\n \"
1129
\\n\",\n \"
1185
\\n\",\n \"
1308
\\n\",\n \"
10222
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Pakistan
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
978
\\n\",\n \"
972
\\n\",\n \"
1201
\\n\",\n \"
900
\\n\",\n \"
668
\\n\",\n \"
514
\\n\",\n \"
691
\\n\",\n \"
...
\\n\",\n \"
14314
\\n\",\n \"
13127
\\n\",\n \"
10124
\\n\",\n \"
8994
\\n\",\n \"
7217
\\n\",\n \"
6811
\\n\",\n \"
7468
\\n\",\n \"
11227
\\n\",\n \"
12603
\\n\",\n \"
241600
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Sri Lanka
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
185
\\n\",\n \"
371
\\n\",\n \"
290
\\n\",\n \"
197
\\n\",\n \"
1086
\\n\",\n \"
845
\\n\",\n \"
1838
\\n\",\n \"
...
\\n\",\n \"
4930
\\n\",\n \"
4714
\\n\",\n \"
4123
\\n\",\n \"
4756
\\n\",\n \"
4547
\\n\",\n \"
4422
\\n\",\n \"
3309
\\n\",\n \"
3338
\\n\",\n \"
2394
\\n\",\n \"
148358
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
9 rows × 38 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Continent Region DevName 1980 \\\\\\n\",\n \"Afghanistan Asia Southern Asia Developing regions 16 \\n\",\n \"Bangladesh Asia Southern Asia Developing regions 83 \\n\",\n \"Bhutan Asia Southern Asia Developing regions 0 \\n\",\n \"India Asia Southern Asia Developing regions 8880 \\n\",\n \"Iran (Islamic Republic of) Asia Southern Asia Developing regions 1172 \\n\",\n \"Maldives Asia Southern Asia Developing regions 0 \\n\",\n \"Nepal Asia Southern Asia Developing regions 1 \\n\",\n \"Pakistan Asia Southern Asia Developing regions 978 \\n\",\n \"Sri Lanka Asia Southern Asia Developing regions 185 \\n\",\n \"\\n\",\n \" 1981 1982 1983 1984 1985 1986 ... 2005 \\\\\\n\",\n \"Afghanistan 39 39 47 71 340 496 ... 3436 \\n\",\n \"Bangladesh 84 86 81 98 92 486 ... 4171 \\n\",\n \"Bhutan 0 0 0 1 0 0 ... 5 \\n\",\n \"India 8670 8147 7338 5704 4211 7150 ... 36210 \\n\",\n \"Iran (Islamic Republic of) 1429 1822 1592 1977 1648 1794 ... 5837 \\n\",\n \"Maldives 0 0 1 0 0 0 ... 0 \\n\",\n \"Nepal 1 6 1 2 4 13 ... 607 \\n\",\n \"Pakistan 972 1201 900 668 514 691 ... 14314 \\n\",\n \"Sri Lanka 371 290 197 1086 845 1838 ... 4930 \\n\",\n \"\\n\",\n \" 2006 2007 2008 2009 2010 2011 2012 \\\\\\n\",\n \"Afghanistan 3009 2652 2111 1746 1758 2203 2635 \\n\",\n \"Bangladesh 4014 2897 2939 2104 4721 2694 2640 \\n\",\n \"Bhutan 10 7 36 865 1464 1879 1075 \\n\",\n \"India 33848 28742 28261 29456 34235 27509 30933 \\n\",\n \"Iran (Islamic Republic of) 7480 6974 6475 6580 7477 7479 7534 \\n\",\n \"Maldives 0 2 1 7 4 3 1 \\n\",\n \"Nepal 540 511 581 561 1392 1129 1185 \\n\",\n \"Pakistan 13127 10124 8994 7217 6811 7468 11227 \\n\",\n \"Sri Lanka 4714 4123 4756 4547 4422 3309 3338 \\n\",\n \"\\n\",\n \" 2013 Total \\n\",\n \"Afghanistan 2004 58639 \\n\",\n \"Bangladesh 3789 65568 \\n\",\n \"Bhutan 487 5876 \\n\",\n \"India 33087 691904 \\n\",\n \"Iran (Islamic Republic of) 11291 175923 \\n\",\n \"Maldives 1 30 \\n\",\n \"Nepal 1308 10222 \\n\",\n \"Pakistan 12603 241600 \\n\",\n \"Sri Lanka 2394 148358 \\n\",\n \"\\n\",\n \"[9 rows x 38 columns]\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# we can pass mutliple criteria in the same line. \\n\",\n \"# let's filter for AreaNAme = Asia and RegName = Southern Asia\\n\",\n \"\\n\",\n \"df_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\\n\",\n \"\\n\",\n \"# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\\n\",\n \"# don't forget to enclose the two conditions in parentheses\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"data dimensions: (195, 38)\\n\",\n \"Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\\n\",\n \" '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\\n\",\n \" '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\\n\",\n \" '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\\n\",\n \" '2011', '2012', '2013', 'Total'],\\n\",\n \" dtype='object')\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Continent
\\n\",\n \"
Region
\\n\",\n \"
DevName
\\n\",\n \"
1980
\\n\",\n \"
1981
\\n\",\n \"
1982
\\n\",\n \"
1983
\\n\",\n \"
1984
\\n\",\n \"
1985
\\n\",\n \"
1986
\\n\",\n \"
...
\\n\",\n \"
2005
\\n\",\n \"
2006
\\n\",\n \"
2007
\\n\",\n \"
2008
\\n\",\n \"
2009
\\n\",\n \"
2010
\\n\",\n \"
2011
\\n\",\n \"
2012
\\n\",\n \"
2013
\\n\",\n \"
Total
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
Afghanistan
\\n\",\n \"
Asia
\\n\",\n \"
Southern Asia
\\n\",\n \"
Developing regions
\\n\",\n \"
16
\\n\",\n \"
39
\\n\",\n \"
39
\\n\",\n \"
47
\\n\",\n \"
71
\\n\",\n \"
340
\\n\",\n \"
496
\\n\",\n \"
...
\\n\",\n \"
3436
\\n\",\n \"
3009
\\n\",\n \"
2652
\\n\",\n \"
2111
\\n\",\n \"
1746
\\n\",\n \"
1758
\\n\",\n \"
2203
\\n\",\n \"
2635
\\n\",\n \"
2004
\\n\",\n \"
58639
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Albania
\\n\",\n \"
Europe
\\n\",\n \"
Southern Europe
\\n\",\n \"
Developed regions
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
...
\\n\",\n \"
1223
\\n\",\n \"
856
\\n\",\n \"
702
\\n\",\n \"
560
\\n\",\n \"
716
\\n\",\n \"
561
\\n\",\n \"
539
\\n\",\n \"
620
\\n\",\n \"
603
\\n\",\n \"
15699
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
2 rows × 38 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Continent Region DevName 1980 1981 1982 \\\\\\n\",\n \"Afghanistan Asia Southern Asia Developing regions 16 39 39 \\n\",\n \"Albania Europe Southern Europe Developed regions 1 0 0 \\n\",\n \"\\n\",\n \" 1983 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 \\\\\\n\",\n \"Afghanistan 47 71 340 496 ... 3436 3009 2652 2111 1746 1758 \\n\",\n \"Albania 0 0 0 1 ... 1223 856 702 560 716 561 \\n\",\n \"\\n\",\n \" 2011 2012 2013 Total \\n\",\n \"Afghanistan 2203 2635 2004 58639 \\n\",\n \"Albania 539 620 603 15699 \\n\",\n \"\\n\",\n \"[2 rows x 38 columns]\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"print('data dimensions:', df_can.shape)\\n\",\n \"print(df_can.columns)\\n\",\n \"df_can.head(2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"* * *\\n\",\n \"\\n\",\n \"# Visualizing Data using Matplotlib\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"## Matplotlib: Standard Python Visualization Library\\n\",\n \"\\n\",\n \"The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ). As mentioned on their website: \\n\",\n \"\\n\",\n \"> Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\\n\",\n \"\\n\",\n \"If you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### Matplotlib.Pyplot\\n\",\n \"\\n\",\n \"One of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's start by importing `Matplotlib` and `Matplotlib.pyplot` as follows:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# we are using the inline backend\\n\",\n \"%matplotlib inline \\n\",\n \"\\n\",\n \"import matplotlib as mpl\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"*optional: check if Matplotlib is loaded.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Matplotlib version: 3.3.2\\n\"\n ]\n }\n ],\n \"source\": [\n \"print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"*optional: apply a style to Matplotlib.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(plt.style.available)\\n\",\n \"mpl.style.use(['ggplot']) # optional: for ggplot-like style\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### Plotting in _pandas_\\n\",\n \"\\n\",\n \"Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in _pandas_ is as simple as appending a `.plot()` method to a series or dataframe.\\n\",\n \"\\n\",\n \"Documentation:\\n\",\n \"\\n\",\n \"- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) \\n\",\n \"- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Line Pots (Series/Dataframe) \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**What is a line plot and why use it?**\\n\",\n \"\\n\",\n \"A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\\n\",\n \"Use line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Let's start with a case study:**\\n\",\n \"\\n\",\n \"In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\\n\",\n \"\\n\",\n \"**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"First, we will extract the data series for Haiti.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1980 262\\n\",\n \"1981 245\\n\",\n \"1982 260\\n\",\n \"1983 380\\n\",\n \"1984 428\\n\",\n \"Name: Iraq, dtype: object\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"haiti = df_can.loc['Iraq', years] # passing in years 1980 - 2013 to exclude the 'total' column\\n\",\n \"haiti.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"haiti.plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"_pandas_ automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type _string_. Therefore, let's change the type of the index values to _integer_ for plotting.\\n\",\n \"\\n\",\n \"Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\\n\",\n \"haiti.plot(kind='line')\\n\",\n \"\\n\",\n \"plt.title('Immigration from Haiti')\\n\",\n \"plt.ylabel('Number of immigrants')\\n\",\n \"plt.xlabel('Years')\\n\",\n \"\\n\",\n \"plt.show() # need this line to show the updates made to the figure\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's annotate this spike in the plot by using the `plt.text()` method.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAY4AAAEaCAYAAAAG87ApAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABRI0lEQVR4nO3deVzUdf7A8dfnC6gIgjADeKGVYF4oGJqaiimplZW5ltV2iFq21bqrbdfaapebHUpZ2KXZZv3aslW6NQlDDS08yyMRNfNAQQZBFOSYz++PyUkScDjmQN/Px8NH8pnv8Z6vNO/53EprrRFCCCEcZLg7ACGEEI2LJA4hhBC1IolDCCFErUjiEEIIUSuSOIQQQtSKJA4hhBC1IolDNFqDBw9m4sSJbo3h22+/RSnFgQMHXHK/f/7zn4SFhaGU4p133nHJPV3J0ef5xBNPEBER4aKoxB8pmcch6mrcuHEcOHCAlJQUt9zfYrHg7e1NQECAS+7n7e3N/PnzGTdunL2stLQUi8VCaGgohuHc72Hff/89ffv2JTk5mcsvv5zAwEB8fX2dek9HDB48mIiICObPn1+p/JdffuHiiy9m9erVDBgwwKFr/fF5rlmzhoEDB7J3714uuugi+3FFRUWUlJRgNpsb8q0IB3m7OwAh6io4OLje1ygrK8Pb2xulVJ3Ob9KkCa1atap3HI7YtWsXhmFwww03VHtMfd+Puzn6PP39/fH393dBRKIq0lQlGsy4ceOIj4/nlVdeoV27dvj7+zNx4kTKysp4/fXX6dChA0FBQdxzzz2Ulpbazxs8eDATJkzg8ccfJzQ0lJYtWzJt2jSsVitPPfUUYWFhhISEMG3atEr3+2NTVXFxMffccw+BgYEEBQVx33338dhjj1Vq0jgzxosuuoimTZty4sQJVqxYweDBgwkODiYwMJC4uDh++OEH+3kXXXQRFRUVJCQkoJSyfzBX1bSybt06Bg0ahK+vL0FBQdx2223k5OTYXz/dzPLJJ5/QuXNn/Pz8uPLKK9m9e3eNz/aOO+7AarVWun9172fnzp1ce+219g/Y6667jqysLPv13nnnHby9vVm5ciVRUVH4+voSFxfHoUOHWLVqFTExMfj5+REfH8/Bgwcd/h04l2nTptGlSxeaN29OeHg49957LwUFBfbXz3yev/zyCwMHDgTg4osvRinF4MGDKz1D4R6SOESDysjIYP369axYsYL/+7//47333uOGG24gPT2dr776ikWLFrFo0SIWLFhQ6byPP/6YsrIy1qxZw5w5c/j3v//NyJEjKSoqYvXq1bz44ov8+9//5quvvqr23o888giffPIJixYtYt26dQQGBjJv3ryzjvvhhx9ITU0lOTmZLVu20KxZM4qKirj//vtZt24d6enpREZGMmLECPLy8uzvy8vLi5deeons7Gyys7OrjOHw4cMMGzaMdu3a8cMPP/DZZ5+xdetW/vSnP1U6Ljs7m9dee43333+f9PR0jh07xvjx46t9by+//DIvvfQSXl5eZ93/j+9Ha82wYcMoKSkhLS2NtLQ0ioqKGDFiRKWEbbVaefLJJ5k/fz7fffcdhw4dYuzYsUyfPp3XXnuNNWvWcODAAaZOnVptXLXl6+vLm2++yfbt23nnnXf49ttvmTx5cpXHhoeH88knn9jfY3Z2NkuWLGmwWEQ9aCHq6K677tJDhw6t9HNISIg+deqUveyaa67RJpNJl5SU2Muuv/56/ac//cn+c1xcnO7Zs2ela3ft2lV37969UlmPHj30gw8+WOm8CRMmaK21Lioq0k2aNNHz58+vdM7ll1+uO3bsWCnGwMBAffz48RrfW0VFhW7ZsqV+77337GVeXl564cKFlY5buXKlBvT+/fu11lo//vjjum3btpWewebNmzWg09LStNZaz5gxQ3t5eemcnBz7MR988IFWSuni4uJqY1q4cKH28vKqVFbV+5k/f7729fXVubm59rLDhw/rZs2a6f/85z/2awF606ZN9mOef/55Dej169fby+bMmaNNJlO1MWlt+3fw9vbWfn5+lf40b95cA3r16tXVnrtkyRLdpEkTXVFRobU++3muXr1aA3rv3r2VzpsxY0alf1fhWlLjEA2qS5cuNGnSxP5zq1atuPTSS2natGmlsjObbgB69uxZ6edWrVrRo0ePs8r+eN5pWVlZlJaW0rdv30rl/fr1qzLGP7aP7927lzvuuIOIiAgCAgIICAigoKCAffv21fBuz7Zt2zb69u1b6Rn07NmTwMBAtm3bZi9r06YNISEh9p/btm2L1rra91eTP76fbdu20bVr10odx2FhYVx66aWVYlBKERUVZf/5dN/Cmc+9VatW5OXlUVFRUWMMN954I5s3b67058svvzzruCVLljBo0CDatGmDv78/f/7znyktLeXw4cO1ft/CfaRzXDQoHx+fSj8rpaoss1qtDXLeHznSKezn53dW2ciRIzGbzSQlJREeHk6TJk0YMGBApaYdR1UXw5nlZyaWM1871/urSlXvp6oYtNaVyg3DwMvL66xzznzup8v0OQZfBgQEnNXn4O1d+ePl+++/56abbuKxxx7jhRdeICgoiHXr1nHXXXfV6TkL95EahzgvRERE0KRJE9auXVupfN26dec8Ny8vj+3bt/Poo48yfPhwunbtSrNmzc769t+kSZNzfvPu1q0ba9eurfRBuGXLFgoKCujWrVst3lHddevWjW3btnH06FF72ZEjR8jMzHRZDFVZs2YNZrOZZ555hssvv5xOnTqdc77G6QR7rucuXEsShzgv+Pn5MWnSJB5//HE+//xzMjMzmTZtGjt27DhnLSQoKIiQkBDeeustMjMzWbt2LbfeeutZcyQuvvhiVq5cyaFDhyp9KJ/pgQceoLCwkHHjxrF161bWrFnDHXfcwYABA+wjhJzttttuIyQkhLFjx7Jx40Y2bNjALbfcQtu2bRk7dqxLYqjKpZdeSm5uLgsWLGDPnj28++67VQ5eOFOHDh0wDIMvv/ySnJycSiOwhPtI4hDnjeeee47rrruO2267jT59+pCfn8+4ceNo1qxZjecZhsHixYvZvXs3PXr0YNy4cfz973+ndevWlY6bPXs2GzZs4OKLL67UP3GmsLAwvv76aw4cOEDv3r0ZOXIk3bt353//+1+Dvc9z8fX15euvv6Zp06YMGjSIuLg4/Pz8WLZs2VlNZK40cuRIpk2bxj//+U+ioqL473//ywsvvFDjOWFhYTz77LPMmjWL1q1b1ziHRbiOzBwX57UhQ4YQFBTk0g9uIc530jkuzhs//fQTGzdupF+/fpSWlrJo0SJWrlxZ5egeIUTdSeIQ5w2lFK+99hqTJ0/GarXSuXNnli5dytVXX+3u0IQ4r0hTlRBCiFqRznEhhBC1IolDCCFErVwwfRyHDh2q03lms7naMfueTOJ2LYnbtRpr3NC4Ym/Tpk2V5VLjEEIIUSuSOIQQQtSKJA4hhBC1IolDCCFErUjiEEIIUSuSOIQQQtSKJA4hhBC1IolDCCE8hNYaa8ZqdHbNG1y5myQOIS5g+/fvZ8yYMcTFxXHllVcyf/58+2v5+fnccsstXHHFFdxyyy0cO3YMAIvFwpgxY4iMjGTatGmVrvfjjz8ydOhQrrjiCv71r39VueXshx9+SFRUFFdddZX9T2ZmZq3ifu655yq9hyFDhtTq/Lq4/PLLsVgsTru+tlrRH72NfvMF9DLP3gZAEocQFzBvb29mzJhBWloan332Ge+88479QzwpKYkBAwbw3XffMWDAAJKSkgBo1qwZDz/8MP/617/Out5jjz3Gc889x5o1a9i7dy8rV66s8r7XX389K1assP/p1KmTQ/FqrbFarZUSx/lAl5ejF76MTvkEvH3QRw+7O6QaSeIQ4gLWunVroqKiAPD39ycyMpLDh20fWsuXL+emm24C4KabbmLZsmUANG/enD59+tC0adNK1zpy5AjHjx8nNjYWpRRjxoyxn+OIEydOcPPNNzN8+HCGDh3K8uXLAVuNIi4ujscee4zhw4fz4IMPUlxczFVXXcUDDzwA2PYkf+ihh7jyyiu59dZbKS4uBmw1oPj4eK677jqefvppe83kww8/rFRbuvPOO0lPTwfg0Ucf5eqrr+bKK6/kxRdfPCvO4uJi/vznP/P+++9z8uRJpk6dyjXXXMOwYcPsMdeGPnUK67x/o9etRN3wZ1TsADh6pNbXcSVJHEIIwPYBvXXrVmJiYgA4evQoYWFhgG0L17y8vBrPP3z4cKXtdlu3bm1PQn/06aefVmqqKi4upmnTpixYsIDly5ezePFinnrqKXtT1+7duxkzZgxff/01iYmJ+Pr6smLFCl599VUA9u7dy1133cXKlSsJCAiwb941depUnn76aT777DOHn8MjjzzCV199RUpKCuvWrWP79u32106cOMG4ceMYNWoUf/7zn3n55Ze54oor+PLLL1m8eDFPP/00J0+edPhe+kQR1sR/wdYNqNvvwxg5FsxhkJ+HLi9z+Dqu5rJFDk+cOMHrr7/O/v37UUrxl7/8hTZt2pCYmEhubi4hISFMmTIFf39/AJYuXUpqaiqGYZCQkEB0dDQAe/bsISkpidLSUmJiYkhISEAp5aq3IcR56cSJE9x99908+eSTtGjRok7XqKo/o7r/N6+//npmzpxZqaysrIxZs2bx/fffo5Ti8OHD5ObmAtCuXTsuu+yyau8dHh5O9+7dAejRowf79++nsLCQgoIC+vXrB8Cf/vSnapvOzvTZZ5/x/vvvU1FRwZEjR9i1axddu3YFICEhgfvuu4/Ro0cDsGrVKlasWMHrr78OwKlTpzh48CCRkZHnvI8+lof1pSfgyEGMSQ+jLrvC9oI5DLQGSy6EVr3IoLu5LHEsXLiQ6OhoHnzwQcrLyzl16hRLly4lKiqKUaNGkZycTHJyMrfffjsHDhwgPT2dOXPmkJ+fz9NPP83LL7+MYRi89dZbTJo0icjISJ599lk2b95s/4YkhKi9srIy7r77bm688UauueYae7nZbObIkSOEhYVx5MgRTCZTjddp3bo12dnZ9p+zs7PtNRZHLFmyhLy8PL766it8fHy4/PLLOXXqFGBrHqvJmc1mXl5elJSUoLWuNnF5e3tjtVrtP5++z6+//sobb7zBF198QcuWLfn73/9OSUmJ/bjevXuzcuVKbrzxRpRSaK158803iYiIcPh9Augjh7AmToei4xiTZ6C69LS/psxhaLA1V3lo4nBJU9XJkyfZsWOHvX3R29sbPz8/MjIyiIuLAyAuLo6MjAwAMjIy6N+/Pz4+PoSGhtKqVSuysrLIz8+nuLiYTp06oZRi0KBB9nOEELWntebBBx8kIiKCSZMmVXpt2LBhLF68GIDFixczfPjwGq8VFhaGv78/GzZsQGvNxx9/fM5zznT8+HHMZjM+Pj589913HDhQ/ZBUHx8fyspqbsoJDAwkICCAH374AbC1YpwWHh7Otm3bsFqtHDx4kM2bN9tj8PX1JSAggNzc3LNqKA899BBBQUE89thjgO1za+HChfba1tatW8/5Pst278T63CNwqgTjH89UShqArcYBaA/u53BJjSMnJ4eAgADmzZvHvn37uOSSSxg3bhwFBQUEBQUBEBQURGFhIWAb7ndmVS84OBiLxYKXl1elbz0mk6na4XEpKSmkpKQAMGvWLMxmc51i9/b2rvO57iRxu1ZjjXvdunX873//o3v37va92Z966imuvvpqpk+fzm233cZHH31EeHg4H3zwAcHBwQB06tSJwsJCSktLWbFiBV988QVdunThtddeY+LEiRQXFzN8+HBuvvnms771t2jRgs8//5yNGzfay+bOncvEiRMZPXo01113HT179uTSSy+1fz54eXlVer533303w4cPJyYmhieffLLS682bN0drjdls5u233+aee+6hefPmXHXVVfbjrr76aj788EOGDRtGt27d6NWrF4GBgcTFxREbG0t8fDwXX3wxV1xxBS1atMBsNuPl5UVwcDBJSUncc889zJ49m2eeeYYHH3yQ4cOHo7WmQ4cOJCcnV/u8S3/aQP6zj2L4+xM042W827Y/6xgdFESOtze+J47TwkN/p1yy5/ju3buZNm0aTz/9NJGRkSxcuBBfX1+WLVvGO++8Yz8uISGBhQsXMn/+fDp16sSgQYMAeO2114iJicFsNvPBBx/YhwHu2LGDTz75hEcfffScMchGTo2DxO1aF1Lc+/fv56677iI1NdVJUdVMZ+/H+tTf8WrdDv3X6aig6pv+Kv55D+qiSIx7HnJhhGdz60ZOJpMJk8lkr0X07duXvXv3EhgYSH5+PmCbbBQQEGA//swRHBaLheDg4LPK8/Ly7N+AhBDCk1k/eht8fAh66pUakwYAIa08uqnKJYmjZcuWmEwm+7f+n376iXbt2hEbG0taWhoAaWlp9O7dG4DY2FjS09MpKysjJyeH7OxsIiIiCAoKwtfXl8zMTLTWrFq1itjYWFe8BSFEIxceHu6+2sbWjbYht9eOxavlub/sKnOYR8/lcNmoqvHjxzN37lzKy8sJDQ3lvvvuQ2tNYmIiqampmM1mpk6dCtj+gfv168fUqVMxDIMJEyZgGLYcN3HiRObNm0dpaSnR0dEyokoI4dF0RQXWjxZASCvUkJGOnWQOg+MF6JJiVDNf5wZYBy7p4/AE0sfROEjcriVxO5/12y/R77+O8ZfHUL36ORS7NWM1+s0XMJ54BdW2g2sCrYJb+ziEEOJCpE8WoT/5P+jUHWL6Onye+m1Irqc2V0niEEIIJ9FfLIYTxzHGTqjdChcePpdDEocQQjiBzslGf/MZqv8QVPuOtTvZPwCaNoNcz1wlVxKHEEI4gfV/74C3N2rUHbU+VykF5jCpcQghxIVC7/wJNq5FXT0G5cDw2yp58JBcSRxCCNGAtPW34bfBIairbqjzdWxzOXKqXHXY3SRxCCFEA9JrV8Kve1Cj70Q1aXruE6pjDoVTxVB0vOGCayCSOIQQooHokmL00kVwyaWoPoPqdS1PHpIriUMIIRqIXvY/KMjHuLmWw2+r4sFDciVxCCFEA9B5ueivk1F94lAdO9f/glLjEEKI85te8i4AavSdDXI91aw5+LeQxCGEEOcjvW83+oc01LBRKFNIw13Y7JnLq0viEEKIetI7fwRADb2+Qa/rqcurS+IQQoj6OpIN/i1QLQIa9rrmMLDkoK3Whr1uPUniEEKIetJHDkJY24a/sDkMysvhmKXhr10PkjiEEKK+crJRoa0b/LKeOpdDEocQQtSDPnUK8o9CaNWbHtWLh87lkMQhhBD1kfvb7qLOaKoKDgGlpMYhhBDnlSPZAKgwJzRV+fhASxMc9ax9OeqUOEpLSykvL2/oWIQQotHROb/VOJzQxwGAObRxNlW9++67ZGVlAbBx40YSEhIYN24c69evd2pwQgjh8Y4chMBg20xvJzi9vLoncShxrFmzhvDwcAA+/vhj/vrXv/Lwww/zwQcfODU4IYTwdPpINjihmcrOHAbH8tBlZc67Ry15O3LQqVOnaNq0KcePH+fIkSP07dsXgKNHjzo1OCGE8HhHDqJ69nHe9c1hoDVYciHMCSO36sChxNGmTRtWr17N4cOH6dGjBwCFhYU0adLEqcEJIYQn08Un4XiBUz/QlTkMDbaRVY0pcUyYMIF33nkHb29v7r33XgC2bNliTyKOuP/++2nWrBmGYeDl5cWsWbMoKioiMTGR3NxcQkJCmDJlCv7+/gAsXbqU1NRUDMMgISGB6OhoAPbs2UNSUhKlpaXExMSQkJBQ/3XvhRCiLn7rGFfOmMNx2hlzOTzlk86hxGE2m3nmmWcqlQ0cOJCoqKha3WzGjBkEBPy+lktycjJRUVGMGjWK5ORkkpOTuf322zlw4ADp6enMmTOH/Px8nn76aV5++WUMw+Ctt95i0qRJREZG8uyzz7J582ZiYmJqFYcQQjQEffig7S/OrAm0DAYvb4+ay+FQ5/jf/va3KsunTJlSr5tnZGQQFxcHQFxcHBkZGfby/v374+PjQ2hoKK1atSIrK4v8/HyKi4vp1KkTSikGDRpkP0cIIVwuJ9s2QS+kldNuoQwvMIV6VOJwqMahtT6r7OTJkxhG7aaBzJw5E4CrrrqK+Ph4CgoKCAoKAiAoKIjCwkIALBYLkZGR9vOCg4OxWCx4eXlhMpns5SaTCYul6sW/UlJSSElJAWDWrFmYzeZaxXqat7d3nc91J4nbtSRu1/KUuAsK8ig1hxLSxvFZ43WJPb9NO6zH8jB5wHuGcySOv/zlL4Btwt/pv59WVFTEFVdc4fCNnn76aYKDgykoKOCZZ56hTZvqq3ZVJaqayqsSHx9PfHy8/ee6jgAzm82NcvSYxO1aErdreUrcFb/uBXOrWsVSl9itAcHorJ9rdZ4uPAanSlD1qA1V9zldY+L461//itaaZ599lr/+9a+VXmvZsmWNH/5/FBwcDEBgYCC9e/cmKyuLwMBA8vPzCQoKIj8/397/YTKZyMvLs59rsVgIDg4+qzwvL89+XSGEcLkjh1B9Bjr/PuYwKCpElxSjmvk6dIpe/TX6k/cxXvwPKqBlg4ZTY1tT165d6datGwsWLKBr166V/tQmaZSUlFBcXGz/+48//kj79u2JjY0lLS0NgLS0NHr37g1AbGws6enplJWVkZOTQ3Z2NhEREQQFBeHr60tmZiZaa1atWkVsbGxd37sQQtSZLiqEk0XOWRX3j+qwvLretA4uimzwpAEO9nF4eXmRkpLCL7/8QklJSaXXHnjggXOeX1BQwIsvvghARUUFAwYMIDo6mo4dO5KYmEhqaipms5mpU6cCEB4eTr9+/Zg6dSqGYTBhwgR7f8rEiROZN28epaWlREdHy4gqIYR7HPltKK4L5lZUmsvR7qJzHq/zcmFfFupPdzklHocSx6uvvsq+ffu47LLLCAwMrPVNwsLCeOGFF84qb9GiBdOnT6/ynNGjRzN69Oizyjt27Mjs2bNrHYMQQjQkfeT04oauq3E4OpdDb14HgIrp55RwHEocW7Zs4dVXX8XPz88pQQghRKNz5BAYxu/NSM7k3wKa+jrcVKU3roU27Z1WG3JoPK3ZbKbMgxbYEkIIt8s5BOYwlLdD37/rRSnl8PLq+ngB7NqOiunrtHgceseDBg3ihRde4Oqrr6Zly5aVXuvevbsz4hJCCI+mcw65ppnqNHOYQzUOveUH0FZUL+c0U4GDiWPZsmUAZy2jrpTi1VdfbfiohBDCg2mtbUNxI7u57J7KHIb++Ue01jWuz6c3rbPNNA+/xGmxOJQ4kpKSnBaAEEI0OgX5cKrEtavVmsNs9ywqhBZVD1LSJSdh+ybU4Gucuvir7DkuhBC15YpVcf9AOTCXQ/+0EcrLndq/AQ7WOE6ePMnixYvZvn07x48fr7T0x2uvvea04IQQwhP9PhTXiTv//dGZQ3Iv7lT1MZvW2mojEV2cGopDNY758+ezd+9exowZQ1FREePHj8dsNnPttdc6NTghhPBIRw6BtzeYQlx3z3PUOHRZGfqn9ajoy20r6jqRQ4njxx9/5MEHH6R3794YhkHv3r2ZMmUKq1evdmpwQgjhiXTOIQhp7fQP6DOpZr622kR1TVU/b4GSYqc3U4GDiUNrTfPmzQFo1qwZJ06coGXLlhw+fNipwQkhhEc6csi1zVSnmcOqncuhN62DZr7QuafTw3Coj6NDhw5s376dqKgoOnfuzIIFC2jWrBmtW7vhwQkhhBtpqxVyslHdL3P5vZU5DL0vq4qYKtCbv0dFxaJ8fJweh0M1jkmTJhESYmvLGz9+PE2aNOHEiRMOLXAohBDnlfyjUF4GYe6ocYRCXi7aWlG5POtnOF4ATlqb6o/OWeOwWq18++239gUHAwICuPfee50emBBCeKQjrh+Ka2cOg4pyOGaB4N875vWmteDtg4rq5ZIwzlnjMAyD5cuX4+Xluk4gIYTwVPq3ORyEOb5dbEOpai6H1trWv9GlJ6pZc5fE4VBTVVxcHCtWrHB2LEII4fmOHIImTaGlG3YfPWMuh93+PZCX45LRVKc51DmelZXFsmXL+PTTTzGZTJWmsj/55JNOC04IITyN/m1ElTOX9KhWcAgoVbnGsWkdKAMVfbnLwnAocQwdOpShQ4c6OxYhhPB8OdnQroNbbq28fSDIVDlxbFwLkV1R1axf5QwOJY7Bgwc7OQwhhPB8uqICjh526pLl53TGXA595BAc+hU1dqJLQ3AocaSmplZZ7uPjg8lkIjIyEh8XjB0WQgi3yjsCFRWuXRX3D5QpDL1jC/DbaCpwaf8GOJg4Vq1aRWZmJoGBgZhMJvLy8igoKKBjx47k5OQA8PDDD9OxY0enBiuEEG51eiiuGxMH5jAosNjWptq0DjpEoEyhLg3BocTRrl07+vTpwzXXXGMvW7ZsGQcPHuSpp55iyZIlvP3228ycOdNpgQohhLv9viqumxOH1rDnZ9izEzXqdpeH4NBw3O+++44RI0ZUKhs2bBhr1qxBKcX111/PgQMHnBKgEEJ4jJxD4OtX7UZKrnB6Loc15VPbzy5upgIHE0dgYCAbNmyoVLZx40YCAgIAKCsrw9sFG7YLIYQ7uXUo7mmnJwFu+cE2CbF1uMtDcOjTPiEhgTlz5tC+fXt7H8evv/7K1KlTAdi1a9dZNRIhhDjvHDmE6tjZvTG0DLbtBVJejurV1y1JzKHE0bNnT1555RU2b96MxWIhJiaGXr160aJFC/vrPXueeylfq9XKo48+SnBwMI8++ihFRUUkJiaSm5tLSEgIU6ZMwd/fH4ClS5eSmpqKYRgkJCQQHR0NwJ49e0hKSqK0tJSYmBgSEhLcm/2FEBcEXVYGllzoP8StcSjDAFMYHDmIctGihn/k8J7jAQEBDBo0iFGjRhEXF2dPGrXx5Zdf0rbt7+u7JCcnExUVxdy5c4mKiiI5ORmAAwcOkJ6ezpw5c5g2bRoLFizAarUC8NZbbzFp0iTmzp3L4cOH2bx5c63jEEKIWsvNtnVKu7Nj/LTQ1hBkhg4Rbrl9tTWOmTNnMm3aNACmT59e7bd6R5ccycvLY+PGjYwePZrPP/8cgIyMDJ544gnAth7WE088we23305GRgb9+/fHx8eH0NBQWrVqRVZWFiEhIRQXF9Opk22/3UGDBpGRkUFMTIzDb1gIIeokxwOG4v7GuOVuKCu11T7coNrEERcXZ//7kCH1r5q988473H777RQXF9vLCgoKCAoKAiAoKIjCwkIALBYLkZGR9uOCg4OxWCx4eXlhMpns5SaTCYvFUuX9UlJSSElJAWDWrFmYzeY6xe3t7V3nc91J4nYtidu13BH3iaJCigBTl+4Y/gF1vk6DxO7mf7NqE8eAAQPsf6/vkiMbNmwgMDCQSy65hG3btp3zeK11rcqrEh8fT3x8vP3no0ePOnzumcxmc53PdSeJ27UkbtdyR9zWPZngH4ClpBRK6n7vxvTM27Spunbl8BjaHTt2sHfvXkpKSiqVn97gqSY7d+5k/fr1bNq0idLSUoqLi5k7dy6BgYHk5+cTFBREfn6+fXjv6ZFbp1ksFoKDg88qz8vLIzjYDUsbCyEuODon261LjXgShxLH22+/zdq1a+ncuTNNmjSxlzs6mum2227jtttuA2Dbtm189tlnTJ48mUWLFpGWlsaoUaNIS0ujd+/eAMTGxjJ37lxGjhxJfn4+2dnZREREYBgGvr6+ZGZmEhkZyapVq2QYsBDCNY4cRHWJdncUHsGhxLF69Wpmz57d4N/uR40aRWJiIqmpqZjNZvu8kPDwcPr168fUqVMxDIMJEyZg/NYJNHHiRObNm0dpaSnR0dHSMS6EcDp9qsS2XavUOAAHE4fZbG6w1W+7detGt27dAGjRogXTp0+v8rjRo0dX2QzWsWNHZs+e3SCxCCGEQ3Kybf/1hKG4HsChxHHvvffyxhtvcMUVVxAYWHmNlq5duzolMCGE8BhHDgKeMRTXEziUOPbs2cOmTZvYsWNHpT4OgNdee80pgQkhhKf4fVXc1u4NxEM4lDg++OADHnnkEXr06OHseIQQwvPkZENgMKqZr7sj8QgOTTts2rSpNEkJIS5Y+shB6Rg/g0OJY+zYsbzzzjscO3YMq9Va6Y8QQpz3crKlf+MMDjVVne7HWLFixVmvffjhhw0bkRBCeBB9sgiOF0j/xhkcShyvvvqqs+MQQgjPdMQ2FFeFtT3HgRcOhxJHSEiIs+MQQgiPpHM8YJ9xD+NQ4jh58iRffvklv/zyy1lrVT3++ONOCUwIITzCkYOgFIS2cnckHsOhxDFnzhysVit9+vQ5ax6HEEKcz/SveyCkNcpHPvtOcyhx7Nq1iwULFuDt7fBiukII0ehpqxV2bUf1cs8WrZ7KoeG4nTt35uDBg86ORQghPMvBfXCyCDp1d3ckHsWhKsR9993Hs88+S0REBC1btqz02pgxY5wRlxBCuJ3OtG08pzp1c3MknsXhJUfy8vLse36f5uh+HEII0RjpzK1gCkWZQt0dikdxKHGkp6fz8ssv2/cHF0KI853WGnZtQ3Xv5e5QPI5DfRxhYWF4eXk5OxYhhPAchw/YZoxHSjPVHzlU4xg4cCDPP/88I0aMOKuPo3t36TQSQpx/9M6tAKhL5TPujxxKHMuXLwdsfR1nUkrJciRCiPNT5lYIDIYQWaPqjxxKHElJSc6OQwghPIbWGr1rG6pTNxkEVAWH+jiEEOKCkpsNxywyf6MaNdY4pk+ffs5s++STTzZoQEII4W72/g2Zv1GlGhPHkCFDXBWHEEJ4jl3boEUgtA53dyQeqcbEMXjwYBeFIYQQnkNnboNI6d+ojvRxCCHEGXReDuTlSDNVDVyy3G1paSkzZsygvLyciooK+vbty80330xRURGJiYnk5uYSEhLClClT8Pf3B2Dp0qWkpqZiGAYJCQlER0cDsGfPHpKSkigtLSUmJoaEhAT5ViCEaDC/r08lHePVcUmNw8fHhxkzZvDCCy/w/PPPs3nzZjIzM0lOTiYqKoq5c+cSFRVFcnIyAAcOHCA9PZ05c+Ywbdo0FixYgNVqBeCtt95i0qRJzJ07l8OHD7N582ZXvAUhxIUicys094O2HdwdiceqNnFMmzbN/vfFixfX6yZKKZo1awZARUUFFRUVKKXIyMggLi4OgLi4ODIyMgDIyMigf//++Pj4EBoaSqtWrcjKyiI/P5/i4mI6deqEUopBgwbZzxFCiIagM7fa+jcMacmvTrVNVYcOHaK0tJQmTZrw+eefc9NNN9XrRlarlUceeYTDhw8zfPhwIiMjKSgosC+cGBQURGFhIQAWi4XIyEj7ucHBwVgsFry8vDCZTPZyk8mExWKp8n4pKSmkpKQAMGvWLMxmc53i9vb2rvO57iRxu5bE7VrOirvCksvRnGz8r/kTfk56Lo31mZ+p2sTRu3dv/va3vxEaGmrvo6iKo/M4DMPghRde4MSJE7z44ov8+uuv1R6rta5VeVXi4+OJj4+3/3z06FGHzz2T2Wyu87nuJHG7lsTtWs6K2/rDagBOtr2YYic9l8b0zNu0aVNlebWJ47777uPnn38mJyeHrKwsrrzyygYJxM/Pj65du7J582YCAwPJz88nKCiI/Px8AgICAFtNIi8vz36OxWIhODj4rPK8vDyCg4MbJC4hhCBzKzTzhfBL3B2JR6txVFXnzp3p3Lkz5eXl9ZrTUVhYiJeXF35+fpSWlvLTTz9xww03EBsbS1paGqNGjSItLY3evXsDEBsby9y5cxk5ciT5+flkZ2cTERGBYRj4+vqSmZlJZGQkq1atYsSIEXWOSwghzqQzt0FEF5RsI1Ejh4bjDhkyhK1bt7Jq1Sp7DWHQoEEOL6men59PUlISVqsVrTX9+vXjsssuo1OnTiQmJpKamorZbGbq1KkAhIeH069fP6ZOnYphGEyYMAHjt46qiRMnMm/ePEpLS4mOjiYmJqaOb10IIX6njxdA9n5Uv4ZpXTmfKe1Ax8E333zDBx98wJAhQwgJCeHo0aOkpqYyduzYSv0InuzQoUN1Oq8xtUeeSeJ2rfM1bl2Qj172P9SQkaiQVi6MrGbOeN56QzrW12dhPPIcKqJLg177TI3pd6XWfRxn+vTTT3n88ce56KKL7GX9+/dn9uzZjSZxCCFqR+/bjTVpJuQfRe/ajvHocyhvH3eH5TR61zZo0gQuinB3KB7PoYHKx48fp127dpXK2rRpQ1FRkVOCEkK4l16/Buvzj4ACNfpO2JeFTn7f3WE5ld65FTp2Oa+TY0NxKHF07tyZd999l1OnTgFQUlLCokWL6NSpk1ODE0K4lrZasX76f1jfeB7CL8GYNhvj6jGoQSPQXy9F79ji7hCdQp8ogoO/oGR/cYc41FR1991389JLLzFu3Dj8/f0pKiqiU6dO/O1vf3N2fEIIF9GnSrC+/RJsTEf1H4q6/T6Uj+3bt7p5AjpzK9a3EzFmzEX5B7g32IaWtR20lvWpHORQ4ggKCuLJJ58kLy/PPqrqzBncQoia6dzDWP/zCqpLT9QV8aiWnjX/SOflYk16Bg78gropAXXVqEqLh6qmTTHufhDrvx/C+p9XMe577LxaXFRnbgVvb7hEWlEcUavVcU0mkyQMIepAf50MmdvQO39Cf/p/0LMPxqDh0DUaZbh3zoDO2oF13r+hvAzjr/9CRcVWeZxq3xE1+g704oXo1ctRg86fOVR651a4uBPKp4m7Q2kUXLKsuhAXMn2yCL02FdXvStS1N6FXfY1O/wbrpnVgCkUNuAo1IB7V0vVfyopTv8T62iwIMmM8MBPVpn2Nx6v4G9DbNqE/nI+O7I5q3a7G4xsDXXISft2DumaMu0NpNGT5RyGcTK9ZAadKUEOvQ4W2wRgzDuP5tzEmPQyhrdGfvI/1kQlUJM1E/7S+Vmuy1Yc1/RsKX3kGOnbB+OeL50waAMowMBL+Bk2aYp3/IrqszAWROlnWDtBW6d+ohXMmDqvVytatWykvL3dFPEKcV3RFBTr1C+jUDdX+9/WPlLcPKnYAXlOfxpj5BmrYjbD7Z6xzn0J/8aHz4zpmQX84H58uPTH+/mStOrtVSxPGXZPh1z3o5PecGKVr6Myt4OUFHTu7O5RG45yJwzAMnn/+eby9pVVLiFrb8j3k5WAMva7aQ1Roa4w/3YXx/NsQfTl62VJ04TGnhmX94E0oLSXg/kdRdfh/W0Vfjhp8tW2I7vZNDR+gC+nMbdAhAtW0mbtDaTQcaqrq0qULmZmZzo5FiPOO9ZvPwBQK0Zef81jl7YMx+i4oPYX+sn6bp9VEb1xrG3I7cize9djlTo0ZD63Dsb79Mvp4YQNG6Dr61Cn4JUuaqWrJoa8aISEhPPvss8TGxmIymSoNwxs7dqzTghOiMdO/7oHMbbbhrQ6OnFKt26H6D0GnfYW+6gaUKbRhYzpZhPX/3oB2F6OGj67XtWxDdP+B9d8PYv3PXIz7pzW+Ibp7foaKclQnmfhXGw7VOEpLS+nduzdKKSwWC3l5efY/Qoiq6W8+g6bNUAOuqtV56rpbAYX+7IOGj+njd6DwGMZdD9SpieqPVPjFqNF3wZYf0Onf1D9AF9OZ20AZENHV3aE0Kg795tx3333OjkOI84ouPIb+IQ01YBiquX+tzlWmENTga9DffIYePhrVOrxhYtr5E3r116hhN6Iuijz3CQ5SQ69Dp3+DXv01XNG4Fj3VP2+B9pegfJu7O5RGxeHhuAcOHODjjz9mwYIFgG2Z8n379jktMCEaM71qGZSXo4aOrNP56poxtiGvDbSwoC49hfXdVyGkFer62xrkmqcpw0DF9IM9O53eqd+Q9P69kLUDddkV7g6l0XEocaxdu5YZM2ZgsVhYtWoVAMXFxbz77rtODU6IxkiXl6G//Qq690K1qtsEOdUiEDXsBtiYjt67q/4xffoB5GRj3HE/qmnTel/vj1TPPqA1+qcNDX5tZ9FfJ9uaEgcNd3cojY5DieOjjz7iX//6F/fcc499J74OHTrwyy+/ODM2IRolvX4NFOTXOATXEeqqUeAfgHVp/b6g6X270SuSbTPUu/Ss17Wq1f4SaGlCb/neOddvYDo/D52xyvZM/GrXlCgcTBwFBQV06FB52J5SqvGNoBDCybTW6JTPoFU76Fq/bY2Vb3PUNTfBji11Xs5cl5dj/c9caBGIGpNQr3hqopRCRfeBbZvQZaVOu09D0amfg1Wj6pncL1QOJY5LLrnE3kR12nfffUdEhOyUJUQlu3+GfVmooSNRRv1X9FGDr4ZgM9ali+q0FIlekQz792LcNsnp36xVjz5Qegp+/smp96kvXXISnbYM1aufR22H25g49JudkJDAf//7X2bMmMGpU6eYOXMmH374IXfddZez4xOiUdHffAbN/VD9hjTI9ZRPE9TIW2BvJmyuXTOQPnzQ1rfRqx+qV/8GiadGnaOgaTOnN1fpX3aRN+Uu9L7ddTt/TQoUn0ANG9WwgV1AHBqO27ZtW1566SU2bNjAZZddhslk4rLLLqNZM5miL8Rp2pKL3piOir+hQZevUP2Hor9einXpIoyevR2aTKitVqyLXoUmTTBundRgsdRE+TSBbjHoLRnoP2unNGXr8jKsC1/GeuhXeGcuxrTZtZqPoisq0CmfQkQX1CWXNnh8FwqH69JNmzalc+fOdO3alS5dukjSEOIP9LdfggY15NoGva7y8sIYdTtk70ev+/bccRyzoP/7lm3W+pgEl24apXr0gWN58Osep1xff/kxHPoV3xE3woG96JRPanf+xrW2tcOG3eiU+C4UDqXqo0ePMnfuXHbt2oWfnx8nTpwgIiKCyZMnExIS4uwYhfB4+lQJetXXEHN5gy8TAkCv/tAhAv3pB+jeg+xbulaKYd9udMqn6IzVYK1AXTG01rPW60v1iEUrhd7yPapDxwa9tj74K/rLxag+cQRMeojiw9m259GrPyq09bnP19rW5xPaGnr2btDYLjQO1TiSkpK45JJLWLhwIfPnz2fhwoV07NiRpKQkZ8cnhFNoq5WK12dh/a5hlskoTlsOJ47XewhudZRSGDfeAXk56FXL7eXaWoHeuJaKFx7D+swU9KZ1qMFXYzzzGsa4v7l85KNqEQgdO6O3/NCg19XWCtvoMF9f1C0TATBumwTe3ljfm+fYwIGsHbA309aU6OZdFxs7h2oce/bsYdq0afal1Zs1a8btt9/O+PHjHbrJ0aNHSUpK4tixYyiliI+P55prrqGoqIjExERyc3MJCQlhypQp+PvbRn4sXbqU1NRUDMMgISGB6OhoeyxJSUmUlpYSExNDQkKCDAsWtffTBtiQjs7age4zsF5bhmqtOfn5R7a5DJFOXCyvazRcGoX+4kP0Zf3Q69egv/kcjh6x7SR403jbvITmfs6LwQGqZx/0//6DthxFBZsb5Jo69XPbh/7EB23JCVBBJtToO9Hvv45euxLVv+YBCdavk8GvBar/0AaJ6ULmUI0jMjKSrKysSmW7d++mUyfHNnb38vLijjvuIDExkZkzZ7J8+XIOHDhAcnIyUVFRzJ07l6ioKJKTkwHb8ibp6enMmTOHadOmsWDBAqxWKwBvvfUWkyZNYu7cuRw+fJjNmzc7/m6F+I316yXQtBkU5DvUb1CjHVuo2L/XtsOfE7/E2GsdxwuwPjwB/eECCDJh/OVRjJlvYAwb5fakAb/NIgf0jw1T69C5h9FL34OoWFSfQZXvNWiErYazeAH6eEH11zhyCLZ8jxp8tVNmzl9oqq1xfPjh77uQhYWF8eyzz9KrVy9MJhN5eXls2rSJAQMGOHSToKAggoKCAPD19aVt27ZYLBYyMjJ44oknAIiLi+OJJ57g9ttvJyMjg/79++Pj40NoaCitWrUiKyuLkJAQiouL7Qlr0KBBZGRkEBNTv4lW4sKid/9s6zi+eQJ63bfo5UvRV8TXad6F1hrr5//FCDJB70HnPqGeVMfOtuXQC/NtiaqDB86latXOtiXulh9g8DX1upTWGuuiJDAMjNv/clZiVoaBcecDWJ/6O/qjBagJU6u+Tson4OWFurJhBy5cqKpNHH9cMv3yy20b0RQWFuLj40OfPn0oLa39DNGcnBz27t1LREQEBQUF9oQSFBREYaFtMxiLxUJk5O+rdwYHB2OxWPDy8sJkMtnLTSYTFoulyvukpKSQkpICwKxZszCb61Zl9vb2rvO57iRxV+/Ygi8p9W+BedStlIZ3oGD2dFrs3UGzy+Nqfa1Tm9ZxbNd2Wtz7MM1an7uDtkHc+48Gu5SznvfxvnGc/PJ/BPv5YvjWvRZU/M3nFO7YQotJ/6B5py728kpxm80U/elOTnz0Ni2GXU/TmL6VrmEtPEZueirN4kYQ2LHhVgWuq8b6/+aZqk0czlhKvaSkhNmzZzNu3DiaN69+GePqOrpqM3M2Pj6e+Pjfl3g+evSo44GewWw21/lcd5K4q6aPHML6fRrq6jFYTpxER0ZBSCsKPnqH45d0rVVTk9Ya63/mgSmUpkOuled9Bt0pCj79L3mrv6nz5EN9zIL17Zchsisneg3g5Blx/jFuPfgaWLWcY0mzMJ58tdI8Guvn/4XSU5QOHOER/0aN6f/NNm3aVFnucN381KlT7Nu3j507d1b646jy8nJmz57NwIED7bWXwMBA8vPzAcjPzycgIADA3hx2msViITg4+KzyvLw8goNdN0ZdNH7662Tw8kYNsS13rry8bIsJ7tkJu7bX7mKb1tqWF7n+1iqHx17QOnaB5v7ozXXv5zi9L7px51/P2YyofJpg3HG/bdTZp79vgKXLStGpX0D3y1Bt29c5FlGZQ6Oq0tLSePvtt/H29qZJk8qjT1577bVznq+15vXXX6dt27aMHPn7/gSxsbGkpaUxatQo0tLS6N27t7187ty5jBw5kvz8fLKzs4mIiMAwDHx9fcnMzCQyMpJVq1YxYsSI2rxfcQHThfno9G9Q/YegAoPs5ar/UPSn/4d1+RK8HNxCVFsrbHtltGqH6jvYSRE3XsrbGxV1Gfqn9WhrRa2Hv+oN6bZ90UffhWrV1rF7duqOGjgMveITdJ9BqA4dbQMfjhdgyPIiDcqhxPHee+/x4IMP0qNHjzrdZOfOnaxatYr27dvz0EMPAXDrrbcyatQoEhMTSU1NxWw2M3WqrWMrPDycfv36MXXqVAzDYMKECfbl3CdOnMi8efMoLS0lOjpaOsaFw/Q3X9j2l75qVKVy1bQpashI9Kf/hz74q0PfTPX3qyB7P8akh2VOQHV69oHv02y1uVpszapPFGH94A1o37HW60mpMePQP2ZgffcVjMdeRK/4BNpdDJ3r9tklquZQ4vD29qZr17rvydu5c2c++uijKl+bPn16leWjR49m9OjRZ5V37NiR2bNn1zkWcWHSJcW2JUFi+lb5DVZdeQ162f/QXy9FJfyt5muVl9n2A29/iW1Gt6iS6tYL7eWF3vwDqjaJY/HbtlrC5Bkor9olZdXcH+PWe7C+/hzWef+G7P2oCVNkrlcDc6iPY+zYsbz77rv2UU9CNDZ6zQo4WVTtGkXKPwA14Cr092loS80dl3pNCuQexhh1e4MsnX6+Us39oFP3Ws0i11sy0N+loIaPRrW/pG437tXfVtv5aT20NKFiB9btOqJaDtU42rRpw0cffcTy5cvPeu3M+R5CeCJdXm5rsojsiurYudrj1FU3oL/9Ev3NZ6ibqt70SJeeQn/xIUR0ge6XOSvk84bq2Qf937fQRw6hwqoeoXOa3r4Z6xvPQfjFqOtuqfs9lcK4bRLWfbtR142t1eq5wjEOPdFXXnmFQYMG0b9//7M6x4XwdHr9GrDkYtx2b43HKXMYKnYAetUy9LU3oZqfvfGR/vYrOGbBmPgPaf5wgOrR25Y4tvxQY3+F3rEFa9IzENoaY8rT9VoCBkAFh2A8t0BqhE7i0FMtKipi7NixtG/fnlatWlX6I4Qn01qjly+F1uEQde4agho+GkqK0Wln1651yUn0Vx9D12jUpd2dEe55R4W0grYdamyu0jt/wvrq0xDSGuPBZ1AtAhrm3pI0nMahJzt48OCzto4VolHYvhkO7EUNv9GhDxLV/hLoGoP+5tOz9s7WKZ9CUSHGqDucFOz5SfW8HLK2o08cP+s1nbkV69ynwBSGMfVp+wKGwrM51FSVlZXFsmXLWLJkCS1btqz02pNPPumMuIRoENblS6BlMKqP48uJGCNGY53zL/S6b1EDhwGgTxy3TR6M7ou62P3LVjQmqmdv9JcfoX/aUGnOi87c9lvSCMX4xzOogJZui1HUjkOJY+jQoQwdKksRi8ZF79sNO7agxoyr3czuzj2gfcdKix/qZUugpBhj1J+dF/D56qJICGgJW36A3xKHztqOde6TEGS2NU8FBNV4CeFZHEocgwcPdnIYQjQ8vXwJ+DZHDRxeq/OUUqgRo9FvvgBbfkBf3Amd+hmqzyBU2w5Oivb8pQzDNrpq/Rp0eRn8koX1pSehpcmWNAIlaTQ2DiWO1NTUal8bMqTmzVOEcAedexi9/jvUsBvqtEeF6tUfbQ7Duux/ti1QKypQ19/qhEgvDKpHb/Tqr221uGX/g8AgW/OUC/dDFw3HocSxevXqSj8fO3aMw4cP07lzZ0kcwiPpFZ+AYaCGXl+n85WXF2rYKPT/vYHeuws18CpUaM3zEEQNukSDTxN08nu2Ibf/mIlqaTrnacIzOZQ4ZsyYcVZZamoqBw8ebPCAhKgvXXgM/d0KVN84VFDdP5xU/3jbSqslxahrxzZghBce1bQp6rIr0HszbaOn6vHvItyvzlMqBw8ezIQJE7jjDhmaKDyL/uR9W9PSiD/V6zqqaVOM8X9HlxQ32N7ZFzI1brKt/0jmVzR6DiWO0/t9n1ZaWsqqVavw83P//sZCnEnv34tevQI1dCSqVbt6X09FxSLzwxtGbRcsFJ7LocRx661ndwoGBwczadKkBg9IiLrSWmP9cD74+aFG1n2tIyFEzRxKHK+++mqln5s2bWrfrU8Ij7FpLez8CXXbvSi/s9eZEkI0DIcSR0hIiLPjEKJedFkp1sULoW0H1KDazdsQQtROjYnjXMuJKKWq3YhJCFfSKZ/C0SMYU56StnQhnKzGxDFwYNUboFgsFr766itOnTrllKCEqA19zIL+YjH07IPqGu3ucIQ479WYOP44ue/48eMsXbqUb775hv79+zNmzBinBieEI3TyIigvw7hpvLtDEeKC4FAfx8mTJ/n0009Zvnw5vXr14rnnnpO9OIRH0Puy0OmpqKtGnXOHOSFEw6gxcZSWlvLFF1/w+eef07VrV5566inCw8NdFZsQNdJaY/3vW+AfgLr2ZneHI8QFo8bEcf/992O1Wrn++uvp2LEjBQUFFBQUVDqme3fZCU24h16/BrJ2oO64v04LGQoh6qbGxHF6f/Gvv/66yteVUmfN8RDCFXTpKfTH70C7i1ED4t0djhAXlBoTR1JSkqviEKJW9NfJYMnFGD8FZcjwWyFcqc6LHNbGvHnz2LhxI4GBgcyePRuAoqIiEhMTyc3NJSQkhClTpuDvb5vtu3TpUlJTUzEMg4SEBKKjowHYs2cPSUlJlJaWEhMTQ0JCAkrJSkIXGp2fh/7qY+jVH3WpNJUK4WouWaZy8ODB/POf/6xUlpycTFRUFHPnziUqKork5GQADhw4QHp6OnPmzGHatGksWLDAvsjiW2+9xaRJk5g7dy6HDx9m8+bNrghfeBi95F2wVmCMGefuUIS4ILkkcXTt2tVemzgtIyODuLg4AOLi4sjIyLCX9+/fHx8fH0JDQ2nVqhVZWVnk5+dTXFxMp06dUEoxaNAg+zniwqH37ESvW4m66gZUiAwJF8IdXNJUVZWCggKCgmx7DQcFBVFYWAjYZqVHRkbajwsODsZiseDl5YXJ9PvmLyaTCYvFUu31U1JSSElJAWDWrFmYzXXbT8Hb27vO57rT+Ri3rqjA8uFbEGTGdMe9GL6eM5LqfHzenqyxxg2NO/bT3JY4qqO1rlV5deLj44mP/320zdGjR+sUj9lsrvO57nQ+xm1N/Ry9JxN1z8NYThTDiWIXR1e98/F5e7LGGjc0rtjbtKl6Uq3btuIKDAwkPz8fgPz8fPsy7SaTiby8PPtxFouF4ODgs8rz8vIIDpaN7hsLfXAfOi+37ucfs9j2q+4ajYq9ogEjE0LUltsSR2xsLGlpaQCkpaXRu3dve3l6ejplZWXk5OSQnZ1NREQEQUFB+Pr6kpmZidaaVatWERsb667wRS3oHVuwPjMV66yH0YXH6naNxQuhrBTj1kkykk4IN3NJU9VLL73E9u3bOX78OPfeey8333wzo0aNIjExkdTUVMxmM1OnTgUgPDycfv36MXXqVAzDYMKECRi/7VE8ceJE5s2bR2lpKdHR0cTExLgifFEPOms71lefAXMo5OViffOFWi99rndsQf+Qhhp5C6pVWydGK4RwhNK17TxopA4dOlSn8xpTe+SZPCFuvW831tnToEVLjIefRW/bhF74EmrYjRg3JVR5zh/j1uVlWJ+cDBUVGE+8gmrS1EXR144nPO+6kLhdrzHF7nF9HOL8pg/+ivWl6dDcH+PBp1GBQRj9h6AGX4P+einWjDWOXWf5Ujh80NZE5aFJQ4gLjSQO0eB0ziGsif8CLx+MqU+hgn/feliNnQAdO6P/Mxd98Near5N7GP3FR7YZ4lGXOTtsIYSDJHGIBqXzcrHO/hdUlNuSRmjlqq7y9sGY9Ag0bYb1tWfRJ09Uey3rh/PBMDDGTnB22EKIWpDEIRqMPmbBOudxKD6J8fenUG3aV3mcCjJhTHoYcrOxLnwJ/duSMpWutXkdbPkBdd2tlWosQgj3k8QhGoQuKsSaOB0K8jEmT0d16Fjj8apTd9RNCbD5e9uChWde61QJ1g/egrYdUEOvc2bYQog6kMQh6k2fPIH1pScgJxvj/mmoiC4OnaeGXo/qPRD9yfvobZt+v94XH9qWTP/zX1DeHre4gRAXPEkcol70iSKsrzwFB/Zi/OVRVJeeDp+rlELd9Vdo0x7rWy+ijx6hfP9e9NfJqP5DUZFdnRi5EKKuJHGIOtNHDmF99iHYuwvj7n+gevSu9TVU02YY9z0GVivW12ZR+PoL0NQXJUumC+GxJHGIOtE7t9qSxolCjKlPoy6r+/pRKrQNxoSp8OtuyrZvRo2+E9UisAGjFUI0JGlAFrVm/S4FvWgehLTC+Ou/UKGt631N1bM3auxEmh7eT+nAYQ0QpRDCWSRxCIdpqxW95F308iXQNRpj0sOo5v7nPtFBRvz1BDai5RiEuFBJ4hAO0adKsM6fA5vXoeJGoG65R0Y8CXGBkv/zGwltrYCcw3DkIHSIQLV03V4k2nIUa9IzsP8X1C13o4aMlKXNhbiASeLwMFprsByFQ/vQB/fBwV/Rh/bBof1QXmY7qLkf6vb7MXoPcH48+7Jsy6IXF2M8MK1OI6eEEOcXSRweQJeVon9YjU5Pgf17ofjk7y+2NEHb9qghPaBNB1SwGevSReg3n8f6Uwbq1kko3+YNH1NONnrll+hVX9mWRX/0OVS7ixr8PkKIxkcShxvpY3nob79Cr1oOxwugVTvU5YNtiaJNB9uSG35ndz4bkd3QX3xk+7NrO8aEqQ7P1q4xHq1h+2asqZ/DT+vBMFCXXYEaOwEVEFTv6wshzg+SONxA7/4Znfo5esN3YLVCj94YQ6+Dzj0c6jtQ3t6oG25Dd4vBumAO1ucfQ117M2rk2FrtrGePp+Qkeu1KdOoXcPgAtAhEXTsWFTcc1dJUl7cohDiPSeJwEV1ehl7/HTr1c9ibCb7NUVeORF15TZ3nQaiILhjTX0Z/8Cb68/+it23EmDj1rKXMq40p5xA69Qt0+je25rGLIlHjp6BiB6B8fOoUkxDi/CeJowYVL83gaOExKpo0heb+KF8/aN4cfP2g+W9/fP1QXl7okmIoLoaSk7YP4ZKTUFL8W/lJ2zf5wmMQ1hZ12yRUvytRzerfN6F8m6PG/x1rVCz6vSSsT/0ddcvd6BtuQZeVgSUX8nLQeTmQlwN5uWiL7b/k5YCXF+qyAaihI1GXXFr/hyaEOO9J4qiBancx3gUWKo5ZoPAY+vABWxIoPgEVFfbjztq03dsHfJtDM9/f/tscdWkUqv8Q6BqDMhp+pRej9wB0x0uxvv0S+j+vkLvkXfTxgj+8IQOCgiE41LaAYNwIVL8hLh3aK4Ro/CRx1MAYM46WVcxk1lpDaSkUF8HJ35KIPUn4orzd08yjgkMwpj6N/vZLmuYc4pRfAJhCUKYwMIVAS5NM2hNC1Jt8itSBUgqaNrX98bDOY2UYqCEjZekOIYTTyOq4QgghakUShxBCiFpplE1VmzdvZuHChVitVoYOHcqoUaPcHZIQQlwwGl2Nw2q1smDBAv75z3+SmJjId999x4EDB9wdlhBCXDAaXeLIysqiVatWhIWF4e3tTf/+/cnIyHB3WEIIccFodInDYrFgMv0+kslkMmGxWNwYkRBCXFgaXR+H1mdNt6tyfaeUlBRSUlIAmDVrFmazuU738/b2rvO57iRxu5bE7VqNNW5o3LGf1ugSh8lkIi8vz/5zXl4eQUFnr9waHx9PfHy8/ee6zmkwN9L5EBK3a0ncrtVY44bGFXubNlWve9foEkfHjh3Jzs4mJyeH4OBg0tPTmTx58jnPq+4BOKI+57qTxO1aErdrNda4oXHHDo2wj8PLy4vx48czc+ZMpkyZQr9+/QgPD3fa/R599FGnXduZJG7Xkrhdq7HGDY079tMaXY0DoFevXvTq1cvdYQghxAWp0dU4hBBCuJckjnM4s4O9MZG4XUvidq3GGjc07thPU7qq8a1CCCFENaTGIYQQolYkcQghhKiVRjmqqj7mzZvHxo0bCQwMZPbs2QD88ssvvPXWW5SUlBASEsLkyZNp3rw55eXlvP766+zduxer1cqgQYO48cYbAdizZw9JSUmUlpYSExNDQkJClTPYPS3uJ554gvz8fJo0aQLA448/TmBgoNPirkvsb775Jrt378YwDMaNG0e3bt0Az3/m1cXtymd+9OhRkpKSOHbsGEop4uPjueaaaygqKiIxMZHc3FxCQkKYMmUK/v7+ACxdupTU1FQMwyAhIYHo6GjAtc+7IeN29e94bWM/fvw4c+bMISsri8GDBzNhwgT7tVz9O15n+gKzbds2vXv3bj116lR72aOPPqq3bdumtdb6m2++0R988IHWWuvVq1frxMRErbXWJSUl+r777tNHjhyxn7Nz505ttVr1zJkz9caNGxtF3DNmzNBZWVlOjbU+sX/11Vc6KSlJa631sWPH9MMPP6wrKirs53jqM68pblc+c4vFonfv3q211vrkyZN68uTJev/+/XrRokV66dKlWmutly5dqhctWqS11nr//v36H//4hy4tLdVHjhzRDzzwgFued0PG7erf8drGXlxcrHfs2KGXL1+u58+fX+larv4dr6sLrqmqa9eu9m8spx06dIguXboA0KNHD77//nv7ayUlJVRUVFBaWoq3tzfNmzcnPz+f4uJiOnXqhFKKQYMGOX2F3oaI211qE/uBAwfo3r07AIGBgfj5+bFnzx6Pf+bVxe1qQUFBXHLJJQD4+vrStm1bLBYLGRkZxMXFARAXF2d/dhkZGfTv3x8fHx9CQ0Np1aoVWVlZLn/eDRW3O9Q29mbNmtG5c2d7jeg0d/yO19UFlziqEh4ezvr16wFYt26dfS2svn370qxZM+655x7uu+8+rrvuOvz9/T1mhd7axn3avHnzeOihh/j444+rXDTSnbFfdNFFrF+/noqKCnJyctizZw9Hjx71+GdeXdynueOZ5+TksHfvXiIiIigoKLCv6RYUFERhYSFw9mrTwcHBWCwWtz7v+sR9mrt+xx2JvTqe8jvuiAuuj6Mqf/nLX1i4cCEff/wxsbGxeHvbHktWVhaGYfDGG29w4sQJpk+fTlRUlNs+bP+otnGHhYUxefJkgoODKS4uZvbs2axatcr+rcgTYr/yyis5cOAAjz76KCEhIVx66aV4eXl5/DOvLm7ALc+8pKSE2bNnM27cuBprm9U9V3c97/rGDe553uB47NXxlN9xR0jiANq2bcvjjz8O2JoiNm7cCMCaNWuIjo7G29ubwMBALr30Unbv3k2XLl3OWqE3ODjY4+MOCwuzx+nr68uAAQPIyspyS+KoLnYvLy/GjRtnP+7xxx+ndevW+Pn5efQzry5uwOXPvLy8nNmzZzNw4EAuv/xywNZ8lp+fT1BQEPn5+QQEBABnrzZtsVgIDg6uchVqZz/vhogbXP+8axt7ddzxzOtKmqqAgoICwLYt7ZIlS7jqqqsA2/LHW7duRWtNSUkJu3btom3btgQFBeHr60tmZiZaa1atWkVsbKzHx11RUWGvLpeXl7NhwwanLhBZl9hPnTpFSUkJAD/++CNeXl60a9fO4595dXG7+plrrXn99ddp27YtI0eOtJfHxsaSlpYGQFpaGr1797aXp6enU1ZWRk5ODtnZ2URERLj8eTdU3O74Ha9t7NXxlN9xR1xwM8dfeukltm/fzvHjxwkMDOTmm2+mpKSE5cuXA9CnTx9uu+02lFKUlJQwb948Dhw4gNaaK6+8kuuvvx6A3bt3M2/ePEpLS4mOjmb8+PFOHTbXEHGXlJQwY8YMKioqsFqtREVFcdddd2EYzv3+UJvYc3JymDlzJoZhEBwczL333ktISAjg2c+8urhd/cx//vlnpk+fTvv27e3P5tZbbyUyMpLExESOHj2K2Wxm6tSp9n6vJUuWsHLlSvsw4piYGMC1z7uh4nbH73hdYr///vs5efIk5eXl+Pn58fjjj9OuXTuX/47X1QWXOIQQQtSPNFUJIYSoFUkcQgghakUShxBCiFqRxCGEEKJWJHEIIYSoFUkcQgghakUShxD1MHfuXObNm1epbPv27YwfP578/Hw3RSWEc0niEKIeEhIS2LRpEz/++CMApaWlvPHGG9x55532Be7qo6Kiot7XEKKhyQRAIepp7dq1vPfee8yePZslS5bwyy+/MGbMGN59910OHDhASEhIpY2dVq5cyaeffkpeXh4BAQHccMMN9qVLtm3bxiuvvMKIESP44osv6NGjB3fddRfz5s3j559/RilFeHg4TzzxhNNn/AtRHVnkUIh66tevH+np6bz88svs3LmT5557jkceeYQHHniA6Ohotm7dyuzZs3nppZcICAggMDCQRx55hLCwMHbs2MG///1vOnbsaN/T4dixYxQVFTFv3jy01nz88ccEBwczf/58AHbt2uWRy1CIC4d8ZRGiAUyYMIGtW7cyZswYvvvuO2JiYujVqxeGYdCjRw86duxoX0m3V69etGrVCqUUXbt2pUePHvz888/2aymluPnmm/Hx8aFJkyZ4eXlx7Ngxjh49ire3N126dJHEIdxKahxCNICWLVsSEBBAu3bt+OGHH1i3bh0bNmywv15RUWFvqtq0aRMff/wxhw4dQmvNqVOnaN++vf3YgICASrvDXX/99SxevJhnnnkGgPj4eEaNGuWaNyZEFSRxCNHATCYTAwcO5N577z3rtbKyMmbPns0DDzxg3wjq+eefr3TMH2sTvr6+3Hnnndx5553s37+fJ598ko4dOxIVFeXU9yFEdaSpSogGNnDgQDZs2MDmzZuxWq2Ulpaybds28vLyKC8vp6ysjICAALy8vCqNyKrOhg0bOHz4MFprfH19MQxDOsaFW0mNQ4gGZjabefjhh3nvvfd4+eWXMQyDiIgI7r77bnx9fUlISCAxMZGysjIuu+yyc27Wk52dzdtvv01hYSF+fn4MGzbM3uwlhDvIcFwhhBC1IvVdIYQQtSKJQwghRK1I4hBCCFErkjiEEELUiiQOIYQQtSKJQwghRK1I4hBCCFErkjiEEELUyv8DjdVgrSn5dMUAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"haiti.plot(kind='line')\\n\",\n \"\\n\",\n \"plt.title('Immigration from Haiti')\\n\",\n \"plt.ylabel('Number of Immigrants')\\n\",\n \"plt.xlabel('Years')\\n\",\n \"\\n\",\n \"# annotate the 2010 Earthquake. \\n\",\n \"# syntax: plt.text(x, y, label)\\n\",\n \"plt.text(2000, 6000, '2010 Earthquake') # see note below\\n\",\n \"\\n\",\n \"plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\\n\",\n \"\\n\",\n \"Quick note on x and y values in `plt.text(x, y, label)`:\\n\",\n \"\\n\",\n \"```\\n\",\n \" Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\\n\",\n \"```\\n\",\n \"\\n\",\n \"```python\\n\",\n \" plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\\n\",\n \"```\\n\",\n \"\\n\",\n \"```\\n\",\n \"If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg 20th index is year 2000 since it is the 20th year with a base year of 1980.\\n\",\n \"```\\n\",\n \"\\n\",\n \"```python\\n\",\n \" plt.text(20, 6000, '2010 Earthquake') # years stored as type int\\n\",\n \"```\\n\",\n \"\\n\",\n \"```\\n\",\n \"We will cover advanced annotation methods in later modules.\\n\",\n \"```\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We can easily add more countries to line plot to make meaningful comparisons immigration from different countries. \\n\",\n \"\\n\",\n \"**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Get the data set for China and India, and display dataframe.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"df_CI.plot(kind='line')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Click here for a sample python solution\\n\",\n \"\\n\",\n \"```python\\n\",\n \" #The correct answer is:\\n\",\n \" df_CI.plot(kind='line')\\n\",\n \"```\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"That doesn't look right...\\n\",\n \"\\n\",\n \"Recall that _pandas_ plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
China
\\n\",\n \"
India
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
1980
\\n\",\n \"
5123
\\n\",\n \"
8880
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1981
\\n\",\n \"
6682
\\n\",\n \"
8670
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1982
\\n\",\n \"
3308
\\n\",\n \"
8147
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1983
\\n\",\n \"
1863
\\n\",\n \"
7338
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1984
\\n\",\n \"
1527
\\n\",\n \"
5704
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" China India\\n\",\n \"1980 5123 8880\\n\",\n \"1981 6682 8670\\n\",\n \"1982 3308 8147\\n\",\n \"1983 1863 7338\\n\",\n \"1984 1527 5704\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_CI = df_CI.transpose()\\n\",\n \"df_CI.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"_pandas_ will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"df_CI.plot(kind='line')\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Click here for a sample python solution\\n\",\n \"\\n\",\n \"```python\\n\",\n \" #The correct answer is:\\n\",\n \" df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\\n\",\n \" df_CI.plot(kind='line')\\n\",\n \"\\n\",\n \" plt.title('Immigrants from China and India')\\n\",\n \" plt.ylabel('Number of Immigrants')\\n\",\n \" plt.xlabel('Years')\\n\",\n \"\\n\",\n \" plt.show()\\n\",\n \"```\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \" From the above plot, we can observe that the China and India have very similar immigration trends through the years. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"_Note_: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\\n\",\n \"\\n\",\n \"That's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below. \\n\",\n \"\\n\",\n \"```python\\n\",\n \"print(type(haiti))\\n\",\n \"print(haiti.head(5))\\n\",\n \"```\\n\",\n \"\\n\",\n \"> class 'pandas.core.series.Series' \\n\",\n \"> 1980 1666 \\n\",\n \"> 1981 3692 \\n\",\n \"> 1982 3498 \\n\",\n \"> 1983 2860 \\n\",\n \"> 1984 1418 \\n\",\n \"> Name: Haiti, dtype: int64 \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Line plot is a handy tool to display several dependent variables against one independent variable. However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Area Plots\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"In the last module, we created a line plot that visualized the top 5 countries that contribued the most immigrants to Canada from 1980 to 2013. With a little modification to the code, we can visualize this plot as a cumulative plot, also knows as a **Stacked Line Plot** or **Area plot**.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
India
\\n\",\n \"
China
\\n\",\n \"
United Kingdom of Great Britain and Northern Ireland
\\n\",\n \"
Philippines
\\n\",\n \"
Pakistan
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
1980
\\n\",\n \"
8880
\\n\",\n \"
5123
\\n\",\n \"
22045
\\n\",\n \"
6051
\\n\",\n \"
978
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1981
\\n\",\n \"
8670
\\n\",\n \"
6682
\\n\",\n \"
24796
\\n\",\n \"
5921
\\n\",\n \"
972
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1982
\\n\",\n \"
8147
\\n\",\n \"
3308
\\n\",\n \"
20620
\\n\",\n \"
5249
\\n\",\n \"
1201
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1983
\\n\",\n \"
7338
\\n\",\n \"
1863
\\n\",\n \"
10015
\\n\",\n \"
4562
\\n\",\n \"
900
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1984
\\n\",\n \"
5704
\\n\",\n \"
1527
\\n\",\n \"
10170
\\n\",\n \"
3801
\\n\",\n \"
668
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" India China United Kingdom of Great Britain and Northern Ireland \\\\\\n\",\n \"1980 8880 5123 22045 \\n\",\n \"1981 8670 6682 24796 \\n\",\n \"1982 8147 3308 20620 \\n\",\n \"1983 7338 1863 10015 \\n\",\n \"1984 5704 1527 10170 \\n\",\n \"\\n\",\n \" Philippines Pakistan \\n\",\n \"1980 6051 978 \\n\",\n \"1981 5921 972 \\n\",\n \"1982 5249 1201 \\n\",\n \"1983 4562 900 \\n\",\n \"1984 3801 668 \"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_can.sort_values(['Total'], ascending=False, axis=0, inplace=True)\\n\",\n \"\\n\",\n \"# get the top 5 entries\\n\",\n \"df_top5 = df_can.head()\\n\",\n \"\\n\",\n \"# transpose the dataframe\\n\",\n \"df_top5 = df_top5[years].transpose() \\n\",\n \"\\n\",\n \"df_top5.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Area plots are stacked by default. And to produce a stacked area plot, each column must be either all positive or all negative values (any NaN values will defaulted to 0). To produce an unstacked plot, pass `stacked=False`. \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\\n\",\n \"df_top5.plot(kind='area', \\n\",\n \" stacked=False,\\n\",\n \" figsize=(20, 10), # pass a tuple (x, y) size\\n\",\n \" )\\n\",\n \"\\n\",\n \"plt.title('Immigration Trend of Top 5 Countries')\\n\",\n \"plt.ylabel('Number of Immigrants')\\n\",\n \"plt.xlabel('Years')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"The unstacked plot has a default transparency (alpha value) at 0.5. We can modify this value by passing in the `alpha` parameter.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df_top5.plot(kind='area', \\n\",\n \" alpha=0.25, # 0-1, default value a= 0.5\\n\",\n \" stacked=False,\\n\",\n \" figsize=(20, 10),\\n\",\n \" )\\n\",\n \"\\n\",\n \"plt.title('Immigration Trend of Top 5 Countries')\\n\",\n \"plt.ylabel('Number of Immigrants')\\n\",\n \"plt.xlabel('Years')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### Two types of plotting\\n\",\n \"\\n\",\n \"As we discussed in the video lectures, there are two styles/options of ploting with `matplotlib`. Plotting using the Artist layer and plotting using the scripting layer.\\n\",\n \"\\n\",\n \"**Option 1: Scripting layer (procedural method) - using matplotlib.pyplot as 'plt' **\\n\",\n \"\\n\",\n \"You can use `plt` i.e. `matplotlib.pyplot` and add more elements by calling different methods procedurally; for example, `plt.title(...)` to add title or `plt.xlabel(...)` to add label to the x-axis.\\n\",\n \"\\n\",\n \"```python\\n\",\n \" # Option 1: This is what we have been using so far\\n\",\n \" df_top5.plot(kind='area', alpha=0.35, figsize=(20, 10)) \\n\",\n \" plt.title('Immigration trend of top 5 countries')\\n\",\n \" plt.ylabel('Number of immigrants')\\n\",\n \" plt.xlabel('Years')\\n\",\n \"```\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Option 2: Artist layer (Object oriented method) - using an `Axes` instance from Matplotlib (preferred) **\\n\",\n \"\\n\",\n \"You can use an `Axes` instance of your current plot and store it in a variable (eg. `ax`). You can add more elements by calling methods with a little change in syntax (by adding \\\"_set__\\\" to the previous methods). For example, use `ax.set_title()` instead of `plt.title()` to add title, or `ax.set_xlabel()` instead of `plt.xlabel()` to add label to the x-axis. \\n\",\n \"\\n\",\n \"This option sometimes is more transparent and flexible to use for advanced plots (in particular when having multiple plots, as you will see later). \\n\",\n \"\\n\",\n \"In this course, we will stick to the **scripting layer**, except for some advanced visualizations where we will need to use the **artist layer** to manipulate advanced aspects of the plots.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 0, 'Years')\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# option 2: preferred option with more flexibility\\n\",\n \"ax = df_top5.plot(kind='area', alpha=0.35, figsize=(20, 10))\\n\",\n \"\\n\",\n \"ax.set_title('Immigration Trend of Top 5 Countries')\\n\",\n \"ax.set_ylabel('Number of Immigrants')\\n\",\n \"ax.set_xlabel('Years')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question**: Use the scripting layer to create a stacked area plot of the 5 countries that contributed the least to immigration to Canada **from** 1980 to 2013. Use a transparency value of 0.45.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABci0lEQVR4nO29eZhcVZ3w/zn33qrqJQuddCBDAgzIEhaVYFheQRbThsCgMj6oAw6KyiKMYhQZERH0FeYNYEDySgSVweVFxg1Uhi0E/RnHKBIDooBAAjNZIGvvtdzlnPP741ZVd1fX3rV0pc/neXgeUnXr1rdvV3/r3O/5ns8RWmuNwWAwGFoeq9kBGAwGg6E2mIRuMBgMewkmoRsMBsNegknoBoPBsJdgErrBYDDsJZiEbjAYDHsJTjPf/PXXX6/qdd3d3ezevbvG0TSGVo3dxN1YTNyNpZXi3n///Qs+Z0boBoPBsJdgErrBYDDsJZiEbjAYDHsJTa2h56K1JpVKoZRCCFHwuB07duC6bgMjqx2tGnulcWutsSyLtra2or9Lg8FQOyZVQk+lUkQiERyneFiO42DbdoOiqi2tGns1cQdBQCqVor29vU5RGQyG0UyqkotSqmQyN7QOjuOglGp2GAbDlKFk9vQ8jxtuuIEgCJBSctJJJ/GBD3xgzDFaa+69916eeeYZYrEYV1xxBYccckjFwZhb870P8zs1GBpHyYQeiUS44YYbaGtrIwgCrr/+eo499lgOP/zw7DHPPPMM27dvZ+XKlbzyyit85zvf4d/+7d/qGrjBYDAYxlIyoQshaGtrA0BKiZRy3Khr/fr1nHrqqQghOPzww4nH4/T19dHV1TWh4NSfn4K+3nGPB7aNkrLyE3bNwnrriUUPOeCAA1iwYAFSSg499FDuuOOOgjXgH/3oRzz33HPcdNNNlcdiMBjyMrRnJ/0b/z/mHHM2bZ3Tmh1OS1FWwVopxec//3m2b9/OmWeeyWGHHTbm+d7eXrq7u7P/nj17Nr29veMS+po1a1izZg0Ay5cvH/MaCDspRtfQg8EBCPzxAQV+dcX/wYGSNfq2tjZ+/etfA3D55Zdz33338YlPfCLvsbZtY1lWxXX/Vp0nqCbuWCw27vfcSBzHaer7V8tUjju5awvKGyS+/RXmn/iuGkVWnFa93rmU9RdqWRa33nor8Xicr33ta2zevJkDDzww+3y+TY/y1U57enro6enJ/jt3qa3rumM6KZSUoMaPxC3LRuV5vCRSEgRBycMyxxx//PG8+OKLPPLII6xcuRLP8+jq6uIb3/gGc+bMQUqJUoogCFi9enXeY1asWEFnZ2f2S+Gd73wn3/ve9zjggAMqj7+JOI5T1rXLxXXdpi6pbqUl3aOZynEP9PeiZMBQ/86GXYNWut41W/rf2dnJUUcdxbPPPjvm8dmzZ4+5GHv27JlwuaXZBEHAr3/9axYsWMAJJ5zAQw89xOrVq3nve9/LqlWrxh1fzjEGg6E0SvqgNcpPNDuUlqPkCH1wcBDbtuns7MTzPP7yl7/w3ve+d8wxixYt4rHHHuPkk0/mlVdeoaOjo2UTeiqV4l3vCm/zTjzxRM4//3w2bdrE5Zdfzs6dO/E8b8zdSYY33nij5DEGg6E0OnABjQ6SzQ6l5SiZ0Pv6+rjzzjtRSqG15n/9r//F2972NlavXg3AkiVLWLhwIRs2bODKK68kGo1yxRVX1D3wetHW1sYTTzwx5rEvfelLXHrppSxZsoR169Zx2223jXtdoWNs2x7Ti92Kq0QNhkailQcaUF6zQ2k5Sib0gw46iFtuuWXc40uWLMn+vxCCiy++uLaRTSIGBweZO3cuAD/5yU8qOuaAAw7ITgT/5S9/YfPmzXWO1mBobbQMGyF0NfNkU5zJ3W7RNStv2yK2DVW2LVbDVVddxWWXXcbcuXM57rjj2LJlS9nHnH322fz0pz/lXe96F8ceeyxvetObqorBYJgqaOVn/qe5gbQgQudrUWkQuRtcJBIJOjo6Sr6u2o6LyUCrxl5t3OX+TutFK3UvjGYqx7356Z8jh15HYnPoOy+rUWTFaaXrbTa4MBgMLYNIj9AFTRtrtiwmoRsMhsmFTt8JClNyqRST0A0Gw6Qh8D3QEg1YmIReKSahGwyGSUPgeWitUNoBU3KpGJPQDQbDpCHwkmil8HUMAQRevNkhtRQmoRsMhklDZoQudTtaWwTJ/maH1FJM6j70p7YO0Zcc3ypXrZyrq93hxPnTCz5/ww03MH/+fC655BIALrjgAvbff3++9rWvAfCVr3yFuXPnctll5bdSrVu3jkgkwvHHH19xvOXwH//xH3z7299GCJG1Yp555pkFj1+2bBk9PT2cc845dYnHYJgI0k+BVgR0oBAEqUGYOa/ZYbUMkzqh9yUDUsH4iRHLElVtbZbvy2E0ixYt4j//8z+55JJLUErR29vL0NBQ9vn169fzla98paL3/P3vf09nZ2dFCT0IgrJUta+//jorV67kscceY8aMGcTjcfbs2VNRfJWgtc5u/mww1AMV+Git0NY0tBZob7DZIbUUkzqhN5rjjz+eL3/5ywC89NJLHHHEEezcuZP+/n7a29vZuHEjxxxzDM899xxf+cpXiMfjzJo1i9tvv5399tuPe+65hx/84Ac4jsNhhx3Gtddeyw9+8ANs2+ZnP/sZN954I0cccQRXX30127ZtA8JR//HHH8+KFSvYsWMHW7ZsYdasWRxyyCFs27aNzZs3s23bNi6++GI+/vGPj4l3z549dHZ20tnZCTDm/++77z7uu+8+PM/j4IMPZuXKldmNOp566im+9a1vsWvXLr74xS9yzjnnEI/H+ehHP8rAwABBEPCv//qvnHnmmWzZsoV//ud/5pRTTuHpp5/m3//93/nGN77Bn//8Z1KpFP/wD//A5z73uQb9hgx7O9J3wxWi9j5oaSHdodIvMmQxCX0Uc+fOxXEctm3bxvr163nb297G9u3b+dOf/sSMGTM48sgjEUJw3XXXce+99zJ79mx+8YtfcPPNN3Pbbbdx55138vvf/55YLMbAwAAzZ87kwgsvHOND/+QnP8kll1zCCSecwLZt27jgggv4zW9+A8Bzzz3Hgw8+SHt7OytWrGDjxo385Cc/IR6P8453vIMPf/jDRCKRbLxHHXUUc+bM4aSTTuKUU07hrLPOyjp2zjrrLD70oQ8BcPPNN3P//ffzsY99DAg3Evn5z3/Oxo0b+ehHP8o555xDLBbjnnvuYfr06fT29vLud787e65NmzZxxx13ZHdm+vznP09XVxdSSj74wQ/ywgsvcNRRRzXml2TYq1HSBa2xYl3opIX0zaRoJZiEnsPxxx/P+vXrWb9+PZdeeinbt29n/fr1zJgxg0WLFrFp0yZeeukl/umf/gkId3Pad999ATjyyCP55Cc/ydKlS1m6dGne869du5aXXnop++/h4WGGh4eBUHg2eru7xYsXE4vFsrv+7Nq1a8yyX9u2ue+++3j22Wf5r//6L77yla/wl7/8hauuuoqXXnqJW265hcHBQeLxOKeddlr2dUuXLsWyLA4//HB27doFhOWU5cuX89RTTyGEYPv27dnn5s+fz6JFi7JL/x966CHuu+8+pJTs2LGDV155xSR0Q03QgY9GICIzIClQpsulIkxCz2HRokWsX7+ev/3tbyxYsID999+fu+++m+nTp/PBD34QrTWHH344Dz300LjXfv/73+cPf/gDq1ev5utf/3p2K7vRaK355S9/mXef0lznSSwWy/6/bdvIPEIyIQQLFy5k4cKFnHrqqXz2s5/lqquu4jOf+Qz33HMPRx99ND/60Y/4/e9/n31NNBodEw/AAw88wJ49e3j00UeJRCKceOKJWdXv6Lg2b97M3XffzcMPP8w+++zDsmXLSKVSBa+nwVAJWnloLRBWBI0dTpIaysbMbuWwaNEi1qxZwz777INt23R1dTE4OMif/vQnFi1axJve9CZ6e3tZv349AL7v89JLL6GU4vXXX+fkk0/muuuuy46MOzs7syNwgNNOO43vfve72X//9a9/rTrW7du385e//CX77+eff55588KOgOHhYfbbbz983+fBBx8sea6hoSG6u7uJRCL87ne/Y+vWrQWPa29vZ8aMGezatSvvl5bBUC1a+igsLMtC4aBaUGTXTCb1CL2r3SnQtmihVOWryLraS/+4Rx55JL29vZx77rnZxxYsWJCdAAW4++67uf766xkcHERKycUXX8whhxzCpz71KYaGhtBac8kllzBz5kze9a53cdlll/H4449z4403ctNNN/H5z3+enp4egiDgxBNP5Oabb674Z4GwG+Z//+//zY4dO4jFYsyePZvly5cDcPXVV3POOecwf/58FixYMOZLJR/ve9/7+MhHPsJZZ53F0UcfzaGHHpr3uKOPPppjjjmGM844gwMPPLBu7ZiGqYlWPmiBEKB0BKVMyaUSjD63wbRq7Eaf21imatybn36Q1FAvcvb5yF2PMKN9JweeXP8d0Frpeht9rsFgaAmECtDaChfK6QiYXYsqwiR0g8EwedABYIUlFxGB5hUQWhKT0A0Gw6Qgq84VgABNFG22oasIk9ANBsOkIHBTaXVuBJFO6GbXosowCd1gMEwKAt9FKwVWLEzoIopGG4VuBZiEbjAYJgXZzS1EG0IARI1Ct0ImdR/69m0+qWQe26Lto2TltbW2dou58yJFj5k3bx7ve9/7+L//9/8CYa93ZiXm97///YrfM5eDDz6YV155pezjf/SjH/Hcc89x0003sWLFijFemAyFHs93jolS6r0MhmrJqHOxOxBCgIiitVHoVsKkTuippEIG42toWmuUrLy2lu/LIZeOjg5eeuklkskk7e3trF27lrlz51b0PuXqbw0GwwgZdS52es8CK4pSllHoVkDJrLN7927uvPNO+vv7EULQ09PD2WefPeaY559/nltuuSUrqTrxxBM577zz6hNxAzjjjDN48sknOeecc/j5z3/Oueeey1NPPQXAM888ww033EAqlaKtrY3bbruNQw89lB/96Ec8+eSTuK5LIpHgG9/4BpdffjlDQ0NIKfk//+f/cOKJJwKwfPly1qxZQ1tbG/feey9z5sxh9erVrFy5Es/z6Orq4hvf+AZz5sypOPZche83v/nNMc8Xep8VK1YU1PXecccd/OxnP+Pv/u7vmD17Nm95y1vKei+DoRIy6lwrOhMAbcVQyih0K6FkQrdtmwsvvJBDDjmEZDLJNddcw1ve8hbmz58/5rgjjzySa665pm6BNpL3vve93H777fT09PDiiy/yT//0T9mEfuihh/LAAw/gOA5r167l5ptv5tvf/jYAf/rTn1izZg1dXV3cddddnHbaaXz6059GSkkymQTClZPHHXcc11xzDTfeeCP33Xcfy5Yt44QTTuChhx5CCMEPf/hDVq1axQ033FBx7LkK31yKvU8+Xe+LL77IL3/5S9asWYPruixdujSb0Eu9l8FQCSPq3FCxgYgCFtIvrq0wjFAyoXd1ddHV1QVAe3s78+bNo7e3d1xC35s46qij2Lp1K7/4xS945zvfOea5wcFBli1bxmuvvYYQAt/3s8+deuqp2Wt17LHHctVVVxEEAWeeeSbHHHMMEJoO3/WudwHw5je/md/+9rcAvPHGG1x++eXs3LkTz/M48MADq4q9lMK32Pvk0/U+9dRTLF26lI6OjjGxl/NeBkMl6MBDI4hEw01ahIihtYXyE02OrHWoqNC7c+dOXnvttbzippdffpmrr76arq4uLrzwQg444IBxx6xZs4Y1a9YAYdmhu7t7zPM7duwYU3u2bJ9CqhnLrrxBx7JFWbVtx3FYunQpX/3qV3nggQfo6+tDiPC1K1as4JRTTuF73/semzdv5n3vex+O42DbNtOmTcue/5RTTuEXv/gFTzzxBJ/+9Kf5l3/5Fz7wgQ/gOE52k4poNIpSCsdxuP7667nssstYunQpv/vd7/ja176WPa9lWTiOg2VZ2f8f83ONevyHP/whv//973n88ce54447WLt27ZhzFHofy7Job2/Pntu27ey5M/+fG0O+98qNLfPl0Cwcx2nq+1fLVIx7p2PhYmd33UolHfBtHCHrfi1a9XrnUnZCT6VSrFixgosuumicbOnggw9m1apVtLW1sWHDBm699VZWrlw57hw9PT309PRk/50rw3FdN5s8AJRUeSc/LduqqstFCFGWYCoIAt7//vfT2dnJ4Ycfzrp169BaEwQBAwMD7LvvvgRBwP333599XEqJUip7/q1btzJ37lzOP/98hoeHefbZZ3nf+96XPT8w5jWjz/sf//Efec+rlBrzHtnrlH7c8zy2bdvGSSedxNve9jYeeOABBgYGynqffOeWUnLCCSfwmc98hk996lO4rsvjjz/OhRdeWPC9Zs6cOSY213WbKj1qJenSaKZi3G5yGKkgHg/7zj1XIqSD5ybrfi1a6XoXk3OVldCDIGDFihW84x3vyE7sjWZ0gj/uuOO45557GBwcZMaMGVWEO0Jbu1WgbVGEbU1VnK9c9t9/fy6++OJxj19++eUsW7aMb33rW5x88skFX79u3TruuusuHMehs7OTO+64o+j7XXXVVVx22WXMnTuX4447ji1btpQdawYpZV6F70Te581vfjPvfve7Wbx4MfPmzcv+/st5L4OhIpQHjPxdCwEK2yz/r4CS+lytNXfeeSfTpk3joosuyntMf38/M2fORAjBxo0bWbFiBatWrSqZdI0+t3Uw+tzGMhXj3vz0gyQH+3DmXQDA0KBE9P+S/Wb2MvfEf6llmONopes9oRH6Sy+9xNq1aznwwAO5+uqrATj//POzP/ySJUuy267Ztk00GmXZsmVVjaANBsPURaiAcSN0HQFtFLrlUjKhL1iwgB//+MdFjzFdDgaDYcKk1bkZhBBI7ZiSSwUYl4vBYGg6GXXuqAF6KOjSkVAHYCgLk9ANBkPTyapzGXEtjWxyYRJ6uZiEbjAYmo7nhepcJWLZx8IRehStMQrdMjEJ3WAwNB3pu2it0KIt+1g4Qo+htEWQ6GtidK3DpFYCvvrqq9lFBqOxbRspK5/57uzs5JBDDil6zLx587j00kuzfpO77rqLeDzOVVddVfH7FWLTpk18+ctf5tVXX8VxHBYsWMCNN95YUMa1ZcsWPvKRj/CrX/2q7PdYtmwZPT09nHPOORXHt3r1al5++WU++clPVvxag6EaVFrMhT2qxTXtRJdaIN0BYO/VjdSKSZ3Q4/H4GFdKhszKx2rOV4pYLMajjz7Kpz71KWbNmlXxe5QilUrx4Q9/mBtuuIElS5YA8Lvf/Y49e/ZUZVesB0uWLMnGZjA0gswIXTjTs49lRuhaC5RnjIvlYEouOdi2zYc+9CG+9a1vjXtuz549XHLJJZx99tmcffbZPP3000AotRoYGEBrzdFHH81PfvITAD71qU+xdu3aMed44IEHeNvb3jYmYZ588sksWLCALVu28I//+I+ceeaZnHnmmdnzj0ZKyVe/+lXOPvtsenp6+MEPfgCEC8C++MUvcvrpp3PhhReyZ8+e7Gt++9vfsmTJEhYvXsxnP/tZXNcFQs3x1772Nc4880wWL17Mxo0bgXBDjC9+8YtAOFo/55xzWLx4MR/84AfZtWtX1dfWYCiECjzQChEZWW0cbnIRQ2qj0C0Xk9DzcNFFF/Hggw8yODhWrH/99ddzySWX8Mgjj/Dtb3+bz33ucwAsWrSIp59+mpdeeomDDjqIP/7xjwBs2LCBt73tbWPO8be//S2rn82lu7ub+++/n8cff5xvfvObXH/99eOOuf/++5k+fTqPPPIIDz/8MD/84Q/ZvHkzjz76KJs2beLJJ5/k1ltvZf369UB4R/CZz3yGb37zmzz55JMEQTBm56VZs2Zl/Sx33XXXuPfL6HaffPJJ3vve97Jq1aoKrqTBUB5KemPVuYQjdEG4a5H0zaRoOUzqkkuzmD59Oueddx733HMP7e3t2cd/+9vf8vLLL2f/PTw8zPDwMCeeeCJPPfUUW7du5cMf/jD/7//9P9544w26urqy5rhy8H2fL37xi7zwwgtYlsWrr7467pjf/OY3vPjiizz88MMADA0N8dprr/GHP/yBc889F9u2mTt3btYzs2nTJg488EDe9KY3AfD+97+f733ve1xyySUAnHXWWQC85S1v4dFHHx33frXS+hoMxchV50K6y0W0obGNQrdMTEIvwMUXX8zSpUv54Ac/mH1MKcUvf/nLMUkewtLFd7/7XebPn8/nP/95Hn30UR5++GFOOOGEcec94ogj+N3vfpf3Pb/97W8zZ84cnnjiCZRSBSdwb7zxRk4//fQxjz355JN5dQslVD3EYmGbWKGJ5i996UtceumlnH322axdu5bbbrut6PkMhmpQ0kdpMXpdEQKw7BgKC4JUs0JrKUzJpQBdXV28+93v5v77788+dtppp/Hd7343+++//vWvANlNP1577TUOOuggTjjhBO666668Zsr3ve992Z2NMvz617/mxRdfZHBwkH333RfLsvjZz36WN8GedtppfP/7389OFm/atIlEIsFJJ53EL37xC6SU7Nixg3Xr1gHhDktbtmzhtddeA+BnP/sZJ510UtnXYXBwMLunamZuwGCoOcpH56QjIUBYAqkclGo9oV0zmNQJvbOzk0gkMu6/aDSa9/FS/1VS/gC47LLL6O3tzf77q1/9Kn/+85/p6enh9NNPz05IAixcuDA7oj7hhBPYvn07xx9//Lhztre3873vfY97772Xk08+mdNPP50f//jHdHd385GPfISf/vSnnHPOObz66qt5LYUXXHABhx12GEuXLuWd73wnn//85wmCgLPOOouDDz6YxYsX84UvfCGbtDP7nl522WUsXrwYy7K48MILy74GGd3ue97znrp0/RgMQKjO1Tl3mCLjc7FAGUFXOZTU59YTo89tHYw+t7FMtbhz1bkZ9uwKiKUeYL8ZQ3VV6LbS9S6mz53UI3SDwTBFyFHnZggVuo5R6JaJSegGg6HpiBx1bvZxAZKIUeiWiUnoBoOhqfieO06dm0EI0MoxxsUyMQndYDA0Fem549S5GYQQSCIITEIvB5PQDQZDU8moc/UodW4GIUDqKBqj0C0Hk9ANBkNTyYi51Ch1bpb0rkUaYRS6ZTCpV4pGh1/ACsZLeWzbwpGV34IpZzretKOKHnPHHXfw85//HNu2EUJw8803c9xxx1X8XqPZsmULJ510Ep/+9Ke59tprAejt7WXhwoX88z//MzfddFPZ5/rzn//MT3/6U7761a9OKCaDYbIwos6dNu65zCYXShmFbjlM6oRuBUMI7Y17XCgLUcUkSb4vh9GsX7+eNWvW8NhjjxGLxejt7cXzxr9/NRx00EGsWbMmm9AfeughDj/88IrOEQQBb33rW3nrW99ak5gMhsnAiDo3f0KXRNBYSKPQLcmkTuiNZufOncyaNSvrNxm9MvL222/niSeeIJVKsWjRIm6++WaEEJx33nksXLiQdevWMTAwwIoVK/Iu+W9ra+Owww7j2Wef5ZhjjuGhhx7i3e9+Nzt27ABCTe3KlSvxPI+uri6+8Y1vMGfOHFasWMGOHTvYsmULs2bN4kMf+hB33XUX3//+91mxYgXbtm1j8+bNbNu2jYsvvpiPf/zjANx999386Ec/AuD888/PyrgMhsnGiDp3xrjnMhtFKwTKKHRLYmroozjttNN4/fXXOeWUU/jCF77A73//++xzF110EY888gi/+tWvSCaTPPHEE9nngiDg4Ycf5itf+UpRedV73/tefv7zn/P6669jWRb77bdf9rmMpnb16tXjNLXPPfcc//7v/86dd9457pwbN27kvvvu4+GHH+a2227D932ee+45fvzjH/Of//mfPPTQQ/zwhz/MemcMhslGmNA1Vtvscc8JBJJYeoRuJkVLYUboo+js7OSxxx7jqaeeYt26dVx++eV84Qtf4IMf/CDr1q3jm9/8Jslkkv7+fo444ojsJhVnn302ECpot27dWvD8p59+OrfeeiuzZ8/mPe95z5jnimlqlyxZMs7wmGHx4sXEYjFisRjd3d3s2rWLP/7xjyxdujS75P6ss87iqaee4phjjpnQ9TEY6oGWHloLIpHxighhAcTQ2kIbJ3pJzAg9B9u2efvb387nPvc5brzxRh555BFSqRTXXnstd999N08++SQXXHBBdtcfgGg0mn1tMd9JNBrlLW95C3fffXf2SyDDl770JT760Y/y5JNPcvPNN485fzEXSqY8lHl/KWVJZa7BMJlQ0kflW1WURlgxNDZaGoVuKUxCH8XGjRvHbCrx/PPPM3/+/GxynTVrFvF4PLu5RDVcfvnlXHvttePMhbXU1J500kk8/vjjJJNJEokEjz32WN66vsEwKVDeOHVuBiEAYRFoC1XFxvBTjZIll927d3PnnXfS39+PEIKenp5xo0utNffeey/PPPMMsViMK664ouDmDJWgnOl5O1O0ZVXldlCjNqDNRyKR4LrrrmNwcBDHcfj7v/97brnlFmbOnMkFF1xAT08P8+fPn1CXyYIFCzj00EPHPZ7R1M6dO5fjjjuOLVu2VP0eb37zm3n/+9/PP/zDPwDhpKgptxgmLcpH56pz0wgBlrCQyoY8HW+GsZTU5/b19dHX18chhxxCMpnkmmuu4eqrr2b+/JF+0A0bNvDYY4/xhS98gVdeeYXvfve7/Nu//VvJNzf63NbB6HMby1SKu5A6F8BzFTt2DNMdeYTujnjdFLqNvN5KBlh29dOXE9LndnV1ZUfb7e3t2d15RrN+/XpOPfVUhBAcfvjhxONx+vrMqi6DYdKjPNoG/oiQTZxwLKDOhdDlIoQVbnKxlyh0+//2AP2b/1iXc1f0NbFz505ee+21cSWD3t5euru7s/+ePXs2vb29dHV1jTluzZo12a3Xli9fPuY1ADt27MBxygup3OMmI60aezVxZ7pvmoXjOE19/2ppWNzuHnADpkUHoOugCZ+umrhftzXCsvPuKOZHFNGoj9BtWBZ1uyaNut5KBgw7Njrx33R3n136BRVS9l9oKpVixYoVXHTRReNuofNVbfJtWNzT00NPT0/237m3OKlUCtu2SwfdomULaN3Yq407lUo1tXQwlUoX1WB7u2mLDxB4b+DKwrfy5VJp3L7nIn0PrTXx+Pi7BBloZBDgqTAZ1uuaNOp6B24c102itaj6/Sa8Y1EQBKxYsYJ3vOMdebslZs+ePSa4PXv2jBudl4NlWS2Z7Az5CYIAyzKNVJMaHSB0gFDJprx94BZW5wLpfUUtJKETXavW1ugq6YGWyEI/7wQpOULXWnPXXXcxb948zjnnnLzHLFq0iMcee4yTTz6ZV155hY6OjqoSeltbG6lUCtd1847wM8RisTF92q1Eq8ZeadxaayzLoq0tj0HPMGkQ2gcdYKnm9Hj7fkadm/9zIgRYlkD6kVCh6yeIxMY7X1oF6afQKkA541XBtaBkQn/ppZdYu3YtBx54IFdffTUQtsFlRuRLlixh4cKFbNiwgSuvvJJoNMoVV1xRVTBCiIIrIkfTqrfR0Lqxt2rchuIILcP/lN+U98+IubRVOKELIZDKQSOQyb6WTujhCF0hqc9Ap2RCX7BgAT/+8Y+LHiOE4OKLL65ZUAaDoUHoIN090pxSp/JSYSnFGj8hCpkuF4HSdlqhO9jgCGuLlh6oAGnVZ4RuCpwGwxRGaB/QzRuhBx5aS0QeF3oGyxIoHUVhEXitndBV4IYlFzv/F9hEac3+OYPBUBPCkosCmpPQVeCB0ojoPgWPEQKkiKERaHe4ccHVAZ0puVj1WWxnErrBMJXRfrhbUOEehLqiAg9QWLHiTRSaKOwFCl0tfTQaTMnFYDDUGqEDmllyCdW5Vl51bgZhCSASGhdbXKGrpYuu47enSegGwxRGqABQCC3DkXqDKaXOhfDmQac3uVAtrtDVyqeet0MmoRsMUxWtwy6XTIJpRi96EXVuhnBJikAJB2SLLzxUPkrXL+2ahG4wTFkkoNPbAmks1fgFb7qIOjdDmNBDQVc12uxJRZ1LWyahGwxTFJHuQdfYgEY0o5yh/PJG6FqENfS9IKGbGrrBYKg9KkCg0MIJB+pNKbkUVudmSY/QlbDRra7Q1WX8vBPAJHSDYYoidBD2oAsbhMBqgqArXNhUquQi0Fqg04KuVkXJAK0kuoinaqKYhG4wTFHCkkuAFqEnRcjGJnTfc8Nl/2WUXIQQKGwELZzQAzc0Ler6Lf8xCd1gmKqk1blKxACr4QrdjDpXi+IJTmQUutpqaYVuVp2r66POBZPQDYYpS0adixUBYTe8y6WUOjeDECAIN4rOKHRbkYw6NyBat/cwCd1gmKKENXQJwgFhpevZjaOUOjdD6ES3kHpEoduKZEboQZ02twCT0A2GqUum40IItLBCr0sDKaXOzZAZoSuVUegONCjC2qKCFCiJtErv+VAtJqEbDFMUoQNGdgO2Gu5zCfcSLa7OBUAIhGUhlZNW6A41JsAao6UXllx0/XbxMgndYJiiiNE90cJueMlFyYw6t7hpMTspmt61SLmtmtBDs6W066POBZPQDYapix7xooStg41dtJNR59qxfYoeJwRYQhBoOxR0ea3pRFfSQ6MRthmhGwyGGiN0kBGlhJOiDS65qLQ61ymizoXRfegxwEb7jV8AVROkV9dl/2ASusEwZQnVuWEVXWM1XKEbbvZQOgVlEjqENfRWVejqOntcwCR0g2FqklHn6pEaOtBYha7ySrrQIZ3QLYFSIlyE1KoKXeVDHdW5YBK6wTBFyahzM/9uvEK3HHVuhozPpaUVusqnzB+3akxCNximIEKF6txMRtdpJ3pDFbrKp5wUJITAsizQVmsrdCv4AqsWk9ANhqmI9kN1biYFCKvhCl1RQU05u7hI2GjdoiUXHZQ1ZzARTEI3GKYgQsu0Ojf8t6YJCt0K3OBChBO3oUJXl37BJENJP1Tn1vl9SnocV61axYYNG5g5cyYrVqwY9/zzzz/PLbfcwr777gvAiSeeyHnnnVf7SA0GQ83IiLnC3YoAYaUVuo0RX5Wrzs0QdrmEuxa1okJXBR7oAFU65U6Ikmc//fTTWbp0KXfeeWfBY4488kiuueaamgZmMBjqyBh1LoQ3641T6Jarzs2QWS2qsLMKXWG1ToFB+qG3pp7qXCij5HLUUUcxbVoJ14LBYGgpMptbkEmoQqQVul5D3j/wUmilUCXUuRnCfUUttBVJK3TjdY2v1ijp1l2dC2WM0Mvh5Zdf5uqrr6arq4sLL7yQAw44IO9xa9asYc2aNQAsX76c7u7uqt7PcZyqX9tsWjV2E3djqXvcg73gRqBt5shqUd1ONObQMYH3LTdumRykD3Ai0+nsLG5bBPBcj4gTJRrrANehM6qZUcPrU+/r3R/04doCKzqNzs5Opk+fXpf3m3BCP/jgg1m1ahVtbW1s2LCBW2+9lZUrV+Y9tqenh56enuy/d+/eXdV7dnd3V/3aZtOqsZu4G0u9444k9tCWcgnUSM3c8XyUHCQ+gfctN+7+3TtRMsC3YsTjpUfbrivxfUnKk0yTir4d/4MnZlQdZy71vt5DvTsJPJdkYOHF49i2XfX77b///gWfm3ARqqOjg7a28LbpuOOOQ0rJ4ODgRE9rMBjqyFh1bobG+Vyk76GRCGd6WceHNxHhSlGFReC3lnExq86lfi50qEFC7+/vR6fbiDZu3IhSiunTy/slGQyG5iDytQw2UKGbVedGZpZ1vBCgtQXCQWMhW0yh2wh1LpRRcvn617/OCy+8wNDQEJ/4xCf4wAc+QBCEjf1LlizhD3/4A6tXr8a2baLRKMuWLUu3GBkMhklLnoQeCroas/S/XHVuhkzbosJGI9Beq02KeoBG2LGSx06Ekgl92bJlRZ9funQpS5curVU8BoOhAQgVjF/TI6z0yL3+lKvOzZAxLkptAzaq1RS6sjwR2URpnUZOg8FQM8LEPbaKrrFDha6q/0YXWnoVL4MXwkbrsIauW02h2wB1LpiEbjBMPbLq3JzH04IuGlB20dKvaMQqrHDXIqVoSYWuboA6F0xCNximIBl1bu6kaJgOGqLQ1ZWZB7MrRRVIbaFazbio/Lp7XMAkdINhypGrzs2QKYE0RKEry1PnZhCAsCyU0qF/piUTuim5GAyGWpNW545vW7RA64YodIWuLMEJIRDCQkqNwmk9hW4D1LlgErrBMOXIqHO1yB2hN1Chq4MKEzpYwkIrgRZ2Syl0G6XOBZPQDYYpx4g6N6drWVhhL3qdFboZdW5FJZd026JSrafQHVHn1te0CCahGwxTj7Q6d7y61gJE3RW6I+rcChJcOqFrLJRwsgrdVmBEnVtfFzqYhG4wTDnGqXOzTzRGoZtR52pR/qpJIcCyBKTbFjXge8P1C7KGNEqdCyahGwxTDqGDcAFRvs0lhFV3n0vgu2gt0Vb5oqrckovWApkaqGOUtUMFLihpErrBYKgDmQ6RPM4l3YCELj0XlAa7/I1zhBBYlgU6bVzUApXqr1+QNURJD7RElrmZx0QwCd1gmGKIoi10NtRZoauCUJ1bSUKH9Cgda0Sh67WGcbFR6lwwCd1gmHIUE3A1ZIQuwxG6FdmnotcJkV78lFHotkxC98MRep3VuWASusEw9Si6KKf+xkUV+IDCaZ9d0evCCpGVVeiqFlHohupc6q7OBZPQDYYpR6jOLbCoR1glEv7E0dJDaQvLrnCSUAgEAqxIODHq17dfvmY0SJ0LJqEbDFOOfOrcDI1Q6GrpUU3qCVOihbDCkgutotBVfrjbUgMwCd1gmEoUUudmSBsX66nQ1dJHVWBazJDZVxRACQct6+9trwVa+YzfTaQ+mIRuMEwpCqhzM6Sd6HVV6Gq/KlFVpoYO4Z1Eyyh0G6TOBZPQDYYpRSF1boYRhW4dl//L6kasQgDpkb3SVusodFV1X2DVYBK6wTCVKKTOzZBV6NZvhF6pOjf7ulEj9JZS6DbIhQ4moRsMU4rMsv9cdW6GrEK3nsbFKhM6QmR3OWoVha6Sfqg5aND7mYRuMEwhQjGXHK/OzR6QVujWybjou6l0Iq6uhi6ECOd1W0ShG5oWZUPUuWASusEwtSiozs0QKnTrtcmF76XVuVUkuMzSfylVyyh0Qxe6bIg6F0xCNximFAXVudkDQoVuvWro0nNDda5VuahKiHBfUSl1yyh0Q3VuY0yLYBK6wTClKKrOzR5UP59LRp2rqkzolrBQknD5vxbISW5cbKQ6F0xCNximFkXUudlDRP18Lhl1rqjQtAgjJRelgLRCd7I70TPq3KAB6lyg0MzICKtWrWLDhg3MnDmTFStWjHtea829997LM888QywW44orruCQQw6pS7AGg2FiFFfnZqifQrdadS6EE6LCstDpXYsUFmqSGxcz6lwl6m9ahDJG6KeffjrXXnttweefeeYZtm/fzsqVK7n00kv5zne+U9MA86JVS7QsGQyTjXDkXbxlsJ4K3Yw61452Vf5iASJdcskqdN3BmsdYSzLq3KCC3ZkmQskR+lFHHcXOnTsLPr9+/XpOPfVUhBAcfvjhxONx+vr66Oqq4hdWBra3C7ZvQFhHou3GXKSGoTWx4T8TxPZHRvdtdjQhWtI2+CfwI7THC+tKn3/5DZKxgzj6rSc0MLipy8C2Z1FuP12HnF7ZC3PEXJ4vefG/+zn8wJm0xzLpwELUyeUyMJwkkBZPDUYRw/nLJcJz6dizmbcsOIpo28jINqyhCwINWkTQCGQNFLpOagv0bQPmTfhcuWgZXsdGqHOhjIReit7eXrq7u7P/nj17Nr29vXkT+po1a1izZg0Ay5cvH/O6snEtrF1/Y/Z0BdOqeH2TcRyn8M8tU+AHwBboPqqhcRXEGwBXY8X/h06Rf2JHKU18OE6q70W6F5/d4ACLU/R6T2JKxZ3cMkCy7690d59X2YlVG+h2aOsEwB1I4vseQ0NDdM+aHx5jJ8Bzq7pupeJWysNVNipaeJIwmhxk51Cc3bt6OWzBnOzjQaAYiElQbXRMd7BkDJvUxH+/e17DGthI99+/dWLnyYO7PUJgOXR2do55fPr06XX5XE44oes8pQ9RYMKlp6eHnp6e7L93795d8fsJ5TI78BncuQkv1Vn6BZOM7u7ugj+3FQzQNjyA5fczHK382tQD29tF23Af7W0zifv5Py47B2Ew1U+bnarqd1pPil3vyUypuJPDvXhuitc3byTasU/Z520f7MP2UkgZjmz7BhKkUkn29AXs2x0+Zvkejp9kYOcOsOyaxu0nBgiUzZw5hcs+M5ID/FV77N69h/1H3RUqpQl8SSrwSCR87MAhlhia8O+3bWAX07xd7N6xBWp8158YHiCQmnjO3a1t21XHvf/++xd8bsJdLrNnzx4T2J49e+pWbgHQIgpCYAe9dXuPZiFUCqE8LDl5dmIRKhn2JBf5oO+Ja5KBjWMFBEF9ty8zhMvJkSmU0niDm8t/odahmGvUIMz1JFIqEu6oBTp1VOjaOoHUxb8k2v0kES1JpsYubgr70AVahV8GUkTDGvVE0Cr8fKsUdlD7jhmtPBqlzoUaJPRFixaxdu1atNa8/PLLdHR01DWhh4W0trpc/GYjZCpM6jqYNCY5S4VLl7EKr+zrH4akbyGA17e93rjgpiiBO4xWYVuhP/RG+S/UAaDGtCymfEmgBAlv1J12RqFb4w0kkqkUlvZRJdJOxE0wTbn4gTfmcSFEeul/+HpJNO0arx6h3LR9Eqygf0LnyksD1blQRsnl61//Oi+88AJDQ0N84hOf4AMf+ABBEH6YlixZwsKFC9mwYQNXXnkl0WiUK664ou5BY8cQavKMYmuFpZII5QEaEQyjIzOaHRJCpjISjYLHDCU1KT/sOtj1xhYOPOigBkY49fBTg2jporAIErvKfp3QMj1QGPldup5EKkFyVELPKnRVbRP6QF8/SqmSPdmRVIJO5dGnxvfCW5YAbQEKKdoQKAIvgROtri1QqBSWDgAb2++l5veXqpw20dpRMqEvW7as6PNCCC6++OJaxVMedgxL1lHA3yTC0bkGNHYwQDAZErpKUeqWMZ7SpJSNEhap4b2vFDbZkO4QWnr4xIhW0uWRVudmTIdaa1xfogjn4oMgwHEcSJsMa738Px4fRClJyi78ubZ8D2TANO2xI882eOHionT8VgwpLdyBrThzDq8qJkslQaXAcupz16/8gmbLetCaK0XttvD2cS9L6kK54dZaWFj+JEiM6fpivonv0SRclXZr2Ihgcrs19gaUFwfp4zEdVUHJYWTZf5hg/EChlMSxIwTKJpEMa9YaC4RV87mcVHwItMKNFe7uiLgJZCCZ7gBakYiP/TwJMXIHoUQMqW38oW1VxxQOojxwOsK70RqSVec2sObSmgndagM0VjC5V4lVhJZhucWyQNjYQV+zI8rWFwt1LWVwPYVlCVzasGiNfR5bGeXHUQikaKtoRWeuOjflSbQMsKKd+NohnkhPQgoLjai5Qjdwh1AadFfhfu+Il0QFAbNiNhEk/b39Y38GIchuQ2e3o7ArKjvlki0rWbGa/7yNVudCqyZ0O0zokyHp1QqhUqADNDZaRCZFp0tmglaLwh+ThKuRUuEIcGnHpkV2kWllgkSYyEQsTBjldnrkqHNdX6KUxIp0okWUeDJznoxCt7YjVvwEvnLAKdyD7qQSiMBnnxlRoiJgcHDsStDRJResCJIo0u2vOqRw4leAFQn3UdW1G5Bk1LlBg9S50LIJPQbYWHtR66Klwg4AhAUiUjcfdaUxCZVCicKr3HYOgZIKnAi+aMOxTEKvOzKB0gItYigE7kB5nUW56tywZVHixKZh2RESqVHirjoodIVKEKgSHS5eAoWgq7ONmKWJx8funDR6GzqAgBh6Aq2y2VG5FQEUooZ3/TJIoZVENsi0CK2a0IWNtiLY/uT2OFRC2LLookQb2orUd5PecmNSybC+WCSh98UFSitkdAae3YEtFH19e8+d02QjcONoFaBxUFY7Utl4Q1vLem1YQ1cjCd0Py2nCjhKJRIiPzt819rnIIMBWSSTFe9AjbgIcB8eymOEoXH/sl0puQpciVr1ITPnpvnwBVhTQ2H7tPrtaeqCChqlzoVUTOqFtzdqLWheFSoUfMCuGFk74x9TkSd+R+mLhP8LBuEYpjdM1k8DpRGPx+pb/aVCEUw8/NQjSw6MdZcVQ2PjDO8p7ccbjkp4TcT2Jnf7d2rY9tnWxxgrdweE4aIUSxRO64yax0jF1WJogt5wkCBNwGkk0nBeoYuciSyXTJSgBIoLGqmkZV0kP3UB1LrR4Qq91n2wzyf4sQqRrnBoraO4dSFhfLP4RGUopQBOZ2YWKdKIRDPWVmWAMFSO9YbR0SeppCDuGJIpyyys9hurcsT3owgp/v47jkPT0qI6m2ip0Bwf70EriiiJL65XC8VJhrznQaYMvx36phN9FIz+DsmJowItXPjGaWZmtiYSDFuFg+/0Vn6cQ4QhdNkydCy2c0BFO2GY0SVZUTpSwZp7Z0TwCmqZP+oZfMiVaFlMa2xJYtoNum44WFsEkV5q2MmEPuovvzAIgIBpOvpVBOOJOr7JUGj+Q2Qlv27aRyiKZCu8Ka63QTcUHUUrhRWYVPCbiJpBS0ZYexHc4IFFjdBJCkF0pCmHrYqAs3P4KFAiZc6XniHTahKitSE0XLGbUudI2Cb0kWjgI1N7R96x1mDwzAw/hoEWTe9GVH96i6+Iti0lPZSsyVjSGTxS7TvtRGkB5w2itUfZ0oMIa8ih1ruuFHS4qbdB0HAdPOSSTmUnI2iZ0L5HuQZ9WWCzleElkEDA93eXX6QgsLRno688eE7Ytimxvt7bSrYvx7RXHZGUGhFZ4DcLuskSJV5VPRp2bOX8jaOmEDgprL3C6CO2l26XSyVNYYEWa6quxymhZlFLjeRLbGkn6nmhDCNOLXi90umUxQ1hDLq/WLVSQ/Yi5nkSrgCBdDnAcB4nDcCKdhIRV2xY+P0GgbazphT1PETcJgc+0tjCjd0Qsokj6+0bu+DJOdJWumWs7hiKCTO6pOKZwEDXq8y2c9NqL2qwE0tJHNVDMBS2e0DUW9mRYUTlBsv3eo34dusmtiyLtldFF7BB74gKtNJY1ErdLOxHTi14/gsSYJCFFG1prvETp8lw44k5PiPqSIAh70AEsy0JYERLJ8HenscNVpXl8KlXhJ/BL9GNH3ARISUd7WALpjNnELEl81GrRUCtkk7ECCGEREEMFlf+thGs/Rk8ER8JOoFr93Sl/THmoEbRsQgd70qyonChhy6IX1s7TNHvSN7e+mI89cY1SCh0dmejyrHYcSxqNbh1Q0gflokfpZ7XVhlQW3uCW4i/OqnPDf7pemBGFE/7uhBA4kSjxjEY3q9Atrz5fCluX7kF33ATadnDs8Lg2x6bD0mM1uiKMVamRRCxFtPzFVRkyWovRD2WbEWpzZ6yV11CPC7RyQhdi0qyonCjZ5GmNtDdpEWnqpG9ufTEf/cOgtEJ3zMw+FtgdgOYNo9GtOaE21ycYtZRcWTEkNv5QieudVeeG/0z5YalMjCo52LY9otGtoUI3o80tZR2MuAmEPXYUP83R+HLkSyV0olvIUdUgSbTiFsusNndUws0MqKxadboov1RPQc1p3YQOk2ZF5USxVDIcQVmjPszCQSCbNuk7rr6Yh6GkRiuIzBzpXAgiYS/6ztf/u84RTj1Cba5HipGdurTdjiZCkCi++82IY3+kB13krC9wHIdUOqHXUqE70NePkoqgwBaGGSJuEsseO6LttDW+HNvlIoTF6LZzKdpAhxrdchEqGWpzR/fFpwVzdlB5PT4fWpX+Eqs1LZ3QteVMihWVEyX8oxn7qwhHC7ppE6Pl/CEPpzSWBZFpoxJMNGxdTMX76xjd1CSjzXUZmVgUwiIQMZRX4otfB1l1bkabK3K+sB3HwfUFvh+MUuhOPKHHhwdQWuI6hbW5tu+CDHCssTF1OgJfqezCIUF4VzHarKusGLLC1kVLpSBXayFEuhmhNsv/hQpMyaUSwkmM5q+onCiWSo27M9NWRqNbm9FCRZSpzU26atyIilhHWqPb+qWwyYby4mjpIyNjO0UCHUXL4iWH0epcL63NzUi6Mti2TaBtksnkKIXuxNv4Uonhktpcx00iA0lnzrxpR1qjm9mTM+xyscY0oijRli47lb97k5Bpba41do4oLHVOfJAoA6/h6lxo+YQ+OVZUTggtQXkIkfObF07TJn3z1Rfz4XoK2xobt2XbuKINYTpdao7yh1GIcYlYihiU6Bkfrc5109rcIGfVpuM4+NomnkzWVKEr3UGUBrlPEW2um0AFATMjOSUXRxBFMtAX3qmK7KToqOPstrRGd2fZMY2szM69M65NM4IK3Iarc6HFEzqTZEXlRAhbFmXeWpsW0ZoudKgkpnH1xRyGUqE2186T9F3djmO86LUnSKDzyK2kCH0m0i9ypzpKnet6EiUl2hqb0G3bHqXRrZ1CV/tJfOUgnMLJzXGTCN+jo2PsiLkjahMVAUND4aAt04c+5u8lrdFVbvnlyXCv3DyxitpodLPqXJPQy0dPhhWVE8SSybBslG8C0nKaktAz23IV0+buGQYpJUTGT3R5ot1odOuBTI5xsWTIaHS9Ip0u4QjdB+Hg+hKpJFa0c8wxlmVhO9GwF72GCl2hwkVFxYi4CZRl0RYbmwDbIxZRS2dLLggQloCc5q+AWLjUvuyYknn/5rRVmwWLWXWuMgm9fLIrKlu35CKUm9Xm5qJFpCm96Nltuazi2lytNCoyfdxzvt2e1ui27hftZCOjzVV5FueMaHQLb8UW1tB1NqFbaW1uLo7jEB/VujjR5f9ZbW6JBTYRb3zLIoBjWcx0FK4fti7mLbkQlp10uQoE5Y3pyR9DuhnB8ieW0DPqXL9EZ0+tae2ETuYWqfGj2FoRanPzJ89maXSzG0MXaVsciGuU1thdM8c9l9XobjYa3VqR0ea6jLcVKiszKVjEcqlHNq9wPZlV1OYSanTTLxH2hBW6g4Ohw6VU+57jJhF2/phGa3SFEOHK5BzH0IhGt3SpZERrMf79sivQJ7h5TkadK4vZJevAXpDQW1ujG/bRW3knIJvVuhguKip+zFAyXIA+ugc9g4pOCzW6/UajWysCdxAtXVw9/o5I2FEUUZRbeC4pVEuEpEZpc3PJaHTDlZjWhBW6g8MDaCVJic6CxwglQ21ugUn4Dhv8UQqCcBu6sfGHbnhNaqi0Rjerzc03RyRqo9HNqnMtk9ArotkrKidKsS+jTDeD1eBJ37C+WLzDJZFuWbTyjKp0bJrR6NaYsGXRxXPyy618YihZeJl+Rp0rpSIIZMG7L9u2CZRFynVrotBNDqe1udF9Ch7juEmkVLQXUL10OgKpRzS6QjBuxK9EDKls/FIKBPKvzB6NtqKICa5A1+kuF2k1Tp0Le0FCb/aKygmR1eYWSJ4i04vewFp0dluu4oelPI1l5T9oRKNbGw+IYbw2d9zzIlq0hiyUD+jsxtCqQG3XcRx8ZZNIJKiFQtdPprW50wu3LDpeEukHTC8wb9rhgKUl/b394c+Ssw0djGh0/eHSvegjWov8E5bhbmgTa9cMV4nSUHUu7AUJvdkrKifCiDa30AEWWNGGTvqObMtVuCshyKPNzcWlDctodGtGrjY3l5I+Ex2qc11PoVWAX2AXnVCjGyGedNMK3YnV0JWfwFc21rRi2twESJ/O9vwJtjNqERWS/v7034EQiJxun4xGV6VKD35Kai0yzQgTWBWkpZ+3I6neFPdZpnn22We59957UUqxePFizj333DHPP//889xyyy3su+++AJx44omcd955NQ82HyMrKnuh/YCGvGetyKfNzUWLxrYuFq0vpunNaHMjheP2RDsdtN6X7KQlR5ubS1ajG+8l2jl+XiNM9iJsWQwkVnv+mnZGoxtPxNG0h5tKq2CsZ6gChB8nKJFmIm4SpMpqc3PpiNrEhCSR1uiGV2HsZ68SjW6pZB02I4QaXV3lbkNaeQ1X50IZCV0pxT333MN1113H7Nmz+cIXvsCiRYuYP3/+mOOOPPJIrrnmmroFWpjW1ejm0+bmEn64Gjfpm6kvFrq1h7AHPdTmFp7oGq3RdYosKDGURsmw00kX6eXWVhsysPAGtoxP6KPUuSlPohnR5uaS0egmvKHsKDasN0+rKnahkgSqdA+6tu2sNjeXNsem0/YZTmt0w7DGf7kFIoqSpZw2cpw2d9wh2bmrgeq3j1N+wz0uUEbJZePGjcydO5f99tsPx3F4+9vfztNPP92I2MqjhTW6lkoWnZyBzKTvxFeulR1TGdrcvmGd1uYWli0Fdieg2bJlax2inFr4yUG09ovaCrUdti56w3kWF41S57qexLascWKu0di2TcLV6d2qNKLKuZBEMolN6cTmuImCLYsZptkQBCO96PlSlyJWss2yHK1FZoBl+xMYJDZBnQtljNB7e3uZPXt29t+zZ8/mlVdeGXfcyy+/zNVXX01XVxcXXnghBxwwvvyxZs0a1qxZA8Dy5cvp7i4s6ymGlXqDzo4OyGy+IKYDivYqz9dIHMcZ+bn73gAvBu3je7mz+BLiA3TPjEK0cB2ydrwGsh3axo6+bcuiszN8LBWk0Bpm7D+fWGf+UbqfnAWezXDvDrqPP77uURdizPVuIUbH3R/0kRIK5czK/g5y0boNEcSw/IHxP2+QAL8N3A6wJNFYG06B8wCkUin84d10dEwDFWHWjHaYVt41HB33//zP/6C1JhCxgnEDdCIJ2tqKHrNPm+R/hiWdnZ0EnodjR8cfr2dgeZrpnTFi7QXuMFO7wItAMB1iI68f/fkO72jaiURdqPKzE49Y+Hak4M80ffr0unwuSyb0fMY9kfPtdvDBB7Nq1Sra2trYsGEDt956KytXrhz3up6eHnp6erL/3r27uMO5EN3tAfFEAtLLy21fYgVDDFZ5vkbS3d2d/bljQzuIuC6yyE7jQvlEfJf4jtcI2us/Sm8b2IXjusicO57Ozs7s8uv+YYklIBCCIJ4/dqUjKATDfdur/j3XgtHXu5UYHffgrq14boJhuT9+gesN4KgIbqJ/3M8rgiHa48PgpxgcDpCBxC1yniAI8FKS/oE4ndIjvud1glR5/dSj497++lakDHDt6SQLvJ/tu3iJBEKNGBXzEdE+buAzPDSE62qCQI473vJhmhRsf+3PdO67IO95HPcNYsN9YZfPKCPo6M83QMTXyMHtxKPVfXbcZJxAFv6ZbNuu+nO5//6FN9ouWXKZPXs2e/aMKFz37NlDV9fYkWJHRwdtbWHZ4LjjjkNKyeBg4zozmrWicqJYKoXWxW9HdSNbF7Pa3OKHJVN5tLm5xDpQwoYmuGj2NqQXR0sPv0APeoZCGt2MOteXOq82NxfbtvG1Rcr1QoVulS185WlzE0hfMq3E0LLDAQidLkKIcStFYbRGt4jTpoA2N5eJaHQz6txmUDKhv+lNb+KNN95g586dBEHAunXrWLRo0Zhj+vv7syP5jRs3opRi+vTCk2q1JqPRbanWxULa3FzSGl2nAZO+4ey/zDffNAbXl+O0ublYto1HG/YE+5gNgB9HYRW1X0JhjW5GnZvyLbQM8ItI1yAsmwQ6QjyZmpBCtzxtbhIlA2aUmDfPaHT7+/rDz6cIF0mNRtvpXvQiGt1C2txcwhXo1f3cGXWubLBpEcooudi2zcc+9jFuuukmlFKcccYZHHDAAaxevRqAJUuW8Ic//IHVq1dj2zbRaJRly5aNK8vUk+xegEE/MrZvw953ImS1uSX+SCHU6E505Vq5MZXU5iY1Uuq82txcXN1Bp2j9HaWajS6gzc1FiVhWo2tHRpK2SKtzU74ItblO4Vo1ZDS6kbCrZEb1Cl3tJ/BlcW1uxE2ktbnFB4CdWY3uELO7w52LtILRl0VYDgFRdJEVyuF2j6U/u1pEsNVAVS2bzVLnQpl96McddxzHHXfcmMeWLFmS/f+lS5eydOnS2kZWCc1YUTlBstrcctZ2NUijm3dbrhx2p7W5dnvpFXCeaGMfYTS6E6aANnfcYSKG0qFGt33WwSNPaB+0TyqIEiiJHS3eipfR6MaT8QkpdIVMEJT4InLcJEqM1+bm0haxiFk+8XhiZKNopcclMEmsaOti0ZXZowg1uhpLDqKs8X39xRhR5zbW4wJ7wUpRYJRGt3VKLqE2t3jyzNAojW459cWMNldHS5fUfLsDy2h0J0RWm1vG2EtbbQTKxh0c2yoqtERoheuFd1b5tLm5OI5DwtNphW7lbYsyCLB1qixtLk7pn82xLGY4Gs93s9vQ5RMrShErLBRTJVZmj0ZEAFWVRrdZ6lzYWxI6GY1u69zeC5VM386Vk9DDlWvI+ib1cuqLg4m0NnefIq2WaQKnA43FNqPRrRo/NQDSxS1jtKes9FZsw2Mtl+GdoMANVF6ZWj4yGt1qFbqDg0OgVFFdAYQlF6vAgqJcMhrd0IluofL4+EKNbpBXo5vV5pZRvpqIRrdZ6lzYqxJ69ZMYzcDKOsfLq+eFk7717Rwqp744lAz/ivJpc3PJanT7ttckvqlI4A6hpUuK0ndEwo6mt2LrH/tEWp2b8iSijDkbyGh0VdiFVYVCd2CwH60lbgltru25WCUmKDN0pjW6QgiEsNBq/MS8smJoBKmh8ROj4Spot6x5q1CjG6lqcVGz1LmwVyX0zIrK1tDoZjeRKIORpcj1LV2E9cXixxTT5o6jfTpaWCi3BU2YkwSVbln0nNmlDybcik3ltO8KLVEKgkCWvRzdcRwCZeP5sqoReioxNGFtbi4djkAqReC7CCt/yUVZ7QQFNLqltLm5aCuCqGLuqlnqXNiLEnpLaXSz2twyjxcOGhtrIkuRS1FsW65RpFyNU+anRjhRfGINddHsbWS0udou3pmSQYpoWMobhVA+vlShNreMEh+ke9GVTcrzq1Lo+snBktrciJtA+n5BbW4unRGBjWRoaAgLgcpTnw/3V7Xxh8ffFVoyGa4CLaDNHX+u6jS6zVLnwl6U0FtJozuizS1T9tCAvVNHtuUqPFwKpA5HbBV8alxi2KI17pomI6W0ubkoEQMVoEcXmHWAF4Ta3CDPFnb5yGh0kylZlUJXefG0Nnefwu/hJRHSp7OMjimAjoggIgIG+wdCF02e8mCo0XVQqT3jngu7dSpop65So9ssdS7sTQk9q9Ed/4ucbIgKJmcy1FujW442d08ctNLYBbYvy4cnOoiY1sXqCRIVJQdJDI3GTYzczYl0QpeBxCrRg57Btm2EHSXuBiMK3QoQQRK/DG2ulpqOjjITetQmJhSJRDzvRtEQTpb6xFDB+LtCoZIVGRBHa3QroVnqXNiLEnojV1ROFCGT6cmZ8hcehK2L9Zv0DRN6smh9cc+QQCmFKtHHPBrPasMWEs8zuxdVigw8kC6qiDY3F221IZWFP7A5/UCozvU8FWpzI+VP1DlOhIQ3otCtBKGSyHK1uWUOEEKNribhJrEsUXDgLEU0VA6PRsvQGlnBYHtkwWKFd/1NUufC3pTQyayonPzuEEulENIte3IGMl089dPollNf7I+H2lyKaHNzCZzQo71ty7YJxzjVCFJDaO2XXKo/mlCj6+DH01uxpdW5bqBLanNzcRyHhAuhQrf8xUWVaHOxK1uFOc3WoUZXiIKj4Hwa3fBvJyi55H80mfKjXemCReWXtRq1HuxVCb1RKyonSjja0ZUtKRYRBAoRDNUpptL1xaGURimIdJXXcQGZXnTBnh2mF71SAncQLT1cyiuTQKYX3UEmQpNfuOxfhXvAltnvncG2bRLpG6tKRugD/QMoqQgo/kUUcRNFtzHMR4et8ZUfvq5AQpcihtYKf1R31YjWooJrUGUzgjYj9NrQqBWVE6Xc5cej0enkX6+JUaFSJe9G40mNbUOko/ySi45OQwsLN9E/ofimItIbRksXl33Kfo0QFoGIofxwYCN0EPaD+znikzJwHIeUbyOlQlSwqG14qB+lJa5d+E7O9l2QEqfChN7pgC/DhoLcfUUzKCuGUuHuTRlCrYVbcHPsvAgR7ukrKxtECeWXtKjWi70soWc0upM7qVuqPDfHaLSIoBHY9Zj0TW/LVWqEnnQVVoV/gBmNrggm/53TZCPU5gYEJbS5416no+jMYiAdoKSPlKBKaHNzcRyHlHLwfb+i9j03ORS2LHbMKXzujDa3Qn9VhyMQKDzPTe+oNB5ttY/T6I5oLcovc0I6p1Rw1x+qc5vX1bWXJfTGrKicECoA5Vfe1CQye6f21zykcrblgsz2ZZWd27JtXN2OhdHoVowfDzcQL3N1Z4bRPhOhA6TvIZVCisqSmW3beCqCF6iKJuRlahilBHLm3xU8JuIm0trcSkfoggiKRCKBKDAKzpSd/PjIatEw/vJWZo8mnLsqf4DYTHUu7HUJPbOichJ3ushkWptb+aWvl0Y3rC/6RRPHYEIjlabSATqAR7vpRa8CHcRRVfQzhxpdReCF7bFB4IabW1S4ctFxQh2t66vKFtgECXxlI+xi2txkqM3trOxLJqPRTSWTFEpfoUY3gvZGSiWWSlU1URk6oryy9QfNVOfCXpbQRyYxJrHdL6vNrWzUBYAVqcukbzn1xZ2DCiklRMrvuMjgWW2mF71CtFIgUxWtVciQ0ei6Q9tA+8jAw5eU1ObmIoTAdmK4fmUbRYfa3FI96IlQmxutLPG1R2xilsL1XIqlr1CjO5KEK1qZPYpw7kphlVlHH1HnNn6VKOx1Cb3+KyonTJBMa3Mr/4VXevtXLuVoc3uHFVppVGxaxef3rVCju6e39fb2bBZeahitfGQVCV1bbQQ6rCELLQmCUMpVjjY3F8dxSAWibIXuiDa3VMtiElGGNjcX2xLMcDR+4FEsQ4/R6CqvqtWuQFqjq8vW6I6oc80IvSbUe0XlhJGJsrW5uYxodGu7wCgrCitSBuobViitcWbuU/H5sxrdLaZ1sVy85ABID6+KTRJGa3SF9vF9hWVVcUdIWEdPeaJsQdee3r7ytLleAlHphEyaTksjZVCwbRHGanQr0ebmUqlGN6POVaLxYi7YSxP6pJZBBQnSQueKX1qvSd9Qm1v8mKGkAgGRmZV1XADI2HQ0gnhf4b0eDWPxk/2hNleUv4grw4hGty/tcdFQZUIPR+ihn6Qc+nr3hG2SVu20ubl02OCX2O0r/FITuEPby9JaFA62Mo1uRp0rK+ymqRV7YULPaHSbs+t2SWR5exrmo16TvuXUF+PJsMOl3A0Sxpy/bVpao1ufRVF7I34qvajIrmz7swxStKGkhwpc/EBXtqBmFI7j4EubwCtvMj4+2BtqcyOFv/hDba4sW5ubS2ckraCQhSfaldWGVDbe4Na0aqN8bW4ulWh0m6nOhb0woYcrKiepRlfrsEdeVGZvy5Ke9K14KXIxsvXFUj3osuJVfRlGNLrV7U05FQlSQyitoUxt7rjXEwEVIH0XqWRVczYQllxcFSHwyivzpRKDaK1xp+9f8JhQmxswo8qE3uEILAJ8Txb0uaisRndHusOlfG1uLpVodJupzoW9MKGPrKicfBpdob20ta7KVWR1mPQtp74YSI3nVabNzcWlDZtJetc0CZHecFU13wwZja70UyglCYqUQIrhOA6+cvC98r6Mg9QgQQltbsRNVKTNzaUzYuHoAN/zUfn2oQOwY0gcZGp35drcXCrQ6DZTnQt7Y0IXDhoxKVsXhUyC9sPFIlUSrlyr3aRoOfXF3UOglKpIm5uLJ9qJWCahl4v2hyeUGDIaXS/ZTxAoLLu67dBs20aLKL7vl6XQVX4cv8QXkZPW5rZXmdA7ohYRSyIDmXfXIkgrEIihg1TF2txcwnk5iVClyy7NVOfCXpjQJ7NGV6hU2Fs8gduxWmt0y6kv9sYFUsqKtLm5uFYHjgiMRrcMZOChA7eijS1yyWh03fggGlWRNnf8uaL4sjxBlyUTqLK0uU7Z2txcYo5NuyWRSqHy7CuaQRID6VfUQ5+PTDNCWRrdJoq5YG9M6NRvReVEsTIJvQIdai611uiWU1/sj+vwD6dzZtXvI9PWxS2bN1d9jqlCkBpCKQ9fV/85URmNrhdHUJk2dxx2BC8ordBNJBII5aFKaXO9JBU7JHLotBRSa2SRPwMlomG7ZRlzRMUY0eiWMUhsojoX9tKEHmp067cZRLWEdTgq0+aOO0ltNbrl1BcHkxqtNdGu6jouYESj27tza9XnmCoE7iA6cHGp/o5Ip30mSDe8a50IIkJQxgi9v78fJSV+WdrciaWedluhdahzLoS02pBah50n1bQsZqhgBXoz1bmwlyb0yarRrUabm0utJ32FSpbU5iZSGssCp30CCSY2HS0sPKPRLYn0hpF+CldU3vOfIaPR1UqhJvhnLuwogRYEqeKT8fHhQbSSpOzpBY/JanPtif0dtNsKhUYGhT+9SoQa3SC5O5wkrpasRrd051wz1blACeFCmmeffZZ7770XpRSLFy/m3HPPHfO81pp7772XZ555hlgsxhVXXMEhhxxSj3jLYoxG125Og38+qtlBPJcRjW4vQftBEzxZeluuEl8yCVdiT/APkGh7qNH1J/Eq3kmC8sJl/5Vqc3OROorWuqKtDvNh2VGktHHjO2nb58iCx3mJQZRWeB37FTwmkgq1uV2xKlt303Q6gKtxkx505U9j2mpDYhOkBolMP3BC71eORrfZ6lwoY4SulOKee+7h2muv5fbbb+d3v/sdW7eOvW1+5pln2L59OytXruTSSy/lO9/5Tt0CLoeRFZWTqHVRh9rcCXc0CRuEU/bKtaKnymzLVSIo16vCg56D0eiWj6pSm5tLKOlSeHqCi1ysCFLbePHiJYfAjSO1QM2cW/AYx0ugAp8ZsYndNXTYYKFJxAtPeGY0uilPTfzOWDglmxEy6txmmRahjBH6xo0bmTt3LvvtF37rvv3tb+fpp59m/vz52WPWr1/PqaeeihCCww8/nHg8Tl9fH11dExth5OO59X/jry+8Fi6xLUDMCTh6Pw/FL5l4Bq0dltC8tmcGfcmJLbA5cl+PtsgraF6tSUwv7diHeJH6oAK6vAEO2PDEhN4rPt1nWiTO86sb94UvhNX0UVOlCKEYTM7gf4YmFvfsWBSmdfDinv2QVH8urR3auiPEX9sO/134d+egSaUUh/z5/yt2NoLAp72CfWnzMS0GjlAMxzt55cVCP5uF1RXl9d2C19YX/zISor/o52S/aUnmzRxC/aX4Z1cg2dw/jwG/+PWevU8fhx5a9JCqKJnQe3t7mT17ZA/J2bNn88orr4w7pru7e8wxvb294xL6mjVrWLNmDQDLly9n//0LryYrxP7v2Z+l76n4ZYYiLG52AIa68q6anOXcmpylVswHji7ryKmVLEre9+g8q6NEzu1LOccA9PT0sHz5cpYvX15JjOO45pprJvT6ZtKqsZu4G4uJu7G0aty5lEzos2fPZs+ekX0s9+zZM27kPXv2bHbv3l30GIPBYDDUl5IJ/U1vehNvvPEGO3fuJAgC1q1bx6JFi8Ycs2jRItauXYvWmpdffpmOjg6T0A0Gg6HBlKyh27bNxz72MW666SaUUpxxxhkccMABrF69GoAlS5awcOFCNmzYwJVXXkk0GuWKK66oa9A9PT11PX89adXYTdyNxcTdWFo17lyEzlcANxgMBkPLsVeuFDUYDIapiEnoBoPBsJcwQWtP7Vi1ahUbNmxg5syZrFixAoD//u//5tvf/japVIo5c+Zw5ZVX0tHRQRAE3HXXXbz22msopTj11FP5x3/8RwBeffVV7rzzTjzPY+HChXz0ox/N20I52eL+8pe/TF9fH9FoqNa97rrrmDmzerthreP+1re+xaZNm7Asi4suuoijjw67gCf79S4Ud6Ov9+7du7nzzjvp7+9HCEFPTw9nn302w8PD3H777ezatYs5c+bwmc98hmnTpgHw4IMP8qtf/QrLsvjoRz/KscceCzT2mtcy7kZe80rjHhoa4rbbbmPjxo2cfvrpfPzjH8+eq9Gf8QmhJwnPP/+83rRpk/7sZz+bfeyaa67Rzz//vNZa6yeffFLff//9Wmutf/vb3+rbb79da611KpXSV1xxhd6xY0f2NS+99JJWSumbbrpJb9iwoSXivuGGG/TGjRvrGmu1cT/66KP6zjvv1Fpr3d/fr//1X/9VSymzr5ms17tY3I2+3r29vXrTpk1aa60TiYS+8sor9ZYtW/QPfvAD/eCDD2qttX7wwQf1D37wA6211lu2bNGf+9zntOd5eseOHfqTn/xkU655LeNu5DWvNO5kMqlffPFF/fjjj+vvfOc7Y87V6M/4RJg0JZejjjoq+w2f4fXXX+fII0MZ0Fve8haeeuqp7HOpVAopJZ7n4TgOHR0d9PX1kUwmOfzwwxFCcOqpp/L0009P+ribQSVxb926lWOOOQaAmTNn0tnZyauvvjrpr3ehuJtBV1dXVljX3t7OvHnz6O3t5emnn+a0004D4LTTTstev6effpq3v/3tRCIR9t13X+bOncvGjRsbfs1rFXejqTTutrY2FixYkL17yNCMz/hEmDQJPR8HHHAA69evB+APf/hDdoHTSSedRFtbG5deeilXXHEF7373u5k2bVpeTUFvb+O3oqs07gyrVq3i6quv5qc//Wne1bfNivvv//7vWb9+PVJKdu7cyauvvsru3bsn/fUuFHeGZl3vnTt38tprr3HooYcyMDCQXbPR1dXF4GCoqM29trNmzaK3t7ep13wicWdoxjUvJ+5CTJbPeLlMmhp6Pi6//HLuvfdefvrTn7Jo0SIcJwx348aNWJbF3XffTTwe5/rrr+fNb35zU5JgPiqNe7/99uPKK69k1qxZJJNJVqxYwdq1a7MjiWbHfcYZZ7B161auueYa5syZwxFHHBHuNTnJr3ehuIGmXe9UKsWKFSu46KKLit6dFbq2zbrmE40bmnPNy427EJPlM14ukzqhz5s3j+uuuw4Ib6s3bNgAwH/9139x7LHH4jgOM2fO5IgjjmDTpk0ceeSR4zQFs2ZVv8tOo+Leb7/9snG2t7dzyimnsHHjxoYn9EJx27bNRRddlD3uuuuu4+/+7u/o7Oyc1Ne7UNxAU653EASsWLGCd7zjHZx44olAWArKmEn7+vqYMSO0EOYqN3p7e5k1a1ZeFUe9r3kt4obGX/NK4i5EM673RJjUJZeBgdBnrpTigQce4F3vCr1x3d3d/PWvf0VrTSqV4pVXXmHevHl0dXXR3t7Oyy+/jNaatWvXjtMUTMa4pZTZW78gCPjTn/7EAQccMGnidl2XVCrcAeq5557Dtm3mz58/6a93obibcb211tx1113MmzePc845J/v4okWL+M1vfgPAb37zG44//vjs4+vWrcP3fXbu3Mkbb7zBoYce2vBrXqu4G33NK427EJPlM14uk2al6Ne//nVeeOEFhoaGmDlzJh/4wAdIpVI8/vjjAJxwwglccMEFCCFIpVKsWrWKrVu3orXmjDPO4D3vCTWZmzZtYtWqVXiex7HHHsvHPvaxurYY1SLuVCrFDTfcgJQSpRRvfvOb+chHPoI1wX0XaxX3zp07uemmm7Asi1mzZvGJT3yCOXPmAJP7eheKuxnX+29/+xvXX389Bx54YPb6nH/++Rx22GHcfvvt7N69m+7ubj772c9m51UeeOABfv3rX2dbLhcuXAg09prXKu5GX/Nq4v6Xf/kXEokEQRDQ2dnJddddx/z58xv+GZ8IkyahGwwGg2FiTOqSi8FgMBjKxyR0g8Fg2EswCd1gMBj2EkxCNxgMhr0Ek9ANBoNhL8EkdIPBYNhLMAndYDAY9hL+f1M5/uNnOsV5AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"df_least5=df_can.sort_values(['Total'], ascending=True, axis=0)\\n\",\n \"df_least5=df_least5[years]\\n\",\n \"df_least5=df_least5.iloc[0:5,:]\\n\",\n \"df_least5=df_least5.transpose()\\n\",\n \"df_least5.head()\\n\",\n \"df_least5.plot(kind='area',alpha=0.45)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question**: Use the artist layer to create an unstacked area plot of the 5 countries that contributed the least to immigration to Canada **from** 1980 to 2013. Use a transparency value of 0.55.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"ax = df_least5.plot(kind='area', alpha=0.35, figsize=(20, 10))\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Histograms\\n\",\n \"\\n\",\n \"A histogram is a way of representing the _frequency_ distribution of numeric dataset. The way it works is it partitions the x-axis into _bins_, assigns each data point in our dataset to a bin, and then counts the number of data points that have been assigned to each bin. So the y-axis is the frequency or the number of data points in each bin. Note that we can change the bin size and usually one needs to tweak it so that the distribution is displayed nicely.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question:** What is the frequency distribution of the number (population) of new immigrants from the various countries to Canada in 2013?\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Before we proceed with creating the histogram plot, let's first examine the data split into intervals. To do this, we will us **Numpy**'s `histrogram` method to get the bin ranges and frequency counts as follows:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"India 33087\\n\",\n \"China 34129\\n\",\n \"United Kingdom of Great Britain and Northern Ireland 5827\\n\",\n \"Philippines 29544\\n\",\n \"Pakistan 12603\\n\",\n \"Name: 2013, dtype: int64\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# let's quickly view the 2013 data\\n\",\n \"df_can['2013'].head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[178 11 1 2 0 0 0 0 1 2]\\n\",\n \"[ 0. 3412.9 6825.8 10238.7 13651.6 17064.5 20477.4 23890.3 27303.2\\n\",\n \" 30716.1 34129. ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# np.histogram returns 2 values\\n\",\n \"count, bin_edges = np.histogram(df_can['2013'])\\n\",\n \"\\n\",\n \"print(count) # frequency count\\n\",\n \"print(bin_edges) # bin ranges, default = 10 bins\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"By default, the `histrogram` method breaks up the dataset into 10 bins. The figure below summarizes the bin ranges and the frequency distribution of immigration in 2013. We can see that in 2013:\\n\",\n \"\\n\",\n \"- 178 countries contributed between 0 to 3412.9 immigrants \\n\",\n \"- 11 countries contributed between 3412.9 to 6825.8 immigrants\\n\",\n \"- 1 country contributed between 6285.8 to 10238.7 immigrants, and so on..\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We can easily graph this distribution by passing `kind=hist` to `plot()`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAf0AAAFRCAYAAAB+EnQdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAA+qklEQVR4nO3deVwTd/4/8FfCjREIhKOgoiiK1qveoogKVm3VqosH1irqelRtPVp7WK1t1UrX4o3WWvFqtx7fFevaw98CCh5YrHijIN6uIEKQQ0Guz+8PH2QNl1ESgszr+Xj4kEwmM+/3TJJXZjIzkQkhBIiIiKjOkxu7ACIiIqoZDH0iIiKJYOgTERFJBEOfiIhIIhj6REREEsHQJyIikgiGfg1r3LgxlixZYuwyXipFRUWYOHEiHBwcIJPJcPjwYWOXVM6NGzcgk8lw9OhRo9YRFBQEf3//GpnX7du34efnh3r16kEmk9XIPMnwauI5tHXrVpiamhp0HlQJQdU2fvx44efnV+F9AMSOHTs0t9PS0kRubq5O0z1y5IgAIK5fv66PMl9aO3fuFBYWFuLYsWMiJSVFPH78uMLxyi7rmlRUVCRSUlJEQUFBjcxvx44doqKX74MHD4Rara6RGqZNmyZat24tkpKSREpKSo3MUxezZs0SXbp0EVZWVsLExKTCcc6dOyfeeOMN4eDgIBQKhXjrrbfKvc58fX0FAK1/bm5uOtXw66+/iv79+wt7e3thaWkpmjdvLqZOnSoSExOr295ze973kZp4Dj169EikpqZWezphYWGid+/eQqVSCYVCITp06CB+/PHHcuMlJiaK119/XVhZWQkHBwcxdepUrffhvLw8ERQUJNq3by/MzMxE06ZNK6z5zTffFA0bNhQWFhbCyclJvPXWW+LixYvV7qMmcUu/hjk6OqJevXrGLqNCBQUFxi6hQleuXIGbmxu8vb3h4uICc3NzY5dUjomJCVxcXGBmZlat6VR3Hdja2kKpVFZrGrq6cuUKunTpAk9PT7i4uFQ4jjGeU8XFxRgzZgymT59e4f2pqano06cP7O3tERMTg6NHj6K4uBj+/v7Iy8vTGnfMmDFISUnR/Dt9+vQz5//VV19h8ODB8PDwwN69e3H58mWEhYXB3NwcCxYs0EuPhlBYWAghRI08h6ysrODs7Fzt6URGRmLIkCH47bffcPr0aYwePRrvvPMOdu3apRknNzcXfn5+MDU1xfHjx7F792788ccfmDRpkmac4uJimJubY8qUKRg9enSF85LJZOjXrx92796NxMRE/PrrrygqKoKfnx/y8/Or3UuNMfanjrrgebb03d3dxeLFizW39+3bJ9q3by+srKyEra2t6Ny5s4iPjxfXr18vt5Xh6+srhBCipKRELF++XDRp0kSYmZkJDw8PsXLlSq35pqeni4CAAGFtbS2cnJzEggULxLhx47Tq9PX1FRMnThQLFiwQLi4uQqVSCSGE+Omnn0SXLl2EjY2NcHBwEG+88YbWFkppbT/99JPm03OLFi3E4cOHxZ07d8TAgQOFtbW1aNmypYiJialy2T2rl7JbW+7u7pVOq+yyBiDWrFkjRo4cKaytrUXDhg3Fnj17xIMHD8SYMWOEQqEQTZo0Ef/3f/9X7d5KH3fkyBHNsPj4eNG1a1dhYWEhPD09xZ49e8qtfwBi9erVIjAwUNjY2IiAgAAhhBDz588XXl5ewsrKSjRo0EBMnTpVPHjwQAghxKFDh8o9N8aPHy+EKP9c1OW54u7uLhYuXCjef/99oVQqhZOTk/jggw9EUVFRlcu6ovlX1s/WrVtFy5Ythbm5uXBzcxOfffaZKCws1FrPEydOFJ999plwdHQUtra2Yv78+aK4uFh8+eWXwsnJSahUKjF//vxKaypry5YtFW7pb9q0SSgUCq29MhkZGQKACAsL06pp0qRJOs9PCCH++usvAUAsW7aswvuf3oKOjY0VPj4+wtLSUtjZ2YnAwEBx7949zf2LFi0qt8VZdqu9tMejR4+K1157TVhZWYlOnTqJv/76SwghqnwfKX2urFmzRri7uwuZTCZycnIqfD/7+eefRbt27YSFhYVwd3cXc+bM0dpSPnLkiPD29hYKhUIoFArRtm1b8ccff1S6nMqum2f18TwGDRokhg8frrm9ceNGYWlpqXn9CCHEgQMHBABx7dq1co+vaLlX5syZMwKAOHPmzHPXaSwMfT140dBPSUkRZmZm4ptvvhHXrl0TCQkJ4qeffhLnzp0TRUVF4pdffhEARFxcnEhJSREZGRlCCCHWrVsnLC0txcaNG0VSUpLYsGGDsLCwED/88INmPoMHDxaenp4iKipKXLhwQQQFBQkbG5tyoa9QKMTUqVPFxYsXxblz54QQT3aZ/fvf/xbJyckiPj5eDB48WDRr1kyzW730jcTDw0OEh4eLxMREMXToUPHKK68IPz8/sXfvXpGYmCiGDx8uGjRoUOUu72f1kpGRIT744APRuHFjkZKSItLS0iqdVkWh7+zsLLZu3SquXLki3n33XWFlZSUGDBggtmzZIq5cuSJmzpwprK2tRXp6erV6Kxv6Dx8+FC4uLmLQoEHi7NmzIjY2VnTv3l1YWVmVC317e3uxZs0akZycrPlwtXjxYhETEyOuX78uIiIiRIsWLcS4ceOEEEI8fvxYrFu3TgAQKSkpIiUlRfOGVva5qMtzxd3dXdjZ2Ylly5aJpKQksXPnTmFiYqIVgGWlpKSI7t27izFjxmjNv6J+Dhw4IORyufj6669FYmKi2Llzp7CzsxMLFizQTM/X11fY2NiIjz76SCQmJorNmzcLAGLgwIFi3rx5IjExUWzdulUAEL/99luldT2tstBfu3atUCqVoqSkRDPs4cOHQi6Xi6CgIK2a7O3thUqlEp6enmL8+PHi5s2bVc5z1qxZwtrautKvoJ5efvXr1xeBgYHi3Llz4siRI6JNmzaiZ8+emnF0DX2ZTCZ8fHxETEyMuHTpkujXr5/w8PAQhYWFVb6PjB8/XtSvX18MHTpUnD59Wpw7d04UFhaWew5t2bJF2NnZie3bt4urV6+K6Oho0aZNGzF27FghxJOvtpRKpZgzZ45ISkoSSUlJYu/evVV+4K8o9Kvq43n4+PiIKVOmaG6PGzdO9OnTR2ucgoICIZfLK/w6UNfQz87OFjNnzhRubm7i4cOHz1WjMTH09WD8+PHCxMRE1KtXr9y/qkI/Pj6+yu/aKvsurkGDBmLevHlaw2bPni2aNGkihBAiKSlJABARERGa+wsKCkSDBg3Khb6np6coLi6usr/SraCjR48KIf4XcE9vMcbFxQkA4ttvv9UMK+3v/PnzlU77Wb0IofuLsKLQnzVrluZ2WlqaACBmzpypGaZWqwUA8e9//7tavZUN/e+//17Uq1dPa+vi0qVLAkC50J84ceIze9u7d68wNzfXrKvKvtMv+4aty/J1d3cXgwcP1hqnf//+YvTo0VXWVNGWcEX99OzZU4wYMUJr2KpVq4SlpaUmHH19fUW7du20xmnVqpVo3bq11rC2bduKDz74oMq6SlUW+pcuXRJmZmZiwYIFIi8vT+Tk5Ihp06YJAOL111/XjLdx40bxxx9/iPPnz4v9+/eLTp06CUdHxyqPXxg4cKBo06bNM2tbsGCBcHNz0/pwULrVGB0dLYTQPfQBiFOnTmnGiY2NFQDE5cuXK3xMqfHjxwtbW1uRk5NTbvjTzyF3d3exYcMGrXGio6MFAKFWqzWvoUOHDj2z71IVhf6z+tDFjh07hJmZmdZ0+vXrJwIDA8uNq1KpxD/+8Y9yw5/1fvPRRx9p3tu9vLzElStXdK6vNuB3+nrStWtXnDlzpty/qrRt2xb9+/dH69atMWzYMKxevRq3b9+u8jHZ2dm4c+cOevXqpTXc19cXN27cwKNHj5CQkAAA6Natm+Z+MzMzdOrUqdz0OnbsCLlc+2lw5swZDBs2DE2aNEH9+vXRqFEjAMDNmze1xmvXrp3m79LvdNu2bVtuWFpa2gv3Ul1P1+jo6AgTExOtGpVKJczNzcvVWN3eEhIS0LJlS9ja2mqGeXl5wc7Orty4Xbp0KTds79696NWrF1xdXaFQKPD222+joKAAqampVbWr5XmWb/v27bXGcXNzw71793Se19PK9nPx4sUKa8jPz8fVq1c1w55e5sCTZfz0Mi8dVtky15WXlxd++uknbNmyBfXq1YNSqUR+fj46duwIExMTzXhTpkzRvD4HDx6MgwcPoqCgAGFhYZVOWwih05kMFy9eRLdu3bSOT2nXrh1sbW1x8eLF5+pHJpNpLTs3NzcA0Gn9tWzZEgqFotL779+/j5s3b2Lu3LlQKBSafwMHDgQAJCcnQ6lU4u9//zv69++PgQMHIjg4GImJic/VQ3X7AIBffvkFkydPxubNm9GhQwed5/m85s2bh9OnT+PQoUNo2rQphg0bhpycnOeejrEw9PXEysoKzZo1K/evKiYmJvj9998RFRWFzp0741//+heaN2+OAwcOPHN+ZZ+sooIfS9TlCV32oMJHjx7h9ddfh0wmQ1hYGOLi4nDy5EnIZLJyB2U9fdBa6bwqGlZSUlLtXl5URQfWlR0mk8nK1aiP3nR9Qym7Dv7880+MGDECvXr1Qnh4OOLj4/Hdd98BeLED43RZvmUPjqxomeiqogNVK6vh6eEVrRdd1tWLGDFiBO7cuYOUlBSkp6djy5YtuH37Npo2bVrpY+zt7eHl5YUbN25UOk6LFi2QnJys03qq7PlROlwul5dbV4WFheXGl8vlWh9WdH3dARWvq6eVTmP16tVaGzNnz57FlStX0KZNGwDApk2bcOrUKfTr1w/R0dFo3bo1Nm7c+Mz566uPnTt3YtSoUfj+++/xzjvvaN33yiuvlPuwXFhYCLVaXekBqFVRqVTw9PRE7969sXfvXty4cQM//fTTc0/HWBj6RiaTydClSxfMnz8fMTEx8PX1xZYtWwD87424uLhYM76NjQ0aNGiA6OhorenExMSgSZMmsLa2RqtWrQAAsbGxmvuLiopw6tSpZ9Zz6dIl3L9/H0uXLkWfPn3QsmVLZGZm6jWIS+nSy8uqVatWuHTpErKysjTDEhMT8eDBg2c+9ujRo1CpVFiyZAm6du2K5s2b486dO1rjVPTcKKu2LN9XX321whqsrKzg4eFRIzVUxsnJCba2toiIiEBaWhqGDx9e6bi5ubm4cuUKGjZsWOk4Y8eOxaNHj7BixYoK78/MzATwZJnExsZqfTg4e/YssrKy8Oqrr2pqS0tL01rH8fHxz9UfoNtzpTLOzs5o2LAhEhMTK9yosbS01IzbunVrzJ07F7///jsmTZqE77///rnn9yI2bdqEoKAgbNu2rVzgA0CPHj0QGxuL7OxszbD//Oc/KCkpQY8ePao9fyEEHj9+XO3p1BSGvhEdP34cixcvxp9//olbt24hMjIS586d04S2u7s75HI5fvvtN6SlpWkC5NNPP8XatWuxadMmXLlyBRs3bsSGDRswf/58AICnpycGDx6MGTNmIDo6GgkJCZg6dSqys7OfufXp7u4OCwsLrF27FlevXkVkZCRmzZplsIuvPKuXl9Xbb78NhUKBcePG4dy5c/jzzz8xadIkWFlZPXNZtmjRAvfv38fmzZtx7do1bN++HevXr9cap0mTJgCA/fv34/79+8jNza1wWrVh+X766af417/+heDgYCQlJWH37t344osv8MEHHxjk9Mvk5GScOXMGt27dAgDN1unTyyg0NBR//fUXkpOTsXXrVowYMQJjx45Fnz59AABXr17FokWLEBcXh5s3byImJgZDhgyBEAITJkyodN6dOnXC559/jvnz52PGjBmIiYnBzZs3ERsbizlz5mDq1KkAgJkzZyI7OxtBQUG4cOECjh49infeeQc9e/aEj48PAKBPnz549OgRFi5ciKtXr2LPnj0IDQ197uVR2fuIrpYuXYo1a9ZgyZIluHDhAhITE7Fv3z5NL8nJyfj4449x9OhRTa9HjhzRvI8Z0sqVK/Huu+9i9erV8PX1RWpqKlJTU6FWqzXjjBkzBiqVCmPGjMHZs2dx6NAhzJgxA6NGjdK8joAnX8mdOXMGqampKCgo0DxvSj+YHT58GBs2bMDZs2dx69YtHDt2DAEBAZDL5Rg2bJjBe9Ubox1NUIe86NH7Fy5cEAMHDhTOzs7C3NxcNGrUSHz44YdaB/d88803wtXVVcjlcq1T9v7xj3+Ixo0bC1NTU9GkSZMKT9n729/+JqysrISjo6NYuHChCAgIEIMGDdKMU9kpSXv27BHNmjUTFhYWon379uLw4cPCxMREbNmyRQhR8elpt2/fLncwT0pKigAg/vOf/1S67HTppToH8pU9OvfpPkpZWFiITZs2Vau3qk7ZMzc3F82aNRN79uwRjo6OWgcEVlSjEE8O9HJychLW1tZi4MCB4p///Ge5g7FmzZolnJychEwmq/KUvWct37KnEQohxKRJkzTPt8pUdiBfRf1s3bpVeHl5CTMzM+Hq6irmz59f7pS9stPy8/PT9FWqf//+4u23335mXShzmlrZ9TdhwgShUqmEmZmZ8PT0FMuWLdM6RfHWrVuai76YmZmJRo0aiTFjxuh80Nb+/ftFv379hJ2dnbCwsBDNmzcX7777rtbjnz5lz9bWttwpe0IIsXnzZtGkSRNhaWkpBgwYIH7++ecKT9l7WkXP14reRyp736poeHh4uOjWrZuwsrIS9evXF+3atRNffvmlEEKIu3fvimHDhgk3Nzdhbm4uXnnlFfH3v/9d6yDWsio7Ze9ZfZTl7u5e4bou+9y9fPmy6Nevn7CyshL29vZiypQp5S6SVtm0Spf1iRMnRK9evYS9vb3m/frtt99+6S7OIxPCAPttqdYpLi6Gl5cXhgwZgpCQEGOXI0k3b95E48aNsX//fgwePNjY5RCRBPHix3VUTEwM0tLS8NprryEnJwcrV67EjRs3EBQUZOzSJOPHH3+Em5sbmjRpgps3b+Kjjz6Cu7s7Xn/9dWOXRkQSxdCvo4qLi7FkyRIkJyfDzMwMrVu3xqFDhzRH25LhZWRkYNGiRfjvf/8Le3t79OjRA3v27IGFhYWxSyMiieLufSIiIong0ftEREQSwdAnIiKSCIY+ERGRRNSpA/nu3r2r1+mpVCqkp6frdZq1lZR6BaTVL3utu6TUr5R6BXTv19XV9bmmyy19IiIiiWDoExERSQRDn4iISCIY+kRERBLB0CciIpIIhj4REZFEMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCLq1LX39e3eMG9jl1Alk037jV0CERG9RGok9NevX4/4+HjY2toiJCQEALBy5UrND+Q8evQI1tbWWL58OdLS0jBnzhzNjwh4enpiypQpNVEmERFRnVYjod+7d28MGDAAoaGhmmFz5szR/L19+3ZYW1trbru4uGD58uU1URoREZFk1Mh3+q1atYJCoajwPiEEYmNj0aNHj5oohYiISLKM/p3+pUuXYGtri1deeUUzLC0tDR999BGsrKwwevRotGzZssLHRkREICIiAgAQHBwMlUql19ru6XVq+qfPfk1NTfW+/GozKfXLXusuKfUrpV4Bw/Vr9NA/duyY1la+UqnE+vXrUb9+fVy7dg3Lly9HSEiI1u7/Uv7+/vD399fcTk9Pr5Gaawt99qtSqSS1/KTUL3utu6TUr5R6BXTvt/T4N10Z9ZS94uJixMXFwdv7f0fJm5mZoX79+gAADw8PODs7IyUlxVglEhER1RlGDf3z58/D1dUVDg4OmmHZ2dkoKSkBANy7dw8pKSlwdnY2VolERER1Ro3s3l+1ahUSEhKQk5ODadOmYeTIkejbt2+5XfsAkJCQgN27d8PExARyuRyTJ0+u9CBAIiIi0l2NhP7s2bMrHD5jxoxyw7p164Zu3boZuCIiIiLp4WV4iYiIJIKhT0REJBEMfSIiIolg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+ERGRRDD0iYiIJIKhT0REJBEMfSIiIolg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+ERGRRDD0iYiIJIKhT0REJBEMfSIiIolg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+ERGRRDD0iYiIJIKhT0REJBEMfSIiIokwrYmZrF+/HvHx8bC1tUVISAgAYPfu3YiMjISNjQ0AIDAwEB06dAAAhIeHIyoqCnK5HBMmTED79u1rokwiIqI6rUZCv3fv3hgwYABCQ0O1hr/55psYMmSI1rA7d+7g+PHjWLFiBTIzM7F48WKsXr0acjl3ShAREVVHjSRpq1atoFAodBr35MmT8Pb2hpmZGZycnODi4oLk5GQDV0hERFT31ciWfmUOHjyImJgYeHh4YNy4cVAoFFCr1fD09NSMY29vD7VabcQqiYiI6gajhf7rr7+OgIAAAMCuXbuwfft2TJ8+HUIInacRERGBiIgIAEBwcDBUKpVea7yn16npnz77NTU11fvyq82k1C97rbuk1K+UegUM16/RQt/Ozk7zt5+fH7755hsAgIODAzIyMjT3qdVq2NvbVzgNf39/+Pv7a26np6cbpthaSp/9qlQqSS0/KfXLXusuKfUrpV4B3ft1dXV9ruka7ei4zMxMzd9xcXFo2LAhAKBTp044fvw4CgsLkZaWhpSUFDRr1sxYZRIREdUZNbKlv2rVKiQkJCAnJwfTpk3DyJEjcfHiRdy4cQMymQyOjo6YMmUKAKBhw4bo3r075s6dC7lcjkmTJvHIfSIiIj2okdCfPXt2uWF9+/atdPzhw4dj+PDhBqyIiIhIergJTUREJBEMfSIiIolg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+ERGRRDD0iYiIJIKhT0REJBEMfSIiIolg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+ERGRRDD0iYiIJMJUl5Hu3LkDhUIBOzs75OfnY//+/ZDL5Rg8eDAsLCwMXSMRERHpgU5b+qtXr8ajR48AANu3b8elS5eQlJSE77//3qDFERERkf7otKV///59uLq6QgiBkydPIiQkBObm5pg5c6ah6yMiIiI90Sn0zczMkJeXhzt37sDBwQE2NjYoLi5GYWGhoesjIiIiPdEp9Hv06IGvvvoKeXl5GDBgAADg+vXrcHJyMmhxREREpD86hX5QUBDOnj0LExMTtG7dGgAgk8kwfvx4gxZHRERE+qNT6ANAu3btkJ6ejqSkJDRv3hxNmzY1ZF1ERESkZzqFfnp6OlavXo0bN24AAHbs2IETJ07gzJkzmDZtmiHrIyIiIj3R6ZS977//Hq+99hq2bdsGU9MnnxPatm2Lc+fOGbQ4IiIi0h+dQj85ORlDhw6FXP6/0a2trTXn7hMREVHtp1Po29raIjU1VWvYnTt3oFKpDFIUERER6Z9O3+kPHjwY33zzDYYOHYqSkhIcPXoU4eHhGDp0qE4zWb9+PeLj42Fra4uQkBAAT44LOHXqFExNTeHs7Izp06ejXr16SEtLw5w5c+Dq6goA8PT0xJQpU16sOyIiItLQKfT79u0LhUKByMhIODg4IDo6GqNGjUKXLl10mknv3r0xYMAAhIaGaoa1bdsWY8aMgYmJCX788UeEh4dj7NixAAAXFxcsX778BdohIiKiyuh8yl6XLl10DvmyWrVqhbS0NK1h7dq10/zdvHlznDhx4oWmTURERLqpNPRjYmLQq1cvAEBUVFSlE+jbt2+1i4iKioK3t7fmdlpaGj766CNYWVlh9OjRaNmyZbXnQUREJHWVhv6xY8c0oX/kyJFKJ1Dd0N+7dy9MTEzg4+MDAFAqlVi/fj3q16+Pa9euYfny5QgJCYG1tXW5x0ZERCAiIgIAEBwcrPcDC+/pdWr6p89+TU1NJXVgppT6Za91l5T6lVKvgOH6rTT0P/30UwCAEALTpk2DSqWCiYmJXmd++PBhnDp1Cp9//jlkMhmAJz/uY2ZmBgDw8PCAs7MzUlJSKrwCoL+/P/z9/TW309PT9VpfbafPflUqlaSWn5T6Za91l5T6lVKvgO79lh70rqtnnrInk8nw4YcfakJZX86cOYNffvkFH3/8MSwsLDTDs7OzUVJSAgC4d+8eUlJS4OzsrNd5ExERSZFOB/I1btwYKSkpcHNze6GZrFq1CgkJCcjJycG0adMwcuRIhIeHo6ioCIsXLwbwv1PzEhISsHv3bpiYmEAul2Py5MlQKBQvNF8iIiL6H51C/9VXX8XXX38NX1/fct8x6PKd/uzZs8sNq+xx3bp1Q7du3XQpi4iIiJ6DTqGfmJgIJycnXLp0qdx9+jh6n4iIiAxPp9BftGiRoesgIiIiA9Pp2vsfffRRhcM/+eQTvRZDREREhqNT6Jf9sR3gyal89+7V9jPZiYiIqFSVu/fXrVsHACgqKtL8Xer+/fto2LCh4SojIiIivaoy9J8+P/7pv2UyGVq0aIHu3bsbrjIiIiLSqypDf8SIEQCenEPfvn37mqiHiIiIDESno/fbt2+Pu3fv4saNG8jPz9e6j6fsERERvRx0Cv29e/fiX//6F9zd3bUumQsw9ImIiF4WOoX+b7/9hq+//hru7u6GroeIiIgMRKdT9szNzV/4uvtERERUO+gU+qNGjUJYWBgyMzNRUlKi9Y+IiIheDjrt3l+/fj0AIDIystx9u3bt0m9FREREZBA6hX7ZC/MQERHRy0en0Hd0dDR0HURERGRgOoX+2rVrIZPJKrxv5syZei2IiIiIDEOn0HdxcdG6/eDBA5w4cQI+Pj4GKYqIiIj0T6fQL70c79P69u2LPXv26L0gIiIiMgydTtmrSOPGjXHp0iV91kJEREQGpNOW/oULF7RuP378GMeOHUODBg0MUhQRERHpn06hv2HDBq3blpaWcHd3x6xZswxSFBEREemfTqEfGhpq6DqIiIjIwHQKfQDIzc3FqVOnoFarYW9vj44dO0KhUBiyNiIiItIjnQ7kS0pKwnvvvYf//Oc/uHnzJiIiIvDee+8hKSnJ0PURERGRnui0pb9161b8/e9/R48ePTTDjh8/ji1btmDZsmUGK46IiIj0R6ct/ZSUFHTv3l1rWLdu3ZCammqQooiIiEj/dAp9FxcXHD9+XGtYbGwsnJ2dDVIUERER6Z9Ou/eDgoIQHByM33//HSqVCvfv30dKSgo++eQTQ9dHREREeqJT6Ldo0QJr165FfHw8MjMz0bFjR3To0EHno/fXr1+P+Ph42NraIiQkBMCTswFWrlyJ+/fvw9HREXPmzNFMLzw8HFFRUZDL5ZgwYQLat2//Yt0RERGRRpW79wsKCnDr1i0AgEKhQK9evfDWW2+hV69eUKvVKCgo0GkmvXv3xvz587WG7du3D23atMGaNWvQpk0b7Nu3DwBw584dHD9+HCtWrMBnn32GzZs3o6Sk5AVaIyIioqdVGfq//PILoqKiKrzv8OHD2L9/v04zadWqVbm9AidPnoSvry8AwNfXFydPntQM9/b2hpmZGZycnODi4oLk5GSd5kNERESVq3L3/vHjx7Fw4cIK7xs0aBAWL16MgICAF5pxVlYWlEolAECpVCI7OxsAoFar4enpqRnP3t4earW6wmlEREQgIiICABAcHAyVSvVCtVTmnl6npn/67NfU1FTvy682k1K/7LXuklK/UuoVMFy/VYZ+6dX3KlJVGFeHEELncf39/eHv76+5nZ6ervd6ajN99qtSqSS1/KTUL3utu6TUr5R6BXTv19XV9bmmW+XufUtLy0pnmp6eDgsLi+ea2dNsbW2RmZkJAMjMzISNjQ0AwMHBARkZGZrxqvrgQURERLqrMvRfe+01/PzzzxXet3PnTnTo0OGFZ9ypUydER0cDAKKjo9G5c2fN8OPHj6OwsBBpaWlISUlBs2bNXng+RERE9ESVu/dHjx6Nzz77DPPmzUOXLl2gVCqRmZmJuLg45OXlYcmSJTrNZNWqVUhISEBOTg6mTZuGkSNHYujQoVi5ciWioqKgUqkwd+5cAEDDhg3RvXt3zJ07F3K5HJMmTYJcrtM1hIiIiKgKMvGML9Fzc3Nx4MABnD9/Hrm5uVAoFGjTpg0GDRpU635l7+7du3qdXvHkIXqdnr6ZbNLt7Ald8Puyuou91l1S6ldKvQKG+07/mRfnUSgUGD16NEaPHv1cEyYiIqLahfvNiYiIJIKhT0REJBEMfSIiIomoNPQ/++wzzd979uypkWKIiIjIcCoN/bt372p+UOfAgQM1VhAREREZRqVH73fu3BmzZs2Ck5MTCgoKsGjRogrH+/LLLw1WHBEREelPpaE/ffp0XL58GWlpaUhOTkafPn1qsi4iIiLSsyrP0/fy8oKXlxeKiorQu3fvGiqJiIiIDOGZF+cBgL59++LChQuIiYlBZmYmlEolevXqhdatWxu6PiIiItITnU7Zi4yMxKpVq2BnZ6e5Bv/q1as1v2VPREREtZ9OW/r79+/HggUL0LhxY80wb29vhISEaP2ePREREdVeOm3p5+TkoEGDBlrDXF1dkZuba5CiiIiISP90Cn0vLy9s374djx8/BgDk5+djx44daN68uUGLIyIiIv3Raff+5MmTsWrVKgQFBUGhUCA3NxfNmzfHrFmzDF0fERER6YlOoa9UKvHll18iIyNDc/S+g4ODoWsjIiIiPdIp9Es5ODgw7ImIiF5S/JU9IiIiiWDoExERScQzQ7+kpAQXLlxAUVFRTdRDREREBvLM0JfL5fjHP/4BU9Pn+vqfiIiIahmddu+3bNkSSUlJhq6FiIiIDEinzXdHR0csW7YMnTp1goODA2Qymea+UaNGGaw4IiIi0h+dQr+goACdO3cGAKjVaoMWRERERIahU+hPnz7d0HUQERGRgel8dN6dO3dw4sQJZGVlYdKkSbh79y4KCwvh7u5uyPqIiIhIT3Q6kC82NhaLFi2CWq1GTEwMACAvLw/bt283aHFERESkPzpt6e/evRsLFy5E48aNERsbCwBwd3fHjRs3qjXzu3fvYuXKlZrbaWlpGDlyJB4+fIjIyEjY2NgAAAIDA9GhQ4dqzYuIiEjqdAr9rKyscrvxZTKZ1lH8L8LV1RXLly8H8OQiQFOnTkWXLl1w6NAhvPnmmxgyZEi1pk9ERET/o9PufQ8PD81u/VLHjh1Ds2bN9FbI+fPn4eLiAkdHR71Nk4iIiP5Hpy39CRMmYMmSJYiKisLjx4+xdOlS3L17FwsWLNBbIceOHUOPHj00tw8ePIiYmBh4eHhg3LhxUCgUepsXERGRFMmEEEKXER8/foxTp04hPT0dDg4O6NixIywtLfVSRFFREaZOnYqQkBDY2dnhwYMHmu/zd+3ahczMzApPG4yIiEBERAQAIDg4GAUFBXqpp9S9Yd56nZ6+OYcf19u0TE1NJfX7ClLql73WXVLqV0q9Arr3a25u/nzT1XVECwsLeHl5Qa1Ww97eXm+BDwCnT59GkyZNYGdnBwCa/wHAz88P33zzTYWP8/f3h7+/v+Z2enq63mp6GeizX5VKJanlJ6V+2WvdJaV+pdQroHu/rq6uzzVdnUI/PT0da9aswZUrV1CvXj08fPgQzZo1w/vvv6+X7+DL7trPzMyEUqkEAMTFxaFhw4bVngcREZHU6RT6oaGh8PDwwPz582FpaYn8/Hzs3LkToaGh+OKLL6pVwOPHj3Hu3DlMmTJFM+zHH3/EjRs3IJPJ4OjoqHUfERERvRidQv/atWv47LPPND+va2lpibFjx2LixInVLsDCwgJhYWFaw957771qT5eIiIi06XTKnqenJ5KTk7WGXb16Fc2bNzdIUURERKR/lW7p79q1S/O3s7Mzli1bhg4dOsDBwQEZGRk4ffo0evbsWSNFEhERUfVVGvoZGRlat7t27QoAyM7OhpmZGbp06aL3U+SIiIjIcCoNff6cLhERUd2i83n6jx8/RmpqKvLz87WGt2jRQu9FERERkf7pFPrR0dEICwuDqalpuav/bNiwwSCFERERkX7pFPo//vgjPvjgA7Rt29bQ9RAREZGB6HTKnqmpKVq1amXoWoiIiMiAdAr9UaNGYfv27cjOzjZ0PURERGQgOu3ed3V1xe7du3Hw4MFy9z19Pj8RERHVXjqF/tq1a9GrVy94e3s/98/4ERERUe2gU+jn5uZi1KhRkMlkhq6HiIiIDESn7/R79+6NmJgYQ9dCREREBqTTln5ycjL++OMP7N27F3Z2dlr3ffnll4aoi4iIiPRMp9D38/ODn5+foWshIiIiA9Ip9Hv37m3gMoiIiMjQdAr9qKioSu/r27ev3oohIiIiw9Ep9I8cOaJ1+8GDB0hNTYWXlxdDn4iI6CWhU+gvWrSo3LCoqCj897//1XtBREREZBg6nbJXkd69e1e525+IiIhqF5229EtKSrRuFxQUICYmBvXq1TNIUURERKR/OoV+YGBguWH29vaYOnWq3gsiIiIiw9Ap9NetW6d128LCAjY2NgYpiIiIiAxDp9B3dHQ0dB1ERERkYFWG/rMusSuTyfD555/rtSAiIiIyjCpD38fHp8LharUav//+Ox4/fmyQooiIiEj/qgz9shfeycnJQXh4OCIjI+Ht7Y2AgACDFkdERET6o9N3+o8ePcL+/ftx8OBBdOjQAd988w1cXFz0UsCMGTNgaWkJuVwOExMTBAcHIzc3FytXrsT9+/fh6OiIOXPmQKFQ6GV+REREUlVl6BcUFODXX3/FgQMH0KpVK3z11Vdo2LCh3otYtGiR1tkA+/btQ5s2bTB06FDs27cP+/btw9ixY/U+XyIiIimp8op8M2bMwIEDBzBkyBD0798fWVlZuHDhgtY/Qzh58iR8fX0BAL6+vjh58qRB5kNERCQlVW7pm5ubAwD+3//7fxXeL5PJyp3D/yKWLl0KAOjXrx/8/f2RlZUFpVIJAFAqlcjOzq72PIiIiKSuytAPDQ01eAGLFy+Gvb09srKysGTJEri6uur82IiICERERAAAgoODoVKp9FrbPb1OTf/02a+pqanel19tJqV+2WvdJaV+pdQrYLh+dTqQz5Ds7e0BALa2tujcuTOSk5Nha2uLzMxMKJVKZGZmVnr1P39/f/j7+2tup6en10jNtYU++1WpVJJaflLql73WXVLqV0q9Arr3+zwbykA1fmVPH/Lz85GXl6f5+9y5c2jUqBE6deqE6OhoAEB0dDQ6d+5szDKJiIjqBKNu6WdlZeHbb78FABQXF6Nnz55o3749mjZtipUrVyIqKgoqlQpz5841ZplERER1glFD39nZGcuXLy83vH79+ry8LxERkZ4Zdfc+ERER1RyGPhERkUQw9ImIiCSCoU9ERCQRDH0iIiKJYOgTERFJBEOfiIhIIhj6REREEsHQJyIikgiGPhERkUQw9ImIiCSCoU9ERCQRDH0iIiKJYOgTERFJBEOfiIhIIhj6REREEsHQJyIikgiGPhERkUQw9ImIiCSCoU9ERCQRDH0iIiKJYOgTERFJBEOfiIhIIhj6REREEsHQJyIikgiGPhERkUQw9ImIiCSCoU9ERCQRpsaceXp6OkJDQ/HgwQPIZDL4+/vjjTfewO7duxEZGQkbGxsAQGBgIDp06GDMUomIiF56Rg19ExMTvPPOO/Dw8EBeXh4++eQTtG3bFgDw5ptvYsiQIcYsj4iIqE4xaugrlUoolUoAgJWVFdzc3KBWq41ZEhERUZ1l1NB/WlpaGq5fv45mzZrh8uXLOHjwIGJiYuDh4YFx48ZBoVCUe0xERAQiIiIAAMHBwVCpVHqt6Z5ep6Z/+uzX1NRU78uvNpNSv+y17pJSv1LqFTBcvzIhhND7VJ9Tfn4+Fi1ahOHDh6Nr16548OCB5vv8Xbt2ITMzE9OnT3/mdO7evavXuoon1+6vF0w27dfbtFQqFdLT0/U2vdpOSv2y17pLSv1KqVdA935dXV2fa7pGP3q/qKgIISEh8PHxQdeuXQEAdnZ2kMvlkMvl8PPzw9WrV41cJRER0cvPqKEvhMB3330HNzc3DBo0SDM8MzNT83dcXBwaNmxojPKIiIjqFKN+p5+YmIiYmBg0atQI8+bNA/Dk9Lxjx47hxo0bkMlkcHR0xJQpU4xZJhERUZ1g1ND38vLC7t27yw3nOflERET6Z/Tv9ImIiKhmMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBLB0CciIpIIhj4REZFEMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBJhauwC6MUVTx6it2nd09uUtJls2m+gKRMR0fPilj4REZFEMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBLB0CciIpIIhj4REZFEMPSJiIgkgqFPREQkEbX62vtnzpzBli1bUFJSAj8/PwwdOtTYJREREb20am3ol5SUYPPmzViwYAEcHBzw6aefolOnTmjQoIGxS6M6RJ8/WmQo/NEiIt3U9tdzbXgt19rQT05OhouLC5ydnQEA3t7eOHnyJEP/JVNbX4SG+lVBorrK2K9lvmb1o9Z+p69Wq+Hg4KC57eDgALVabcSKiIiIXm61dktfCFFumEwm07odERGBiIgIAEBwcDBcXV31W8Svf+l3ekRGovfXRi0mpV6BGuyX74c1zhDrttZu6Ts4OCAjI0NzOyMjA0qlUmscf39/BAcHIzg42CA1fPLJJwaZbm0kpV4BafXLXusuKfUrpV4Bw/Vba0O/adOmSElJQVpaGoqKinD8+HF06tTJ2GURERG9tGrt7n0TExNMnDgRS5cuRUlJCfr06YOGDRsauywiIqKXVq0NfQDo0KEDOnToYLT5+/v7G23eNU1KvQLS6pe91l1S6ldKvQKG61cmKjpijoiIiOqcWvudPhEREelXrd69byx15fK/M2bMgKWlJeRyOUxMTBAcHIzc3FysXLkS9+/fh6OjI+bMmQOFQgEACA8PR1RUFORyOSZMmID27dsDAK5du4bQ0FAUFBTgtddew4QJE8qdPmkM69evR3x8PGxtbRESEgIAeu2vsLAQ69atw7Vr11C/fn3Mnj0bTk5OtabX3bt3IzIyEjY2NgCAwMBAzddhL3Ov6enpCA0NxYMHDyCTyeDv74833nijzq7byvqti+u3oKAAixYtQlFREYqLi9GtWzeMHDmyzq7byvo16roVpKW4uFjMnDlTpKamisLCQvHhhx+K27dvG7usFzJ9+nSRlZWlNWzHjh0iPDxcCCFEeHi42LFjhxBCiNu3b4sPP/xQFBQUiHv37omZM2eK4uJiIYQQn3zyiUhMTBQlJSVi6dKlIj4+vkb7qMzFixfF1atXxdy5czXD9NnfH3/8ITZu3CiEEOLo0aNixYoVNdidtop63bVrl/jll1/Kjfuy96pWq8XVq1eFEEI8evRIvP/+++L27dt1dt1W1m9dXL8lJSUiLy9PCCFEYWGh+PTTT0ViYmKdXbeV9WvMdcvd+2U8fflfU1NTzeV/64qTJ0/C19cXAODr66vp7eTJk/D29oaZmRmcnJzg4uKC5ORkZGZmIi8vD82bN4dMJkOvXr1qzfJo1aqVZmuglD77++uvv9C7d28AQLdu3XDhwoUKLxpVEyrqtTIve69KpRIeHh4AACsrK7i5uUGtVtfZdVtZv5V5mfuVyWSwtLQEABQXF6O4uBgymazOrtvK+q1MTfTL3ftlVHT53ytXrhixoupZunQpAKBfv37w9/dHVlaW5iJHSqUS2dnZAJ707enpqXmcvb091Go1TExMXqrLIeuzv6efCyYmJrC2tkZOTo5ml1xtcPDgQcTExMDDwwPjxo2DQqGoU72mpaXh+vXraNasmSTW7dP9Xr58uU6u35KSEnz88cdITU1F//794enpWafXbUX9nj592mjrlqFfRkWfkGrD99cvYvHixbC3t0dWVhaWLFlS5SUdK/tkaKxPyPr2Iv3V9ufC66+/joCAAADArl27sH37dkyfPr3O9Jqfn4+QkBAEBQXB2tq60vHqar91df3K5XIsX74cDx8+xLfffotbt25VOu7L3itQcb/GXLfcvV+GLpf/fVnY29sDAGxtbdG5c2ckJyfD1tYWmZmZAIDMzEzNp8GyfavVatjb21e4PEqnWxvps7+n7ysuLsajR4903sVeE+zs7CCXyyGXy+Hn54erV68CqBu9FhUVISQkBD4+PujatSuAur1uK+q3Lq9fAKhXrx5atWqFM2fO1Ol1W+rpfo25bhn6ZdSVy//m5+cjLy9P8/e5c+fQqFEjdOrUCdHR0QCA6OhodO7cGQDQqVMnHD9+HIWFhUhLS0NKSgqaNWsGpVIJKysrJCUlQQiBmJiYWr089Nlfx44dcfjwYQDAiRMn8Oqrrxp9a/BppW+SABAXF6e5YuXL3qsQAt999x3c3NwwaNAgzfC6um4r67curt/s7Gw8fPgQwJMj28+fPw83N7c6u24r69eY65YX56lAfHw8tm3bprn87/Dhw41d0nO7d+8evv32WwBPPv317NkTw4cPR05ODlauXIn09HSoVCrMnTtX86lw7969OHToEORyOYKCgvDaa68BAK5evYr169ejoKAA7du3x8SJE2tF+K1atQoJCQnIycmBra0tRo4cic6dO+utv4KCAqxbtw7Xr1+HQqHA7Nmz4ezsXGt6vXjxIm7cuAGZTAZHR0dMmTJFs1fqZe718uXL+Pzzz9GoUSPN8ywwMBCenp51ct1W1u+xY8fq3Pq9efMmQkNDUVJSAiEEunfvjoCAAL2+L9WWXqvqd+3atUZbtwx9IiIiieDufSIiIolg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+0UsgNDQUO3fuNMq8hRBYv349JkyYgE8//dQoNZQ6cuQIlixZYtQaiF5mDH2iFzBjxgxMnjwZ+fn5mmGRkZH44osvjFeUgVy+fBnnzp3Dhg0bsGzZsnL3Hz58GAsXLqyRWnx8fLBgwYIamVdZFy9exLRp04wybyJ9YegTvaDi4mL89ttvxi7juZWUlDzX+KW/cV76a2Evq+LiYmOXQGR0/MEdohc0ZMgQ/PLLL+jfvz/q1aundV9aWhpmzpyJn3/+GSYmJgCAL774Aj4+PvDz88Phw4cRGRmJpk2b4vDhw1AoFHjvvfeQkpKCXbt2obCwEGPHjtX8ZCbw5JKeixcvxpUrV9CkSRPMnDkTjo6OAID//ve/CAsLw7Vr12BjY4NRo0bB29sbwJOvBszNzZGeno6EhATMmzcPbdu21apXrVZj06ZNuHz5MhQKBd566y34+/sjKioKmzdvRlFREd555x0MHjwYI0eOrHK5zJgxA/3790dMTAzu3bsHb29vBAYGYv369bh8+TI8PT0xZ84cKBQKzXJ69913sXv3buTn5yMwMBAeHh747rvvkJ6eDh8fH0yaNAkANMtt8eLFAICzZ88iLCwMDx48gI+PD27fvo1evXqVW8bR0dHo378/evfujY0bN+LmzZuQyWRo164dJk2apFl/T9d+//59tG/fHjNmzEBJSQm+/vprzXIAgNWrV0OtVuOHH35ASkoKzM3N0bNnT4wfP/5Fnk5ENYJb+kQvyMPDA6+++ir+/e9/v9Djr1y5And3d4SFhaFnz55YtWoVkpOTsWbNGrz33nsICwvT+vrg6NGj+Nvf/obNmzejcePGWLNmDYAnv62wZMkS9OzZEz/88ANmzZqFzZs34/bt21qPHTZsGLZt2wYvL69ytaxevRoODg7YuHEjPvjgA/z88884f/48+vbti8mTJ6N58+bYsWPHMwO/1J9//okFCxZg9erVOHXqFJYtW4bAwEBs3rwZJSUl+P3338sti9WrV2P27NnYtm0b9u7di4ULF2LFihWIjY1FQkJCuXlkZ2djxYoVGDNmDMLCwuDq6oqkpKRy03V2dsYPP/yguZz2sGHDsHHjRqxcuRIZGRnYs2eP1mNiY2Mxf/58hIaG4tatWzh8+DAsLS0xf/58KJVK7NixAzt27IC9vT22bNmCN954A9u2bcPatWvRvXt3nZYPkbEw9ImqYeTIkfj99981v//9PJycnNCnTx/I5XJ4e3sjIyMDAQEBMDMzQ7t27WBqaorU1FTN+B06dECrVq1gZmaGwMBAJCUlIT09HfHx8XB0dESfPn1gYmICDw8PdO3aFSdOnNA8tnPnzvDy8oJcLoe5ublWHenp6bh8+TLefvttmJubo3HjxvDz80NMTMwLL5cBAwbAzs4O9vb28PLyQrNmzdCkSROYmZmhS5cuuH79utb4AQEBMDc3R7t27WBhYYGePXvC1tZW8/iy4wPA6dOn0aBBA3Tt2hUmJiYYOHAg7OzstMZRKpUYOHAgTExMYG5uDhcXF7Rt2xZmZmawsbHBm2++We4DxcCBA2Fvbw+FQoGOHTvixo0blfZZuo6ys7NhaWmJ5s2bv/AyI6oJ3L1PVA2NGjVCx44dsW/fPri5uT3XY21tbTV/lwbx06Flbm6utaXv4OCg+dvS0hIKhQKZmZm4f/8+rly5gqCgIM39xcXF6NWrV4WPLSszMxMKhQJWVlaaYSqVSvNzny+ibG9lbz9+/Pi5xn96OTxd99N9yWSycj/7rFKptG5nZWVhy5YtuHTpEvLz81FSUlLuZ0jLrgO1Wl1pn9OmTcOuXbswZ84cODk5ISAgAB07dqx0fCJjY+gTVdPIkSPx8ccfa/0saulBb48fP4a1tTUA4MGDB9Waz9O/p52fn4/c3FwolUo4ODigVatWVR5BX9WvIiqVSuTm5iIvL08T/Onp6eUCtLaxs7PTCmQhRJUBDQD//Oc/AQDffvst6tevj7i4OISFhek0v4qW4SuvvILZs2ejpKQEcXFxWLFiBTZv3vzSH/RIdRd37xNVk4uLC7p37671PbWNjQ3s7e1x5MgRlJSUICoqCvfu3avWfE6fPo3Lly+jqKgIO3fuhKenJ1QqFTp27IiUlBTExMSgqKgIRUVFSE5Oxp07d3SarkqlQosWLfDPf/4TBQUFuHnzJg4dOgQfH59q1WtoHTp0wK1btxAXF4fi4mIcPHjwmR+s8vLyYGlpiXr16kGtVj/X8Ri2trbIycnBo0ePNMNiYmKQnZ0NuVyu+XAnl/NtlWovbukT6UFAQACOHDmiNWzq1Kn44Ycf8PPPP6Nv377V/r63R48e2LNnD5KSkuDh4YH3338fAGBlZYUFCxZg27Zt2LZtG4QQcHd3f66jyGfNmoVNmzZh6tSpUCgUGDFiRLkj/GsbGxsbzJ07F1u2bEFoaCh8fHzg4eEBMzOzSh8zYsQIrFu3DuPHj4eLiwt69eqFX3/9Vaf5ubm5oUePHpg5cyZKSkqwYsUKnDlzBtu3b8fjx4/h6OiIWbNmlTtmgqg2kQkhhLGLICKqrpKSErz77rt477330Lp1a2OXQ1QrcT8UEb20zpw5g4cPH6KwsBDh4eEQQvAIeqIqcPc+Eb20kpKSsGbNGhQVFaFBgwaYN28ed68TVYG794mIiCSCu/eJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBLx/wFy29h1g0R+JAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df_can['2013'].plot(kind='hist', figsize=(8, 5))\\n\",\n \"\\n\",\n \"plt.title('Histogram of Immigration from 195 Countries in 2013') # add a title to the histogram\\n\",\n \"plt.ylabel('Number of Countries') # add y-label\\n\",\n \"plt.xlabel('Number of Immigrants') # add x-label\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"In the above plot, the x-axis represents the population range of immigrants in intervals of 3412.9. The y-axis represents the number of countries that contributed to the aforementioned population. \\n\",\n \"\\n\",\n \"Notice that the x-axis labels do not match with the bin size. This can be fixed by passing in a `xticks` keyword that contains the list of the bin sizes, as follows:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# 'bin_edges' is a list of bin intervals\\n\",\n \"count, bin_edges = np.histogram(df_can['2013'])\\n\",\n \"\\n\",\n \"df_can['2013'].plot(kind='hist', figsize=(8, 5), xticks=bin_edges)\\n\",\n \"\\n\",\n \"plt.title('Histogram of Immigration from 195 countries in 2013') # add a title to the histogram\\n\",\n \"plt.ylabel('Number of Countries') # add y-label\\n\",\n \"plt.xlabel('Number of Immigrants') # add x-label\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"_Side Note:_ We could use `df_can['2013'].plot.hist()`, instead. In fact, throughout this lesson, using `some_data.plot(kind='type_plot', ...)` is equivalent to `some_data.plot.type_plot(...)`. That is, passing the type of the plot as argument or method behaves the same. \\n\",\n \"\\n\",\n \"See the _pandas_ documentation for more info [http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We can also plot multiple histograms on the same plot. For example, let's try to answer the following questions using a histogram.\\n\",\n \"\\n\",\n \"**Question**: What is the immigration distribution for Denmark, Norway, and Sweden for years 1980 - 2013?\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# generate histogram\\n\",\n \"df_can.loc[['Denmark', 'Norway', 'Sweden'], years].plot.hist()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"That does not look right! \\n\",\n \"\\n\",\n \"Don't worry, you'll often come across situations like this when creating plots. The solution often lies in how the underlying dataset is structured.\\n\",\n \"\\n\",\n \"Instead of plotting the population frequency distribution of the population for the 3 countries, _pandas_ instead plotted the population frequency distribution for the `years`.\\n\",\n \"\\n\",\n \"This can be easily fixed by first transposing the dataset, and then plotting as shown below.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# generate histogram\\n\",\n \"df_t.plot(kind='hist', figsize=(10, 6))\\n\",\n \"\\n\",\n \"plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\\n\",\n \"plt.ylabel('Number of Years')\\n\",\n \"plt.xlabel('Number of Immigrants')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's make a few modifications to improve the impact and aesthetics of the previous plot:\\n\",\n \"\\n\",\n \"- increase the bin size to 15 by passing in `bins` parameter\\n\",\n \"- set transparency to 60% by passing in `alpha` paramemter\\n\",\n \"- label the x-axis by passing in `x-label` paramater\\n\",\n \"- change the colors of the plots by passing in `color` parameter\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# generate histogram\\n\",\n \"df_t.plot(kind='hist', figsize=(10, 6))\\n\",\n \"\\n\",\n \"plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\\n\",\n \"plt.ylabel('Number of Years')\\n\",\n \"plt.xlabel('Number of Immigrants')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's make a few modifications to improve the impact and aesthetics of the previous plot:\\n\",\n \"\\n\",\n \"- increase the bin size to 15 by passing in `bins` parameter\\n\",\n \"- set transparency to 60% by passing in `alpha` paramemter\\n\",\n \"- label the x-axis by passing in `x-label` paramater\\n\",\n \"- change the colors of the plots by passing in `color` parameter\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"# let's get the x-tick values\\n\",\n \"count, bin_edges = np.histogram(df_t, 15)\\n\",\n \"\\n\",\n \"# un-stacked histogram\\n\",\n \"df_t.plot(kind ='hist', \\n\",\n \" figsize=(10, 6),\\n\",\n \" bins=15,\\n\",\n \" alpha=0.6,\\n\",\n \" xticks=bin_edges,\\n\",\n \" color=['coral', 'darkslateblue', 'mediumseagreen']\\n\",\n \" )\\n\",\n \"\\n\",\n \"plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\\n\",\n \"plt.ylabel('Number of Years')\\n\",\n \"plt.xlabel('Number of Immigrants')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Tip:\\n\",\n \"For a full listing of colors available in Matplotlib, run the following code in your python shell:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"import matplotlib\\n\",\n \"for name, hex in matplotlib.colors.cnames.items():\\n\",\n \" print(name, hex)\\n\",\n \"```\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"If we do no want the plots to overlap each other, we can stack them using the `stacked` paramemter. Let's also adjust the min and max x-axis labels to remove the extra gap on the edges of the plot. We can pass a tuple (min,max) using the `xlim` paramater, as show below.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"count, bin_edges = np.histogram(df_t, 15)\\n\",\n \"xmin = bin_edges[0] - 10 # first bin value is 31.0, adding buffer of 10 for aesthetic purposes \\n\",\n \"xmax = bin_edges[-1] + 10 # last bin value is 308.0, adding buffer of 10 for aesthetic purposes\\n\",\n \"\\n\",\n \"# stacked Histogram\\n\",\n \"df_t.plot(kind='hist',\\n\",\n \" figsize=(10, 6), \\n\",\n \" bins=15,\\n\",\n \" xticks=bin_edges,\\n\",\n \" color=['coral', 'darkslateblue', 'mediumseagreen'],\\n\",\n \" stacked=True,\\n\",\n \" xlim=(xmin, xmax)\\n\",\n \" )\\n\",\n \"\\n\",\n \"plt.title('Histogram of Immigration from Denmark, Norway, and Sweden from 1980 - 2013')\\n\",\n \"plt.ylabel('Number of Years')\\n\",\n \"plt.xlabel('Number of Immigrants') \\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question**: Use the scripting layer to display the immigration distribution for Greece, Albania, and Bulgaria for years 1980 - 2013? Use an overlapping plot with 15 bins and a transparency value of 0.35.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"df_three_con=df_can.loc[['Greece','Albania','Bulgaria'],years]\\n\",\n \"df_three_con=df_three_con.transpose()\\n\",\n \"df_three_con.plot(kind='hist',bins=15,alpha=0.35,stacked=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Bar Charts (Dataframe) \\n\",\n \"\\n\",\n \"A bar plot is a way of representing data where the _length_ of the bars represents the magnitude/size of the feature/variable. Bar graphs usually represent numerical and categorical variables grouped in intervals. \\n\",\n \"\\n\",\n \"To create a bar plot, we can pass one of two arguments via `kind` parameter in `plot()`:\\n\",\n \"\\n\",\n \"- `kind=bar` creates a _vertical_ bar plot\\n\",\n \"- `kind=barh` creates a _horizontal_ bar plot\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Vertical bar plot**\\n\",\n \"\\n\",\n \"In vertical bar graphs, the x-axis is used for labelling, and the length of bars on the y-axis corresponds to the magnitude of the variable being measured. Vertical bar graphs are particuarly useful in analyzing time series data. One disadvantage is that they lack space for text labelling at the foot of each bar. \\n\",\n \"\\n\",\n \"**Let's start off by analyzing the effect of Iceland's Financial Crisis:**\\n\",\n \"\\n\",\n \"The 2008 - 2011 Icelandic Financial Crisis was a major economic and political event in Iceland. Relative to the size of its economy, Iceland's systemic banking collapse was the largest experienced by any country in economic history. The crisis led to a severe economic depression in 2008 - 2011 and significant political unrest.\\n\",\n \"\\n\",\n \"**Question:** Let's compare the number of Icelandic immigrants (country = 'Iceland') to Canada from year 1980 to 2013. \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1980 17\\n\",\n \"1981 33\\n\",\n \"1982 10\\n\",\n \"1983 9\\n\",\n \"1984 13\\n\",\n \"Name: Iceland, dtype: object\"\n ]\n },\n \"execution_count\": 60,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# step 1: get the data\\n\",\n \"df_iceland = df_can.loc['Iceland', years]\\n\",\n \"df_iceland.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# step 2: plot data\\n\",\n \"df_iceland.plot(kind='bar', figsize=(10, 6))\\n\",\n \"\\n\",\n \"plt.xlabel('Year') # add to x-label to the plot\\n\",\n \"plt.ylabel('Number of immigrants') # add y-label to the plot\\n\",\n \"plt.title('Icelandic immigrants to Canada from 1980 to 2013') # add title to the plot\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"The bar plot above shows the total number of immigrants broken down by each year. We can clearly see the impact of the financial crisis; the number of immigrants to Canada started increasing rapidly after 2008. \\n\",\n \"\\n\",\n \"Let's annotate this on the plot using the `annotate` method of the **scripting layer** or the **pyplot interface**. We will pass in the following parameters:\\n\",\n \"\\n\",\n \"- `s`: str, the text of annotation.\\n\",\n \"- `xy`: Tuple specifying the (x,y) point to annotate (in this case, end point of arrow).\\n\",\n \"- `xytext`: Tuple specifying the (x,y) point to place the text (in this case, start point of arrow).\\n\",\n \"- `xycoords`: The coordinate system that xy is given in - 'data' uses the coordinate system of the object being annotated (default).\\n\",\n \"- `arrowprops`: Takes a dictionary of properties to draw the arrow:\\n\",\n \" - `arrowstyle`: Specifies the arrow style, `'->'` is standard arrow.\\n\",\n \" - `connectionstyle`: Specifies the connection type. `arc3` is a straight line.\\n\",\n \" - `color`: Specifes color of arror.\\n\",\n \" - `lw`: Specifies the line width.\\n\",\n \"\\n\",\n \"I encourage you to read the Matplotlib documentation for more details on annotations: \\n\",\n \"[http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df_iceland.plot(kind='bar', figsize=(10, 6), rot=90) # rotate the xticks(labelled points on x-axis) by 90 degrees\\n\",\n \"\\n\",\n \"plt.xlabel('Year')\\n\",\n \"plt.ylabel('Number of Immigrants')\\n\",\n \"plt.title('Icelandic Immigrants to Canada from 1980 to 2013')\\n\",\n \"\\n\",\n \"# Annotate arrow\\n\",\n \"plt.annotate('', # s: str. Will leave it blank for no text\\n\",\n \" xy=(32, 70), # place head of the arrow at point (year 2012 , pop 70)\\n\",\n \" xytext=(28, 20), # place base of the arrow at point (year 2008 , pop 20)\\n\",\n \" xycoords='data', # will use the coordinate system of the object being annotated \\n\",\n \" arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='blue', lw=2)\\n\",\n \" )\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's also annotate a text to go over the arrow. We will pass in the following additional parameters:\\n\",\n \"\\n\",\n \"- `rotation`: rotation angle of text in degrees (counter clockwise)\\n\",\n \"- `va`: vertical alignment of text [‘center’ | ‘top’ | ‘bottom’ | ‘baseline’]\\n\",\n \"- `ha`: horizontal alignment of text [‘center’ | ‘right’ | ‘left’]\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df_iceland.plot(kind='bar', figsize=(10, 6), rot=90) \\n\",\n \"\\n\",\n \"plt.xlabel('Year')\\n\",\n \"plt.ylabel('Number of Immigrants')\\n\",\n \"plt.title('Icelandic Immigrants to Canada from 1980 to 2013')\\n\",\n \"\\n\",\n \"# Annotate arrow\\n\",\n \"plt.annotate('', # s: str. will leave it blank for no text\\n\",\n \" xy=(32, 70), # place head of the arrow at point (year 2012 , pop 70)\\n\",\n \" xytext=(28, 20), # place base of the arrow at point (year 2008 , pop 20)\\n\",\n \" xycoords='data', # will use the coordinate system of the object being annotated \\n\",\n \" arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='blue', lw=2)\\n\",\n \" )\\n\",\n \"\\n\",\n \"# Annotate Text\\n\",\n \"plt.annotate('2008 - 2011 Financial Crisis', # text to display\\n\",\n \" xy=(28, 30), # start the text at at point (year 2008 , pop 30)\\n\",\n \" rotation=72.5, # based on trial and error to match the arrow\\n\",\n \" va='bottom', # want the text to be vertically 'bottom' aligned\\n\",\n \" ha='left', # want the text to be horizontally 'left' algned.\\n\",\n \" )\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Horizontal Bar Plot**\\n\",\n \"\\n\",\n \"Sometimes it is more practical to represent the data horizontally, especially if you need more room for labelling the bars. In horizontal bar graphs, the y-axis is used for labelling, and the length of bars on the x-axis corresponds to the magnitude of the variable being measured. As you will see, there is more room on the y-axis to label categetorical variables.\\n\",\n \"\\n\",\n \"**Question:** Using the scripting layter and the `df_can` dataset, create a _horizontal_ bar plot showing the _total_ number of immigrants to Canada from the top 15 countries, for the period 1980 - 2013. Label each country with the total immigrant count.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Get the data pertaining to the top 15 countries.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 64,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer\\n\",\n \"df_can.sort_values(['Total'], ascending=False, axis=0,inplace=True)\\n\",\n \"df_can_top15=df_can['Total'].head(15)\\n\",\n \"df_can_top15.plot(kind='barh')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 2: Plot data:\\n\",\n \"\\n\",\n \"1. Use `kind='barh'` to generate a bar chart with horizontal bars.\\n\",\n \"2. Make sure to choose a good size for the plot and to label your axes and to give the plot a title.\\n\",\n \"3. Loop through the countries and annotate the immigrant population using the anotate function of the scripting interface.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"df_can.sort_values(by='Total', ascending=True, inplace=True)\\n\",\n \"\\n\",\n \"# get top 15 countries\\n\",\n \"df_top15 = df_can['Total'].tail(15)\\n\",\n \"df_top15\\n\",\n \"\\n\",\n \"df_top15.plot(kind='barh', figsize=(12, 12), color='steelblue')\\n\",\n \"plt.xlabel('Number of Immigrants')\\n\",\n \"plt.title('Top 15 Conuntries Contributing to the Immigration to Canada between 1980 - 2013')\\n\",\n \"# annotate value labels to each country\\n\",\n \"for index, value in enumerate(df_top15): \\n\",\n \" label = format(int(value), ',') # format int with commas\\n\",\n \"\\n\",\n \"# place text at the end of bar (subtracting 47000 from x, and 0.1 from y to make it fit within the bar)\\n\",\n \"plt.annotate(label, xy=(value - 47000, index - 0.10), color='white')\\n\",\n \"\\n\",\n \"plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Pie Charts \\n\",\n \"\\n\",\n \"A `pie chart` is a circualr graphic that displays numeric proportions by dividing a circle (or pie) into proportional slices. You are most likely already familiar with pie charts as it is widely used in business and media. We can create pie charts in Matplotlib by passing in the `kind=pie` keyword.\\n\",\n \"\\n\",\n \"Let's use a pie chart to explore the proportion (percentage) of new immigrants grouped by continents for the entire time period from 1980 to 2013. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Gather data. \\n\",\n \"\\n\",\n \"We will use _pandas_ `groupby` method to summarize the immigration data by `Continent`. The general process of `groupby` involves the following steps:\\n\",\n \"\\n\",\n \"1. **Split:** Splitting the data into groups based on some criteria.\\n\",\n \"2. **Apply:** Applying a function to each group independently:\\n\",\n \" .sum()\\n\",\n \" .count()\\n\",\n \" .mean() \\n\",\n \" .std() \\n\",\n \" .aggregate()\\n\",\n \" .apply()\\n\",\n \" .etc..\\n\",\n \"3. **Combine:** Combining the results into a data structure.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1980
\\n\",\n \"
1981
\\n\",\n \"
1982
\\n\",\n \"
1983
\\n\",\n \"
1984
\\n\",\n \"
1985
\\n\",\n \"
1986
\\n\",\n \"
1987
\\n\",\n \"
1988
\\n\",\n \"
1989
\\n\",\n \"
...
\\n\",\n \"
2005
\\n\",\n \"
2006
\\n\",\n \"
2007
\\n\",\n \"
2008
\\n\",\n \"
2009
\\n\",\n \"
2010
\\n\",\n \"
2011
\\n\",\n \"
2012
\\n\",\n \"
2013
\\n\",\n \"
Total
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Continent
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
Africa
\\n\",\n \"
3951
\\n\",\n \"
4363
\\n\",\n \"
3819
\\n\",\n \"
2671
\\n\",\n \"
2639
\\n\",\n \"
2650
\\n\",\n \"
3782
\\n\",\n \"
7494
\\n\",\n \"
7552
\\n\",\n \"
9894
\\n\",\n \"
...
\\n\",\n \"
27523
\\n\",\n \"
29188
\\n\",\n \"
28284
\\n\",\n \"
29890
\\n\",\n \"
34534
\\n\",\n \"
40892
\\n\",\n \"
35441
\\n\",\n \"
38083
\\n\",\n \"
38543
\\n\",\n \"
618948
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Asia
\\n\",\n \"
31025
\\n\",\n \"
34314
\\n\",\n \"
30214
\\n\",\n \"
24696
\\n\",\n \"
27274
\\n\",\n \"
23850
\\n\",\n \"
28739
\\n\",\n \"
43203
\\n\",\n \"
47454
\\n\",\n \"
60256
\\n\",\n \"
...
\\n\",\n \"
159253
\\n\",\n \"
149054
\\n\",\n \"
133459
\\n\",\n \"
139894
\\n\",\n \"
141434
\\n\",\n \"
163845
\\n\",\n \"
146894
\\n\",\n \"
152218
\\n\",\n \"
155075
\\n\",\n \"
3317794
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Europe
\\n\",\n \"
39760
\\n\",\n \"
44802
\\n\",\n \"
42720
\\n\",\n \"
24638
\\n\",\n \"
22287
\\n\",\n \"
20844
\\n\",\n \"
24370
\\n\",\n \"
46698
\\n\",\n \"
54726
\\n\",\n \"
60893
\\n\",\n \"
...
\\n\",\n \"
35955
\\n\",\n \"
33053
\\n\",\n \"
33495
\\n\",\n \"
34692
\\n\",\n \"
35078
\\n\",\n \"
33425
\\n\",\n \"
26778
\\n\",\n \"
29177
\\n\",\n \"
28691
\\n\",\n \"
1410947
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Latin America and the Caribbean
\\n\",\n \"
13081
\\n\",\n \"
15215
\\n\",\n \"
16769
\\n\",\n \"
15427
\\n\",\n \"
13678
\\n\",\n \"
15171
\\n\",\n \"
21179
\\n\",\n \"
28471
\\n\",\n \"
21924
\\n\",\n \"
25060
\\n\",\n \"
...
\\n\",\n \"
24747
\\n\",\n \"
24676
\\n\",\n \"
26011
\\n\",\n \"
26547
\\n\",\n \"
26867
\\n\",\n \"
28818
\\n\",\n \"
27856
\\n\",\n \"
27173
\\n\",\n \"
24950
\\n\",\n \"
765148
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Northern America
\\n\",\n \"
9378
\\n\",\n \"
10030
\\n\",\n \"
9074
\\n\",\n \"
7100
\\n\",\n \"
6661
\\n\",\n \"
6543
\\n\",\n \"
7074
\\n\",\n \"
7705
\\n\",\n \"
6469
\\n\",\n \"
6790
\\n\",\n \"
...
\\n\",\n \"
8394
\\n\",\n \"
9613
\\n\",\n \"
9463
\\n\",\n \"
10190
\\n\",\n \"
8995
\\n\",\n \"
8142
\\n\",\n \"
7677
\\n\",\n \"
7892
\\n\",\n \"
8503
\\n\",\n \"
241142
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
5 rows × 35 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" 1980 1981 1982 1983 1984 1985 \\\\\\n\",\n \"Continent \\n\",\n \"Africa 3951 4363 3819 2671 2639 2650 \\n\",\n \"Asia 31025 34314 30214 24696 27274 23850 \\n\",\n \"Europe 39760 44802 42720 24638 22287 20844 \\n\",\n \"Latin America and the Caribbean 13081 15215 16769 15427 13678 15171 \\n\",\n \"Northern America 9378 10030 9074 7100 6661 6543 \\n\",\n \"\\n\",\n \" 1986 1987 1988 1989 ... 2005 \\\\\\n\",\n \"Continent ... \\n\",\n \"Africa 3782 7494 7552 9894 ... 27523 \\n\",\n \"Asia 28739 43203 47454 60256 ... 159253 \\n\",\n \"Europe 24370 46698 54726 60893 ... 35955 \\n\",\n \"Latin America and the Caribbean 21179 28471 21924 25060 ... 24747 \\n\",\n \"Northern America 7074 7705 6469 6790 ... 8394 \\n\",\n \"\\n\",\n \" 2006 2007 2008 2009 2010 \\\\\\n\",\n \"Continent \\n\",\n \"Africa 29188 28284 29890 34534 40892 \\n\",\n \"Asia 149054 133459 139894 141434 163845 \\n\",\n \"Europe 33053 33495 34692 35078 33425 \\n\",\n \"Latin America and the Caribbean 24676 26011 26547 26867 28818 \\n\",\n \"Northern America 9613 9463 10190 8995 8142 \\n\",\n \"\\n\",\n \" 2011 2012 2013 Total \\n\",\n \"Continent \\n\",\n \"Africa 35441 38083 38543 618948 \\n\",\n \"Asia 146894 152218 155075 3317794 \\n\",\n \"Europe 26778 29177 28691 1410947 \\n\",\n \"Latin America and the Caribbean 27856 27173 24950 765148 \\n\",\n \"Northern America 7677 7892 8503 241142 \\n\",\n \"\\n\",\n \"[5 rows x 35 columns]\"\n ]\n },\n \"execution_count\": 66,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# group countries by continents and apply sum() function \\n\",\n \"df_continents = df_can.groupby('Continent', axis=0).sum()\\n\",\n \"\\n\",\n \"# note: the output of the groupby method is a `groupby' object. \\n\",\n \"# we can not use it further until we apply a function (eg .sum())\\n\",\n \"print(type(df_can.groupby('Continent', axis=0)))\\n\",\n \"\\n\",\n \"df_continents.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\\n\",\n \"\\n\",\n \"- `autopct` - is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\\n\",\n \"- `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\\n\",\n \"- `shadow` - Draws a shadow beneath the pie (to give a 3D feel).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# autopct create %, start angle represent starting point\\n\",\n \"df_continents['Total'].plot(kind='pie',\\n\",\n \" figsize=(6, 6),\\n\",\n \" autopct='%1.1f%%', # add in percentages\\n\",\n \" startangle=40, # start angle 90° (Africa)\\n\",\n \" shadow=True, # add shadow \\n\",\n \" )\\n\",\n \"\\n\",\n \"plt.title('Immigration to Canada by Continent [1980 - 2013]')\\n\",\n \"plt.axis('equal') # Sets the pie chart to look like a circle.\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\\n\",\n \"\\n\",\n \"- Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\\n\",\n \"- Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\\n\",\n \"- Pass in a custom set of colors for continents by passing in `colors` parameter.\\n\",\n \"- **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\\n\",\n \"explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\\n\",\n \"\\n\",\n \"df_continents['Total'].plot(kind='pie',\\n\",\n \" figsize=(15, 6),\\n\",\n \" autopct='%1.1f%%', \\n\",\n \" startangle=90, \\n\",\n \" shadow=True, \\n\",\n \" labels=None, # turn off labels on pie chart\\n\",\n \" pctdistance=1.15, # the ratio between the center of each pie slice and the start of the text generated by autopct \\n\",\n \" colors=colors_list, # add custom colors\\n\",\n \" explode=explode_list # 'explode' lowest 3 continents\\n\",\n \" )\\n\",\n \"\\n\",\n \"# scale the title up by 12% to match pctdistance\\n\",\n \"plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \\n\",\n \"\\n\",\n \"plt.axis('equal') \\n\",\n \"\\n\",\n \"# add legend\\n\",\n \"plt.legend(labels=df_continents.index, loc='upper left') \\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\\n\",\n \"\\n\",\n \"**Note**: You might need to play with the explore values in order to fix any overlapping slice values.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\\n\",\n \"explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\\n\",\n \"df_continents['2013'].plot(kind='pie',\\n\",\n \" figsize=(15, 6),\\n\",\n \" autopct='%1.1f%%', \\n\",\n \" startangle=90, \\n\",\n \" shadow=True, \\n\",\n \" labels=None, # turn off labels on pie chart\\n\",\n \" pctdistance=1.2, # the ratio between the pie center and start of text label\\n\",\n \" colors=colors_list,\\n\",\n \" explode=explode_list # 'explode' lowest 3 continents\\n\",\n \" )\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Click here for a sample python solution\\n\",\n \"\\n\",\n \"```python\\n\",\n \" #The correct answer is:\\n\",\n \" explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\\n\",\n \"\\n\",\n \" df_continents['2013'].plot(kind='pie',\\n\",\n \" figsize=(15, 6),\\n\",\n \" autopct='%1.1f%%', \\n\",\n \" startangle=90, \\n\",\n \" shadow=True, \\n\",\n \" labels=None, # turn off labels on pie chart\\n\",\n \" pctdistance=1.12, # the ratio between the pie center and start of text label\\n\",\n \" explode=explode_list # 'explode' lowest 3 continents\\n\",\n \" )\\n\",\n \"\\n\",\n \" # scale the title up by 12% to match pctdistance\\n\",\n \" plt.title('Immigration to Canada by Continent in 2013', y=1.12) \\n\",\n \" plt.axis('equal') \\n\",\n \"\\n\",\n \" # add legend\\n\",\n \" plt.legend(labels=df_continents.index, loc='upper left') \\n\",\n \"\\n\",\n \" # show plot\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"```\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Box Plots \\n\",\n \"\\n\",\n \"A `box plot` is a way of statistically representing the _distribution_ of the data through five main dimensions: \\n\",\n \"\\n\",\n \"- **Minimun:** Smallest number in the dataset.\\n\",\n \"- **First quartile:** Middle number between the `minimum` and the `median`.\\n\",\n \"- **Second quartile (Median):** Middle number of the (sorted) dataset.\\n\",\n \"- **Third quartile:** Middle number between `median` and `maximum`.\\n\",\n \"- **Maximum:** Highest number in the dataset.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"To make a `box plot`, we can use `kind=box` in `plot` method invoked on a _pandas_ series or dataframe.\\n\",\n \"\\n\",\n \"Let's plot the box plot for the Japanese immigrants between 1980 - 2013.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Japan
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
1980
\\n\",\n \"
701
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1981
\\n\",\n \"
756
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1982
\\n\",\n \"
598
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1983
\\n\",\n \"
309
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1984
\\n\",\n \"
246
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Japan\\n\",\n \"1980 701\\n\",\n \"1981 756\\n\",\n \"1982 598\\n\",\n \"1983 309\\n\",\n \"1984 246\"\n ]\n },\n \"execution_count\": 82,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# to get a dataframe, place extra square brackets around 'Japan'.\\n\",\n \"df_japan = df_can.loc[['Japan'], years].transpose()\\n\",\n \"df_japan.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We can immediately make a few key observations from the plot above:\\n\",\n \"\\n\",\n \"1. The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and median number of immigrants is around 900 (median).\\n\",\n \"2. 25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\\n\",\n \"3. 75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\\n\",\n \"\\n\",\n \"We can view the actual numbers by calling the `describe()` method on the dataframe.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Japan
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
count
\\n\",\n \"
34.000000
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
mean
\\n\",\n \"
814.911765
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
std
\\n\",\n \"
337.219771
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
min
\\n\",\n \"
198.000000
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
25%
\\n\",\n \"
529.000000
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
50%
\\n\",\n \"
902.000000
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
75%
\\n\",\n \"
1079.000000
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
max
\\n\",\n \"
1284.000000
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Japan\\n\",\n \"count 34.000000\\n\",\n \"mean 814.911765\\n\",\n \"std 337.219771\\n\",\n \"min 198.000000\\n\",\n \"25% 529.000000\\n\",\n \"50% 902.000000\\n\",\n \"75% 1079.000000\\n\",\n \"max 1284.000000\"\n ]\n },\n \"execution_count\": 83,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_japan.describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyize these two countries further using box plots.\\n\",\n \"\\n\",\n \"**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Get the dataset for China and India and call the dataframe **df_CI**.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"df_CI.plot(kind='box',figsize=(10,7))\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We can observe that, while both countries have around the same median immigrant population (~20,000), China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to _False_. You can also specify a different color in case you are not a big fan of the default red color.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# horizontal box plots\\n\",\n \"df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\\n\",\n \"\\n\",\n \"plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\\n\",\n \"plt.xlabel('Number of Immigrants')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Subplots**\\n\",\n \"\\n\",\n \"Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\\n\",\n \"\\n\",\n \"To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**. \\n\",\n \"\\n\",\n \"Typical syntax is : \\n\",\n \"\\n\",\n \"```python\\n\",\n \" fig = plt.figure() # create figure\\n\",\n \" ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\\n\",\n \"```\\n\",\n \"\\n\",\n \"Where\\n\",\n \"\\n\",\n \"- `nrows` and `ncols` are used to notionally split the figure into (`nrows` * `ncols`) sub-axes, \\n\",\n \"- `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` * `ncols` as shown below.\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure() # create figure\\n\",\n \"\\n\",\n \"ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\\n\",\n \"ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\\n\",\n \"\\n\",\n \"# Subplot 1: Box plot\\n\",\n \"df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\\n\",\n \"ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\\n\",\n \"ax0.set_xlabel('Number of Immigrants')\\n\",\n \"ax0.set_ylabel('Countries')\\n\",\n \"\\n\",\n \"# Subplot 2: Line plot\\n\",\n \"df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\\n\",\n \"ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\\n\",\n \"ax1.set_ylabel('Number of Immigrants')\\n\",\n \"ax1.set_xlabel('Years')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"** * Tip regarding subplot convention **\\n\",\n \"\\n\",\n \"In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that the a 3 digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\\n\",\n \"\\n\",\n \"```python\\n\",\n \" subplot(211) == subplot(2, 1, 1) \\n\",\n \"```\\n\",\n \"\\n\",\n \"produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's try something a little more advanced. \\n\",\n \"\\n\",\n \"Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\\n\",\n \"\\n\",\n \"**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the _decades_ `1980s`, `1990s`, and `2000s`.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1980
\\n\",\n \"
1981
\\n\",\n \"
1982
\\n\",\n \"
1983
\\n\",\n \"
1984
\\n\",\n \"
1985
\\n\",\n \"
1986
\\n\",\n \"
1987
\\n\",\n \"
1988
\\n\",\n \"
1989
\\n\",\n \"
...
\\n\",\n \"
2004
\\n\",\n \"
2005
\\n\",\n \"
2006
\\n\",\n \"
2007
\\n\",\n \"
2008
\\n\",\n \"
2009
\\n\",\n \"
2010
\\n\",\n \"
2011
\\n\",\n \"
2012
\\n\",\n \"
2013
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
India
\\n\",\n \"
8880
\\n\",\n \"
8670
\\n\",\n \"
8147
\\n\",\n \"
7338
\\n\",\n \"
5704
\\n\",\n \"
4211
\\n\",\n \"
7150
\\n\",\n \"
10189
\\n\",\n \"
11522
\\n\",\n \"
10343
\\n\",\n \"
...
\\n\",\n \"
28235
\\n\",\n \"
36210
\\n\",\n \"
33848
\\n\",\n \"
28742
\\n\",\n \"
28261
\\n\",\n \"
29456
\\n\",\n \"
34235
\\n\",\n \"
27509
\\n\",\n \"
30933
\\n\",\n \"
33087
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
China
\\n\",\n \"
5123
\\n\",\n \"
6682
\\n\",\n \"
3308
\\n\",\n \"
1863
\\n\",\n \"
1527
\\n\",\n \"
1816
\\n\",\n \"
1960
\\n\",\n \"
2643
\\n\",\n \"
2758
\\n\",\n \"
4323
\\n\",\n \"
...
\\n\",\n \"
36619
\\n\",\n \"
42584
\\n\",\n \"
33518
\\n\",\n \"
27642
\\n\",\n \"
30037
\\n\",\n \"
29622
\\n\",\n \"
30391
\\n\",\n \"
28502
\\n\",\n \"
33024
\\n\",\n \"
34129
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
United Kingdom of Great Britain and Northern Ireland
\\n\",\n \"
22045
\\n\",\n \"
24796
\\n\",\n \"
20620
\\n\",\n \"
10015
\\n\",\n \"
10170
\\n\",\n \"
9564
\\n\",\n \"
9470
\\n\",\n \"
21337
\\n\",\n \"
27359
\\n\",\n \"
23795
\\n\",\n \"
...
\\n\",\n \"
7533
\\n\",\n \"
7258
\\n\",\n \"
7140
\\n\",\n \"
8216
\\n\",\n \"
8979
\\n\",\n \"
8876
\\n\",\n \"
8724
\\n\",\n \"
6204
\\n\",\n \"
6195
\\n\",\n \"
5827
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Philippines
\\n\",\n \"
6051
\\n\",\n \"
5921
\\n\",\n \"
5249
\\n\",\n \"
4562
\\n\",\n \"
3801
\\n\",\n \"
3150
\\n\",\n \"
4166
\\n\",\n \"
7360
\\n\",\n \"
8639
\\n\",\n \"
11865
\\n\",\n \"
...
\\n\",\n \"
14004
\\n\",\n \"
18139
\\n\",\n \"
18400
\\n\",\n \"
19837
\\n\",\n \"
24887
\\n\",\n \"
28573
\\n\",\n \"
38617
\\n\",\n \"
36765
\\n\",\n \"
34315
\\n\",\n \"
29544
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Pakistan
\\n\",\n \"
978
\\n\",\n \"
972
\\n\",\n \"
1201
\\n\",\n \"
900
\\n\",\n \"
668
\\n\",\n \"
514
\\n\",\n \"
691
\\n\",\n \"
1072
\\n\",\n \"
1334
\\n\",\n \"
2261
\\n\",\n \"
...
\\n\",\n \"
13399
\\n\",\n \"
14314
\\n\",\n \"
13127
\\n\",\n \"
10124
\\n\",\n \"
8994
\\n\",\n \"
7217
\\n\",\n \"
6811
\\n\",\n \"
7468
\\n\",\n \"
11227
\\n\",\n \"
12603
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
5 rows × 34 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" 1980 1981 1982 \\\\\\n\",\n \"India 8880 8670 8147 \\n\",\n \"China 5123 6682 3308 \\n\",\n \"United Kingdom of Great Britain and Northern Ir... 22045 24796 20620 \\n\",\n \"Philippines 6051 5921 5249 \\n\",\n \"Pakistan 978 972 1201 \\n\",\n \"\\n\",\n \" 1983 1984 1985 1986 \\\\\\n\",\n \"India 7338 5704 4211 7150 \\n\",\n \"China 1863 1527 1816 1960 \\n\",\n \"United Kingdom of Great Britain and Northern Ir... 10015 10170 9564 9470 \\n\",\n \"Philippines 4562 3801 3150 4166 \\n\",\n \"Pakistan 900 668 514 691 \\n\",\n \"\\n\",\n \" 1987 1988 1989 ... \\\\\\n\",\n \"India 10189 11522 10343 ... \\n\",\n \"China 2643 2758 4323 ... \\n\",\n \"United Kingdom of Great Britain and Northern Ir... 21337 27359 23795 ... \\n\",\n \"Philippines 7360 8639 11865 ... \\n\",\n \"Pakistan 1072 1334 2261 ... \\n\",\n \"\\n\",\n \" 2004 2005 2006 \\\\\\n\",\n \"India 28235 36210 33848 \\n\",\n \"China 36619 42584 33518 \\n\",\n \"United Kingdom of Great Britain and Northern Ir... 7533 7258 7140 \\n\",\n \"Philippines 14004 18139 18400 \\n\",\n \"Pakistan 13399 14314 13127 \\n\",\n \"\\n\",\n \" 2007 2008 2009 \\\\\\n\",\n \"India 28742 28261 29456 \\n\",\n \"China 27642 30037 29622 \\n\",\n \"United Kingdom of Great Britain and Northern Ir... 8216 8979 8876 \\n\",\n \"Philippines 19837 24887 28573 \\n\",\n \"Pakistan 10124 8994 7217 \\n\",\n \"\\n\",\n \" 2010 2011 2012 2013 \\n\",\n \"India 34235 27509 30933 33087 \\n\",\n \"China 30391 28502 33024 34129 \\n\",\n \"United Kingdom of Great Britain and Northern Ir... 8724 6204 6195 5827 \\n\",\n \"Philippines 38617 36765 34315 29544 \\n\",\n \"Pakistan 6811 7468 11227 12603 \\n\",\n \"\\n\",\n \"[5 rows x 34 columns]\"\n ]\n },\n \"execution_count\": 89,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"df_can.sort_values(by='Total',inplace=True, ascending=False,axis=0)\\n\",\n \"df_top15=df_can[years].head(15)\\n\",\n \"df_top15.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\\n\",\n \"\\n\",\n \"1. Create a list of all years in decades 80's, 90's, and 00's.\\n\",\n \"2. Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\\n\",\n \"3. Merge the three series into a new data frame. Call your dataframe **new_df**.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1980s
\\n\",\n \"
1990s
\\n\",\n \"
2000s
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
India
\\n\",\n \"
82154
\\n\",\n \"
180395
\\n\",\n \"
429355
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
China
\\n\",\n \"
32003
\\n\",\n \"
161528
\\n\",\n \"
466431
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
United Kingdom of Great Britain and Northern Ireland
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"new_df.plot(kind='box',figsize=(10, 6))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. In order to be an outlier, the data value must be: \\n\",\n \"\\n\",\n \"- larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\\n\",\n \"- smaller than Q1 by at least 1.5 times the IQR.\\n\",\n \"\\n\",\n \"Let's look at decade 2000s as an example: \\n\",\n \"\\n\",\n \"- Q1 (25%) = 36,101.5 \\n\",\n \"- Q3 (75%) = 105,505.5 \\n\",\n \"- IQR = Q3 - Q1 = 69,404 \\n\",\n \"\\n\",\n \"Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\\n\",\n \"\\n\",\n \"Outlier > 105,505.5 + (1.5 * 69,404) \\n\",\n \"Outlier > 209,611.5\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1980s
\\n\",\n \"
1990s
\\n\",\n \"
2000s
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
India
\\n\",\n \"
82154
\\n\",\n \"
180395
\\n\",\n \"
429355
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
China
\\n\",\n \"
32003
\\n\",\n \"
161528
\\n\",\n \"
466431
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Philippines
\\n\",\n \"
60764
\\n\",\n \"
138482
\\n\",\n \"
312145
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" 1980s 1990s 2000s\\n\",\n \"India 82154 180395 429355\\n\",\n \"China 32003 161528 466431\\n\",\n \"Philippines 60764 138482 312145\"\n ]\n },\n \"execution_count\": 93,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# let's check how many entries fall above the outlier threshold \\n\",\n \"new_df[new_df['2000s']>209611]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"China and India are both considered as outliers since their population for the decade exceeds 209,611.5. \\n\",\n \"\\n\",\n \"The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) on box plots for more information.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Scatter Plots \\n\",\n \"\\n\",\n \"A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the datapoints are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\\n\",\n \"\\n\",\n \"Let's start by exploring the following:\\n\",\n \"\\n\",\n \"Using a `scatter plot`, let's visualize the trend of total immigrantion to Canada (all countries combined) for the years 1980 - 2013.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 94,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
year
\\n\",\n \"
total
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
1980
\\n\",\n \"
99137
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
1981
\\n\",\n \"
110563
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
1982
\\n\",\n \"
104271
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
1983
\\n\",\n \"
75550
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
1984
\\n\",\n \"
73417
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year total\\n\",\n \"0 1980 99137\\n\",\n \"1 1981 110563\\n\",\n \"2 1982 104271\\n\",\n \"3 1983 75550\\n\",\n \"4 1984 73417\"\n ]\n },\n \"execution_count\": 94,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# we can use the sum() method to get the total population per year\\n\",\n \"df_tot = pd.DataFrame(df_can[years].sum(axis=0))\\n\",\n \"\\n\",\n \"# change the years to type int (useful for regression later on)\\n\",\n \"df_tot.index = map(int, df_tot.index)\\n\",\n \"\\n\",\n \"# reset the index to put in back in as a column in the df_tot dataframe\\n\",\n \"df_tot.reset_index(inplace = True)\\n\",\n \"\\n\",\n \"# rename columns\\n\",\n \"df_tot.columns = ['year', 'total']\\n\",\n \"\\n\",\n \"# view the final dataframe\\n\",\n \"df_tot.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 95,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\\n\",\n \"plt.title('Total Immigration to Canada from 1980 - 2013')\\n\",\n \"plt.xlabel('Year')\\n\",\n \"plt.ylabel('Number of Immigrants')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Notice how the scatter plot does not connect the datapoints together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit). \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"So let's try to plot a linear line of best fit, and use it to predict the number of immigrants in 2015.\\n\",\n \"\\n\",\n \"Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\\n\",\n \"\\n\",\n \"- `x`: x-coordinates of the data. \\n\",\n \"- `y`: y-coordinates of the data. \\n\",\n \"- `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 96,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 5.56709228e+03, -1.09261952e+07])\"\n ]\n },\n \"execution_count\": 96,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = df_tot['year'] # year on x-axis\\n\",\n \"y = df_tot['total'] # total on y-axis\\n\",\n \"fit = np.polyfit(x, y, deg=1)\\n\",\n \"\\n\",\n \"fit\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a*x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 and intercept in position 1. \\n\",\n \"\\n\",\n \"Step 2: Plot the regression line on the `scatter plot`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 97,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"'No. Immigrants = 5567 * Year + -10926195'\"\n ]\n },\n \"execution_count\": 97,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\\n\",\n \"\\n\",\n \"plt.title('Total Immigration to Canada from 1980 - 2013')\\n\",\n \"plt.xlabel('Year')\\n\",\n \"plt.ylabel('Number of Immigrants')\\n\",\n \"\\n\",\n \"# plot line of best fit\\n\",\n \"plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\\n\",\n \"plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\\n\",\n \"\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# print out the line of best fit\\n\",\n \"'No. Immigrants = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1]) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"No. Immigrants = 5567 * Year - 10926195\\n\",\n \"No. Immigrants = 5567 * 2015 - 10926195\\n\",\n \"No. Immigrants = 291,310\\n\",\n \"```\\n\",\n \"\\n\",\n \"When compared to the actuals from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\\n\",\n \"\\n\",\n \"As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \\\"assisted relatives\\\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Get the data:\\n\",\n \"\\n\",\n \"1. Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_countries**.\\n\",\n \"2. Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\\n\",\n \"3. Reset the index in place.\\n\",\n \"4. Rename the columns to **year** and **total**.\\n\",\n \"5. Display the resulting dataframe.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 98,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"### type your answer here\\n\",\n \"df_countries=df_can.loc[['Denmark','Norway','Sweden'],years]\\n\",\n \"df_total=pd.DataFrame(df_countries.sum(axis=0))\\n\",\n \"df_total.head()\\n\",\n \"df_total.reset_index(inplace=True)\\n\",\n \"df_total.columns = ['year','total']\\n\",\n \"df_total['year'] = df_total['year'].astype(int)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 2: Generate the scatter plot by plotting the total versus year in **df_total**.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 99,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"df_total.plot(kind='scatter',x='year',y='total',figsize=(10, 6))\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Bubble Plots \\n\",\n \"\\n\",\n \"A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The datapoints are replaced with bubbles, and the size of the bubble is determined by the third variable 'z', also known as the weight. In `maplotlib`, we can pass in an array or scalar to the keyword `s` to `plot()`, that contains the weight of each point.\\n\",\n \"\\n\",\n \"**Let's start by analyzing the effect of Argentina's great depression**.\\n\",\n \"\\n\",\n \"Argentina suffered a great depression from 1998 - 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002. \\n\",\n \"\\n\",\n \"Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the _normalized_ value of the population for each year.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and bring it in the dataframe.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 100,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Year
\\n\",\n \"
India
\\n\",\n \"
China
\\n\",\n \"
United Kingdom of Great Britain and Northern Ireland
\\n\",\n \"
Philippines
\\n\",\n \"
Pakistan
\\n\",\n \"
United States of America
\\n\",\n \"
Iran (Islamic Republic of)
\\n\",\n \"
Sri Lanka
\\n\",\n \"
Republic of Korea
\\n\",\n \"
...
\\n\",\n \"
Kiribati
\\n\",\n \"
Vanuatu
\\n\",\n \"
Sao Tome and Principe
\\n\",\n \"
Tuvalu
\\n\",\n \"
American Samoa
\\n\",\n \"
San Marino
\\n\",\n \"
New Caledonia
\\n\",\n \"
Marshall Islands
\\n\",\n \"
Western Sahara
\\n\",\n \"
Palau
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
1980
\\n\",\n \"
8880
\\n\",\n \"
5123
\\n\",\n \"
22045
\\n\",\n \"
6051
\\n\",\n \"
978
\\n\",\n \"
9378
\\n\",\n \"
1172
\\n\",\n \"
185
\\n\",\n \"
1011
\\n\",\n \"
...
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
1981
\\n\",\n \"
8670
\\n\",\n \"
6682
\\n\",\n \"
24796
\\n\",\n \"
5921
\\n\",\n \"
972
\\n\",\n \"
10030
\\n\",\n \"
1429
\\n\",\n \"
371
\\n\",\n \"
1456
\\n\",\n \"
...
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
1982
\\n\",\n \"
8147
\\n\",\n \"
3308
\\n\",\n \"
20620
\\n\",\n \"
5249
\\n\",\n \"
1201
\\n\",\n \"
9074
\\n\",\n \"
1822
\\n\",\n \"
290
\\n\",\n \"
1572
\\n\",\n \"
...
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
1983
\\n\",\n \"
7338
\\n\",\n \"
1863
\\n\",\n \"
10015
\\n\",\n \"
4562
\\n\",\n \"
900
\\n\",\n \"
7100
\\n\",\n \"
1592
\\n\",\n \"
197
\\n\",\n \"
1081
\\n\",\n \"
...
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
1984
\\n\",\n \"
5704
\\n\",\n \"
1527
\\n\",\n \"
10170
\\n\",\n \"
3801
\\n\",\n \"
668
\\n\",\n \"
6661
\\n\",\n \"
1977
\\n\",\n \"
1086
\\n\",\n \"
847
\\n\",\n \"
...
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
5 rows × 196 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Year India China United Kingdom of Great Britain and Northern Ireland \\\\\\n\",\n \"0 1980 8880 5123 22045 \\n\",\n \"1 1981 8670 6682 24796 \\n\",\n \"2 1982 8147 3308 20620 \\n\",\n \"3 1983 7338 1863 10015 \\n\",\n \"4 1984 5704 1527 10170 \\n\",\n \"\\n\",\n \" Philippines Pakistan United States of America \\\\\\n\",\n \"0 6051 978 9378 \\n\",\n \"1 5921 972 10030 \\n\",\n \"2 5249 1201 9074 \\n\",\n \"3 4562 900 7100 \\n\",\n \"4 3801 668 6661 \\n\",\n \"\\n\",\n \" Iran (Islamic Republic of) Sri Lanka Republic of Korea ... Kiribati \\\\\\n\",\n \"0 1172 185 1011 ... 0 \\n\",\n \"1 1429 371 1456 ... 0 \\n\",\n \"2 1822 290 1572 ... 0 \\n\",\n \"3 1592 197 1081 ... 1 \\n\",\n \"4 1977 1086 847 ... 0 \\n\",\n \"\\n\",\n \" Vanuatu Sao Tome and Principe Tuvalu American Samoa San Marino \\\\\\n\",\n \"0 0 0 0 0 1 \\n\",\n \"1 0 0 1 1 0 \\n\",\n \"2 0 0 0 0 0 \\n\",\n \"3 0 0 0 0 0 \\n\",\n \"4 0 0 1 0 0 \\n\",\n \"\\n\",\n \" New Caledonia Marshall Islands Western Sahara Palau \\n\",\n \"0 0 0 0 0 \\n\",\n \"1 0 0 0 0 \\n\",\n \"2 0 0 0 0 \\n\",\n \"3 0 0 0 0 \\n\",\n \"4 0 0 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 196 columns]\"\n ]\n },\n \"execution_count\": 100,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_can_t = df_can[years].transpose() # transposed dataframe\\n\",\n \"\\n\",\n \"# cast the Years (the index) to type int\\n\",\n \"df_can_t.index = map(int, df_can_t.index)\\n\",\n \"\\n\",\n \"# let's label the index. This will automatically be the column name when we reset the index\\n\",\n \"df_can_t.index.name = 'Year'\\n\",\n \"\\n\",\n \"# reset index to bring the Year in as a column\\n\",\n \"df_can_t.reset_index(inplace=True)\\n\",\n \"\\n\",\n \"# view the changes\\n\",\n \"df_can_t.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 2: Create the normalized weights. \\n\",\n \"\\n\",\n \"There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) to bring all values into the range [0,1]. The general formula is:\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"where _`X`_ is an original value, _`X'`_ is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# normalize Brazil data\\n\",\n \"norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\\n\",\n \"\\n\",\n \"# normalize Argentina data\\n\",\n \"norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 3: Plot the data. \\n\",\n \"\\n\",\n \"- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \\n\",\n \"- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\\n\",\n \" - multiply weights by 2000 to scale it up on the graph, and,\\n\",\n \" - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 102,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 102,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAA1kAAAH0CAYAAADPHOcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAADimUlEQVR4nOzdd3xUVfo/8M+9d+7U9A4htCT0FmmCUgQERWyIoq6uYi/rrusWd113XbZ8Xfe3KgpYFuwdLFgXlC69hiItoYWQkDbp0+ee3x+RkZCETJKZzAQ+79crL8mde+95Zu5MnOeec54jCSEEiIiIiIiIKCDkUAdARERERER0PmGSRUREREREFEBMsoiIiIiIiAKISRYREREREVEAMckiIiIiIiIKICZZREREREREAcQki4gCavz48bjnnntCGsPq1ashSRLy8/Pbpb0nnngCycnJkCQJb775Zru0Ge7Ofh/ceeedmDRpUsji6d69O/7xj3+ErP32IEkS3n333XZpa/HixUhPT4eiKLjzzjvbpU0ioo6ESRbReSbUX2Y//fRTPPfcc+3Wnk6na5DYjB49GoWFhejcuXPQ29+8eTOefvpp/Pe//0VhYSFmzpwZ9Db9MX78eEiS5PuJi4vDxIkTsXHjxnZpv73fB4E0bdo0KIqCL774ItShNGrSpEmNJjaFhYWYMWNG0Nv3er246667cNNNNyEvLw8vvPBC0Nv0xw8//IAbb7wRmZmZkGW5yZs9CxcuxMCBA2E2m9G1a1f89a9/haZp9fZZuXIlxowZg+joaMTFxeHyyy/Htm3b6u1TXV2Ne++9F/Hx8bBYLLjyyitx+PDhgDyXN954A5dddhkSExMRGRmJoUOH4r333muw36FDhzBlyhSYzWYkJCTggQceQG1tre9xh8OBWbNmISsrC3q9HhkZGQ3OYbfbMW3aNHTt2hVGoxHJycm47rrrsG/fvoA8F6ILFZMsIgqouLg4REVFtekcbrcbbVknXa/XIyUlBbIc/D9xOTk5kGUZ1157LVJSUmAymRrs09bn01q33norCgsLUVhYiFWrViEuLg5XXnklampqmjzG5XIFpO1AvA9C4cSJE1i5ciV++9vf4r///W+z+2uaBq/X2w6RNS8lJQVGozHo7RQWFqKmpgZTp05FamoqoqOjG+wjhIDb7Q56LGey2Wzo2rUr/vKXv2Dw4MGN7rNgwQI88sgj+O1vf4u9e/di3rx5eOWVV/DnP//Zt09eXh6mTZuGIUOGYOvWrVi7di2io6MxZcqUegnM7bffjhUrVuDjjz/GunXrIITA5ZdfDrvd3ubnsmLFClxzzTX45ptvsHPnTtx88824/fbb8dFHH/n2qampwcSJE6HT6bBhwwYsWrQIS5cuxd133+3bx+v1Qq/X47777sPNN9/caFuSJOHyyy/HokWLcPDgQXz99dfweDyYOHEiHA5Hm58L0QVLENF55Y477hATJ05s8PuLL74oUlNThcViEXfffbdwuVzi5ZdfFl27dhUxMTHi3nvvFU6n03fcuHHjxF133SX+9Kc/icTERBEdHS2eeOIJ4fV6xezZs0VSUpJISEgQTzzxRL32x40bJ+6++27f7zabTdx7770iKipKxMTEiAcffFD84Q9/EOnp6Y3G2K1bNyFJkqiurhbffvutGDdunIiNjRVRUVFi7NixYvPmzb7junXrJgDU+xFCiFWrVgkA4sSJE759N27cKMaMGSOMRqOIiYkRt9xyiygqKvI9/tRTT4n09HSxZMkS0bt3b2E2m8X48eNFbm7uOV/rxtpv6vkcOHBATJ06VVgsFmGxWMS0adNETk6O73xvvPGGUBRFrFy5UgwYMEAYjUYxduxYcfLkSbFmzRoxZMgQYTabxcSJE0V+fv453wdnXwchhNi9e7cAIHbs2OHbBkC88MIL4pZbbhFRUVFixowZQgghnnjiCdGnTx9hMplEly5dxP333y8qKirO+doDEKtWrWq0/bPfl42ZM2eOGDx4sLBYLCI5OVnMnDlTFBQU+B4/fV2//fZbMWbMGGEymUTfvn3F0qVL650nOztbjBo1ShgMBpGZmSk++ugj0a1bN/H3v//9nO0LIcRf/vIXMX36dFFQUCD0er3Iy8ur9/jp98mHH34oevfuLRRFEXv27BGlpaVixowZwmw2i6SkJPHkk0+Kn//85w2e84svvih69+4tDAaDyMjIEP/4xz+E2+2u97r++c9/Fr/85S9FbGysSEpKEr/5zW+Ex+PxvY5NveYAxDvvvOM7FwAxf/58cdttt4mIiAjRpUsX8cwzz9SL57333hMjRowQUVFRIj4+XkydOlUcPHiwydfnjTfeaLT9M9+7Q4YMEaqqii+//FJUVVWJ++67TyQkJAiDwSCGDh0qli1b5jvf0aNHBQDx3nvvicmTJwuTySR69+4tVq9eLfLz88WVV14pzGaz6Nu3r1i7dm2z1++0xt7/QghxySWXiPvvv7/etueee06YzWZRU1MjhBDis88+EwBEVVWVb5/Tn53s7GwhhBAHDx4UAOo9F6vVKvR6vXjjjTf8jrMlpk2bJqZPn+77/dVXXxVGo7He5/Krr74SAMSRI0caHH/6veuP7Ozses+XiFqOPVlEF4CtW7di27Zt+O677/D+++/j3XffxbXXXosNGzbgf//7H9555x288847eO211+od9/HHH8PtdmPdunV47rnn8H//93+YNm0aampq8P333+M///kP/u///g//+9//mmz78ccfx+eff4533nkHmzZtQnR0NF566aUG+23ZsgUrV67EkiVLsGvXLhiNRtTU1ODhhx/Gpk2bsGHDBmRmZuKKK65AWVmZ73kpioI5c+b4emwac+rUKUyePBldunTBli1b8OWXX2Lv3r244YYb6u1XWFiIl19+Ge+99x42bNiAiooK3HXXXU0+txdeeAFz5syBoigN2j/7+QghMHnyZDgcDqxZswZr1qxBTU0Nrrjiinq9R5qmYfbs2Vi4cCHWr1+PgoICzJw5E3/5y1/w8ssvY926dcjPz8djjz3WZFyNsdlsePPNN5GQkIDMzMx6j82ePRujRo3Cjh078M9//hMAYDKZ8N///hf79u3Dm2++idWrV+OXv/yl75itW7f6nnNhYSHuuOMOpKSkoE+fPi2K62z/+c9/sGfPHnz22WfIy8tr9O77b3/7WzzxxBPYtWsXhg0bhpkzZ6KiogJA3dCnqVOnIiYmBps3b8Zbb72F//f//h+Ki4ubbdvr9eK1117DHXfcgU6dOmHixIkNPhMAUFBQgJdeeglvvvkm9u3bh27dumHWrFnYtWsXvvrqK6xcuRL5+flYsmRJveP++te/4j//+Q+efvpp7N+/Hy+88AJeffVVzJ49u95+c+fORadOnbB582a8+OKLmDNnDt5++20Ade+5MWPG4KabbvK99qNHj27yOc2ePRtjx45FdnY2fve73+Hxxx/HqlWrfI87nU78+c9/xo4dO/Ddd99BURRcddVVTfZozpw5E1u2bAEAfP755/Xa1zQNv//97/Hss8/iwIEDGDlyJO666y4sW7YM7777Lnbu3IlLLrkE06ZNw4EDB+qd989//jMefPBBZGdno2/fvrjllltwxx134N5778XOnTvRt29f3HrrrW3uHXM4HA16+0wmE2w2m2844NChQ33vf7fbDbvdjtdeew3p6em+9/f69euhqiomTpzoO09sbCxGjBiBdevWtSnGplRWViIhIcH3+/r16zFq1Kh6PYmTJ0+GLMtYv359q9uprq7GwoULkZqa2uBvBRG1QKizPCIKrMZ6shITE+v1Uk2dOlXEx8cLh8Ph23bNNdeIG264wff7uHHjxODBg+udu1+/fmLAgAH1tg0aNEj85je/qXfc6TvINTU1Qq/Xi4ULF9Y7ZuTIkQ16sqKjo0V1dfU5n5vX6xUxMTHi3Xff9W1TFKXBneOze7KefPJJkZqaWu81OH2nds2aNUKIuru8iqKI4uJi3z4ffPCBkCRJ2O32JmM6fQf/TI09n4ULFwqTySRKSkp8206dOiWMRqN46623fOcCIHbu3Onb59///rcAILZt2+bb9txzz4n4+PgmYxKi7jrodDpfrxkAkZCQIFavXl1vPwDirrvuOue5hBDi008/FXq9Xni93gaPLViwQJjNZrFly5Z67be0J+tsO3bsEAB8vXanr+snn3zi26ewsFAA8PVmLViwQFgsFmG1Wn377NmzRwBotidryZIlIjExUbhcLiGEEB999JHo0qWLrxdJiLr3iSRJ4vjx475thw4dEgDE8uXLfdtcLpfo0qWL7znX1tYKk8kk/ve//9Vr86233hLR0dG+37t16yauvvrqevtMmTJF3Hzzzb7fJ06cKO64444G8aORnqxHHnmk3j69e/cWf/jDH5p8DcrKygQAsW7duib3Od379P333/u2nX7vntnblJOTIwCIr7/+ut7xWVlZYtasWfXO9fzzz/se37JliwAg/vOf//i2nX4v7Nmzp8m4ztRUT9aTTz4pYmNjxbp164SmaWLfvn2iV69eAoB4//33fftt3LhRpKWlCUVRhCRJonfv3uLw4cO+x//5z3+KTp06NTj/jBkzxNSpU/2KsSXeeecdoaqq2L59u2/b5ZdfLm655ZYG+yYkJIh///vfDbY315P1+9//3ve3ok+fPvV62Ymo5diTRXQB6Nu3L/R6ve/3lJQU9O7dGwaDod62s+/2nz2vISUlBYMGDWqwralegtzcXLhcLlx88cX1to8aNarRGCMiIuptO3r0KG6//XZkZGQgKioKUVFRqKysxPHjx8/xbBv64YcfcPHFF9d7DQYPHozo6Gj88MMPvm2dO3dGYmKi7/fU1FQIIfzqBWnu+fzwww/o169fvTvRycnJ6N27d70YJEnCwIEDfb+npKQAQL3XPSUlBWVlZc3OBbr++uuRnZ2N7OxsbNu2DXfccQeuvfZaZGdn19tvxIgRDY799NNPMXbsWHTu3BkRERH42c9+BpfLhVOnTtXbb+XKlfjFL36Bd955B8OHDz9nPM1ZvXo1pkyZgrS0NERGRuLSSy8FgAbXe8iQIb5/p6SkQFEUFBUVAQD27duHvn37IjY21rfPgAEDGp03dLZXX30Vt956K1RVBQBce+21qK2tbdBTm5ycjK5du/p+P10g4Mz3uaqqGDZsmO/3H374AXa7HTfccAMiIiJ8P/fffz8qKytRUlLS6PMD6t6Hp59fSzV3ruzsbFx//fXo0aMHIiMjfc+rpZ+x0858D5x+XcaOHVtvn7Fjx9Z7zwP1/9Y09Z4H0KrP4pmefPJJ3HTTTbjsssugqirGjBmD2267DQCgKIqvjVmzZuGaa67B5s2bsX79evTt2xdTp05FdXV1s21IktTkY2de+yuvvNKvmD///HPce++9eO2113DRRRf5dcy5YmjK7373O+zcuROrVq1Ceno6rr/+er+eLxE1jkkW0QXg9JfG0yRJanTb2RW2Wnvc2fz5H77FYmmwbdq0acjLy8P8+fOxadMmZGdnIykpqVXFGZqK4cztZyZhZz7W3PNrTGPPp7EYhBD1tsuy7Puyd+YxZ77up7eJZoppREVFISMjAxkZGRg6dCj+85//IDk5uUHVv7Nj3bx5M2688UaMHTsWn332GXbs2IFXXnkFQP3CGIcOHcKMGTPw97//HdOnTz9nLM3Jy8vD1KlT0b17d3z44YfYtm2br7rf2df77OsE/HSNzn49W9L+smXLMHfuXOh0Ouh0OlgsFpSXlzcogNHYtQXO/T4/Hd/ixYt9iW92djb27NmDnJwcxMXFNfn8/PmMNeVc57LZbJg8eTIkScLrr7+OLVu2YOvWrZAkqVWfMUVR/Cq80dg1auz93di21r4OpxkMBrzyyiuw2Ww4duwYCgsLMXToUABAeno6AGDevHm+/w4dOhSjRo3Chx9+iLy8PF/hiU6dOqG0tLTBjY6ioiJfQtiYM6/9woULm433ww8/xMyZM/Hf//4Xt99+e73HOnXq1OCmh9vthtVqPWcMTTk9lHj8+PH49NNPcezYsUYrGhKRf5hkEVHQZGRkQK/XNygbvmnTpmaPLSsrw759+/CHP/wBU6ZMQb9+/WA0Ghvcydbr9c326PTv3x8bN26s98Vx165dqKysRP/+/VvwjFqvf//++OGHH1BaWurbVlRUhEOHDrVbDEBdyXubzXbOfdatW4eEhAT84x//wMiRI9GrV68Ga45ZrVZcddVVuOGGG/C73/2uzXFt3boVdrsdc+bMwSWXXILevXu3qvemf//+2Ldvn2+OFlDXi1RZWXnO4xYsWIC+ffti165d9b4IL168GN988w1OnjzZ5LH9+vUDgHrvc4/Hg+3bt9eLy2g04siRI77E98yfMxPr5vjznvfH/v37UVJSgn/+85+47LLL0LdvX5SXlwesEubp9/XatWvrbf/+++/b9T3fGJ1Ohy5dukBVVbz//vvo0aMHsrKyAAC1tbUNKpPKsgxZln2vzSWXXAK3242VK1f69qmoqMDmzZt9PbCNOfOap6amnjPGBQsW4M4778Rbb73VIME6HcPGjRtRVVXl2/bdd99B0zRccsklzb8IzRBCwOl0tvk8RBcqJllEFDQWiwX3338/nnzySXz11Vc4dOgQ/vSnP2H//v3N9jbExsYiMTERCxYswKFDh7Bx40bccsstDUqk9+jRA6tWrUJBQUG9BOZMv/jFL1BVVYU777wTe/fuxbp163D77bfj0ksvxZgxYwL2fM/l1ltvRWJiImbOnIkdO3Zg+/btuPnmm5Gamhq0tbXsdjtOnTqFU6dO4eDBg/jrX/+Kffv24frrrz/ncb1790ZJSQlee+01HDlyBG+//XaDYiXTp09HTEwMnnrqKV8bp06danUJ+MzMTEiShGeffRZHjx7FkiVL8Le//a3F57n11lsRGRmJ2267Dbt27cKmTZtw1113NVpa/zSPx4PXX38dM2fOxIABA+r9zJgxA126dGm0AMaZsV999dV4+OGHsWbNGuzbtw/3338/qqqqfO/ziIgIPPHEE3jiiScwb948HDx4ED/88AM+/PBDPP744y16jj169MD27dtx+PBhlJaWtroYRLdu3WAwGDB37lwcPnwYK1aswK9+9atW9QQ2Jj09HTfeeCMeeughLFu2DAcOHMCvfvUr7N27NyCJ+dlcLpcvOa6pqYHVakV2dna99Z5yc3Px1ltv4dChQ9i+fTseeughfPTRR3jppZd8idU111yD/fv34w9/+AMOHjyIPXv24M477/SVOgeAXr164dprr8WDDz6INWvWIDs7G7feemvAPs/PP/88HnzwQbzwwgsYN26c7/NltVp9+9x6661ISEjArbfeil27dmHVqlV4+OGHMXPmTPTo0cO33759+5Cdne37fJ5+jU5/VlevXo2XX34Zu3btQl5eHtavX48ZM2ZAluVm/1YQUdOYZBFRUD3zzDO4+uqrceutt2LEiBEoLy/HnXfe2eywIlmWsXjxYhw+fBiDBg3CnXfeiUcffRSdOnWqt9+zzz6L7du3o0ePHvXmU50pOTkZ3377LfLz8zF8+HBMmzYNAwYMwCeffBKw59kck8mEb7/9FgaDAWPHjsW4ceNgsViwdOnSRoe/BcL777+PTp06oVOnThg+fDi++OILvP766/jZz352zuOmTZuGP/3pT3jiiScwcOBAfPjhh/h//+//1dtnzZo12LZtG9LS0nxtdOrUCRs2bGhVrIMGDcLcuXPx6quvol+/fvjPf/6DOXPmtPg8ZrMZ33zzDcrKyjBixAj87Gc/w69//WskJSU1ecyXX36JgoIC3HTTTY0+fuONN+K1114751C1N954AwMGDMCVV16J8ePHIzU1FZdffnm99/mf//xnPP/881i4cCEGDx6MSy+9FM8//zy6d+/eouf4m9/8BgkJCRg8eDASExNbXUkuISEB7777Lr777jv0798fv/3tb/Gf//wnoOvLLVy4EFOmTMFtt92GwYMHY/369fjqq6/aXIWyMQUFBcjKykJWVha2b9+Ozz77DFlZWZg6dapvH03TMHfuXGRlZWHcuHHYv38/VqxYgSuuuMK3z7hx47B48WKsXLkSw4cPx7hx41BYWIj//e9/9a7VO++8g/Hjx+P666/H6NGjoWkavv3223Mm9P564YUX4PV68cADD9T7fJ05LDciIgLLly+Hy+XCqFGjMGPGDEyePLnBDYGpU6ciKysLr776Kk6cOOF7jQoKCgDU/W368MMPMWHCBGRmZuLWW29FVFQUNm3aVG/uIRG1jCQCNS6AiMhPEyZMQGxsbLsmOUTtyev1ok+fPrjmmmvw7LPPhjocIiJqZ7pQB0BE57c9e/Zgx44dGDVqFFwuF9555x2sWrUK33zzTahDIwqYtWvXori4GFlZWaiursbzzz+PY8eO4c477wx1aEREFAJMsogoqCRJwssvv4xf/vKX0DQNffr0wWeffeZ3+WKijsDr9eIf//gHcnNzoaoqBgwYgFWrVtUrx09ERBcODhckIiIiIiIKIBa+ICIiIiIiCiAmWURERERERAHEJIuIiIiIiCiALpjCF6fXg6DQSEhIaHKhWGo/vA7hgdch9HgNwgOvQ3jgdQicvaV78fXRryEgoMqq38eZzWbYbDa/97e77egR3QPTM6fDpGv72mxUp3PnzgE7F3uyiIiIiIjaQAiBL498ic9yP4NO1rUowWoNk2rCiZoTmLtzLkpsJUFti1qHSRYRERERUSsJIfBJzifYVbILZtXcbu2qsgpZkrFg7wIU1HDEVri5YIYLBoMQAtXuapyoOoHcilzYPDZoQoMsyTDrzMiIyUDXqK6IUCMgSVKowyUiIiKiABJC4PPDn+NA+YGQDNuTJAl6WY+39r2FewfeiwRTQrvHQI1jktUKDo8DGws3YnfpblQ6KwFR120rSz91DGpCw66SXZAkCVGGKAxKGIRRnUbBqDOGMHIiIiIiCpQ1+Wuwt3QvTGro5kVJkgRVVvHGD2/gkSGP8LtmmGCS1QJezYtlx5dhV8kuaEKDUWeERbU0uq8sybDo6x7zaB5sKNiATYWbMCRxCCZ3mwxFVtozdCIiIiIKoFJ7KdYVrAtpgnWaJEnweD1YcngJbu59c6jDITDJAgB4vV44HA4AaHJYX0FtAT478hlsHhsMigEKFLjdbr/bUKAAAthSsAV7i/fi+p7Xo7MlcBVMwl1RURGcTme7timEAAAYjUYoCpNaIiIiCgxNaPjo4EcwKIZQh+KjKipyKnKwv2w/+sb3DXU4F7wLPsnyer2w2+2wWCxNJli7Snbhi8NfwKQzIUIX0ab2VFWFEAIfHP4A16Zfi0GJg9p0vo5Cp9OFJNERQqC2thYmk4mJFhEREQXE6vzVKHeWh135dLPOjC+Pfoke0T04bDDELvjqgg6Ho9kE68vDX8KsmgNWvEKSJJhVM744/AV2l+wOyDmpcZIkwWKx+HoqiYiIiNrC5XVhc+HmsEuwTvNqXqzKXxXqMC54F3ySBTQ9RPBkzcm6HqwgjbU1qSZ8fvhznKw5GZTzUx1WdiQiIqJA2XJqCzShhTqMJukVPfaV7gvrGC8EF3yS1dQXcK/mxceHPg76XQqTzoRPcj6BV/MGtZ0LHRMtIiIiagun14n86nysyFuBalc1Su2lqHBWwOFx+OaBh4taTy32le0LdRgXtAt+TlZTvj3+LWo9tUGf0ChJEmrcNfg271tc2f3KoLZFRERERP6pdlVjy6ktKKwthNVh9SVW+6z7oFf0gAAEBGRJhk7WwaSYYFJNSDYlI9YYG9IbvGadGRsLN2JAwoCQxXChY5LVCIfHgeyS7HarGGNQDMguzsZlXS7rMJMUH330URQWFuKjjz4CADz77LP49NNPsX79+hBHRkRERNQ6QggcqTyCtSfXIr86HzpZV5dQATDqjKhyVcGia3wuv8PrgN1rR7GtGAbFgGRzMrpEdoEqq+39NCBJEopsRXB4HB3mu+X5hklWIzYWbmz3caya0LD51GaM6zLO72MeffRRLF682Pd7ZGQkMjMz8eijj2LixInBCNPnb3/7GzSNY32JiIjo/JBTnoNvjn6DClcFLDoLzKq5wT52j/2cPVQS6hYG1oSGE9UnkF+djyRzEjJjMyFL7TtLx625UVhbiB7RPdq1Xapzwc/JOpsQArtLd7d71m/UGZFdkt3iMb0jR47Ezp07sXPnTnz55ZcYOHAg7r77bhw7dqzR/V0uVwCiBaKiohATExOQcxERERGFisvrwseHPsYHBz+AW3MjQo1oNJESQsDusft9Xp2sgyIrKLYVY/Opzah0VgYy7GaZdWbkVOS0a5v0EyZZZ6l2V7f7h+C0KmcVatw1LTpGVVUkJSUhKSkJmZmZeOKJJ+B2u7F//34AdUnYM888gz/+8Y/o378/rrvuOgDAwoULcfnllyMzMxNDhgzBgw8+iKKiIt95Z8yYgdTU1AY/p4cHPvroo5g5c2ZgnjgRERFRCBypPIK52XORU5EDi9r0kj5A3XBAj/C0uA1FViCEQHZJNg5aD7ZbkQydrENRbVHzO1JQMMk6y4mqE0CICsQIIXCi+kSrj3e5XHjvvfdgMBgwcOBA3/bXX38dCQkJ+OKLLzBnzhzf9j//+c9YsWIFFi5ciJMnT+Khhx7yPbZgwQJfD9nOnTvxq1/9CmazGUOGDGl1fEREREThYk/pHrx/4H0IIXzzrs6l0lnZpu+Iqqyi2F6MXSW72m1aSoWzol3aoYY4J+ssuRW5QVsXqzkm1YTcilz0i+/n9zEbN25EZmYmAMBut8NkMmHOnDno0qWLb5/BgwfjN7/5Tb3j7rnnHt+/u3btiv/7v//DlClTUFhYiE6dOiE2Ntb3+OrVq/Hyyy/j1VdfRe/evVv79IiIiIjCwu6S3fj88OeNzrtqiltzt3lelSIpqHZXI7skG0MShwR9npZXcImgUGGSdRabx9buExNPkyUZte7aFh2TlZXl652qra3F2rVr8etf/xpRUVEYP368b5+zbdiwAfPmzcOhQ4dQVVXlK2KRn5+PTp06+fY7ePAgHnjgATzxxBOYPHly654YERERUZg4WH4QXxz5okUJFlBXpExC28uyK5KCWlct9pTuwaCEQUEt9c4kK3SYZJ0l1Ktjt7R9o9GIHj1+qhozYMAArFu3Di+++KIvyTKZ6vfMnTx5ErfffjtuuOEG/PrXv0ZsbCwKCwtx8803w+12+/YrLS3FHXfcgenTp+Pee+9t/ZMiIiIiCgN2jx1LcpfApGv5qCUJEkSA5pQosoJKZyXyqvPQLapbQM7ZGJkzg0KGSdZZQtWLFcj2FUWB3d509Zvs7Gw4HA7Mnj3bl4Dt2bOn3j5OpxN33303MjIy8Pe//73NMRERERGF2uJDi5vfqQmKrAQsyQLqClMcrzqORFMizGhZr5q/ZJlJVqgwyTqLWWeGJrSQJFua0GBRLS06xu12o7i4GABgs9mwevVqrFmzpsEcrDP16NEDkiTh1VdfxfTp07Fv3z48//zz9fZ5/PHHUVRUhOeeew5lZWW+7ZGRkQ16xoiIiIjC3a6SXThedbzFwwRPi1AjAl4ZUJEV/FD2A8ZGjQ3oeU+z6Fr2vZICh0nWWTJiMrCrZBcs+vZ/U9rddmTEZLTomM2bN/vmXBmNRnTt2hVPPPEE7rvvviaP6devH/7xj39g3rx5mDt3LgYOHIjZs2fjtttu8+2zceNG5OfnY+zY+h/65557jqXbiYiIqENxeV1YemxpqxMsoC7JCvRNeAkS7B478irzkKhPDOi5NaEh3hQf0HOS/yTRXsX6Q6ygoKDR7TabDWbzTx+4KlcV5uyY0+IepUCwuW149KJHEamPbPe2g02n08HjafnaEoFy9nW+UCUkJKC0tDTUYVzweB1Cj9cgPPA6hIcL4TpsKNiAlSdWtmou1pk2n9oMrxb4YhJGgxFZcVkBLYJR667FdRnXYVDCoICd83zXuXPngJ2LPVlniVQjEW2IhkdrPiEQAnDZjKgsjoX1RBLcDgOEkCBJAqrRibi0YkQnW6E3OeHPZybKEIUINSIAz4KIiIiIgLp1SHcU72hzggUAJsWEGq0mAFHVZ3fbUeGsQKwxtvmdW6BrZNeAno/8xyTrLJIkYVDCIGwo2ACjztjoPh6XDif2pKP4cBfYa0yQhASdwQVJ/qlTUGgSinK6ArKA0WJHUno+0gYehk7fePLm8DhwaeqlQS3jSURERHShya/Jh9VhDcgopThTHCqcFdDJgf0KrVf0yKvOC2iSFamPRLQ+OmDno5ZhktWIUZ1GYVPhpgbbNa+Ew5v741RuGoQmQaf3QG90NXoOSRZQTU4AgNej4MSedOT/0BMpmXlIH7EPslJ/lKYsyRiZMjLwT4aIiIjoArb+5PqA9GIBQCdLJxyrPBaQc52tylkFj+YJSALn8rowNHkob96HEOs6NsKoM2Jw4mA4vU7ftuqSaGz5eAIKc7pC0Xmb7JFqik7vgaLzovBQN2z5eAKqS366s+D0OjEkaUiTPWdERERE1DqljtKAFaxQJAVxxrigrKvqER5Uu6oDci5NaLi086UBORe1DpOsJkzpNgUWnQVCCJzKScXOry6F16NAp7ateINO9cDrUbDzq0tRlJsKIQQi1AhM7jo5QJETEREREQC4NTeqXFUBPWf3qO7wisAXv1BlFWWOsuZ3bIYQAmlRaW2qpEhtxySrCYqsYEavGcg7kIhD64dAZ3D7VbzCH5IE6AxuHFw3BHkHE3BD5g1QZCUwJyciIiIiAEBxbTFc3sandrSWWTUj2hAd8N4sWZJR4257UQ27x46JaRMDEBG1BZOsc6lMA/bNBHT24JxfZwd+uLmuHSIiIiIKqJyKHBgUQ8DP2y+uHwQCvwqSw+No0/FOT90UlNSI1ABFRK3VLoUvSktLMX/+fFRUVECSJEyaNAlTp07FokWLsGLFCkRFRQEAbrnlFlx00UUAgM8++wwrV66ELMuYNWsWhgwZAgA4cuQI5s+fD5fLhaysLMyaNSsok/q8XuDjj83oGmeBwebFwfKDUGU1YOd3a270ie2DJHMiPvlEw8MP10JhZxYRERFRwFQ6KwP6/e00vaJHz+ieOFxxOKCVBtsyDFEIAZNqwpXdrwxYPNR67ZJkKYqC22+/HT179oTdbscf/vAHDBpUtzDaVVddhWuuuabe/vn5+diwYQOee+45lJeX4+9//zteeOEFyLKMBQsW4P7770dmZiaefvppZGdnIysrK+Axf/utAbW1gMEApFhSYFbN2Fe2D27NDUVqfTbkFV7oZT0GJA1AlL4uuaypkfDttwZceaWzmaOJiIiIyF9eeINWYa+zpTOKbcWoddcGrLCGEK3vHbN5bJjVf1bAy8tT67TLcMHY2Fj07NkTAGAymZCamgqr1drk/lu3bsXo0aOhqiqSkpKQkpKC3NxclJeXw263o1evXpAkCWPHjsXWrVsDHq/DAWRn62Ew/PShjNJHYUTKCKSYU+AVXr8WKz6TR/PAK7xIsaRgeMpwX4IFAAaDhOxsPRxt6yHuUD766CN07coF8oiIiKhjkiQJAxIGQJGUNiVHgWB323FZ2mVIi+QUlHDR7qlucXExjh49ioyMDBw4cADLli3D2rVr0bNnT/z85z9HREQErFYrMjMzfcfExcXBarVCURTEx8f7tsfHxzeZrC1fvhzLly8HAPzrX/9CQkJCo/sVFRVBp6v/MmzZogKQIZ+VgsqQ0Tu+N9Jj03Gi+gSKaovg8DoAAegUHST8lJQJCHi8HkACjIoRqZGpSItMO+fdha1bTbjsMneTjzeluLgYw4YNQ1RUFHbu3AlVDXy3eGsVFBQgKysLn376KS655BLf9unTp+Pyyy9v8NoHi8FgaPI9cCHR6XR8HcIAr0Po8RqEB16H8HA+X4fY4liccp0K6npRl3S7BJsKNgECrW5HkiSoqgoBAbO5ZVUBbW4bLk+/HFdlXNWqtik42jXJcjgcePbZZ3HnnXfCbDZj8uTJmDFjBoC6no23334bDz30UJN3A1pyl2DSpEmYNGmS7/fS0tJG93M6nVDOmAwlBLBzpxF6vQatiaIxMmR0i+yGrhFd4dJcqHRWotxRDpfmghACkiRBL+sRZ4xDlCEKelnv+9BpTZxUrwd27FBw6aX2FlcxfPfddzFx4kTk5ubi66+/xrRp05rc1+VyQa/Xt6yBNvB4PI3+W1VVxMXF1dsWTE6ns8n3wIUkISGBr0MY4HUIPV6D8MDrEB7O5+ugulVU1FQEpfjFmQbFDsLO4p3wCm+rhg6qqgq3u25Kis1m8/s4m9uGMaljMDJm5Hl7DdtT586dA3audqsu6PF48Oyzz2LMmDEYOXIkACAmJgayLEOWZUycOBGHDx8GUNdDVVb20zoBVqsVcXFxDbaXlZUhLi4uoHFWV0uorPTvZZEkCQbFgCRzEnrH9cbAhIEYlDgIAxMGondcbySaE2FQDH7f1aiqklFT07IMS9M0vP/++7jxxhtx44034r333qv3+MiRI/HMM8/gj3/8I/r374/rrrsOALB27VpMnDgRPXv2xKRJk7Bx40akpqbik08+8R1bUlKCRx99FAMHDkSvXr1w7bXXYtOmTb7HN2zYgNTUVKxduxbTp09Heno6xo8fj9WrV/v2GT58OIC6nqvU1FTftT97uODp37du3YopU6YgPT0dU6dOxe7du337CCHwu9/9DqNHj0Z6ejpGjRqFp59+Gk4n57IRERFRQ+nR6XB7Wz5KqKWMOiOGpQxDhBrR4iklZzLpTH7t59E8cHlduLrn1RifNr7V7VHwtEuSJYTAK6+8gtTU1Hq9LOXl5b5/b9myBWlpdeNIhw0bhg0bNsDtdqO4uBiFhYXIyMhAbGwsTCYTDh06BCEE1q5di2HDhgU01hMnFCAIJTn9IYT4sX3/rVmzBjabDRMmTMANN9yAjRs34vjx4/X2ef3115GQkIAvvvgCc+bMQWFhIWbNmoWsrCwsXboUTz31FGbPnl3vGLvdjhtvvBG1tbV49913sWzZMkyYMAG33HILcnJy6u37t7/9DY888gi+++47DBo0CA8++CAqKysBAMuWLfPFsHPnTnzzzTdNPhdN0/D000/jb3/7G5YtW4bo6Gg88MADvt4uIQQSEhIwb948rF69GrNnz8aiRYswd+7cFr1mREREdGFINCdCVdpnGoUqqxicOBgZMRnQhNbidbQ0oSFCH9Hsfja3DcmWZPwy65cYkjSkldFSsLXLcMGDBw9i7dq16Nq1K373u98BqCvXvn79ehw7dgySJCExMRH33XcfACAtLQ2jRo3CY489BlmWcffdd0P+cYLUPffcg5deegkulwtDhgwJeGXB3FwdTP7dRAg4k6mu/X79/L8D8u677+L666+HTqdDcnIyxowZg/fffx9//OMfffsMHjwYv/nNb3y/n56j9swzz0BRFPTq1QuPP/44brvtNt8+X3zxBaqrq/Hyyy/75k396le/wrp16/DOO+/gb3/7m2/fxx57DJdddhkA4Mknn8Qnn3yCnTt3Yvz48b6expiYGCQlJZ3zuQghMHv2bAwcOBAA8Nvf/hbXXHMNjh07hoyMDMiyjMcff9y3f1paGo4dO4a33noLv/3tb/1+zYiIiOjCoJN1iDHEwO4J0pqnZ5EkCZ0jOiPBlID91v2ocFZAkRS/hhC6NTfijfGNPiaEgM1jg1k1Y2qPqbgo6aKgzjOjtmuXJKtPnz5YtGhRg+2n18RqzPTp0zF9+vQG29PT0/Hss88GNL4z2WxSg4IX7UWWgdpa/z8wRUVFWL58Ob7++mvfthtvvBFPPfUUfve73/mSo7MT0ZycHAwePLjeXLShQ4fW22fXrl0oKSlB37596213uVwwGo31tvXv39/376SkJCiKgpKSEr+fx2mSJKFfv36+31NSUgDUzafLyMgAALz33nv44IMPcOLECdhsNni93ibnuRERERElmZJwpOpIm5bgaSm9osfgxMFweBw4VnUMVocVHs0DRVKaTI50sg4Rav2erNPDAlMsKbii+xXoF98vYOXiKbhYSP8sof6+3pL2P/jgA3g8HkydOrXedq/Xi2+//da33dRI19zZH/Czf9c0DZmZmVi4cGGDY88+X2PVDFuT+MiyXC/xO7tYyJdffok//elP+OMf/4hRo0YhIiICX331FZ555pkWt0VEREQXhjGpY7DPug8W1dLubRt1RvSJ6wNNaCi2FaPUXgqHxwG71w6vVrfwsAQJQhYw6UyweWxQZAWRaiRiDDFIMifh4pSLEWcKbA0CCj4mWWcJVS9WS9vXNA0ffPABHnnkEV8xi9Pmz5+P9957r0HydVpmZiaWLFkCr9frS2q2b99eb5/Bgwfj448/RmRkZJvKup6uZOj1tn4F89M2b96MAQMG4P777/dty8/Pb/N5iYiI6PyVbElGsjkZNe6akMUgSzJSLClIsdSN0jk9/M/utsMLLzyyB9d0vQbdorohxZLidwEMCl/sbzyL2SxC1pulaYDF4l/RjVWrVuHkyZO4/fbb0adPn3o/M2fOxNq1a3HixIlGj73jjjtQUlKCP/7xj8jJycH69et9vUGne4+uv/56dO3aFT//+c+xZs0anDhxAjt27MDcuXOxdOlSv59TXFwcLBYL1qxZg+LiYlRUVPh97NnS09Oxf/9+LFu2DMeOHcPChQvPWUiDiIiICABGpIyAze1/afRgkyQJFtWCBHMCks3JGJw8GOPTxqNHdA8mWOcJJllnycjwwN4+cyMbsNvr2vfHu+++i6ysLKSmpjZ4bNSoUYiPj8f777/f6LGdOnXCG2+8gW3btmHy5Ml46qmn8Pvf/x5A3aK9AGA0GvHxxx9j0KBBeOyxxzBmzBjce++9yM7ORpcuXfx+TrIs45///Cc+//xzDB8+HFOmTPH72LPddtttuOGGG/DYY49hypQp2LlzZ72CHkRERESNGZQwCGbV3KI1V9uLzW3DuG7jQh0GBZgkwvHdFgQFBQWNbrfZbPVW1q6qkjBnTqTfPUqBZLNJePTRakRGtn/bmzZtwg033IDly5c3KHYRCDqdrt0WHm7M2df5QnU+LzjZkfA6hB6vQXjgdQgPF8p1yCnPwQcHPwjJ3KymeDQPksxJ+O3Y39ZbC5ZCI5CLEXNO1lkiIwWiozV4PO1fFjMqSkNERPskWG+99Rb69euHlJQUHDp0CH/961+RlZUVlASLiIiIKNQyYzPRL64fcipyoFf0oQ4HAKBBw029bmI59vMQhwueRZKAQYPccDjat12HAxgyxI32+oydPHkSDz30EMaOHYs//vGPGDlyJN5+++32aZyIiIgoBK5JvwZ6RR8WwwZtbhsu73p5WPWsUeCwJ6sRo0Y5sWlT+97hkGVg5Ehnu7X3xBNP4Iknnmi39oiIiIhCTa/ocXPvm/HmvjdhkA0h60Gye+wYED8AQ5OGNr8zdUjsyWqE0QgMHuyC09k+dzmcToEhQ1w4a41fIiIiIgqw1IhU3NL7Fji8jpD0aDk8DmTEZOC6jOs4TPA8xiSrCVOmOGGxAMH+7AkBREQITJ7cfr1YRERERBeyntE9cVuf2+D0Ots10bJ77OgT1wcze81kgnWeu+CTrKY+WIoCzJhhC3o5d7sduOEGO35cE5iCJBzGXhMREVH46B7dHfcNvA9GnREOT3An42tCg8PjwLgu4zA9YzoTrAvABZ9kAU1/AU9N1XDttfagJVp2O3DttXakpoZo9eMLBBMsIiIiakyiOREPDX4IIzuNhN1jD8p3Brvbjgg1Ag8MegBjUscwwbpAXPCFL4xGI2pra2GxWBp90w8a5IEQDnzxhREmEwJS/U+InxKsQYNCt3bUhUAIgdraWphMXD2diIiIGpIlGZO6TsLA+IH4/MjnOFV7CkbFCEVu/TAjIQTsHjtMOhPGpY3DpZ0vZXJ1gbngkyxFUWAymWCz2QCg0Q9AZiZwyy01+OyzSNTWyjAYWt+e0wlERGi45ZZqdO7sxY/NnvcMBgOczvadd3b6bpTJZILC8ZhERER0DsmWZNw38D6U2cuwOn81city4fQ4YVJNkCX/Bn+5vC64NBc6WTrh8m6XY0D8gDYla9RxXfBJFlCXaFks516jICMDeOwxF7791oDsbD00DS2qBuhw1JVpHzHChcmTXVCUNmRqHdCFspo8ERERdWzxpnjckHkD3Job+637kVuRC6vdCqvDCqfXCU1oEKi7kStLMlRZRZQ+CjGGGKRGpGJgwkDEm+JD/Cwo1JhktYCiAFde6cRllzmxaZMBu3apqKqSIYSAyVSXRJ2maXVDAiVJQlSUhksvdWPkSCfLtBMRERF1AKqsYlDCIAxKGASgboRMtbsabq8bHuGBDBmKrCBSHwlVVkMcLYUbJlmtYDQC48c7MW6cEzU1EvLyFBw+rENtrQRNq0u2LBaBjAwP0tK8iIgQAZnLRUREREShIUkSovRRoQ6DOggmWW0gSUBkpED//h70788CFkRERERExBLuREREREREAcUki4iIiIiIKICYZBEREREREQUQkywiIiIiIqIAYpJFREREREQUQEyyiIiIiIiIAohJFhERERERUQAxySIiIiIiIgogJllEREREREQBxCSLiIiIiIgogJhkERERERERBRCTLCIiIiIiogBikkVERERERBRAulAHQEREREQUDEIIuDQXPJoHQggosgKDYoAssZ+BgotJFhERERGdF9yaG7kVuThkPYQyZxmsdiscXgcgAEh1++hkHaIN0YgzxCEtMg394/sj2hAd0rjp/MMki4iIiIg6NKvdijUn1yCnIgcOjwMmnQmyJEOSJJh0pgb729w21LpqkVuRixV5K9ApohMu7Xwpesf2hiRJIXgGdL5hkkVEREREHVKVswqf5n6KvOo8GBQDdLIOFtXi17GSJMGoMwIAKhwVWHRoESLUCFzR/Qr0i+8XzLDpAsAki4iIiIg6FCEEtpzaghUnVrQosWqKJEmwqBYICHyS8wl2lezCdRnXNdoLRuQPzvojIiIiog7D6XXizX1v4ru872BQDFAkJaDnN6tmHK8+jrk75+JI5ZGAnpsuHEyyiIiIiKhDsLlteHX3qyiyFQW1l0mVVciSjPcPvI99ZfuC1g6dv5hkEREREVHYc3gcWLB3AeweO1RZDXp7p4tmfJL7CQ5YDwS9PTq/MMkiIiIiorAmhMDb+9+G3WOHTm7fkgJmnRkf53yMotqidm2XOjYmWUREREQU1tbmr0WxrbhderAaY1SMWJSzCJrQQtI+dTxMsoiIiIgobJXZy7CuYF1IK/1JkoQqZxWW5y0PWQzUsTDJIiIiIqKwJITA4kOLoVf0oQ4FRp0Rm09tRnFtcahDoQ6ASRYRERERhaXj1cdRbCuGLIXHV1ajYsTyE+zNouaFxzuWiIiIiOgsa/PXwqyaQx2GjyzJOF51HHaPPdShUJhjkkVEREREYafWXYu86jxIkhTqUOoRENhYuDHUYVCYY5JFRERERGFnU+EmKJIS6jAaMCgG7CvlAsV0bkyyiIiIiCjs5Nfkh0XBi8ZUuirh9DpDHQaFMSZZRERERBR2KpwVoQ6hSW7NzcWJ6ZyYZBERERFRWHF4HKh2VYc6jCYZdUbkVOSEOgwKY0yyiIiIiCislDnK4Pa6Qx1Gk1RZRbGN62VR05hkEREREVFYcXldQHgVFWzAK7yhDoHCGJMsIiIiIgorHs0DKcyzLE1ooQ6BwhiTLCIiIiIKK3pFDyFEqMM4p3AsL0/hg0kWEREREYUVo84YdosQn0kIAVVWQx0GhTEmWUREREQUVuKN8dDL4blGFgA4vU50iewS6jAojDHJIiIiIqKwopN1iDZEhzqMJrk1NzJiMkIdBoUxJllEREREFHbijHGhDqFJBsWAeGN8qMOgMMYki4iIiIjCTkZMBuwee6jDaFScMQ6KzMIX1DQmWUREREQUdoYkDQnL4hK17lqMTBkZ6jAozDHJIiIiIqKwo8oqMmIy4NE8oQ6lHpPOhIEJA0MdBoU5JllEREREFJYu63IZnF5nqMPwcWtu9I3ry6GC1CwmWUREREQUluJMcegT2wdurzvUoQAAJEiY2HViqMOgDoBJFhERERGFrWszroUiKxBChDQOm9uGqd2nwqQzhTQO6hiYZBERERFR2DIoBlzd82rYPLaQxeDW3OgR3QMDEzkXi/zDJIuIiIiIwlqfuD4YkjgkJCXdNaFBJ+lwQ+YN7d42dVxMsoiIiIgo7F3d82qkx6TD4XG0W5ua0KAJDfcMuIfDBKlFmGQRERERUdiTJAkze81Er9he7dKj5dW8kCDh/oH3I8YYE/T26PzCJIuIiIiIOgRZkjEjcwZGdxoNu8cOTWhBacfusSPeFI8HBz/IBItaRRfqAIiIiIiI/CVJEiZ0nYBBiYPw0cGPUO4sD9hQPq/mhUfz4PKul2NEyghIkhSQ89KFh0kWEREREXU4CaYEPDj4Qaw7uQ7bi7ej2lUNs87cqsTI6XVCgoTuUd0xrec0ROojgxAxXUjaJckqLS3F/PnzUVFRAUmSMGnSJEydOhU1NTV4/vnnUVJSgsTERPz6179GREQEAOCzzz7DypUrIcsyZs2ahSFDhgAAjhw5gvnz58PlciErKwuzZs3iXQYiIiKiC5AsyRjbZSzGpI7BAesBbCjcgFO1p+DSXDDrzNDJjX/VFUL4SsJH6aNwcaeLMarTKBa3oIBplyRLURTcfvvt6NmzJ+x2O/7whz9g0KBBWL16NQYOHIjrrrsOS5YswZIlS3DbbbchPz8fGzZswHPPPYfy8nL8/e9/xwsvvABZlrFgwQLcf//9yMzMxNNPP43s7GxkZWW1x9MgIiIiojAkSRL6xvdF3/i+cHgcKKwtxKHyQyiyFaHGXeOrEqhICgyKAXGmOKRHp6NbVDdE66N5w54Crl2SrNjYWMTGxgIATCYTUlNTYbVasXXrVvz1r38FAIwbNw5//etfcdttt2Hr1q0YPXo0VFVFUlISUlJSkJubi8TERNjtdvTq1QsAMHbsWGzdupVJFhEREREBAIw6I3pE90CP6B6hDoUuYO0+J6u4uBhHjx5FRkYGKisrfclXbGwsqqqqAABWqxWZmZm+Y+Li4mC1WqEoCuLj433b4+PjYbVaG21n+fLlWL58OQDgX//6FxISEoL1lMgPOp2O1yAM8DqEB16H0OM1CA+8DuGB1yH0eA3OP+2aZDkcDjz77LO48847YTabm9xPCNGi7Y2ZNGkSJk2a5Pu9tLTU/0Ap4BISEngNwgCvQ3jgdQg9XoPwwOsQHngdQo/XIDx07tw5YOdqt3WyPB4Pnn32WYwZMwYjR44EAERHR6O8vBwAUF5ejqioKAB1PVRlZWW+Y61WK+Li4hpsLysrQ1xcXHs9BSIiIiIioma1S5IlhMArr7yC1NRUTJs2zbd92LBhWLNmDQBgzZo1GD58uG/7hg0b4Ha7UVxcjMLCQmRkZCA2NhYmkwmHDh2CEAJr167FsGHD2uMpEBERERER+aVdhgsePHgQa9euRdeuXfG73/0OAHDLLbfguuuuw/PPP4+VK1ciISEBjz32GAAgLS0No0aNwmOPPQZZlnH33XdDluvywXvuuQcvvfQSXC4XhgwZwqIXREREREQUViTRkolOHVhBQUGoQ7igcaxxeOB1CA+8DqHHaxAeeB3CA69D6PEahIcOOSeLiIiIiIjoQsAki4iIiIiIKICYZBEREREREQUQkywiIiIiIqIAYpJFREREREQUQEyyiIiIiIiIAohJFhERERERUQAxySIiIiIiIgogJllEREREREQBxCSLiIiIiIgogJhkERERERERBRCTLCIiIiIiogDShToAIiIiovOJEEBNjYTSUhl2uwS3W4JOJ2A0CiQkaIiKEpCkUEdJRMHEJIuIiIioDYQA8vNl7NypR0mJjIoKGTabDK9XQJIkyDKgaXU/iiJgMgnExgrEx3sxeLAbPXp4mXQRnWeYZBERERG1gtsNbNumYvt2PaxWGSYTIP84EcNsFj/uJRo5UkJlpYTychm7d+sRG6thyBAXRo50wWBor+iJKJiYZBERERG1gKYBK1casH27Hm43YDIBFkvLzyPLQESEgNstYd06PdavN2DQIBemTHFCx29oRB0aP8JEREREfioqkrFokQlVVTKMRgQsGTIY6sYL7tqlIidHxQ032JCWpgXm5B2IEALV7mrUumvh1byQJRkGxYAYQwwUWQl1eER+Y5JFRERE1AwhgBUrDNi0SQ+jETAag9OOwSDB4wHefNOCrCw3pk51+IYgnq/K7GVYX7AeJfYSlDvKYffY4RGeugcFoMgK9Ioe0YZoxBniMDhxMHrH9obEiWwUxphkEREREZ2DpgEffWTC4cM6mEzBb0+SALMZ2L1bRVmZjJ/9zHbeDR/UhIa9ZXuxqWATCm2FMCkmKLICSZJgVs2NHmNz21DrqsXB8oOI1EdiYMJAXNL5Eph07XBRiFroPL83QkRERNR6mga8/74ZR4+2T4J1JoMBKCiQ8eabFng87dt2MJXaS/HyrpexJHcJqlxViFAj/B4KKEkSLKoFmtCw5dQWvLDjBWQXZwc3YKJWYJJFRERE1AghgI8/NuH4cSVkVf/0egklJRLee88MrYNP0dKEhpV5K/HK7ldg89hgUS1tGvJnUAxQFRVfHfkKb+17Cza3LYDRErUNkywiIiKiRmzYoOLQIV3Q5l/5S6+XcOKEguXLO259d6/mxQcHPsCGwg0w6UyQpcB9BTWpJpyqPYWXdr2Eckd5wM5L1Bbn2QhfIiIiorarqJCwZo2x3YcINsVkAjZv1mPwYDeSkztWl5YmNLx34D2crDkZtPlTOlkHTWj4757/4v6B9yPGGBOUdsh/VocVORU5OF55HFanFTa3DR7hgQQJqqwiyhCFeEM8MmIy0DOm53k3t45JFhEREdEZhAAWLTJDVUMdSX0mE7BokQkPP1zboSoOfpb7GfJr8mFQgtsTd7p37LUfXsPDgx+GURfiLsgLkFfzYnfpbmw+tRnFtmLIkgyjYvQNC1Wkurl3XuFFuaMcZfYy7CrZBZ2iQ9fIrrisy2VIjUwN5VMImA70ESUiIiIKvk2bVBQXy2GXyEgSUFUlY9WqjjNscF/ZPuwr2xf0BOs0WZLh9rrx+eHP26U9+smukl14bsdz+OrIV6h118KiWmDSmc45706WZFj0FhgUAwprC/Ha3tfw8q6XUWYva8fIgyPM/nwQERERhY4QwJYthrAZJng2oxHYsUOF1xvqSJpn99jx1ZGvmizJHiyqouJQ+SHst+5v13YvVDa3DW/vextfHP4CsiS3+nqfTrhq3DV4effLWHViFTTRsYbGnolJFhEREdGPDh9WUFkZ3ovcOhwSdu8Os7GMjfjyyJfQEJovyWbVjG+OfAOPdh7Vvg9DBTUFeDH7RRTWFgYsmZYlGSadCesL1uP1va/D5XUF5LztjUkWERER0Y++/94Ac/t2vLSY2VxXBCOc2T125FbkQpVDlwzavDbsKtkVsvbPd/nV+Xjjhzegk3TQyYEv82DSmVBqL8Vre1/rkIkWkywiIiIiADU1EvLzFbRh6aZ2U1wso7Q0fL/GbSjYEOoQYNaZsaVoS6jDOC+V2Erw9v63YVAMbVrrrDmqoqLSWYk3970Jr9YBxsieoVWfTpfLBc/5tPQ4ERERXfByc3UQogNkWABUFdi3LzyLRAshsLt0d7sVuziX4tpiFNUWhTqM84pX8+LDQx9CL+uDmmCdpioqSuwlWJ63POhtBZJfSdbbb7+N3NxcAMCOHTswa9Ys3Hnnndi2bVtQgyMiIiJqL4cPKzCbRajD8IteD5w4oYQ6jEZVuipR5aoKdRgAAIPOgN2lu0MdxnllxYkVqHJVtUuCdZpRMWJL0Racqj3Vbm22lV9J1rp165CWlgYA+Pjjj/HII4/g97//PT744IOgBkdERETUXqzWjjFU8LTy8vAcLphXnQcJ4fFCqrKKU7aO88U83JXYSrD51GYYlfZfg8ykmLD40GII0TFuhPjVz+x0OmEwGFBdXY2ioiJcfPHFAIDS0tKgBkdERETUHoQAysslKOHZOdSoqioZbjfCbtHkwxWHYdKFTw38CkdFqEM4byzPWx6SBAsAJElCubMchyoOoXds75DE0BJ+3QLp3Lkzvv/+eyxduhSDBg0CAFRVVUGvD+/KNkRERET+cDgAlys8el/85fEA1dXhF3OFswKyFD69bFWuqg693lK4sHvsOFp1NKTX1qwzY93JdSFrvyX86sm6++678eabb0Kn0+GBBx4AAOzatcuXcBERERF1ZF6vhA4yCslH0yR4vRKA8Ao83KrACQi4NXdYFOIIZ06vEwesB3C48jCsdisqnZVwa25oQoMsyyioKUCJvQQxhhgkmhIRpY9q13lZQF1vVkFNASqdlYg2RLdr2y3lV5KVkJCAf/zjH/W2jRkzBgMHDgxKUERERETtSdPQ4ZIsAPCGVz4DoC6pCSdCiA4zjycUim3FWJ2/Gkcqj8DtdcOkM9UlT1JdZb/TSu2lcHldOFV7CvnV+TDqjEixpKBLRJegrJPVFJ2sw87inRifNr7d2mwNv/r7fvWrXzW6/de//nVAgyEiIiIKBUUB5PAZ4eYXWRbQhWEVd0UKr4ltsiS3axLQUdg9dryz/x28svsVHKs6BlVWYVbNjfZOCSHg8DgA1L2eekUPTWjIq8rDxsKNyKvKa7dEVq/ocbLmZLu01RZ+veMae9FsNhvkjvbXiIiIiKgRqio6VGXB01Q1/HpozDozrA5ruw8la4pBMYRd4hdqu0p2YemxpQAAi2ppdn+H1wGP5qnXswXAl7weqzqGEnsJ+sf3h1EX/MIYFc6KoLfRVudMsh588EEAdYsPn/73aTU1NbjkkkuCFxkRERFRO9HrAYtF/DjHKXwIAdhsEsrLJdTWStC0uvhkWcBg0FBTI8FiCa8erS6RXXCk6kjYzIGKM8WFTcIXakIIfHP0G+wo3gGzavb7uGpX9TmHgepkHRxeB7ae2oqBiQMRY4gJQLRNq3RV1s0VC6MCK2c750fykUcegRACTz/9NB555JF6j8XExKBz585BDY6IiIiovcTGaigtDX2Ph9MJHD+uoKpKht1eV9xCloWvvLwk1SVfOp2MBQsioNcLxMRoSE72Ytw4FxISQltJLzM2E8vzlodFkqUJDfGG+FCHERaEEPjyyJfYXbq7RQkWALi8rmYTGgkSFFnB7pLdGJQwCDHGmDZEe26a0ODRPNAr4Vvp/JxJVr9+/QAAr732GgyG0H9QiIiIiIIlKcmLU6eUkPQKCQGUlUk4caIuuTo9R0xRAEVp2IMgSUBkpEBkZN1jdruMw4dl7N2rIiVFw8UXuzBggDsk634lGBPaZciYP2xuG3rHhf+aSu1h1YlV2F26u1VrmLVkvpVO1mF36W4MSx7W4mSuJbwiDKu+nMGvPyOKomD58uU4duwYHA5Hvcd+8YtfBCUwIiIiovbUq5cHmzfrERnZvu3a7cAPP6iorZWg0/m3uLDbLSE2tn6PlaIAERFAdbWMzz83YvVqA2bMsCE1tX17thRZQY+oHjhefTzkc6Ei1Aj0iu0V0hjCQUFNAdYXrG910iPLcouqRiqygh/KfsCw5GFBGaopQQr7YiZ+DWScN28evv76axiNRiQnJ9f7ISIiIjofdOvmhdncfoUkhKgbFrh1qx5OpwRVhd/FN2RZNDksUJIAi6UuEXv9dQu++cbQ7qXex3cZD7vb3r6NnsXtdaNffL+wnrfTHryaF4tzFreqB+s0s87cot4sCRLsHjuOVR1rdZvnoioqdFJ4J1l+Rbdr1y7MmzcPFkvz1UeIiIiIOiKdrq436+BBNehDBj0eYPduFTU1UovbEgKIidGa7fGSJMBsBnbuVJGbq8Odd9oQFdU+SWSyJRnJ5mTUuGtCVnTCIzwYkzomJG2HkzX5a1Drrm3THLlIfWSLk1WdrMOJ6hPoHNE54PPz4gzhX8zEr1crISEBbrc72LEQERERhdT48U64XMFtw+0Gduz4aXhga47v3t3/rimjUYLDIePVVyNgtbbfF9Ore14Nh9fR/I5B4PQ6MTR5KCL0ESFpP1xoQkN2SXabkxydrGtVkQlZknG86nib2j6bEAKxxtiAnjMY/Eqyxo4di//3//4f1q1bh71799b7ISIiIjpfxMQIdOrkRbDWVfV46nqWXC6p1UUpzOafCl7463Rbr70Wgaqq9km0UiNTkZWUBafH2S7tnSaEgEW1YHLXye3abjjab92PGndNQM5l0plavOCwLMkotZdCE4GbF2jz2DrEPDu/7p8sXVq3WNkHH3xQb7skSZg3b17goyIiIiIKkSuvdOCNNywwtX4KS6OEAPbsUeF0tj7BcruBPn08rTpWlgGvF3j9dTN+8YvadqmiOKXbFORW5PpVAjxQ7B47bu1zKxQ59OX4Q21T4SaYdYGp8JcWkYY9ZXugSn5UZjmDW3OjxF6CZHNgajkYFSP6xvUNyLmCya+P1/z584MdBxEREVFYSE3VMHSoCzt36hHIFWzy82VUV7duiCBQlyAlJmqIi2t9N5ui1C1u/L//GXH11cEfyqeTdbij7x14dc+rkCAFfR6NzW3DVT2uQucIruUqhECZvSxgyWasMRYGxdDiXilVVlFqLw1IkuXRPOgT2yfsKwsCfg4XJCIiIrqQTJ7shMWiBWzYoMMBHDuma1PvkaIAvXu3rhfrTAaDhOxsFXl57fM1MMYYg3sG3ANNaAEdNnY2u8eOK7pfgYuSLwpaGx1JubMcdk/gKjxKkoRkczI8ouXvwUDF4fQ6MT5tfEDOFWx+fdRtNhsWL16Mffv2obq6ut54zJdffjlowRERERGFgqIAM2bY8frrFpjbONpKiLp1sNrSieN2AwMHBm5xYZMJ+PRTM37xi8DM12lOvCke9w+6H+/ufxflzvI2lRM/m0fzQEDg+vTr0T+hf8DO29Edrzoe8CGa3aK64VTtKWhCa1GvpMPjgBCiTT2ZLq8LgxMHI9oQ3epztCe/XvmFCxfi6NGjmDFjBmpqanDXXXchISEBV111VbDjIyIiIgqJ1FQN111nh72NN+ErKyXU1MiQW/l91+0G0tM9bRomeDZJAmpqJOzc2bL5NW0RbYjGg4MfxOhOo2H32APSq2V325FiScEvh/ySCdZZCmoKYNQZA3pOWZLRN75vi3uzNKHBrbW+UrkQAgbFgKk9prb6HO3Nr4/77t278Zvf/AbDhw+HLMsYPnw4fv3rX+P7778PdnxEREREITNwoAdXXOFoU6J1/LgCna51CZLbDfTo4UWXLoEfZmcyAdu2tbwsd1vIkowJXSfggUEPINmcDLvHDre3ZV++NaGh1l0Lo2LE1elX445+d8CsBqa4w/nErbkhIfBz4GIMMUixpLQo0RIQbUqq7R47pmdMhyq3302BtvJruKAQAuYf+8qNRiNqa2sRExODU6dOBTU4IiIiolAbNswNk0ngs89MMBrRomF/bjdQWSm3ej2sjAwPUlODN4+puFhGfj5gDGyHR7MSTAn4eb+fo9Zdi7X5a3Go4hCqXdVwa26YdeZ6hQ00ocHmsUGGDKPOiM4RnTE+dTxSI1PbN+gOJpjVHHvF9ILdbUe1uxqK5N8Y1tYOFbS5bbi82+XoHt29VceHil8f+W7dumHfvn0YOHAg+vTpg9deew1GoxGdOnUKdnxEREREIde/vwdJSbVYtMiE8nLZ7/LueXlKi+dieTyAXg9kZbkRFRWkBbt+ZDYDy5fLmDYtqM00yaJacGWPK3ElroTD40BhbSFyKnJQ7aqGV/NCkiQYFSN6RPdA16iuiFQjg16h8Hxh1BnhFV7opMBX4pMkCYMSB2F3yW5Uuav8asPfZOxMNo8Nl6Vdhos7XdyaMEPKr1f9/vvv9xW7uOuuu/D++++jtrYWv/jFL4IaHBEREVG4SEzU8OCDtVi1yoCNG+vKuzc3z6qyUm5RsQq3G+jc2Yv0dG+r53C1hCwDhYXBb8cfRl1dMtUjukeoQzkv9IjugY2FGxEhRwTl/LIkY1DiIBwsP4hiWzF0kq7JBFgv61tUdt2reeEVXkzrMQ1ZSVmBCrldNftsNU3D6tWrMX36dABAVFQUHnjggaAHRkRERBRuZBmYONGJwYPdWLrUgLy8uq9Sja2nJQTgcDTf66JpdT9RUQI9e3qC3nt1turquhLz7T1kkIKrS0SXoC8ALUsy+sb1RZIpCQfLD8KreRtdl6slBThsHhtSLam4qddNiNAHJ0FsD80mWbIsY9myZbjxxhvbIx4iIiKisJeQoOG22+yw2yWsX6/Hnj0qqqokKAp887ZcrrqeKbWRufoeD6BpEvR6geRkL7p180LfvjUofNxuoLBQQY8e3tAEQK2mCQ1WhxVHKo/gWNUx1Lpr4dE8kCUZqqziZM1JxBnjEKVGQVWCVzQi3hSPkYaROFJ5BCX2Eng0j6/nSkA0W5hEExrsbjviTHG4rMtlyErK6vDDQv3qtxs3bhy+++47TJkyJdjxEBEREXUYJpPApElOTJzohNUq48gRBUePKigvV3DqlAKvt673S5LqfgwGAZNJICpKQ1ycgNks2rR+ViCYzRJycnRMsjqQGlcNvj/5PfZb96PaVQ1FVmBUjA0SE6fbid3VuyFLMiLUCKRFpiHRlBiUBEaRFWTGZiI9Jh1FtUUoqC2A3WOH3WNHXExc3dpaP1Y71IQGu8cOWZJhVs3nZTETv5Ks3NxcLF26FF988QXi4+PrXZjZs2cHLTgiIiKijkCSgPh4DfHxGoYPrytJvmaNHnFxImQ9VP7S6YCqqo7da3ChcHqd+OLwFzhYfhA6SQdVUc85pK5HdA+UOcugyiqcXif2W/fjsHwY6THpSDInBSVGWZLRKaITOkV0ghACAgLju4zHKdspeDQPJEgwKAZ0j+6OtIi0Dj0k8Fz8SrImTpyIiRMnBjsWIiIiovOG0ym1S/GKQNA0Jlnh7qD1IL448gW8mhcmnX/lLS16CyyqBU6PE5IkQZVVCAgcsB5Aka0IfeL6BHXtKYfXgSndpmB4yvCgtRGu/Eqyxo8fH+QwiIiIiIioMcvzlmNDwQaYdeYWz63qEdUDe8v2QpV+Ok4n61DprMSWU1uQlZgVlMWchRAw68wdtjpgW/mVZK1cubLR7aqqIj4+HpmZmVAbm9VJREREdIEyGAS04K0jHFCy3L4VDcl/S48txbaibbCollYdH2+KR4IxAVaHtV7lv9OVB7cXb8fQpKEBT7TsXjtu73N7i0q3n0/8etZr167FoUOHEB0djfj4eJSVlaGyshLp6ekoLi4GAPz+979Henp6UIMlIiIi6iji4zW4XHUVBMOZx4N2LxtP/llfsB7bi7b7PTywKb3jemPzqc0QQjQoeqFICnYW78SITiMCNnTQ5XVhUMIgdI/uHpDzdUR+JVldunTBiBEjMHXqVN+2pUuX4uTJk/jb3/6GTz/9FK+//jr++c9/Bi1QIiIioo6ka1cvJCn8kxebTSAz0xPqMOgsZfYyrDqxqs0JFlA3PHBA3ADsKt0FndTw67+AwL6yfRicOLjNbbk1N2KNsZjaY2rzO5/H/JqOuX79elxxxRX1tk2ePBnr1q2DJEm45pprkJ+fH5QAiYiIiDqiyMi6Eu3hTlWBTp1Yvj2cCCGw6NAiGJRGVrlupWhjNPrF94NH80CI+u9LWZJR4azAqdpTbWrD5XUhSh+FWf1nBbWgRkfgV5IVHR2N7du319u2Y8cOREVFAQDcbjd0ugtzvCURERFRYyQJiIsL/yQrMrJuAWUKH1uLtqLMXuabNxUoCaYEDEoYBAEBTdSfMKjKKg5XHm6w3V92tx0plhTcM+CegCaHHZVfmdGsWbPw3HPPoWvXrr45WXl5eXjssccAADk5OQ16uoiIiIgudGlpHhQV6cN2rSxNA1JTwz8RvNBsPbUVJrXtwwQbE2OMwchOI7G/bD/KneX1epw8mgdFtiJ0snTy+3xe4YVbc2Nit4m4OOXioCx03BH5lWQNHjwYc+fORXZ2NqxWK7KysnDRRRchMjLS9/jgwW0fw0lERER0PrnkEhe2bAnTDAuAzQZMmsQkK5ycrD6JMkdZq6sJ+kOVVQxKHISi2iIcqz4Gu8cOVVKhyirya/L9SrLcXjc8woO0yDRcl34dog3RQYu3I/J7jF9UVBTGjh0bzFiIiIiIzitms0DXrh4UFekQjjf4k5I0dO4MlJaGOhI6bX3h+oAUu/BHsiUZSeYkVLgqkFeVhypXFWqdtahwVCDaEF2vV0oTGmxuG2RJhkW1YGDiQIxLHYcIfUS7xNrRNJlk/fOf/8Sf/vQnAMBf/vKXJrv+Zs+eHZzIiIiIiM4D48Y58eabKiyW8OoxstuBceNcANrnCz35Jxhzsc5FkiTEGmIRmxgLTWgos5chMzYTRp0Rbs0NIYQvscqIzkDXqK6IUCM4LLAZTSZZ48aN8/17woQJ7RIMERER0fkmLU1Dp05elJfLUJTm928PQgAREQJDhrhDHQqdQRMaKpwV0CuhGWIqSzISTAmwqBbckHlDSGI4XzSZZF166aW+f48fP75Njbz00kvYsWMHoqOj8eyzzwIAFi1ahBUrVvgqFN5yyy246KKLAACfffYZVq5cCVmWMWvWLAwZMgQAcOTIEcyfPx8ulwtZWVmYNWsWs2giIiIKa5IE3HSTDfPmRYRNkmW3A3feaQOLQ4cXq8MKl9cVsiQLqOvZKneWh6z984XfH639+/fj6NGjcDgc9bZPnz692WPHjx+PK664AvPnz6+3/aqrrsI111xTb1t+fj42bNiA5557DuXl5fj73/+OF154AbIsY8GCBbj//vuRmZmJp59+GtnZ2cjKyvL3KRARERGFRHS0wIQJDqxYYYQpxKPznE6BrCwP0tJaV6qbgsfpdUJD6K+L28sezrbyK8l6/fXXsXHjRvTp0wf6M2qQ+tuL1K9fPxQXF/u179atWzF69GioqoqkpCSkpKQgNzcXiYmJsNvt6NWrFwBg7Nix2Lp1K5MsIiIi6hAuvtiNAwdUFBUpUEO0TqvXC1gsAldc4Wh+Z2p3mtCAMJi619q1sugnfiVZ33//PZ599lnExcUFtPFly5Zh7dq16NmzJ37+858jIiICVqsVmZmZvn3i4uJgtVqhKAri4+N92+Pj42G1WgMaDxEREVGwSBJw6602LFhgQW2t3O5D9TStLoa77uIwwXClSOExnlSRwyOOjsyvj1hCQgLUAN9ymTx5MmbMmAEA+Oijj/D222/joYceghCNp+9NbW/K8uXLsXz5cgDAv/71LyQkJLQtYGoTnU7HaxAGeB3CA69D6PEahIcL9To8/jgwb56M6mqp3Xq0vF5AkgR+8QsN8fGGeo9dqNchnJy+BoZIAyIPR8KsN4c0niRLEt8TbeRXkvXAAw/g1VdfxSWXXILo6PoLjfXr169VDcfExPj+PXHiRDzzzDMA6nqoysrKfI9ZrVbExcU12F5WVnbOnrVJkyZh0qRJvt9LuQBESCUkJPAahAFeh/DA6xB6vAbh4UK+DrfeCrz7rgWFhXLQ52g5nQKRkQJ33mmDEKLBmlgX8nUIF2deA8kjweaxhSwWt+ZGujn9gnxPdO7cOWDn8ivJOnLkCHbu3In9+/fXm5MFAC+//HKrGi4vL0dsbCwAYMuWLUhLSwMADBs2DC+++CKmTZuG8vJyFBYWIiMjA7Isw2Qy4dChQ8jMzMTatWtxxRVXtKptIiIiolAyGIC77qrFunUqvv/eCFUF5AAvjSQE4HAAQ4e6MXmyM2wqG9K5xRpjUe4IXXU/u8eOzJjM5nekc/Iryfrggw/w+OOPY9CgQa1qZM6cOdi3bx+qq6vxwAMP4KabbsIPP/yAY8eOQZIkJCYm4r777gMApKWlYdSoUXjssccgyzLuvvtuyD/+1bnnnnvw0ksvweVyYciQISx6QURERB2WJAFjxrgxYIAHixaZUVQkw2yu294WQgA2GxAdreGWW+xITWURg46ke1R3nKo9BYNiaH7nIDDpTEiNSA1J2+cTSfgx2enBBx/E3LlzoevAsyQLCgpCHcIFjUMRwgOvQ3jgdQg9XoPwwOvwEyGAw4cVfP+9Afn5ddUHWzpfy+ut67lKSdEwapQL/fu7/eod43UIvTOvgd1jx/M7ng9JkuUVXnSL7IaZvWe2e9vhoN2HC86cORNvvvkmZsyY4Vs8+DQ50H3bRERERBcYSQIyMrzIyLChulrC2rUGnDihoLJShsMBKErdEMPTvVxCAG434HJJ0OsFYmI0pKR4MW6cC/Hx7LnqyEw6E7pHdcfJmpOQpfb9nu3wODC+y/h2bfN85VeSdXre1XfffdfgsY8++iiwERERERFdwCIjBa66qm4dK00DrFYZR48qKCqS4fFIEALQ6QRiYzVkZHiRmKhxvtV5ZmLaRPx3739h1rVflUGv8KJLRBckW5Lbrc3zmV9J1rx584IdBxERERGdRZaBhAQNCQnsnbqQJFuSMTx5OLYXb4dRMbZLmx7NgxmZM9qlrQuBX0lWYmJisOMgIiIiIqIfXd71chwqPwSX1wWprdVQmmFz23B5t8sRZYhqfmfyi19Jls1mwzfffINjx47B4XDUe+zJJ58MSmBERERERBcqRVbwsz4/w4K9C6CDLmiJlsPjQO+43hiZMjIo579Q+ZVkPffcc9A0DSNGjGiwThYREREREQVevCked/S7A2/+8CZUWQ14ouXwONAzpidmZM4Iem/ZhcavJCsnJwevvfZahy7hTkRERETU0XSydMJ9A+/Du/vfRa27FgZd20u7CyFg99pxUdJFmNp9KhOsIPCrLmSfPn1w8uTJYMdCRERERERniTfF4+EhD2Nw0mDYPXb4scxtk1xeF2RJxm19bsNVPa5ighUkfnVNPfTQQ3j66aeRkZGBmJiYeo/NmMEqJEREREREwaSTdbiqx1UYkjgEK/JWIK86DzpJB1VpftVqIQRsHhsi1AgMTR6Ky9Iugyq3cLVrahG/kqwPPvgAZWVlSExMhN1u921n5ktERERE1H5SI1Lx834/R627FmtPrsWJ6hOocFTA7q3r4ZIlGUIIaNCgyioi9ZGIMcRgWPIw9I3r2+4LHF+o/EqyNmzYgBdeeAGxsbHBjoeIiIiIiJphUS24svuVAOp6qsqd5bA6rHB4HFBkBWadGUnmJJh0phBHemHyK8lKTk6GwqXEiYiIiIjCjiRJiDPGIc4YF+pQ6Ed+JVljxozBv//9b1xxxRUN5mQNGDAgGHERERERERF1SH4lWcuWLQNQNzfrTJIkYd68eYGPioiIiIiIqIPyK8maP39+sOMgIiIiIiI6L7C8CBERERERUQCdsyfrL3/5S7Nl2mfPnh3QgIiIiIiIiDqycyZZEyZMaK84iIiIiIiIzgvnTLLGjx/fTmEQERERERGdHzgni4iIiIiIKICYZBEREREREQUQkywiIiIiIqIAajLJ+tOf/uT79+LFi9slGCIiIiIioo6uySSroKAALpcLAPDVV1+1W0BEREREREQdWZPVBYcPH45f/epXSEpKgsvlwlNPPdXoflwni4iIiIiI6CdNJlkPPfQQDhw4gOLiYuTm5uKyyy5rz7iIiIiIiIg6pHOuk9WnTx/06dMHHo+Ha2YRERERERH54ZxJ1mkTJkzA3r17sXbtWpSXlyM2NhZjx47FgAEDgh0fERERERFRh+JXCfcVK1Zgzpw5iImJwYgRIxAbG4sXXngBy5cvD3Z8REREREREHYpfPVlffPEFnnzySXTv3t23bfTo0Xj22WcxadKkYMVGRERERETU4fiVZFVXV6NLly71tnXu3Bk1NTVBCYqIiIiIOgZNaLA6rHB6nRBCQCfrEGeMg17Rhzo0opDxK8nq06cP3n77bfzsZz+DwWCAw+HA+++/j169egU7PiIiIiIKM1WuKqw5sQb5NfmodFbCpbngFV4IISBLMvSyHpGGSCQYEzA2dSw6R3SGJEmhDpuo3fiVZN17772YM2cO7rzzTkRERKCmpga9evXCr371q2DHR0REREQB4HYDxcUycnJUnDwpw+mUIISEiAgZLpcZSUle9OrlQadOXhiNjZ+jxFaCL49+iZM1J6HKat2PUvdzNpfXhfyafLy29zXEmeIwIW0C+sX3C/KzJAoPfiVZsbGxmD17NsrKynzVBePj44MdGxERERG1gRBAQYGMVasMOHFCB6dTgsEgoD9jJJ/LJcFmU1BQoGDDBj30eiA52YtLLnGid28vZLluSOCqE6uwsXAjjIoRZp3Zr/ZlSYZFb4HT68SnOZ8iuyQb12dcD5POFKRnTBQe/EqyTouPj2dyRURERNQBHD2q4OuvjbBaZZhMgF4P6PWiyf11OiAqqu7fFRUKFi82IzJS4OJLarDX+DqK7KfalByZVBPyqvMwL3se7up/F+JN/E5J5y+/SrgTERERUcfgdgNLlhjx7rtmOBwyLBZAbuE3PkkCLBbA7dXw7HtHsPLTPlDc0W2OTZVVSJDw3z3/RZm9rM3nIwpXTLKIiIiIzhMVFRLmzYvA/v06mM11yVJb7CvbB69SDUdFDDYvmgTrybb3PkmSBJ2sw1v73oLL62rz+YjCUbNJlqZp2Lt3LzweT3vEQ0REREStUFYm4ZVXIuDxSNDr217Jr6i2CFaHFbIkQ1YEZJ0He7+9GKXHk9t8blmS4fA68PXRr9t8LqJw1GySJcsy/v3vf0Ona9H0LSIiIiJqJ1VVEl57LQI6XcuHBjbGo3mQU5EDVf6paqAkATqDG/tWDUV5YVyb2zAoBuwt3YtjlcfafC6icOPXx7Bv3744dOhQsGMhIiIiohYSAvjww7pqf4FaiupkzUloQmv0MUX1YN/KYfC4lTa3Y9KZsCp/VZvPQxRu/OqeSkxMxNNPP41hw4YhPj6+3mJyM2fODFpwRERERHRuGzaoKC6uqyAYCEIInKo9BZ3c+NdESQK8HgUH1w5B/4nb29SWJEnIr8lHtasakfrINp2LKJz41ZPlcrkwfPhwSJIEq9WKsrIy3w8RERERhUZlpYQ1a4wBS7AAoMZdA7vHfs59FJ0Xpcc7oexEUpvbU2UVGws3tvk8ROHEr56shx56KNhxEBEREVELrVxpgNL2UXv1WB1WKHLzJ9UZXDi2ow/i04rb1J4qqyisLWzTOYjCjd9TI/Pz8/Hxxx/jtddeAwAUFBTg+PHjQQuMiIiIiJrmdgM5OToEujZZpbMSitR8kiVJQE1ZFGyVlja3We4ob/M5iMKJX0nWxo0b8dRTT8FqtWLt2rUAALvdjrfffjuowRERERFR43buVOFyBajSxRlcmv9rVymqG8e2925zm7XuWng1b5vPQxQu/Lr3sWjRIvz5z39G9+7dsXFj3ZjZbt264dixY8GMjYiIiIiasH+/GtC5WD7C/11lRaCqpO3l3AUEPMIDBQEe+0gUIn71ZFVWVqJbt271tkmSVK/KIBERERG1n/LyIH0Pa+FpXTYD3M62j1n0Z4giUUfhV5LVs2dP3zDB09avX4+MjIygBEVERERETbPbgerqAKw63Aid1LKESfPKqCmNaVObekXPJIvOK359imbNmoV//OMfWLlyJZxOJ/75z3+ioKAATz75ZLDjIyIiIqKzWK0K3O7gnNusmlHrrvV7xJKid6O8MB6xqaWtbjPWEMsRUnRe8SvJSk1NxZw5c7B9+3YMHToU8fHxGDp0KIxGY7DjIyKidiJE3UQMftEhCn92OyDLElo0gcpP8aZ4FNQWQC/p/dpflgU8Tv/2bYwQAnHGts/rIgonfvcHGwwG9OnTB1arFXFxcUywiIg6MLfmxo7iHThccRjljnJUuaqgCQ2SJMGoGBFjiEGSOQmjOo1CvCk+1OES0VlE4HMrnxh9TAuHDAoI0fqbMza3DRclXdTq44nCkV+foNLSUrz44ovIycmBxWJBbW0tMjIy8Mtf/hKJiYnBjpGIiALE5rbhf8f+h9yKXLi9bhh1RkiSBL3y011oAYFyZzlK7aXYUbwDKeYUjO0yFn3i+oQwciI6k14PaFpwzq3ICmKNsahwVPjVsy2EBEX1tLq9WGMsukd1b/XxROHIrxmT8+fPR8+ePfHGG29g4cKFeOONN5Ceno758+cHOz4iIgoAIQR2Fu3Ei9kvIrciFzpZB5NqOucXKEVWYFEtqHJVYXHOYnx08CM4PI52jJqImhIdrUFRgted1SOqBzzCv8TJ41IRGV/RqnbsHjsuSrqIw5TpvONXknXkyBHcdtttviGCRqMRt912G44cORLU4IiIqO00oeGTnE/w1dGvoMoqdHLLKodJkgSzzoyjVUcxN3suSu2tn9xORIERHS1gMgUvyTKrZqSYU/xaIFiSgOgUa4vbEEIg1hCLUZ1HtSZEorDm1/9pMzMzkZubiz59fhoqcvjwYfTq1StogRERUdsJIbDo0CIcrjgMs2o+YzvgrDWiqjgO1hNJcDkM0LwyJElAUb2ITChHXJdiWGJrICt1Y5JUWYUQAgv2LMB9A+9rMFfL6QSKihTk5upQUyNB0wBVFUhN1dCtmwcxMQK8WU0UGJIExMUJVFYG70OVGZsJq9PqK4rTFJ3BDYOl5b3cDq8Dd/S7A7IUnFL0RKHUZJL10Ucf+f6dnJyMp59+GhdddBHi4+NRVlaGnTt34tJLL22XIImIqHVWnFiB3IpcmHQmAIDHpUP+Dz1RlJMGR40JQgCqwYUzv+MIAVhPJuDo9r5QVA9iOpWix0UHERFfBUmSoMoq3tr/Fh4Z8gg8Tj02bNBj3z4VlZUS3G7AZAJ0P/7fRdOA7dvrKqAZjQIpKV6MG+dC165eJlxEbZSa6kFpqR6qGpzzy5KMAfEDsLN4Z5M94EIA5ujqFn+ebW4bpnSfgkQz5/bT+anJJKusrKze7yNHjgQAVFVVQVVVjBgxAi6XK7jRERFRqxXXFmNT4SaYdCZoXgmHN/fHqdw0CE2CTu+Bamz8b7gkATrVC6h1w4SqiuKwbck4RMRVot9l22GOqUVNrYYn5x1GdM1wAIDBUJdcmUz1z6UogMVy+i64hOJiHd58U0VcnIbLLnNgwIDWT5YnutBdeqkL27YFL8kCgEh9JAYnDsbu0t2NLhbsceqRNvBwi85p89gwoesEjEgZEagwicJOk0nWQw891J5xEBFRgH2S+wmMihHVJdH4YeUwuOxG6PQtX71UkgX0JiectUZs+2w8LPHlsFdGwa05MKxTNSL1kf6fSwIiIgRcLglLlpiwa5cH06fbGyRnRNS8yEiBLl28KCtTgtozHG2IxkVJF2Fv2V44Pc56vVp6swPxXYv8Oo9bc0OChOnp09E/oX+wwiUKC34PgnU6nTh+/DgOHjxY74eIiMLPyeqTKLGXoPBgN+z4agy8HqVVCVY9ElBekIADq4ei6HAXqIqCY1XHWn06kwk4cUKHF1+MREEB52QQtca4cU7YbMFvx6JaMCJ5BFIjUuHVvPAKLzwuHVJ65TWb4Hk1L2rdtegS0QW/zPolEyy6IPhV+GLNmjV4/fXXodPpoNfXX9H75ZdfDkpgRETUemtOroH1UF8c2TIIqqHtQ7uFAEoOp8JeY4bO4IHbbkBRTjdoGUfh1txQ5daNV1LVunO/8YYFd9xRiy5dgrTwD9F5qmdPL3r18uDYMV1Qhw0CdZVG02PS0TWqK/KqTsDqOYmEftmodQuYdCZfAQshBBxeBzRNg1FnRHpMOi7rchniTHHBDZAojPiVZL377rv4zW9+g0GDBgU7HiIiaiMhBHbv8+DIlosCkmABQOnxFNhrzL5Kg5Kswe3Qo+hIKsriypBiSWn1uSWpbk7X229bcP/9NYiPD15ZaqLz0XXX2fHiixEA2qeajCqr6GzoiT8/lARzXA8crz6OIxVH4PK6ICCgyArSItOQHp2OOGMcqwfSBcmvJEun06Ffv37BjoWIiAKgpLoaP6wdAL2hjcMDf1RbHgFbRRRkpf56OZKswVMTgcMn7Ejp08TBfpKkul6tRYvMeOCBWlYeJGoBoxG45ho7Pv7Y3C7zG+12YPRoFzp1EgCiMcgwCIMSeCOe6Ex+3VqYOXMm3n77bVRVVQU7HiIiaqP3Fnvg9QTmzrHmlVF2IqVBgnWarNNQeDwSgSg2K8tAWZmMtWv1ze9MRPX06ePFFVc4gj4/y24HBg1y47LLnMFtiKiD86snq3Pnzli0aBGWLVvW4LEz19MiIqLQKiqSceCgHvoA5SnWE0kQAufsWRLwIidHh/79216O3WQC1q0zYMQIN0wmDhskaolhw9yQJOCbb4wwmc79uW0Nm03CRRc5MXWqk73NRM3wK8maO3cuxo4di9GjRzcofEFEROFj9WpD3TDBACw/pXkl2CojIMvnTnYkCbBaZXg8Py1C3BaSBKxbp8fll/NOOVFLDR3qRmKiF598YobdDhgMbc+GPJ66AjXTptmRlRWYYchE5zu//ndYU1ODmTNnQuJtCyKisOV0AkeO6GBU9dCE1ujCoS1RXRoDISRIaC7Jqvt/w8mTCrp1a3xYYUsYDMDevSomTeLdcqLW6NpVwyOP1OB//zNi504VBkPrboBoGmCzAd27e3HjjXaYzexdJvKXXx+58ePHY+3atRg3blyw4yEiolY6cECFx1O3cKhoJjHyR215lK+a4LnoZB0UBSgtlQOSZAFAZaWM/HwFaWmBOR/RhUanA66+2oExY5xYs8aAgwd1cDgkKAYnyp1WWB1WuLwuaEKDQW+A8ApE66MRb0yA7LVAlgW6d/dg3DgnUlO5tAJRS/mVZOXm5mLp0qX49NNPERMTU++x2bNnByMuIiJqodxc5cfKYkbopLaN2xMC8DhVSM0MFdSEBoNiAAA4HFKz87f8ZTQKHDigY5JF1EYxMQLXXuvA/pIcfLz+IA4c1MHhioDHHgPNXfd3wuVxw+N1olY6hWLDFnTv6cFVo1MwulsWy68TtZJf/xeeOHEiJk6cGOxYiIioDaxW5ccER4JFtcDusbf6XB6nCk2TocjNJzkW1QIAcLvrEq1AFKxQ1boiHkTUNja3DYtzFuN41XGYu5gxJK3uLogQgNAkaJqMiAgT7I6aejdIVhXtQHbFRtyUeROSLEkhip6o4/J7uCAREYW3qqqfviGlRabhh7IfoMpqq87lqDUBfgw51Cv6M+Z+SaiuDkySBdQNGSSi1jtedRzvH3gfsiT7boacJkmApAjIiheyojXogTbrzHB4HHh1z6u4ovsVGJ4yvB0jJ+r4/EqyVq5c2eRjEyZMCFgwRETUel4voPyY78Qb46GX9a2em+V16yBJ5z7WK7yIN8T7fpdlAU8Aqhqe5naz6gVRax2rPIZ3D7wLo2JsdeEySZJgVs1YemwpvMKLiztdHOAoic5ffiVZ33//fb3fKyoqcOrUKfTp08evJOull17Cjh07EB0djWeffRZAXcXC559/HiUlJUhMTMSvf/1rREREAAA+++wzrFy5ErIsY9asWRgyZAgA4MiRI5g/fz5cLheysrIwa9YsVjwkIvrRmX8OJUlCZmxmm3qzzkVAwKgzwqwzB/zcvjZYyIyoVWrdtfjg4AdtSrDOZFbN+O74d0gxp6B7dPe2B0h0AfAryXrqqacabFu5ciVOnjzpVyPjx4/HFVdcgfnz5/u2LVmyBAMHDsR1112HJUuWYMmSJbjtttuQn5+PDRs24LnnnkN5eTn+/ve/44UXXoAsy1iwYAHuv/9+ZGZm4umnn0Z2djaysrL8fKpEROc3RamfmCSYEpBgSoDVYW1xOXdZ1gAh4VxDBhNNifV+1zQpIOtknaYozLKIWkoIgcWHFkOW5IDeiDbpTPg091M8kvVIUG7cEJ1vWj3gffz48eccRnimfv36+XqpTtu6dauvJPy4ceOwdetW3/bRo0dDVVUkJSUhJSUFubm5KC8vh91uR69evSBJEsaOHes7hoiIgKiohklJn9g+MCpGaKJlJZj1FgeEaPwLmiY0JJoSG0ncBCIjA1fqOSKCSRZRSx2wHkBedR50cgDveKCud9zpdWJF3oqAnpfofOVXkqVpWr0fh8OB5cuXw2KxNH9wEyorKxEbGwsAiI2NRVVVFQDAarUiPv6nMf5xcXGwWq0NtsfHx8Nqtba6fSKi801cnLfBEDtFVpCVlAWDzgCv5n85dL3JCamRniRNaEg0J8KkMzV4TFUBo7HFYTfK6wUSE7k2D9HZhBAQ5xhLu65gXdCG8eoVPfaW7m3R3xKiC5VftzluueWWBtvi4uJw//33Bzygpv5wnOsPSmOWL1+O5cuXAwD+9a9/ISEhoc2xUevpdDpegzDA6xAegnUdhg0DDh+WYbE07IEaYxmDA6UHcLL6JPSK3q/z6Q0ahFbXW6UJDTpZh2RLcpPHGwyAxRKYu+fV1QLDhxuQkBDR/M6twM9CeOB1aF5JbQnW5K3BqZpTsDqscHqcAABVVhFnikOCOQFj0sYgLToN5fZyVGqVLb4JLssyzGb/ErNady1Oek/ioqSLWvxcqGn8LJx//Pq/4bx58+r9bjAYEBUV1aaGo6OjUV5ejtjYWJSXl/vOFx8fj7KyMt9+VqsVcXFxDbaXlZUhLi6uyfNPmjQJkyZN8v1eWlrapnipbRISEngNwgCvQ3gI1nVISQG83kjYbI0/3t3SHbG6WORU5KDGXQNVViGh6TkbenMNqq0WKIqECH0EYg2xgAa4NXeDfT0eICnJC5stMHe4NQ2Ija1GsN6u/CyEB16HpuVV52Hp0aU4ZTsFo2KEItcfnuuEE4WOQpy0nsSmY5sQZ4xDgjEBTocTkqdlc7HMZjNsTf3hOJsANh3ZhK5q1xa1QefGz0J46Ny5c8DO5ddwwcTExHo/bU2wAGDYsGFYs2YNAGDNmjUYPny4b/uGDRvgdrtRXFyMwsJCZGRkIDY2FiaTCYcOHYIQAmvXrsWwYcPaHAcR0flCrwd69nTDe448J9oQjWHJwzA8eThiDbFQZRVe4YVbc8PldcHldcGtuSFJElK61CLGEI8ukV3qEqxzkCQgNTUwCZbXW/c8DIaAnI6oQ/FoHnx5+Eu89cNbqHJVwaJaGiRYZzq9BpbT68SXR77EofJDjd4ICRRJkmB1croGUXPO2ZM1e/bscx4sSRL+8pe/NNvInDlzsG/fPlRXV+OBBx7ATTfdhOuuuw7PP/88Vq5ciYSEBDz22GMAgLS0NIwaNQqPPfYYZFnG3XffDVmuywXvuecevPTSS3C5XBgyZAgrCxIRnWXCBBdeeklFc6OFLKoF/eL7AagbCujwOKAJDZIkQS/roSp11cN2luhgs0k4R4cXNA2Ii9MCVlnQ4ah7HkQXGqfXiTd/eBNl9jKY1dbNq6pyVWFb0TZclHgRDLrg3KmoclUF5bxE55Nz/i9xzJgxjW63Wq343//+B6fT6Vcjjz76aKPbm0rQpk+fjunTpzfYnp6e7ltni4iIGkpI0NCvnxs5OSr0/k29gizJTX6hy8z0YscOudkEqlevwKxC7HYDmZkeJCSw6AVdWLyaF2/tewvljvJWJ0ea0CBLMjShYUfJDgxLHhaUcussfEHUvHP+b/PshYarq6vx2WefYcWKFRg9ejRmzJgR1OCIiKjlrrnGgblzdRBCQluXyYmIEOjc2YuCAqXRRMvtBnr39kAN0Pc4WRa4/np7YE5G1IF8e/xblNpLYVBa3/t0el0sCRK8mhcHyg5gYOLAQIXoI0utXgGI6ILh1+AOm82GL774AsuWLcNFF12EZ555BikpKcGOjYiIGiGEQLmzHFXOKniFFyadCQmmBF/VP70euO46Oz74wAxTw0rrLZae7oXVKsPlkiCf8d3K660bJpiSEpheJ5sNmDHDEbAy8ET+stuBvDwFOTkqyspkeL11yYqiCMTHa8jMdKNrVw0mU3DWbiuoKcD2ou0wqW37wKqyCqe3bpSRLMmwOq0othUjyZwUiDB9glUinuh8cs4ky+Vy4euvv8ZXX32Ffv364W9/+xvS0tLaKzYiIvqRV/NiV+kubC/aDqvDCofXAU3TfHOlDLIBkYZI9IzuiXGp45CeHoGpUx345htjmxMtSQKystzYulWFptUlWl4vYLEI9O8fmGGCNhswfrwTffsG5nxEzRGiLrFas0aPEyd0cLsBsxlQzqoxUVSkYNs2FaoKpKV5MHasC926edvcS3ymb49/C6Ou7XcXIvQRqHRV+hYKV2UVxyqPIdGU6OvlCoRY07kL4RBRM0nWww8/DE3TcM011yA9PR2VlZWorKyst8+AAQOCGiAR0YVMCIHs4mysOLECdo8dZtUMnaxDhNxw/SiX14U9JXuws3gn+sb1xdWDr4aiAF98UZdoteU7lqoCw4a5sX27CodDQmyswKBB7no9W6djqHJVodZdC01o0Ct6xBhiYNKZGh1iJERdgjVhggOXXhq8imhEZ6qqkrBokQkFBTqYzQJGY9MLaSsKEBlZ9+/iYh3eektF585e3HSTDdHRbe/ZqnZV40T1iVYXujhTojEReVV5UM7IFO1eO6pcVYg2RLf5/ADg8DjQLbJbQM5FdD47Z5Kl/3HW9Lffftvo45IkNVhDi4iIAsPldeHDgx8iryoPJtXk15cwVVGhQsWh8kOYmz0XN2beiPvu645Fi0yoqpLbNBRPpwMGD/4pETqdYLk1N/Kq8lBiL4HT64QQAoqsQIIETWjQhAZFVhCpj0S3yG6IMcRAkiS4XHXJ280329CrFyfSU/vYtk3Ft98aodPV9ca2hCTVzVMsL5cxb14EJk92YPjwtt0c2Fa0DTo5MKU5I/QRMKmmeoUpVFlFfk1+wJIsWZIxLLluCR1NA8rKZOTmKr7eQCEkKIpAcrKGzEwPkpO9fhfhITqfnPNTPX/+/PaKg4iIzuD0OrFwz0JUu6pbNU9Dr+ghhMA7+9/BLX1uwcMP98Ty5Qbs2KGHx4MWDSH0euvmrKSmenHDDXbExgrs36/g669NOFR8EqfcRyBLEhRJaVDJ7MzeK5vbht0lu2FSIpER0R9DBsi45hoH18OidrN6tR7ff2+AuY2dRopS97NsmRHV1TImTPCv2nJjTtac9M2nbCtJkpBiTsHx6uPQST99xbO7A1NMxiu86B7VHV6HGV+v0WP/fhW1tTIUpa438Mze8uPHgTVrDDAaBbp39+Cyy5wBm79J1BEEaFUTIiIKFCEE3t3/Lqrd1b71qlpDkiQYFAM+OPABHhz0ICZPjsP48U5s26Zixw4DysslaFpdwnXmPBQh6taq8noBo1GgZ08PJkxwIT7+py9I3TNrEDHpFRj3qYjY3x811ih4BaAaXGis8JjXrcDr1kFvAiK7HIVuyFdI7zcOBsNFrX5+REDdDYkKZwU8mgc6WYdYQ2yjScuGDSrWrWt7gnUmkwnYuFEPvV7g0ktbt7ZbubM8cAEBSItMQ0FNgW/dOwBweB0QQrR5XpbT7YF68CY8vzgCqlrXEx0R0XhvYN3jdY/l5+vw6qs6dOvmxY032lvcg0jUETHJIiIKMxsKNqCwthAmXdtLA0qSBL2ix0c5H+GBgQ9Ar5cwerQbo0e7YbNJyM+XkZOjQ1VVXUU1WRZQVYHu3b3o0cOLuDitwbwru8eO/+75b93cjN46dOu9Fs5aIyoK42A9mQx7lRmaVwdoAGQNepMTkYkViO9SjIj4KshKXbL29dGv4REejEgZ0ebnSeHP7rFjR/EOHK86jgpnBVxeF8wWMzwOD2INsciIycDgxMF+9eqU2cuwOn81TtacRJWrCh7N40si9LIekfpIdInsgnGp4xBnikNRkYwVK4wBTbBOMxqBVasMyMjwtKqnxuMNbLEXWZLRL74fskuyoUp1N2k0oUFAQDrXquLNKC1S4doxC4fk6Ba/jrIMRETUFRF58cW6ojxnDj0mOh8xySIiCiN2jx1rTq4JSIJ1mizJKLOVYfOpzbi408W+7WazQK9e3hbNhxJC4L0D78HhcdSbR2KwOJCcUYDkjAK/z2VWzVh2bBlSLCnoGtnV7+OoY6lyVuHro1/jWNUxaEKrV0XPq3lh99hhc9twuOIwVuStQGZsJqb2mNroZ6DGVYOPcz5GXnUejIoRiqw0up9Lc+FQ+SHsLd2LtIhuKF9xT0CWM2iKyQQsXmzCQw/VQlGAU7WnsKN4B0psJahy1S21IEsyItQIJJoSkZWUhdSIVEiSBIHA9+pEG6LRLbIbjlcfD8hixEUnLShcczUGpXSFLLc+UTu91t4XXxhRVSVhzJjW9f4RdQRMsoiIwsi6k+uCcl6TasLWU1sxMmVkm4YMbSrchFO1pwKWBJp0JnyS8wkeGfJIwCb/U3gQQmDzqc1YeWIldLLunD1UkiT55h7mVOTgxZ0vYmr3qfUW0t1ZvBNLjy2FIimwqJZm29fJOuhkHbZvMePYoZ3ol9Iz4OtF/RQ/UFUl44OlhajusgTF9mKYFBMU+adxuF7hRbmzHKWOUuws2Yk4Yxwu6XQJ9Iret7ZVIHWP7g4AvvlZre3FKi5Sf0ywercpwTqT2QysXm2AqgpcfDF7tOj8xCW7iYjChBACe8v2wqAEpxJEubMcJ2tOtvp4l9eFNfmB7WWTJAl2tx2r81cH7JwUekIIfHnkS3yX9x0MisG3bpM/VFmFTtZhyeElWJm3EgCwsWAjvjr6FfSKvl7i0nwcQHFOT+iNGg5YD6Cgxv+e1pZwa27k1OzBJ6tOoNZtQ4Qa0WScp5NEp9eJr499jf1l++HyBqdHp3t0d/SP7w+DYoBHa9mwRE1osDvdcG/7WUATrNPMZvz/9u47uq7qWvT/d7dT1Ztly1WW3Cu2ARuDMZjQAwkEQoAEkpCXQpJf8nvv3vyRcXPHuC/vd+8bj3Dz7gjpFQIJoSWhh2JjMDbGBXew5N5kq0un773X74+DhIUkW+ecLat4fjIyhrGOlre0z95nz7XmmpNXXgnQ2CiPomJ0kne2EEIME52pTjqSHYM2vt/ws71xe9bfv7FhI47yvtS630wfl6uk8tho8eKBF9nWuI2Qmf0mqJAVYt3xdTy25zFeOfxKVmO1nywm1pbuKWfqJntb99IS97bQRMJO8G7Du7Ql2rBjhbQeLxnw93ZNWLx97G2iqainx9WlJFDCTTU3MbNkZnd65pkknSQJO8HY8FhqG/4HZeYkzwOsLn4//PnPQVy59MUoJEGWEEIME4faDzEI2zO6mbrJydjJrL//vVPv9dhP46X2ZDv72/YPytji3NrXto93G971ZMXT0i0ee/8xlMruwji+ZxKm/6NVIku32N2827PJAtu12XJqC65y0TUdy5fkxAeZNeqdkD8BtHQ65GCsaEXtKJdVXcZNNTfxnUXf4epJVzM2PJaAESDpJInbcRJ2AlM3qQhWcNHYi/jOou9w47i7+WBHEX7/4ARYkC6I0dyss2lT7vvGhBhuJAFeCCGGiaORo/jNwW0a1ZnszOr7bNemNdE6aKmMQTPInpY9TC2aOijji3PDdm2eqXvGs5TSutY6dHR2Ne1i8ZjFGe8njLbloek9AzRHOdS31jOteFrOx7eneQ+2a3f3g9N0iLaffb/Y6SzDothfTHuinV1Nu5hfPj/nUutdlFKUB8sZlzcu/W/pFksql7Ckckn3a1zlUl5WTlNTU4/vfXm1/5w0EQ6F4J13fDk3dRZiuJGVLCGEGCYc1/Hs4ao/2VYyOxk9OWj7RiC9T+VkNPtVNjE8vHfqPSJ2xJP3saMcGmONGLpBxI7QmmjNeIx4Z+8UQ0MzOBU7lXN6alOsiaZ4U4+G2wDxjsyCLICaohpcXNqT7TREG3I6rtPF7BirJq4642t0Te91vhwHPvjA7K4GONgaG3WOHZNHUjG6yDtaCCGGCb/pxx3kzQmZFA04XVuybdADwMGosCbOrXca3slpH9bpTkROdAdCPt3HwY6DGX2/UuCk+k5Ds12bU7FTOR3fgfYDfZZHd1Imys3sWglZIcbnjwfgUMehrNMjT5d0kswomZHVit2JEzqdnefuETEQgK1bJWVQjC4SZAkhxDAxuWAycSc+qP9GgVWQ1fcNdvAHePJgKYZOwknQFGs6+wsHqDne3KMqYSQVyew9orR+X2/pFo2xxqyPLZaK0ZnqP/VWqcwnJKYUTKHAV0BnMvcCOLZrk2flcdPUm7L6/ro6i0Dg3F2PpgmnTmU3ASTEcCVBlhBCDBPjwuMGtVdU0klSlVeV1ffm+fIGvfqfZchM9kh2vPN4xmXCzyRmx3qsnqacVEaTEJqueu3H+vj42ToZO9krTbD739UUmp75taJpGvPK51HkL+JQ56Gsjy3pJAlbYb4898tn7E12JseO6edkP9bpWlsHd6VciHNNCl8IIcQwETADlAZKidpnLuWsFCQiQVJxH0ppGKZNsCCKbpz5wc52beaXz8/q2CpCFX2mRnlFKUWxv3jQxheD73Dn4awf6vvSV8DWmerMqKiGLxjvN2XQcbOvMNiebO+395cvGCfbzFpd01lYsZBT0VNEU1GCZnDAabpKKaJ2lHll87i++vqcrtdk8twHPEPxbwoxmCTIEkKIYeTCygt5/sDzvfa1OCmDY3smcWr/OGIdYey4hevqaAC6wrAc/OEo+WVtTF64h2BBz1l6pRRjw2MpDmQXyATNIPlWPrbybqXidDE7Rk1RzaCMLc6NlJPqd3XHC5qmZRwYBfOjdDYX9vm1bIvAwJn3DwYKcut3pWkaVflV3DH9Dl448AKHOg5haEa/Aazt2iScdF+rW2pvYUrhlJz+fWBI+lZlk2IpxHAmQZYQQgwj88vns/rIapRSaJqG6+jUbZjNyfoqXMfA9KXQNIUV7F3pz074aDpUwcl9VeSXtjLjsi2EiiJAulfOp2o+ldOxVRdWs7N556CsaFm6xcySmZ6PK84dy7A8TSnVNb3HeEqpjAu3FIxpobWhBNPqHZzlEhD2t9fLSRkUlLVmPe7p45eHyvn8rM/TmezknYZ3ONZ5jOZ4c3eVT1M3KQ4UUxGq4KIxF1ESHHgT5LM5V1UFT2cYsidTjC4SZAkhxDBi6iafqvkUf9z9R1LN49j1+mLshA/Dss+aDgigGwrdSBJrD/HuMyuYMK+OcfN2MKtkVs49qC6fcDlbG7d6HmQ5rkN1UbWnqWbi3JuQN4Gkk/Ssl1rACPRKnc2z8jIao2rmAQ5v63uFNGBk31i7vxQ+5epUzd6X9bhdTg8A83x5XDHhipzHzERxscPx4wbGOaxFUVAgQZYYXaTwhRBCDDPVhdWUtXyCd5+9CNfRMazMU/Q0HUyfzaH3plK/+lKun3JjzseV78tndulsz/tl2crmmknXeDqmOPfG5o31NADPs3oWW7EMK+PAyAokKRzTxMcXnhzlkO/Lz/rY+goklYLCMc34+lhlzlQux+aFadNsYtnXBcmYUlBSkv0eOSGGIwmyhBBimNm926Tx3VVUFRdjq1TW47jKxed3GZ+8kr89VdTrQTMbN0y5AZ/h86zcejQV5YoJV1Dgz660vBg+/Iaf0mCpZ+ONzx+Poz568A5b4ax6tU1ZtAc70XOVVCl1xkqbSqkzvscLfYU9jg3S6bqTF+3O+Pg+znZtKkIVOY+TiwkT3HO6ihWNwrRpEmSJ0UWCLCGEGEYiEY1nngkSCsH04ulMLpiM7doZb9K3XZt8Xz6LKxdTEPKxd6/JO+/kvsrgM3x8dvpnSbiJnAOtmB1jesl0Lqq8KOfjEsPDkjFLzlodc6ACZoB8Xz5KKZJukkn5k7IaJ7+8jbHTD+KkPtohUeAv6JGemnJS7Gvbx5aTW1h3bB1rj65l7dG1rDu2ji0nt1DfWk/K+WjCY0x4TI9VNidlMqbmMIVjWrM6xtPF7TgXVFyQ8zi5CAYV48c7nkzMDPTfmzUr+wklIYYjCbKEEGKYUAoefzzYPYOsaRqTCiaxpHIJYStMyk2dtbBA0kliaAbTiqcxv3x+d/pWMAivvhqgvT33Cl5VeVV8fubnSbmprMtgR1NRphdP59baW7NanRDD0/zy+YTMkGcrnbVFtdjKJmSGKPIXZT1OzcU7sQIJlJuegKgtqgXSf97ZtJO3j7/N0c6j3b2zTN3s7lkXs2Mcixzj7eNvs6NxByk3hd/wU+BLr74qF0x/ktpl23P7YT9UEa6gMlzpyVi5uPTSBNHo4F+bqRTMmGGf05UzIc4FCbKEEGKY2L/f4NAhs9fDRtAMsqB8ARePvZgx4THdfYJsZWO7djotUPdR6C9kQcUCLqy8sM+HNNOE557LfrP/6SbkT+CbC75JeaicSCoy4IfqlJsi6Sa5bsp13Fp766CW/BbnXlfhllwa/Z4u35dPWbCMmqKanIJx3XCZf91bpByXqvB4wlaY5lgzG05soCXegqmb/fa9AjA0A1M3aU20suH4BhpjjVQXVpN0UihlsOC6dRhm7pUVo6kol1ddnvM4XqiudqiocAa9nLtScMUV/ZfEF2KkkuqCQggxTKxd6ycc7j9Y8Rv+7hl4+KiM9EAfPg0D9u83icch4EGslefL455Z97CreRdvHXuLE5ET6JpOwAj0OKaUmyJuxwlbYWaXzOaKiVcQtsK5H4AYlqoLq1k8ZjGbTm7KqHFwX2J2jHtn30tdax0nIidyqkCph9q4/rN76XhrGcfbGtnbsQtTNzMK3romBXY27aS2YBYVwTFUXfEkwYLcU91Sboqaohpmlg6PVgaaBrfdFuWnP83z5H7Rl1gMrrkmfsb7nhAjlQRZQggxDMRiGkeOGBk9zGQzs68UvPOOj8su86ZCoKZpzC6dzezS2bQmWqlrqWNf+z5iqRgKhambVIQqqC2qZWL+xIz7HImR6ZrJ15ByU2xr3JZ1oBVNRblk3CVcWnUpS8cu5dE9j3Ko/RBBK/PxonaUmsIabpv2KY7PPMQ/PbQV1TQeAlleB8kwx8x1/Nv903npqEFnKtadXpgNRzn4DX/Ovey8VlKiWLEizurVAYK5xcu9JJOKyZNdFi2SvVhidJIgSwghhoGDB3XszCu1ZywQgIMHTcDbMuwARf4iFlcuZnHlYs/HFiOLpmncWH0jY0JjePXwq2dNxztdyk2hoXHz1JuZWz4XSKch3j3zbtYfX8/rR15H1/QBlYvvajdw7eRrWVSxCIXirwf/xMKrozQeaGD/uzOJd4Qw/UnONmehFKQSPoJ5Uaau2ElF9RFePLqLe2ffy+92/o72ZHtWK20pJ0XADPCl2V8iYA7SklEOli9P0dGhs2mTz7NAK5lUlJUpPvvZ6Fl/70KMVBJkCSHEMLB3r+X5THF/mpvlqUYMPk3TuGjsRcwqncVz+59jX9s+XOX2SieFdOpr3I5jGRa1RbVcN+W6XitgmqaxdNxS5pbNZc3RNexu2k0kFcHUTfyGH03TUEqRcBKk3BQFvgIWVCxgRdUKQlYIgM0Nm2mKNxG2wlRMOU755OO0nyzm4JZpRFvzSMYCOI6BprkfHpeOYTj4gnFCRZ1MmL+XosrmDwMDjZZEC7uadvGVeV/h+f3Pp1fujOCAVpmVUkTtKDNKZnDT1Js8a+I8GK69NoHPB+vWpQOtXAKjWAwmTXK5444opjyFilFM3t5CCDEMtLbq56y6VmenjuuCLjUnxDmQ78vns9M/S8yOseXkFg62H6Ql0ULSSWLqJiEzREmghOrCauaXzz/ralCeL4/rp1zPNZOuoTHeSH1rPUc7j6KUQtd1qsJVTC2aSlmwrFdhlY0NG3vsB9Q0KBzTwrxrNgCQSlhEmvNJJXwoBb5AinBJO5a/75S2kBni3YZ3WVK5hJum3sTCioW8euhVjnQewdAMfLqvV8CVdJLYrs3Y8FhumnoTtcW1fY493Fx5ZYJp01I8+WSISAQCgcwiLdsG14Wrr46zeHFKVrDEqCdBlhBCDAODXcHr42wbfNnXEBAiY0EzyLJxy1g2bln335WVldHY2JjVeIZuMCY0hjGhMQN6fWeyk1PRU92rWn2x/CmKxjZndByNsUbaEm0U+guZmD+Re2ffS3uynW2ntnG44zAdyQ5c5aJpGvm+fMbnjWdO6RxKgiUZ/TvDwYQJLvff38mbb/p57z2LtjaNYLD/CRulIB4Hn08xbZrNVVclyMuTIhfi/CBBlhBCDCKloLlZ/7A8u4HjaOTnawQCfmprU1RUuFhWbuk32ZCeNOJ8c7jz8Fn7zGXDxeVQxyHm+ud2/12Br4DlVcs9/7eGA9OEyy9PsGJFgvp6g+3bLZqaDFpaNFIprfs1+fkuJSUuNTU28+alsHLvhS7EiCJBlhBCDIJYDN56y8/27Rbt7RqGoREIKDQNQiGd1lYfq1f7CQYVU6bYOE46IDsXwVYgoCTIEuedfa37sqpMeDYhM8SB9gPMLZt79hePIpoGNTUONTU9G5Kfq/uYEMOdBFlCCOEhpWDTJotXXgnguuD3Q14eQM8UGZ8vnUIDcOCAycGDBh0dGnPm2IMeABUXn+PcRCGGgaSTRMf7jYi6ppNypQx5FwmwhEiTbc9CCOER24bHHgvy4osBLCsdYA2EYcDYsQ5NTTrvvOMjFhvcY6yokCBLnH98hg+XQUgXVC6mJnPWQoieJMgSQggPOA48/HCIAweMrEqxB4NQUKBwXdi0yUc87v0xAiQSGsuWJQZncCGGsckFk4mnvL+wYnaMyYWTPR9XCDGySZAlhBAeePbZAMeP6/j92efKjB3rYNvpdJutWy3PKw4qBWPH2pSUSHUvcf6ZWDARBimVbWL+xMEZWAgxYkmQJYQQOdq/32DbNiunAAtg7Fi3e59WKqVRX+/t5qxoFK66apCWyIQY5vJ9+ZQFyzwftyRQQpG/yPNxhRAjmyQRCyHEaZRS7G/fz6aGTbTEW4ikIigUATNAkb+ImSUzmVc2D0M3Pnx9ehWrvxTBlJviWOcxWhOtxO04rnLx+Xzork7IDDEhf0J33x5dh1mzbLZutbAsOHbMYMIEh0Ag958rmVTMm2czebLsxxKji1IQi2nYdrp0eDCo+i2+sGTMEl48+CIhs/9eWZmI2TGWjV129hcKIc47EmQJIcSHPmj5gBf3v0hLsoWwGUY77UktZseIpqLUt9bzyqFXuHjsxSwft5zDhw1aWgzC4Z4peI7rsLd1L6eip1AoTP2j262rXBJ2gmgqyonoCfKsPGaWzCRkhSgsVIwf73D0qIFhwMGDJtOn2zn9XI4D4bDi+utlFUuMDrEYvP22n337TJqbNRIJrbt0eCCgKC5W1NbaXHRRosckxaIxi1h/Yj0JO9Hj+s6GUooCXwEXVl6Y408jhBiNJMgS5x3XhcZGnfp6g2PHDFwXLAsmTXKYNMmmuLj/WVAxOrnK5Zn6Z9jRuIOQGSLPyuvzdZqmda86rTm8hl1Nuwhs+zqhUM8Aqz3Zzo7GHTjK6V7x6m88S7OIO3HebXiXKYVTmJA/gepqh1QKTp40aGrSc+o7Y9vpUvFf/nJUmoGKES+ZhOefD7B7t4VS6QqehgGhHgtTGm1tGm++6WPdOh9z5qS49to4ppkut35b7W38Yscvcl7NitpR7pxx5xmvcSHE+UuCLHHeiEY11qzxsXu3RUeHjmkq/P70w6tSsH27D6XSM6ALFiS56KIkPt9QH7UYbK5yeXTPoxzqOETYCg/4+4JWkNZEK5t3bGJh6cVYWjqCaU+0s/XUVkzdxNAG9vCloWHqJvvb9uO4DpMLJzN9uoPfD/X1BrHYxx8iByYWg3HjHO64I0YwKMUuxMh26JDOX/4SIpHQBtQeoWsFa/t2k7q6PG67LUpVlcuY8Bhunnozf63/K0Ezu+bEsVSMm6pvYmze2Ky+X/QvGtU4ckSnvt4kHtcwTaiqcpg8WSZBxcgiQZYY9bqaw/7jHwE0Lb1qlZfX84FT0+hejUgmNd54w8c77/i4+eYYU6c6fQ0rRonXDr/GgfYDWT1saY6faMRih9rBgvIFOMphe+P2HqmBmTB1k4MdBynwF1ASKGHKFIdw2CWR0HDddMrfQB4wYjGwLMVVVyW48MKUPJSIrMXtOJtObuJg+0Hak+04roOpmxT5i5hWPI25ZXOzfr9noq7O4E9/ChEIDLz/XBe/X8Nx4He/C3PnnREmT3aZWzYXS7f4a/1fAQb8M9iujVKKT9d+mlmlszL9MUQ/XBe2b7dYv97HyZM6rptua6Hr6c/wrVt9OI6isFAxe3aKSy9NysSRGPYkyBKjmuvC448H2bvXzGglIBBI5/f/8Y8hli1LsmqV9BUajRpjjbx97O3uFMBMJWMBcCw6kh0ci6SLWygUWg51oi3dYk/zHi4aexGGZlBSkl5Zra21WbvWz9GjBsmkhs+nsKz0BIHjpAMrw4DiYsXSpQkWL07JSqzIWsJJ8Oy+Z/mg5QNc5RIwT9vY5EBHsoO9rXv5x6F/sKBsAVdOvHLQ0uYaG3X+9KdQVv3numhaOjj74x/DfOMbnRQVKWaUzGBSwSSeqXuGutY6LMPC0vvOqU25KVJuiuqCaj5V86ms7xmit5MndR5/PEhLi04w2HvV/vRJUNfV2LTJx+bNPj7xiTgLF8okkhi+JMgSo5ZS8Oc/B9m/P7MAq4umQTgM69f70DS48koJtEabfxz8R8+HxwypDydSTd3kQPuB7ln+XNmuzfHIccbnjUfT0pMF1dUO1dVRHAdOndKpq0sX3FAqvdF/6lSbceNcmd0VOTvQdoC/7P0LjuvgM/qO1DVN6179fffku+xp2cNdM++iJFDi6bG4bvo+nunqVV80DXw+ePzxEPfdF0HTIGgGuWPGHXQkO3jz6Jsc7TxKc7yZpJsEwKf7KAmUMDZvLJdVXUa+Lz/3AxHdNm+2eO65QJ/BVX+6Jo+eey5AXZ3JrbfG0KUhkRiGJMgSo9abb1rU15s5zX5COmVh3TofU6bYVFdL6uBokXSSHGg/0O9D5EAYpoOmpYOaU9FT5Fl5ngRZpm5yvDMdZDkOPQprGAZUVrpUVrpAKud/S4jT7Wvbx6N7HiVgBLCMgVVK8Rt+Ek6CX2z/BV+Z+xVPA62337a6Vzi8oOvplZNNmywWL/7o+sn35XPtlGuBdNVAV7koFIZm5FyFUPRty5Z0gJXNJCikg7K9e03+/Ocgn/1sTFa0xLAjsb8YlVpbNdau7b93UaaCQXj66SApeaYdNY51HiPh5LY66QvFMX3pN4WjHGJOzItDA9J7YRzlkEggwb04J2J2jMc/eJyAEcg4sNA1HUMzeGT3IziuN+9XpWDzZr9n9/EuwSBs2ND/5IqmaRi6gambEmANkqYmLacAq0sgAPX1Jm++KaVTxfAjQZYYlV5+OYDp4TqtpkE8rvHOO3IjHy3q2+pzShWED3vy5EeBdIpfyvEuCneUQzSVLrs+ZowEWWLwPbX3KTS0rAMLXdPpTHXy6qFXPTmeI0d0mpsH5zGlsVGnoUEegYaCUvCXv4Q8SQGFdNC8dm2A1lYJiMXwIumCYtRJJGDfPtPznkCBAGzZ4mfZMtloOxp0JDswtdxvgaUTGzj0Xg3qw/95KemkGFfhSAGLYU4pldOKRzQVZf2J9RzpOJKu4KccLN2i2F/MjJIZ56SCX1uijX1t+3Iu6OA3/Gw9tZUrJl6R8zHv2mURCAzOHkOfD95/32TMmOSgjC/6V19vcPKkTnjgHTPOyjTh5Zf93HabNFwXw4cEWWLU2bvXJJlkUBqvNjenm1wWFUlxgZHO0I2cKwECjJ+9j8Pbp3p0VB9RKJJxk2XL5CFwuHGVy86mnaw/vp62RBtJJ4mu6/h0H1V5VVw+/nLGhMecdZy4Hefv+/7OBy0foGt6j/2BtmsTTUWpa6vjlUOvcPHYi1k+bvmgpa+tPbo2p/2Jp0u6SbY3bmdhxcKcxmloMDzNSDidzweHD0sT4aGwdq0/5zTBjzMMqK+3SCTinq2QCZErCbLEqFNXl3uxi/5pHDxoUlQkm7NGugl5E9h6cmvOM/emz6Z8yjGOb/djGt6lH2noVJT4mDVL3mvDhatcXjn0CttPbSdiRwiZITRNw2+mn+oUioMdB/nZtp9REa5g5fiVzCiZ0edYRzqO8Oj7j+K6br9pq5qmETLT7881h9ewu3k3d8+8O+sGumdyPHLcs9WygBFgb8venIOseHxwUwYSCUkXPNccB44fNwYlEEom0/uzZs2yvR9ciCzIHUaMOq2t+qCVcw2FFAcOyOznaDCpYJJn6X21S7dj+Gx8hndPDpod4nO32VKa2EPNsWae2/ccj3/wOL9773c8ufdJNpzYQMo9eyCbdJL8dudvebfhXdAgbIX7XFUyNIM8Xx7RVJS/fPAX3jjyRq/XHOs8xu92/Q4dfcAV/IJWkJZ4C7/a/qucC7b0pS3R5tlYmqbRkmjJeRw1yAkDgz2+6K2xUSeZHJzgORRKT7IKMVzIu1GMOo4zeLOfmoZUGBwlivxFlARKPHlgNUyXeVe+x4HXrvbgyCCZMFh8YQfjx5/fT4GO67CzeSf7WvelU/I0nbAV5qLKiygJDqxMuKtcdjXtYt3xdTREGvAZPkzdJOSEiEaj7G7azerDq6kpqmHl+JV9juu4Dr/f9XuaYk34MwikQ1aINUfWoGs6y6uWA+mmto+9/1hWFfxM3SSSivBM3TPcPv32jL73bGzXHnDANxBeVBi0rMF9/w/2+KK3w4cHLwVU16GlRWalxPAhQZYYdbr6Fg0GpRi0DwhxbmmaxpLKJbx88OXulKxsOa7DZbMnUGbuZPsrizB82RdHSSUsSmve5xu3Ls/pmEayjmQHbxx9g11Nu4jZse60PEgHAxsbNlIZrmTZ2GXMKp2FrvX9YBW34zy8+2GOR44TMkN9poYGrXTqXX1rPTubdnLlxCu5ZNwlPV7z/P7nORU7lVGA1SVkhXj98OtMLJjIxPyJvHzwZZJOMuv9T5Zh8X7L++xv28+UwilZjdEX3eMlUy/GKylxaWoyBmU117ahvFyqdp5riQTo+uB9RjtySsUwIiG/GHUKC91BSwOJx6GqSu7io8WSMUso9ZfiKjencRwcbqq5iS+suJDp17yMYTnYycxWBVxXw0mZTFr8Hp+6EcpCpTkd00i1vXE7P97yY7Y3bu9euTp9xcfUTcJWmPZEO0/ufZJf7fgVcbt3RbGEk+CX239JU7yp39S+0xm6QdgK8/rh13uUIE84CXY278wqwOoSNIOsPrwa27XZ2bgz5wITITO9QualAqvAs7GUUhT4ch9v2rQUMe9az/UQi8H06bJ351zz+8HN7XZ7RoZk84thRIIsMepMmeIQiw1OyqDjSGPY0UTXdG6ffjtJJ4nqIzK3kyaRljwiLfkkov4+g/eYHeO6SdcRtsLUFteyfOZk5t30AuNm7Ee5OqnEmYMtxzawExYFZa1c8OmXqZ3fwHVTrvPqRxxR3m14l2fqnyFoBrH0M//eNE0jz5dHc7yZn2//eY9ASynFw7sfJmJHzjrOxwXNIOuOrWPLyS0AvHPinZxT3zRN41DHIbae3Ercyb3EtKZpHO08StLxrvJkeag858mGLnEnzqSCSTmPM22aM2gl3PPzFZMny738XBs3zsG2B+fz2XXTk6wf/7sTJ3TefNPiyScDPPFEgBde8FNfbxCXau9ikEnikxh1ZsyweeGFwflgLihQFBcP4jScOOdKg6XcPfNuHtnzCJZuEW/P58Dm6bSfLCYZDeC6OigNTXexggnChZ1MXPABhZWNxJ0YqyauYn7F/O7xbqy+Mb3Pa9G7VC/Zw/EPJtB4YByx9hDJ6IdV5FwT9BSB/AgF5S1MnFcP/g6CVpAvzflyxoHBaLCvbR8vHngx49RNS7eI2TH+sPsP3DfnPjRNo761nmOdxwhb2TXiCVkhVh9Zzfyy+Ww9uTXnptWQLojx3IHnck5N7ZJyUzREG5iQP8GT8ZaPW86Oxh3k+fJyHsvQDBZVLMp5HNNM38937bI8bcmRTMKCBSkpKjMEKipcfL7B+XyORjWqq9Ork4kErFnjZ8cOi/Z2HZ9P4fOl91XbNmzY4MPvhwkTbFatSlBZKZ/rwnsSZIlRJxRSTJhgc/Kk6WnT4GQSFi2SD+bRaGLBRL48435+8MvdHNibRyCg0A2F6f9YlROl0dmSz6bnl1BYEuOfvjyGC8b1fMjVNI1ba2/lnRPv8OrhV6mYVkfVzIMAuI6OY+vk5QWJJzvQtHRhhqgdZU7xHG6svtGzXkUjzT8O/oOAkV0wY+kWJyIn+KD1A6YXT2ftsbU5BzMdyQ52NO+gNdGac5l/AJ/h40D7AaYVT8t5LEj/zEc6jngWZI0Jj2Fs3ljaEm397nEbiKSTZFrxNE8CU4Crroqze7cJOfaz66JUuofiFVd4X6FRnJ1pQkWFQ2ur4ennM4DPp6ittdm71+Cvfw2RTKbTE/PzewZ1pgn5+ek/Hz9u8qtfmVxwQZKrr05IuqHwlDwuilHpyisTnqcMahpceqk0hh2NTp7U+f3Pq5iYvJL5VVMJ+fw4yiHpJLFdG9u1STpJUm4KyzCZPqaKBYWX8fwfZ/Hee72n2DVN46KxF/Hthd9mfvl8NDSiqSgRpw3b6CCu2omkOkm5Karyqrhvzn3cUnvLeRtgNUYbaYg25NRoN2SGeOvoW3QkOzjSeSTnpr0hM8TaI2s9K/MP6aIdXtE0rd/0PqUUdS11PF33NH9+/888/v7j/H3f32mINJxxzFtqbsmp2qZSClM3uaH6hqzH+LhgEG64IU406s140Sh88pNRaVg7hC65JOHZ+eziujBpks22bRZ/+lN6UmQg51jX0++xrVt9/P73YWzZpic8JCtZYlSqqnJZuDDBtm0Wfn/uwVYsBtdfHycYlJK/XlAKDh/WefddH83NRvcHrt8PJSUO8+almDbN8Xymsy9NTRq//nUYywKfT6PcV055qBxHOUSSESJ2BKUUATNAvi+/RyqfFYS//z2Arivmzu396Ry2wlw75VqumXwNHakOjnYepTPZSUVpBaFUiNJgaU6rBqPFa0dey3nlo2uf0htH3sDUcv9o0zSNE5ETnjXohXTRDqVUzgEgQMpJURwo7vF3cTvOuuPr2HZqG+3J9h5VGR3lsLlhM2PCY7io8iLmlc3D0HtO25cGS7l28rU8v//5jFfvlFLEnTh3z7w7pyIhfZk92+bYsSQbNvhyajQfi8GKFQmmTZO9WENp+nSH4mKXREL37B4fj8PEiQ4vvxwglMXCs98PDQ0af/pTiDvvjJ6Tzx4x+kmQJUata65JcOSISWurllM+fzwO06bZLFwoDbK88MEHBi+9FKSlRScYVD3SL1Mp6OjQ2b3bIj9fsXJlggULBu/37rrwpz+FsCx6fagamkGBv4AC/5mrpAWD8OyzQaZM6SQvr+8gXNM0CnwFFJSkxyorK6OxsdGTn2E0ONJxBEPLPU/H0i22ntrq2UO+rum4eLdXozxYTsJJeJJKp2t6j1TBU9FT/H7X77vH//h+tK4myZFUhGf3PcvGho18fubnex3LojHpvVQvHHgBv+Ef0CSA7dooFJ+b8TlPCl705aqrEliW4s03/QQCva/XM1EqHWCtXJlg+XLJRhhqug6f+UyMX/4ynFVA9HGxGFxwQZK33vLlNJ7Pp3HggMGmTRaLF8vnvcidTKGKUcs04d57IxQVuSQS2a1AxWJQU2Nz662DVEf4POK68PTTAf785xCJhEY4rPrc36ZpEA6nS5o/+2yARx5J59YPhjfe8NHSkvtsqmHAk096swflfORVlbyuZr1e0TWdfCvfk7GSTpLFYxZ7FrQV+gvJs9JFKppiTfxyxy8BBhTAhawQzfFmfrXjV33+7heNWcTX5n2NQn8hncnOftMSbdcmmooyPm883174baoLq3P4ic7u8suT3HtvBJ/PJRLhrK06lIJIBPx+l698JSIB1jBSWemyalU85xL9yaSiqipdUVip3JefgkF49VW/pA0KT0iQJUY1vx++/OUI8+en+60MtH+W46RXsK68Ms5tt8Wk2EWOXBcefTTE7t0WodDAZ6GDQTh82OA3vwmT8nhi0XVh8+bc0o+6GAYcPGjR0iI5JplSSnm6WmTqpmfj6ZrO4jGLidm5T7K4yuUTkz7B+LzxfbYLyETMjrGwYiGappFyU/xu1+/w6b6M0hAt3aIz2clj7z/W59dLg6V8Ze5X+Oq8r1JdWE2elYejHFJOCqUUhb5C5pTN4VsLv8WdM+8kaHpwIQ1AVZXL/fdHuOWWKEVFDokEdHRoxGIaiQTEYhodHek/Fxc73H57lPvvjzBmjFSPG26WLk1x+eXp/XbZXBKxGIwd63L77VHq672rQJlKaWzdev5VeBXek3RBMeqZJlx/fYKFC1O8+qqfQ4dMHEcjFFI9HvZdN70p2u+HqVNTfOITCQoLZQ+WF155xc+BA0ZWAY3PBy0tOs88E+Azn/GusUldnUFHh0Ze7hWrAfD706lMN94ozVcyoWmaZ/ueXFwm5k/kWCT78u2ny/PlsXTsUjac2JDTOEopJhZMJGSFuH7y9fx8+8+zrliolCLfl8/SsUsBePfEu8RSMYJW5heXZVgcbD9IY7SRslBZn68ZEx7DLbW3ZHWsg0XXYfZsh9mzo9g2nDqlc/SoQTKZvl9MmOBQVuZKpbgRYPnyFOPHOzz1VIhYTCMwgIQA205PhK5YkU7/3L7dJJFIf9Z7IRiE996TlEGROwmyxHlj3DiXu++OEY1q7N1rfviQreO66ZWIkhKXmpoUU6c6+M7PIm+D4uRJnQ0bcs2Vh927LfbuTVFb682m9T17LE/2A3QxTThxQp7qslHkK6I92Z5zQYi4HWdF9QqePfBszo11k06SRWMW4Tf9zCmbw3un3st6r1fMjnHF+CsAqAhXcGnVpbx57M2sVn/iTpw7ZtzRXURj08lNWQVYXQJmgNeOvMZt027LeoyhZJrp1YyxY2WlaqSaPNnlm9/sZP16H1u2+Ghp0TAMeuy9S6UgHtcIhVymTbNZuTJBUVF6ErSuzvQkI+F0ra06SmW290+Ij5MgS5x3QiHF/Pkp5s+XWapz4ZVX/AOanTybUAhefz1Aba03e26amnTP00BbW+UTORtLxy3l6bqnc159KvAVUFNcw5yOOWxs2JhTAQxXuSwftxyAaydfS0OkgYZoQ8Zl9qOpKFdNuoqq/Kruv1sxfgVRO8qmhk0DXtHqqt53a+2tVOWlxzrWeYzmWDNhX/a/N0Mz2Ne6j6STPG9bCIihZ1npFinLlydpbtbZt8/g0CED29bQNCgrc5k2LUVlpdtrxaq93ft7eVf6qRefXeL8JUGWEGLQJBJw8KDpycqgpkFDg05Li0Zxce5pnLbtfUCUSkmQlY3ZpbN5+cDLOY2RcBIsHbsUTdNYXrWcdxreyXqslJuiurC6OwDSNZ27Z93NH3f/kSOdRwa0AtUVFF058UouHntxj69pmsZ1U65jQt4Enj/wPLZr91uwQilF1I5SGijlntn3UBb8KK3v/Zb38Zm5X1wxJ0ZzvJnKcGXOYwmRC02D0lKX0lKXJUsGNhHqDsIiplLp4kt42CdPnH9kO78QYtAcO2YQ93CLkqbBBx94Mzc0GMVMpEBKdnRNZ0HFAuJ2dm8WpRQaWncwEzSDfHrqp4mlMi9Y4SiHgBng07Wf7vH3lm7x+VmfZ9m4ZeiaTiQV6bOAheM6RFIRCnwFfHbaZ1k2blm//9bc8rl8e+G3ubTqUgJGgISToCPZQXuinc4Pm1UX+Yu4pfYWvjb/az0CLIDOVKcnPcEAorbH3WGFOEe8KnhxOsMAy5IAS+RGVrKEEINm717T031PgUB6Zeyii3JP9czPdz0p3/7xMUV2rphwBUc7j3K082jGaWtxJ84d0+/oscI0s3Qm1zvX89z+5wgYgQHt90o5KYJWkC/O+mKfqYa6pnPFhCu4fPzlvN/8PuuOr6M90U7STaJpGj7dx6SiSawcv5LSYOmAjj1gBrhs/GVcNv4yUm6K5ngztmsTNIMU+YvO2Keqq5KigQc9xjSppiZGprIyh2PHDM8KXwCEw26fwVssBhs2+Dl+XCeZTO8dCwZdLr44SVWV3P9FT0MeZH3jG98gEAig6zqGYfDv//7vdHZ28uCDD3Lq1CnKy8v5zne+Q96HJcCefvppXnvtNXRd595772XBggVD+wMIIfoViWiefvBpGp71zJo82eaDD7zbMK0UFBfLh2y2NE3jczM+x2N7HuNQx6EB9XtSSpFwEtxSewtTi6b2+vrCioUU+gt5+eDLNEQaCFmhPoOWuB1H0zRqimq4aepNZ93LpWs6M0tnMrN05sB/wAGwdIsxoTEDfn2xv5ikk8y5fLqGltO+LiGG0owZNhs2+MnP92blSal0yuLpjh3TWb3az8GDJkqlqxB3cV2DnTstSktdFi1KsnhxKqPPvdZWjUhEo6MDYjGdoiJXim+NEkMeZAH84Ac/oKCgoPu/n3nmGebOncvNN9/MM888wzPPPMNdd93FkSNHWLduHT/60Y9oaWnh3/7t3/jxj3+MLjk6QgxLhoHnFZq8utznzk3xyive7WqORuHCC6XZaS5M3eSumXex+shqtp7aSnuynbAZ7rUKlXJTpNwU4/PGc/WkqxmXN67fMasLq/nqvK9yInKCNUfWcLD9ICk3Ban0Pq48K4/FVYu5qPKiAQV2w8mCigW8fvj1nMcpDZRS7C/24IiEOPcmT3YoLHQ/3EOVu2hU45JLPrqXr13r4/XXAwSDqs/gR9chHIZ4XOeVVwJs3uzj3nujBIP9B32pFGzaZLFpk4/GxnSV40BAJ5HIIxBQTJlic/nlCenvNsINiyDr4zZu3Mi//uu/ArBixQr+9V//lbvuuouNGzeybNkyLMuioqKCyspK6urqmDZt2tAesBCiTxMnOrz3no9QyJsZRttOl9r3QjAIU6bYHD5s5txPRykoKFBMnepNefnzmaZprJywkhXjV/RKyTM0A8uwmFY0jRUTVlDgKzj7gB+qDFdy+/TbgfS+qcKSQjpaOnIuGz+UgmaQiQUTOR45fsa0wjOJ23EuHH/hiP49iPObpsHixUnWrPHnnJmgFJSUOEyenL6Xv/aan3XrfITDA/sMCwaho0Pn5z8P89/+W2efx7Nhg8Xq1QFSqfTru3o1hkIa0Wj63zl40ORnPzOpqnK4887YGQM2MXwNiyDrhz/8IQBXXXUVq1atoq2tjeLi9KxacXEx7e3tADQ3N1NbW9v9fSUlJTQ3N5/7AxZCDMikSTau692HQywG06bZno13441x/uu/8nIOsqJRuP32mPRU8dBgpeQBGLpBwAzQqXV6Pva5dvn4y/n1jl+T58uuq7au6VxYeaHHRyXEuXXJJUm2bbOIRnMr5x6LwZ13pu/l27ebvPVW5j0eTRMSCY2HHw5z332RHp8Lr7ziZ/16H8HgmZsnG0Y6+GpqMvjJT8J85SsRCgok0BpphjzI+rd/+zdKSkpoa2vjf/7P/8m4cf2nffRVyak/r7zyCq+88goA//7v/05ZWd/d7MW5YZqmnINh4Fyfh9JSmDhRJx73JvoIhRSLFvlzDoq6lJXB7bdrPP20RiiU3TEmEoqlSxWXXDLwnkxyPQy90XIOSktLuThyMbtO7co43TGSivDpWZ+makzV2V88SEbLeRjpRsN5+MY34Mc/NrCs7FLUIxHFJz+pmDvXj1KwcaNOWVn2n12trYr29iBTP9wu+uabGlu3apSW9j2mruuE+ojoXBf+9KcQ/+//6/bYCyaGvyEPskpKSgAoLCxkyZIl1NXVUVhYSEtLC8XFxbS0tHTv1yotLaWpqan7e5ubm7u//+NWrVrFqlWruv+7sbFxEH8KcTZlZWVyDoaBoTgPs2dbvPxyIOc0jmRSsWBBipaWhDcH9qHqaliyxMeaNf6MZyxjMaiudrjyyiiZ/Frlehh6o+kcXDXmKk41n+Jg+8EBB1pRO8olYy9henD6kP4eRtN5GMlGy3m44w6d3/0ujOsOvLS7UulshEsvTTJ/foLGRjh8WOfw4Tzy8rJfPdJ1eOYZhy98IUo8Dk89lY/fn/63+hIKhYj288WODnjssRQ33OBhTxTRpzMt9mRqSCtGxONxYrFY95+3bdvGxIkTWbx4MWvWrAFgzZo1LFmyBIDFixezbt06UqkUJ0+e5Pjx49TU1AzZ8YtzKxaD3bsNNmywePddi/p6A0e2wAx7S5akKC11c2oYqRT4fLBqlbcBVpfLLktyyy0xHEeRGkB1eNdNvx+XLUvy2c9GpT+WGFK6pnPnzDtZWLGQhJ0g4fR/nUTtKK5yuXbytVwx8YpzeJQfOXFC57nn/DzxRIA//lHjxRf9NDdLrq3IXUWFyze/2cGkSTadnWdvVByJgGEoPv/5CCtXfnTdrF7tz3kvsabB4cMGkYjGhg3p1bFsWRbs3m3KM88IM6QrWW1tbfyf//N/AHAch+XLl7NgwQKmTp3Kgw8+yGuvvUZZWRnf/e53AZgwYQJLly7lu9/9Lrqu86UvfUkqC54Hjh79qHRqMqlhWQql0kUQwmHFjBk2K1YkJF95mNI0uO22KD//eR4+X3ZpHPE43H13dFCaTnaZNctmypROXn/dz65dFp2dOj6f6j5m207PQAYC6YIZV10Vp7RU3nNieNA0jeumXMfKCSt5+/jbbDu1jY5kB65y00UtNKgIVnD5+MuZXz4fUz+3H/+OA1u2WGzc6OPkSZ1gML3vJBTSaW/3sXGjjzFjHJYtSzJ7ti37G0XWgkG4444YDQ3pZ4fDhw2iUR3HUSiV7m3l8ylKS11WrUoyZ06qVwr6yZOGJ5NnSmns3m2ydatFIMfipfG4xvbtFgsW5N4nUpwbmspko9MIduzYsaE+hPNaNqkIjgN/+Uuwu5dRfze8VCrdO2nlygSXXioltM9kKFNCjh7V+cMfQhiGNuA9Va6b/mD5zGcizJhx7qbwHAcaGnTq601Onky/8fLzFbW1NuPGOTnnxY+W1JyRbLSfA6UUnalO4nYcXdMJmkFCloedwTOQSMAf/hDmxAm9V0ru6SlSXWlbU6c63H571NMee+LMRvP1oBR0dmq0tekoBYGAoqTE7fdzSCn4//6/fE/2P6VSMG1aim3bfGft43WmdMEuxcUOX/zimV8jcuNluqDcwsSw5Lrwhz+EOHFCJ3yWHpmWlf7/mjV+Eglt0FLKRG6qqly++c0If/lLkMOHTUIh1e9sddfDVnm5yxe/GD3nK0aGAePGuYwbJ0G7GJk0TSPfl0++L39IjyOVgt/8JkxbW+8A6+M0Ld1v6NAhnYcfDvGFL0gqrsidpqUnyfLzhybXrrPTu2XZZFKWeEcSCbLEsPT3vwc4dszIaHk9GIS33/ZRXu4wf753Zb6Fd/LyFPfcE2X/foO1a/0cP26QSKRTKrr4/Yrycofrr08wfbojD1lCjGBPPx2krU3LKNXX59M4dszgxRf9XHedTJqJc0vTwLIUkHtAY9vp1ESvKuIOZM+wGD4kyBLDTjSqsWNHdvnLoRCsXetn3jzJ6R+uNC1dka+6OorrQkuLTnOzhlJQVJTOk/fqA0mIc6Er6V7uOT1FIlp3unemAgHYscPiqqsSg7oXU4i+lJW5NDUZOV/TSsGMGTZ79vg8OS65FkYWCbLEsPPmm76cVi+amgyOHNGZMCGHcnbinNB1KC11KS0d6iMRYuCUSpd4XrMmvRpr2xqalt7rMW1aihUrklmVflYqvX/JtjV8PpV1v5/h4o03fDlNmCSTGps3W1x0kUzfi3PrsssS/PGPobNuVzgTpWDcOIfqagfDyD3l3XWhsFCea0YSCbLEsKIU7Nxp4cth0icUUqxZ4+euu2LeHZgY1pRS7G/fz9qjazkVPUXKTWHqJkX+Ii4eezGzS2eja5J3KHLX0KDz+ONBWloMgsF0GlBXIOG6Gtu3+9iyxUdtrc2nPx0b0Mxze7vGmjV+3n/fJBbTAA1NUxQVKRYtSrBoUSqne+JQ2bMnt3t5MAibN/skyBLnXHW1Q2Ghwrazn+WIRuHGG9OVj8eNc2hpyW1lLBaDFSskfXYkkacOMawkEukUk1zoOrS1yVv7fNEUa+K/tv4XD+9+mFPRU2iahs/woWs6bYk2nq57mh9t+hH72vYN9aGKYcB10ynJbW3p+00mDh7U+dWvwiQSOuGw6nPF3bLSwcG+fQa/+lWY5Blqp7guPPNMgB//OI+dOy00TSMUSk8UBYOQSGi8+mqAH/0on82bR1aekOOkf8+5yvXzQIhsaBpcfHGCWJZztY4DxcUu06ali20sW5botwnxQJWVuYwbJytZI4msZIlhJZnUcmrY1yWVkg/m80FjrJFfbv8lpm6SZ+UB6dVQ19HRjXR/oLAVRinFH/f8kc/UfoYZJTOG+KjFUGht1Vi92s/evSbxuEYgoJNI5FNS4rJkSZKFC1NnLBne2qrx6KNh/P6BpfD5fBqtrRp//GOIe+6J9voe14VHHw1x8KBxxqp7XfuZnnsuQCwGl1wyMlZ1ksmzN4IdiFxWEoTIxUUXpWhoMNi2zcpoX6Hrpj+HTr/uZ8xwKChQ3anFmYrF4PLLpdrtSCNBlhhWvNqDYJrnRfu385rjOvxh1x+wdAuUTsO+sRzZUUO0LYxydTRN4Q/HGDv9IONmHCRkhXhi7xN8e+G3h7ystTh3XBf++tcAO3akU9dMkw9XizR0HWIxnZdeCvDaawE+/ekotbV9l3n+xz/8GEZm9yefDw4fNjl8WGfixJ4Rx/PPBzhwwBjww1soBK+9FqC8/KPZ8eHM7++/t2Em0lXehBgaN94YxzQVmzb5CAbPfv0nk+kKuffdF+nRF0vT4K67ovziF+HuBvcDFY/DrFkpLrhgZEywiI9IkCUGheOkK0O9846P9nYNn8/AtvMYM8ZhxYoEVVV9T3H6/QqfL7fSqUpBMCgfzKPdjsYddKY6SZ6ayO7XF5FK+jB9SQzTAdIPoXbSYv+mmRzcMp3qJbson17H6sOruXHqjUN67GLgjh7VefddH/G4hq4rKivTK08DqT6qFDz2WIgDBwa2WvSnP4W49dYYM2f2bAGRTMK+fVZWzXG79ojeffdHeUfxOBnPjncd5+rVAaZNi2R+IOeYrkM4rHCc3GbNsikgIoRXNA2uuy5BdbXNm2/6OXbMxO9XPe4FXX0d8/IUF1yQYsWKRJ/3p7Iyly98IcLDD4cwDG1AkxCxGMycmeLmm+MjugjO+UqCLOG5nTtNXnghQCymdc/8mGY6FfDoUZNf/9qkvNzljjuiFBX1/ADVdZg61aa+3sq6KlU0CldfLTM+o936E+uJHZ/CrleXYPhSWP6+UylMX/q9ULd+DnbSwpi3h+vc6zB0qRM/nG3ZYrFunY+mJp1g8KNVkQ8+SLdpmDrV5tpr4xQU9P8Q/sorfvbvz2y16Mkng3zzm50UFn407qZNFqkUWQVZmpZezUrfD9Njrl/vzyotWtPgxAmdpiad0tLhvzdj3rwU69f78Puz+/5IBK64QlKkxNCbMcNhxowoLS3pIjXNzTrJpIZppvtgLVqUYObMs/d1rKpy+epXIzz3XICDBw1A63V9KJXei1hc7LJsWYKLL05JgDVCSZAlPPXuuxYvvhggGKTPmeP07CZ0dur87Gd53HdfJ6WlPZ82Lr88yc6dVtalU8NhxezZEmSNZnE7zpHGDna9tgLTP7BzbfpT7N80A5V/hBMzTlCVVzXIRymyoVR6/9GWLRahEL3uA10zxAcPmjz0UJgvfCHK2LG9Aw7bTgdqma4WWRa8/rqfm2+Od//d0aMDD9T6kkym92d1BVnZ9gGE9M+/dq2vx/ENV0uXJtiwIfvygsGgYt48uZeL4aO4WOV87RUVKe68M0YsprFunY/33zdJJjVAYVkuRUUul16aZPJkR4KrEU6CLOGZo0d1XnghcMa0nC5dsz2//32Yb32rs8cMcWmpy/jxDqdOGRnPHMdicPHFKU/2AojhK+EkOLh5JrqR2d4U05/k2Htzab+ynSokyBqOXnvNz9at1lnvI4YBuq7xhz+E+NrXIr1WtN57zyKZ1DIOjkwTPvjAxLY/WrnKtZCOUurDh6i0WCz7/UqGkZ6kOptYDLZutWhq0jGM9Az6rFlnLu7htWAwneq0a5eJ35/Z7zAWg2XLUtKYXIxawaDiyisTXHllusxpWVmAxsbhnwosBk4eRYVnXn3Vn9EDja6nl8Tfe693aeI77ohhWQong2foREIxYYLTfcMSo5hr0nJ4HLqRWcqUpkFnUxGJjgHMBIhzLhrVWL/eN+D7iKaBpmm8+GLvZaH33st8FatLLKZRV/fR032uxRc0TcPv/2iMXPcp2Xb/X2tq0nj00SD/+Z/5vPpqgN27fWzf7uOvfw3w4IN5/O1vgazLUmfjxhvjVFYqksmB/w7jcaipsVm5Uu7lQoiRS4Is4YlIROPQITPjpe1QCDZu7J1OEgymq/P4/S7xAazMRyIwebLLnXf2LpUsRp9jB/JQyewCJdNyqd823uMjEl54801fxis8hgH19WavnleJRPY3AsuC5uaPDqS21iGSwwRzIKAoKfloQiBd3Cd7/QV9+/YZ/PzneRw9auLz0b2XzTDS91pd19i1y+KnP82jvf3c3CgNAz7/+QgTJrh0dp65RYdS0NmZrqR2220xuZcLIUY0CbKEJzZvtrJOfzl1Sqejo/enaX6+4mtfi3DJJUlMUxGJ0OMD2nHSH8h5eS433RTnjjui5zQVRgyd1laTsnAhrsp8839hIIwbz3LDnxhUO3emy6xnSinFO+9kv/en93g9/3vOnBShUHaBkeNAdXWqx89VWelk3UMqEoE5c3ovZZ06pfPYYyF8vjOnIlpWeiXtN785c6NkL5km3HlnlDvvjFBa6hCNplerbDv9/1gs/d9jxtjce2+Em2+OS8q3EGLEk0dS4YmWFj2rhyNIf+BHo1qPnhJdfD64/PIEK1YkqK83uvdZaFq6wMXSpUnKyoZ/lS3hLV2HCfkTaG49hq4N/Gks6SapLaqVGfJhyHXT6YLZFITw+zUaGnq+DwIBRTSa3bHYNj2q9xkGzJxps327hdU7u/mMYrHeFfJWrEjw61+bWRX3CQYVc+b0Lgbx/PP+ATdK7krVfvttPytWnJuUPE2DmhqHmpoo7e0aO3aYtLfrFBUF0LQ4c+faWQeyQggxHEmQJTyhaQqlsm8kfLZZy48+oId/E04x+MrKXAwVYlzeOI53HsfUz34rc5RDkb+IQrOMoiKpWDbcZLL/si+u2/Pms2BBir//3cgqkAmFFFOn9jygK66Is2ePiesOrL8NpFdn5s5N9ZoIqqpyKS11icX0jO6ZyWS62t7Hi0FEIhqHD5sZ7UELBNKFMS67LHHOJx0KChTLlqWvwbIyRWOjXI9CiNFHFuSFJyor3V57IgbKNJU0nBQZmTLFIT9fUVNYw5jwGFLumR/SbNcmz8pjbtlcHEfj4oul985wY5pkXUnOdelRWALSPZqyaUqeSsH06Xav1ONgEO69N70xayABYSyWfp/2V+75tttiJJO9UxP7Y9tQWKj4xCd632g3bPBl9btradE5fvzsjwGRiMZLL/n55S/D/PSnYX71qzBr1/qyvucLIcT5QIIs4Yn587MrDawUVFU5WT0MifOXrqc3x9u2xvTi6cwqmYXf8JNyUzjKQSmFoxxSbgpTN5lSOIUF5QvQMZgwwZGgfhjSNBg71s6qSW80CkuW9AycDQMWLUplXEnPcdIpyn0pKVF8/eudjB1rE4vR556maBRcN53KfMcd0X5XvcrLXT7/+Qi2rc5YLRDSK2IFBS733hvpM12xpUXLKl1b03oW+Pg414W//S1dlXDzZh/t7TrRqE5bm84bb/j40Y/yWbvWu71wQggxmki6oPCEz5fe3H3ggJXRjGo0Cp/8pEyHisytWJFk61YLpTTKQ+WUh8qJpqKcip0i6SaxNIuSQAn5vny0D/OhYjG48srh38T1fHXZZUkeftgiHM4s0qqocBk3rvfezJUrE5w8qVNfb551r5dS6ffH7bdHe/XcOl04rLj77hiRiMbatT4OHDBJpdKBfyCguPLKFHPnDqy/0/jxLl//eoRXX/Wzd2+6IWkgkA5+XDddSr6wMN2YdNmy5BknsrJN1zaMvn9WpeDxx4PU1/edhhgIpP+xNWv8JBIaq1bJfVwIIU4nQZbwzCc+keCnP01XGRzIh30qlU4zrK2VfVYic6GQ4u67o/z+9+HuDf8hK8Qka1Kfr4/F4Prr44wfL4VShqvJkx1KSx0iEX3AkzWxmMaVV/ad/qlpcPvtMZ59NsDWremiFX2tBEWj6T1Kd94Zpbp6YPejcFhxzTUJILfgoqBA8alPxUml0r29jhwxSKXSxzN7doopU5yz3k8rKlz27CHjoiFKpVfn+rJli8XeveZZm0IHg/DWWz7mzElRWSnXlhBCdJF0QeGZoiLF5z4XIZE4+z6DZBLy89PpMlLpTWRr/HiX++6LEAi4RCL0KoutVLrktaYpbr01ysKFssF+ONM0uPvudEnAgZQ4j8VgwYIECxb0f141Ld0Q9//5fzo/rMqniMUgGlXE4+kWEDfeGOe73+0YcIA1GCwLFi9OcfPNcT7zmTg33hinuvrsARbA4sXJrEqel5U5VFT0/YvesMF31gCrSygEr7/uz/wAhBBiFJOVLOGpSZPSD73PPBPgxAmTQED1mJGOx9MPPdOmpfjkJ+NZl30XoktFhcs3vhHh6FGd1av9NDamVwIsK72P5YYbEkyfPrCHVTH0CgoUX/1qJw8/HKKpySAUUr3OXTKZDsKWLk1yxRUDW0nKz1fccEOc669Pf39RkZ+Ojo5R0VsvGIRJk2yOHjUHHGzFYrBsWbLP66K5WefkSZ28vIGNpetw4ICJbTMqfp9CCOEFuR0Kz1VUuHzlK1GamzVWr/bT2mrg8yn8foepUx0uuiiRVS8cIfqjaelVrbvuyrDKgRiWCgoUX/96hAMHDNau9XHsWPoBvqs/3uLFKS65JJlVXyVNA78fwmEyLooxnF1/fZyf/jSMpmlnnVDo6gO2ZEnfK4AtLRpKacDAf7+2ne5zdqb9bEKI85tSdO9hPR8mZM6DH1EMlZISxac/nS4yUFYWpLExy86gQojzjqalS6BPmZKOhGw7/cGcTVrc+aCwUHHPPek9ikr1vfcMIB5XFBcr7rkn0u9Djq4PvLT8RwbeP0wIMXiam3UaGzUsK129eThkDEUiGqtX+9i92yIeT88ClZS4LFmS5IILBlYoaCSSIEsIIcSwdz7MeuaqstLl/vs7ee01P3v2mEQi6QctpcBxNIqKBlapsLTUxTQzi7L8fldacQgxhN5/3+D11wM0NOgopaGUIhiEmpoUN9wQH7IMouPHdX7/+xCQvh91VSuNxXRefDHAe+/5+MIX+m5PMdLJx5YQQggxSoTDihtvjHPddVBXZ9DcnK7UWFnpMmHCwPYmFhQoxo51aG01BvR62043cB6ts9FCDHebNlm88EKAYJAP91J+NOFRV2fxs58Z/Lf/FumzHcNgSiTgkUfCmGbfVadDITh1SuOpp4Lcfvsoyt/+kCzuCyGEEKOMYcD06Q5Ll6a48MIUEydmVvxlxYoE0QFmeCeT/TdwFkIMrs5OjeefD/QbQFkWJBI6zzyTeYQVi2ls3GixerWf9983BlT19XTvvOMjmTxzWx+fT+ODD0w6O0dfdSpZyRJCCCFED7W1DkuXJtmwwXfG2e90Q/k4RUWSKijEUFi71nfWVDvThP37TWIxbUBpvbYNf/1rgPfft3BdDdNUJBI+8vMVV1xx5rYZp9u+3RpQmqJlpfvtXX316JqskZUsIYQQQvRy1VUJVq2Ko2mKSOSjYhjp/nMalqX4zGdizJ8v/eeEGCr795sD2s/kOBq7d599bUUpePjhEB98YOL3QzCosKx0GqJSGn//e4DNmwe2gaqryMXZWBa0tY2+kERWsoQQQ0YpRX1rPTuadgBQU1TDrNJZ6Nrou9kKMRJdfHE63XDXLpNduyySSfD5FEuWpJg8WfrPCTHUnAH2UDcMNaAU4D17DA4dMvrtk9fVfHz+/LNXBdR1heOc/SahFBkX2xkJJMgSwiNds7u6Tlb9e843RzqO8MTeJ2hPthMyQwBsa9zGSwdf4rop1zGzZOYQH6EQAtIl3efMsZkzxx7qQxFCfEwwmN4XeTapVLqP6dmsX+8nHD7zayIRjT17DGbPPnOEN2GCQ329ftZgLBrVWLhw9K2IS5AlhAc2brR4+20/bW0f9X9YuTLBrFnZPZQoBQ0NOk1NOuXl7oBujCPJicgJfrfrdwSMAGHro7t515+f2PsEn532WWqLa4fqEIUQQohhb9GiJM8+GyAUOvPr8vIUNTVnX/bq7Dx7Q3O/H44cMc8aZK1cmWDHDh95ef1PPCsFRUUukycPcEluBJEgS4gcvf66j7fe8hMM0n2Ti8d1nnwySCIRz3h2pqFB5/HHgzQ1dZVPVlRUuNx2W5TS0tGxQvbCgRcIGAG0fu7kITPESwdfoqaopt/XCCGEEOe7efNSrF7tx7b7bwgei8GKFckBNQwfSCsGx2FABTRKShQrVsR5801/nwV0lIJ4HD73ueioTD2WjQ9iRLFdm6ZYE83xZpQa+oAjHoe33+775tGVt5xJydNYTOO3vw2TSOjk5yvy8hR5edDZqfPb34YHlBIw3HUmOznSceSswVNTrInjkePn6KiEEEKIkccw4N57IxiGIh7v+TXXhc5OWLAgxfLlA3uAmDLFPuuzhuMw4Ankyy9PcvXVcXRd0dmZTm1MJCASgYICh/vuizBu3OjK1ukiK1liREi5KV7c/yK7m3cTs9MN6wr9hVxYeSFLxy4dstWO7dvT5U1Pb/x3urY2jSNHDCZOHNgy+Nq1PpTq3VNC19NVejZutLjkkpGdt9yWbMNWZ0+jNHSD45HjjMsbdw6OSgghhBiZiooU99/fyTvvWLz3no9IRPuwCbnD5ZcnmDBh4EHMZZcl2bzZ1+/XUymorrbJzx/4RPeSJSkWL05RX29w7JiBYShmzHAoLR2dwVUXCbLEoGpPtrP++Ho4AYUUckHFBVj6wEp/dnFch9/t/B2NsUZ8ho88X7rkjaMcXjv8Gi2JFq6fcv1gHP5ZdXbqZ6yIo2nagBt6Ahw+bOLr594WCEB9/cgPsgzNQOPsQbHjOvgN/zk4IiGEEGJk8/lg+fIUy5fn9owQDituvz3Kn/8cwjDoUR4+EkkXz7j11ljG42oa1NQ4A9oXNlpIkCUGhVKKZ/c9y5bGLfh0H4V5hTS1NbH68GpumHIDs8tmD3isd068Q0O0gaDZOycvaAbZ1LCJpZVLKQmWePgTDMzUqSnWrvVjWX0HWoahMloG17Qzbw7V9aFPkXRch40H6tlVH6F2UpBlNbUY+gCSuD9UEaog38o/6+ssw6KmqCaXQxVCCCFEhmpqHL797U7WrPGxf7+JbWuEw4orr0wyd+7ZS7eLNAmyxKB4/fDrvNf4HmHzo8pxQSsdJD1V9xQlgRLG5o0d0FhbG7f2GWB1CRgB1hxdw6dqPpXbQWdhwgSX0lKHaFTvtaHUtmHSJJuCgoEHRrNnp/jHP4w+93hFowy4y/pg2dP8Pg/84SBH3h+HoQWwXZux1av5zr0VzCufO6AxdE1nbvlcNjZsJGD03Qo+6SSpLaolYA6gVbwQQgghPJWXp7j++gSQGOpDGbGk8IXwnOM6bDq5qd/AKGAGeO3IawMeL5o8c76doRu0J9szOsYuSilclX1OsKbBXXdFMU1FJNI1ZnpJPT/f5TOfyWxJfdGiFEVFLqmPxVLJZHqJPtuS8F5oiDTwy9WvcWLvJEIhhT9oEw4rmg6O59f/2MjhjsMDHmvVxFVMyJtA1O59bhN2gqJAETdNvcnLwxdCCCGEOGdkJUt47mTsJJ2pTgp8BX1+Xdd0jncOvGqcz/SRdPovdaOUwqf3v0mzL0knyUsHXmJPyx6STpKQFWJh+UJWjF+RcRGNwkLFt77VyY4dFrt2mWgaLFiQZPp0J+OSpKYJX/5yhL//PUB9vUUqlc6znjkzxXXXxQdUfnWwvHb4NVr2TcPy9zwXpj9J+6GpvHroVe6Zfc+AxtI1nbtm3sWG4xvYdHITLfEWNE0jz8pj6bilLK9anvHePSGEEEKI4UKCLOE5pVR/xfayUltUy5aTW/AZfQdSkVSEpeOWDng8x3X4zc7f0BJvwWf4CJgBXOXy1rG3OBU7xWemfSbjYzQMmD8/xfz5uafzBQLwmc/Ese04iYRGIKByzn9WCvbuNTh1CsrKelcvHIjjkeOECvNoOmhi+j5aUXMdHX9enIZoQ0bj6ZrO0nFLWTpuKUkniSIdLEtfLCGEEEKMdJIuKDxXHionZPXfelwpRVmwbMDjrRi/AlM3+0zrSzpJJuRPYFL+pAGP917je5yKnuoVtAXMALuad9EYbRzwWIPJNNNVfrzYYPrccwEefTTEb3+r89xz2e9zGj97H7rhotx0IKRcUEpn0vy9OR2fz/DhN/wSYAkhhBBiVJAgS3jO0i1ml84m4fS9WTJqR1kxfsWAxwuaQe6bcx/5vnw6k50knAQxO0Y0FaW6sJq7Z92d0cP59sbt/QaBQTPI2yfeHvBYI8WhQwb5+ZCfr3HoUHZR25jwGAxfkkU3r6ZwTDNWIEl+eSuLPrkGKxinIlTh8VELIYQQQoxMki4oBsW1k6+lNdHKvtZ93VUFU26KlJPiqolXMaVwSkbjFQWK+Oq8r3IicoK61josw2J2yezunlmZOFOhCx0dxx19PRymTbN5800/qZRi8eLsimdcOeFKfrn9l4TydeZctbHH16KpGCvHr/TiUIUQQgghRjwJssSgMHSDu2bexdGOo7x1/C18QR+TA5O5tOrSrAKjLpXhSirDlTkd25SCKRzrPNZnefCIHWFe+bycxh+OVq1KMHt2ipISH35/duVYK8OV3FJ7C3/f93eSbhK/7ifhJLB0ixuqb2By4WRvD1oIIYQQYoSSIEsMqqr8Km7Lv42ysjIaG4fHXqeLx17MxoaNuMpF1z7KmE25KSpDlUwpyGyVbaQYO9alrAxyOQ2zSmcxrXgaOxt3cix6jPJAOfMr5kslQCGEEEKI00iQJc47ATPAl+d8mb/s/QsNkQZsZePTfVQXVvOpmk9J8YWzMHWT+RXzmc/8oT4UIYQQQohhSYIscV4qDhTzlblfoT3ZTmeykyJ/0RkrIgohhBBCCDFQEmSJ81qBr6DfpslD6VT0FI9/8DgRO0KBr4Dbp91OcaB4qA9LCCGEEEIMgJRwF8Ij+9r28dudv+X3u35PQySzxrync5XLw7sfJmbHMDSDjmQHj+x+xMMjFUIIIYQQg0mCLCE80Bxr5pHdj9AUa+JU9BS/2fkbYnYsq7EiqQiRVKR7b5iu6bQl20i5KS8PWQghhBBCDBJJFxTCA/va9qFrendgFHfiNMebqcqrynisoBnE0Hs2DPbpPkwt+8u1KdbE03VPk5eXx1WVV1EaLM16LCGEEEIIcWaykiWEB8aGx+Koj5oYW7qV9V4vUze5fsr1xO04HckOEk6CT079ZE5VD5+se5LWRCvNsWaerHsy63GEEEIIIcTZyUqWEB6oyq/iExM/wYYTG9DQuHnqzeT78rMeb375fGqLamlPtlPoLyRoBnM6PqVUn38WQgghhBDekyBLCI8sHbeUpeOWejZeyAp5Vlb+pqk38XTd04SsEFdPutqTMYUQQgghRN8kyBLiPFAZruRr879GWVkZjY2NQ304QgghhBCjmuzJEkIIIYQQQggPSZAlhBBCCCGEEB6SIEsIIYQQQgghPCRBlhBCCCGEEEJ4SIIsIYQQQgghhPCQBFlCCCGEEEII4SEJsoQQQgghhBDCQxJkCSGEEEIIIYSHJMgSQgghhBBCCA9JkCWEEEIIIYQQHpIgSwghhBBCCCE8JEGWEEIIIYQQQnhIgiwhhBBCCCGE8JAEWUIIIYQQQgjhIQmyhBBCCCGEEMJDEmQJIYQQQgghhIckyBJCCCGEEEIID2lKKTXUByGEEEIIIYQQo8V5sZL1ve99b6gP4bwn52B4kPMwPMh5GHpyDoYHOQ/Dg5yHoSfnYHjw8jycF0GWEEIIIYQQQpwrEmQJIYQQQgghhIfOiyBr1apVQ30I5z05B8ODnIfhQc7D0JNzMDzIeRge5DwMPTkHw4OX50EKXwghhBBCCCGEh86LlSwhhBBCCCGEOFfMoT6AbDz00ENs3ryZwsJCHnjgAQAOHDjAL3/5S+LxOOXl5XzrW98iFAqxdu1a/va3v3V/76FDh/iP//gPJk+ezL/+67/S0tKCz+cD4Pvf/z6FhYVD8jONRJmcB9u2+dnPfsb+/ftxXZfLLruMT33qUwDs27ePn/zkJySTSRYuXMi9996LpmlD+aONGF6dA7kWcpPpefjFL35BfX09uq5zzz33MHv2bECuhVx5dR7kesheY2MjP/nJT2htbUXTNFatWsV1111HZ2cnDz74IKdOnaK8vJzvfOc75OXlAfD000/z2muvoes69957LwsWLADkesiFl+dBrofsZHoOOjo6+NGPfkRdXR2XX345X/rSl7rHkmshe16eh4yvBTUC7dy5U9XX16vvfve73X/3ve99T+3cuVMppdSrr76qHnvssV7fd/DgQfWNb3yj+79/8IMfqLq6usE/4FEqk/Owdu1a9eCDDyqllIrH4+rrX/+6amho6P6e999/X7muq374wx+qzZs3n9sfZATz6hzItZCbTM7DCy+8oH7yk58opZRqbW1V//RP/6Qcx+n+HrkWsufVeZDrIXvNzc2qvr5eKaVUNBpV3/rWt9Thw4fVww8/rJ5++mmllFJPP/20evjhh5VSSh0+fFj99//+31UymVQNDQ3q/vvvl+vBA16eB7kespPpOYjFYmr37t3qpZdeUr/61a96jCXXQva8PA+ZXgsjMl1w1qxZ3TMvXY4dO8bMmTMBmDdvHhs2bOj1fW+++SaXXHLJOTnG80Gm5yEej+M4DslkEtM0CYVCtLS0EIvFmDZtGpqmcdlll7Fx48Zz+nOMZF6cA5G7TM7DkSNHmDNnDgCFhYWEw2H27dsn14IHvDgPIjfFxcVUV1cDEAwGqaqqorm5mY0bN7JixQoAVqxY0f3e3rhxI8uWLcOyLCoqKqisrKSurk6uhxx5dR5E9jI9B4FAgBkzZnSvknSRayE3Xp2HbIzIIKsvEyZM4N133wVg/fr1NDU19XrN22+/3SvIeuihh/gf/+N/8MQTT6CkBkjO+jsPF198MYFAgK985St8/etf58YbbyQvL4/m5mZKS0u7v7+0tJTm5uYhOfbRItNz0EWuBW/1dx4mT57Mu+++i+M4nDx5kn379tHY2CjXwiDJ9Dx0keshdydPnmT//v3U1NTQ1tZGcXExkH7oaW9vB+j1vi8pKaG5uVmuBw/lch66yPWQm4Gcg/7IteCdXM5Dl0yuhRG5J6svX/va1/jtb3/LE088weLFizHNnj/a3r178fl8TJw4sfvvvvWtb1FSUkIsFuOBBx7gjTfe6I5qRXb6Ow91dXXous7Pf/5zIpEI//Iv/8LcuXPlZj0IMj0HY8aMkWthEPR3HlauXMmRI0f43ve+R3l5OdOnT8cwDLkWBkmm5wHks8EL8XicBx54gHvuueeMK+b9ve/levBGrucB5HrI1UDPQX/kWvBGrucBMr8WRk2QVVVVxfe//30gnR6yefPmHl9/6623eq1ilZSUAOnlw+XLl1NXVyc3jhz1dx7efPNNFixYgGmaFBYWMn36dOrr65k5c2aPVcempqbu8yKyk+k5GDNmjFwLg6C/82AYBvfcc0/3677//e8zduxYwuGwXAuDINPzAPLZkCvbtnnggQe49NJLueiii4B0SmZLSwvFxcW0tLRQUFAApGflT3/fNzc3U1JS0uvv5XrInBfnAeR6yEUm56A/ci3kzovzAJlfC6MmXbCtrQ0A13V56qmnuOqqq7q/5rou69ev7xFkOY7TvTRo2zabNm1iwoQJ5/agR6H+zkNZWRk7duxAKUU8Hmfv3r1UVVVRXFxMMBjkgw8+QCnFG2+8weLFi4fyRxjxMj0Hci0Mjv7OQyKRIB6PA7Bt2zYMw2D8+PFyLQySTM+DXA+5UUrxs5/9jKqqKm644Ybuv1+8eDFr1qwBYM2aNSxZsqT779etW0cqleLkyZMcP36cmpoauR5y5NV5kOshe5meg/7ItZAbr85DNtfCiGxG/J//+Z/s2rWLjo4OCgsLue2224jH47z00ksAXHjhhXzuc5/rLm+5c+dOHn30UX74wx92jxGPx/nBD36A4zi4rsvcuXP5whe+gK6Pmrhz0GVyHuLxOA899BBHjhxBKcXKlSv55Cc/CUB9fT0PPfQQyWSSBQsW8MUvflFKkw6QF+dAroXcZXIeTp48yQ9/+EN0XaekpISvfvWrlJeXA3It5MqL8yDXQ2727NnDv/zLvzBx4sTu9+4dd9xBbW0tDz74II2NjZSVlfHd7363e0/oU089xeuvv95dSn/hwoWAXA+58Oo8yPWQvWzOwTe+8Q2i0Si2bRMOh/n+97/P+PHj5VrIgVfnoaysLONrYUQGWUIIIYQQQggxXMlUhBBCCCGEEEJ4SIIsIYQQQgghhPCQBFlCCCGEEEII4SEJsoQQQgghhBDCQxJkCSGEEEIIIYSHJMgSQgghhBBCCA9JkCWEEGLE+7//9//y0EMP9fi7Xbt28cUvfpGWlpYhOiohhBDnKwmyhBBCjHj33nsvW7ZsYdu2bQAkk0l+/vOf8/nPf57i4uKcx3ccJ+cxhBBCnD+kGbEQQohR4e233+aRRx7hgQce4KmnnuLAgQPceuut/OEPf+DIkSOUl5dzzz33MHv2bABef/11/va3v9HU1ERBQQE33XQTV111FQA7d+7kv/7rv7jmmmt47rnnmDdvHt/85jeH8scTQggxgphDfQBCCCGEF5YuXcq6dev48Y9/zPvvv89//Md/8M///M/cf//9LFiwgB07dvDAAw/wn//5nxQUFFBYWMg///M/M2bMGHbv3s3/+l//i6lTp1JdXQ1Aa2srnZ2dPPTQQ8h8pBBCiExIuqAQQohR40tf+hI7duzg1ltv5a233mLhwoVccMEF6LrOvHnzmDp1Kps3bwbgggsuoLKyEk3TmDVrFvPmzWPPnj3dY2maxm233YZlWfh8vqH6kYQQQoxAspIlhBBi1CgqKqKgoIDx48fzzjvvsH79ejZt2tT9dcdxutMFt2zZwhNPPMGxY8dQSpFIJJg4cWL3awsKCiS4EkIIkRUJsoQQQoxKpaWlXHrppXz1q1/t9bVUKsUDDzzA/fffz+LFizFNk//9v/93j9domnauDlUIIcQoI+mCQgghRqVLL72UTZs2sXXrVlzXJZlMsnPnTpqamrBtm1QqRUFBAYZh9KhMKIQQQuRKVrKEEEKMSmVlZfzTP/0TjzzyCD/+8Y/RdZ2amhruu+8+gsEg9957Lw8++CCpVIpFixaxePHioT5kIYQQo4SUcBdCCCGEEEIID0m6oBBCCCGEEEJ4SIIsIYQQQgghhPCQBFlCCCGEEEII4SEJsoQQQgghhBDCQxJkCSGEEEIIIYSHJMgSQgghhBBCCA9JkCWEEEIIIYQQHpIgSwghhBBCCCE8JEGWEEIIIYQQQnjo/wdoP6Vsq36vAwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Brazil\\n\",\n \"ax0 = df_can_t.plot(kind='scatter',\\n\",\n \" x='Year',\\n\",\n \" y='Brazil',\\n\",\n \" figsize=(14, 8),\\n\",\n \" alpha=0.5, # transparency\\n\",\n \" color='green',\\n\",\n \" s=norm_brazil * 2000 + 10, # pass in weights \\n\",\n \" xlim=(1975, 2015)\\n\",\n \" )\\n\",\n \"\\n\",\n \"# Argentina\\n\",\n \"ax1 = df_can_t.plot(kind='scatter',\\n\",\n \" x='Year',\\n\",\n \" y='Argentina',\\n\",\n \" alpha=0.5,\\n\",\n \" color=\\\"blue\\\",\\n\",\n \" s=norm_argentina * 2000 + 10,\\n\",\n \" ax = ax0\\n\",\n \" )\\n\",\n \"\\n\",\n \"ax0.set_ylabel('Number of Immigrants')\\n\",\n \"ax0.set_title('Immigration from Brazil and Argentina from 1980 - 2013')\\n\",\n \"ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble, the more immigrants in that year.\\n\",\n \"\\n\",\n \"From the plot above, we can see a corresponding increase in immigration from Argentina during the 1998 - 2002 great depression. We can also observe a similar spike around 1985 to 1993. In fact, Argentina had suffered a great depression from 1974 - 1990, just before the onset of 1998 - 2002 great depression. \\n\",\n \"\\n\",\n \"On a similar note, Brazil suffered the _Samba Effect_ where the Brazilian real (currency) dropped nearly 35% in 1999. There was a fear of a South American financial crisis as many South American countries were heavily dependent on industrial exports from Brazil. The Brazilian government subsequently adopted an austerity program, and the economy slowly recovered over the years, culminating in a surge in 2010. The immigration data reflect these events.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013. You can use **df_can_t** that we defined and used in the previous example.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Normalize the data pertaining to China and India.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 103,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"india_normalized=(df_can_t['India']-df_can_t['India'].min())/(df_can_t['India'].max()-df_can_t['India'].min())\\n\",\n \"china_normalized=(df_can_t['China']-df_can_t['China'].min())/(df_can_t['China'].max()-df_can_t['China'].min())\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 2: Generate the bubble plots.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 104,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 104,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"### type your answer here\\n\",\n \"\\n\",\n \"ax0 = df_can_t.plot(kind='scatter',\\n\",\n \" x='Year',\\n\",\n \" y='India',\\n\",\n \" figsize=(14, 8),\\n\",\n \" alpha=0.5, # transparency\\n\",\n \" color='green',\\n\",\n \" s=india_normalized * 2000 + 10, # pass in weights \\n\",\n \" xlim=(1975, 2015)\\n\",\n \" )\\n\",\n \"\\n\",\n \"# Argentina\\n\",\n \"ax1 = df_can_t.plot(kind='scatter',\\n\",\n \" x='Year',\\n\",\n \" y='China',\\n\",\n \" alpha=0.5,\\n\",\n \" color=\\\"blue\\\",\\n\",\n \" s=china_normalized * 2000 + 10,\\n\",\n \" ax = ax0\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"ax0.set_ylabel('Number of Immigrants')\\n\",\n \"ax0.set_title('Immigration from india and china from 1980 - 2013')\\n\",\n \"ax0.legend(['India', 'China'], loc='upper left', fontsize='x-large')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Waffle Charts \\n\",\n \"\\n\",\n \"A `waffle chart` is an interesting visualization that is normally created to display progress toward goals. It is commonly an effective option when you are trying to add interesting visualization features to a visual that consists mainly of cells, such as an Excel dashboard.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Unfortunately, unlike R, `waffle` charts are not built into any of the Python visualization libraries. Therefore, we will learn how to create them from scratch.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Step 1.** The first step into creating a waffle chart is determing the proportion of each category with respect to the total.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's revisit the previous case study about Denmark, Norway, and Sweden.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 107,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Continent
\\n\",\n \"
Region
\\n\",\n \"
DevName
\\n\",\n \"
1980
\\n\",\n \"
1981
\\n\",\n \"
1982
\\n\",\n \"
1983
\\n\",\n \"
1984
\\n\",\n \"
1985
\\n\",\n \"
1986
\\n\",\n \"
...
\\n\",\n \"
2005
\\n\",\n \"
2006
\\n\",\n \"
2007
\\n\",\n \"
2008
\\n\",\n \"
2009
\\n\",\n \"
2010
\\n\",\n \"
2011
\\n\",\n \"
2012
\\n\",\n \"
2013
\\n\",\n \"
Total
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
Denmark
\\n\",\n \"
Europe
\\n\",\n \"
Northern Europe
\\n\",\n \"
Developed regions
\\n\",\n \"
272
\\n\",\n \"
293
\\n\",\n \"
299
\\n\",\n \"
106
\\n\",\n \"
93
\\n\",\n \"
73
\\n\",\n \"
93
\\n\",\n \"
...
\\n\",\n \"
62
\\n\",\n \"
101
\\n\",\n \"
97
\\n\",\n \"
108
\\n\",\n \"
81
\\n\",\n \"
92
\\n\",\n \"
93
\\n\",\n \"
94
\\n\",\n \"
81
\\n\",\n \"
3901
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Norway
\\n\",\n \"
Europe
\\n\",\n \"
Northern Europe
\\n\",\n \"
Developed regions
\\n\",\n \"
116
\\n\",\n \"
77
\\n\",\n \"
106
\\n\",\n \"
51
\\n\",\n \"
31
\\n\",\n \"
54
\\n\",\n \"
56
\\n\",\n \"
...
\\n\",\n \"
57
\\n\",\n \"
53
\\n\",\n \"
73
\\n\",\n \"
66
\\n\",\n \"
75
\\n\",\n \"
46
\\n\",\n \"
49
\\n\",\n \"
53
\\n\",\n \"
59
\\n\",\n \"
2327
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Sweden
\\n\",\n \"
Europe
\\n\",\n \"
Northern Europe
\\n\",\n \"
Developed regions
\\n\",\n \"
281
\\n\",\n \"
308
\\n\",\n \"
222
\\n\",\n \"
176
\\n\",\n \"
128
\\n\",\n \"
158
\\n\",\n \"
187
\\n\",\n \"
...
\\n\",\n \"
205
\\n\",\n \"
139
\\n\",\n \"
193
\\n\",\n \"
165
\\n\",\n \"
167
\\n\",\n \"
159
\\n\",\n \"
134
\\n\",\n \"
140
\\n\",\n \"
140
\\n\",\n \"
5866
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
3 rows × 38 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Continent Region DevName 1980 1981 1982 1983 \\\\\\n\",\n \"Denmark Europe Northern Europe Developed regions 272 293 299 106 \\n\",\n \"Norway Europe Northern Europe Developed regions 116 77 106 51 \\n\",\n \"Sweden Europe Northern Europe Developed regions 281 308 222 176 \\n\",\n \"\\n\",\n \" 1984 1985 1986 ... 2005 2006 2007 2008 2009 2010 2011 \\\\\\n\",\n \"Denmark 93 73 93 ... 62 101 97 108 81 92 93 \\n\",\n \"Norway 31 54 56 ... 57 53 73 66 75 46 49 \\n\",\n \"Sweden 128 158 187 ... 205 139 193 165 167 159 134 \\n\",\n \"\\n\",\n \" 2012 2013 Total \\n\",\n \"Denmark 94 81 3901 \\n\",\n \"Norway 53 59 2327 \\n\",\n \"Sweden 140 140 5866 \\n\",\n \"\\n\",\n \"[3 rows x 38 columns]\"\n ]\n },\n \"execution_count\": 107,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# let's create a new dataframe for these three countries \\n\",\n \"df_dsn = df_can.loc[['Denmark', 'Norway', 'Sweden'], :]\\n\",\n \"\\n\",\n \"# let's take a look at our dataframe\\n\",\n \"df_dsn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 108,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Denmark: 0.32255663965602777\\n\",\n \"Norway: 0.1924094592359848\\n\",\n \"Sweden: 0.48503390110798744\\n\"\n ]\n }\n ],\n \"source\": [\n \"# compute the proportion of each category with respect to the total\\n\",\n \"total_values = sum(df_dsn['Total'])\\n\",\n \"category_proportions = [(float(value) / total_values) for value in df_dsn['Total']]\\n\",\n \"\\n\",\n \"# print out proportions\\n\",\n \"for i, proportion in enumerate(category_proportions):\\n\",\n \" print (df_dsn.index.values[i] + ': ' + str(proportion))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Step 2.** The second step is defining the overall size of the `waffle` chart.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 109,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Total number of tiles is 400\\n\"\n ]\n }\n ],\n \"source\": [\n \"width = 40 # width of chart\\n\",\n \"height = 10 # height of chart\\n\",\n \"\\n\",\n \"total_num_tiles = width * height # total number of tiles\\n\",\n \"\\n\",\n \"print ('Total number of tiles is ', total_num_tiles)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Step 3.** The third step is using the proportion of each category to determe it respective number of tiles\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 110,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Denmark: 129\\n\",\n \"Norway: 77\\n\",\n \"Sweden: 194\\n\"\n ]\n }\n ],\n \"source\": [\n \"# compute the number of tiles for each catagory\\n\",\n \"tiles_per_category = [round(proportion * total_num_tiles) for proportion in category_proportions]\\n\",\n \"\\n\",\n \"# print out number of tiles per category\\n\",\n \"for i, tiles in enumerate(tiles_per_category):\\n\",\n \" print (df_dsn.index.values[i] + ': ' + str(tiles))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Based on the calculated proportions, Denmark will occupy 129 tiles of the `waffle` chart, Norway will occupy 77 tiles, and Sweden will occupy 194 tiles.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Step 4.** The fourth step is creating a matrix that resembles the `waffle` chart and populating it.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 111,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"130\\n\",\n \"207\\n\",\n \"Waffle chart populated!\\n\"\n ]\n }\n ],\n \"source\": [\n \"# initialize the waffle chart as an empty matrix\\n\",\n \"waffle_chart = np.zeros((height, width))\\n\",\n \"\\n\",\n \"# define indices to loop through waffle chart\\n\",\n \"category_index = 0\\n\",\n \"tile_index = 0\\n\",\n \"\\n\",\n \"# populate the waffle chart\\n\",\n \"for col in range(width):\\n\",\n \" for row in range(height):\\n\",\n \" tile_index += 1\\n\",\n \"\\n\",\n \" # if the number of tiles populated for the current category is equal to its corresponding allocated tiles...\\n\",\n \" if tile_index > sum(tiles_per_category[0:category_index]):\\n\",\n \" # ...proceed to the next category\\n\",\n \" category_index += 1 \\n\",\n \" print(tile_index)\\n\",\n \" \\n\",\n \" # set the class value to an integer, which increases with class\\n\",\n \" waffle_chart[row, col] = category_index\\n\",\n \" \\n\",\n \"print ('Waffle chart populated!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's take a peek at how the matrix looks like.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 112,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.],\\n\",\n \" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.],\\n\",\n \" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.],\\n\",\n \" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.],\\n\",\n \" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.],\\n\",\n \" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.],\\n\",\n \" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.],\\n\",\n \" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.],\\n\",\n \" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.],\\n\",\n \" [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2.,\\n\",\n \" 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\\n\",\n \" 3., 3., 3., 3., 3., 3., 3., 3.]])\"\n ]\n },\n \"execution_count\": 112,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"waffle_chart\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"As expected, the matrix consists of three categories and the total number of each category's instances matches the total number of tiles allocated to each category.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Step 5.** Map the `waffle` chart matrix into a visual.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 113,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 113,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.patches as mpatches\\n\",\n \"\\n\",\n \"# instantiate a new figure object\\n\",\n \"fig = plt.figure()\\n\",\n \"\\n\",\n \"# use matshow to display the waffle chart\\n\",\n \"colormap = plt.cm.coolwarm\\n\",\n \"plt.matshow(waffle_chart, cmap=colormap)\\n\",\n \"plt.colorbar()\\n\",\n \"\\n\",\n \"# get the axis\\n\",\n \"ax = plt.gca()\\n\",\n \"\\n\",\n \"# set minor ticks\\n\",\n \"ax.set_xticks(np.arange(-.5, (width), 1), minor=True)\\n\",\n \"ax.set_yticks(np.arange(-.5, (height), 1), minor=True)\\n\",\n \" \\n\",\n \"# add gridlines based on minor ticks\\n\",\n \"ax.grid(which='minor', color='w', linestyle='-', linewidth=2)\\n\",\n \"\\n\",\n \"plt.xticks([])\\n\",\n \"plt.yticks([])\\n\",\n \"\\n\",\n \"# compute cumulative sum of individual categories to match color schemes between chart and legend\\n\",\n \"values_cumsum = np.cumsum(df_dsn['Total'])\\n\",\n \"total_values = values_cumsum[len(values_cumsum) - 1]\\n\",\n \"\\n\",\n \"# create legend\\n\",\n \"legend_handles = []\\n\",\n \"for i, category in enumerate(df_dsn.index.values):\\n\",\n \" label_str = category + ' (' + str(df_dsn['Total'][i]) + ')'\\n\",\n \" color_val = colormap(float(values_cumsum[i])/total_values)\\n\",\n \" legend_handles.append(mpatches.Patch(color=color_val, label=label_str))\\n\",\n \"\\n\",\n \"# add legend to chart\\n\",\n \"plt.legend(handles=legend_handles,\\n\",\n \" loc='lower center', \\n\",\n \" ncol=len(df_dsn.index.values),\\n\",\n \" bbox_to_anchor=(0., -0.2, 0.95, .1)\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"And there you go! What a good looking _delicious_ `waffle` chart, don't you think?\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now it would very inefficient to repeat these seven steps every time we wish to create a `waffle` chart. So let's combine all seven steps into one function called _create_waffle_chart_. This function would take the following parameters as input:\\n\",\n \"\\n\",\n \"> 1. **categories**: Unique categories or classes in dataframe.\\n\",\n \"> 2. **values**: Values corresponding to categories or classes.\\n\",\n \"> 3. **height**: Defined height of waffle chart.\\n\",\n \"> 4. **width**: Defined width of waffle chart.\\n\",\n \"> 5. **colormap**: Colormap class\\n\",\n \"> 6. **value_sign**: In order to make our function more generalizable, we will add this parameter to address signs that could be associated with a value such as %, $, and so on. **value_sign** has a default value of empty string.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now to create a `waffle` chart, all we have to do is call the function `create_waffle_chart`. Let's define the input parameters:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 122,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"width = 40 # width of chart\\n\",\n \"height = 10 # height of chart\\n\",\n \"\\n\",\n \"categories = df_dsn.index.values # categories\\n\",\n \"values = df_dsn['Total'] # correponding values of categories\\n\",\n \"\\n\",\n \"colormap = plt.cm.coolwarm # color map class\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"And now let's call our function to create a `waffle` chart.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 123,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Total number of tiles is 400\\n\",\n \"Denmark: 129\\n\",\n \"Norway: 77\\n\",\n \"Sweden: 194\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"create_waffle_chart(categories, values, height, width, colormap)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"There seems to be a new Python package for generating `waffle charts` called [PyWaffle](https://github.com/ligyxy/PyWaffle), but it looks like the repository is still being built. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Word Clouds \\n\",\n \"\\n\",\n \"`Word` clouds (also known as text clouds or tag clouds) work in a simple way: the more a specific word appears in a source of textual data (such as a speech, blog post, or database), the bigger and bolder it appears in the word cloud.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Luckily, a Python package already exists in Python for generating `word` clouds. The package, called `word_cloud` was developed by **Andreas Mueller**. You can learn more about the package by following this [link](https://github.com/amueller/word_cloud/).\\n\",\n \"\\n\",\n \"Let's use this package to learn how to generate a word cloud for a given text document.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"First, let's install the package.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 127,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting wordcloud\\n\",\n \" Downloading wordcloud-1.8.1-cp37-cp37m-win_amd64.whl (154 kB)\\n\",\n \"Requirement already satisfied: pillow in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from wordcloud) (8.0.1)\\n\",\n \"Requirement already satisfied: matplotlib in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from wordcloud) (3.3.2)\\n\",\n \"Requirement already satisfied: numpy>=1.6.1 in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from wordcloud) (1.19.2)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.1 in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from matplotlib->wordcloud) (2.8.1)\\n\",\n \"Requirement already satisfied: cycler>=0.10 in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from matplotlib->wordcloud) (0.10.0)\\n\",\n \"Requirement already satisfied: kiwisolver>=1.0.1 in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from matplotlib->wordcloud) (1.3.0)\\n\",\n \"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from matplotlib->wordcloud) (2.4.7)\\n\",\n \"Requirement already satisfied: certifi>=2020.06.20 in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from matplotlib->wordcloud) (2021.5.30)\\n\",\n \"Requirement already satisfied: six>=1.5 in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from python-dateutil>=2.1->matplotlib->wordcloud) (1.15.0)\\n\",\n \"Installing collected packages: wordcloud\\n\",\n \"Successfully installed wordcloud-1.8.1\\n\",\n \"Note: you may need to restart the kernel to use updated packages.\\n\"\n ]\n }\n ],\n \"source\": [\n \"pip install wordcloud\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 128,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Wordcloud is installed and imported!\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\n\",\n \"# import package and its set of stopwords\\n\",\n \"from wordcloud import WordCloud, STOPWORDS\\n\",\n \"\\n\",\n \"print ('Wordcloud is installed and imported!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"`Word` clouds are commonly used to perform high-level analysis and visualization of text data. Accordinly, let's digress from the immigration dataset and work with an example that involves analyzing text data. Let's try to analyze a short novel written by **Lewis Carroll** titled _Alice's Adventures in Wonderland_. Let's go ahead and download a _.txt_ file of the novel.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Using countries with single-word names, let's duplicate each country's name based on how much they contribute to the total immigration.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 168,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'India India India India India India India India India China China China China China China China China China Philippines Philippines Philippines Philippines Philippines Philippines Philippines Pakistan Pakistan Pakistan Poland Lebanon France Jamaica Romania Haiti Guyana Portugal Egypt Morocco Colombia '\"\n ]\n },\n \"execution_count\": 168,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"total_immigration = df_can['Total'].sum()\\n\",\n \"total_immigration\\n\",\n \"max_words = 90\\n\",\n \"word_string = ''\\n\",\n \"for country in df_can.index.values:\\n\",\n \" # check if country's name is a single-word name\\n\",\n \" if len(country.split(' ')) == 1:\\n\",\n \" repeat_num_times = int(df_can.loc[country, 'Total']/float(total_immigration)*max_words)\\n\",\n \" word_string = word_string + ((country + ' ') * repeat_num_times)\\n\",\n \" \\n\",\n \"# display the generated text\\n\",\n \"word_string\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We are not dealing with any stopwords here, so there is no need to pass them when creating the word cloud.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 170,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Word cloud created!\\n\"\n ]\n }\n ],\n \"source\": [\n \"# create the word cloud\\n\",\n \"from wordcloud import WordCloud\\n\",\n \"\\n\",\n \"wordcloud = WordCloud(background_color='white').generate(word_string)\\n\",\n \"\\n\",\n \"print('Word cloud created!')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 171,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Choropleth Maps \\n\",\n \"\\n\",\n \"A `Choropleth` map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region. Below is a `Choropleth` map of the US depicting the population by square mile per state.\\n\",\n \"\\n\",\n \" \\n\",\n \"# display the cloud\\n\",\n \"fig = plt.figure()\\n\",\n \"fig.set_figwidth(14)\\n\",\n \"fig.set_figheight(18)\\n\",\n \"\\n\",\n \"plt.imshow(wordcloud, interpolation='bilinear')\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Choropleth Maps \\n\",\n \"\\n\",\n \"A `Choropleth` map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region. Below is a `Choropleth` map of the US depicting the population by square mile per state.\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now, let's create our own `Choropleth` map of the world depicting immigration from various countries to Canada.\\n\",\n \"\\n\",\n \"\\n\",\n \"```\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"In order to create a `Choropleth` map, we need a GeoJSON file that defines the areas/boundaries of the state, county, or country that we are interested in. In our case, since we are endeavoring to create a world map, we want a GeoJSON that defines the boundaries of all world countries. For your convenience, we will be providing you with this file, so let's go ahead and download it. Let's name it **world_countries.json**.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 175,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"GeoJSON file downloaded!\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"'wget' is not recognized as an internal or external command,\\n\",\n \"operable program or batch file.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# download countries geojson file\\n\",\n \"!wget --quiet https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/world_countries.json\\n\",\n \" \\n\",\n \"print('GeoJSON file downloaded!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now that we have the GeoJSON file, let's create a world map, centered around **[0, 0]** _latitude_ and _longitude_ values, with an intial zoom level of 2.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 177,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Folium installed and imported!\\n\"\n ]\n }\n ],\n \"source\": [\n \"import folium\\n\",\n \"\\n\",\n \"print('Folium installed and imported!')\\n\",\n \"world_geo = r'world_countries.json' # geojson file\\n\",\n \"\\n\",\n \"# create a plain world map\\n\",\n \"world_map = folium.Map(location=[0, 0], zoom_start=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"And now to create a `Choropleth` map, we will use the _choropleth_ method with the following main parameters:\\n\",\n \"\\n\",\n \"1. geo_data, which is the GeoJSON file.\\n\",\n \"2. data, which is the dataframe containing the data.\\n\",\n \"3. columns, which represents the columns in the dataframe that will be used to create the `Choropleth` map.\\n\",\n \"4. key_on, which is the key or variable in the GeoJSON file that contains the name of the variable of interest. To determine that, you will need to open the GeoJSON file using any text editor and note the name of the key or variable that contains the name of the countries, since the countries are our variable of interest. In this case, **name** is the key in the GeoJSON file that contains the name of the countries. Note that this key is case_sensitive, so you need to pass exactly as it exists in the GeoJSON file.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 185,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 185,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_can['Total'] = df_can.sum(axis=1)\\n\",\n \"\\n\",\n \"world_geo = r'world_countries.json'\\n\",\n \"\\n\",\n \"# create a numpy array of length 6 and has linear spacing from the minium total immigration to the maximum total immigration\\n\",\n \"threshold_scale = np.linspace(df_can['Total'].min(),\\n\",\n \" df_can['Total'].max(),\\n\",\n \" 6, dtype=int)\\n\",\n \"threshold_scale = threshold_scale.tolist() # change the numpy array to a list\\n\",\n \"threshold_scale[-1] = threshold_scale[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration\\n\",\n \"\\n\",\n \"# let Folium determine the scale.\\n\",\n \"world_map = folium.Map(location=[0, 0], zoom_start=2)\\n\",\n \"world_map.choropleth(\\n\",\n \" geo_data=world_geo,\\n\",\n \" data=df_can,\\n\",\n \" columns=['Country', 'Total'],\\n\",\n \" key_on='feature.properties.name',\\n\",\n \" threshold_scale=threshold_scale,\\n\",\n \" fill_color='YlOrRd', \\n\",\n \" fill_opacity=0.7, \\n\",\n \" line_opacity=0.2,\\n\",\n \" legend_name='Immigration to Canada',\\n\",\n \" reset=True\\n\",\n \")\\n\",\n \"world_map\\n\",\n \"\\n\",\n \"Much better now! Feel free to play around with the data and perhaps create `Choropleth` maps for individuals years, or perhaps decades, and see how they compare with the entire period from 1980 to 2013.\\n\",\n \"\\n\",\n \"# generate choropleth map using the total immigration of each country to Canada from 1980 to 2013\\n\",\n \"world_map.choropleth(\\n\",\n \" geo_data=world_geo,\\n\",\n \" data=df_can,\\n\",\n \" columns=['OdName', 'Total'],\\n\",\n \" key_on='feature.properties.name',\\n\",\n \" fill_color='YlOrRd', \\n\",\n \" fill_opacity=0.7, \\n\",\n \" line_opacity=0.2,\\n\",\n \" legend_name='Immigration to Canada'\\n\",\n \")\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"As per our `Choropleth` map legend, the darker the color of a country and the closer the color to red, the higher the number of immigrants from that country. Accordingly, the highest immigration over the course of 33 years (from 1980 to 2013) was from China, India, and the Philippines, followed by Poland, Pakistan, and interestingly, the US.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Notice how the legend is displaying a negative boundary or threshold. Let's fix that by defining our own thresholds and starting with 0 instead of -6,918!\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"world_geo = r'world_countries.json'\\n\",\n \"\\n\",\n \"# create a numpy array of length 6 and has linear spacing from the minium total immigration to the maximum total immigration\\n\",\n \"threshold_scale = np.linspace(df_can['Total'].min(),\\n\",\n \" df_can['Total'].max(),\\n\",\n \" 6, dtype=int)\\n\",\n \"threshold_scale = threshold_scale.tolist() # change the numpy array to a list\\n\",\n \"threshold_scale[-1] = threshold_scale[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration\\n\",\n \"\\n\",\n \"# let Folium determine the scale.\\n\",\n \"world_map = folium.Map(location=[0, 0], zoom_start=2)\\n\",\n \"world_map.choropleth(\\n\",\n \" geo_data=world_geo,\\n\",\n \" data=df_can,\\n\",\n \" columns=['Country', 'Total'],\\n\",\n \" key_on='feature.properties.name',\\n\",\n \" threshold_scale=threshold_scale,\\n\",\n \" fill_color='YlOrRd', \\n\",\n \" fill_opacity=0.7, \\n\",\n \" line_opacity=0.2,\\n\",\n \" legend_name='Immigration to Canada',\\n\",\n \" reset=True\\n\",\n \")\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Much better now! Feel free to play around with the data and perhaps create `Choropleth` maps for individuals years, or perhaps decades, and see how they compare with the entire period from 1980 to 2013.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "Data Visualization/Python/Readme.dm",
"content": "Data visulaization projects implemented in python.\n"
},
{
"path": "Data Visualization/Python/Spatial visualization of San Francisco incidents.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"## Spatial visualization of San Francisco Police Department Incidents for 2016\\n\",\n \"\\n\",\n \"## Objectives\\n\",\n \"\\n\",\n \"After completing this lab you will be able to:\\n\",\n \"\\n\",\n \"- Visualize geospatial data with Folium\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"In this notebook, we will create maps for different objectives. To do that, we will part ways with Matplotlib and work with another Python visualization library, namely **Folium**. What is nice about **Folium** is that it was developed for the sole purpose of visualizing geospatial data. While other libraries are available to visualize geospatial data, such as **plotly**, they might have a cap on how many API calls you can make within a defined time frame. **Folium**, on the other hand, is completely free.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Exploring Datasets with _pandas_ and Matplotlib\\n\",\n \"\\n\",\n \"Toolkits: This Notebook heavily relies on [_pandas_](http://pandas.pydata.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) and [**Numpy**](http://www.numpy.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) for data wrangling, analysis, and visualization. The primary plotting library we will explore in this lab is [**Folium**](https://github.com/python-visualization/folium/).\\n\",\n \"\\n\",\n \"Dataset: \\n\",\n \"\\n\",\n \"1. San Francisco Police Department Incidents for the year 2016 - [Police Department Incidents](https://data.sfgov.org/Public-Safety/Police-Department-Incidents-Previous-Year-2016-/ritf-b9ki?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) from San Francisco public data portal. Incidents derived from San Francisco Police Department (SFPD) Crime Incident Reporting system. Updated daily, showing data for the entire year of 2016. Address and location has been anonymized by moving to mid-block or to an intersection. \\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Downloading and Prepping Data \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Import Primary Modules:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np # useful for many scientific computing in Python\\n\",\n \"import pandas as pd # primary data structure library\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Introduction to Folium \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Folium is a powerful Python library that helps you create several types of Leaflet maps. The fact that the Folium results are interactive makes this library very useful for dashboard building.\\n\",\n \"\\n\",\n \"From the official Folium documentation page:\\n\",\n \"\\n\",\n \"> Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in on a Leaflet map via Folium.\\n\",\n \"\\n\",\n \"> Folium makes it easy to visualize data that's been manipulated in Python on an interactive Leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing Vincent/Vega visualizations as markers on the map.\\n\",\n \"\\n\",\n \"> The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. Folium supports both GeoJSON and TopoJSON overlays, as well as the binding of data to those overlays to create choropleth maps with color-brewer color schemes.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Generating the world map is straigtforward in **Folium**. You simply create a **Folium** _Map_ object and then you display it. What is attactive about **Folium** maps is that they are interactive, so you can zoom into any region of interest despite the initial zoom level. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"#### Let's install **Folium**\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"**Folium** is not available by default. So, we first need to install it before we are able to import it.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"'conda' is not recognized as an internal or external command,\\n\",\n \"operable program or batch file.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Folium installed and imported!\\n\"\n ]\n }\n ],\n \"source\": [\n \"!conda install -c conda-forge folium=0.5.0 --yes\\n\",\n \"import folium\\n\",\n \"\\n\",\n \"print('Folium installed and imported!')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# define the world map\\n\",\n \"world_map = folium.Map()\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Go ahead. Try zooming in and out of the rendered map above.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"You can customize this default definition of the world map by specifying the centre of your map and the intial zoom level. \\n\",\n \"\\n\",\n \"All locations on a map are defined by their respective _Latitude_ and _Longitude_ values. So you can create a map and pass in a center of _Latitude_ and _Longitude_ values of **[0, 0]**. \\n\",\n \"\\n\",\n \"For a defined center, you can also define the intial zoom level into that location when the map is rendered. **The higher the zoom level the more the map is zoomed into the center**.\\n\",\n \"\\n\",\n \"Let's create a map centered around Canada and play with the zoom level to see how it affects the rendered map.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# define the world map centered around Canada with a higher zoom level\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=8)\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map# define the world map centered around Canada with a low zoom level\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4)\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create the map again with a higher zoom level\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# define the world map centered around Canada with a higher zoom level\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=8)\\n\",\n \"\\n\",\n \"# display world map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"As you can see, the higher the zoom level the more the map is zoomed into the given center.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"### Create a map of Mexico with a zoom level of 4.\\n\",\n \"world_map = folium.Map(location=[23.6345, -102.5528], zoom_start=4)\\n\",\n \"world_map\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# create a Stamen Toner map of the world centered around Canada\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Toner')\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"world_mapAnother cool feature of **Folium** is that you can generate different map styles.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### A. Stamen Toner Maps\\n\",\n \"\\n\",\n \"These are high-contrast B+W (black and white) maps. They are perfect for data mashups and exploring river meanders and coastal zones. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create a Stamen Toner map of canada with a zoom level of 4.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# create a Stamen Toner map of the world centered around Canada\\n\",\n \"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Toner')\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"world_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### B. Stamen Terrain Maps\\n\",\n \"\\n\",\n \"These are maps that feature hill shading and natural vegetation colors. They showcase advanced labeling and linework generalization of dual-carriageway roads.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"### B. Stamen Terrain Maps\\n\",\n \"\\n\",\n \"These are maps that feature hill shading and natural vegetation colors. They showcase advanced labeling and linework generalization of dual-carriageway roads.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's create a Stamen Terrain map of Canada with zoom level 4.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \" IncidntNum Category Descript \\\\\\n\",\n \"0 120058272 WEAPON LAWS POSS OF PROHIBITED WEAPON \\n\",\n \"1 120058272 WEAPON LAWS FIREARM, LOADED, IN VEHICLE, POSSESSION OR USE \\n\",\n \"2 141059263 WARRANTS WARRANT ARREST \\n\",\n \"3 160013662 NON-CRIMINAL LOST PROPERTY \\n\",\n \"4 160002740 NON-CRIMINAL LOST PROPERTY \\n\",\n \"\\n\",\n \" DayOfWeek Date Time PdDistrict Resolution \\\\\\n\",\n \"0 Friday 01/29/2016 12:00:00 AM 11:00 SOUTHERN ARREST, BOOKED \\n\",\n \"1 Friday 01/29/2016 12:00:00 AM 11:00 SOUTHERN ARREST, BOOKED \\n\",\n \"2 Monday 04/25/2016 12:00:00 AM 14:59 BAYVIEW ARREST, BOOKED \\n\",\n \"3 Tuesday 01/05/2016 12:00:00 AM 23:50 TENDERLOIN NONE \\n\",\n \"4 Friday 01/01/2016 12:00:00 AM 00:30 MISSION NONE \\n\",\n \"\\n\",\n \" Address X Y \\\\\\n\",\n \"0 800 Block of BRYANT ST -122.403405 37.775421 \\n\",\n \"1 800 Block of BRYANT ST -122.403405 37.775421 \\n\",\n \"2 KEITH ST / SHAFTER AV -122.388856 37.729981 \\n\",\n \"3 JONES ST / OFARRELL ST -122.412971 37.785788 \\n\",\n \"4 16TH ST / MISSION ST -122.419672 37.765050 \\n\",\n \"\\n\",\n \" Location PdId \\n\",\n \"0 (37.775420706711, -122.403404791479) 12005827212120 \\n\",\n \"1 (37.775420706711, -122.403404791479) 12005827212168 \\n\",\n \"2 (37.7299809672996, -122.388856204292) 14105926363010 \\n\",\n \"3 (37.7857883766888, -122.412970537591) 16001366271000 \\n\",\n \"4 (37.7650501214668, -122.419671780296) 16000274071000 \"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_incidents.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"So each row consists of 13 features:\\n\",\n \"\\n\",\n \"> 1. **IncidntNum**: Incident Number\\n\",\n \"> 2. **Category**: Category of crime or incident\\n\",\n \"> 3. **Descript**: Description of the crime or incident\\n\",\n \"> 4. **DayOfWeek**: The day of week on which the incident occurred\\n\",\n \"> 5. **Date**: The Date on which the incident occurred\\n\",\n \"> 6. **Time**: The time of day on which the incident occurred\\n\",\n \"> 7. **PdDistrict**: The police department district\\n\",\n \"> 8. **Resolution**: The resolution of the crime in terms whether the perpetrator was arrested or not\\n\",\n \"> 9. **Address**: The closest address to where the incident took place\\n\",\n \"> 10. **X**: The longitude value of the crime location \\n\",\n \"> 11. **Y**: The latitude value of the crime location\\n\",\n \"> 12. **Location**: A tuple of the latitude and the longitude values\\n\",\n \"> 13. **PdId**: The police department ID\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's find out how many entries there are in our dataset.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(150500, 13)\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_incidents.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"So the dataframe consists of 150,500 crimes, which took place in the year 2016. In order to reduce computational cost, let's just work with the first 100 incidents in this dataset.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# get the first 100 crimes in the df_incidents dataframe\\n\",\n \"limit = 100\\n\",\n \"df_incidents = df_incidents.iloc[0:limit, :]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Let's confirm that our dataframe now consists only of 100 crimes.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(100, 13)\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_incidents.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now that we reduced the data a little bit, let's visualize where these crimes took place in the city of San Francisco. We will use the default style and we will initialize the zoom level to 12. \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# San Francisco latitude and longitude values\\n\",\n \"latitude = 37.77\\n\",\n \"longitude = -122.42\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# create map and display it\\n\",\n \"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\\n\",\n \"\\n\",\n \"# display the map of San Francisco\\n\",\n \"sanfran_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now let's superimpose the locations of the crimes onto the map. The way to do that in **Folium** is to create a _feature group_ with its own features and style and then add it to the sanfran_map.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# instantiate a feature group for the incidents in the dataframe\\n\",\n \"incidents = folium.map.FeatureGroup()\\n\",\n \"\\n\",\n \"# loop through the 100 crimes and add each to the incidents feature group\\n\",\n \"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\\n\",\n \" incidents.add_child(\\n\",\n \" folium.CircleMarker(\\n\",\n \" [lat, lng],\\n\",\n \" radius=5, # define how big you want the circle markers to be\\n\",\n \" color='yellow',\\n\",\n \" fill=True,\\n\",\n \" fill_color='blue',\\n\",\n \" fill_opacity=0.6\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"# add incidents to map\\n\",\n \"sanfran_map.add_child(incidents)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"You can also add some pop-up text that would get displayed when you hover over a marker. Let's make each marker display the category of the crime when hovered over.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# instantiate a feature group for the incidents in the dataframe\\n\",\n \"incidents = folium.map.FeatureGroup()\\n\",\n \"\\n\",\n \"# loop through the 100 crimes and add each to the incidents feature group\\n\",\n \"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\\n\",\n \" incidents.add_child(\\n\",\n \" folium.CircleMarker(\\n\",\n \" [lat, lng],\\n\",\n \" radius=5, # define how big you want the circle markers to be\\n\",\n \" color='yellow',\\n\",\n \" fill=True,\\n\",\n \" fill_color='blue',\\n\",\n \" fill_opacity=0.6\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \"# add pop-up text to each marker on the map\\n\",\n \"latitudes = list(df_incidents.Y)\\n\",\n \"longitudes = list(df_incidents.X)\\n\",\n \"labels = list(df_incidents.Category)\\n\",\n \"\\n\",\n \"for lat, lng, label in zip(latitudes, longitudes, labels):\\n\",\n \" folium.Marker([lat, lng], popup=label).add_to(sanfran_map) \\n\",\n \" \\n\",\n \"# add incidents to map\\n\",\n \"sanfran_map.add_child(incidents)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Isn't this really cool? Now you are able to know what crime category occurred at each marker.\\n\",\n \"\\n\",\n \"If you find the map to be so congested will all these markers, there are two remedies to this problem. The simpler solution is to remove these location markers and just add the text to the circle markers themselves as follows:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# create map and display it\\n\",\n \"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\\n\",\n \"\\n\",\n \"# loop through the 100 crimes and add each to the map\\n\",\n \"for lat, lng, label in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\\n\",\n \" folium.CircleMarker(\\n\",\n \" [lat, lng],\\n\",\n \" radius=5, # define how big you want the circle markers to be\\n\",\n \" color='yellow',\\n\",\n \" fill=True,\\n\",\n \" popup=label,\\n\",\n \" fill_color='blue',\\n\",\n \" fill_opacity=0.6\\n\",\n \" ).add_to(sanfran_map)\\n\",\n \"\\n\",\n \"# show map\\n\",\n \"sanfran_map\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"The other proper remedy is to group the markers into different clusters. Each cluster is then represented by the number of crimes in each neighborhood. These clusters can be thought of as pockets of San Francisco which you can then analyze separately.\\n\",\n \"\\n\",\n \"To implement this, we start off by instantiating a _MarkerCluster_ object and adding all the data points in the dataframe to this object.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Make this Notebook Trusted to load map: File -> Trust Notebook
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from folium import plugins\\n\",\n \"\\n\",\n \"# let's start again with a clean copy of the map of San Francisco\\n\",\n \"sanfran_map = folium.Map(location = [latitude, longitude], zoom_start = 12)\\n\",\n \"\\n\",\n \"# instantiate a mark cluster object for the incidents in the dataframe\\n\",\n \"incidents = plugins.MarkerCluster().add_to(sanfran_map)\\n\",\n \"\\n\",\n \"# loop through the dataframe and add each data point to the mark cluster\\n\",\n \"for lat, lng, label, in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\\n\",\n \" folium.Marker(\\n\",\n \" location=[lat, lng],\\n\",\n \" icon=None,\\n\",\n \" popup=label,\\n\",\n \" ).add_to(incidents)\\n\",\n \"\\n\",\n \"# display map\\n\",\n \"sanfran_map\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/Readme.md",
"content": "# Melenoma Classification\n\n## 1. Problem Statment\n\nClassfying Melenoma skin lesion images into 9 classes of diagnostics using deep learning models. \n\n## 2. Methods\n\n### 2.1. Dataset\nThe dataset used is the [Skin Lesion Images for Melanoma Classification on Kaggle](https://www.kaggle.com/datasets/andrewmvd/isic-2019). This dataset contains the training data for the ISIC 2019 challenge, note that it already includes data from previous years (2018 and 2017). The dataset for ISIC 2019 contains 25,331 images available for the classification of dermoscopic images among nine different diagnostic categories:\n\n* Melanoma\n* Melanocytic nevus\n* Basal cell carcinoma\n* Actinic keratosis\n* Benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis)\n* Dermatofibroma\n* Vascular lesion\n* Squamous cell carcinoma\n* None of the above\n\n\n\n### 2.2. Data Preprcoessing \n\n\n### 2.3. Feature Engineering \n\n\n### 2.4. Models\nFinetuned pretrained CNN based models. The models used are: \n\n* VGG-16\n* VGG-19\n* ResNet-50 \n* Mobile-Net\n\n\n## 3. Results \nSince the data is imbalanced so the F1_score.\n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/deep-learning-models/CNN_model.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 16 19:00:48 2021\n\n@author: youss\n\"\"\"\nimport tensorflow.compat.v1 as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten\nfrom tensorflow.keras.layers import Dense, BatchNormalization, Dropout, Activation\n\n# https://keras.io/api/applications/\ndef simple_CNN(train_data_shape,n_classes):\n # building a linear stack of layers with the sequential model\n\n model = Sequential()\n model.add(Conv2D(32, (3, 3), input_shape=train_data_shape[1:]))\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n \n model.add(Conv2D(64, (3, 3)))\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n \n model.add(Conv2D(128, (3, 3)))\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n \n model.add(Flatten())\n model.add(Dense(64))\n model.add(Activation('relu'))\n model.add(Dropout(0.5))\n model.add(Dense(n_classes))\n model.add(Activation('sigmoid'))\n \n model.summary()\n return model\n\ndef MobileNet(num_classes, is_trainable ): \n \n pretrained_model=tf.keras.applications.MobileNet(\n input_shape=(224, 224, 3),\n alpha=1.0,\n depth_multiplier=1,\n dropout=0.001,\n include_top=False,\n weights=\"imagenet\")\n\n for layer in pretrained_model.layers[0:18]:\n layer.trainable = is_trainable\n\n model = Sequential()\n # first (and only) set of FC => RELU layers\n model.add(Flatten())\n model.add(Dense(200, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n model.add(Dense(400, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n\n # softmax classifier\n model.add(Dense(num_classes,activation='softmax'))\n pretrainedInput = pretrained_model.input\n pretrainedOutput = pretrained_model.output\n output = model(pretrainedOutput)\n model = tf.keras.models.Model(pretrainedInput, output)\n model.summary() \n return model \n\ndef VGG_16(num_classes,is_trainable):\n from tensorflow.keras.applications.vgg16 import VGG16\n\n pretrained_model = VGG16(\n include_top=False,\n input_shape=(224, 224, 3),\n weights='imagenet')\n \n for layer in pretrained_model.layers:\n layer.trainable = is_trainable\n\n model = Sequential()\n # first (and only) set of FC => RELU layers\n model.add(Flatten())\n model.add(Dense(200, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n model.add(Dense(400, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n\n # softmax classifier\n model.add(Dense(num_classes,activation='softmax'))\n pretrainedInput = pretrained_model.input\n pretrainedOutput = pretrained_model.output\n output = model(pretrainedOutput)\n model = tf.keras.models.Model(pretrainedInput, output)\n model.summary() \n return model \n\ndef Inception_v3(num_classes,is_trainable):\n pretrained_model= tf.keras.applications.InceptionV3(\n include_top=False,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=(224, 224, 3),\n pooling='max')\n for layer in pretrained_model.layers[0:150]:\n layer.trainable = is_trainable\n model = Sequential()\n # first (and only) set of FC => RELU layers\n model.add(Flatten())\n model.add(Dense(200, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n model.add(Dense(400, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n\n # softmax classifier\n model.add(Dense(num_classes,activation='softmax'))\n pretrainedInput = pretrained_model.input\n pretrainedOutput = pretrained_model.output\n output = model(pretrainedOutput)\n model = tf.keras.models.Model(pretrainedInput, output)\n model.summary() \n return model \n\ndef InceptionResNetV2(num_classes,is_trainable):\n pretrained_model=tf.keras.applications.InceptionResNetV2(\n include_top=False,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=(224,224,3))\n \n for layer in pretrained_model.layers[0:450]:\n layer.trainable = is_trainable\n model = Sequential()\n # first (and only) set of FC => RELU layers\n model.add(Flatten())\n model.add(Dense(32, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n \n model.add(Dense(64, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n \n model.add(Dense(128, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n\n model.add(Dense(256, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n\n model.add(Dense(512, activation='relu'))\n model.add(Dropout(0.5))\n model.add(BatchNormalization())\n \n\n # softmax classifier\n model.add(Dense(num_classes,activation='softmax'))\n pretrainedInput = pretrained_model.input\n pretrainedOutput = pretrained_model.output\n output = model(pretrainedOutput)\n model = tf.keras.models.Model(pretrainedInput, output)\n model.summary() \n return model \n \n \n \n "
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/deep-learning-models/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 16 19:29:24 2021\n\n@author: youss\n\"\"\"\n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/deep-learning-models/main.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 22 04:58:28 2021\n\n@author: youss\n\"\"\"\nimport sys \n\nsys.path.insert(0,'D:/work & study/Nawah/Datasets/codes/deep learning models')\nfrom CNN_model import Inception_v3\nfrom CNN_model import VGG_16\nfrom CNN_model import simple_CNN\nfrom CNN_model import MobileNet\nfrom CNN_model import InceptionResNetV2\n\ndef select_CNN_model(model_name,num_classes,trainable,input_shape):\n \n if model_name == 'simple_CNN':\n model= simple_CNN(input_shape,num_classes)\n \n elif model_name=='MobileNet':\n model=MobileNet(num_classes,trainable)\n \n elif model_name=='VGG-16':\n model=VGG_16(num_classes,trainable)\n \n elif model_name=='Inception-v3':\n model=Inception_v3(num_classes,trainable)\n \n elif model_name=='InceptionResNetV2':\n model=InceptionResNetV2(num_classes,trainable)\n \n else:\n print(\"Error value : There is no model with the following name\",model_name)\n return \n \n return model\n \ndef getLayerIndexByName(model, layername):\n \n for idx, layer in enumerate(model.layers):\n if layer.name == layername:\n return idx\n return None \n "
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/deep-learning-models/readme.md",
"content": "The deep learning models used are \n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/deep-learning-models/training.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 16 19:31:01 2021\n\n@author: youss\n\"\"\"\nimport numpy as np\nimport sys\n\nimport matplotlib.pyplot as plt\nfrom keras.callbacks import EarlyStopping\nfrom sklearn.utils import class_weight\nfrom tensorflow.keras.optimizers import SGD\n\nsys.path.insert(0,'D:/work & study/Nawah/Datasets/codes/evaluation metrics')\nfrom f1_score import f1,f1_micro\nfrom classification_metrics import confusion_matrix_calc\n\ndef training_model(model,train_data,train_labels,val_data,val_labels,test_data,test_labels,num_epoch,batch_size,num_classes,evaulation_metric):\n \n class_weights = class_weight.compute_class_weight('balanced',np.unique(train_labels.values.argmax(axis=1)),train_labels.values.argmax(axis=1))\n sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n es = EarlyStopping(monitor='val_'+evaulation_metric, mode='max', verbose=1,patience=10,baseline=0.5,min_delta=0.1)\n \n if evaulation_metric=='accuracy':\n model.compile(loss='binary_crossentropy',optimizer=sgd,metrics=['accuracy'])\n elif evaulation_metric=='f1':\n model.compile(loss='binary_crossentropy',optimizer=sgd,metrics=[f1])\n elif evaulation_metric=='f1_micro':\n model.compile(loss='binary_crossentropy',optimizer=sgd,metrics=[f1_micro])\n \n \n history=model.fit(train_data,train_labels, validation_data=(val_data,val_labels),epochs=num_epoch, batch_size=batch_size,class_weight=class_weights,callbacks=[es])\n score=model.evaluate(test_data,test_labels)\n print(f'Test loss: {score[0]} / Test' + ' ' + evaulation_metric + f'score: {score[1]}')\n plotting_train_val_metrics(history,evaulation_metric) \n predicted_train_labels=model.predict(train_data)\n predicted_val_labels=model.predict(val_data)\n predicted_test_labels=model.predict(test_data)\n confusion_matrix_calc(predicted_train_labels,train_labels,num_classes,'confusion matrix of the training data') \n confusion_matrix_calc(predicted_val_labels,val_labels,num_classes,'confusion matrix of the validation data') \n confusion_matrix_calc(predicted_test_labels,test_labels,num_classes,'confusion matrix of the test data') \n \n return None\n\ndef plotting_train_val_metrics(history,evaulation_metric):\n plt.plot(history.history[evaulation_metric])\n plt.plot(history.history['val_'+ evaulation_metric])\n plt.title('training and val' + evaulation_metric)\n plt.ylabel(evaulation_metric +'score')\n plt.xlabel('epoch')\n plt.legend(['train', 'val'], loc='upper left')\n plt.show()\n # summarize history for loss\n plt.plot(history.history['loss'])\n plt.plot(history.history['val_loss'])\n plt.title('model loss')\n plt.ylabel('loss')\n plt.xlabel('epoch')\n plt.legend(['train', 'val'], loc='upper left')\n plt.show()\n return None\n "
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/evaluation-metrics/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 16 19:21:12 2021\n\n@author: youss\n\"\"\"\n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/evaluation-metrics/classification_metrics.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 17 13:57:42 2021\n\n@author: youss\n\"\"\"\nfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef confusion_matrix_calc(predicted_labels,true_labels,num_classes,title):\n positions = np.arange(0,num_classes)\n classes = np.arange(0,num_classes)\n cm=confusion_matrix(predicted_labels.argmax(axis=1), true_labels.values.argmax(axis=1),labels=classes) \n disp=ConfusionMatrixDisplay(cm,display_labels=classes)\n classes_name = true_labels.columns.values\n plt.figure(figsize=(10,10))\n disp.plot()\n plt.xticks(positions, classes_name)\n plt.yticks(positions, classes_name)\n plt.title(title)\n return \n \n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/evaluation-metrics/f1_score.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 16 19:10:17 2021\n\n@author: youss\n\"\"\"\nimport tensorflow.keras.backend as K\nimport tensorflow.compat.v1 as tf\n\ndef f1(y_true, y_pred):\n y_pred = K.round(y_pred)\n tp = K.sum(K.cast(y_true*y_pred, 'float'), axis=0)\n tn = K.sum(K.cast((1-y_true)*(1-y_pred), 'float'), axis=0)\n fp = K.sum(K.cast((1-y_true)*y_pred, 'float'), axis=0)\n fn = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=0)\n\n p = tp / (tp + fp + K.epsilon())\n r = tp / (tp + fn + K.epsilon())\n\n f1 = 2*p*r / (p+r+K.epsilon())\n f1 = tf.where(tf.is_nan(f1), tf.zeros_like(f1), f1)\n return K.mean(f1)\n\ndef f1_loss(y_true, y_pred):\n \n tp = K.sum(K.cast(y_true*y_pred, 'float'), axis=0)\n tn = K.sum(K.cast((1-y_true)*(1-y_pred), 'float'), axis=0)\n fp = K.sum(K.cast((1-y_true)*y_pred, 'float'), axis=0)\n fn = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=0)\n\n p = tp / (tp + fp + K.epsilon())\n r = tp / (tp + fn + K.epsilon())\n\n f1 = 2*p*r / (p+r+K.epsilon())\n f1 = tf.where(tf.is_nan(f1), tf.zeros_like(f1), f1)\n return 1 - K.mean(f1)\n\ndef f1_micro(y_true, y_pred):\n y_pred = K.round(y_pred)\n tp_per_class = K.sum(K.cast(y_true*y_pred, 'float'), axis=1)\n tn_per_class = K.sum(K.cast((1-y_true)*(1-y_pred), 'float'), axis=1)\n fp_per_class = K.sum(K.cast((1-y_true)*y_pred, 'float'), axis=1)\n fn_per_class = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=1)\n\n p_per_class = tp_per_class / (tp_per_class + fp_per_class + K.epsilon())\n r_per_class = tp_per_class / (tp_per_class + fn_per_class + K.epsilon())\n\n f1_per_class = 2*p_per_class*r_per_class / (p_per_class+r_per_class+K.epsilon())\n f1_total= K.sum(f1_per_class*K.sum(y_true,axis=1))/ K.sum(y_true)\n \n return f1_total\n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/evaluation-metrics/readme.md",
"content": "The evaluation metrics used to evaluate the classificaiton models\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/loading and storing/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 16 17:23:51 2021\n\n@author: youss\n\"\"\"\n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/loading and storing/loading_images.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 24 02:40:27 2021\n\n@author: youss\n\"\"\"\nimport cv2\nimport os\n\n \ndef load_images_from_folder(folder,width,height):\n images = []\n i=0\n \n for filename in os.listdir(folder):\n img = cv2.imread(os.path.join(folder,filename))\n img=cv2.resize(img,(width,height))\n if img is not None:\n images.append(img)\n i=i+1\n return images\n\n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/loading and storing/loading_storing_h5py.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 16 16:55:49 2021\n\n@author: youss\n\"\"\"\nimport numpy as np\nimport h5py\nimport os \n\n\ndef storing_h5py(input_data,hdf5_dir):\n for i in range (len(input_data)):\n image_id=i\n image= input_data[i]\n file = h5py.File(os.path.join(hdf5_dir,str(image_id)+'.h5'), \"w\")\n dataset = file.create_dataset(\"image\", np.shape(image), h5py.h5t.STD_U8BE, data=image)\n file.close()\n\ndef read_h5py(hdf5_dir,num_images):\n images=[]\n for i in range(num_images):\n image_id=i\n file = h5py.File(os.path.join(hdf5_dir,str(image_id)+'.h5'), \"r+\")\n image = np.array(file[\"/image\"]).astype(\"uint8\")\n images.append(image)\n return images\n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/loading and storing/readme.md",
"content": "Loading the inputs and storing the output \n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/main.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 16 16:54:05 2021\n\n@author: youssef Hosni\n\"\"\"\nimport pandas as pd\nimport numpy as np\nimport sys\n\nimport tensorflow.compat.v1 as tf\n\nsys.path.insert(0,'D:/work & study/Nawah/Datasets/codes/loading and storing')\nfrom loading_images import load_images_from_folder\nsys.path.insert(0,'D:/work & study/Nawah/Datasets/codes/preprocessing')\nfrom exploration import bar_plot,class_counts_proportions\nfrom preprocessing import splitting_normalization\nfrom preprocessing import splitting_classes\nsys.path.insert(0,'D:/work & study/Nawah/Datasets/codes/deep learning models')\nfrom main import select_CNN_model\nsys.path.insert(0,'D:/work & study/Nawah/Datasets/codes/deep learning models')\nfrom training import training_model \n\n\nprint('Using:')\nprint('\\t\\u2022 TensorFlow version:', tf.__version__)\nprint('\\t\\u2022 tf.keras version:', tf.keras.__version__)\nprint('\\t\\u2022 Running on GPU' if tf.config.list_physical_devices('GPU') else '\\t\\u2022 GPU device not found. Running on CPU')\n\n#%% Loading the png data and split and normalize it \nimages_dir = \"D:\\\\work & study\\\\Nawah\\\\Datasets\\\\ISIC_2019_Training_Input\\\\ISIC_2019_Training_Input\"\nwidth = 224\nheight = 224\ninput_data = load_images_from_folder(images_dir,width,height)\n#%% preprocessiing \nlabels = pd.read_csv(\"D:/work & study/Nawah/Datasets/ISIC_2019_Training_GroundTruth.csv\")\nlabels=labels.iloc[:,1:]\nlabels.head()\n#%% splitting the data and normalizing it \ntrain_data,train_labels, val_data,val_labels,test_data,test_labels = splitting_normalization(\n input_data, \n labels\n )\n\n#%% dividing the data into datasets one with two classes and one with 8 classes \n[train_data_small_classes, \ntrain_labels_small_classes,\ntrain_data_labels_two_classes] = splitting_classes(train_data,train_labels)\n\n[val_data_small_classes, \nval_labels_small_classes,\nval_data_labels_two_classes] = splitting_classes(val_data,val_labels)\n\n[test_data_small_classes, \ntest_labels_small_classes,\ntest_data_labels_two_classes] = splitting_classes(test_data,test_labels)\n\n\n#%% underesampling the data \nsys.path.insert(0,'D:/work & study/Nawah/Datasets/codes/preprocessing')\nfrom preprocessing import resampling\nresampling_stragey = {1:4500}\nresampled_training_data, resampled_labels = resampling(\n train_data,\n train_labels,\n 'under_sampling',\n resampling_stragey\n )\nresampled_training_data = resampled_training_data.reshape(\n resampled_training_data.shape[0],\n 224,\n 224,\n 3\n )\nresampled_labels=pd.DataFrame(resampled_labels)\nresampled_labels.columns = train_labels.columns[0:-1]\nresampled_labels['UNK'] = 0\n\n#%% Oversampling the data\nresampling_stragey = {2:2000,4:1700,5:1700,6:2000}\nresampled_training_data_small, resampled_labels_small= resampling(\n train_data_small_classes,train_labels_small_classes,\n 'over_sampling',resampling_stragey\n )\nresampled_training_data_small = resampled_training_data_small.reshape(\n resampled_training_data_small.shape[0],\n 224,224,3\n )\nresampled_labels_small = pd.DataFrame(resampled_labels_small)\nresampled_labels_small.columns=train_labels_small_classes.columns[0:-1]\nresampled_labels_small['UNK']=0\n#%% Building the simple CNN model and trainning it\nmodels_name_list = [\n 'simple_CNN',\n 'MobileNet',\n 'VGG-16',\n 'Inception-v3',\n 'InceptionResNetV2'\n ]\nmodel_name=models_name_list[3]\nis_trainable=False\nepoch_num=100\nbatch_num=32\nevaluation_metrics_list=[\n 'accuracy',\n 'f1',\n 'f1_micro'\n ]\nevaluation_metric = evaluation_metrics_list[2]\nmodel = select_CNN_model(model_name,8,is_trainable, np.shape(resampled_training_data_small))\ntraining_model(model,resampled_training_data_small,resampled_labels_small,val_data_small_classes,val_labels_small_classes,test_data_small_classes,\n test_labels_small_classes,epoch_num,batch_num,8,evaluation_metric)\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/preprocessing/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 21 06:45:28 2021\n\n@author: youss\n\"\"\"\n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/preprocessing/exploration.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 21 04:05:02 2021\n\n@author: youss\n\"\"\"\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndef bar_plot(input_data,title):\n input_data.columns\n df=pd.DataFrame()\n df[\"disease\"]=input_data.columns\n number_cases_per_class=input_data.sum()\n df[\"number_of_cases\"]=number_cases_per_class.values\n plt.figure()\n plt.bar(df[\"disease\"],df[\"number_of_cases\"])\n plt.title(title)\n \ndef class_counts_proportions(labels):\n df=pd.DataFrame()\n df[\"Label\"]=labels.columns\n number_cases_per_class=labels.sum()\n df[\"number_of_cases_each_class\"]=number_cases_per_class.values\n df[\"percentage_of_classes\"]=number_cases_per_class.values/sum(number_cases_per_class.values) \n return df\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/preprocessing/preprocessing.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 16 22:20:13 2021\n@author: youss\n\"\"\"\n\nimport numpy as np\nimport time\nimport cv2\n\nfrom multiprocessing.dummy import Pool\nfrom multiprocessing.sharedctypes import Value\nfrom ctypes import c_int\nfrom sklearn.model_selection import train_test_split\nfrom imblearn.over_sampling import SMOTE, RandomOverSampler\nfrom imblearn.under_sampling import RandomUnderSampler, TomekLinks,NearMiss\nfrom imblearn.under_sampling import OneSidedSelection\n\ndef splitting_normalization(input_data, labels):\n train_data,val_data,train_labels,val_labels = train_test_split(input_data,labels, test_size=0.3, random_state=42)\n val_data,test_data,val_labels,test_labels = train_test_split(val_data,val_labels, test_size=0.33, random_state=42)\n \n # building the input vector from the 28x28 pixels\n train_data=np.array(train_data)\n val_data=np.array(val_data)\n test_data=np.array(test_data)\n \n train_data = train_data.astype('float32')\n val_data = val_data.astype('float32')\n test_data = test_data.astype('float32')\n print(train_data.shape)\n print(val_data.shape)\n print(test_data.shape)\n \n # normalizing the data to help with the training\n train_data /= 255\n val_data /= 255\n test_data /= 255\n return train_data,train_labels, val_data,val_labels,test_data,test_labels\n\n\n\ndef splitting_classes(input_data,input_labels):\n \n \"\"\"\n Parameters\n ----------\n input_data : Array \n The input data with all classes .\n input_labels : DataFrame \n The input labels of data with all classes.\n\n Returns\n -------\n ouput_data_small_classes : Array \n The input data with all classes .\n output_labels_small_classes : DataFrame \n The input labels of data with all classes.\n output_labels_two_classes : DataFrame \n The input labels of data with all classes.\n\n \"\"\"\n input_labels.reset_index(drop=True,inplace=True)\n output_labels_small_classes=input_labels[input_labels['NV']==0]\n output_labels_small_classes.drop(columns='NV',inplace=True)\n ouput_data_small_classes=input_data[output_labels_small_classes.index.values]\n \n \n output_labels_two_classes=input_labels.copy()\n output_labels_two_classes['other_classes']=0\n output_labels_two_classes.iloc[output_labels_small_classes.index.values,9]=1\n\n labels_to_drop_index=[0,2,3,4,5,6,7,8]\n output_labels_two_classes.drop(columns=output_labels_two_classes.columns[labels_to_drop_index],inplace=True)\n\n return ouput_data_small_classes, output_labels_small_classes,output_labels_two_classes\n\ndef resizing_data(input_data,width, height):\n resized_data=[]\n def read_imagecv2(img, counter):\n img = cv2.resize(img, (width, height))\n resized_data.append(img)\n with counter.get_lock(): #processing pools give no way to check up on progress, so we make our own\n counter.value += 1\n\n # start 4 worker processes\n with Pool(processes=2) as pool: #this should be the same as your processor cores (or less)\n counter = Value(c_int, 0) #using sharedctypes with mp.dummy isn't needed anymore, but we already wrote the code once...\n chunksize = 4 #making this larger might improve speed (less important the longer a single function call takes)\n resized_test_data = pool.starmap_async(read_imagecv2, ((img, counter) for img in input_data) , chunksize) #how many jobs to submit to each worker at once \n while not resized_test_data.ready(): #print out progress to indicate program is still working.\n #with counter.get_lock(): #you could lock here but you're not modifying the value, so nothing bad will happen if a write occurs simultaneously\n #just don't `time.sleep()` while you're holding the lock\n print(\"\\rcompleted {} images \".format(counter.value), end='')\n time.sleep(.5)\n print('\\nCompleted all images')\n return resized_data \n\n\n\ndef resampling(train_data,train_labels,resampling_type,resampling_stragey):\n train_data_new=np.reshape(train_data,(train_data.shape[0],train_data.shape[1]*train_data.shape[2]*train_data.shape[3]))\n if resampling_type == 'SMOTE':\n train_data_resampled,train_labels_resampled = SMOTE(random_state=42).fit_resample(train_data_new, train_labels.values)\n \n elif resampling_type=='over_sampling':\n over_sampler=RandomOverSampler(sampling_strategy=resampling_stragey)\n train_data_resampled, train_labels_resampled = over_sampler.fit_resample(train_data_new,train_labels.values) \n \n elif resampling_type== 'under_sampling':\n under_sampler=RandomUnderSampler(sampling_strategy=resampling_stragey)\n train_data_resampled, train_labels_resampled = under_sampler.fit_resample(train_data_new,train_labels.values)\n \n elif resampling_type == 'tomelinks':\n t1= TomekLinks( sampling_strategy=resampling_stragey)\n train_data_resampled, train_labels_resampled = t1.fit_resample(train_data_new,train_labels.values )\n \n elif resampling_type=='near_miss_neighbors':\n undersample = NearMiss(version=1, n_neighbors=3)\n train_data_resampled, train_labels_resampled = undersample.fit_resample(train_data_new,train_labels.values )\n \n elif resampling_type=='one_sided_selection':\n undersample = OneSidedSelection(n_neighbors=1, n_seeds_S=200)\n train_data_resampled, train_labels_resampled = undersample.fit_resample(train_data_new,train_labels.values )\n \n return train_data_resampled, train_labels_resampled \n\n\n\n\n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/preprocessing/readme.md",
"content": "The preprocessing for the data \n"
},
{
"path": "Deep Learning/Classification/Melenoma_Classification/readme.md",
"content": "# Melenoma Classification \n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/Best_mask.py",
"content": "#This file is for obtaining confidence interval of the used procedure and to get the best mask that specifies the maximum AUC\n\nimport numpy as np\nfrom hyper_opt import create_mask,model_1D,model_1D_calibrate\nimport load_data\nimport data_preprocessing\nimport generate_result\nfrom sklearn.model_selection import train_test_split\n\ndef main():\n # define input file names, directories, and parameters\n train_Con_file_name = 'CV_con.npz'\n train_AD_file_name = 'CV_pat.npz'\n #test_Con_file_name = 'CV_ADNI_CON.npz'\n #test_AD_file_name = 'CV_ADNI_AD.npz'\n mask_name = '4mm_brain_mask_bin_epl.nii.gz'\n feature_selection_type = 'L2_penality'\n results_directory = 'newResults'\n results_path = load_data.find_path(results_directory)\n from sklearn.model_selection import train_test_split\n\n #defining variables\n number_of_cv = 5\n Hyperparameter_model__1 = 5000\n Hyperparameter_model__3 = 5000\n number_of_neighbours = 1\n np.random.seed(1)\n accuracy_total_list = list()\n F1_score_total_list = list()\n auc_total_list = list()\n Best_AUC = 0\n\n # loading input data and mask\n train_data,train_labels=load_data.train_data_3d(train_Con_file_name,train_AD_file_name)\n #test_data, test_labels = load_data.test_data_3d(test_Con_file_name, test_AD_file_name)\n mask_4mm = load_data.mask(mask_name)\n original_mask=mask_4mm.get_fdata()\n\n\n # data preprocessing\n train_data = np.moveaxis(train_data.copy(), 3, 0)\n #test_data = np.moveaxis(test_data.copy(), 3, 0)\n train_data = train_data * original_mask\n #test_data = test_data * original_mask\n shape = np.shape(train_data)\n train_data_flattened = data_preprocessing.flatten(train_data.copy())\n #test_data_flattened = data_preprocessing.flatten(test_data.copy())\n orignal_mask_flatten = data_preprocessing.flatten(original_mask[np.newaxis, :, :, :].copy())\n orignal_mask_flatten = np.reshape(orignal_mask_flatten, (-1))\n train_data_flattened = data_preprocessing.MinMax_scaler(train_data_flattened.copy())\n #test_data_flattened = data_preprocessing.MinMax_scaler(test_data_flattened.copy())\n \n #confidence_interval using bootstraping\n for _ in range(100):\n train_data_flattened, test_data_flattened, train_labels, test_labels = train_test_split(train_data_flattened, train_labels, test_size=0.2, random_state=42)\n train_data_inlir, train_labels_inlir, outlier_indices_train = data_preprocessing.outliers(train_data_flattened,\n train_labels,\n number_of_neighbours)\n test_data_inlier, test_labels_inlier, outlier_indices_test = data_preprocessing.novelty(train_data_inlir,\n train_labels_inlir,\n test_data_flattened,\n test_labels,\n number_of_neighbours)\n\n train_data_inlier,train_labels_inlier=data_preprocessing.resampling(train_data_inlir.copy(), train_labels_inlir.copy())\n train_data_outliers, trian_labels_outliers = data_preprocessing.resampling(train_data_flattened[outlier_indices_train].copy(),\n train_labels[outlier_indices_train].copy())\n\n train_data_inlier_unflattened = data_preprocessing.deflatten(train_data_inlier, shape)\n train_data_outlier_unflattened = data_preprocessing.deflatten(train_data_outliers, shape)\n train_data_inlier_unflattened = np.moveaxis(train_data_inlier_unflattened.copy(), 0, 3)\n train_data_outlier_unflattened = np.moveaxis(train_data_outlier_unflattened.copy(), 0, 3)\n train_data_inlier_noised = data_preprocessing.apply_noise_manytypes(train_data_inlier_unflattened.copy())\n train_data_inlier_filtered = data_preprocessing.apply_filter_manytypes(train_data_inlier_unflattened.copy())\n train_data_inlier_more = data_preprocessing.concatination(train_data_inlier_noised, train_data_inlier_filtered)\n train_labels_inlier_more = data_preprocessing.dublicate(train_labels_inlier.copy(), 29)\n train_data_outlier_noised = data_preprocessing.apply_noise_manytypes(train_data_outlier_unflattened.copy())\n train_data_outlier_filtered = data_preprocessing.apply_filter_manytypes(train_data_outlier_unflattened.copy())\n train_data_outlier_more = data_preprocessing.concatination(train_data_outlier_noised, train_data_outlier_filtered)\n train_labels_outlier_more = data_preprocessing.dublicate(trian_labels_outliers.copy(), 29)\n train_data_inlier_more = np.moveaxis(train_data_inlier_more.copy(), 3, 0)\n train_data_outlier_more = np.moveaxis(train_data_outlier_more.copy(), 3, 0)\n train_data_inlier_more_flattened = data_preprocessing.flatten(train_data_inlier_more.copy())\n train_data_outlier_more_flattened = data_preprocessing.flatten(train_data_outlier_more.copy())\n # train_data_inlier_inlier, train_labels_inlier_inlier, inlier_outlier_indices_train = data_preprocessing.novelty(\n # train_data_inlier, train_labels_inlier,\n # train_data_inlier_more_flattened,\n # train_labels_inlier_more,\n # number_of_neighbours)\n train_data_outlier_inlier, train_labels_outlier_inlier, outlier_outlier_indices_train = data_preprocessing.novelty(train_data_outliers, trian_labels_outliers,\n train_data_outlier_more_flattened,\n train_labels_outlier_more,\n number_of_neighbours)\n\t\n train_data_inlier, train_labels_inlier = data_preprocessing.upsampling(train_data_inlier,train_labels_inlier)\n if train_data_inlier is None: continue\n\n train_data_inlier, train_labels_inlier = data_preprocessing.shuffling(train_data_inlier,train_labels_inlier)\n if np.shape(train_data_inlier)[0]>0:\n\n train_data_outlier_inlier, train_labels_outlier_inlier = data_preprocessing.upsampling(\n train_data_outlier_inlier,\n train_labels_outlier_inlier)\n train_data_outlier_inlier, train_labels_outlier_inlier = data_preprocessing.shuffling(train_data_outlier_inlier,\n train_labels_outlier_inlier)\n if train_data_outlier_inlier is None: continue\n\n \n\n\n #Brain extraction of data\n train_data_inlier_brain=train_data_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n test_data_inlier_brain=test_data_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n train_data_outlier_inlier_brain=train_data_outlier_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n test_data_outlier_brain=(test_data_flattened[outlier_indices_test])[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n concated_data = data_preprocessing.concat(train_data_inlier, train_data_outlier_inlier)\n concated_labels = data_preprocessing.concat(train_labels_inlier[:, np.newaxis],train_labels_outlier_inlier[:, np.newaxis])\n\n\n #Model stage 1 with high certainity\n model1_created_mask, model1_, model1_name, model1_weights = create_mask(train_data_inlier_brain, train_labels_inlier,\n number_of_cv, feature_selection_type,\n Hyperparameter_model__1, mask_threshold=4,\n model_type='gaussian_process')\n train_data_inlier_CVspace = data_preprocessing.coefficient_of_variance(train_data_inlier_brain * model1_created_mask)[:,np.newaxis]\n test_data_inlier_CVspace = data_preprocessing.coefficient_of_variance(test_data_inlier_brain * model1_created_mask)[:,np.newaxis]\n model1_, model1_name = model_1D(train_data_inlier_CVspace, train_labels_inlier, model1_created_mask,\n data_validation=None, labels_validation=None,\n model_type='gaussian_process')\n model1_test_accuracy,model1_F1_score,model1_auc,low_confidence_indices=generate_result.out_result_highprob(test_data_inlier_CVspace,\n test_labels_inlier,\n original_mask,model1_created_mask,\n model1_)\n\n #Model stage 2 with low certainity\n if (low_confidence_indices!=0):\n model2_, model2_name = model_1D_calibrate(train_data_inlier_CVspace, train_labels_inlier, model1_created_mask,\n data_validation=None, labels_validation=None,\n model_type='gaussian_process')\n model2_test_accuracy, model2_F1_score, model2_auc = generate_result.out_result(test_data_inlier_CVspace[low_confidence_indices],\n test_labels_inlier[low_confidence_indices],\n original_mask,\n model1_created_mask,\n model2_)\n else:\n model2_test_accuracy, model2_F1_score, model2_auc=0,0,0\n\n\n #Model stage 3 with outliers\n model3_created_mask, model3_, model3_name, model3_weights = create_mask(concated_data,\n concated_labels, number_of_cv,\n feature_selection_type, Hyperparameter_model__3,\n mask_threshold=4,\n model_type='gaussian_process')\n concated_data_cv = data_preprocessing.coefficient_of_variance(\n concated_data[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)].copy() * model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])[:, np.newaxis]\n test_data_outlier_cv = data_preprocessing.coefficient_of_variance(test_data_outlier_brain *model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])[:, np.newaxis]\n model3_, model3_name = model_1D(concated_data_cv, concated_labels, model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)],\n data_validation=None, labels_validation=None, model_type='gaussian_process')\n model3_test_accuracy,model3_F1_score,model3_auc = generate_result.out_result(np.nan_to_num(test_data_outlier_cv) ,\n test_labels[outlier_indices_test], np.nan_to_num(original_mask),\n np.nan_to_num(model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)]), model3_)\n\n #stacking procedure of bootstapping and printing models\n div = 3\n if (model2_test_accuracy==0): div=div-1\n if (model1_test_accuracy==0): div=div-1\n avg_accuracy = (model1_test_accuracy + model2_test_accuracy + model3_test_accuracy) / div\n avg_F1_score = (model1_F1_score + model2_F1_score + model3_F1_score) / div\n avg_auc = (model1_auc + model2_auc + model3_auc) / div\n accuracy_total_list.append(avg_accuracy)\n F1_score_total_list.append(avg_F1_score)\n auc_total_list.append(avg_auc)\n if (model2_auc>Best_AUC):\n Best_AUC=model2_auc\n data_preprocessing_method = \"Seperating outlier of training set and test set, then synthethise more data from training-outliers, then appling probability predictions. High probability \" \\\n \"samples model is used with predictions with high probability, then apply low probability model. Finally add noise to outliers and concatinate with inlier data\" \\\n \"to be used for outlier model\"\n generate_result.print_result_3models(mask_4mm, results_path, model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)], model3_, model3_name,\n model3_weights[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)],model3_test_accuracy,model3_auc, model3_F1_score, Hyperparameter_model__3,\n model2_, model2_name, model2_test_accuracy, model2_auc,model2_F1_score,\n model1_, model1_created_mask, model1_name, model1_weights,model1_test_accuracy, model1_auc, model1_F1_score,Hyperparameter_model__1,\n feature_selection_type, data_preprocessing_method)\n\n # total_performance_confidence_level\n generate_result.confidence_interval_model_99(accuracy_total_list, F1_score_total_list, auc_total_list, 'total_performance')\n\n\n\nif __name__=='__main__':\n main()"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/Best_mask2.py",
"content": "#This file is for obtaining confidence interval of the used procedure and to get the best mask that specifies the maximum AUC\n\nimport numpy as np\nfrom hyper_opt import create_mask,model_reduced,model_1D,model_1D_calibrate\nimport load_data\nimport data_preprocessing\nimport generate_result\nfrom sklearn.model_selection import train_test_split\n\ndef main():\n # define input file names, directories, and parameters\n train_Con_file_name = 'CV_con.npz'\n train_AD_file_name = 'CV_pat.npz'\n #test_Con_file_name = 'CV_ADNI_CON.npz'\n #test_AD_file_name = 'CV_ADNI_AD.npz'\n mask_name = '4mm_brain_mask_bin_epl.nii.gz'\n feature_selection_type = 'L2_penality'\n results_directory = 'newResults'\n results_path = load_data.find_path(results_directory)\n from sklearn.model_selection import train_test_split\n\n #defining variables\n number_of_cv = 5\n Hyperparameter_model__1 = 1000\n Hyperparameter_model__3 = 1000\n number_of_neighbours = 1\n np.random.seed(1)\n accuracy_total_list = list()\n F1_score_total_list = list()\n auc_total_list = list()\n Best_AUC = 0\n model_name='decison tree classifer'\n\n # loading input data and mask\n train_data,train_labels=load_data.train_data_3d(train_Con_file_name,train_AD_file_name)\n #test_data, test_labels = load_data.test_data_3d(test_Con_file_name, test_AD_file_name)\n mask_4mm = load_data.mask(mask_name)\n original_mask=mask_4mm.get_fdata()\n\n\n # data preprocessing\n train_data = np.moveaxis(train_data.copy(), 3, 0)\n #test_data = np.moveaxis(test_data.copy(), 3, 0)\n train_data = train_data * original_mask\n #test_data = test_data * original_mask\n shape = np.shape(train_data)\n train_data_flattened = data_preprocessing.flatten(train_data.copy())\n #test_data_flattened = data_preprocessing.flatten(test_data.copy())\n orignal_mask_flatten = data_preprocessing.flatten(original_mask[np.newaxis, :, :, :].copy())\n orignal_mask_flatten = np.reshape(orignal_mask_flatten, (-1))\n train_data_flattened = data_preprocessing.MinMax_scaler(train_data_flattened.copy())\n #test_data_flattened = data_preprocessing.MinMax_scaler(test_data_flattened.copy())\n \n #confidence_interval using bootstraping\n for _ in range(100):\n number_of_neighbours = 1\n train_data_flattened, test_data_flattened, train_labels, test_labels = train_test_split(train_data_flattened, train_labels, test_size=0.2, random_state=42)\n train_data_inlier, train_labels_inlier, outlier_indices_train = data_preprocessing.outliers(train_data_flattened,\n train_labels,\n number_of_neighbours)\n if len(train_data_inlier)<=1: \n continue\n test_data_inlier, test_labels_inlier, outlier_indices_test = data_preprocessing.novelty(train_data_inlier,\n train_labels_inlier,\n test_data_flattened,\n test_labels,\n number_of_neighbours)\n\n train_labels_inlier=train_labels_inlier[:, np.newaxis]\n #train_data_outliers, trian_labels_outliers = data_preprocessing.resampling(train_data_flattened[outlier_indices_train].copy(),\n # train_labels[outlier_indices_train].copy())\n train_data_outliers, trian_labels_outliers =train_data_flattened[outlier_indices_train],(train_labels[outlier_indices_train])[:, np.newaxis]\n\n train_data_inlier_unflattened = data_preprocessing.deflatten(train_data_inlier, shape)\n train_data_outlier_unflattened = data_preprocessing.deflatten(train_data_outliers, shape)\n train_data_inlier_unflattened = np.moveaxis(train_data_inlier_unflattened.copy(), 0, 3)\n train_data_outlier_unflattened = np.moveaxis(train_data_outlier_unflattened.copy(), 0, 3)\n train_data_inlier_noised = data_preprocessing.apply_noise_manytypes(train_data_inlier_unflattened.copy())\n train_data_inlier_filtered = data_preprocessing.apply_filter_manytypes(train_data_inlier_unflattened.copy())\n train_data_inlier_more = data_preprocessing.concatination(train_data_inlier_noised, train_data_inlier_filtered)\n train_labels_inlier_more = data_preprocessing.dublicate(train_labels_inlier.copy(), 29)\n train_data_outlier_noised = data_preprocessing.apply_noise_manytypes(train_data_outlier_unflattened.copy())\n train_data_outlier_filtered = data_preprocessing.apply_filter_manytypes(train_data_outlier_unflattened.copy())\n train_data_outlier_more = data_preprocessing.concatination(train_data_outlier_noised, train_data_outlier_filtered)\n train_labels_outlier_more = data_preprocessing.dublicate(trian_labels_outliers.copy(), 29)\n train_data_inlier_more = np.moveaxis(train_data_inlier_more.copy(), 3, 0)\n train_data_outlier_more = np.moveaxis(train_data_outlier_more.copy(), 3, 0)\n train_data_inlier_more_flattened = data_preprocessing.flatten(train_data_inlier_more.copy())\n train_data_outlier_more_flattened = data_preprocessing.flatten(train_data_outlier_more.copy())\n # train_data_inlier_inlier, train_labels_inlier_inlier, inlier_outlier_indices_train = data_preprocessing.novelty(\n # train_data_inlier, train_labels_inlier,\n # train_data_inlier_more_flattened,\n # train_labels_inlier_more,\n # number_of_neighbours)\n if len(train_data_outliers)<=1: \n continue\n train_data_outlier_inlier, train_labels_outlier_inlier, outlier_outlier_indices_train = data_preprocessing.novelty(train_data_outliers, trian_labels_outliers,\n train_data_outlier_more_flattened,\n train_labels_outlier_more,\n number_of_neighbours)\n\t\n train_data_inlier, train_labels_inlier = data_preprocessing.upsampling(train_data_inlier,train_labels_inlier)\n if len(train_data_inlier)==0: continue\n\n train_data_inlier, train_labels_inlier = data_preprocessing.shuffling(train_data_inlier,train_labels_inlier)\n print('np.shape(train_data_outlier_inlier)',np.shape(train_data_outlier_inlier))\n print('np.shape(train_data_outlier_inlier)[0]',np.shape(train_data_outlier_inlier)[0])\n if np.shape(train_data_inlier)[0]>0:\n\n train_data_outlier_inlier, train_labels_outlier_inlier = data_preprocessing.upsampling(\n train_data_outlier_inlier.copy(),\n train_labels_outlier_inlier.copy())\n train_data_outlier_inlier, train_labels_outlier_inlier = data_preprocessing.shuffling(train_data_outlier_inlier.copy(),\n train_labels_outlier_inlier.copy())\n if len(train_data_outlier_inlier)==0: continue\n\n \n\n\n #Brain extraction of data\n train_data_inlier_brain=train_data_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n test_data_inlier_brain=test_data_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n train_data_outlier_inlier_brain=train_data_outlier_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n test_data_outlier_brain=(test_data_flattened[outlier_indices_test])[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n concated_data = data_preprocessing.concat(train_data_inlier, train_data_outlier_inlier)\n print('train_labels_inlier[:, np.newaxis]',np.shape(train_labels_inlier[:, np.newaxis]))\n print('train_labels_outlier_inlier[:, np.newaxis]',np.shape(train_labels_outlier_inlier[:, np.newaxis]))\n if (len(np.shape(train_labels_outlier_inlier[:, np.newaxis]))<=2):\n concated_labels = data_preprocessing.concat(train_labels_inlier[:, np.newaxis],train_labels_outlier_inlier[:, np.newaxis])\n else:\n concated_labels = data_preprocessing.concat(train_labels_inlier[:, np.newaxis],train_labels_outlier_inlier)\n #Model stage 1 with high certainity\n model1_created_mask, model1_, model1_name, model1_weights = create_mask(train_data_inlier_brain, train_labels_inlier,\n number_of_cv, feature_selection_type,\n Hyperparameter_model__1, mask_threshold=4,\n model_type='gaussian_process')\n #train_data_inlier_CVspace = data_preprocessing.coefficient_of_variance(train_data_inlier_brain * model1_created_mask)[:,np.newaxis]\n #test_data_inlier_CVspace = data_preprocessing.coefficient_of_variance(test_data_inlier_brain * model1_created_mask)[:,np.newaxis]\n #model1_, model1_name = model_1D(train_data_inlier_CVspace, train_labels_inlier, model1_created_mask,\n # data_validation=None, labels_validation=None,\n # model_type='gaussian_process')\n train_data_inlier_CVspace = (train_data_inlier_brain * model1_created_mask)\n test_data_inlier_CVspace = (test_data_inlier_brain * model1_created_mask)\n\n model1_, model1_name = model_reduced(train_data_inlier_CVspace, train_labels_inlier, model1_created_mask,\n data_validation=None, labels_validation=None,\n model_type='gaussian_process')\n\n model1_test_accuracy,model1_F1_score,model1_auc,low_confidence_indices=generate_result.out_result_highprob(test_data_inlier_CVspace,\n test_labels_inlier,\n original_mask,model1_created_mask,\n model1_)\n\n #Model stage 2 with low certainity\n if (low_confidence_indices!=0):\n model2_, model2_name = model_reduced(train_data_inlier_CVspace, train_labels_inlier, model1_created_mask,\n data_validation=None, labels_validation=None,\n model_type='gaussian_process')\n model2_test_accuracy, model2_F1_score, model2_auc = generate_result.out_result(test_data_inlier_CVspace[low_confidence_indices],\n test_labels_inlier[low_confidence_indices],\n original_mask,\n model1_created_mask,\n model2_)\n else:\n model2_test_accuracy, model2_F1_score, model2_auc=0,0,0\n\n\n #Model stage 3 with outliers\n model3_created_mask, model3_, model3_name, model3_weights = create_mask(concated_data,\n concated_labels, number_of_cv,\n feature_selection_type, Hyperparameter_model__3,\n mask_threshold=4,\n model_type='gaussian_process')\n #concated_data_cv = data_preprocessing.coefficient_of_variance(\n # concated_data[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)].copy() * model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])[:, np.newaxis]\n #test_data_outlier_cv = data_preprocessing.coefficient_of_variance(test_data_outlier_brain *model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])[:, np.newaxis]\n #model3_, model3_name = model_1D(concated_data_cv, concated_labels, model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)],\n # data_validation=None, labels_validation=None, model_type='gaussian_process')\n \n #model3_test_accuracy,model3_F1_score,model3_auc = generate_result.out_result(np.nan_to_num(test_data_outlier_cv) ,\n # test_labels[outlier_indices_test], np.nan_to_num(original_mask),\n # np.nan_to_num(model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)]), model3_)\n concated_data_cv = (concated_data[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)].copy() * \n model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])\n test_data_outlier_cv = (test_data_outlier_brain *model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])\n\n model3_, model3_name = model_reduced(concated_data_cv, concated_labels, model3_created_mask,\n data_validation=None, labels_validation=None, model_type=model_name)\n model3_test_accuracy,model3_F1_score,model3_auc = generate_result.out_result(np.nan_to_num(test_data_outlier_cv) ,\n np.nan_to_num(test_labels[outlier_indices_test]), np.nan_to_num(original_mask),\n np.nan_to_num(model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)]), model3_)\n\n #stacking procedure of bootstapping and printing models\n div = 3\n print('np.shape(test_labels_inlier[low_confidence_indices])',np.shape(test_labels_inlier[low_confidence_indices]))\n print('length(test_labels_inlier[low_confidence_indices])',len(test_labels_inlier[low_confidence_indices]))\n print('np.shape(test_labels_inlier[low_confidence_indices])[0]',np.shape(test_labels_inlier[low_confidence_indices])[0])\n if np.logical_and((model1_test_accuracy==0),(len(test_labels_inlier[low_confidence_indices])==len(test_labels_inlier))): div=div-1\n if np.logical_and((model3_test_accuracy==0),(len(test_labels[outlier_indices_test]))==0): div=div-1\n testnum=len(test_labels)\n highcernum=(len(test_labels_inlier)-len(test_labels_inlier[low_confidence_indices]))/testnum\n lowcernum=(len(test_labels_inlier[low_confidence_indices]))/testnum\n outnum=(len(test_labels[outlier_indices_test]))/testnum\n avg_accuracy = (highcernum*model1_test_accuracy + lowcernum*model2_test_accuracy + outnum*model3_test_accuracy) \n avg_F1_score = (highcernum*model1_F1_score + lowcernum*model2_F1_score + outnum*model3_F1_score) \n avg_auc = (highcernum*model1_auc + lowcernum*model2_auc + outnum*model3_auc) \n accuracy_total_list.append(avg_accuracy)\n F1_score_total_list.append(avg_F1_score)\n auc_total_list.append(avg_auc)\n if (avg_auc>Best_AUC):\n Best_AUC=avg_auc\n data_preprocessing_method = \"Seperating outlier of training set and test set, then synthethise more data from training-outliers, then appling probability predictions. High probability \" \\\n \"samples model is used with predictions with high probability, then apply low probability model. Finally add noise to outliers and concatinate with inlier data\" \\\n \"to be used for outlier model\"\n generate_result.print_result_3models(mask_4mm, results_path, model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)], model3_, model3_name,\n model3_weights[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)],model3_test_accuracy,model3_auc, model3_F1_score, Hyperparameter_model__3,\n model2_, model2_name, model2_test_accuracy, model2_auc,model2_F1_score,\n model1_, model1_created_mask, model1_name, model1_weights,model1_test_accuracy, model1_auc, model1_F1_score,Hyperparameter_model__1,\n feature_selection_type, data_preprocessing_method,highcernum,lowcernum,outnum)\n\n # total_performance_confidence_level\n generate_result.confidence_interval_model_99(accuracy_total_list, F1_score_total_list, auc_total_list, 'total_performance')\n\n\n\nif __name__=='__main__':\n main()"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/Model.py",
"content": "import numpy as np\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_validate\nfrom sklearn.feature_selection import RFECV\nfrom sklearn.svm import SVR\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.svm import LinearSVC\nfrom numpy import load\nimport sklearn\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn import tree\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn.ensemble import RandomForestClassifier\ndef inner_loop(data,labels,number_of_cv,feature_selection_type,Hyperparameter,mask_threshold):\n feature_weight=np.zeros(np.shape(data)[1])\n weights = np.zeros(np.shape(data)[1])\n if (feature_selection_type=='recursion'):\n svc = SVC(kernel=\"linear\")\n rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(number_of_cv),\n scoring='accuracy',n_jobs=-1,min_features_to_select=Hyperparameter)\n rfecv=rfecv.fit(data,labels)\n index_of_max_accuracy=np.argmax(rfecv.grid_scores_)\n accuracy=rfecv.grid_scores_[index_of_max_accuracy]\n #print(\"Optimal number of features : %d\" % rfecv.n_features_)\n indecies=rfecv.get_support( indices=True)\n weights[indecies]= np.absolute(rfecv.estimator_.coef_)\n weights=weights[np.newaxis, :]\n feature_weight[indecies]=np.array(feature_weight[indecies]+1,dtype=int)\n mask=feature_weight\n return mask,accuracy,weights\n if (feature_selection_type=='L2_penality'):\n #model_threshold=.000005\n lsvc = LinearSVC(C=Hyperparameter, penalty=\"l2\", dual=True,max_iter=40000)\n lsvc = cross_validate(lsvc, data,labels, cv=number_of_cv, scoring = 'accuracy', return_estimator =True)\n index_of_max_accuracy=np.argmax(lsvc['test_score'])\n accuracy=lsvc['test_score'][index_of_max_accuracy]\n weights= np.absolute(lsvc['estimator'][index_of_max_accuracy].coef_)\n for i,estimator in enumerate(lsvc['estimator']):\n model = SelectFromModel(estimator, prefit=True,threshold=\"mean\")\n indecies=model.get_support(indices=True)\n T_new = model.transform(data)\n nfeatures=T_new.shape[1]\n feature_weight[indecies]=feature_weight[indecies]+1\n mask=np.array(feature_weight>mask_threshold,dtype=int)\n #print('Optimal number of features:',np.shape(mask[mask>0]))\n return mask,accuracy,weights\n\n\ndef model(data_train,labels_train,data_validation,labels_validation,mask,model_type,sample_weights):\n if (model_type=='gaussian_process'):\n kernel = 1.0 * RBF(len(mask[mask>0])+1)\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc=gpc.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n validation_accuracy=gpc.score(data_validation[:,np.squeeze(np.where(mask>0),axis=0)],labels_validation)\n return gpc,validation_accuracy,model_type\n if (model_type=='svm'):\n clf = SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf=clf.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train,sample_weight=sample_weights)\n validation_accuracy=clf.score(data_validation[:,np.squeeze(np.where(mask>0),axis=0)],labels_validation)\n return clf,validation_accuracy,model_type\n if (model_type=='decison tree classifer'):\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n validation_accuracy=tree_clf.score(data_validation[:,np.squeeze(np.where(mask>0),axis=0)],labels_validation)\n return tree_clf,validation_accuracy,model_type\n if (model_type=='ensamble classifer'):\n kernel = 1.0 * RBF(len(mask[mask>0]))\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc=gpc.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n clf = SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf=clf.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n estimators=[('Gussian_process',gpc),('svm classifer',clf),('Decision tree',tree_clf)]\n ensemble = VotingClassifier(estimators, voting='hard',)\n ensemble=ensemble.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n validation_accuracy=ensemble.score(data_validation[:,np.squeeze(np.where(mask>0),axis=0)],labels_validation)\n return ensemble,validation_accuracy,model_type\n if (model_type=='Random_forest'):\n clf=RandomForestClassifier(n_jobs=-1,max_depth=len(mask[mask>0]))\n clf=clf.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train,sample_weight=sample_weights)\n validation_accuracy=clf.score(data_validation[:,np.squeeze(np.where(mask>0),axis=0)],labels_validation) \n return clf,validation_accuracy,model_type \n \n\n\ndef create_mask(data,labels,number_of_cv,feature_selection_type,Hyperparameter,mask_threshold,model_type,sample_weights):\n index=0\n models=[]\n masks=np.zeros(np.shape(data)[1])[np.newaxis, :]\n accuracies=np.zeros((number_of_cv+1,1))\n wight_matrix=np.zeros(np.shape(data)[1])[np.newaxis, :]\n weights=np.zeros(np.shape(data)[1])\n kf = KFold(n_splits=number_of_cv)\n for train_index, test_index in kf.split(data):\n X_train, X_test = data[train_index], data[test_index]\n y_train, y_test = labels[train_index], labels[test_index]\n sample_weights_new=sample_weights[train_index]\n mask,accuracy,weights=inner_loop(X_train,y_train,number_of_cv,feature_selection_type,Hyperparameter,mask_threshold)\n if (len(mask[mask>0])==0):\n continue\n model_,validation_accuracy,model_type=model(X_train,y_train,X_test,y_test,mask,model_type,sample_weights_new)\n masks=np.append(masks,mask[np.newaxis, :], axis=0)\n print(masks.shape)\n accuracies[index]=validation_accuracy\n print(validation_accuracy)\n print(np.shape(np.where(mask>0)))\n wight_matrix=np.append(wight_matrix,weights,axis=0)\n models=np.append(models,model_)\n index=index+1\n argument_of_maximum_accuracy=np.argmax(accuracies)\n print(argument_of_maximum_accuracy)\n print('total number of features:',np.shape(masks[argument_of_maximum_accuracy][masks[argument_of_maximum_accuracy]>0]))\n if (len(masks[argument_of_maximum_accuracy][masks[argument_of_maximum_accuracy]>0])==0):\n raise ValueError(\"the mask produces zero array\")\n\n return masks[argument_of_maximum_accuracy+1],models[argument_of_maximum_accuracy],model_type,wight_matrix[argument_of_maximum_accuracy+1]\n\n\n'''\n#unit test\noulu_con_data=load('/data/fmri/Folder/AD_classification/Data/input_data/whole_brain_Oulu_Con.npz')['masked_voxels']\noulu_ad_data=load('/data/fmri/Folder/AD_classification/Data/input_data/whole_brain_Oulu_AD.npz')['masked_voxels']\nlst_oulu=np.hstack((oulu_ad_data,oulu_con_data)).T\noulu_labels_ad=np.ones((np.shape(oulu_ad_data)[1],1))\noulu_labels_con=np.zeros((np.shape(oulu_con_data)[1],1))\noulu_labels=np.vstack((oulu_labels_ad,oulu_labels_con))\nidx = np.random.permutation(len(oulu_labels))\nlst_oulu,oulu_labels = lst_oulu[idx], oulu_labels[idx]\n\n\nmask,model_,model_type,weights=create_mask(data=lst_oulu,labels=oulu_labels,number_of_cv=5,\n feature_selection_type='L2_penality',Hyperparameter=1000,mask_threshold=0,model_type='gaussian_process')\nprint(np.shape(mask))\nprint(model_)\nprint(model_type)\nprint(np.shape(weights))\n'''\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/confidence_interval_mask.py",
"content": "import numpy as np\nfrom hyper_opt import create_mask,model_1D,model_1D_calibrate\nimport load_data\nimport data_preprocessing\nimport generate_result\nimport nibabel as nib\n\n\ndef main():\n # define input file names, directories, and parameters\n train_Con_file_name = 'CV_OULU_CON.npz'\n train_AD_file_name = 'CV_OULU_AD.npz'\n test_Con_file_name = 'CV_ADNI_CON.npz'\n test_AD_file_name = 'CV_ADNI_AD.npz'\n mask_name = '4mm_brain_mask_bin.nii.gz'\n created_mask_high_certainity_file_name = './Output_results_directory/2019-08-09/13/high_certainity_model_mask.nii.gz'\n created_mask_outlier_file_name = './Output_results_directory/2019-08-09/13/outliers_model_mask.nii.gz'\n\n #define variables\n number_of_neighbours = 1\n accuracy_total_list = list()\n F1_score_total_list = list()\n auc_total_list = list()\n\n\n # loading input data and mask\n train_data,train_labels=load_data.train_data_3d(train_Con_file_name,train_AD_file_name)\n test_data, test_labels = load_data.test_data_3d(test_Con_file_name, test_AD_file_name)\n mask_4mm = load_data.mask(mask_name)\n original_mask=mask_4mm.get_fdata()\n created_mask_high_certainity = nib.load(created_mask_high_certainity_file_name)\n created_mask_outlier = nib.load(created_mask_outlier_file_name)\n created_mask_high_certainity = created_mask_high_certainity.get_fdata()\n created_mask_outlier = created_mask_outlier.get_fdata()\n\n\n # data preprocessing\n train_data = np.moveaxis(train_data.copy(), 3, 0)\n test_data = np.moveaxis(test_data.copy(), 3, 0)\n train_data = train_data * original_mask\n test_data = test_data * original_mask\n shape = np.shape(test_data)\n train_data_flattened = data_preprocessing.flatten(train_data.copy())\n test_data_flattened = data_preprocessing.flatten(test_data.copy())\n orignal_mask_flatten = data_preprocessing.flatten(original_mask[np.newaxis, :, :, :].copy())\n orignal_mask_flatten = np.reshape(orignal_mask_flatten, (-1))\n train_data_flattened = data_preprocessing.MinMax_scaler(train_data_flattened.copy())\n test_data_flattened = data_preprocessing.MinMax_scaler(test_data_flattened.copy())\n created_mask_high_certainity_flatten = data_preprocessing.flatten(\n created_mask_high_certainity[np.newaxis, :, :, :].copy())\n created_mask_high_certainity_flatten = np.reshape(created_mask_high_certainity_flatten, (-1))\n created_mask_outlier_flatten = data_preprocessing.flatten(created_mask_outlier[np.newaxis, :, :, :].copy())\n created_mask_outlier_flatten = np.reshape(created_mask_outlier_flatten, (-1))\n train_data_inlir, train_labels_inlir, outlier_indices_train = data_preprocessing.outliers(train_data_flattened,\n train_labels,\n number_of_neighbours)\n test_data_inlier, test_labels_inlier, outlier_indices_test = data_preprocessing.novelty(train_data_inlir,\n train_labels_inlir,\n test_data_flattened,\n test_labels,\n number_of_neighbours)\n model1_created_mask=created_mask_high_certainity_flatten\n model3_created_mask=created_mask_outlier_flatten\n #confidence_interval using bootstraping\n for _ in range(1000):\n\n train_data_inlier,train_labels_inlier=data_preprocessing.resampling(train_data_inlir.copy(), train_labels_inlir.copy())\n train_data_outliers, trian_labels_outliers = data_preprocessing.resampling(train_data_flattened[outlier_indices_train].copy(),\n train_labels[outlier_indices_train].copy())\n\n train_data_inlier_unflattened = data_preprocessing.deflatten(train_data_inlier, shape)\n train_data_outlier_unflattened = data_preprocessing.deflatten(train_data_outliers, shape)\n train_data_inlier_unflattened = np.moveaxis(train_data_inlier_unflattened.copy(), 0, 3)\n train_data_outlier_unflattened = np.moveaxis(train_data_outlier_unflattened.copy(), 0, 3)\n train_data_inlier_noised = data_preprocessing.apply_noise_manytypes(train_data_inlier_unflattened.copy())\n train_data_inlier_filtered = data_preprocessing.apply_filter_manytypes(train_data_inlier_unflattened.copy())\n train_data_inlier_more = data_preprocessing.concatination(train_data_inlier_noised, train_data_inlier_filtered)\n train_labels_inlier_more = data_preprocessing.dublicate(train_labels_inlier.copy(), 29)\n train_data_outlier_noised = data_preprocessing.apply_noise_manytypes(train_data_outlier_unflattened.copy())\n train_data_outlier_filtered = data_preprocessing.apply_filter_manytypes(train_data_outlier_unflattened.copy())\n train_data_outlier_more = data_preprocessing.concatination(train_data_outlier_noised, train_data_outlier_filtered)\n train_labels_outlier_more = data_preprocessing.dublicate(trian_labels_outliers.copy(), 29)\n train_data_inlier_more = np.moveaxis(train_data_inlier_more.copy(), 3, 0)\n train_data_outlier_more = np.moveaxis(train_data_outlier_more.copy(), 3, 0)\n train_data_inlier_more_flattened = data_preprocessing.flatten(train_data_inlier_more.copy())\n train_data_outlier_more_flattened = data_preprocessing.flatten(train_data_outlier_more.copy())\n # train_data_inlier_inlier, train_labels_inlier_inlier, inlier_outlier_indices_train = data_preprocessing.novelty(\n # train_data_inlier, train_labels_inlier,\n # train_data_inlier_more_flattened,\n # train_labels_inlier_more,\n # number_of_neighbours)\n train_data_outlier_inlier, train_labels_outlier_inlier, outlier_outlier_indices_train = data_preprocessing.novelty(train_data_outliers, trian_labels_outliers,\n train_data_outlier_more_flattened,\n train_labels_outlier_more,\n number_of_neighbours)\n train_data_inlier, train_labels_inlier = data_preprocessing.upsampling(train_data_inlier,train_labels_inlier)\n if train_data_inlier is None:\n continue\n\n train_data_inlier, train_labels_inlier = data_preprocessing.shuffling(train_data_inlier,train_labels_inlier)\n train_data_outlier_inlier, train_labels_outlier_inlier = data_preprocessing.upsampling(train_data_outlier_inlier,\n train_labels_outlier_inlier)\n if train_data_outlier_inlier is None:\n continue\n\n train_data_outlier_inlier, train_labels_outlier_inlier = data_preprocessing.shuffling(train_data_outlier_inlier,\n train_labels_outlier_inlier)\n\n\n #Brain extraction of data\n train_data_inlier_brain=train_data_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n test_data_inlier_brain=test_data_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n train_data_outlier_inlier_brain=train_data_outlier_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n test_data_outlier_brain=(test_data_flattened[outlier_indices_test])[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\n concated_data = data_preprocessing.concat(train_data_inlier, train_data_outlier_inlier)\n concated_labels = data_preprocessing.concat(train_labels_inlier[:, np.newaxis],train_labels_outlier_inlier[:,np.newaxis])\n \n #Model stage 1 with high certainity\n train_data_inlier_CVspace = data_preprocessing.coefficient_of_variance(train_data_inlier_brain * model1_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])[:,np.newaxis]\n test_data_inlier_CVspace = data_preprocessing.coefficient_of_variance(test_data_inlier_brain * model1_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])[:,np.newaxis]\n model1_, model1_name = model_1D(train_data_inlier_CVspace, train_labels_inlier, model1_created_mask,\n data_validation=None, labels_validation=None,\n model_type='gaussian_process')\n model1_test_accuracy,model1_F1_score,model1_auc,low_confidence_indices=generate_result.out_result_highprob(test_data_inlier_CVspace,\n test_labels_inlier,\n original_mask,model1_created_mask,\n model1_)\n #Model stage 2 with low certainity\n if (low_confidence_indices!=0):\n model2_, model2_name = model_1D_calibrate(train_data_inlier_CVspace, train_labels_inlier, model1_created_mask,\n data_validation=None, labels_validation=None,\n model_type='gaussian_process')\n model2_test_accuracy, model2_F1_score, model2_auc = generate_result.out_result(test_data_inlier_CVspace[low_confidence_indices],\n test_labels_inlier[low_confidence_indices],\n original_mask,\n model1_created_mask,\n model2_)\n else:\n model2_test_accuracy, model2_F1_score, model2_auc=0,0,0\n #Model stage 3 with outliers\n concated_data_cv = data_preprocessing.coefficient_of_variance(concated_data[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)].copy() * \n model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])[:, np.newaxis]\n test_data_outlier_cv = data_preprocessing.coefficient_of_variance(test_data_outlier_brain *\n model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])[:, np.newaxis]\n model3_, model3_name = model_1D(concated_data_cv, concated_labels, model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)],\n data_validation=None, labels_validation=None, model_type='gaussian_process')\n model3_test_accuracy,model3_F1_score,model3_auc = generate_result.out_result(test_data_outlier_cv ,\n test_labels[outlier_indices_test], original_mask,\n model3_created_mask[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)], model3_)\n\n #stacking part of bootstrapping\n div = 3\n if (model2_test_accuracy==0):\n div=div-1\n if (model1_test_accuracy==0):\n div=div-1\n\n avg_accuracy = (model1_test_accuracy + model2_test_accuracy + model3_test_accuracy ) / div\n avg_F1_score = (model1_F1_score + model2_F1_score + model3_F1_score ) /div\n avg_auc = (model1_auc + model2_auc + model3_auc ) / div\n accuracy_total_list.append(avg_accuracy)\n F1_score_total_list.append(avg_F1_score)\n auc_total_list.append(avg_auc)\n\n # total_performance_confidence_level\n generate_result.confidence_interval_model_95(accuracy_total_list, F1_score_total_list, auc_total_list, 'total_performance')\n\n\n\nif __name__=='__main__':\n main()"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/data_preprocessing.py",
"content": "from sklearn.preprocessing import StandardScaler\nfrom scipy import ndimage\nimport nilearn\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.preprocessing import PowerTransformer\nfrom scipy.stats import variation\nimport numpy as np\nfrom densratio import densratio\nfrom sklearn.neighbors import LocalOutlierFactor\nfrom sklearn.utils import resample\nfrom sklearn.utils import shuffle\nfrom imblearn.over_sampling import ADASYN\nfrom sklearn import preprocessing\nfrom scipy.stats import ks_2samp\nfrom sklearn.decomposition import FastICA, PCA\nimport nibabel as nib\nfrom skimage.util import random_noise\nfrom scipy.signal import wiener\nfrom skimage.filters import unsharp_mask\nfrom scipy import signal\nimport math\nimport load_data\nimport data_augmentation\n\n\n####2D transformation\n\ndef standarization(data):\n scaler = StandardScaler()\n scaler.fit(data)\n data = scaler.transform(data)\n\n return data\n\n\ndef quantile_transform(data, random_state):\n quantile_transformer = preprocessing.QuantileTransformer(n_quantiles=36, random_state=random_state)\n data = quantile_transformer.fit_transform(data)\n return data\n\n\ndef gussian_filter(data, sigma):\n for i in range(len(data)):\n data[i] = ndimage.gaussian_filter(data[i], sigma)\n\n return data\n\n\ndef signal_clean(data):\n data = nilearn.signal.clean(data)\n\n return data\n\n\ndef robust_scaler(data):\n scaler = RobustScaler()\n data = scaler.fit_transform(data)\n\n return data\n\n\ndef MinMax_scaler(data):\n scaler = MinMaxScaler()\n data = scaler.fit_transform(data)\n\n return data\n\n\ndef dublicate(data, number):\n data_stacked = data.copy()\n for i in range(number):\n data_stacked = np.vstack((data, data_stacked))\n\n return data_stacked\n\n\ndef concat(data1, data2):\n data1 = np.vstack((data1, data2))\n\n return data1\n\n\ndef shuffling(data, labels):\n idx = np.random.permutation(len(labels))\n data, labels = data[idx], labels[idx]\n\n return data, labels\n\n\ndef PowerTransform(data):\n power_transform = PowerTransformer()\n data = power_transform.fit_transform(data)\n\n return data\n\n\ndef coefficient_of_variance(data):\n data = MinMax_scaler(data)\n data = variation(data, axis=1)\n\n return data\n\n\ndef density_ratio_estimation(train_data, test_data):\n result = densratio(train_data, test_data)\n sample_weight = result.compute_density_ratio(train_data)\n\n return sample_weight\n\n\ndef outliers(train_data, train_labels, number_of_neighbours):\n neigh = LocalOutlierFactor(n_neighbors=number_of_neighbours)\n indices = neigh.fit_predict(train_data)\n train_data_inlier = train_data[np.where(indices == 1)]\n train_labels_inlier = train_labels[np.where(indices == 1)]\n outlier_indices = np.where(indices == -1)\n\n return train_data_inlier, train_labels_inlier, outlier_indices\n\n\ndef novelty(train_data, train_labels, test_data, test_labels, number_of_neighbours):\n neigh = LocalOutlierFactor(n_neighbors=number_of_neighbours, novelty=True)\n indices = neigh.fit(train_data)\n indices = indices.predict(test_data)\n test_data_inlier = test_data[np.where(indices == 1)]\n print('test_labels[np.where(indices == 1)]',np.shape(test_labels[np.where(indices == 1)]))\n test_labels_inlier = test_labels[np.where(indices == 1)]\n outlier_indices = np.where(indices == -1)\n\n return test_data_inlier, test_labels_inlier, outlier_indices\n\n\ndef upsampling(data,labels):\n X = np.hstack((data, labels))\n if (len(X[X[:, -1] == 0])>len(X[X[:, -1]==1])):\n not_fewsamples = X[np.where(X[:, -1] == 0)]\n fewsamples = X[np.where(X[:, -1] == 1)]\n else:\n not_fewsamples = X[np.where(X[:, -1] == 1)]\n fewsamples = X[np.where(X[:, -1] == 0)]\n if len(fewsamples)==0:\n return data,labels\n print('np.shape(fewsamples)',np.shape(fewsamples))\n if (np.shape((np.unique(fewsamples,axis=0)))[0]<(len(not_fewsamples[:, -1])-len(fewsamples[:, -1]))):\n fewsamples_upsampled = resample(fewsamples,\n replace=True, # sample with replacement\n n_samples=len(not_fewsamples[:, -1]) - len(fewsamples[:, -1]),\n # match number in majority class\n random_state=1) # reproducible results\n else:\n fewsamples_upsampled = resample(fewsamples,\n replace=False, # sample with replacement\n n_samples=len(not_fewsamples[:, -1])-len(fewsamples[:, -1]), # match number in majority class\n random_state=1) # reproducible results\n fewsamples_upsampled=np.vstack((fewsamples_upsampled, fewsamples))\n fewsamples_upsampled = np.vstack((fewsamples_upsampled, not_fewsamples))\n fewsamples_upsampled = shuffle(fewsamples_upsampled, random_state=42)\n labels = fewsamples_upsampled[:, -1]\n data = fewsamples_upsampled[:, 0:np.shape(fewsamples_upsampled)[1] - 1]\n\n return data,labels\n\n\ndef resampling(data, labels):\n AD_data = data[np.where(labels == 1)]\n AD_labels = labels[np.where(labels == 1)]\n Con_data = data[np.where(labels == 0)]\n Con_labels = labels[np.where(labels == 0)]\n indices = np.random.randint(0, len(AD_labels), len(AD_labels))\n AD_data = AD_data[indices].copy()\n AD_labels = AD_labels[indices].copy()\n indices = np.random.randint(0, len(Con_labels), len(Con_labels))\n Con_data = Con_data[indices].copy()\n Con_labels = Con_labels[indices].copy()\n data = concat(Con_data, AD_data)\n labels = concat(Con_labels[:, np.newaxis], AD_labels[:, np.newaxis])\n data, labels = shuffling(data, labels)\n return data, labels\n\n\ndef synthetic(data, labels, num):\n smote = ADASYN(ratio='all', n_neighbors=num)\n data, labels = smote.fit_sample(data, labels)\n\n return data, labels\n\n\ndef KSTest(train_data, test_data, step):\n index = []\n for i in range(0, len(train_data) - step, step):\n for j in range(train_data.shape[1]):\n\n r = ks_2samp(train_data[i:i + step, j], test_data[:, j])\n if r[1] > 0.05:\n index = np.append(index, j)\n if index==[]:\n return train_data,test_data \n index = index[:, np.newaxis]\n index = index.astype(int)\n index = removeDuplicates(index)\n train_data[:, index] = 0\n test_data[:, index] = 0\n\n return train_data, test_data\n\n\ndef removeDuplicates(listofElements):\n # Create an empty list to store unique elements\n uniqueList = []\n\n # Iterate over the original list and for each element\n # add it to uniqueList, if its not already there.\n for elem in listofElements:\n if elem not in uniqueList:\n uniqueList.append(elem)\n\n # Return the list of unique elements\n return uniqueList\n\n\ndef ica(data, number_of_combonents):\n ica = FastICA(n_components=number_of_combonents)\n ICA_combonents = ica.fit_transform(data)\n # ICA_combonents = ica.inverse_transform(ICA_combonents)\n\n return ICA_combonents\n\n\ndef pca(data, number_of_combonents):\n pca_m = PCA(n_components=number_of_combonents)\n PCA_combonents = pca_m.fit_transform(data)\n # ICA_combonents = pca_m.inverse_transform(ICA_combonents)\n\n return PCA_combonents\n\n\n####3D transformation\n\ndef g_po_sk(input=None):\n input = input\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n sigma = 0.155\n input[:, :, :, i] = random_noise(input[:, :, :, i], var=sigma ** 2)\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='poisson')\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='speckle')\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef sp(input):\n input = input\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='s&p')\n\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef po(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='poisson')\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef g_sp(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n sigma = 0.155\n input[:, :, :, i] = random_noise(input[:, :, :, i], var=sigma ** 2)\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='s&p')\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef g_po(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n sigma = 0.155\n input[:, :, :, i] = random_noise(input[:, :, :, i], var=sigma ** 2)\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='poisson')\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef g_sk(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n sigma = 0.155\n input[:, :, :, i] = random_noise(input[:, :, :, i], var=sigma ** 2)\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='speckle')\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef sp_sk(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='s&p')\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='speckle')\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef sp_po(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='s&p')\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='poisson')\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef po_sk(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='poisson')\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='speckle')\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef noise_all(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n sigma = 0.155\n input[:, :, :, i] = random_noise(input[:, :, :, i], var=sigma ** 2)\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='s&p')\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='poisson')\n input[:, :, :, i] = random_noise(input[:, :, :, i], mode='speckle')\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef apply_noise_manytypes(data):\n data_noised = concatination(data, sp(data.copy()))\n data_noised = concatination(data_noised, g_po_sk(data.copy()))\n data_noised = concatination(data_noised, po(data.copy()))\n data_noised = concatination(data_noised, g_sp(data.copy()))\n data_noised = concatination(data_noised, g_po(data.copy()))\n data_noised = concatination(data_noised, g_sk(data.copy()))\n data_noised = concatination(data_noised, sp_sk(data.copy()))\n data_noised = concatination(data_noised, sp_po(data.copy()))\n data_noised = concatination(data_noised, po_sk(data.copy()))\n data_noised = concatination(data_noised, noise_all(data.copy()))\n return data_noised\n\n\ndef g_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = ndimage.gaussian_filter(input[:, :, :, i], .5)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef m_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = signal.medfilt(input[:, :, :, i])\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef us_f(input): # unsharp filter\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = unsharp_mask(input[:, :, :, i], radius=5, amount=2)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef c_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = nilearn.signal.clean(input[:, :, :, i])\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef w_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = wiener(input[:, :, :, i], mysize=7)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef g_m_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = ndimage.gaussian_filter(input[:, :, :, i], .5)\n input[:, :, :, i] = signal.medfilt(input[:, :, :, i])\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef g_us_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = ndimage.gaussian_filter(input[:, :, :, i], .5)\n input[:, :, :, i] = unsharp_mask(input[:, :, :, i], radius=5, amount=2)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef m_us_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = signal.medfilt(input[:, :, :, i])\n input[:, :, :, i] = unsharp_mask(input[:, :, :, i], radius=5, amount=2)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef g_c_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = ndimage.gaussian_filter(input[:, :, :, i], .5)\n input[:, :, :, i] = nilearn.signal.clean(input[:, :, :, i])\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef g_w_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = ndimage.gaussian_filter(input[:, :, :, i], .5)\n input[:, :, :, i] = wiener(input[:, :, :, i], mysize=7)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef c_w_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = nilearn.signal.clean(input[:, :, :, i])\n input[:, :, :, i] = wiener(input[:, :, :, i], mysize=7)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef m_w_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = signal.medfilt(input[:, :, :, i])\n input[:, :, :, i] = wiener(input[:, :, :, i], mysize=7)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef m_c_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = signal.medfilt(input[:, :, :, i])\n input[:, :, :, i] = nilearn.signal.clean(input[:, :, :, i])\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef m_g_c_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = signal.medfilt(input[:, :, :, i])\n input[:, :, :, i] = ndimage.gaussian_filter(input[:, :, :, i], .5)\n input[:, :, :, i] = nilearn.signal.clean(input[:, :, :, i])\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef m_g_us_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = signal.medfilt(input[:, :, :, i])\n input[:, :, :, i] = ndimage.gaussian_filter(input[:, :, :, i], .5)\n input[:, :, :, i] = unsharp_mask(input[:, :, :, i], radius=5, amount=2)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef c_w_us_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = nilearn.signal.clean(input[:, :, :, i])\n input[:, :, :, i] = wiener(input[:, :, :, i], mysize=7)\n input[:, :, :, i] = unsharp_mask(input[:, :, :, i], radius=5, amount=2)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef c_w_us_g_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = nilearn.signal.clean(input[:, :, :, i])\n input[:, :, :, i] = wiener(input[:, :, :, i], mysize=7)\n input[:, :, :, i] = unsharp_mask(input[:, :, :, i], radius=5, amount=2)\n input[:, :, :, i] = ndimage.gaussian_filter(input[:, :, :, i], .5)\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef c_w_us_m_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = nilearn.signal.clean(input[:, :, :, i])\n input[:, :, :, i] = wiener(input[:, :, :, i], mysize=7)\n input[:, :, :, i] = unsharp_mask(input[:, :, :, i], radius=5, amount=2)\n input[:, :, :, i] = signal.medfilt(input[:, :, :, i])\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef all_f(input):\n shape = np.shape(input)\n num_of_inputs = shape[3]\n input = np.moveaxis(input, 1, 2)\n # print('input shape', np.shape(input))\n for i in range(num_of_inputs):\n input[:, :, :, i] = ndimage.gaussian_filter(input[:, :, :, i], .5)\n input[:, :, :, i] = nilearn.signal.clean(input[:, :, :, i])\n input[:, :, :, i] = wiener(input[:, :, :, i], mysize=7)\n input[:, :, :, i] = unsharp_mask(input[:, :, :, i], radius=5, amount=2)\n input[:, :, :, i] = signal.medfilt(input[:, :, :, i])\n # print(np.shape(input))\n input = np.moveaxis(input, 2, 1)\n return input\n\n\ndef apply_filter_manytypes(data):\n data_filter = g_f(data.copy())\n data_filter = concatination(data_filter, m_f(data.copy()))\n data_filter = concatination(data_filter, us_f(data.copy()))\n data_filter = concatination(data_filter, c_f(data.copy()))\n data_filter = concatination(data_filter, w_f(data.copy()))\n data_filter = concatination(data_filter, g_m_f(data.copy()))\n data_filter = concatination(data_filter, g_us_f(data.copy()))\n data_filter = concatination(data_filter, m_us_f(data.copy()))\n data_filter = concatination(data_filter, g_c_f(data.copy()))\n data_filter = concatination(data_filter, g_w_f(data.copy()))\n data_filter = concatination(data_filter, c_w_f(data.copy()))\n data_filter = concatination(data_filter, m_w_f((data.copy())))\n data_filter = concatination(data_filter, m_c_f(data.copy()))\n data_filter = concatination(data_filter, m_g_c_f(data.copy()))\n data_filter = concatination(data_filter, m_g_us_f(data.copy()))\n data_filter = concatination(data_filter, c_w_us_f(data.copy()))\n data_filter = concatination(data_filter, c_w_us_g_f(data.copy()))\n data_filter = concatination(data_filter, c_w_us_m_f(data.copy()))\n data_filter = concatination(data_filter, all_f(data.copy()))\n\n return data_filter\n\n\ndef concatination(data1, data2):\n shape_data1 = np.shape(data1)\n shape_data2 = np.shape(data2)\n matrix_data = np.zeros((shape_data1[0], shape_data1[1], shape_data1[2], shape_data1[3] + shape_data2[3]))\n matrix_data[:, :, :, 0:shape_data1[3]] = data1\n matrix_data[:, :, :, shape_data1[3]:shape_data1[3] + shape_data2[3]] = data2\n return matrix_data\n\n\ndef flatten(data):\n data = np.reshape(data, (np.shape(data)[0], -1))\n return data\n\n\ndef deflatten(data, shape):\n data = np.reshape(data, (-1, shape[1], shape[2], shape[3]))\n return data\n\n\ndef select_max_features(mask, number_of_featrues):\n mask_reduces = np.zeros((len(mask)))\n argsmask = np.argsort((-mask).copy())\n for i in range(number_of_featrues): mask_reduces[argsmask[i]] = mask[argsmask[i]]\n return mask_reduces\n\n\ndef transposnig(input_data, order):\n return input_data.transpose(order)\n\n\ndef size_editing(data, final_height):\n data_length = data.shape[1]\n if (data_length > final_height):\n diff = abs(data_length - final_height) / 2\n if (round(diff) > diff):\n start = round(diff)\n end = data_length - round(diff) + 1\n return data[:, start:end, start:end, :]\n\n else:\n start = int(diff)\n end = int(data_length - diff)\n return data[:, start:end, start:end, :]\n else:\n diff = abs(data_length - final_height) / 2\n if (round(diff) > diff):\n resized_data = np.pad(data,\n ((0, 0), (round(diff), round(diff) - 1), (round(diff), round(diff) - 1), (0, 0)),\n 'constant', constant_values=(0, 0))\n else:\n resized_data = np.pad(data, ((0, 0), (round(diff), round(diff)), (round(diff), round(diff)), (0, 0)),\n 'constant', constant_values=(0, 0))\n\n return resized_data\n\n\ndef depth_reshapeing(data):\n depth = int(data.shape[3])\n\n dim0 = int(data.shape[0])\n\n dim1 = int(data.shape[1])\n\n dim2 = int(data.shape[2])\n\n step = math.floor(depth / 3)\n\n reshaped_data = np.empty((dim0, dim1, dim2, 3))\n\n for i in range(3):\n\n if i == 2:\n reshaped_data[:, :, :, i] = np.mean(data[:, :, :, step * i:depth], axis=3)\n else:\n reshaped_data[:, :, :, i] = np.mean(data[:, :, :, step * i:step * (i + 1)], axis=3)\n return reshaped_data\n\n\ndef converting_nii_to_npz(file_name):\n file_path = load_data.find_path(file_name)\n nii_file = data_augmentation.load_obj(file_path)\n np.savez(file_path[0:len(file_path) - 7] + '.npz', masked_voxels=nii_file)\n\n\ndef labels_convert_one_hot(labels):\n length = len(labels)\n if labels.all() == 0:\n ones = np.ones((length, 1))\n labels = np.hstack((ones, labels))\n elif labels.all() == 1:\n zeros = np.zeros((length, 1))\n labels = np.hstack((zeros, labels))\n\n else:\n zeros = np.zeros((length, 1))\n labels = np.hstack((zeros, labels))\n indecies = np.where(labels[:, 1] == 0)\n labels[indecies[0], 0] = 1\n return labels"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/AlexNet.py",
"content": "import data_preprocessing\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import layers\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential, Input, Model\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import LeakyReLU\nimport os\nimport CNN_feature_extractor\nimport model_evaluation\nfrom sklearn.model_selection import train_test_split\n\ndef model(train_data_whole,train_labels_whole,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters):\n \n \n \n train_data, valid_data, train_labels, valid_labels = train_test_split(train_data_whole, train_labels_whole,test_size=0.1, random_state=13)\n train_labels_one_hot=data_preprocessing.labels_convert_one_hot(train_labels)\n valid_labels_one_hot=data_preprocessing.labels_convert_one_hot(valid_labels)\n test_labels_one_hot=data_preprocessing.labels_convert_one_hot(test_labels)\n batch_size=round(train_data.shape[0]/batch_size_factor)\n\n #Instantiate an empty model\n classification_model = Sequential()\n\n # 1st Convolutional Layer\n classification_model.add(Conv2D(filters=96, input_shape=(train_data.shape[1],train_data.shape[2],train_data.shape[3]), kernel_size=(11,11), strides=(4,4), padding='valid', activation='tanh'))\n #classification_model.add(LeakyReLU(alpha=0.01))\n \n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))\n classification_model.add(Dropout(0.25))\n\n # 2nd Convolutional Layer\n classification_model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding='valid',activation='sigmoid'))\n #classification_model.add(LeakyReLU(alpha=0.01))\n\n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))\n classification_model.add(Dropout(0.25))\n\n # 3rd Convolutional Layer\n classification_model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid',activation='sigmoid'))\n #classification_model.add(LeakyReLU(alpha=0.01))\n\n # 4th Convolutional Layer\n classification_model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid',activation='sigmoid'))\n #classification_model.add(LeakyReLU(alpha=0.01))\n\n\n # 5th Convolutional Layer\n classification_model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid',activation='sigmoid'))\n #classification_model.add(LeakyReLU(alpha=0.01))\n \n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))\n classification_model.add(Dropout(0.25))\n\n # Passing it to a Fully Connected layer\n classification_model.add(Flatten())\n # 1st Fully Connected Layer\n classification_model.add(Dense(4096,activation='sigmoid'))\n #classification_model.add(LeakyReLU(alpha=0.01))\n \n\n # Add Dropout to prevent overfitting\n classification_model.add(Dropout(0.5))\n\n # 2nd Fully Connected Layer\n classification_model.add(Dense(4096,activation='sigmoid'))\n\n # Add Dropout\n classification_model.add(Dropout(0.5))\n\n # 3rd Fully Connected Layer\n classification_model.add(Dense(1000,activation='sigmoid'))\n #classification_model.add(LeakyReLU(alpha=0.01))\n\n # Add Dropout\n classification_model.add(Dropout(0.5))\n\n # Output Layer\n classification_model.add(Dense(num_classes,activation='softmax'))\n\n classification_model.summary()\n\n # Compile the classification_model\n classification_model.compile(loss='mean_squared_error', optimizer=opt, metrics=['accuracy'])\n\n es=keras.callbacks.EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=5000,\n verbose=1, mode='auto')\n\n mc = ModelCheckpoint(os.path.join(result_path,'best_model.h5'), monitor='val_acc', mode='auto', save_best_only=True)\n classification_train = classification_model.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(valid_data, valid_labels_one_hot),callbacks=[mc,es])\n best_model=load_model(os.path.join(result_path,'best_model.h5')) \n file_name=os.path.split(result_path)[1] \n date=os.path.split(os.path.split(result_path)[0])[1]\n classification_model.save(os.path.join(result_path,date+'_'+file_name+'_'+'AlexNet_model.h5')) \n\n \n if feature_extraction==1:\n feature_extractor_parameters['CNN_model']=classification_model\n CNN_feature_extractor.CNN_feature_extraction_classsification(feature_extractor_parameters,result_path)\n return\n model_evaluation.testing_and_printing(classification_model,classification_train,best_model,test_data, test_labels_one_hot, 'Alexnet', result_path,epoch)\n "
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/CNN.py",
"content": "'''\nimport os\nos.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\";\n \n# The GPU id to use, usually either \"0\" or \"1\";\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\";\n'''\nimport os\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" # see issue #152\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n\n\nimport numpy as np\nimport os\nimport sys\nfrom numpy import load\nimport keras\nimport load_data\nimport generate_result_\nimport data_preprocessing\nimport CNN_feature_extractor\nimport LeNet\nimport simple_model\nimport DenseNet121\nimport InceptionResNetV2\nimport ResNet50\nimport VGG_pretrained\nimport VGG\nimport ZFNet\nimport AlexNet\nimport optimizers\n\n\ndef CNN_main(train_data,test_data,result_path,train_labels,test_labels,num_classes,epoch,batch_size_factor,optimizer,CNN_model,train_CNN,feature_extraction,feature_extractor_parameters):\n '''\n Function Description: This function is the main function for the CNN modules and it controls them all,\n it decides which method to be used , CNN as a classifer or as a feature extractor and for each one of \n them which CNN model and architetcture to be used and also the paramteres for each one of the case is \n defined by the variables as described later.\n \n Function Parameters:\n -------------------------------------------------------\n Train_data_file_name:The name of the train data file , this will be differ depeneding on the CNN model \n used,For the following models 'ResNet50','inception_model,'DenseNet121','VGG_pretrained' a resized data \n to diminssion 224*224*3 will be used , this one was resized from the original data and was saved after, \n as this conversion takes alot of time so as to decrease the computation time.\n Test_file_name:The name of the test data file and there are manily two files as mentioned in the previous\n variable.\n results_directory:the directory name where you would like the output files to be generated\n train_data_file_name:This is used to load the training data labels , also you should choose the\n right file as there are different training data files due to differnet augmentation methods used \n and they differ in their number so the lables will differ also\n test_data_file_name: the name of the test labels file , this will also be the same as long as the \n test data is not changable.\n num_classes:the number of the classes the our data will be classifed to.\n epoch:the number of iter through all the training dataset that the CNN model will go through\n batch_size_factor:this will take the batch as a factor of the training data size and since the\n trainig data in our case is small , then it will be alawys =1\n optimizer:this will be the optimizer used for training the CNN model, the variable should have \n the name of one of the optimizer in the code , else error will occur.\n CNN_model:this define which CNN model to be trained on the training dataset or it will be None if a saved\n model will be used as feature extractor \n train_CNN: This selects one of the two modes ,=0 means to train a CNN_model or =1 to use a pretrained model\n or a saved model to extrat features from the training data directly.\n feature_extraction:This is variable wil be used only if train_CNN =0 and it will select between two options;\n =0 to trian the CNN model only without using it for feature extraction and if it is =1 the model after \n being trained it will be saved and used for feature extraction.\n feature_extractor_parameters:\n This is a dict of four variables and they are used as an input parameters for the feature extraction \n functionan they are as the following :\n 'CNN_model': this will be the CNN model used for feature extraction, it may takes different type , it\n may be string , in the case of pretrained model so the string will be equal to the name of the selected model\n or it may take 'all' in this case all the pretrained models will be used , in case of saved model this can take\n two forms, the first to be string if the feature_extraction=1 , this mean that a saved model will be used directly\n without being trained and if feature_extraction=1 it will be th model that have just been trained and saved.\n \n 'model_type':to choose between the pretrained models on imagenet dataset or the saved trained models\n on the training set.\n 'classifer_name': this is the classifer name you would like to use , it can be 'all', or one of them.\n 'hyperprametres': this is another dict in which the classifer hyperparmaters is defined, it contain four main\n parameters , the value of the neigbors to look to for the KNN classifer and the for SVC classifer thera are\n two hyperparamters 'penalty' and 'C' and for the fully conncted classifer there are four hyperparamters\n 'dropouts','activation_functions','opt','epoch'.If any of this classifers is not used then it's hyperparmater should \n be =None.\n '''\n \n opt=optimizers.choosing(optimizer)\n \n if train_CNN==1:\n if CNN_model=='simple_model':\n simple_model.model(train_data,train_labels,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters)\n elif CNN_model=='LeNet':\n LeNet.model(train_data,train_labels,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters)\n elif CNN_model=='AlexNet':\n AlexNet.model(train_data,train_labels,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters)\n elif CNN_model=='ZFNet':\n ZFNet.model(train_data,train_labels,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters)\n elif CNN_model=='VGG':\n VGG.model(train_data,train_labels,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters)\n elif CNN_model=='VGG_pretrained':\n VGG_pretrained.model(train_data,train_labels,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters)\n elif CNN_model=='ResNet50':\n ResNet50.model(train_data,train_labels,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters)\n elif CNN_model=='inception_model':\n InceptionResNetV2.model(train_data,train_labels,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters) \n elif CNN_model== 'DenseNet121':\n DenseNet121.model(train_data,train_labels,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters)\n else:\n print('Value Error: CNN_model took unexpected value')\n sys.exit()\n \n \n elif train_CNN==0:\n CNN_feature_extractor.CNN_feature_extraction_classsification(feature_extractor_parameters,result_path)\n\n else:\n print('Value Error: train_CNN took unexpected value')\n sys.exit()\n \n \n\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/CNN_feature_extractor.py",
"content": "__version__ = '0.10.3'\n\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport load_data\nimport optimizers\nimport data_preprocessing\nimport keras\nfrom keras import layers\nfrom keras import losses\nfrom keras import backend as K\nfrom keras.models import Sequential, Input, Model\nfrom keras.models import load_model\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.applications.vgg16 import VGG16\nfrom keras.applications.vgg19 import VGG19\nfrom keras.applications.inception_v3 import InceptionV3\nfrom keras.applications.densenet import DenseNet169\nfrom keras.applications.densenet import DenseNet201\nfrom keras.applications.mobilenet import MobileNet\nfrom keras.applications.nasnet import NASNetMobile\nfrom keras.applications.mobilenet_v2 import MobileNetV2\nfrom sklearn.metrics import accuracy_score, log_loss\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC, LinearSVC, NuSVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn.model_selection import train_test_split\nfrom itertools import islice\nimport sys\nimport os\n\ndef take(n, iterable):\n \"Return first n items of the iterable as a list\"\n return list(islice(iterable, n))\n\ndef fully_connected_layer(dropouts,activation_function):\n \n if dropouts==None or activation_function==None:\n return\n \n \n classification_model = Sequential()\n # 1st Fully Connected Layer\n classification_model.add(Dense(4096,activation=activation_function[0]))\n # Add Dropout to prevent overfitting\n classification_model.add(Dropout(dropouts[0]))\n # 2nd Fully Connected Layer\n classification_model.add(Dense(4096,activation=activation_function[1]))\n # Add Dropout\n classification_model.add(Dropout(dropouts[1]))\n # 3rd Fully Connected Layer\n classification_model.add(Dense(1000,activation=activation_function[2]))\n # Add Dropout\n classification_model.add(Dropout(dropouts[2]))\n # Output Layer\n classification_model.add(Dense(2,activation=activation_function[3])) \n return classification_model\n\ndef fully_connected_layer_fit(features_train,features_test,train_labels,test_labels,classification_model,epoch,opt): \n train_labels=data_preprocessing.labels_convert_one_hot(train_labels)\n test_labels=data_preprocessing.labels_convert_one_hot(test_labels)\n features_train, features_valid, train_labels, valid_labels = train_test_split(features_train, train_labels,test_size=0.1, random_state=13)\n print(features_train.shape)\n print(features_valid.shape)\n batch_size=features_train.shape[0] \n classification_model.compile(loss=losses.binary_crossentropy, optimizer=opt,\n metrics=['accuracy'])\n classification_train = classification_model.fit(features_train, train_labels, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(features_valid, valid_labels))\n test_eval = classification_model.evaluate(features_test, test_labels, verbose=1)\n print('Test loss:', test_eval[0])\n print('Test accuracy:', test_eval[1])\n #print('AUC on test data:',test_eval[2])\n print('the number of epochs:', epoch)\n accuracy = classification_train.history['acc']\n val_accuracy = classification_train.history['val_acc']\n loss = classification_train.history['loss']\n val_loss = classification_train.history['val_loss']\n epochs = range(len(accuracy))\n plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n plt.title('Training and validation accuracy')\n plt.legend()\n plt.figure()\n plt.plot(epochs, loss, 'bo', label='Training loss')\n plt.plot(epochs, val_loss, 'b', label='Validation loss')\n plt.title('Training and validation loss')\n plt.legend()\n plt.show()\n return test_eval\n\n\ndef features_pretrained_model(model,train_data,test_data):\n features_train=model.predict(train_data)\n features_test=model.predict(test_data)\n length=features_train.shape[1]\n width=features_train.shape[2]\n depth=features_train.shape[3]\n features_train=features_train.reshape(features_train.shape[0],length*width*depth)\n features_test=features_test.reshape(features_test.shape[0],length*width*depth)\n return features_train,features_test\n\ndef features_saved_model(train_data,test_data,model,intermediate_layer,results):\n \n if type(model)==str:\n results['model_is']=model\n model_path=load_data.find_path(model)\n model=load_model(model_path)\n \n \n get_layer_output = K.function([model.layers[0].input, K.learning_phase()],\n [model.layers[intermediate_layer].output])\n # output in test mode = 0\n features_train = get_layer_output([train_data, 0])[0]\n \n # output in train mode = 1\n features_test = get_layer_output([test_data, 1])[0]\n \n length=features_train.shape[1]\n width=features_train.shape[2]\n depth=features_train.shape[3]\n features_train=features_train.reshape(features_train.shape[0],length*width*depth)\n features_test=features_test.reshape(features_test.shape[0],length*width*depth)\n \n return features_train,features_test,results\n \n \ndef classifer_fit_testing(features_train,train_labels,features_test,test_labels,classifer_name,hyperparameters,results):\n opt=optimizers.choosing(hyperparameters['opt'])\n kernel = 1.0 * RBF(features_train.shape[1])\n \n classifers = {'SVC': SVC(gamma='scale'),\n 'NuSVC': NuSVC(probability=True, gamma='scale', class_weight='balanced'),\n 'LinearSVC': LinearSVC(random_state=0, tol=1e-5, penalty=hyperparameters['penalty'], dual=False, C=hyperparameters['C']),\n 'KNN': KNeighborsClassifier(hyperparameters['neighbors'], weights='distance', p=2, leaf_size=100),\n 'Random_forest': RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0),\n 'Decision_tree': DecisionTreeClassifier(),\n 'GaussinanNB': GaussianNB(),\n 'Adaboost': AdaBoostClassifier(n_estimators=100, learning_rate=0.1),\n 'Gradientboos': GradientBoostingClassifier(),\n 'Gradient_boosting': GradientBoostingClassifier(),\n 'LinearDiscriminantAnalysis': LinearDiscriminantAnalysis(),\n 'QuadraticDiscriminantAnalysis': QuadraticDiscriminantAnalysis(),\n 'gpc':GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1),\n 'fully_connected':fully_connected_layer(hyperparameters['dropouts'],hyperparameters['activation_function'])}\n \n if type(classifer_name)==str and classifer_name !='all' :\n classifer=classifers[classifer_name]\n print(classifer_name)\n if classifer_name=='fully_connected':\n test_acc=fully_connected_layer_fit(features_train,features_test,train_labels,test_labels,classifer,hyperparameters['epoch'],opt) \n results['classifer_is']=classifer_name\n results['test_acc_is']=test_acc \n else:\n classifer.fit(features_train,train_labels)\n predicted_labels=classifer.predict(features_test)\n test_acc=accuracy_score(test_labels, predicted_labels)\n results['classifer_is']=classifer_name\n results['test_acc_is']=test_acc \n else:\n if classifer_name=='all':\n #classifer=take(len(classifers)-2, classifers.items())\n classifer=classifers\n else:\n classifer=[classifers.get(clf_name) for clf_name in classifer_name]\n i=0 \n for classifer_name in classifer:\n print(classifer_name)\n clf=classifer[classifer_name]\n if classifer_name=='fully_connected':\n test_acc=fully_connected_layer_fit(features_train,features_test,train_labels,test_labels,clf,hyperparameters['epoch'],opt) \n test_acc=accuracy_score(test_labels, predicted_labels)\n results['classifer_'+str(i)+'is']=classifer_name\n results['test'+str(i)+'acc_is']=test_acc \n else:\n clf.fit(features_train,train_labels)\n predicted_labels=clf.predict(features_test)\n test_acc=accuracy_score(test_labels, predicted_labels)\n results['classifer_'+str(i)+'is']=classifer_name\n results['test'+str(i)+'acc_is']=test_acc \n i=i+1\n return results\n\ndef CNN_feature_extraction_classsification(feature_extractor_parameters,results_path):\n '''\n function description\n the function use the CNN as feature extractor and use this features to train\n and test number of classifers \n \n Function arguments :\n train_data: the training data from which the training features will be extracted\n test_data :the training data from which the training features will be extracted\n train_labels:the labels for the training data in the normal form\n test_labels:the labels for the training data in the normal form\n model_type: there are two values for this variable 'pretrained_model' or\n 'saved_model', the first the pretrained model provided by keras will be used , \n and the second one of the saved models will be used and this will be defined by the \n comming variable\n feature_extractor_parameters: \n This is a dict of four elemnts as the follwoing:\n CNN_model: this variable defines which model to be used , if it is value is 'all'\n this will mean that all the pretrained models will be used , this can be used only if \n \"model_type\" is 'pretrained_model', and if the \"model_type\" is 'pretraiend_model' then\n the value should be one of the pretrained models else it will give error, and it will take \n the model name as it is saved if the \"model_type\" is 'saved_model'.\n classifer_name: this variable is used to choose the classifer to be used to be \n trained by the training data and to be tested by the test_data , it can be equal to \n 'all'm which will mean that all the classifers will be used or it can take the name of one\n of the classifers,the name should match the names defined in the function else there be error.\n hyperprametres: this is another dict in which the classifer hyperparmaters is defined, it contain four main\n parameters , the value of the neigbors to look to for the KNN classifer and the for SVC classifer thera are\n two hyperparamters 'penalty' and 'C' and for the fully conncted classifer there are two hyperparamters\n 'dropouts','activation_functions','epoch'and 'opt'.If any of this classifers is not used then it's hyperparmater should \n be =None.\n results_path:this will be the path where the results file will be generated and the results will be printed. \n \n '''\n \n CNN_model=feature_extractor_parameters['CNN_model']\n model_type=feature_extractor_parameters['model_type']\n classifer_name=feature_extractor_parameters['classifer_name']\n intermediate_layer=feature_extractor_parameters['intermediate_layer']\n hyperparameters=feature_extractor_parameters['hyperparameters']\n data=feature_extractor_parameters['data']\n train_data=data['train_data']\n test_data=data['test_data']\n train_labels=data['train_labels']\n test_labels=data['test_labels']\n if model_type=='pretrained':\n \n if CNN_model=='all':\n model={'Xcception':keras.applications.xception.Xception(include_top=False, weights='imagenet'),\n 'VGG16':VGG16(weights='imagenet',include_top=False),\n 'VGG19':VGG19(include_top=False, weights='imagenet'), \n 'ResNet50':keras.applications.resnet50.ResNet50(include_top=False, weights='imagenet'),\n 'inceptionv2':keras.applications.inception_resnet_v2.InceptionResNetV2(include_top=False, weights='imagenet'), \n 'inceptionv3':InceptionV3(include_top=False, weights='imagenet'), \n 'DensNet121':keras.applications.densenet.DenseNet121(include_top=False, weights='imagenet'),\n 'DenseNeT169':DenseNet169(include_top=False, weights='imagenet'),\n 'DensNet201':DenseNet201(include_top=False, weights='imagenet'),\n 'MobileNet':MobileNet( alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=False, weights='imagenet'),\n 'NASNet':NASNetMobile( include_top=False, weights='imagenet'),\n 'MobileNetV2':MobileNetV2( alpha=1.0, include_top=False, weights='imagenet')}\n \n for model_name in model:\n print(model_name)\n results={}\n results['the_model_is'] =model_name\n features_train,features_test=features_pretrained_model(model[model_name],train_data,test_data)\n results=classifer_fit_testing(features_train,train_labels,features_test,test_labels,classifer_name,hyperparameters,results)\n for key in results:\n f=open(os.path.join(results_path,'CNN_as_feature_extraction_results.txt'),\"a+\")\n line1=[key,results[key]]\n print(line1)\n f.write(\"{}\" \"\\n\" .format(line1)) \n\n \n else:\n if CNN_model=='Xcception':\n model=keras.applications.xception.Xception(include_top=False, weights='imagenet')\n elif CNN_model=='VGG16':\n model= VGG16(weights='imagenet',include_top=False)\n elif CNN_model=='VGG19':\n model=VGG19(weights='imagenet',include_top=False)\n elif CNN_model=='ResNet50':\n model=keras.applications.resnet50.ResNet50(include_top=False, weights='imagenet')\n elif CNN_model=='inceptionV2':\n model=keras.applications.inception_resnet_v2.InceptionResNetV2(include_top=False, weights='imagenet')\n elif CNN_model=='inceptionV3':\n model=InceptionV3(include_top=False, weights='imagenet')\n elif CNN_model=='DenseNet121':\n model=keras.applications.densenet.DenseNet121(include_top=False, weights='imagenet')\n elif CNN_model=='DenseNet169':\n model=DenseNet169(include_top=False, weights='imagenet')\n elif CNN_model=='DenseNet201':\n model=DenseNet201(include_top=False, weights='imagenet')\n elif CNN_model=='MobilNet':\n model=MobileNet(include_top=False, weights='imagenet')\n elif CNN_model=='NASNet':\n model=NASNetMobile(include_top=False, weights='imagenet') \n elif CNN_model=='MobileNetV2':\n model=MobileNetV2(include_top=False, weights='imagenet') \n else:\n print('No such a pretrained model with such a name')\n sys.exit()\n \n model_name=CNN_model\n print(model_name)\n results={}\n results['the_model_is'] =model_name\n features_train,features_test=features_pretrained_model(model,train_data,test_data)\n results=classifer_fit_testing(features_train,train_labels,features_test,test_labels,classifer_name,hyperparameters,results)\n for key in results:\n f=open(os.path.join(results_path,'CNN_as_feature_extraction_results.txt'),\"a+\")\n line1=[key,results[key]]\n print(line1)\n f.write(\"{}\" \"\\n\" .format(line1)) \n\n \n \n elif model_type=='saved_model':\n results={}\n features_train,featuers_test,results=features_saved_model(train_data,test_data,CNN_model,intermediate_layer,results)\n results=classifer_fit_testing(features_train,train_labels,featuers_test,test_labels,classifer_name,hyperparameters,results)\n \n for key in results:\n f=open(os.path.join(results_path,'CNN_as_feature_extraction_results.txt'),\"a+\")\n line1=[key,results[key]]\n print(line1)\n f.write(\"{}\" \"\\n\" .format(line1)) \n\n \n f=open(os.path.join(results_path,'classifers_hyperparameters.txt'),\"w+\")\n line1='the classifers hyperparameters used '\n line2=hyperparameters\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1,line2))\n \n \n \n return \n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/DenseNet121.py",
"content": "import data_preprocessing\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import layers\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential, Input, Model\nfrom keras.layers import Dense, Dropout, Flatten\nimport os\nimport CNN_feature_extractor\nimport model_evaluation\nfrom sklearn.model_selection import train_test_split\n\ndef model(train_data_whole,train_labels_whole,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters):\n \n train_data, valid_data, train_labels, valid_labels = train_test_split(train_data_whole, train_labels_whole,test_size=0.1, random_state=13)\n train_labels_one_hot=data_preprocessing.labels_convert_one_hot(train_labels)\n valid_labels_one_hot=data_preprocessing.labels_convert_one_hot(valid_labels)\n test_labels_one_hot=data_preprocessing.labels_convert_one_hot(test_labels)\n '''\n train_data=data_preprocessing.depth_reshapeing(train_data)\n test_data=data_preprocessing.depth_reshapeing(test_data)\n valid_data=data_preprocessing.depth_reshapeing(valid_data)\n\n train_data = data_preprocessing.size_editing(train_data, 224)\n valid_data= data_preprocessing.size_editing(valid_data, 224)\n test_data = data_preprocessing.size_editing(test_data, 224)\n '''\n batch_size=round(train_data.shape[0]/batch_size_factor)\n input_shape= (224,224,3)\n densenet121_model=keras.applications.densenet.DenseNet121(include_top=False, weights='imagenet', input_shape=input_shape, pooling=None, classes=num_classes)\n\n\n \n layer_dict = dict([(layer.name, layer) for layer in densenet121_model.layers])\n # Getting output tensor of the last VGG layer that we want to include\n x = layer_dict[list(layer_dict.keys())[-1]].output\n x = MaxPooling2D(pool_size=(2, 2))(x)\n\n x = Flatten()(x)\n x = Dense(4096, activation='relu')(x)\n x = Dropout(0.5)(x)\n x = Dense(4096, activation='relu')(x)\n x = Dropout(0.5)(x)\n x = Dense(num_classes, activation='softmax')(x)\n classification_model = Model(input=densenet121_model.input, output=x)\n \n \n for layer in classification_model.layers:\n layer.trainable = True\n classification_model.compile(loss='mean_squared_error',optimizer=opt,metrics=['accuracy'])\n es = keras.callbacks.EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=500,\n verbose=1, mode='auto')\n mc = ModelCheckpoint(os.path.join(result_path, 'best_model.h5'), monitor='val_acc', mode='auto',\n save_best_only=True)\n\n\n classification_train = classification_model.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(valid_data, valid_labels_one_hot),callbacks=[es,mc ])\n file_name=os.path.split(result_path)[1] \n date=os.path.split(os.path.split(result_path)[0])[1]\n classification_model.save(os.path.join(result_path,date+'_'+file_name+'_'+'DenseNet121.h5')) \n #best_model=load_model(os.path.join(result_path,'best_model.h5'))\n best_model=classification_model\n if feature_extraction==1:\n feature_extractor_parameters['CNN_model']=classification_model\n CNN_feature_extractor.CNN_feature_extraction_classsification(feature_extractor_parameters,result_path)\n return\n model_evaluation.testing_and_printing(classification_model,classification_train,best_model,test_data, test_labels_one_hot, 'InceptionResNetV2', result_path,epoch)\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/InceptionResNetV2.py",
"content": "import data_preprocessing\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import layers\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential, Input, Model\nfrom keras.layers import Dense, Dropout, Flatten\nimport os\nimport CNN_feature_extractor\nimport model_evaluation\nfrom sklearn.model_selection import train_test_split\n\n\ndef model (train_data_whole,train_labels_whole,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters):\n \n train_data, valid_data, train_labels, valid_labels = train_test_split(train_data_whole, train_labels_whole,test_size=0.1, random_state=13)\n train_labels_one_hot=data_preprocessing.labels_convert_one_hot(train_labels)\n valid_labels_one_hot=data_preprocessing.labels_convert_one_hot(valid_labels)\n test_labels_one_hot=data_preprocessing.labels_convert_one_hot(test_labels)\n \n '''\n train_data=data_preprocessing.depth_reshapeing(train_data)\n test_data=data_preprocessing.depth_reshapeing(test_data)\n valid_data=data_preprocessing.depth_reshapeing(valid_data)\n\n train_data = data_preprocessing.size_editing(train_data, 224)\n valid_data= data_preprocessing.size_editing(valid_data, 224)\n test_data = data_preprocessing.size_editing(test_data, 224)\n '''\n input_shape= (224,224,3)\n batch_size=round(train_data.shape[0]/batch_size_factor)\n inception_model=keras.applications.inception_resnet_v2.InceptionResNetV2(include_top=False, weights='imagenet',input_shape=input_shape, pooling=None)\n\n \n layer_dict = dict([(layer.name, layer) for layer in inception_model.layers])\n # Getting output tensor of the last VGG layer that we want to include\n x = layer_dict[list(layer_dict.keys())[-1]].output\n x = MaxPooling2D(pool_size=(2, 2))(x)\n\n x = Flatten()(x)\n x = Dense(4096, activation='relu')(x)\n x = Dropout(0.5)(x)\n x = Dense(4096, activation='relu')(x)\n x = Dropout(0.5)(x)\n x = Dense(num_classes, activation='softmax')(x)\n classification_model = Model(input=inception_model.input, output=x)\n \n \n for layer in classification_model.layers:\n layer.trainable = True\n classification_model.compile(loss='mean_squared_error',optimizer=opt,metrics=['accuracy'])\n \n es = keras.callbacks.EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=500,\n verbose=1, mode='auto')\n mc = ModelCheckpoint(os.path.join(result_path, 'best_model.h5'), monitor='val_acc', mode='auto',\n save_best_only=True)\n\n\n classification_train = classification_model.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(valid_data, valid_labels_one_hot),callbacks=[es,mc ])\n \n #best_model=load_model(os.path.join(result_path,'best_model.h5'))\n best_model=classification_model\n file_name=os.path.split(result_path)[1] \n date=os.path.split(os.path.split(result_path)[0])[1]\n classification_model.save(os.path.join(result_path,date+'_'+file_name+'_'+'InceptionResNet_model.h5')) \n \n if feature_extraction==1:\n feature_extractor_parameters['CNN_model']=classification_model\n CNN_feature_extractor.CNN_feature_extraction_classsification(feature_extractor_parameters,result_path)\n return\n model_evaluation.testing_and_printing(classification_model,classification_train,best_model,test_data, test_labels_one_hot, 'InceptionResNetV2', result_path,epoch)\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/LeNet.py",
"content": "import data_preprocessing\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import layers\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential, Input, Model\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import LeakyReLU\nimport os\nimport CNN_feature_extractor\nimport model_evaluation\nfrom sklearn.model_selection import train_test_split\n\ndef model (train_data_whole,train_labels_whole,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters):\n \n train_data, valid_data, train_labels, valid_labels = train_test_split(train_data_whole, train_labels_whole,test_size=0.1, random_state=13)\n train_labels_one_hot=data_preprocessing.labels_convert_one_hot(train_labels)\n valid_labels_one_hot=data_preprocessing.labels_convert_one_hot(valid_labels)\n test_labels_one_hot=data_preprocessing.labels_convert_one_hot(test_labels)\n batch_size=round(train_data.shape[0]/batch_size_factor)\n classification_model = Sequential()\n # C1 Convolutional Layer\n classification_model.add(layers.Conv2D(6, kernel_size=(5, 5), strides=(1, 1),activation='relu', input_shape=(train_data.shape[1], train_data.shape[2], train_data.shape[3]), padding='same'))\n \n classification_model.add(Dropout(0.7))\n\n # S2 Pooling Layer\n classification_model.add(layers.AveragePooling2D(pool_size=(2, 2), strides=(1, 1), padding='valid'))\n # C3 Convolutional Layer\n classification_model.add(layers.Conv2D(16, kernel_size=(5, 5), strides=(1, 1), padding='valid',activation='relu'))\n \n classification_model.add(Dropout(0.7))\n\n # S4 Pooling Layer\n classification_model.add(layers.AveragePooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))\n # C5 Fully Connected Convolutional Layer\n classification_model.add(layers.Conv2D(120, kernel_size=(5, 5), strides=(1, 1), padding='valid',activation='relu'))\n \n classification_model.add(Dropout(0.8))\n #Flatten the CNN output so that we can connect it with fully connected layers\n classification_model.add(layers.Flatten())\n # FC6 Fully Connected Layer\n classification_model.add(layers.Dense(84,activation='relu'))\n \n classification_model.add(Dropout(0.8))\n\n #Output Layer with softmax activation\n classification_model.add(layers.Dense(num_classes, activation='softmax'))\n\n\n classification_model.compile(loss=keras.losses.categorical_crossentropy, optimizer=opt, metrics=['accuracy'])\n\n\n es=keras.callbacks.EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=100,\n verbose=1, mode='auto')\n mc = ModelCheckpoint(os.path.join(result_path,'best_model.h5'), monitor='val_acc', mode='auto', save_best_only=True)\n classification_train = classification_model.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(valid_data, valid_labels_one_hot),callbacks=[es,mc])\n best_model=load_model(os.path.join(result_path,'best_model.h5'))\n file_name=os.path.split(result_path)[1] \n date=os.path.split(os.path.split(result_path)[0])[1]\n classification_model.save(os.path.join(result_path,date+'_'+file_name+'_'+'leNet_model.h5')) \n if feature_extraction==1:\n feature_extractor_parameters['CNN_model']=classification_model\n CNN_feature_extractor.CNN_feature_extraction_classsification(train_data_whole,train_labels_whole,test_data,test_labels,feature_extractor_parameters,result_path)\n return \n model_evaluation.testing_and_printing(classification_model,classification_train,best_model,test_data,test_labels_one_hot,'LeNet',result_path,epoch) "
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/ResNet50.py",
"content": "import data_preprocessing\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import layers\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential, Input, Model\nfrom keras.layers import Dense, Dropout, Flatten\nimport os\nimport CNN_feature_extractor\nimport model_evaluation\nfrom sklearn.model_selection import train_test_split\ndef model(train_data_whole,train_labels_whole,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters):\n \n train_data, valid_data, train_labels, valid_labels = train_test_split(train_data_whole, train_labels_whole,test_size=0.1, random_state=13)\n train_labels_one_hot=data_preprocessing.labels_convert_one_hot(train_labels)\n valid_labels_one_hot=data_preprocessing.labels_convert_one_hot(valid_labels)\n test_labels_one_hot=data_preprocessing.labels_convert_one_hot(test_labels)\n \n '''\n train_data=data_preprocessing.depth_reshapeing(train_data)\n test_data=data_preprocessing.depth_reshapeing(test_data)\n valid_data=data_preprocessing.depth_reshapeing(valid_data)\n\n train_data = data_preprocessing.size_editing(train_data, 224)\n valid_data= data_preprocessing.size_editing(valid_data, 224)\n test_data = data_preprocessing.size_editing(test_data, 224)\n '''\n batch_size=round(train_data.shape[0]/batch_size_factor)\n input_shape= (224,224,3)\n resnet50_model=keras.applications.resnet50.ResNet50(include_top=False, weights='imagenet', input_shape=input_shape, pooling=None)\n \n layer_dict = dict([(layer.name, layer) for layer in resnet50_model.layers])\n #print(layer_dict)\n # Getting output tensor of the last VGG layer that we want to include\n x = layer_dict[list(layer_dict.keys())[-1]].output\n x = MaxPooling2D(pool_size=(2, 2))(x)\n\n x = Flatten()(x)\n x = Dense(4096, activation='relu')(x)\n x = Dropout(0.5)(x)\n x = Dense(4096, activation='relu')(x)\n x = Dropout(0.5)(x)\n x = Dense(num_classes, activation='softmax')(x)\n classification_model = Model(input=resnet50_model.input, output=x)\n \n for layer in classification_model.layers[:len(list(layer_dict.keys()))-50]:\n layer.trainable = False\n classification_model.compile(loss='mean_squared_error',optimizer=opt,metrics=['accuracy'])\n es = keras.callbacks.EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=500,\n verbose=1, mode='auto')\n mc = ModelCheckpoint(os.path.join(result_path, 'best_model.h5'), monitor='val_acc', mode='auto',\n save_best_only=True)\n\n classification_train = classification_model.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(valid_data, valid_labels_one_hot),callbacks=[es,mc])\n #best_model=load_model(os.path.join(result_path,'best_model.h5'))\n best_model=classification_model\n file_name=os.path.split(result_path)[1] \n date=os.path.split(os.path.split(result_path)[0])[1]\n classification_model.save(os.path.join(result_path,date+'_'+file_name+'_'+'ResNet50_model.h5')) \n \n if feature_extraction==1:\n feature_extractor_parameters['CNN_model']=classification_model\n CNN_feature_extractor.CNN_feature_extraction_classsification(feature_extractor_parameters,result_path)\n return \n model_evaluation.testing_and_printing(classification_model,classification_train,best_model,test_data, test_labels_one_hot, 'ResNet50', result_path,epoch)\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/VGG.py",
"content": "import data_preprocessing\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import layers\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential, Input, Model\nfrom keras.layers import Dense, Dropout, Flatten\nimport os\nimport CNN_feature_extractor\nimport model_evaluation\nfrom sklearn.model_selection import train_test_split\ndef model(train_data_whole,train_labels_whole,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters): \n \n \n train_data, valid_data, train_labels, valid_labels = train_test_split(train_data_whole, train_labels_whole,test_size=0.1, random_state=13)\n train_labels_one_hot=data_preprocessing.labels_convert_one_hot(train_labels)\n valid_labels_one_hot=data_preprocessing.labels_convert_one_hot(valid_labels)\n test_labels_one_hot=data_preprocessing.labels_convert_one_hot(test_labels)\n \n \n batch_size=round(train_data.shape[0]/batch_size_factor)\n\n #Instantiate an empty model\n classification_model = Sequential()\n\n # 1st Convolutional Layer\n classification_model.add(Conv2D(filters=64, input_shape=(train_data.shape[1],train_data.shape[2],train_data.shape[3]),activation='relu', kernel_size=(3,3), strides=(1,1), padding='same'))\n classification_model.add(Conv2D(filters=64,activation='relu', kernel_size=(3,3), strides=(1,1), padding='same'))\n classification_model.add(Dropout(0.4))\n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n\n # 2nd Convolutional Layer\n classification_model.add(Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Dropout(0.4))\n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n\n # 3rd Convolutional Layer\n classification_model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Dropout(0.4))\n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n\n # 4th Convolutional Layer\n classification_model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Dropout(0.4)) \n # 5th Convolutional Layer\n classification_model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='same',activation='relu'))\n classification_model.add(Dropout(0.4))\n \n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))\n\n # Passing it to a Fully Connected layer\n classification_model.add(Flatten())\n # 1st Fully Connected Layer\n classification_model.add(Dense(4096,activation='relu'))\n # Add Dropout to prevent overfitting\n classification_model.add(Dropout(0.5))\n\n # 2nd Fully Connected Layer\n classification_model.add(Dense(4096,activation='relu'))\n\n # Add Dropout\n classification_model.add(Dropout(0.5))\n # Output Layer\n classification_model.add(Dense(num_classes,activation='softmax'))\n classification_model.summary()\n classification_model.compile(loss='mean_squared_error', optimizer=opt, metrics=['accuracy'])\n\n es=keras.callbacks.EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=1000,\n verbose=1, mode='auto')\n\n mc = ModelCheckpoint(os.path.join(result_path,'best_model.h5'), monitor='val_acc', mode='auto', save_best_only=True)\n classification_train = classification_model.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(valid_data, valid_labels_one_hot),callbacks=[mc,es])\n best_model=load_model(os.path.join(result_path,'best_model.h5'))\n file_name=os.path.split(result_path)[1] \n date=os.path.split(os.path.split(result_path)[0])[1]\n classification_model.save(os.path.join(result_path,date+'_'+file_name+'_'+'VGG_model.h5')) \n \n if feature_extraction==1:\n feature_extractor_parameters['CNN_model']=classification_model\n CNN_feature_extractor.CNN_feature_extraction_classsification(feature_extractor_parameters,result_path)\n return\n model_evaluation.testing_and_printing(classification_model,classification_train,best_model,test_data, test_labels_one_hot, 'VGG', result_path,epoch)\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/VGG_pretrained.py",
"content": "import data_preprocessing\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import layers\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential, Input, Model\nfrom keras.layers import Dense, Dropout, Flatten\nimport os\nimport CNN_feature_extractor\nimport model_evaluation\nfrom sklearn.model_selection import train_test_split\nfrom keras.applications.vgg16 import VGG16\n\n\ndef model (train_data_whole,train_labels_whole,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters):\n \n train_data, valid_data, train_labels, valid_labels = train_test_split(train_data_whole, train_labels_whole,test_size=0.1, random_state=13)\n train_labels_one_hot=data_preprocessing.labels_convert_one_hot(train_labels)\n valid_labels_one_hot=data_preprocessing.labels_convert_one_hot(valid_labels)\n test_labels_one_hot=data_preprocessing.labels_convert_one_hot(test_labels) \n batch_size=round(train_data.shape[0]/batch_size_factor)\n \n input_shape= (224,224,3)\n vgg_model = VGG16(weights='imagenet',\n include_top=False,\n input_shape=input_shape)\n # Creating dictionary that maps layer names to the layers\n layer_dict = dict([(layer.name, layer) for layer in vgg_model.layers])\n # Getting output tensor of the last VGG layer that we want to include\n x = layer_dict['block2_pool'].output\n \n x = Conv2D(filters=64, kernel_size=(3, 3), activation='relu',padding='same')(x)\n x = MaxPooling2D(pool_size=(2, 2))(x)\n x = Conv2D(filters=128, kernel_size=(3, 3), activation='relu',padding='same')(x)\n x = MaxPooling2D(pool_size=(2, 2))(x)\n x = Conv2D(filters=128, kernel_size=(3, 3), activation='relu',padding='same')(x)\n x = MaxPooling2D(pool_size=(2, 2))(x)\n \n x = Flatten()(x)\n x = Dense(4096, activation='relu')(x)\n x = Dropout(0.5)(x)\n x = Dense(4096, activation='relu')(x)\n x = Dropout(0.5)(x)\n x = Dense(2, activation='softmax')(x)\n \n # Creating new model. Please note that this is NOT a Sequential() model.\n classification_model = Model(input=vgg_model.input, output=x)\n for layer in classification_model.layers[:7]:\n layer.trainable = True\n \n es=keras.callbacks.EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=5000,\n verbose=1, mode='auto')\n\n mc = ModelCheckpoint(os.path.join(result_path,'best_model.h5'), monitor='val_acc', mode='auto', save_best_only=True)\n \n classification_model.compile(loss='mean_squared_error',optimizer=opt,metrics=['accuracy'])\n classification_train = classification_model.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(valid_data, valid_labels_one_hot),callbacks=[mc,es])\n best_model=load_model(os.path.join(result_path,'best_model.h5'))\n file_name=os.path.split(result_path)[1] \n date=os.path.split(os.path.split(result_path)[0])[1]\n classification_model.save(os.path.join(result_path,date+'_'+file_name+'_'+'VGGpretrained_model.h5')) \n\n if feature_extraction==1:\n feature_extractor_parameters['CNN_model']=classification_model\n CNN_feature_extractor.CNN_feature_extraction_classsification(feature_extractor_parameters,result_path)\n return \n model_evaluation.testing_and_printing(classification_model,classification_train,best_model,test_data, test_labels_one_hot, 'VGG_pretrained', result_path,epoch)\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/ZFNet.py",
"content": "import data_preprocessing\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import layers\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential, Input, Model\nfrom keras.layers import Dense, Dropout, Flatten\nimport os\nimport CNN_feature_extractor\nimport model_evaluation\nfrom sklearn.model_selection import train_test_split\n\n\ndef model(train_data_whole,train_labels_whole,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters):\n \n train_data_whole = data_preprocessing.size_editing(train_data_whole, 224)\n test_data = data_preprocessing.size_editing(test_data, 224)\n\n train_data, valid_data, train_labels, valid_labels = train_test_split(train_data_whole, train_labels_whole,test_size=0.1, random_state=13)\n train_labels_one_hot=data_preprocessing.labels_convert_one_hot(train_labels)\n valid_labels_one_hot=data_preprocessing.labels_convert_one_hot(valid_labels)\n test_labels_one_hot=data_preprocessing.labels_convert_one_hot(test_labels)\n \n \n \n batch_size=round(train_data.shape[0]/batch_size_factor)\n\n #Instantiate an empty model\n classification_model = Sequential()\n\n # 1st Convolutional Layer\n classification_model.add(Conv2D(filters=96, input_shape=(train_data.shape[1],train_data.shape[2],train_data.shape[3]),activation='relu', kernel_size=(7,7), strides=(2,2), padding='valid'))\n\n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid'))\n\n # 2nd Convolutional Layer\n classification_model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding='valid',activation='relu'))\n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid'))\n\n # 3rd Convolutional Layer\n classification_model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='valid',activation='relu'))\n\n # 4th Convolutional Layer\n classification_model.add(Conv2D(filters=1024, kernel_size=(3,3), strides=(1,1), padding='valid',activation='relu'))\n\n\n # 5th Convolutional Layer\n classification_model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='valid',activation='relu'))\n # Max Pooling\n classification_model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid'))\n\n # Passing it to a Fully Connected layer\n classification_model.add(Flatten())\n # 1st Fully Connected Layer\n classification_model.add(Dense(4096, input_shape=(train_data.shape[1],train_data.shape[2],train_data.shape[3]),activation='relu'))\n # Add Dropout to prevent overfitting\n classification_model.add(Dropout(0.4))\n\n # 2nd Fully Connected Layer\n classification_model.add(Dense(4096))\n\n # Add Dropout\n classification_model.add(Dropout(0.4))\n\n # 3rd Fully Connected Layer\n classification_model.add(Dense(1000,activation='relu'))\n # Add Dropout\n classification_model.add(Dropout(0.4))\n\n # Output Layer\n classification_model.add(Dense(num_classes,activation='softmax'))\n\n classification_model.summary()\n\n # Compile the classification_model\n classification_model.compile(loss='mean_squared_error', optimizer=opt, metrics=['accuracy'])\n\n es=keras.callbacks.EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=100,\n verbose=1, mode='auto')\n\n mc = ModelCheckpoint(os.path.join(result_path,'best_model.h5'), monitor='val_acc', mode='auto', save_best_only=True)\n classification_train = classification_model.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(valid_data, valid_labels_one_hot),callbacks=[mc,es])\n \n best_model=load_model(os.path.join(result_path,'best_model.h5')) \n file_name=os.path.split(result_path)[1] \n date=os.path.split(os.path.split(result_path)[0])[1]\n classification_model.save(os.path.join(result_path,date+'_'+file_name+'_'+'ZNet_model.h5')) \n \n if feature_extraction==1:\n feature_extractor_parameters['CNN_model']=classification_model\n CNN_feature_extractor.CNN_feature_extraction_classsification(feature_extractor_parameters,result_path)\n return\n model_evaluation.testing_and_printing(classification_model,classification_train,best_model,test_data, test_labels_one_hot, 'ZFNet', result_path,epoch)\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/optimizers.py",
"content": "import keras\nfrom keras import optimizers\nimport sys\n\n\ndef choosing(optimizer):\n if optimizer=='adam':\n opt=keras.optimizers.Adam(lr=0.000001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n elif optimizer=='adamax': \n opt= keras.optimizers.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0)\n elif optimizer=='Nadama': \n opt= keras.optimizers.Nadam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=None, schedule_decay=0.004)\n elif optimizer=='adadelta': \n opt= optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)\n elif optimizer=='adagrad': \n opt= keras.optimizers.Adagrad(lr=0.01, epsilon=None, decay=0.0)\n elif optimizer=='sgd':\n opt = optimizers.SGD(lr=0.01, clipnorm=1.)\n elif optimizer=='RMSprop':\n opt=keras.optimizers.RMSprop(lr=0.0006, rho=0.9, epsilon=None, decay=0.0)\n elif optimizer==None:\n return\n else:\n print('Value Error:Optimizer took unexpected value')\n sys.exit()\n \n return opt "
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/readme.md",
"content": "\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/CNN_based_models/simple_model.py",
"content": "import data_preprocessing\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import layers\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.models import Sequential, Input, Model\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import LeakyReLU\nimport os\nimport CNN_feature_extractor\nimport model_evaluation\nfrom sklearn.model_selection import train_test_split\n\n\ndef model (train_data_whole,train_labels_whole,test_data,test_labels,opt,epoch,batch_size_factor,num_classes,result_path,feature_extraction,feature_extractor_parameters): \n train_data, valid_data, train_labels, valid_labels = train_test_split(train_data_whole, train_labels_whole,test_size=0.2, random_state=13)\n \n train_labels_one_hot=data_preprocessing.labels_convert_one_hot(train_labels)\n valid_labels_one_hot=data_preprocessing.labels_convert_one_hot(valid_labels)\n test_labels_one_hot=data_preprocessing.labels_convert_one_hot(test_labels)\n batch_size=round(train_data.shape[0]/batch_size_factor)\n classification_model = Sequential()\n classification_model.add(Conv2D(32, kernel_size=(3, 3), padding='same',activation='relu',\n input_shape=(train_data.shape[1], train_data.shape[2], train_data.shape[3])))\n #classification_model.add(BatchNormalization())\n \n classification_model.add(MaxPooling2D((2, 2), padding='same'))\n classification_model.add(Dropout(0.5))\n classification_model.add(Conv2D(64, (3,3), padding='same'))\n classification_model.add(LeakyReLU(alpha=0.1))\n #classification_model.add(BatchNormalization())\n \n classification_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n classification_model.add(Dropout(0.5))\n classification_model.add(Conv2D(128, (3,3), padding='same'))\n classification_model.add(LeakyReLU(alpha=0.1))\n #classification_model.add(BatchNormalization())\n classification_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n classification_model.add(Dropout(0.5))\n classification_model.add(Flatten())\n classification_model.add(Dense(128))\n classification_model.add(LeakyReLU(alpha=0.1))\n #classification_model.add(BatchNormalization())\n classification_model.add(Dense(128,))\n classification_model.add(Dropout(0.5))\n classification_model.add(LeakyReLU(alpha=0.1))\n\n classification_model.add(Dense(num_classes, activation='softmax'))\n classification_model.compile(loss=keras.losses.categorical_crossentropy, optimizer=opt,\n metrics=['accuracy'])\n \n es=keras.callbacks.EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=50,\n verbose=1, mode='auto',baseline=0.9)\n\n mc = ModelCheckpoint(os.path.join(result_path,'best_model.h5'), monitor='val_acc', mode='auto', save_best_only=True)\n classification_train = classification_model.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epoch,\n verbose=1, validation_data=(valid_data, valid_labels_one_hot),callbacks=[es,mc])\n best_model=load_model(os.path.join(result_path,'best_model.h5'))\n file_name=os.path.split(result_path)[1] \n date=os.path.split(os.path.split(result_path)[0])[1]\n \n classification_model.save(os.path.join(result_path,date+'_'+file_name+'_'+'simple_model.h5')) \n \n if feature_extraction==1:\n feature_extractor_parameters['CNN_model']=classification_model\n CNN_feature_extractor.CNN_feature_extraction_classsification(train_data_whole,train_labels_whole,test_data,test_labels,feature_extractor_parameters,result_path)\n return\n \n model_evaluation.testing_and_printing(classification_model,classification_train,best_model,test_data,test_labels_one_hot,'simple_architecture',result_path,epoch)\n "
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/evaluation/metrics.py",
"content": "def auc_roc(y_true, y_pred):\n # any tensorflow metric\n value, update_op = tf.contrib.metrics.streaming_auc(y_pred, y_true)\n\n # find all variables created for this metric\n metric_vars = [i for i in tf.local_variables() if 'auc_roc' in i.name.split('/')[1]]\n\n # Add metric variables to GLOBAL_VARIABLES collection.\n # They will be initialized for new session.\n for v in metric_vars:\n tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, v)\n\n # force to update metric values\n with tf.control_dependencies([update_op]):\n value = tf.identity(value)\n return value"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/evaluation/model_evaluation.py",
"content": "import matplotlib.pyplot as plt\nimport generate_result_\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import cohen_kappa_score\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import confusion_matrix\n\n\ndef testing_and_printing(classification_model,classification_train,best_model,test_data,test_labels_one_hot,model_name,results_path,epoch):\n #classification_model.summary()\n test_eval = classification_model.evaluate(test_data, test_labels_one_hot, verbose=1)\n prediction=classification_model.predict(test_data)\n predicted_classes=classification_model.predict_classes(test_data)\n print(predicted_classes)\n test_labels=test_labels_one_hot[:,1]\n \n print(test_labels_one_hot)\n print(prediction)\n print('Test loss:', test_eval[0])\n print('Test accuracy:', test_eval[1])\n precision = precision_score(test_labels, predicted_classes)\n print('Precision: %f' % precision)\n # recall: tp / (tp + fn)\n recall = recall_score(test_labels, predicted_classes)\n print('Recall: %f' % recall)\n # f1: 2 tp / (2 tp + fp + fn)\n f1 = f1_score(test_labels, predicted_classes)\n print('F1 score: %f' % f1)\n \n matrix = confusion_matrix(test_labels, predicted_classes)\n print(matrix)\n \n \n \n #print('AUC on test data:',test_eval[2])\n print('the number of epochs:', epoch)\n accuracy = classification_train.history['acc']\n val_accuracy = classification_train.history['val_acc']\n loss = classification_train.history['loss']\n val_loss = classification_train.history['val_loss']\n epochs = range(len(accuracy))\n plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n plt.title('Training and validation accuracy')\n plt.legend()\n plt.figure()\n plt.plot(epochs, loss, 'bo', label='Training loss')\n plt.plot(epochs, val_loss, 'b', label='Validation loss')\n plt.title('Training and validation loss')\n plt.legend()\n plt.show()\n\n \n test_acc=best_model.evaluate(test_data,test_labels_one_hot)\n print('best model test accaurecy is ',test_acc)\n generate_result_.cnn_save_result(test_eval[1],classification_model,model_name,results_path)\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/evaluation/readme.md",
"content": "\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/main.py",
"content": "import CNN\nimport load_data\nfrom numpy import load\nimport numpy as np\nimport data_preprocessing\nimport preprocessing_methods\nimport generate_result_\nimport os\nfrom scipy.signal import resample_poly \n\ndef main():\n \n \n \n train_data_path='/data/fmri/Folder/AD_classification/Data/input_data/preprocessed_data/CV_OULU_Con_AD_preprocessed.npz'\n train_data_classifer = load(train_data_path)['masked_voxels']\n train_data_path='/data/fmri/Folder/AD_classification/Data/input_data/Augmented_data/CV_OULU_Con_AD_aug.npz'\n train_data_CNN = load(train_data_path)['masked_voxels']\n test_data_path='/data/fmri/Folder/AD_classification/Data/input_data/CV_ADNI_Con_AD.npz'\n test_data_CNN = load(test_data_path)['masked_voxels']\n test_data_path='/data/fmri/Folder/AD_classification/Data/input_data/preprocessed_data/CV_ADNI_Con_AD_preprocessed.npz'\n test_data_classifer = load(test_data_path)['masked_voxels']\n \n transposing_order=[3,0,2,1]\n train_data_CNN=data_preprocessing.transposnig(train_data_CNN,transposing_order)\n test_data_CNN=data_preprocessing.transposnig(test_data_CNN,transposing_order)\n \n train_labels_path='/data/fmri/Folder/AD_classification/Data/input_data/labels/train_labels_aug_data.npz'\n train_labels_CNN=load(train_labels_path)['labels']\n shuffling_indicies = np.random.permutation(len(train_labels_CNN))\n temp = train_data_CNN[shuffling_indicies, :, :, :]\n train_data_CNN=temp\n train_labels_CNN = train_labels_CNN[shuffling_indicies]\n \n \n \n \n \n train_labels_path='/data/fmri/Folder/AD_classification/Data/input_data/labels/train_labels.npz'\n train_labels_classifer=load(train_labels_path)['labels']\n shuffling_indicies = np.random.permutation(len(train_labels_classifer))\n temp = train_data_classifer[shuffling_indicies, :, :, :]\n train_data_classifer=temp\n train_labels_classifer = train_labels_classifer[shuffling_indicies]\n\n #test_data_path = load_data.find_path(test_data_file_name)\n #test_data_path='/data/fmri/Folder/AD_classification/Data/input_data/CV_ADNI_Con_AD.npz'\n #test_data = load(test_data_path)['masked_voxels']\n #test_labels_path=load_data.find_path(test_labels_file_name)\n test_labels_path='/data/fmri/Folder/AD_classification/Data/input_data/labels/test_labels.npz'\n test_labels=load(test_labels_path)['labels']\n shuffling_indicies = np.random.permutation(len(test_labels))\n test_data_CNN = test_data_CNN[shuffling_indicies, :, :, :]\n test_data_classifer = test_data_classifer[shuffling_indicies, :, :, :]\n\n test_labels = test_labels[shuffling_indicies]\n \n train_data_CNN,test_data_CNN,train_labels_CNN,test_labels=preprocessing_methods.preprocessing(train_data_CNN,test_data_CNN,train_labels_CNN,test_labels,4,0,None,None)\n \n factors=[(224,45),(224,45),(3,54)]\n train_data_CNN=resample_poly(train_data_CNN, factors[0][0], factors[0][1], axis=1)\n train_data_CNN=resample_poly(train_data_CNN, factors[1][0], factors[1][1], axis=2)\n #train_data_CNN=resample_poly(train_data_CNN, factors[2][0], factors[2][1], axis=3)\n\n test_data_CNN=resample_poly(test_data_CNN, factors[0][0], factors[0][1], axis=1)\n test_data_CNN=resample_poly(test_data_CNN, factors[1][0], factors[1][1], axis=2)\n #test_data_CNN=resample_poly(test_data_CNN, factors[2][0], factors[2][1], axis=3)\n\n\n train_CNN=0\n feature_extraction=1\n \n if train_CNN==1 and feature_extraction==1:\n line1='CNN model is trained and saved and then used as feature extractor'\n line2='CNN model used for feature extraction is :'\n elif train_CNN==1 and feature_extraction==0:\n line1 ='CNN model is trained and used to test the test data'\n line2='CNN model used is :'\n elif train_CNN==0 and feature_extraction==1:\n line1 ='using a saved model to extract fetaures'\n line2='The model used used is a saved model'\n else:\n print('Value Error: train_CNN and feature_extraction cannnot have these values')\n \n \n results_directory='Results'\n num_classes=2\n epoch=1000\n batch_size_factor=1\n optimizer='adam'\n CNN_models=['VGG16','VGG19']\n #intermedidate_layer=[7,7,7,16]\n hyperparameters={'dropouts':[0.25,0.5,0.5],'activation_function':['relu','relu','relu','sigmoid'],'epoch':10,'opt':'adam','penalty':'l1','C':100,'neighbors':50}\n data={'train_data':train_data_CNN,'test_data':test_data_CNN,'train_labels':train_labels_CNN,'test_labels':test_labels}\n preprocessing_method='method 4'\n i=0\n for CNN_model in CNN_models:\n result_path = generate_result_.create_results_dir(results_directory)\n print(CNN_model)\n feature_extractor_parameters={'data':data,'hyperparameters':hyperparameters,'model_type':'pretrained','CNN_model':CNN_model,'intermediate_layer':7,'classifer_name':'all'}\n CNN.CNN_main(train_data_CNN,test_data_CNN,result_path,train_labels_CNN,test_labels,num_classes,epoch,batch_size_factor,optimizer,CNN_model,train_CNN,feature_extraction,feature_extractor_parameters) \n f = open(os.path.join(result_path, 'README'), \"w+\")\n \n line3=CNN_model \n line4='The preprocessing methods used is '+' '+preprocessing_method\n line5='The number of epochs used to train the CNN_model is '+str(epoch)\n line6='the oprimizer used is '+optimizer\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1,line2,line3,line4,line5,line6)) \n i=i+1\n \nif __name__=='__main__':\n main()\n \n \n "
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/preprocessing/data_augmentation.py",
"content": "import random\nfrom scipy import ndarray\nimport skimage as sk\nfrom skimage import transform\nfrom skimage import util\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.feature_selection import RFECV\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\n#from sklearn import decomposition\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn import decomposition\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.svm import LinearSVC\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.utils import resample\nfrom sklearn.utils import shuffle\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import Normalizer\nfrom sklearn import preprocessing\nfrom scipy import ndimage\nimport nilearn\nimport nibabel as nib\nimport numpy as np\nimport os \nimport load_data\n\ndef load_obj(obj):\n # Load subjects\n in_img = nib.load(obj)\n in_shape = in_img.shape\n print('Shape: ', in_shape)\n in_array = in_img.get_fdata()\n return in_array\n\n \n\ndef flipping(img,axis):\n flipped_img = np.flip(img,axis=axis)\n return flipped_img\n\n\n\ndef flipping_HV(img):\n flipped_img = np.fliplr(img)\n return flipped_img\n\ndef rotate(img,angle):\n \n img=ndimage.interpolation.rotate(img,angle) \n return img\n\ndef shifting(img,shift_amount):\n \n img=ndimage.interpolation.shift(img,shift_amount)\n \n return img\n\ndef zooming(img,zooming_amount):\n img=ndimage.interpolation.zoom(img,zooming_amount)\n return img\n\n\ndef add_gaussian_noise(X_imgs):\n gaussian_noise_imgs = []\n row, col,depth,number_of_samples = X_imgs.shape\n # Gaussian distribution parameters\n mean = 0\n var = 0.1\n sigma = var ** 0.5\n gaussian_noise_imgs=np.empty(X_imgs.shape)\n for i in range(number_of_samples):\n gaussian_img=np.zeros((row,col,depth))\n gaussian = np.random.random((row, col, 1)).astype(np.float64)\n gaussian = np.tile(gaussian,(1,1,depth)) \n gaussian_img = cv2.addWeighted(X_imgs[:,:,:,i], 0.75, 0.25 * gaussian, 0.25, 0 ,dtype=cv2.CV_64F)\n gaussian_noise_imgs[:,:,:,i]=gaussian_img\n gaussian_noise_imgs = np.array(gaussian_noise_imgs, dtype = np.float32)\n return gaussian_noise_imgs\n \n\ndef transposnig(input_data,order):\n return input_data.transpose(order)\n\n\n\ndef mask_print(input,mask,name):\n #remained_feature_indices=np.where(mask==1)\n masking_img = nib.load('/data/fmri/Folder/AD_classification/Data/input_data/4mm_brain_mask_bin.nii.gz')\n\n masking_shape = masking_img.shape\n print(masking_shape)\n masking = np.empty(masking_shape, dtype=float)\n masking[:,:,:] = masking_img.get_data().astype(float)\n for i in range (np.shape(input)[3]):\n input[:,:,:,i]=mask*input[:,:,:,i]\n #input[:,:,:,i]=input[:,:,:,i]\n hdr = masking_img.header\n aff = masking_img.affine\n out_img = nib.Nifti1Image(input, aff, hdr)\n # Save to disk\n out_file_name = '/data/fmri/Folder/AD_classification/Data/input_data/Augmented_data/mask_'+name+'.nii.gz'\n nib.save(out_img, out_file_name)\n \ndef slicing(len1,len2 ):\n diff=abs(len2-len1)/2 \n \n if (round(diff)>diff):\n return round(diff),len2-round(diff)+1\n \n else: \n return int(diff),int(len2-diff)\n\n\n'''\nOulu_data_ad_path = '/data/fmri/Folder/AD_classification/Data/Raw_data/Oulu_Data/CV_OULU_AD.nii.gz'\nOulu_data_con_path='/data/fmri/Folder/AD_classification/Data/Raw_data/Oulu_Data/CV_OULU_CON.nii.gz'\nadni_data_ad_path='/data/fmri/Folder/AD_classification/Data/Raw_data/ADNI_Data/CV_ADNI_AD.nii.gz'\nadni_data_con_path='/data/fmri/Folder/AD_classification/Data/Raw_data/ADNI_Data/CV_ADNI_CON.nii.gz'\nmasking_data='/data/fmri/Folder/AD_classification/Data/input_data/4mm_brain_mask_bin.nii.gz'\n\n\n\nOulu_data_ad=load_obj(Oulu_data_ad_path)\nOulu_data_con=load_obj(Oulu_data_con_path)\nadni_data_ad=load_obj(adni_data_ad_path)\nadni_data_con=load_obj(adni_data_con_path)\nmask=load_obj(masking_data)\n\norder_data=(0,2,1,3)\norder_mask=(0,2,1)\n\nOulu_data_con_transposed=transposnig(Oulu_data_con,order_data)\nOulu_data_ad_transposed=transposnig(Oulu_data_ad,order_data)\nmask_transposed=transposnig(mask,order_mask)\n\n\n#Rotation\nangles=[30,-30,60,-60,45,-45]\nfor i in angles:\n Oulu_data_ad_rotated=rotate(Oulu_data_ad_transposed,i)\n mask_rotated=rotate(mask_transposed,i)\n start,end=slicing(Oulu_data_ad.shape[0],Oulu_data_ad_rotated.shape[0])\n \n Oulu_data_ad_rotated=Oulu_data_ad_rotated[0:Oulu_data_ad.shape[0],0:Oulu_data_ad.shape[0],:,:]\n mask_rotated=mask_rotated[0:Oulu_data_ad.shape[0],0:Oulu_data_ad.shape[0],:]\n\n Oulu_data_ad_rotated_transposed=transposnig(Oulu_data_ad_rotated,order_data)\n mask_rotated_transposed=transposnig(mask_rotated,order_mask)\n print(Oulu_data_ad_rotated_transposed.shape)\n mask_print(Oulu_data_ad_rotated_transposed,mask_rotated_transposed,'rotated_' +str(i)+ '_Oulu_data_ad')\n\n\n\n# adding gussian noise \n\nOulu_data_ad_noised=add_gaussian_noise(Oulu_data_ad_transposed)\nOulu_data_ad_noised_transposed=transposnig(Oulu_data_ad_noised,order_data)\nmask_print(Oulu_data_ad_noised_transposed,mask,'Oulu_data_ad_gussian_noised')\n\n\n#shifting \nshift_amount_data=[0,20,0,0]\nshift_amount_mask=[0,20,0]\nOulu_data_con_shifted=shifting(Oulu_data_con_transposed,shift_amount_data)\nOulu_data_con_shifted_transposed=transposnig(Oulu_data_con_shifted,order_data)\nmask_shifted=shifting(mask_transposed,shift_amount_mask)\nmask_shifted_transposed=transposnig(mask_shifted,order_mask)\nmask_print(Oulu_data_con_shifted_transposed,mask_shifted_transposed,'down_Oulu_data_con')\n\n\n# flipping\nOulu_data_ad_flipped=flipping(Oulu_data_ad_transposed,0)\nOulu_data_ad_flipped_transposed=transposnig(Oulu_data_ad_flipped,order_data)\nmask_tranposed=transposnig(mask,order_mask)\nmask_flipped=flipping(mask_tranposed,1)\nmask_flipped_transposed=transposnig(mask_flipped,order_mask)\nmask_print(Oulu_data_ad_flipped_transposed,mask_flipped_transposed,'vertical_flipped_Oulu_data_ad')\n'''\n\n\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/preprocessing/data_preprocessing.py",
"content": "from sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import Normalizer\nfrom sklearn import preprocessing\nfrom scipy import ndimage\nimport nilearn\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.preprocessing import PowerTransformer\nfrom scipy.stats import variation\nimport numpy as np\nfrom densratio import densratio\nfrom sklearn.neighbors import LocalOutlierFactor\nfrom sklearn.utils import resample\nfrom sklearn.utils import shuffle\nfrom imblearn.over_sampling import SMOTE,ADASYN\nfrom sklearn import preprocessing\nfrom scipy.stats import ks_2samp\nimport tensorflow as tf\nimport math\nimport load_data\nimport data_augmentation\nfrom scipy.signal import resample_poly\n\n\ndef Normalization(data):\n return Normalizer().fit_transform(data)\n\n\ndef standarization(data):\n scaler = StandardScaler()\n scaler.fit(data)\n data = scaler.transform(data)\n return data\n\ndef quantile_transform(data,random_state):\n quantile_transformer = preprocessing.QuantileTransformer(random_state=random_state)\n data = quantile_transformer.fit_transform(data)\n return data\n\ndef gussian_filter(data,sigma):\n for i in range(len(data)):\n data[i] = ndimage.gaussian_filter(data[i], sigma)\n\n return data\n\ndef signal_clean(data):\n data = nilearn.signal.clean(data)\n\n return data\ndef robust_scaler(data):\n scaler = RobustScaler()\n data = scaler.fit_transform(data)\n\n return data\n\ndef MinMax_scaler(data):\n scaler = MinMaxScaler()\n data = scaler.fit_transform(data)\n\n return data\n\ndef dublicate(data,number):\n for i in range(number):\n data=np.vstack((data,data))\n\n return data\n\ndef concat(data1,data2):\n\n data1=np.vstack((data1,data2))\n\n return data1\n\ndef shuffling(data,labels):\n idx = np.random.permutation(len(labels))\n data, labels = data[idx], labels[idx]\n\n return data,labels\n\ndef PowerTransform(data):\n power_transform = PowerTransformer()\n data=power_transform.fit_transform(data)\n \n return data\n\ndef coefficient_of_variance(data):\n #data=MinMax_scaler(data)\n data = variation(data, axis=1)\n\n return data\n\ndef density_ratio_estimation(train_data,test_data):\n result = densratio(train_data,test_data)\n sample_weight=result.compute_density_ratio(train_data) \n\n return sample_weight\n\ndef outliers(train_data,train_labels,number_of_neighbours):\n neigh = LocalOutlierFactor(n_neighbors=number_of_neighbours)\n indices=neigh.fit_predict(train_data)\n train_data_inlier=train_data[np.where(indices==1)]\n train_labels_inlier=train_labels[np.where(indices==1)]\n outlier_indices=np.where(indices==-1)\n\n return train_data_inlier,train_labels_inlier,outlier_indices\n\ndef novelty(train_data,train_labels,test_data,test_labels,number_of_neighbours):\n neigh = LocalOutlierFactor(n_neighbors=number_of_neighbours,novelty=True)\n indices=neigh.fit(train_data)\n indices=indices.predict(test_data)\n test_data_inlier=test_data[np.where(indices==1)]\n test_labels_inlier=test_labels[np.where(indices==1)]\n outlier_indices=np.where(indices==-1)\n \n return test_data_inlier,test_labels_inlier,outlier_indices\n\ndef upsampling(data,labels):\n X = np.hstack((data, labels))\n not_fewsamples = X[np.where(X[:, -1] == 0)]\n\n fewsamples = X[np.where(X[:, -1] == 1)]\n fewsamples_upsampled = resample(fewsamples,\n replace=False, # sample with replacement\n n_samples=len(not_fewsamples)-len(fewsamples), # match number in majority class\n random_state=42) # reproducible results\n fewsamples_upsampled=np.vstack((fewsamples_upsampled, fewsamples))\n fewsamples_upsampled = np.vstack((fewsamples_upsampled, not_fewsamples))\n fewsamples_upsampled = shuffle(fewsamples_upsampled, random_state=42)\n labels = fewsamples_upsampled[:, -1]\n data = fewsamples_upsampled[:, 0:np.shape(fewsamples_upsampled)[1] - 1]\n\n return data,labels\n\ndef synthetic(data,labels,num):\n smote = ADASYN(ratio='all',n_neighbors=num)\n data, labels = smote.fit_sample(data, labels)\n \n return data,labels\n\ndef KSTest(train_data,test_data,step):\n\n index=[]\n for i in range(0,len(train_data)-step,step):\n for j in range(train_data.shape[1]):\n\n r=ks_2samp(train_data[i:i+step,j],test_data[:,j])\n if r[1]>0.05:\n index=np.append(index,j)\n print(train_data.shape)\n if index==[]:\n return train_data,test_data\n index=index[:,np.newaxis]\n index=index.astype(int)\n index=removeDuplicates(index)\n train_data[:,index]=0\n test_data[:,index]=0\n \n\n return train_data,test_data\n\n\ndef removeDuplicates(listofElements):\n\n # Create an empty list to store unique elements\n uniqueList = []\n\n # Iterate over the original list and for each element\n # add it to uniqueList, if its not already there.\n for elem in listofElements:\n if elem not in uniqueList:\n uniqueList.append(elem)\n\n # Return the list of unique elements\n return uniqueList\n\ndef transposnig(input_data, order):\n return input_data.transpose(order)\n\n\ndef size_editing(data, final_height):\n data_length = data.shape[1]\n if (data_length > final_height):\n diff = abs(data_length - final_height) / 2\n if (round(diff) > diff):\n start = round(diff)\n end = data_length - round(diff) + 1\n return data[:, start:end, start:end, :]\n\n else:\n start = int(diff)\n end = int(data_length - diff)\n return data[:, start:end, start:end, :]\n else:\n diff = abs(data_length - final_height) / 2\n if (round(diff) > diff):\n resized_data=np.pad(data,((0,0),(round(diff),round(diff)-1),(round(diff),round(diff)-1),(0,0)),'constant',constant_values=(0, 0))\n else:\n resized_data=np.pad(data,((0,0),(round(diff),round(diff)),(round(diff),round(diff)),(0,0)),'constant',constant_values=(0, 0))\n \n return resized_data\n \ndef depth_reshapeing(data):\n\n depth=int(data.shape[3])\n\n dim0=int(data.shape[0])\n\n dim1=int(data.shape[1])\n\n dim2=int(data.shape[2])\n\n step=math.floor(depth/3)\n\n reshaped_data=np.empty((dim0,dim1,dim2,3))\n \n for i in range(3):\n \n if i==2:\n reshaped_data[:,:,:,i]=np.mean(data[:,:,:,step*i:depth],axis=3) \n else: \n reshaped_data[:,:,:,i]=np.mean(data[:,:,:,step*i:step*(i+1)],axis=3)\n return reshaped_data \n\ndef converting_nii_to_npz(file_path):\n #file_path=load_data.find_path(file_name)\n nii_file=data_augmentation.load_obj(file_path)\n np.savez(file_path[0:len(file_path)-7]+'.npz',masked_voxels=nii_file) \n\n\ndef labels_convert_one_hot(labels):\n length=len(labels)\n zeros=np.zeros((length,1))\n labels=np.hstack((zeros,labels))\n indecies=np.where(labels[:,1]==0)\n labels[indecies[0],0]=1\n \n \n \n return labels \n\ndef data_resampling(data,factors):\n \n for k in range(len(factors)):\n data = resample_poly(data, factors[k][0], factors[k][1], axis=k+1)\n \n \n \n \n \n "
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/preprocessing/preprocessing_methods.py",
"content": "import data_preprocessing\nimport numpy as np\nimport load_data\n\ndef preprocessing(train_data,test_data,train_labels,test_labels,method,save,file_name,output_dir): \n \n \n \n dim0_train=train_data.shape[0]\n dim1_train=train_data.shape[1]\n dim2_train=train_data.shape[2]\n dim3_train=train_data.shape[3]\n \n dim0_test=test_data.shape[0]\n dim1_test=test_data.shape[1]\n dim2_test=test_data.shape[2]\n dim3_test=test_data.shape[3]\n \n if method==0:\n return\n elif method==1:\n train_data=train_data.reshape(dim0_train,dim1_train*dim2_train*dim3_train)\n test_data=test_data.reshape(dim0_test,dim1_test*dim2_test*dim3_test)\n train_data=data_preprocessing.MinMax_scaler(train_data)\n test_data=data_preprocessing.MinMax_scaler(test_data)\n train_data,test_data=data_preprocessing.standarization(train_data,test_data)\n train_data,test_data=data_preprocessing.KSTest(train_data,test_data,800)\n train_data=train_data.reshape(dim0_train,dim1_train,dim2_train,dim3_train)\n test_data=test_data.reshape(dim0_test,dim1_test,dim2_test,dim3_test)\n \n elif method==2:\n for i in range(train_data.shape[0]):\n for j in range(train_data.shape[3]):\n train_data[i, :, :, j] = data_preprocessing.standarization(train_data[i, :, :, j])\n train_data[i, :, :, j] = data_preprocessing.MinMax_scaler(train_data[i, :, :, j])\n \n if i < test_data.shape[0]:\n test_data[i, :, :, j] = data_preprocessing.standarization(test_data[i, :, :, j])\n \n test_data[i, :, :, j] = data_preprocessing.MinMax_scaler(test_data[i, :, :, j]) \n \n\n \n \n \n \n elif method==3: \n train_data=train_data.reshape(dim0_train,dim1_train*dim2_train*dim3_train)\n test_data=test_data.reshape(dim0_test,dim1_test*dim2_test*dim3_test)\n train_data,test_data=data_preprocessing.KSTest(train_data,test_data,500) \n train_data=train_data.reshape(dim0_train,dim1_train,dim2_train,dim3_train)\n test_data=test_data.reshape(dim0_test,dim1_test,dim2_test,dim3_test)\n \n\n \n elif method==4: \n train_data=train_data.reshape(dim0_train,dim1_train*dim2_train,dim3_train)\n test_data=test_data.reshape(dim0_test,dim1_test*dim2_test,dim3_test)\n for i in range (dim3_train): \n train_data[:,:,i]=data_preprocessing.MinMax_scaler(train_data[:,:,i])\n train_data[:,:,i]=data_preprocessing.standarization(train_data[:,:,i])\n \n test_data[:,:,i]=data_preprocessing.MinMax_scaler(test_data[:,:,i])\n test_data[:,:,i]=data_preprocessing.standarization(test_data[:,:,i])\n \n train_data[:,:,i],test_data[:,:,i]=data_preprocessing.KSTest(train_data[:,:,i],test_data[:,:,i],800) \n train_data=train_data.reshape(dim0_train,dim1_train,dim2_train,dim3_train)\n test_data=test_data.reshape(dim0_test,dim1_test,dim2_test,dim3_test)\n \n elif method==5:\n train_data=train_data.reshape(dim0_train,dim1_train*dim2_train*dim3_train)\n train_data,train_labels,index=data_preprocessing.outliers(train_data,train_labels,1)\n train_data=train_data.reshape(dim0_train-np.size(index),dim1_train,dim2_train,dim3_train)\n if save==0:\n \n return train_data,test_data,train_labels,test_labels\n else:\n \n transposing_order = [1,3,2,0]\n train_data = data_preprocessing.transposnig(train_data, transposing_order)\n test_data = data_preprocessing.transposnig(test_data, transposing_order)\n output_path=load_data.find_path(output_dir)\n np.savez(output_path+file_name+'train_data.npz',masked_voxels=train_data)\n np.savez(output_path+file_name+'test_data.npz',masked_voxels=test_data)\n \n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/preprocessing/readme.md",
"content": "\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/storing_loading/generate_result_.py",
"content": "import numpy as np\nimport os\nimport nibabel as nib\nfrom datetime import date\nfrom sklearn import metrics\nfrom sklearn.metrics import f1_score\nimport load_data\n'''\ntest_Con_file_name='whole_brain_ADNI_Con.npz'\ntest_AD_file_name='whole_brain_ADNI_AD.npz'\nroot_dir='/data'\nmask_name='4mm_brain_mask_bin.nii.gz'\nresults_directory=load_data.find_path('results','/data')\nmask,model,model_name,weights=Model.create_mask()\n'''\ndef out_result(test_data,test_labels,original_mask,created_mask,model):\n if (np.shape(test_data)[1]==1):\n test_data=np.reshape(test_data,(-1,1))\n predicted_labels = model.predict(test_data)\n test_accuracy = model.score(test_data,test_labels[:,np.newaxis])\n else:\n masked_test_data = test_data*created_mask\n predicted_labels = model.predict(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)])\n test_accuracy = model.score(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)],test_labels[:,np.newaxis])\n\n F1_score = f1_score(test_labels,predicted_labels, average='weighted')\n fpr, tpr, thresholds = metrics.roc_curve(test_labels, predicted_labels)\n auc=metrics.auc(fpr, tpr)\n return test_accuracy,F1_score,auc\ndef print_result_2models(test_data,test_labels,original_mask,model,model_name,results_directory,model1_accuracy,model1_auc,model1_f1,model1_name,model1_mask,model1_weights,feature_selection_type,Hyperparameter,data_preprocessing_method):\n if (np.shape(test_data)[1]==1):\n test_data=np.reshape(test_data,(-1,1))\n predicted_labels = model.predict(test_data)\n test_accuracy = model.score(test_data,test_labels[:,np.newaxis])\n else:\n masked_test_data = test_data*model1_mask\n predicted_labels = model.predict(masked_test_data[:,np.squeeze(np.where(model1_mask>0),axis=0)])\n test_accuracy = model.score(masked_test_data[:,np.squeeze(np.where(model1_mask>0),axis=0)],test_labels[:,np.newaxis])\n\n F1_score = f1_score(test_labels,predicted_labels, average='weighted')\n fpr, tpr, thresholds = metrics.roc_curve(test_labels, predicted_labels)\n auc=metrics.auc(fpr, tpr)\n\n\n today = str(date.today()) # To save the results in a directory with the date as a name\n\n if os.path.exists(os.path.join(results_directory,today))==0:\n os.mkdir(os.path.join(results_directory,today))\n\n if len(os.listdir(os.path.join(results_directory,today))) ==0:\n file_number = 1\n else:\n\n #latest_file = sorted(os.path.join(results_directory,today),key=x,reverse=True)\n print(os.path.join(results_directory, today))\n dir_list=os.listdir(os.path.join(results_directory,today))\n latest_file=sorted(list(map(int,dir_list)),reverse=True)\n print(latest_file)\n\n file_number = ((latest_file[0]))+1\n\n os.mkdir(os.path.join(results_directory,today,str(file_number)))\n\n line1 = 'Test accuracy on inliers:' + ' ' + str(model1_accuracy)\n line2 = 'F1 score on inliers :' + ' ' + str(model1_f1)\n line3 = 'AUC on inliers :' + ' ' + str(model1_auc)\n line4 = 'Test accuracy on outliers :' + ' ' + str(test_accuracy)\n line5 = 'F1 score on outliers :' + ' ' + str(F1_score)\n line6 = 'AUC on outliers :' + ' ' + str(auc)\n line7 = 'Total test accuracy :' + ' ' + str((model1_accuracy+test_accuracy)/2)\n line8 = 'Total F1_score :' + ' ' + str((model1_f1 + F1_score) / 2)\n line9 = 'Total AUC :' + ' ' + str((model1_auc + auc) / 2)\n f = open(os.path.join(results_directory,today,str(file_number),'Results.txt'),\"w+\")\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1, line2, line3,line4,line5,line6,line7,line8,line9))\n\n\n line1='The model used to obtain first model result is '+ ' '+ model1_name\n line2='The model used to obtain second result is '+ ' '+ model_name\n line3='The feature selection methods is ' + ' '+ feature_selection_type\n line4= 'the hyperparameter used is ' + ' '+ str(Hyperparameter)\n line5= 'The preprocessing method used is '+ ' '+ data_preprocessing_method\n f=open(os.path.join(results_directory,today,str(file_number),'README.txt'),\"w+\")\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1,line2,line3,line4,line5))\n\n mask_print(original_mask,model1_mask,os.path.join(results_directory,today,str(file_number)),'model_')\n weights_print(original_mask ,model1_weights, os.path.join(results_directory,today,str(file_number)),'model_')\n\n return\ndef print_result(test_data,test_labels,original_mask,created_mask,model,model_name,weights,results_directory,feature_selection_type,Hyperparameter,data_preprocessing_method):\n\n\n masked_test_data = test_data*created_mask\n print(masked_test_data.shape)\n print(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)].shape)\n\n predicted_labels = model.predict(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)])\n\n print(predicted_labels)\n\n test_accuracy = model.score(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)],test_labels[:,np.newaxis])\n F1_score = f1_score(test_labels,predicted_labels, average='weighted')\n fpr, tpr, thresholds = metrics.roc_curve(test_labels, predicted_labels)\n auc=metrics.auc(fpr, tpr)\n\n\n today = str(date.today()) # To save the results in a directory with the date as a name\n\n if os.path.exists(os.path.join(results_directory,today))==0:\n os.mkdir(os.path.join(results_directory,today))\n\n if len(os.listdir(os.path.join(results_directory,today))) ==0:\n file_number = 1\n else:\n\n #latest_file = sorted(os.path.join(results_directory,today),key=x,reverse=True)\n print(os.path.join(results_directory, today))\n dir_list=os.listdir(os.path.join(results_directory,today))\n latest_file=sorted(list(map(int,dir_list)),reverse=True)\n print(latest_file)\n\n file_number = ((latest_file[0]))+1\n\n os.mkdir(os.path.join(results_directory,today,str(file_number)))\n\n line1 = 'Test accuracy is:' + ' ' + str(test_accuracy)\n line2 = 'F1 score is:' + ' ' + str(F1_score)\n line3 = 'AUC is :' + ' ' + str(auc)\n f = open(os.path.join(results_directory,today,str(file_number),'Results.txt'),\"w+\")\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1, line2, line3))\n\n\n line1='The model used to obtain this result is '+ ' '+ model_name\n line2='The feature selection methods is ' + ' '+ feature_selection_type\n line3= 'the hyperparameter used is ' + ' '+ str(Hyperparameter)\n line4= 'The preprocessing method used is '+ ' '+ data_preprocessing_method\n f=open(os.path.join(results_directory,today,str(file_number),'README.txt'),\"w+\")\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1,line2,line3,line4))\n mask_print(original_mask,created_mask,os.path.join(results_directory,today,str(file_number)),'output_')\n weights_print(original_mask ,weights, os.path.join(results_directory,today,str(file_number)),'output_')\n return\n\n\ndef mask_print(original_mask,created_mask,output_dir,name):\n\n masking_shape = original_mask.shape\n masking = np.empty(masking_shape, dtype=float)\n masking[:, :, :] = original_mask.get_data().astype(float)\n masking[np.where(masking > 0)] = masking[np.where(masking > 0)] * 0 + created_mask\n hdr = original_mask.header\n aff = original_mask.affine\n out_img = nib.Nifti1Image(masking, aff, hdr)\n nib.save(out_img, os.path.join(output_dir,name+'mask.nii.gz'))\n return\n\ndef weights_print(original_mask, weights, output_dir,name):\n\n masking_shape = original_mask.shape\n masking = np.empty(masking_shape, dtype=float)\n masking[:, :, :] = original_mask.get_data().astype(float)\n masking[np.where(masking > 0)] = masking[np.where(masking > 0)] * 0 + weights\n hdr = original_mask.header\n aff = original_mask.affine\n out_img = nib.Nifti1Image(masking, aff, hdr)\n nib.save(out_img, os.path.join(output_dir, name+'weights.nii.gz'))\n return\n\n\ndef cnn_save_result(test_accuracy,model,model_name,result_path):\n \n line1 = 'Test accuracy is:' + ' ' + str(test_accuracy)\n f = open(os.path.join(result_path, 'Results.txt'), \"w+\")\n f.write(\"{}\" \"\\n\" .format(line1))\n\n model.save(os.path.join(result_path, 'trained_model.h5'))\n\n f = open(os.path.join(result_path, 'README'), \"w+\")\n line1 ='CNN model was used '\n line2 = 'The model used to obtain this result is ' + ' ' + model_name\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1,line2))\n\n \ndef create_results_dir(results_directory):\n today = str(date.today()) # To save the results in a directory with the date as a name\n\n Results_dir_path=load_data.find_path(results_directory)\n if os.path.exists(os.path.join(Results_dir_path, today)) == 0:\n os.mkdir(os.path.join(Results_dir_path, today))\n\n if len(os.listdir(os.path.join(Results_dir_path, today))) == 0:\n file_number = 1\n else:\n\n # latest_file = sorted(os.path.join(results_directory,today),key=x,reverse=True)\n print(os.path.join(Results_dir_path, today))\n dir_list = os.listdir(os.path.join(Results_dir_path, today))\n latest_file = sorted(list(map(int, dir_list)), reverse=True)\n print(latest_file)\n file_number = ((latest_file[0])) + 1\n os.mkdir(os.path.join(Results_dir_path, today, str(file_number)))\n result_path=os.path.join(Results_dir_path, today, str(file_number))\n print(result_path)\n return result_path\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/storing_loading/load_data.py",
"content": "import numpy as np\nfrom numpy import load\nimport os\nimport nibabel as nib\nfrom pathlib import Path\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\ndef find_path(file_name):\n data_path=None\n current_dir_path=os.getcwd()\n p=Path(current_dir_path)\n root_dir=p.parts[0]+p.parts[1]\n for r,d,f in os.walk(root_dir):\n for files in f:\n if files == file_name:\n data_path=os.path.join(r,files)\n\n else:\n for dir in d :\n if dir == file_name:\n data_path = os.path.join(r, dir)\n if data_path is not None:\n return data_path\n else:\n os.makedirs('./'+file_name)\n return './'+file_name\n\n\n\ndef train_data_3d(train_Con_file_name, train_AD_file_name):\n train_data_Con_path = find_path(train_Con_file_name)\n train_data_AD_path = find_path(train_AD_file_name)\n train_data_Con = load(train_data_Con_path)['masked_voxels']\n train_data_AD = load(train_data_AD_path)['masked_voxels']\n train_data=np.concatenate((train_data_Con,train_data_AD),axis=3)\n train_labels = np.hstack((np.zeros(train_data_Con.shape[3]), np.ones(train_data_AD.shape[3])))\n\n\n return train_data, train_labels\n\ndef test_data_3d(test_Con_file_name,test_AD_file_name):\n test_data_Con_path=find_path(test_Con_file_name)\n test_data_AD_path = find_path(test_AD_file_name)\n test_data_Con = load(test_data_Con_path)['masked_voxels']\n test_data_AD = load(test_data_AD_path)['masked_voxels']\n test_data = np.concatenate((test_data_Con, test_data_AD), axis=3)\n test_labels = np.hstack((np.zeros(test_data_Con.shape[3]), np.ones(test_data_AD.shape[3])))\n\n\n return test_data,test_labels\n\n\ndef mask(mask_name):\n mask_path = find_path(mask_name)\n original_mask = nib.load(mask_path)\n return original_mask\n\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/deep learning/storing_loading/readme.md",
"content": "\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/generate_result.py",
"content": "import numpy as np\nimport os\nimport nibabel as nib\nfrom datetime import date\nfrom sklearn import metrics\nfrom sklearn.metrics import f1_score\nimport load_data\n'''\ntest_Con_file_name='whole_brain_ADNI_Con.npz'\ntest_AD_file_name='whole_brain_ADNI_AD.npz'\nroot_dir='/data'\nmask_name='4mm_brain_mask_bin.nii.gz'\nresults_directory=load_data.find_path('results','/data')\nmask,model,model_name,weights=Model.create_mask()\n'''\ndef out_result(test_data,test_labels,original_mask,created_mask,model):\n if (np.shape(test_data)[1]==1):\n test_data=np.reshape(test_data,(-1,1))\n predicted_labels = model.predict(test_data)\n test_accuracy = model.score(test_data,test_labels[:,np.newaxis])\n else:\n masked_test_data = test_data*created_mask\n predicted_labels = model.predict(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)])\n test_accuracy = model.score(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)],test_labels[:,np.newaxis])\n\n F1_score = f1_score(test_labels,predicted_labels, average='weighted')\n fpr, tpr, thresholds = metrics.roc_curve(test_labels, predicted_labels)\n auc=metrics.auc(fpr, tpr)\n return test_accuracy,F1_score,auc\ndef print_result_2models(test_data,test_labels,original_mask,model,model_name,results_directory,model1_accuracy,model1_auc,model1_f1,model1_name,model1_mask,model1_weights,feature_selection_type,Hyperparameter,data_preprocessing_method):\n if (np.shape(test_data)[1]==1):\n test_data=np.reshape(test_data,(-1,1))\n predicted_labels = model.predict(test_data)\n test_accuracy = model.score(test_data,test_labels[:,np.newaxis])\n else:\n masked_test_data = test_data*model1_mask\n predicted_labels = model.predict(masked_test_data[:,np.squeeze(np.where(model1_mask>0),axis=0)])\n test_accuracy = model.score(masked_test_data[:,np.squeeze(np.where(model1_mask>0),axis=0)],test_labels[:,np.newaxis])\n\n F1_score = f1_score(test_labels,predicted_labels, average='weighted')\n fpr, tpr, thresholds = metrics.roc_curve(test_labels, predicted_labels)\n auc=metrics.auc(fpr, tpr)\n\n\n today = str(date.today()) # To save the results in a directory with the date as a name\n\n if os.path.exists(os.path.join(results_directory,today))==0:\n os.mkdir(os.path.join(results_directory,today))\n\n if len(os.listdir(os.path.join(results_directory,today))) ==0:\n file_number = 1\n else:\n\n #latest_file = sorted(os.path.join(results_directory,today),key=x,reverse=True)\n print(os.path.join(results_directory, today))\n dir_list=os.listdir(os.path.join(results_directory,today))\n latest_file=sorted(list(map(int,dir_list)),reverse=True)\n print(latest_file)\n\n file_number = ((latest_file[0]))+1\n\n os.mkdir(os.path.join(results_directory,today,str(file_number)))\n\n line1 = 'Test accuracy on inliers:' + ' ' + str(model1_accuracy)\n line2 = 'F1 score on inliers :' + ' ' + str(model1_f1)\n line3 = 'AUC on inliers :' + ' ' + str(model1_auc)\n line4 = 'Test accuracy on outliers :' + ' ' + str(test_accuracy)\n line5 = 'F1 score on outliers :' + ' ' + str(F1_score)\n line6 = 'AUC on outliers :' + ' ' + str(auc)\n line7 = 'Total test accuracy :' + ' ' + str((model1_accuracy+test_accuracy)/2)\n line8 = 'Total F1_score :' + ' ' + str((model1_f1 + F1_score) / 2)\n line9 = 'Total AUC :' + ' ' + str((model1_auc + auc) / 2)\n f = open(os.path.join(results_directory,today,str(file_number),'Results.txt'),\"w+\")\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1, line2, line3,line4,line5,line6,line7,line8,line9))\n\n\n line1='The model used to obtain first model result is '+ ' '+ model1_name\n line2='The model used to obtain second result is '+ ' '+ model_name\n line3='The feature selection methods is ' + ' '+ feature_selection_type\n line4= 'the hyperparameter used is ' + ' '+ str(Hyperparameter)\n line5= 'The preprocessing method used is '+ ' '+ data_preprocessing_method\n f=open(os.path.join(results_directory,today,str(file_number),'README.txt'),\"w+\")\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1,line2,line3,line4,line5))\n\n mask_print(original_mask,model1_mask,os.path.join(results_directory,today,str(file_number)),'model_')\n weights_print(original_mask ,model1_weights, os.path.join(results_directory,today,str(file_number)),'model_')\n\n return\ndef print_result(test_data,test_labels,original_mask,created_mask,model,model_name,weights,results_directory,feature_selection_type,Hyperparameter,data_preprocessing_method):\n\n\n masked_test_data = test_data*created_mask\n print(masked_test_data.shape)\n print(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)].shape)\n\n predicted_labels = model.predict(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)])\n\n print(predicted_labels)\n\n test_accuracy = model.score(masked_test_data[:,np.squeeze(np.where(created_mask>0),axis=0)],test_labels[:,np.newaxis])\n F1_score = f1_score(test_labels,predicted_labels, average='weighted')\n fpr, tpr, thresholds = metrics.roc_curve(test_labels, predicted_labels)\n auc=metrics.auc(fpr, tpr)\n\n\n today = str(date.today()) # To save the results in a directory with the date as a name\n\n if os.path.exists(os.path.join(results_directory,today))==0:\n os.mkdir(os.path.join(results_directory,today))\n\n if len(os.listdir(os.path.join(results_directory,today))) ==0:\n file_number = 1\n else:\n\n #latest_file = sorted(os.path.join(results_directory,today),key=x,reverse=True)\n print(os.path.join(results_directory, today))\n dir_list=os.listdir(os.path.join(results_directory,today))\n latest_file=sorted(list(map(int,dir_list)),reverse=True)\n print(latest_file)\n\n file_number = ((latest_file[0]))+1\n\n os.mkdir(os.path.join(results_directory,today,str(file_number)))\n\n line1 = 'Test accuracy is:' + ' ' + str(test_accuracy)\n line2 = 'F1 score is:' + ' ' + str(F1_score)\n line3 = 'AUC is :' + ' ' + str(auc)\n f = open(os.path.join(results_directory,today,str(file_number),'Results.txt'),\"w+\")\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1, line2, line3))\n\n\n line1='The model used to obtain this result is '+ ' '+ model_name\n line2='The feature selection methods is ' + ' '+ feature_selection_type\n line3= 'the hyperparameter used is ' + ' '+ str(Hyperparameter)\n line4= 'The preprocessing method used is '+ ' '+ data_preprocessing_method\n f=open(os.path.join(results_directory,today,str(file_number),'README.txt'),\"w+\")\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1,line2,line3,line4))\n mask_print(original_mask,created_mask,os.path.join(results_directory,today,str(file_number)),'output_')\n weights_print(original_mask ,weights, os.path.join(results_directory,today,str(file_number)),'output_')\n return\n\n\ndef mask_print(original_mask,created_mask,output_dir,name):\n\n masking_shape = original_mask.shape\n masking = np.empty(masking_shape, dtype=float)\n masking[:, :, :] = original_mask.get_data().astype(float)\n masking[np.where(masking > 0)] = masking[np.where(masking > 0)] * 0 + created_mask\n hdr = original_mask.header\n aff = original_mask.affine\n out_img = nib.Nifti1Image(masking, aff, hdr)\n nib.save(out_img, os.path.join(output_dir,name+'mask.nii.gz'))\n return\n\ndef weights_print(original_mask, weights, output_dir,name):\n\n masking_shape = original_mask.shape\n masking = np.empty(masking_shape, dtype=float)\n masking[:, :, :] = original_mask.get_data().astype(float)\n masking[np.where(masking > 0)] = masking[np.where(masking > 0)] * 0 + weights\n hdr = original_mask.header\n aff = original_mask.affine\n out_img = nib.Nifti1Image(masking, aff, hdr)\n nib.save(out_img, os.path.join(output_dir, name+'weights.nii.gz'))\n return\n\n\ndef cnn_save_result(test_accuracy,model,model_name,result_path):\n \n line1 = 'Test accuracy is:' + ' ' + str(test_accuracy)\n f = open(os.path.join(result_path, 'Results.txt'), \"w+\")\n f.write(\"{}\" \"\\n\" .format(line1))\n\n model.save(os.path.join(result_path, 'trained_model.h5'))\n\n f = open(os.path.join(result_path, 'README'), \"w+\")\n line1 ='CNN model was used '\n line2 = 'The model used to obtain this result is ' + ' ' + model_name\n f.write(\"{}\" \"\\n\" \"{}\" \"\\n\" .format(line1,line2))\n\n\n\ndef create_results_dir(results_directory):\n today = str(date.today()) # To save the results in a directory with the date as a name\n\n Results_dir_path=load_data.find_path(results_directory)\n if os.path.exists(os.path.join(Results_dir_path, today)) == 0:\n os.mkdir(os.path.join(Results_dir_path, today))\n\n if len(os.listdir(os.path.join(Results_dir_path, today))) == 0:\n file_number = 1\n else:\n\n # latest_file = sorted(os.path.join(results_directory,today),key=x,reverse=True)\n print(os.path.join(Results_dir_path, today))\n dir_list = os.listdir(os.path.join(Results_dir_path, today))\n latest_file = sorted(list(map(int, dir_list)), reverse=True)\n print(latest_file)\n file_number = ((latest_file[0])) + 1\n os.mkdir(os.path.join(Results_dir_path, today, str(file_number)))\n result_path=os.path.join(Results_dir_path, today, str(file_number))\n print(result_path)\n return result_path\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/hyper_opt.py",
"content": "import numpy as np\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_validate\nfrom sklearn.feature_selection import RFE\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.svm import LinearSVC\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn import tree\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn import svm\nfrom sklearn.calibration import CalibratedClassifierCV\nfrom data_preprocessing import select_max_features\n\n\ndef hyperparameter_selection(data,labels,number_of_cv,feature_selection_type,Hyperparameter,mask_threshold):\n feature_weight=np.zeros(np.shape(data)[1])\n weights = np.zeros(np.shape(data)[1])\n if (feature_selection_type=='recursion'):\n masks = []\n accuracies = []\n weights = []\n if Hyperparameter is not None:\n #boundaries between 1e+4 to 1e+10\n Hyperparameter=[Hyperparameter]\n else:\n Hyperparameter=[500,1000,2000,3000,1e+4,5000,1e+6,7000,1e+8,9000,1e+10]\n for i, value in enumerate(Hyperparameter):\n svc = SVC(kernel=\"linear\")\n rfe = RFE(estimator=svc, step=1,n_features_to_select=value)\n rfe = rfe.fit(data, labels)\n lsvc = cross_validate(rfe.estimator_, data, labels, cv=number_of_cv, scoring='accuracy', return_estimator=True)\n index_of_max_accuracy = np.argmax(lsvc['test_score'])\n accuracy = lsvc['test_score'][index_of_max_accuracy]\n weight = np.absolute(lsvc['estimator'][index_of_max_accuracy].coef_)\n for i, estimator in enumerate(lsvc['estimator']):\n model = SelectFromModel(estimator, prefit=True, threshold=\"mean\")\n indecies = model.get_support(indices=True)\n T_new = model.transform(data)\n nfeatures = T_new.shape[1]\n feature_weight[indecies] = feature_weight[indecies] + 1\n mask = np.array(feature_weight > mask_threshold, dtype=int)[np.newaxis, :]\n masks = np.reshape(masks, (-1, np.shape(mask)[1]))\n masks = np.append(masks, mask, axis=0)\n accuracies = np.append(accuracies, accuracy)\n weights = np.append(weights, weight)\n weights = np.reshape(weights, (-1, np.shape(mask)[1]))\n argument_of_maximum_accuracy = np.argmax(accuracies)\n return masks[argument_of_maximum_accuracy], accuracies[argument_of_maximum_accuracy], weights[argument_of_maximum_accuracy]\n if (feature_selection_type=='L2_penality'):\n masks=[]\n accuracies=[]\n weights=[]\n #model_threshold=.00075\n if Hyperparameter is not None:\n #boundaries between 1e-4 to 10000\n Hyperparameter=[Hyperparameter]\n else:\n Hyperparameter=[1e-4,1e-3,1e-2,1e-1,1,10,100,1000,10000]\n for i,value in enumerate(Hyperparameter):\n lsvc = LinearSVC(C=value, penalty=\"l2\", dual=True,max_iter=40000)\n lsvc = cross_validate(lsvc, data,labels, cv=number_of_cv, scoring = 'accuracy', return_estimator =True)\n index_of_max_accuracy=np.argmax(lsvc['test_score'])\n accuracy=lsvc['test_score'][index_of_max_accuracy]\n weight= np.absolute(lsvc['estimator'][index_of_max_accuracy].coef_)\n for i,estimator in enumerate(lsvc['estimator']):\n model = SelectFromModel(estimator, prefit=True,threshold='1.25*median')\n indecies=model.get_support(indices=True)\n T_new = model.transform(data)\n nfeatures=T_new.shape[1]\n feature_weight[indecies]=feature_weight[indecies]+1\n\n mask=np.array(feature_weight>mask_threshold,dtype=int)[np.newaxis, :]\n masks=np.reshape(masks,(-1,np.shape(mask)[1]))\n masks=np.append(masks,mask,axis = 0)\n accuracies=np.append(accuracies,accuracy)\n weights=np.append(weights,weight)\n weights=np.reshape(weights,(-1,np.shape(mask)[1]))\n argument_of_maximum_accuracy=np.argmax(accuracies)\n return masks[argument_of_maximum_accuracy],accuracies[argument_of_maximum_accuracy],weights[argument_of_maximum_accuracy]\n\ndef model(data_train,labels_train,mask,data_validation=None,labels_validation=None,model_type='gaussian_process'):\n if (model_type=='gaussian_process'):\n kernel = 1.0 * RBF(len(mask[mask>0])*40)\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc=gpc.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n if (data_validation is not None):\n validation_accuracy=gpc.score(data_validation[:,np.squeeze(np.where(mask>0),axis=0)],labels_validation)\n return gpc,validation_accuracy,model_type\n return gpc,model_type\n if (model_type=='svm'):\n clf = svm.SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf=clf.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n if (data_validation is not None):\n validation_accuracy=clf.score(data_validation[:,np.squeeze(np.where(mask>0),axis=0)],labels_validation)\n return clf,validation_accuracy,model_type\n return clf,model_type\n if (model_type=='decison tree classifer'):\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n if (data_validation is not None):\n validation_accuracy=tree_clf.score(data_validation[:,np.squeeze(np.where(mask>0),axis=0)],labels_validation)\n return tree_clf,validation_accuracy,model_type\n return tree_clf,model_type\n if (model_type=='ensamble classifer'):\n kernel = 1.0 * RBF(len(mask[mask>0]))\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc=gpc.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n clf = svm.SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf=clf.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n\n estimators=[('Gussian_process',gpc),('svm classifer',clf),('Decision tree',tree_clf)]\n ensemble = VotingClassifier(estimators, voting='hard',)\n ensemble=ensemble.fit(data_train[:,np.squeeze(np.where(mask>0),axis=0)],labels_train)\n if (data_validation is not None):\n validation_accuracy=ensemble.score(data_validation[:,np.squeeze(np.where(mask>0),axis=0)],labels_validation)\n return ensemble,validation_accuracy,model_type\n return ensemble,model_type\n\n\ndef create_mask(data,labels,number_of_cv,feature_selection_type,Hyperparameter,mask_threshold=1,model_type='gaussian_process'):\n index=0\n masks=np.zeros(np.shape(data)[1])[np.newaxis, :]\n accuracies=np.zeros((number_of_cv+1,1))\n wight_matrix=np.zeros(np.shape(data)[1])[np.newaxis, :]\n weights=np.zeros(np.shape(data)[1])\n if Hyperparameter is not None:\n # boundaries between 1e-4 to 10000\n Hyperparameter = Hyperparameter\n else:\n Hyperparameter = np.shape(data)[1]\n kf = KFold(n_splits=number_of_cv)\n for train_index, test_index in kf.split(data):\n X_train, X_test = data[train_index], data[test_index]\n y_train, y_test = labels[train_index], labels[test_index]\n mask,accuracy,weights=hyperparameter_selection(X_train,y_train,number_of_cv,feature_selection_type,Hyperparameter,mask_threshold)\n if (len(mask[mask>0])==0):\n continue\n model_,validation_accuracy,model_type=model(X_train,y_train,mask,X_test,y_test,model_type)\n masks=np.append(masks,mask[np.newaxis, :], axis=0)\n accuracies[index]=validation_accuracy\n wight_matrix=np.append(wight_matrix,weights[np.newaxis, :],axis=0)\n index=index+1\n optimal_mask = select_max_features(np.sum(masks,axis=0).copy(), Hyperparameter)\n optimal_mask=np.array(optimal_mask>mask_threshold,dtype=int)\n if (len(optimal_mask[optimal_mask>0])==0):\n raise ValueError(\"the mask produces zero array\")\n out_model,model_type=model(data,labels,optimal_mask,None,None,model_type)\n argument_of_maximum_accuracy=np.argmax(accuracies)\n return optimal_mask,out_model,model_type,wight_matrix[argument_of_maximum_accuracy]\n\n\ndef model_1D(data_train,labels_train,mask,data_validation=None,labels_validation=None,model_type='gaussian_process'):\n data_train=np.reshape(data_train,(-1,1))\n if (model_type=='gaussian_process'):\n kernel = 1.0 * RBF(len(mask[mask>0]))\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc=gpc.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=gpc.score(data_validation,labels_validation)\n return gpc,validation_accuracy,model_type\n return gpc,model_type\n if (model_type=='svm'):\n clf = svm.SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf=clf.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=clf.score(data_validation,labels_validation)\n return clf,validation_accuracy,model_type\n return clf,model_type\n if (model_type=='decison tree classifer'):\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=tree_clf.score(data_validation,labels_validation)\n return tree_clf,validation_accuracy,model_type\n return tree_clf,model_type\n if (model_type=='ensamble classifer'):\n kernel = 1.0 * RBF(len(mask[mask>0]))\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc=gpc.fit(data_train,labels_train)\n clf = svm.SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf=clf.fit(data_train,labels_train)\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train,labels_train)\n\n estimators=[('Gussian_process',gpc),('svm classifer',clf),('Decision tree',tree_clf)]\n ensemble = VotingClassifier(estimators, voting='hard',)\n ensemble=ensemble.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=ensemble.score(data_validation,labels_validation)\n return ensemble,validation_accuracy,model_type\n return ensemble,model_type\ndef model_1D_calibrate(data_train,labels_train,mask,data_validation=None,labels_validation=None,model_type='gaussian_process'):\n data_train=np.reshape(data_train,(-1,1))\n if (model_type=='gaussian_process'):\n kernel = 1.0 * RBF(len(mask[mask>0])**2)\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc = CalibratedClassifierCV(gpc, cv=5, method='isotonic')\n gpc=gpc.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=gpc.score(data_validation,labels_validation)\n return gpc,validation_accuracy,model_type\n return gpc,model_type\n if (model_type=='svm'):\n clf = svm.SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf = CalibratedClassifierCV(clf, cv=5, method='isotonic')\n clf=clf.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=clf.score(data_validation,labels_validation)\n return clf,validation_accuracy,model_type\n return clf,model_type\n if (model_type=='decison tree classifer'):\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=tree_clf.score(data_validation,labels_validation)\n return tree_clf,validation_accuracy,model_type\n return tree_clf,model_type\n if (model_type=='ensamble classifer'):\n kernel = 1.0 * RBF(len(mask[mask>0]))\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc=gpc.fit(data_train,labels_train)\n clf = svm.SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf=clf.fit(data_train,labels_train)\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train,labels_train)\n\n estimators=[('Gussian_process',gpc),('svm classifer',clf),('Decision tree',tree_clf)]\n ensemble = VotingClassifier(estimators, voting='hard',)\n ensemble=ensemble.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=ensemble.score(data_validation,labels_validation)\n return ensemble,validation_accuracy,model_type\n return ensemble,model_type\ndef model_reduced(data_train,labels_train,mask,data_validation=None,labels_validation=None,model_type='gaussian_process'):\n if (model_type=='gaussian_process'):\n kernel = 1.0 * RBF(1)\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc=gpc.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=gpc.score(data_validation,labels_validation)\n return gpc,validation_accuracy,model_type\n return gpc,model_type\n if (model_type=='svm'):\n clf = svm.SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf=clf.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=clf.score(data_validation,labels_validation)\n return clf,validation_accuracy,model_type\n return clf,model_type\n if (model_type=='decison tree classifer'):\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=tree_clf.score(data_validation,labels_validation)\n return tree_clf,validation_accuracy,model_type\n return tree_clf,model_type\n if (model_type=='ensamble classifer'):\n kernel = 1.0 * RBF(len(mask[mask>0]))\n gpc = GaussianProcessClassifier(kernel=kernel,n_restarts_optimizer=5,random_state=None,\n multi_class=\"one_vs_rest\",max_iter_predict=100,n_jobs=-1)\n gpc=gpc.fit(data_train,labels_train)\n clf = svm.SVC(kernel='linear',gamma='scale', decision_function_shape='ovo')\n clf=clf.fit(data_train,labels_train)\n tree_clf = tree.DecisionTreeClassifier()\n tree_clf = tree_clf.fit(data_train,labels_train)\n\n estimators=[('Gussian_process',gpc),('svm classifer',clf),('Decision tree',tree_clf)]\n ensemble = VotingClassifier(estimators, voting='hard',)\n ensemble=ensemble.fit(data_train,labels_train)\n if (data_validation is not None):\n validation_accuracy=ensemble.score(data_validation,labels_validation)\n return ensemble,validation_accuracy,model_type\n return ensemble,model_type\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/load_data.py",
"content": "import numpy as np\nfrom numpy import load\nimport os\nimport nibabel as nib\nfrom pathlib import Path\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\ndef find_path(file_name):\n data_path=None\n current_dir_path=os.getcwd()\n p=Path(current_dir_path)\n root_dir=p.parts[0]+p.parts[1]\n for r,d,f in os.walk(root_dir):\n for files in f:\n if files == file_name:\n data_path=os.path.join(r,files)\n\n else:\n for dir in d :\n if dir == file_name:\n data_path = os.path.join(r, dir)\n if data_path is not None:\n return data_path\n else:\n os.makedirs('./'+file_name)\n return './'+file_name\n\n\n\ndef train_data_3d(train_Con_file_name, train_AD_file_name):\n train_data_Con_path = find_path(train_Con_file_name)\n train_data_AD_path = find_path(train_AD_file_name)\n train_data_Con = load(train_data_Con_path)['masked_voxels']\n train_data_AD = load(train_data_AD_path)['masked_voxels']\n train_data=np.concatenate((train_data_Con,train_data_AD),axis=3)\n train_labels = np.hstack((np.zeros(train_data_Con.shape[3]), np.ones(train_data_AD.shape[3])))\n\n\n return train_data, train_labels\n\ndef test_data_3d(test_Con_file_name,test_AD_file_name):\n test_data_Con_path=find_path(test_Con_file_name)\n test_data_AD_path = find_path(test_AD_file_name)\n test_data_Con = load(test_data_Con_path)['masked_voxels']\n test_data_AD = load(test_data_AD_path)['masked_voxels']\n test_data = np.concatenate((test_data_Con, test_data_AD), axis=3)\n test_labels = np.hstack((np.zeros(test_data_Con.shape[3]), np.ones(test_data_AD.shape[3])))\n\n\n return test_data,test_labels\n\n\ndef mask(mask_name):\n mask_path = find_path(mask_name)\n original_mask = nib.load(mask_path)\n return original_mask\n\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/load_models.py",
"content": "import pickle\nimport load_data\nimport data_preprocessing\nimport numpy as np\nimport nibabel as nib\nimport generate_result\n\n#define paths\ntrain_Con_file_name = 'CV_OULU_CON.npz'\ntrain_AD_file_name = 'CV_OULU_AD.npz'\ntest_Con_file_name = 'CV_ADNI_CON.npz'\ntest_AD_file_name = 'CV_ADNI_AD.npz'\nmask_name = '4mm_brain_mask_bin.nii.gz'\ncreated_mask_high_certainity_file_name='./Output_results_directory/2019-08-10/1/high_certainity_model_mask.nii.gz'\ncreated_mask_outlier_file_name='./Output_results_directory/2019-08-10/1/high_certainity_model_mask.nii.gz'\nhigh_certainity_model_name='./Output_results_directory/2019-08-10/1/high_certainity_model.sav'\nlow_certainty_model_name='./Output_results_directory/2019-08-10/1/low_certainty_model.sav'\noutliers_model_name='./Output_results_directory/2019-08-10/1/outliers_model.sav'\n#define variables\nnumber_of_neighbours = 1\n\n#load data\ntrain_data,train_labels=load_data.train_data_3d(train_Con_file_name,train_AD_file_name)\ntest_data, test_labels = load_data.test_data_3d(test_Con_file_name, test_AD_file_name)\n\n#load masks\nmask_4mm = load_data.mask(mask_name)\ncreated_mask_high_certainity = nib.load(created_mask_high_certainity_file_name)\ncreated_mask_outlier = nib.load(created_mask_outlier_file_name)\n\n\n#data preprocessing\ntrain_data = np.moveaxis(train_data.copy(), 3, 0)\ntest_data = np.moveaxis(test_data.copy(), 3, 0)\noriginal_mask=mask_4mm.get_fdata()\ntrain_data = train_data * original_mask\ntest_data = test_data * original_mask\ncreated_mask_high_certainity=created_mask_high_certainity.get_fdata()\ncreated_mask_outlier=created_mask_outlier.get_fdata()\norignal_mask_flatten = data_preprocessing.flatten(original_mask[np.newaxis, :, :, :].copy())\norignal_mask_flatten = np.reshape(orignal_mask_flatten, (-1))\ncreated_mask_high_certainity_flatten = data_preprocessing.flatten(created_mask_high_certainity[np.newaxis, :, :, :].copy())\ncreated_mask_high_certainity_flatten = np.reshape(created_mask_high_certainity_flatten, (-1))\ncreated_mask_outlier_flatten = data_preprocessing.flatten(created_mask_outlier[np.newaxis, :, :, :].copy())\ncreated_mask_outlier_flatten = np.reshape(created_mask_outlier_flatten, (-1))\ntrain_data_flattened = data_preprocessing.flatten(train_data.copy())\ntest_data_flattened = data_preprocessing.flatten(test_data.copy())\ntrain_data_flattened = data_preprocessing.MinMax_scaler(train_data_flattened.copy())\ntest_data_flattened = data_preprocessing.MinMax_scaler(test_data_flattened.copy())\n\n\ntrain_data_inlier, train_labels_inlier, outlier_indices_train = data_preprocessing.outliers(train_data_flattened,\n train_labels,\n number_of_neighbours)\ntest_data_inlier, test_labels_inlier, outlier_indices_test = data_preprocessing.novelty(train_data_inlier,\n train_labels_inlier,\n test_data_flattened,\n test_labels,\n number_of_neighbours)\n\ntest_data_inlier_brain=test_data_inlier[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\ntest_data_outlier_brain=(test_data_flattened[outlier_indices_test])[:,np.squeeze(np.where(orignal_mask_flatten>0),axis=0)]\ntest_data_masked_high_certainity=test_data_inlier_brain* created_mask_high_certainity_flatten[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)]\ntest_data_inlier_CVspace = data_preprocessing.coefficient_of_variance(test_data_masked_high_certainity)[:,np.newaxis]\ntest_data_outlier_cv = data_preprocessing.coefficient_of_variance(\n test_data_outlier_brain *created_mask_outlier_flatten[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)])[:, np.newaxis]\n#load models\nhigh_certainity_model = pickle.load(open(high_certainity_model_name, 'rb'))\nlow_certainty_model = pickle.load(open(low_certainty_model_name, 'rb'))\noutliers_model = pickle.load(open(outliers_model_name, 'rb'))\n#output results\ntest_accuracy_high_certainity,F1_score_high_certainity,auc_high_certainity,low_confidence_indices=generate_result.out_result_highprob(test_data_inlier_CVspace,\n test_labels_inlier,orignal_mask_flatten,created_mask_high_certainity_flatten,high_certainity_model)\ntest_accuracy_low_certainty,F1_score_low_certainty,auc_low_certainty=generate_result.out_result(test_data_inlier_CVspace[low_confidence_indices],\n test_labels_inlier[low_confidence_indices],orignal_mask_flatten,created_mask_high_certainity_flatten,low_certainty_model)\ntest_accuracy_outlier,F1_score_outlier,auc_outlier= generate_result.out_result(test_data_outlier_cv ,\n test_labels[outlier_indices_test], orignal_mask_flatten,\n created_mask_outlier_flatten, outliers_model)\n\n\n#print results\nprint('total_test_accuracy>',(test_accuracy_high_certainity+test_accuracy_low_certainty+test_accuracy_outlier)/3)\nprint('total_F1_score>',(F1_score_high_certainity+F1_score_low_certainty+F1_score_outlier)/3)\nprint('total_AUC_score>',(auc_high_certainity+auc_low_certainty+auc_outlier)/3)"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/main.py",
"content": "#from hyper_opt import create_mask,model,model_1D\nimport load_data\nimport data_preprocessing\nimport generate_result\nfrom Model import create_mask\nfrom pathlib import Path\n\n\n\ndef main():\n train_Con_file_name = 'whole_brain_Oulu_Con.npz'\n train_AD_file_name = 'whole_brain_Oulu_AD.npz'\n test_Con_file_name = 'whole_brain_ADNI_Con.npz'\n test_AD_file_name = 'whole_brain_ADNI_AD.npz'\n root_dir='/data'\n mask_name='4mm_brain_mask_bin.nii.gz'\n results_directory='Results'\n results_path=load_data.find_path(results_directory)\n number_of_cv=5\n feature_selection_type='recursion'\n data_preprocessing_method='kstest and standarization and Normalization and Density ratio estimation'\n Hyperparameter=(4000,10)\n train_data,train_labels=load_data.train_data(train_Con_file_name,train_AD_file_name)\n test_data, test_labels = load_data.test_data(test_Con_file_name, test_AD_file_name)\n #sample_weight = data_preprocessing.density_ratio_estimation(train_data,test_data)\n original_mask=load_data.mask(mask_name,root_dir)\n\n #created_mask,model_,model_name,weights=create_mask(train_data,labels_train,number_of_cv,feature_selection_type,\n #Hyperparameter,mask_threshold=2,model_type='gaussian_process')\n\n #test_data = data_preprocessing.coefficient_of_variance(test_data)\n \n #model_, model_name=model_1D(train_data,labels_train,created_mask,data_validation=None,labels_validation=None,model_type='gaussian_process')\n train_data,test_data=data_preprocessing.KSTest(train_data,test_data,step=Hyperparameter[1])\n\n\n train_data = data_preprocessing.standarization(train_data)\n test_data = data_preprocessing.standarization(test_data)\n train_data = data_preprocessing.standarization(train_data)\n test_data = data_preprocessing.standarization(test_data)\n\n train_data = data_preprocessing. MinMax_scaler(train_data)\n test_data= data_preprocessing. MinMax_scaler(test_data)\n\n sample_weights=data_preprocessing.density_ratio_estimation(train_data,test_data)\n\n created_mask,model,model_name,weights =create_mask(train_data,train_labels,number_of_cv,feature_selection_type,Hyperparameter[0],1,model_type='Random_forest', sample_weights=sample_weights)\n \n generate_result.print_result(test_data, test_labels, original_mask, created_mask, model, model_name, weights,\n results_path,feature_selection_type,Hyperparameter,data_preprocessing_method)\n\n\n\n\n\n\nif __name__=='__main__':\n main()"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/pykliep.py",
"content": "import numpy as np\nimport warnings\n\nclass DensityRatioEstimator:\n \"\"\"\n Class to accomplish direct density estimation implementing the original KLIEP \n algorithm from Direct Importance Estimation with Model Selection\n and Its Application to Covariate Shift Adaptation by Sugiyama et al. \n \n The training set is distributed via \n train ~ p(x)\n and the test set is distributed via \n test ~ q(x).\n \n The KLIEP algorithm and its variants approximate w(x) = q(x) / p(x) directly. The predict function returns the\n estimate of w(x). The function w(x) can serve as sample weights for the training set during\n training to modify the expectation function that the model's loss function is optimized via,\n i.e.\n \n E_{x ~ w(x)p(x)} loss(x) = E_{x ~ q(x)} loss(x).\n \n Usage : \n The fit method is used to run the KLIEP algorithm using LCV and returns value of J \n trained on the entire training/test set with the best sigma found. \n Use the predict method on the training set to determine the sample weights from the KLIEP algorithm.\n \"\"\"\n \n def __init__(self, max_iter=5000, num_params=[.1,.2], epsilon=1e-4, cv=3, sigmas=[.01,.1,.25,.5,.75,1], random_state=None, verbose=0):\n \"\"\" \n Direct density estimation using an inner LCV loop to estimate the proper model. Can be used with sklearn\n cross validation methods with or without storing the inner CV. To use a standard grid search.\n \n \n max_iter : Number of iterations to perform\n num_params : List of number of test set vectors used to construct the approximation for inner LCV.\n Must be a float. Original paper used 10%, i.e. =.1\n sigmas : List of sigmas to be used in inner LCV loop.\n epsilon : Additive factor in the iterative algorithm for numerical stability.\n \"\"\"\n self.max_iter = max_iter\n self.num_params = num_params\n self.epsilon = epsilon\n self.verbose = verbose\n self.sigmas = sigmas\n self.cv = cv\n self.random_state = 0\n \n def fit(self, X_train, X_test, alpha_0=None):\n \"\"\" Uses cross validation to select sigma as in the original paper (LCV).\n In a break from sklearn convention, y=X_test.\n The parameter cv corresponds to R in the original paper.\n Once found, the best sigma is used to train on the full set.\"\"\"\n \n # LCV loop, shuffle a copy in place for performance.\n cv = self.cv\n chunk = int(X_test.shape[0]/float(cv))\n if self.random_state is not None:\n np.random.seed(self.random_state)\n X_test_shuffled = X_test.copy()\n np.random.shuffle(X_test_shuffled)\n \n j_scores = {}\n \n if type(self.sigmas) != list:\n self.sigmas = [self.sigmas]\n \n if type(self.num_params) != list:\n self.num_params = [self.num_params]\n \n if len(self.sigmas) * len(self.num_params) > 1:\n # Inner LCV loop\n for num_param in self.num_params:\n for sigma in self.sigmas:\n j_scores[(num_param,sigma)] = np.zeros(cv)\n for k in range(1,cv+1):\n if self.verbose > 0:\n print('Training: sigma: %s R: %s' % (sigma, k))\n X_test_fold = X_test_shuffled[(k-1)*chunk:k*chunk,:] \n j_scores[(num_param,sigma)][k-1] = self._fit(X_train=X_train, \n X_test=X_test_fold,\n num_parameters = num_param,\n sigma=sigma)\n j_scores[(num_param,sigma)] = np.mean(j_scores[(num_param,sigma)])\n\n sorted_scores = sorted([x for x in j_scores.items() if np.isfinite(x[1])], key=lambda x :x[1], reverse=True)\n if len(sorted_scores) == 0:\n warnings.warn('LCV failed to converge for all values of sigma.')\n return self\n self._sigma = sorted_scores[0][0][1]\n self._num_parameters = sorted_scores[0][0][0]\n self._j_scores = sorted_scores\n else:\n self._sigma = self.sigmas[0]\n self._num_parameters = self.num_params[0]\n # best sigma\n self._j = self._fit(X_train=X_train, X_test=X_test_shuffled, num_parameters=self._num_parameters, sigma=self._sigma)\n\n return self # Compatibility with sklearn\n \n def _fit(self, X_train, X_test, num_parameters, sigma, alpha_0=None):\n \"\"\" Fits the estimator with the given parameters w-hat and returns J\"\"\"\n \n num_parameters = num_parameters\n \n if type(num_parameters) == float:\n num_parameters = int(X_test.shape[0] * num_parameters)\n\n self._select_param_vectors(X_test=X_test, \n sigma=sigma,\n num_parameters=num_parameters)\n \n X_train = self._reshape_X(X_train)\n X_test = self._reshape_X(X_test)\n \n if alpha_0 is None:\n alpha_0 = np.ones(shape=(num_parameters,1))/float(num_parameters)\n \n self._find_alpha(X_train=X_train,\n X_test=X_test,\n num_parameters=num_parameters,\n epsilon=self.epsilon,\n alpha_0 = alpha_0,\n sigma=sigma)\n \n return self._calculate_j(X_test,sigma=sigma)\n \n def _calculate_j(self, X_test, sigma):\n return np.log(self.predict(X_test,sigma=sigma)).sum()/X_test.shape[0]\n \n def score(self, X_test):\n \"\"\" Return the J score, similar to sklearn's API \"\"\"\n return self._calculate_j(X_test=X_test, sigma=self._sigma)\n\n @staticmethod \n def _reshape_X(X):\n \"\"\" Reshape input from mxn to mx1xn to take advantage of numpy broadcasting. \"\"\"\n if len(X.shape) != 3:\n return X.reshape((X.shape[0],1,X.shape[1]))\n return X\n \n def _select_param_vectors(self, X_test, sigma, num_parameters):\n \"\"\" X_test is the test set. b is the number of parameters. \"\"\" \n indices = np.random.choice(X_test.shape[0], size=num_parameters, replace=False)\n self._test_vectors = X_test[indices,:].copy()\n self._phi_fitted = True\n \n def _phi(self, X, sigma=None):\n \n if sigma is None:\n sigma = self._sigma\n \n if self._phi_fitted:\n return np.exp(-np.sum((X-self._test_vectors)**2, axis=-1)/(2*sigma**2))\n raise Exception('Phi not fitted.')\n\n def _find_alpha(self, alpha_0, X_train, X_test, num_parameters, sigma, epsilon):\n A = np.zeros(shape=(X_test.shape[0],num_parameters))\n b = np.zeros(shape=(num_parameters,1))\n\n A = self._phi(X_test, sigma)\n b = self._phi(X_train, sigma).sum(axis=0) / X_train.shape[0] \n b = b.reshape((num_parameters, 1))\n \n out = alpha_0.copy()\n for k in range(self.max_iter):\n out += epsilon*np.dot(np.transpose(A),1./np.dot(A,out))\n out += b*(((1-np.dot(np.transpose(b),out))/np.dot(np.transpose(b),b)))\n out = np.maximum(0,out)\n out /= (np.dot(np.transpose(b),out))\n \n self._alpha = out\n self._fitted = True\n \n def predict(self, X, sigma=None):\n \"\"\" Equivalent of w(X) from the original paper.\"\"\"\n \n X = self._reshape_X(X)\n if not self._fitted:\n raise Exception('Not fitted!')\n return np.dot(self._phi(X, sigma=sigma), self._alpha).reshape((X.shape[0],))"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/readme.md",
"content": "This project is about utilizint resting_state FMRI to classify patients with Alzheimer's disease form controls.\nThe project started on June 2019, as a part of summer internship in Oulu universtiy, in collaboration with Oulu-university \nhospital.\n\n---\n## Installation\n___\n### Dependencies\n* Python(>=3.5)\n* Keras==2.2.4\n* nilearn==0.5.2\n* scipy==1.2.1\n* nibabel==2.4.1\n* numpy==1.16.2\n* imbalanced_learn==0.5.0\n* imblearn==0.0\n* scikit_learn==0.21.2\n* densratio==0.2.2\n* skimage==0.0\n* matplotlib==3.0.3\n\nDownload all using:\n\n```bash\npip3 install -r requirements.txt\n```\n\n## Data\n\nIn this project Oulu university data where used for trainign the model and [ADNI](http://adni.loni.usc.edu/data-samples/) data were used in testing the performance.\nThe 4mm Brain mask that was used for extracting brain from scalp had been extracted using [FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki)\n\n---\n## Model\n\nCoefficient of variation over time for BOLD signal was used as the input data for the model. The model Consists of three main \nsteps:\n1. Loading and preprocessing the input data\n1. Voxel Selection using cross validation for creating mask of most effective voxels\n1. Classify the masked data using many classifiers and printing the results (Gaussian process classifier provides the best \nperformance in high dimensional space.\n\n\n---\n### Running the code\n```python3\npython3 main.py\n```\nChange input parameters and variables in the main.py file to fit your requirements and preferences\nThe default script runs under 10 minutes on an average laptop using under 1 GB of memory.\n\n---\n## Results\n\nThe results folder sould be created by you and give it name to the variable 'Results_directory' , it contains the measured performace in **Results.txt**, the used parameters and chosed classifier **README.txt**, mask of effective voxels, and voxel importance weight.\nThe provided pretrained model in **Output_results_directory** gives the following confidence level:\n\n| | Median | Min(.95 CL) | Max(.95 CL) | \n|----------|--------|-------------|-------------|\n| Accuracy | .702 | .555 | .835 | \n| F1_score | .723 | .595 | .841 | \n| AUC | .781 | .721 | .856 | \n\n#### Note the mask and weights files images are in NIFTI format, you can use FSL utils or nibabel library to process the data or FSLeyes to visualize.\n#### Note try always to get unique name for the *Results* directory (unique in your root directory)\n\n\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/sample_test.py",
"content": "import pickle\nimport load_data\nimport data_preprocessing\nimport numpy as np\nimport nibabel as nib\nfrom sklearn.neighbors import LocalOutlierFactor\nfrom scipy.stats import variation\n\ntrain_Con_file_name = 'CV_OULU_CON.npz'\ntrain_AD_file_name = 'CV_OULU_AD.npz'\nmask_name = '4mm_brain_mask_bin.nii.gz'\ncreated_mask_high_certainity_file_name='./Output_results_directory/2019-08-10/1/high_certainity_model_mask.nii.gz'\ncreated_mask_outlier_file_name='./Output_results_directory/2019-08-10/1/high_certainity_model_mask.nii.gz'\nhigh_certainity_model_name='./Output_results_directory/2019-08-10/1/high_certainity_model.sav'\nlow_certainty_model_name='./Output_results_directory/2019-08-10/1/low_certainty_model.sav'\noutliers_model_name='./Output_results_directory/2019-08-10/1/outliers_model.sav'\nscaler_name='scaler.sav'\nnumber_of_neighbours = 1\nmodel_type='gaussian_process'\n\n#load nii file\nsample_name='CV_ADNI_AD.nii.gz'\nsample_path=load_data.find_path(sample_name)\nsample = nib.load(sample_path)\nsample = sample.get_fdata()\nsample=sample[:,:,:,7] #comment if using 3d data\n\n#load necessary files\nmask_4mm = load_data.mask(mask_name)\noriginal_mask=mask_4mm.get_fdata()\norignal_mask_flatten = data_preprocessing.flatten(original_mask[np.newaxis, :, :, :].copy())\norignal_mask_flatten = np.reshape(orignal_mask_flatten, (-1))\ncreated_mask_high_certainity = nib.load(created_mask_high_certainity_file_name)\ncreated_mask_outlier = nib.load(created_mask_outlier_file_name)\ncreated_mask_high_certainity=created_mask_high_certainity.get_fdata()\ncreated_mask_outlier=created_mask_outlier.get_fdata()\ncreated_mask_high_certainity_flatten = data_preprocessing.flatten(created_mask_high_certainity[np.newaxis, :, :, :].copy())\ncreated_mask_high_certainity_flatten = np.reshape(created_mask_high_certainity_flatten, (-1))\ncreated_mask_outlier_flatten = data_preprocessing.flatten(created_mask_outlier[np.newaxis, :, :, :].copy())\ncreated_mask_outlier_flatten = np.reshape(created_mask_outlier_flatten, (-1))\ntrain_data,train_labels=load_data.train_data_3d(train_Con_file_name,train_AD_file_name)\ntrain_data = np.moveaxis(train_data.copy(), 3, 0)\ntrain_data = train_data * original_mask\ntrain_data_flattened = data_preprocessing.flatten(train_data.copy())\ntrain_data_flattened = data_preprocessing.MinMax_scaler(train_data_flattened.copy())\n\n\n\n#preprocessing\nsample_pre=np.array(sample)*original_mask\nsample_pre=np.reshape(sample_pre,(1,-1))\nscaler = pickle.load(open(scaler_name, 'rb'))\nsample_pre=scaler.transform(sample_pre)\n\n\n#load_models\nhigh_certainity_model = pickle.load(open(high_certainity_model_name, 'rb'))\nlow_certainty_model = pickle.load(open(low_certainty_model_name, 'rb'))\noutliers_model = pickle.load(open(outliers_model_name, 'rb'))\n\n#prediction\nneigh = LocalOutlierFactor(n_neighbors=number_of_neighbours,novelty=True)\nneighbours=neigh.fit(train_data_flattened)\ninlier_outlier_state=neighbours.predict(sample_pre.copy())\nif (inlier_outlier_state==1):#inlier\n sample_pre = variation(sample_pre[:,np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)]*\n created_mask_high_certainity_flatten[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)],axis=1)[:,np.newaxis]\n\n\n if (model_type!='ensamble classifer'):\n sample_prob=high_certainity_model.predict_proba(sample_pre)\n if ((sample_prob[0,0]>.64)|(sample_prob[0,0]<.35)):\n sample_pred=high_certainity_model.predict(sample_pre)\n print('high certainty prediction')\n\n else:\n sample_pred = low_certainty_model.predict(sample_pre)\n print('low certainty prediction')\n\n else:\n sample_pred = low_certainty_model.predict(sample_pre)\n\nelse:\n sample_pre=variation(sample_pre[:,np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)]*\n created_mask_outlier_flatten[np.squeeze(np.where(orignal_mask_flatten > 0), axis=0)],axis=1)[:,np.newaxis]\n sample_pred = outliers_model.predict(sample_pre)\n print('an outlier prediction')\nif sample_pred:\n print('sample-prediction: AD')\nelse:\n print('sample-prediction: CON')\n\n\n"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/shuffle.py",
"content": "import numpy as np\nfrom numpy import load\nimport os\n\n\noulu_con_data=load('/data/fmri/Folder/AD_classification/Data/input_data/whole_brain_Oulu_Con.npz')['masked_voxels']\noulu_ad_data=load('/data/fmri/Folder/AD_classification/Data/input_data/whole_brain_Oulu_AD.npz')['masked_voxels']\nadni_con_data=load('/data/fmri/Folder/AD_classification/Data/input_data/whole_brain_ADNI_Con.npz')['masked_voxels']\nadni_ad_data=load('/data/fmri/Folder/AD_classification/Data/input_data/whole_brain_ADNI_AD.npz')['masked_voxels']\n\n\n\n\n\n\n\nidx = np.random.permutation(np.shape(oulu_con_data)[0])\noulu_con_data= (oulu_con_data)[idx,:]\noulu_ad_data=(oulu_ad_data)[idx,:]\nadni_con_data=(adni_con_data)[idx,:]\nadni_ad_data=(adni_ad_data)[idx,:]\n\nprint(idx)\nprint(np.shape(idx))\nos.mkdir('./data')\nnp.savez('./data/oulu_con_data', masked_voxels=oulu_con_data)\nnp.savez('./data/oulu_ad_data',masked_voxels=oulu_ad_data)\nnp.savez('./data/adni_con_data',masked_voxels=adni_con_data)\nnp.savez('./data/adni_ad_data',masked_voxels=adni_ad_data)\nnp.savez('./data/key',idx)\n\nnpzfile = np.load('data/key.npz')\nnpzfile=np.asarray(npzfile['arr_0'])\nprint(npzfile)\nprint(np.shape(npzfile))"
},
{
"path": "Machine Learning/Classification/Alzhimers CV-BOLD Classification/writing.py",
"content": "import numpy as np\nfrom numpy import load\nimport nibabel as nib\n\nmasking_img = nib.load('/data/fmri/Folder/AD_classification/Data/input_data/4mm_brain_mask_bin.nii.gz')\nmasking_shape = masking_img.shape\n\nmasking = np.empty(masking_shape, dtype=float)\nmasking[:,:,:] = masking_img.get_data().astype(float)\nprint(masking.shape)\ntmp=np.where(masking[2,:,:]>0)\nprint(((tmp)))\n\n\n\n'''\nimport os \nfrom datetime import date\nimport glob\n\ntoday = date.today()\nx='hello'\nf=open('test.txt',\"w+\")\nf.write(x+'world')\n\nos.mkdir('writing')\nLatestFile = sorted(os.listdir('/data/fmri/Folder/AD_classification/codes/model/writing'),reverse = True)\n\nx=int(LatestFile[0][0])\nx=x+1\nprint(x)\n\n'''"
},
{
"path": "Machine Learning/Classification/Sensor-activity-recognition/codes/classes_accuarcy.m",
"content": "function [class_accuarcy] = classes_accuarcy(test_labels,predicted_labels)\r\n% giving the predicted labels and the groun truth labels, this function\r\n% returns the class(activity) that is best classified with the classifier\r\n\r\nmisclassified_index=find(test_labels~=predicted_labels);\r\n\r\nmisclassified_labels=test_labels(misclassified_index);\r\n\r\nunique_labels=unique(test_labels);\r\nclass_accuarcy=[]; \r\nfor i =1: length(unique_labels)\r\ncounter=length(find(misclassified_labels==unique_labels(i)));\r\nclass_accuarcy=[class_accuarcy counter];\r\nend \r\nclass_accuarcy=1-(class_accuarcy/length(test_labels));\r\n\r\n\r\nend\r\n\r\n"
},
{
"path": "Machine Learning/Classification/Sensor-activity-recognition/codes/classification.m",
"content": "function [acc,class_accuarcy] = classification(train_features,train_labels,test_features,test_labels,classifier_name)\r\n%Apply the croos validation on the data and use Knn classifier \r\n% Detailed explanation goes here\r\n\r\n\r\n if strcmp(classifier_name,'KNN')\r\n clear classifer_model;\r\n classifer_model=fitcknn(train_features,train_labels,'NumNeighbors',5);\r\n predicted_labels=predict(classifer_model,test_features);\r\n performance_evaluaion=classperf(test_labels);\r\n classperf(performance_evaluaion,predicted_labels);\r\n acc=length(find(predicted_labels==test_labels))/length(predicted_labels);\r\n class_accuarcy = classes_accuarcy(test_labels,predicted_labels);\r\n \r\n elseif strcmp(classifier_name,'LDA')\r\n clear classifer_model;\r\n classifer_model=fitcdiscr(train_features,train_labels,'DiscrimType','linear');\r\n predicted_labels=predict(classifer_model,test_features);\r\n acc=length(find(predicted_labels==test_labels))/length(predicted_labels);\r\n class_accuarcy = classes_accuarcy(test_labels,predicted_labels);\r\n\r\n \r\n elseif strcmp(classifier_name,'QDA')\r\n classifer_model=fitcdiscr(train_features,train_labels,'DiscrimType','quadratic');\r\n predicted_labels=predict(classifer_model,test_features); \r\n acc=length(find(predicted_labels==test_labels))/length(predicted_labels);\r\n class_accuarcy = classes_accuarcy(test_labels,predicted_labels);\r\n\r\nend\r\n\r\n"
},
{
"path": "Machine Learning/Classification/Sensor-activity-recognition/codes/create_feature_map.m",
"content": "function [feature_map] = create_feature_map(activity_data,window_size)\r\n%UNTITLED2 Summary of this function goes here\r\n% Detailed explanation goes here\r\nstruct_fields=fieldnames(activity_data);\r\nfeature_map=struct;\r\nfor i=1:length(struct_fields)\r\n participant=getfield(activity_data,cell2mat(struct_fields(i)));\r\n labels=participant.labels;\r\n new_label_index=find((labels(2:end)-labels(1:end-1))~=0);\r\n new_label_index=[1 ; new_label_index ; length(labels)];\r\n participant_field_names=fieldnames(participant);\r\n labels_col=[];\r\n feat_map_participant=[];\r\n feature_map_all_positions=[];\r\n for j=1:length(participant_field_names)\r\n feat_map_all_labels_one_position=[];\r\n if ~strcmp(cell2mat(participant_field_names(j)), 'labels') && ~strcmp(cell2mat(participant_field_names(j)), 'time')\r\n position_data=getfield(participant,cell2mat(participant_field_names(j)));\r\n labels_col=[];\r\n for k=1:length(new_label_index)-1\r\n feat_map_all_axis_one_label=[];\r\n for c=1:9\r\n if k==length(new_label_index)-1\r\n strip=position_data(new_label_index(k):new_label_index(k+1),c);\r\n current_label=labels(new_label_index(k+1));\r\n \r\n else \r\n strip=position_data(new_label_index(k):new_label_index(k+1),c);\r\n current_label=labels(new_label_index(k+1));\r\n end\r\n feat_map_axis=features(strip,window_size);\r\n feat_map_all_axis_one_label=horzcat(feat_map_all_axis_one_label,feat_map_axis);\r\n \r\n end \r\n labels_col_temp=ones(size(feat_map_all_axis_one_label,1),1)*current_label;\r\n labels_col=vertcat(labels_col,labels_col_temp);\r\n feat_map_all_labels_one_position=vertcat(feat_map_all_labels_one_position,feat_map_all_axis_one_label);\r\n end\r\n feature_map_all_positions=horzcat(feature_map_all_positions,feat_map_all_labels_one_position);\r\n \r\n end\r\n end\r\n feat_map_participant=horzcat(labels_col,feature_map_all_positions);\r\n feature_map.(cell2mat(struct_fields(i)))=feat_map_participant;\r\nend\r\nsave([ 'feature_map_' int2str(window_size) 's.mat'],'feature_map')\r\nend\r\n\r\n"
},
{
"path": "Machine Learning/Classification/Sensor-activity-recognition/codes/main.m",
"content": "%% create new feature map and use it \r\nclear all\r\nclc;\r\nactivity_data=load('dataActivity');\r\nclassification_accuarcy=[];\r\nk_folds=10; % number of folds used for cross validation\r\nwindow_size=8; % widnow size for creating the feature map \r\ncreate_new_feature_map=1; % if ==1 then a new feature map will be created , else a saved one will be used \r\nsaved_feature_map_file_name='feature_map_3s.mat'; % the name of the feature map file \r\nscaling=0; % scaling should be ==1 if you would like to scale the data and 0 if not\r\noutliers=0; % outliers should be ==1 if you would like to remove the outliers and zero if ypu donot like.\r\n%[activity_data] = scalingANDoutliers(activity_data,scaling,outliers);\r\n\r\n% Check whether a new feature map will be created or a save one should be\r\n% used\r\nif create_new_feature_map==1\r\nfeature_map=create_feature_map(activity_data,window_size);\r\nparticipant_names=fieldnames(feature_map);\r\nelse \r\nfeature_map=load(saved_feature_map_file_name);\r\nfeature_map=feature_map.feature_map;\r\nparticipant_names=fieldnames(feature_map); \r\nend\r\n\r\n\r\nclassifier_name='KNN'; % classifier name, to change the classifier used check the names of the avaliable classifier from classification function\r\ntrain_labels=[];\r\ntrain_features=[];\r\ncross_validation_each_posiiton_acc=[];\r\nmax_class_acc_each_position=[];\r\nmax_class_acc_value=[];\r\n\r\n\r\ntime_starting_feature_index=1;\r\nfrequency_starting_feature_index=7;\r\n\r\ntime_ending_feature_index=6;\r\nfrequency_ending_feature_index=11;\r\n\r\ntime_features=0;\r\nclassification_accuarcy=[];\r\nclass_acc_all_folds=[];\r\nfor j=1:k_folds\r\n train_features=[];\r\n test_features=[];\r\n train_labels=[];\r\n test_labels=[];\r\n for i=1:length(participant_names)\r\n \r\n if i==length(participant_names)-(j-1)\r\n participant=getfield(feature_map,cell2mat(participant_names(i)));\r\n test_labels= participant(:,1);\r\n test_features=participant(:,2:end);\r\n else\r\n participant=getfield(feature_map,cell2mat(participant_names(i)));\r\n train_labels=vertcat(train_labels,participant(:,1));\r\n train_features=vertcat(train_features,participant(:,2:end));\r\n end \r\nend\r\n%%%% selecting certain feature for example certain positions or certain\r\n%%%% axis should be done here before giving the data to the classifier.\r\nposition_feature_index_all=[];\r\nfor sensor=0:8\r\nfor position=0:4\r\nif time_features==1\r\nstarting_index=time_starting_feature_index;\r\nending_index= time_ending_feature_index; \r\nelseif time_features==0\r\nstarting_index=frequency_starting_feature_index;\r\nending_index= frequency_ending_feature_index; \r\nelse\r\n starting_index=1;\r\n ending_index=11;\r\nend\r\nposition_feature_index=linspace(starting_index,ending_index,ending_index-starting_index+1)+11*sensor+position*99;\r\nposition_feature_index_all=[position_feature_index_all position_feature_index];\r\n end\r\n end\r\n\r\ntrain_features_certain_positions=train_features(:,position_feature_index_all);\r\ntest_features_certain_positions=test_features(:,position_feature_index_all);\r\n[train_features_certain_positions] = scalingANDoutliers(train_features_certain_positions,scaling,outliers);\r\n[test_features_certain_positions] = scalingANDoutliers(test_features_certain_positions,scaling,outliers);\r\n[acc,class_acc] =classification(train_features_certain_positions,train_labels,test_features_certain_positions,test_labels,classifier_name);\r\nclassification_accuarcy=[classification_accuarcy acc];\r\nclass_acc_all_folds=[class_acc_all_folds ; class_acc];\r\nend\r\ncross_validation_acc=mean(classification_accuarcy);\r\nclass_acc_one_position=mean(class_acc_all_folds,1);\r\n[max_class_acc,max_class_label]=max(class_acc_one_position);\r\n\r\n\r\n\r\n\r\n\r\n\r\n"
},
{
"path": "Machine Learning/Classification/Sensor-activity-recognition/codes/performance_evaluation.m",
"content": "function [cp] = performance_evaluation(classifier_model,test_data,test_labels)\r\n%UNTITLED3 Summary of this function goes here\r\n% Detailed explanation goes here\r\n predicted_labels=predict(classifer_model,test_data);\r\n cp=classperf(test_labels);\r\n classperf(cp,predicted_labels)\r\n \r\n\r\n\r\nend\r\n\r\n"
},
{
"path": "Machine Learning/Classification/Sensor-activity-recognition/codes/readme.md",
"content": "\n"
},
{
"path": "Machine Learning/Classification/Sensor-activity-recognition/codes/scalingANDoutliers.m",
"content": "function [scaledANDcleanedData] = scalingANDoutliers(data,scaling,outliers)\n%Scales the activity data person-wise and postition-wise\n% Detailed explanation goes here\n\n%positions = {'leftPocket','rightPocket','belt','wrist','upperArm'};\n\n%scaling\n\n \n if scaling == 1 %standardization\n \n minVal = min(data,[],2);\n maxVal = max(data,[],2);\n\n data = (data - minVal)./maxVal;\n \n \n else\n ;\n end\n \n if outliers == 1\n \n if max(data.(positions{f})) > 2\n \n data.(positions{f})(data.(positions{f}) > 2) = NaN; \n \n end\n \n else\n ;\n end\n\n\nscaledANDcleanedData = data;\n\nend\n\n\n"
},
{
"path": "Machine Learning/Classification/Sensor-activity-recognition/readme.md",
"content": "# Sensor Activity Recogniation \n\n\n### Dataset\nThe dataset can be downloaded __[here](https://www.kaggle.com/youssef19/sensor-activity-dataset)__\n\nThe feature matrix has 381 columns and n of rows depending on the widnow size \n\nThe first column is the labels column , so after that there are 380 columns which is the following :\n380= 9*5*8\n\n9:(3 axis for the three sensors: Accelometer , Linear Accelometer and Gyroscope)\n\n5:(five positions and they are in this order: left pocket,right pocket,belt,wrist,upper arm )\n\n3:(eight features an dthey are in these order:\n\n 1.Mean\n\n 2.Standard Deviation \n\n 3.Median \n\n 4.Variance \n\n 5.Zero crossings\n\n 6.Root mean square value \n\n 7.Sum of FFT coefficients\n\n 8.Signal energy medium )\n\n\nSo the first 8 columns are the features of the xasis of the accelometer for the left pocket position and so on . \n\n"
},
{
"path": "Machine Learning/Clustering/Customer identification for mail order products/Identify Customer Segments.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Project: Identify Customer Segments\\n\",\n \"\\n\",\n \"In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.\\n\",\n \"\\n\",\n \"This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. **Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks.** The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.\\n\",\n \"\\n\",\n \"It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. **There will be times in the project where you will need to make and justify your own decisions on how to treat the data.** These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.\\n\",\n \"\\n\",\n \"At the end of most sections, there will be a Markdown cell labeled **Discussion**. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. **Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'\\\\nImport note: The classroom currently uses sklearn version 0.19.\\\\nIf you need to use an imputer, it is available in sklearn.preprocessing.Imputer,\\\\ninstead of sklearn.impute as in newer versions of sklearn.\\\\n'\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# import libraries here; add more as necessary\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"# magic word for producing visualizations in notebook\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"'''\\n\",\n \"Import note: The classroom currently uses sklearn version 0.19.\\n\",\n \"If you need to use an imputer, it is available in sklearn.preprocessing.Imputer,\\n\",\n \"instead of sklearn.impute as in newer versions of sklearn.\\n\",\n \"'''\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 0: Load the Data\\n\",\n \"\\n\",\n \"There are four files associated with this project (not including this one):\\n\",\n \"\\n\",\n \"- `Udacity_AZDIAS_Subset.csv`: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).\\n\",\n \"- `Udacity_CUSTOMERS_Subset.csv`: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).\\n\",\n \"- `Data_Dictionary.md`: Detailed information file about the features in the provided datasets.\\n\",\n \"- `AZDIAS_Feature_Summary.csv`: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns\\n\",\n \"\\n\",\n \"Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.\\n\",\n \"\\n\",\n \"To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the `.csv` data files in this project: they're semicolon (`;`) delimited, so you'll need an additional argument in your [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.\\n\",\n \"\\n\",\n \"Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load in the general demographics data.\\n\",\n \"azdias = pd.read_csv('Udacity_AZDIAS_Subset.csv',sep=';')\\n\",\n \"\\n\",\n \"# Load in the feature summary file.\\n\",\n \"feat_info = pd.read_csv('AZDIAS_Feature_Summary.csv',sep=';')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(891221, 85)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.shape(azdias)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \" attribute information_level type missing_or_unknown\\n\",\n \"0 AGER_TYP person categorical [-1,0]\\n\",\n \"1 ALTERSKATEGORIE_GROB person ordinal [-1,0,9]\\n\",\n \"2 ANREDE_KZ person categorical [-1,0]\\n\",\n \"3 CJT_GESAMTTYP person categorical [0]\\n\",\n \"4 FINANZ_MINIMALIST person ordinal [-1]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"feat_info.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"> **Tip**: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut `esc --> a` (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and `esc --> b` adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use `esc --> m` and to convert to a code cell, use `esc --> y`. \\n\",\n \"\\n\",\n \"## Step 1: Preprocessing\\n\",\n \"\\n\",\n \"### Step 1.1: Assess Missing Data\\n\",\n \"\\n\",\n \"The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the **Discussion** cell with your findings and decisions at the end of each step that has one!\\n\",\n \"\\n\",\n \"#### Step 1.1.1: Convert Missing Value Codes to NaNs\\n\",\n \"The fourth column of the feature attributes summary (loaded in above as `feat_info`) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. `[-1,0]`), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.\\n\",\n \"\\n\",\n \"**As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def unknown_col_correction(unknown_col):\\n\",\n \" unknown_values_corr=pd.DataFrame()\\n\",\n \" for x in unknown_col:\\n\",\n \" x=list(x)\\n\",\n \" x.remove('[')\\n\",\n \" x.remove(']')\\n\",\n \" while ',' in x: \\n\",\n \" x.remove(',')\\n\",\n \" unknown_values=[]\\n\",\n \" flag=0\\n\",\n \" for i in range(len(x)):\\n\",\n \" if flag==1:\\n\",\n \" flag=0\\n\",\n \" continue\\n\",\n \" if x[i]=='-':\\n\",\n \" unknown_values.append(int(x[i+1])*-1)\\n\",\n \" flag=1\\n\",\n \" elif x[i]=='X':\\n\",\n \" unknown_values.append(\\\"XX\\\")\\n\",\n \" flag=1\\n\",\n \" else:\\n\",\n \" unknown_values.append(int(x[i]))\\n\",\n \" unknown_values_corr=unknown_values_corr.append(pd.Series([unknown_values]),ignore_index=True) \\n\",\n \" return unknown_values_corr \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
attribute
\\n\",\n \"
information_level
\\n\",\n \"
type
\\n\",\n \"
missing_or_unknown
\\n\",\n \"
corrected_unknown
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
AGER_TYP
\\n\",\n \"
person
\\n\",\n \"
categorical
\\n\",\n \"
[-1,0]
\\n\",\n \"
[-1, 0]
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
ALTERSKATEGORIE_GROB
\\n\",\n \"
person
\\n\",\n \"
ordinal
\\n\",\n \"
[-1,0,9]
\\n\",\n \"
[-1, 0, 9]
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
ANREDE_KZ
\\n\",\n \"
person
\\n\",\n \"
categorical
\\n\",\n \"
[-1,0]
\\n\",\n \"
[-1, 0]
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
CJT_GESAMTTYP
\\n\",\n \"
person
\\n\",\n \"
categorical
\\n\",\n \"
[0]
\\n\",\n \"
[0]
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
FINANZ_MINIMALIST
\\n\",\n \"
person
\\n\",\n \"
ordinal
\\n\",\n \"
[-1]
\\n\",\n \"
[-1]
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" attribute information_level type missing_or_unknown \\\\\\n\",\n \"0 AGER_TYP person categorical [-1,0] \\n\",\n \"1 ALTERSKATEGORIE_GROB person ordinal [-1,0,9] \\n\",\n \"2 ANREDE_KZ person categorical [-1,0] \\n\",\n \"3 CJT_GESAMTTYP person categorical [0] \\n\",\n \"4 FINANZ_MINIMALIST person ordinal [-1] \\n\",\n \"\\n\",\n \" corrected_unknown \\n\",\n \"0 [-1, 0] \\n\",\n \"1 [-1, 0, 9] \\n\",\n \"2 [-1, 0] \\n\",\n \"3 [0] \\n\",\n \"4 [-1] \"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"corrected_unknow_values=unknown_col_correction(feat_info.iloc[:,3])\\n\",\n \"feat_info['corrected_unknown']=corrected_unknow_values\\n\",\n \"feat_info.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" \\\"\\\"\\\"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Identify missing or unknown data values and convert them to NaNs.\\n\",\n \"def missing_values_convert(df_input):\\n\",\n \" for i in range(len(feat_info)):\\n\",\n \" for unknown_value in feat_info.iloc[i,4]:\\n\",\n \" df_input.iloc[:,i][df_input.iloc[:,i]== unknown_value]=np.nan\\n\",\n \" return df_input\\n\",\n \"\\n\",\n \"azdias=missing_values_convert(azdias)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Step 1.1.2: Assess Missing Data in Each Column\\n\",\n \"\\n\",\n \"How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's [`hist()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html) function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)\\n\",\n \"\\n\",\n \"For the remaining features, are there any patterns in which columns have, or share, missing data?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[]], dtype=object)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Perform an assessment of how much missing data there is in each column of the\\n\",\n \"# dataset.\\n\",\n \"missing_data = pd.DataFrame(azdias.isnull().sum().reset_index())\\n\",\n \"missing_data.columns = ['Column_name','Count_missing_value']\\n\",\n \"missing_data.hist(bins=85)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2 ANREDE_KZ\\n\",\n \"4 FINANZ_MINIMALIST\\n\",\n \"5 FINANZ_SPARER\\n\",\n \"6 FINANZ_VORSORGER\\n\",\n \"7 FINANZ_ANLEGER\\n\",\n \"8 FINANZ_UNAUFFAELLIGER\\n\",\n \"9 FINANZ_HAUSBAUER\\n\",\n \"10 FINANZTYP\\n\",\n \"13 GREEN_AVANTGARDE\\n\",\n \"24 SEMIO_SOZ\\n\",\n \"25 SEMIO_FAM\\n\",\n \"26 SEMIO_REL\\n\",\n \"27 SEMIO_MAT\\n\",\n \"28 SEMIO_VERT\\n\",\n \"29 SEMIO_LUST\\n\",\n \"30 SEMIO_ERL\\n\",\n \"31 SEMIO_KULT\\n\",\n \"32 SEMIO_RAT\\n\",\n \"33 SEMIO_KRIT\\n\",\n \"34 SEMIO_DOM\\n\",\n \"35 SEMIO_KAEM\\n\",\n \"36 SEMIO_PFLICHT\\n\",\n \"37 SEMIO_TRADV\\n\",\n \"42 ZABEOTYP\\n\",\n \"Name: Column_name, dtype: object\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# features with no missing or unknow values \\n\",\n \"complete_features=missing_data[(missing_data['Count_missing_value']==0)]['Column_name']\\n\",\n \"complete_features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \" Column_name Count_missing_value\\n\",\n \"0 AGER_TYP 685843\\n\",\n \"11 GEBURTSJAHR 392318\\n\",\n \"40 TITEL_KZ 889061\\n\",\n \"43 ALTER_HH 310267\\n\",\n \"47 KK_KUNDENTYP 584612\\n\",\n \"64 KBA05_BAUMAX 476524\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"missing_data[ (missing_data['Count_missing_value']>200000) ]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 AGER_TYP\\n\",\n \"11 GEBURTSJAHR\\n\",\n \"40 TITEL_KZ\\n\",\n \"43 ALTER_HH\\n\",\n \"47 KK_KUNDENTYP\\n\",\n \"64 KBA05_BAUMAX\\n\",\n \"Name: Column_name, dtype: object\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"outliers_features=missing_data[(missing_data['Count_missing_value']>200000)]['Column_name']\\n\",\n \"outliers_features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[]], dtype=object)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXgAAAEICAYAAABVv+9nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAGCRJREFUeJzt3XuUZWV55/HvE1qgobRBW0tsiK3G4EI6ErriNZoqhcht1FnLUQg6kJjpZYgZRlGDYrytcYbgkEFXTJAYR0cuJSJeAiZKGErGNQat5tYgV6GVRmxApLGwM9L6zB97d3OqqKpz21Xn8M73s9ZZvc++vPs57znnV7vevWt3ZCaSpPL82qALkCQtDQNekgplwEtSoQx4SSqUAS9JhTLgJalQBryKFhHvjYhP9bH98RHxjSZrakpErI2IjIgVg65Fwym8Dl5zRcQfAO8Angf8DLgW+EhmfmsJ95nAczPz9qXaR2kiYi1wJ/CEzNwx2Go0jDyC1ywR8Q7gLOC/AKPArwN/A7x2kHVJ6p4Br10iYhXwYeBPM/PizHw4Mx/JzH/IzHdFxB4RcVZE/Kh+nBURe9TbnhgR35rTXkbEb9TTn4mIT0TEpRHxs4i4KiKeUy+7st7kuoiYiYg3LlLjeERsiYh3R8S9EXFPRLwuIo6KiFsj4oGIeG/L+h+MiHPr6T0j4tyI+ElEPBgR342I0Zb676hruzMijp/vddWv6a0RcVtE/LR+TVEv2y0izoyI++s23tZuCCUijo2I6Tnz3h4RX62nj46IayLioYi4KyI+uEhbmyPisPlee/38xRHxf+rXfl1EjC/UlspgwKvVS4A9gS8tsPw04MXAIcALgBcC7+ui/eOADwH7ArcDHwHIzFfUy1+QmSOZ+fk27Ty9rnMN8H7g74A3AeuBlwPvj4hnz7PdCcAq4ADgKcBbge0RsTfwceDIzHwi8FKqYamFHAP8DlUfvAF4dT3/PwBHUvXPocDr2rwOgK8CB0bEc1vm/QFwfj39MPDvgX2Ao4E/iYhO2p0lItYAlwL/GXgy8E7gixHx1G7b0uOHAa9WTwHuX2Q893jgw5l5b2beRxXWb+6i/Ysz8zt1++dRBWEvHqE6J/AIMAmsBj6WmT/LzBuBG4HfWmC7pwC/kZm/zMyNmflQvexXwMERsTIz76nbWcjpmflgZv4QuKLldbyhrmNLZv4UOL3dC8nMnwNfofrhRx30z6MKfjJzKjM3ZeavMvN64ALg99q1O483AV/LzK/VbV0GTANH9dCWHicMeLX6CbB6kSGFZwA/aHn+g3pep37cMv1zYKS78nb5SWb+sp7eXv+7tWX59gXa/hzwdWCyHmI6IyKekJkPA2+kOqK/px5Get4i+1/odTwDuKtlWev0Ys6nDniqo/cv18FPRLwoIq6IiPsiYltd4+oO2231TODf1cMzD0bEg8DvAvv10JYeJwx4tfo28K8sPLTwI6qg2OnX63lQDSXstXNBRDx9KQrsR30+4UOZeRDVMMwxVMMfZObXM/NwqsC7mWrYp1v3APu3PD+gw+2+QfWD9RCqoD+/Zdn5VEfzB2TmKuBsIBZoZ9Z7QDWUtdNdwOcyc5+Wx96Z2fa3DD1+GfDaJTO3UY1pf6I+cblXRDwhIo6MiDOohgfeFxFPjYjV9bo7T+JdBzw/Ig6JiD2BD3a5+63AfOPmjYmIiYhYFxG7AQ9RDdn8MiJGI+I19Vj8/wVmgF8u1tYCLgROjog1EbEP8OedbFQPWV0EfJRqfPyylsVPBB7IzH+NiBdSHeEv5Frg2Po9GwNe37LsXODfRMSr65PBe9YnrPefvymVwIDXLJn5V1TXwL8PuI/qyO9twJepTtBNA9cDm4Cr63lk5q1UV+D8M3Ab0O018x8EPlsPH7yh7xcyv6dTBelDwE3AN6mC79eAU6h+G3mAaoz7pB7a/zuqo/HrgWuArwE76OyHxfnAYcAX5pwDOQn4cET8jOoH6oWLtPEXwHOAn1KdH9n1m0Bm3kV1qet7efR9fRdmQNH8QydpiUTEkcDZmfnMtitLS8Cf3lJDImJlfT3+ivqyxA+w8CWn0pIz4DV0orp/zMw8j38cdG1tBNXQyE+phmhuohpWYYHXMxMRLx9gvSqcQzSSVCiP4CWpUMt6m9HVq1fn2rVr26738MMPs/feey99QT2wtt5YW++GuT5r6023tW3cuPH+zOz+thKZuWyP9evXZyeuuOKKjtYbBGvrjbX1bpjrs7bedFsbMJ09ZK5DNJJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVKhlvVWBpMdae+qliy4/Zd0OTmyzTi82n350421quHgEL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhWob8BHx6Yi4NyJumGfZOyMiI2L10pQnSepVJ0fwnwGOmDszIg4ADgd+2HBNkqQGtA34zLwSeGCeRf8deDeQTRclSepfT2PwEfEa4O7MvK7heiRJDYnM9gfgEbEWuCQzD46IvYArgN/PzG0RsRkYy8z7F9h2A7ABYHR0dP3k5GTb/c3MzDAyMtLpa1hW1tYba1vYpru3Lbp8dCVs3d78ftetWdV3G4Puu8WUVNvExMTGzBzrdj+9BPw64HLg5/Xi/YEfAS/MzB8v1s7Y2FhOT0+33d/U1BTj4+Nt1xsEa+uNtS2sk9sFn7mp+Tt7N3G74EH33WJKqi0iegr4rj81mbkJeFrLjjezyBG8JGkwOrlM8gLg28CBEbElIt6y9GVJkvrV9gg+M49rs3xtY9VIkhrjX7JKUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JherkP93+dETcGxE3tMz7aETcHBHXR8SXImKfpS1TktStTo7gPwMcMWfeZcDBmflbwK3AexquS5LUp7YBn5lXAg/MmfeNzNxRP/0XYP8lqE2S1IfIzPYrRawFLsnMg+dZ9g/A5zPz3AW23QBsABgdHV0/OTnZdn8zMzOMjIy0XW8QrK031rawTXdvW3T56ErYur35/a5bs6rvNgbdd4spqbaJiYmNmTnW7X5WdLtBq4g4DdgBnLfQOpl5DnAOwNjYWI6Pj7dtd2pqik7WGwRr6421LezEUy9ddPkp63Zw5qa+vqrz2nz8eN9tDLrvFmNtfQR8RJwAHAO8Kjv5NUCStKx6CviIOAL4c+D3MvPnzZYkSWpCJ5dJXgB8GzgwIrZExFuAvwaeCFwWEddGxNlLXKckqUttj+Az87h5Zv/9EtQiSWqQf8kqSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFaqT/3T70xFxb0Tc0DLvyRFxWUTcVv+779KWKUnqVidH8J8Bjpgz71Tg8sx8LnB5/VySNETaBnxmXgk8MGf2a4HP1tOfBV7XcF2SpD5FZrZfKWItcElmHlw/fzAz92lZ/tPMnHeYJiI2ABsARkdH109OTrbd38zMDCMjI53Uv+ysrTfWtrBNd29bdPnoSti6vfn9rluzqu82Bt13iymptomJiY2ZOdbtflZ0u0G3MvMc4ByAsbGxHB8fb7vN1NQUnaw3CNbWG2tb2ImnXrro8lPW7eDMTc1/VTcfP953G4Puu8VYW+9X0WyNiP0A6n/vba4kSVITeg34rwIn1NMnAF9pphxJUlM6uUzyAuDbwIERsSUi3gKcDhweEbcBh9fPJUlDpO3AXmYet8CiVzVciySpQf4lqyQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQS36zMenxYG2bG35Jj0cewUtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqH6CviIeHtE3BgRN0TEBRGxZ1OFSZL603PAR8Qa4D8CY5l5MLAbcGxThUmS+tPvEM0KYGVErAD2An7Uf0mSpCZEZva+ccTJwEeA7cA3MvP4edbZAGwAGB0dXT85Odm23ZmZGUZGRmbN23T3tp7r7Ne6Nat2Tc9X27Cwtt7MzMxw57ZfDrqMBY2uhK3bm2+39XPdq2F/X0upbWJiYmNmjnW7n54DPiL2Bb4IvBF4EPgCcFFmnrvQNmNjYzk9Pd227ampKcbHx2fNG+Td/jaffvSu6flqGxbW1pupqSlO/KeHB13Ggk5Zt4MzNzV/49fWz3Wvhv19LaW2iOgp4PsZojkMuDMz78vMR4CLgZf20Z4kqUH9BPwPgRdHxF4REcCrgJuaKUuS1K+eAz4zrwIuAq4GNtVtndNQXZKkPvU1sJeZHwA+0FAtkqQG+ZesklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEI1fwcjqQ+DuKncKet28P/jV6GJvj5l3Q5O7KGdJm50pvY8gpekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUqL4CPiL2iYiLIuLmiLgpIl7SVGGSpP70ewOOjwH/lJmvj4jdgb0aqEmS1ICeAz4ingS8AjgRIDN/AfyimbIkSf2KzOxtw4hDgHOA7wEvADYCJ2fmw3PW2wBsABgdHV0/OTnZtu2ZmRlGRkZmzdt097ae6mzCujWrdk3PV9uwKKG2QbzPoyth6/Zl323Hhrm+x1ttrd/lQer2uzoxMbExM8e63U8/AT8G/Avwssy8KiI+BjyUmX+x0DZjY2M5PT3dtu2pqSnGx8dnzRvEbWR3ar216Xy1DYsSahvU7YLP3DS8twse5voeb7UNy22Ku/2uRkRPAd/PSdYtwJbMvKp+fhFwaB/tSZIa1HPAZ+aPgbsi4sB61quohmskSUOg39+t/gw4r76C5g7gD/svSZLUhL4CPjOvBboeF5IkLT3/klWSCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqH6DviI2C0iromIS5ooSJLUjCaO4E8GbmqgHUlSg/oK+IjYHzga+FQz5UiSmtLvEfxZwLuBXzVQiySpQZGZvW0YcQxwVGaeFBHjwDsz85h51tsAbAAYHR1dPzk52bbtmZkZRkZGZs3bdPe2nupswro1q3ZNz1fbsCihtkG8z6MrYev2Zd9tx4a5vsdbba3f5UHq9rs6MTGxMTPHut1PPwH/X4E3AzuAPYEnARdn5psW2mZsbCynp6fbtj01NcX4+PiseWtPvbSnOpuw+fSjd03PV9uwKKG2QbzPp6zbwZmbViz7fjs1zPU93mpr/S4PUrff1YjoKeB7HqLJzPdk5v6ZuRY4Fvhfi4W7JGl5eR28JBWqkd+tMnMKmGqiLUlSMzyCl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBVqOG8iIUlLYFjuabVcPIKXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVKieAz4iDoiIKyLipoi4MSJObrIwSVJ/+rkXzQ7glMy8OiKeCGyMiMsy83sN1SZJ6kPPR/CZeU9mXl1P/wy4CVjTVGGSpP5EZvbfSMRa4Erg4Mx8aM6yDcAGgNHR0fWTk5Nt25uZmWFkZGTWvE13b+u7zl6tW7Nq1/R8tQ2LEmobxPs8uhK2bl/23XZsmOuzts71kyMTExMbM3Os2332HfARMQJ8E/hIZl682LpjY2M5PT3dts2pqSnGx8dnzRuW23zOV9uwKKG2QbzPp6zbwZmbhvfO2cNcn7V1rp8ciYieAr6vq2gi4gnAF4Hz2oW7JGl59XMVTQB/D9yUmX/VXEmSpCb0cwT/MuDNwCsj4tr6cVRDdUmS+tTzAFVmfguIBmuRJDXIv2SVpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCtVXwEfEERFxS0TcHhGnNlWUJKl/PQd8ROwGfAI4EjgIOC4iDmqqMElSf/o5gn8hcHtm3pGZvwAmgdc2U5YkqV+Rmb1tGPF64IjM/OP6+ZuBF2Xm2+astwHYUD89ELilg+ZXA/f3VNjSs7beWFvvhrk+a+tNt7U9MzOf2u1OVnS7QYuYZ95jflpk5jnAOV01HDGdmWO9FraUrK031ta7Ya7P2nqzXLX1M0SzBTig5fn+wI/6K0eS1JR+Av67wHMj4lkRsTtwLPDVZsqSJPWr5yGazNwREW8Dvg7sBnw6M29sqK6uhnSWmbX1xtp6N8z1WVtvlqW2nk+ySpKGm3/JKkmFMuAlqVSZOTQP4Aiq6+RvB05dwv0cAFwB3ATcCJxcz38ycBlwW/3vvvX8AD5e13U9cGhLWyfU698GnNAyfz2wqd7m49TDYV3UuBtwDXBJ/fxZwFX1fj4P7F7P36N+fnu9fG1LG++p598CvLqJfgb2AS4Cbq777yXD0m/A2+v38wbgAmDPQfYb8GngXuCGlnlL3lcL7aOD2j5av6/XA18C9um1T3rp98Vqa1n2TqrLsVcPot8Wqw/4s7ovbgTOGETfPabWbr7cS/mgCrTvA88GdgeuAw5aon3tt/ODADwRuJXqdgtn7Oxo4FTgL+vpo4B/rD9MLwauavlA3FH/u289vfML+x2q8It62yO7rPEdwPk8GvAXAsfW02cDf1JPnwScXU8fC3y+nj6o7sM96g/M9+s+7qufgc8Cf1xP704V+APvN2ANcCewsqW/ThxkvwGvAA5ldogueV8ttI8Oavt9YEU9/ZcttXXdJ932e7va6vkHUF3U8QMeDfhl7bdF+m4C+Gdgj/r50wbRd4+ptanQ7PdRd/jXW56/B3jPMu37K8DhVD9N96vn7QfcUk9/EjiuZf1b6uXHAZ9smf/Jet5+wM0t82et10E9+wOXA68ELqk/iPfz6JdvV1/VH/iX1NMr6vVibv/tXK+ffgaeRBWiMWf+wPuNKuDvovpCr6j77dWD7jdgLbODYMn7aqF9tKttzrJ/C5w332tt1ye9fF47qY3qN8cXAJt5NOCXvd8WeF8vBA6bZ71l77vWxzCNwe/8gu60pZ63pCJiLfDbVL/yjGbmPQD1v09rU9ti87fMM79TZwHvBn5VP38K8GBm7pinvV011Mu31et3W3Mnng3cB/yPiLgmIj4VEXszBP2WmXcD/w34IXAPVT9sZDj6rdVy9NVC++jGH1Ed3fZSWy+f10VFxGuAuzPzujmLhqXffhN4eURcFRHfjIjf6bG+RvtumAK+o1sfNLrDiBHgi8B/ysyHFlt1nnnZw/xOajoGuDczN3aw/2Wtjeqo4VDgbzPzt4GHqX6VXchy9tu+VDe7exbwDGBvqjudLtTecvZbJ4amnog4DdgBnLcEtXVdd0TsBZwGvH++xQ3W1o8VVENBLwbeBVwYEdFwfV3XPkwBv6y3PoiIJ1CF+3mZeXE9e2tE7Fcv34/qRMpitS02f/955nfiZcBrImIz1R06X0l1RL9PROz8w7TW9nbVUC9fBTzQQ82d2AJsycyr6ucXUQX+MPTbYcCdmXlfZj4CXAy8lOHot1bL0VcL7aOtiDgBOAY4PuuxgB5qu5/u+30xz6H6wX1d/b3YH7g6Ip7eQ21L0m91uxdn5TtUv32v7qG+Zvuu3Rjicj2ofgLeQfVG7jzp8Pwl2lcA/xM4a878jzL7JMsZ9fTRzD6R8516/pOpxqT3rR93Ak+ul323XnfniZyjeqhznEdPsn6B2SdeTqqn/5TZJ14urKefz+yTO3dQndjpq5+B/w0cWE9/sO6zgfcb8CKqqxf2qrf9LNVVDQPtNx47VrvkfbXQPjqo7Qjge8BT56zXdZ902+/tapuzbDOPjsEve78t0HdvBT5cT/8m1VBKDKLvZtXZbegs5YPqjPitVGeXT1vC/fwu1a821wPX1o+jqMazLqe6POnylg9EUP3nJt+nurxqrKWtP6K6bOl24A9b5o9RXa73feCv6fIyybqNcR4N+GdTnf2/vf4A7Dxbv2f9/PZ6+bNbtj+t3v8ttFyN0k8/A4cA03Xffbn+8gxFvwEforrM7wbgc/WXamD9RnWp5j3AI1RHX29Zjr5aaB8d1HY7VTDt/E6c3Wuf9NLvi9U2Z/lmZl8muWz9tkjf7Q6cW7d7NfDKQfTd3Ie3KpCkQg3TGLwkqUEGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSrU/wOs5Acln0tHPwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Investigate patterns in the amount of missing data in each column.\\n\",\n \"\\n\",\n \"missing_data[(missing_data['Count_missing_value']>0) & (missing_data['Count_missing_value']<200000) ].hist()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Remove the outlier columns from the dataset. (You'll perform other data\\n\",\n \"# engineering tasks such as re-encoding and imputation later.)\\n\",\n \"\\n\",\n \"azdias.drop(labels=outliers_features ,axis=1,inplace=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Discussion 1.1.2: Assess Missing Data in Each Column\\n\",\n \"\\n\",\n \"(Double click this cell and replace this text with your own text, reporting your observations regarding the amount of missing data in each column. Are there any patterns in missing values? Which columns were removed from the dataset?)\\n\",\n \"\\n\",\n \"My observation regarding the number of missing data:\\n\",\n \"\\n\",\n \"The columns which have no missing or unknown are the Personality typology and the Financial typology and both are the core features, so they cannot be missing.\\n\",\n \"\\n\",\n \"Most of the missing values are in the range between 0 and 200,000 and they are centered around 100,000.\\n\",\n \"\\n\",\n \"The columns removed from the dataset are the following:\\n\",\n \"* AGER_TYP\\n\",\n \"* GEBURTSJAHR\\n\",\n \"* TITEL_KZ\\n\",\n \"* ALTER_HH\\n\",\n \"* KK_KUNDENTYP\\n\",\n \"* KBA05_BAUMAX\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Step 1.1.3: Assess Missing Data in Each Row\\n\",\n \"\\n\",\n \"Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.\\n\",\n \"\\n\",\n \"In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.\\n\",\n \"- You can use seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) function to create a bar chart of code frequencies and matplotlib's [`subplot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html) function to put bar charts for the two subplots side by side.\\n\",\n \"- To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.\\n\",\n \"\\n\",\n \"Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. **Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# How much data is missing in each row of the dataset?\\n\",\n \"azdias['number_missing_values']=azdias.isnull().sum(axis=1)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAY0AAAD8CAYAAACLrvgBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAFx9JREFUeJzt3H+MXeV95/H3Jzg03rSUHw0jZHuXVLWyoWFDwCJeZbWaQguGRDF/BImILW7EyquIVInKqnX6DypZJPIHTQNqkazgxaxoEpQ2ayshcS2SUXelhEASGoeQxA6lMIsXb2IgWFETOfnuH/cxvQx3Zh4PQ+7Y9/2Sru453/Oc85xnGPO55znnTqoKSZJ6vGbcJyBJOnEYGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6LhkaSNyV5ZOj14yQfSnJmkr1J9rf3M1r7JLk9yYEk30py4dCxtrT2+5NsGapflGRf2+f2JGn1kX1IksZj0dCoqu9V1QVVdQFwEfAT4LPANuCBqloPPNDWAa4A1rfXVuBOGAQAcBPwduBi4KahELiztT2236ZWn68PSdIYHO/01KXAD6rqn4DNwM5W3wlc1ZY3A/fUwFeB05OcA1wO7K2qw1X1LLAX2NS2nVZVX6nBNw3vmXOsUX1IksZg1XG2vwb4ZFueqqqDAFV1MMnZrb4GeGpon9lWW6g+O6K+UB8vkWQrgysVVq9efdG6deuOc1gDv/jFL3jNaybvNo/jniyTOm6Y3LH3jPv73//+D6vqDYsdqzs0kpwKvBv48GJNR9RqCfVuVbUd2A6wYcOGevjhh49n9xfNzMwwPT29pH1PZI57skzquGFyx94z7iT/1HOs44ncK4BvVNUzbf2ZNrVEez/U6rPA8Ef9tcDTi9TXjqgv1IckaQyOJzTey79MTQHsBo49AbUF2DVUv649RbUReL5NMe0BLktyRrsBfhmwp217IcnG9tTUdXOONaoPSdIYdE1PJflXwO8B/2WofCtwX5LrgSeBq1v9fuBK4ACDJ63eB1BVh5N8BHiotbu5qg635fcDdwOrgS+010J9SJLGoCs0quonwFlzaj9i8DTV3LYF3DDPcXYAO0bUHwbeMqI+sg9J0nhM3mMEkqQlMzQkSd0MDUlSN0NDktTN0JAkdTvePyNyUtv3f57nD7Z9fkn7PnHrO5f5bCRp5fFKQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVK3rtBIcnqSzyT5bpLHkvz7JGcm2Ztkf3s/o7VNktuTHEjyrSQXDh1nS2u/P8mWofpFSfa1fW5PklYf2YckaTx6rzQ+Dnyxqv4t8FbgMWAb8EBVrQceaOsAVwDr22srcCcMAgC4CXg7cDFw01AI3NnaHttvU6vP14ckaQwWDY0kpwH/EbgLoKp+VlXPAZuBna3ZTuCqtrwZuKcGvgqcnuQc4HJgb1Udrqpngb3AprbttKr6SlUVcM+cY43qQ5I0Bj1XGr8J/D/gvyf5ZpJPJHk9MFVVBwHa+9mt/RrgqaH9Z1ttofrsiDoL9CFJGoNVnW0uBP6wqh5M8nEWnibKiFotod4tyVYG01tMTU0xMzNzPLu/aGo13Hj+0SXtu9Q+V4IjR46c0Oe/VI578kzq2Jdz3D2hMQvMVtWDbf0zDELjmSTnVNXBNsV0aKj9uqH91wJPt/r0nPpMq68d0Z4F+niJqtoObAfYsGFDTU9Pj2q2qDvu3cVt+3p+JC/3xLVL63MlmJmZYak/sxOZ4548kzr25Rz3otNTVfV/gaeSvKmVLgW+A+wGjj0BtQXY1ZZ3A9e1p6g2As+3qaU9wGVJzmg3wC8D9rRtLyTZ2J6aum7OsUb1IUkag96P1X8I3JvkVOBx4H0MAue+JNcDTwJXt7b3A1cCB4CftLZU1eEkHwEeau1urqrDbfn9wN3AauAL7QVw6zx9SJLGoCs0quoRYMOITZeOaFvADfMcZwewY0T9YeAtI+o/GtWHJGk8/Ea4JKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnq1hUaSZ5Isi/JI0kebrUzk+xNsr+9n9HqSXJ7kgNJvpXkwqHjbGnt9yfZMlS/qB3/QNs3C/UhSRqP47nS+J2quqCqNrT1bcADVbUeeKCtA1wBrG+vrcCdMAgA4Cbg7cDFwE1DIXBna3tsv02L9CFJGoNXMj21GdjZlncCVw3V76mBrwKnJzkHuBzYW1WHq+pZYC+wqW07raq+UlUF3DPnWKP6kCSNQW9oFPB3Sb6eZGurTVXVQYD2fnarrwGeGtp3ttUWqs+OqC/UhyRpDFZ1tntHVT2d5Gxgb5LvLtA2I2q1hHq3FmRbAaamppiZmTme3V80tRpuPP/okvZdap8rwZEjR07o818qxz15JnXsyznurtCoqqfb+6Ekn2VwT+KZJOdU1cE2xXSoNZ8F1g3tvhZ4utWn59RnWn3tiPYs0Mfc89sObAfYsGFDTU9Pj2q2qDvu3cVt+3pz9KWeuHZpfa4EMzMzLPVndiJz3JNnUse+nONedHoqyeuT/NqxZeAy4NvAbuDYE1BbgF1teTdwXXuKaiPwfJta2gNcluSMdgP8MmBP2/ZCko3tqanr5hxrVB+SpDHo+Vg9BXy2PQW7CvjrqvpikoeA+5JcDzwJXN3a3w9cCRwAfgK8D6CqDif5CPBQa3dzVR1uy+8H7gZWA19oL4Bb5+lDkjQGi4ZGVT0OvHVE/UfApSPqBdwwz7F2ADtG1B8G3tLbhyRpPPxGuCSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6tYdGklOSfLNJJ9r629M8mCS/Uk+neTUVv+Vtn6gbT936BgfbvXvJbl8qL6p1Q4k2TZUH9mHJGk8judK44PAY0PrHwU+VlXrgWeB61v9euDZqvot4GOtHUnOA64BfhvYBPxVC6JTgL8ErgDOA97b2i7UhyRpDLpCI8la4J3AJ9p6gEuAz7QmO4Gr2vLmtk7bfmlrvxn4VFX9tKr+ETgAXNxeB6rq8ar6GfApYPMifUiSxqD3SuMvgD8GftHWzwKeq6qjbX0WWNOW1wBPAbTtz7f2L9bn7DNffaE+JEljsGqxBkneBRyqqq8nmT5WHtG0Ftk2X31UcC3UftQ5bgW2AkxNTTEzMzOq2aKmVsON5x9dvOEIS+1zJThy5MgJff5L5bgnz6SOfTnHvWhoAO8A3p3kSuB1wGkMrjxOT7KqXQmsBZ5u7WeBdcBsklXArwOHh+rHDO8zqv7DBfp4iaraDmwH2LBhQ01PT3cM6+XuuHcXt+3r+ZG83BPXLq3PlWBmZoal/sxOZI578kzq2Jdz3ItOT1XVh6tqbVWdy+BG9peq6lrgy8B7WrMtwK62vLut07Z/qaqq1a9pT1e9EVgPfA14CFjfnpQ6tfWxu+0zXx+SpDF4Jd/T+BPgj5IcYHD/4a5Wvws4q9X/CNgGUFWPAvcB3wG+CNxQVT9vVxEfAPYweDrrvtZ2oT4kSWNwXHMxVTUDzLTlxxk8+TS3zT8DV8+z/y3ALSPq9wP3j6iP7EOSNB5+I1yS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHVbNDSSvC7J15L8Q5JHk/xZq78xyYNJ9if5dJJTW/1X2vqBtv3coWN9uNW/l+TyofqmVjuQZNtQfWQfkqTx6LnS+ClwSVW9FbgA2JRkI/BR4GNVtR54Fri+tb8eeLaqfgv4WGtHkvOAa4DfBjYBf5XklCSnAH8JXAGcB7y3tWWBPiRJY7BoaNTAkbb62vYq4BLgM62+E7iqLW9u67TtlyZJq3+qqn5aVf8IHAAubq8DVfV4Vf0M+BSwue0zXx+SpDHouqfRrggeAQ4Be4EfAM9V1dHWZBZY05bXAE8BtO3PA2cN1+fsM1/9rAX6kCSNwaqeRlX1c+CCJKcDnwXePKpZe8882+arjwquhdq/TJKtwFaAqakpZmZmRjVb1NRquPH8o4s3HGGpfa4ER44cOaHPf6kc9+SZ1LEv57i7QuOYqnouyQywETg9yap2JbAWeLo1mwXWAbNJVgG/Dhweqh8zvM+o+g8X6GPueW0HtgNs2LChpqenj2dYL7rj3l3ctu+4fiQveuLapfW5EszMzLDUn9mJzHFPnkkd+3KOu+fpqTe0KwySrAZ+F3gM+DLwntZsC7CrLe9u67TtX6qqavVr2tNVbwTWA18DHgLWtyelTmVws3x322e+PiRJY9DzsfocYGd7yuk1wH1V9bkk3wE+leS/Ad8E7mrt7wL+R5IDDK4wrgGoqkeT3Ad8BzgK3NCmvUjyAWAPcAqwo6oebcf6k3n6kCSNwaKhUVXfAt42ov44gyef5tb/Gbh6nmPdAtwyon4/cH9vH5Kk8fAb4ZKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqduioZFkXZIvJ3ksyaNJPtjqZybZm2R/ez+j1ZPk9iQHknwryYVDx9rS2u9PsmWoflGSfW2f25NkoT4kSePRc6VxFLixqt4MbARuSHIesA14oKrWAw+0dYArgPXttRW4EwYBANwEvB24GLhpKATubG2P7bep1efrQ5I0BouGRlUdrKpvtOUXgMeANcBmYGdrthO4qi1vBu6pga8Cpyc5B7gc2FtVh6vqWWAvsKltO62qvlJVBdwz51ij+pAkjcFx3dNIci7wNuBBYKqqDsIgWICzW7M1wFNDu8222kL12RF1FuhDkjQGq3obJvlV4G+AD1XVj9tth5FNR9RqCfVuSbYymN5iamqKmZmZ49n9RVOr4cbzjy5p36X2uRIcOXLkhD7/pXLck2dSx76c4+4KjSSvZRAY91bV37byM0nOqaqDbYrpUKvPAuuGdl8LPN3q03PqM62+dkT7hfp4iaraDmwH2LBhQ01PT49qtqg77t3Fbfu6c/Qlnrh2aX2uBDMzMyz1Z3Yic9yTZ1LHvpzj7nl6KsBdwGNV9edDm3YDx56A2gLsGqpf156i2gg836aW9gCXJTmj3QC/DNjTtr2QZGPr67o5xxrVhyRpDHo+Vr8D+H1gX5JHWu1PgVuB+5JcDzwJXN223Q9cCRwAfgK8D6CqDif5CPBQa3dzVR1uy+8H7gZWA19oLxboQ5I0BouGRlX9b0bfdwC4dET7Am6Y51g7gB0j6g8DbxlR/9GoPiRJ4+E3wiVJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUrdFQyPJjiSHknx7qHZmkr1J9rf3M1o9SW5PciDJt5JcOLTPltZ+f5ItQ/WLkuxr+9yeJAv1IUkan54rjbuBTXNq24AHqmo98EBbB7gCWN9eW4E7YRAAwE3A24GLgZuGQuDO1vbYfpsW6UOSNCaLhkZV/T1weE55M7CzLe8Erhqq31MDXwVOT3IOcDmwt6oOV9WzwF5gU9t2WlV9paoKuGfOsUb1IUkak6Xe05iqqoMA7f3sVl8DPDXUbrbVFqrPjqgv1IckaUxWLfPxMqJWS6gfX6fJVgZTXExNTTEzM3O8hwBgajXceP7RJe271D5XgiNHjpzQ579UjnvyTOrYl3PcSw2NZ5KcU1UH2xTToVafBdYNtVsLPN3q03PqM62+dkT7hfp4maraDmwH2LBhQ01PT8/XdEF33LuL2/Yt7UfyxLVL63MlmJmZYak/sxOZ4548kzr25Rz3UqendgPHnoDaAuwaql/XnqLaCDzfppb2AJclOaPdAL8M2NO2vZBkY3tq6ro5xxrVhyRpTBb9WJ3kkwyuEn4jySyDp6BuBe5Lcj3wJHB1a34/cCVwAPgJ8D6Aqjqc5CPAQ63dzVV17Ob6+xk8obUa+EJ7sUAfkqQxWTQ0quq982y6dETbAm6Y5zg7gB0j6g8DbxlR/9GoPiRJ4+M3wiVJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUrfl/tPoWoJzt33+Fe3/xK3vXKYzkaSFeaUhSermlYYkrRCvZNbhlzXj4JWGJKmboSFJ6mZoSJK6eU9jmbzSJ6Ak6URgaJwEToSbZ9KJxH9T83N6SpLUzdCQJHUzNCRJ3bynIWlBzu9rmFcakqRuK/5KI8km4OPAKcAnqurWMZ+SloF/pFE6Ma3o0EhyCvCXwO8Bs8BDSXZX1XfGe2Ynj3O3fZ4bzz/KH/g9E0kdVnRoABcDB6rqcYAknwI2A4bGhHOe/cQwzi+9+t/51ZGqGvc5zCvJe4BNVfWf2/rvA2+vqg/MabcV2NpW3wR8b4ld/gbwwyXueyJz3JNlUscNkzv2nnH/m6p6w2IHWulXGhlRe1nKVdV2YPsr7ix5uKo2vNLjnGgc92SZ1HHD5I59Oce90p+emgXWDa2vBZ4e07lI0sRb6aHxELA+yRuTnApcA+we8zlJ0sRa0dNTVXU0yQeAPQweud1RVY++il2+4imuE5TjniyTOm6Y3LEv27hX9I1wSdLKstKnpyRJK4ihIUnqZmg0STYl+V6SA0m2jft8Xi1JdiQ5lOTbQ7Uzk+xNsr+9nzHOc3w1JFmX5MtJHkvyaJIPtvpJPfYkr0vytST/0Mb9Z63+xiQPtnF/uj1octJJckqSbyb5XFs/6ced5Ikk+5I8kuThVlu233NDg5f8uZIrgPOA9yY5b7xn9aq5G9g0p7YNeKCq1gMPtPWTzVHgxqp6M7ARuKH9Nz7Zx/5T4JKqeitwAbApyUbgo8DH2rifBa4f4zm+mj4IPDa0Pinj/p2qumDouxnL9ntuaAy8+OdKqupnwLE/V3LSqaq/Bw7PKW8GdrblncBVv9ST+iWoqoNV9Y22/AKD/5Gs4SQfew0caauvba8CLgE+0+on3bgBkqwF3gl8oq2HCRj3PJbt99zQGFgDPDW0Pttqk2Kqqg7C4H+uwNljPp9XVZJzgbcBDzIBY29TNI8Ah4C9wA+A56rqaGtysv6+/wXwx8Av2vpZTMa4C/i7JF9vf2IJlvH3fEV/T+OXqOvPlejEl+RXgb8BPlRVPx58+Dy5VdXPgQuSnA58FnjzqGa/3LN6dSV5F3Coqr6eZPpYeUTTk2rczTuq6ukkZwN7k3x3OQ/ulcbApP+5kmeSnAPQ3g+N+XxeFUleyyAw7q2qv23liRg7QFU9B8wwuKdzepJjHxpPxt/3dwDvTvIEg+nmSxhceZzs46aqnm7vhxh8SLiYZfw9NzQGJv3PlewGtrTlLcCuMZ7Lq6LNZ98FPFZVfz606aQee5I3tCsMkqwGfpfB/ZwvA+9pzU66cVfVh6tqbVWdy+Df85eq6lpO8nEneX2SXzu2DFwGfJtl/D33G+FNkisZfBI59udKbhnzKb0qknwSmGbwp5KfAW4C/idwH/CvgSeBq6tq7s3yE1qS/wD8L2Af/zLH/acM7muctGNP8u8Y3Pg8hcGHxPuq6uYkv8ngE/iZwDeB/1RVPx3fmb562vTUf62qd53s427j+2xbXQX8dVXdkuQslun33NCQJHVzekqS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEnd/j8K1sFxPE1cbgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"azdias['number_missing_values'].hist(bins=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Write code to divide the data into two subsets based on the number of missing\\n\",\n \"# values in each row.\\n\",\n \"azdias_below_threshold=azdias[azdias['number_missing_values']<30]\\n\",\n \"azdias_above_threshold=azdias[azdias['number_missing_values']>30]\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def distrubition_compare(features,input1,input2):\\n\",\n \" fig, axes = plt.subplots(5, 2, figsize=(10, 30))\\n\",\n \" \\n\",\n \" for i,feature in enumerate(features):\\n\",\n \" sns.countplot(ax=axes[i,0],data=input1[feature],x=input1[feature])\\n\",\n \" axes[i,0].set_title(feature)\\n\",\n \" sns.countplot(ax=axes[i,1],data=input2[feature],x=input2[feature])\\n\",\n \" axes[i,1].set_title(feature)\\n\",\n \"\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Compare the distribution of values for at least five columns where there are\\n\",\n \"# no or few missing values, between the two subsets.\\n\",\n \"no_missing_values_features=['SEMIO_SOZ','SEMIO_MAT','FINANZTYP','FINANZ_HAUSBAUER', 'SEMIO_TRADV']\\n\",\n \"distrubition_compare(no_missing_values_features,azdias_above_threshold,azdias_below_threshold)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Discussion 1.1.3: Assess Missing Data in Each Row\\n\",\n \"\\n\",\n \"(Double-click this cell and replace this text with your own text, reporting your observations regarding missing data in rows. Are the data with lots of missing values are qualitatively different from data with few or no missing values?)\\n\",\n \"\\n\",\n \"yes, the data with lots of missing values are concetated in one value, while the data with few missing values are evenly distrubeted around all the values.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 1.2: Select and Re-Encode Features\\n\",\n \"\\n\",\n \"Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (`feat_info`) for a summary of types of measurement.\\n\",\n \"- For numeric and interval data, these features can be kept without changes.\\n\",\n \"- Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).\\n\",\n \"- Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.\\n\",\n \"\\n\",\n \"In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.\\n\",\n \"\\n\",\n \"Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ordinal 49\\n\",\n \"categorical 21\\n\",\n \"mixed 7\\n\",\n \"numeric 7\\n\",\n \"interval 1\\n\",\n \"Name: type, dtype: int64\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# How many features are there of each data type?\\n\",\n \"\\n\",\n \"type_count=feat_info['type'].value_counts()\\n\",\n \"type_count\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Step 1.2.1: Re-Encode Categorical Features\\n\",\n \"\\n\",\n \"For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:\\n\",\n \"- For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.\\n\",\n \"- There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.\\n\",\n \"- For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" errors=errors)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Assess categorical variables: which are binary, which are multi-level, and\\n\",\n \"# which one needs to be re-encoded?\\n\",\n \"\\n\",\n \"cat_features=feat_info[(feat_info['type']== 'categorical')]\\n\",\n \"\\n\",\n \"for i in range(len(outliers_features.values)):\\n\",\n \" cat_features=cat_features[cat_features['attribute']!=outliers_features.values[i]]\\n\",\n \" \\n\",\n \"cat_feat_unique_values=pd.DataFrame(azdias_below_threshold[cat_features['attribute']].nunique())\\n\",\n \"cat_feat_unique_values=cat_feat_unique_values.reset_index()\\n\",\n \"cat_feat_unique_values.rename(columns={'index':'features', 0:'number_unique_values'},inplace=True)\\n\",\n \"\\n\",\n \"binary_features= cat_feat_unique_values[cat_feat_unique_values['number_unique_values']==2] \\n\",\n \"multi_level_features= cat_feat_unique_values[cat_feat_unique_values['number_unique_values']>=3]\\n\",\n \"\\n\",\n \"azdias_below_threshold.drop(labels=multi_level_features['features'],axis=1,inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:3140: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" self[k1] = value[k2]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Re-encode categorical variable(s) to be kept in the analysis.\\n\",\n \"feature_reencoded=azdias_below_threshold[binary_features['features']].select_dtypes(include=['object']).columns\\n\",\n \"azdias_below_threshold[feature_reencoded]=azdias_below_threshold[feature_reencoded].replace({'W': 1,'O':0})\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Discussion 1.2.1: Re-Encode Categorical Features\\n\",\n \"\\n\",\n \"(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding categorical features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)\\n\",\n \"\\n\",\n \"I kept the following features :\\n\",\n \"\\n\",\n \"* ANREDE_KZ\\n\",\n \"* GREEN_AVANTGARDE\\n\",\n \"* SOHO_KZ\\n\",\n \"* VERS_TYP\\n\",\n \"* OST_WEST_KZ\\n\",\n \"\\n\",\n \"Removed the following:\\n\",\n \"\\n\",\n \"* CJT_GESAMTTYP\\n\",\n \"* FINANZTYP\\n\",\n \"* GFK_URLAUBERTYP\\n\",\n \"* LP_FAMILIE_FEIN\\n\",\n \"* LP_FAMILIE_GROB\\n\",\n \"* LP_STATUS_FEIN\\n\",\n \"* LP_STATUS_GROB\\n\",\n \"* NATIONALITAET_KZ\\n\",\n \"* SHOPPER_TYP\\n\",\n \"* ZABEOTYP\\n\",\n \"* GEBAEUDETYP\\n\",\n \"* CAMEO_DEUG_2015\\n\",\n \"* CAMEO_DEU_2015\\n\",\n \"\\n\",\n \"For the feature OST_WEST_KZ , it has non numeric value \\\"W\\\" and it was replaced with numeric value 1 instead and \\\"O\\\" was replaced with 0\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Step 1.2.2: Engineer Mixed-Type Features\\n\",\n \"\\n\",\n \"There are a handful of features that are marked as \\\"mixed\\\" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:\\n\",\n \"- \\\"PRAEGENDE_JUGENDJAHRE\\\" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.\\n\",\n \"- \\\"CAMEO_INTL_2015\\\" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).\\n\",\n \"- If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.\\n\",\n \"\\n\",\n \"Be sure to check `Data_Dictionary.md` for the details needed to finish these tasks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mixed_features=feat_info[(feat_info['type']== 'mixed') ]\\n\",\n \"for i in range(len(outliers_features.values)):\\n\",\n \" mixed_features=mixed_features[mixed_features['attribute']!=outliers_features.values[i]]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" \\n\",\n \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" This is separate from the ipykernel package so we can avoid doing imports until\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Investigate \\\"PRAEGENDE_JUGENDJAHRE\\\" and engineer two new variables.\\n\",\n \"azdias_below_threshold['PRAEGENDE_JUGENDJAHRE_age']= azdias_below_threshold['PRAEGENDE_JUGENDJAHRE'].replace({1:1,2:1,3:2,4:2,5:3,6:3,7:3,8:4,9:4,10:5,11:5,12:5,13:5,14:6,15:6})\\n\",\n \"azdias_below_threshold['PRAEGENDE_JUGENDJAHRE_movment']=azdias_below_threshold['PRAEGENDE_JUGENDJAHRE'].replace({1:0,3:0,5:0,8:0,10:0,12:0,14:0,2:1,4:1,6:1,7:1,9:1,11:1,13:1,15:1})\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" import sys\\n\",\n \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" \\n\",\n \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" # This is added back by InteractiveShellApp.init_path()\\n\",\n \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" if sys.path[0] == '':\\n\",\n \"/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" errors=errors)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Investigate \\\"CAMEO_INTL_2015\\\" and engineer two new variables.\\n\",\n \"\\n\",\n \"unique_values=azdias_below_threshold['CAMEO_INTL_2015'].unique()\\n\",\n \"unique_values=unique_values.astype(float)\\n\",\n \"unique_values=np.delete(unique_values,np.argwhere(np.isnan(unique_values)))\\n\",\n \"unique_values=unique_values.astype(int)\\n\",\n \"azdias_below_threshold['CAMEO_INTL_2015_wealth']=azdias_below_threshold['CAMEO_INTL_2015']\\n\",\n \"azdias_below_threshold['CAMEO_INTL_2015_family_stage']=azdias_below_threshold['CAMEO_INTL_2015']\\n\",\n \"\\n\",\n \"for unique_value in unique_values:\\n\",\n \" azdias_below_threshold['CAMEO_INTL_2015_wealth']=azdias_below_threshold['CAMEO_INTL_2015_wealth'].replace({str(unique_value):int(unique_value/10)})\\n\",\n \" azdias_below_threshold['CAMEO_INTL_2015_family_stage']=azdias_below_threshold['CAMEO_INTL_2015_family_stage'].replace({str(unique_value):int(str(unique_value)[1])})\\n\",\n \" \\n\",\n \"azdias_below_threshold.drop(labels=mixed_features['attribute'],axis=1,inplace=True) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Discussion 1.2.2: Engineer Mixed-Type Features\\n\",\n \"\\n\",\n \"(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding mixed-value features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)\\n\",\n \"\\n\",\n \"The features that were kept PRAEGENDE_JUGENDJAHRE and CAMEO_INTL_2015, but after being splitted into 4 features.\\n\",\n \"\\n\",\n \"The following were removed:\\n\",\n \"\\n\",\n \" LP_LEBENSPHASE_FEIN\\n\",\n \"* LP_LEBENSPHASE_GROB\\n\",\n \"* WOHNLAGE\\n\",\n \"* PLZ8_BAUMAX\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Step 1.2.3: Complete Feature Selection\\n\",\n \"\\n\",\n \"In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:\\n\",\n \"- All numeric, interval, and ordinal type columns from the original dataset.\\n\",\n \"- Binary categorical features (all numerically-encoded).\\n\",\n \"- Engineered features from other multi-level categorical features and mixed features.\\n\",\n \"\\n\",\n \"Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep \\\"PRAEGENDE_JUGENDJAHRE\\\", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from **the subset with few or no missing values**.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# If there are other re-engineering tasks you need to perform, make sure you\\n\",\n \"# take care of them here. (Dealing with missing data will come in step 2.1.)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Do whatever you need to in order to ensure that the dataframe only contains\\n\",\n \"# the columns that should be passed to the algorithm functions.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 1.3: Create a Cleaning Function\\n\",\n \"\\n\",\n \"Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def removing_columns_rows(df,outliers_features ,row_threshold):\\n\",\n \" missing_data = pd.DataFrame(df.isnull().sum().reset_index())\\n\",\n \" missing_data.columns = ['Column_name','Count_missing_value']\\n\",\n \" \\n\",\n \" #outliers_features=missing_data[(missing_data['Count_missing_value']>column_threshold)]['Column_name']\\n\",\n \" \\n\",\n \" df.drop(labels=outliers_features ,axis=1,inplace=True)\\n\",\n \"\\n\",\n \" df['number_missing_values']=df.isnull().sum(axis=1)\\n\",\n \" df=df[df['number_missing_values']=3]\\n\",\n \" df=df.drop(labels=multi_level_features['features'],axis=1)\\n\",\n \" feature_reencoded=df[binary_features['features']].select_dtypes(include=['object']).columns\\n\",\n \" df[feature_reencoded]=df[feature_reencoded].replace({'W':1,'O':0})\\n\",\n \" \\n\",\n \" # mixed features re-encoding \\n\",\n \" mixed_features=feat_info[(feat_info['type']== 'mixed') ]\\n\",\n \" for i in range(len(outliers_features.values)):\\n\",\n \" mixed_features=mixed_features[mixed_features['attribute']!=outliers_features.values[i]]\\n\",\n \"\\n\",\n \" df['PRAEGENDE_JUGENDJAHRE_age']= df['PRAEGENDE_JUGENDJAHRE'].replace({1:1,2:1,3:2,4:2,5:3,6:3,7:3,8:4,9:4,10:5,11:5,12:5,13:5,14:6,15:6})\\n\",\n \" df['PRAEGENDE_JUGENDJAHRE_movment']=df['PRAEGENDE_JUGENDJAHRE'].replace({1:0,3:0,5:0,8:0,10:0,12:0,14:0,2:1,4:1,6:1,7:1,9:1,11:1,13:1,15:1})\\n\",\n \"\\n\",\n \" unique_values=df['CAMEO_INTL_2015'].unique()\\n\",\n \" unique_values=unique_values.astype(float)\\n\",\n \" unique_values=np.delete(unique_values,np.argwhere(np.isnan(unique_values)))\\n\",\n \" unique_values=unique_values.astype(int)\\n\",\n \" df['CAMEO_INTL_2015_wealth']=df['CAMEO_INTL_2015']\\n\",\n \" df['CAMEO_INTL_2015_family_stage']=df['CAMEO_INTL_2015']\\n\",\n \"\\n\",\n \" for unique_value in unique_values:\\n\",\n \" df['CAMEO_INTL_2015_wealth']=df['CAMEO_INTL_2015_wealth'].replace({str(unique_value):int(unique_value/10)})\\n\",\n \" df['CAMEO_INTL_2015_family_stage']=df['CAMEO_INTL_2015_family_stage'].replace({str(unique_value):int(str(unique_value)[1])})\\n\",\n \" \\n\",\n \" df=df.drop(labels=mixed_features['attribute'],axis=1) \\n\",\n \" return df \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def clean_data(df,row_threshold):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Perform feature trimming, re-encoding, and engineering for demographics\\n\",\n \" data\\n\",\n \" \\n\",\n \" INPUT: Demographics DataFrame\\n\",\n \" OUTPUT: Trimmed and cleaned demographics DataFrame\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # Put in code here to execute all main cleaning steps:\\n\",\n \" # convert missing value codes into NaNs, ...\\n\",\n \" df=missing_values_convert(df)\\n\",\n \" # remove selected columns and rows, ...\\n\",\n \" df_col_row_removed=removing_columns_rows(df,outliers_features ,row_threshold)\\n\",\n \" # select, re-encode, and engineer column values.\\n\",\n \" df_rencoded=removing_rencoding_features(df_col_row_removed,outliers_features) \\n\",\n \" # Return the cleaned dataframe.\\n\",\n \" return df_rencoded\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Feature Transformation\\n\",\n \"\\n\",\n \"### Step 2.1: Apply Feature Scaling\\n\",\n \"\\n\",\n \"Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the [API reference page for sklearn](http://scikit-learn.org/stable/modules/classes.html) to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:\\n\",\n \"\\n\",\n \"- sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an [Imputer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html) to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.\\n\",\n \"- For the actual scaling function, a [StandardScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) instance is suggested, scaling each feature to mean 0 and standard deviation 1.\\n\",\n \"- For these classes, you can make use of the `.fit_transform()` method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# If you've not yet cleaned the dataset of all NaN values, then investigate and\\n\",\n \"# do that now.\\n\",\n \"\\n\",\n \"column_miising=pd.DataFrame(azdias_below_threshold.isna()).sum(axis=0)\\n\",\n \"rows_miising=pd.DataFrame(azdias_below_threshold.isna()).sum(axis=1)\\n\",\n \"\\n\",\n \"column_miising.hist()\\n\",\n \"plt.figure()\\n\",\n \"rows_miising.hist()\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import Imputer\\n\",\n \"simple_imp=Imputer(missing_values=np.nan, strategy='most_frequent')\\n\",\n \"simple_imp_model=simple_imp.fit(azdias_below_threshold)\\n\",\n \"azdias_imputed=simple_imp_model.transform(azdias_below_threshold)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Apply feature scaling to the general population demographics data.\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"stand=StandardScaler()\\n\",\n \"stand.fit(azdias_imputed)\\n\",\n \"azdias_scaled=stand.transform(azdias_imputed)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Discussion 2.1: Apply Feature Scaling\\n\",\n \"\\n\",\n \"(Double-click this cell and replace this text with your own text, reporting your decisions regarding feature scaling.)\\n\",\n \"\\n\",\n \"* The missing value was replaced witht the most frequent value for each column and then standarization was applied on the data \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 2.2: Perform Dimensionality Reduction\\n\",\n \"\\n\",\n \"On your scaled data, you are now ready to apply dimensionality reduction techniques.\\n\",\n \"\\n\",\n \"- Use sklearn's [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).\\n\",\n \"- Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's [`plot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html) function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.\\n\",\n \"- Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 3.97396281, -2.44366123, -2.87347163, ..., 0.08552273,\\n\",\n \" 0.10382552, 0.19582701],\\n\",\n \" [-0.93890899, 0.28804214, -3.04606656, ..., -0.39087167,\\n\",\n \" -0.41388522, 0.26953475],\\n\",\n \" [-3.83100162, 1.2601343 , -0.75170454, ..., -0.71143082,\\n\",\n \" 0.55387812, 0.04355387],\\n\",\n \" ..., \\n\",\n \" [-0.83553671, -3.27144018, -2.91422624, ..., 0.44650075,\\n\",\n \" -0.89370843, 0.16138679],\\n\",\n \" [ 5.88800919, -3.1428129 , 2.39557296, ..., -0.56207293,\\n\",\n \" 0.84436168, -0.25425928],\\n\",\n \" [-0.83922522, 1.02478108, 3.11778881, ..., -0.7348376 ,\\n\",\n \" -0.45280773, -0.09923875]])\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Apply PCA to the data.\\n\",\n \"from sklearn.decomposition import PCA\\n\",\n \"pca=PCA(n_components=50)\\n\",\n \"pca.fit_transform(azdias_scaled)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5,1,'Explained Variance Per Principal Component')\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Investigate the variance accounted for by each principal component.\\n\",\n \"\\n\",\n \"num_components=len(pca.explained_variance_ratio_)\\n\",\n \"ind = np.arange(num_components)\\n\",\n \"vals = pca.explained_variance_ratio_\\n\",\n \" \\n\",\n \"plt.figure(figsize=(25, 6))\\n\",\n \"ax = plt.subplot(111)\\n\",\n \"cumvals = np.cumsum(vals)\\n\",\n \"ax.bar(ind, vals)\\n\",\n \"ax.plot(ind, cumvals)\\n\",\n \"for i in range(num_components):\\n\",\n \" ax.annotate(r\\\"%s%%\\\" % ((str(vals[i]*100)[:4])), (ind[i]+0.15, vals[i]), va=\\\"bottom\\\", ha=\\\"center\\\", fontsize=8)\\n\",\n \" \\n\",\n \"ax.xaxis.set_tick_params(width=0) \\n\",\n \"ax.yaxis.set_tick_params(width=2, length=12)\\n\",\n \"ax.set_xlabel(\\\"Principal Component\\\")\\n\",\n \"ax.set_ylabel(\\\"Variance Explained (%)\\\")\\n\",\n \"plt.title('Explained Variance Per Principal Component')\\n\",\n \" \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Re-apply PCA to the data while selecting for number of components to retain.\\n\",\n \"pca=PCA(n_components=10)\\n\",\n \"pca_model=pca.fit(azdias_scaled)\\n\",\n \"azdias_pca=pca_model.transform(azdias_scaled)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Discussion 2.2: Perform Dimensionality Reduction\\n\",\n \"\\n\",\n \"(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding dimensionality reduction. How many principal components / transformed features are you retaining for the next step of the analysis?)\\n\",\n \"\\n\",\n \"* The number of principal components retain for the next step is 10\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 2.3: Interpret Principal Components\\n\",\n \"\\n\",\n \"Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.\\n\",\n \"\\n\",\n \"As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.\\n\",\n \"\\n\",\n \"- To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.\\n\",\n \"- You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the *i*-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_feature_importance(pca_model,i):\\n\",\n \" features_names=azdias_below_threshold.columns.values\\n\",\n \" compnents_df=pd.DataFrame(pca_model.components_)\\n\",\n \" compnents_df.columns=features_names\\n\",\n \" compnents_df=compnents_df.transpose()\\n\",\n \" compnents_name=[]\\n\",\n \" for j in range(len(pca_model.components_)):\\n\",\n \" compnents_name=np.append(compnents_name,'compnent_'+str(j))\\n\",\n \" compnents_df.columns=compnents_name\\n\",\n \" sorted_df_component=compnents_df.sort_values(by=['compnent_'+str(i-1)],axis=0,ascending=False)\\n\",\n \" return sorted_df_component['compnent_'+str(i-1)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"PLZ8_ANTG3 0.225356\\n\",\n \"PLZ8_ANTG4 0.216901\\n\",\n \"CAMEO_INTL_2015_wealth 0.204642\\n\",\n \"HH_EINKOMMEN_SCORE 0.202210\\n\",\n \"ORTSGR_KLS9 0.196785\\n\",\n \"EWDICHTE 0.194674\\n\",\n \"FINANZ_HAUSBAUER 0.159538\\n\",\n \"KBA05_ANTG4 0.154002\\n\",\n \"PLZ8_ANTG2 0.153695\\n\",\n \"FINANZ_SPARER 0.153073\\n\",\n \"ARBEIT 0.142582\\n\",\n \"KBA05_ANTG3 0.136740\\n\",\n \"ANZ_HAUSHALTE_AKTIV 0.136067\\n\",\n \"RELAT_AB 0.134964\\n\",\n \"SEMIO_PFLICHT 0.121139\\n\",\n \"SEMIO_REL 0.118562\\n\",\n \"PRAEGENDE_JUGENDJAHRE_age 0.112215\\n\",\n \"SEMIO_RAT 0.099551\\n\",\n \"SEMIO_TRADV 0.093702\\n\",\n \"SEMIO_MAT 0.082416\\n\",\n \"FINANZ_UNAUFFAELLIGER 0.081043\\n\",\n \"SEMIO_FAM 0.080993\\n\",\n \"SEMIO_KULT 0.075509\\n\",\n \"FINANZ_ANLEGER 0.075070\\n\",\n \"REGIOTYP 0.060324\\n\",\n \"SEMIO_SOZ 0.043304\\n\",\n \"PLZ8_HHZ 0.042258\\n\",\n \"HEALTH_TYP 0.041211\\n\",\n \"KKK 0.039582\\n\",\n \"SEMIO_KAEM 0.039413\\n\",\n \" ... \\n\",\n \"ANREDE_KZ 0.007526\\n\",\n \"SEMIO_KRIT 0.003804\\n\",\n \"number_missing_values -0.001244\\n\",\n \"SOHO_KZ -0.001989\\n\",\n \"ANZ_TITEL -0.004227\\n\",\n \"RETOURTYP_BK_S -0.021712\\n\",\n \"SEMIO_VERT -0.040622\\n\",\n \"ONLINE_AFFINITAET -0.041300\\n\",\n \"MIN_GEBAEUDEJAHR -0.043042\\n\",\n \"OST_WEST_KZ -0.053669\\n\",\n \"WOHNDAUER_2008 -0.061219\\n\",\n \"KBA13_ANZAHL_PKW -0.073955\\n\",\n \"SEMIO_LUST -0.076870\\n\",\n \"ANZ_PERSONEN -0.078060\\n\",\n \"SEMIO_ERL -0.080156\\n\",\n \"PRAEGENDE_JUGENDJAHRE_movment -0.110204\\n\",\n \"GREEN_AVANTGARDE -0.110204\\n\",\n \"GEBAEUDETYP_RASTER -0.117193\\n\",\n \"FINANZ_VORSORGER -0.120150\\n\",\n \"CAMEO_INTL_2015_family_stage -0.125158\\n\",\n \"ALTERSKATEGORIE_GROB -0.125585\\n\",\n \"BALLRAUM -0.127358\\n\",\n \"INNENSTADT -0.164654\\n\",\n \"PLZ8_GBZ -0.166388\\n\",\n \"KONSUMNAEHE -0.167411\\n\",\n \"KBA05_ANTG1 -0.214420\\n\",\n \"KBA05_GBZ -0.216066\\n\",\n \"FINANZ_MINIMALIST -0.223083\\n\",\n \"MOBI_REGIO -0.224789\\n\",\n \"PLZ8_ANTG1 -0.225675\\n\",\n \"Name: compnent_0, Length: 65, dtype: float64\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Map weights for the first principal component to corresponding feature names\\n\",\n \"# and then print the linked values, sorted by weight.\\n\",\n \"# HINT: Try defining a function here or in a new cell that you can reuse in the\\n\",\n \"# other cells.\\n\",\n \"first_component_weitghs=get_feature_importance(pca_model,1)\\n\",\n \"first_component_weitghs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ALTERSKATEGORIE_GROB 0.256048\\n\",\n \"SEMIO_ERL 0.229339\\n\",\n \"FINANZ_VORSORGER 0.229293\\n\",\n \"SEMIO_LUST 0.180080\\n\",\n \"RETOURTYP_BK_S 0.161561\\n\",\n \"FINANZ_HAUSBAUER 0.122137\\n\",\n \"SEMIO_KRIT 0.117056\\n\",\n \"SEMIO_KAEM 0.115813\\n\",\n \"W_KEIT_KIND_HH 0.114503\\n\",\n \"PLZ8_ANTG3 0.098290\\n\",\n \"EWDICHTE 0.098110\\n\",\n \"ORTSGR_KLS9 0.096787\\n\",\n \"PLZ8_ANTG4 0.096682\\n\",\n \"ANREDE_KZ 0.092482\\n\",\n \"CAMEO_INTL_2015_wealth 0.079272\\n\",\n \"KBA05_ANTG4 0.075302\\n\",\n \"SEMIO_DOM 0.074236\\n\",\n \"ARBEIT 0.072053\\n\",\n \"RELAT_AB 0.069492\\n\",\n \"PLZ8_ANTG2 0.068161\\n\",\n \"ANZ_HAUSHALTE_AKTIV 0.066506\\n\",\n \"HH_EINKOMMEN_SCORE 0.062335\\n\",\n \"FINANZ_MINIMALIST 0.059513\\n\",\n \"WOHNDAUER_2008 0.058989\\n\",\n \"KBA05_ANTG3 0.050849\\n\",\n \"VERS_TYP 0.032129\\n\",\n \"ANZ_HH_TITEL 0.032055\\n\",\n \"PLZ8_HHZ 0.016151\\n\",\n \"REGIOTYP 0.011365\\n\",\n \"ANZ_TITEL 0.006828\\n\",\n \" ... \\n\",\n \"PRAEGENDE_JUGENDJAHRE_movment -0.017977\\n\",\n \"OST_WEST_KZ -0.027553\\n\",\n \"number_missing_values -0.029443\\n\",\n \"KBA13_ANZAHL_PKW -0.038618\\n\",\n \"GEBAEUDETYP_RASTER -0.047307\\n\",\n \"MIN_GEBAEUDEJAHR -0.051478\\n\",\n \"HEALTH_TYP -0.057077\\n\",\n \"ANZ_PERSONEN -0.063638\\n\",\n \"BALLRAUM -0.064801\\n\",\n \"SEMIO_VERT -0.072325\\n\",\n \"KBA05_ANTG1 -0.073487\\n\",\n \"KONSUMNAEHE -0.074971\\n\",\n \"PLZ8_GBZ -0.075068\\n\",\n \"MOBI_REGIO -0.079763\\n\",\n \"INNENSTADT -0.079788\\n\",\n \"KBA05_GBZ -0.092074\\n\",\n \"PLZ8_ANTG1 -0.096217\\n\",\n \"SEMIO_SOZ -0.102348\\n\",\n \"SEMIO_MAT -0.161809\\n\",\n \"ONLINE_AFFINITAET -0.165368\\n\",\n \"SEMIO_RAT -0.167242\\n\",\n \"SEMIO_FAM -0.183932\\n\",\n \"FINANZ_ANLEGER -0.203033\\n\",\n \"SEMIO_KULT -0.219010\\n\",\n \"SEMIO_PFLICHT -0.225470\\n\",\n \"FINANZ_UNAUFFAELLIGER -0.226069\\n\",\n \"SEMIO_TRADV -0.227959\\n\",\n \"FINANZ_SPARER -0.231805\\n\",\n \"PRAEGENDE_JUGENDJAHRE_age -0.238825\\n\",\n \"SEMIO_REL -0.253591\\n\",\n \"Name: compnent_1, Length: 65, dtype: float64\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Map weights for the second principal component to corresponding feature names\\n\",\n \"# and then print the linked values, sorted by weight.\\n\",\n \"\\n\",\n \"second_component_weitghs=get_feature_importance(pca_model,2)\\n\",\n \"second_component_weitghs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SEMIO_VERT 0.344392\\n\",\n \"SEMIO_SOZ 0.262644\\n\",\n \"SEMIO_FAM 0.249003\\n\",\n \"SEMIO_KULT 0.234749\\n\",\n \"FINANZ_MINIMALIST 0.154073\\n\",\n \"RETOURTYP_BK_S 0.107846\\n\",\n \"FINANZ_VORSORGER 0.101009\\n\",\n \"W_KEIT_KIND_HH 0.084219\\n\",\n \"ALTERSKATEGORIE_GROB 0.078494\\n\",\n \"SEMIO_REL 0.068086\\n\",\n \"SEMIO_LUST 0.063855\\n\",\n \"SEMIO_MAT 0.055693\\n\",\n \"ORTSGR_KLS9 0.050371\\n\",\n \"EWDICHTE 0.049586\\n\",\n \"PLZ8_ANTG4 0.049555\\n\",\n \"PLZ8_ANTG3 0.047969\\n\",\n \"GREEN_AVANTGARDE 0.047718\\n\",\n \"PRAEGENDE_JUGENDJAHRE_movment 0.047718\\n\",\n \"ARBEIT 0.037520\\n\",\n \"RELAT_AB 0.034491\\n\",\n \"WOHNDAUER_2008 0.033260\\n\",\n \"PLZ8_ANTG2 0.032499\\n\",\n \"CAMEO_INTL_2015_wealth 0.030020\\n\",\n \"KBA05_ANTG4 0.029813\\n\",\n \"ANZ_HAUSHALTE_AKTIV 0.026622\\n\",\n \"ANZ_HH_TITEL 0.013798\\n\",\n \"KBA05_ANTG3 0.012177\\n\",\n \"ANZ_TITEL 0.009607\\n\",\n \"PLZ8_HHZ 0.005959\\n\",\n \"VERS_TYP 0.001568\\n\",\n \" ... \\n\",\n \"KKK -0.015838\\n\",\n \"HH_EINKOMMEN_SCORE -0.015943\\n\",\n \"OST_WEST_KZ -0.016306\\n\",\n \"KBA05_ANTG1 -0.020482\\n\",\n \"MIN_GEBAEUDEJAHR -0.022024\\n\",\n \"KBA13_ANZAHL_PKW -0.024724\\n\",\n \"MOBI_REGIO -0.024891\\n\",\n \"KBA05_GBZ -0.028281\\n\",\n \"number_missing_values -0.029613\\n\",\n \"GEBAEUDETYP_RASTER -0.032031\\n\",\n \"HEALTH_TYP -0.033988\\n\",\n \"BALLRAUM -0.037251\\n\",\n \"FINANZ_HAUSBAUER -0.039332\\n\",\n \"PLZ8_GBZ -0.040180\\n\",\n \"KONSUMNAEHE -0.040517\\n\",\n \"INNENSTADT -0.045750\\n\",\n \"PLZ8_ANTG1 -0.048929\\n\",\n \"ONLINE_AFFINITAET -0.055130\\n\",\n \"SEMIO_TRADV -0.078078\\n\",\n \"SEMIO_PFLICHT -0.079358\\n\",\n \"FINANZ_UNAUFFAELLIGER -0.101196\\n\",\n \"FINANZ_SPARER -0.106609\\n\",\n \"PRAEGENDE_JUGENDJAHRE_age -0.111275\\n\",\n \"SEMIO_ERL -0.176088\\n\",\n \"FINANZ_ANLEGER -0.190484\\n\",\n \"SEMIO_RAT -0.217075\\n\",\n \"SEMIO_KRIT -0.275798\\n\",\n \"SEMIO_DOM -0.312589\\n\",\n \"SEMIO_KAEM -0.335150\\n\",\n \"ANREDE_KZ -0.367370\\n\",\n \"Name: compnent_2, Length: 65, dtype: float64\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Map weights for the third principal component to corresponding feature names\\n\",\n \"# and then print the linked values, sorted by weight.\\n\",\n \"third_component_weitghs=get_feature_importance(pca_model,3)\\n\",\n \"third_component_weitghs\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Discussion 2.3: Interpret Principal Components\\n\",\n \"\\n\",\n \"(Double-click this cell and replace this text with your own text, reporting your observations from detailed investigation of the first few principal components generated. Can we interpret positive and negative values from them in a meaningful way?)\\n\",\n \"* Yes, the postive and negative values arenegatively coorelated for example in teh first compnent, the heighest postive weights features are related for the dreamful, clutur and family orinted personalities while the the heighest negative weights features are for the retional, critical thinking personalities which shows that they are negatively coorelated as one of them increase the other will decrease.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Clustering\\n\",\n \"\\n\",\n \"### Step 3.1: Apply Clustering to General Population\\n\",\n \"\\n\",\n \"You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.\\n\",\n \"\\n\",\n \"- Use sklearn's [KMeans](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans) class to perform k-means clustering on the PCA-transformed data.\\n\",\n \"- Then, compute the average difference from each point to its assigned cluster's center. **Hint**: The KMeans object's `.score()` method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.\\n\",\n \"- Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. **Warning**: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.\\n\",\n \"- Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import KMeans\\n\",\n \"\\n\",\n \"# Over a number of different cluster counts...\\n\",\n \"K=[10,15,20,25,30]\\n\",\n \"scores=[]\\n\",\n \"for k in K:\\n\",\n \" # run k-means clustering on the data and...\\n\",\n \" kmeans = KMeans(n_clusters=k)\\n\",\n \" model_k=kmeans.fit(azdias_pca)\\n\",\n \" labels=model_k.predict(azdias_pca)\\n\",\n \" # compute the average within-cluster distances.\\n\",\n \" score=model_k.score(azdias_pca)\\n\",\n \" scores=np.append(scores,score)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Investigate the change in within-cluster distance across number of clusters.\\n\",\n \"# HINT: Use matplotlib's plot function to visualize this relationship.\\n\",\n \"plt.plot(K,-1*scores,linestyle='--', marker='o', color='b')\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Re-fit the k-means model with the selected number of clusters and obtain\\n\",\n \"# cluster predictions for the general population demographics data.\\n\",\n \"from sklearn.cluster import KMeans\\n\",\n \"\\n\",\n \"best_k=30\\n\",\n \"kmeans = KMeans(n_clusters=best_k)\\n\",\n \"model_k=kmeans.fit(azdias_pca)\\n\",\n \"labels_demo=model_k.predict(azdias_pca)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Discussion 3.1: Apply Clustering to General Population\\n\",\n \"\\n\",\n \"(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding clustering. Into how many clusters have you decided to segment the population?)\\n\",\n \"k used is 30\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 3.2: Apply All Steps to the Customer Data\\n\",\n \"\\n\",\n \"Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.\\n\",\n \"\\n\",\n \"- Don't forget when loading in the customers data, that it is semicolon (`;`) delimited.\\n\",\n \"- Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the `clean_data()` function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)\\n\",\n \"- Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a `.fit()` or `.fit_transform()` method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load in the customer demographics data.\\n\",\n \"customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv',sep=';')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" \\\"\\\"\\\"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Apply preprocessing, feature transformation, and clustering from the general\\n\",\n \"# demographics onto the customer data, obtaining cluster predictions for the\\n\",\n \"# customer demographics data.\\n\",\n \"customers_cleared=clean_data(customers,30)\\n\",\n \"customer_imputed=simple_imp_model.transform(customers_cleared)\\n\",\n \"customers_stand=stand.transform(customer_imputed)\\n\",\n \"customers_pca=pca_model.transform(customers_stand)\\n\",\n \"labels_customers=model_k.predict(customers_pca)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 3.3: Compare Customer Data to Demographics Data\\n\",\n \"\\n\",\n \"At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.\\n\",\n \"\\n\",\n \"Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.\\n\",\n \"\\n\",\n \"Take a look at the following points in this step:\\n\",\n \"\\n\",\n \"- Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) or [`barplot()`](https://seaborn.pydata.org/generated/seaborn.barplot.html) function could be handy.\\n\",\n \" - Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!\\n\",\n \"- Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the `.inverse_transform()` method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.\\n\",\n \"- Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compare the proportion of data in each cluster for the customer data to the\\n\",\n \"# proportion of data in each cluster for the general population.\\n\",\n \"from collections import Counter\\n\",\n \"y=Counter(labels_demo)\\n\",\n \"x=Counter(labels_customers)\\n\",\n \"subjects_per_labels_demo=pd.DataFrame(index=list(y.keys()),data=list(y.values()),columns=['demo_data'])\\n\",\n \"subjects_per_labels_demo.sort_index(axis=0,inplace=True)\\n\",\n \"\\n\",\n \"subjects_per_labels_customers=pd.DataFrame(index=list(x.keys()),data=list(x.values()),columns=['customers_data'])\\n\",\n \"subjects_per_labels_customers.sort_index(axis=0,inplace=True)\\n\",\n \"subjects_per_labels=pd.concat([subjects_per_labels_demo,subjects_per_labels_customers],axis=1)\\n\",\n \"\\n\",\n \"subjects_per_labels['demo_data_prop']=100*(subjects_per_labels['demo_data']/sum(subjects_per_labels['demo_data']))\\n\",\n \"subjects_per_labels['customer_data_prop']=100*(subjects_per_labels['customers_data']/sum(subjects_per_labels['customers_data']))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"subjects_per_labels.plot(kind='bar',figsize=(10,10),y=['demo_data_prop','customer_data_prop'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# What kinds of people are part of a cluster that is overrepresented in the\\n\",\n \"# customer data compared to the general population?\\n\",\n \"\\n\",\n \"customers_overrepresented_labels=[2,4,15,16,21,25]\\n\",\n \"overrepresented_subjects_index=np.where(np.in1d(labels_customers,customers_overrepresented_labels))[0]\\n\",\n \"overrepresented_subject_index_25=np.where(np.in1d(labels_customers,customers_overrepresented_labels[5]))[0]\\n\",\n \"overrespsented_comp=customers_pca[overrepresented_subject_index_25,:]\\n\",\n \"average=overrespsented_comp.mean(axis=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"PLZ8_ANTG3 0.225356\\n\",\n \"PLZ8_ANTG4 0.216901\\n\",\n \"CAMEO_INTL_2015_wealth 0.204642\\n\",\n \"HH_EINKOMMEN_SCORE 0.202210\\n\",\n \"ORTSGR_KLS9 0.196785\\n\",\n \"EWDICHTE 0.194674\\n\",\n \"FINANZ_HAUSBAUER 0.159538\\n\",\n \"KBA05_ANTG4 0.154002\\n\",\n \"PLZ8_ANTG2 0.153695\\n\",\n \"FINANZ_SPARER 0.153073\\n\",\n \"ARBEIT 0.142582\\n\",\n \"KBA05_ANTG3 0.136740\\n\",\n \"ANZ_HAUSHALTE_AKTIV 0.136067\\n\",\n \"RELAT_AB 0.134964\\n\",\n \"SEMIO_PFLICHT 0.121139\\n\",\n \"SEMIO_REL 0.118562\\n\",\n \"PRAEGENDE_JUGENDJAHRE_age 0.112215\\n\",\n \"SEMIO_RAT 0.099551\\n\",\n \"SEMIO_TRADV 0.093702\\n\",\n \"SEMIO_MAT 0.082416\\n\",\n \"FINANZ_UNAUFFAELLIGER 0.081043\\n\",\n \"SEMIO_FAM 0.080993\\n\",\n \"SEMIO_KULT 0.075509\\n\",\n \"FINANZ_ANLEGER 0.075070\\n\",\n \"REGIOTYP 0.060324\\n\",\n \"SEMIO_SOZ 0.043304\\n\",\n \"PLZ8_HHZ 0.042258\\n\",\n \"HEALTH_TYP 0.041211\\n\",\n \"KKK 0.039582\\n\",\n \"SEMIO_KAEM 0.039413\\n\",\n \" ... \\n\",\n \"ANREDE_KZ 0.007526\\n\",\n \"SEMIO_KRIT 0.003804\\n\",\n \"number_missing_values -0.001244\\n\",\n \"SOHO_KZ -0.001989\\n\",\n \"ANZ_TITEL -0.004227\\n\",\n \"RETOURTYP_BK_S -0.021712\\n\",\n \"SEMIO_VERT -0.040622\\n\",\n \"ONLINE_AFFINITAET -0.041300\\n\",\n \"MIN_GEBAEUDEJAHR -0.043042\\n\",\n \"OST_WEST_KZ -0.053669\\n\",\n \"WOHNDAUER_2008 -0.061219\\n\",\n \"KBA13_ANZAHL_PKW -0.073955\\n\",\n \"SEMIO_LUST -0.076870\\n\",\n \"ANZ_PERSONEN -0.078060\\n\",\n \"SEMIO_ERL -0.080156\\n\",\n \"PRAEGENDE_JUGENDJAHRE_movment -0.110204\\n\",\n \"GREEN_AVANTGARDE -0.110204\\n\",\n \"GEBAEUDETYP_RASTER -0.117193\\n\",\n \"FINANZ_VORSORGER -0.120150\\n\",\n \"CAMEO_INTL_2015_family_stage -0.125158\\n\",\n \"ALTERSKATEGORIE_GROB -0.125585\\n\",\n \"BALLRAUM -0.127358\\n\",\n \"INNENSTADT -0.164654\\n\",\n \"PLZ8_GBZ -0.166388\\n\",\n \"KONSUMNAEHE -0.167411\\n\",\n \"KBA05_ANTG1 -0.214420\\n\",\n \"KBA05_GBZ -0.216066\\n\",\n \"FINANZ_MINIMALIST -0.223083\\n\",\n \"MOBI_REGIO -0.224789\\n\",\n \"PLZ8_ANTG1 -0.225675\\n\",\n \"Name: compnent_0, Length: 65, dtype: float64\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"first_component_weitghs=get_feature_importance(pca_model,1)\\n\",\n \"first_component_weitghs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SEMIO_VERT 0.344392\\n\",\n \"SEMIO_SOZ 0.262644\\n\",\n \"SEMIO_FAM 0.249003\\n\",\n \"SEMIO_KULT 0.234749\\n\",\n \"FINANZ_MINIMALIST 0.154073\\n\",\n \"RETOURTYP_BK_S 0.107846\\n\",\n \"FINANZ_VORSORGER 0.101009\\n\",\n \"W_KEIT_KIND_HH 0.084219\\n\",\n \"ALTERSKATEGORIE_GROB 0.078494\\n\",\n \"SEMIO_REL 0.068086\\n\",\n \"SEMIO_LUST 0.063855\\n\",\n \"SEMIO_MAT 0.055693\\n\",\n \"ORTSGR_KLS9 0.050371\\n\",\n \"EWDICHTE 0.049586\\n\",\n \"PLZ8_ANTG4 0.049555\\n\",\n \"PLZ8_ANTG3 0.047969\\n\",\n \"GREEN_AVANTGARDE 0.047718\\n\",\n \"PRAEGENDE_JUGENDJAHRE_movment 0.047718\\n\",\n \"ARBEIT 0.037520\\n\",\n \"RELAT_AB 0.034491\\n\",\n \"WOHNDAUER_2008 0.033260\\n\",\n \"PLZ8_ANTG2 0.032499\\n\",\n \"CAMEO_INTL_2015_wealth 0.030020\\n\",\n \"KBA05_ANTG4 0.029813\\n\",\n \"ANZ_HAUSHALTE_AKTIV 0.026622\\n\",\n \"ANZ_HH_TITEL 0.013798\\n\",\n \"KBA05_ANTG3 0.012177\\n\",\n \"ANZ_TITEL 0.009607\\n\",\n \"PLZ8_HHZ 0.005959\\n\",\n \"VERS_TYP 0.001568\\n\",\n \" ... \\n\",\n \"KKK -0.015838\\n\",\n \"HH_EINKOMMEN_SCORE -0.015943\\n\",\n \"OST_WEST_KZ -0.016306\\n\",\n \"KBA05_ANTG1 -0.020482\\n\",\n \"MIN_GEBAEUDEJAHR -0.022024\\n\",\n \"KBA13_ANZAHL_PKW -0.024724\\n\",\n \"MOBI_REGIO -0.024891\\n\",\n \"KBA05_GBZ -0.028281\\n\",\n \"number_missing_values -0.029613\\n\",\n \"GEBAEUDETYP_RASTER -0.032031\\n\",\n \"HEALTH_TYP -0.033988\\n\",\n \"BALLRAUM -0.037251\\n\",\n \"FINANZ_HAUSBAUER -0.039332\\n\",\n \"PLZ8_GBZ -0.040180\\n\",\n \"KONSUMNAEHE -0.040517\\n\",\n \"INNENSTADT -0.045750\\n\",\n \"PLZ8_ANTG1 -0.048929\\n\",\n \"ONLINE_AFFINITAET -0.055130\\n\",\n \"SEMIO_TRADV -0.078078\\n\",\n \"SEMIO_PFLICHT -0.079358\\n\",\n \"FINANZ_UNAUFFAELLIGER -0.101196\\n\",\n \"FINANZ_SPARER -0.106609\\n\",\n \"PRAEGENDE_JUGENDJAHRE_age -0.111275\\n\",\n \"SEMIO_ERL -0.176088\\n\",\n \"FINANZ_ANLEGER -0.190484\\n\",\n \"SEMIO_RAT -0.217075\\n\",\n \"SEMIO_KRIT -0.275798\\n\",\n \"SEMIO_DOM -0.312589\\n\",\n \"SEMIO_KAEM -0.335150\\n\",\n \"ANREDE_KZ -0.367370\\n\",\n \"Name: compnent_2, Length: 65, dtype: float64\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"third_component_weitghs=get_feature_importance(pca_model,3)\\n\",\n \"third_component_weitghs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"PRAEGENDE_JUGENDJAHRE_movment 0.398993\\n\",\n \"GREEN_AVANTGARDE 0.398993\\n\",\n \"EWDICHTE 0.259710\\n\",\n \"ORTSGR_KLS9 0.247238\\n\",\n \"ONLINE_AFFINITAET 0.135588\\n\",\n \"PLZ8_HHZ 0.133654\\n\",\n \"SEMIO_DOM 0.117534\\n\",\n \"KBA05_ANTG1 0.110556\\n\",\n \"ANZ_PERSONEN 0.108251\\n\",\n \"OST_WEST_KZ 0.107269\\n\",\n \"PLZ8_ANTG2 0.104550\\n\",\n \"RELAT_AB 0.099040\\n\",\n \"PLZ8_ANTG3 0.094760\\n\",\n \"MOBI_REGIO 0.081531\\n\",\n \"PLZ8_ANTG4 0.081328\\n\",\n \"SEMIO_KAEM 0.077719\\n\",\n \"FINANZ_UNAUFFAELLIGER 0.074461\\n\",\n \"SEMIO_TRADV 0.067081\\n\",\n \"CAMEO_INTL_2015_family_stage 0.062640\\n\",\n \"KBA05_GBZ 0.061882\\n\",\n \"FINANZ_MINIMALIST 0.059445\\n\",\n \"ARBEIT 0.056918\\n\",\n \"SEMIO_RAT 0.056374\\n\",\n \"ANZ_TITEL 0.047913\\n\",\n \"ANREDE_KZ 0.038644\\n\",\n \"KBA13_ANZAHL_PKW 0.035220\\n\",\n \"ANZ_HH_TITEL 0.034988\\n\",\n \"PLZ8_GBZ 0.034748\\n\",\n \"PRAEGENDE_JUGENDJAHRE_age 0.029460\\n\",\n \"SEMIO_PFLICHT 0.021464\\n\",\n \" ... \\n\",\n \"SEMIO_SOZ 0.008709\\n\",\n \"HEALTH_TYP 0.006593\\n\",\n \"SOHO_KZ 0.002360\\n\",\n \"FINANZ_SPARER -0.000493\\n\",\n \"SEMIO_LUST -0.000555\\n\",\n \"SEMIO_REL -0.001748\\n\",\n \"RETOURTYP_BK_S -0.003935\\n\",\n \"FINANZ_VORSORGER -0.019646\\n\",\n \"SEMIO_FAM -0.021309\\n\",\n \"SEMIO_VERT -0.021623\\n\",\n \"SEMIO_ERL -0.027427\\n\",\n \"SEMIO_KULT -0.029460\\n\",\n \"ANZ_HAUSHALTE_AKTIV -0.031451\\n\",\n \"KBA05_ANTG4 -0.035266\\n\",\n \"PLZ8_ANTG1 -0.045632\\n\",\n \"ALTERSKATEGORIE_GROB -0.050897\\n\",\n \"KBA05_ANTG3 -0.068268\\n\",\n \"MIN_GEBAEUDEJAHR -0.068858\\n\",\n \"number_missing_values -0.072011\\n\",\n \"GEBAEUDETYP_RASTER -0.084942\\n\",\n \"FINANZ_HAUSBAUER -0.095237\\n\",\n \"W_KEIT_KIND_HH -0.098056\\n\",\n \"FINANZ_ANLEGER -0.119672\\n\",\n \"CAMEO_INTL_2015_wealth -0.121090\\n\",\n \"KONSUMNAEHE -0.149070\\n\",\n \"INNENSTADT -0.218148\\n\",\n \"REGIOTYP -0.219971\\n\",\n \"BALLRAUM -0.231948\\n\",\n \"HH_EINKOMMEN_SCORE -0.233552\\n\",\n \"KKK -0.273046\\n\",\n \"Name: compnent_3, Length: 65, dtype: float64\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\n\",\n \"fourth_component_weitghs=get_feature_importance(pca_model,4)\\n\",\n \"fourth_component_weitghs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ALTERSKATEGORIE_GROB 0.256048\\n\",\n \"SEMIO_ERL 0.229339\\n\",\n \"FINANZ_VORSORGER 0.229293\\n\",\n \"SEMIO_LUST 0.180080\\n\",\n \"RETOURTYP_BK_S 0.161561\\n\",\n \"FINANZ_HAUSBAUER 0.122137\\n\",\n \"SEMIO_KRIT 0.117056\\n\",\n \"SEMIO_KAEM 0.115813\\n\",\n \"W_KEIT_KIND_HH 0.114503\\n\",\n \"PLZ8_ANTG3 0.098290\\n\",\n \"EWDICHTE 0.098110\\n\",\n \"ORTSGR_KLS9 0.096787\\n\",\n \"PLZ8_ANTG4 0.096682\\n\",\n \"ANREDE_KZ 0.092482\\n\",\n \"CAMEO_INTL_2015_wealth 0.079272\\n\",\n \"KBA05_ANTG4 0.075302\\n\",\n \"SEMIO_DOM 0.074236\\n\",\n \"ARBEIT 0.072053\\n\",\n \"RELAT_AB 0.069492\\n\",\n \"PLZ8_ANTG2 0.068161\\n\",\n \"ANZ_HAUSHALTE_AKTIV 0.066506\\n\",\n \"HH_EINKOMMEN_SCORE 0.062335\\n\",\n \"FINANZ_MINIMALIST 0.059513\\n\",\n \"WOHNDAUER_2008 0.058989\\n\",\n \"KBA05_ANTG3 0.050849\\n\",\n \"VERS_TYP 0.032129\\n\",\n \"ANZ_HH_TITEL 0.032055\\n\",\n \"PLZ8_HHZ 0.016151\\n\",\n \"REGIOTYP 0.011365\\n\",\n \"ANZ_TITEL 0.006828\\n\",\n \" ... \\n\",\n \"PRAEGENDE_JUGENDJAHRE_movment -0.017977\\n\",\n \"OST_WEST_KZ -0.027553\\n\",\n \"number_missing_values -0.029443\\n\",\n \"KBA13_ANZAHL_PKW -0.038618\\n\",\n \"GEBAEUDETYP_RASTER -0.047307\\n\",\n \"MIN_GEBAEUDEJAHR -0.051478\\n\",\n \"HEALTH_TYP -0.057077\\n\",\n \"ANZ_PERSONEN -0.063638\\n\",\n \"BALLRAUM -0.064801\\n\",\n \"SEMIO_VERT -0.072325\\n\",\n \"KBA05_ANTG1 -0.073487\\n\",\n \"KONSUMNAEHE -0.074971\\n\",\n \"PLZ8_GBZ -0.075068\\n\",\n \"MOBI_REGIO -0.079763\\n\",\n \"INNENSTADT -0.079788\\n\",\n \"KBA05_GBZ -0.092074\\n\",\n \"PLZ8_ANTG1 -0.096217\\n\",\n \"SEMIO_SOZ -0.102348\\n\",\n \"SEMIO_MAT -0.161809\\n\",\n \"ONLINE_AFFINITAET -0.165368\\n\",\n \"SEMIO_RAT -0.167242\\n\",\n \"SEMIO_FAM -0.183932\\n\",\n \"FINANZ_ANLEGER -0.203033\\n\",\n \"SEMIO_KULT -0.219010\\n\",\n \"SEMIO_PFLICHT -0.225470\\n\",\n \"FINANZ_UNAUFFAELLIGER -0.226069\\n\",\n \"SEMIO_TRADV -0.227959\\n\",\n \"FINANZ_SPARER -0.231805\\n\",\n \"PRAEGENDE_JUGENDJAHRE_age -0.238825\\n\",\n \"SEMIO_REL -0.253591\\n\",\n \"Name: compnent_1, Length: 65, dtype: float64\"\n ]\n },\n \"execution_count\": 61,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"second_component_weitghs=get_feature_importance(pca_model,2)\\n\",\n \"second_component_weitghs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# What kinds of people are part of a cluster that is underrepresented in the\\n\",\n \"# customer data compared to the general population?\\n\",\n \"customers_underrepresented_labels=[0,1,3,5,6,7,17,18,19,20,24,29]\\n\",\n \"underrepresented_subjects_index=np.where(np.in1d(labels_customers,customers_underrepresented_labels))[0]\\n\",\n \"underrepresented_subject_index_5=np.where(np.in1d(labels_customers,customers_underrepresented_labels[3]))[0]\\n\",\n \"underrepresented_comp=customers_pca[underrepresented_subject_index_5,:]\\n\",\n \"average=underrepresented_comp.mean(axis=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 2.56805224, 3.17728803, 3.54341946, 0.03484543, -0.33722702,\\n\",\n \" 1.59798928, 2.09505676, 0.80920504, -0.03768405, 0.6091663 ])\"\n ]\n },\n \"execution_count\": 60,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Discussion 3.3: Compare Customer Data to Demographics Data\\n\",\n \"\\n\",\n \"(Double-click this cell and replace this text with your own text, reporting findings and conclusions from the clustering analysis. Can we describe segments of the population that are relatively popular with the mail-order company, or relatively unpopular with the company?)\\n\",\n \"\\n\",\n \"* Yes, it could be described. For example the most over-represented cluster in the customer data is cluster 25. For this label the first, third and fourth compnent are dominant. Since the first compnent had negative value, the feature with negative weights in this compnents will be the most important. These features are PLZ8_ANTG1, MOBI_REGIO, FINANZ_MINIMALIST. They are related to the low rate of movment and very low financial minmalization. For the third and fourth compnent these features had the heighest weight are SEMIO_VERT, SEMIO_SOZ, SEMIO_FAM, SEMIO_KULT and the lowest weights was SEMIO_RAT, SEMIO_KRIT, SEMIO_DOM and SEMIO_KAEM which are related to the personality the customers are more of retional and critical thinking. for the Fourth component the feature with the heighest weights are PRAEGENDE_JUGENDJAHRE_movment, GREEN_AVANTGARDE and EWDICHTE, which is related to that to be in the west and to be a member of Membership in environmental sustainability and the high denisty of the houses.\\n\",\n \"\\n\",\n \"* For the most under-respsented cluster in the customer data is 5. For this cluster the third, second and first compnent had the heghest values. The third compnent was mentioned before, for the second compnent the heighest weights are of the following feature ALTERSKATEGORIE_GROB, SEMIO_ERL, FINANZ_VORSORGER. The first feature is related to the age and it means that the customers are less of aged people and the second is people that are less even oriented and thirldy the people that are very low finaiclly perepared. The first compenet highest weight features are PLZ8_ANTG3, PLZ8_ANTG4, and CAMEO_INTL_2015_wealth. The first two is related to the number of 6-10 and 10+ families and this might be related to the low income, so that's why there are no much customers in this regions. The third feaature is related to the wealthy house hold and there are small customers of poor house holds.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"> Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in **Discussion** cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to **File -> Download as -> HTML (.html)**. You will submit both that document and this notebook for your project submission.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "Machine Learning/Clustering/Customer identification for mail order products/LICENSE",
"content": "MIT License\n\nCopyright (c) 2021 youssefHosni\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
},
{
"path": "Machine Learning/Clustering/Customer identification for mail order products/README.md",
"content": "# project summary\nIn this project, real-life data from Bertelsmann partners AZ Direct and Arvato Finance Solution were used. The data here concerns a company that performs mail-order sales in Germany. Their main question of interest is to identify facets of the population that are most likely to be purchasers of their products for a mailout campaign. As a data scientist i will use unsupervised learning techniques to organize the general population into clusters, then use those clusters to see which of them comprise the main user base for the company. Prior to applying the machine learning methods, i cleaned the data in order to convert the data into a usable form.\n\n## steps\n### Step 1: Preprocessing\nWhen you start an analysis, you must first explore and understand the data that you are working with. In this (and the next) step of the project, you’ll be working with the general demographics data. As part of your investigation of dataset properties, you must attend to a few key points:\n\nHow are missing or unknown values encoded in the data? Are there certain features (columns) that should be removed from the analysis because of missing data? Are there certain data points (rows) that should be treated separately from the rest?\nConsider the level of measurement for each feature in the dataset (e.g. categorical, ordinal, numeric). What assumptions must be made in order to use each feature in the final analysis? Are there features that need to be re-encoded before they can be used? Are there additional features that can be dropped at this stage?\nYou will create a cleaning procedure that you will apply first to the general demographic data, then later to the customers data.\n\n### Step 2: Feature Transformation\nNow that your data is clean, you will use dimensionality reduction techniques to identify relationships between variables in the dataset, resulting in the creation of a new set of variables that account for those correlations. In this stage of the project, you will attend to the following points:\n\nThe first technique that you should perform on your data is feature scaling. What might happen if we don’t perform feature scaling before applying later techniques you’ll be using?\nOnce you’ve scaled your features, you can then apply principal component analysis (PCA) to find the vectors of maximal variability. How much variability in the data does each principal component capture? Can you interpret associations between original features in your dataset based on the weights given on the strongest components? How many components will you keep as part of the dimensionality reduction process?\nYou will use the sklearn library to create objects that implement your feature scaling and PCA dimensionality reduction decisions.\n\n### Step 3: Clustering\nFinally, on your transformed data, you will apply clustering techniques to identify groups in the general demographic data. You will then apply the same clustering model to the customers dataset to see how market segments differ between the general population and the mail-order sales company. You will tackle the following points in this stage:\n\nUse the k-means method to cluster the demographic data into groups. How should you make a decision on how many clusters to use?\nApply the techniques and models that you fit on the demographic data to the customers data: data cleaning, feature scaling, PCA, and k-means clustering. Compare the distribution of people by cluster for the customer data to that of the general population. Can you say anything about which types of people are likely consumers for the mail-order sales company?\n\n## Requirments\n* NumPy\n* pandas\n* Sklearn / scikit-learn\n* Matplotlib (for data visualization)\n* Seaborn (for data visualization)\n\n## Data used \n\nDemographic data for the general population of Germany; 891211 persons (rows) x 85 features (columns).\nDemographic data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).\nThe data is not provided as it is private data.\n\n"
},
{
"path": "Machine Learning/Clustering/Finding-the-best-Tornoto-neighborhood-to-open-a-new-gym/Finding best neighborhood for new gym opening in toronto city.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Download all the needed dependices and libraries \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 179,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: geopy in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (2.1.0)\\n\",\n \"Requirement already satisfied: geographiclib<2,>=1.49 in c:\\\\users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages (from geopy) (1.50)\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import folium # map rendering library\\n\",\n \"!pip install geopy\\n\",\n \"from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\\n\",\n \"import requests\\n\",\n \"from pandas.io.json import json_normalize\\n\",\n \"import matplotlib.cm as cm\\n\",\n \"import matplotlib.colors as colors\\n\",\n \"from sklearn.cluster import KMeans\\n\",\n \"import requests\\n\",\n \"import seaborn as sn\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Loading and exploring an demograhics dataset \\n\",\n \"The neighbourhood-profiles-2016-csv.csv contains demographics infromation for each neighborhood. The demogrpahics information that are important to our problem is the total population, the 15-45 poulation, the number of educated people and the number of employers in each neighborhood\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 341,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The shape of the demograhiics 2016 extra dataset (140, 4)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Category
\\n\",\n \"
Topic
\\n\",\n \"
Attribute
\\n\",\n \"
City of Toronto
\\n\",\n \"
Agincourt North
\\n\",\n \"
Agincourt South-Malvern West
\\n\",\n \"
Alderwood
\\n\",\n \"
Annex
\\n\",\n \"
Banbury-Don Mills
\\n\",\n \"
Bathurst Manor
\\n\",\n \"
...
\\n\",\n \"
Willowdale West
\\n\",\n \"
Willowridge-Martingrove-Richview
\\n\",\n \"
Woburn
\\n\",\n \"
Woodbine Corridor
\\n\",\n \"
Woodbine-Lumsden
\\n\",\n \"
Wychwood
\\n\",\n \"
Yonge-Eglinton
\\n\",\n \"
Yonge-St.Clair
\\n\",\n \"
York University Heights
\\n\",\n \"
Yorkdale-Glen Park
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
Population
\\n\",\n \"
Population
\\n\",\n \"
Population, 2011
\\n\",\n \"
2615060.0
\\n\",\n \"
30279.0
\\n\",\n \"
21988.0
\\n\",\n \"
11904.0
\\n\",\n \"
29177.0
\\n\",\n \"
26918.0
\\n\",\n \"
15434.0
\\n\",\n \"
...
\\n\",\n \"
15004.0
\\n\",\n \"
21343.0
\\n\",\n \"
53350.0
\\n\",\n \"
11703.0
\\n\",\n \"
7826.0
\\n\",\n \"
13986.0
\\n\",\n \"
10578.0
\\n\",\n \"
11652.0
\\n\",\n \"
27713.0
\\n\",\n \"
14687.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
Population
\\n\",\n \"
Population
\\n\",\n \"
Population, 2006
\\n\",\n \"
2503281.0
\\n\",\n \"
30156.0
\\n\",\n \"
21562.0
\\n\",\n \"
11656.0
\\n\",\n \"
27482.0
\\n\",\n \"
25439.0
\\n\",\n \"
14945.0
\\n\",\n \"
...
\\n\",\n \"
12517.0
\\n\",\n \"
20907.0
\\n\",\n \"
52461.0
\\n\",\n \"
11550.0
\\n\",\n \"
8051.0
\\n\",\n \"
14194.0
\\n\",\n \"
10497.0
\\n\",\n \"
11235.0
\\n\",\n \"
26140.0
\\n\",\n \"
14830.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
Population
\\n\",\n \"
Population
\\n\",\n \"
Population percentage change, 2006 to 2011
\\n\",\n \"
4.5
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
...
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
Population
\\n\",\n \"
Population
\\n\",\n \"
Population density per square kilometre
\\n\",\n \"
4149.5
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
...
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
NaN
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
Population
\\n\",\n \"
Dwellings
\\n\",\n \"
Total private dwellings
\\n\",\n \"
1107851.0
\\n\",\n \"
9341.0
\\n\",\n \"
7861.0
\\n\",\n \"
4840.0
\\n\",\n \"
17172.0
\\n\",\n \"
12118.0
\\n\",\n \"
6320.0
\\n\",\n \"
...
\\n\",\n \"
6931.0
\\n\",\n \"
8336.0
\\n\",\n \"
19181.0
\\n\",\n \"
5391.0
\\n\",\n \"
3645.0
\\n\",\n \"
6002.0
\\n\",\n \"
5550.0
\\n\",\n \"
7128.0
\\n\",\n \"
11722.0
\\n\",\n \"
5444.0
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
5 rows × 144 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Category Topic Attribute \\\\\\n\",\n \"0 Population Population Population, 2011 \\n\",\n \"1 Population Population Population, 2006 \\n\",\n \"2 Population Population Population percentage change, 2006 to 2011 \\n\",\n \"3 Population Population Population density per square kilometre \\n\",\n \"4 Population Dwellings Total private dwellings \\n\",\n \"\\n\",\n \" City of Toronto Agincourt North Agincourt South-Malvern West Alderwood \\\\\\n\",\n \"0 2615060.0 30279.0 21988.0 11904.0 \\n\",\n \"1 2503281.0 30156.0 21562.0 11656.0 \\n\",\n \"2 4.5 NaN NaN NaN \\n\",\n \"3 4149.5 NaN NaN NaN \\n\",\n \"4 1107851.0 9341.0 7861.0 4840.0 \\n\",\n \"\\n\",\n \" Annex Banbury-Don Mills Bathurst Manor ... Willowdale West \\\\\\n\",\n \"0 29177.0 26918.0 15434.0 ... 15004.0 \\n\",\n \"1 27482.0 25439.0 14945.0 ... 12517.0 \\n\",\n \"2 NaN NaN NaN ... NaN \\n\",\n \"3 NaN NaN NaN ... NaN \\n\",\n \"4 17172.0 12118.0 6320.0 ... 6931.0 \\n\",\n \"\\n\",\n \" Willowridge-Martingrove-Richview Woburn Woodbine Corridor \\\\\\n\",\n \"0 21343.0 53350.0 11703.0 \\n\",\n \"1 20907.0 52461.0 11550.0 \\n\",\n \"2 NaN NaN NaN \\n\",\n \"3 NaN NaN NaN \\n\",\n \"4 8336.0 19181.0 5391.0 \\n\",\n \"\\n\",\n \" Woodbine-Lumsden Wychwood Yonge-Eglinton Yonge-St.Clair \\\\\\n\",\n \"0 7826.0 13986.0 10578.0 11652.0 \\n\",\n \"1 8051.0 14194.0 10497.0 11235.0 \\n\",\n \"2 NaN NaN NaN NaN \\n\",\n \"3 NaN NaN NaN NaN \\n\",\n \"4 3645.0 6002.0 5550.0 7128.0 \\n\",\n \"\\n\",\n \" York University Heights Yorkdale-Glen Park \\n\",\n \"0 27713.0 14687.0 \\n\",\n \"1 26140.0 14830.0 \\n\",\n \"2 NaN NaN \\n\",\n \"3 NaN NaN \\n\",\n \"4 11722.0 5444.0 \\n\",\n \"\\n\",\n \"[5 rows x 144 columns]\"\n ]\n },\n \"execution_count\": 341,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"demographics_data=pd.read_csv('neighbourhood-data2011.csv', sep=',' , encoding='latin-1')\\n\",\n \"print(\\\"The shape of the demograhiics 2016 extra dataset\\\",demographics_2016_extra_data.shape) \\n\",\n \"demographics_data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Selecting the important features and dropping the rest of the features.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 342,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Neighborhood
\\n\",\n \"
Total population
\\n\",\n \"
age 15-24
\\n\",\n \"
age 25-34
\\n\",\n \"
age 35-44
\\n\",\n \"
people with income
\\n\",\n \"
20-30 thousand
\\n\",\n \"
30-40 thousand
\\n\",\n \"
40-50 thousand
\\n\",\n \"
50-60 thousand
\\n\",\n \"
60-80 thousand
\\n\",\n \"
80-90 thousand
\\n\",\n \"
100 thousand and more
\\n\",\n \"
Total population over 15 for education
\\n\",\n \"
High school diploma or equivalent
\\n\",\n \"
Postsecondary certificate, diploma or degree
\\n\",\n \"
In the labour force
\\n\",\n \"
Employed
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
Agincourt North
\\n\",\n \"
30280
\\n\",\n \"
4165
\\n\",\n \"
3840
\\n\",\n \"
3845
\\n\",\n \"
25770
\\n\",\n \"
3800
\\n\",\n \"
3000
\\n\",\n \"
1915
\\n\",\n \"
1105
\\n\",\n \"
1195
\\n\",\n \"
325
\\n\",\n \"
185
\\n\",\n \"
25765
\\n\",\n \"
7480
\\n\",\n \"
12325
\\n\",\n \"
14925
\\n\",\n \"
13230
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
Agincourt South-Malvern West
\\n\",\n \"
21990
\\n\",\n \"
3110
\\n\",\n \"
2775
\\n\",\n \"
2955
\\n\",\n \"
18600
\\n\",\n \"
3170
\\n\",\n \"
2310
\\n\",\n \"
1340
\\n\",\n \"
965
\\n\",\n \"
805
\\n\",\n \"
200
\\n\",\n \"
180
\\n\",\n \"
18595
\\n\",\n \"
4840
\\n\",\n \"
9695
\\n\",\n \"
11025
\\n\",\n \"
9860
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
Alderwood
\\n\",\n \"
11900
\\n\",\n \"
1325
\\n\",\n \"
1520
\\n\",\n \"
1675
\\n\",\n \"
10210
\\n\",\n \"
1580
\\n\",\n \"
1475
\\n\",\n \"
1110
\\n\",\n \"
970
\\n\",\n \"
925
\\n\",\n \"
260
\\n\",\n \"
190
\\n\",\n \"
10210
\\n\",\n \"
2560
\\n\",\n \"
5355
\\n\",\n \"
6740
\\n\",\n \"
6240
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
Annex
\\n\",\n \"
29180
\\n\",\n \"
3915
\\n\",\n \"
7190
\\n\",\n \"
3990
\\n\",\n \"
25180
\\n\",\n \"
2975
\\n\",\n \"
2660
\\n\",\n \"
2345
\\n\",\n \"
1950
\\n\",\n \"
2225
\\n\",\n \"
1125
\\n\",\n \"
2490
\\n\",\n \"
25185
\\n\",\n \"
4275
\\n\",\n \"
19220
\\n\",\n \"
18085
\\n\",\n \"
16770
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
Banbury-Don Mills
\\n\",\n \"
26910
\\n\",\n \"
2715
\\n\",\n \"
2930
\\n\",\n \"
3970
\\n\",\n \"
22855
\\n\",\n \"
2910
\\n\",\n \"
2670
\\n\",\n \"
2405
\\n\",\n \"
1900
\\n\",\n \"
2245
\\n\",\n \"
1060
\\n\",\n \"
1770
\\n\",\n \"
22855
\\n\",\n \"
4715
\\n\",\n \"
15840
\\n\",\n \"
14000
\\n\",\n \"
13030
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Neighborhood Total population age 15-24 age 25-34 \\\\\\n\",\n \"0 Agincourt North 30280 4165 3840 \\n\",\n \"1 Agincourt South-Malvern West 21990 3110 2775 \\n\",\n \"2 Alderwood 11900 1325 1520 \\n\",\n \"3 Annex 29180 3915 7190 \\n\",\n \"4 Banbury-Don Mills 26910 2715 2930 \\n\",\n \"\\n\",\n \" age 35-44 people with income 20-30 thousand 30-40 thousand 40-50 thousand \\\\\\n\",\n \"0 3845 25770 3800 3000 1915 \\n\",\n \"1 2955 18600 3170 2310 1340 \\n\",\n \"2 1675 10210 1580 1475 1110 \\n\",\n \"3 3990 25180 2975 2660 2345 \\n\",\n \"4 3970 22855 2910 2670 2405 \\n\",\n \"\\n\",\n \" 50-60 thousand 60-80 thousand 80-90 thousand 100 thousand and more \\\\\\n\",\n \"0 1105 1195 325 185 \\n\",\n \"1 965 805 200 180 \\n\",\n \"2 970 925 260 190 \\n\",\n \"3 1950 2225 1125 2490 \\n\",\n \"4 1900 2245 1060 1770 \\n\",\n \"\\n\",\n \" Total population over 15 for education High school diploma or equivalent \\\\\\n\",\n \"0 25765 7480 \\n\",\n \"1 18595 4840 \\n\",\n \"2 10210 2560 \\n\",\n \"3 25185 4275 \\n\",\n \"4 22855 4715 \\n\",\n \"\\n\",\n \" Postsecondary certificate, diploma or degree In the labour force Employed \\n\",\n \"0 12325 14925 13230 \\n\",\n \"1 9695 11025 9860 \\n\",\n \"2 5355 6740 6240 \\n\",\n \"3 19220 18085 16770 \\n\",\n \"4 15840 14000 13030 \"\n ]\n },\n \"execution_count\": 342,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"important_features_index=[9,14,17,20,692,699,700,701,702,703,704,705,1368,1370,1371,1410,1411]\\n\",\n \"important_features_index=[x-2 for x in important_features_index]\\n\",\n \"demographics_data=demographics_data.iloc[important_features_index,:]\\n\",\n \"\\n\",\n \"\\n\",\n \"demographics_data=demographics_data.set_index('Attribute')\\n\",\n \"demographics_data=demographics_data.transpose()\\n\",\n \"new_columns_name=['Total population' ,'age 15-24','age 25-34','age 35-44','people with income','20-30 thousand','30-40 thousand','40-50 thousand','50-60 thousand','60-80 thousand','80-90 thousand','100 thousand and more','Total population over 15 for education','High school diploma or equivalent','Postsecondary certificate, diploma or degree','In the labour force','Employed']\\n\",\n \"demographics_data.columns=new_columns_name\\n\",\n \"\\n\",\n \"\\n\",\n \"demographics_data=demographics_data.reset_index(drop=False)\\n\",\n \"demographics_data.rename(columns={'index':'Neighborhood'},inplace=True)\\n\",\n \"demographics_data.drop(index=[0,1,2],axis=1,inplace=True)\\n\",\n \"\\n\",\n \"demographics_data.reset_index(drop=True,inplace=True)\\n\",\n \"demographics_data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 343,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Neighborhood
\\n\",\n \"
Total population
\\n\",\n \"
number of educated people
\\n\",\n \"
number of 15-45
\\n\",\n \"
number of employers
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
Agincourt North
\\n\",\n \"
30280
\\n\",\n \"
19805
\\n\",\n \"
11850
\\n\",\n \"
13230
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
Agincourt South-Malvern West
\\n\",\n \"
21990
\\n\",\n \"
14535
\\n\",\n \"
8840
\\n\",\n \"
9860
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
Alderwood
\\n\",\n \"
11900
\\n\",\n \"
7915
\\n\",\n \"
4520
\\n\",\n \"
6240
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
Annex
\\n\",\n \"
29180
\\n\",\n \"
23495
\\n\",\n \"
15095
\\n\",\n \"
16770
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
Banbury-Don Mills
\\n\",\n \"
26910
\\n\",\n \"
20555
\\n\",\n \"
9615
\\n\",\n \"
13030
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Neighborhood Total population number of educated people \\\\\\n\",\n \"0 Agincourt North 30280 19805 \\n\",\n \"1 Agincourt South-Malvern West 21990 14535 \\n\",\n \"2 Alderwood 11900 7915 \\n\",\n \"3 Annex 29180 23495 \\n\",\n \"4 Banbury-Don Mills 26910 20555 \\n\",\n \"\\n\",\n \" number of 15-45 number of employers \\n\",\n \"0 11850 13230 \\n\",\n \"1 8840 9860 \\n\",\n \"2 4520 6240 \\n\",\n \"3 15095 16770 \\n\",\n \"4 9615 13030 \"\n ]\n },\n \"execution_count\": 343,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# calcaulating the number of 15-45 population and change the name of the columns\\n\",\n \"demographics_data['number of educated people']=(demographics_data['High school diploma or equivalent']+demographics_data['Postsecondary certificate, diploma or degree'])\\n\",\n \"demographics_data['number of 15-45']=(demographics_data['age 15-24']+demographics_data['age 25-34']+demographics_data['age 35-44'])\\n\",\n \"demographics_data['number of employers']=demographics_data['Employed']\\n\",\n \"demographics_data.drop(columns=new_columns_name[1:],axis=1,inplace=True)\\n\",\n \"demographics_data.sort_values(by='Neighborhood')\\n\",\n \"demographics_data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Loading and preprocessing the geographical data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The graphical data (lat,long) for each neighborhood is used to get the venues information for each neighborhood from Foursquare API.\\n\",\n \"The Neighbourhood Crime Rates.csv is downloaded from the toronta open data portal.\\n\",\n \"The data is preprocessed by removing the unneeded part and convering it from strings into float.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 135,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"We use pandas.read_csv() function to read the csv file. In the bracket, we put the file path along with a quotation mark, so that pandas will read the file into a data frame from that address. The file path can be either an URL or your local file address. \\n\",\n \"Because the data does not include headers, we can add an argument headers = None inside the read_csv() method, so that pandas will not automatically set the first row as a header. \\n\",\n \"You can also assign the dataset to any variable you create.\\n\",\n \"
\\n\",\n \"Thus, we have to add headers manually.\\n\",\n \"
\\n\",\n \"
\\n\",\n \"Firstly, we create a list \\\"headers\\\" that include all column names in order.\\n\",\n \"Then, we use dataframe.columns = headers to replace the headers by the list we created.\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" symboling normalized-losses make fuel-type aspiration num-of-doors \\\\\\n\",\n \"0 3 ? alfa-romero gas std two \\n\",\n \"1 3 ? alfa-romero gas std two \\n\",\n \"2 1 ? alfa-romero gas std two \\n\",\n \"3 2 164 audi gas std four \\n\",\n \"4 2 164 audi gas std four \\n\",\n \"5 2 ? audi gas std two \\n\",\n \"6 1 158 audi gas std four \\n\",\n \"7 1 ? audi gas std four \\n\",\n \"8 1 158 audi gas turbo four \\n\",\n \"9 0 ? audi gas turbo two \\n\",\n \"\\n\",\n \" body-style drive-wheels engine-location wheel-base ... engine-size \\\\\\n\",\n \"0 convertible rwd front 88.6 ... 130 \\n\",\n \"1 convertible rwd front 88.6 ... 130 \\n\",\n \"2 hatchback rwd front 94.5 ... 152 \\n\",\n \"3 sedan fwd front 99.8 ... 109 \\n\",\n \"4 sedan 4wd front 99.4 ... 136 \\n\",\n \"5 sedan fwd front 99.8 ... 136 \\n\",\n \"6 sedan fwd front 105.8 ... 136 \\n\",\n \"7 wagon fwd front 105.8 ... 136 \\n\",\n \"8 sedan fwd front 105.8 ... 131 \\n\",\n \"9 hatchback 4wd front 99.5 ... 131 \\n\",\n \"\\n\",\n \" fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\\\\n\",\n \"0 mpfi 3.47 2.68 9.0 111 5000 21 \\n\",\n \"1 mpfi 3.47 2.68 9.0 111 5000 21 \\n\",\n \"2 mpfi 2.68 3.47 9.0 154 5000 19 \\n\",\n \"3 mpfi 3.19 3.40 10.0 102 5500 24 \\n\",\n \"4 mpfi 3.19 3.40 8.0 115 5500 18 \\n\",\n \"5 mpfi 3.19 3.40 8.5 110 5500 19 \\n\",\n \"6 mpfi 3.19 3.40 8.5 110 5500 19 \\n\",\n \"7 mpfi 3.19 3.40 8.5 110 5500 19 \\n\",\n \"8 mpfi 3.13 3.40 8.3 140 5500 17 \\n\",\n \"9 mpfi 3.13 3.40 7.0 160 5500 16 \\n\",\n \"\\n\",\n \" highway-mpg price \\n\",\n \"0 27 13495 \\n\",\n \"1 27 16500 \\n\",\n \"2 26 16500 \\n\",\n \"3 30 13950 \\n\",\n \"4 22 17450 \\n\",\n \"5 25 15250 \\n\",\n \"6 25 17710 \\n\",\n \"7 25 18920 \\n\",\n \"8 20 23875 \\n\",\n \"9 22 ? \\n\",\n \"\\n\",\n \"[10 rows x 26 columns]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# replace headers and recheck our data frame\\n\",\n \"df.columns = headers\\n\",\n \"df.head(10)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Identify and handle missing values
\\n\",\n \"\\n\",\n \"
Identify missing values
\\n\",\n \"
Convert \\\"?\\\" to NaN
\\n\",\n \"In the car dataset, missing data comes with the question mark \\\"?\\\".\\n\",\n \"We replace \\\"?\\\" with NaN (Not a Number), which is Python's default missing value marker, for reasons of computational speed and convenience. Here we use the function: \\n\",\n \"
.replace(A, B, inplace = True)
\\n\",\n \"to replace A by B\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"we can drop missing values along the column \\\"price\\\" as follows \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
symboling
\\n\",\n \"
normalized-losses
\\n\",\n \"
make
\\n\",\n \"
fuel-type
\\n\",\n \"
aspiration
\\n\",\n \"
num-of-doors
\\n\",\n \"
body-style
\\n\",\n \"
drive-wheels
\\n\",\n \"
engine-location
\\n\",\n \"
wheel-base
\\n\",\n \"
...
\\n\",\n \"
engine-size
\\n\",\n \"
fuel-system
\\n\",\n \"
bore
\\n\",\n \"
stroke
\\n\",\n \"
compression-ratio
\\n\",\n \"
horsepower
\\n\",\n \"
peak-rpm
\\n\",\n \"
city-mpg
\\n\",\n \"
highway-mpg
\\n\",\n \"
price
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
3
\\n\",\n \"
NaN
\\n\",\n \"
alfa-romero
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
two
\\n\",\n \"
convertible
\\n\",\n \"
rwd
\\n\",\n \"
front
\\n\",\n \"
88.6
\\n\",\n \"
...
\\n\",\n \"
130
\\n\",\n \"
mpfi
\\n\",\n \"
3.47
\\n\",\n \"
2.68
\\n\",\n \"
9.0
\\n\",\n \"
111
\\n\",\n \"
5000
\\n\",\n \"
21
\\n\",\n \"
27
\\n\",\n \"
13495
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
3
\\n\",\n \"
NaN
\\n\",\n \"
alfa-romero
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
two
\\n\",\n \"
convertible
\\n\",\n \"
rwd
\\n\",\n \"
front
\\n\",\n \"
88.6
\\n\",\n \"
...
\\n\",\n \"
130
\\n\",\n \"
mpfi
\\n\",\n \"
3.47
\\n\",\n \"
2.68
\\n\",\n \"
9.0
\\n\",\n \"
111
\\n\",\n \"
5000
\\n\",\n \"
21
\\n\",\n \"
27
\\n\",\n \"
16500
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
NaN
\\n\",\n \"
alfa-romero
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
two
\\n\",\n \"
hatchback
\\n\",\n \"
rwd
\\n\",\n \"
front
\\n\",\n \"
94.5
\\n\",\n \"
...
\\n\",\n \"
152
\\n\",\n \"
mpfi
\\n\",\n \"
2.68
\\n\",\n \"
3.47
\\n\",\n \"
9.0
\\n\",\n \"
154
\\n\",\n \"
5000
\\n\",\n \"
19
\\n\",\n \"
26
\\n\",\n \"
16500
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
2
\\n\",\n \"
164
\\n\",\n \"
audi
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
four
\\n\",\n \"
sedan
\\n\",\n \"
fwd
\\n\",\n \"
front
\\n\",\n \"
99.8
\\n\",\n \"
...
\\n\",\n \"
109
\\n\",\n \"
mpfi
\\n\",\n \"
3.19
\\n\",\n \"
3.40
\\n\",\n \"
10.0
\\n\",\n \"
102
\\n\",\n \"
5500
\\n\",\n \"
24
\\n\",\n \"
30
\\n\",\n \"
13950
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
2
\\n\",\n \"
164
\\n\",\n \"
audi
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
four
\\n\",\n \"
sedan
\\n\",\n \"
4wd
\\n\",\n \"
front
\\n\",\n \"
99.4
\\n\",\n \"
...
\\n\",\n \"
136
\\n\",\n \"
mpfi
\\n\",\n \"
3.19
\\n\",\n \"
3.40
\\n\",\n \"
8.0
\\n\",\n \"
115
\\n\",\n \"
5500
\\n\",\n \"
18
\\n\",\n \"
22
\\n\",\n \"
17450
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
5 rows × 26 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" symboling normalized-losses make fuel-type aspiration num-of-doors \\\\\\n\",\n \"0 3 NaN alfa-romero gas std two \\n\",\n \"1 3 NaN alfa-romero gas std two \\n\",\n \"2 1 NaN alfa-romero gas std two \\n\",\n \"3 2 164 audi gas std four \\n\",\n \"4 2 164 audi gas std four \\n\",\n \"\\n\",\n \" body-style drive-wheels engine-location wheel-base ... engine-size \\\\\\n\",\n \"0 convertible rwd front 88.6 ... 130 \\n\",\n \"1 convertible rwd front 88.6 ... 130 \\n\",\n \"2 hatchback rwd front 94.5 ... 152 \\n\",\n \"3 sedan fwd front 99.8 ... 109 \\n\",\n \"4 sedan 4wd front 99.4 ... 136 \\n\",\n \"\\n\",\n \" fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\\\\n\",\n \"0 mpfi 3.47 2.68 9.0 111 5000 21 \\n\",\n \"1 mpfi 3.47 2.68 9.0 111 5000 21 \\n\",\n \"2 mpfi 2.68 3.47 9.0 154 5000 19 \\n\",\n \"3 mpfi 3.19 3.40 10.0 102 5500 24 \\n\",\n \"4 mpfi 3.19 3.40 8.0 115 5500 18 \\n\",\n \"\\n\",\n \" highway-mpg price \\n\",\n \"0 27 13495 \\n\",\n \"1 27 16500 \\n\",\n \"2 26 16500 \\n\",\n \"3 30 13950 \\n\",\n \"4 22 17450 \\n\",\n \"\\n\",\n \"[5 rows x 26 columns]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"# replace \\\"?\\\" to NaN\\n\",\n \"df.replace(\\\"?\\\", np.nan, inplace = True)\\n\",\n \"df.head(5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Identify_missing_values\\n\",\n \"\\n\",\n \"
Evaluating for Missing Data
\\n\",\n \"\\n\",\n \"The missing values are converted to default. We use the following functions to identify these missing values. There are two methods to detect missing data:\\n\",\n \"\\n\",\n \"\\n\",\n \"
.isnull()
\\n\",\n \"
.notnull()
\\n\",\n \"\\n\",\n \"The output is a boolean value indicating whether the value that is passed into the argument is in fact missing data.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"Using a for loop in Python, we can quickly figure out the number of missing values in each column. As mentioned above, \\\"True\\\" represents a missing value, \\\"False\\\" means the value is present in the dataset. In the body of the for loop the method \\\".value_counts()\\\" counts the number of \\\"True\\\" values. \\n\",\n \"
\\n\",\n \"How to deal with missing data?\\n\",\n \"\\n\",\n \"\\n\",\n \"
drop data \\n\",\n \" a. drop the whole row \\n\",\n \" b. drop the whole column\\n\",\n \"
\\n\",\n \"
replace data \\n\",\n \" a. replace it by mean \\n\",\n \" b. replace it by frequency \\n\",\n \" c. replace it based on other functions\\n\",\n \"
\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Whole columns should be dropped only if most entries in the column are empty. In our dataset, none of the columns are empty enough to drop entirely.\\n\",\n \"We have some freedom in choosing which method to replace data; however, some methods may seem more reasonable than others. We will apply each method to many different columns:\\n\",\n \"\\n\",\n \"Replace by mean:\\n\",\n \"\\n\",\n \"
\\n\",\n \"
\\\"normalized-losses\\\": 41 missing data, replace them with mean
\\n\",\n \"
\\\"stroke\\\": 4 missing data, replace them with mean
\\n\",\n \"
\\\"bore\\\": 4 missing data, replace them with mean
\\n\",\n \"
\\\"horsepower\\\": 2 missing data, replace them with mean
\\n\",\n \"
\\\"peak-rpm\\\": 2 missing data, replace them with mean
\\n\",\n \"
\\n\",\n \"\\n\",\n \"Replace by frequency:\\n\",\n \"\\n\",\n \"
\\n\",\n \"
\\\"num-of-doors\\\": 2 missing data, replace them with \\\"four\\\". \\n\",\n \"
\\n\",\n \"
Reason: 84% sedans is four doors. Since four doors is most frequent, it is most likely to occur
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"\\n\",\n \"Drop the whole row:\\n\",\n \"\\n\",\n \"
\\n\",\n \"
\\\"price\\\": 4 missing data, simply delete the whole row\\n\",\n \"
\\n\",\n \"
Reason: price is what we want to predict. Any data entry without price data cannot be used for prediction; therefore any row now without price data is not useful to us
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df['peak-rpm'].replace(np.nan, avg_peakrpm, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To see which values are present in a particular column, we can use the \\\".value_counts()\\\" method:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"four 114\\n\",\n \"two 89\\n\",\n \"Name: num-of-doors, dtype: int64\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['num-of-doors'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see that four doors are the most common type. We can also use the \\\".idxmax()\\\" method to calculate for us the most common type automatically:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'four'\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['num-of-doors'].value_counts().idxmax()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The replacement procedure is very similar to what we have seen previously\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#replace the missing 'num-of-doors' values by the most frequent \\n\",\n \"df[\\\"num-of-doors\\\"].replace(np.nan, \\\"four\\\", inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, let's drop all rows that do not have price data:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# simply drop whole row with NaN in \\\"price\\\" column\\n\",\n \"df.dropna(subset=[\\\"price\\\"], axis=0, inplace=True)\\n\",\n \"\\n\",\n \"# reset index, because we droped two rows\\n\",\n \"df.reset_index(drop=True, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
symboling
\\n\",\n \"
normalized-losses
\\n\",\n \"
make
\\n\",\n \"
fuel-type
\\n\",\n \"
aspiration
\\n\",\n \"
num-of-doors
\\n\",\n \"
body-style
\\n\",\n \"
drive-wheels
\\n\",\n \"
engine-location
\\n\",\n \"
wheel-base
\\n\",\n \"
...
\\n\",\n \"
engine-size
\\n\",\n \"
fuel-system
\\n\",\n \"
bore
\\n\",\n \"
stroke
\\n\",\n \"
compression-ratio
\\n\",\n \"
horsepower
\\n\",\n \"
peak-rpm
\\n\",\n \"
city-mpg
\\n\",\n \"
highway-mpg
\\n\",\n \"
price
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
3
\\n\",\n \"
122
\\n\",\n \"
alfa-romero
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
two
\\n\",\n \"
convertible
\\n\",\n \"
rwd
\\n\",\n \"
front
\\n\",\n \"
88.6
\\n\",\n \"
...
\\n\",\n \"
130
\\n\",\n \"
mpfi
\\n\",\n \"
3.47
\\n\",\n \"
2.68
\\n\",\n \"
9.0
\\n\",\n \"
111
\\n\",\n \"
5000
\\n\",\n \"
21
\\n\",\n \"
27
\\n\",\n \"
13495
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
3
\\n\",\n \"
122
\\n\",\n \"
alfa-romero
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
two
\\n\",\n \"
convertible
\\n\",\n \"
rwd
\\n\",\n \"
front
\\n\",\n \"
88.6
\\n\",\n \"
...
\\n\",\n \"
130
\\n\",\n \"
mpfi
\\n\",\n \"
3.47
\\n\",\n \"
2.68
\\n\",\n \"
9.0
\\n\",\n \"
111
\\n\",\n \"
5000
\\n\",\n \"
21
\\n\",\n \"
27
\\n\",\n \"
16500
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
122
\\n\",\n \"
alfa-romero
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
two
\\n\",\n \"
hatchback
\\n\",\n \"
rwd
\\n\",\n \"
front
\\n\",\n \"
94.5
\\n\",\n \"
...
\\n\",\n \"
152
\\n\",\n \"
mpfi
\\n\",\n \"
2.68
\\n\",\n \"
3.47
\\n\",\n \"
9.0
\\n\",\n \"
154
\\n\",\n \"
5000
\\n\",\n \"
19
\\n\",\n \"
26
\\n\",\n \"
16500
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
2
\\n\",\n \"
164
\\n\",\n \"
audi
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
four
\\n\",\n \"
sedan
\\n\",\n \"
fwd
\\n\",\n \"
front
\\n\",\n \"
99.8
\\n\",\n \"
...
\\n\",\n \"
109
\\n\",\n \"
mpfi
\\n\",\n \"
3.19
\\n\",\n \"
3.40
\\n\",\n \"
10.0
\\n\",\n \"
102
\\n\",\n \"
5500
\\n\",\n \"
24
\\n\",\n \"
30
\\n\",\n \"
13950
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
2
\\n\",\n \"
164
\\n\",\n \"
audi
\\n\",\n \"
gas
\\n\",\n \"
std
\\n\",\n \"
four
\\n\",\n \"
sedan
\\n\",\n \"
4wd
\\n\",\n \"
front
\\n\",\n \"
99.4
\\n\",\n \"
...
\\n\",\n \"
136
\\n\",\n \"
mpfi
\\n\",\n \"
3.19
\\n\",\n \"
3.40
\\n\",\n \"
8.0
\\n\",\n \"
115
\\n\",\n \"
5500
\\n\",\n \"
18
\\n\",\n \"
22
\\n\",\n \"
17450
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
5 rows × 26 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" symboling normalized-losses make fuel-type aspiration num-of-doors \\\\\\n\",\n \"0 3 122 alfa-romero gas std two \\n\",\n \"1 3 122 alfa-romero gas std two \\n\",\n \"2 1 122 alfa-romero gas std two \\n\",\n \"3 2 164 audi gas std four \\n\",\n \"4 2 164 audi gas std four \\n\",\n \"\\n\",\n \" body-style drive-wheels engine-location wheel-base ... engine-size \\\\\\n\",\n \"0 convertible rwd front 88.6 ... 130 \\n\",\n \"1 convertible rwd front 88.6 ... 130 \\n\",\n \"2 hatchback rwd front 94.5 ... 152 \\n\",\n \"3 sedan fwd front 99.8 ... 109 \\n\",\n \"4 sedan 4wd front 99.4 ... 136 \\n\",\n \"\\n\",\n \" fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\\\\n\",\n \"0 mpfi 3.47 2.68 9.0 111 5000 21 \\n\",\n \"1 mpfi 3.47 2.68 9.0 111 5000 21 \\n\",\n \"2 mpfi 2.68 3.47 9.0 154 5000 19 \\n\",\n \"3 mpfi 3.19 3.40 10.0 102 5500 24 \\n\",\n \"4 mpfi 3.19 3.40 8.0 115 5500 18 \\n\",\n \"\\n\",\n \" highway-mpg price \\n\",\n \"0 27 13495 \\n\",\n \"1 27 16500 \\n\",\n \"2 26 16500 \\n\",\n \"3 30 13950 \\n\",\n \"4 22 17450 \\n\",\n \"\\n\",\n \"[5 rows x 26 columns]\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Good! Now, we obtain the dataset with no missing values.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Correct data format
\\n\",\n \"We are almost there!\\n\",\n \"
The last step in data cleaning is checking and making sure that all data is in the correct format (int, float, text or other).
\\n\",\n \"\\n\",\n \"In Pandas, we use \\n\",\n \"\\n\",\n \"
As we can see above, some columns are not of the correct data type. Numerical variables should have type 'float' or 'int', and variables with strings such as categories should have type 'object'. For example, 'bore' and 'stroke' variables are numerical values that describe the engines, so we should expect them to be of the type 'float' or 'int'; however, they are shown as type 'object'. We have to convert data types into a proper format for each column using the \\\"astype()\\\" method.
\\n\",\n \"Data is usually collected from different agencies with different formats.\\n\",\n \"(Data Standardization is also a term for a particular type of data normalization, where we subtract the mean and divide by the standard deviation)\\n\",\n \"
\\n\",\n \" \\n\",\n \"What is Standardization?\\n\",\n \"
Standardization is the process of transforming data into a common format which allows the researcher to make the meaningful comparison.\\n\",\n \"
In our dataset, the fuel consumption columns \\\"city-mpg\\\" and \\\"highway-mpg\\\" are represented by mpg (miles per gallon) unit. Assume we are developing an application in a country that accept the fuel consumption with L/100km standard
\\n\",\n \"
We will need to apply data transformation to transform mpg into L/100km?
Normalization is the process of transforming values of several variables into a similar range. Typical normalizations include scaling the variable so the variable average is 0, scaling the variable so the variance is 1, or scaling variable so the variable values range from 0 to 1\\n\",\n \"
In our dataset, \\\"horsepower\\\" is a real valued variable ranging from 48 to 288, it has 57 unique values. What if we only care about the price difference between cars with high horsepower, medium horsepower, and little horsepower (3 types)? Can we rearrange them into three ‘bins' to simplify analysis?
\\n\",\n \"\\n\",\n \"
We will use the Pandas method 'cut' to segment the 'horsepower' column into 3 bins
\\n\",\n \" Check the dataframe above carefully, you will find the last column provides the bins for \\\"horsepower\\\" with 3 categories (\\\"Low\\\",\\\"Medium\\\" and \\\"High\\\"). \\n\",\n \"
\\n\",\n \"
\\n\",\n \" We successfully narrow the intervals from 57 to 3!\\n\",\n \"
\\n\",\n \"What is an indicator variable?\\n\",\n \"
\\n\",\n \" An indicator variable (or dummy variable) is a numerical variable used to label categories. They are called 'dummies' because the numbers themselves don't have inherent meaning. \\n\",\n \"
\\n\",\n \"\\n\",\n \"Why we use indicator variables?\\n\",\n \"\\n\",\n \"
\\n\",\n \" So we can use categorical variables for regression analysis in the later modules.\\n\",\n \"
\\n\",\n \"Example\\n\",\n \"
\\n\",\n \" We see the column \\\"fuel-type\\\" has two unique values, \\\"gas\\\" or \\\"diesel\\\". Regression doesn't understand words, only numbers. To use this attribute in regression analysis, we convert \\\"fuel-type\\\" into indicator variables.\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" We will use the panda's method 'get_dummies' to assign numerical values to different categories of fuel type. \\n\",\n \"
When visualizing individual variables, it is important to first understand what type of variable you are dealing with. This will help us find the right visualization method for that variable.
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"symboling int64\\n\",\n \"normalized-losses int32\\n\",\n \"make object\\n\",\n \"aspiration object\\n\",\n \"num-of-doors object\\n\",\n \"body-style object\\n\",\n \"drive-wheels object\\n\",\n \"engine-location object\\n\",\n \"wheel-base float64\\n\",\n \"length float64\\n\",\n \"width float64\\n\",\n \"height float64\\n\",\n \"curb-weight int64\\n\",\n \"engine-type object\\n\",\n \"num-of-cylinders object\\n\",\n \"engine-size int64\\n\",\n \"fuel-system object\\n\",\n \"bore float64\\n\",\n \"stroke float64\\n\",\n \"compression-ratio float64\\n\",\n \"horsepower int32\\n\",\n \"peak-rpm float64\\n\",\n \"city-mpg int64\\n\",\n \"highway-mpg int64\\n\",\n \"price float64\\n\",\n \"city-L/100km float64\\n\",\n \"horsepower-binned category\\n\",\n \"fuel-type-diesel uint8\\n\",\n \"fuel-type-gas uint8\\n\",\n \"dtype: object\\n\"\n ]\n }\n ],\n \"source\": [\n \"# list the data types for each column\\n\",\n \"print(df.dtypes)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For example, we can calculate the correlation between variables of type \\\"int64\\\" or \\\"float64\\\" using the method \\\"corr\\\":\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Continuous numerical variables are variables that may contain any value within some range. Continuous numerical variables can have the type \\\"int64\\\" or \\\"float64\\\". A great way to visualize these variables is by using scatterplots with fitted lines.
\\n\",\n \"\\n\",\n \"
In order to start understanding the (linear) relationship between an individual variable and the price. We can do this by using \\\"regplot\\\", which plots the scatterplot plus the fitted regression line for the data.
As the engine-size goes up, the price goes up: this indicates a positive direct correlation between these two variables. Engine size seems like a pretty good predictor of price since the regression line is almost a perfect diagonal line.
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" We can examine the correlation between 'engine-size' and 'price' and see it's approximately 0.87\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
As the highway-mpg goes up, the price goes down: this indicates an inverse/negative relationship between these two variables. Highway mpg could potentially be a predictor of price.
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can examine the correlation between 'highway-mpg' and 'price' and see it's approximately -0.704\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Peak rpm does not seem like a good predictor of the price at all since the regression line is close to horizontal. Also, the data points are very scattered and far from the fitted line, showing lots of variability. Therefore it's it is not a reliable variable.
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can examine the correlation between 'peak-rpm' and 'price' and see it's approximately -0.101616 \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
These are variables that describe a 'characteristic' of a data unit, and are selected from a small group of categories. The categorical variables can have the type \\\"object\\\" or \\\"int64\\\". A good way to visualize categorical variables is by using boxplots.
We see that the distributions of price between the different body-style categories have a significant overlap, and so body-style would not be a good predictor of price. Let's examine engine \\\"engine-location\\\" and \\\"price\\\":
Here we see that the distribution of price between these two engine-location categories, front and rear, are distinct enough to take engine-location as a potential good predictor of price.
Here we see that the distribution of price between the different drive-wheels categories differs; as such drive-wheels could potentially be a predictor of price.
Let's first take a look at the variables by utilizing a description method.
\\n\",\n \"\\n\",\n \"
The describe function automatically computes basic statistics for all continuous variables. Any NaN values are automatically skipped in these statistics.
\\n\",\n \"\\n\",\n \"This will show:\\n\",\n \"\\n\",\n \"
\\n\",\n \"
the count of that variable
\\n\",\n \"
the mean
\\n\",\n \"
the standard deviation (std)
\\n\",\n \"
the minimum value
\\n\",\n \"
the IQR (Interquartile Range: 25%, 50% and 75%)
\\n\",\n \"
the maximum value
\\n\",\n \"
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" We can apply the method \\\"describe\\\" as follows:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Value-counts is a good way of understanding how many units of each characteristic/variable we have. We can apply the \\\"value_counts\\\" method on the column 'drive-wheels'. Don’t forget the method \\\"value_counts\\\" only works on Pandas series, not Pandas Dataframes. As a result, we only include one bracket \\\"df['drive-wheels']\\\" not two brackets \\\"df[['drive-wheels']]\\\".
Examining the value counts of the engine location would not be a good predictor variable for the price. This is because we only have three cars with a rear engine and 198 with an engine in the front, this result is skewed. Thus, we are not able to draw any conclusions about the engine location.
The \\\"groupby\\\" method groups data by different categories. The data is grouped based on one or several variables and analysis is performed on the individual groups.
\\n\",\n \"\\n\",\n \"
For example, let's group by the variable \\\"drive-wheels\\\". We see that there are 3 different categories of drive wheels.
From our data, it seems rear-wheel drive vehicles are, on average, the most expensive, while 4-wheel and front-wheel are approximately the same in price.
\\n\",\n \"\\n\",\n \"
You can also group with multiple variables. For example, let's group by both 'drive-wheels' and 'body-style'. This groups the dataframe by the unique combinations 'drive-wheels' and 'body-style'. We can store the results in the variable 'grouped_test1'.
This grouped data is much easier to visualize when it is made into a pivot table. A pivot table is like an Excel spreadsheet, with one variable along the column and another along the row. We can convert the dataframe to a pivot table using the method \\\"pivot \\\" to create a pivot table from the groups.
\\n\",\n \"\\n\",\n \"
In this case, we will leave the drive-wheel variable as the rows of the table, and pivot body-style to become the columns of the table:
Often, we won't have data for some of the pivot cells. We can fill these missing cells with the value 0, but any other value could potentially be used as well. It should be mentioned that missing data is quite a complex subject and is an entire course on its own.
The heatmap plots the target variable (price) proportional to colour with respect to the variables 'drive-wheel' and 'body-style' in the vertical and horizontal axis respectively. This allows us to visualize how the price is related to 'drive-wheel' and 'body-style'.
\\n\",\n \"\\n\",\n \"
The default labels convey no useful information to us. Let's change that:
Visualization is very important in data science, and Python visualization packages provide great freedom. We will go more in-depth in a separate Python Visualizations course.
\\n\",\n \"\\n\",\n \"
The main question we want to answer in this module, is \\\"What are the main characteristics which have the most impact on the car price?\\\".
\\n\",\n \"\\n\",\n \"
To get a better measure of the important characteristics, we look at the correlation of these variables with the car price, in other words: how is the car price dependent on this variable?
Correlation: a measure of the extent of interdependence between variables.
\\n\",\n \"\\n\",\n \"
Causation: the relationship between cause and effect between two variables.
\\n\",\n \"\\n\",\n \"
It is important to know the difference between these two and that correlation does not imply causation. Determining correlation is much simpler the determining causation as causation may require independent experimentation.
Pearson Correlation is the default method of the function \\\"corr\\\". Like before we can calculate the Pearson Correlation of the of the 'int64' or 'float64' variables.
What is this P-value? The P-value is the probability value that the correlation between these two variables is statistically significant. Normally, we choose a significance level of 0.05, which means that we are 95% confident that the correlation between the variables is significant.
\\n\",\n \"\\n\",\n \"By convention, when the\\n\",\n \"\\n\",\n \"
\\n\",\n \"
p-value is $<$ 0.001: we say there is strong evidence that the correlation is significant.
\\n\",\n \"
the p-value is $<$ 0.05: there is moderate evidence that the correlation is significant.
\\n\",\n \"
the p-value is $<$ 0.1: there is weak evidence that the correlation is significant.
\\n\",\n \"
the p-value is $>$ 0.1: there is no evidence that the correlation is significant.
\\n\",\n \"
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" We can obtain this information using \\\"stats\\\" module in the \\\"scipy\\\" library.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from scipy import stats\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Wheel-base vs Price
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's calculate the Pearson Correlation Coefficient and P-value of 'wheel-base' and 'price'. \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The Pearson Correlation Coefficient is 0.5846418222655083 with a P-value of P = 8.076488270732873e-20\\n\"\n ]\n }\n ],\n \"source\": [\n \"pearson_coef, p_value = stats.pearsonr(df['wheel-base'], df['price'])\\n\",\n \"print(\\\"The Pearson Correlation Coefficient is\\\", pearson_coef, \\\" with a P-value of P =\\\", p_value) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Conclusion:
\\n\",\n \"
Since the p-value is $<$ 0.001, the correlation between wheel-base and price is statistically significant, although the linear relationship isn't extremely strong (~0.585)
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" Let's calculate the Pearson Correlation Coefficient and P-value of 'horsepower' and 'price'.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The Pearson Correlation Coefficient is 0.8096068016571052 with a P-value of P = 6.273536270650862e-48\\n\"\n ]\n }\n ],\n \"source\": [\n \"pearson_coef, p_value = stats.pearsonr(df['horsepower'], df['price'])\\n\",\n \"print(\\\"The Pearson Correlation Coefficient is\\\", pearson_coef, \\\" with a P-value of P = \\\", p_value) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Conclusion:
\\n\",\n \"\\n\",\n \"
Since the p-value is $<$ 0.001, the correlation between horsepower and price is statistically significant, and the linear relationship is quite strong (~0.809, close to 1)
\\n\",\n \"\\n\",\n \"Let's calculate the Pearson Correlation Coefficient and P-value of 'length' and 'price'.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The Pearson Correlation Coefficient is 0.6906283804483644 with a P-value of P = 8.016477466158188e-30\\n\"\n ]\n }\n ],\n \"source\": [\n \"pearson_coef, p_value = stats.pearsonr(df['length'], df['price'])\\n\",\n \"print(\\\"The Pearson Correlation Coefficient is\\\", pearson_coef, \\\" with a P-value of P = \\\", p_value) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Conclusion:
\\n\",\n \"
Since the p-value is $<$ 0.001, the correlation between length and price is statistically significant, and the linear relationship is moderately strong (~0.691).
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" Let's calculate the Pearson Correlation Coefficient and P-value of 'width' and 'price':\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The Pearson Correlation Coefficient is 0.7512653440522674 with a P-value of P = 9.200335510481516e-38\\n\"\n ]\n }\n ],\n \"source\": [\n \"pearson_coef, p_value = stats.pearsonr(df['width'], df['price'])\\n\",\n \"print(\\\"The Pearson Correlation Coefficient is\\\", pearson_coef, \\\" with a P-value of P =\\\", p_value ) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Conclusion:\\n\",\n \"\\n\",\n \"Since the p-value is < 0.001, the correlation between width and price is statistically significant, and the linear relationship is quite strong (~0.751).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Curb-weight vs Price\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" Let's calculate the Pearson Correlation Coefficient and P-value of 'curb-weight' and 'price':\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The Pearson Correlation Coefficient is 0.8344145257702849 with a P-value of P = 2.1895772388933803e-53\\n\"\n ]\n }\n ],\n \"source\": [\n \"pearson_coef, p_value = stats.pearsonr(df['curb-weight'], df['price'])\\n\",\n \"print( \\\"The Pearson Correlation Coefficient is\\\", pearson_coef, \\\" with a P-value of P = \\\", p_value) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Conclusion:
\\n\",\n \"
Since the p-value is $<$ 0.001, the correlation between curb-weight and price is statistically significant, and the linear relationship is quite strong (~0.834).
\\n\",\n \"\\n\",\n \"Let's calculate the Pearson Correlation Coefficient and P-value of 'engine-size' and 'price':\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The Pearson Correlation Coefficient is 0.8723351674455185 with a P-value of P = 9.265491622198389e-64\\n\"\n ]\n }\n ],\n \"source\": [\n \"pearson_coef, p_value = stats.pearsonr(df['engine-size'], df['price'])\\n\",\n \"print(\\\"The Pearson Correlation Coefficient is\\\", pearson_coef, \\\" with a P-value of P =\\\", p_value) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Conclusion:
\\n\",\n \"\\n\",\n \"
Since the p-value is $<$ 0.001, the correlation between engine-size and price is statistically significant, and the linear relationship is very strong (~0.872).
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" Let's calculate the Pearson Correlation Coefficient and P-value of 'bore' and 'price':\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The Pearson Correlation Coefficient is 0.5431553832626606 with a P-value of P = 8.049189483935032e-17\\n\"\n ]\n }\n ],\n \"source\": [\n \"pearson_coef, p_value = stats.pearsonr(df['bore'], df['price'])\\n\",\n \"print(\\\"The Pearson Correlation Coefficient is\\\", pearson_coef, \\\" with a P-value of P = \\\", p_value ) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Conclusion:
\\n\",\n \"
Since the p-value is $<$ 0.001, the correlation between bore and price is statistically significant, but the linear relationship is only moderate (~0.521).
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" We can relate the process for each 'City-mpg' and 'Highway-mpg':\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
City-mpg vs Price
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The Pearson Correlation Coefficient is -0.6865710067844681 with a P-value of P = 2.3211320655673773e-29\\n\"\n ]\n }\n ],\n \"source\": [\n \"pearson_coef, p_value = stats.pearsonr(df['city-mpg'], df['price'])\\n\",\n \"print(\\\"The Pearson Correlation Coefficient is\\\", pearson_coef, \\\" with a P-value of P = \\\", p_value) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Conclusion:
\\n\",\n \"
Since the p-value is $<$ 0.001, the correlation between city-mpg and price is statistically significant, and the coefficient of ~ -0.687 shows that the relationship is negative and moderately strong.
The Analysis of Variance (ANOVA) is a statistical method used to test whether there are significant differences between the means of two or more groups. ANOVA returns two parameters:
\\n\",\n \"\\n\",\n \"
F-test score: ANOVA assumes the means of all groups are the same, calculates how much the actual means deviate from the assumption, and reports it as the F-test score. A larger score means there is a larger difference between the means.
\\n\",\n \"\\n\",\n \"
P-value: P-value tells how statistically significant is our calculated score value.
\\n\",\n \"\\n\",\n \"
If our price variable is strongly correlated with the variable we are analyzing, expect ANOVA to return a sizeable F-test score and a small p-value.
Since ANOVA analyzes the difference between different groups of the same variable, the groupby function will come in handy. Because the ANOVA algorithm averages the data automatically, we do not need to take the average before hand.
\\n\",\n \"\\n\",\n \"
Let's see if different types 'drive-wheels' impact 'price', we group the data.
We now have a better idea of what our data looks like and which variables are important to take into account when predicting the car price. We have narrowed it down to the following variables:
As we now move into building machine learning models to automate our analysis, feeding the model with variables that meaningfully affect our target variable will improve our model's prediction performance.
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Model Development\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Some questions we want to ask in this module\\n\",\n \"\\n\",\n \"
\\n\",\n \"
do I know if the dealer is offering fair value for my trade-in?
\\n\",\n \"
do I know if I put a fair value on my car?
\\n\",\n \"
\\n\",\n \"
Data Analytics, we often use Model Development to help us predict future observations from the data we have.
\\n\",\n \"\\n\",\n \"
A Model will help us understand the exact relationship between different variables and how these variables are used to predict the result.
One example of a Data Model that we will be using is
\\n\",\n \"Simple Linear Regression.\\n\",\n \"\\n\",\n \" \\n\",\n \"
Simple Linear Regression is a method to help us understand the relationship between two variables:
\\n\",\n \"
\\n\",\n \"
The predictor/independent variable (X)
\\n\",\n \"
The response/dependent variable (that we want to predict)(Y)
\\n\",\n \"
\\n\",\n \"\\n\",\n \"
The result of Linear Regression is a linear function that predicts the response (dependent) variable as a function of the predictor (independent) variable.
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we saw above, we should get a final linear model with the structure:\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"$$\\n\",\n \"Yhat = a + b X\\n\",\n \"$$\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Plugging in the actual values we get:\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"price = 38423.31 - 821.73 x highway-mpg\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
What if we want to predict car price using more than one variable?
\\n\",\n \"\\n\",\n \"
If we want to use more variables in our model to predict car price, we can use Multiple Linear Regression.\\n\",\n \"Multiple Linear Regression is very similar to Simple Linear Regression, but this method is used to explain the relationship between one continuous response (dependent) variable and two or more predictor (independent) variables.\\n\",\n \"Most of the real-world regression models involve multiple predictors. We will illustrate the structure by using four predictor variables, but these results can generalize to any integer:
From the previous section we know that other good predictors of price could be:
\\n\",\n \"
\\n\",\n \"
Horsepower
\\n\",\n \"
Curb-weight
\\n\",\n \"
Engine-size
\\n\",\n \"
Highway-mpg
\\n\",\n \"
\\n\",\n \"Let's develop a model using these variables as the predictor variables.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 103,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Z = df[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Fit the linear model using the four above-mentioned variables.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 104,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression()\"\n ]\n },\n \"execution_count\": 104,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lm.fit(Z, df['price'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"What is the value of the intercept(a)?\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 105,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"-15811.86376772925\"\n ]\n },\n \"execution_count\": 105,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lm.intercept_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"What are the values of the coefficients (b1, b2, b3, b4)?\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 106,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([53.53022809, 4.70805253, 81.51280006, 36.1593925 ])\"\n ]\n },\n \"execution_count\": 106,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lm.coef_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" What is the final estimated linear model that we get?\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we saw above, we should get a final linear function with the structure:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"Yhat = a + b_1 X_1 + b_2 X_2 + b_3 X_3 + b_4 X_4\\n\",\n \"$$\\n\",\n \"\\n\",\n \"What is the linear function we get in this example?\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Price = -15678.742628061467 + 52.65851272 x horsepower + 4.69878948 x curb-weight + 81.95906216 x engine-size + 33.58258185 x highway-mpg\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
2) Model Evaluation using Visualization
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we've developed some models, how do we evaluate our models and how do we choose the best one? One way to do this is by using visualization.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
When it comes to simple linear regression, an excellent way to visualize the fit of our model is by using regression plots.
\\n\",\n \"\\n\",\n \"
This plot will show a combination of a scattered data points (a scatter plot), as well as the fitted linear regression line going through the data. This will give us a reasonable estimate of the relationship between the two variables, the strength of the correlation, as well as the direction (positive or negative correlation).
We can see from this plot that price is negatively correlated to highway-mpg, since the regression slope is negative.\\n\",\n \"One thing to keep in mind when looking at a regression plot is to pay attention to how scattered the data points are around the regression line. This will give you a good indication of the variance of the data, and whether a linear model would be the best fit or not. If the data is too far off from the line, this linear model might not be the best model for this data. Let's compare this plot to the regression plot of \\\"peak-rpm\\\".
Comparing the regression plot of \\\"peak-rpm\\\" and \\\"highway-mpg\\\" we see that the points for \\\"highway-mpg\\\" are much closer to the generated line and on the average decrease. The points for \\\"peak-rpm\\\" have more spread around the predicted line, and it is much harder to determine if the points are decreasing or increasing as the \\\"highway-mpg\\\" increases.
A good way to visualize the variance of the data is to use a residual plot.
\\n\",\n \"\\n\",\n \"
What is a residual?
\\n\",\n \"\\n\",\n \"
The difference between the observed value (y) and the predicted value (Yhat) is called the residual (e). When we look at a regression plot, the residual is the distance from the data point to the fitted regression line.
\\n\",\n \"\\n\",\n \"
So what is a residual plot?
\\n\",\n \"\\n\",\n \"
A residual plot is a graph that shows the residuals on the vertical y-axis and the independent variable on the horizontal x-axis.
\\n\",\n \"\\n\",\n \"
What do we pay attention to when looking at a residual plot?
\\n\",\n \"\\n\",\n \"
We look at the spread of the residuals:
\\n\",\n \"\\n\",\n \"
- If the points in a residual plot are randomly spread out around the x-axis, then a linear model is appropriate for the data. Why is that? Randomly spread out residuals means that the variance is constant, and thus the linear model is a good fit for this data.
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 109,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages\\\\seaborn\\\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\\n\",\n \" FutureWarning\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
We can see from this residual plot that the residuals are not randomly spread around the x-axis, which leads us to believe that maybe a non-linear model is more appropriate for this data.
How do we visualize a model for Multiple Linear Regression? This gets a bit more complicated because you can't visualize it with regression or residual plot.
\\n\",\n \"\\n\",\n \"
One way to look at the fit of the model is by looking at the distribution plot: We can look at the distribution of the fitted values that result from the model and compare it to the distribution of the actual values.
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First lets make a prediction \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 110,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Y_hat = lm.predict(Z)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 111,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages\\\\seaborn\\\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\\n\",\n \" warnings.warn(msg, FutureWarning)\\n\",\n \"C:\\\\Users\\\\youss\\\\anaconda3\\\\envs\\\\new_enviroment\\\\lib\\\\site-packages\\\\seaborn\\\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\\n\",\n \" warnings.warn(msg, FutureWarning)\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
We can see that the fitted values are reasonably close to the actual values, since the two distributions overlap a bit. However, there is definitely some room for improvement.
We saw earlier that a linear model did not provide the best fit while using highway-mpg as the predictor variable. Let's see if we can try fitting a polynomial model to the data instead.
We can already see from plotting that this polynomial model performs better than the linear model. This is because the generated polynomial function \\\"hits\\\" more of the data points.
The analytical expression for Multivariate Polynomial function gets complicated. For example, the expression for a second-order (degree=2)polynomial with two variables is given by:
Data Pipelines simplify the steps of processing the data. We use the module Pipeline to create a pipeline. We also use StandardScaler as a step in our pipeline.
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 123,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.pipeline import Pipeline\\n\",\n \"from sklearn.preprocessing import StandardScaler\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We create the pipeline, by creating a list of tuples including the name of the model or estimator and its corresponding constructor.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 124,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Input=[('scale',StandardScaler()), ('polynomial', PolynomialFeatures(include_bias=False)), ('model',LinearRegression())]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"we input the list as an argument to the pipeline constructor \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 125,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Pipeline(steps=[('scale', StandardScaler()),\\n\",\n \" ('polynomial', PolynomialFeatures(include_bias=False)),\\n\",\n \" ('model', LinearRegression())])\"\n ]\n },\n \"execution_count\": 125,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pipe=Pipeline(Input)\\n\",\n \"pipe\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can normalize the data, perform a transform and fit the model simultaneously. \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 126,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Pipeline(steps=[('scale', StandardScaler()),\\n\",\n \" ('polynomial', PolynomialFeatures(include_bias=False)),\\n\",\n \" ('model', LinearRegression())])\"\n ]\n },\n \"execution_count\": 126,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pipe.fit(Z,y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" Similarly, we can normalize the data, perform a transform and produce a prediction simultaneously\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 127,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([13102.93329646, 13102.93329646, 18226.43450275, 10391.09183955])\"\n ]\n },\n \"execution_count\": 127,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ypipe=pipe.predict(Z)\\n\",\n \"ypipe[0:4]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
When evaluating our models, not only do we want to visualize the results, but we also want a quantitative measure to determine how accurate the model is.
\\n\",\n \"\\n\",\n \"
Two very important measures that are often used in Statistics to determine the accuracy of a model are:
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's calculate the R^2\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 128,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The R-square is: 0.4965911884339175\\n\"\n ]\n }\n ],\n \"source\": [\n \"#highway_mpg_fit\\n\",\n \"lm.fit(X, Y)\\n\",\n \"# Find the R^2\\n\",\n \"print('The R-square is: ', lm.score(X, Y))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can say that ~ 49.659% of the variation of the price is explained by this simple linear model \\\"horsepower_fit\\\".\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's calculate the MSE\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can predict the output i.e., \\\"yhat\\\" using the predict method, where X is the input variable:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 129,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The output of the first four predicted value is: [16236.50464347 16236.50464347 17058.23802179 13771.3045085 ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"Yhat=lm.predict(X)\\n\",\n \"print('The output of the first four predicted value is: ', Yhat[0:4])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"lets import the function mean_squared_error from the module metrics\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 130,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import mean_squared_error\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"we compare the predicted results with the actual results \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 131,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The mean square error of price and predicted value is: 31635042.944639895\\n\"\n ]\n }\n ],\n \"source\": [\n \"mse = mean_squared_error(df['price'], Yhat)\\n\",\n \"print('The mean square error of price and predicted value is: ', mse)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Model 2: Multiple Linear Regression
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's calculate the R^2\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 132,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The R-square is: 0.8093732522175299\\n\"\n ]\n }\n ],\n \"source\": [\n \"# fit the model \\n\",\n \"lm.fit(Z, df['price'])\\n\",\n \"# Find the R^2\\n\",\n \"print('The R-square is: ', lm.score(Z, df['price']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can say that ~ 80.896 % of the variation of price is explained by this multiple linear regression \\\"multi_fit\\\".\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's calculate the MSE\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" we produce a prediction \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 133,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Y_predict_multifit = lm.predict(Z)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" we compare the predicted results with the actual results \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 134,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The mean square error of price and predicted value using multifit is: 11979300.34981888\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('The mean square error of price and predicted value using multifit is: ', \\\\\\n\",\n \" mean_squared_error(df['price'], Y_predict_multifit))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
Model 3: Polynomial Fit
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's calculate the R^2\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"let’s import the function r2_score from the module metrics as we are using a different function\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 135,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import r2_score\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We apply the function to get the value of r^2\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 136,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The R-square value is: 0.6741946663906516\\n\"\n ]\n }\n ],\n \"source\": [\n \"r_squared = r2_score(y, p(x))\\n\",\n \"print('The R-square value is: ', r_squared)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can say that ~ 67.419 % of the variation of price is explained by this polynomial fit\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
In the previous section, we trained the model using the method fit. Now we will use the method predict to produce a prediction. Lets import pyplot for plotting; we will also be using some functions from numpy.
Now that we have visualized the different models, and generated the R-squared and MSE values for the fits, how do we determine a good model fit?\\n\",\n \"
\\n\",\n \"
What is a good R-squared value?
\\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"
When comparing models, the model with the higher R-squared value is a better fit for the data.\\n\",\n \"
\\n\",\n \"
What is a good MSE?
\\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"
When comparing models, the model with the smallest MSE value is a better fit for the data.
\\n\",\n \"\\n\",\n \"
Let's take a look at the values for the different models.
\\n\",\n \"
Simple Linear Regression: Using Highway-mpg as a Predictor Variable of Price.\\n\",\n \"
\\n\",\n \"
R-squared: 0.49659118843391759
\\n\",\n \"
MSE: 3.16 x10^7
\\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
Multiple Linear Regression: Using Horsepower, Curb-weight, Engine-size, and Highway-mpg as Predictor Variables of Price.\\n\",\n \"
\\n\",\n \"
R-squared: 0.80896354913783497
\\n\",\n \"
MSE: 1.2 x10^7
\\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
Polynomial Fit: Using Highway-mpg as a Predictor Variable of Price.\\n\",\n \"
Usually, the more variables you have, the better your model is at predicting, but this is not always true. Sometimes you may not have enough data, you may run into numerical problems, or many of the variables may not be useful and or even act as noise. As a result, you should always check the MSE and R^2.
\\n\",\n \"\\n\",\n \"
So to be able to compare the results of the MLR vs SLR models, we look at a combination of both the R-squared and MSE to make the best conclusion about the fit of the model.\\n\",\n \"
\\n\",\n \"
MSEThe MSE of SLR is 3.16x10^7 while MLR has an MSE of 1.2 x10^7. The MSE of MLR is much smaller.
\\n\",\n \"
R-squared: In this case, we can also see that there is a big difference between the R-squared of the SLR and the R-squared of the MLR. The R-squared for the SLR (~0.497) is very small compared to the R-squared for the MLR (~0.809).
\\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"This R-squared in combination with the MSE show that MLR seems like the better model fit in this case, compared to SLR.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
MSE: We can see that Polynomial Fit brought down the MSE, since this MSE is smaller than the one from the SLR.
\\n\",\n \"
R-squared: The R-squared for the Polyfit is larger than the R-squared for the SLR, so the Polynomial Fit also brought up the R-squared quite a bit.
\\n\",\n \"
\\n\",\n \"
Since the Polynomial Fit resulted in a lower MSE and a higher R-squared, we can conclude that this was a better fit model than the simple linear regression for predicting Price with Highway-mpg as a predictor variable.
Comparing these three models, we conclude that the MLR model is the best model to be able to predict price from our dataset. This result makes sense, since we have 27 variables in total, and we know that more than one of those variables are potential predictors of the final car price.
\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "Machine Learning/Regression/Automobile price prediction/Readme.md",
"content": "# Automobile Price Prediction #\n\n## 1.Background ##\n\nIn this project I will predict the price of old automobile. In this project the following steps were excluded:\n\n* Loading the data\n* Preprocessing the data\n* Explore features or charecteristics to predict price of car\n* Develop prediction models\n* Evaluate and refine prediction models\n\n## 2. Methods\n\n### 2.1. Data\nThe dataset used is the [Automobile Dataset](https://www.kaggle.com/datasets/premptk/automobile-data-changed) from Kaggle. The data\n\n### 2.2. Data Preprocessing \n\n#### Identify and handle missing values ####\n\n#### Correct data format ####\n\n### 2.3. Feature Engineering ###\n\n#### Data Standardization ####\n\n\n#### Data Normalization ####\n\n\n#### Binning ####\n\n\n### 2.3. Data Exploration ###\n\n\n\n"
},
{
"path": "Machine Learning/Regression/Readme.md",
"content": "Regression projects \n"
},
{
"path": "Natural_Language_processing/Data-Science-Resume-Selector/Resume Selector with Naive Bayes .ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Resume selector using naive bayes \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Selecting the resume that are eligbile to data scientist postions, the dataset used contains 125 resumes, in the resumetext column. Resumes were queried from Indeed.com with keyword 'data scientist', location 'Vermont'. If a resume is 'not flagged', the applicant can submit a modified resume version at a later date. If it is 'flagged', the applicant is invited to interview.\\n\",\n \"The data can be downloaded from __[here](https://www.kaggle.com/samdeeplearning/deepnlp)__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"4VulXquoOxhD\"\n },\n \"source\": [\n \"### IMPORT LIBRARIES AND DATASETS\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: nltk in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (3.4.5)\\n\",\n \"Requirement already satisfied: six in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from nltk) (1.14.0)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# install nltk\\n\",\n \"!pip install nltk\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: gensim in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (3.8.3)\\n\",\n \"Requirement already satisfied: scipy>=0.18.1 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from gensim) (1.4.1)\\n\",\n \"Requirement already satisfied: six>=1.5.0 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from gensim) (1.14.0)\\n\",\n \"Requirement already satisfied: numpy>=1.11.3 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from gensim) (1.18.1)\\n\",\n \"Requirement already satisfied: Cython==0.29.14 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from gensim) (0.29.14)\\n\",\n \"Requirement already satisfied: smart-open>=1.8.1 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from gensim) (4.1.0)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# install gensim\\n\",\n \"!pip install gensim\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: wordcloud in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (1.8.1)\\n\",\n \"Requirement already satisfied: matplotlib in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from wordcloud) (3.1.3)\\n\",\n \"Requirement already satisfied: pillow in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from wordcloud) (7.0.0)\\n\",\n \"Requirement already satisfied: numpy>=1.6.1 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from wordcloud) (1.18.1)\\n\",\n \"Requirement already satisfied: kiwisolver>=1.0.1 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from matplotlib->wordcloud) (1.1.0)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.1 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from matplotlib->wordcloud) (2.8.1)\\n\",\n \"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from matplotlib->wordcloud) (2.4.6)\\n\",\n \"Requirement already satisfied: cycler>=0.10 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from matplotlib->wordcloud) (0.10.0)\\n\",\n \"Requirement already satisfied: setuptools in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from kiwisolver>=1.0.1->matplotlib->wordcloud) (45.2.0.post20200210)\\n\",\n \"Requirement already satisfied: six>=1.5 in c:\\\\users\\\\administrator\\\\anaconda31\\\\lib\\\\site-packages (from python-dateutil>=2.1->matplotlib->wordcloud) (1.14.0)\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install wordcloud\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"executionInfo\": {\n \"elapsed\": 8660,\n \"status\": \"ok\",\n \"timestamp\": 1601677250737,\n \"user\": {\n \"displayName\": \"Kukeshajanth Kodeswaran\",\n \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14Gj_hF0QlKjkAZvh3_nIU1Fj3LuIyLifAN3KKIdI7A=s64\",\n \"userId\": \"01021579274124875186\"\n },\n \"user_tz\": 240\n },\n \"id\": \"bpiddPjsl_4Q\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"from wordcloud import WordCloud, STOPWORDS\\n\",\n \"import nltk\\n\",\n \"from nltk.stem import PorterStemmer, WordNetLemmatizer\\n\",\n \"from nltk.corpus import stopwords\\n\",\n \"from nltk.tokenize import word_tokenize, sent_tokenize\\n\",\n \"import gensim\\n\",\n \"from gensim.utils import simple_preprocess\\n\",\n \"from gensim.parsing.preprocessing import STOPWORDS\\n\",\n \"from sklearn.metrics import classification_report, confusion_matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from jupyterthemes import jtplot\\n\",\n \"jtplot.style(theme='monokai', context='notebook', ticks=True, grid=False) \\n\",\n \"# setting the style of the notebook to be monokai theme \\n\",\n \"# this line of code is important to ensure that we are able to see the x and y axes clearly\\n\",\n \"# If you don't run this code line, you will notice that the xlabel and ylabel on any plot is black on black and it will be hard to see them. \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"executionInfo\": {\n \"elapsed\": 9549,\n \"status\": \"ok\",\n \"timestamp\": 1601677251631,\n \"user\": {\n \"displayName\": \"Kukeshajanth Kodeswaran\",\n \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14Gj_hF0QlKjkAZvh3_nIU1Fj3LuIyLifAN3KKIdI7A=s64\",\n \"userId\": \"01021579274124875186\"\n },\n \"user_tz\": 240\n },\n \"id\": \"8QiTczEunJNx\",\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
resume_id
\\n\",\n \"
class
\\n\",\n \"
resume_text
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
resume_1
\\n\",\n \"
not_flagged
\\n\",\n \"
\\\\rCustomer Service Supervisor/Tier - Isabella ...
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
resume_2
\\n\",\n \"
not_flagged
\\n\",\n \"
\\\\rEngineer / Scientist - IBM Microelectronics ...
\\\\rWaterbury VT - Email me on Indeed: indeed.co...
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
124
\\n\",\n \"
\\\\rResearch and Development Scientist - Burling...
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
125 rows × 2 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" resume_text class\\n\",\n \"0 \\\\rCustomer Service Supervisor/Tier - Isabella ... 0\\n\",\n \"1 \\\\rEngineer / Scientist - IBM Microelectronics ... 0\\n\",\n \"2 \\\\rLTS Software Engineer Computational Lithogra... 0\\n\",\n \"3 TUTOR\\\\rWilliston VT - Email me on Indeed: ind... 0\\n\",\n \"4 \\\\rIndependent Consultant - Self-employed\\\\rBurl... 1\\n\",\n \".. ... ...\\n\",\n \"120 \\\\rBrattleboro VT - Email me on Indeed: indeed.... 0\\n\",\n \"121 \\\\rResearch and Teaching Assistant - University... 0\\n\",\n \"122 \\\\rMedical Coder - Highly Skilled - Entry Level... 0\\n\",\n \"123 \\\\rWaterbury VT - Email me on Indeed: indeed.co... 1\\n\",\n \"124 \\\\rResearch and Development Scientist - Burling... 0\\n\",\n \"\\n\",\n \"[125 rows x 2 columns]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"resume_df['class'] = resume_df['class'].apply(lambda x:1 if x == 'flagged' else 0)\\n\",\n \"resume_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The number of 0 class 92\\n\",\n \"The number of 1 class 33\\n\"\n ]\n }\n ],\n \"source\": [\n \"#Divide the DataFrame into two, one that belongs to class 0 and 1. Do we have a balanced dataset?\\n\",\n \"resume_df_0 = resume_df[resume_df['class']==0]\\n\",\n \"resume_df_1 = resume_df[resume_df['class']==1]\\n\",\n \"print('The number of 0 class',len(resume_df_0))\\n\",\n \"print('The number of 1 class',len(resume_df_1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"jgkZHO3CPnsp\"\n },\n \"source\": [\n \"### DATA CLEANING\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\Administrator\\\\anaconda31\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:1: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" \\\"\\\"\\\"Entry point for launching an IPython kernel.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
resume_text
\\n\",\n \"
class
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
Customer Service Supervisor/Tier - Isabella Ca...
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
Engineer / Scientist - IBM Microelectronics Di...
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
LTS Software Engineer Computational Lithograph...
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
TUTORWilliston VT - Email me on Indeed: indee...
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
Independent Consultant - Self-employedBurlingt...
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
...
\\n\",\n \"
...
\\n\",\n \"
...
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
120
\\n\",\n \"
Brattleboro VT - Email me on Indeed: indeed.co...
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
121
\\n\",\n \"
Research and Teaching Assistant - University o...
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
122
\\n\",\n \"
Medical Coder - Highly Skilled - Entry LevelSu...
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
123
\\n\",\n \"
Waterbury VT - Email me on Indeed: indeed.com/...
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
124
\\n\",\n \"
Research and Development Scientist - Burlingto...
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
125 rows × 2 columns
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" resume_text class\\n\",\n \"0 Customer Service Supervisor/Tier - Isabella Ca... 0\\n\",\n \"1 Engineer / Scientist - IBM Microelectronics Di... 0\\n\",\n \"2 LTS Software Engineer Computational Lithograph... 0\\n\",\n \"3 TUTORWilliston VT - Email me on Indeed: indee... 0\\n\",\n \"4 Independent Consultant - Self-employedBurlingt... 1\\n\",\n \".. ... ...\\n\",\n \"120 Brattleboro VT - Email me on Indeed: indeed.co... 0\\n\",\n \"121 Research and Teaching Assistant - University o... 0\\n\",\n \"122 Medical Coder - Highly Skilled - Entry LevelSu... 0\\n\",\n \"123 Waterbury VT - Email me on Indeed: indeed.com/... 1\\n\",\n \"124 Research and Development Scientist - Burlingto... 0\\n\",\n \"\\n\",\n \"[125 rows x 2 columns]\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"resume_df['resume_text'] = resume_df['resume_text'].apply(lambda x:x.replace('\\\\r', ''))\\n\",\n \"resume_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 68\n },\n \"executionInfo\": {\n \"elapsed\": 1889,\n \"status\": \"ok\",\n \"timestamp\": 1601424280497,\n \"user\": {\n \"displayName\": \"Kukeshajanth Kodeswaran\",\n \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14Gj_hF0QlKjkAZvh3_nIU1Fj3LuIyLifAN3KKIdI7A=s64\",\n \"userId\": \"01021579274124875186\"\n },\n \"user_tz\": 240\n },\n \"id\": \"51VMI7E2ODX8\",\n \"outputId\": \"69d173e6-7f51-47f5-eedf-bf82e9f65861\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[nltk_data] Downloading package punkt to\\n\",\n \"[nltk_data] C:\\\\Users\\\\Administrator\\\\AppData\\\\Roaming\\\\nltk_data...\\n\",\n \"[nltk_data] Package punkt is already up-to-date!\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# download nltk packages\\n\",\n \"nltk.download('punkt')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 68\n },\n \"executionInfo\": {\n \"elapsed\": 1528,\n \"status\": \"ok\",\n \"timestamp\": 1601424280500,\n \"user\": {\n \"displayName\": \"Kukeshajanth Kodeswaran\",\n \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14Gj_hF0QlKjkAZvh3_nIU1Fj3LuIyLifAN3KKIdI7A=s64\",\n \"userId\": \"01021579274124875186\"\n },\n \"user_tz\": 240\n },\n \"id\": \"YDtZAF0l8qx4\",\n \"outputId\": \"65faf0c5-3e96-491f-dea0-c93f69e60b3a\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[nltk_data] Downloading package stopwords to\\n\",\n \"[nltk_data] C:\\\\Users\\\\Administrator\\\\AppData\\\\Roaming\\\\nltk_data...\\n\",\n \"[nltk_data] Package stopwords is already up-to-date!\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# download nltk packages\\n\",\n \"nltk.download(\\\"stopwords\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"id\": \"xlZjHcAb8t91\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Get additional stopwords from nltk\\n\",\n \"from nltk.corpus import stopwords\\n\",\n \"stop_words = stopwords.words('english')\\n\",\n \"stop_words.extend(['from','subject','reply','use','email','com'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"id\": \"-Sl0PEtkKhfn\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Remove stop words and remove words with 2 or less characters\\n\",\n \"def preprocess(text):\\n\",\n \" result = []\\n\",\n \" for token in gensim.utils.simple_preprocess(text) :\\n\",\n \" if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 2 and token not in stop_words:\\n\",\n \" result.append(token)\\n\",\n \" \\n\",\n \" return ' '.join(result)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"id\": \"Xhh3-a4tRgIR\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Cleaned text\\n\",\n \"resume_df['cleaned'] = resume_df['resume_text'].apply(preprocess)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"customer service supervisor tier isabella catalog companysouth burlington aecf work service supervisor tierisabella catalog company shelburne august present customer service visual set display website maintenance supervise customer service team popular catalog company manage day day issues resolution customer upset ensure customer satisfaction troubleshoot order shipping issues lost transit order errors damages manage resolve escalated customer calls ensure customer satisfaction assist customers order placing cross selling upselling catalog merchandise set display sample merchandise catalog library customer pick area facility website clean adding images type product information assistant events coordinator office services assistanteileen fisher irvington february july support director architecture architecture coordinator daily activities including preparing monthly expense reports scheduling calendar maintenance arranging aspects travel logistics catering interior design research projects manage event set ups entire process eileen fisher corporate locations catering overseeing set walk space client review event forms facilities team daily management professional calendars require heavy scheduling office services include companywide room reservations office supply orders filtered calls chief creative officer owner companytemp white plains december february office services assistant receptionist managed heavy volume orthopedic specialty benefit management company directed heavy daily incoming mail flow processed daily checks entered data excel generate totals accounting personal january december home office assistant personal assistant provided professional office support established psychologists new york area carefully handled personal confidential patient information organized uncluttered simplified office space create user friendly atmosphere coordinated researched travel related details flights hotels visas cars managed personal errands phone calls emails responsible mail processing bank deposits psychologists service representative account managercm almy sons greenwich january january greenwich january january customer service representative account manager provided high level customer service clergy church members denominations answered heavy volume assisted customers highly efficient manor assisted customers overall design garments final decision making church item purchases managed maintained large account database daily phone calls customer accounts responsible tracking large shipments replacement lost damaged items dorset street south burlington aimeerblair gmail assistant chief financial salute americas heroes ossining january january ossining january january administrative assistant chief financial officer interviewed military veterans families considered financial aid reviewed highly confidential database candidate mediated discussions military veterans collectors arrange final payouts debt incurred time injury finalized paperwork award payouts coordinated travel logistics large sponsored events assisted disabled veterans events provided basic administrative assistant sales team trade coordinatorleo electron microscopy thornwood august thornwood august administrative assistant sales team trade coordinator communicated general information provided quotes high end buyers worked closely team sales associates arranging meetings potential buyers prepared final proposals closing sale information purchased electron microscopes arranged aspects travel logistics trade shows united states canada attended trade shows sales associates scientists insure electron microscopes arrived safely set assisted demonstrations close sales trade floorartist charles fazzino pop artistcharles fazzino new rochelle freelance artist assembled dimensional piece art weekly basis home office responsible detailed finishing work making pieces presentable purchase galleries world visual artswestchester community college new york school years combined office services focus administrative assistance customer service event coordination trade coordination facilitating\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(resume_df['cleaned'][0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 54\n },\n \"executionInfo\": {\n \"elapsed\": 483,\n \"status\": \"ok\",\n \"timestamp\": 1601424285357,\n \"user\": {\n \"displayName\": \"Kukeshajanth Kodeswaran\",\n \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14Gj_hF0QlKjkAZvh3_nIU1Fj3LuIyLifAN3KKIdI7A=s64\",\n \"userId\": \"01021579274124875186\"\n },\n \"user_tz\": 240\n },\n \"id\": \"r2fyiTSEFvfK\",\n \"outputId\": \"67e6f5c4-be77-48d0-f397-6aa394d31ce8\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Customer Service Supervisor/Tier - Isabella Catalog CompanySouth Burlington VT - Email me on Indeed: indeed.com/r//49f8c9aecf490d26WORK EXPERIENCECustomer Service Supervisor/TierIsabella Catalog Company - Shelburne VT - August 2015 to Present2 Customer Service/Visual Set Up & Display/Website Maintenance¢ Supervise customer service team of a popular catalog company¢ Manage day to day issues and resolution of customer upset to ensure customer satisfaction¢ Troubleshoot order and shipping issues: lost in transit order errors damages¢ Manage and resolve escalated customer calls to ensure customer satisfaction¢ Assist customers with order placing cross-selling/upselling of catalog merchandise¢ Set up and display of sample merchandise in catalog library as well as customer pick-up area of the facility ¢ Website clean-up: adding images type up product information proofreadingAdministrative Assistant /Events Coordinator/Office Services AssistantEileen Fisher Inc - Irvington NY - February 2014 to July 2015Support to Director of Architecture and Architecture Coordinator in all daily activities including: preparing monthly expense reports scheduling calendar maintenance arranging all aspects of travel/logistics catering interior design research projects¢ Manage event set ups through entire process for two Eileen Fisher corporate locations¢ Catering overseeing set up walk-thru of space with client review event forms with facilities team ¢ Daily management of two professional calendars that require heavy scheduling¢ Office services that include: companywide room reservations office supply orders¢ Filtered calls to the Chief Creative Officer/Owner of the companyTemp AssignmentOrthoNet - White Plains NY - December 2013 to February 2014Office Services Assistant/Receptionist¢ Managed heavy call volume for orthopedic specialty benefit management company¢ Directed heavy daily incoming mail flow¢ Processed daily checks and entered data into Excel to generate totals for accounting reportsExecutive Personal AssistantWestchester NY - January 2012 to December 2013Home Office Assistant/ Personal Assistant¢ Provided professional office support to three established Psychologists in the New York area ¢ Carefully handled personal and confidential patient information¢ Organized uncluttered and simplified office space to create a more user-friendly atmosphere ¢ Coordinated and researched all travel related details (flights hotels visas cars etc.)¢ Managed personal errands phone calls and emails.¢ Responsible for mail processing and bank deposits while Psychologists were travelingCustomer Service Representative/ Account ManagerCM Almy & Sons Inc - Greenwich CT - January 2007 to January 2012 Greenwich CT January 2007 - January 2012Customer Service Representative/ Account Manager¢ Provided a high level of customer service to clergy and church members of all denominations¢ Answered heavy call volume and assisted customers in a highly efficient manor¢ Assisted customers with overall design of garments final decision making of church item purchases ¢ Managed and maintained a large account database with daily phone calls to customer accounts¢ Responsible for tracking large shipments and also replacement of lost or damaged items435 Dorset Street * South Burlington VT 05403 \",\n \"_ 914.564.4381 _ Aimeerblair319@gmail.comAdministrative Assistant to Chief Financial OfficerCoalition to Salute Americas Heroes - Ossining NY - January 2005 to January 2007Ossining NY January 2005 - January 2007Administrative Assistant to Chief Financial Officer¢ Interviewed military veterans and their families to be considered for financial aid ¢ Reviewed a highly confidential database for candidate¢ Mediated discussions between military veterans and collectors¢ Arrange final payouts for debt incurred during time of injury¢ Finalized paperwork for award payouts¢ Coordinated travel and logistics for large sponsored events¢ Assisted disabled veterans during events¢ Provided basic administrative supportAdministrative Assistant to Sales Team/ Trade Show CoordinatorLeo Electron Microscopy - Thornwood NY - May 2000 to August 2003Thornwood NY May 2000 - August 2003Administrative Assistant to Sales Team/ Trade Show Coordinator¢ Communicated general information and provided quotes to high end buyers¢ Worked closely with a team of sales associates arranging meetings with potential buyers¢ Prepared final proposals and closing sale information on purchased electron microscopes¢ Arranged all aspects of travel and logistics for trade shows within the United States and Canada.¢ Attended trade shows with sales associates and scientists to insure all electron microscopes arrived safely for set up¢ Assisted with demonstrations and close of sales on trade show floorArtist Charles Fazzino 3D Pop ArtistCharles Fazzino - New Rochelle NY - 1993 to 1996and 2003-2005Freelance Artist¢ Assembled 3 dimensional piece-art on a weekly basis from home office¢ Responsible for detailed finishing work and making pieces presentable for purchase in galleries world-wideEDUCATIONAAS in Visual ArtsWestchester Community College - New York NY School knowledgeADDITIONAL INFORMATIONProviding more than 15 years of combined office services with a focus on Administrative Assistance Customer Service Event Coordination Trade Show Coordination and Facilitating\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(resume_df['resume_text'][0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"tf4bOLzfPc78\"\n },\n \"source\": [\n \"### VISUALIZE CLEANED DATASET\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 296\n },\n \"executionInfo\": {\n \"elapsed\": 1229,\n \"status\": \"ok\",\n \"timestamp\": 1601270473021,\n \"user\": {\n \"displayName\": \"Kukeshajanth Kodeswaran\",\n \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14Gj_hF0QlKjkAZvh3_nIU1Fj3LuIyLifAN3KKIdI7A=s64\",\n \"userId\": \"01021579274124875186\"\n },\n \"user_tz\": 240\n },\n \"id\": \"VXoj-rLAGGpn\",\n \"outputId\": \"23c2a36c-d3c0-4728-f93b-07fbcd15088a\",\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Predicting the Test set results\\n\",\n \"y_predict_test = NB_classifier.predict(x_test)\\n\",\n \"cm = confusion_matrix(y_test, y_predict_test)\\n\",\n \"sns.heatmap(cm, annot = True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 170\n },\n \"executionInfo\": {\n \"elapsed\": 599,\n \"status\": \"ok\",\n \"timestamp\": 1601277379671,\n \"user\": {\n \"displayName\": \"Kukeshajanth Kodeswaran\",\n \"photoUrl\": \"https://lh3.googleusercontent.com/a-/AOh14Gj_hF0QlKjkAZvh3_nIU1Fj3LuIyLifAN3KKIdI7A=s64\",\n \"userId\": \"01021579274124875186\"\n },\n \"user_tz\": 240\n },\n \"id\": \"sTSddgb6F_o6\",\n \"outputId\": \"54d97fc8-9969-4179-b7b2-9d88be88ca5e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.95 0.95 0.95 21\\n\",\n \" 1 0.91 0.91 0.91 11\\n\",\n \"\\n\",\n \" accuracy 0.94 32\\n\",\n \" macro avg 0.93 0.93 0.93 32\\n\",\n \"weighted avg 0.94 0.94 0.94 32\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# classification report\\n\",\n \"print(classification_report(y_test, y_predict_test))\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"authorship_tag\": \"ABX9TyOo/ghNd1zcohbzjmvnCLRz\",\n \"collapsed_sections\": [],\n \"mount_file_id\": \"1HiCpotiWB3NF2LqZMog9pqLOSf3IxNc6\",\n \"name\": \"resume_selector.ipynb\",\n \"provenance\": [],\n \"toc_visible\": true\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
},
{
"path": "Natural_Language_processing/Data-Science-Resume-Selector/readme.md",
"content": "## Data Science Resume Selector \n\nSelecting the resume that are eligbile to data scientist postions, the dataset used contains 125 resumes, in the resumetext column. Resumes were queried from Indeed.com with keyword 'data scientist', location 'Vermont'. If a resume is 'not flagged', the applicant can submit a modified resume version at a later date. If it is 'flagged', the applicant is invited to interview.\nThe data can be downloaded from __[here](https://www.kaggle.com/samdeeplearning/deepnlp)__\n"
},
{
"path": "Natural_Language_processing/Data-Science-Resume-Selector/resume.csv",
"content": "resume_id,class,resume_text\r\nresume_1,not_flagged,\"\rCustomer Service Supervisor/Tier - Isabella Catalog Company\rSouth Burlington VT - Email me on Indeed: indeed.com/r//49f8c9aecf490d26\rWORK EXPERIENCE\rCustomer Service Supervisor/Tier\rIsabella Catalog Company - Shelburne VT - August 2015 to Present\r2 Customer Service/Visual Set Up & Display/Website Maintenance\r Supervise customer service team of a popular catalog company\r Manage day to day issues and resolution of customer upset to ensure customer satisfaction\r Troubleshoot order and shipping issues: lost in transit order errors damages\r Manage and resolve escalated customer calls to ensure customer satisfaction\r Assist customers with order placing cross-selling/upselling of catalog merchandise\r Set up and display of sample merchandise in catalog library as well as customer pick-up area of the facility Website clean-up: adding images type up product information proofreading\rAdministrative Assistant /Events Coordinator/Office Services Assistant\rEileen Fisher Inc - Irvington NY - February 2014 to July 2015\rSupport to Director of Architecture and Architecture Coordinator in all daily activities including: preparing monthly expense reports scheduling calendar maintenance arranging all aspects of travel/logistics catering interior design research projects\r Manage event set ups through entire process for two Eileen Fisher corporate locations\r Catering overseeing set up walk-thru of space with client review event forms with facilities team Daily management of two professional calendars that require heavy scheduling\r Office services that include: companywide room reservations office supply orders\r Filtered calls to the Chief Creative Officer/Owner of the company\rTemp Assignment\rOrthoNet - White Plains NY - December 2013 to February 2014\rOffice Services Assistant/Receptionist\r Managed heavy call volume for orthopedic specialty benefit management company\r Directed heavy daily incoming mail flow\r Processed daily checks and entered data into Excel to generate totals for accounting reports\rExecutive Personal Assistant\rWestchester NY - January 2012 to December 2013\rHome Office Assistant/ Personal Assistant\r Provided professional office support to three established Psychologists in the New York area Carefully handled personal and confidential patient information\r Organized uncluttered and simplified office space to create a more user-friendly atmosphere Coordinated and researched all travel related details (flights hotels visas cars etc.)\r Managed personal errands phone calls and emails.\r Responsible for mail processing and bank deposits while Psychologists were traveling\rCustomer Service Representative/ Account Manager\rCM Almy & Sons Inc - Greenwich CT - January 2007 to January 2012\r \rGreenwich CT January 2007 - January 2012\rCustomer Service Representative/ Account Manager\r Provided a high level of customer service to clergy and church members of all denominations\r Answered heavy call volume and assisted customers in a highly efficient manor\r Assisted customers with overall design of garments final decision making of church item purchases Managed and maintained a large account database with daily phone calls to customer accounts\r Responsible for tracking large shipments and also replacement of lost or damaged items\r435 Dorset Street * South Burlington VT 05403 _ 914.564.4381 _ Aimeerblair319@gmail.com\rAdministrative Assistant to Chief Financial Officer\rCoalition to Salute Americas Heroes - Ossining NY - January 2005 to January 2007\rOssining NY January 2005 - January 2007\rAdministrative Assistant to Chief Financial Officer\r Interviewed military veterans and their families to be considered for financial aid Reviewed a highly confidential database for candidate\r Mediated discussions between military veterans and collectors\r Arrange final payouts for debt incurred during time of injury\r Finalized paperwork for award payouts\r Coordinated travel and logistics for large sponsored events\r Assisted disabled veterans during events\r Provided basic administrative support\rAdministrative Assistant to Sales Team/ Trade Show Coordinator\rLeo Electron Microscopy - Thornwood NY - May 2000 to August 2003\rThornwood NY May 2000 - August 2003\rAdministrative Assistant to Sales Team/ Trade Show Coordinator\r Communicated general information and provided quotes to high end buyers\r Worked closely with a team of sales associates arranging meetings with potential buyers\r Prepared final proposals and closing sale information on purchased electron microscopes\r Arranged all aspects of travel and logistics for trade shows within the United States and Canada.\r Attended trade shows with sales associates and scientists to insure all electron microscopes arrived safely for set up\r Assisted with demonstrations and close of sales on trade show floor\rArtist Charles Fazzino 3D Pop Artist\rCharles Fazzino - New Rochelle NY - 1993 to 1996\rand 2003-2005\rFreelance Artist\r Assembled 3 dimensional piece-art on a weekly basis from home office\r Responsible for detailed finishing work and making pieces presentable for purchase in galleries world-wide\rEDUCATION\rAAS in Visual Arts\rWestchester Community College - New York NY School knowledge\rADDITIONAL INFORMATION\rProviding more than 15 years of combined office services with a focus on Administrative Assistance Customer Service Event Coordination Trade Show Coordination and Facilitating\r\"\r\nresume_2,not_flagged,\"\rEngineer / Scientist - IBM Microelectronics Division\rWestford VT - Email me on Indeed: indeed.com/r/Albert-Gregoritsch/b105a8b2b40f9eca\rWORK EXPERIENCE\rEngineer / Scientist\rIBM Microelectronics Division - June 2007 to Present\rResponsible for Process and Equipment engineering for multiple lines including: o Multiple Bake processes\ro Leaded and lead free solder reflow\ro Thermal cycling\r Wrote specifications and procured capital for equipment purchases and upgrades Project management for equipment installation and equipment upgrades\r Developed methods for acquiring and tracking critical data metrics\r Drove production efficiency gains through data-driven decision making\r Implemented and maintained Lean Manufacturing initiatives o Root cause analysis and Structured Problem solving\ro Continuous Improvement activities\ro Standard Work and Job Breakdown Sheets\r Utilized statistical process controls on critical process indicators\r Worked with cross functional teams to implement and communicate changes\r Improved process flow and reduced cycle time through waste elimination\r Identified opportunities for and drove implementation of technical improvements to current manufacturing processes and procedures\r Oversight and implementation of multiple complex projects simultaneously\r Responsible for quality inspection strategy including inspection methods sample plans and defect criteria (June 2007 - July 2008)\r Oversight of materials transport processes (June 2007 - July 2008)\rEngineering Technician\rIBM Microelectronics Division - June 2007 to June 2007\rJune 2007)\r Tooling and Process support for:\ro Metal mask cleaning and inspection processes o Multiple Bake processes\ro Leaded and lead free solder reflow\ro Thermal cycling\r Documented manufacturing processes\r Trained production operators\r Qualified new equipment and processes\rProduction Operator\rIBM Microelectronics Division - May 1995 to May 1998\rSupplemental Operator\rIBM Microelectronics Division - September 1994 to May 1995\r \rSupervisor\rEndicott Contract Manufacturing - October 1993 to September 1994\rSupervised 19 employees working in quality control\r Handled personnel issues including scheduling vacation planning hiring firing performance evaluations and resource actions\rMicroscope Inspector\rEndicott Contract Manufacturing - March 1993 to October 1993\rSupplemental Operator\rIBM - February 1991 to October 1992\rEDUCATION\rBachelors in Business Technology and Management\rVermont Technical College\rAssociates in General Engineering Technologies\rVermont Technical College\rADDITIONAL INFORMATION Skills\r Microsoft Office Suite Lotus Symphony Windows AIX familiar with QMF SQL other database software and Microsoft Project\r PLC controllers Fluke Dataloggers Omega Dataloggers Laser Particle Counters Microscopes Automated Inspection Equipment Belt furnaces Blue M ovens Thermal Cycling chambers\r Lean Manufacturing initiatives Structured Problem solving 5S continuous improvement value stream analysis standard work visual pull systems\r Excellent presentation skills public speaking power point\r\"\r\nresume_3,not_flagged,\"\rLTS Software Engineer Computational Lithography - IBM Corporation\rBolton VT - Email me on Indeed: indeed.com/r/Albert-Nemethy/a21b1e4ebf733793\rIn 1981 started as an equipment technician responsible for maintaining IBM's proprietary device test systems took an instant liking to developing tester applications written in basic. I a steady and productive climb into equipment engineering developing many software systems and applications along the way. From 87C52 device microcode for high speed servo applications to multi-process multi-threaded desktop graphical applications I have to hone my skills building industrial strength software for the global market. My most recent project involved developing blob analysis applications for wafer map processes using MS Visual C#. I continue to development my own products maintaining my software skills using the latest leading edge technology.\rWORK EXPERIENCE\rLTS Software Engineer Computational Lithography\rIBM Corporation - September 2011 to Present\rRe-engineering of unit test processes that guarentee functionality of all Perl based applications used for IBM's Computational Lithography Processes. The goal of this work is to automatically generate test files which will verify that all applications are coded to their functional specifications.\rContractor for IBM. Software Engineer\rCTG INC - September 2010 to Present\rComputational Lithography\rDeveloper of Perl based applications that preprocess mask data for IBM's Computational Lithography Processes. These applications incorporate the use of Mentor Graphics - Calibre - products to provide Optical Proximity Correction for IBM's 200 - 300mm wafer fab mask operations.\rCustomer Engineer\rAPPLIED MATERIALS INC - February 2007 to March 2010\rDeveloped C# based application to graphically analyze wafer maps in an attempt to isolate hotspots on 300 mm wafers which would allow repositioning for optimum process operation. These programs used blob analysis to identify key areas of the wafer that indicated xy corrections direction and theta rotation.\rMaterials Coordinator\rNSTAR GLOBAL SERVICES - April 2004 to February 2007\rMaterials coordinator. Handled spare parts for Applied Materials operations in Fishkill NY\rSoftware Engineer\rIBM Corporation - Burlington VT - May 1997 to September 2003\rDeveloped a real time multithreaded Java based SAS command processor for wafer final test use. Using AWT I built a multi-window real time based Process scheduler that was run on an AIX platform. All code was in Java. FailData Analysis System. I Developed logic diagnostic applications for semiconductor device support at wafer final test. The purpose of one of these applications is to automatically pinpoint final test failures on all chips tested. The AIX/C/Motif based application FAST (Faildata Analysis SysTem) was an integral part. Version 2.x was developed using Java and swing for the GUI.\rIBM Corporation - Poughkeepsie NY - March 1981 to September 2003\r \rSoftware Engineer/Scientist\rIBM Corporation - Boca Raton FL - April 1992 to May 1997\rHigh Speed Serial Prober. Tester interfaces. Built software interfaces for measurement devices like:SMU DMM Capacitance meter etc. Developed code in C to interface a 40 inch 8 axis Anorad gantry to drive our device measurement systems. Developed diagnostic code for all systems as well.\rBoard Display Program. I Developed the Board Display Program. This was a 20000 line program written in C using OS2's presentation engine which allowed the user to graphically display the part under test and manipulate it to produce alignment and height mapping points. It also allowed an off-line method of optimizing a test sequence.\rVisual Test Generator. I invented an offline OS/2 graphical application to generate a test file from the device's net list called the Visual Test Generator. This required many optimization algorithms and enabled the test engineer to graphically refine a test program for maximum optimization.\rProgramming Technician\rIBM Corporation - July 1983 to June 1986\rPC Chip In Place Test Automation. Our team transformed an existing tester into a fully automatic functional chip tester. Utilizing ARTIC technology and Intel 87C51 embedded microcontrollers we developed multitasking real time software to control various pieces of the tester using C and PLM.\rCIPT Automatic Alignment. Automatic alignment of test probe to pads was accomplished using Cognex machine vision and custom application code that we developed using C. This project also required us to develop a pre Windows GUI screen management system.\rModule Assembly Tool Diagnostics. Developed maintenance diagnostic programs for automatic second generation module assembly tool. This was an Instron based servo driven compression tool that was used to compress the various pieces of a TCM together as it was being built. This was the first use of the C programming language at this site.\rEquipment Maintenance Technician\rIBM Corporation - March 1981 to July 1983\rResponsible for repair of all LT-1280 and LT-128 Test equipment.\rANN v2.3 01202013\rEDUCATION\rBachelor of Science in Electrical Engineering\rKennedy Western University\rEngineering\rState University of NY\rAssociates of Applied Science in Electrical Technology\rState University of NY\rLINKS http://users.gmavt.net/anemethy\rADDITIONAL INFORMATION\rTECHNICAL SKILLS:\rEnvironments: MS Windows/Office AIX/Unix/Linux MSVisual C++ MSVisual C# Languages: C/C++/C# Java JavaScript Perl HTML XML Basic and Assembler Development Tools: Orcad Visual Age VC++ VC#\rHardware: Motion control Anorad Kensington Ormac\rMeasurement: Keithly HP NI Data Acquisition. IEEE Equipment Microcontrollers: Atmel 328 Arduino 80x51 family of\rSystem Controllers: IBM HP Industrial PCs\rTest Systems: Teradyne [...] Advantest 66xx\r \"\r\nresume_4,not_flagged,\" TUTOR\rWilliston VT - Email me on Indeed: indeed.com/r/Alec-Schwartz/7177c11327372c0a\rWORK EXPERIENCE\rTutor\rDickinson College Biology Department - Carlisle PA - March 2016 to May 2016\rI was invited to tutor three students enrolled in Biology 120: Life at the Extremes. I helped them learn as independently as possible while still acting as a mentor and guide.\rTeaching Assistant\rDickinson College Biology Department - Carlisle PA - January 2016 to May 2016\rTaught by Professor Scott Boback this comparative physiology course explored how extremophiles are capable of surviving and maintaining\rhomeostasis in harsh environments. I helped students perform hypothesis-driven physiology experiments and vertebrate dissections.\rQA/QC Laboratory Coordinator\rAlliance for Aquatic Resource Monitoring (ALLARM) - Carlisle PA - August 2015 to May 2016\rALLARM a small NGO housed at Dickinson college engages communities to use science as a tool to investigate the health of their streams. I helped\rmentor organizations to teach them to use the data they generate for aquatic protection and restoration efforts. I mainly worked in the lab performing\rchemical tests for quality assurance verification. I also currently research and test equipment to verify precision accuracy and accessibility for citizen\rscientists as well as conducting bimonthly baseline analysis of our local Retort Spring Run.\rFirst-Year Mentor Wilderness Introduction to Life at Dickinson (WILD) Leader\rCampus Life at Dickinson College - Carlisle PA - August 2013 to May 2016\rThe First-Year Mentor program provides an opportunity for upper-level students to help new students experience a positive transition to college. I met with my First-Year Interest Group weekly to check in on their personal social and intellectual development at Dickinson. Last Fall I led a 3-day\rbackpacking trip through a section of the Delaware Water Gap during orientation. After one of my first-year's suddenly passed away in the Fall '15 I co- led a partnership between Dickinson and our local YMCA's Safety Around Water Program to provide help during outreach enrollment and swim lessons. It has given our community a meaningful way to commemorate the life of Jigme Nidup.\rCustomer Experience Intern\rNaviNet - Boston MA - June 2015 to August 2015\rNaviNet is America's leading healthcare collaboration network connecting over 40 health plans and 60% of the nation's physicians. I worked as a\rCustomer Experience Intern drafting user-experience reports and presentations for NaviNet and health insurance plans to showcase how health providers were using different software products.\rStudent Researcher\rDickinson College Chemistry Department - Carlisle PA - March 2015 to May 2015\r \rProfessor Mike Holden's interests are in the area of organotransitionmetal-mediated synthesis of organic compounds. In his lab I worked to synthesize\rknown biologically active compounds with the incorporation of ferrocene (an iron compound) to potentially boost efficacy of antimalarials.\rStudent Researcher\rBen-Gurion University of the Negev Jacob Blaustein Institute for Desert Research - Midreshet Israel - November 2014 to January 2015\rSpecies adapted to fasting sequentially oxidize fuels in three discrete phases. Professor Berry Pinshow's lab is exploring the physiological\rresponses to food deprivation in House and Spanish sparrows (birds not adapted to prolonged fasting). For two months I helped prepare dietary isotopic tracers run 32 hour-long experiments requiring hourly measurements for stable isotope signatures in real-time using a 13C-infrared analyzer (HeliFANplus) and interpret collected data using repeated measures (RM)-ANOVA.\rPaper: Khalilieh A McCue MD Pinshow B. Physiological responses to food deprivation in the house sparrow a species not adapted to prolonged fasting. Am J Physiol Regul Integr Comp Physiol. 303: R551-R561 2012.\rEDUCATION\rBS in Biochemistry and Molecular Biology\rDickinson College - Carlisle PA 2012 to 2016\rSKILLS\rNMR LCMS HPLC qPCR Gel Electrophoresis Western Blot Transfection various microscopy techniques (DIC Phase Contrast Brightfield Darkfield Fluorescent) recombinant DNA Plasmid Cloning Pymol CRISPR Cas9\rLINKS http://blogs.dickinson.edu/writingsciencenewssp14/author/schwaral\rAWARDS\rCum Laude for BS in Biochemistry and Molecular Biology\rMay 2016\rMaintained an overall GPA of >3.50 throughout my time at Dickinson College.\r \"\r\nresume_5,flagged,\"\rIndependent Consultant - Self-employed\rBurlington VT - Email me on Indeed: indeed.com/r/Alex-Reutter/2c4a904a891a6fef\rWORK EXPERIENCE\rIndependent Consultant\rSelf-employed - Burlington VT - October 2016 to Present Projects in progress.\rSenior Data Scientist\rIBM - 2015 to 2016\rDeveloped product strategies for Data Science Experience (datascience.ibm.com) for machine learning algorithms and end-to- end usage for data scientists. Patient Zero for ensuring product design matches typical data scientist workflows.\r Enabled connection with tens-of-thousands of customers onsite and in social media impressions by crafting story of data\rscientist in auto industry and how use of SPSS and Spark evolved from analysis of spreadsheet data on defect rates of auto\rparts to integration of PySpark into analytic work streams. Story was featured at 2015 Spark Signature Moment in IBM\rSpeaker Presenter\rIBM - 2013 to 2015\rand in customer-facing videos and presentations.\r Increased sales by evangelizing IBM analytics solutions to hundreds of customers onsite as Speaker Presenter at IBM\rInsight 2013 - 2015.\r Improved development of new product features including Data Science Experience (DSX) Watson Analytics SPSS\rAdvisory Software Engineer (Data Scientist)\rIBM - 2009 to 2015\rLed team as Agile product owner for Analytic Server working with offering management and internal and external clients to coordinate and prioritize the development of new software features.\r Facilitated quarterly (rather than yearly) release cycles and single integrated system for project tracking by transitioning\r100+ developers from Waterfalls to Agile development processes as project focal.\r Aided transformation of integrated supply chain (ISC) code base from R to more-easily maintainable SPSS by mentoring\rcolleague on SPSS products from model prototyping to advanced forecasting techniques. Success story of collaboration between software group and ISC was presented to IBM analytics and inspired further internal partnerships.\r Delivered Analytic Server 1.0 - 2.1 product by keeping development focused on important stories designing and implementing scripts to track sprint progress and analyze product backlog culling dead work items and celebrating\rsuccesses.\r \rR. ALEXANDER (ALEX) REUTTER alex.reutter@gmail.com\rMaster Statistical Writer\rSPSS INC - 2006 to 2009\rAuthored and contributed to designs integration standards and user-facing documentation of common statistical components across SPSS products.\r Gave customers access to item response models otherwise unavailable in SPSS Statistics product by creating custom\rcommands and dialogs using open source Python and R programmability.\r Ensured quality and consistency of thousands of pages of documentation training and sales and marketing presentations by writing cross-product standards for developing examples and sample data that prove product functionality.\r Reduced time to create command syntax documentation 80% and ensured designs / documentation were in sync by writing standards for single-sourcing command syntax designs and user-facing documentation.\rSenior Statistical Writer\rSPSS INC - 1998 to 2006\rGave customers access to 1K+ pages of algorithms documentation in easily accessible online Help and PDF format by leading project to convert from assorted collection of MS Word documents to single-sourced XML.\rResearch Assistant to Dr. Giovanni Parmigiani\rDUKE UNIVERSITY - Durham NC - 1997 to 1998\rDesigned hierarchical models / wrote programs in R for Center for Health Policy Research. Identified defects in algorithms and underlying assumptions of Stroke Policy Model leading to creation of web portal that allowed practicing physicians to more\reasily assess effects of potential treatment strategies utilizing model.\rEDUCATION\rMaster of Science in Statistics and Decision Sciences\rDuke University - Durham NC 1998\rBaccalaureate in (AB) Mathematics\rPrinceton University - Princeton NJ 1994\rMSc in General Strategies for Assessing Convergence of MCMC Algorithms Using Coupled Sample Paths\rfair assignment of university class rank\rLINKS http://www.linkedin.com/in/alex-reutter\r \"\r\nresume_6,not_flagged,\"\rPoultney VT - Email me on Indeed: indeed.com/r//d47cbf764c27fba3\rI am an organized independent worker with strong time management skills. Detail-oriented and able to learn new tasks quickly and effectively.\rI am a strong and hard working employee who strives to do my best work. I pride myself in respecting my superiors and following directions.\rHighlights\rHighly responsible and reliable Upbeat outgoing and positive Works well under pressure Food safety understanding Exceptional interpersonal skills Strongly self motivated Incredibly good at organizational tasks\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rMate\rOsprey Fishing Fleet - New York NY - April 2014 to August 2014\rPort Jefferson NY\rNY Osprey Fishing Fleet. Lowest level worker on the boats. Tasks included: basic knowledge of fishing and fishing laws associated with charter boats in this area cutting bait cleaning fish setting up fishing\rrods (ie. rigging cleaning untangling etc.) general boat upkeep and customer service.\rVolunteer Project Intern\rAvalon Park and Preserve - February 2013 to August 2014\rStony Brook NY\rAssisted with the Truck Farm local food and gardening educational demonstrations. Jobs included: Organizing events teaching about gardening and edible plants attending local fairs and festivals working on the truck and garden bed doing educational demonstrations.\rProject Intern\rAvalon Park and Preserve - October 2013 to December 2013\rStony Brook NY\rWorked with research scientists Zosia Baumann and Daniel Madigan in the SBU Marine and\rAtmospheric Science Department in laboratory. My jobs include subsampling various fish tissues for further tissue analysis among other organizational tasks.\rStony Brook Dept. of New Literacy and Bhutan Centre for Media and Democracy\rIntern assistant\rParo and Thimphu - December 2012 to January 2013\rBhutan\rTook photographs of the teacher workshop and happenings around the BCMD office. I also processed data and made that data into usable graphs and video taped interviews of teachers attending the news literacy workshop. I was also required to sort through and count papers. While helping with a four day teacher workshop I learned important elements of news and media literacy.\rOffice Intern\r \rStony Brook University Center for News Literacy - New York NY - August 2012 to August 2012\rStony Brook New York\rOrganized and alphabetized office records took calls sorted papers etc.\rEDUCATION\rBachelor of Arts in Sustainable Agriculture and Food Production\rGreen Mountain College - Poultney VT May 2017 to Present\rWard Melville High School 2014\rHigh School Diploma\rGeneral College Preparatory Education -\rSKILLS\rSustainable Agriculture (3 years)\rEast Setauket NY\r\"\r\nresume_7,not_flagged,\"\rMedical Laboratory Scientist (Special Chemistry) - Dartmouth College Giesel School of Medicine\rSouth Royalton VT - Email me on Indeed: indeed.com/r//bbf6b080f47051cd\rTo secure a position with a reputable organization where I can use my current skills and expertise while developing new skills and utilize all possible available opportunities to pursue a long term career.\rPERSONAL QUALITIES\rSelf-motivated result-oriented energetic dedicated\rCompassionate to patients and their families strong relationship building skills Authorized to work in the US for any employer\rWORK EXPERIENCE\rMedical Laboratory Scientist (Special Chemistry)\rDartmouth College Giesel School of Medicine - Lebanon NH - July 2013 to Present\r Serve as informational resource and lab assistant for UNH students.\r Performs accurate and appropriate testing of specimens received in the laboratory according to established laboratory protocol and procedures.\r Helped with the implementation of new allergy testing on the Thermo Scientific Phadia 250.\r Executed and analyzed tests such as antinuclear antibody through fluorescent microscopy and detection of specific analytes (TTG IgA Quantiferon TB anti-neutrophil cytoplasmic antibody) through enzyme linked immunosorbent assay (ELISA).\r Prepare samples and reagents for immunosuppressant drug monitoring on the Waters Mass Spectrometer. Integrated instrument data quality control and tested principles for accurate result reporting.\r Participate in proficiency testing of unknowns and internal and external continuing education programs to keep current of developments within field.\r Perform clerical and support services as needed such as answering the telephone calling STATS and alerting values to the appropriate department or clinician disposing of contaminated specimens control of inventory etc.\r Aided in the physical move of the laboratory to a new location revalidation of tests and instruments.\rMedical Laboratory Scientist (Generalist)\rRutland Regional Medical Center - Rutland VT - June 2012 to July 2013\rPerforms accurate and appropriate testing of specimens received in the laboratory according to established laboratory protocol and procedures.\r Reports results for stats abnormal assays critical values and other categories of special requests as defined by laboratory policy in clinical areas including chemistry hematology coag blood bank microbiology and reference.\r Accurately analyzed and evaluated QC results obtained before accepting and reporting patient test results. Records results obtained for quality control testing as defined in test procedure.\r Performed maintenance and calibrations according to the schedule for the instrument/equipment.\r Troubleshoots instruments equipment reagents and patient specimens when problems occur.\r Demonstrates respect and regard for the dignity of all patients families visitors and fellow employees to ensure a professional responsible and courteous environment.\r \r Rotating charge technologist.\r Lead tech on a team that implemented a new lab-wide specimen tracking system.\rLaboratory Assistant\rGifford Medical Center - Randolph VT - June 2008 to June 2012\r Perform phlebotomy in outpatient clinic and inpatient areas.\r Trained & competent in all age ranges including pediatrics\r Perform EKG testing in outpatient clinic\r Set up and sanitize laboratories complying with OSHA & other regulatory standards\r Prepare specimens Analyze fluid chemical content collect blood samples & examine elements. Prepare specimens that are required to be sent out to other facilities.\r Well versed in electronic medical records and computerized charting systems\r Clerical skills including answering phones greeting patients and receiving and collecting data entry of specimens\r Highly trained in customer service skills including diffusing agitated patients etc.\rMedical Laboratory Technologist - Student Intern\rGlens Falls Hospital - Glens Falls NY - January 2012 to May 2012\r Performed and analyzed tests in clinical areas including chemistry hematology blood bank urinalysis serology histology and bacteriology.\r Ensured test-result validity before recording/reporting results.\r Evaluated quality control within laboratory using standard laboratory test and measurement controls and maintained compliance with CLIA OSHA safety and risk-management guidelines.\r Operated and calibrated an assortment of laboratory/testing equipment and performed various chemical microscopic and bacteriologic tests.\r Analyzers used: Abbott Cell Dyne Sapphire Siemens Dimension Vista Advia Centaur Variant II etc.\r Hands on experience with Cerner Millennium computer system\rEDUCATION\rBachelor of Science in Medical Laboratory Science\rUniversity of Vermont - Burlington VT August 2007 to May 2012\rCERTIFICATIONS/LICENSES\rASCP\rAugust 2018\rMedical Laboratory scientist\r\"\r\nresume_8,flagged,\"Statistician\rBurlington VT - Email me on Indeed: indeed.com/r//0d7d4fd5131c8662\rTo secure a position that would allow for growth and development. To work in an environment supportive of reaching goals that would allow me to make a contribution to the organization.\rWORK EXPERIENCE\rStatistician\rIBM - Essex Jct VT - 2010 to July 2013\rStatistical consulting for semiconductor manufacturing fabricator\r* Provide statistical consulting services to engineering community.\r* Statistical process control (SPC) support and guidance.\r* Measurement systems analysis and support.\r* Instruction in classes (statistical methods SPC design of experiment) for engineering community\rSoftware Engineer\rIBM - Essex Jct VT - 1997 to 2010\rDevelopment of statistical applications used by engineers to analyze data\r* Customer support. Help hundreds of data warehouse users access and analyze their data. * Statistical consulting. Provide engineers with the best way to do their analysis.\r* Education support for proprietary data analysis software.\r* Technical writing in support of software\rStaff Engineer /Scientist\rIBM - Essex Jct VT - 1991 to 1997\rStatistician for IBM photomask facility. Provide statistical analysis and consulting to the general photomask community.\r* Engineering community data analysis support. Help the engineers understand the trends in and complexities of process data.\r* Yield model development. Predict product yield from processing results.\rEngineer /Scientist\rIBM - Essex Jct VT - 1990 to 1991\rCapacity planner - IBM site mainframe usage (VM/MVS). * Usage forecasting out one two and five years\r* Development of capital plan to meet forecast\rEDUCATION\rMS in Statistics\rUniversity of Vermont - Burlington VT 1991 to 1993\r BA in Geography\rMiddlebury College -\rMiddlebury VT\r1969 to 1973\rADDITIONAL INFORMATION SKILLS\rData Analysis Programming - SAS (http://www.sas.com/) SQL SPSS Technical Writing software customer support and education HTML MS EXCEL\r\"\r\nresume_9,not_flagged,\"Research technician\rBurlington VT - Email me on Indeed: indeed.com/r//37468526e5f0909d\rI am a young enthusiastic scientist with previous experience in academic research who wants to enter into the biotechnology industry to utilize my scientific skills to investigate molecular disease pathways with the goal of advancing protein therapeutics.\rWORK EXPERIENCE\rLaboratory/Research Technician\rUniversity of Vermont - Burlington VT - August 2011 to Present\rSupervisor: Maria Roemhildt Ph.D.\rAssist the PI in performing laboratory experiments focused on an experimental in vivo rat model evaluating primary osteoarthritis disease development. Performed quantification and documentation of histological and genetic investigations. Responsible for manuscript drafting and submission for publication. Submitted and presented research to academic conferences. Maintained position as laboratory safety officer for multiple laboratories within the Orthopaedics Department of the University.\rSenior Honors Research\rBiology Department Saint Michaels College - Winooski VT - September 2010 to May 2011\rSupervisor: Douglas Green Ph.D.\rConducted literature review of primary and secondary sources on the evolution of virulence of viruses and the trade-off hypothesis. Developed a working host and pathogen system with E. coli and T4 bacteriophage in order to evaluate the tempo and mode of virulence transmission under various conditions. Prepared poster presentation of results.\rResearch Grant Recipient\rBiology Department Saint Michaels College - Winooski VT - June 2010 to September 2010\rSupervisor: Douglas Green Ph.D.\rAdapted standard laboratory techniques in order to develop a standard growth curve currently used to evaluate bacteria population data and MOI.\rResearch Project\rSchool for Field Studies - San Carlos CA - September 2009 to December 2009\rSupervisor: AJ Schneller Ph.D.\rResearched assessed and documented the outcomes of an experiential environmental learning course designed to engage local seventh grade students in community environmental issues while facilitating their participation in community conservation actions. Exposed to the application process of research submission to academic conferences. Research accepted to be presented at the NAAEE conference.\rEDUCATION\rBS in Biology\rSaint Michael's College - Winooski VT 2007 to 2011\r \rSKILLS\rLab Skills: Brightfield and fluorescent microscopy immunohistochemistry real time RT-PCR analysis\rslide preparation and staining processing specimens for histological analyses gene expression analyses histological analyses NMR UV/VIS spectroscopy IR spectroscopy gel electrophoresis (SDS-PAGE) paper chromatography column chromatography bacteria culture techniques genotyping/phenotyping Vidana video analysis software ANOVA analysis of variance sterile technique autoclaving dissection staining and culture of microorganisms centrifugation extraction and isolation of DNA/RNA laboratory safety training and implementation. Computer experience: Microsoft Word Microsoft Excel Microsoft PowerPoint. Languages: Proficient in Spanish 7 years of courses 1 semester in Mexico Strong writing analytical and math skills\rLINKS http://www.ncbi.nlm.nih.gov/pubmed/23123358\rADDITIONAL INFORMATION\rHONORS\r Phi Beta Kappa inducted Spring 2011\r Sigma Xi The Scientific Research Society inducted Spring 2011\r Deans List Honors Saint Michaels College [...]\r Saint Michaels College Honors Scholarship [...]\r Saint Michaels College Scholarship [...]\r Member of Delta Epsilon Sigma National Honor Society inducted Spring 2010 Member of Saint Michaels Honor Program [...]\r Hartnett Grant Recipient for academic study in science 2010\rPEER-REVIEWED\rPUBLICATIONS\rRoemhildt M. L. Beynnon B. D. Gauthier A. E. Gardner-Morse M. Ertem F. & Badger G. J. (2012). Chronic In Vivo Load Alteration Induces Degenerative Changes in the Rat Tibiofemoral Joint. Osteoarthritis and Cartilage.\r \"\r\nresume_10,not_flagged,\"Barbara Hennessey-Elzohairy\rNewport VT - Email me on Indeed: indeed.com/r/Barbara-Hennessey-Elzohairy/a1166565d7bf3268\rI am looking for an administrative/management or research position in medical/health and wellness field. I have excellent writing skills experience in doing Phase three clinical trials and have advanced training in psychology as well as 25 plus years of experience in this field.\rWORK EXPERIENCE\rPsychotherapist\rPrivate Practice - Psychotherapist - 1988 to Present\rProvide therapeutic services to clients with range of Diagnosis including Clinical Depression Borderline Personality Disorder PTSD. Specialty in Adolescent And Family therapy.\rCamp Nurse\rBerwich Boys Foundation - June 2008 to August 2008\rWork camp for 30 adolescent boys from inner city Massachusetts. I was the only medical staff present on 760 acre Camp on Dyer Island in Maine. Boys developed many skills including building mechanical and electrical. They maintained all facilities using heavy equipment and power tools.\rTravel Nursing - Augusta ME - 2007 to 2007 2007)\rForensic Psychiatric Hospital\rRutland Medical Center - Rutland VT - 2007 to 2007 2007)\rGlendale Adventist Hospital - Glendale CA - 2004 to 2004 2004)\rInstructor\rCommunity College of Vermont (CCV) - Saint Johnsbury VT - September 2000 to November 2000\rTaught Introduction to Psychology to adult students with varying degrees of academic preparation. Course was a 3 hour\rtwice per week overview of neuropsychology social psychology and treatment modalities.\rAdjunct Faculty\rNortheast Correctional Center - Saint Johnsbury VT - June 1999 to December 1999\rTaught Biology and Health to incarcerated\rstudents ranging in age from 16 to 45 years of age. Most students had not completed high school and were working on GEDs.\r \rUnited Nations\rNew York City\rAdministrative Officer\rEmployed by UNCTAD (United Nations Committee on Trade\rAnd Development). Managed international conferences in\rNew York Geneva Switzerland and Washington D.C.\rCoordinated document production with translators in the 6 official\rlanguages of the U.N. Managed time tables interfaced and coordinated with delegates from the 200 member countries. Position required\rfrequent travel to Europe (Geneva Switzerland) and Washington D.C.\rRepresented the Secretary General of this department as needed.\rClinical Research Coordinator\rSt. Lukes/Roosevelt Hospital - 1992 to 1994\rCoordinated research protocol for evaluation of anti-retroviral Study drug. This was a Phase II study in the treatment of HIV/ AIDS. Counseled patients on management of symptoms monitored side effects of study drug; performed physicals blood draws and lab preparations for evaluation of viral load. Maintained all clinical data and documentation according to FDA requirements and in compliance with GCP (Good Clinical Practice). This study was funded by a German company and Involved giving quarterly feedback to funding source;\rInterfacing with other research team members: bench scientists biologists and statisticians.\rResearch Nurse/Clinical Therapist\rNew York State Psychiatric Institute - 1988 to 1991\rConducted pharmaceutically funded research studies with\rHigh risk suicidal adolescents involving implementation of protocols recruitment and identification of study subjects and administrative research instruments. Screened evaluated and\rprovided psychotherapy and counseling to adolescents with\rClinical depression Mood disorders and suicidal behaviors.\rAlso jointly served as a clinical therapist in the Suicide Disorders\rClinic at Columbia Presbyterian Hospital.\rPsychiatric Nurse\rMobile Psychiatric Emergency Service - Jersey City NJ - 1987 to 1987\r1987 - l988)\rConducted psychiatric evaluations on clients in crises\rIn their homes and other field locations. Coordinated with Jersey City Police Dept. and community agencies in containment Of suicidal citizens violent patients and hostage negotiations. Provided grief counseling and crises intervention to children Families social work agencies and businesses.\rEDUCATION\rMasters in Psychology\rNew School for Social Research - New York NY 1988\rA.A.S. in Nursing\rPace University - New York NY 1979\rBlack American Literature\rColumbia Teachers College 1969 to 1970\rBachelor of Science in Philosophy/Psychology\rColumbia University - New York NY 1968\rSKILLS\rwriting; computer literate; research skills\r\"\r\nresume_11,flagged,\"Barbara Kurth\rResearch Assistant Professor Clinical Research Navigation at Tulane University Health Sciences Center & Louisiana State University\rCharlotte VT - Email me on Indeed: indeed.com/r/Barbara-Kurth/f36e1f10874fab52\rWORK EXPERIENCE\rResearch Assistant Professor Clinical Research Navigation\rTulane University Health Sciences Center & Louisiana State University - New Orleans LA - 2008 to Present\rHealth Sciences Center- New Orleans LA\rResearch Assistant Professor Clinical Research Navigation\rProvide regulatory expertise advice and administration for Medical Faculty and Staff to ensure that clinical research protocols meet all institutional and governmental regulations from inception to approval\rThese services include:\r Advising and assisting investigators in the drafting and generation of research protocols using ICH-GCP guidelines\r Ensuring appropriate input from biostatisticians bioinformaticists intellectual property officers and contracting officers on clinical research proposals\r Guiding researchers by oversight of informed consents for studies using IRB guidelines\r Guiding researchers in processing of all necessary paperwork to activate Investigator Initiated & pharmaceutical studies including site agreements budgets informed consents 1572's license requirements etc.\r Facilitating input for electronic protocol management\rWork Experience (continued)\r Providing editorial assistance in writing/revising protocol and consent forms as requested by various committees\r Assisting researchers as needed to ensure submissions satisfy regulatory criteria for IRB FDA- IND IDE 510(k) etc.\r Assisting researchers in regulations regarding GLP and cGMP as needed\r Working with researchers to oversee the establishment of study budget\r Working with researchers to strategize proper accrual of patients data acquisition and data analysis and reporting\r Teaching Protocol Design and Writing Masters of Science in Clinical Research Program\r \rResearch Assistant Professor Clinical Trials Scientist\rUniversity of Virginia Cancer Center - Charlottesville VA - 2006 to 2008\rClinical trial development for physicians needing assistance and guidance including protocol writing and regulatory submissions\rCancer Trials Developed:\r Photodynamic therapy for non-resectable cholangiocarcinoma: A phase II pilot study.\r Use of High Dose Estradiol in Women with Breast Cancer: Role of Apoptosis.\r A Single Arm Phase II Clinical Trial of Neoadjuvant Chemotherapy in the Treatment of Advanced Stage Epithelial Ovarian Primary Peritoneal and Fallopian Tube Cancers.\r Pilot Study Evaluating Massage Therapy in Cancer Patients Undergoing Treatment for Acute Myeloid Leukemia.\r Use of and Attitudes toward Complementary and Alternative Medicine in Lung Cancer Patients in an Academic Medical Center.\r A Pilot Feasibility and Dosimetry Study of Topotherapy for Whole Breast Irradiation in Patients Undergoing Breast Conservation Therapy for Stages 0 I and IIA Breast Carcinoma.\r Hormone-Sensitive Progressive Metastatic Breast Cancer (Group B). Work Experience (continued)\r Phase I/II Study of Dasatinib plus Capecitabine for Paclitaxel-Refractory Metastatic Breast Cancer (Group A) and Dasatinib plus Fulvestrant for\r Effector Mechanisms in the Targeting of mAb-opsonized Malignant B cells: Killing/Shaving of either circulating B cells or B cells in fixed tissue after treatment with rituximab (RTX).\r Chart Review of Minority Patients with Newly Diagnosed Lung and Gynecological Cancers Seen at UVA in the Calendar Year 2005.\rSenior Scientist\rUniversity of Virginia - Charlottesville VA - 2003 to 2006\r Meetings with and presentations to funding agencies and the private sector Responsible for all compliance issues in the laboratory\r Responsible for writing and keeping current Human Use Protocols and Consents\r Responsible for writing and keeping current Animal Use Protocols\r Publishing\r Bench work\rAssistant Director of Primate Models Core\rUniversity of Virginia - Charlottesville VA - 1995 to 2003\rCharlottesville VA\rAssistant Director of Primate Models Core\r Established and maintained colony of cynomolgus macaque for use in vaccine development\r Prepared vaccine samples of SP-10 for immunization of female macaques using conjugation to an immunogenic protein carrier oil:water and water:oil emulsions and aluminum salt adjuvants\r Explored various routes of immunization: intramuscular subcutaneous oral (salmonella sp.) intracervical wall and intranasal to determine the most efficient means of vaccine delivery\r Performed immunizations with other sperm-specific and egg-specific vaccine preps from investigators from other institutions\rWork Experience (continued)\r Determined the immunogenicity of vaccine preps in macaque cervical mucus oviductal fluid and serum (IgA IgG)\r Determined the activity of fluids from immunized macaques by In vitro assays on isolated peripheral blood lymphocytes\rResearch Associate; Research Fellow\rUniversity of Virginia - Charlottesville VA - 1995 to 2003\r Isolated and localized the SP-10 protein of sperm to determine tissue-specificity at the protein and mRNA level\r Employed Western blots to determine purity of isolated SP-10 protein prior to vaccine preparation\r Employed Northern blot technique to determine appropriate animal model for vaccine efficacy\r Employed various forms of microscopy to elucidate the localization and timing of expression of the SP-10 protein during spermatogenesis\rRESEARCH EXPERIENCE\r Cell & Tissue Culture\r Vaccine Preparation and Delivery (adjuvanted and nonadjuvanted emulsions vehicle carriers such as Salmonella sp; PO SQ IM)\r Electrophoresis - Protein and nucleic acid 1 and 2-D\r Microscopy - Optical bright field phase contrast fluorescence dark field TEM SEM photography\r Preparation for Microscopy - Fixation embedding sectioning (for LM EM) slide prep\r In situ Hybridization - H3 S35 BrDU\r Immunoassays - ICH ELISA Western\r Cell DNA RNA Isolation - Gradient Northern\rOTHER TRAINING EXPERIENCE\r 1999: AWIC (Animal Welfare Information Center) workshop Beltsville MA\r 1999: FDLI Educational Conference Washington DC\r 1999: Clinical Investigations: Changing Expectations and Regulatory Requirements Washington DC\r 1999: The Changing Regulatory Environment for IRBs: Its Impact on IRBs Sponsors and CROs Washington DC\r 2001: Medical Technology Course for Students in Law and Medical School UVA Charlottesville VA\r 2005: Clinical Research Coordinators' Continuing Education Series UVA Charlottesville VA\rEDUCATION\rPh.D. in Molecular and Cellular Biology and Pathobiology\rMedical University of South Carolina - Charleston SC\r1984 to 1988\rB.S. in Biology\rTrinity College - Burlington VT 1980 to 1983\r\"\r\nresume_12,not_flagged,\"Benjamin Alexander Research Scientist\rCraftsbury Common VT - Email me on Indeed: indeed.com/r/Benjamin-Alexander/9a26aa047fbf5df8\r Performed fragrance analysis and recreation using GC/MS and field analytical tools Compounded commercial fragrances for a range of products\r Oversaw a commercial research greenhouse with over 1000 plant varieties\r Maintained a fully stocked biology laboratory for 6-10 research personnel\r Maintained lab records including computer data entry and written reports Performed quality assurance and pre-inspection protocols\rWORK EXPERIENCE\rResearch Scientist\rInternational Flavors and Fragrance - Union Beach NJ - March 2004 to June 2009\rINTERNATIONAL FLAVORS AND FRAGRANCES INC. Union Beach NJ\rResearch Scientist Mar 2003-Jun 2009\r Oversight and planting of a research greenhouse and outdoor gardens with over 1000 plant varieties.\r Research of cultural historical cosmetic health and folk uses of plants and other natural materials.\r Creation of nature identical fragrance accords from analytical data.\r Organization of fragrance accords in a multi-media database (Living Library) to provide enhanced access.\r Addressed regulatory concerns customer standards and price/material availability during fragrance creation.\r Design and implementation of integrated pest management program in research greenhouse and gardens. Botanical garden tour guide for external and internal customers to showcase plant collection demonstrate company capabilities and provide inspiration for new products and sales.\r Photography of natural collections to support documentation and marketing of fragrance accords.\r.\rU.S. Plant Soil and Nutrition Library Ithaca NY\rSupervising Lab Technician Aug 1996-Jun 1999\r Maintained a fully stocked botanical research laboratory for 6-10 research personnel.\r Responsible for greenhouse maintenance and plant propagation to support multiple research programs.\r Constructed and maintained hydroponic systems for research greenhouses.\r Created and implemented an integrated pest management program for research greenhouses.\rCornell University Department of Plant Biology Ithaca NY\rSupervising Lab Technician Aug 1994-May1998\r Plant propagation and greenhouse maintenance to support botanical research\r Conducted field trials of new crop varieties.\r Testing of new integrated pest management techniques to reduce the use of synthetic pesticides. Sterile growth media preparation and bacterial and fungal inoculation.\rMILL VILLAGE CONSTRUCTION Craftsbury Common VT\rCustom Builder Jun 1999-Dec 2003 and Jun 2009-present\r Design renovation and construction of residential and light commercial facilities.\rCustom Builder\rCraftsbury Common VT - June 1998 to December 2003\r \rDesigned renovated and constructed residential and commercial facilities; researched low-toxicity building materials and alternative energy systems\rLaboratory Technician\rCornell University Department of Plant Biology - Ithaca NY - August 1994 to May 1996\rPrepared growth media using sterile technique; maintained greenhouse plantings; conducted field surveys and collected specimens of experimental crops and fungal pathogens\rEDUCATION\rBS in Agriculture and Life Sciences\rCornell University - Ithaca NY May 1998\rADDITIONAL INFORMATION\rOther Skills:\r Experienced at carrying out independent work and research activities Proficient in the use of MS Office Windows Excel and the internet Enjoy working as part of a larger team to achieve high-quality results\r\"\r\nresume_13,not_flagged,\"Benjamin Symonds\rEssex Junction VT - Email me on Indeed: indeed.com/r/Benjamin-Symonds/733ef61140b208f9\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rProgrammer Contractor\rTechnical Connection - Burlington VT - October 2016 to Present\rDesigned and built SQL database architecture and MS Access front-end for a client company requiring a software solution for reporting employee union membership twice yearly.\rWrote training documentation and trained all software users and support\rAvionics Apprentice\rVT Air National Guard - South Burlington VT - February 2014 to Present\rEnsured proper operation of electro-mechanical avionics equipment on F-16 aircraft. Achieved on over ten units performing maintenance and repair procedures on all units.\rPromoted to self-supervising role after 11 months of performing duties under supervision.\rData Technician\rProduction Advantage - Williston VT - December 2013 to June 2016\rLead all weekly monthly and annual financial analysis reporting for all business units.\rMade operating procedures for all user changes to SQL back-end of business ERP system.\rDesigned and built SQL database architecture and C# front-end for an in-house custom software solution for managing product group pricing updates.\rContractor\rResource Systems Group Inc. - White River Junction VT - October 2013 to December 2013\rAided in sound analysis of wind turbine farm surveying. Shadowed acoustic engineers as they determined noise output trends to help the client develop wind turbine installation standards and best practices.\rWeb Survey Programmer\rResource Systems Group Inc. - White River Junction VT - August 2013 to October 2013 Created online state transportation research surveys using in-house web API technology.\rMade SQL query optimizations for an oversized database of wind turbine research data.\rIT Professional\rUVM College of Medicine - Winooski VT - September 2012 to May 2013\rManaged analytic reports of medical research data to research fellows. Designed and created a C# full-stack application with a universal storage model for research projects.\r \rEnsured proper operation of medical sample analysis equipment networking capabilities.\rDatabase & Project Developer\rCSL Software Solutions - Burlington VT - July 2009 to May 2012\rManaged all work for primary clients. Fulfilled all software development lifecycle phases for full-stack applications using C# .NET ASP .NET and HTML5 as front-end technologies.\rBuilt and tested custom analytics software solutions for clients using in-house API tools. Regularly performed edits to API tools involving XML JavaScript and HTML5 technologies.\rEDUCATION\rApprentice in Avionics\rCommunity College of The Air Force - Wichita Falls TX 2015 to 2015\rApprentice in Avionics Electrical Principles\rCommunity College of the Air Force - Wichita Falls TX 2015 to 2015\rMinor in Computer Science\rUniversity of Vermont - Burlington VT 2006 to 2009\rBS in Applied Mathematics\rUniversity of Vermont - Burlington VT 2004 to 2009\rMILITARY SERVICE\rService Country: US Branch: Air National Guard Rank: E-3\rFebruary 2014 to Present\rADDITIONAL INFORMATION\rI want to become a data scientist and have started building a portfolio for mathematics graduate studies beginning with the (subject) Math GRE. I am currently preparing for the exam with practice books and through continuing education on Open Course Ware at MIT. I am taking calculus courses to strengthen my basis for high-level algebra and statistics courses. This course load aids me in preparing for the exam and will introduce me to data science technologies like the R and Python languages. My current goal is to become well-practiced for the exam so that I can handle practice exams in a time-efficient manner (and score in the 90th percentile) and to become familiar enough with data science to know what areas of the exam are most relevant to the field so as to determine what areas should be emphasized in my studies. While pursuing these educational goals I hope to learn more about the data science field professionally through my career. My full- stack development experience assists me towards this end but my drive to learn will lead me to become a data scientist professional in the future.\r\"\r\nresume_14,not_flagged,\"Bonnie Mae Savage Ph.D.\rRESEARCH ENGINEER / SCIENTIST AND DEVELOPMENT ADMINISTRATOR\rWheelock VT - Email me on Indeed: indeed.com/r/cf31d4f032b64974\rSuperior skills at bringing biological science innovative engineered systems and people together for successful outcomes.\rCore Skills\rEngineering Research & Technical Analyses Business Development Strategist\rExtensive High-Profile Public Speaking Multiple Discipline Innovator\rNegotiations for Research & Financial Support Partnership with World-Renowned Researchers\rWORK EXPERIENCE\rEntrepreneur & Chief Executive Officer\rSTARFIRE RANCH VERMONT - Wheelock VT - 2005 to Present\r* Developed land designed/constructed ranch infrastructure e.g. designing/constructing barn exercise arena lands for pasture herd/breeding program and Developed Gentleness Training Program (one of its kind).\r* Bred/developed elite quality horses for reining competition.\r* Produced 2 World Champion Reining horses each in 1st year of training!\r* Acheived notarity in reining horse world with two of the World's Top Reining Trainers/Riders have requested to work with SFR horses. The start-up operation rapidly earned professional community rapport is considered one of best up-and-coming ranches nationally & internationally for reining horses.\r* Invited Member of the Board of Directors of the National Appaloosa Reining Horse Association.\rEngineering Research Initiator & Head Development Administrator\rPURDUE UNIVERSITY Civil Engineering - West Lafayette IN - October 2005 to June 2006\r* Development of Tracking Monitoring and Traceability Systems Developing technology systems that allow one to: 1) track food source materials from farm to processors 2) monitor quality of food product and appropriate ambient environments including determination of biological and chemical contaminants and 3) trace food contaminants or other concerns back to origin of substance or critical condition.\r* Research project independently created concepts and put together technical team of 15 diverse experts in areas such as agriculture IT transportation logistics and communications.\r* Development of integrated wireless technologies biological and chemical sensors and legacy supply chain systems involved. Developed to launch off stage.\r \r* Received promises of financial support from companies; e.g. Kraft $3M Del Monte $1.5M and several other companies for $0.5M each.\rEngineering Research Developer & Administrator\rPURDUE UNIVERSITY Civil Engineering - West Lafayette IN - May 2004 to June 2006\r* Development of Optimized Freight Exchange Centers. Developed freight exchange center designs to be integrated with multimodal transportation system in US.\r* Projections found with freight optimization in place entire Updated National Transportation System would ensure ROI of 4 years as result of fuel/energy savings time savings and efficiencies in freight transport.\r* $38M was set aside by Congress to study logistics of national plan roll out.\rVisiting Assistant Professor\rPURDUE UNIVERSITY Civil Engineering - West Lafayette IN - June 2003 to March 2006 Civil Engineering\r* Produced set of research initiatives and developed curriculum as visiting member of staff.\r* Research initiatives each had potential for significant national and worldwide impact on American economy transportation industry and biomedical communities.\rBioMedical Engineering\r* Research in Biomedical Engineering on topics involving application of photonic energy applied to cells aimed at cellular stimulation blood vessel creation and bone regeneration as well as tumor relocation exposition.\rBioMedical Research Initiator & Administrator Volunteerism\rPURDUE UNIVERSITY BioMedical Engineering - West Lafayette IN - July 2004 to August 2005\r* Investigation of Effects of Photonic on Osteoblastic Cells. Pursued self-funded investigations of effects of photonic energy on human osteoblastic cells. Supervised 2 student researchers.\r* Effects of photonic energy applications on osteoblastic cells quantified in terms of growth indicators proliferation morphology and adherence properties.\r* Successful in showing how to initiate larger rates of osteoblastic cell growth responsible for bone growth.\r* Identified particular characterized photonic energies and co-varying energy amounts which detracted were neutral or increased osteoblastic cellular responses.\r* Identified possible generalized cellular responses to characterized and applied photonic energies based upon results of work done with osteoblastic cells and previous work completed with vascular cell lines.\r* Negotiated $10M to further research extending through clinical trials and to develop minimally invasive instrument for treating bone growth issues in humans.\rBioMedical Engineering Research Initiator & Administrator Volunteerism\rPURDUE UNIVERSITY BioMedical Engineering - West Lafayette IN - June 2003 to September 2004\r* Investigation of Effects of Monochromatic Light on Vascular Cells. Pursued self-funded research investigating the effects of monochromatic photonic energy on human vascular cell lines.\r* Procured use of laboratory facilities and equipment efforts of technicians for aiding in development of needed equipment and training and permissions for use of designated-use equipment.\r* Research quantified effects of characterized photonic energy applications in terms of growth indicators growth proliferation morphology and adherence properties.\r* Observed polarity effects on vascular structure growth initiated vascular growth of singular vascular and branching vascular systems.\r* Identified mechanism which has potential to explain tumor cell distribution throughout body.\r* Publicly presented results of growth indicators proliferation and morphology changes due to photonic energy applications. Permission given to further use facilities to pursue independent research.\rEngineering Researcher\rPURDUE UNIVERSITY Civil Engineering - Lafayette IN - November 2003 to May 2004 * Transportation Distribution and Logistics Investigation for Indiana's Economy.\r* Data contribution resulted in necessary planning tool for Governor and The Central Indiana Partnership to plan for and encourage economic changes that would benefit businesses and overall state economy.\rEngineering Technical Research Developer & Administrator\rPURDUE UNIVERSITY Civil Engineering - West Lafayette IN - June 2003 to May 2004\r* Development of Updated National Transportation System. Developed multimodal transportation system plan for both passenger and freight transportation in US.\r* Developed modeled and cost-estimated physical and systematic components of proposed national transportation system integrated suggested systems and identified emerging and existing technology needed to achieve the total transportation system communications and safety systems resulting.\r* Held press conferences to speaking to national media.\r* Presented to United States Congress Department of Transportation where it was enthusiastically accepted and supported with standing ovation and presented to Senate International Economic Affairs group.\r* Research went to President George Bush's desk on 3 separate occasions.\rEngineering Researcher\rPURDUE UNIVERSITY Civil Engineeering - Seattle WA - January 2002 to 2003\r* Experimental Analysis of Temperature Distributions in PCC Pavements Using Simultaneous Equations Methodology.\r* Produced solution to long time industry problem of predicting temperature gradients in PCC pavements.\rEngineering Researcher\rPower Thought LLC - Woodinville WA - May 2001 to November 2002\r* Neural Networks as Tool for Modeling Temperatures in PCC Pavements: Created collaboration with Power Thought LLC. to research and conduct work to investigate possibility of using neural network computing framework to model/predict dynamic temperature distributions in Portland cement concrete pavements.\rEngineering Researcher; Instructor; Assistant Research Administrator\rUNIVERSITY OF WASHINGTON Civil Engineering - Seattle WA - January 1995 to June 2001 Analytical & Laboratory Research\r* Characterization of Gradients in Rigid Pavements: Established database of temperature/moisture profiles expected to exist in array of typical in-use and new PCC pavements.\r* Supervised/coordinated effort of 3 universities involved in field data collection to establish normal and extreme conditions found in typical pavements throughout United States.\r* Presented results to FHWA in Washington D.C. and to industry groups ~200 persons for critical review.\rInstructor\r*Taught undergraduate and graduate courses in Construction Materials Civil Engineering History Construction Economics Biophysics and Cryophysics. Created a laboratory course graduate courses and suggested curriculum.\rEntrepreneur & Head Technical Analyst\rROCKET RIDE INVESTMENTS - July 1996 to December 1999\r* Managed elite high-risk stock portfolios for 2-5 clients and informally advised major stock firm on choices. * Clients made returns of ~200% annually resulting in multi-million dollars.\rEngineering Field Research Engineering Supervisor\rUNIVERSITY OF WASHINGTON / STATE OF WASHINGTON Dept. of Transportation - Seattle WA - August 1997 to March 1998\rEvaluation of Highway Reconstruction Practices\r* Paving quality assessment study and urban primary route closure viability study. Participated in overall planning/execution of pavement and public reaction study.\r* Established that full baseline set of data for future pavement life cycle issues could be collected safely in major total shut down live construction settings.\rEngineering Research Team Member\rAnalytical & Laboratory Research - June 1995 to March 1998\r* Interpreting FWD Tests of Curled and Warped PCC Pavements: Investigation of effects of curling and warping in PCC pavements on results falling weight deflectometer tests.\r* Data was subsequently available for investigations aimed at improving longevity of pavement life cycles.\rField Research Data Collection & Safety Supervisor\rUNIVERSITY OF WASHINGTON Civil Engineering - Seattle WA - June 1997 to July 1997\r* Supervised ~10 persons in collection of data and maintained safety in live construction traffic situations for both day and night research activities for Washington State Department of Transportation.\r* Data collected and contributed to nationally watched experiments' success.\rResearch Volunteer-Cryo Processes\rUNIVERSITY OF WASHINGTON Quaternary Research Center - Seattle WA - March 1993 to 1995\r* Freezing in Porous Media: Theoretical model describing freezing processes generalized to include freezing processes in other porous building materials.\r* Worked with head of Quaternary Research Center on development and head of prestigious German Institute on his own theory development for 3 months after completing personal research.\r* Received invitation from 3 of 6 members of deciding committee to go to Germany under prestigious Humboldt Fellowship for Post-Doctoral Research to continue research of choice. Offered full support full use of facilities and 3 Ph. D. students at disposal-unprecedented offer in history of this fellowship.\rEngineering Forensic Research Team Member\rUNIVERSITY OF WASHINGTON - Seattle WA - June 1993 to August 1993\rEvaluation of PCC Pavement Condition Using FWD Testing: Pavement rehabilitation investigation conducted including forensic study condition assessment and rehabilitation recommendations for municipal facility experiencing untimely pavement structure failure.\rField Research Supervisor\rUNIVERSITY OF WASHINGTON Engineering Master's Thesis-Analytical Research - Seattle WA - July 1991 to December 1992\r* Unsaturated Moisture Movement in Portland Cement Concrete: Validated use of porting soil physics principles known to dominate in other porous structures for use in modeling unsaturated moisture movement in Portland Cement Concrete. 1st successful model for unsaturated moisture movement in PCC.\r* Method was presented to engineering community and followed up on by renowned Los Alamos National Laboratory for employment as tool in related future research.\rField Research Supervisor\rState of Washington Polyfelt Geosynthetics Inc. & University of Washington; 6/1991\r* Directed ~35 persons in installation of moisture/temperature monitoring equipment and geosynthetics in roadway. In-situ soil sampling and testing geosynthetic strain measurements.\r* New roadway with sensors subsequently used as source of research data for State of Washington DOT.\rEngineering Research Assistant\rUNIVERSITY OF ALASKA FAIRBANKS - Fairbanks AK - September 1987 to September 1990 Engineering Master's Thesis-Applied Research\r* Full-scale Field and Laboratory Modeling of Road Embankment Experiencing Thaw-Strain. Results subsequently employed by Alaska DOT for highway construction and rehabilitation.\r* Research implementation extended life of concerned pavements by 10 years and estimated to have saved State of Alaska DOT over $1M in pavement embankment restorations.\rAgricultural Research Station Environmental Biophysics Research Investigator Volunteerism\r* Investigated albedo values for differing crop and terrain surfaces dependent upon radiant energy inputs.\r* Information forwarded to Alaska Department of Agriculture; considered in ongoing effort to establish crop growth in Delta Junction region and State of Alaska.\rGeophysical Institute Radiant Energy Volunteerism\r* Initiated/conducted research to identify critical actors in timing of ice breakup on northern rivers.\r* Information employed in subsequent biophysical research for arctic conditions proving valuable for those receiving goods/services via river transportation in northern regions particularly circumpolar nations and northern portions of former Soviet Union.\rEnvironmental Field Officer\rSTATE OF ALASKA Department of Environmental Conservation; Kenai District - Kenai AK - August 1988 to January 1989\r* Provided technical and managerial assistance for major groundwater clean-up project using technical expertise in infiltration and transport behavior of multiple fluids (ground water and hydrocarbon derivatives). Initiated and set up computerized data processing and storage system.\rEnvironmental Engineer\rNortech Engineering - Fairbanks AK - May 1988 to August 1988\r* Designed commercial onsite waste water systems. Information gathering for analytical procedures; re: hydrocarbon contaminated surface/groundwater soils/degradation processes in sub-arctic environment Radon gas detection.\rResearch Engineer\rSTATE OF ALASKA DOTPF-Research Section - Fairbanks AK - May 1987 to August 1987\r* Conducted forensic investigations for highways containing experimental installations of geosynthetics and recommend designs for installation improvements. Recommendations were adopted by State of Alaska DOT saving over $1M in first several years.\rEnvironmental Intern Level III\rSTATE OF ALASKA Dept. of Environmental Conservation - Kenai AK - May 1986 to August 1986\r* Coordinated 2 state investigations of benzene contaminated groundwater. Investigated/determined technical needs for remediation identified multiple pollutants types/sources communicated with public to alleviate fears and hired contractors for clean-up operations.\r* Worked with Alaska Attorney General to suggest punitive damages to be levied against oil/chemical companies responsible for environmental damages.\r* Both projects closely watched by public interest groups (environmental and oil/chemical companies). Reports of situation made to State Legislator and general public.\rMicrobiology Laboratory Assistant\rUNIVERSITY OF ALASKA Institute of Arctic Biology - Fairbanks AK - June 1983 to 1984\r* Directly assisted Research Veterinarian and Head Microbiologist in Brucella Suis vaccine research.\r* Use of code 3 laboratory (air lock-controlled environment chamber) knowledge of specific cell level transport processes.\r* Contributed to research team effort in identifying critical markers and developing vaccine.\rEDUCATION\rPh.D. in Engineering\rUniversity of Washington 2002 to 2003\rMS in Research Engineering\rUniversity of Washington 1990 to 1992\rMS in Applied Engineering\rUniversity of Alaska Fairbanks - Fairbanks AK 1985 to 1990\rBS in Human & Natural Resources Management Studies\rUniversity of Alaska Fairbanks - Fairbanks AK January 1981 to January 1985\rAD in Business Administration\rCommunity College of Vermont - Saint Johnsbury VT January 1980 to January 1981\rSKILLS\rMS Word Suite Statistical Evaluation Data Analyses Public Speaking Presentations Reports Innovation Team Building\rADDITIONAL INFORMATION\rInterests in Alternative Medicine Equine Research Pets Versatile Skills Set\r\"\r\nresume_15,not_flagged,\"Brad Gallant\rSENIOR ENGINEER/METROLOGIST - East Coast Metrology\rBurlington VT - Email me on Indeed: indeed.com/r/Brad-Gallant/79dad2ccf9ecf296\rWORK EXPERIENCE\rSENIOR ENGINEER/METROLOGIST\rEast Coast Metrology - Topsfield MA - June 2012 to Present\rOperate very precise metrology equipment to perform alignments and dimensional inspections for large and small clients around the world in industries such as aerospace defense automotive heavy equipment antenna and radio telescopes power generation and visual arts.\r Coordinate with onsite Project and Quality Engineers to quickly assess and execute their specific measurement/alignment needs and provide them with a detailed report of the findings.\r Entrusted to train and mentor new employees using clear and logical practices and hands-on methods in real world industry settings in order to quickly prepare them for taking on projects independently with\rconfidence.\r Project: Lead Metrology Engineer for the installation of Mayo Clinic's new Proton Beam Therapy\rCenter in Rochester MN. Used precision laser tracker equipment to align 100+ ton multi-million dollar magnets drive equipment support structures and robotic patient positioning systems; all to within 0.5mm of their nominal location from a total-system isocenter in another part of the building. Highly\rregarded by project engineers and installation crew for the ability to describe a parts position and orientation comprehensibly and clearly communicate the movements necessary to align it.\rENVIRONMENTAL ACOUSTICS ENGINEER/SCIENTIST\rEpsilon Associates Inc - Maynard MA - November 2009 to June 2012\rMaynard MA November 2009 thru June 2012\rENVIRONMENTAL ACOUSTICS ENGINEER/SCIENTIST\r Design coordinate and implement numerous noise impact assessment studies throughout the U.S. and Canada for various clients in the energy industrial and development industries.\r Display expertise in the use and care of highly precise and sophisticated sound level and meteorological equipment\r Work closely with multidisciplinary clients engineers and co-workers to develop final consulting\rproducts while staying within time and monetary budgets\r Frequently conduct analyses outside the realm of acoustics (including shadow flicker daylight and meteorological analyses) in order to maximize billable time and contribute to the overall success of the business\rBOTTLING TECHNICIAN\rMercury Brewing Company - Ipswich MA - June 2009 to November 2009\rOperated and maintained multiple-component machines of bottling-line for maximum performance and efficiency\r Ensured bottling process remained above normal level of sanitary and safety standards\r Consistently exhibited positive influence upbeat attitude and motivation of others\rSALES ASSOCIATE\rPutnam's Ski & Snowboard - Portsmouth NH - December 2005 to 2009 Delivered sales averaging in excess of $70000 per season\r \r Successfully evaluated customers' skill levels and preferences then recommend equipment representing the best match of fit and performance for their needs\r Proficient in understanding applying and explaining technologies of products\rEDUCATION\rBS in Mechanical Engineering\rUniversity of New Hampshire - Durham NH December 2008\rADDITIONAL INFORMATION\rMotivated and proactive Mechanical Engineer with incisive analytical capabilities and strong collaborative skills. Demonstrates accuracy in scientific measurements and data analysis. Highly regarded for attention to detail commitment to quality and enthusiasm. Areas of strength include:\rExpertise with Technical Equipment\rProficient with setup operation data collection and analysis using a variety of highly precise and sophisticated tools and software both in the field and office including laser trackers laser scanners portable articulating arm CMMs sound level meters meteorological monitors and software packages including Spatial Analyzer Rhinoceros 3D and AutoCAD.\rCommunications and Training\rHighly regarded for ability to clearly and succinctly explain processes and technologies utilizing easy-to- understand approaches and hands-on experience.\rTeam Player\rProven success working in teams in a broad array of situations: clients engineers co-workers and trainees in engineering consulting retail and service-oriented establishments.\r\"\r\nresume_16,flagged,\"Caroline Clauson Work Study Student\rBrattleboro VT - Email me on Indeed: indeed.com/r/Caroline-Clauson/a9b10c9d443e9b8c\rWORK EXPERIENCE\rWork Study Student\rFox Common - Lowell MA - September 2013 to May 2015\rSign out pool equipment and board games\r Do basic sound board operation set up microphones and stage equipment Assist with event management\rfood prep gas pumps sales floor\rCumberland Farms - Brattleboro VT - June 2013 to August 2014\rBrattleboro VT June 2013 - August 2014\r Promote merchandise by creating displays of promotional and seasonal items\r Successfully manage multiple tasks in a high energy environment (cash register food prep gas pumps sales floor)\r Complete a series of regular reports (daily and weekly logs service requests inventory reports)\r Handle closing or opening tasks\rWork Study Student\rLowell MA - February 2011 to May 2013\rTrained individuals on use of assistive technology\r Participate in events designed to increase department awareness Maintained confidentiality of students\r Gathered data for research on student disabilities\rIntern\rEIC Laboratories - Norwood MA - May 2011 to August 2011\rAnalyzed spectroscopy data for use by others Presented data findings to senior scientists Gathered data during experiments\rSKILLS:\rAdvanced in Microsoft Office and Proficient in Adobe Dreamweaver Fireworks and Sony Vegas\rACTIVITIES:\rSecretary of Pride Alliance\rUmass Lowell - 2010 to 2011\rExhibited my animated sculpture in an Arbotics gallery show at 119 Gallery\rFour years training in Martial Arts\rCompeted in DECA District and State conferences; placed third at the State Competition in the economics portion.\r \rEDUCATION\rBS in Business Administration Marketing\rUniversity of Massachusetts Lowell - Lowell MA July 2015\r\"\r\nresume_17,not_flagged,\"Catherine Maddox Molecular Biologist\rBurlington VT - Email me on Indeed: indeed.com/r/Catherine-Maddox/735029ba63d17816\rToxicologist with excellent written and oral communication. Manage multiple projects simultaneously and work independently in variety of environments. Learn laboratory procedures quickly. Generate maintain and analyze data.\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rGeneral Biologist\rIAP World International - Beltsville MD - November 2010 to September 2015\rWork independently on various research projects to determine the genetic toxicological effect of compounds on avian species using DNA/RNA purification and quantification qPCR automated qPCR set-up primer testing and optimization. Other projects included geneotyping field-collected mice genetic determination of sex for multiple avian species and ELISAs.\rMaintain data from own project as well as maintain inventory and data for the lab as a whole for multiple studies including two multi-generational studies in Japanese quail.\rProtocol development and modifications for a variety of projects in the lab\rData analysis using Excel and other statistical analysis programs\rStudy Analyst\rWIL Research Laboratories LLC - Ashland OH - February 2008 to January 2010\rAuthored clear reports concisely summarizing technical findings\r Used keen attention to detail tracked ongoing results from 2-3 studies each week Collaborated with technical writers life scientists and quality assurance personnel\rMade oral presentations of progress to entire staff\rSummer Scholar Life Plus LLC - West Lafayette IN - May 2006 to August 2006\rWest Lafayette IN May-August 2006\r Worked with the staff toxicologist to create protocol templates that maintain Good Laboratory Practices Researched international toxicology standards and created reference library for internal use\r Made oral presentations of progress to entire staff\rEDUCATION\rMS in Occupational and Environmental Health Thesis in Genetic Toxicology\rUniv. of Iowa - Iowa City IA 2005 to 2007\rBS in Natural Resources and Environmental Science\rPurdue Univ - West Lafayette IN 2001 to 2005\r \rADDITIONAL INFORMATION\rTechnical Skills\r DNA/RNA isolation purification and sequencing\r DNA amplification (PCR)\r DNA/RNA gel electrophoresis\r DNA/ RNA quantification\r RealTime PCR/qPCR\r Automated PCR set-up\r Primer testing and optimization\r DNA sequence analysis\r Tissue collection from various avian species and mice/rats\r Blood collection and lymphocyte isolation\r Cell staining and blood cell typing\r Maintenance of bacterial stock\r Big Blue Transgenic Assay\r Aseptic Technique\r Water Quality Analysis\r Familiarity with OECD testing guidelines\r Proficiency in Microsoft Office Suite: Word Excel PowerPoint and Access Entrez tools from National Center of Biotechnology\rPublications:\rFernie K.J. P.F.P. Henry R.J. Letcher C.M. Maddox B. Rattner D. Sprague E. Sverko D. Zaruk and N.K. Karouna-Renier. 2014. Exposure and potential effects of technical Short Chain Chlorinated Paraffins (SCCPs; C10-13 55.5% Cl) on captive American kestrels (Falco sparverius): Preliminary Findings. An Interim Report from an Ongoing Study Prepared for the United Nations Persistent Organic Pollutant Review Committee Request for Information for 31 July 2014.\rKarouna-Renier N.K. P.F.P. Henry C.M. Maddox S. Schultz and D. Sprague. 2014. Genomic hormonal and physiological responses in Japanese quail (Coturnix japonica) exposed over multiple generations to the flame retardant HBCD. SETAC 35th Annual Meeting Vancouver BC.\rKarouna-Renier N.K. Y. Chen P.F.P. Henry C.M. Maddox D. Sprague. 2013. Gene Expression Changes Across Multiple Generations of Japanese Quail Exposed to the Endocrine Active Chemical 17-Trenbolone. SETAC 34th Annual Meeting Nashville TN.\rKarouna-Renier N.K. C.M. Maddox P.F.P. Henry Y. Chen D. Sprague J.A. Green. 2012. Genomic effects of dietary exposure to [...] (HBCD) in Japanese quail (Cortunix japonica). Society of Environmental Toxicology and Chemistry North America. 33rd Annual Meeting; Long Beach CA.\rHenry P.F.P. N.K. Karouna-Renier D. Sprague J. Green C.M. Maddox Y. Chen M.R. Bakst. 2012. Effects of [...] dietary exposure on reproductive measures in Japanese quail. Society of Environmental Toxicology and Chemistry North America. 33rd Annual Meeting; Long Beach CA.\rChen Y. N.K. Karouna-Renier P.F.P. Henry C.M. Maddox D. Sprague. 2012. Expression of estradiol- responsive genes does not correspond with circulating steroid concentrations in Japanese quail (Coturnix japonica)exposedtotheandrogenicendocrinedisruptor17_-trenbolone.SocietyofEnvironmentalToxicology and Chemistry North America. 33rd Annual Meeting; Long Beach CA.\rChen Y. N.K. Karouna-Renier C.M. Maddox D. Sprague and P.F.P. Henry. 2011. Effects of maternal dietary exposure to 17_ trenbolone on the growth and development of Japanese quail embryo. Society of Environmental Toxicology and Chemistry North America 32nd Annual Meeting Boston MA.\rKarouna-Renier N.K. Y. Chen C.M. Maddox D. Sprague and P.F.P. Henry. 2011. A multigenerational study investigating effects of 17_ trenbolone exposure on the expression of steroid-responsive genes in Japanese quail. Society of Environmental Toxicology and Chemistry North America 32nd Annual Meeting Boston MA.\rJacobus JA B. Wang C. Maddox H. Esch L. Lehmann L.W. Robertson K. Wang P. Kirby G. Ludewig. 2010. 3-Methylcholanthrene (3-MC) and 4-chlorobiphenyl (PCB3) genotoxicity is gender-related in Fischer 344 transgenic rats. Environment International vol. 36 (2010) pages 970-979\rMaddox C. B. Wang P. Kirby K. Wang G. Ludewig. 2008. Mutagenicity of 3-methylcholanthrene PCB3 and 4-OH-PCB3 in the lung of transgenic BigBlue rats. Environmental Toxicology and Pharmacology vol. 25 no. 2 (2008 Mar) pages 260-266\r\"\r\nresume_18,flagged,\"Chelsea Martin\rEnvironmental Scientist - Vanasse Hangen Brustlin (VHB)\rBurlington VT - Email me on Indeed: indeed.com/r/Chelsea-Martin/41d81792c41faa37\rWORK EXPERIENCE\rEnvironmental Scientist\rVanasse Hangen Brustlin (VHB) - Bedford NH - 2007 to Present\rDiverse experience in studies of the ecology and management of wetlands and other aquatic systems. Responsibilities include field work and reporting of projects involving wetland delineations functional evaluations botanical surveys and general ecological assessments. Field investigations involve detailed wetland/water delineations rare flora surveys wildlife habitat assessments vernal pool assessments rare or irreplaceable natural community identifications and RTE plant monitoring. Experience in technical writing of Natural Resource Assessment Reports GIS based mapping and federal and state environmental permitting.\rRiver Assessment Program Intern\rVanasse Hangen Brustlin (VHB) - Concord NH - June 2007 to August 2007\rAssisted with Outreach education and training of volunteers in basic river ecology and water quality sampling techniques. Collected and analyzed water quality samples in the field and laboratory. Participated in biomonitoring macroinvertibrate studies and stream morphology assessments. Conducted deployment and retrieval of mulitparameter dataloggers and temperature loggers. Assisted with data input and review into the Environmental Monitoring Database and Annual Water Quality Reports and Surface Water Quality Assessments.\rUndergraduate Research Assistant\rUniversity of Vermont Fisheries Department - Burlington VT - 2006 to 2007\rResearched biological impediments for lake trout restoration in Lake Champlain. Using gillnets assisted in identifying the predator community of fry and how predation rates varied seasonally and diurnally by conducting stomach analysis identifying gut contents including macroinvertebrates. Assisted with deep-water egg trap and tested techniques for assessing lake trout reproduction in Lake Champlain. Assisted with data management analysis and modeling. Contributed to sea lamprey surveys habitat analysis and pheromone study and used multi probe to collect abiotic parameters and stream gauging techniques.\rEnvironmental Educator\rUniversity of Vermont Watershed Alliance S Forest Service - Burlington VT - 2005 to 2006\rTaught watershed basics in Vermont Middle and High Schools classroom discussions. Led students on field surveys including watershed testing and analysis. Educated students on basic water quality parameters and macroinvertibrate analysis. Surveyed local schools on current watershed education and interest in agencies service.\rEDUCATION\rBS\rUniversity of Vermont - Burlington VT May 2007\rEnvironmental Science\r \rSchool of Arts and Sciences\rADDITIONAL INFORMATION\rRESEARCH SKILLS\r Wetland and Stream Delineation and Data Collection\r GIS/GPS Mapping\r Proficiency with Trimble Software and Pathfinder Office\r Vernal Pool Assessments\r Stream fish survey techniques: electroshocking\r Natural Community Mapping/ Habitat Assessment\r Assessment of plant populations\r Aquatic macroinvertebrate sampling and identification\r GPS and Arcpad experience\r Logistical field planning while supervising a field crew\r Water Quality Multiparameter tools\r Extensive experience with Microsoft Excel Word and PowerPoint Proficiency in ArcGIS 9.2 9.3 and AutoCAD\r\"\r\nresume_19,flagged,\"Christopher Fusting Data Science Consultant\rBurlington VT - Email me on Indeed: indeed.com/r/Christopher-Fusting/99e69d05f35c7ad2\rChris Fusting is a quantitative researcher interested in modeling environmental and social systems. He has experience working on a diverse set of projects including: developing an SDK for wireless sensor network nodes in Java quantifying carbon sequestration in the Panamanian jungle and predicting corn soy and wheat crop yields around the globe.\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rData Science Consultant\rAquent - July 2015 to August 2015\rAccomplishments\r Translated a desire for more effective recruitment and greater fill rate of orders into a quantitative data driven solution.\r Found latent skill-clusters describing data driven market segments using natural language processing techniques and Latent Dirichlet Allocation.\r Worked with the CTO to identify high value use of the research and passed the methodology and findings off to an internal team.\rJava Web Applications Developer\rAquent - Internal - Asheville NC - September 2011 to December 2014\rAccomplishments\r Developed a successful candidate suggestion web application feature that matches job seekers with employers.\r Prototyped a talent similarity index.\r Helped migrate legacy application framework to the Spring framework.\r Brought to the team a big data and analysis perspective to help drive the development of intelligent software to make our business more efficient.\rDeveloper / Analyst\rResource Data Inc (RDI) - Asheville NC - October 2008 to September 2011\rAccomplishments:\r Developed a data repository and analysis system housing well over 4 billion climatic observations capable of quickly analyzing large regions of the world\r Implemented a variant of Canonical Component Analysis (Shen et al 2002) in R to model annual crop yield\r Optimized crop yield regression models\r Researched the implementation of NDVI in crop yield prediction models\r Developed models in use by RenRe the USDA and the World Bank\rRemote Sensing Analyst\rShort term Contracts - 2008 to 2011 Conservation Strategy Fund\r \r_ Assessment of land use change in the Exuma Cays Island Chain from 1980 - 2010 using Landsat TM data.\r US Forest Service\r_ Mapping the Roan Mountain balds shrubs lines and canopy lines using LiDAR data\r The National Modeling and Analysis Center (NEMAC)\r_ Mapping impervious surface change in Buncombe county NC over time using Landsat TM data. Resource Data Inc.\r_ Mapping the Bent Creek canopy using LiDAR data.\r Warren Wilson College\r_ Mapping the French Broad River District (Asheville NC) using LiDAR data.\rDeveloper / Analyst\rResource Data Inc (RDI) - December 2009 to April 2010\rAccomplishments:\r Quantified carbon content of a large watershed in Panama using Landsat 5 TM satellite imagery and field data gathered from forest plots I helped construct\r Programmed and deployed wireless sensors to gather field data in the rainforest.\r Provided carbon sequestration data to CREA capable of making the argument that the rainforest should be protected for monetary reasons\rPaid Intern - Developer\rSun Microsystems Inc - Menlo Park CA - June 2008 to October 2008 http://blogs.oracle.com/cfusting/\rAccomplishments:\r Researched the efficacy of the Sun SPOT as a ecological tool capable of teaching scientists more about the natural environment and provide conservationists with a valuable tool to detect micro-climates and\rhabitats available to endangered species and therefore argue for an area's conservation.\r Collaborated on the development of a Software Development Kit (SDK) specializing in interfacing the\rSun SPOT with different sensors logging their data and sending it to a database.\r Developed solar energy sources rainforest-proof enclosures and other retrofits to the Sun SPOT and deployed a wireless sensor network in the Cocobolo nature reserve Panama.\rEDUCATION\rMasters of Science in Mathematics\rThe University of Vermont\rMath\rUniversity of North Carolina Asheville -\rIntegrative Studies\rWarren Wilson College\rLINKS http://cfusting.wordpress.com\rAsheville NC\r \rADDITIONAL INFORMATION\rKEY SKILLS\rDevelopment\rSeven years of professional development experience using Java C R Julia Scala SQL Javascript Jquery HTML CSS Ruby. Database experience includes PostreSQL SQLServer MySQL. Build systems including Ant\rand Maven. Some experience with embedded systems and wireless sensor networks. I work in Linux.\rAnalysis\rProficient in the automation of data analysis and visualization in R Julia Apache Spark with EC2 using machine\rlearning algorithms such as regression decision trees PCA random forests and Glmnet. Experience cleaning and mining text data with regression nai_ve bayes and Latent Dirichlet Allocation.\rGeospatial\rExtensive experience using open source GIS software including: R's spatial extensions GRASS QGIS and PostGIS. Solid understanding of projection systems and data handling. High level understanding of open source\rgeospatial libraries such as GeoTools.\r\"\r\nresume_20,not_flagged,\"Chuck Pace CEO - Predictive Machines\rManchester Center VT - Email me on Indeed: indeed.com/r/Chuck-Pace/13bc3ca5924c62a7\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rCEO\rPredictive Machines - January 2016 to Present\rDeveloping Deep Learning Data Science autonomous robotics IoT and Distributed Systems. (iOS Android OSX Windows C++ C Matlab Python Caffe TensorFlow SciPy/NumPy/SciLearn Spark Hadoop). Application domains include: aerial drone platforms healthcare remote patient monitoring sub-container orchestration of highly secure distributed systems high throughput sensor fusion machine intelligence.\rData Scientist Founder at Corista\rObjectiveC C - February 2005 to Present\rPython Java Javascript Meteor ReactJS/Native Cordova MongoDB Ubuntu/OSX/ Windows GPU FPGA)\rData Scientist Founder at Corista\rFebruary 2005 - Present (12 years)\rDeveloped petabyte-scale high throughput image repository. Wrote distributed Deep Learning algorithms for Content-based Image Retrieval (CBIR) and tissue quantification/classification algorithms (Matlab C+\r+ Python Java OpenCV ITK Caffe). Created cloud-scale machine learning and production environments with both Apache Spark and custom distributed processing (RabbitMQ Celery Python/Scipy/NumPy Java). Designed and implemented full streaming query interface for pathologists and cancer screening. Utilized many AWS facilities including EC2 orchestration (Python C++ C# Javascript Ruby RoR Tomcat Postgres MongoDB) Docker Containers.\rSystem selected by among others Partner's MGH Pathology Department Dartmouth-Hitchcock Medical Center and Johns Hopkins. In addition to developing machine learning algorithms worked to mature the digital pathology market wrote key patents to facilitate the adoption of digital pathology and was\rinstrumental in securing customers and investors.\rChief Technologist\rObjectiveC C - February 2007 to June 2012\rLed research and development team of top engineers and researchers to create vision systems negotiated partnerships and technology acquisition/licensing. Video processing image understanding and machine intelligence algorithms for specific domains. Domain specific focus typically yielded at least an order of magnitude increase in capabilities over state-of-the-art. C++ Matlab GPU FPGA OpenCV ITK VTK OpenGL iOS Android Windows Ubuntu.\rCTO\rInformation Theory at Euclid Discoveries LLC - October 2001 to February 2007\r \rLed research and engineering resources to develop novel Computer Vision & Artificial Intelligence systems utilizing Facial Recognition Neural Networks Structure-from-Motion (Matlab C++ OpenCV OpenGL GPU). Also directed team to develop sophisticated Intellectual Property portfolio management systems (PHP RDBMS). Defined and executed intellectual property portfolio strategy and wrote many Computer Vision patents.\rCTO VP\rR&D - Containerization at Op40 - January 2000 to October 2001\rDeveloped core of cloud application containerization system (SaaS/IaaS) similar to the Docker Container and AWS Lambda serverless technologies. Implemented in C++/Java on server desktop web and mobile platforms. Went on to hire 35 FTEs to expand market and support the system's adoption into Fortune 20 companies including Boeing and IBM. Personally facilitated $18M in angel/VC raise and closing $100M in booked business before the 2001 dot bomb crash. (J2EE Tomcat Weblogic Websphere Windows CE Red Hat Linux Windows Microsoft SQL Server DB2)\rAlgorithmic Engineer Systems Engineer\rVarious - Manchester NH - June 1994 to December 1999\rarchitected implemented built team around large scale CRM system that resulted in $4.2B acquisition by Kana. Written in Java C with heavy analytics distributed system.\rNovasoft (San Francisco CA) - Development covered all advanced aspects of GUI/DBMS/network/ graphics programming focused on creating fault-tolerant highly-scalable systems. Heavy C++ UI and server development GPU/OpenCV Java RDBMS (DB2).\rTheatrix Interactive (Berkley/Emeryville CA) - architected and implemented real-time C++/ObjectiveC game engine for music and animation that led to acquisition by Electronic Arts\rSprint (Dallas TX) - team lead and chief architect for innovation prototype team on the $10B ION project largest map/reduce enterprise messaging & transaction processing system - much like a hand-coded Hadoop system. C++ C RDBMS (Oracle)\rDoD/Defense Contractor/Airport Security (Austin TX)(Boston MA)(Los Angeles CA)\rStealth destroyer friend/foe identification system handprint identification system for SFO mentored battlefield technology group. C kernel development on Mach distributed autonomous machine intelligence.\rDavis Instruments (Hayward CA) - Internet-of-Things weather sensor station system analytics dashboards remote device monitoring. C++ distributed database embedded systems.\rAlgorithm Developer\rVarious - Machine Intelligence & Distributed Systems - Oakland CA - June 1991 to June 1994\rled development of Content-based Image Retrieval and understanding system. C++ imaging algorithms machine intelligence.\rMarCole Enterprises (Walnut Creek CA) - consumer retail analytics Image sensor system design image/ video database/filesystem. C++ DB2 heavy statistical forecasting models.\rLevi Strauss (San Francisco) - CRM analytics system customer analytics system. Smalltalk C Lisp RDBMS statistical algorithmic development.\rUPS (Paramus NJ & Greenwich CT) - worked with CFO to create Financial Modeling/Projection System Executive Decision Support System. C RDBMS statistical algorithmic development.\rMayo Clinic (Rochester MN) - Led team and deployed tablet-based EHR and image management system pilot. C++ RDBMS mobile machine intelligence algorithms.\rIBM ASTC/Global Services - big data DSS/OLAP/BoM for all Deutsche Bank operations; custom 3D rendering pipeline & OpenInventor/OpenGL framework; semiconductor fabrication IoT for: process control inspection systems anomaly detection and visualization. C C++ Java RDBMS embedded systems remote processing control & monitoring (robotic systems) fault detection algorithms.\rEDUCATION\rBachelor's in Computer Science & Mathematics\rClarkson University\rADDITIONAL INFORMATION\rSkills & Expertise Distributed Systems Image Processing Algorithms\rVideo Compression Video Processing Java\rUnix\rSoftware Development Embedded Systems C++\rInvention\rComputer Vision Cloud Computing Python\rPatent Prosecution Patent Strategy Intellectual Property\r\"\r\nresume_21,not_flagged,\"Cynthia Williamson\rLicensed Master Teacher Manager Technology Specialist\rCambridge VT - Email me on Indeed: indeed.com/r/Cynthia-Williamson/78cbeb3f4ecb277c\r Demonstrated and proven record of superior teaching presentation and leadership skills. Consistently maintain excellent rapport with students parents and colleagues.\r Entrusted to capably represent my school on the School Advisory Committee\r Active and committed volunteer in my community\r Dedicated to creating the best learning experience possible for each and every student.\r Professional Teacher Certification with an ESOL Special Endorsement in Florida and Highly Qualified Endorsement Certification in Vermont.\rWORK EXPERIENCE\rAdministrative Assistant to Philip C Smith District Retail Leader\rKeyBank - Burlington VT - September 2010 to Present\rCreator and administrator of the KeyBank Vermont District Website\r Proxy for District President and District Retail Leader Payroll and District Recognition administrator\r Technology support to executives and managers (EXCEL PowerPoint WORD 2010 Lotus Notes Lotus Survey Lotus Mailmerges Client Experience Desktop MAPP Reports)\r Created and maintained Lotus Notes VT Business Meeting and Calendar database for executives across all lines of business\r Phone support for executive office - District Retail Leader District Operations Manager Key Investment Relationship Manager Public Affairs)\r Training and Banquet Luncheon Coordinator for District\r Travel Coordinator for district executives managers tellers - Travel Solutions\r Security badge and building access for district\r Purchasing invoice processing for district retail and executive office\r Licensed Notary - mortgage discharge research and processing\r Coordinated KeyBank American Cancer Society Daffodil Days Campaigns 2011 2012 and technical support for KeyBank community sponsorships (i.e. Marathon Maple Festival Key for Women Event).\rAdministrative Assistant to Dana Poverman Director of Outpatient Services\rHoward Center - Burlington VT - October 2009 to September 2010\rProvide caring service to our mental health and substance abuse outpatient adult population; Intake Registration Financial Briefings Insurance Authorizations\r Developed and launched in collaboration with the Billing and IT departments the Financial Briefing Electronic Filing system and Form 45 Federal system\r Developed and Launched the Standing Order Electronic filing system\r Super User technology specialist provide technology support to clinician staff as we transition into a paperless system (PowerPoint EXCEL Outlook PsychConsult Dominion)\r Federal Probation and Parole Point Person Howard Center developed and maintained new health information filing system providing seamless immediate service and responsible for hosting and passing the Federal Audit\r Research and graph data for state compliance reports (using Excel)\r Coordinate meetings and site visits (using Outlook)\r \r Front Desk coverage and ShoreTel Switchboard providing caring service to mental health and substance abuse clients\r3rd -5th Grade Teacher\rMilton Elementary School - Milton VT - August 2006 to 2009\rSupervised and trained UVM student teachers\r Plan lead and facilitated grade level literacy planning and evaluation. Develop with teacher input grade level action plans to meet Vermont Literacy Standards monitored with measurable results\r Ensured consistency and quality of instruction as literacy leader leading weekly meetings/trainings/ maintaining WIKI resource website\r As Literacy Leader focused efforts and attention on achieving reaching our school's student NECAP Literacy Reading and Writing goals utilizing Google docs to analyze performance school-wide\r Ensured consistency and quality of instruction of scoring On Demand Writing Assessments and Developmental Reading Assessment scoring (Running Record instruction and support)\r Developed collaborated with grade levels to create SmartBoard Lesson plans using open web sources ELMO that meet the Vermont and National Grade level Standards\r Develop and maintain effective parent partnerships through educational curriculum nights newsletters parent educational trainings and hosting parent conference nights\r Organized community fundraising and classroom budget (fieldtrips educational materials technology)\r Developed Classroom Blog (4Writers.21classes.com) a forum for students to publish and comment on peer writing. A community of writers who each created a personal weblog page captivating even the most reluctant readers and writers.\r Designed unique writer's craft/personal student word walls expanding the personal word boxes to remediate high risk students in writing presented\r Lucy Calkins Ralph Fletcher Nancy Atwell Reggie Routman and Linda Rief are strong influences in my instructional writer's craft lessons and teaching philosophy.\r Authored an economics unit curriculum manual and presentation including classroom economy governing system 21st Century Vermont Economics Student Research reports Cornel 2 column note taking published on 4Writers.21classes.com blog and class yearbook.\rLong Term Substitute Teacher\rCharlotte Central School - Charlotte VT - May 2006 to June 2006\rFifth Grade Teacher\rLong Term Substitute - May 2006 to June 2006\rCreate and develop lessons that meet each child's individual needs and meet state and national grade level expectations.\r Interface with fifth grade team teachers to ensure a smooth transition.\rFourth Grade Teacher\rHillsborough County Schools - July 2005 to November 2005\rCreate and develop lessons that meet each child's individual needs and meet county state and national grade level expectations. Lessons designed to stay current with technology and national trends and standards.\r Develop information sessions for parents that promote parent involvement in their child's education through volunteer programs within the class and school information training sessions to provide tools parents need to understand the curriculum and detail ways parents can support their child's academics throughout the year. Nurture and maintain parent communication and involvement. Create and organize weekly newsletters bi-weekly progress reports quarterly curriculum overviews reading and math goal contracts to clarify expectations and involve parents in their child's academic goals and progress.\r Host and coordinate Parent Conference Nights.\rElementary Teacher Grades 1-5\rHillsborough County Schools - Tampa FL - July 2000 to November 2005\rFirst Grade Teacher\rHillsborough County Schools - July 2001 to June 2005\r3 years service on the 5 Star Committee to develop and write our schools application. Develop and monitor community and business partnerships\r Served on the Curriculum Committee developed creative school-wide enrichment programs\r Interfaced with guidance counselors independent educational consultants speech pathologists school psychologists ESOL specialists to develop individualized programs to meet all my student's special needs. Served on Child Study Teams to diagnose and develop Individualized Educational Programs (IEP's) for struggling students.\rTeam Leader\rHillsborough County Schools - 2004 to 2005\rLead grade level planning sessions and mentor new staff members.\r Attend county and state conventions to stay current. Present training session sharing information with grade level teams.\r Develop and coordinate weekly homework packets parent newsletter quarterly curriculum overviews class websites and seasonal events for grade level team.\r Parent/Student advocate during child study team placement meetings by parent request. Parents and administration requested my presence in meetings as an advocate even in subsequent years when their child was no longer in my class.\r Created and presented Powerpoint presentation for grade level Curriculum Night designed to promote parents involvement in their child's education.\r Hosted parent outreach events located in satellite neighborhood to improve parent attendance on conference days. Business partners sponsored hotdog dinners and carnival event during parent workshops and teacher conferences.\r Lead collaborative student performance feedback sessions between grade levels.\r3rd - 5th Grade Writer's Workshop Teacher\rHillsborough County Schools - 2003 to 2004\rDeveloped and nurtured intensive individualized instruction for at risk students after school.\r3rd - 5th Grade Language Impaired Teacher\rHillsborough County Schools - July 2000 to May 2001\rCollaborated with the Speech Pathologist to co-teach a self-contained language impaired classroom.\r Collaborated with guidance counselor school psychologist outside educational specialists ESE specialist and county administration to develop creative and intensive instruction to meet each child's special needs.\rFront Office Manager\rDesmond Americana Inn - Albany NY - 1982 to 1985\rInterviewed hired trained and supervised reservation clerks front desk clerks bellmen security and switchboard operators.\r Programmed the energy conservation computer that was housed within my office.\r Developed hotel marketing promotions.\r Represented the Front Office Department at Board Meetings.\r Interfaced with Department Heads General Manager Resident Manager Corporate Business Managers vendors hotel guests Secret Service Security for government officials and General Electric Research and Development Scientists entertainers convention leaders and bus tours.\r Processed work orders and invoices.\r Maintained department reports and budget.\r Scheduled staff and assisted with payroll.\r Acting Manager On Duty during holidays evenings weekends.\rEDUCATION\rMaster of Arts in Elementary Education\rUniversity of South Florida 1998 to 2000\rPsychology\rMount Holyoke College - 1979 to 1982\rDiploma\r- Tampa FL\rSouth Hadley MA\rChamplain Valley Union High School - Hinesburg VT 1973 to 1977\rSKILLS\rEXCEL WORD2010 LOTUS NOTES (mailmergescalendarsurvey) OUTLOOK HR PAYROLL POWERPOINT TRAVEL SOLUTIONS NOTARY Licensed\r\"\r\nresume_22,not_flagged,\"Daniel Iliescu\rR&D and Product Development Manager\rWhite River Junction VT - Email me on Indeed: indeed.com/r/Daniel-Iliescu/35ec7d6392bbb3ec\r Experienced materials scientist and R&D manager with a versatile multi-disciplinary skill set and a pragmatic customer-focused operational approach. Highly innovative and resourceful.\r Led and directed the innovation R&D efforts of a high-tech materials company aimed at creating disruptive technologies and products.\r Skilled in managing complex technical projects and driving the decision making process across several simultaneous projects. Strong ability to anticipate possible outcomes or roadblocks and create contingency plans to minimize project disruptions and ensure progress in a timely manner along the critical path.\r Detail oriented. Data and pragmatic return-on-investment driven decision making process.\r Managed complex projects involving large US and foreign companies legal firms (regulatory and intellectual property) regulatory governmental agencies universities consultants and suppliers.\r Strong interpersonal skills honesty professional integrity and a pragmatic entrepreneurial mindset have allowed connecting effectively to both technical and business leaders and build strong long-lasting professional relationships.\r Proven expertise in leading and enabling collaborative work and managing multi-disciplinary technical and non-technical personnel in a cross-functional environment. Built cross-functional relationships with technical and business leaders in large US and foreign companies with legal counsels scientists and regulatory personnel in governmental agencies military personnel universities national accredited certification laboratories etc.\r Designed development plans and negotiated the technical and legal framework of joint experimental programs and development agreements with US and foreign companies.\r Skilled in working interactively with customers to understand establish and prioritize requirements expectations and necessary resources. Collected the voice of customers and developed applications knowledge. Produced prototypes to demonstrate potential to interested customers (OEMs US military).\r Promoted technologies to large companies in the US Germany Korea Japan India The Netherlands and Dubai. Led technical negotiations with a Mexico-based company for a $20M contract.\r Worked extensively with Washington DC law firm on government regulatory issues approvals for large-scale manufacturing and import authorizations.\r Worked closely with Boston-based law firm on issues related to Intellectual Property (IP) protection.\r Strong advanced-mathematics and analytical skills.\r Advanced computer skills. Programming experience.\r Expert in statistical data analysis statistical process control and multi-variable experiment design and optimization using the statistical design of experiment methodology (DOE).\r Lean Six Sigma Green Belt certified.\r Kaizen continuous process improvement Lean process optimization 5S. Willing to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rTechnology Consultant\rUS Naval Sea Systems Command (NAVSEA) - 2016 to Present\r \r Partner with a CA-based company on a project for the US Naval Sea Systems Command (NAVSEA). Author of the experimental protocol for the validation of an on-board technology for arresting and securing fatigue cracks in structural elements.\r Adviser for a nanomaterials company on the due diligence process for the purpose of acquisition/investment and IP acquisition/licensing.\r Adviser for South African company on the sale of a US-based technology company. Responsible for generating interest in company's IP assets and for providing technical advice during the due diligence process. Consultant with international company on the emerging nanomaterials science and technologies.\r Contractor performing mechanical testing on ceramic materials at low temperatures. Overseeing the design acquisition installation and qualification of high-value infrastructure and testing equipment.\rR&D Manager\rSeldon Technologies Inc - Windsor VT - September 2005 to December 2015\rManaged the company's innovation and R&D programs. Worked closely with customers to understand and prioritize requirements (technical cost manufacturing). Produced prototypes to demonstrate feasibility and potential. Promoted and demonstrated new applications and products to the US military and domestic and foreign OEMs and established joint development agreements or testing programs with the goal of bringing the technologies and products to the market.\r Established strategic partnership with industry leaders in the US and overseas and negotiated joint testing plans and development agreements (legal framework scope procedure deliverables and schedules) alongside legal and members of the senior management team. Ensured that goals and deliverables were clearly defined especially in cases when broader competing interests needed to be taken into account.\r Coordinated the development efforts in a cross-functional environment with stakeholders within the company and with commercial partners in the US Germany Korea Japan Mexico India The Netherlands and Dubai. Planned prioritized and executed the R&D and NPD projects to meet the company's strategic commercial goals and the customer's development cycles or delivery schedules. Worked daily alongside technical and non- technical personnel to provide leadership and hands-on problem-solving skills.\r Responsible for developing the groundwork necessary for a full commercial launch for a nanomaterial- containing product including obtaining necessary government approvals for large-scale manufacturing setting up the supply chain and identifying developing and training contract manufacturers.\r In charge of setting up the international supply chain and obtaining necessary government regulatory approvals and necessary import authorizations.\r Promoted technology and products to key decision makers and was part of the team which closed a $20M contract with a Mexico-based company.\r Advised by Washington DC law firm successfully lobbied government representatives to accept the terms submitted in applications for approval of large-scale manufacturing commercialization and imports.\r Responsible for managing the intellectual property. Worked closely with Boston-based firm to develop protection strategies monitor competitive IP and identify business-critical jurisdictions for patent filing.\r Interacted closely with the US Army Public Health Command the USAF Human Effectiveness Directorate and special operations community at Fort Bragg NC to capture and implement very specific functionalities and performance requirements for technologies sold to the US military.\r Worked with Unilever (Mumbai India) to develop a technology for the chemical and microbiological purification of drinking water. Experience with the business and R&D culture in India.\r Ensured that all R&D efforts activities and data were properly documented and reported to internal and external stakeholders. Tailored external reports mindful of possible competing interests and in accordance with the specific legal framework governing the agreements.\r Authored high-quality scientific articles published in peer-reviewed journals reports to regulatory or funding agencies technical documents for intellectual property protection and reports on potential technological advantages in competing products and technologies.\rConsultant\rNASA Glenn Research Center - Cleveland OH - 2004 to 2005\rConsulted on high-velocity ballistic impact testing of the reinforced carbon heat shield panels on the leading edges of the orbiter's wings to determine cause of failure (Columbia Space Shuttle investigation). Responsible for producing and testing specialized high-velocity projectiles with specific structural properties.\rEDUCATION\rPh.D. in Materials Science\rDartmouth College Thayer School of Engineering 2001\rCERTIFICATIONS/LICENSES\rLean Six Sigma Green Belt\rOctober 2016\rPATENTS\r- Hanover NH\rElectrodes and applications (#WO2013126840 A1)\r2013\rADDITIONAL INFORMATION SKILLS AND CERTIFICATIONS\r Expertise in materials science and mechanical engineering. Proficiency in the mechanical behavior of materials tri-axial mechanical testing structural design physical chemistry organic and inorganic chemistry microstructure and surface design and characterization carbon nanotubes carbon fibers plastics metal alloys.\r Expert in using and customizing servo-hydraulic testing systems including multi-axial systems with independently controlled loading axes. Built custom data acquisition systems including sensors and transducers for testing systems.\r Proficient in microstructure and surface characterization techniques using electron microscopy (SEM) EDS EDXA XPS EBSD Raman FTIR XRD. Also TGA BET DSC GC-MS UV-visible spectrophotometry laser particle size measurements computerized tomography.\r Programming experience. Advanced computer skills. Proficient in SolidWorks 3D Design.\r Strong advanced-mathematics and analytical skills.\r Skilled in statistical data analysis statistical process control and statistical design of experiments for multi- variable process / product optimization (DOE).\r Resourceful innovative and versatile with multi-disciplinary technical skill set.\r Customer-focused with a pragmatic entrepreneurial mindset. Driven by the desire to create successful new business through innovative hands-on collaborative work.\r Skilled in managing simultaneous complex multi-disciplinary projects involving scientific engineering business\rand legal groups in large companies. Pragmatic organized and detail oriented.\r Ability to anticipate and prioritize possible outcomes and roadblocks and prepare and implement contingency plans to ensure progress along the critical path with minimal interruptions.\r Skilled in leading technical and non-technical personnel with multi-disciplinary backgrounds in collaborative work in a cross-functional environment.\r Practical experience interacting with legal firms on issues related to contracts and agreements scope and framework of joint testing and research programs regulatory and intellectual property corporate governance. Skilled in working with legal counsels to minimize expenses while maximizing the return.\r Conducted frequent business development and technical sales activities in support of the larger sales team. Promoted technologies and products and conducted technical negotiations related to technology licensing and distribution A strong scientific background augmented by the ability to connect on a personal level to both technical and business leaders have enabled effective communications between groups and the development of long-lasting professional relationships.\r Excellent proposal writing presentation and reporting skills.\r Experience with SalesForce CRM software.\r Expert user of Base Camp software for collaborative work. Worked with on-site and off-site parties to generate updated contents and manage documents in real time.\r Lean Six Sigma Green Belt certification.\r Kaizen continuous improvement methodology.\r Lean process optimization.\r\"\r\nresume_23,not_flagged,\"Data Entry\rData Entry - JP Morgan Chase\rS Burlington VT - Email me on Indeed: indeed.com/r/9e6812d87b4ddb70\rTo be employed as a bench scientist in either the private or public sector to apply my skills and training to practice good science and solve problems of economic scientific and/or social importance.\rWORK EXPERIENCE\rData Entry\rJP Morgan Chase - South Burlington VT - June 2014 to Present\rAu Pair\rRoma Lazio - March 2014 to May 2014\rTeaching Assistant HORT\rClemson University School of Agricultural - Clemson SC - May 2013 to December 2013\rClemson SC May 2013 - December 2013\rTeaching Assistant HORT 455/655: Just Fruits Fall 2013\r-Involved in lecture preparation and the gathering of materials for demonstrations and sampling -Managed and updated the Blackboard Learning System page for the course\r-Reviewed and revised quiz exam and syllabus material for the course\rResearch Assistant\rClemson University School of Agricultural - May 2013 to December 2013\rOrganized and prepared samples for analysis through freeze drying using a lyophilizer and grinding samples using liquid nitrogen\r-Used a BioPhotometer to calculate DNA concentration in a sample and vacuum centrifuge to reconcentrate samples\r-Performed enzyme digestions PCR (including basic knowledge of Real Time PCR) and gel electrophoresis -Performed DNA extraction procedures\rField Work\r-Collected compiled and analyzed field samples and data\r-Performed laboratory analysis of fruit for qualities such as firmness size coloration chlorophyll content pH and soluble solids\r-Collected data using the Fruit Texture Analyzer refractometer titration sampler DA meter and colorimeter\rEDUCATION\rBachelor of Science in Genetics\rClemson University - Clemson SC December 2013\r \"\r\nresume_24,flagged,\"David Grass\rCity Research Scientist - Environmental Emergency Information Program\rBurlington VT - Email me on Indeed: indeed.com/r/David-Grass/4e726672b2989e5e\rWORK EXPERIENCE\rCity Research Scientist\rEnvironmental Emergency Information Program - New York NY - September 2007 to Present\rBureau of Environmental Disease Prevention\rNYC Department Health and Mental Hygiene (DOHMH)\r Serve as second-in-command for 15 person multi-disciplinary program supporting the data collection analysis and reporting needs of the Environmental Health Division\r Serve as project manager and/or business lead for multiple federally funded\rtechnology projects.\r Served as project manager for the development of a web-based GIS warehouse for electronic floor plans and building information. It has become the largest repository of\rits kind in the world.\r Supervise three environmental health scientists. Direct activities of cross-functional\rproject teams including research scientists IT specialists and GIS analysts.\r Directed design development and implementation of the NYC Environmental\rInvestigations Tracking System an application used to track indoor environmental and food-borne illness outbreak investigations. Adoption of the system has reduced\rresponse time by more than 20%.\r Co-investigator for DOHMH's Climate Change Adaptation Program. Program aims to build capacity of the public health system to prevent and respond to climate-related\rhealth risks\r Provide statistical consultations for study design by clinical programs.\r Serve as member of DOHMH's Incident Command System (ICS) leadership as one of the leads for the Environmental Data Assessment and Analysis sub-section.\rPrincipal Investigator\rOccupational Exposures - Palisades NY - January 2003 to September 2007\rSubway Columbia University\r Coordinated design and execution of exposure assessment biological\rmonitoring and biomarker analyses\r Coordinated study logistics with labor management and 3 to 5 scientists and technicians during recruitment and field work\r Analyzed biological and environmental data synthesized and published results\rTeaching Assistant / Instructor\rColumbia University - New York NY - September 2002 to December 2004\rPublic Health Impacts of Climate Change P9300 Fall 2003\r Dynamics of Climate Variability and Climate Change W4400 Spring 2002 The Climate System EESC 2100 Fall 2002\rData Analyst\rNASA/Goddard Space Flight Center - Greenbelt MD - June 1999 to May 2000\r \rHydrological Sciences Branch\r Correlated remotely-sensed climatologic variables with malaria incidence in hypo- endemic regions of Bolivia and South Africa.\rResearch and Teaching Assistant\rMiddlebury College - Middlebury VT - June 1998 to May 1999\rAnalyzed fish flesh for Mercury using Cold-Vapor AA Spectroscopy\r Assisted a high school chemistry class to design and carry out lead (Pb) detection projects using the college's analytical equipment\rIntern\rUniversidad de Chile - Santiago de Chile RegioՁn Metropolitana - 1998 to January 1998\rChile\r Designed and conducted study of the role of tree-dwelling plants in the nutrient cycles of a pristine temperate rainforest in Southern Chile\r Used caving techniques to access forest canopy to conduct ecological research\rIntern\rSmithsonian Environmental Research Center - Edgewater MD - September 1997 to December 1997\rAnalyzed the transport of nutrients through functional forest strata using SAS statistical\rsoftware. Organized and supervised 3 to 4 volunteers during field work deployments\r Provided logistical support maintenance and data transfer for experimental deployment of a NASA sun photometer on a 50 meter meteorological tower\rEDUCATION\rM.A. in Climate Science Minors\rCOLUMBIA UNIVERSITY - New York NY September 2001 to October 2008\rPhD in Applied Mathematics and Earth & Environmental Science\rUNIVERSIDAD DE CHILE - Santiago de Chile RegioՁn Metropolitana July 2000 to May 2001\rB.A. in Chemistry and Environmental Studies\rMIDDLEBURY COLLEGE - Middlebury VT September 1995 to May 1999\rADDITIONAL INFORMATION\rIT: Project Management Requirements Gathering Business Analysis Systems Integration Health: Environmental Epi Disaster Epi Biomonitoring Risk Assessment Risk Communication Statistics: Biostatistics Parametric and Non-parametric data analysis in R Excel Matlab\r\"\r\nresume_25,flagged,\"David Lucero Public Health Analyst\rBurlington VT - Email me on Indeed: indeed.com/r/David-Lucero/1d742930ed41b354\rWORK EXPERIENCE\rGraduate Researcher Vermont USA\rUniversity of Vermont - Burlington VT - August 2008 to July 2013\rPrimary research focused on monitoring indicators of disease risk across time and space in 2 indigenous communities in rural Latin America. Secondary responsibility included managing the Stevens laboratory (e.g. negotiating with vendors QA/QC experiments and equipment etc.). Trained and supervised 48 scientists (4 international 3 American 3 PhDs 30 international undergraduates and 8 American undergraduates) in biological assays and interpretation techniques.\r1. Sustainable Public Health Initiative in Guatemala\rSustainable interventions to control Chagas disease insect vectors were applied between 2002 and 2009 in conjunction with partners in the Guatemalan Ministry of Health and local University. Person-hour surveys of disease risk (e.g. abundance of insect vectors cleanliness index wall/ floor construction etc.) were administered 5 times during this period.\ro Positive Control Impact: Household reinfestation of insect vectors maintained below 5% over 9 years despite previous studies reporting a rapid 4 month reinfestation with traditional intervention methods.\ro Positive Educational Impact: Residents were trained in developing a cement-like compound from local resources. Findings shared with scientific community via peer-reviewed publication.\ro Analytical tools: Esri ArcGIS 10.1 high-res imagery MATLAB-coded spatial models SAS JMP Pro 10 MS Access PCR\r2. Secondary Hosts and Sylvatic Reinfestation in Bolivia\rBetween 2011 and 2012 person-hour surveys of disease risk (e.g. abundance of insect vectors primary hosts secondary hosts etc.) were administered in conjunction with Bolivian Ministry of Health and local University researchers. Sylvatic insect vectors were collected with traps and blood meals were subsequently analyzed to monitor migration.\ro Positive Control Impact: Sylvatic vectors are potential sources of reinfestation. Secondary hosts vary in their statistical correlation with household infestation. Roads may aid in passive migration of insect vectors.\ro Positive Educational Impact: Government officials and Bolivian University students were trained in the use of ESRI Arc GIS 10.1 GPS acquisition and advanced molecular biology techniques. Findings were shared with stakeholders (e.g. public health officials community members etc.) via presentations and reports.\ro Analytical tools: Esri ArcGIS 10.1 MATLAB-coded spatial models Graph Pad Prizm Sequencher Sequencing Cloning qPCR\rTeaching Assistant Vermont USA\rUniversity of Vermont - Burlington VT - August 2009 to January 2013\rPrepared and presented PhD and undergraduate level coursework for 20 to 250 students per semester across 5 courses.\rEvaluated progress weekly with assignments and grades.\rPerformed troubleshoot on various statistical and molecular ecology software across OS platforms.\r1. Graduate Teaching Assistant Genetics (Spring 2012)\r \r2. Guest Lecturer and Graduate Teaching Assistant Population Genetics (Fall 2009 2011 2012) 3. Graduate Teaching Assistant Biology 2 (Spring 2010 2011)\r4. Guest Lecturer and Graduate Teaching Assistant Biology 1 (Fall 2010)\r5. Undergraduate Teaching Assistant Sustainable Community Development (Spring 2008)\rResearch Fellow\rEcoHealth - ___ - June 2011 to September 2011\rResearch focused on H5N1 evolution (avian flu) post-vaccination of the avian host nationally.\ro Positive Control Impact: H5N1 strains are rapidly evolving; therefore public health officials should consider these findings before administering another blanket vaccination.\ro Positive Educational Impact: Scientific writing workshops in English for non-native speakers led to numerous reports and publication drafts.\ro Analytical tools: RT-PCR Gel Electrophoresis MS Excel MS Word Google Translate\rUndergraduate Researcher\rUniversity of Vermont - Burlington VT - December 2004 to July 2008\rPrimary research focused on monitoring Chagas disease insect vector movement within and between indigenous communities in rural Bolivia with kinship and bloodmeal analysis.\ro Positive Control Impact: One rural town in Bolivia displayed high vector movement and was surveyed in my Graduate Researcher position.\ro Positive Educational Impact: Research led to 3 scientific publications. Findings were shared with the University at least 2 times a year via presentations and posters.\ro Analytical tools: MS Powerpoint Structure Genepop Multiplex PCR Microsatellites\rEDUCATION\rPh.D. in Biology (Public Health concentration)\rUniversity of Vermont (College of Arts and Sciences) - Burlington VT 2008 to 2013\rB.S. in Natural Resources Self-designed (Infectious diseases concentration)\rUniversity of Vermont (Rubenstein School of Natural Resources) - Burlington VT 2004 to 2008\rSKILLS\rFluent in Spanish Research Scientific Writing Molecular Biology (e.g. qPCR Cloning Sequencing Gel Electrophoresis) Spatial Statistics GIS Windows Apple OSX Apple iOS Android\rPUBLICATIONS\rEcohealth interventions limit triatomine reinfestation in La Brea Guatemala. American Journal of Tropical Medicine and Hygiene\rhttp://www.ajtmh.org/content/88/4/630.short\rFebruary 4 2013\rPublished Abstract\rIn this study we evaluate the effect of participatory Ecohealth interventions on domestic reinfestation of the Chagas disease vector Triatoma dimidiata after village-wide suppression of the vector population using a residual insecticide. The study was conducted in the rural community of La Brea Guatemala between 2002\r \rand 2009 where vector infestation was analyzed within a spatial data framework based on entomological and socio-economic surveys of homesteads within the village. Participatory interventions focused on community awareness and low-cost home improvements using local materials to limit areas of refuge and alternative blood meals for the vector within the home and potential shelter for the vector outside the home. As a result domestic infestation was maintained at __ 3% and peridomestic infestation at __ 2% for 5 years beyond the last insecticide spraying in sharp contrast to the rapid reinfestation experienced in earlier insecticide only interventions.\rHousehold model of Chagas disease vectors (Hemiptera: Reduviidae) considering domestic peridomestic and sylvatic vector populations. Journal of Medical Entomology http://www.bioone.org/doi/abs/10.1603/ME12096\rMay 7 2013\rPublished Abstract\rDisease transmission is difficult to model because most vectors and hosts have different generation times. Chagas disease is such a situation where insect vectors have 12 generations annually and mammalian hosts including humans can live for decades. The hemataphagous triatominae vectors (Hemiptera: Reduviidae) of the causative parasite Trypanosoma cruzi (Kinetoplastida: Trypanosomatidae) usually feed on sleeping hosts making vector infestation of houses peridomestic areas and wild animal burrows a central factor in transmission. Because of difficulties with different generation times we developed a model considering the dwelling as the unit of infection changing the dynamics from an indirect to a direct transmission model. In some regions vectors only infest houses; in others they infest corrals; and in some regions they also infest wild animal burrows. We examined the effect of sylvatic and peridomestic vector populations on household infestation rates. Both sylvatic and peridomestic vectors increase house infestation rates sylvatic much more than peridomestic as measured by the reproductive number R0. The efficacy of manipulating parameters in the model to control vector populations was examined. When R0 > 1 the number of infested houses increases. The presence of sylvatic vectors increases R0 by at least an order of magnitude. When there are no sylvatic vectors spraying rate is the most influential parameter. Spraying rate is relatively unimportant when there are sylvatic vectors; in this case community size especially the ratio of houses to sylvatic burrows is most important. The application of this modeling approach to other parasites and enhancements of the model are discussed.\rVector blood meals and Chagas disease transmission potential United States. Emerging Infectious Diseases\rhttp://wwwnc.cdc.gov/eid/article/18/4/11-1396_article.htm\rApril 2012\rPublished Abstract\rA high proportion of triatomine insects vectors for Trypanosoma cruzi trypanosomes collected in Arizona and California and examined using a novel assay had fed on humans. Other triatomine insects were positive for T. cruzi parasite infection which indicates that the potential exists for vector transmission of Chagas disease in the United States.\rChagas Disease: Trypanosoma cruzi the first hundred years Triatomine Biology Chapter 8\rhttp://www.sciencedirect.com/science/bookseries/0065308X/75\r2011\rPublished Abstract\rA complete picture of Chagas disease requires an appreciation of the many species of kissing bugs and their role in transmitting this disease to humans and other mammals. This chapter provides an overview of the taxonomy of the major species of kissing bugs and their evolution. Knowledge of systematics and biological kinship of these insects may contribute to novel and useful measures to control the bugs. The biology of kissing\r \rbugs their life cycle method of feeding and other behaviours contributing to the transmission of Trypanosoma cruzi are explained. We close with a discussion of vector control measures and the allergic complications of kissing bug bites a feature of particular importance in the United States.\rA method for the identification of guinea pig blood meal in the Chagas disease vector Triatoma infestans\rhttp://www.kinetoplastids.com/content/6/1/1\rPublished Abstract\rBackground\rIn a SINE-based PCR assay a primer set specific for guinea pig genome short interspersed elements DNA was used to test the utility of genomic markers for identifying the source of vertebrate blood meals of Triatoma infestans.\rMethods\rThe investigation consisted of two assays. In Assay 1 thirty-six insects collected from the Province of ZudaՁnez in Chuquisaca Bolivia were frozen 140 hours after feeding under controlled conditions on guinea pigs. The species of the vertebrate host was confirmed from dissection of the posterior part of the abdomen of each insect followed by DNA extraction and PCR amplification. Assay 2 investigated whether the technique worked under field conditions. We analyzed the bloodmeal of 34 insects collected from households and peri- domestic structures from communities where wild and captive guinea pigs occur. After collection the insects were maintained at room temperature for 2 months without feeding and then analyzed.\rResults\rIn Assay 1 each of the 36 insects allowed to feed on guinea pig blood tested positive for guinea pig DNA. The guinea pig DNA was reliably identified in as little as 1 hour and up to 40 hours after feeding. For Assay 2 8 out of the 34 samples (23%) showed positive results with guinea pig specific primers.\rConclusion\rThe results in assay 1 demonstrated that DNA from the vertebrate host can be amplified 140 hours post feeding from the abdomen of the blood-feeding Chagas disease vector Triatoma infestans. The results in assay 2 confirmed that the procedure works on insects collected from households and peri-domestic structures and that the source of a blood meal can be determined at least 2 months post feeding.\rPCR reveals significantly higher rates of Trypanosoma cruzi infection than microscopy in the Chagas vector Triatoma infestans: High rates found in Chuquisaca Bolivia http://www.biomedcentral.com/1471-2334/7/66\rJune 27 2007\rPublished Abstract\rBackground\rThe Andean valleys of Bolivia are the only reported location of sylvatic Triatoma infestans the main vector of Chagas disease in this country and the high human prevalence of Trypanosoma cruzi infection in this region is hypothesized to result from the ability of vectors to persist in domestic peri-domestic and sylvatic environments. Determination of the rate of Trypanosoma infection in its triatomine vectors is an important element in programs directed at reducing human infections. Traditionally T. cruzi has been detected in insect vectors by direct microscopic examination of extruded feces or dissection and analysis of the entire bug. Although this technique has proven to be useful several drawbacks related to its sensitivity especially in the case of small instars and applicability to large numbers of insects and dead specimens have motivated researchers to look for a molecular assay based on the polymerase chain reaction (PCR) as an alternative\r \rfor parasitic detection of T. cruzi infection in vectors. In the work presented here we have compared a PCR assay and direct microscopic observation for diagnosis of T. cruzi infection in T. infestans collected in the field from five localities and four habitats in Chuquisaca Bolivia. The efficacy of the methods was compared across nymphal stages localities and habitats.\rMethods\rWe examined 152 nymph and adult T. infestans collected from rural areas in the department of Chuquisaca Bolivia. For microscopic observation a few drops of rectal content obtained by abdominal extrusion were diluted with saline solution and compressed between a slide and a cover slip. The presence of motile parasites in 50 microscopic fields was registered using 400 magnification. For the molecular analysis dissection of the posterior part of the abdomen of each insect followed by DNA extraction and PCR amplification was performed using the TCZ1 (5' CGA GCT CTT GCC CAC ACG GGT GCT 3') and TCZ2 (5' CCT CCA AGC AGC GGA TAG TTC AGG 3') primers. Amplicons were chromatographed on a 2% agarose gel with a 100 bp size standard stained with ethidium bromide and viewed with UV fluorescence.\rFor both the microscopy and PCR assays we calculated sensitivity (number of positives by a method divided by the number of positives by either method) and discrepancy (one method was negative and the other was positive) at the locality life stage and habitat level. The degree of agreement between PCR and microscopy was determined by calculating Kappa (k) values with 95% confidence intervals.\rResults\rWe observed a high prevalence of T. cruzi infection in T. infestans (81.16% by PCR and 56.52% by microscopy) and discovered that PCR is significantly more sensitive than microscopic observation. The overall degree of agreement between the two methods was moderate (Kappa = 0.43 α 0.07). The level of infection is significantly different among communities; however prevalence was similar among habitats and life stages.\rConclusion\rPCR was significantly more sensitive than microscopy in all habitats developmental stages and localities in Chuquisaca Bolivia. Overall we observed a high prevalence of T. cruzi infection in T. infestans in this area of Bolivia; however microscopy underestimated infection at all levels examined.\rADDITIONAL INFORMATION SOFTWARE PROFICIENCY\rEndNote X6 5 years\rEsri ArcGIS 10.1 8 years Genepop 6 years\rGraph Pad Prizm 6 2 years MATLAB R2012 5 years Microsoft Office 11 years Sequencher 5 years\rSAS JMP V10 Pro 6 years Structure 7 years\rINVITED PRESENTATIONS\rNovember 2012. Probing for indicators of Chagas disease risk. Meegid XI: 11th International Conference on Molecular Epidemiology and Evolutionary Genetics of Infectious Diseases Loyola University New Orleans Louisiana.\rSeptember 2012. Landscape risk factors associated with Chagas disease infestation in Zurima Bolivia. Biology Symposium at the Universidad San Francisco Xavier de Chuquisaca Sucre Bolivia.\rJanuary 2011. Home alone? Understanding the Spatial Distribution of a Chagas Disease Insect Vector Across Traditional and Sustainable Public Health Initiatives in La Brea Guatemala. Ecological Lunch Symposium at the University of Vermont Burlington Vermont.\rAugust 2010. What Can We Gain From Interdisciplinary Research? A Focus on Geographic Information Systems Spatial Statistics and Population Genetics. Biology Symposium at the Universidad San Francisco Xavier de Chuquisaca Sucre Bolivia.\rJune 2010. Introduction to Spatial Analyses for La Brea Guatemala. Biology Symposium at the Universidad de San Carlos Guatemala City Guatemala.\rJune 2010 Genetic Variability and Population Structure of Bolivian Triatoma infestans Across Communities. Biology Symposium at the Universidad de San Carlos Guatemala City Guatemala.\rApril 2010. Spatial and Genetic Variability in Chagas Disease Vectors An Insight Into Possible Drivers. American Association of Geographers Symposium Washington District of Columbia.\rOctober 2009. Chagas Disease in Southern Bolivia: A Spatial Insight. Ecological Lunch Symposium at the University of Vermont Burlington Vermont.\rSeptember 2009. A Comparison of Microscopy and Molecular Biology Techniques in Detecting T. cruzi in Triatoma infestans. University of Maine Orono Maine.\rOctober 2007. Analyzing Chagas Disease Transmission via Population Structure and Feeding Preferences. Ohio State University Columbus Ohio.\rAugust 2006. Feeding Preferences of Chagas Vectors in Chuquisaca Bolivia. McNair Scholars Seminar at the University of Vermont Burlington Vermont.\rApril 2005. Transmission Dynamics of Chagas Disease Vectors Using Bloodmeal Analysis. URECA! Symposium at the University of Vermont Burlington Vermont.\r\"\r\nresume_26,not_flagged,\"Deborah Ploof\rLaboratory Information System & Administrative Manager - Porter Hospital Inc\rBridport VT - Email me on Indeed: indeed.com/r/Deborah-Ploof/b160caae7a1ec4af\rTo obtain a position that utilizes my knowledge of CLIA JCAHO and CAP regulations LIS computer skills customer service experience and the medical technology skills gleaned from 38 years of experience as a clinical laboratory scientist.\rWORK EXPERIENCE\rLaboratory Information System & Administrative Manager\rPorter Hospital Inc - Middlebury VT - September 1994 to Present\rManaged all aspects of maintaining a laboratory information system (LIS) including the following:\r* Maintain various data bases - client test dictionary message codes HIS and reference lab cross-references and instrument interfaces.\r* Troubleshoot LIS problems; prepare and submit documentation to the LIS vendor explaining the problem.\r* Worked with LIS vendor to create and test new reference lab interfaces the first one for Mayo Medical Laboratories (first lab in NECLA group to be interfaced). This was replaced in November of 2004 with another interface called OCMS developed by Fletcher Allen Hospital of Vermont that connects Porter Hospital Laboratory with FAHC and Mayo Medical Laboratories. This one was replaced in November 2009 with one to Mayo Access.\r* Helped develop and test the laboratory interface (ADT in Orders in and out Results out) with the hospital information system (HIS). Maintain the laboratory databases on the HIS and troubleshoot problems with interfaces.\r* Established and manage the extensive system of printing and/or faxing of laboratory reports throughout the hospital facility and physician office network tailoring it to the unique needs of each nursing unit or clinical practice.\r* Tested and implemented multiple upgrades on the LIS.\r* Attended training sessions offered by the LIS vendor prepared written operating procedures and training manuals; trained all lab employees on LIS.\r* Prepared documentation in NCCLS format for all testing within the laboratory including Chemistry Specimen Handling Hematology Coagulation Blood Banking and Microbiology.\r* Prepared and maintained Porter Hospital Specimen Manual (Test Catalog) that includes laboratory information on preparing requisitions medical necessity documentation proper collection of specimens storage and transportation CPT codes and prices.\r* Participated as a member of the laboratory team that worked to obtain Porter Hospital Laboratory's accreditation by the College of American Pathologists and on the team to maintain those high standards in order to retain accreditation. I have also been a member of three CAP inspection teams.\r* Collected and prepared statistical and quality assurance data/reports for the laboratory manager and hospital administration.\r Worked as Laboratory Director and Consultant for Porter Hospital owned physician's offices from 1996 to 2000. The office laboratories were all inspected by CLIA and passed their first and subsequent inspections as moderately complex laboratories.\rBench Technologist\rPorter Hospital Inc - Middlebury VT - September 1986 to September 1994\r \rFor personal/family reasons requested a transfer from lab manager to bench technologist where I worked in blood banking chemistry hematology coagulation and microbiology performing tests commonly performed in a community hospital laboratory. I worked a variety of shifts was on call and worked part-time and full-time.\rLaboratory Manager\rPorter Hospital Inc - Middlebury VT - May 1974 to September 1986\rPerformed all the administrative functions of a laboratory manager in a small 45-bed community hospital laboratory.\r* Prepared laboratory for JCAHO certification. Obtained AABB certification.\r* Moved laboratory from all manual hematology coagulation and chemistry procedures to automated instrumentation.\r* Introduced computers into the laboratory setting up training implementing that was essential for the growth of the laboratory services.\r* Established an outreach service for the laboratory complete with courier service.\r* to work as both laboratory manager and bench technologist pulling a variety of shifts weekends and on call.\rEDUCATION\rBachelor Of Science in Medical Technology\rUniversity of Vermont - Burlington VT September 1970 to May 1974\rSKILLS\rWord Excel LEAN SQL Crystal Reports Meditech CyberLab\r\"\r\nresume_27,not_flagged,\"Denis ChasseՁ\rReading Specialist and Professional Outdoor Landscape Painter\rChester VT - Email me on Indeed: indeed.com/r/Denis-Chasse/a48de5c7fd62df5d\rTo teach the principles of outdoor landscape painting focusing on design values and color harmony in creating a scene . I wish to enhance my art education and teaching experiences in new endeavors. .\rWORK EXPERIENCE\rTitle1Reading Specialist\rWindsor Southeast School District - 2011 to Present\rState Street School. In my role as Reading Specialist I am working in the Middle School grades 5 thru 8. I work in the classrooms\rassisting students in understanding the content area material through the use of Thinking Maps to outline text information. In addition I conduct small group reading classes assisting students in using before during and after reading strategies to engage them in connecting with text. This year I initiated the SERP (strategic education research program) adopted in the Boston Public Schools and promoted by Harvard Univ. research on the use of high utility words in the middle school curriculum. Also I administer the DRA2 to assess student reading ability and meet with teachers to analyze the data during the PLC and EST meetings. Then interventions are planned and implemented by the teachers and the reading specialist to assist students in achieving high success reading activities as recommended in R.Allington's research on best practices. .\rReading Specialist/Literacy Coach\rWindham Northeast - Westminster VT - 2009 to 2011\rI coordinated the Four Blocks Literacy Program. I assisted in the Aimsweb assessments of students being progress monitored bimonthly in early literacy and reading skills. As a member of the RTI team and EST team we reviewed the NECAP results and subsequently plan in the further assessing of students at risk; and recommended implemented effective intervention programs for those students.\rReading Specialist and Literacy Coach\rSAU - Hillsboro NH - 2007 to 2009\rI worked with students in small groups as well as within classrooms assisting students with reading study skills and\rwriting strategies in the content areas.\rAlso I organized and conducted professional development\rworkshops on the Key Three Routine on Comprehension with an emphasis on main idea note taking and summarizing content\rmaterials. Also I implemented a year long online learning program via Virtual High School to enable students to complete semester course work.\rMarketing Agent\rMarketing Agent for the Eagle Times Publication Co - Claremont NH - 2005 to 2007\rI worked with clients on a weekly basis to develop effective advertising campaigns. Some of my clients included Mary Hitchcock Hospital Dartmouth College Hopkins Center in Hanover N.H. as well as local Vermont and N.H. businesses in the Upper Valley Region of the Conn. River.\rReading Specialist\r \rRawls Byrd Elementary - Williamsburg VA - 1996 to 2005\rCoordinated the school's balanced literacy reading program.\rConducted in-service workshops for classroom teachers on the five strands of reading phonemic awareness phonics word recognition fluency and comprehension.\rCoordinated the school testing for the Standards of Learning for Va. as well as the Stanford Achievement Test.\rPurchased reading materials for classrooms i.e. novels teacher manuals Accelerated Reader books. Conducted in-service teacher workshops on literacy groups guided reading reading workshop peer reading and literature circles.\rTaught reading strategies to students in classrooms as well as small groups.\rMember of the school child study team advising on student assistant plans.\rSupervised the America Reads tutorial program involving college students tutoring at risk students. In addition supervised twenty-five senior citizen volunteer readers tutoring at risk students.\rBuilt a data base on every student tracking their progress in the areas of reading testing and remediation. Initiated a minority speaker program for at risk students.\rEncouraged parental involvement through the Virginia Read-a-loud Program.\rReading Specialist\rAberdeen Elementary - Hampton VA - 1992 to 1996\rImplemented remedial and advanced reading programs which\rincorporated a balanced literacy approach to reading and writing..\rPresented thematic programs i.e. sea life artists and their art plays and inventions which encouraged children to explore their own creativity.\rOrganized a Read to Me program which brought into the school throughout the year forty people from local businesses universities and military bases to discuss careers and to read children's books to students. Promoted the Multiple Sclerosis Read-a-thon which challenged students to get sponsors to donate amounts of monies for numbers of books that students read.\rReading Specialist\rK.A. Brett Elementary - Tamworth NH - 1990 to 1992\rImplemented a Chapter 1 reading program to assist at risk students in acquiring reading skills in vocabulary building comprehension and\rcritical thinking.\rInitiated an enrichment program which encouraged students to explore their talents and interests. Students developed projects on a wide range of topics; i.e. puppetry storytelling and art\rappreciation.\rPromoted and implemented a Business Partners in Education\rProgram which brought over thirty business people into the school to visit classrooms and discuss career choices.\rDeveloped a parent volunteer program in which parents visited the school on a weekly basis and read storybooks to students.\rContracted thirty businesses to donate $50. each toward the purchase of a satellite dish for our school so as to beam in\reducational programs for students and adults.\rReading Specialist\rBethlehem Elementary School - Bethlehem NH - 1988 to 1989\rWorked with students in remedial and developmental groups. Attended balanced literacy workshops at the Dept. of Education Concord N.H. Wrote a grant and received $3000 for a Robert Frost Symposium in Bethlehem Elem. School\rReading Specialist\rMelrose Public Schools - 1972 to 1988\rMa. Through effective\rtesting and diagnosis I developed remediation programs in phonics\rcomprehension and study skills for students. I coordinated an advanced reading and enrichment program into the reading program to challenge all students as well as the gifted. I also wrote and received several grants to foster the arts and literature in the Melrose\rPublic Schools. In addition I implemented programs which brought\rartists authors poets and scientist into the Melrose schools.\rEDUCATION\rHillsboro-Deering High School - 2009\rEnglish\rHigh School Literacy 2008\rLynnfield MA\rcontent learning via writing strategies and techniques\rContent Area Writing sponsored by Univ. of NH - 2007\rTechnology\rUniv. of Va. 2005\rreading b\rsponsored by James Madison Univ 2004\rClassroom\rUniv. of Va. 2003\rEducation for Teachers\rChristopher Newport Univ - Newport News VA 1998\rLesley College - Cambridge MA 1997\rFrench\rHarvard University 1984 to 1985\rArt History\rHarvard University\rDurham NH\r1983 to 1984\rCertificate\rArt Institute of Boston 1976 to 1978\rM.Ed. in Reading\rSalem Teacher's College - 1972 to 1975\rB.A. in Philosophy\rSalem MA\rSt John's Seminary - 1965 to 1970\rDiploma\rMelrose High School 1965\rSKILLS\r-\rBrighton MA\rMelrose MA\rProfessional 'plein air artist. Attended the Art Institiute of Boston achieving Certificate of Merit. Continued studies in the Gloucester School of Outdoor Painting founded by Emile Gruppe'. My wife Katheryn and I\rown and operate the Chasse' Fine Art Gallery of Chester Vt. I have exhibited in art shows in Jackson New Hampshire Williamsburg Va. Southern Vermont Art Center Manchester Vt. In addition I have run painting workshops in New England.\r\"\r\nresume_28,flagged,\"Drew Burkhard\rEnvironmental Scientist Meets Mechanical Engineer\rColchester VT - Email me on Indeed: indeed.com/r/Drew-Burkhard/2f30c52ae97d210d\rWORK EXPERIENCE\rTransportation Research Center Intern\rUniversity of Vermont - Burlington VT - September 2011 to December 2011\rI spent the majority of my time analyzing data from several devices measuring: gases particle size concentrations etc. from the emissions of an engine using petroleum diesel and biodiesel fuels. I did extensive work with Excel spreadsheets made graphs and manipulated data for representation.\rBrewer's Assistant\rSwitchback Beerworks - Burlington VT - January 2010 to December 2010\rEmployees of this brewery split time between many tasks. This requires a cohesive team environment to ensure success. Running the kegging machine was among my duties and required a diligent effort to maintain production. Quality control is a major concern at the brewery and my attention to detail ensured that our product remained in top condition. It was this ability that earned me an early raise and also new responsibilities around the brewery.\rDelivery Driver\rJunior's Italian - Colchester VT - April 2009 to February 2010\rThis job demanded juggling tasks on busy weekend nights at one of the areas most popular Italian restaurants. Coordinating orders from the staff in front food from the chefs and deliveries with the other drivers was a challenge met on every shift.\rPersonal Lines-Customer Service Assistant\rWinooski Insurance - Winooski VT - May 2007 to August 2008\rWorked in a fast-paced team environment assisting agents in providing customer service duties. Skills included problem solving in answering customer questions answering telephones responding to inquiries and entering customer data on management system.\rSecurity\rGreen Mountain Concert Services - Vermont - April 2007 to August 2007\rCreated a safe environment for event goers. Collaborated with coworkers to ensure success of securing the entire event.\rBusser\rThree Tomatoes Restaurant - Burlington VT - February 2005 to August 2005\rThis position requires quick feet and a quick mind. While running around setting and clearing tables I offered customer service to patrons and cooperation with other staff members.\rBusser\rCannon's Restaurant - Burlington VT - April 2004 to July 2005\r \rEDUCATION\rEnvironmental Science\rUniversity of Vermont - Burlington VT January 2009 to January 2011\rMechanical Engineering\rUniversity of Colorado - Boulder CO 2005 to 2008\rColchester High School - Colchester VT January 2001 to January 2005\rSKILLS\rEfficiency Creativity Problem Solving Microsoft Office Proficiency Writing Communicating With People Leadership Quick Learner Team Worker Organized\rADDITIONAL INFORMATION\rI am a motivated and enthusiastic recent college graduate looking for an entry level position to get my career started. I was one of my class's top students at the University of Vermont where I obtained my Bachelor's of Science. I look forward to applying my education to meaningful projects and research in the field.\r\"\r\nresume_29,not_flagged,\"Elizabeth Conway\rSeeking a Part time Admin/receptionist postition\rEast Hardwick VT - Email me on Indeed: indeed.com/r/Elizabeth-Conway/f0620353200ab872\rWORK EXPERIENCE\rAssistant Calf Manager\rFairvue Farm - January 2014 to Present\rDaily calf care on a 2000 cow dairy farm including but not limited to feeding vaccines record keeping to maintain and raise healthy replacement calves assisting dairy manager in creating needed reports.\rOwner/Operator of a fiber mill\rFibers - 2007 to 2013\rResponsibilities included bookkeeping record keeping\rshipping/receiving customer service maintenance sales daily operations and advertising\rBookkeeper/Administration\rE.F. Jones LLC - 2007 to 2013\rResponsibilities included AP/AR reconciliation and daily administration duties\rSenior Associate Scientist\rPfizer Inc - 2004 to 2007\rResponsibilities included cell culture conducting In Vivo studies\rtissue collection western blots data analysis and reporting and presenting data to the team leaders.\rEDUCATION\rMaster of Animal Science\rUniversity of Connecticut 2001 to 2003\rBachelor of Animal Science\rUniversity of Connecticut 1999 to 2001\rAnimal Science\rUniversity of Connecticut 1997 to 1999\rSKILLS\rWord Excel Outlook Quickbooks data entry customer service bookeeping filing animal care and sewing\r \"\r\nresume_30,not_flagged,\"Elizabeth Klepner Pharmacovigilance Scientist\rKillington VT - Email me on Indeed: indeed.com/r/Elizabeth-Klepner/b820d246f81a6805\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rVermont State Program Director\rQualidigm - Barre VT\rResponsibilities\rVermont State Program Director December 2014- Present\r Accountable for overall project and operational management of contract\r Sets strategic direction managing resource allocation achieving contract deliverables and managing stakeholder relationships\rAccomplishments\rMet all required Federal Government deliverables on time as required by contract. Completed Leadership & Organizing in Action course.\rSkills Used\rLeadership skills in overseeing a diverse staff of professionals.\rWriting skills used to produce timely reports to the Center for Medicare and Medicaid (CMS). Analytical skills to capture review and discuss data to stakeholders.\rMarketing skills to develop marketing material used for recruitment of stakeholders. Experienced in Microsoft office.\rRN Care Coordinator\rUniversal American - 2012 to December 2014\r2014 to 2015 Manage medication reconciliation home care coordination and resources for ACO beneficiaries. Coordinate ACO care between clients and eight practices.\rCase Manager/ Liaison Rutland County April 2013 - February 2014\rSTATE OF VERMONT DEPARTMENT OF VERMONT HEALTH ACCESS - 2013 to 2014\rResponsibilities\r Manage and establish working agreements between Hospital Practices and Clients. Manage medication reconciliation transition of care and coordination for clients.\rPharmacovigilance Scientist\rSANOFI PASTEUR PHARMACEUTICAL COMPANY - Swiftwater PA - April 2010 to 2012\r Process cases in accordance with FDA timeline from triage to submission.\r Participate in internal and external global audits and Periodic Safety Update Reports.\rPharmacovigilance Safety Associate/Occupational Health Clinician\rBRISTOL MEYERS SQUIBB COMPANY - Hopewell NJ - January 2006 to April 2010\r \r Validate data triage and process adverse event cases for investigational and licensed products within FDA timelines using CARES data base.\r Occupational/Corporate Health Nurse responsible for safety surveillance emergency response team and OSHA recordkeeping immunization for foreign/domestic travel.\rRadio Frequency Electrical Engineer/ Safety Officer/Marketing Manager\rLINEARIZER TECHNOLOGY INC - Hamilton NJ - November 1997 to May 2001\rResponsible for production and testing of KA-band Linearizers for satellite use. Developed OSHA compliance program including safety and health regulations.\rProduction Engineer\rFORD AEROSPACE - Wayne NJ - May 1986 to January 1989\rResponsible for production and testing of guidance and navigation systems for the B1B Space Shuttle and Military Programs.\r Developed Design Test Fixture: Manufacturing Cost reduction $250000.\rAdjunct Professor of Engineering\rTHE COLLEGE OF NEW JERSEY - Ewing NJ - 1989 to 1989 Responsible for four Electrical Engineering laboratories.\rCritical Care Registered Nurse\rROBERT WOOD JOHNSON UNIVERSITY HOSPITAL - Hamilton NJ\rResponsibilities\r Emergency Critical Care PACU Pediatric Critical Care expertise. Proficiency in Phlebotomy Advanced IV Therapy. Started immunization for foreign travel clinic.\rAccomplishments\rAward: Employee of Quarter nominated for Employee of The Year\rEDUCATION\rEngineering\rTHE COLLEGE OF NEW JERSEY - Ewing NJ May 1986\rNursing\rUNIVERSITY OF ARIZONA - Tucson AZ May 1980\rHELENE FULD SCHOOL OF NURSING - Trenton NJ May 1977\rSKILLS\rArisG Database for drug safety Medra Coding expert Microsoft Office including Excel and Word Critical care phlebotomy immunizations for foreign travel (10+ years)\rAWARDS\rIEEE Engineering award\rMay 1986\rEmployee of Quarter for Robert Wood Johnson University Hospital\r\"\r\nresume_31,not_flagged,\"Ellen Kujawa\rSenior Research Scientist - Wisconsin Department of Natural Resources\rBurlington VT - Email me on Indeed: indeed.com/r/Ellen-Kujawa/ac529b7a231f8ee6\rMotivated conservation professional with over a decade of environmental research and communication experience. Skills include statistical analysis writing editing and public speaking. Particularly passionate about natural resources management science-based conservation strategies and public policy.\rWilling to relocate to: Boston MA\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rSenior Research Scientist\rWisconsin Department of Natural Resources - June 2015 to Present\rDeveloping a data-driven framework of best management practices for Wisconsin's lakes modeling future invasive species success analyzing a decade of longitudinal survey data and preparing and submitting papers for publication. June 2015 - present.\rFaculty Associate University of Wisconsin-Madison.\rMentored students through a semester-long biology research project graded student work and assisted with curriculum development. January 2015 - present.\rEditor Environmental Resources Center University of Wisconsin-Madison.\rEdited and wrote content for a variety of environmental publications including land management plans species conservation reports course materials and blog posts. August 2014 - May 2015.\rTeaching Assistant University of Wisconsin-Madison.\rTaught two introductory biology/geography labs per week designed curriculum provided writing feedback and mentored students. August 2013 - May 2015.\rInstructor PEOPLE Program University of Wisconsin-Madison.\rDesigned and implemented three weeks of science and writing coursework for fifteen middle school students of color and/or from low-income backgrounds and coordinated two undergraduate teaching assistants. Summers 2013 2014.\rAnalyst Self-Employed.\rResearched and analyzed biomass as a sustainable fuel source. Contributed to a large-scale report on construction and demolition waste submitted to the U.S. Environmental Protection Agency in 2012. September 2011- March 2012.\rField Technician Harvard Forest.\rCompleted a 35-hectare tree survey of biodiversity succession and carbon sequestering. May-August 2011. Mentor Mount Holyoke College.\rMentored an intermediate-level college ecology course independently planned and led structured help sessions and assisted with labs. September 2009 - May 2011.\rEDUCATION\rM.S. in Conservation Biology and Sustainable Development\rUniversity of Wisconsin - Madison WI May 2015\rA.B. in Environmental Studies\r \rMount Holyoke College May 2011\rSKILLS\rStatistical Analysis (6 years) Research (8 years) Written Communication (10+ years) Public Speaking (8 years) Resource Management (5 years)\r\"\r\nresume_32,flagged,\"Eric Anderson Data Scientist\rBrattleboro VT - Email me on Indeed: indeed.com/r/Eric-Anderson/3adfe47953693b8a\rI am looking for a full time position in the data analysis data mining and machine learning domain. I am especially interested in data modeling simulation and visualization challenges.\rWORK EXPERIENCE\rTeaching Assistant\rCognitive and Neural Systems Department - Boston MA - 2004 to 2005 Lecture and grading CN570 Models of Learning and Reinforcement\rTechnical Research Assistant\rMood and Anxiety Disorders Program - Bethesda MD - 2000 to 2002 Co-authored two articles in peer-reviewed journals\rResearch Assistant\rYale University - New Haven CT - January 2000 to October 2000\rResponsible for structural MRI analysis for four clinical studies Part time 1996 and 1997:\rWorked on literature reviews for grant proposals\rSupported PET image analysis\rComputational Modeling Internship\rMillennium Institute - Arlington VA - 1999 to 1999 Worked on data fitting and documentation for the T21 model\rEDUCATION\rPhD in Cognitive and Neural Systems\rBoston University - Boston MA 2005 to 2012\rBA in Environmental Design\rHampshire College - Amherst MA 1996 to 2000\rSKILLS\rMachine learning Neural Networks Matlab Data mining\rADDITIONAL INFORMATION\rSkills and Interest\r Trained in machine learning techniques including neural networks Research focus: adaptive decision making models\r \r Expertise in large-scale simulations using empirical data Data mining graph theory and visualization\r Proficient in Matlab/Simulink (10+ year of experience) Familiar with Java Python R SQL C/C++ Perl\r\"\r\nresume_33,flagged,\"Eric Hedeman\rBurlington VT - Email me on Indeed: indeed.com/r/Eric-Hedeman/5e5da1582420708e\rWORK EXPERIENCE\rVolunteer Research Assistant\rKessler Foundation - West Orange NJ - May 2013 to August 2014\rResponsibilities\rCollaborated with top scientists to investigate use of prism adaptations on individuals with hidden disabilities related to stroke. Interpreted and analyzed data utilizing SPSS. Assisted researchers to conduct neuropsychological on research participants.\rAccomplishments\rTaught eighth grade science students about stroke including prevention and related impairments\rSkills Used\rInterpersonal skills when collaborating with researchers and patients SPSS\rNeuropsychological methods\rResearch Volunteer\rUniversity of Vermont - Burlington VT - 2013 to 2014\rAssisted in animal model experiments that involve understanding the neurobiological mechanisms of stress emotion and resilience.\rValet\rCountry Club Valet Services - Millburn NJ - 2012 to 2014 Provided services for many different types of clients\rLaborer\rGrant Homes Construction - Mendham NJ - 2011 to 2013\rIntern\rNorthern Lights Post Production Studio - New York NY - June 2010 to 2011\rInterned at a prominent postproduction film and television studio. Learned industry software programs and assisted in editing initiatives.\rCashier\rMendham Apothecary - October 2008 to May 2009\rEDUCATION\rBS in Psychology\rUniversity of Vermont - Burlington VT 2011 to 2015\r \rSKILLS\rContent manager for cognitiveworks.com (@cogntiveWx) SPSS Immunohistochemistry Data analysis CPR + AED certified Skiing Backpacking Film\rADDITIONAL INFORMATION References available upon request\r\"\r\nresume_34,flagged,\"Erik McCullen Senior Scientist/Engineer\rFairfax VT - Email me on Indeed: indeed.com/r/Erik-McCullen/33076d4b716d1b29 Authorized to work in the US for any employer\rWORK EXPERIENCE\rTEM Engineer\rGlobalfoundries - 2015 to Present\rTEM Engineer\rIBM - 2011 to 2015\rSenior Research Scientist\rWayne State University - 2001 to 2011\rEDUCATION\rPhD Physics\rTufts University\rBachelor's Physics\rUniversity of Michigan\rSKILLS\rFailure analysis (5 years) Electron microscopy (10+ years) Matlab (10+ years) Laboratory management (10+ years) Data Analysis (10+ years) Data Mining (8 years) Lean Manufacturing (2 years) material characterization (10+ years)\r \"\r\nresume_35,not_flagged,\"Eugene Fuller Product Engineering Technician\rJeffersonville VT - Email me on Indeed: indeed.com/r/Eugene-Fuller/34230398d3b57621\rWORK EXPERIENCE\rProduct Engineering Technician\rIBM - 1999 to 2013 1999-2013)\rDeveloped and tested solutions to solve technical problems in areas of research and development manufacturing sales construction inspection and maintenance. Assisted engineers inspected products conducted tests and collected data.\r_ Collaborated with engineers and scientists to create modify and test leading edge customized products _ Streamlined production process by facilitating meetings with design team and content matter experts to determine cost-effective corrective actions\r_ Contributed to research and development (R&D) during early product development; verified the first ASIC test sites in CU45 technology\r_ Ensured manufacturing schedule remained on track by collaborating with design team to create verification flow test design in lab and verify the design prior to production\r_ Prepared and conducted experiments and calculated recorded and reported all results _ Anticipated and diagnosed problems and performed scheduled maintenance procedures\rEUGENE FULLER * pawstrucking@yahoo.com\rIBM Product Engineering Technician\r_ Utilized computer-aided design and drafting equipment including GYM-IBM's internal CAD system for design verification purposes\r_ Assured ultimate product quality by monitoring and auditing line operators and addressing and troubleshooting production issues as they arose\r_ Developed expertise on ASIC products including Sony-Ulala Huawai-SD5 and SD5 product lines Cisco- Doppler Apple U3 Heavy and U4Kodiak chipset\r_ Reduced incidences of down time by monitoring available inventory and assuring adequate numbers of spare parts were available\r_ Expedited provision of customer prototypes by directly delivering to vendor and customer sites\rSenior VLSI Mask Designer\rIBM - 1999 to 2013\rForecasted and maintained a reliable schedule in the creation of complex technical designs; successfully met or exceeded each products established turn-around-time (TAT)\r_ Collaborated with Circuit Team for optimal planning and construction of proposed design\r_ Verified design plans against physical and electrical checks and guidelines; work with design team to clarify guidelines and ensure solutions that will pass quality checks\r_ Championed improvements to analytical tools and overall processes to drive timely production IBM - Essex Junction VT - 1987 to 2013\rSenior Facilities Maintenance Technician\r \rIBM - 1991 to 1999\rMaintenance and installation of air liquid and gas related products. Monitor controls and activation processes for products including Johnson Controls Modicon PLC.\rManufacturing Operations (1987-1991)\rProvided maintenance support to systems both within and outside of the clean room. Developed expertise on Back End of the Line (BEOL) manufacturing and testing and wafer fabrication processes.\rEDUCATION\rEngineering\rVermont Technical College - Randolph VT 1996\rADDITIONAL INFORMATION\rTechnical Proficiencies:\rMicro Soft Office Unix Calibre Mentor Synopsis Hercules Files Systems (AFS DFS GSA)\r\"\r\nresume_36,not_flagged,\"Evan Hurley\rAssistant Professor of Chemistry - LINDSEY W ILSON COLLEGE\rBomoseen VT - Email me on Indeed: indeed.com/r/Evan-Hurley/51ea113ac3f85a2f\rWORK EXPERIENCE\rAssistant Professor of Chemistry\rLINDSEY W ILSON COLLEGE - Columbia KY - August 2013 to Present\rLINDSEY W ILSON COLLEGE - Columbia KY - 2014 to 2015\rIndependent research project\rJacob Giordano: Synthesis of multidimensional coordination polymers. Jacob is currently working on synthesizing pyridine-based ligands and using them to make novel coordination polymers with various metals.\rBrittany Dean\rLINDSEY W ILSON COLLEGE - Columbia KY - 2014 to 2015\rImproving the solubility of anti-epileptic drugs. Brittany is continuing the project started by\rWendy Price and studying how to develop new forms of anti-epileptic drugs to potentially improve solubility.\rLINDSEY W ILSON COLLEGE - Columbia KY - 2013 to 2014\rHonors program research project\rPaige M. Lewis: Increasing the solubility of Tamoxifen an anticancer drug. Paige completed her honors project and successfully presented her results while receiving one of the highest scores in the history of the college honors program.\rLINDSEY W ILSON COLLEGE Columbia KY USA 2013-2014\rHonors program research project\rWendy Price: Improving the solubility of anti-epileptic drugs. Wendy completed her honors project and successfully presented her results.\rGraduate Research Assistant\rKANSAS STATE UNIVERSITY - Manhattan KS - 2008 to 2013\rAdvisor: Christer Aakero_y\r Crystal engineering: co-crystal synthesis and screening of as-prepared small molecules with different hydrogen and halogen bond donor molecules.\r Organic synthesis: C-C bond forming cross-coupling reactions (Suzuki-Miyaura and Sonagashira) and small molecule synthesis.\r Synthesis of gold and silver nanoparticles for making superlattice assemblies and studying particle interaction and aggregation.\r Photochromic spiropyran molecules: studies on crystal growth and dynamic behavior influenced by light.\r Use of Spartan (molecular modeling computer program) to construct electrostatic potential maps on small molecules in order to predict probable binding sites.\r Single crystal growth of organic co-crystals held together via non-covalent interactions.\rSOLID STATE CHEMICAL INFORMATION (NOW PART OF ALBANY MOLECULAR RESEARCH INC.) West Lafayette IN\rSr. Research Specialist\r- 2007 to 2008\r \rSupervisor: Jason Hanko\r Intellectual property (IP) work with active pharmaceutical ingredients (API) molecules including polymorph salt and co-crystal screening to determine the most thermodynamically stable form.\r Single crystal growth studies of API molecules for structure elucidation.\r Analysis of thermodynamically stable forms of client API's including X-ray diffraction (powder and single crystal) thermal gravimetric analysis differential scanning calorimetry hot stage microscopy dynamic\r1 13\rvapor sorption analysis Raman spectroscopy NMR spectroscopy ( H and C) and infrared analysis.\r Wrote reports summarizing the research findings and participated on conference calls with clients.\r Promoted from research specialist to sr. research specialist after three month review.\rGraduate Research Assistant\rUNIVERSITY OF NEBRASKA - LINCOLN - Lincoln NE - 2004 to 2007\rAdvisor: Wonyoung Choe\r Crystal engineering of porphyrin-based materials for making potentially porous organic-based solids. Synthesis of various porphyrin ligands for interaction with metals.\r Single crystal growth of the materials for structure elucidation.\r Variable temperature x-ray analysis on single crystals to test for changes in lattice parameters.\rUndergraduate Research Assistant\rHOBART COLLEGE - Geneva NY - 2003 to 2004\rAdvisors: Martel Zeldin and Bradley Kraft\r Synthesis of polyhedral oligomeric silesquioxane (POSS) compounds with an organic moiety tethered to the POSS cage to form a potential catalytic complex.\r Synthesis of zirconocene complexes possessing di-schiff base ligands of 2-hydroxy-5- methylisopthaldehyde as potential catalysts for olefin epoxidation reactions.\rUNDERGRADUATE ST UDE NTRESEARCH\rEDUCATION\rPh.D. in Chemistry\rKANSAS STATE UNIVERSITY August 2013\rM.S. in Chemistry\rUNIVERSITY OF NEBRASKA 2007\rB.S. in Chemistry\r-\rManhattan KS\rLincoln NE\r-\rHOBART COLLEGE - Geneva NY 2004\rADDITIONAL INFORMATION\rResearch scientist with experience in organic synthesis pharmaceutical chemistry and nanoparticle synthesis. Practical expertise in project management laboratory research data collection and analysis.\r\"\r\nresume_37,not_flagged,\"Felicity Milton\rMechanical Engineer Sports Biomechanist Entrepreneur\rRutland VT - Email me on Indeed: indeed.com/r/Felicity-Milton/4e2e43f9270dc9c7\rI am determined to lay a foundation of excellence for a career at the cutting edge of biomechanical sports engineering and technical product development by taking every opportunity that is before me to enhance my professional people and life skills. My driving ambition is to strengthen the understanding between the need and the solution for athletic performance recovery and enjoyment at all levels of sporting activity. I aim to bridge the disciplines of entrepreneurial sports engineering with international distance running. Above all I want to achieve my potential as an excellent sports engineer communicator and leader at the heart of innovative sporting creation.\rWORK EXPERIENCE\rSport Scientist educator\rPowerade Sports Vision - National WV - February 2012 to Present\rRepresent Isotonic Sports Drink Powerade ION4 and Powerade Zero across various campaigns including the 2012 Goals Center Tour Great Swim series Bannatyne and Virgin Active Gyms.\r- Attending Goals Centers Virgin Active and Bannatyne gyms and Great Swim/Run Events - General set up of sampling sites at events\r- Undertaking sweat rate testing with gym users and athletes\r- Delivering advice on hydration techniques and key brand messages\r- Explaining the importance of hydration and its related techniques in a sporting context\rProject Manager\rBarn Solutions - Rutland VT - July 2010 to August 2010\rOutbuilding Development - Overall responsibility for the successful initiation planning design execution monitoring controlling and closure of the Building Project.\rRunning Footwear Performance Engineering & Product Development Intern\rAdidas - Herzogenrath - July 2008 to August 2008\rWork on assigned projects related to performance engineering\rAssist with biomechanical mechanical and sensory data collection and analysis Footwear Materials Development\rProduct Creation Technologies PCT\rRunning Footwear Development\rCommercialization and Production\rCosting\rDocument findings and recommendations\rMechanical Engineering Internship\rOve Arup & Partners Multinational Engineering company - Newcastle WA - June 2006 to July 2006\rSustainable development and energy efficiency.\rI worked on a feasibility study for the construction of a new detached house in a conservation area including reviewing the local utilities and issues affecting the current design renewable and low energy technologies (biomass heat pumps solar and PV) controls Part L and funding for renewables.\r \rEDUCATION\rMaster of Science in Entrepreneurship\rOklahoma State University 2010 to 2011\rMasters in General Engineering\rDurham University 2005 to 2009\rA-Levels & GCSE's in Maths Physics Design Technology Sports Science (+13 GCSE's)\rOakham School 1998 to 2005\rMaster of Science in Sports Biomechanics\rLoughborough University 2012\rAWARDS\rListed Below\rThe Creativity Innovation and Entrepreneurship (CIE) Scholarship 2010\rA distinguished initiative developed to recognize and engage the top students enrolled in the MBA Program at the Spears School of Business ... Selection criteria will include previous academic performance and achievements leadership experiences extra-curricular or community engagement activity and unique life accomplishments.\rEngineering Leadership Award 2007\rAwarded by the Royal Academy of Engineering for outstandingly able engineering undergraduates with marked leadership potential to undertake an accelerated personal development program.\rYoung Engineers of Britain National Award Winner 2005\rMy design for a tuned percussion instrument for special needs children won the U18 category and the Special Needs category in the national Finals for the Young Engineer for Britain awards 2005.\rIMechE undergraduate Scholarship to study Engineering at Durham 2005 Officer Selection for Royal Military Academy Sandhurst\rSponsored by the Royal Engineers for a short service commission\rIKB National Award Winner 2006 Innovation award in Design\rBlueprint Business Plan Competition Finalist 2006\rFor feasibility study and business plan to market my innovative design\rBUSA X Country Gold (British Universities national winner)\rBUSA Track 5000m Gold\rAthletics and Cross Country Womens GB representation\rSenior World Cross-Country Championships in Mombassa 2007\rSenior European Cross-Country Championships in San Giorgio Legnano Italy on December 10th 06 Team Silver.\rU23 European Cross-Country Championships in Torro Spain Italy on December 10th 07 individual 5th Team Gold.\rWorld University Cross-Country Championships 2008\rUniversity of Durham:\r2007 Sportswoman of the Year\rFull Palatinate\rTeam Durham Life Membership\rAthletic Scholarship Oklahoma State University 2010-2012 All American: Indoor Track 2010\rCrest Gold Award\rDesigning and making a prototype for a water recycling and waste reduction system for Masterfoods.\rOld Oakhamian Cup 2005\rFor contribution to sport at Oakham including representing the School in the 1st Hockey Netball Tennis Athletics and Swimming. National girls hockey and netball finalist. 800 meter record holder.\rDesign Scholarship Oakham School 1998\rLAMDA Bronze Medallion & grade 8: Acting and spoken English.\rADDITIONAL INFORMATION Study and research interests\rMASTERS PROJECT [...] RUNNING SIMULATOR\rThis report presented a detailed design of a Running Simulator that aimed to precisely mimic forces and motions of the foot and ankle collected over the running cycle and critically analyzed. A dynamic model has been designed to track the desired torque about the ankle and regulate the force-pressure behavior inside a pair of pneumatic muscle-tendon type actuators enabling the mean pressure (stiffness) of the pneumatic actuators to be inflated slowly but deflated quickly mimicking the energy return of biological muscles and tendons. A robot module and linearly actuated knee pivot have been designed to ensure the cyclic mass (energy) transfer from heel strike to toe off.\rApplications of the Running Simulator: Will increase the realism of footwear tests at an economic cost while maintaining a high repeatability. It will enable manufacturers to design shoes or prosthetic feet to reduce stresses inflicted on the runner by simulating the performance of the shoe or foot over its life cycle in a factory environment.\rFuture work: The next step is to build a working model to validate the entire system design. Once validated the presented Computer Aided Design can be manipulated in a simulation package and controlled by the dynamic model in a pure computational simulation. This reduces the time and costs of making speculative shoe or prosthetic foot prototypes and testing them on several Running Simulators.\rMASTERS OF ENTREPRENEURSHIP [...]\r1POINT6 LLC FAR INFRARED COMPRESSION SOCK PATENTED\rA Far Infrared emitted compression sock for localized seamless heating and cooling of muscles for recovery rehabilitation and preparation\rTo the end user: On the spot wearable muscular Recovery Rehabilitation & Preparation\rOriginality: Seamlessly integrates three effective therapeutic modalities (heating cooling and compression) into a wearable portable and functional sock at an economic price.\r\"\r\nresume_38,not_flagged,\"Finley Losch\rBurlington VT - Email me on Indeed: indeed.com/r/Finley-Losch/3294654ed9564e36\r Excellent ability to adapt to new situations technology assignments with ease and minimal supervision Created problem-specific databases to streamline paperwork and regulatory reports\r Outstanding organizational multitasking and communication skills\r Fantastic sense of humor with an upbeat outlook while remaining both professional and personable\rWORK EXPERIENCE\rGRADUATE TEACHING ASSISTANT\rJohnson State College - Johnson VT - January 2015 to May 2016\rCo-facilitate undergraduate courses; grading; liaison for student questions and issues; lesson plan creation\rSOFTWARE IMPLEMENTATION SPECIALIST\rVermont Information Processing Inc. (VIP) - Colchester VT - January 2014 to December 2014\rTravel around US training and installing database/inventory software for warehouses and beer companies; Tailor software to needs of client; Customer service; Troubleshooting during live software transition\rENVIRONMENTAL SCIENTIST\rECS INC - Waterbury VT - 2011 to 2013\rWork closely with a wide variety of clients and personalities with the ability to defuse tense situations.\r Conduct on-site inspections in various locations including residential university commercial and rental properties\r Collect environmental samples of building materials from vacant and inhabited properties with high accuracy and turnover - allowing for quick response for client\r Complete large scale projects consistently under budget and on time while following State and Federal regulations while working in a team or alone and frequently self-directed\r Summarize projects in reports detailing construction materials and regulatory requirements\rHAZARDOUS MATERIALS SPECIALIST\rUNIVERSITY OF VERMONT - Burlington VT - 2010 to 2011\rMonitored environmental projects in buildings throughout the University and hospital campus\ridentifying regulatory issues and addressing community concerns\r Collect environmental samples and communicating laboratory results to interested parties in an understandable manner\r Created and maintained an extensive database containing hazardous material tracking information.\r Investigated staff and student complaints regarding building safety and health issues and provided\rfollow up monitoring\rENVIRONMENTAL HEALTH TECHNICIAN\rHISTOTECHNICIAN - Burlington VT - 2007 to 2010\rExcelled at on the job training for medical technician position mastering all tasks one year earlier than average OJT employees\r Performed histochemical and immunohistochemical stains on a variety of tissues to assist in diagnosis Maintained accurate records for quality assurance control\r Assisted with teaching and implementation of new procedures\r \r Organized handling of hazardous materials and hazard communications Assisted with database management and data entry\rINDUSTRIAL HYGIENE INTERN\rRANSOM ENVIRONMENTAL - Portland ME - 2006 to 2007\rPerformed airborne exposure monitoring sound level and mold surveys using appropriate sampling protocols techniques and analytical standards\r Calibrated industrial hygiene equipment\r Created database to log all client information\rEDUCATION\rMaster's in Clinical Mental Health\rJohnson State College - Johnson VT 2015 to 2017\rB.S. in Environmental Safety and Health\rUniversity of Southern Maine - Gorham ME May 2007\r\"\r\nresume_39,flagged,\"Geoffrey Abbott Software / Research Engineer\rBurlington VT - Email me on Indeed: indeed.com/r/Geoffrey-Abbott/4a599d43a820fe6d\rI bring over 15 years of industry experience as a Software Engineer. I have worked for small startups as well as Fortune 500 companies.\rWORK EXPERIENCE\rAdjunct Faculty\rChamplain College - Burlington ON - January 2016 to Present Teaching Introduction to Programming and Advanced Programming in C++.\rPartner\rCassisCorp LLC - Burlington ON - May 2015 to Present\rCassisCorp provides a range of contract services including Software Development Analytics Consulting Research & Development Advertising and Business Consulting.\rSoftware Development Manager / Architect of Measurement\rMyWebGrocer - Winooski VT - August 2014 to May 2015\rManagement of a team of developers in the Rapid Application Development group. Also serving as the Architect of Measurement for the internal Analytics Guild providing measurement and analysis solutions for all customer properties.\rChief Scientist\rAppZorz Inc - San Diego CA - March 2012 to August 2014\rResponsibilities included software development and research into the technologies and methodologies used for all core projects. Technologies utilized range from mobile phone APIs web servers databases programming languages third party software packages and more.\rLead Research Engineer\rNtrepid Corporation - San Diego CA - December 2010 to February 2012\rResponsible for helping to steer the technical direction of the company and leading research efforts on various security related projects for both government and enterprise clients. Projects involved integrating several types of secure communication systems into large-scale deployments of software and infrastructure. Provided custom software development for a variety of platforms including mobile devices. Additional responsibilities included strategic planning and development of future product offerings to keep the company competitive going forward.\rResearch Engineer\rAnonymizer Inc - San Diego CA - November 2009 to December 2010\rWorked as a researcher on various security related projects. Designed and developed the Nevercookie Firefox extension to protect against web tracking exploits utilizing Flash LSO browser caching exploits etc. Developed a suite of tools for various client projects involving establishment monitoring and analysis of secure communications. Directed several research projects involving the analysis of security vulnerabilities in deployed systems.\r \rSoftware Engineer\rIBM - Williston VT - June 2005 to November 2009\rWas responsible for architecture coding and maintenance of fully integrated process tracking tool using Ruby on Rails AJAX and DB2. Developed suite of density data analysis tools for Windows and AIX allowing engineers to optimize transistor distribution on IBM chips significantly improving heat efficiency and speed. Designed and developed numerous other software systems in support of the IBM semiconductor development process.\rEDUCATION\rBS in Mathematics\rUniversity of Vermont - 2004 to 2008\rSKILLS\rBurlington ON\rSoftware Development (10+ years) Programming (10+ years) C++ (10+ years) Agile (7 years) Ruby On Rails (7 years) Linux (10+ years)\rLINKS https://www.linkedin.com/in/geoffreyabbott\rPATENTS\rRegional pattern density determination method and system (#7703053)\rhttp://www.google.com.gt/patents/US7703053\rApril 2010\rA method and system of determining a localized measure of regional pattern density in a fabrication process of a chip are disclosed. In one embodiment the method includes determining pattern density values for each cell of a plurality of cells of interest; averaging the pattern density values for each cell within a first selected region about a target cell to determine the localized measure of regional pattern density for the target cell; storing the localized measure of regional pattern density for the target cell; and repeating the averaging and the storing for each of the plurality of cells. The simplification of data allows for a localized measure of regional pattern density determination in much less time than conventional techniques.\r \"\r\nresume_40,flagged,\"George Gallant Staff Engineer / Scientist\rUnderhill VT - Email me on Indeed: indeed.com/r/George-Gallant/0f47f41a6e3c1f79\r1) Results driven and enthusiastic Engineer with demonstrated strength guiding software and hardware teams through diverse projects including:\r- circuit modeling and simulation software\r- artificial intelligence\r- web page development\r- desktop support\r- data analysis and reporting and\r- improving processes.\r2) Recognized as highly-effective teacher and mentor and established reputation for being reliable resource for creating debugging and managing software applications on UNIX/Linux and Windows systems improving customer satisfaction.\r3) Proven results facilitating enablement initiatives efficiency activities and utilizing tools managing scheduling and prioritizing projects through development build and testing improving quality\r4) Experienced on AIX/UNIX Linux and MS Windows hardware and software platforms\r- Programming languages include: Perl C C++ SAS and shell script languages.\r- Reputation as a resource for support education and diagnosis of software problems.\r- Windows applications: MS Office Excel Word Visio and PowerPoint experience for engineering presentations to management and peers to educate or provide status of ongoing projects. Recently completed courses on Network+ and Installing and Configuring Microsoft Server 2012.\r5) Recent work supported the Semiconductor Development and Research Community at IBM.\r- Integrated and developed software used by Integrated Chip circuit designers.\r- Problem solver who interfaced with clients software support teams and software vendors.\r- Used Rational Team Concert to manage work tasks and schedules.\r6) A web server administrator: created or edited html-based web pages on secure web servers.\r7) Formal education in Electronics and Computer programming and applications.\rWORK EXPERIENCE\rStaff Engineer / Scientist\rIBM - Essex Junction VT - 2006 to 2014\rSoftware lead collaborating with company teams and clients developing architecture designing testing and delivering solutions involving modeling and simulation software for leading edge technologies.\r Supported emerging features in new technologies by installing software applications used on UNIX / Linux platforms ensuring timely completion.\r Simplified maintenance and inclusion of new features by merging disparate software applications from 2 separate design teams into 1 application.\r Enhanced client satisfaction by maintaining websites and web server which were available 24x7 delivering critical documents to worldwide clients.\r Prevented data loss by managing data storage and allocation shared by organization ensuring adequate storage capabilities.\r Improved new employee onboarding by mentoring and training on systems and applications allowing faster adapting to new email systems ensuring higher productivity in engineering environment within first month.\r \r Enriched quality by designing and implementing software version control application ensuring improved collaboration within software development teams.\rEngineer / Scientist\rIBM - 2002 to 2006\rSupported imperatives efficiency activities and structured problem solving involving software release and providing models for various design communities.\r Delivered on time software models on UNIX / Linux based systems for simulation applications.\r Enabled rapid use of latest UNIX / Linux applications for development team with timely installs.\r Administered web sites and server by providing secured confidential data to worldwide clients.\r Ensured adequate capacity by managing Data Storage and allocation shared by organization.\r Mentored new employees enabling efficient adaption to culture and applications vital to productivity.\rSenior Specialist\rIBM - 1993 to 2002\rCreator and primary support for menu-driven data analysis system for analyzing data from manufacturing line. Provided office support for computer systems.\r Achieved improvements to manufacturing quality by developing software enabling analysis of data collected characterizing trends and statistics with menu-driven application.\r Improved response to computer problems by providing PC support which included software and hardware planning for engineering organization.\r Surpassed commitments by delivering software supporting modeling and simulation applications. Previously Held Positions:\rArtificial Intelligence and Software Applications\r Researched and applied emerging Artificial Intelligence technologies to engineering and manufacturing situations particularly with respect to Knowledge Based and Expert Systems. Creator and primary support for menu-driven data analysis system used to analyze data from a manufacturing line.\r Published several (AI) technical papers in both internal company and external publications and conferences. Designed built and demonstrated feasible applications capturing expert knowledge.\r Saved time by implementing data analysis improvements with user interface.\r Reduced information loss by creating data capture application tracking experiments.\rEDUCATION\rMicrosoft Products MS Server and Network support\rKnowledgeWave - South Burlington VT 2014\rComputer Science\rSt. Michael's College - Colchester VT\rAAS in Electronics\rEastern Maine Community College - Bangor ME\rSKILLS\rNetwork + course recently completed. Installing and configuring MS Server 2010.\rLINKS http://www.linkedin.com/in/georgegallant\rADDITIONAL INFORMATION www.linkedin.com/in/georgegallant/\r \"\r\nresume_41,not_flagged,\"George Slusser Advisory Engineer\rMilton VT - Email me on Indeed: indeed.com/r/George-Slusser/8a0955fc924a4020\rTo obtain an exciting and challenging laboratory position that focuses on both analytical and research excellence.\rWORK EXPERIENCE\rAdvisory Engineer\rIBM - Essex Junction VT - July 2010 to May 2013\rResponsible for operating three surface analysis tools and using the data from these tools to solve manufacturing line problems. This process required interfacing with line engineers and technicians interpretation of data documentation of results and presentations to engineers and line management.\rTeacher\rMiddle School - Burlington VT - August 2009 to June 2010\rTaught math and science to grades 67 and 8. Taught technology to grades 1to 8.\rScience Teacher\rMilton High School - Milton VT - November 2008 to June 2009\rTaught conceptual physics to grade 9 students. Taught Earth Science and Human Anatomy to grade 11and 12 students.\rSenior Engineer/ Manager\rIBM - Essex Junction VT - October 1988 to July 2008\rManaged a team of chemists engineers chemical technicians and build to print technicians responsible for building specifications for 150+ chemicals gases and raw materials analyzing incoming chemicals gases and raw materials to those specifications and solving manufacturing line problems. This process became a key part of IBM's ISO9001 certification. I was responsible for hiring firing appraising and promoting lab personnel.\rChemist/ Engineer\rIBM - Essex Junction VT - November 1978 to October 1988\rResponsible for interfacing with customers analyzing data and reporting results to engineers as well as management. Tools utilized included surface analysis tools such as Secondary Ion Mass Spectrometry (SIMS) Scanning Tunneling Microscopu (STM) and X-ray Photoelectron Spectroscopy (XPS) and inorganic chemistry tools such as ion chromatographs autotitrimeters and UV visible spectrometers.\rEDUCATION\rMS in education\rSt. Michael's College - Colchester VT 2004 to 2014\rPh.D in Analytical Chemistry\rPurdue University - West Lafayette IN 1973 to 1979\r \rBachelor of Science in Chemistry\rKing's College - Wilkes-Barre PA 1969 to 1973\rADDITIONAL INFORMATION\rHighly motivated creative and flexible laboratory scientist with over 20 years experience with both analytical chemistry and surface chemistry equipment. Excellent computer skills with various types of software including Microsoft Word Excel and PowerPoint Oracle SQL Lotus Notes IBM dB2 and HTML. Ten years experience as manager of Chemistry Laboratory.\rADDITIONAL QUALIFICATIONS\rDesigned a Laboratory Information Management System (LIMS) for use in a laboratory environment. Interfaced with programmers to assure a user friendly system.\rTeach basic computers basic Internet and basic Microsoft Word to adults at Milton Public Library two times per month.\rMentor and key interface with Milton 5/6 grade science class of 88 students and 22 mentors\r\"\r\nresume_42,not_flagged,\"Geovanny Rodriguez Automation Engineer\rAlburgh VT - Email me on Indeed: indeed.com/r/Geovanny-Rodriguez/9b657616d919e365\rExceptionally talented Electrical / Software Engineer specializing in hardware testing programming engineering support and automation. Effectively created and implemented software tools to collect and analyze data as well as reduce turn around time. Proven success enhancing existing test systems capabilities through design and interface of external electronic hardware. Proficient in sourcing and acquiring test equipment through development of vendor relationships. Fluent in speaking and reading Spanish.\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rAutomation Engineer\rFujitsu FrontTech - Plattsburgh NY - 2014 to 2015\rAutomation of processes through software tool creation. Expanding capabilities of current software code. Maintenance of production database. Data extraction and reporting. Hardware system support (maintenance installation). Production support through hardware repair and build.\r Extract data (serial numbers part numbers production dates and the likes) for sales reporting.\r Created and implement data tracker tool which collected time stamp data used for production process time studies.\r Modified existing code to expand label changes. This automated label choice and met the new ISO standards based of product delivery zone.\r Automated software to extract mac address data and automatically included it in the customer report. This eliminated hand written report error and saved report creation time.\r Reorganized and mapped database deployment. Saved money by eliminating redundant external (undocumented) database and fix access permissions.\r Worked in the creation of point of sale work bench joining three separate production areas into one continuous lane. Saved floor space and streamlined the production process.\r Installed and relocated floor assets to streamline production and increase output time.\rGeovanny Rodriguez geovannyjobs@yahoo.com\rEngineer / Scientist Retrainee\rIBM - 2008 to 2013\rConduct testing of power PC chips to find root cause failures. Report failures and make fix recommendations. Test modules complete tool calibration and bring up equipment repair and / or modifications programming of tools to conduct tests and expedite results.\r Save time and ensure proper functioning by calibrating equipment including tester tools measuring devices and temperature controllers.\r Reduce down time by ensuring equipment ready for work before parts arrive by completing tester bring-up process and debugging.\r Identify root cause of failures by conducting failure analysis testing isolating and documenting problems in collaboration with designers.\r Reduce test report turn-around time by creating software tools (GUIs scripts etc.) simplifying and automating pattern creation and results analysis.\r \r Saved money by adapting current assets to work with new technology supporting project testing requirements by programming complex tools and software or interfacing hardware.\r Incremented test capabilities by enabling other departments (manufacturing) to do test and failure analysis. Serve as consultant to project engineering staff and manufacturing to set up testing equipment enabling engineers to conduct tests and experiments and thus enhancing site test capabilities.\r Acquire sophisticated test equipment through established external vendors and comprehensive web searches meeting project specifications budget and schedule constraints.\r Ensure specifications met by analyzing boards and consulting with designers to modify and / or correct specifications prior to 2nd review saving money and improving test capabilities enhancing capabilities to meet future test requirements.\r Research suitability of service contacts for test equipment submit requests for quotes from new vendors acquire approvals and obtain purchase orders. Maintain documentation for future auditing purposes.\rLab Technician Specialist\rIBM - 2003 to 2008\rSet up tools to run test modules created test programs and conducted high volume of reliability testing. Analyzed test results reported findings to management and supervised colleagues conducting testing.\rTech Lab Specialist Stress Operations\rIBM - 2001 to 2003\rConducted product testing and repaired boards and tools in lab setting including qualification reliability.\rEDUCATION\rBS in Electrical Engineering\rUniversity of Vermont - Burlington VT 2010\rBS in Engineering Technology\rInter-American University - BayamoՁn PR 2000\rLINKS http://www.linkedin.com/in/geovannyrodriguez\rADDITIONAL INFORMATION TECHNICAL SKILLS\rSoftware PHP MySQL Javascript HTML C C++ Java Perl Python and ActionScript\rOperating Systems Windows Linux\rHardware\rPrinted circuit board design and testing debugging and repair; calibration testing soldering and repair of test equipment and computers\r \"\r\nresume_43,flagged,\"Guang Zeng\rResearch Scientist/Imaging Specialist - MBF Bioscience Inc\rWilliston VT - Email me on Indeed: indeed.com/r/Guang-Zeng/0890876cc481ccaa\rSeeking a challenging position in the area of image processing computer vision and medical image analysis\rCurrent Job Position\r Company: MBF Bioscience Inc.\r Title: Research Scientist/Imaging Specialist [...] - Present)\r Responsibilities: Developing algorithms for stereology neuron morphology brain mapping and quantitative analysis microscopy image analysis virtual slice montage. Designing software solutions for biomedical research.\rPrevious Job Position\r Company: Mayo Clinic.\r Title: Biomedical Imaging Software Program Developer [...] - [...]\r Responsibilities: Automate and integrate various neuroimaging algorithms into Mayo's workflows. Design and implement efficient algorithms for MRI image analysis.\r Company: Nanyou Engineering Design Co.Ltd China\r Title: Assistant Electrical Engineer [...]\r Responsibilities: Design power supply and distribution systems for civil buildings. Design fire alarm and CCTV systems for civil buildings.\rWORK EXPERIENCE\rResearch Scientist/Imaging Specialist\rMBF Bioscience Inc - Williston VT - July 2010 to Present\rThis work is focused on developing algorithms for stereology neuron morphology brain mapping and quantitative analysis microscopy image analysis virtual slice montage designing software solutions for biomedical research.\rImage Stitching\rDeveloped the Montage module for MBF software Neurolucida\r Developed multi-scale KLT feature points detection and matching algorithms Developed fast multi-image composition method based on Dijkstra' algorithm Studied and implemented phase correlation method for image stitching\rNeuron Tracing\rDeveloped algorithm for neuron segmentation and tracing in Brainbow images\r Developed algorithm for automatic neuron segmentation based on HSV color histogram Implemented the Livewire algorithm for semi-automatic neuron tracing\r3D Cells Labeling\rDeveloped algorithms for 2D and 3D cell segmentation from various types of microscopy images Implemented Watershed algorithm for 2D cell detection\r \r Developed multi-scale LoG filtering method for detecting cells with different size in various types of 2D microscopy images.\r Designed robust cell classification method based on SVM\r Integrated the 2D cell detection and classification methods for 3D cell labeling.\rImage Registration\rDesign algorithms for image registration between stained histological virtual slices and light microscopy images\r Developed automatic brain contour detection method based on Otsu's method\r Implemented Iterative Closes Points (ICP) method for contour matching\r Implemented mutual information method for image registration\r Integrated ICP and Thin-Plate Spline (TPS) for image registration\rAll the above development work is in C++.\rBiomedical Imaging Software Program Developer\rThe Aging and Dementia Imaging Research Laboratory Mayo Clinic 2010\r- Rochester MN - July 2009 to July\rThis work focuses on automating and integrating various neuroimaging algorithms into larger workflows. Designing implementing and documenting efficient algorithms and validation experiments for scientific data analysis.\r Integrate FreeSurfer pipeline into Mayo's workflows for automatic reconstruction of brain's cortical surface from structural MRI data\r Integrate REST toolkit into Mayo's workflows for RESTing fMRI data analysis\r Develop active contour-based algorithm for automatic white matter hyperintensities segmentation in MRI images.\rAll the above development work is in MATLAB.\rImaging Research Intern\rEigen Corporation - Grass Valley CA - May 2008 to August 2008\rParticipated in QCA project; this project is to develop a computer aided diagnosis system for stenosis detection with X-ray Digital Subtraction Angiography (DSA).\r Developed an automatic computer aided diagnosis algorithm for stenosis detection and catheter measurement in DSA images based on linear feature detection and linear discriminate analysis.\r Designed and built software GUI framework for the automatic stenosis estimation in DSA\rResearch Assistant\rDepartment of Electrical and Computer Engineering Clemson University - January 2004 to May 2008\rAutomatic linear feature analysis\rConducted research in Automatic Linear Feature Analysis this work is focused on developing a fast and automatic algorithm for detecting linear structures such as plant root retinal vessel palmprint 2D barcode and urban road in imagery.\r Developed a method for linear feature detection in images based on matched filtering and local entropy thresholding.\r Developed a strong classifier for discriminating detected linear features from light-colored background objects using linear discriminate analysis (LDA) perceptron learning and Adaboost algorithm.\r Developed a marked Gibbs point process algorithm for fast and automatic linear feature detection.\rVehicle Detection & Tracking\rThis work is focused on developing a fast and automatic algorithm for automatic detecting and tracking vehicles.\r Integrated SIFT and Haar-like feature detector for vehicle detection\r Applied Kalman filtering for tracking SIFT features\rBiometric Analysis\r Developed a palm print detector based on linear feature detection\r Implemented Haar-like feature detector for face detection\r Implemented a face and skin detection method using HSV color segmentation Implemented a Eigenface-based facial recognition method\rResearch Intern\rRadiology Support Unit Mayo Clinic - Rochester MN - May 2007 to August 2007\rParticipated in IMT project; the goal is to automate the ultrasound measurements of intima-media thickness (IMT) of carotid arteries for diagnosing cardiovascular diseases.\r Developed an algorithm to distinguish longitudinal clips from transverse clips based on edge detection\r Developed CALEX algorithm for automatic IMT measurements\rAssistant Electrical Engineer\rNanyou Engineering Design Co.Ltd - ShenZhen CN - August 1998 to August 2002\rShenZhen China\r Design power supply and distribution systems for civil buildings. Design fire alarm and CCTV systems for civil buildings.\rEDUCATION\rPh.D. in Electrical Engineering\rClemson University - Clemson SC December 2008\rM.S. in Electrical Engineering\rClemson University - Clemson SC May 2005\rB.S. in Industrial Automation\rXiangtan University - Xiangtan CN August 1998\rADDITIONAL INFORMATION\rExpertise\r Object Segmentation\ro Object Segmentation based on Matched filtering Watershed Gabor filtering GMM o Active contour-based segmentation\ro HSV color segmentation\r Object Tracking\ro Point tracking based on Kalman filter\ro Kernel tracking based on KLT SIFT and Meanshift\r Pattern Recognition and machine learning\ro Linear feature recognition\ro Biometric Analysis\ro Computer aided diagnostic\ro Supervised and unsupervised classification clustering Image Registration\ro Intensity-based registration\ro Feature-based registration\ro Affirm transformation and Spline-based transformation Image Stitching\ro Phase correlation based method\ro Feature based method\r Software development object-oriented programming\rComputer Skills\r Programming Language: C/C++ Matlab Simulink Maple Prolog Soar. Image Libraries: OpenCV IPL OpenGL ITK VTK CImg Blepo.\r Brain Image Analysis Tool: FreeSurfer SPM FSL\r Operating Systems: GNU/Linux Unix Windows.\r\"\r\nresume_44,not_flagged,\"Guy Wilson\rGeosciences and Environmental Liability Management Consultant\rBradford VT - Email me on Indeed: indeed.com/r/Guy-Wilson/0083a3bd1f4c8117\rI seek a professional role that draws upon the versatile combination of experience skills and talents I bring to the mission of a dynamic and socially responsible organization with substantial involvement in environmental management natural resources use and conservation and/or health promotion.\rAttributes: I am motivated by a strong disposition toward responsive and high quality support services and am an effective and diligent consultant and advocate for the interests of the clients and organizations that I serve. I have operated successfully in a variety of contexts through timely and appropriately client-focused services technical competence effective interpersonal skills concise and discrete documentation and reporting a collaborative approach and professional integrity.\rSummary of Knowledge Skills and Abilities: I have served clients in a variety of arenas including oil & gas production and natural gas transmission environmental and natural resource consulting and liability management and (most recently) as a critical care RN at a large Level 1 trauma center and academic teaching hospital. I have cultivated excellent interpersonal skills and communicate positively in writing via presentations and in negotiations under varied and often sensitive and stressful circumstances. . Through academic and experiential learning I have developed versatile knowledge and skills including:\r Ability to initiate and direct focused research rapidly assimilate information relevant to context and apply information pursuant to strategic objectives;\r Providing liability management and litigation support services in complex matters including expert testimony through reports depositions and courtroom trials;\r Effective project management and cost control under adverse and fluid circumstances;\r Context-appropriate data collection and analysis discrete documentation reporting and presentation;\r Prioritizing and maintaining appropriate attention to relevant details while working under pressure;\r Relating effectively to clients and colleagues in process so as to assess and appropriately respond to varying needs under fluid and stressful circumstances.\r Fostering effective collaboration among multi-disciplinary team members and optimizing performance pursuant to achieving both short-term and strategic objectives;\r Client development and client relations and oversight of other consulting support agencies;\r Mentoring and facilitating skill development in less experienced colleagues; and\r Skillful and concise written and verbal communication reporting and presentation.\rWilling to relocate to: Winchester VA - Staunton VA - Harrisonburg VA\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rCritcal Care RN\rDartmouth-Hitchcock Medical Center - Lebanon NH - 2006 to Present\rAdult Intensive Care - direct patient care patient/family advocate in a multidisciplinary critical care setting (trauma neurological cardio-pulmonary medical and surgical) - ACLS experienced in CRRT and other specialties early progressive mobility initiation project leader.\rInterventional Radiology - Sedation and procedure support RN - inpatient and outpatient\r \rGastroenterology and Hepatology - Outpatient clinic triage RN (per diem)\rStaff RN (per diem)\rCentral Vermont Medical Center - Berlin NH - April 2011 to August 2013\rRN Staff in Adult Progressive and Intensive Care Unit. Provided direct patient care services in a multidisciplinary acute care setting (cardio-pulmonary medical/surgical psychiatric care patient populations).\rEnvironmental Manager\rBurgess & Niple Ltd. - Akron OH - 2001 to 2004\rResponsibilities\r Provided consulting support services to private and publicly held organizations in various matters including environmental assessment and remediation environmental liability and risk assessment regulatory compliance natural resource assessment and management environmental cost analysis for acquisitions and divestitures of commercial and industrial assets and environmental cost recovery litigation.\r Served a broad cross-section of clients including international and national corporations small and medium- sized business utility and energy services companies oil and gas exploration and production interests law firms banking and venture capital interests governmental agencies and private citizens.\r Operated effectively in multiple roles including multi-disciplinary team assembly and management technical guidance client development and communications public information and staff development.\r Provided litigation support and expert witness services in a variety of civil matters was often charged with independent reviews and oversight of other consultants and had in-depth participation in developing legal- technical strategies in connection with environmental damage and cost recovery claims.\r Designed and directed geologic and hydrogeologic investigations risk-based assessment and remediation projects and liability analysis toward various objectives (e.g. industrial sites cleanup brownfields redevelopment environmental cost allocation in acquisitions and divestitures soil and groundwater cleanup solid and hazardous waste disposal and facilities siting and cost recovery litigation).\r Prepared and delivered presentations to clients and other professionals at closed meetings at educational meetings for various professional services organizations and at public hearings.\rEnvironmental Liability Manager\rEarth Sciences Consultants Inc. - Akron OH - 1989 to 2001\rResponsibilities\r Provided consulting support services to private and publicly held organizations in various matters including environmental assessment and remediation environmental liability and risk assessment regulatory compliance natural resource assessment and management environmental cost analysis for acquisitions and divestitures of commercial and industrial assets and environmental cost recovery litigation.\r Served a broad cross-section of clients including international and national corporations small and medium- sized business utility and energy services companies oil and gas exploration and production interests law firms banking and venture capital interests governmental agencies and private citizens.\r Operated effectively in multiple roles including multi-disciplinary team assembly and management technical guidance client development and communications public information and staff development.\r Provided litigation support and expert witness services in a variety of civil matters was often charged with independent reviews and oversight of other consultants and had in-depth participation in developing legal- technical strategies in connection with environmental damage and cost recovery claims.\r Designed and directed geologic and hydrogeologic investigations risk-based assessment and remediation projects and liability analysis toward various objectives (e.g. industrial sites cleanup brownfields redevelopment environmental cost allocation in acquisitions and divestitures soil and groundwater cleanup solid and hazardous waste disposal and facilities siting and cost recovery litigation).\r Prepared and delivered presentations to clients and other professionals at closed meetings at educational meetings for various professional services organizations and at public hearings.\rSales and Customer Service Agent\rVermander-Jaslow Inc. - Canton OH - 1983 to 1989\rResponsibilities\rOil & Gas Production and Transmission Equipment - Marketing sales and service of oil and natural gas well completion and production equipment natural gas transmission equipment and upstream petroleum product storage and distribution equipment to oil and gas exploration and production interests and to natural gas transmission and distribution companies in Michigan New York Ohio Pennsylvania and West Virginia.\rEDUCATION\rMaster of Science in Geosciences\rUniversity of Louisiana at Monroe (formerly Northeast Louisiana University)\rBachelor of Arts in Geology\rThe College of Wooster - Wooster OH\rBachelor of Science in Nursing\rUniversity of Maine at Fort Kent - Fort Kent ME\rCERTIFICATIONS/LICENSES\rACLS BLS\rMay 2017\rNurse's License: Class: RN State: NH Expires: March 2016\rADDITIONAL INFORMATION\r- Monroe LA\rContinuing Education Certificates - Environmental and Natural Resources Management: National Brownfield Association Meetings and Seminars\r Ohio EPA Voluntary Action Program Certified Professional Training Seminars\r Environmental Law Institute Seminars Cleveland Bar Association\r In Situ Remediation/Reductive De-chlorination Seminar Regenesis Cleveland OH Environmental Compliance Health and Safety Training Orlando FL\r Risk Based Corrective Action Seminar ASTM Philadelphia PA\r Monitored Natural Attenuation Seminar Parsons Inc. and U.S. EPA\r Urban Properties Redevelopment Seminar RMI New York NY\r Groundwater Pollution and Hydrology The Princeton Course Princeton NJ\r HAZWOPER Health and Safety Training per OSHA 29 CFR [...] (40-hour course) HAZWOPER Health and Safety Annual Refresher Training per OSHA 29 CFR [...]\rContinuing Education Certificates - Healthcare: CRRT Advanced user\r BIRT Safe Patient Handling trainer\r ENA Trauma Nursing Core Course (TNCC)\r ENA Trauma Care After Resuscitation (TCAR)\r Gero-Palliative Care Fellowship (Agewise)\r AHA ACLS and BLS training and updates\r AACN Continuing Education Units - various healthcare topics (24-36 hours yearly)\rPast or Present Professional Affiliations and Community Organizations:\r American Association of Petroleum Geologists\r American Association of Petroleum Geologists - Division of Environmental Geosciences American Institute of Professional Geologists Certified Professional Geologist\r National Ground Water Association of Groundwater Scientists and Engineers\r Ohio Environmental Protection Agency Ohio Certified Professional\r College of Wooster/University of Sydney joint archaeological expedition to Pella Jordan American Association of Critical Care Nurses\r Commissioner Town of West Fairlee VT Conservation Commission\r Chairman Town of West Fairlee VT Planning Commission\r\"\r\nresume_45,not_flagged,\"Haleigh Marshall Staff - Heart of the Village Inn\rWilliston VT - Email me on Indeed: indeed.com/r/Haleigh-Marshall/5849302e5e2156e7\rI am interested in the long-term environmental impacts of natural resource extraction processes. I seek a professional role as an environmental scientist that focuses on mitigating these impacts. My undergraduate experience focused primarily on monitoring water quality of the Finger Lakes of Western NY through routine sampling and analytical procedures and how regional bedrock geology and industry impacts water quality.\rProfile\r Deliberative thinker who employs a thorough and conscientious approach to research\r Project leader who delivers scope and quality on time\r Productive team builder who develops strong one-on-one relationships with collaborative partners Strong field background in water quality assessment\rWORK EXPERIENCE\rStaff\rHeart of the Village Inn - June 2011 to Present\rnonconsecutive\rCreated a welcoming guest experience by greeting guests and ensuring their needs were met during their stay. Shared local tourism knowledge managed bookings and served as manager on duty for a small bed and breakfast.\rField Assistant\rHobart & William Smith Colleges - New York NY - April 2011 to May 2014\rAssisted Professor John Halfman in the study of water quality indicators in the Finger Lakes of Western New York with an emphasis on working independently. Performed analyses for Dissolved Oxygen concentration total and Calcium hardness and Chloride concentration as well as using a probe to record pH TDS and conductivity.\rResearch Assistant/ Teaching Assistant\rHobart & William Smith Colleges - January 2011 to May 2014\rAs a teaching assistant I facilitated activities in geoscience lectures and labs. I was in charge of ensuring that the chemical analyses performed by students followed proper protocols and were done using correct laboratory technique. I also assisted staff in the student academic services office by entering and analyzing data gathered from peer tutoring and study workshop programs and facilitated the production of the Colleges' annual senior showcase.\rField Experience\rGIS Internship\rHobart & William Smith Colleges - December 2012 to February 2013\rThis internship provided me with a basic and functional knowledge of Geographic Information Software (GIS) its applications and the wide range of questions it can be used to answer. Worked on multiple assignments and projects designed to further my working knowledge of the program while also contributing to a larger project a staff member was working on by researching compiling data and making an interference map.\r \rVT EPSCoR - May 2012 to August 2012\ron an agricultural field with previous heavy Phosphorous application analyzed soils by sifting and hydrograph analyzed soils groundwater and surface water for Phosphorous and Nitrogen content.\rEDUCATION\rBachelor of Science in Geoscience\rWilliam Smith College May 2014\rADDITIONAL INFORMATION\rSkills\rSurface Water Quality Analysis\rSoil Core Characterization Micro-piezometer installation\rBedrock Characterization and Mapping GIS Map Creation\rData Analysis MS Excel\rLab and Research Instructor\r\"\r\nresume_46,not_flagged,\"Heath Evola Project Consultant\rWaitsfield VT - Email me on Indeed: indeed.com/r/Heath-Evola/c3c93283bef6a223\rI am seeking a position that allows me to utilize my experience in application design and imaginative problem- solving skills. I have always been motivated in my work by the opportunity to make the world a better place through contributions ranging from big ideas to the granular detail of conscientious labor.\rWORK EXPERIENCE\rProject Consultant\rCreative Microsystems - Waitsfield VT - 2012 to 2012\r2012 Facilitated and researched equipment upgrades and maintenance and managed lab resources. Worked directly with lead scientist to organize and initiate new projects.\rDigital Project Manager\rChooseco - Waitsfield VT - 2009 to 2010\rDeveloped 33 electronic books in a new format for Kindle with a creative team. Responsible for QA and working directly with Amazon on interface programming and design. Additionally I researched and presented business development strategies to bring their world-famous children's brand into the gaming space.\rY-Not Chemical and Development Foundation\rStrategic Business Solutions - Baton Rouge LA - 2007 to 2007\rBaton Rouge LA\rQA and Software Dual Testing 2007 Developed software and created reports including a FEMA contract for the Federal Government and testing software packages prior to client installation.\rY-Not Chemical and Development Foundation Baton Rouge LA\rResearch Assistant - Department of Chemistry and Physics\rStudent Robotics Laboratory - Industrial Technology Department - 2005 to 2006\r2005-2006 Helped install and maintain new or donated robots and automated equipment including repair and replacement of lathes mills robots and automated conveyors. Research Assistant - Department of Chemistry and Physics 2005-2006 Developed a system of software development and hardware integration to automate chemical reactions that had previously been controlled manually using National Instruments hardware and software repaired Gas Chromatographs integrated new computing systems.\rIndependent Contractor\rSoutheastern Louisiana University - Hammond LA - 2005 to 2005\rEDUCATION\rMaster of Science in Computer Science and Chemistry\rSoutheastern Louisiana University - Hammond LA January 2005 to December 2007\rBachelor of Science in Computer Science\rSoutheastern Louisiana University - Hammond LA\r \rDecember 2004\rADDITIONAL INFORMATION\rSoftware & Skills\rLabVIEW C++ Docklight BalanceLink Quickset Pump Control Expert level Windows and Mac user and technical troubleshooter critical thinking\rEfficient Data entry and analysis\r\"\r\nresume_47,not_flagged,\"Howard Alford Laboratory Assistant Intern\rMc Indoe Falls VT - Email me on Indeed: indeed.com/r/Howard-Alford/53d75c52120ff1ea\rWORK EXPERIENCE\rLaboratory Assistant Intern\rEMSL Analytical - New York NY - April 2009 to April 2009\r* Learned and performed Chain of Custody Procedures\r* Observed performance of Atomic Absorbance Flame Spectrophotometer * Prepared wipes for lead analysis through acid and water digestion\r* Filtered samples\rSelf Employed - Orange NJ - January 2007 to August 2008\rHandyman\r* Prepared and submitted budget estimates progress and cost tracking reports\r* Scheduled projects in logical steps and budget time required to meet deadlines\r* Read and interpreted plans instructions and specifications to determine work activities\r* Measured marked and recorded openings and distances to layout areas where construction performed\r* Loaded and identified building materials machinery tools and distributed them to the appropriate locations according to project plans and specifications\r* Performed and oversaw the construction of homes\rVice President\rT & C Inc - June 2001 to January 2007\r* Oversaw activities directly related to building contracts\r* Directed and coordinated activities of business specifically production pricing sales and distribution of products\r* Reviewed financial statements sales activity reports and other performance data to measure productivity and goal achievement\r* Managed staff prepared work schedules and assigned specific duties\rLaboratory Technician\rAmerican Cyanamid - February 1997 to March 2001\r* Discarded and rejected products materials and equipment not meeting specifications\r* Inspected tested and measured materials and work for conformance to specifications\r* Recorded inspection and test data such as weights temperatures grades moisture content\r* Observed and monitored production operations and equipment to ensure conformance to specifications Pilot lab/ Junior.\r* Compounder: responsible for weighing and mixing raw materials soap lotions deodorants and perfumes.\rLaboratory Technician\rGarden State Labs - Irvington NJ - January 1983 to February 1997\r* Set up and conducted chemical experiments tests and analysis using techniques such as chromatography spectroscopy physical and chemical separation techniques and microscopy\r* Conducted chemical and physical laboratory tests to assist scientists in making qualitative and quantitative analysis of solids liquids and gaseous materials\r \r* Compiled and interpreted results of tests and analysis\r* Provided technical support and assistance to chemists and engineers * Maintained clean and sterilized laboratory equipment\r\"\r\nresume_48,not_flagged,\"Ileene Schmidt\rOwner sole proprietor Jeffersonville Pottery/ environmental researcher\rJeffersonville VT - Email me on Indeed: indeed.com/r/Ileene-Schmidt/2da206b69b87001d\rPosition working in physical sciences\rWilling to relocate to: Woodstock VT - Woodstock VT\rWORK EXPERIENCE\rSoul Propriator\rJeffersonville Pottery - Jeffersonville VT - April 2009 to Present\rMake a line of hand thrown High and low Fire stone ware or earthenware Pottery I research Vt clays and chemically manipulate the chemistry depending\ron whether it is used for a glaze tile making or hand thrown cylinders.\rPurchase store measure specific amounts of glaze chemicals to formulate different glazes to fuze in a kiln. I market and retail the products I manufacture.\rOwner sole proprietor\rIleene's Carpentry - Jeffersonville VT - 2002 to June 2010\rin top of the line refinishing of residential homes. Design consultation and remodeling high end energy efficient homes. All aspects of construction from sills to roofs and sofets.\rGeology Laboratory Technician Sorting\rJohnson State College - Johnson VT - March 2004 to May 2004\rand categorizing all specimens and maintaining a working rock laboratory.\rOperations Manager Chef\rAramark Company - Johnson VT - 2000 to 2002\rManaging operations within the Base Lodge section including: cash transactions and records food service employee supervision and scheduling and menu planning. Maintaining inventory and server operations in the cafeteria. Customer service and providing an optimal dining experience.\rOwner soul Propriator\rRunning Bear Pottery and Residential Restoration - South Royalton VT - 1995 to 2000\rSouth Royalton Vermont 1995 - 2000. Producing and marketing a line of hand-thrown pottery both artistic and functional. I served 15 accounts in addition to private orders. Developing and producing original glazes. Applying glazes and firing pottery ware. Creating and incorporating process engineering and recycling techniques as part of the manufacturing process. Advertising and filling orders and maintaining sales log accounts payable and receivable. Design consultation and remodeling high end energy efficient homes. All aspects of construction from sills to roofs and sofets.\rResidant Potter\rSimon Pierce Pottery and Glass - Quechee VT - 1993 to 1994\rCreating hand-thrown stock of pottery for commercial sale. Working as part of a team to demonstrate the pottery process for the public. Teaching children to make pottery. Loading and firing kilns. Adhering to OSHA regulations.\r \rCarpenter\rShepard Construction Company - Quechee VT - 1990 to 1993\rWorking with construction crew to complete frames and finish work of homes.\rStaff Scientist Receive\rUSGS Bureau of Mines - Douglas AK - 1978 to 1980\rlog and process rock samples for X- ray spec analysis. Determine geochemistry with a Kevex 2000 analyzer. Wet and dry chemistry procedures and gold panning\rEDUCATION\rBachelor of Science in Integrated Environmental Science\rJohnson State College - Johnson VT 2005\rEnvironmental Clay Mineralogy\rBachelor of University Studies Program University of New Mexico\rADDITIONAL INFORMATION QUALIFICATIONS:\r- Albuquerque NM\r_ Designing scientific experiments based on ASTM standards.\r_ Experience and agility in local State and Federal communications.\r_ Ability to recognize long-term trends or changes.\r_ Logging and analyzing scientific data and standard sampling methods (ASTM).\r_ Environmental and lab experience in rock and sediment analysis.\r_ Comprehensive business experience.\r_ GPS and map experience.\r_ Promoting quality products projects and team applications based on Environmental Conservation _ Macro and Micro Process Engineering and Analysis.\r\"\r\nresume_49,not_flagged,\"Jacob Miller Environmentalist\rLyndonville VT - Email me on Indeed: indeed.com/r/Jacob-Miller/7c6bcbe4e07b479d\rI'm an ambitious and young man in the field of science. My work is a compilation of varying work so as to have the broadest perspective of science as a whole. My long term goal is to compile enough time and work in the field so as to have a credible name in science. My work is in the hopes that my generation won't just be know as the technological generation rather a green generation coming to meet the needs of the world brought upon by the all the problems of the industrial revolution.\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rCook/Dishwasher/Delivery Driver\rKingdom Crust - Saint Johnsbury VT - August 2016 to Present\rKingdom Crust is a local pizza joint that is unique because they source all main ingredients from local farms. Job consists of:\r-Cooking pizza\r-Delivering\r-Dishwashing -Customer Service -20-30 hours per week\rLaborer/Equipment Operator\rBirds Eye View Farm - Lyndonville VT - September 2015 to Present\rWork for John Miller who has 20000 laying hens and is a contract farmer for Pete & Gerry's organic eggs. I operate the egg packer and have routine chores throughout the barn.\rCFI Intern\rState Department of Forrests Parks & Recreation - Saint Johnsbury VT - June 2016 to September 2016\rInternship through the State of Vermont. The job was surveying 40 random CFI plots through out Wiloughby State Forrest. Each plot would count the amount of trees in the plot in extreme precision and accuracy and then be entered into a data base as to track global image change and how it's affecting tree growth.\rJob consisted of:\r-Tree Identification -Hiking\r-GPS/compass navigation -Data Entry\r-Excel work\r-Plot set up\r-50 Hours per week\rLaborer/Operator\rGeorge Henry's Jerseys - Malone NY - June 2010 to September 2013 My first job and probably the most defining for my future job choices.\r \rJob consisted of:\r-Milking twice a day\r-Haying\r-Heavy machinery operation -Barn chores\r-35-45 hours per week\rThis was a summer job that I went back to for four summers.\rEDUCATION\rBachelor's in Natural Science/Envirmentalist\rLyndon State College - Lyndonville VT 2016 to 2017\rGED in Science\rSt. Johnsbury Academy - Saint Johnsbury VT 2012 to 2016\rSKILLS\rExcel (2 years) Data Entry (1 year) Milking (6 years) Driving (3 years) Public Speaking (1 year) Powerpoint (4 years) Cooking (1 year) Research (1 year)\rAWARDS\rScience Fair\rApril 2010\rAward winning science fair project competed in local region.\rPolevaulting\rFebruary 2016\r3rd place at track indoor states for 2016 Tied with 4th place for outdoor states 2016\rCERTIFICATIONS/LICENSES\rDriver's License\rJanuary 2016 to January 2018\rGROUPS\rIndoor/outdoor Track & Field\rNovember 2012 to June 2016\rPractice 6 days a weeks during the season.\r-pole vaulter\r-mid distance runner\rWorking with the team really gave me an idea about how the results you get are a direct result from time out in.\rPUBLICATIONS\rSupporting The Lamoille Valley Rail Trail\rhttp://static1.squarespace.com/static/557ac930e4b002c66e9c416d/ t/57a26ab5be6594bf8c83501a/1470261980964/2016_May_newsletter.pdf\rMay 2016\rThis article describes my final research project at St. Johnsbury Academy as tried to support the Rail Trail so that it would gain traction as an alternative transportation solution. My idea was to promote the Trail locally through work on the trail with kids from my school.\rADDITIONAL INFORMATION\rI want to start finding more jobs that will give me experience out in the field of science. More jobs that are in the field of science will boost my credentials as a scientist. This in the long run will help me grow to be respected in the scientific community. My long term goals is to eventually gain enough influence in the scientific community which will eventually gain my name respect so that my opinions are valued. Once my name is respected and my opinions valued I can start influencing regulation and policy with what the world needs it to be so the world can achieve environmental sustainable. I want our generation to be rembered as the green generation not just the technological generation.\r \"\r\nresume_50,flagged,\"Jake Kittell\rPrincipal - Happiness Tool Co. Inc\rMorrisville VT - Email me on Indeed: indeed.com/r/Jake-Kittell/2f01f0efe234bd2b\rDevelop Great Engineered Products and Information Systems Willing to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rPrincipal\rHappiness Tool Co. Inc - Morrisville VT - December 2010 to Present\rPrincipal Engineering Machine Design and Build and Software\rHappiness Tech LLC - Morrisville VT - June 2004 to Present\rsolutions. Jake Kittell 802-888-1679 *\r6/04 - Present Happiness Tech LLC Morrisville VT: Contract Machine Design and\rCustom Software. Principal.\rHappiness Tech LLC was founded to provide solutions for customers based on my principals of direct communication honesty and hard work. My experience at larger companies\rconvinced me that I could best serve my customers from a small client oriented company.\rOver the first several years in business Happiness Tech LLC has produced several complete and functional machines a few complete software/database applications and a wide array of design and SolidWorks consulting and contract services for happy customers. Customers\rinclude Pratt & Whitney Suss Micro-Tec SB Electronics Northern Power Systems and iRobot\rCorp.\rPrincipal Engineer Contract\rMoscow Mills Manufacturing Service - Moscow VT - April 2003 to June 2004\rDesign Engineering. President Anderson Leveille. 802-253-2036 x 108 *\r4/03-6/04 Moscow Mills Manufacturing Service. Moscow VT: Contract Engineering and Machine Shop Services. Principal Engineer.\rWhile at this small company I was involved in several projects from concept through happy\rcustomers. These included a new enclosure design for a flat panel computer to be used as an operator interface. The new design reduced weight by a factor of three while also reducing\rmanufacturing cost and addressing major cooling issues for the LCD and computer. As well I designed and built an X-Ray enclosure to house an in-process Electron Beam system. This enclosure was a complex labyrinth path for a printing web to pass through while keeping X- Rays in. Construction was formed and welded sheet metal to form a mold that then had lead cast in place.\rLead R&D Staff\riRobot Corp - Jaffrey NH - April 1997 to April 2003\r \rMember (E5) Design of mobile robots. Engineering oversight of manufacturing and quality. Operations Manager Jim Lenahan. (now at R&G Bank Note) 603-654-6185\r*\r4/97-4/03 iRobot Corp. (formerly Real World Interface Inc). Milford NH: Manufacturer of intelligent mobile robots. Lead R&D Staff Member.\rReal World Interface was a young company of fifteen people hoping to move from a start up business to a profitable manufacturing company when I started. My first mission was to\restablish engineering controls on the manufacturing process.\rThis included establishing a part numbering and naming system with revision control for everything from software to resistors and gears. Many assemblies were only sketched on scraps of paper without dates or titles. From this we grew to a state where it was clearly known what\rwe were building and what we needed to do it. Lead time for robot orders went from 1 year to just a few weeks. Cost of Goods sold on these products went from 85% to less than 45%.\rJake Kittell 722 Darling Rd.\r802-888-1679 Jake@HappinessTech.biz Morrisville VT 05661\rHaving achieved this I moved on to design work for the next generation of robots. I had\rprimary design responsibility for our line of three All Terrain Robotic Vehicles. These are\rrugged outdoor vehicles that can navigate serious terrain while carrying up to two powerful\rPCs. There are now several hundred of them serving the needs of robotic researchers from universities to national laboratories and US military defense programs.\riRobot Corp - 1999 to 1999\rmerged with I.S. Robotics to become iRobot Corp. The\rcompany is now 200 people strong. Work at iRobot included implementation of Oracle ERP system companywide importing half a million records from many assorted information systems. Mechanical leadership for iRobot's Packbot EOD robot. Design work included prototype design of iRobot's 7 Ft. long 6 axis robotic arm for the robot. EOD Robot This arm was designed to full military specifications able to resist all weather hi pressure wash down hi voltage electric shocks rigorous vibration and the sand of the Iraqi desert. It could lift 5 pounds at 7 feet horizontally yet weighed only 19 lbs.\rDesign Engineer\rREHAU Inc - Springfield VT - October 1996 to April 1997\rUntil recently REHAU designed and built all fabrication machinery and tooling for its North\rAmerican operations in Germany. The fairly new group that I joined has the mission to take over this machinery production. I participated in the development of new gauging concepts to better measure what was produced. I gained hands on experience with machine wiring and\rpneumatics.\rDesign Engineer\rREHAU Inc - Springfield NH - September 1996 to April 1997\rDesign of fabrication machinery for PVC extrusions. Plant Manager: George Trombly 802-886-8595 *\rEngineer\rAdvanced Manufacturing & Development - Willits CA - April 1994 to August 1996\rEngineering responsibility for infrared paint dryer line. Engineering Manager: Ron Budish 707-459-9451.\r*\rEngineer\rBio-Plexus - Tolland CT - February 1993 to June 1993\rDesigned and fabricated automatic test\rmachine participated in product development. Director R&D: Rick Kearns. (Company is out of business now)\r*\rMember Board of Directors\rUniversity of CT Engineering Dept - Willimantic CT - June 1992 to May 1993\rBusiness\rsupervision Manager: Alice Rubin 860-456-3611. *\rStudent Research Specialist\rUniversity of CT Engineering Dept - Storrs CT - September 1990 to January 1993\rPrototype machining and shop maintenance. Manager: Tom Marcellino 860-486-2011. *\rProprietor Contractor Carpenter\rYankee Roots Craftsmen - Mansfield CT - May 1988 to August 1990\rHome building additions remodeling. *\rJake Kittell 722 Darling Rd.\r802-888-1679 Jake@HappinessTech.biz Morrisville VT 05661\rApprenticeship and Background\rPerkin Elmer - Stamford CT - 1970 to 1985\rCT.\rAn apprentice in the most traditional sense of the word participating in engineering projects on a regular basis. My father a Principal Scientist (engineering) at Hughes/ Perkin Elmer\rCorporation worked mostly from our home. He is among the world's experts on micro\rpositioning having done such things as achieve positional stability of good sized mirrors to the angstrom level for 12 hour periods. Together we made assemblies that traveled in the space\rshuttle and designed the machine that has polished many of the world's largest telescope\rmirrors (including the Hubble's). Facilities included a precision machine shop as well as thorough electronics and metrology capabilities. During this period I invented a machine to\rdistribute mercury in arc lamps in a few seconds' time for Perkin-Elmer's lamp factory. This\rprocess was previously done by hand requiring skilled workers ten minutes per lamp.\rOpportunity was not spared to educate me in the ways of a craftsman. Craftsmanship is a\rnever-ceasing desire to improve the product process and methods of what we do. I learned\rthat attention to detail is the path to a quality finished product. Craftsmen look to the next\rlevel of performance even as the present one is being developed. I learned the skills to take a\rproblem and develop solutions using sketches and analytical techniques to build precision parts and produce finished machinery and to make detailed drawings from the point of view of a\rshop person. This has been a great aid in winning the respect of shop personnel in my\rprofessional work.\rMy Grandfather on my mother's side invented the first process to separate color images into dots for printing by Time magazine that is the foundation of modern color printing. He was an\rearly pioneer in television and made the first non-military radar installation aboard a sea vessel while at GE. From him I gained the courage to invent whatever needs to be invented. This\rthree generation long tradition of breaking new ground continues through my work.\rRelated Skills\rPeople who work with me believe in my abilities to solve tough problems and to deal with them in a fair and honest way. Patience allows me to be methodical and thorough in my work and is the basis for a strong knack for having inventions work well on the first attempt. I enjoy\rworking in group settings and utilize creative vision and listening skills to be a leader.\rJake Kittell 722 Darling Rd.\r802-888-1679 Jake@HappinessTech.biz Morrisville VT 05661 Experience and Education\rEDUCATION\rBSME in design\rUniversity of Connecticut - Morrisville VT\rADDITIONAL INFORMATION\rDESIGN SKILLS GROUP & PERSONAL\r Certified SolidWorks Professional 17years * Flexible and cheerful!\r FEA 10 Years * Diplomatic\r Photo Rendering 15 Years * Maintain successful working relationships\r Design for Manufacture / Assembly in OEM * Able to inspire craftsmanship in teammates. capital equipment market for 16 years. * High frustration threshold.\r Design of 7 Ft. long 4 axis lightweight robotic * Conflict resolution.\rarm for Explosive Ordinance Disposal.\r(iRobot PackBot EOD) MANUFACTURING\r Expert precision sheet metal design.\r Frame weldment Design. * Designed engineering document control\r Designed several production all terrain and new part numbering system radically\rmobile robots. improving product consistency and gross\r Designed new generation industrial gas margin.\rInfrared oven developing technology that * Technical lead and data conversion\rimproved fuel efficiency 200%. programmer for companywide Oracle ERP\rimplementation at iRobot Corp.\rCOMPUTER SKILLS * Mfg. Engineering responsibility for $2 million per year industrial gas dryer line.\r* 35 years programming experience\r* Reduced warrantee returns for gas burners\r* Application Development using VB.NET by improving production methods saving\rVisual BASIC VBA and PLCs.\r$55k per year.\r* Database Development on Oracle MySQL\rusing SQL PL/SQL VB SHOP EXPERIENCE\r* Company Infrastructure Development.\rWrote user-friendly visual Inventory Bill of * Lifelong machine shop experience enables Material MRP system that ran $2M/ year an intimate understanding of tolerancing configured robot manufacturing operation. and dimensioning.\r* 19 years with SolidWorks * Competent Machinist CNC and Manual.\r* Expert Excel and Word application * Experienced with all machine shop\rdevelopment skills. processes manual and CNC.\r* High quality technical writing and\rdocumentation skills. PROJECT MANAGEMENT\r\"\r\nresume_51,not_flagged,\"Jake McGrew\rPresident and Principal Search Consultant - Symbiont Sourcing\rNorwich VT - Email me on Indeed: indeed.com/r/Jake-McGrew/5de8615a52d165b0\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rPresident and Principal Search Consultant\rSymbiont Sourcing - Norwich VT - April 2015 to Present\rSymbiont Sourcing is a boutique executive search and recruitment firm\rspecializing in the life sciences with a focus on medicine and biotechnology.\rWe provide customized search strategies and managed recruitment campaigns on a global scale.\rRecruitment Strategy Consultant\rSymbiont Sourcing - Lebanon NH - August 2016 to September 2016\rAssisted Global Rescue a medical evacuation and travel medicine insurance firm implement a long-term recruitment strategy for physicians based in Lebanon NH and Manila Philippines.\rDirector of Talent Acquisition\rShirley Parsons LLC - Boston MA - January 2014 to March 2015\rRecruited by a UK based group of search firms to conduct market research on the viability of a specialist EHS (environment health and safety) recruitment\ragency in the US. Following positive results of the survey I co-founded Shirley\rParsons LLC and served as Director Of Talent Acquisition. This role involved\rall aspects of business development including brainstorming and authoring a\rbusiness plan; selection of our U.S. base of operations; and all pre-launch\rlogistics. As Director of Talent Acquisition I worked to build our brand by developing relationships with prospective clients and industry leaders while\ropening new business and building the company network. Within 12 weeks of opening our U.S. office we placed a senior executive with a manufacturing\rcompany and performed several retained searches for new clients.\rWildlife technician\rVermont Department of Fish and Wildlife and Vermont Cooperative Fish - Burlington VT - June 2007 to December 2009\rand Wildlife Reseaerch Unit - Springfield and Burlington Vermont\rWildlife technician for the Vermont Department of Fish and Wildlife. Operated the beaver conflict management program with the purpose of reducing conflicts\rarising from beaver related flooding of private and municipal lands. My\rresearch position involved analyzing data and identifying correlations between housing density and habitat fragmentation and seeking development strategies\rthat would minimize this effect.\rTeaching assistant for Ecology 1000 Fall of 2002 to Spring 2004\r \rUniversity of Georgia - Athens GA - August 2002 to May 2007 Teaching\rAssistant for Ecology\r- September 2005 to March 2006\rMy responsibilities\rinvolved instruction of three laboratory sections per week grading of laboratory\rreports and exams assisting the instructor with lectures and acting as a resource for student questions and concerns.\rContract recruiter specializing in the placement of high-level RF design\rKineticom Inc - San Diego CA - May 2005 to September 2005\rSan Diego California Contract recruiter specializing in the placement of high-level RF design engineers with clients such as T-Mobile and Sprint.\rIndependent Research - June 2004 to August 2004\rCosta Rica\rConducted interviews in San Jose Costa Rica with representatives from non- profit organizations and government agencies involved with the Eastern\rTropical Pacific Seascape conservation corridor. Scouted potential research\rsites and developed contacts with local research scientists conservation\ractivists and community leaders.\rInternational Center for Research in Agroforestry - Meru District - KE - June 2003 to August 2003\rKenya\rConducted research on tree biodiversity on private land holdings around the\rImenti Forest protected area. Research techniques involved detailed tree counts on informants' land as well as in-depth interviews.\rSenior Technical Recruiter and Account Manager for a recruitment firm\rKineticom Inc - San Diego CA - April 2000 to June 2002\rspecializing in the placement of high-level wireless and optics engineers in permanent and contract positions with major telecommunications\rcompanies. Provided full life-cycle recruitment services for our clients that\rincluded identification of potential candidates interview coordination\rsalary/benefits negotiation and signing of candidates. Responsible for the recruitment of approximately one- half of all of the engineers placed during my\rtenure. Was responsible for procurement and oversight of recruiting resources\rincluding on-line databases and recruitment management software.\rChimborazo Province - July 2001 to August 2001\rdevelopment project that involved the development of a local canyon into a rock\rclimbing site. Provided instruction for a group of aspiring guides from the indigenous community of San Pablo (courses included rock-climbing safety\rmountain rescue techniques wilderness first aid and the how-to of operating a\rbusiness catering to clients from the U.S. and Europe.) Recruited a total of seven project team members from the United States and Europe to help with\rcanyon development and to provide first-aid instruction. Raised over fifteen\rhundred dollars in donations from individuals and corporations to purchase\requipment and offset expenses for team members.\rAccount executive managing client relations\rS-Com Inc - San Francisco CA - March 1999 to February 2000\rfor S-Com's third largest account while providing 360 recruitment for senior level positions.\rTechnical Recruiter for an international telecommunications recruitment\rS-Com Inc - San Francisco CA - October 1998 to March 1999\rSan Francisco California Technical Recruiter for an international telecommunications recruitment firm. Recruited and placed over forty contract employees and maintained a\rconsistent base of twenty-five to thirty engineers in the field.\rSELECTIONS OF OTHER EXPERIENCE INCLUDING RESEARCH AND VOLUNTEER PROJECTS\rEDUCATION\rDoctor of Medicine in attended for 3 years\rFlinders University School of Medicine February 2010 to November 2014\rPh.D. in Ecology\rUniversity of Georgia - Athens GA August 2002 to May 2009\rEcology\rSchool of International Training - Brattleboro VT August 1998 to December 1998\rB.A. in Anthropology\rPomona College - Claremont CA August 1994 to May 1998\rSKILLS\rMicrosoft Office (10+ years) Recruiting (7 years) Sales (7 years) Management (4 years)\rCERTIFICATIONS/LICENSES\rCPR\r\"\r\nresume_52,not_flagged,\"Job Seeker\rStaff Scientist/ Field Geologist - Stone Environmental / Cascade Technical Services\r- Email me on Indeed: indeed.com/r/e54c23f1aa124657\rWORK EXPERIENCE\rStaff Scientist/ Field Geologist\rStone Environmental / Cascade Technical Services - Montpelier VT - 2014 to Present\rConducted site investigations using the COREDFN WaterlooAPS and Geoprobe Membrane Interface Probe. Trained new geologist employees on field services.\rSeasonal employee\rStone Environmental - Montpelier VT - 2013 to 2014\rWorked for the AgChem and WRM departments as a lead field sampler. Also worked with many ArcGIS projects for the AIM AgChem and Water Resources departments.\rUndergraduate Teaching Assistant\rUniversity of Vermont - Burlington VT - 2012 to 2014 Helped teach Geology 1 labs.\rEDUCATION\rB.S. in Geology\rUniversity of Vermont 2014\rCERTIFICATIONS/LICENSES\rCPR & First Aid\rADDITIONAL INFORMATION\rTechnical Skills\r-Rock core logging.\r-Soil core logging.\r-Split spoon logging.\r-Water sampling.\r-Soil and rock sampling.\r-Field Mapping.\r-Surveying.\r-Data management and graphing. -Vehicle maintenance / engine repair. -Small electronics repair.\rJacob Vincent\r \rI joined Stone Environmental in the spring of 2013 working with the Agricultural Chemistry Water Resources and Applied Information Management Groups. Upon graduating from the University of Vermont in 2014 with a B.S. in Geology and a minor in Geospatial Technologies I became a member of the Investigation and Remediation Team at Stone Environmental. I am responsible for performing Waterloo advanced profiling core discrete fracture network and membrane interface probe investigations as well as post-fieldwork data management.\r\"\r\nresume_53,flagged,\"John Klein\rCastleton VT - Email me on Indeed: indeed.com/r/John-Klein/a9eaa3a81e5e8713\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rAnalyst/Independent Contractor\rCora Group - New York NY - January 2009 to Present\rIndependent Contractor/Analyst - Part Time January 2009 - Present\rSkills: Study Design Data Collection Data Analysis Report Writing\r Duties included: study/instrument design; data cleaning and analysis with R; writing.\r Quantifying growth of a Social Network required: research of methods; descriptive statistics from the network analysis and visualization software UCINet/NetDraw\r I learned that researching and applying statistical techniques to real world problems can be very rewarding. I also learned about the difficulties of working remotely.\rBiostatistician\rVermont Genetics Network - Northfield VT - June 2015 to June 2016\rSkills: Study design and analysis project management\r Collaborating with college professors and students in Vermont on: study design; data analysis and sample size calculations in R; and assessments of past analyses.\r Study topics included: Beech Bark Disease (logistic regression); Cognitive Biofeedback (Mixed factor regression); Effects of BPA on cow ovary cells (Regression LOESS modeling etc)\r I learned about working under a grant and with academics.\rStaff Member\rFortySeven Main Street Inc - Castleton VT - August 2013 to May 2015\rSkills: Leadership Interpersonal Relations Conflict Mediation Record Keeping\r Duties at the therapeutic community residence included: leading work crews during the work program; dispensing medications; cooking meals; and other general tasks.\r I learned how to unplug and clean toilets efficiently and how to work smoothly with people whose perception of reality differs greatly from mine.\rAssistant Professor of Mathematics\rCastleton State College - Castleton VT - January 2014 to May 2014\rAssistant Professor Intro to Biostatistics Spring Semester 2014\rSkills: Interaction with Class Presenting Statistical topics to students\r Duties included: demonstrations in R Minitab and SPSS; creating and grading homework handouts quizzes and tests; teaching the class on ANOVA\r I learned that mathematics is an infinitely creative endeavor especially when students are figuring out ways to get test questions wrong.\rPhysical Scientist\rCRREL: Cold Regions Reseatch and Engineering Laboratory - Hanover NH - June 2007 to September 2008\r \rSkills: Study Design Data Collection Data Analysis Report Writing\r My duties involved: wetland mapping; study design data collection and analysis; explaining the Regression and Bayesian modeling approaches to the problem to the PI and other wetland scientists including a group at the Society of Wetland Scientists National Conference.\r I was bitten by many mosquitoes.\r I learned that professional scientists have a very interesting job and work hard. I learned I wanted to be a professional scientist/statistician.\rEDUCATION\rMaster's in Biostatistics\rColumbia University in the City of New York August 2011 to May 2013\rBA in Natural Science\rCastleton State College - Castleton VT September 2002 to May 2007\rSKILLS\r- New York NY\rR project (2 years) Microsoft Office (10+ years) SAS (Less than 1 year) SPSS (1 year) JIRA (Less than 1 year) Confluence (Less than 1 year)\r\"\r\nresume_54,not_flagged,\"John McCann Proprietor - North Branch Vineyards\rMontpelier VT - Email me on Indeed: indeed.com/r/John-McCann/e10274d19df1c209 Authorized to work in the US for any employer\rWORK EXPERIENCE\rProprietor - North Branch Vineyards\rVintner - Montpelier VT - 2007 to 2015\r Produce unique wines using locally and regionally grown grapes\r Analyze wine chemistry data provided by Virginia Tech Enology to determine the wines stability and aging process\r Responsible for all daily winery operations including equipment inspection and sterilization\rQuality Assurance Engineer\rVintner - Lebanon NH - 2007 to 2009\r Collaborated with United States Government and Boeing personnel during inspection on products delivered Worked with customer auditors providing evidence on recorded documents to ensure compliance with policies and procedures\rQuality Assurance Manager\rLiquid Measurement Systems - Georgia VT - 2006 to 2007\r Developed Incoming Inspection Reports (IIR) providing process control measures on purchased products\r Provided Material Review Board activities by providing root cause analysis and implementing corrective actions while maintaining quality standards and contract compliance\rQuality Assurance Engineer\rGW Plastics Inc - Bethel VT - 2005 to 2006\r Recorded and maintained quality batch records and supporting documentation\r Generated non-conformance material reports to ensure complete containment and segregation of conforming goods\rSenior Test Engineer\rGeneral Dynamics ATP - Burlington VT - 2004 to 2005\r Performed data reduction and analysis tasks on F/A-22 Linear Linkless composite 20mm Ammunition Handling System tests using Microsoft Excel\r Provided technical assistance by reviewing test plans for the (JSF) Joint Strike Fighter Gun System Control Unit environmental development test\rEngineer/Scientist Specialist\rBoeing - Lompoc CA - 1998 to 2004\r Prepared process control documents to support vehicle processing for satellite communication system deployment\r Assistant Test Conductor that directed technicians and quality assurance personnel during Delta II and Delta IV vehicle ordnance installation processing\r Composed product qualification documents and presented to customer during test readiness reviews\r \rEDUCATION\rBachelor of Science in Civil and Environmental Engineering\rUniversity of Vermont May 1998\rAssociate in Mechanical Engineering Technology\rVermont Technical College May 1992\rADDITIONAL INFORMATION\r Extensive experience in developing and writing procedures standards and reports\r Proficient with Microsoft Office including Word Excel Publisher PowerPoint and E-mail Excellent communication and organizational skills\r Energetic team member with outstanding interpersonal skills\r\"\r\nresume_55,not_flagged,\"Jong Yu Staff Microbiologist\rWilliston VT - Email me on Indeed: indeed.com/r/Jong-Yu/f7d7a5b646de5500\rMicrobiologist working in an envriomental monitoring laboratory for biosolids microscopic particulate analysis and microbiological procedures in adherence to EPA for detecting pathogenic microbes in these environmental samples.\rAlso have academic research experience working with Vibrios in shellfish particularly in oysters and testing treatment methods to eliminate high levels of Vibrios.\rWORK EXPERIENCE\rStaff Microbiologist\rAnalytical Services Inc - Williston VT - August 2013 to Present\rResponsibilities\rProcessing biosolids microscopic particulate analysis (MPA) filters and bacteriological analysis on environmental samples as a part of an environmental surveillance programs for potential pathogens in these environmental samples.\rSkills Used\rAseptic Technique\rFollowing SOPs that adhere to EPA standards Time efficiency\rAbility to adapt quick to situations\rResearch Technician\rUniversity of New Hampshire - Durham NH - May 2011 to Present\r-Michael Taylor Ph.D. Candidate (Summer 2011 Present)\r Processed oysters and water according to the FDA Bacteriological Analytical Manual (BAM) Vibrio enrichment method to detect naturally occurring Vibrio parahaemolyticus in NH oysters after relay treatments using SmartCycler II real-time PCR machine\r Compared new chromogenic media to standard TCBS media used in FDA BAM for V. parahaemolyticus detection in oyster water and sediment samples\r Extracted genomic materials from oyster homogenate for metagenomic sequencing using a commercial kit Supervised an undergraduate research student to perform basic laboratory procedures (media preparation laboratory upkeep and autoclaving biohazardous waste) and process samples for V. parahaemolyticus detection in oysters using culture-based and real-time PCR method\rResearch Technician\rUniversity of New Hampshire - Durham NH - January 2011 to Present\r-Dr. Stephen Jones Research Associate Professor (Spring 2011 Present)\r Lead the Vibrio surveillance project to detect V. parahaemolyticus V. vulnificus and V. cholera in oyster water and sediment samples collected from two oyster beds in the Great Bay Estuary following the FDA BAM enrichment method for Vibrio isolation and detection\r Prepared media and reagents needed for Vibrio enrichment isolation cultivation and preservation\r \r Managed the laboratory by ordering supplies and reagents needed for the surveillance and relay projects routine laboratory upkeep and maintenance and familiarizing with new laboratory equipment such as SmartCycler II and HOBO temperature and salinity data logger\rLaboratory Technician\rUniversity of New Hampshire - Durham NH - August 2012 to June 2013\r-Dr. Aaron Margolin Professor of Microbiology (Summer 2012 present)\r Maintaining different mammalian cell lines (Buffalo green Monkey Kidney Human Embryonic Kidney and African Green Monkey Kidney cells) for propagation and detection of Astrovirus Poliovirus Rotavirus and Adenovirus 40/41 in sludge samples\r Preparing and splitting mammalian cells for neutral red plaque assay and crystal violet plaque assay for viral detection and quantification and tube suspension cell culture method to propagate and detect viruses using ABI real-time PCR machine\rLaboratory Assistant for Microbiology Labs\rUniversity of New Hampshire - January 2012 to December 2012\rBMS 708: Virology Lab\rBMS 715: Immunology Lab\rBMS 602: Pathogenic Microbiology Lab BMS 407: Germs\r Presented lab lectures and lab exercises to students\r Aiding students in laboratory exercises\r Supervising undergraduate teaching assistants\r Ordering media reagents and supplies for future lab exercises\r Answering student emails about lab materials grades and any other inquiries Troubleshooting lab protocols\r Preparing lab quizzes and lab practical for testing students\rTeaching Assistant\rUniversity of New Hampshire - Durham NH - 2008 to 2011\rUniversity of New Hampshire\r-BMS 503: General Microbiology (Fall 2011)\r-BMS 703: Infectious Disease and Health (Fall 2011)\r-BMS 602: Pathogenic Microbiology (Spring 2011 Summer 2011) -MLS 721: Mycology Parasitology Virology (Spring 2009)\r-BMS 407: Germs (Fall 2008 Fall 2010)\r Presented lab lectures and lab exercises to students\r Aiding students in laboratory exercises\r Supervising undergraduate teaching assistants\r Ordering media reagents and supplies for future lab exercises\r Answering student emails about lab materials grades and any other inquiries Troubleshooting lab protocols\r Preparing lab quizzes and lab practical for testing students\rGraduate Research Assistant\rUniversity of New Hampshire - Durham NH - 2009 to 2010 -Dr. Stephen Jones Masters Degree Adviser\r Coordinated and performed depuration and relaying treatments at Spinney Creek Shellfish Inc. to eliminate Vibrio parahaemolyticus and Vibrio vulnificus in NH oysters\r Optimized a real-time PCR protocol to detect V. parahaemolyticus and V. vulnificus in oysters using Bio-Rad iCycler 5 following a modified protocol used by Dr. Jones previous work\rUndergraduate Teaching Assistant\rUniversity of New Hampshire - September 2006 to March 2008\rMICR 501: Microbes for Human Diseases (Fall 2006 2007)\r-MICR 602: Pathogenic Microbiology (Spring 2007 2008)\r Helped aliquot cultures experiment and equipment set-ups clean ups and as well as helping students whenever possible during the course of the laboratory session.\rPoster Abstracts and Presentation:\r-Jones S.H. J. Yu B. Schuster C. Ellis J. Mahoney V. Cooper and C. Whistler. 2011. Environmental Conditions and the dynamics of Different Pathogenic Vibrio Species in Northern New England Shellfish\rUndergraduate Researcher\rUniversity of New Hampshire - 2006 to 2008\r-Dr. Frank Rodgers Professor of Microbiology\r Analyzed stx-2 toxin production from Escherichia coli O157:H7 after co-culture experiments with probiotics and antibiotics by using African Green Monkey Kidney cells for cytotoxicity assays\r Maintained the African Green Monkey Kidney cell lines for cytoxicity assays and establish a protocol for properly splitting cells using a hemocytometer\rLaboratory Assistant\rUniversity of New Hampshire - Durham NH - 2006 to 2008\r-Robert Mooney Instrumentation Scientist\r Responsible for preparing reagents and media for all Microbiology teaching laboratories properly handling and disposing biohazardous waste cleaning and calibrating (if needed) laboratory equipment such as microscopes and pipettors and ensuring all reagents and materials are refilled and accounted for on each laboratory bench for students to use\rEDUCATION\rM.S. in Microbiology\rUniversity of New Hampshire 2011\rB.S. in Microbiology\rUniversity of New Hampshire March 2008\r- Durham NH\r- Durham NH\r\"\r\nresume_56,not_flagged,\"Jordan Armstrong\rAmerican Society for Clinical Pathology (ASCP) -- Medical Laboratory Scientist (MLS)\rSouth Burlington VT - Email me on Indeed: indeed.com/r/Jordan-Armstrong/6086ccf990a53cd7\rI am an American Society for Clinical Pathology (ASCP) -- Medical Laboratory Scientist (MLS) (Passed examination certification pending receipt of transcript). I am excited to be beginning my career as a Medical Laboratory Scientist and I am looking for a great place to start my career.\rWORK EXPERIENCE\rDelivery Driver\rVermont Wine Merchants - Burlington VT - May 2013 to December 2014\rLab Assistant\rDr. Yolanda Chen's agroecology lab - May 2011 to January 2013\rPerformed PCR and gel electrophoresis\r Maintained Colorado potato beetle colonies as well as stock of colony host plants\r Assisted in preparation and data collection for diapause choice feeding and larval growth rate studies\rIndependent Research\rDr. Yolanda Chen's agroecology lab - August 2011 to May 2012\rOvipositional ability of geographic populations of Colorado potato beetle\r Independently designed and conducted an oviposition experiment\r Dissected beetles and prepared ovaries for analysis collected and analyzed data\rLab Assistant\rDr. Alison Brody's ecology and evolution lab - August 2010 to August 2011\rIndependently prepared experimental set up Processed plant and soil samples and audio data from pollinator data and catalogued data\rIndependent Research\rDr. Alison Brody's ecology and evolution lab - November 2010 to May 2011\rExploring the benefits of male sterility in Polemonium foliosissimum: do females make bigger and better offspring?\r Independently performed a germination experiment\r Collected and analyzed growth data on experimental plants\rSKILLS\r Genetics lab: DNA extraction & purification gel electrophoresis PCR Southern blot\r Computing: Basic programming in R and HTML data analysis in JMP Microsoft Office\r Medical lab: Phlebotomy immunoassays culturing bacteria spectrophotometry blood typing hematology differentials microscopy sterile technique\rRELEVANT COURSEWORK Clinical Chemistry\r Clinical Microbiology\r Immunology\r \r Immunohematology Hematology\r Advanced Genetics Genetics\r Molecular and Cell Biology\rServer and Food Preparer Nunyuns\rThe CafeՁ at Myer's - Burlington VT - September 2007 to October 2009\rRespite Worker Howard Center Burlington VT (6/09-9/09)\r Buyer Whole Foods Market Boston MA Seattle WA Fort Lauderdale FL (11/97-6/01 & 3/05-6/07) Buyer City Market Burlington VT (3/02- 2/05)\rEDUCATION\rPost Baccalaureate Medical Laboratory Science Certificate in Medical Laboratory Science\rThe University of Vermont - 2012 to 2014\rBA in Biology\rThe University of Vermont - 2014\rBurlington VT\rBurlington VT\rCommunity College of Vermont - Winooski VT 2007 to 2009\r\"\r\nresume_57,flagged,\"Joseph Lea\rUndergraduate Research Assistant - Research in Microchip Manufacturing\rPawlet VT - Email me on Indeed: indeed.com/r/Joseph-Lea/c477339a8e1291e9\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rUndergraduate Research Assistant\rResearch in Computational Genomics - August 2016 to Present\r Currently working to find functional relationships between environmental data and genomic information at the base pair level\r Translating previously created scripts from C++ to R\rUndergraduate Research Assistant\rResearch in Microchip Manufacturing - February 2016 to Present\r Implementing predictive analytics on trace data collected during the epitaxial process to identify potentially malfunctioning chips\r Working to adapt the control system to find problems as they occur rather than post-production\rIntern in Data Analysis\rAllen Institute for Brain Science - June 2016 to August 2016\r Developed a system for mapping genomic data between electrophysiological and transcriptomic modalities that is now being applied to current research\r Networked across research teams to impact as many ongoing projects as possible\r Worked alongside both lab and computational scientists to develop an understanding of both the collection and uses of RNAseq data\rUndergraduate Research Assistant\rResearch in Computational Neuroscience RPI - June 2014 to August 2014\r Partnered with RPI professors and the Neural Stem Cell Institute to investigate stem cell data Created a model for temporally ordering frontal lobe brain cell development\rEDUCATION\rB.S. in Mathematics of Operations Research\rRensselaer Polytechnic Institute - Troy NY May 2017\rSKILLS\rR (2 years) Microsoft Office (5 years) Data Analysis (2 years)\rADDITIONAL INFORMATION RELATED SKILLS\r \r Proficient in R Microsoft Office and Matlab Experience in Python and SQL\r\"\r\nresume_58,not_flagged,\"Joseph YoungYoung\rPolice Officer - Norwich University Varsity Soccer\rBurlington VT - Email me on Indeed: indeed.com/r/Joseph-YoungYoung/8926eb04d8690820\rWORK EXPERIENCE\rPolice Officer\rNorwich University Varsity Soccer - 2011 to Present\r2011-present\rMe: I am a motivated and hardworking individual with a propensity for improving and growing no matter the scenario. I am an extremely capable person who can handle\rproblems independently. I am not perfect but I do anything I can to be the best in everything I do.\rJoseph Young\rReferences\rReferences\r1. Eric Nordenson\rPolice Officer\rCity of Montpelier Enord13@msn.com (802)-272-2975\r2. Dr. Seth Frisbie\rAsst Professor of Chemistry Norwich Univeristy\r158 Harmon Dr.\rNorthfield VT 05663 Sfrisbie@norwich.edu (802)-485-2614\r3. Dr. Richard Milius\rSelf-employed Central Vermont - 2012 to 2014 Painting/Landscaping/Odd-Jobs\rNorwich University Varsity Soccer - 2010 to 2014\rMVP 2011 2014\r Team Player Award 2012 2013\rStudent Athletic Advisory Committee\rNorwich University Varsity Soccer - 2010 to 2014\rResearcher/Scientist\r- June 2012 to August 2012 Summer-2012\r \r Proposed and received funding myself\r Collected and analyzed 1400 hours of Data\rVOLUNTEER\rMalia Crushes Cancer Fundraiser - March 2012 to May 2012\rRaised $1020.00\r Malia is now cancer-free\rLiaison\rNorwich University Varsity Soccer - Burlington VT - 2011 to 2011\r05401 Phone: (571)-274-4288 Email: Jyoung3@stu.norwich.edu Joseph W. Young\r Organized School Events and Fundraisers\rLifeguard\rFairfax County Park Authority - Fairfax VA - 2009 to 2011\rCPR & First aid certified\r 5 Emergency situations 100% save rate\rWounded Warrior Project Walter Reed Medical Center - March 2010 to May 2010 Built and decorated a Rec room for wounded soldiers\rEDUCATION\rB.A. in Biochemistry\rNorwich University - Northfield VT 2010 to 2014\r\"\r\nresume_59,not_flagged,\"Karl Johnson\rProvide counsel - PRIVATE CONSULTING\rMontpelier VT - Email me on Indeed: indeed.com/r/Karl-Johnson/f54184a7fb3f91c5\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rProvide counsel\rPRIVATE CONSULTING - 2007 to Present\rpublic representation permitting assistance financial analysis & budgeting and senior management to clients on a broad range of issues including but not limited to:\r Performed due diligence on Renewable Energy projects under consideration for funding through loans or grants.\r Conducted statewide heating oil and propane pricing data collection & quality assurance.\r Prepared filings required under FERC regulations.\r Provided representation in business transactions including performance of due diligence financial evaluation of assets for acquisition and client representation with sellers regulators and financiers suppliers and subcontractors.\r Conducted technical and financial evaluation of potential acquisitions for purchase and development.\r Wrote and compiled ARRA compliant RFPs Grant and Contracts.\r Responsible for development of business plans and pro forma for clients' use in raising capital.\r Performed quality assurance on assessments and reports prior to release to client's customer.\r Participated on the Permitting Committee of the Vermont 25X'25 Committee charged to investigate the permitting requirements for various renewable energy technologies and make recommendations related to permitting to assure safeguards are in place to protect the public while allowing for as streamlined a permitting process as practicable.\r Developed workflow processes & process documentation for the Vermont Dig Safe program investigations. Administered and presented Underground Facility Damage Prevention & Training Seminar.\r Responsible for the development of both software and process documentation for the Vermont Dig Safe program.\r Consulted on numerous varied permitting matters in Federal State & Local jurisdictions.\r Represented client's interests before the Vermont Natural Resources Board's Water Panel in various proceedings related to permitting of energy projects.\r Provided cost reduction consulting services to commercial and industrial customers focused on waste and recycling program optimization cost reduction and cost control management resulting in quantifiable cost savings and profit improvements.\rNORTHERN POWER SYSTEMS Waitsfield Vermont\rNorthern Power Systems a Distributed Energy Systems Company (DESC) designed built and serviced highly reliable power systems for a wide variety of applications and clients world-wide.\rDirector of Project Management\rNorthern Power Company - Waitsfield VT - 2004 to 2006\r \rPrepared and managed annual department budget of ~$2.2M. Hired directed and coordinated resources to effectively obtain positive outcomes on internal efforts chartered to drive organizational growth as well as deliver quality results on commercial contracts.\r Responsible for oversight on $40M+ of projects domestically and internationally.\r Senior representative for major clients.\r Implemented organizational changes and personnel management yielding improved margin performance and reduction in business risks:\r_ Coached Project Managers to improve performance individual effectiveness professionalism and morale of department.\r_ Tripled capacity while maintaining staffing levels reducing costs and expanding ability to manage higher volumes of projects while growing business from $7M to >$40M.\r_ Established uniform monthly budget procedure & review process.\rSenior Project Manager\rNorthern Power Systems - Waitsfield VT - 2001 to 2004\rLed project teams on projects comprised of the design procurement of materials equipment and contracted services production and installation of distributed generation facilities and remote power systems. Managed purchasing and service contracts and negotiated with equipment vendors and suppliers to reduce costs while improving quality and delivery timelines.\rSpecific projects include:\r Distributed Energy MicroGrid Waitsfield Vermont - Responsible for the design permitting and construction of a MicroGrid distributed generation system at the Mad River Industrial Park in Waitsfield Vermont using utility-owned distribution lines to convey power generated by multiple non-co-located distributed generation assets to distributed loads.\r EOP One Market Street California - Managed all aspects of the design procurement and testing of a 1.5 Megawatt Combined Heat and Power Distributed Cogeneration Project for large office complex in San Francisco.\r Corridor Pipeline Alberta Canada - Directed $1.6M design and production project for 22 power systems for remote block valve installations on the Corridor oil pipeline in Alberta Canada.\r CORRPRO Chad & Cameroon West Africa - Completed major United Kingdom client project encompassing design and construction of 12 remote power systems for cathodic protection of an oil pipeline based in Chad and Cameroon. Project delivered on time and under budget in spite of 5-week design and materials approval delays by client.\r Lankahuasa 1 Offshore Platform Gulf of Mexico - Head of project team for design and construction of complex 60kw solar & natural gas-fired micro turbine hybrid power system providing energy to offshore oil and gas extraction platform based in the Gulf of Mexico.\rTHE JOHNSON COMPANY INC. Montpelier Vermont\rAn Environmental Sciences and Engineering Services company providing consulting and design services to a diverse clientele across multiple markets including hazardous site investigation and remediation energy development and environmental litigation support.\rSenior Manager\rCharged with responsibility for distressed contracts and handling delicate and controversial issues including litigation: Formulated strategy provided expert testimony; propelled research exhibit preparation quality assurance pre-filed testimony and expert witness preparation. Managed teams of scientists engineers attorneys contractors and vendors to optimize performance. Prepared statements of work cost estimates schedules and budgets; tracked ROI for business development ventures.\r Directed marketing managers developed corporate marketing plan and proposals created and produced advertising campaigns and conducted market assessment & completed business development missions to 6 Eastern European countries.\r Expedited and obtained environmental permits and construction certifications for $35M power plant project in under one year.\r Supervised the removal of 101 tractor trailer loads of contaminated material and managed State and Federal inspections negotiations and field sampling in sensitive elementary school setting.\r Expedited construction of groundwater collection and treatment system in time for school opening; managed midwinter installation of 1000 feet of pipe without school disruption.\r Led meetings and successfully handled public relations with state town school board PTA teachers administrators and nearby landowners; ensured regulatory compliance for 12+ years.\rSCIENCE APPLICATIONS INTERNATIONAL CORP. McLean Virginia / Palo Alto California\rAs the largest employee-owned research and engineering firm in the United State SAIC works to solve complex technical problems in national security homeland security energy the environment space telecommunications health care & logistics.\rEngineer\rPlanned directed and coordinated time-critical projects relating to municipal industrial and steam- electric generating plant wastewater discharge permitting. Steered procedures to coordinate technical and administrative operations HR management and principal client relations.\r Consultant to USEPA in Washington D.C. Dallas Reno Seattle San Francisco Oakland and San Diego. Established Division office and acted as liaison between 5 entities including SAIC EPA Region IX the state environmental agency the permittee (municipality industry) and regional water quality control boards for each drainage basin; managed permit application review permit drafting and technical review program for wastewater discharge.\r Performed work flow analysis for Permitting and Compliance Section of EPA Region VI. Participated in numerous operational and compliance diagnostic evaluations of publicly owned waste water treatment plants. Coordinated reviewed and wrote 100+ wastewater discharge permits for municipalities industry and steam electric generating facilities in under 1 year.\rEDUCATION\rBachelor of Science in Engineering\rUniversity of Massachusetts - Amherst MA\rADDITIONAL INFORMATION Qualifications:\r Business:\r_ Facilitated successful interactions between clients municipalities and state & federal agencies in permitting proceedings including land use for commercial and industrial development air pollution & wastewater discharge.\r_ Participated in start-up and growth initiatives in Energy Systems Environmental Services & Specialty Markets.\r_ Knowledgeable & experienced in personnel resource management hiring performance evaluation coaching & mentoring.\r_ Performed all aspects of small business brokering: Developed materials applicable to sale negotiated terms drafted P & S agreements prepared pro forma and conducted financial analyses.\r Regulatory:\r_ Experienced obtaining numerous regulatory approvals including local state and federal government jurisdictions.\r_ Chaired Municipal Budget Review Committee as appointed by Montpelier Vermont City Council\r_ Served on City-wide Property Reappraisal Committee as appointed by Montpelier Vermont City Council _ Member of the Permitting Committee of the Vermont 25X'25 Committee\r_ Appointed by the Governor as Chair of Vermont's District #5 Environmental Commission for 'Act 250':\ro Chaired hundreds of quasi-judicial hearings in contested cases.\ro Reviewed applications and evidentiary record; facilitated deliberations & decisions under Statutory 10 Criteria.\ro Issued state land use development permits under Vermont's Act 250\"\" Land Use Development Law. _ Performed as Expert Witness providing testimony involving science and engineering applied to land use and development projects; noise measurement, impacts and mitigation; hazardous waste investigation and remediation; groundwater hydrology, and water quality issues in both judicial & quasi-judicial proceedings.\r Energy\r_ Skilled with Natural Gas, Solar, Hydrogen, Wind, Hydroelectric, Distillate, Waste-to-Energy Technologies & Systems.\r_ Knowledgeable across multiple energy market sectors including manufacturing, regulatory, and commercial development.\r_ Responsible for management of cogeneration, distributed, and hybrid generation projects worldwide generating $40M+ revenue annually.\r_ Managed the design , development, negotiation of the utility interconnection agreement, and permitting of a 'Micro Grid' distributed generation R&D project to be built in Waitsfield, Vt. by client in collaboration with local utility; and gained Vermont Public Service Board Certificate of Public Good for project.\r Environment:\"\r\nresume_60,not_flagged,\"Kelley Zilembo Human Resources Coordinator\rBristol VT - Email me on Indeed: indeed.com/r/Kelley-Zilembo/0a67c72520fcdb29\rProactive HR Operations professional oversees recruitment payroll benefits compensation safety and compliance along with employee relations. Passionate leader specializing in addressing issues before they become a critical. Thrives in an environment where my contributions improve processes that have a positive impact on the overall goals of the organization and a healthy work life balance for employees.\rWORK EXPERIENCE\rHuman Resources University Recruiter\rMathWorks.Inc - Natick MA - 2014 to Present\rFacilitate high volume of phone screening video interviews and resume review for full-time/internship Engineering & Computer Scientists positions.\r Assist with recruiting events such as college days interviews ad virtual career fairs.\r Schedule interviews technical interviews and travel (hotel & flight) accommodations for onsite interviews.\r Develop candidate pipelines from academia for candidates with Master's & Doctoral degrees. Manage high volume requisitions queues in Application Tracking System.\r Oversee interviewing schedule/calendar for up to seven interviewers daily.\rHuman Resources Coordinator\rSd Associates LLC - Williston VT\r Oversee all HR operations of the company (110 employees in three locations in Vermont and Massachusetts) in accordance with MA & VT laws.\r Payroll: Researched and implemented Paychex payroll system. Manage the employee record retention process including employee schedules time cards status changes. Submission of semi-monthly payroll adjustments and process. Manage HR administrative team.\r Benefits & Compensation: Researched implemented and manage company wide health insurance plans in two states (Vermont & Massachusetts). Partnered with a broker to purchase plans and negotiate rates. Manage company open enrollment process for health dental vision and accident insurances while managing payments through payroll deductions. Manage Personal Time Off (PTO) program company wide. Completed yearly anniversary letter and performance review process. Manage all tuitions reimbursements for employees. Facilitate in wellness programs.\r Recruitment: Manage recruiting advertising interviewing hiring and onboarding. Responsible for all job postings while partnering with supervisors to meet their staffing needs. Review all resumes conduct initial phone screenings setup onsite interviews and check candidate references. Complete onboarding process including new hire paperwork and familiarization of company policies and procedures. Manage recruiting external portals for advertising. Update and write all job descriptions and company forms for internal use and employee files.\r Compliance: Partner with company legal team and attorneys to write and update company policy manual including compliance with all insurance policies (General Liability Professional Liability Workers Compensation Liability Coverage and 125 Cafeteria Plans). Manage all employee promotions exit interviews terminations and turnover rate documentation. Research company legal issues and provide guidance to company Directors to collaborate with. Manage compliance processes in reference to background checks DMV checks and fingerprints.\r \r Safety: Oversee all workers compensation claims for employees and work with claims adjusters doctors employees and supervisors to maintain the companys workers compensation mod. Ensure all employees are CPR & First Aid certified and maintain database for renewals. Follow OSHA protocol and conduct safety trainings as needed. Ensure that all employees working with clients understand they are mandated reporters and teach signs of abuse and neglect of clients. Maintain all Federal & Vermont Medical Leave cases and facilitate meetings between supervisors and employees. Manage HIPAA breach log. Manage all unemployment claims for the company providing the VT DOL with documentation for appeal hearings. Complete and process any property damage claims for employees.\r Employee Relations: Communicate and guide employees who seek assistance on issues impacting their employment. Answer all employee questions and train employees regarding the policy manual and procedures. Manage employee disciplinary action process and partner with supervisors on all performance improvement plans. Provide guidance to supervisors for managing employees.\r Training & Development: Monitor employees probationary periods and guide supervisors through employee evaluations. Oversee SUPPORT Training database. Guide employees for professional development. Works with Training Development Coordinator to unsure employees working direct service with clients are RBT certified and complete Relias training coursework.\r IT: Work with IT department to make sure the companies Internet and server are HIPAA compliant. Work with IT to improve company server help ticketing system and website building.\rHuman Resources Assistant\rSeaChange International - Acton MA - 2014 to 2014\r2014\r Data entry for new employees and updating changes for existing employees.\r Enrolling new employees in benefits programs and making benefits changes.\r Create and provide all executive staff with monthly Headcount report.\r Setup employees with worker's compensation claims short term or long term disability claims.\r Prepare interview schedules offer letters and complete background checks.\r Provide support to all employee questions.\r Entering termination of benefits and notifications for employees that qualify for COBRA.\r Maintain and update vendor service agreements for third party sourcing.\r Execute PowerPoint slides for FY'15 Succession Plan.\r Maintain data integrity by auditing investigating errors and taking corrective action regarding data issues for both American and International data entry.\rEmployment Coordinator\rRight at Home - Westborough MA - 2013 to 2013\r2013\r Looking over resumes phone screening & interviewing applicants to see if they would be a good fit caring for the elderly in their home unsupervised.\r Maintaining all employees files and keeping their files up to date.\r Running CORIs background checks and DMVs on all employees; reviewing these reports to ensure they were fit to care for or to drive the elderly.\r Researched health insurance plans and signing employees up that qualify.\r Managing an assistant to help with booking appointments answering phone calls and filing paperwork.\rCustomer Service Administrative Assistant\rChaves Heating & Air Conditioning - Hudson MA - 2011 to 2012\rResponsibilities included: assisting customers with scheduling appointments confirming appointments maintaining the service technicians with daily scheduling and dispatching answering phones entering service\rinvoices preparing timecards selling and explaining yearly contract plans to customers and any general office support.\rStaff Counselor- Boys & Girls Club\rThe Youth Center - Marlborough MA - 2011 to 2012\rAfter school program for children from 6-18. The youth center is located in a section 8 housing facility.\r Responsible for leading programs implementing daily activities running games & tournaments homework help teaching how to cook/making group dinners taking small groups on community outings managing & facilitating children for their actions and helping them make positive choices (when age appropriate).\rTherapist\rMarlborough Public Schools - Marlborough MA - 2006 to 2011\rOne-to-one aide for children on the Autism Spectrum from ages 2.9-12.\r Following the principles of Applied Behavioral Analysis.\r Implementing behavior intervention plans running discrete trials increasing independent and leisure skills language opportunities making data sheets meeting with families for monthly clinics and graphing data for students success rate.\r Collaborating with occupational and physical therapists.\r In home one-to-one care working on bathing toilet training dressing and family participation skills including community outing; dining out and grocery shopping.\rWaitress Bartender\rRuby Tuesday's - Marlborough MA - 2003 to 2006\rEDUCATION\rM.A in Psychology\rUniversity of the Rockies 2012 to 2013\rB.A in Psychology Dean College\rGreen Mountain College - UK 2003 to 2006\rA.A in Liberal Studies\rDean College - UK 2001 to 2003\rADDITIONAL INFORMATION\rSkills\rComputer Proficient- Microsoft Office (Word Excel Outlook & PowerPoint) ADP & HRIS Databases Third Party vendor data entry (Blue Cross Blue Shield Unum EyeMed )\r Excellent interpersonal multi-tasking skills and excellent organizational skills.\r Adaptable flexible dependable and extremely efficient.\r Ability to work and problem-solve within a group or independently.\r Strong communication skills and phone skills.\r Social Media experience Website experience.\r CPR & First Aid certified.\r Scholarship & All American Softball Pitcher for Dean College - 2002 & 2003.\r\"\r\nresume_61,flagged,\"Kenneth Sikora Entrepreneur research scientist student\rBrookfield VT - Email me on Indeed: indeed.com/r/Kenneth-Sikora/ac846771d799d345\rI am looking to gain clinical experience become more familiar with the hospital environment and develop valuable health-care related skills while actively applying and expanding both my teamwork skills and my knowledge of the biological and chemical sciences and in the process helping enable positive change in patients lives. Some medical shadowing experience and excellent people skills.\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rStudent Library Advisory Committee\rNorwich University - Northfield VT - September 2014 to Present\rCommittee member (working with the committee to allocate over $1000 for purchasing library materials)\rAssistant Editor\rNorwich University Chameleon Literary Journal - August 2014 to Present Reviewing/critiquing submissions discussing journal layout photographing public functions\rStudent Ambassador\rNorwich University Chameleon Literary Journal - August 2014 to Present\rPromoting undergraduate research across campus serving as a resource for students)\rPrimary Investigator\rSu Hui's Stargauge - May 2014 to Present\rSelf-initiated research required me to teach myself several skills needed to pursue it\rCoo_rdinator (Identifying and working with a mentor collaborating with a Chinese literature professor from Changzhou University in China to translate ancient Chinese working with students and professors to construct an interactive website\rVermont Genetics Network At-Large Summer Intern\rUniversity of Vermont - Burlington VT - June 2015 to August 2015\rBSL-2 training and certification training in proper record keeping (lab notebooks) and data storage (computer files)\rIsolating RNA library building performing quality control and bioinformatics analysis presenting findings at monthly lab meetings\rUndergraduate Research Fellow\rNorwich University - Northfield VT - May 2014 to July 2014\rResponsibilities\rWorked with mentor to develop and propose a research project presented a poster of the project to a diverse audience\r \rAccomplishments\rDeveloped and wrote a protocol for the expression of H.pylori NDGluRS in E.coli\rEDUCATION\rBachelor of Science in Biochemistry\rNorwich University - Northfield VT 2016\rSKILLS\rAcid washing vacuum filtration recrystallization distillation interpretation of IR/NMR spectra retrosynthesis titration micropipetting transformation of bacteria extraction of protein PCR DNA sequence analysis protein denaturation protein trypsinization 2- and 3-D SDS-PAGE spot picking\r(3 years) Microsoft Excel (including macros and Visual Basic) PowerPoint _Torrent Adobe Photoshop Blender and Google SketchUp (4 years) PDB NCBI OMIM Biology Workbench BLAST Galaxy Server Quiagen IPA and Bioconductor packages in R (2 years) SYBL-X the Macintosh\rOS HTML CSS JQuery R DOS and the Ruby on Rails framework (Less than 1 year) Designing and manufacturing 3D-printed objects Microsoft PowerPoint and poster presentations public speaking experienced with manuscript preparation and the peer-review process network security Kali Linux and pen testing Designing and manufacturing 3D-printed objects Microsoft PowerPoint and poster presentations public speaking experienced with manuscript preparation and the peer-review process network security Kali Linux and pen testing Library building for sequencing primer design RT-QPCR statistical error analysis control charting GC-MS FTIR NMR extensive microbiology methods (1 year) PyMOL VMD Cn3D ChemDraw ChemSketch MiniTabTM MinitabExpressTM (2 years)\rAWARDS\rKreitzburg Library Prize for Best Student Scholarship in the Junior Humanities/ Liberal Arts category\rSeptember 2015\rAwarded for original research paper Git vs Ge: The Importance of the Dual Pronoun in Beowulf\rNorwich University College of Science & Mathematics Board of Fellows Student Research Award\rGiven for significant research-related scholarship in the examination evaluation and creation of technical knowledge as demonstrated by my undergraduate research project Investigating the Activity of H.pylori Non- discriminating Glutamyl-tRNA Synthetase\rThe American Institute of Chemists Student Award Certificate for 2016\rMay 2016\rRecognizing the recipient as an Outstanding Student for this academic year majoring in Biochemistry on the basis of a demonstrated record in leadership ability character scholastic achievement and advancement potential in the chemical professions.\rOutstanding Senior Award\rMay 2016\rAwarded by the Green Mountain Section of the American Chemical Society\rPUBLICATIONS\rGit vs Ge: The Importance of the Dual Pronoun in Beowulf\rhttp://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1150&context=tor\rSeptember 2015\rSikora Kenneth R. III (2015) Git vs Ge: The Importance of the Dual Pronoun in Beowulf The Oswald Review: an International Journal of Undergraduate Research and Criticism in the Discipline of English. Vol. 17: Iss. 1 Article 3.\rUsing Polynomial Regression to Objectively Test the Fit of Calibration Curves in Analytical Chemistry\rhttp://article.sciencepublishinggroup.com/pdf/10.11648.j.ijamtp.20150102.11.pdf\rFebruary 2015\rSeth H. Frisbie Erika J. Mitchell Kenneth R. Sikora Marwan S. Abualrub Yousef Abosalem Using Polynomial Regression to Objectively Test the Fit of Calibration Curves in Analytical Chemistry International Journal of Applied Mathematics and Theoretical Physics. Vol. 1 No. 2 2015 pp. 14-18.\rADDITIONAL INFORMATION\rLanguages: English (native); French (intermediate); German Castilian Spanish Mandarin Korean Russian (basic)\rCellist in small ensemble and string quartet playing seasonally at nursing/assisted living homes and hospitals Chorale guitar Highland pipes composing and arranging music amateur photography stop motion animation short filmmaking\rReading ancient and classical literature history linguistics/semantics watercolors poetry (reverse dactyl free verse and haiku)\rRaising livestock beekeeping maple sugaring gardening stone wall building/repair/maintenance post-and- beam construction\rHiking running skiing hunting horseback riding sailing fencing (foil eՁpeՁe sabre) historical reenacting English country dancing\r \"\r\nresume_62,not_flagged,\"Kerry Monahan\rIntern - Vermont Department of Fish & Wildlife\rBennington VT - Email me on Indeed: indeed.com/r/Kerry-Monahan/565d1c1e84eed844\rWORK EXPERIENCE\rIntern\rVermont Department of Fish & Wildlife - May 2012 to Present\rConducted wide array of bat fieldwork including: trapping; telemetry; tracking and technical assistance. Oversight of summer bat maternity colony monitoring efforts.\r Assisted with the Department's Got Bats! campaign.\r Applied knowledge of bat anatomy and behavior in monitoring and management efforts.\r Provided technical assistance to homeowners.\r Assisted with tracking endangered Timberland rattlesnakes. Independent biological research.\rVolunteer Citizen Scientist\rVermont Center for Ecostudies - May 2011 to August 2011\rMap vernal pool locations in Southern Vermont by conducting field visits to potential pools.\r Used GPS and mapping skills to locate identify and document potential pools.\r Collect biological and physical data and report locations of unmapped vernal pools. The purpose of this information collection effort is to improve conservation planning help protect species that depend upon vernal pools and preserve the ecological values associated with these wetland habitats.\rEDUCATION\rB.S. in Natural Resource Management\rGreen Mountain College - Poultney VT 2013\rA.A. in Liberal Arts\rSouthern Vermont College - Bennington VT 2011\rSKILLS\rBat surveying trapping identification and handling; acoustic data interpretation; technical assistance; nuisance wildlife control and exclusion; electro-fishing; telemetry; animal behavior; GIS/GPS; biology lab work; mammalogy lab work; chainsaw certification; forestry; forest ecology; watershed management; land- use management; navigation of rough terrain; timber rattlesnake knowledge; CPR; moderate ornithological identification skills; independent biological research. Rabies vaccinated.\r \"\r\nresume_63,not_flagged,\"Laura Sinofsky-Bohm\rSENIOR REGIONAL SCIENTIFIC MANAGER Northeast at ASTRAZENECA PHARMACEUTICALS\rWilliston VT - Email me on Indeed: indeed.com/r/Laura-Sinofsky-Bohm/28cf8c1e829a96bb\rDedicated decisive and self-motivated professional offering diverse background in building high-profile relationships developing and executing successful strategies and exceeding corporate objectives across the pharmaceutical industry within Sales and Medical Affairs. Strong technical orientation scientific aptitude and negotiation skills combined with a passion and stamina to lay foundation for success. Equipped with reputable ability to rapidly transition to new business and therapeutic areas. Adept at applying business planning approaches to achieve successful results. Highly capable of grasping and translating the impacts of healthcare trends and other business drives in a customer friendly language. A quick learner capable of effectively multitasking in a fast-paced and challenging environment. Demonstrates strong time management skills and is able to successful handle multiple tasks. Extremely flexible; adaptable to any working condition. Exhibits strong interpersonal skills and is accustomed to relating well with clients of diverse cultures and backgrounds in a highly stressful and challenging environment. Articulate communicator with fundamental knowledge of German and French and fluent in Spanish languages. Powered with unparalleled work ethic and exceptional organizational skills in effectively managing priority initiatives and critical projects. Proficient in Microsoft Office Suite (Word PowerPoint Excel Outlook and MS Office Publisher).\rWORK EXPERIENCE\rSENIOR REGIONAL SCIENTIFIC MANAGER Northeast\rASTRAZENECA PHARMACEUTICALS - October 2009 to Present\rOne of 6 RSMs elected based on leadership capabilities to represent a novel model supporting VIMOVO(TM). First group in Medical Affairs (MA) to use a phased approach to launch a new product thereby serving as guide for formation molding and development of future groups. Identified investigated defined and shaped customer base in unexplored area void of collaborations. Provided expert oversight to customer development and pre- launch activities. Forged participation in speaker meetings advertising board launch meetings and clinical trials. Spearheaded the development and alignment of key opinion leaders in accordance with AstraZeneca's therapeutic focus.\r Actively participate in 7 workgroups - 3 national level RSM trainings (i.e. Success Circles Diversity Team and MAWLI (Medical Affairs Leadership Initiative).\r RSM Lead for VIMOVO Training Team responsible for identification and executing training on priority topics for education of team.\r Sole RSM of the VIMOVO Navigator Team collaborating with senior management Strategy and Operations RDSA and Legal to unite and modify VIMOVO Team objectives for efficient and consistent field documentation.\r Lead American College of Rheumatology (ACR) RSM responsible for identification of key symposia posters collection and consolidation of reactive clinical insight.\r Held accountability for pipeline and clinical trial support identifying 10 trial sites for OSKIRA 3.\r Scientifically supported trained and reinforced regulatory guidance for 10 speakers auditing over 19 programs.\r Lead Executive Committee for Field Non-Sales Membership and Communications for AZNOW.\r Received distinction as the VIMOVO Highlights champion instructing coaching and driving interest in monthly submission process on a one-on-one and national basis to achieve VIMOVO metrics and objectives.\r \r Elected as 1 of 6 designers for MAWLI (Medical Affairs Leadership Initiative) logo.\r Handpicked to work with a group of women in driving performance and establishing people and organization MAWLI.\r Chosen by the senior leadership to highlight VIMOVO Team accomplishments during a national Medical Affairs meeting.\r Recognized as 1 of 5 members of (MA) Diversity Team in collaborating identifying and educating persons regarding MA cultural activities.\rSENIOR REGIONAL SCIENTIFIC MANAGER Northeast\rCNS/ Neuroscience - April 2009 to October 2009\rField liaison providing comprehensive educational support and scientific and clinical information to academic institutions and healthcare practitioners. Managed clinical trial development in Northern New England for investigator started studies in neuroscience (specifically schizophrenia bipolar mania and depression). Worked collaboratively with Clinical Development Team to support trial recruitment and site and investigator identification for SEROQUEL trials (such as AZD3480 SEROQUEL XRΩ); presented and assisted investigator commencing sponsored research by expediting the review assessment and follow-up of clinical proposals and research. Reinforced Brand Team activities by identifying thought leaders for symposia medical meetings and advisory boards. Instructed and scientifically updated the DURB members; liaised between the regional account and state government directors.\r Worked as speaker in roughly 3 dozen promotional speaker programs.\r Rendered support to Latin American Marketing Team through self-accord.\r Developed and maintained positive relationship with internal stakeholders (Brand Team and sales and scientific affairs) by meeting their expectations through application of strong comprehension of the business medical professionals and clinical investigators at key academic institutions such as Dartmouth Medical Center Maine Medical University of Massachusetts and Fletcher Allen Hospital located across Maine New Hampshire and Vermont.\r Increased geographical investigator sponsored study (ISS) activity through KOL identification supervising 6 PIs supporting business critical goals for SEROQUEL(TM) and SEROQUEL XR(TM).\r Co-Lead delivering critical intelligence to the Brand Team Emerging Brands and MASLT (Medical Affairs Senior Leadership Team) as RSM Team representative.\r APA Executive Summary Lead responsible for designing and rolling out novel format for collecting organizing CI from National Congress to Brand in two day real time.\r Investigated and educated peers on new technology Trial Trove.\r Monthly Highlight RSM Champion responsible for designing highlight guide mentoring and motivating peers. Co-Lead Logistics Team for country wide personal development forum (PDF).\r Facilitated Speaker Training live and web-based.\r Audited and supported 25 speaker programs.\r Partnered with National Account Manager Miguel Cotto conducting a conference calls series to educate medical professionals at Veteran Association Hospitals across United States.\r Success Circle Member contributing to cross-therapeutic area mentoring across the organization such as CV RDSA Senior Associate Scientist and Associate Director of Health Economics Research (HEOR).\rREGIONAL SCIENTIFIC MANAGER\rCNS/ Neuroscience - February 2006 to April 2009\rDeveloped and maintained comprehensive understanding and demonstrated proficiency in areas of CNS Franchise and Medical Affairs (MA) mission vision and operating principles; relevant operating company scientific data and objectives; relevant market dynamics and competitive landscape; regulatory and healthcare compliance guidelines affecting Medical Affairs and the pharmaceutical industry; corporate policies on\rappropriate business conduct and ethical behavior; all SOPs and guidelines affecting MA; as well as all requirements for appropriate behavior and documentation of activities through planned training. Utilized clinical/scientific and organizational knowledge to facilitate collaborations with investigators. Prepared and presented data and information in a logical manner and in accordance with the audience's request. Played a lead role in carrying out plans concerning proactive outreaches as permitted through legal exceptions process. Intelligently planned and implemented an integrated Medical Affairs/SAL strategic plan particularly on clinical investigator sites working to determine sites and resolve issues with enrolled sites that posed a barrier in conducting studies and accepted proactive activities. Provided professional oversight in instructing expert speakers per request through one-on-one interactions. Assumed full responsibility in providing general response(s) to scientific inquiries of local and regional health care providers investigators health care systems academic medical centers and payer systems. Held accountability in listening vigorously and recording scientific voice of customer. Established and maintained a regional scientific landscape plan determining major systems of care research capabilities and others.\r Provided support to the implementation of the XR mania and depression and relapse prevention program.\r Functioned as Co-Training Lead for 3 indications facilitating instructions on generalized anxiety disorder (GAD) emerging competitive intelligence (CI) as well as other topics in pediatrics\r Rendered assistance in the breakdown of competition during Journal Club Conference calls.\r Worked closely with regional/district Sales Leadership in providing regional and local support to improve sales training initiatives and to develop competencies of field personnel.\r Gained acknowledgment as the only RSM selected to support in the construction of Regional Director of Scientific Affairs (RDSA) Guidebook with 2 RDSAs (2009).\r Recognized as 1 of the lead US contributors CNS Innovation event across Medical Affairs; submitted 9 to the Medical Affairs (MA) Review Team 3 of which garnered national attention and was implemented (cardiovascular risk prevention best practice collaborative and consolidated reference site).\r Integral member of 4 person American Psychiatric Academy (APA) Planning Committee generating reporting documents and gaining approval from legal for distribution and use.\r Selected as 1 of 7 RSMs to join the CNS MA Teleconference Planning Team.\r Lead designer for capturing examples and rational of National Team activity in a comprehensive document. Lead for gathering competitive intelligence/insight (CI) for emerging clinical data.\r Formulated CNS CI Blitz a comprehensive update on the completive environment distributed biweekly.\rActive lead of CNS RSM Training Team\rRegional Team Monthly Highlight - August 2001 to February 2006\rpoint person for gathering organizing formatting and reporting individual submissions. Supported the instigation of storyboard on misuse or abuse of SEROQUEL.\r Active lead of CNS RSM Training Team.\rCNS * (ME MA NH VT) AUG 2001-FEB 2006 SPECIALTY CARE SALES SPECIALIST\rComprehended evaluated and implemented training on healthcare industry trends applicable laws and regulations and market conditions; directed the healthcare environment with compliant daily implementation of sales calls. Made use of analytics to determine and prioritize business drivers and offered solutions to problems and challenges. Assigned resources to meet various customer needs and opportunities; supervised performance plan and created real-time adjustments. Easily understood and communicated complex technical information and scientific concepts. Handled territory team matrix to identify new business opportunities establishing appropriate tactics and strategies. Handled all aspects of submitting honoraria setting up\rinformational booths and communicating with medical professionals to educate and serve as knowledgeable product resource.\r Collaborated with peers to solicit 4 panel speakers (2 thought leader neurologists and psychiatrists); planned and implemented infrastructure including assistance of MIS (2001).\r Collaborated with the P&T Committee for UMASS Medical Center and Saint Vincent's Hospital to grant formulary status of SEROQUELΩ (2001-2002).\r Designed and implemented 23 programs 2 customized solutions 3 Grand Rounds and various meetings with MIS to establish 2 thought leaders in Neurology and Psychiatry as well as 12 programs for the promotion of ZOMIGΩ (2001).\r Placed 2nd in sales volume for the Northeast ZOMIGΩ and remarkably surpassed SEROQUEL Ω sales performance expectations by 107.5% (2002).\r Expedited the acquisition of a $.25M research grant for use of SEROQUELΩ in pediatric patients in collaboration with the regional MIS and University of Massachusetts Medical Research Director (2002).\r Endorsed and directed 29 programs for SEROQUELΩ 2 programs for ZOMIGΩ (2002).\r Presented and published the Spanish translation of Dr. Weiden's Approach to Schizophrenia Communications (ASC-SR) which obtained approval to the National Quick List in (2002).\r Substantially improved quality time with medical professional through the establishment of lunch and learns and appointments- 60 and 10 in 2003 and 119 and 42 respectively (2002).\r Successfully exceeded ZOMIG sales performance expectations over 2 years by 107% surpassing the national 16% market share by 14% finishing at 108% (2003).\rBusiness Manager\rRegional Team Monthly Highlight - Manhattan NY - 2003 to 2003\ras Customer Solutions Champion for the district; spearheaded the New England West District Team in the New England region finishing first out of all the districts with utilization 3.5 times greater than the second district Manhattan New York District (2003).\r Gained acknowledgment as the second highest representative in the Northeast region for National Quick List by creating 61individual items for customers (2003).\r Distinguished at a national and regional level for a 5.8 and 2.4 point change ranking 7th nationally for SEROQUELΩ (106%) and ZOMIGΩ (120%) (2004).\r Nationally and regionally recognized for an increased average daily dose of SEROQUELΩ 200mg and 300mg tablets at Dartmouth Medical Center the number 1 ranked non-retail account (2004).\r Represented the organization as 1 of 23 PSS during Career Day at the Boston Business Center in New England.\rPRIMARY CARE SALES SPECIALIST\rCNS - April 1998 to August 2001\r Featured in the Regional Newsletter for November 1998.\r Earned recognition for outstanding sales achievements and was inducted into the 1999 President's Club.\r Exceeded forecast expectations for ZOMIG by 43.40% in 1998 and 48.20% (1999).\r Gained recognition as district leader for ACCOLATEΩ (106.26%) PULMICORT TURBUHALERΩ (118.84%) and ZOMIGΩ (143.89%) (2000). Functioned as anti-migraine disease specialist for the Providence District upon selection by the District Sales Manager (2002).\r Developed clinician questionnaire to identify the best times days and locations for lectures with medical professionals.\r Worked closely with CNS specialty long-term care and hospital counterparts to develop the Brighter Beginnings Program at 3 sites (Community Health Link UMASS Medical Center and Lipton Center-3 of largest\rnon-retail accounts) which engaged the mental health community in flower planting and beautification projects (2001).\ractive representative for the 17th 18th\rFramingham and Clinton Nurses Association - Boston MA - 2001 to 2001\r Forged participation in Mental Health Awareness Week by donating patient education materials to the Lipton Center (2001).\r Served as active representative for the 17th 18th and 19th Annual Public Sector Conference as well as for the Harrington Memorial Hospital display (2003); Framingham and Clinton Nurses Association (2003); VA Head Nurses Planning Committee (2002); American Association for the Study of Headache (1999 2002); Prime Medical Boston (1999) and Family Practice Conference for University of Massachusetts Medical Center (1999-2000).\rEARLIER CAREER\rVOLUNTEER (Paul Hart M.D )\rWorcester Evening Free Medical Service Program (WEFMSP) - Worcester MA - May 1998 to October 2000\rCLINICAL RESEARCH COORDINATOR\rMcKesson HBOC - Westborough MA - October 1997 to March 1998\rRESEARCH ASSISTANT\rBeth Israel Deaconess Hospital Department of Gerontology - Boston MA - October 1996 to May 1997\rRichard Glew M.D. Chief of Immunology\rUniversity of Massachusetts Memorial Medical Center Department of Immunology MA - 1997 to 1997\r1997\rRADIOLOGICAL TECHNICIAN INTERN\rUniversity of Massachusetts Memorial Medical Center Department of Radiology - MA - 1992 to 1993\r- Worcester\rWorcester\rObstetrician/ Gynecologist\rAlan J. Albert - Worcester MA - 1989 to 1993\rEDUCATION\rMaster of Science in Biology\rHarvard University - Cambridge MA 2006\rMaster of Science in Health Systems\rUniversity of Medicine and Dentistry of New Jersey School - 2005\rBachelor of Arts in Spanish\rBrandeis University - Waltham MA 1997\rNew Brunswick NJ\r\"\r\nresume_64,not_flagged,\"Leonid Alexander Norsworthy PhD IEMBA Former online Professor/Knowledge Manager\rSaint Johnsbury VT - Email me on Indeed: indeed.com/r/106492cb37e54e97\rInternational educator/economic development professional with over 30 years of experience. Offers extensive experience in knowledge management and learning corporate strategy and learning/informatics program design and implementation.\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rAdjunct Professor/Collegiate Professor\rUniversity of Maryland - 2008 to 2014\rGraduate School of Management and Technology\rAdelphi Maryland\rTaught Seminar 670 Contemporary/Competitive Strategy Analysis the final seminars in 42 semester hour Master of Business Administration program. Mentored fellow adjunct professor to assist in improvements in student evaluation scores. Assisted with transition of syllabus from 13 week to 10 week format for two final 6 graduate credit hour seminars.\rTaught 660 Environment and Organizations.\rAdjunct Professor\rUniversity of Maryland - 2008 to 2014\rTaught 6672 Germany in World Affairs 6642 Russia and Eastern Europe in World Affairs 6647 Western Europe in World Affairs 5524 Contemporary American Foreign Policy International Political Economy\rAdjunct Online Professor/Senior Online Instructor\rNorwich University - Northfield VT - 2007 to 2013\rCollege of Graduate and Continuing Studies\rTeach Seminar One: Theory in the International System Seminar Three: Law in the International System Seminar Four Commerce and the International System. Grade comprehensive examinations. Participate in June residencies culmination of six 6 semester hour seminars for Master of Diplomacy program. Graded comprehensive examinations. Supervised Masters theses. Participated in annual June residencies for Master of Arts in Diplomacy graduating students in Northfield Vermont.\r2008 Visiting adjunct professor\rIUKB Institut Universitaire Kurt Bosch\rNew York College Athens Greece/SUNY Empire State\rMBA in International Business and MBA in Marketing programs.\rInvited to teach 4 graduate credit EU based seminar with English as the language of instruction.\rAdjunct Professor\rDepartment of Business Administration and Department of Social Studies - Lyndonville VT - 2004 to 2010\rTaught 3240 International Business required course for Bachelor of Business Administration program. Introduced case methods and systematic development of critical thinking and other competencies\r \rConsultant/Writer-Editor/Knowledge Manager\rWORLD BANK GROUP - Washington DC - October 1995 to March 2005\rInformatics and Knowledge Manager conducted public policy and economic research on transitional economies of Europe Central Asia Latin America and Caribbean and lower income countries in Africa. Authored and edited strategy documents reports best practice notes and issue briefings. Led teams responsible for project and tasks in global development learning project research and analysis web applications and content development news services records and document management publications and corporate communications. Managed and implemented knowledge initiatives developed publications and communications strategies to reach stakeholders specialists and development partners.\rSenior Consultant--Writer/Editor\rWORLD BANK GROUP - 2002 to 2005\rLatin America Caribbean Region Operations Evaluation Department\rEnvironmentally and Socially Sustainable Development\r Through interviews with specialists and document reviews and synthesis exercises launch publication En Breve to disseminate information on noteworthy Bank lending and non-lending activities in the Latin America Caribbean region.\r Developed Results at a Glance publication for external audiences on the impact of Bank work in the areas of clean energy biodiversity and natural resources management water and agricultural productivity.\r Review sector operations and lessons from experience to draft sector guidelines for the use of Social Analysis in natural resources management and rural development projects and activities.\r Interview sector specialists to identify priorities and strategic thrusts of operations including relevant analytical frameworks.\r Coordinate drafting of overviews and sample terms of reference with sector specialists and social scientists for inclusion in Social Analysis Sourcebook.\rAdjunct Professor\rGlobal Development Program - Washington DC - 2001 to 2001\rKnowledge Manager\rWORLD BANK GROUP - 1998 to 2001\r Managed projects to establish distance learning centers in 11 countries. Oversaw market studies to determine demand for distance learning centers in Bosnia Bulgaria Lithuania Poland Russia and Ukraine. Content areas were in public sector management privatization and economic reform.\r In consultation with clients developed governance and resource mobilization strategies for these centers to achieve sustainable operations in knowledge services for economic development. Facilitated dialogue with high-ranking government officials and corporate leaders and other stakeholders on ICT issues relevant to knowledge economy and other Bank initiatives.\r Pioneered innovative approach to leverage existing national technological and human capital bases to reduce costs and integrate knowledge services within a given country in the region. Contributed to Bank science and technology strategy.\r Developed technical platforms for disseminating knowledge products and provided guidance and support to the task teams and thematic groups. Promoted and developed knowledge initiatives and partnerships with external client institutions. Implemented integrated web site knowledge system and promoted the use of these tools among staff. Provided guidance on operational and budgetary issues developing custom applications for project tracking.\r After training and certification served as coordinator for introduction of new financial products for lending operations and introduction of SAP and report development to support operations.\r Supervised knowledge management and distance learning teams of information analysts operations analysts and project consultants.\rAssociate Adjunct Professor\rSchool of International Service & School of Public Affairs -\rConsultant Economist/Social Scientist-other\rWORLD BANK GROUP - 1995 to 1997\rWashington DC - 1988 to 2001\rSub-Saharan Africa Region\rEnvironmentally and Socially Sustainable Development & Human Development Departments\r Designed and implemented new Rural Development and Environment knowledge management system including the publishing of web pages and the synthesizing of key documents on lessons learned in the field. Reviewed and analyzed findings of NRM and Rural Development projects.\r Developed new knowledge management systems for Health Population and Nutrition Affinity Groups in the Africa Region. Updated and designed new Poverty Monitoring knowledge management system.\r Wrote proposals to bilateral aid and multilateral donors in support of population and poverty projects for the African Population Advisory Committee located in the World Bank raising US$ 2 million.\r Developed and implemented document dissemination strategy for case studies training materials newsletters and videos for public health and poverty reduction activities in West and East Africa.\r Developed Staff Appraisal Report for Senegal Sector Investment Project integrating consultant studies and materials from preparatory field work. Wrote political component of extended poverty study of the Republic of Guinea. Developed presentations on Bamako Initiative public health financing health care and education reforms for the region as a whole. Authored press briefings for World Bank President's visit to Africa.\rDirector Foreign Policy Studies\rDaiwa Institute of Research/Daiwa Securities America - Washington DC - April 1991 to June 1995\rAs a senior policy analyst and manager in the consulting subsidiary of the world's second largest securities firm responsible for business development and monitoring the areas of U.S. foreign policy financial services regulation international trade defense and science and technology policy.\r Wrote daily weekly and monthly reports and books on U.S. policies affecting investor interests in the United States and around the world. Organized fact-finding missions for Daiwa personnel and clients to meet with industry leaders association executives and key federal government officials.\r Monitored and responded to World Bank Inter-American Development Bank and US AID contract opportunities for Daiwa and U.S. joint venture partners. Developed proposals for joint ventures and federal procurement contracts in the areas of information technology privatization technical assistance and financial services modernization.\r Developed successful proposal for ADB Indonesia workforce study. Participated in successful bid for EBRD Far East Venture Capital Fund in NIS. Promoted EBRD IBRD-funded training program in privatization and financial services for NIS managers and government officials\r Organized and conducted Washington components of postal service information systems procurement practices. Conducted and disseminated findings of study on U.S. systems integration services. Investigated and documented joint venture opportunities between US and Japanese companies in Asia and Latin America. Conceptualized and implemented fact finding tours for Japanese financial executives to study reforms in the U.S. banking and securities industries.\rInstructor\rSchool of International Service & School of Public Affairs - Washington DC - 1992 to 1993\rDirector Development Serivices\rCatholi University of America - Washington DC - November 1988 to April 1991\rAs a member of the capital campaign management team managed fund accounting prospect research and systems in support of a $100 million centennial campaign and $12 million in annual development revenues.\rLecturer\rCatholic University of America--University College - Washington DC - 1989 to 1990 Taught two sections of Europe and 1992\rManagerMarketing Research\rAmerican Enterprise Institute - Washington DC - June 1988 to October 1988\rFirst as a consultant and then as a member of the development staff responsible for corporate relations representing two-thirds of a $12 million annual budget conducting extensive marketing research and managing a data conversion from a IBM System/38 to a Novell network using Clipper and dBase software.\rAssistant Director\rDevelopment - Washington DC - 1984 to 1988\rResponsible for managing prospect research for a $100 million campaign. Coordinated direct mail solicitations fund raising scripts proposals to foundations corporations and individuals..\rEDUCATION\rInternational Master of Business Administration in Business Adminisration\rMcDonough School of Business - Washington DC 1997 to 1999\rDoctor of Philosophy in International Relations\rAmerican University - Washington DC 1985 to 1989\rMaster of Arts in International Affars\rAmerican University - Washington DC January 1985 to December 1986\rBachelor of Arts in International Studies\rAmerican University - Washington DC September 1980 to December 1984\rSKILLS\rForeign Lanagues: French German Russian Spanish and Portuguese (10+ years) Microsoft Office (10+ years) Lotus Notes/Domino (10+ years) Foreign Language: Russian (10+ years) Foreign Language German (10+ years) Foreign Language Spanish (10+ years) Graduate online graduate school instruction (10+ years)\rAWARDS\rBeta Gamma Sigma Georgetown University Washington D.C.\rMay 1999\rTop 20% of 55 student graduating cohort in IEMBA program\rPhi Kappa Phi\rMay 1989\rTop 1% of all graduating graduate students at American University\rDeans List Amerivan University\rDecember 1984\rGraduate with a 3.3 grade point average for all undergraduate coursework completed at American University\rPUBLICATIONS\rRussian Views of the Transition\rApril 2000\rCo-wrote and co-edited compendium of Russian authors views of the transition to a market economy.\rLessons from Experience: Agriculture Natural Resources and the Environemnt\rMarch 2000\rco-wrote adn co-edited compendium of best practices in the rural development natural resources management and environment management sector forr the Environmentally and socially Sustainable Development professional network in the Europe Central Asia region of the World Bank Group (International Bank for Reconstruction and Development)\rThe Clinton Revolution: an Insider's Look at the New Administration\rJanuary 1993\rCo-wrote and co-edited review of teh foreign and domestic policies of the incoming Clinton Administration the first book on Bill Clinton's administration published in Japan and appearing in the United States of America.\rDawn of a New Era\rOctober 1992\rCo-wrote and co-edited a policy review/rounup of the bush Administration in preparation for the 192 presidential election.\rNon-Profit Computer Sourcebook.\r1988\rFor the Taft Group researched wrote and edited a directory of computer software and hardware systems available for non-profits\rFRI Prospect Research Resource Directory\rOctober 1991\rResearched wrote and edited directory of resources for prospect research for non-profitd engaged in annual and capital fund raising campaigns.\rPolitical Economy of Science and Technolgy in the German Democratic Republic\rMay 1989\rDoctoral dissertation on science and technology in the German Democratic Republic. Findings based on literature reviews and multivariate regressions of foreign trade and domestic production in the micro-elecronics sector in East Germany. Originally predicted reunification going into defense in February 1989 but was counseled to change this prediction to a more neutral set of findings without predictions of political economic policy predictions\rEast Germany Trade and Economic Policies\rMaster's thesis written in support of Master of Arts degree from the Scholl of Interntional Service awarded in December 1986\rADDITIONAL INFORMATION\rFull list of publications presentations and references available upon request\r\"\r\nresume_65,not_flagged,\"Louis Larosiliere\rSenior Director Aero & Hydrodynamics Engineering\rQuechee VT - Email me on Indeed: indeed.com/r/Louis-Larosiliere/bf0d65b129796ae6\rWORK EXPERIENCE\rSenior Director Aero & Hydrodynamics Engineering\rConcepts NREC - Wilder VT - January 2006 to April 2014\rPlan and direct all turbomachinery aerodynamic/hydraulic design and R&D activities for a diverse cross-section of industrial and government clients.\r Provide technical and programmatic direction to over a dozen engineers engaged in the engineering of advanced turbomachinery components and systems.\r Expertise in most turbomachines especially advanced axial fan and compressor designs. Consistently deliver the highest quality engineering designs with 100% client satisfaction.\r Maintain core competency and relevancy by leveraging government sponsored R&D efforts in technology innovation.\r Develop new business through marketing and sales campaigns Recruit mentor and develop engineering staff.\rSenior Researcher/Technologist\rU.S. Army Research Lab at NASA GRC - Cleveland OH - February 1992 to December 2005\rResponsible for advocating planning and executing turbomachinery and propulsion\rsystems technology development aimed at enhancing performance. Developed state-of- the-art simulation methods in support of advanced turbomachinery aero design and\ranalysis. Established appropriate technology investment areas for resolving U.S. Army\rpropulsion and power logistics problems in collaboration with technologists and other\rpersonnel from NASA GRC industry and universities. Basic specialties and core\rcompetencies include: aerodynamic shape design and performance optimization;\rapplication of active flow-control to intelligent propulsion and power systems; unsteady\rgas dynamics of fluid machinery; high speed aerodynamics and reacting flows;\raeropropulsion system design and analysis; thermofluids modeling and simulation of hybrid gas turbine and fuel cell power systems. Directed a team of researchers in\rassembling and developing the technology base for the next generation (NASA's UEET) of high-performance compact multi-stage compressors in aerospace propulsion and power. Other duties included serving as a technical focal point for air-breathing\rpropulsion aerodynamic modeling and design at the U.S. ARMY Research Lab.\rResearch Associate\rUniversity of Tennessee Space Institute - Tullahoma TN - January 1989 to December 1991\rResearched and applied CFD algorithms for multi-phase chemically reactive flows. Developed a bipropellant spray combustion analysis tool based on the Los Alamos KIVA code to support rocket thrust chamber design. Wrote a winning proposal for conducting computational research in rocket engine bipropellant spray combustion under NASA sponsorship. Performed a comprehensive parametric study of the combustion characteristics of a small satellite rocket thruster equipped with a pintle spray injector resulting in a substantial reduction of the development time.\r \rLead Engineer\rWilliams International Walled - Lake MI - August 1987 to December 1989\rResponsible for the design analysis and testing of gas turbine compressors. Directed technicians in development testing for numerous high-performance small axial compressors including instrumentation definition data reduction and synthesis. Executed the aerodynamic design of an advanced highly-loaded three-stage axial compressor from concept development to detailed flow path and blading design including iterations with aeromechanics specialists. Created and developed conceptual design and performance evaluation software for axial compressors. Consulted on gas turbine aerodynamics development issues and thermodynamic cycle performance assessment.\rTurbomachinery Engineer\rGE-Aircraft Engines - Lynn MA - July 1985 to August 1987\rResponsibilities included the aerodynamic design and development of advanced\rcompressors. Produced conceptual and detailed designs for several innovative aero- engine axial and centrifugal compressors. Instrumental in the successful aerodynamic\rredesign of two production fans and compressors. Substantially reduced the design and development time of a high-performance fan through the expert application of state-of- the practice computational aerodynamics (CFD) and structural mechanics tools.\rResearch Associate\rKarman Institute for Fluid Dynamics - September 1984 to July 1985\rPrincipal investigator on the modeling of rotating stall in axial-flow compressors.\rDesigned instrumentation system for measuring the flow structure within a rotating\rblade row in deep stall. Performed theoretical and experimental investigations on the inception and evolution of rotating stall. Tested analyzed and gained insight into the\rdevelopment of centrifugal pumps airfoil cascades turbochargers and axial blowers.\rObtained valuable international collaborative R&D experience via interactions with\rEuropean/NATO scientists.\rEDUCATION\rDoctor of Philosophy (Ph.D.) in Aerospace & Mechanical Engineering\rUniversity of Tennessee - Knoxville TN\rPost Graduate Diploma in Fluid Dynamics\rKarman Institute for Fluid Dynamics\rMaster of Science in Aerospace Engineering\rUniversity of Tennessee - Knoxville TN\rB.S. in Aerospace Engineering\rBoston University - Boston MA\r\"\r\nresume_66,not_flagged,\"Marcus Pante Safety and Training Specialist\rWestford VT - Email me on Indeed: indeed.com/r/Marcus-Pante/c5c4028fac25efb4 Authorized to work in the US for any employer\rWORK EXPERIENCE\rTown Health Officer\rTown of Westford VT - Westford VT - October 2016 to Present\rInvestigate possible public health hazards and risks within the municipality.\rTake action to prevent remove or destroy any such hazards.\rTake action to mitigate significant public health risks.\rEnforce health laws rules and permit conditions and taking the steps necessary to enforce orders.\rSafety and Training Manager\rThe Oryza Group / FCI Federal - Saint Albans VT - February 2015 to September 2016\r* Establish safety programs and introduce a safety culture to an office environment with a large amount of repetitive work and material handling.\r* Manage all Risk Management Workers Compensation and return to work programs.\r* Interface on a regular basis with our governmental customers with regards to environmental health safety and security.\r* Developed and launched an out of service and maintenance program for all equipment.\r* Developed and delivered a driver safety training program.\r* Managed a safety program for 85 seasonal employees performing office work in an active warehouse.\r* Perform regular safety inspections off all work spaces.\r* Schedule proctor track and report all annual training required by the company and the customer.\r* Develop and conduct HAZMAT training forklift training and Sexual harassment/workplace violence training. * Manage ADA accommodations.\r* Perform Environmental Health and Safety audits of other contracted sites.\r* Developed weekly safety training materials for all team meetings.\r* Managed all aspects of Occupant Emergency planning for 450 contract staff in a federal facility including planning and assessing fire/active shooter and shelter in place drills.\rRegional Safety Specialist\rMultiband Inc - June 2013 to September 2014\r* Develop and support a Safety culture in 12 markets from Maine to Michigan encompassing 1200+ associates. * Develop training materials on a weekly basis for technician safety meetings.\r* Develop and implement Safety programs policies and training.\r* Oversee and support all Risk Management Activities for assigned sites.\r* Perform quarterly site safety audits.\r* Conduct post incident root cause investigations for all accidents and near misses.\r* Evaluate new tools and work practices for their possible effect on associate safety.\r* Interact with local and state agencies with regard to occupational health and safety related concerns. * Support other departments as required to help ensure the safety of all associates.\r* Travel regionally 70% of the time.\r \rSite Training and Safety Manager\rMultiband Inc - June 2011 to June 2013\r* Develop and support a Safety culture in a market consisting of 50 associates conducting independent telecommunications field services work.\r* Manage all site and field training activities.\r* Maintain all associates training records including both company provided and 3rd party certifications. Conduct weekly technician safety meetings. Conduct 6-week new hire technical training classes for all new employees. * Conduct specialty classes and new technology training as required by federal agencies and customer contracts.\r* Train the Trainer certified for all DirecTV and Viasat technical quality and audit certifications. * Perform all First Aid and CPR instruction in accordance with NSC requirements.\rService Technician\rMultiband Inc - March 2009 to June 2011\r* Troubleshooting specialist for existing and VIP DirecTV Systems. * Provide technical support for installation technicians.\rInstallation Technician\rMultiband Inc - November 2008 to March 2009\rConduct installations for new DirecTV customers and upgrade existing DirecTV customers.\rStaff Scientist\rStone Environmental Inc - Montpelier VT - April 2005 to March 2008\r* Operate and maintain the company's Geoprobe 6610DT Drill Rig.\r* Operate and maintain the company's Waterloo Profiler system.\r* Operate and maintain the Membrane Interface Probe and associated tooling.\r* Collect and handle groundwater and soil samples in accordance with Stone Environmental SOP.\r* Interpret Data in the field as it is generated to assist the client in refining of the conceptual site model and guide further investigation.\r* Process and QA/QC Data generated in the field for client deliverables.\r* Develop and review SOPs to improve productivity efficiency and safety.\r* Travel on short notice nationally and internationally to meet client needs.\r* Generate and implement site specific Health and Safety Plans.\rField Technician\rPrecision Industrial Maintenance - Barre VT - 2004 to 2005\r* UST and AST cleaning and removal.\r* Hazmat spill response and cleanup.\r* Organized and maintain spill response equipment.\r* Industrial Health and Safety compliance assistance.\r* Safely work with hazardous materials in industrial facilities.\r* Confined space work.\r* Manage and transport hazardous waste in accordance with DOT regulations.\rField Services Technician\rClean Harbors Inc - Bow NH - 2003 to 2004\r* Hazmat spill response and cleanup.\r* Confined space work and rescue in level C B and A PPE. * Work safely for long periods of time in extreme conditions.\r* Travel throughout New England on short notice.\rEDUCATION\rEnvironmental Science / Biology\rNorwich University - Northfield VT 1997 to 2002\rSKILLS\rMicrosoft Office Suite (10+ years) Root Cause Analysis (10+ years) Training & Development (7 years) Safety (10+ years) Continuous Improvement (10+ years)\rMILITARY SERVICE\rService Country: US Branch: US Navy Rank: E3\rJuly 1993 to July 1995\rADDITIONAL INFORMATION Interests\rProfessional Interests\r Occupational Safety\r Adult Career Development Failure Analysis\rPersonal Interests\r Scuba Diving\r Woodworking\r Home-brewing\r Fly Fishing\r\"\r\nresume_67,not_flagged,\"Margaret McEnroe\rMonkton VT - Email me on Indeed: indeed.com/r/Margaret-McEnroe/e602259e804a2445\rWORK EXPERIENCE\rAll Breed Cat and dog Grooming - Verona NJ - September 2008 to July 2014\rGrooming dogs; advising clientele on proper care of their animals; managing grooming aspect of the business; managing employees; answering phones; planning appointments.\rVeterinary Technician Assistant\rAll Breed Cat and dog Grooming - Caldwell NJ - January 2010 to September 2011\rAssisted veterinarian with various tasks including restraining animals for examination shots blood drawing x-rays and anesthesia administration; cleaned equipment cages etc; administered medication to various animals; cared for boarded animals; answered phones and booked appointments.\rField Biologist\rU.S. Geological Survey - Camp Pendleton CA - March 2008 to September 2008\rConducted stream surveys for the sensitive Arroyo Toad tadpole on Marine Corps Base Camp Pendleton and throughout San Diego County; conducted water quality assessments including dissolved oxygen pH and conductivity; conducted stream vegetation and habitat assessments; conducted night surveys for Arroyo Toads; trapped and collected data for Western Pond Turtles on MCB Camp Pendleton.\rBrown-headed Cowbird Technician\rU.S. Geological Survey - San Diego CA - March 2008 to May 2008\rTrapped and exterminated Brown-headed cowbirds in an effort to cull the population due to its negative effects on sensitive and endangered bird populations. Traps were set up and managed along the San Luis Rey River in San Diego County CA.\rAvian Surveyor\rU.S. Fish and Wildlife Service - Carlsbad CA - March 2007 to June 2007\rLocated randomly selected points with the use of a GPS unit throughout San Diego County CA to survey the endangered California Gnatcatcher; spoke with landowners and interested public in order to gain access to specific points and to educate about our efforts; conducted point counts to determine the presence/absence and success of gnatcatchers; conducted vegetation surveys and habitat assessments at all survey points; entered data with the use of Microsoft Access.\rAvian Research Field Assistant\rOhio State University Natural Resources Department - Columbus OH - April 2006 to June 2006\rMist-netted and banded migratory land birds along the coast of Lake Erie in Ohio; took blood samples from white-throated sparrows yellow-rumped warblers magnolia warblers and red-eyed vireos for metabolite and DNA research; prepared blood samples for laboratory analysis; radio-tracked yellow-rumped warblers and red- eyed vireos; conducted vegetation surveys at banding bleeding and tracking sites.\rAvian Research Field and Lab Assistant\rUniversity of Rhode Island Department of Natural Resource Sciences - Kingston RI - September 2005 to December 2005\r \rMist-netted banded and took blood samples from various species of migratory land birds at the Kingston Wildlife Research Station; prepared blood samples for laboratory analysis; monitored and recorded the weights and nutritional status of over 20 captive white-throated sparrows for use in metabolite research; released captive sparrows at the conclusion of the field season.\rCoastal Research Fellow\rUniversity of Rhode Island Department of Plant Sciences - Kingston RI - May 2005 to October 2005\rWorked with plant scientists to investigate organic bio-solid fertilizers and their effects on soil pH nitrates and crop production while farming four types of vegetable plants; planted and cared for crops; monitored and maintained insect damage; extracted and prepared soil samples for laboratory analysis; prepared plant samples for analysis; as part of the university's Coastal Fellowship program I created an original research project determining effects of bio-solids on the common earthworm Eisenia fetida.\rTurf grass Research Assistant\rUniversity of Rhode Island Department of Plant Sciences - Kingston RI - May 2005 to September 2005\rAssisted turf grass scientist on an ongoing research project; conducted various maintenance procedures including seeding fertilizing and mowing; entered data; collected plant and turf samples for analysis; monitored insect damage.\rEDUCATION\rBachelor of Science in Wildlife and Conservation Biology\rUniversity of Rhode Island - Kingston RI September 2001 to December 2005\r\"\r\nresume_68,not_flagged,\"Maria E Ramos-Nino\rMedical Laboratory Scientist-Charge - Department of Pathology and Laboratory Sciences\rColchester VT - Email me on Indeed: indeed.com/r/Maria-E-Ramos-Nino/523bebbcc55737fa\rWORK EXPERIENCE\rMedical Laboratory Scientist-Charge\rDepartment of Pathology and Laboratory Sciences - 2009 to Present\rSupervise medical laboratory personnel Execute medical microbiological testing\rAffiliate Assistant Professor of Pathology\rUniversity of Vermont - 2009 to Present\rLecturer Department of Nursing and Health Sciences\rUniversity of Vermont - 2008 to Present\rTeach Advance Clinical Microbiology (4cr/1semester)\r Teach Principles of Microbiology (3cr/4 semesters)\r Teach Clinical Chemistry I (4cr/1 semester)\r Teach Advanced Clinical Chemistry II (4cr/3 semester) Teach Applied Molecular Biology (3cr/2 semester)\r Teach Immunology (3cr/1semester)\rMedical Laboratory Scientist\rDepartment of Pathology and Laboratory Sciences - 2009 to 2009\r2009\r Execute medical microbiological testing\rResearch Assistant Professor\rUniversity of Vermont - 2004 to 2009\rResearch: Environmental pathology.\r-Cell signaling pathways that regulate Fra-1 expression and genes transcriptionally regulated by Fra-1 during the development of metastasis.\r-Endogenous factors that may act as modifiers of susceptibility to cancer caused by exposure to viruses and environmental toxicants or drugs.\r Team-teach Environmental Pathology: Microarray technology in environmental sciences.\r Supervise Post Doctoral Fellows research\rVolunteer work in emergency services\rMilton Rescue - Milton VT - 2003 to 2009\rClinical Research Fellow\rUniversity of Vermont - 2006 to 2008\rCommittees - 2004 to 2008\rResearch Associate\rUniversity of Vermont - 2001 to 2004\r \rConsulting Scientist\rIngenuity Systems - 2001 to 2002 Knowledge Acquisition for data bases.\rResearch Associate\rSchool of Medicine Center for Molecular Medicine and Genetics\r- Detroit MI - 1999 to 2001\rWayne State University Detroit Michigan. 1999-2001\r Coordinated microarray project. Managed high-throughput robotic equipment\rthat manufactured microarrays; maintained clone libraries; optimized and controlled plasmid and PCR products production; established parameters and supervised microarrays function and quality; labeled hybridized and analyzed researchers' samples.\r Trained graduate and undergraduate research assistants.\r Created and organized cloning lab to make controls for high-throughput genotyping assays.\r Researched on ribosomal RNA using instant evolution techniques.\rP.P.I - 1997 to 1999\rAACR Minority Scholar Award 2006\rProfessor of General and Applied Microbiology\rUniversidad Nacional - 1990 to 1999\rCreated directed and managed multidisciplinary Biotechnology Center. Coordinated work of biologist chemists artificial intelligence and electronic engineers to develop technologies in animal production and plant sciences.\r Directed many undergraduate and graduate theses leading to the development of new protocols and instruments for the dairy farms and milk industry in the region.\r Assisted animal farms in implementing microbiological quality control programs\r Designed and managed undergraduate laboratories\r Researched in mastitis: diagnostic tools auto-vaccines host-microbial interaction.\rCONICIT - 1993 to 1996\rLecturer Food Microbiology\rInstituto Universitario de Tecnologia - 1989 to 1992\rDesigned and implemented new curriculum and laboratory exercises. Developed projects in food safety.\rEDUCATION\rBachelor in Medical Laboratory Sciences\rUniversity of Vermont 2009\rClinical Research Fellow\rUniversity of Vermont 2006 to 2008\rDoctoral in Molecular Biology\rWayne State University 1999 to 2001\rPh.D. in Microbiology and Molecular Biology\rUniversity of Surrey - Surrey UK 1996\rMaster in Business Administration\rUniversidad Nacional del Tachira 1992\rB.S. in Biology\rUniversity of Massachusetts - Boston MA 1982\r\"\r\nresume_69,not_flagged,\"Marie Alvin\rWhite River Junction VT - Email me on Indeed: indeed.com/r/Marie-Alvin/8e74552432f3a93f Authorized to work in the US for any employer\rWORK EXPERIENCE\rResearch Support Assistant\rEngineer Research and Development Center (ERDC) / Cold Regions Research Engineering Laboratory (CRREL) - Lyme NH - June 2016 to September 2016\rValidated time entry and processed in the CEFMS database meeting payroll deadlines and keeping proper records for 25 Researchers and Scientists. Prepared travel orders and submitted travel vouchers for reimbursement. Facilitated arrival of new employees. Maintained travel / leave calendar and administrative files. Scheduled conference rooms and attended branch meetings as needed. Submitted DPW work orders and followed through until completion.\rCommand Staff Division - ERDC - Cold Region Research and Engineering Laboratory\rKatmai Technical Services LLC - Hanover NH - June 2008 to June 2016\rHanover NH\rCommand Staff Division - ERDC - Cold Region Research and Engineering Laboratory Administrative Assistant - Contractor (32 hours Week)\r Prepare review and edit timekeeping using the Corp of Engineers Financial Management System (CEFMS) Obtain proper documents for employees leave and maintain accurate timekeeping files for audits\r Prepare travel orders and vouchers for employees based on specific requests provided by traveler in accordance with appropriate travel regulations\r Input and process employee travel requests and vouchers into Corp of Engineers Financial Management System (CEFMS) for travel\r Submit local vouchers for mileage reimbursements\r Serve as an organizational resource for current training classes scheduled\r Developed and maintain several excel spreadsheets tracking supervisor's training\r Developed and maintain CP 18 Intern excel spreadsheet\r Coordinate and manage development assignments for the CP18 Intern program obtaining proper documents and maintaining files\r Main point of contact for the CP18 Interns providing guidance and direction as needed\r Ensure the organization's administrative operations run smoothly including maintaining files performance and personnel support\r Organize and provide needed supervisory training of the Delegation of Training Authority maintaining proper documents and record files\r Schedule and arrange conferences rooms using Outlook calendar\r Receive telephone calls and correspondence\r Building property supervisor submitting work orders as needed and requested\r Assist with other duties as assigned\rProgram Coordinator\rWhite River SD - June 2005 to August 2008 P/T 20 hours week)\r \r Coordinated reading times with teachers and mentors\r Supervised and developed relationships between mentors and students\r Facilitated communication between principal teachers mentors and children Maintained data on excel spreadsheet\r Organized annual events\r Schedule and ran orientations for new mentors\r Assured guidelines and program protocol were followed\r Provided support to mentors and students as needed\rOffice Administrator\rUpper Valley Haven - Homeless Shelter - Hartford VT - June 2005 to April 2008 30 Hours Week)\r Ensure the organization's administrative operations run smoothly including maintaining files performance and personnel support\r Prepare correspondence reports and monthly packets for board meetings\r Maintained donor data base\r Recorded gift donations in database and prepared daily deposits (75 - 100 Checks a day during the Holiday season)\r Prepared gift acknowledgements to send to donors\r Prepared annual newsletter mailings\r Responsible for accounts receivable and accounts payable\r Prepared payroll\r Assisted with quarterly and annual tax documents\r Created an excel spreadsheet for Tacking memorial contributions for families Assist clients in obtaining their month's supply of food\r Stocked food shelves\r Entered data in the computer tracking system\r Taught volunteers how to use tracking system\r Maintaining adequate stock of supplies\r Contacted companies for equipment repairs\r General office duties answering phone photo copying\rAdministrative Assistant\rWindsor County Partners - Windsor VT - February 2006 to July 2006\rP/T 10 hours Week)\r Maintained Mentor and mentee database\r Generated gift acknowledgments\r Prepare and organize partnership packets\r Assisted in guiding mentors with mentee as needed\r Prepared monthly packets for board meetings\r Organized and established a process to help maintain the smooth operation within the office Answered and directed phone call opened and distributed mail\rParaprofessional\rHartford School District - White River SD - August 1995 to June 2004\rF/T 35 hours week)\r Assisted in Reading Recovery Program for 5 years\r Provided individual reading instruction for first and second graders\r Reported progress and needs head of department\r Implemented speech and language activities for students in need of services for three years Assisted speech/language pathologist as assigned\r Entered progress or needs in students files\r Provided one on one assistance for a second grader for 1 year\r Qualified typist 50 WPM\rEDUCATION\ryes\rHartford High School - Hartford VT\rSKILLS\rTyping 50 WPM familiar with Microsoft Word Excel and Outlook. (7 years)\r\"\r\nresume_70,not_flagged,\"Mariel Cykon\rASCP certified Medical Laboratory Scientist - MLS(ASCP)cm\rBurlington VT - Email me on Indeed: indeed.com/r/Mariel-Cykon/932af590b8289a19\rHighly trained Medical Laboratory Scientist with strong abilities in laboratory technique\rspecimen handling and equipment use. My education has made me successful in carrying out Laboratory tasks and techniques in my internship at Rhode Island Hospital. I am motivated and organized with a passionate commitment to first-rate patient care. Licensed Medical Laboratory Technologist through the American Society for Clinical Pathologists (ASCP) with general expertise in Hematology Immunohematology Chemistry Urinalysis and Microbiology.\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rMedical Laboratory Science Student Intern\rRhode Island Hospital - Providence RI - January 2015 to May 2015\rDemonstrated successful technique and knowledge in the areas of Hematology Coagulation\rUrinalysis Chemistry Microbiology and Blood Bank. Proper handling of blood collection tubes for testing and analysis of specimens for acceptability. Data was evaluated by recognizing abnormal and\rnormal results. Experience with Beckman Coulter Centaur Vitek 2 Maldi Blood gas analyzers\rRemisol.\rEDUCATION\rBachelor of Science in Microbiology Molecular Biology\rUniversity of Vermont - Burlington VT May 2015\rAssociate of Arts in Psychology\rCommunity College of Vermont - Montpelier VT May 2011\rADDITIONAL INFORMATION\rSkills\rASCP Certified MLS Specimen Processing\rCritical Thinking Public Safety Security and Confidentiality\rJudgment and Decision Making Science\rLaboratory Testing Technique Operations and Quality Control Analysis\r \"\r\nresume_71,flagged,\"Mark Mckenna\rChief Scientist Unmanned Systems Division - APPLIED RESEARCH ASSOCIATES INC\rWhite River Junction VT - Email me on Indeed: indeed.com/r/Mark-Mckenna/7ec9e42dca7d6a0a\rWORK EXPERIENCE\rSenior Scientist\rAPPLIED RESEARCH ASSOCIATES INC - Randolph VT - 2009 to Present\rRandolph Vermont 2009-current\rSensing instrumentation and application development organization with 1220 employees.\rSenior Scientist\rLead scientist for nonlinear acoustic detection of concealed weapons for Department of Defense Joint IED Defeat Organization using phased microphone array. Lead scientist for project for Department of Homeland Security in continuation of project from Luna Innovations. Improvements to system with hardware integration of multiple axis scanning system for receivers and software improvements to speed scanning feature extraction and classification using Matlab and Labview.\rSenior Research Scientist\rLUNA INNOVATIONS INC - Hampton VA - 2005 to 2009\rWin more than $2M in program contracts by collaborating with prospects and current clients to determine sensor needs and building business case through data collection analysis and the development of technical reports and proposals. Direct team of up to 3 to manage all measurement science and instrumentation development activities; orchestrate new laboratory techniques Ensure achievement of programs by effectively managing relationships with vendors defense department prime contractors and industrial aerospace companies including NASA Boeing Aerojet and EPRI.\rKey Contributions:\r Served as lead scientist resulting in office securing multi-million dollar contract from DHS.\r Asked to serve as technical reviewer within NASA's peer review process.\r Led office in program wins for past two years with highest proposal value\r Research for Boeing 787 Dreamliner lead to patent application on fastener characterization\rKey Projects:\r Enabled measurements of stress to characterize aerospace fasteners and cold worked zones including transducer development;\r Partnered with the Department of Homeland Security to discover a way to detect concealed weapons explosives and IED devices from a distance;\r Leveraged nonlinear acoustics to characterize welds for NASA's ARES launch vehicle;\r Developed high-power RF test and measurement electronics - featuring instrument control and utilizing both analog and digital circuitry - via LabView.\rMARK MCKENNA PhD mark.j.mckenna@gmail.com\rExecutive Vice President\rRITEC - Warwick RI - 1995 to 2005\rServed in dual role as senior executive and research program manager to grow annual sales from $350K to $1M during tenure; partnered with international clients on various semi-conductor and RF\r \rprojects and provided training and foreign sales development. Oversaw day-to-day operations of 10-person office including sales customer development customer support software development and instrumentation engineering activities.\rKey Contributions:\r Delivered additional revenues by securing win of SBIR Phase II grant for microcavitation controlled ultrasonic cleaning of semiconductor wafers.\r Realized development and launch of 4 new instruments and revised 2 additional instruments during tenure; including spearheading 2 custom instruments for Lockheed and Sumitomo Metals Technology.\r Commanded expertise in Visual Basic Labview and C/C++ to develop RF test and measurement electronics with analog and digital circuitry.\rAssociate Lecturer Physics Department\rUNIVERSITY OF WISCONSIN-MILWAUKEE - Milwaukee WI - 1993 to 1995\rEnhanced school credibility and grew enrollments in introductory physics course while serving in joint position as course lecturer and researcher of material properties of high-temperature superconductors. Published several research advancements and received multiple distinctions in peer-reviewed scientific journals including Physical Review Letters.\rKey Contributions:\r Developed precision ultrasonic measurement system.\r Authored solutions manual for intermediate thermodynamics textbook.\r Realized increased enrollment in introductory course after completing survey project and recommending improvements to the undergraduate supervisor.\r Selected for numerous honors including serving as session chair for the national conference and publication of physics demonstration video throughout media nationwide.\rEDUCATION\rMaster of Science in Physics\rBrown University - Providence RI\rBachelor of Science in Physics\rGeorgetown University - Washington DC\rSKILLS\rLabview Matlab RF Circuit Design High Power RF\rAWARDS\rARA Scientist of the Year 2013\rMay 2013\r\"\r\nresume_72,not_flagged,\"Mark Sydorenko Strategic management - PefectJob\r- Email me on Indeed: indeed.com/r/Mark-Sydorenko/9d2397cb78579d5c\r* Sharp - PhD in Biomedical Engineering from #1 ranked Johns Hopkins BME dept. successful management leader of enterprise software development former Bell Labs scientist author of blocking patents and technical publications.\r* Technophile - Shell Oil Co. AT&T Bell Labs biotech start-up strategic business consultant and serial technology venture founder.\r* Entrepreneur - Leader of software product innovation business development and organizational management. Founded and managed three software technology companies while attracting more than $25M in private funding over a 15 year period.\r* Versatile - Goal focused team player eager to take charge of any value generating role.\rWORK EXPERIENCE\rStrategic management\rPefectJob - Burlington VT - 2013 to Present\rmarketing sales technology infrastructure software application development product C/AL code Microsoft Dynamics NAV SQL client-server production processes barcodes\rManaging Director of Technology\r[Custom Enterprise Resource Planning (ERP) systems for the construction supply industry]\r* Developed C/AL code modules for Microsoft Dynamics NAV to extend platform's functionality to custom stone supply industry sales and operations processes. Marketing & Sales. Microsoft Partner.\rManaging Director of Technology\rNEM Technologies - New York NY - 2008 to Present\rNew York NY\rManagement software application development product LAMP Linux Microsoft Windows Eclipse Visual Studio Apache TomCat MySQL SQL database Java J2ME Java SE Java Script XML C C++ Struts network HTML5 IP TCP UDP packet wireless cellular mobile phone client server DSP digital signal processing content music audio video codec data compression psychoacoustics psychophysics model physiology hearing auditory neurophysiology compression real-time ARM Symbian UIQ BREW Windows Mobile Android\rManaging Director of Technology\r* Continuation of BrainMedia's business and model (NEM Technologies obtained all sales contracts and intellectual property formerly owned by BrainMedia).\r* Developed & released successor NEMx digital audio codec incorporating spectral extension.\r* Struck agreements with marketing and sales partners.\rCTO and Principal Board Member\rBrainMedia - 2006 to 2008\rproviding technical and organizational leadership with greater focus on software technology products. Grew organization to 78 people / 42 FTEs.\r* Advanced software product & technology position and launched five wireless music services.\r \rExecutive management manager founder leader start-up technology fund raising venture capital investor relations stakeholders road show travel profit & loss P&L balance sheet accounting budget strategic planning business development legal contract marketing sales operations quality assurance QA customer support tier 3 recruiting hiring partner contact rolodex reports speaker conference presentation presenter pitch codec\rTeam management\rBrainMedia - New York NY - 1999 to 2008\rmanager executive lead product recruiting hiring mentor patents intellectual property software architecture application development developer perception computational neurophysiology offshore management SCRUM Agile Waterfall ISO 9000 UML cloud computing EC2 LAMP Linux Microsoft Windows FPGA Eclipse Visual Studio CodeWarrior Perforce SourceSafe Apache Axis TomCat MySQL SQL Pearl script Java J2ME Java SE JavaScript XML C C++ Matlab compiler bug debug Bugzilla Struts HTML network cellular 3G 4G IP TCP UDP RTP RTSP RTCP packet payload wireless cellular mobile phone client server multi-threaded parallel computing DSP digital signal processing optimization HPC high performance computing fixed point DCT content music audio video codec transform coding lossy lossless parametric vector psychoacoustics psychophysics QA unit testing MOS Mushra model physiology compression real-time ARM Symbian UIQ BREW Windows Mobile\rCEO and President\rBrainMedia - 1999 to 2005\rLed all $25M of private funding.\r* Built organization to 46 people including 25 FTEs in technology research & software development quality assurance marketing and sales and operations.\r* Managed the evolution from a team of researchers and technology innovators to an ISO9000 & Agile practices driven software development company marketing end-to-end software technologies enabling delivery of music and content services to cell phones.\rResearch development\rOtowave LLC - Plainfield NJ - 1995 to 1999\rsoftware product executive founder leader start-up technology fund raising capital angel blocking patent strategic planning legal contract marketing software Matlab C DSP digital signal processing DCT FFT audio video codec compression psychoacoustic psychoacoustics psychophysics model physiology hearing auditory neurophysiology compression Matlab probability theory statistics point process stochastic acoustics\rCTO\r* Invented revolutionary digital audio and video compression technology based on the Neural Encoding Model NEM (for communications & Internet applications); granted blocking patents in the US and worldwide.\r* Developed business strategy and plan. Recruited business partners. Nurtured client & support network.\r* Marketed business and technology to development partners clients and investors. Landed three seed investment rounds.\rStrategic management\rOtowave LLC - Basking Ridge NJ - 1997 to 1998\ranalysis consultant biotech biotechnology pharma pharmaceutical industry biochip life science\rStrategic Management Consultant\r* Biotechnology / pharmaceutical business development.\r* Contracted by senior officers at Fortune 100 companies to provide market business and product analyses and opportunity assessments.\rChief Technical Officer\rMimosa Acoustics - Mountainside NJ - 1995 to 1996\rMountainside NJ\rClinical hospital patients product hearing physiology cochlea evoked otoacoustic emissions software hardware customer support EOAE acoustics\rChief Technical Officer\r* Developed and manufactured software / hardware clinical product (hearing diagnostics).\r* Marketed product to end-users customer development and education and organized shows.\rMember of Technical Staff in the Information Principles Research Center\rAT&T Bell Laboratories - Murray Hill NJ - 1992 to 1995\rMurray Hill NJ\rResearch DSP digital signal processing audio speech codec compression ASR speech recognition psychoacoustics psychophysics model physiology hearing auditory neurophysiology brain cochlea experiment anechoic chamber acoustics loudness Fletcher Munson probability theory statistics stochastic point process acoustics journal publication publish patent reviewer programmer C Matlab\rMember of Technical Staff in the Information Principles Research Center\r* Researcher / Expert in Psychoacoustics and DSP Algorithms: Investigated advanced technological solutions for digital voice communications and audio compression issues.\r* Granted two US Patents in digital audio compression that substantially contributed to the evolution of the AAC codec (iTunes / iPod audio).\rResearch DSP digital signal processing automatic speech recognition ASR Markov HMM psychoacoustic models CELP\rConsultant to the Speech Research Department (NSA funded)\r* Developed novel technological solutions to automatic / computer speech recognition problems.\rJohns Hopkins School of Medicine Baltimore MD\rResearch lab hearing auditory neurophysiology neuroanatomy physiology neuron neural brain brainstem cochlea molecular biology bio biochemistry histology experiment surgery neurosurgery mammalian cat dorsal cochlear nucleus synapse electrode in vivo analysis models data collection programming C Unix Windows Apple mathematician probability theory statistics stochastic point process Martingale acoustics journal publication publish science\rGraduate Researcher: Center for Hearing Sciences and Neural Encoding Labs\r* Investigated hearing (audio) and visual (video) signal processing in the central nervous system.\r* Conducted neurosurgery and advanced probabilistic / statistical models of neurological processes.\rShell Oil Co. New Orleans LA\rGeophysicist geophysics seismologist seismology geology geophone array design beam forming hydrocarbon oil gas trap DSP digital signal processing data collection field interpretation research UNIX Exploration Geophysicist\r* Produced oil exploration maps based on seismic data acquisition advanced digital signal processing algorithms and geologic interpretation.\rEDUCATION\rPh.D. in Biomedical Engineering\rJohns Hopkins School of Medicine - Baltimore MD 1985 to 1992\rBA in top that\rNorthwestern University - Evanston IL\rADDITIONAL INFORMATION SKILLS\rBUSINESS\r* Organizational leadership strategic business planning and development; product development; market analysis; fund raising.\rCOMMUNICATION\r* Excellent public speaking abilities (university lecturer trade show marketing presentations invited speaker at international scientific and technical conferences company leadership & inspiration).\r* Effective interpersonal communicator and negotiator (marketing & relationship development).\r* Advanced writing skills (published works in books and prestigious scientific and trade journals).\r\"\r\nresume_73,not_flagged,\"Matthew Wojcik\rIT professional and cybersecurity expert with 20 years of experience.\rBrattleboro VT - Email me on Indeed: indeed.com/r/Matthew-Wojcik/8c8cd0d543bf4b4d\rRoots in systems administration and network design. Specialized for thirteen years in development of open community standards supporting IT security working in a Federally Funded Research & Development Center (FFRDC). Contributor to U.S. Federal and DoD security management specifications including SCAP FDCC USGCB. Experienced UNIX and TCP/IP administrator. Knowledge of vulnerability management configuration & compliance IDS. Noted presenter consensus builder strategic thinker and analyst. Creative intuitive innovative personable.\rCurrently seeking a challenging and rewarding cybersecurity or general technologist position: product management customer solutions engineering strategic leadership tech evangelism or other specialty which would benefit from my skills energy and expert knowledge.\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rCybersecurity and IT Specialist\rIndependent Contractor - Brattleboro VT - January 2015 to Present IT security technical tasks and project management\rHome and small-office network and computer support\ro Available in some cases on a time-trade / pro-bono basis in local community\rTechnical Product Manager Security Benchmarks\rCenter for Internet Security - December 2013 to August 2014\rManaged all aspects of creation and publication of CIS Consensus Benchmarks o Product Owner for multiple Benchmarks in an Agile environment\r- Initial portfolio: all Linux and UNIX operating systems and major applications\r- Transitioned to manage all Microsoft software benchmarks\ro Led online communities of volunteer and compensated cybersecurity experts\ro Negotiated consensus secure configuration profiles for operating systems and applications\ro Created updated and released CIS Benchmark documents (.doc .xls .pdf) and XML automation content (XCCDF OVAL and CIS proprietary)\ro Authored recommendation prose created artifacts for automated compliance assessment\ro Managed service requests meeting aggressive goals for response time and resolution\ro Owned contractor relationships: led negotiations determined compensation developed Statements of Work oversaw delivery\rProduct Manager Benchmark Analytics and Security Research\rnCircle / Tripwire - March 2013 to June 2013\rResponsible for security content strategy to drive functions of all nCircle / Tripwire products\ro Operations analysis of content development efforts and priorities\ro Developed post-acquisition roadmap for integration of expanded product line with PM team o Created and presented content strategy to approval of CEO and other company officers\r \rLead INFOSEC Scientist\rThe MITRE Corporation - Bedford MA - February 2009 to October 2012\rLeading contributor Making Security Measurable\ro Set direction with other seniors for cybersecurity standards including CVE OVAL CCE CWE CEE and CPE\ro High-level engagements with government sponsors OS vendors IT security industry\ro Balanced requirements of various user communities working objectively in public interest\ro Sought by colleagues for strategic input vision technical analysis big picture insight\rProject and technical lead for the Common Configuration Enumeration (CCE)\ro Lead CCE list growth from 2000 to over 10000 configuration identifiers in two years\ro Responsible for all aspects of CCE publication\ro Product manager CCE analysis & content management tools\ro Led expansion of CCE to include coverage of UNIX configuration concepts\ro Education and outreach; collaboration with software vendors industry experts contributors\rTechnical lead enterprise cybersecurity remediation standards\ro Principal designer proposed standard remediation framework\ro Ensured compatibility with Security Content Automation Protocol (SCAP) other assessment options\ro Analyzed requirements for flexible enterprise response to vulnerabilities mis-configurations compliance issues missing patches\ro Identified critical technical challenges crafted solutions obtained community feedback\ro Incorporated insights from overview & deep-dive discussions with users vendors SMEs\ro Directed efforts of core remediation standards team\ro Product manager reference implementation\ro Primary author NIST Interagency Report 7670 (DRAFT) Proposed Open Specifications for an Enterprise Remediation Automation Framework\rSenior INFOSEC Engineer\rThe MITRE Corporation - April 2001 to February 2009\rOriginal designer Open Vulnerability and Assessment Language (OVAL)\ro Established core concepts set direction and priorities\ro Created SQL schema for standardized assessment of security-relevant computer states\ro Authored and published OVAL queries for Solaris Windows; led content team\ro Established OVAL Repository; set OVAL Compatibility test criteria & evaluation procedure\rOVAL Board Moderator\ro Led OVAL Board of cybersecurity experts including C-level executives\ro Analyzed technical & social challenges; proposed solutions; built consensus\ro Education and outreach through technical conferences trade show expos cold calls\rCVE analyst\ro Analyzed public vulnerability information to ensure correct CVE ID assignment\ro Determined vulnerability type scope impact using disclosures and code analysis o Wrote CVE descriptions under review of CVE Editor\rIDS analyst\ro Investigated exploit attempts possible data exfiltration; detected malware o Incident resolution escalation and documentation\rConfiguration guide author\ro Wrote secure configuration guides for HP-UX and Solaris for government sponsors\rINFOSEC Engineer/Scientist\rThe MITRE Corporation - Bedford MA - December 1999 to April 2001\rSecurity content and application developer\ro Created Outpost a client/server prototype developed for the DoD on a team of five\ro Contributed to Outpost applications including vulnerability assessment patch and config management trust relationship & reachability analysis attack simulation\ro Principal developer Outpost host-based checks for Solaris 2.x and Windows vulnerabilities\ro Primary content creator Outpost Penetrability Analysis Application\ro DBA and primary sysadmin Outpost development and demo environment\rNetwork Design Consultant\rCollege of Computer Science (CCS) Northeastern University - January 1999 to February 1999\rPrincipal network architect NSF research project\ro Designed fault-tolerant ATM network with FastEthernet failover for 32-node Beowulf cluster\ro Evaluated ATM and Ethernet switches negotiated purchase of under strict budget via competitive bid process\rNetwork Administrator\rCCS Northeastern University - 1996 to 1997\rNetwork design operations disaster planning & recovery\ro Orchestrated network redesign replaced shared-media Ethernet with switch fabric o Responsible for all TCP/IP functions supporting a network of over 400 nodes\ro Installed hubs switches and routers; managed DNS NFS NIS servers and clients o Configured physical networking routing access lists VLANs\ro Supported legacy AppleTalk and IPX communications\ro Resolved user requests via helpdesk email phone and in-person\rSystems Administrator\rCCS Northeastern University - 1993 to 1996\rIntegral team member redesign of UNIX environment\ro Installed hardware multiple UNIX variants patches user accounts open source software o Specialized in network and system security NIS and DNS\rSupported UNIX Windows and Macintosh networks\ro Maintained desktop and server systems\ro Configured routers and hubs managed physical network o Responded to user support requests\rEDUCATION\rComputer Science\rCollege of Computer Science Northeastern University - Boston MA 1992 to 1995\rSKILLS\rMicrosoft Windows desktop and server Linux Red Hat Fedora Debian CentOS Ubuntu UNIX Solaris HP-UX AIX Apple OS X iOS Android Cisco IOS Cisco CatOS Microsoft Office LibreOffice SharePoint Oracle CRM JIRA Confluence Remedy ARS SVN snort tcpdump nagios SourceFire StealthWatch Intellitactics TCP/IP Network Administration UNIX/Linux Systems Administration Windows Administration Amazon Web Services Cisco Routers & Switches Firewalls IDS IPS System Hardening VPN DNS Virtualization SCAP PC Mac and Network Hardware Agile Development CVE CCE CPE XML HTML SQL Perl Bash XCCDF OVAL C C++ Java\rADDITIONAL INFORMATION\rRelevant College Courses: Algorithms & Data Structures I and II Functional Programming Digital Engineering Operating Systems Compiler Design Software Design and Development\rNotable co-operative education position: Research assistant to Professor Karl Lieberherr Northeastern University. Contributor to the Isthmus project an Adaptive Programming implementation integrating Demeter/ C++ with Tcl/Tk. Lead developer of system that extends Tcl/Tk interpreter with functions corresponding to users Demeter application facilitating GUI development.\r\"\r\nresume_74,not_flagged,\"Megan Phillips Chemist - I AC\rWestford VT - Email me on Indeed: indeed.com/r/Megan-Phillips/c5565f1bf1f486d5\rWORK EXPERIENCE\rChemist\rI AC - Burlington VT - September 2013 to Present\rJob Duties:\r Wet chemistry analyses of water samples including parameters such as alkalinity turbidity total suspended solids TKN phosphorus nitrogen conductivity turbidity BOD COD mercury pH and MPN of Total Coliform and E. coli using colilert.\r Analysis of water samples using ICP analyzer including sample digestion and standard preparation.\rLevel II Rugby Coach\rUSA Rugby - March 2014 to May 2014\rMember of Student Orientation Staff (SOS) Castleton State College Summer of 2008\ro Aided incoming students in multiple aspects of college life such as choosing a class schedule moving into dormitories and general knowledge of the college policies.\ro Lead student group over the course of weekends and days of orientation at the beginning of the fall semester. o Answered any questions that the new students may have had and provided useful information to help students adjust to college life.\r UVM-Extension Master Gardener Intern Spring of 2014\ro Completed the coursework and have started the required community service hours to become a Certified Master Gardener.\rStaff Scientist\rUniversity of New Hampshire Department of Microbiology Laboratory - Durham NH - January 2012 to July 2013\rJob Duties:\r Research with state of the art incubation equipment being used to develop an alternative test procedure for the detection of Total Coliforms and E. coli in water samples.\r Ran analyses using equipment collected data from units recorded all data into designated laboratory notebooks or onto data sheets and input data into spreadsheets in an organized and easily navigated format. Daily laboratory duties included autoclave use for making several types of liquid and agar based medias pouring media plates and following all necessary quality control actions including logging reception of samples logging of reception of products ordering more products as needed knowledge of proper handling and disposal of potentially hazardous materials logs of media preparation logs of incubator water bath freezer and refrigerator temperatures use of the autoclave to decontaminate any viable organisms after use testing of medias using control cultures to ensure any prepared media is not contaminated and works as expected calibration of thermometers pH meters autoclaves incubators water baths pipets etc.\rLaboratory Analyst\rAquacheck Laboratory - Perkinsville VT - June 2010 to December 2011\rJob Duties:\r Checked in samples packaged and mailed sample kits.\r \r Drinking Water: Experience analyzing samples for chloride hardness calcium alkalinity turbidity TDS/ conductivity nitrate and nitrite pH hydrogen sulfide and other general inorganic parameters.\r Tested several samples daily for total coliform and E. coli presence.\r Experience using flame spectrometer analysis for metals such as iron manganese copper and sodium in drinking water samples and also experience using a furnace spectrometer to measure arsenic and lead in water samples.\r Wastewater: Experience using membrane filtration to enumerate E. coli counts in wastewater samples running 5-day biological oxygen demand total solids total suspended solids total dissolved solids phosphorus and other parameters.\r Familiar with following Standard Operating Procedures daily to ensure quality work and attention to detail.\r Familiar with a laboratory setting autoclaving quality control titrations glassware scales spectrophotometer calibrating pH meter and other general laboratory duties.\rCommunity Advisor\rCastleton State College - August 2009 to May 2010\rJob Duties:\r Managed three residence halls which each housed 36 students.\r Demonstrated the qualities of a Community Facilitator and leader. Performed as a referral agent.\r Became a team member.\r Preformed as an administrator.\r Operated as a college representative.\rVolunteer Experience and Certifications\rPresident and Captain\rCastleton State College - September 2008 to March 2010\rWomen's Rugby Football Club Castleton State College Fall 2008 - Spring 2010\ro Facilitated weekly officer meetings.\ro Proposed and managed Women's Rugby Club budget.\ro Facilitated monthly team meetings.\ro Coordinated all travel and accommodations for away games.\rEDUCATION\rBachelor of Arts in Biology\rCastleton State College - Castleton VT May 2010\rCastleton State College\r\"\r\nresume_75,not_flagged,\"Meghan Lout\rEcologist/Project Manager - Western EcoSystems Technology Inc\rColchester VT - Email me on Indeed: indeed.com/r/Meghan-Lout/1552f741900a3722\rMy objective is to obtain an employment opportunity as an Environmental Scientist with Green Seal Environmental Inc in Massachusetts.\rWORK EXPERIENCE\rEcologist/Project Manager\rWestern EcoSystems Technology Inc - Burlington VT - October 2011 to Present\rLeads and assists in studies researching impacts of development on wildlife Hires employees assists with accounting and administrative tasks\r Designs coordinates supervises and conducts field studies\r Writes and reviews budgets proposals and technical reports\rAdjunct Professor of Ecology\rSpringfield College - Springfield MA - August 2011 to December 2011 Biology 260 (Ecology) 261 (Ecology lab)\rWood Thrush Telemetry Technician\rUniversity of Massachusetts - Amherst MA - June 2011 to June 2011\rAssisted with nest searches mounting transmitters and tracking fledglings for research investigating population decline\rAvian and Bat Migration Field Supervisor\rStantec Consulting - Topsham ME - April 2010 to January 2011\rSupervised pre and post-construction wildlife surveys at proposed and operational wind energy facilities.\r Assisted with bat acoustic data analysis and writing technical reports\rGraduate Research Student\rPurdue University - West Lafayette IN - August 2006 to May 2009\rResearch conducted in Costa Rica)\r Investigated song differentiation of thrushes at range boundaries to test hypotheses addressing\rforces underlying patterns of beta diversity and whether such interactions could affect range shifts and subsequent extinction rates predicted by climate change models.\r Recorded and analyze avian vocalizations and mapped territories of thrushes\rTeaching Assistant\rBiology I - West Lafayette IN - August 2006 to May 2009\rBiology 121: Biology I - Diversity Ecology and Behavior\rBiology 131: Biology II - Development Structure and Function II\rBiology 205/206: Biology and Ecology for Elementary Education Majors I and II Biology 283: Conservation Biology\rBat Research Technician\r \rUniversity of Kentucky - Lexington KY - March 2005 to August 2005\rSupervised a research project investigating the roosting and foraging ecology of Myotis volans in response to various forest management practices\r Mist-netted processed and mounted transmitters on and tracked bats to roosts using radio\rtelemetry equipment\r203 Deer Lane Unit 4 Colchester VT 05446 phone: 802-377-2719 e-mail: szwenia@hotmail.com\rSandhill Crane Research Technician\rUnited States Geological Survey/Northern Prairie Wildlife Research Center - Jamestown ND - January 2005 to March 2005\rJamestown North Dakota Jan. - Mar. 2005\rSandhill Crane Research Technician\r Assisted with rocket-netting processing mounting transmitters on and tracking foraging and roosting sandhill crane using null-peak telemetry to understand use of the North Platte River by\rcranes\rFerruginous Hawk Research Assistant\rUtah State University - Logan UT - April 2004 to August 2004\rConducted aerial and ground-based hawk and eagle surveys for research assessing the impact of oil and gas well developments on ferruginous hawk\r Banded mounted transmitters on and tracked nestlings until dispersal\rCalifornia Condor Field Technician\rThe Peregrine Fund - Boise ID - April 2003 to January 2004\rAssisted with captive breeding and reintroduction efforts for the California condor Tracked handled chelated sick birds and monitored behavior in Northern AZ\rOrnithology Intern\rU.S. Fish and Wildlife Service - North Chatham MA - December 2000 to January 2001\rand April - Aug. 2001\r Participated in shorebird monitoring staging counts and censuses\r Conducted passerine marsh-bird waterfowl and horseshoe crab surveys\r Assisted with gull population and nocturnal predator control; mapped distribution of invasive plants at Great Meadows Wildlife Refuge\rEDUCATION\rBiostatistics\rUniversity of Massachusetts May 2011\rM.S. in Ecology\rPurdue University May 2009\rB.S.\rUniversity of Massachusetts May 2003\rCertificate in Tropical Reforestation\rSchool for Field Studies July 2002\r\"\r\nresume_76,flagged,\"Melissa Burbank\rBrattleboro VT - Email me on Indeed: indeed.com/r/Melissa-Burbank/3374044f54dc82a3\rData scientist researcher and analyst with extensive experience conducting and delivering fundamental and complex statistical analyses to a wide variety of audiences through clear and concise client deliverable and professional quality presentations. Significant accomplishment in developing and launching new data products and implementing new analytical techniques to strengthen existing data offerings to clients. Demonstrated ability in utilizing industry research and complex statistical analyses to provide clear insight and guidance to clients. Strong teaching and mentoring skill set utilized to introduce statistical concepts and best practices to individuals with varying degrees of exposure and knowledge of statistics. Five years of extensive graduate level training in statistics advanced statistics and research methodology with a focus on quantitative research design including survey instrument construction. Expertise in the management of large databases and statistical analysis in Excel SPSS and STATA. Familiarity with R SAS and SQL.\rWilling to relocate to: Keene NH - Springfield MA - Brattleboro VT Authorized to work in the US for any employer\rWORK EXPERIENCE\rData Scientist Data Insights and Innovation\rFORRESTER RESEARCH - Cambridge MA - 2015 to 2016\rCambridge MA 2015 - 2016\rOne of the most influential research and advisory firms in the world that works with business and technology leaders to develop customer-obsessed strategies to drive growth.\rData Scientist Data Insights and Innovation\rResponsible for implementing and guiding the use of advanced analytics across data and research teams conduct and often deliver analysis in the form of documents and PowerPoint deliverables to win and retain clients and development of new frameworks and data product offerings.\r Designed and launched the new Business-to-Business Customer Experience Index including developing proprietary algorithms.\r Introduced new analytical techniques based on industry trends to research analysts and provided guidance on use of these statistics in various research reports.\r Developed and disseminated statistical best practice documents across Forrester's data group as well as instructed several clients on these best practices during custom engagements.\r Generated and retained several large clients by conducting additional data analysis beyond Forrester's standard offerings.\r Strengthened and standardized existing data products to increase validity and efficiency of offerings to clients.\r Mentored both junior and senior colleagues on statistics and research methodology\rSupervisor/Trainer\rUNIVERSITY OF NEW HAMPSHIRE SURVEY CENTER - Durham NH - September 2009 to February 2011\rQualitative Interviewer\rUNIVERSITY OF NEW HAMPSHIRE SURVEY CENTER - Goffstown NH - 2010 to 2010\rResponsible for extensive hour-long interviews of parolees and program participants for a grant funded evaluation of the efficacy of an offender re-entry program in Hillsborough County NH. Project Directors: Dr. John Humphrey & Dr. Peter Cordella\r \r Assisted in thru development of the interview protocol\r Conducted fifty hour long in-depth interviews with parolees and program participants Transcribed interview notes into full interview manuscripts\r Supported the qualitative data analysis for inclusion in final report\rInstructor\rUNIVERSITY OF NEW HAMPSHIRE AND CASTLETON STATE COLLEGE - 2004 to 2009\rResponsible for instructing courses in a wide variety of subject matters to graduate and undergraduate students with primary focus on statistics and research methodology.\r Designed course syllabi to meet departmental goals and standards\r Developed and presented lesson plans to students introducing both fundamental and advanced concepts. Mentored and provided additional support to students experiencing difficulties\r Tracked and monitored students' progress via self-designed assessments (exams homework assignments and projects)\rCourses:\rTeaching Assistant\rResearch Methods (SOC601) - September 2007 to November 2007 Fall 2007)\rEDUCATION\rM.A. in Forensic Psychology\rCastleton State College - Castleton VT 2004\rB.A. in Psychology\rCastleton State College - Castleton VT 2002\rPh.D. in Sociology Program\rUniversity of New Hampshire\rSKILLS\rAdvanced analytics Research Teaching Business Analysis Competitive Intelligence Database Management Marketing Research\r\"\r\nresume_77,not_flagged,\"Melissa Harding Phlebotomist/ Laboratory Assistant\rBurlington VT - Email me on Indeed: indeed.com/r/Melissa-Harding/f5cfaba319473a4a Authorized to work in the US for any employer\rWORK EXPERIENCE\rPhlebotomist/ Laboratory Assistant\rThomas Chittenden Health Center - Williston VT - 2012 to 2013\rManaged the blood urine and fecal specimens in LIS (Laboratory Information System) and tracked tests conducted in external labs. Collected and labeled test on patients of this community health center. Coordinated patient care and tests with team of lab and medical professionals including accurate billing.\r Clinical test included: rapid strep- A pregnancy mono urinalysis blood cells counts proteome hemmocult and TSH levels in accordance with clinic and regulatory standards set forth by the CLIA guidelines.\r Maintained quality control for kit lot numbers managed supply inventory performed daily function checks and proper equipment maintenance.\r Assisted new and less experienced lab technicians and phlebotomists with technical and procedural questions.\rLaboratory Technologist/ Technician\rCommunity Health Center of Burlington - Burlington VT - 2010 to 2012\rper diem)\rCollected and labeled blood urine and fecal tests for this community health center in accordance with clinic and regulatory standards set forth by the CLIA guidelines. Coordinated tests with team of lab and medical professionals including accurate billing. Manage specimens sent to external laboratories. Tests include: rapid strep- A pregnancy HIV urinalysis drug screening blood cells counts and blood chemistries.\r Cultured bacteriological tests performing microscopic examinations and preparing any necessary reagents and documents specimens and results.\r Maintained quality control for kit lot numbers managed supply inventory performed daily function checks and proper equipment maintenance.\rTechnician II Collections\rCommunity Health Center of Burlington - Burlington VT - 2010 to 2012\rProvided phlebotomy services for blood drives throughout the region. Interviewed and screened donors to determine eligibility for blood donation. Collaborated with team members to maintain safety quality identity potency and purity of the blood products as outlined in the Code of Federal Regulation and OSHA.\r Cross trained in use of MCS+ from Haemonetics for collection of red cells and in post collection operations to package and ship blood blood products and sample tubes.\r Member of blood drive team including collecting and inventorying supplies set-up and tear-down of temporary donation sites greeting donors collecting and labeling samples providing donor reaction care and operating fleet vehicles.\r Kept focus on donor and blood safety while meeting sponsor expectations.\rLaboratory Research Technician\rDr. Xiaoli Fu - 2009 to 2010\rpost- doctor): Department of Microbiology & Molecular Genetics\r \rAssisted principle investigator in her research in determining transcriptional levels through RNA isolation of a membrane protein of the oral pathogen Aggregatibacter actinomycetemcomitans in different stress conditions as well as various mutants related to the absence of the protein. Assist the principle investigator in performing laboratory experiments to include Western Blotting SDS-PAGE phenotype designation and immunodot blots of a HIS- tagged protein; to decipher the mechanisms of a novel membrane protein and how it effects the secretion of virulence determinants of Aggregatibacter actinomycetemcomitans. Also preformed blue- white screening membrane isolation of a gram negative bacteria genomic isolation and plasmid purification. Maintained notes and files to include research results protocols and methods. Collected and compiled research data; preformed literature searches.\rPrepared support materials including solutions medias cultures and strains.\r Assisted in the developing new laboratory protocols.\r Mentored undergraduate in a short- term research project including training of techniques and reinforcement of theory as well as managing projects costs.\r Ordered supplies for the laboratory including managing costs of supplies and negotiating with vendors and being mindful of an overall budget.\rQuality Assurance Analyst\rPBM Nutritionals LLC - Georgia VT - 2008 to 2009\rwith QA in Microbiology\rAnalyzed environmental samples raw ingredients in- process and finished samples using approved microbiological testing methods in accordance with current company procedures GMP's and analytical methods. Basic Microbiological skills include: Plating methods isolating pure cultures preserving lab cultures pipetting serial dilutions aerobic plate and direct microscopic counts turbid metric estimation of bacterial numbers identifying bacterial cultures basic staining techniques preparing and sterilizing media and reagents. Routine operation skills include: calibration maintenance and simple repair of lab instruments and autoclaves data entry of completed test results into LIMS (Laboratory Information Management System).\r Audited plant GMP safety SOPs and work instructions as outlined by the plant standards.\rLaboratory Technician\rDr. Judith Van Houten Department of Biology - 2008 to 2008\rPrepared class experiments for Genetics Course including bacterial culture plasmid isolation plasmid transformation Southern Blot gel electrophoresis SDS- PAGE Western Blot bacterial libraries restriction enzymes PCR and microarray. Calibration upkeep of supplies routine cleaning of laboratory equipment. Maintained detailed budget listing of classroom materials reagents and expense reports.\r Coordinated with professor teaching assistants and students to ensure timely preparation of experiments. Problem-solved various experiments and edited an existing manual to clarify protocols.\r Directly tutored and taught students in correct methods for conducting experiments while emphasizing classroom theory. Creating presentations and handouts for classroom and tutoring support.\rUniversity of Vermont - Burlington VT - 2006 to 2008 2009 to 2010\rUniversity of Vermont - Ouro Preto MG - 2007 to 2007\rGenomic responses of the bovine mammary gland and epithelial cells to acute LPS challenge. Veterinary Immunology and Immunopathology: abstracted present at the 8th IVIS Ouro Preto Brazil; publication pending.\rMicroarray Scientist\rUniversity of Vermont - Collegeville PA - 2007 to 2007 with Safety Assessment/ Jessica Schroeck\rProcessed large capacity of samples using the Nugen/ Affymetrix SOP and running agilents.\rProcessed RNA for Affymetrix target preparation hybridization and scanning. Analyzed gene expression profiling data. Accurately maintained electronic notebook and electronically tracked samples (LIMS) throughout the processing. Used statistical analytical tools to determine outcome of samples testing and significance. Prepared and submitted expense reports for project budget. Calibrated and performed routine laboratory equipment cleaning and maintenance.\r Mentored and managed interns.\rLab Technician/Research Intern\rUniversity of Vermont - June 2006 to August 2006\rCharacterized LPS-induced mastitis utilizing cell culture the Affymetrix microarray platform and RT-Q-PCR. Maintained accurate lab data and interpreted the biological significance of statistics obtained by using Excel and Affymetrix's Gene Ontology. Maintained cell cultures and cell lines; created new passages and storing. Isolated RNA and prepared target cDNA or cRNA for hybridization on Affymetrix GeneChips. Assisted in maintaining the laboratory including ordering supplies producing media and maintaining calibration of the instrumentation and maintenance of ATCC organisms.\rEDUCATION\rBachelor of Science in Biomedical Technologies\rNorwich University - Northfield VT 2006\rSKILLS\rPhlebotomy (3 years) Medical (3 years) Research (3 years)\rADDITIONAL INFORMATION\r Phlebotomist & Laboratory Technician: More than 8 years' experience in providing quality phlebotomy and technician services in the context of research laboratories public health food/pharma production and education.\r Quality Assurance: Demonstrated ability in applying the standards and procedures of ISO9001 and other quality measurements within the food production field.\r Mentoring & Teaching: Adept in providing structured laboratory instruction to students and interns mentoring and guiding young professionals in technical procedures and assisting patients in maintaining safety.\r\"\r\nresume_78,not_flagged,\"Michael Boylan\rVice President Registered Investment Adviser\rEssex Junction VT - Email me on Indeed: indeed.com/r/Michael-Boylan/40c689d3475509c3\rTo use my knowledge experience and entrepreneurial background to be high producing insurance adjuster.\rWORK EXPERIENCE\rProperty Adjuster\rEBERL Claims Service/State Farm - Charleston SC - April 2016 to November 2016\rTrained and employed as property adjuster for Eberl most recently in Charleston SC working claims for Hurricane Matthew doing all aspects of claims inspection ECS entry and Xactimate estimating. Used Eagle View when available for roof estimates drawing roofs when not available.\rVice President Registered Investment Adviser\rStifel Nicolaus and Company - Garden City NY - May 2009 to August 2016\r Managed account asset base of approximately $50 million for high net worth individuals. Responsibilities include identifying client goals asset allocation risk tolerance.\r Operate diversified asset base bonds stocks international investments currency hedging.\r Use Bloomberg terminals Excel spreadsheets Compustat financial database to evaluate financial data track and manage accounts. High level Excel and MS Office user.\rVice President Registered Investment Adviser\rAG Edwards/Wachovia Securities - Huntington NY - January 2008 to April 2009\r Managed account asset base of approximately $50 million for high net worth individuals. Diversified asset base to reduce/spread risk using modern portfolio methods.\rVice President Securities Analyst Registered Investment Adviser\rMcGinn Smith & Co - Albany NY - March 2003 to January 2008\r Developed financial models of publicly traded companies.\r Obtained Securities Analyst license by passing Series 86 & 87 exams.\r Wrote research reports on healthcare companies as licensed securities analyst interacting with senior management including CEO's CFO's PhD scientists covering pharmaceutical biotechnology and medical device companies.\r Developed operated and managed quantitative investment fund for approximately 25 high net worth accounts.\r Advised small healthcare companies on financing options market opportunities.\r Negotiated with principals of top national healthcare venture capital funds in representing startup biotechnology companies' efforts to raise capital.\rCurrent Insurance Adjuster Licenses\r NY(home state) TX GA NC SC WV\rEDUCATION\rBachelor of Arts in Economics\rState University of NY at Oswego - Oswego NY\r \r1976\rcertification\rMIT\rSKILLS\rMicrosoft Office (10+ years) Xactimate Level 2 certification (1 year)\rCERTIFICATIONS/LICENSES\rAdjuster licenses for property in NY and 5 other states\r6 current licenses FL pending.\rXactimate Level 2\rJanuary 2017 to Present\rRecently certified. Level 1 in December. Used Xactimate to close 30 claims in Hurricane Matthew.\rADDITIONAL INFORMATION\rTwenty plus years of financial industry experience 1 year of claims adjusting training and field experience. Most recent insurance adjuster experience working Hurricane Matthew out of Charleston SC handling claims for State Farm in October/November 2016 including about 25% 2 story steep claims. Closed approximately 30 claims in 21 days. Level 1 & 2 Xactimate certified. State Farm ECS certified in property and estimatics.\r\"\r\nresume_79,not_flagged,\"Michael Cichanowski freelance - Beth Israel Deaconess Medical Center\rShaftsbury VT - Email me on Indeed: indeed.com/r/Michael-Cichanowski/ac146c271e6bbad3\rSeasoned Senior Graphic Designer/Artist with extensive experience in both print and multimedia with the ability to take\rmedical/scientific concept and bring it to visual life.\rCareer focus has been with hospital/medical and scientific research as well as higher education.\rExpert level Mac and PC skills in the following:\r Adobe CS6 CS5 CS4 Scientific Illustration (Medical) Photoshop Traditional Illustration Animation\r Indesign MS Office/Desktop Publishing (Excel\r Illustrator Powerpoint etc.)\r Quark Xpress Freehand\r Flash HTML\r Dreamweaver Vendor and Project Management\r Imageready\rWORK EXPERIENCE\rfreelance\rBeth Israel Deaconess Medical Center - 2009 to Present\rSenior Graphic Designer\rResponsible for planning analyzing and creating visual solutions to communications problems. Use a variety of methods such as color type illustration photography animation and various print and layout techniques to develop compelling presentations; and\rwork with scientists communications staff and hospital personnel to develop material for publication.\r Create graphics tables and charts for publication in marketing materials community posters scientific journals text\rbooks educational handouts presentations scientific posters and web-based communication tools.\r Excellent project management production and professional design experience.\r Work with information design as applied to statistical data\r Manage and maintain file archive of images and presentations.\r Work with Adobe CS5/6 design tools to turn concepts into figures for publication in leading scientific journals Digital photography including microscopic imaging\r Traditional Illustration\r Intensive use of graphics peripherals including scanners slide scanners digital cameras and printers (including extensive\rlarge format printing Kodak 1200i 60 inch printer Epson large format printers). Maintain all equipment.\r Work with print houses/vendors through the bidding proofing and printing process.\rfreelance\rNational Institutes of Health-Vaccine Research Center - 2006 to Present Senior Graphic/Medical Designer\r \rResponsible for planning analyzing and creating visual solutions to communications problems. Use a variety of methods such as color type illustration photography animation and various print and layout techniques to develop compelling presentations; and\rwork with scientists communications staff and hospital personnel to develop material for publication.\r Create graphics tables and charts for publication in scientific journals marketing materials community posters text\rbooks educational handouts presentations scientific posters and web-based communication tools.\r Manage and maintain file archive of images and presentations.\r Web design/animation using Flash Dreamweaver Adobe CS4.\r Work with Adobe CS4 design tools to turn concepts into figures for publication in leading scientific journals (Nature\rScience Journal of Virology PLoS Cell etc.)\r Digital photography including microscopic imaging\r Intensive use of graphics peripherals including scanners slide scanners digital cameras and printers (including extensive\rlarge format printing Kodak 1200i 60 inch printer Epson large format printers). Maintain all equipment.\r Work with print houses/vendors through the bidding proofing and printing process.\rSr. Graphic Designer\rGVH Studios - Bennington VT\rGraphic Design Specialist\rNational Institutes of Health-Vaccine Research Center - May 2006 to October 2006\rDevelopment of Graphic Design in support of NASA including marketing materials posters signs mailers and published\rwork\r Communication with clients in the design of artwork and layouts.\r Delivery of artwork from concept through completion on time and on budget delivery. Create images for publication in journals and books\r Coached selection as to the best approach to a project or presentation\r Work with print houses/vendors through the bidding proofing and printing process\r Desktop Publishing\r Use of graphics peripherals including scanners slide scanners digital cameras and printers\rFreelance and Stay\rHome Father - May 2005 to May 2006\rFreelance work with National Clients: GE Healthcare Everbank Dept. of Education Imagilin Kelliher Samets and Volk\rSenior Graphic/Medical Designer\rNational Institutes of Health - 2005 to 2005\rrelocated to VT for spouse's promotion)\r Development of graphic design in support of the National Human Genome Research Institute including figures for scientific journals text books educational handouts signs posters mailers packaging and published work Worked with Branch Chiefs Deputy Chiefs PI's staff scientists and the other members of the research team to display\rresearch findings and scientific concepts in new and interesting ways that assist the communication of research findings\r Work with Adobe CS3 design tools to turn concepts into stylized or schematic visuals and create figures to display\rscientific and project portfolio data\r Intensive use of graphics peripherals including scanners slide scanners digital cameras and printers (including extensive\rlarge format printing). Maintain all equipment\r Work with print houses/vendors through the bidding proofing and printing process\r Digital photography including microscopic imaging\r Manage file archive of images and presentations\rGraphic Artist/Art Director\rClassic E.S.P - 2001 to 2002\rcompany was sold)\r Director of in-house art department for screen printing/embroidery and marketing promotional facility Delivery of artwork form concept to completion\r Customer Communication and Guidance\r Preparation of digital files for print including color separations and color correction\r Out-putting mechanical separations separations for spot color and 4-color process\rGraphic Artist/Production Artist\rJager Di Paola and Kemp Design - 2000 to 2001\ryear contract)\r Produced mechanical separations for screen printing and die cutting using Adobe Photoshop and Illustrator for Burton\rsnowboards\r Preparation of digital files for print including mechanicals color separations and color correction\r Work through technical issues in order to accurately produce a design in print\r Out-putting mechanical separations separations for spot color and 4-color process\r Maintain accurate color and print information for Burton production facilities\rPre-press Operator / Product Flow Supervisor\rSelect Design Ltd - 1998 to 2000\rPre-press department: Responsible for the preparation of silk screens for production and the mixing of inks (utilizing the\rPantone color matching system). Fully trained to assemble and maintain Newman roller frames\r Product Flow Supervisor: Responsibilities included supervision of receiving shipping returns/credit handling inventory\rcontrol and departmental planning (focusing on updating systems and implementing new systems). Responsible for 4\remployees\rEDUCATION\rBachelor of Arts\rUniversity of Vermont 1998\r\"\r\nresume_80,flagged,\"Michael Fink\rData Scientist/TPM - Smart Resource Labs\rColchester VT - Email me on Indeed: indeed.com/r/Michael-Fink/de1318cf5e7cc74c\rI am a data scientist with an extensive history in math chemistry and physics. I have experience with R iPython Mathematica MATLAB SQL MongoDB and Javascript. My last three annual reviews cited my teamwork attitude and accountability as being particularly strong. I aspire to become an expert data scientist fluent in Python and R.\rWORK EXPERIENCE\rData Scientist/TPM\rSmart Resource Labs - Burlington VT - October 2015 to Present\r Solely responsible for data analysis for a 1 MW solar farm in Vergennes VT.\r Wrote Python scripts to analyze and create visualizations to identify and predict inverter faults degradation snow losses performance relative to model subarray peer-to-peer performance and much more.\r Wrote Python scripts to analyze data and provide a 10 MW solar plant with a calculation awarding\rthem a Make-Whole payment from a lengthy legal document.\r Responsible for delivery of data products and energy-measurement packages.\rProcess Engineer\rChroma Technology - Bellows Falls VT - April 2013 to October 2014\rResponsible for discovering initiating improving verifying and standardizing manufacturing processes in Chroma's reactive sputtering department.\r Wrote Mathematica program for visualization of gas composition vs. sputter rate gas flow rates vs. pressure and several other metrics.\r Extensive recording modification and use of Excel macros and SQL views for analysis of process metrics especially arcing gas pressures gas composition vacuum statistics and resulting cosmetic quality.\r Analyzed hundreds of anneals with JMP to find a characteristic equation to find correct anneal temperature to induce needed spectral shift of product based on the product's chemical composition.\rContract Chemist\rSelf-employed Antarctica/Vermont - August 2012 to March 2013\r Programmed electronic models of organic semiconductor photovoltaics for comparison to experimental results.\r Conducted calibrations of spectrophotometers under adverse conditions on the Antarctic Ocean.\rThin Film Scientist\rOmega Optical - Brattleboro VT - July 2008 to July 2012\rModeled built and tested tens of organic photovoltaic designs culminating in a near-record\rbreaking organic PV cell.\r Developed a Mathematica program that determines the risk of late delivery based on yield of process steps and historical precedent with the requested product.\r Authored white papers on polarization anti-reflective technology and Shack-Hartmann wavefront\r \rdetection.\r Singlehandedly developed and wrote 12 educational laboratory exercises emphasizing\rconnections between photonics and biology chemistry and other fields in physics resulting in a\rnew product.\r Scored 53/55 and 54/55 in 2010 and 2011 annual employee reviews (no other reviews given during my tenure at Omega).\rPhysics Teacher\rChemistry - Saint Johnsbury VT - August 2006 to June 2008\rTaught ~50 students per semester in subjects of chemistry and physics emphasizing practical\ruses of science.\r Developed seven student directed labs on topics including identification of unknowns separating of mixtures calorimetry and stoichiometry.\r Wrote Mathematica demonstrations on physics topics including wave interference Fourier\ranalysis diffusion and random walks.\rEDUCATION\rDoctorate in Chemistry\rUniversity of Oregon - Eugene OR March 2006\rMaster of Science in Chemistry\rUniversity of Oregon - Eugene OR June 2001\rBachelor of Science in Chemistry\rBates College - Lewiston ME June 1999\rMechanics\rSolid-State Physics\r\"\r\nresume_81,not_flagged,\"Michael Streeter Quality Control Scientist at PFIZER INC\rSouth Burlington VT - Email me on Indeed: indeed.com/r/Michael-Streeter/84274c77e752bef1\rQuality Control Professional with extensive experience as scientist dedicated to process improvement in highly regulated manufacturing environments known as a go-to problem solver with exceptional trouble-shooting skills with a proven ability to utilize superior communication skills to coordinate project activities between sites. Key areas of expertise include:\r Process Review & Reengineering Technical Writing for Compliance Implementation of New Technologies Data Organization and Review Coordination of Projects\rWORK EXPERIENCE\rQuality Control Scientist\rPFIZER INC - Rouses Point NY - 1989 to Present\rAs integral member of quality operations team responsible for various high level tasks including Method Development Optimization and Validation; Coordinating the transfer of methods between sites ; Research & Implementation of new technologies; Regulatory Change Activities; Laboratory Supervision; and Development and Implementation of Information Management Systems.\rMethod Development / Optimization & Validation / Transfer\r Developed and Validated methods for analyzing multiple drug products and incoming materials to current industry standards.\r Optimized multiple processes in the laboratory leading to a reduction in cycle time and consumable costs.\r Coordinated the transfer of analytical methods to and from both internal and external sites in support of technology transfer of multiple drug products ensuring regulatory compliance.\r Authored multiple protocols and reports to support development validation and transfer activities.\rTechnical / Analytical Contributions\r Provided estimated analytical laboratory costs for multiple drug product manufacturing projects working within a technology transfer team.\r Researched and implemented the use of new technologies leading to a marked reduction in cycle time required to move raw materials from receipt to designated departments.\r802.238.8065\r Performed Forensic Analysis to identify foreign materials found in product. Introduced new equipment and techniques which resulted in an increase from 10% to nearly 100% successful identification of the foreign material.\r Primary reviewer of multiple pharmaceutical industry reference publications to ensure the site was informed of all proposed changes with potential to impact the site and a member of the team responsible for implementing any changes.\r Successfully proposed revisions to one of these publications to improve the performance of a test.\r Worked within a team building a Laboratory Information Management System (Quality Module within SAP) and integrating the system with various data acquisition software.\rLaboratory Supervision\r \r Supervised a group of analysts in an analytical laboratory supporting process validation testing optimizing methods and coordinating schedules with multiple departments resulting in increased efficiencies which enabled a reduction in cycle time and laboratory staffing levels.\rEDUCATION\rBA in Chemistry\rSTATE UNIVERSITY OF NEW YORK - Potsdam NY\r\"\r\nresume_82,not_flagged,\"Michelle Alexander\rBennington VT - Email me on Indeed: indeed.com/r/Michelle-Alexander/f96518f919b71a54\rWORK EXPERIENCE\rSubstitute Teacher (K-12)\rSouthwestern Vermont Supervisory Union - Bennington VT - January 2006 to Present\rResponsibilities\rMy duties include working as a substitute teacher for all subjects grades and programs in the schools located in Bennington VT. (k-12th grade).\rProgram Assistant\rNatural Resources Conservation Service - Rutland VT - October 2013 to January 2014\rResponsibilities\rMy duties included processing reviewing assembling and tracking documentation and certification of Farm Bill applications contracts payments and technical service provider projects. During processing of applications and certifications of eligibility for the field office zone I collected eligibility documents from applicants from other USDA agencies then entered data into agency-specific software programs and prepared reports on the findings. I also analyzed problems with applications and took remedial actions as well as recommended changes in procedures when it was necessary to prevent recurrence of similar problems that may have delayed obtaining certifications of eligibility. I also examined agency files to confirm that information on each document was complete and then adjusted and corrected obvious errors as needed. For payments to contracted participants I reviewed payment requests and verified payment documents to ensure that complete financial personal and contract information was provided then I input data into files and matched files properly. I also drafted and finalized contract administration letters and documents related to status reviews contract implementation contract modifications and potential cancellations or terminations while bringing files up- to-date and maintaining contract documents in case files. I also made procurement requests for all office supply needed for the field office zone and documented all supplies received. I serviced and performed monthly vehicle logs on all field office zone vehicles as well as assisted field staff with planning workload by developing customer files and attributing land base in the Toolkit program. I also provided customer service aid to customers by guiding them through the application contract and payment process and provided customers with resource information.\rHumane Investigative Agent\rSecond Chance Animal Shelter - Shaftsbury VT - May 2007 to December 2007\rResponsibilities\rMy duties included investigating alleged animal abuse cases at residences within Bennington County. When abuse had occurred I worked with state and local police officers animal control officers and the animal owners to resolve the abuse and poor living conditions. Sometimes this had involved confiscating the animal(s). Often with a case performed follow-up visits and educated the public about animal care. With each case I kept files up-to-date maintained documents in case files and entered data into specific software programs and prepared reports.\rRegulatory Specialist\rDept of the Army US Army Corps of Engineers - Troy NY - February 2000 to April 2002 Responsibilities\r \rI worked as a Regulatory Specialist within the Compliance and Enforcement Section of the Regulatory Branch. This work entailed opening enforcement cases and conducting investigations on alleged violations where a Department of the Army permit had not been obtained prior to impacting navigable waters and/or wetland areas. I reviewed and assembled documentation met with property owners consultants attorneys state and town officials and other customers to determine the prior site conditions the purpose of the project and the degree of environmental degradation that had occurred. I then worked with these individuals to resolve each violation on a case-by-case basis. During this process I conducted wetland delineations reviewed permits project designs and engineering plans and provided resource information to customers including soils maps NWI wetland maps aerial photographs and wetlands confirmations. With each case I prepared a report on the findings of the unauthorized activities prepared reports on the violators outlining the nature of their violation coordinated the data obtained with concerned agencies drafted and finalized administration letters and documents kept files up-to-date maintained documents in case files and entered data into agency- specific software programs. I also performed compliance inspections on wetland creation areas to observe if permit conditions were followed by permit applicants. I reviewed potential violation and non-compliance determinations including verifying submitted documents to ensure complete information was provided all data was added to the files and matched field observations. I also analyzed problems with applications and took remedial actions. In addition I recommended changes in procedure when necessary to prevent recurrence of similar problems that may delay processing enforcement actions.\rHydrologist\rUSDA Forest Service Northeastern Research Station - Durham NH - March 1999 to February 2000\rResponsibilities\rMy principal duties included reviewing and assembling documentation of 40 years of water quality data from streams ponds and lakes on the White Mountain National Forest and entered data into agency-specific software programs and prepared reports including performing summaries and statistical analyses. I am the secondary author on a Forest Service General Technical Report published in year 2002 that displays this data. My other duties included: using biomass equations to calculate biomass for a large numbers of trees on two study watersheds and incorporating nutrient analyses using Arcedit to digitize regional geology and ice- storm damage maps creating professional slide presentations using various graphics programs and scanners redesigning and creating web pages for the Hubbard Brook Ecosystem Study editing and creating tables for 40 years of snow depth and snow water equivalent data from the Hubbard Brook Experimental Forest and providing support services for scientists in Project 4352. In addition as a temporary assignment I worked with other resource specialists at the Army Environmental Center at the Aberdeen Proving Grounds writing and editing a Natural Resources and Cultural Plan for the U.S. Military Installations on Okinawa Japan.\rHydrologist\rUSDA Forest Service Hubbard Brook Experimental Forest - Campton NH - March 1997 to March 1999\rResponsibilities\rMy duties included participation as a member of an interdisciplinary team that performed research projects using watershed ecosystem analysis as the major experimental approach. In the field on a weekly basis I collected soils meteorological and hydrologic data in addition to installing operating and servicing rain gages weather stations stream gaging stations sensors and data loggers. After collection of the data I reviewed and assembled documentation including generating daily and monthly tables of the experimental data and entering it into agency-specific software programs. I examined files to confirm that information on each document was complete and adjusted and corrected obvious errors as needed to verify the accuracy of all data. This data was used to view long-term trends from the various experimental treatments performed over the last thirty years on the forest. I also analyzed and created graphs and tables for 40 years of stream- flow data collected at Hubbard Brook. On a weekly basis I also operated a National Atmospheric Deposition Program wet deposition collector and performed laboratory chemical analyses on the samples. I also assisted\rwith surveys of benchmark levels for weirs with level and rod and conducted surveys of vegetation to estimate biomass. In addition I gave oral presentations to several college classes on Acid Precipitation/Atmospheric Pollution and gave tours of the studies and instruments used at Hubbard Experimental Forest.\rHydrologist\rUSDA Forest Service Rocky Mtn. Research Station - Flagstaff AZ - October 1993 to June 1996\rResponsibilities\rAt this research station I reviewed and assembled documentation primarily on five different studies. The first study I worked on was a laboratory study examining the effects of fine sediment on Apache Trout egg survival and fry emergence. I set-up and maintained the entire laboratory part of this study which included cultivating Apache Trout in large runways. During the field section of this study I collected and analyzed sediment samples from study streams and collected vegetation samples from study plots. The second study I worked on was a set of predation studies in the field and laboratory with Brown Brook Rainbow and Apache Trout and Spinedace. The third study I worked on involved sampling native and non-native fish populations on the Verde River. The last study involved examining the transportation and accumulation of large woody organic debris on three burned and one unburned watershed in Arizona. I co-authored a professional paper on this last study and presented these studies at three professional meetings.\rFisheries Biologist\rUSDA Forest Service LoLo National Forest - Missoula MT - June 1995 to January 1996\rResponsibilities\rMy duties involved providing advice and reviewing and assembling documentation of plans related to the protection and management of the aquatic resources including anadromous fish migrations stream surveys stream productivity populations stream utilization physical and biological characteristics endangered or critical species and habitat improvements on rehabilitation programs. I participated in management investigations and surveys necessary for the protection mitigation of damage to and restoration of fish habitat. I reviewed and assembled documentation required for contracts and payments for timber sale projects grazing allotments due for re-issuance and past and present mining claim operations.\r\"\r\nresume_83,not_flagged,\"Mitch Krauss Program Manager\rBurlington VT - Email me on Indeed: indeed.com/r/Mitch-Krauss/95cf69d293ee59bb\rHighly accomplished and versatile program and project manager with a proven track record managing projects and inventories in excess of $24M/year. Over 17 years of versatile experience in a blend if industries. Leadership of teams of 5 - 20 individuals. Intuitive and customer focused with proven ability to translate analytics into executable plans that drive results. Creative and innovative professional with a strong skill set for research development implementation of new business processes and performance enhancement of existing processes and programs. A unifier recognized with the ability to establish trust and credibility at many organizational levels build and lead highly effective teams and external partnerships.\rWORK EXPERIENCE\rProgram Manager\rIBM - Essex Jct VT - 2011 to 2014\rfor IBM Burlington Chemical Management and Project Manager for Product Stewardship\r- Supply chain procurement warehouse management delivery logistics quality specifications.\r- EH&S Environmental regulatory affairs emergency response site security chemical and HazCom audits and education.\r- Data & analytics budget and spend forecasting of $24M/year.\r- ISO14001 and Department of Homeland Security audits.\r- Hardware installations and infrastructure capital projects.\rAwards - An Ounce of Prevention - for site chemical spending dashboard Real Time Chemical Spending.\rProject Manager for IBM Product Stewardship\rFluor Corporation - Essex Jct VT - 2005 to 2011\rInterpretation technical support and tactical response to national and international environmental laws including Cal Prop65 RoHS and REACH.\r- Developed and managed supplier and product material declarations.\r- Contract Negotiation of RoHS Supplier agreements. Translated to a cost savings of over 70% to the total analytical testing program costs.\r- Support of Sales agreements Statements of work and customer warranties.\r- Managed analytical testing program > $500K comprised of hundreds of raw materials thousands of finished products and packaging across 11 sites around the globe. Management and data analysis.\r- Price negotiation resulting in 45% reduction in unit cost of testing. (Value Awareness awards 2006 & 2009). - Position White paper to the Norwegian government Arsenic Use in the Semiconductor Industry.\rAssistant Coach for Women's Soccer\rMiddlebury College - Middlebury VT - 2004 to 2011\rAssist with all facets of training and managing a nationally-ranked collegiate athletic program.\rPrepare and conduct individual and group training sessions for advanced athletes. Coordinate efforts of training personnel to rehabilitate injured players. Recruiting player evaluations travel arrangements and budgets.\rConsultant DBA Mitch Krauss\r \rSeventh Generation - Burlington VT - 2008 to 2008\rInvestigation in Air Care - An in depth research and analysis for new business development in the area of air filtration systems air fresheners cleansers and other similar products for household use. Provided the client with data and a risk analysis to determine if this segment is aligned to their core values.\rProject & People Manager\rQuapaw Information Systems Inc - Tulsa OK - May 2004 to August 2004\rQuapaw Trust Analysis Project (QTAP) performed historical accounting of the department of the Interiors (DOI) management of the Quapaw Tribal Trust in relation to Tar Creek Superfund site Picher Oklahoma. Analysis evaluated extent to which Federal policies procedures and customs were performed in manner consistent with then current applicable trust obligations and accepted industry practices.\r Managed field teams for digital capture encoding and analysis of documents related to case.\rProduct R&D and Sales\rMaven Peal Instruments Inc - Calais VT - 2002 to 2004\rManufacture of the highest quality guitar amplifiers to worldwide professionals. Conducted presentations and seminars privately in stores and at trade shows for professional musicians and dealers. Clientele included Warren Hanes Joe Perry Neil Young and Sonny Landreth.\rEnvironmental Programs Staff Member\rFluor Corporation - Essex Jct VT - 1999 to 2003 Environmental Programs (for IBM)\rTool Connect - Implementation and integration of Environmental Impact Assessments (EIA) EH&S facilities utilities and manufacturing process and equipment data into one system. Proven cost savings in excess $2 million.\r- Process Development Data Management and Training for multiple IBM sites. - ISO14001 and corporate audits.\r- Tool inspections.\r- Mass balance modeling of site chemical and utilities usage and emissions.\rAnalyst of organic and inorganic compounds pesticides and herbicides\rEndyne Inc - Williston VT - 1998 to 1999\rResearch and Development Scientist for Pharmaceutical Sciences\rWyeth Pharmaceuticals - Pearl River NY - 1996 to 1998 NY 1996 - 1998\rResearch and Development Scientist for Pharmaceutical Sciences\rOther Professional Work of Note\rEDUCATION\rMaster of Arts in Natrual Sciences - Environmental Chemistry\rState University of New York at Plattsburgh - Plattsburgh NY 1996\rBA in Environmental Studies & Chemistry\rState University of New York at Plattsburgh - Plattsburgh NY 1993\rSKILLS\rMediation HAZWOPER40\r\"\r\nresume_84,not_flagged,\"Morgan Melekos Wetlands/Compliance Scientist\rMontpelier VT - Email me on Indeed: indeed.com/r/Morgan-Melekos/52df5876cf2b35b9 Authorized to work in the US for any employer\rWORK EXPERIENCE\rWetlands/compliance Scientist\rVarious - Nationwide - August 1999 to April 2016\rI'm a career contract scientist helping firms around the country that need temporary up-staffing in the areas of baseline biological surveys (wetlands streams protected species) and construction compliance. I'm available for assignments of any duration or location at rates below retail.\rEDUCATION\rB.A. in Philosophy/Science of Religion\rWinthrop University - Rock Hill SC 1995\rInstitute for Wetland and Environmental Education and Research\rSKILLS\rWetland Delineation and Assessment (10+ years) Permit Compliance Inspection for Major Construction (10+ years) Protected Wildlife and Plants Surveys (10+ years)\rADDITIONAL INFORMATION SKILLS AND EXPERIENCE\rConstruction Compliance Monitoring\r Extensive experience monitoring construction on major energy projects for permit compliance.\r Knowledge of typical permit rules constraints and construction methods of compliance.\r Experience building positive relationships with agency inspectors site managers and project owners.\r Experience monitoring coastal avifauna for dredging and other near-shore heavy equipment operations. Examples of compliance assignments:\r1. Construction monitoring for listed wildlife on pipeline construction in south FL 2004\r2. Construction monitoring on SW Border Fence Construction Project CA 2007\r3. Monitoring pipeline construction in northern LA 2007\r4. Environmental Compliance Coordinator wind farm construction WV 2011\r5. Environmental Inspector natural gas pipeline construction northern NY 2012 2013\rPipeline Routing Environmental Advisement\r Worked with routing engineers and surveyors staking corridor through Redstone Arsenal in AL 2001 Re-route advisement as EI on pipeline installation in northern NY [...]\r Routing assistance with civil survey crews in SE OH 2012.\r \rWetlands and Streams\r Experience delineating WOTUS in OH WV PA VT NY FL GA LA SC NC AL TX WI IN OK.\r 40 hours of training by an established educational institute in wetland delineation (Ralph Tiner's IWEER).\r Extensive ORAM/HHEI/QHEI assessment experience in OH.\r Extensive experience in monitoring the recovery of streams and wetlands following pipeline installation.\r Experience conducting jurisdictional stream determinations: assessing aquatic habitat quality using assessment methods based on condition of substrates banks macroinvertebrates and other characteristics. Over 900 miles of linear corridor on 33 different projects walked for wetland/stream work since 1999.\r Experience defending wetland jurisdictionality/boundaries in the field to regulatory agency officers.\rAvifauna\r General knowledge of bird species behavior and habitats.\r Experience micro-siting setting up and operating mobile radar observation stations for migratory avian surveys related to wind farm siting.\r Conducted raptor nest assessment on cell phone towers.\r Experience working with Anabat units.\r Southwestern raptor identification training from the U. S. Forest Service (USFS).\r Trained/experienced in USFS methods to survey for Mexican spotted owls.\r Trained/experienced in capturing handling and banding red-cockaded woodpeckers.\r Extensive large-tract red-cockaded woodpecker surveys performed from helicopters and ground transects. Played leading role on [...] survey in south Florida planning and conducting searches for protected nesting wading birds. Surveys included helicopter searches plus ground surveys by ATV and pedestrian transects. Botanical\r General knowledge of dominant plant species in many communities and regions.\r Expertise in hydrophytic species.\r Knowledge of plant anatomy and dichotomous keys for identifying unknown species.\r Experience with several vegetative plot sampling methodologies on several botanical projects.\r Knowledge of Natural Heritage Inventory methodologies for evaluating native community integrity.\r Botanical work performed mainly in the Southeast and Florida but also north to Wisconsin and Vermont and west to the Dakotas Wyoming and New Mexico.\rOther Wildlife\r Numerous suitable habitat surveys for threatened and endangered species since 2000 in several regions. Collected data on VT black bear habitat usage: beech stand mapping tree marking nests etc.\r Conducted suitable habitat surveys for Indiana bats as part of linear survey corridor project in IN and OH. Extensive experience surveying for gopher tortoises in Florida Georgia South Carolina and Alabama.\r Trained/experienced in gopher tortoise excavation handling biometric data collection and mitigation.\r Limited experience in other herpetological capture/survey methods.\r Experience electro-shock surveying aquatic fauna.\rEnvironmental Disaster Response\r Mobilized by EPA to Kalamazoo River oil spill August 2010 to map extents of oiling in floodplains.\r Mobilized by EPA to LA to locate and coordinate recovery of HAZMATs in marsh following Katrina/Rita. Mobilized by NPS to eastern TX to fight wildfires as a Federal firefighter August 1998.\rNautical/Water Craft\r Deck Hand/pilot on 35' power troller fishing vessel Alaska fisheries. Deck Hand on 48' long-line halibut fishing vessel Alaska fisheries.\r Air boat work in coastal marsh for oil spill and hurricane response mobilizations.\r Coastal cruising and day sailing experience small craft trailering and operation sea kayaking. Strong swimmer usually not a puker.\rReporting Management and Business Development\r 15 years of free-lance work marketing own services to firms and agencies.\r Experience bidding on and winning US Forest Service botanical survey project.\r Wrote reports for EPA documenting Hurricanes Katrina/Rita HAZMAT debris removal from LA wetlands. Wrote protocols/created data sheets/ maps for wildlife survey efforts on [...] survey site in Florida.\r Managed more than 15 wetland delineation and protected species projects since 2002.\r Authored and prepared numerous environmental reports for submission to Federal/State agencies.\r Co-wrote report on native prairie communities in North Dakota for the Parks and Recreation Department. Acted as crew leader managed field operations for trail crews numbering up to 10 subordinates.\r All daily fieldwork activities documented in logbooks from 2000 to present.\rOther Skills/Training/Professional Activities\r Service on Board of Academic Advisors Winthrop University Department of Environmental Studies. Current 24-hour HAZMAT/HAZWOPER training (Hazardous Waste Materials/Operations).\r Current OSHA 10-hour training.\r Current First Aid/CPR training.\r Two seasons of work in Pacific commercial fisheries on small vessels and dockside.\r Recurrent work from helicopters.\rdoghandling /training veterinary technician for three years prior to consulting career.\r Wildlife landscape and botanical photography.\r Extensive sub-meter and handheld GPS operation some GIS data processing.\r Formal training and extensive experience in map/compass orienteering.\r Extensive ATV 4x4 operation off-road (good driving record).\r Wilderness First Aid training.\r Federal wildland fire suppression training and experience; prescribed burn training and experience. USFS Wildfire Powersaw Training.\r Forestry metrics experience.\r Experience using civil survey equipment and methods.\rEmployers Since 1996\r Self-employed Ecological Consultant Aug. 03 - present\r Geomarine Inc. (Wildlife Biologist/Botanist) Mar. 03 - Jun. 03\r Birkitt Environmental Inc. (Wetland/Wildlife Biologist) Nov. 02\r North Dakota State Parks and Recreation Department (Field Biologist) Jul. 2002 - Oct. 02\r Colorado State Univ.{Center for the Eco. Mngmnt. of Military Lands}(Botanist) Jul. 02\r Typha Ecological Field Services (Self-employed Biologist) Oct. 01 - Oct. 02\r ENSR International {GA FL and CO offices} (Biologist) Jul. 99 - Oct. 01\r South Carolina State Park Service (Trails Education Specialist) Dec. 98 - Jun. 99\r Palmetto Trails Association (Trail Crew Chief) Oct. 98 - Dec. 98\r National Park Service Congaree Swamp National Park (Trail Crew Firefighter) May 1996 - Nov. 1998\r\"\r\nresume_85,not_flagged,\"Nawras Abureehan Growth & Data Scientist\rMiddlebury VT - Email me on Indeed: indeed.com/r/Nawras-Abureehan/1dcc19e7ff6433ac\rWORK EXPERIENCE\rTreasurer\rMiddlebury Arabesque - Middlebury VT - 2011 to Present\rAdministered $5000 budget for sponsoring events purchasing utilities and promotional materials\r Initiate visits of cultural and political speakers to enhance cultural diversity and political awareness\r Advertised for 5 events of 400 attendees each semester through posters Facebook and College E-mail Lead and coordinate 10 students for the annual trip to the sociopolitical Harvard Arab Conference\rTechnical Assistant\rMiddlebury Arabesque - Middlebury VT - 2011 to Present\r2011-present\r Set up sound system and light board for dances concerts plays and speaker events in the Social Hall\r Assist with event management such as security set-up and break-down\r Act as liaison with bands and performers and advice McCullough staff of problems concerns and damage\rGrowth & Data Scientist\rAccelerate Product Partners - New York NY - January 2014 to February 2014\rAccomplishments\r APP makes investment management more accessible for investors through technology based due diligence. Assisted in launching the startup APP by content marketing product testing and data analysis.\r Collected data from consumers activity on APP and analyzed them using Google Analytics to find the Key Performance Indicator (KPI) in order to drive growth.\rSkills Used Analytical skills.\rDeveloper of Optimization Engine\rInfosys Consulting Inc - Bangalore Karnataka - June 2013 to September 2013\rConducted market research on existing commercial optimization solvers and free open sources.\r Developed the interface to extract data from the data source automate the process of generating mathematical\rformulation for the supply chain problem and present the results in graph and charts and enabled the user to carry out what-if analysis.\r Developed an optimization tool to solve vehicle routing linear programming problems using statistical techniques programming languages optimization algorithms and simulation models in practice.\rOffice Assistant\rMiddlebury Arabesque - Middlebury VT - December 2011 to December 2012\rAnswer questions and explains admissions procedures by phone and office e-mails during admission high season\r Classify highly confidential application documents and enter information to Nolij database in a timely fashion\r \rADDITIONAL\r Achievements and Honors: Deans Honors List (spring'11) United World Colleges (UWC) two years-IB scholarship '08 5th place in Palestinian Olympiad for Chemistry '07-08 3rd place in Palestinian Mathematical Olympiad '05-06.\rEDUCATION\rBA in Biochemistry Economics\rMiddlebury College - Middlebury VT 2010 to 2014\rSKILLS\rData Analysis Programming Finance Biochemistry Laboratory\rLINKS http://www.linkedin.com/in/nawrasabureehan/\rADDITIONAL INFORMATION\r Computer Skills: STATA R JAVA Python MAPLE MATLAB Bloomberg MS Excel and Google Analytics.\r \"\r\nresume_86,not_flagged,\"Nicholas Sindorf\rFounding Father - Sigma Phi Epsilon NH Gamma Chapter\r- Email me on Indeed: indeed.com/r/Nicholas-Sindorf/a05a57829ea6875a\rWORK EXPERIENCE\rYouth Conservation Corps Crew Leader\rNorthWoods Stewardship Center - East Charleston VT - June 2016 to August 2016\r Partnered with US Fish & Wildlife Service to lead and educate crews on conservation and wildlife principles and practices including basic forestry skills habitat management and data recording\r Learned forestry practices including timber sale preparation and stand management from USFWS forester at the Silvio O. Conte National Wildlife Refuge Nulhegan Basin Division\rRiver Steward\rNorthWoods Stewardship Center - Errol NH - June 2016 to August 2016\r Ran water quality tests such as DO and turbidity at sites along the Androscoggin River NH\r Inventoried data on community member's usage of the Androscoggin to obtain preliminary census data of the river\rFIRE team member\rPeter T. Paul College Business and Economics - Durham NH - June 2015 to August 2015\rworked closely with UNH Paul College faculty to develop a year-round class for incoming first-year business students at UNH\r Compiled concise research reports on real world problems that can be solved using emerging technologies and practices\r Assisted computer scientists in database creation for 700+ enrolled FIRE students\rDivemaster Program Intern\rSubway Watersports - June 2014 to July 2014\r Assisted Divemasters with leading customers on dives and maintenance of customer's equipment\r Performed diving techniques with customers including how to properly handle risks in multiple underwater situations\rCampus Involvement\rEDUCATION\rBachelor of Science in Wildlife and Conservation Biology\rUniversity of New Hampshire - Durham NH May 2016\rLINKS https://www.linkedin.com/in/nicksindorf\r \rADDITIONAL INFORMATION\rSkills Proficient in Microsoft Word Excel Access PowerPoint and ArcGIS\r\"\r\nresume_87,not_flagged,\"Nicholas Snelling Leader - NES Rentals\rColchester VT - Email me on Indeed: indeed.com/r/Nicholas-Snelling/ffb5dbd717819958\rWORK EXPERIENCE\rLeader\rNES Rentals - Williston VT - 2014 to Present\rin $28B equipment rental industry with 80 locations across Central and Eastern United States.\rIntern\rWorking with Environmental Compliance Group at different locations nationwide to ensure compliance is being met at all sites.\r Completed thorough safety audit at Plattsburgh NY location.\r Compiled data from all branches to compile Compliance Safety and Accountability (CSA) scores of third party haulers.\r Edited all safety webinars and Material Safety Data Sheet (MSDS) for new hire webinar.\r Compiled data from all branches on waste receptacles.\r Created multiple surveys in Survey Monkey sent to all branch managers.\r Completed Aerial Work Platform Training (AWPTA) and CPR/AED training.\r Helped roll out new mobile applications for all drivers allowing them to log hours digitally.\r Reviewed Spill Prevention Control and Countermeasure (SPCC) plan proposal and suggested recommendations.\rBurlington Country Club - Burlington VT - June 2013 to August 2013\rPrivate family friendly country club offering highest standards of golf and hospitality in New England\rGreens Staff\r- June 2013 to August 2013\rHelped maintain country club grounds. Cleaned bunkers mowed lawn and helped maintain native grass areas.\rChipotle Restaurant - South Burlington VT - June 2012 to August 2012\rOpened and closed entire kitchen area. Prepared all food served in restaurant. Helped open new store in area. Worked with management team to ensure excellent customer dining experience.\rPrep Cook\rCity Market - Burlington VT - June 2011 to August 2011\rBurlington VT Summer 2011\rCommunity-owned grocery store in offering local organic and conventional products as well as hot bar and made to order counter.\rPrep Cook\rCreated and executed a new menu for customers everyday along with maintaining a clean working kitchen. Took inventory and helped create food orders. Maintained presentable buffet style counter area.\r \rKitchen Manager\rNew York Pizza Oven - 2010 to 2011\rNew York Pizza Oven - Colchester VT - 2009 to 2011\rNew York Style pizzeria that also served Italian foods such as pasta along with chicken wings and subs.\rFry Cook\rNew York Pizza Oven - 2009 to 2010\rManaged and maintained working kitchen by providing guidance to all staff members as well as taking full responsibility for all earnings that were acquired during shift.\r Helped design and introduce new menu.\r Made nightly cash deposits along with accounting for card/cash earnings. Selected as part of group of employees used to open new restaurant.\r Managed 3-6 employees during shift.\rEDUCATION\rMarine Science\rMaine Maritime Academy\rADDITIONAL INFORMATION QUALIFICATIONS\rAnalytical organized and efficient Marine Scientist with laboratory experience in:\r Monitoring Chemical Systems Preparing & Analyzing Samples Operating Spectrophotometer Centrifuge Usage\r Chemical Analysis Properly Disposing Hazardous Materials\r Solution Mixtures Running SPSS Analysis Tests\r Maintaining Laboratory Inventory Compiling Test Results Writing Technical Reports Interpreting Test Results\rAccomplish tasks in timely and accurate ways as instructed by team leaders. Able to analyze tasks to produce efficient results. Quick learner who enjoys incorporating new learning into practical applications. Resolve problems using critical thinking skills. Work well independently and in team environments.\r\"\r\nresume_88,flagged,\"Nisha Chaube Graduate Teaching Assistant\rColchester VT - Email me on Indeed: indeed.com/r/Nisha-Chaube/7b4477be57bc4b6c\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rGraduate Teaching Assistant (GTA)\rUniversity of Vermont - Burlington VT - August 2016 to Present\rAssisting professor in teaching the subjects Computer organization [CS120] and Advanced Programming in C++ [CS121].\rHolding office hours for students. Grading homeworks and projects.\rIntern\rDynamica India Pvt Ltd - Noida Uttar Pradesh - June 2015 to August 2015\rProgramming Project: Examination Portal.\rCreated a responsive and dynamic quizzing website to conduct online tests at universities using Oracle 10g at the back end and Java/J2EE technologies (Servlet JSP) HTML CSS JS and JDBC at the front end.\rIntern\rHonda Siel Power Products - Noida Uttar Pradesh - June 2014 to August 2014\rAnalysis Project: Feasibility study for design software for product traceability using barcode.\rAnalyzed data processing software and performed an intensive study of the requirements of overseas clients (America and\rCanada Honda) for barcode labeling.\rPerformed an integrated study of manufacturing stages from capturing engine number to quality inspection till product dispatch.\rCourse Project\rUniversity of Vermont - Burlington VT - November 2016\rAnalysis and Programming Project: Fetal Death Analysis. Performed data analysis to understand the cause of fetal death by selected characteristics such as mothers age mothers origin race mothers residence status fetal sex period of gestation birth weight mothers height risk factors on the US fetal death data set.\rCourse Project\r- 2014\rProgramming Project: Calculator.Developed a calculator that could perform all operations using JAVA programming language.\rCourse Project\r- 2012\rProgramming Project: Quiz game. Developed a quiz game in C++ to test the IQ level of the player.\r \rEDUCATION\rMaster of Science in Computer Science\rUniversity of Vermont - Burlington VT August 2016 to May 2018\rSKILLS\rMicrosoft Office (8 years) C C++ Python Java HTML 5 Oracle 10G SQL\rAWARDS\rSeventh National Grassroots Technological Innovations and Traditional Knowledge award India\r2013\rSeventh National Grassroots Technological Innovations and Traditional Knowledge award (2013) by scientist Mr. R.A Mashelkar and recognized by the President of India Sri. Pranab Mukherjee for my idea of innovation.\rEF tour scholarship recipient\r2012\rEF Global citizen scholarship recipient who was awarded to travel to China in 2012 on the basis of an essay writing.\rIgnite 2009 (National Innovation Foundation)\r2009\rAwardee for the idea of innovation Travel Bag with folding seat awarded by the ex-president of India late Dr. A.P.J Abdul Kalam. Also filed a national patent of product design.\rCleared assessment on IT FOUNDATION SKILLS which is a cognizant certified student program.\rMarch 2016\rSuccessfully completed Aricent employment enhancement program.\rApril 2016\rCompleted training program requirement for Lucideus certified cyber security expert grade 1.\r2013\rCleared Aspiring Minds Computer Adaptive Test an employability assessment test.\rApril 2016\rComplete scholarship in Computer Science engineering under-graduation course.\r2013\rCERTIFICATIONS/LICENSES\rAspiring Minds Computer Adaptive Test an employability assessment test.\r2016\rPUBLICATIONS\rResearch on Dual cryptography based data security in cloud computing\rApril 2016\rA publication in International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 3 Issue 4 April. Proposed an architecture in cloud computing to ensure secure data transfer using Rijndael and Serpent algorithms.\rADDITIONAL INFORMATION\r Technical Expertise: JAVA SE C C++ Python HTML CSS Oracle 10g SQL Microsoft Office.\r Relevant coursework: Database System Data Science Operating System Data Structures Cryptography Software Engineering.\rUndergraduation (Bachelor of Technology in Computer Science): Galgotias College of Engineering & Technology (GCET) Greater Noida India\r\"\r\nresume_89,not_flagged,\"Patrick Raymond\rStaff Engineer / Scientist - International Business Machines\rEnosburg Falls VT - Email me on Indeed: indeed.com/r/Patrick-Raymond/39e936afa72c013a\rSeeking a challenging exciting and energetic engineering position with a growing and/or cutting edge company corporation or entity. New process start-up R&D transfer to manufacturing or exclusive R&D activities are key areas of interest for employment.\rWORK EXPERIENCE\rStaff Engineer / Scientist\rInternational Business Machines - Essex Junction VT - 2009 to Present\rSource new or used equipment provide specifications perform pre-acceptance oversee installation perform qualification and perform post acceptance. Create documentation pertaining to operations and standard work. Transfer processes from research and development to a completely setup manufacturing line.\r Develop and introduce new processes to continuously drive the next advancement in technology improve current production quality lower costs and improve throughput / turn around time.\r Train new engineers and technicians to take over ownership of new equipment and processes.\r Develop preventative maintenance plans and standard work to maintain and support new equipment and processes.\r Continuously monitor product signals and feedback from customers to resolve issues or find opportunities to add value and improve quality for the customer.\r Determine root causes of failures using statistical methods and recommend changes in designs tolerances or processing methods.\r Provide technical expertise or support related to manufacturing\r Supervise technicians and other engineers\r Troubleshoot new or existing product problems involving designs materials or processes.\r Review product designs for manufacturability or completeness.\r Train production personnel in new or existing methods.\r Communicate manufacturing capabilities production schedules or other information to facilitate production processes.\r Design install or troubleshoot manufacturing equipment.\r Apply continuous improvement methods such as lean manufacturing to enhance manufacturing quality reliability or cost-effectiveness.\r Investigate or resolve operational problems such as material use variances or bottlenecks.\r Estimate costs production times or staffing requirements for new designs.\r Evaluate manufactured products according to specifications and quality standards.\r Purchase equipment materials or parts.\r Design layout of equipment or workspaces to achieve maximum efficiency.\r Design testing methods and test finished products or process capabilities to establish standards or validate process requirements.\r Read current literature converse with colleagues participate in educational programs attend meetings attend workshops or participate in professional organizations or conferences to keep abreast of developments in the technology field.\r Develop sustainable manufacturing technologies to reduce greenhouse gas emissions minimize raw material use replace toxic materials with non-toxic materials replace non-renewable materials with renewable materials or reduce waste.\r \r Evaluate current or proposed manufacturing processes or practices for environmental sustainability considering factors such as green house gas emissions air pollution water pollution energy use or waste creation.\rEngineering Technician\rInternational Business Machines - Essex Junction VT - 2006 to 2009\rCreated and optimized equipment process programs.\r Dispositioned product after a process deviation or tool malfunction.\r Provided corrective actions for out of control processes.\r Set up and operated production equipment in accordance with current good manufacturing practices and standard operating procedures.\r Monitored and adjusted production processes or equipment to maintain/improve quality and productivity.\r Troubleshot problems with equipment devices or products.\r Trained technicians.\r Measured and recorded data associated with equipment operations.\r Assisted engineers in developing new products processes or procedures.\r Prepared production documents such as standard operating procedures.\r Provided production progress and changeover reports to shift supervisors and management.\rEngineering Technician\rInternational Business Machines - East Fishkill NY - 2003 to 2006 Same as above.\rProduction Associate\rInternational Business Machines - Essex Junction VT - 2000 to 2003\rOperated production equipment.\r Trained operators on production equipment. Performed required equipment qualifications. Safety representative.\rBookkeeper\rTravers Forest Products - MontreՁal QC - 1997 to 2000\rAccounts Receivable Accounts Payable Payroll Financial Forecasting\rEDUCATION\rM.E. in Microelectronics Manufacturing Engineering\rRochester Institute of Technology - Rochester NY 2010 to 2013\rB.S. in Electrical & Mechanical Engineering\rRochester Institute of Technology - Rochester NY 2006 to 2009\rA.S. in Engineering Science\rDutchess Community College - Poughkeepsie NY 2003 to 2005\rA.S. in Business Management\rChamplain College - Burlington VT\r2001 to 2003\rHigh School Diploma\rEnosburg Falls High School - Enosburg Falls VT 1997\rADDITIONAL INFORMATION\rSkills\r Root Cause Analysis\r Failure Mode Effect Analysis (FMEA)\r Lean Manufacturing Principles\r Statistical Process Control (SPC)\r Design of Experiments (DOE)\r MS Word Excel PowerPoint Project\r PLC Programming (Rockwell Automation) AutoCAD\r SolidWorks\r Dreamweaver (website building)\r C/C++\r\"\r\nresume_90,not_flagged,\"Peter Klinger\rNorthfield VT - Email me on Indeed: indeed.com/r/Peter-Klinger/55cbfc87289ce027\rExperienced versatile Quality Professional with extensive experience in Bipolar and CMOS products in the Semiconductor industry.\rKnown for analytical and problem solving skills. Achieved yield loss reduction to lower costs and increase customer satisfaction. Highly organized detailed oriented efficient production planning and accurate project costing. Led through example supervised motivated and built thorough Quality Control teams and got bottom- line results.\rWORK EXPERIENCE\rMicro-Manipulator Probe Stations - 2010 to Present\rIdentified root cause semiconductor failures.\r Used RIE etching front/backside cross sectional polishing techniques chemical delayering.\r Utilized highly sophisticated equipment such as Scanning Electron Microscopes Focused Ion Beam Micro- Manipulator Probe Stations.\r Performed Electrical Analysis to isolate failing devices.\r Identified semiconductor defects/mechanisms.\r Increased yield 5% - 8% customized design rules altered specifications per product function.\r Identified systematic issues within layout.\r Increase work efficiencies. Streamlined quantified Characterization Lab workflow.\r Published article in Electronic Device Failure Analysis trade magazine.\rStaff Scientist Quality Assurance\rApplied Research Associates - Randolph VT - January 2009 to June 2010\rOversaw and Maintained the Quality program for ~3 million dollar Cone Penetrometer Technology contracts. Assured delivery of Trucks and Track Rigs were on time and accurate to customer specifications.\r Performed internal ISO audits.\r Update ISO Documentation from ISO 9001:2001 to 9001:2008\r Created Draft policies for Software Asset Management.\r Troubleshoot Data acquisition enclosures and computers.\r Created Quality Assurance Work Instructions and Procedures. Created Customer Computer setup Procedure\r Assistant to the IT Manager\rTechnical Laboratory Specialist\r- 1999 to 2008\rOwner/Operator\rSHOWTIME VIDEO - Northfield VT - 1998 to 2000\rMedia vender for 8000 population 10 mile radius of Northfield VT. With 5 employees 75K annual revenue. Maintained all aspects of small business ownership: Interview hire evaluate discipline and terminate employees. Purchasing inventory control sales customer service payroll etc.\rProduction Scheduler\rCAPITAL CITY PRESS - Berlin VT - 1997 to 1998\rCoordinated and scheduled 1440 production run for 7 presses generated over 1 million in sales annually.\r \r Met 98% of print production schedules. Coordinated personal schedules and shop resources. 100% compliance to Internal Quality Standards for production.\r Increased Customer Satisfaction. Resolved ~98 customer issues annually.\rIBM - Burlington VT - 1990 to 1992\rEDUCATION\rBS in Management and Information Security\rChamplain College May 2009\rASEE in Electrical and Electronics Engineering\rVermont Technical College - Randolph Center VT 2009\rADDITIONAL INFORMATION TECHNICAL SUMMARY\rDesk Top Windows 95 98 XP Seven\rApplications Word Excel Power Point Project Viso Operating Systems Windows Server2003 Linux Equipment Routers Switches Hubs Firewalls\rWeb Design Dream Weaver\r\"\r\nresume_91,not_flagged,\"Rebecca Mulheron\rBurlington VT - Email me on Indeed: indeed.com/r/Rebecca-Mulheron/8c150d2673fd81dc\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rMedical Research Laboratory Technician II\rUniversity of Vermont Pathology Department - Burlington VT - January 2015 to Present\rDuties focused on maintaining a large clinical research biorepository through client sample receipt (tracked shipments sample log in) prepped samples for testing aliquoted samples for repository placed samples in repository maintained off site repository storage database management data entry created excel spreadsheets order materials communication with sampling sites (supply material trouble shoot problems) trained new employees manage day to day activities of the study and ensure the study runs smoothly. Tested samples for Glucose Calcium and Creatinine levels on a Roche Cobas c311 analyzer. Regularly accession patient samples in the Fletcher Allen Hospital Sunquest LIS system. Experience working in a fast paced heavy work load CLIA certified Biosafety Level 2 laboratory. Well versed with keeping projects on task and meeting deadlines.\rMolecular & Genetics Lab Research Scientist\rNova Southeastern University - Dania Beach FL - December 2011 to August 2014\rResponsibilities\rLaboratory techniques utilized regularly were: gel electrophoresis PCR PCR gel band purification DNA extraction RNA extraction and field tissue sampling. General experience with qPCR and microarray analysis. Other laboratory duties included ordering equipment logging inventory preparing solutions equipment calibration mentoring undergraduate students assistant faulty and visiting research scientists.\rAccomplishments\rMy research focused on the Sponge Orange Band Disease outbreak in Xestospongia muta (giant barrel sponge) during 2012 by examining the etiology of the disease microbiomes of diseased and healthy sponges. A total of 51 distinct 16S rRNA amplicon libraries were constructed from universal V4 primers (515F /806R) and sequenced using the 454 Titanium GS FLX platform. The sequences produced were analyzed using the Quantitative Insights into Microbial Ecology v.1.8.0 (QIIME). The goal was to determine if the disease was caused by a possible microbial causative agent. This research was also part of the Earth Microbiome Project.\rOther research positions involved participating on the Porifera Tree of Life Project which focused on defining the phylogenetic relationships within Porifera that still remain unsolved. In particular the monophyly of the Porifera phylum and its largest class; Demospongiae has been questioned and the relationships among its major lineages. The lack of a robust phylogenetic hypothesis for this phylum hampers progress in basic studies of sponge biology biodiversity comparative evolutionary studies and efforts to conserve sponges. The goal was to provide a phylogenetic context that would improve the understanding of all aspects of sponge biology (https://www.portol.org/). I was specifically working on identifying the genetic differences between sponge species using 18S rRNA and 28S rRNA analysis. The analyses included using software such as Geneious to evaluate sequences.\r \rI also assisted on both sub-projects of the BP oil and dispersant exposure experiment. The first looked at the effect of BP oil and dispersant on the microbial communities of sponge Cinachyrella australiensis. I evaluated a region of the 28S gene to determine is the sponge samples themselves all belonged to the sample species or if some represent a unknown subspecies. I also completed the RNA extraction for samples which looked at the Cinachyrella australiensis gene expression change to the BP oil and dispersant\rSkills Used\rWhile I was employed I demonstrate the ability to work independently and within a team. I took leadership roles within different research projects and general laboratory maintenance. I am well versed with computer software: Mega Excel Word PowerPoint Geneious Quantitative Insights Into Microbial Ecology (QIIME) and National Center for Biotechnology Information (NCBI) Database.\rCoral Nursery and Coral Restoration Scientist\rNova Southeastern University - Dania Beach FL - August 2011 to December 2012\rResponsibilities\rDuties included taking care of Nova Southeastern Oceanographic Centers outdoor nurseries. This included general maintenance such as water quality measurements tank cleaning coral growth measurements coral propagation coral out planting and field coral monitoring assessments.\rLine Operator\rIBM - Essex Junction VT - December 2009 to July 2011\rResponsibilities\rJob duties included loading and unloading of product increased product output through trouble shooting tools and computer log analysis. Training new employees on selected tools to maintain and increase output.\rCoral Laboratory Technician\rMotes Tropical Research Laboratory - Summerland Key FL - October 2009 to December 2009\rResponsibilities\rDuties included maintaining coral aquariums through cleaning feeding and monitoring water quality. Water quality tests included measuring pH salinity and temperature. Cleaning involved the removal of algae and parasites from both individual corals and the overall tanks. Coral mounting bases were made by hand and the experience of cutting and mounting new corals was acquired. I helped renovated the indoor laboratory by replacing all of the filtration and water flow systems. I updated and organized the coral photographic inventory. I used Sigma Scan and the new photos to measure the area of the corals which was compared with the previous year to measure coral growth. I used both Excel and PowerPoint to present findings and research.\rLab Technician I\rWaterbury State Environmental Lab State of Vermont - Waterbury VT - June 2009 to October 2009\rResponsibilities\rTasks included completing water testing on samples brought into the lab equipment calibration making reagents filling orders cleaning glassware training staff and using Sample Master / Lims (software) for data entry. Daily water quality tests included alkalinity chlorophyll coliform conductivity dissolved oxygen pH total suspended solids turbidity and total phosphorus. Some experience with carbonyl compounds total petroleum hydrocarbons (diesel range) volatile organics and volatile organics (gasoline range).\rEDUCATION\rMS in Biological Sciences\rNova Southeastern University - Dania Beach FL\r2011 to 2014\rBS in Natural Resources Ecology\rUniversity of Vermont - Burlington VT 2005 to 2009\rSKILLS\rMicrobiome Analysis (2 years) Genomic Sequencing (2 years) Clinical Research (2 years) PCR (3 years) DNA Extraction (2 years) RNA Extraction (2 years) qPCR (1 year) Water Quality Anaylsis (1 year) Microarray Analysis (1 year)\rAWARDS\rPresident's Faculty Research & Development Grant at Nova Southeastern University 2013\rDecember 2013\rPUBLICATIONS\rMicrobial communities reveal sub-population of the sponge Cinachyrella\rJuly 2014\r\"\r\nresume_92,not_flagged,\"Ruth Willis Family Nurse Practitioner\rWilliston VT - Email me on Indeed: indeed.com/r/Ruth-Willis/2e72f15ddfd48499\rCreative and versatile APRN with FNP credentials. Skilled in direct patient care medical and scientific investigations complex medical data analysis and diverse program management. Clinical rotations in Family Pediatric and Obstetrics/Gynecology medical practices. Demonstrated flexibility and adaptability to new environments with shifting requirements including work as a Medical Data Analyst Nurse Scientific Researcher and an Environmental Health Peace Corps volunteer.\rAs a Nurse Practitioner worked in outpatient clinics with patients of all ages delivering a broad range of clinical nursing skills. An ability to recognize client and community concerns. Advanced computer electronic medical record system and data analysis skills. Adept in design and development of reports communicating complex data and concepts.\r Willing to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rPublic Health Data Analyst\rUNIVERSITY OF VERMONT COLLEGE OF MEDICINE DEPARTMENT OF PEDIATRICS VT - 2012 to Present\r- Burlington\rCore member of team designing piloting and implementing data collection as part of longitudinal tracking of clinically important measures to quantify improvements in Vermont children's health care.\r- Devise quality improvement measures.\r- Train data collection specialists for chart reviews. Oversee paper and multiple EMR systems data extractions. - Conduct descriptive analyses on new and existing datasets and create detailed aggregate individual practice- level and longitudinal data reports.\r- Anticipate issues during the data collection process and evaluate solutions to meet research objectives while ensuring confidentiality of research materials and compliance with applicable regulations.\r* Provide individual education and coaching to participating practices on implementation of system changes to track and demonstrate improvements in care. Develop and host annual learning sessions. Accomplishments:\rRapid-Cycle QI - Adolescent Depression Screening: Provided ongoing technical assistance and QI support including final month of practice data collection and feedback reporting soliciting feedback from participants on practice successes and barriers aggregation and finalization of MOC data and submission of final reports to credentialing bodies.\r* Rapid-Cycle QI Asthma Management Children 5 - : Contributed to ongoing planning and development of the new project scheduled to start Fall 2015. Conducted stakeholder meetings and focused activities to develop project aim goals measures and data collection tool.\r* 2014-2015 Rapid-Cycle QI Improving Weight Nutrition & Physical Activity in Primary Care: Launched project to improve assessment and counseling of nutrition and physical activity supporting achievement/maintenance of a healthy weight status. Engaged 28 providers from 23 practices who are participating in monthly conference call learning opportunities monthly data collection and review.\r* Preventative Health Services Data Analyses: Performed data aggregation cleaning and validation for three age-based segments/cohorts for preventive health services in each of 33 practices (in excess of 5000 unique records).\r* 2014 - 2014 CHAMP Learning Session Reports: Developed practice and network level reports of selected preventive health services data presented to practices and stakeholders.\r* CHAMP Database Development: design of a comprehensive practice database to support longitudinal practice participation.\r* CHAMP Annual Needs Assessments: Assessed participating practices and verified inter-rater reliability of data collectors.\r* Practice Report Design & Enhancements: Began the review and modification of the template to include emphasis on asthma. Developing a new template and database tool to provide practice level appendices.\r* CHAMP Data Dictionaries: Created and maintained updated data dictionaries to include modifiable views examples of acceptable charting for variables (and unacceptable examples) and an easy search function for reviewers in the field using the electronic version.\r* HPV & Childhood immunization Rates: Collaborated with participating practices and Immunization program staff at VDH to support activities related to increasing HPV completion rates and improving childhood immunization rates.\rSchool Nurse Substitute\rChittenden Central Supervisory Union - Essex Junction VT - 2010 to Present\rSchool Nurse Substitute\r* Dispensed first aid care and medically prescribed services to students and staff.\r* Provided health education to students and documented health encounters immunizations and other health data.\r* Prepared health reports for education district supervisor and health department.\rProject Coordinator\rUNIVERSITY OF VERMONT COLLEGE OF MEDICINE DEPARTMENT OF PEDIATRICS - Burlington VT - March 2010 to June 2012\r* Recruited community pediatric and family medicine offices to build state and regional capacity for enhancing developmental and preventive services for children 0-3 years old.\r* Implemented chart reviews to develop screening surveillance and referral of children. Analyzed data for QI.\rCLINICAL ROTATIONS\rLittle Rivers Health Centers - Bradford VT - 2012 to 2012\r2012\rFederally Qualified Health Center office setting providing a wide variety of short-term and long-term medical care to patients consisting of pediatric young adult middle-aged and geriatric populations.\r- Performed initial history and physical exam of patients and developed tentative diagnosis and treatment with preceptor prior to reviewing with patient.\rFamily Primary Care Clinial Rotation\rEnosburg Health Center (FQHC) - Enosburg Falls VT - 2012 to 2012\r2012\rFederally Qualified Health Center office providing short and long term medical care to pediatric young adult middle-aged and geriatric populations.\r- Conducted comprehensive history and physicals developed diagnoses formulated and instituted an appropriate treatment plan under supervision wrote prescription medications and charted all visits on network EMR.\rTrained on the Vermont Prescription Monitoring System for tracking the prescription of controlled substances.\rPediatric Clinical Rotation\rSouth Royalton Health Clinic - South Royalton VT - 2012 to 2012\r2012\rAn out-patient practice serving children and adolescents ranging from 5 days to 19 years old. Also worked in South Royalton and South Strafford school based clinics.\r- Performed comprehensive history and physicals including supervised well child checks and acute visits.\r- Developed tentative diagnosis and treatment with preceptor wrote prescription medications and charted visits.\rWorked extensively with patients to manage asthma and attain healthy nutrition and physical activity.\rFamily Primary Care Clinical Rotation\rBerlin Family Health - Berlin VT - 2011 to 2011\r2011\rA hospital based out-patient practice providing short and long term medical care to patients consisting of young adult middle-aged and geriatric populations.\r- Assisted taking patients' history and conducting physicals performing IM injections providing wound care and delivering patient education.\rPediatric Clinical Rotation\rBrookside Pediatrics & Adolescent Medicine - Bennington VT - 2011 to 2011\r2011\rAn out-patient practice serving children 5 days to 19 years old.\r- Performed comprehensive H&Ps including supervised well child checks and acute visits.\r- Developed tentative diagnosis and treatment with preceptors wrote prescription medications and charted visits.\r- Experience in IM and subcutaneous injections peak flow performance and spirometry.\rFletcher Allen Womens Health Care Services\rOBSTETRICS & GYNECOLOGY - Burlington VT - 2011 to 2011\rBurlington VT 2011\rAn out-patient office providing comprehensive gynecologic care to women of all ages with an emphasis in low- risk obstetrics and gynecological health of post-menopausal women.\r- Conducted comprehensive assessments and performed speculum/cervical exams initiated and interpreted nitrazine test or ferning NSTs assist biopsy and fetal movement.\r- Provided patient education and counseling on wide variety of health-related topics.\rImmunization Nurse Seasonal Position\rMollen Immunization Clinics - Northwestern VT - August 2009 to December 2010\r* Set-up and managed seasonal influenza regional immunization clinics in north central Vermont.\r* Administered seasonal influenza immunizations and educated clients on seasonal influenza immunization.\rTRC Scholar Student Researcher\rUniversity Of Vermont TRC Center Department Of Hematology & Oncology - Burlington VT - 2008 to 2009\rAnalyzed patient interview data for projects relating to diabetes management and familial breast cancer risk.\rEARLIER PROFESSIONAL EXPERIENCE\rHealth Scientist Respiratory Disease Division\rCDC National Institute Of Occupational Safety & Health - Morgantown WV - 2004 to 2007 Analyzed and managed national occupational asthma and work-related pneumoconiosis data.\r* Contributed to multiple facets of project planning for state-based occupational asthma/pneumoconiosis programs.\rComputer Specialist & User Support Montana Water Resource Division\rU.S. DEPARTMENT OF INTERIOR GEOLOGICAL SURVEY - Helena MT - 1998 to 2004\rManaged the computer and network support for the Montana Water Resources division including network system security database management and end-user education.\r- Supervised computer assistants and verified adequate back-up of critical computer systems.\r- Developed end-use training and education for a range of commercial software packages.\r* Provided expertise in budgeting for computer and network obsolescence and growth\rResearch Assistant\rOregon State University Department Of Forest Science - Corvallis OR - 1994 to 1998\rImplemented water temperature studies in the Oregon Coast range. Trained technicians and analyzed survey data.\r* Designed database and computerized Road Sediment Survey funded by Oregon Department of Forestry.\rInorganic Chemist Region IV\rU.S. Environmental Protection Agency Analytical Services Division - Athens GA - 1992 to 1993\rProcessed and analyzed drinking waters waste water effluents sediments wastes fish tissue and air-filters using EPA analytical and quality control procedures for the region.\rResearch & Teaching Assistant\rUniversity Of Georgia Soil Science Department - Athens GA - 1990 to 1992\rDesigned implemented and analyzed laboratory and field soil erosion studies on the effectiveness of anionic polyacrylamide application to three soil series.\r* Taught theory and practice to Introductory Soils Laboratory and two laboratory sections of upper division Soil Fertility.\rMEDICAL VOLUNTEER\rU.S. Peace Corps Environmental Health - Morgantown WV - 1987 to 1989 Healthy | United Way (Helena MT & Morgantown WV)\rEDUCATION\rMaster of Science in Nursing\rUniversity of Vermont College of Nursing & Health Sciences 2012\rMaster of Science in Soil Science\rUniversity of Georgia College of Agriculture & Environment 1993\rBachelor of Science in Soil Science\rUniversity of Georgia College of Agriculture & Environment 1986\r-\rBurlington VT\rAthens GA\rAthens GA\r-\r-\rSKILLS\rSpanish (Working Proficiency 2+) Extensive skills in Excel Power Point and other computer software Electronic Medical Record programs: EPIC Allscripts PCC Centricity NextGen\rADDITIONAL INFORMATION\rAdvanced Practice Registered Nurse (APRN) Licensed by the State of Nevada\rFamily Nurse Practitioner (FNP) Certified by the American Academy of Nurse Practitioners Certification Program (AANPCP)\rCardiopulmonary Resuscitation (CPR) | Basic Life Support (BLS)\r\"\r\nresume_93,not_flagged,\"Samantha Gagnon Data Entry - JP Morgan Chase\rS Burlington VT - Email me on Indeed: indeed.com/r/Samantha-Gagnon/9e6812d87b4ddb70\rTo be employed as a bench scientist in either the private or public sector to apply my skills and training to practice good science and solve problems of economic scientific and/or social importance.\rWORK EXPERIENCE\rData Entry\rJP Morgan Chase - South Burlington VT - June 2014 to Present\rAu Pair\rRoma Lazio - March 2014 to May 2014\rTeaching Assistant HORT\rClemson University School of Agricultural - Clemson SC - May 2013 to December 2013\rClemson SC May 2013 - December 2013\rTeaching Assistant HORT 455/655: Just Fruits Fall 2013\r-Involved in lecture preparation and the gathering of materials for demonstrations and sampling -Managed and updated the Blackboard Learning System page for the course\r-Reviewed and revised quiz exam and syllabus material for the course\rResearch Assistant\rClemson University School of Agricultural - May 2013 to December 2013\rOrganized and prepared samples for analysis through freeze drying using a lyophilizer and grinding samples using liquid nitrogen\r-Used a BioPhotometer to calculate DNA concentration in a sample and vacuum centrifuge to reconcentrate samples\r-Performed enzyme digestions PCR (including basic knowledge of Real Time PCR) and gel electrophoresis -Performed DNA extraction procedures\rField Work\r-Collected compiled and analyzed field samples and data\r-Performed laboratory analysis of fruit for qualities such as firmness size coloration chlorophyll content pH and soluble solids\r-Collected data using the Fruit Texture Analyzer refractometer titration sampler DA meter and colorimeter\rEDUCATION\rBachelor of Science in Genetics\rClemson University - Clemson SC December 2013\r \"\r\nresume_94,not_flagged,\"Samuel Lagor\rBurlington VT - Email me on Indeed: indeed.com/r/Samuel-Lagor/cfe31faad83b0522 Authorized to work in the US for any employer\rWORK EXPERIENCE\rTeacher's Assistant\rUniversity of Vermont - Burlington VT - August 2013 to Present\rResponsibilities\r Taught three section of an Introduction to Geology course's lab in addition to a full academic course load\r Led discussions tutorials and laboratory exercises\r Assisted and led lectures in a class of more than 200 students in addition to a full academic course load\r Proctored examinations; evaluated and graded examinations assignments and papers in a timely manner; recorded grades\r Scheduled and maintained office hours to meet with students\r Informed students of procedures for completing and submitting laboratory work such as lab reports and field write-ups\rAccomplishments\r Successfully helped over 100 students learn about the basic principles of geology via instruction in the field and in the lab\r Helped over 200 students learn about the natural hazards humanity may encounter in a classroom setting\r Helped a dozen students hone their skills identifying minerals on the petrographic microscope in a petrology laboratory\r Volunteered periodically with a local group of third graders to teach them basic skills of an observational scientist\r Skills Used\r Student Teaching\r Microsoft Office\r Scanning Electron Microscopy Adobe Illustrator\r ArcGIS\r Geologic Mapping\rAssistant Faculty\rThe Governors Institute of Vermont Environmental Science and Technology Institute VT - June 2013 to June 2014\rResponsibilities\r Taught high school students about the quality of soil and water environments\r Acted as a residential assistant to students during week-long stay in campus housing Helped students conduct field research analyze data and prepare reports\r Led students in exercises in scanning electron microscopy for additional data\r- Burlington\rAccomplishments\rHelped high school students understand the value of field-based science research.\rSkills Used\r Student Teaching\r Microsoft Office\r Scanning Electron Microscopy\rOffice and Field Intern\rPioneer Engineering & Environmental Services Inc. - Chicago IL - June 2010 to July 2010\rResponsibilities\r Conducted research on potentially hazardous sites at the University of Illinois at Chicago's library by retrieving historical documents for given addresses and photocopied USGS aerial photographs of given areas over several decades\r Assisted in many field excursions including site investigations at industrial residential and underground storage tank locations needing remediation\r Assisted in many drilling projects ranging from soil and sediment sampling to installing groundwater monitoring wells\rAccomplishments\rThis month-long summer internship taught me the skills necessary to work in a professional office setting. Assisting with background research site assessments and contamination remediation taught me the importance of flexibility and drive in the environmental remediation field.\rSkills Used\r Microsoft Office\r Archival research Soil sampling\r Water sampling\r Site assessment\rEDUCATION\rMS in Geology\rUniversity of Vermont 2013 to 2015\rBS in Geology\rSt. Lawrence University 2009 to 2013\rSKILLS\r- Burlington VT\r- Canton NY\rMicrosoft Office Public Speaking Scanning Electron Microscopy Adobe Illustrator ArcGIS Geologic Mapping\rLINKS https://www.linkedin.com/in/samlagor\r \"\r\nresume_95,not_flagged,\"Sara Ritter\rScientist and Instrumentation Specialist - Burlington Labs Burlington Vt\rUnderhill VT - Email me on Indeed: indeed.com/r/Sara-Ritter/132152f2340ee17b\rWORK EXPERIENCE\rScientist and Instrumentation Specialist\rBurlington Labs Burlington Vt - MS MS LC - December 2014 to Present Specializing in maintenance and management of 10 LC/MS/MS systems.\rChemist\rPerrigo Inc - Georgia VT - November 2011 to December 2014\rQuality Analysis Instrumentation Chemistry\rConducts finished product analysis of baby formula for oil and water-soluble vitamins by HPLC. Conducts new method development and validation. Developed 5S workstation for the Instrumentation Laboratory. Validates and approves laboratory data and records per FDA regulations. Attends Lean 6 Sigma training cGMP HACCP and 5S training. Plant Safety Team member. Conducts regular maintenance on Agilent 1100 and 1260 series HPLC instruments.\rAssistant Operator Jericho Underhill Water District\rState of Vermont Class - November 2008 to August 2012\r3 Water System Operator. Performs daily monthly and quarterly water testing duties security checks regular maintenance of chemical pumps and facilities. Monitors water and chemical meters and well pump times. Orders chemicals and supplies as needed. Reports to the chief operator and board of trustees. Assists chief operator with customer concerns and maintenance of system.\rChemist I Vermont Agency\r- August 2001 to September 2002\rAnalyzed feed and fertilizer samples for mineral and nutrient compliance using ICP and nitrogen analyzer. Prepared and ran maple syrup samples for lead and formaldehyde. Prepared and ran meat samples for fat content and moisture.\rChemist Severn Trent Services\r- December 2000 to August 2001\rAnalyzed water wastewater and soil samples in accordance with EPA standards. Experience in Wet Chemistry Lab and Metals Lab. Analysis included TKN pH Total Phosphorus Ammonia Nitrate Cyanide Turbidity Hg. COD.\rEDUCATION\rB.A. in Biology\rSecondary Ed - Potsdam NY 2000\rADDITIONAL INFORMATION Technical Expertise:\r \rSoftware: LIMS Gensuite Citrix Ensure Masshunter LabSolutions Analyst Multiquant\rInstrument Maintenance and Troubleshooting: Spectrophotometer Centrifuge Oxygen sensor Dionex IC Leeman Analyzer PD 200 ICP LECO nitrogen analyzers HPLC and GC units Shimadzu Sciex and Agilent LC-MS-MS systems.\r\"\r\nresume_96,not_flagged,\"Sarah Froebel Registered Nurse\rFairfax VT - Email me on Indeed: indeed.com/r/Sarah-Froebel/4442cfba43bf66ac Authorized to work in the US for any employer\rWORK EXPERIENCE\rRN Staff Nurse\rMaple Leaf Farms - Underhill VT - June 2016 to August 2016\r* Collaborates to develop implement evaluate and revise individual treatment plans. * Supports staff and monitors staff for compliance with policies and procedures\r* Performs nursing duties under the direction of MD\r* Maintains integrity thinks proactively and is result oriented\rRegistered Nurse\rMHM Services Inc - South Burlington VT - October 2015 to June 2016\r* Collaborates to develop implement evaluate and revise individual treatment plans. * Supports staff and monitors staff for compliance with policies and procedures\r* Performs nursing duties under the direction of MD\r* Maintains integrity thinks proactively and is result oriented\rLicensed Practical Nurse\rMANSFIELD PLACE - Essex Junction VT - 2014 to 2015\r* Supervises resident assistant staff and assists staff with resident care\r* Supports staff and monitors staff for compliance with policies and procedures * Performs nursing assessments under the direction of RN\r* Participates in the change of shifts report\rLicensed Nursing Assistant\rTHE LODGE at SHELBURNE BAY - Shelburne VT - 2013 to 2014\r* Provides patients' personal hygiene by giving baths backrubs shampoos and shaves; assisting with ambulation to the bathroom; helping with showers.\r* Provides for activities of daily living by assisting with serving meals feeding patients as necessary; ambulating turning and positioning patients; providing fresh water and nourishment between meals.\r* Provides patient comfort by utilizing resources and materials; transporting patients; addressing patients' requests; reporting observations of the patient to the Med Tech.\r* Documents actions by completing forms reports and records.\r* Maintains work operations by following policies and procedures.\r* Protects organization's value by keeping patient information confidential.\rLicensed Nursing Assistant\rGENESIS HEALTHCARE - Saint Albans VT - 2012 to 2013\rVeterinary Assistant\rBERLIN VETERINARY CLINIC - Montpelier VT - 2010 to 2011\r* Analyzed lab results vaccinated small animals set up IV's and collected blood samples.\r \r* Assisted with and developed radiographs.\rRecruiting Assistant\rUS CENSUS BUREAU Washington County VT - 2010 to 2010\r2010\rEnumerator\r* Planned work by reviewing assignment area.\r* Conducted interviews with residents within the assigned area by following stringent guidelines and confidentiality laws.\r* Maintained records of hours worked miles driven quality control results in the performance of duties. Recruiting Assistant\r* Met with people local groups and agencies within the Washington County area to promote US Census employment agencies.\r* Acquired support of local organizations to donate space for census testing ensuring space met specific requirements.\r* Arranged and conducted census employment testing sessions for various position scored tests and reviewed application materials for completeness and accuracy\rDirector of Animal Programs and Laboratory Support\rMIDDLEBURY COLLEGE - Middlebury VT - 2004 to 2009\r* Hired trained and supervised laboratory support staff including animal care staff Senior Laboratory Technician and student assistants ensuring that related facilities were managed properly.\r* Served as a liaison between faculty IACUC Attending Veterinarian outside consultants and government inspectors.\r* Managed the operations of the Animal Care Facility * Served as IACUC administrator\r* Spearheaded AAALAC Accreditation\rInternal Sales Representative\rMED ASSOCIATES - Georgia VT - 2002 to 2003\rResearch Associate III Pharmacology Department Immunology Group\rBERLEX BIOSCIENCES - Richmond CA - 2001 to 2002\r* Managed all lab functions including the creation of dosing solutions and the consistent use of various ELISA kits.\r* Entered information into electronic notebook wrote reports and presented data.\rAnimal Resource Manager\rXOMA (US) LLC - 2000 to 2001\rManaged all functions of a biopharmaceutical animal research facility including supervision of research technicians and animal care staff in the pharmacology/toxicology department.\r* Serves as a liaison between faculty IACUC Attending Veterinarian outside consultants and government inspectors\r* Participated in IACUC inspections and submitted annual reports to AAALAC international.\r* Provided information and resources for the development of new animal models for testing the safety and efficacy of XOMA products\rXOMA (US) LLC - Berkeley CA - 1996 to 2001\rResearch Associate Scientist I & II\rXOMA (US) LLC - 1996 to 2000\rManaged the daily administrative operations of the animal care facility and maintained the Pharmacology Department's online historical database.\r* Trained and supervised new employees and educated staff in the humane treatment of animals technician safety and the FDA's Good Laboratory Practices (GLP) regulations.\r* Provided information and resources for the development of new animal models for testing safety and efficacy of XOMA products.\r* Performed QA/QC audits on all research data in accordance with GLP and GMP standards Served as a liaison between principal investigators and IACUC during protocol review.\r* Participated in the development revision and implementation of Standard Operating Procedures (SOP) for Pharmacology Department\rEDUCATION\rB.S. in Nursing\rVERMONT TECHNICAL COLLEGE 2018\rAssociates in Nursing\rVERMONT TECHNICAL COLLEGE - 2015\rCertificate\rVERMONT TECHNICAL COLLEGE 2014\rAttended Nursing Program\rNORWICH UNIVERSITY 2012\rDelta CA\rCertificate in Leadership and Management\rUNIVERSITY OF VERMONT 2007\rB.A. in Secondary Education\rTRINITY COLLEGE OF VERMONT 1993\rB.S. in Animal Science\rUNIVERSITY OF VERMONT September 1991\rCERTIFICATIONS/LICENSES\rAEMT\rMay 2018\rCPR\rApril 2018\rNurse's License: Class: RN State: VT Expires: March 2017\rADDITIONAL INFORMATION\rEffective communicator and manager with strong organizational problem solving and interpersonal skills. Proficient in prioritizing tasks and managing multiple responsibilities simultaneously.\rHard-working and dedicated employee with proven expertise in data collection and analysis and documentation.\r\"\r\nresume_97,not_flagged,\"Sarah Hale\rResearch Associate (faculty) at Department of Obstetrics Gynecology and Reproductive Sciences\rEssex Junction VT - Email me on Indeed: indeed.com/r/Sarah-Hale/d1aa7dc004864199\rHighly motivated and independent PhD professional with clinical research and oncologic-focused basic science experience with strong communication collaborative and analytical skills is seeking a clinical research scientist position within the life sciences industry.\rWORK EXPERIENCE\rResearch Associate (faculty)\rDepartment of Obstetrics Gynecology and Reproductive Sciences - September 2010 to Present\rThe University of Vermont College of Medicine\rProject: Cardiovascular contribution of prepregnancy physiology to pregnancy physiology and the development of preeclampsia\rClinical research: Evaluation of cardiovascular physiologic parameters including but not limited to vascular compliance uterine blood flow flow-mediated vasodilation of the brachial artery coagulation profiles inflammatory cytokine profile cardiac output and sympathetic tone in women prior to pregnancy during pregnancy and postpartum\r Monitoring research patient visits\r Preparation of manuscripts\r Analysis and reporting of clinical research data in abstracts and presentations at scientific conferences\r General project management including but not limited to: Monitoring and maintaining the study protocol in accordance with the Institutional Review Board\r Interacting with and directing the nursing staff\r Managing and maintaining large clinical data sets\r Organizing collaborations with co-investigators\r Liasion with statistician\r Supervision of medical student clinical research projects\r Supervision of clinical research coordinator\rPostdoctoral Associate\rDepartment of Obstetrics Gynecology and Reproductive Sciences - 2008 to 2010\rThe University of Vermont College of Medicine\rAdvisors: Ira M. Bernstein MD and George J. Osol PhD\rPostdoctoral work included both clinical research under the supervision of Dr. Bernstein and basic science research under the supervision of Dr. Osol.\rBasic science research involved determining the effect of nitric oxide on matrix metalloproteinases and the expression of vascular endothelial growth factor receptors during pregnancy.\rResponsibilities included:\r Management of projects\r Management of undergraduate students Preparation of manuscripts\r \r Analysis and reporting of data in abstracts and presentations at scientific conferences\rEDUCATION\rPh.D. in Cell and Molecular Biology Dept. of Pharmacology\rThe University of Vermont College of Medicine - Burlington VT January 2002 to January 2008\rSKILLS\rclinical research project management molecular biology cell culture westerns\r\"\r\nresume_98,not_flagged,\"Sarah Locknar\rWoodstock VT - Email me on Indeed: indeed.com/r/Sarah-Locknar/6862461e87653a91 Authorized to work in the US for any employer\rWORK EXPERIENCE\rStaff Scientist and Technical Business Development Manager\rOmega Optical - Brattleboro VT - August 2014 to Present\rResponsibilities\rFluorescence product management\rContinuation of R&D projects\rRoutine sample analysis of narrow bandpasses total wavefront distortion etc. Writing reports grants and marketing materials\rAccomplishments\rRevamped the fluorescence filter set offerings for microscopy\rLed a team to redesign and relaunch one of Omega's dormant product lines\rEstablished collaboration with University of Vermont to obtain access to human tissue samples.\rSkills Used\rTeambuilding communication problem solving product management analytical skills\rProject Scientist\rOmega Optical - Brattleboro VT - August 2009 to August 2014\rResponsibilities\rResearch and development in high-speed multispectral imaging for biomedical applications. My role was in data acquisition image analysis algorithms software user interface suggestions and literature review. Research and development in small-molecule organic photovoltaics. My role was mostly literature review and direction of the project and thin-film characterization by light microscopy AFM SEM and profilometer. I also characterized workfunction of materials via Kelvin Probe.\rHelped identify and implement process improvements in Omega's production departments.\rAttended workshops (instructor at AQLM Woods Hole) and tradeshows (ACS MRS PittCon) and educated the sales team about new applications and markets. Wrote grants peer and non-peer reviewed papers and content for website and marketing materials.\rAccomplishments\rSeveral publications and grant proposals in the areas of in-vivo multispectral biomedical imaging small- molecule organic photovoltaics and thin-film optics.\rI was instrumental in establishing collaborations with researchers and veterinary clinics in VT and NH.\rA process change of using a different polishing slurry composition increased yield by 50%. Developed a method to estimate blocking in filters with very low light transmission. Built laser-scattering measurement systems and a Shack-Hartmann wavefront-measuring instrument.\rDeveloped an online laser safety course for the company and served on the safety committee.\rSkills Used\r \rcreativity problem solving communication team building project management writing and editing MS Word Excel Powerpoint data analysis some LabView SEM EDS profilometer micrsocopy optics\rDirector of Marketing and Webmaster\rGreen Technologies - Windsor VT - May 2003 to April 2013\rResponsibilities\rFormerly the largest used-vegetable oil biodiesel production facility in VT. Currently offering consulting services in the areas of green chemistry oxidation catalysis biodiesel and bioesters.\rDesigned developed content for and maintained the website prepared brochures cold-calling for used-oil pickup and biodiesel customers.\rHelped with strategic planning business plan and grant writing.\rAccomplishments\rWrote several grants\rDeveloped test protocols for oil feedstock analysis\rSkills Used\rCritical thinking and analysis creativity HTML PowerPoint MS Word FrontPage\rTechnical Assistance Center Engineer\rBiotek Instruments - Winooski VT - May 2007 to August 2009\rResponsibilities\rPhone and email support for Biotek Instruments' entire product line of microplate washers and spectrophotometric microplate readers.\rServed on the green team committee and the safety committee\rAccomplishments\rWrote tech notes for the website\rHelped write and edit a handbook for our Technical Support group\rSkills Used\rCritical thinking troubleshooting communication customer management systems MS Word\rResearch Assistant Professor and Director COBRE Imaging and Physiology Core Facility\rDepartment of Anatomy and Neurobiology University of Vermont College of Medicine - Burlington\rVT - January 2003 to May 2007\rResponsibilities\rDirector of the Center of Biomedical Research Excellence in Neuroscience a multiuser facility containing five microscope imaging systems. Systems included multiphoton laser scanning confocal high-speed fluorescence deconvolution microscopy total internal reflection microscopy and a high-speed fluorescence ratiometric imaging system.\rHelped researchers optimize and design imaging experiments including manuscript and grant preparation\rMaintained equipment usage statistics and scheduling\rAccomplishments\rSeveral publications in the areas of calcium regulation in neurons of guinea pig and mudpuppy and acupuncture\rDeveloped online laser safety course still in use\rDeveloped and taught a course entitled Techniques in Optical Microscopy for graduate and medical students\rSkills Used\rTeamwork teaching communication logistics critical thinking editing and writing MS Word Excel HTML Microcal Origin project management microscopy and optics\rPostdoctoral Fellow\rDepartment of Anatomy and Neurobiology University of Vermont - Burlington VT - August 1999 to January 2003\rResponsibilities\rMaintained the high-speed Noran confocal microscope\rPerformed research on calcium regulation in cardiac parasympathetic neurons of mudpuppy and guinea pig using ratiometric and non-ratiometric fluorescent indicators. Performed imaging on immuno-fluorescently labelled tissue sections.\rAccomplishments\rSeveral publications in neuroanatomy and calcium imaging in neurons.\rSkills Used\rteamwork writing and editing data and image analysis IDL image analysis Microcal Origin project management\rGraduate Student\rDepartment of Chemistry Carnegie Mellon University - Pittsburgh PA - August 1991 to August 1999\rResponsibilities\rOriginal research in the area of optical spectroscopy- electroabsorption fluorescence and resonance Raman. Molecules of interest were polyenes and other aromatics embedded in polymer melts. Also attempted frozen glasses at liquid nitrogen temperatures.\rAccomplishments\rAbout 7 peer-reviewed publications came from this work.\rSkills Used\rComputer/ instrument interfacing HTML Matlab programming data analysis and fitting chemical synthesis MS Word MS Excel MS Powerpoint teamwork chemical modeling software (Hyperchem Argus Gaussian Mopac etc)\rEDUCATION\rPhD in Physical Chemistry\rCarnegie Mellon University - Pittsburgh PA 1991 to 1999\rBS in Biology and Chemistry\rButler University - Indianapolis IN 1987 to 1991\rGROUPS\rGreen Mountain Section of the American Chemical Society\rJanuary 1992 to Present\rGovernment Affairs Committee Chair 2007-present\rChittenden County Transit Authority\r2007 to 2009\rCommissioner from Winooski\rServed on the Finance Committee during driver contract negotiations\rADDITIONAL INFORMATION\rI worked at Amoco Research Center during summer and winter breaks [...] in the FTIR group. I performed routine FTIR analysis of liquids (oil-in-water gasoline additives) and hot plastic melts.\rPublication list is available upon request.\r\"\r\nresume_99,not_flagged,\"Sebastian Castro\rEnosburg Falls VT - Email me on Indeed: indeed.com/r/Sebastian-Castro/bccf7477d82a5cbd\rWilling to relocate\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rProject consultant\r- 2013 to Present\rDesign of the Monitoring and Evaluation\r\rM\rphase in the ejoramiento de la Seguridad Alimentaria de Productores y Productoras de Caf. This project was coordinated eՁ with Food4Farmers and SOPPEXCA R.L. in Nicaragua.\rConsultant\rEcoAgriculture Partners - 2015 to 2015\rSix month position with EcoAgriculture Partners\rdeveloping a biodiversity impact assessment of the CAMBio project \rcarried out by the anco Centroamericano de Integraci\rBoՁ n\roՁ\rm(\rEcon ica BCIE). Tasks in this position included field work data gathering field data and GIS analysis and final report writing.\rGIS Specialist\rGovernment of Liberia - 2015 to 2015\rSix month position at Tetra Tech ARD. Tasks\rcarried out in this position included contributing to the GIS analysis of a climate change mitigation and forest conservation strategic\rassessment prepared for the Government of Liberia codeveloping an automated registration eform using the iFormBuilder platform and developing professional cartography for numerous reports\rtechnical brochures and international development project\rproposals.\rProject consultant\rMitigation and Smallholder Livelihoods in Coffee Landscapes - 2014 to 2014\rI wrote a literature review titled limate Change\rMitigation and Smallholder Livelihoods in Coffee Landscapes:\r\rSynergies and Tradeoffs. This desk review was led by Dr. V. Ernesto Mendez and Dr. Peter Laderach CIAT Colombia.\r \rProject consultant\rClimate Community and Biodiversity Alliance Standards - 2014 to 2014\rTechnical review of the application documents\rput together by a rubber plantation in Colombia to comply with the Climate Community and Biodiversity Alliance Standards 2do edition. This audit was led by Environmental Services Inc.\rResearch assistant\r- 2013 to 2013\rerennial Grass Biomass for biofuel\r\rr\rproduction esearch project with Dr. Sidney C. Bosworth \rExtension Associate Professor Plant and Soil Science Department University of Vermont.\rResearch assistant\r- 2013 to 2013\revisiting the Thin Months\rR\rstudy. Data\rcollection in Mexico data processing analysis and writing for final\rCoPrincipal Investigator\rARLG and International Center for Tropical Agriculture (CIAT) - 2012 to 2013\rustainable management of ycena\r\rcitricolor\riՁ a in Coffee. This project was done with Mar del\rMilagro Granados MSc. phytopathologist at the Crop Protection Department University of Costa Rica.\rARLG and International Center for Tropical Agriculture (CIAT) - 2007 to 2013\rof 150 smallholder coffee\rhouseholds in Mexico Nicaragua and Guatemala to assess livelihood changes (with a focus on food security). Joint project ARLG and International Center for Tropical Agriculture (CIAT)\rCoPrincipal Investigator\rARLG and International Center for Tropical Agriculture (CIAT) - 2011 to 2012\rustainable nutrient management in \rp\rcoffee farms under the risk of climate change roject. Funding for this project was provided by the Arthur Riggs Emerging Scientist\rR\rfellowship and coordinated by Earthwatch and CoopeTarraz .L.\ruՁ\rScience Coordinator and field scientist\rEarthwatch Institute - 2007 to 2012\rCosta Rica. My work included coordinating workshops which provided results from our science program including our research on agroforestry practice soil fertility improvement and wild habitat\rmanagement for biodiversity conservation.\rCoPrincipal Investigator\rARLG and International Center for Tropical Agriculture (CIAT) - 2007 to 2011\rManagement in Tarraz uՁ r\r Costa Rica esearch project. This project was funded by Starbucks Coffee Company.\r2006 V\rParticipating alumni: egetation Mapping and wood resource\r\rp\ruse in Rufu Forest Tanzania roject. This research project was\r\rorganized by ITC as a requisite for graduating in the Professional Masters program.\rTeaching Experience\rDirector\rGeospatial Lab - 2006 to 2007\reՁ\rInstituto del Caf e Costa\rd\rRica\r San Jos eՁ\r Costa Rica. I coordinated the application of geospatial technologies for field research in the seven coffee growing regions of Costa Rica.\rProject Manager\rVerde Urbano - 2004 to 2005\rS.A. a consulting company\rspecialized in spatial planning and geoinformation technologies\rapplications for agriculture and natural resource management and urban development. Costa Rica.\rTechnical Advisor\r- 2003 to 2004\rPURDIVE Forestal native tree species\rnursery for high end landscaping projects. Guanacaste Costa Rica.\rProject Manager Tr ica\r- 2000 to 2004\rTica landscaping company San Rafael de Alajuela Costa Rica.\rStudent Research Assistant\rPlant Biotechnology Laboratory - 1998 to 2000\roՁ m(\rCentro de Investigaciones Agron icas CIA) UCR. \rResearch Experience\rEDUCATION\recology and conservation\rUniversity in Vermont 2015\rGeographic Information Systems\rChamplain College 2015\rTeaching Assistant\rUniversity of Vermont 2013\rlimate Champions\rUniversity of Vermont 2012\rStudent Research Conference\rUniversity of Vermont 2012\rPh.D. in Plant and Soil Science / Agroecology. Plant and Soil\rUniversity of Vermont 2009\rMSc. in Geography\rUniversity of Costa 2004\rBS in Agronomy\rUniversity of Costa Rica 1997 to 2002\rADDITIONAL INFORMATION Expertise\rQuantitative data analysis techniques\r Linear and non linear modeling techniques: ANOVA OLS GLM GLMM and non linear regression\r Multivariate statistics: PCA and canonical correlations.\r Structural equation modeling (SEM).\r-p\r Non arametric and resampling techniques Advance general data processing\r Database design query and maintenance Computation of biodiversity metrics\rComputer Skills\r Microsoft Office Suit\r Static and computational software: R (proficient) SPSS and SAS (basic) Database management software: MS Access and PostGRESQL (basic) Adobe Photoshop\r AUTO CAD\r MS Project Manager\rGeographical Information Systems (GIS) and Remote Sensing GIS: Arc View 3.2 ArcGIS 8 10\r QGIS and GRASS\r Image analysis: ENVI / ERDAS\r Spatial applications and geostatistics with R\r Python applications in QGIS\r Basic web map creation (CartoDB and Google maps)\r Mobile data collection with Fulcrumapp and iFormBuilder\r\"\r\nresume_100,not_flagged,\"Shadi Bakhtiari\rQA Associate at Olympus Biotech Corporation\rWhite River Junction VT - Email me on Indeed: indeed.com/r/Shadi-Bakhtiari/1fbb8c28b7d7fb03\rSeeking a full-time Quality Assurance or Compliance Auditor position within the Biotechnology or Pharmaceutical industry where my technical knowledge and experience could contribute to advancement of quality product.\rWORK EXPERIENCE\rQA Associate\rOlympus Biotech Corporation - West Lebanon NH - March 2004 to Present\r* As a certified lead auditor perform supplier and internal audits and provide audit reports to management\r* Review and approve audit responses and corrective actions\r* Participate and assist during regulatory inspections (FDA/EMA/GMED/TGA)\r* As a CAPA Specialist facilitate problem solving and investigation of issues; ensure effective and timely communication of status to involved parties evaluate root cause/corrective action and perform effectiveness checks on the implemented actions\r* Evaluate and approve change controls and update product market disposition accordingly * Review regulatory submissions associated with change controls\r* Review and approve validations for methods processes systems and equipment\r* Perform comprehensive review and assessment of data for product release\r* Provide quality assurance support for primary customers including incoming material testing validation manufacturing sales and pharmacovigilance\r* Perform product risk assessment through evaluation of applicable quality systems to ensure risks associated with product and patient safety are continuously evaluated\r* Use operational excellence and lean manufacturing principles to improve and enhance processes increase productivity and reduce cost\r* Author and/or update standard operating procedures as needed\r* Support management with compliance initiatives and quality system projects including Annual Product Review\rQC Analyst\rOlympus Biotech Corporation - West Lebanon NH - April 2003 to March 2004\rSupported functions of chemistry and microbiology laboratories. Performed environmental monitoring sterility bioburden and endotoxin assays for in-process and finished products. Prepared trend reports for Annual Product Review.\rQuality Control Chemist\rAbbott Laboratories - North Chicago IL - November 2002 to March 2003\rPerformed physical and chemical analysis of bulk drug products according to USP/NF and standard monographs. Used HPLC MS IR and FTIR for detection and analysis.\rAssociate Scientist\rAbbott Laboratories - North Chicago IL - April 2001 to November 2002\rConducted Erythromycin strain development and screening assays. Analyzed secondary metabolite production using HPLC TLC and FTIR methods. Performed physical and chemical mutagenesis of S.\r \rerythreae for strain improvement and assisted in developing analytical assays for identification of efficient strains. Conducted safety audits and provided safety training for the department.\rProduct Specialist\rAbbott Laboratories - Abbott Park IL - May 1995 to April 2001\rCoordinated scheduling activities and interacted with Planning Production Activity Control and Quality Assurance business units for timely release of product. Performed manufacturing and testing activities for STD/ RSV business team within the Diagnostic Division. Provided testing and manufacturing training to business team personnel. Assisted with identifying cost reduction and improvement opportunities. Performed periodic internal GMP and Safety audits and followed up on corrective actions. Performed peer review of testing and manufacturing records. Performed enzyme immunoassay small-scale manufacturing protein purification antibody conjugation and microparticle coating. Investigated deviations implemented corrective actions and initiated electronic document change. Developed and implemented procedures for control of documents and records for business teams within the Diagnostic division.\rQuality Support Specialist\rAbbott Laboratories - Abbott Park IL - March 1994 to May 1995\rCoordinated and communicated testing activities for Matrix/Allergy and Hepatitis C business teams in support of product release. Performed in-process and final release testing of finished product. Coordinated calibration schedules. Assisted in troubleshooting used problem solving and root cause analysis tools.\rAnalytical Quality Assurance Technician\rAbbott Laboratories - Abbott Park IL - July 1992 to March 1994\rPerformed microbiological testing of in-process and finished products. Monitored air and surface bioburden of manufacturing areas. Maintained microbial stock cultures and performed growth promotion assays. Assisted with operational qualification of gas chromatography system (MIDI) for microbial identification by fatty acid analysis. Performed microbial sensitivity studies. Trained laboratory personnel on assays. Updated testing and instrument work instructions.\rQuality Control Microbiologist/Chemist\rBlistex Inc - Oak Brook IL - October 1990 to July 1992\rPerformed bioburden testing of in-process and finished products. Conducted antimicrobial preservative effectiveness and antibiotic assay studies. Prepared media maintained microbial stock cultures and performed growth promotion assays. Assisted with operational qualification of the Microbiology lab autoclave. Performed stability testing of product (hand creams and lip moisturizers). Used HPLC GC IR and UV Spectrophotometer for analysis.\rQuality Assurance Coordinator\rSmith & Nephew Solopak Laboratory - Franklin Park IL - September 1989 to October 1990\rCoordinated weekly responsibilities of QC Inspection team members and trained personnel on standard operating procedures. Approved document changes. Ensured all manufacturing and testing components drug product containers enclosures packaging materials and labels met stated specifications. As a team leader assisted in performance evaluations of QC Inspection personnel.\rQuality Control Microbiologist\rSmith & Nephew Solopak Laboratory - Franklin Park IL - June 1987 to September 1989\rPerformed sterility and endotoxin testing of parentheral drug products and water. Monitored surface and air bioburden of clean rooms. Performed sterility testing of finished product. Identified microbial contaminants by biochemical assays. Assisted with operational qualification of Steritest unit. Trained laboratory personnel.\rEDUCATION\rBachelors of Science\rWestern Illinois University - Macomb IL December 1983\rADDITIONAL INFORMATION\rQUALIFICATIONS\rAn active contributor with strong work ethics. Detail oriented adaptable and a team player with excellent problem solving communication and negotiation skills. Highly organized and able to manage multiple projects. Experienced in applying quality management systems within operations and enforcing compliance with company policies and applicable industry regulations. Strong knowledge of US and international regulations pertaining to product complaints and adverse event investigations and reporting related to medical devices [...] [...] European MDD). Experienced in performing internal and supplier audits. Extensive experience in performing investigations root cause analysis CAPA and evaluating CAPA effectiveness change controls and complaints.\r\"\r\nresume_101,not_flagged,\"Shana Kane\rCompliance Management Professional\rEast Fairfield VT - Email me on Indeed: indeed.com/r/Shana-Kane/8cafdb13c8836a07\rWORK EXPERIENCE\rSystems Assurance Advisor\rBP Exploration (Alaska) Inc. - 2009 to Present\rResponsible for managing process and personnel associated with database of procedures and records that support compliance and a safe operating environment. Activities includes KPI reporting and management reviews training related to Management of Change and document control processes quality assurance of data entry budgeting and contractor oversight.\rEnvironmental Management System Advisor\rBP Exploration (Alaska) Inc. - Anchorage AK - 2007 to 2009\rManaged activities associated with maintaining ISO 14001 certification including annual assessment of environmental impacts cross-functional team meetings development of annual objectives and targets and documentation. Coordinated biannual audits for complex multi-site business. Conducted internal audits. Lead performance review meetings with management.\rEnvironmental Scientist\rBP Exploration (Alaska) Inc. - Anchorage AK - 2000 to 2007\rProvided environmental regulatory support. Activities included obtaining environmental permits submittal of reports to regulatory agencies interpretation of regulations for operations project management of environmental remediation projects. Extensive communications with regulatory agencies.\rEnvironmental Consultant\rEMCON Alaska - 1994 to 2000\rProvided multi-media environmental services. Experience includes emergency planning and response technical writing site investigation data analysis and reporting.\rEDUCATION\rBachelor of Arts in Earth & Environmental Science\rAlaska Pacific University\rSKILLS\rRegulatory Compliance Auditing Management of Change ISO 14001 Project Management Training\rADDITIONAL INFORMATION AREAS OF EXPERTISE\r Management of Change\r Compliance Management Systems\r \"\r\nresume_102,flagged,\"Shannon Warburton Senior Scientist - MERCK AND CO. /GLYCOFI\rThetford VT - Email me on Indeed: indeed.com/r/Shannon-Warburton/8387a1ee5c766e6c\rWORK EXPERIENCE\rSenior Scientist\rMERCK AND CO. /GLYCOFI - June 2008 to Present\rEnabled expression of novel and follow-on human therapeutic proteins and peptides in glyco-engineered Pichia pastoris\r Supported key improvements to the glyco-engineered Pichia pastoris platform which included cell line robustness recombinant protein yield improvement and increasing N-glycosylation occupancy to 100%\r Provided data and support for manuscripts and patent submissions and presented data biannually at departmental meetings\r March 2010 - February 2013 Department Safety Representative\r March 2012 - February 2013 5S Committee Department Representative\r Awarded Merck Research Labs Award of Excellence for outstanding efforts and contributions preparing for the 2013 Environmental Health and Safety Audit.\r Awarded Merck Research Labs Award of Excellence for outstanding efforts and contributions on the N-glycan occupancy improvement to 100% (November 2009)\rStaff Biologist-Strain Development\rMERCK AND CO. /GLYCOFI - June 2006 to January 2008\rSupported glyco-engineered Pichia pastoris platform improvement studies Provided data and support for manuscripts patent submissions\rResearch Associate II-Strain Development\rMERCK AND CO. /GLYCOFI - January 2004 to June 2006\rEnabled production of therapeutic proteins in glyco-engineered Pichia pastoris\r Supported Principal Scientists in glyco-engineering and humanization of Pichia pastoris cell lines.\rResearch Associate I-Strain Development\rMERCK AND CO. /GLYCOFI - October 2003 to January 2004\rExecuted experiments to genetically engineer Pichia pastoris to perform human N-glycosylation.\r Supported and designed experiments to increase yield of recombinant proteins in glyco-engineered Pichia pastoris\rLaboratory Technician-Department of Pharmacology and Toxicology\rDARTMOUTH COLLEGE - Hanover NH - June 2001 to September 2003\rManaged laboratory day to day operations\r Provided technical direction to laboratory staff\r Assisted in the design of and the execution of experiments for analysis of cellular and molecular effects of synthetic and naturally occurring derivatives of retinoids\rLaboratory Technician-Department of Medicine\rDARTMOUTH COLLEGE - Hanover NH - June 1999 to May 2001 Performed mouse breeding experiments\r \r Utilized molecular biological techniques to genotype mice colonies Placed lab orders and handled paper work.\rIntern-Department of Pathology\rDARTMOUTH COLLEGE - Hanover NH - January 1999 to April 1999\rInterned and completed Senior Project required to graduate from Vermont Technical College\rEDUCATION\rBachelor of Science in Biotechnology\rGRANITE STATE COLLEGE - West Lebanon NH May 2010\rAssociate's in Sciences Biotechnology\rVERMONT TECHNICAL COLLEGE-Randolph - Randolph VT May 1999\rSKILLS\rMolecular biology and biochemical techniques and assays including: DNA manipulations; microbiology\rof bacterial and fungal systems; PCR; RT-PCR; chemical and UV mutagenesis; Protein A Purification\rSDS PAGE Western Blot; Northern and Southern Blots; sterile technique; yeast and mammalian cell cultures; and microscopy Recombinant therapeutic protein production in Glyco-engineered Pichia\rpastoris Engineering of heterologous metabolic pathways in yeast Environmental Health and Safety departmental representative for period of three years Experienced in new lab set-up and management; and 5S certification process\rADDITIONAL INFORMATION\rPUBLICATIONS\r(1) Kim S Warburton S Boldoth I Svensson C Pon L d'Anjou M Stadheim TA Choi BK.\rRegulation of alcohol oxidase 1 (AOX1) promoter and peroxisome biogenesis in different fermentation processes in Pichia pastoris. J Biotechnol. 2013 Jul [...]\r(2) Choi BK Warburton S Lin H Patel R Boldoth I Meehl M d'Anjou M Pon L Stadheim TA Sethuraman N. Improvement of N-glycan site occupancy of therapeutic glycoproteins produced in Pichia pastoris. Appl Microbiol Biotechnol. 2012 [...]\r(3) Choi BK Actor JK Rios S d'Anjou M Stadheim TA Warburton S Giaccone E Cukan M Li H Kull A Sharkey N Gollnick P Kocieba M Artym J Zimecki M Kruzel ML Wildt S.\rGlycoconj J. Recombinant human lactoferrin expressed in glycoengineered Pichia pastoris: effect of terminal N-acetylneuraminic acid on in vitro secondary humoral immune response.2008 [...]\r(4) White KA Yore MM Warburton SL Vaseva AV Rieder E Freemantle SJ Spinella MJ.\rNegative feedback at the level of nuclear receptor coregulation. Self-limitation of retinoid signaling by RIP140.J Biol Chem. 2003 Nov [...]\r(5) Freemantle SJ Kerley JS Olsen SL Gross RH Spinella MJ. Developmentally-related candidate retinoic acid target genes regulated early during neuronal differentiation of human embryonal carcinoma. Oncogene. 2002 Apr [...]\r(6) Kerley JS Olsen SL Freemantle SJ Spinella MJ. (co-author) Transcriptional activation of the nuclear receptor corepressor RIP140 by retinoic acid: a potential negative-feedback regulatory mechanism. Biochem Biophys Res Commun. 2001 Jul [...]\r\"\r\nresume_103,not_flagged,\"Stephanie Locke\rHead Cashier/Equipment Technician - Three Mountain Equipment\rJeffersonville VT - Email me on Indeed: indeed.com/r/Stephanie-Locke/13aa49acaeefbc52\rWORK EXPERIENCE\rHead Cashier/Equipment Technician\rThree Mountain Equipment - Jeffersonville VT - December 2013 to Present\r4323 VT Rt. 108 South Jeffersonville VT 05464 Dates of Employment: December 2013-present Supervisor: Lynn Provost (802) 644-1174\rHours per Week: 40\rDuties:\r Responsible for informing guests of equipment rental options and policies; sell lift tickets and book ski/ snowboard lessons.\r Sign guests up for and take payment for appropriate packages; issue discounts refunds or additional charges if needed.\r Handle large amounts of cash and receipts; responsible for performing cash out and close up at end of shifts. Train new staff supervise cashiers and make staff schedules.\r Fit renters for boots and helmets; sanitize and store returned equipment.\rPark Attendant\rVermont State Parks - Underhill VT - May 2014 to October 2014\rSupervisor: Jacob Partlow (802) 779-4106 Hours per Week: 40\rDuties:\r Buildings and grounds maintenance including cleaning campsites bathroom facilities and pavilions; lawn/ garden care.\r Office and customer service duties including answering phones taking reservations collecting park usage fees assisting visitors with questions about trail networks and the local area.\r Responsible for general upkeep and cleanliness of the park to make it presentable to day use and overnight guests.\rZip Line Tour Guide\rArbortrek Canopy Adventures - Jeffersonville VT - December 2011 to May 2014\rSupervisor: Mike Smith (248) 321-4968 Hours per Week: 40\rDuties:\r Instruct tour participants of risk factors to zip lining and teach proper zip lining technique prior to beginning the course.\r Complete daily course/gear inspections; fit seat harnesses chest harnesses and helmets to participants.\r Oversee participants on tour; responsible for giving instruction clipping/unclipping participants into zip lines and managing rappels from tree platforms.\r Educate and entertain guests on the natural history and ecology of the area.\r \r Trained in and perform rescues on the course in the event of minor/emergency incidents.\rProject Assistant\rCary Institute of Ecosystem Studies - Millbrook NY - May 2009 to October 2011\rSupervisor: Dr. Rick Ostfeld (845) 677-7600 ext 136 Hours per Week: 35\rDuties:\r Assisted scientist in a long term study of the ecology of Lyme disease in relation to mammals and human risk. Conducted wildlife trapping and processing of small to medium sized mammals and performed animal husbandry of these animals in a holding facility; anesthetized animals to collect blood samples; applied oral vaccination and collected blood samples from mammals; conducted nest box surveys for white footed mice. Worked at a deer checkpoint during fall hunting season to assess kills and collect tick data off of carcasses before returning to hunters.\r Conducted density drags for ticks using drag cloths and collected ticks for analysis of Lyme disease bacterium.\r Prepared microscope slides using fluorescent antibodies and performed microscopy to identify Lyme disease bacterium in tick specimens; identified tick species; collected and processed blood samples from mammals. Entered and corrected data in several large databases from multiple projects.\rDeer Technician\rSouthern Illinois University - Carbondale IL - January 2011 to March 2011\rSupervisor: Matt Springer (724) 541-5498 Hours per Week: 37.5\rDuties:\r Assisted PhD student with study of white tailed deer dispersal in central Illinois.\r Set and deployed clover traps drop nets rocket nets and dart guns to capture deer at specific bait sites.\r Occupied tree stands during both day and nighttime hours to observe deer approaching bait sights and to deploy nets.\r Physically restrained deer so that anesthesia could be applied in order to ear tag radio collar and collect tissue samples.\r Injected anesthetic drugs and corresponding reversals into deer and monitored conditions while processing.\rS. Locke:\rEDUCATION\rBachelor of Science in Environmental Studies Biology\rSt. Lawrence University - Canton NY May 2009\rEnvironmental Studies Biology\rJames Cook University February 2007 to June 2007\rADDITIONAL INFORMATION RELEVANT SKILLS\rCoursework: General Biology Ecology Environmental Security Aquaculture Marine Ecology Chemistry Intro Environmental Studies Biodiversity of Tropical Australia Energy and the Environment Mammalogy GIS Computer: Microsoft Word Microsoft Excel Microsoft Access Microsoft PowerPoint Fathom ArcGis Resmark Point of Sales Resort Suites data entry\rField work: Wildlife trapping and handling (birds and mammals) animal husbandry wildlife immobilization/ anesthesia deer restraint clover traps rocket nets drop nets dart guns tree stand setup nest box surveys camera trapping night vision radio telemetry radio frequency identification GPS tick collection vegetation surveys tree mapping First Aid and CPR certified\rLab work: GIS mapping autoclaving centrifuging tick processing blood and tissue sampling microscopy fluorescent antibodies flash freezing statistical analyses data entry\r\"\r\nresume_104,not_flagged,\"Stephen Schad\rSenior Computer Operator - STATE OF NEW YORK\rSaint Albans VT - Email me on Indeed: indeed.com/r/Stephen-Schad/648c9b19a5dc987b\rIT professional with experience in troubleshooting and administering Windows Apple Android iOS Active Directory Office Lotus Notes Red Hat Linux UNIX systems and printers.\rWORK EXPERIENCE\rSenior Systems Administrator\rUnited States Citizenship and Immigration Services(under contract to) - Williston VT - August 2014 to Present\rResponsibilities\r1. Manages the functionality and efficiency of a group of computers running on one or more operating systems. 2. Maintains the integrity and security of servers and systems.\r3. Sets up administrator and service accounts.\r4. Maintains system documentation\rSenior Computer Operator\rSTATE OF NEW YORK - June 2011 to September 2014\rResponsibilities\r Capitalize on the opportunity to support Comptrollers office with monitoring of IBM mainframes including training mentoring and supervising a top-performing staff to meet or exceed objectives.\r Utilize broad scope of industry knowledge and dynamic technical acumen toward accurately reviewing and analyzing documentation to find error codes as well as documenting errors and other news in detailed daily reports.\r Ensure proper completion of print work for Comptroller including printing key confidential and secure documents\rComputer Operator\rSTATE OF NEW YORK - June 2008 to June 2011\rSupervise and train staff while also monitoring consoles printers and peripheral equipment.\r Provide help desk support via telephone communications with end-users.\r Monitor the IBM mainframe console and direct jobs to printers.\r Read and interpret instructions relating to the execution of computer programs.\rComputer Operator\rRENSSELAER POLYTECHNIC INSTITUTE - Troy NY - February 2007 to June 2008 Provide support to faculty and students in the operation of computer and peripheral equipment.\r Monitor and maintain the campus' computers printers and networking equipment.\r Monitor the network print and telephony consoles for any problems that might occur.\rTemporary Scientist\rWYETH PHARMACEUTICALS - Rouses Point NY - March 2005 to July 2006\rCoordinated experiments at production scale in preparation for FDA approval and clinical studies.\r \r Responsible for supporting pharmaceutical re-formulation project by monitoring trials sustaining process control collecting data and performing data analyses.\r Assisted staff scientists with writing and checking reports for accuracy.\rComputer Manager\rWORCESTER POLYTECHNIC INSTITUTE - May 2001 to March 2005\rComputer Technician\rWORCESTER POLYTECHNIC INSTITUTE - September 1997 to May 2001\rSupport computer systems for a department of 60+ people while overseeing a staff of 6 people.\r Maintained and repaired computers printers and networking equipment.\r Trained users with no prior experience to use Windows Office and other software. Recommended and purchased computers and other equipment for the department.\rEDUCATION\rBachelor of Science in Chemistry\rState University of New York Empire State College - New York NY\r\"\r\nresume_105,flagged,\"Steven Brady\rPostdoctoral Fellow - VT Cooperative Fish & Wildlife Research Unit\r- Email me on Indeed: indeed.com/r/Steven-Brady/6796871a632d8bbc\rWORK EXPERIENCE\rPostdoctoral Fellow\rVT Cooperative Fish & Wildlife Research Unit - Burlington VT - 2014 to Present\r Leading 25 international professionals in working group to explore novel trends in applied evolution and tackle challenging theoretical problems in the study of contemporary evolution\r Developing open source (R framework) software for harvest data analysis and adaptive management\r Editor of special issue on 'Evolutionary Toxicology' in Evolutionary Applications a top biology journal\r Reviewed/edited book ('R for Fledglings') on open source data analysis\r Authored scientific paper describing 'pitfalls' in the field of landscape genetics\r Authoring scientific paper highlighting critical overlooked issues in typical harvest analyses\r Singlehandedly instructing two undergrad./graduate courses (Animal Behavior; Ecology); responsibilities include student coaching lecture development. Received very positive feedback on teaching style and accessibility. Routine use of wit keeps students alert/entertained (occasional chuckles can be heard in class)\rFounding team member in startup\rVT Cooperative Fish & Wildlife Research Unit - 2015 to 2015\r Conceived and worked up concept\r Collaborated with three MBA students to develop business plan (semifinalist in MIT 100k competition) Composed and analyzed preliminary market surveys\rVisiting Scholar\rDartmouth College - Hanover NH - 2014 to 2014\r Led team of 5 professionals and students to design execute and analyze large-scale field/lab experiments Developed and modeled novel evolutionary biology theory showing that adaptation to environmental change can cause the unexpected outcome of local extinction\r Conducted physiological and acoustic assays of amphibians\r Mentored students in optimization of data collection processing and interpretation\rPostdoctoral Researcher\rNortheast Fisheries Science Center - Woods Hole MA - 2013 to 2014\r Analyzed big data (millions of acoustic data points) to infer patterns in an endangered species\r Team leader in visual whale survey at sea (two week cruise: Great South Channel / Georges Bank)\r Lead author on manuscript reporting first use of density estimation from acoustic data in the Right Whale. Discovered acoustic surveys can be effective complement to visual surveys for detection of whales.\rHixon Fellow / Mianus Fellow\rYale University - New Haven CT - 2007 to 2013\r Published first evidence of a vertebrate adapting to roads (featured in international media outlets)\r Independently executed aquatic toxicological assays (e.g. LC50) across multiple species and life stages Analyzed genetic ecological and toxicological data with frequentist and Bayesian methods in R / Matlab - Conducted phylogenetic analyses to examine effect of species relationship on toxicity tolerance\r- Composed de novo phylogenetic trees using multiple mitochondrial genes sequences from GenBank\r \r- Compiled database of toxicity values to assess tolerance across taxa\r- Implemented mixed (i.e. fixed and random effects) generalized models with both univariate and multivariate data; used dimension reduction techniques (e.g. PCA RDA NMDS) to analyze high\rdimension data; honed Matlab particle detection algorithms to analyze moving objects\r Extracted amplified and sequenced animal DNA/RNA in numerous experimental contexts\r Developed and managed animal care/use protocols and standard operating procedures for amphibians\r Developed knowledge of GLP compliance ICH guidance and various lab safety trainings/protocols\r Developed novel technique for sperm and egg extraction in amphibians\r Conducted full-sib / half-sib breeding design to infer patterns of genetic inheritance in an amphibian\r Managed and motivated teams of researchers in lab and field (through adverse weather and terrain)\r Designed and constructed novel infrastructure for aquatic animal husbandry and toxicological assays\r Monitored water quality at 100s of sites for abiotic parameters and biotic indicators (e.g. EPT Index)\r Analyzed water samples in lab to measure nutrient composition and heavy metal contaminants\r Screened amphibians for disease using microscopic dissection\r Delivered dozens of research presentations at international regional and local scientific conferences\r Organized 3-day international conservation science conference (American Museum of Natural History)\r- Solely responsible for coordinating 60 preeminent scientists to serve as student mentors\r Participated in 3-day EPA workshop on advancing understanding of chloride toxicity\rMacClean Fellow / Carpenter Fellow\rYale University - New Haven CT - 2005 to 2007\r Used Matlab to model trait responses to dynamic selection regimes with quantitative genetics approach Used ERDAS Imagine remote sensing software to analyze high resolution remotely sensed IKONOS imagery (1 m panchromatic and 4 m multi-spectral) and digital elevation models to estimate winter ice cover on vernal pools as a predictor of marbled salamander occupancy\r Surveyed wetland bird communities in response to land use; found unexpectedly high biodiversity in highly developed landscapes including suburban/urban developments and trailer park communities\r Delineated 100s of wetlands and surveyed for biodiversity amphibian performance zoonotic disease and water quality. Used GIS to estimate composition of land use/land cover in buffers surrounding wetlands\rWeb Developer\rOutdoor Gear Exchange - Burlington VT - 2004 to 2005\r Overhauled and managed back end and front end of commercial website (gearx.com)\r Following launch of improved website increased monthly online sales from $1000 to $20000\rEDUCATION\rPh.D. in Ecology and Evolutionary Biology\rYale University School of Forestry & Environmental Studies 2013\rEcology\rYale University School of Forestry & Environmental Studies 2007\r- New Haven CT\r- New Haven CT\rcertification in Wilderness Emergency Medical Technician\rAcadia Mountain Guides Climbing School 2001 to 2003\rB.A. in Fine Arts\rSaint Michael's College - Colchester VT 2001\rLINKS http://stevenpbrady.weebly.com\rADDITIONAL INFORMATION\rCore Competencies\r Toxicology/ecotoxicology Data synthesis & meta-analysis Publishing scientific literature\r Experimental design & inference High dimensional data analysis Translating science to application Field & bench science Computer coding (esp. in R) Presentations/public speaking\r \"\r\nresume_106,not_flagged,\"Susan Kennedy\rResearch Laboratory Assistant - Department of Veterans Affairs\rWhite River Junction VT - Email me on Indeed: indeed.com/r/Susan-Kennedy/1a61b3ed6bcc22b8 Authorized to work in the US for any employer\rWORK EXPERIENCE\rClinical Laboratory Scientist\rDHMC - Hanover NH - January 2014 to Present\rResponsibilities\rLaboratory technical assistant for Ryan Ratts MD. Characterizing the proteome of endosomal transport mechanism involved in the delivery of diphtheria toxin and its potential for a drug delivery system.\rResearch Laboratory Assistant\rDepartment of Veterans Affairs - September 2013 to November 2013\rLaboratory assistant for Sue Eszterhas Duties include; molecular biology immunohistochemistry flow cytometry and other research tasks as needed.\rResearch Laboratory Assistant\rDepartment of Veterans Affairs - April 2013 to October 2013\rLaboratory technical assistant for Brenda Petrella PhD- Duties included immunohistochemistry fluorescent microscopy flow cytometry real time PCR.\rResearch Laboratory Assistant\rDepartment of Veterans Affairs - June 2013 to September 2013\rLaboratory technical assistant for Pierre Pascal Lenck-Santini PhD. Duties included: whole rat brain dissection coronal sectioning of the hippocampus followed by free floating and traditional immunohistochemistry or fluorescent in situ hybridization and fluorescent microscopy.\rResearch Laboratory Assistant\rDepartment of Veterans Affairs - June 2010 to March 2013\rServing as laboratory manager for the research lab of Roy Fava Ph.D. Current research techniques applied in this project included:\rLaboratory management: Statistical data analysis literature searches chemical inventory management biosafety compliance record keeping human subjects consent.\rImmunology: Flow cytometry Elisa in situ hybridization for mRNA localization production of digoxigenin- labeled riboprobes histological staining isolation of RNA and real-time PCR.\rAnalytical instrumentation: Nikon fluorescent microscopes BD FACSCanto Flow Cytometer Iq5 Multicolor Real-time PCR Detection System BioTek Take 3 Spectrophotometer and the Michrom HM505E microtome for sectioning of frozen tissue.\rLaboratory Technical Assistant\rDepartment of Veterans Affairs - White River Junction VT - 2010 to March 2013\rResponsibilities\rServing as laboratory manager for the research lab of Roy Fava Ph.D. Current research techniques applied in this project included:\r \rLaboratory management: Statistical data analysis literature searches chemical inventory management biosafety compliance record keeping human subjects consent.\rImmunology: Flow cytometry Elisa in situ hybridization for mRNA localization production of digoxigenin- labeled riboprobes histological staining isolation of RNA and real-time PCR.\rAnalytical instrumentation: Nikon fluorescent microscopes BD FACSCanto Flow Cytometer Iq5 Multicolor Real-time PCR Detection System BioTek Take 3 Spectrophotometer and the Michrom HM505E microtome for sectioning of frozen tissue.\rAssistant Manager for Protein Services\rDartmouth College - Hanover NH - 2001 to 2009\rMolecular Biology & Proteomics Shared Resource Facility\rManaged day-to-day operation of the proteomics shared resource facility.\rProteomics Techniques: Protein expression analysis by mass spectroscopy & liquid chromatography including HPLC nano-scale ESI Mass Spectroscopy (MS) LCMSMS & MALDI TOF MS. Preformed all aspects of sample intake documentation preparation purification analysis by LC MSMS multiple database searches and reporting of results.\rApplied computational skills: Xcalibur Bioworks Mascot Sequest Peaks and GPMAW.\rProteomics protocol development: Protein purification by TCA precipitation 1D & 2D SDS-PAGE enzymatic and chemical digestion peptide extraction gel filtration and post translational modification.\rSupervised users in the use of the MALDI mass spectrometer other core facility instruments. Trained new proteomics researchers in proper technics via seminars web based tutorials and individual consultations. Equipment maintenance: Routine maintenance quality control and trouble-shooting of the following: Thermo Finnigan LTQ Eksigent Nanoflow HPLC and Beckman Coulter PF2D Agilent 1200 HPLC Sutter Laser Capillary Column Puller Thermo Savant Ultra Low Temperature Freeze Dryer and Lyophilizer.\rSoftware: Managed webpage content instrument purchases chemical and lab supply inventory billing and EHS compliance with chemical and biosafety requirements for safe handling of biohazards including personal protection and hazard containment participated in development of an Oasis integrated web-based sample tracking billing accounting and budgeting database application.\rEDUCATION\rMaster of Science in Veterinary Science & Technical Education\rVirginia Polytechnic Institute and State University - Blacksburg VA\rAssociate in Applied Science in Research and Veterinary Animal Science\rState University of New York - Delhi NY\rBachelor of Science in Psychology\rState University of New York - New Paltz NY\rADDITIONAL INFORMATION\rTechnical expertise:\rFlow cytometry\rElisa assays for detection of cytokines and cell signaling molecules\rProtein purification\rSDS-page gels and Western blots\rChromatography Methods HPLC IEX SCX HILIC RPLC Thin layer and affinity Mass Spectroscopy Methods: nano-scale LCMS LCMSMS & MALDI TOF MS.\rIn Situ Hybridization for tissue localization of mRNA Production of digoxigenin labeled riboprobes Histological & Immunological staining fluorescent and Fluorescent & Confocal Microscopy\rIsolation of RNA & DNA\rReal-time PCR\rDNA sequencing\rComputer Skills: Windows & Macintosh computer software including Excel Word Power Point Prism Sigma Plot Photoshop Thermo Scientific Proteome Discoverer (originally known as Sequest) Quick Books Reference Update Reference Manager Delta Graph ChemStation and 32 Karat Gene Construction Kit FlowJo\rAMT Eligibility: Medical Technologist Certification as specified by AMT Board\r\"\r\nresume_107,flagged,\"Swaminathan Prasanna Senior UI Developer - ATS\rTexas VT - Email me on Indeed: indeed.com/r/Swaminathan-Prasanna/b347ebfe59d7ab67\rIT Professional with proven analytical abilities and organizational skills with more than 8+ years of expertise in developing implementing products and solutions in D3 JavaScript Core Java (Version 1.6 and 1.7) C and Python. An excellent team player with good leadership qualities with strong oral and written communication skills and a vision to excel in everything I do.\r Experience on architecture of Core Java and J2EE Core Design Patterns Android Object Oriented Analysis and Design/Development Methodologies (OOAD) Object Modeling with Use Cases Sequence and Class Diagrams using UML with Rational Rose.\r Worked on providing intuitive dashboards in D3 JS.\r Developed Single page applications on Angular 2 and Aurelia JavaScript framework\r Experience of over 2 years is developing multi-tier applications using Java/J2EE technologies (Servlets JSP JDBC XML XSD CSS and HTML).\r Proficiency in Web technologies like Thymeleaf PHP JavaScript HTML 5 AJAX JQUERY.\r Proficient in working with various tools/IDEs like MyEclipse Jetbrains Webstorm Notepad++ Eclipse Juno Dreamweaver Sublime and NetBeans.\r Experience in developing Java based Web Services using REST.\r Web technologies like D3 JS HTML CSS JavaScript AngularJS JQuery Ajax are part of my armory.\r Have good experience in Shell Scripting.\r Have experience in Bootstrap and foundation and SASS libraries.\r Secured First place in many paper presentation events in the year [...]\r Ability to work in tight schedules and efficient in meeting deadlines.\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rSenior UI Developer\rATS - Dallas TX - October 2016 to Present\rRemote from Eagan MN)\rW3G is developing a job posting application wherein major vendors can come and post their jobs. Candidates will be able to apply for positions that match their current skillset. This application leverages on Kendo UI and Angular JS framework. I am part of the team that is handling the UI requirements of this application. Responsibilities:\r Built the Single page applications on leading JavaScript frame work.\r Provided Bug fixes and UI enhancement in the current Kendo UI application.\r Experienced in using Photoshop for UX purposes.\r Developed plans and setup the migration from Kendo UI to Angular 2 application.\r Used HTML and CSS to provide styling to the current web application.\r Used REST calls to get Data from the Server Used Elastic search and Kibana for these purposes. Environment: JavaScript HTML5 CSS Angular 2 Kendo UI Photoshop\rSenior Software Developer - UI\rAllThings IoT Thomson Reuters - Eagan MN - February 2016 to August 2016\r \rThomson Reuters's Allthings IoT team wanted to create a one-stop platform for trusted external and internal IoT related data sets Application development tools to quickly validate datasets and build visualizations which demonstrate business values and insights. I was instrumental in proposing and developing various visualizations and the application by itself.\rResponsibilities:\r Performed analysis and developed various visualizations and related datasets to use on those visualizations. Built the Single page applications on leading JavaScript frame work.\r Provided option to choose multiple visualizations in real-time to be displayed to the user.\r Used Foundation framework to work the look and feel of the application.\r Extensive experience with SVG's.\r Provided code maintenance and worked with data scientist to develop aggregated data for the visualization. Used REST calls to provide data to the front-end application.\r Presented the visualization to the Stakeholders.\rEnvironment: Aurelia JavaScript D3 JS HTML Foundation CSS Sami Git REST\rD3 Dashboard Developer\rUSAA - June 2015 to December 2015\rBusiness intelligence team wanted a D3 dashboard that provided employee job satisfaction. I created an enterprise dashboard that contained multiple visualizations to support all business requirements for displaying data. We got good responses on the visuals we used for the dashboard.\rResponsibilities:\r Designed and created Proof of concept on the d3 Dashboard.\r Used REST web service calls to get data to the dashboard visuals in the form of JSON. Application works in all devices.\r Worked extensively on svg.\r Built the Single page applications on leading JavaScript frame work.\rEnvironment: JavaScript D3 JS REST HTML Bootstrap CSS\rSenior Consultant\rUSAA - San Antonio TX - December 2014 to December 2015\rSenior UI Developer\rUSAA - December 2014 to May 2015\rBrowser Independence Research was an effort to attain browser agnostics at the company. The company was dependent on IE 8 for all its operations and wanted to expand its operations to other browsers.\rThey had implemented their infrastructure by using IE specific BHO helper objects and disabled Tabs to create independent session. We researched and proposed corrective solutions for their products to be implemented on all browsers with better Usability.\rResponsibilities:\r Designed and Created Proof of concept's (Javascript HTML 5 CSS and Super-Web sockets) applications for the project.\r Created applications to highlights issue on existing applications.\r Researched dependencies and developed alternatives for each feature with new solution.\r Implemented effective layout using component functions in AngularJS.\r Used filters custom directives for the better implementation of application and bind data with model for two- way data binding using AngularJS.\r Created documentation of our proposed solutions.\r Presented solution to client.\rEnvironment: AngularJS C# .Net HTML 5 CSS\rSoftware Developer\rRolls Roys Marine Aeroxchange - Irving TX - August 2014 to October 2014\rRolls Roys Marine project is a quoting/ordering system where Aeroxchange acts as a broker between the user and the client. We generate quote for the customers based on the input from the user and pass the quote to the appropriate teams in the client office. We Process the order based on the responses and provide the user with the shipping details.\rResponsibilities:\r Created the Main HTML page for the application using Thymeleaf.\r Created Rest web services for the UI which needs to be called based on user action.\r Wrote the controller classes to call repository to retrieve data from Oracle database.\r Created JPQL queries for independent repositories.\r Wrote JPA entity classes to store data for the database tables.\r Worked on EDI messaging with client ERP systems.\r Wrote Annotations to provide validations and extend hibernate functions for the entity objects.\r Worked with the client on setting the requirement timelines and was part of scrum meetings to discuss the same.\r Bootstrap used along with AngularJS for developing application\rEnvironment: Java 1.6/1.7 Spring MVC Rest Web Services JPA Thymeleaf AngularJS Annotations Hibernate Maven MyEclipse 2014 CSS Agile Oracle SCRUM.\rSoftware Developer\rUSC forecasting tool AirCom International - Irving TX - April 2014 to August 2014\rUI Development)\rAIRCOM is the largest independent provider of network planning optimization and OSS software and consultancy services for mobile networks.\rWith offices in 14 countries we provide local and regional viewpoints and resource as well as ensuring that our customers benefit from our global knowledge. By looking ahead of the market and sharing intelligence we develop the skills and tools that network operators need to remain competitive whatever the economic climate. Responsibilities:\r Part of the team responsible for collecting forecasted data across various geographical locations.\r Created Java tool to render reports for all collected data across various nodes.\r Designed the application to analyze historical data and come up with a forecast for the upcoming year.\r Developed using UNIX Operating system.\r The code base was versioned and maintained on SVN.\rEnvironment: Java 1.6 J2EE JavaScript Oracle WebLogic Server CSS SVN Agile SQL Oracle. Unix\rSoftware Developer\rUSC forecasting tool AirCom International - Addison TX - September 2013 to April 2014\rUI Development)\rSecurus is the premier provider of innovative communications solutions for the corrections industry. S-Gate is the new software which enables the primary software tool which is called the secure Call Platform to accept grievances from inmates at the correctional facilities. The grievances could be of different types like Medical Personal or Administrative. Based on requests changes are authorized by officials (Assigner Processor or Viewer). It will then be communicated to the inmate. The inmate has the option to reject a decision made by authorities. The system also allows the option to track a request and audits can be made to check on the efficiency of the system.\rResponsibilities:\r Participated in Sprint meetings to gather the requirements for the projects and also helped in building the wireframes and the requirement document for the project.\r Designed the framework for the UI with technologies such as JSP JavaScript CSS and HTML 5.\r Used JSON to transfer data from UI to the Application Framework which was based on Struts.\r Helped in designing and coding the application over many Sprint cycles and coded using J2EE technologies like EJBV3.0.\r Consumed Web Services for validating the entitlement information for the user.\r Used JUnit Test cases and Jasmine tool to validate/test my java/JavaScript code and also got acquired to Mokito test framework.\r Used Oracle and SQL to communicate with Databases for data related operations.\r Created generic JavaScript files to use it over the project to implement many functions.\r Used SVN for versioning the code.\rEnvironment: Java J2EE JSP Struts JavaScript Tomcat Server Web Services CSS Agile My Eclipse SVN Jasmine Agile SQL Oracle\rSoftware Engineer\rUSC forecasting tool AirCom International - Irving TX - April 2013 to August 2013\rUI Development)\rvMobile Construction application provides National Operations Construction Technicians across the entire Verizon footprint the ability to electronically via a Laptop/Tablet review update and status work activities view associated work prints attached work related remarks physically inventory GPS co-ordinates for IPID (Item of Plant Identifier) locations and fiber splice points test and collect test results for the various splice points and process daily time sheets.\rResponsibilities:\r Participated in initial requirement analysis phase to gather all Use-cases from the client.\r Worked on sending and receiving JSON request/responses from Client systems (laptop or tablet) to tablet server which transmits the data.\r Used MVC architecture to code the entire solution. Used JavaScript and HTML for the view Java for the Model and Controller.\r Used twitter bootstrap framework.\r Created Java Apps to communicate the solution to the UI using Containers.\r Installed and used CVS code repository for parallel development with the Indian team on different time zones. Handled various test cases from the client and delivered on time on Agile Environments.\rEnvironment: Java J2EE HTML 5 CSS JavaScript Agile Eclipse CVS Agile MVC\rSoftware /Commissioning Engineer\rSamsung Telecom - Frisco TX - June 2012 to January 2013\rSamsung commissioning engineer is responsible for commissioning or building a cell site from base to becoming a full commercial site that passes commercial traffic. We co-ordinate with many teams like router team and field engineers to get the commercial site functioning. Troubleshooting becomes an essential part of this process. When there are about 30 sites to work on at once software engineers are required to provide support with tools to provide faster solutions.\rResponsibilities:\r Generated CORE JAVA tools to provide the commissioning engineer with an Interface to communicate with the program.\r Developed Java tools to validate the different parameters associated with a commercial cell site.\r Developed code to check the current values present in the site from the database.\r Deployed test cases to verify the different functionalities associated with building and commissioning a commercial cell site. Used MVC Architecture.\r Used Ant scripts to verify the software that was used in the commissioning process.\r Worked on open stack technology like red hat.\r Responsible for adding neighbors updating parameters and borders for the particular site.\r Maintain Site information in our database using Oracle.\r Troubleshoot various complications that arise in a cell site.\rEnvironment: Core Java 1.6/1.7 HTML 5 CSS Java Script Eclipse Oracle Red hat Ant scripts\rAndroid Developer\rMotorola Mobility - Libertyville IL - March 2010 to May 2012\rWorked on automating the testing process on mobile devices using Python Scripts. After every software release a mobile device is subject to testing process like Stability testing Monkey testing and manual testing. Devices need to be cleared by the quality assurance department for the software version to be released. Android Applications were used to perform all the testing activities. Test suites for SMS email and Multimedia are developed to assist this process.\rResponsibilities:\r Developed Java Code snippets to change the automation script depending on the software version that is released. Worked on the migration from Gingerbread to Ice-Cream Sandwich (ICS).\r Developed applications to perform prolonged quality assurance with over 30 test suites.\r Experience in using tools like PMD Dalvik and other Testing tools.\r Experience in developing test cases.\r Refer to the different layer logs according to the requirement in the test case.\r Extended the work to the stability automation testing. Setting up the stability rack and configure the initial settings in the rack and the phone to run the automated script.\r Customized PYTHON scripts for change in requirements.\r Participated in the team meeting and interacted with the development team and the team lead and understand the feature requirement and developed the test cases and test plan accordingly.\r Testing apps using android based on scripted and exploratory use cases covering all real-time user scenarios.\rEnvironment: CORE JAVA Java Script Linux Python scripts HTML PHP\rSoftware Developer\rIllinois Institute of Technology - Chicago IL - September 2009 to November 2009\rWorked as an intern to develop android tools to provide grades for courses and manage student records. Used Client - Server modeling to develop formulas that can be used to extract information based on the requirements. CSS scripts were developed to show data on the webpage as per user request.\rResponsibilities:\r Designed Use case sequence control flow and Class diagrams.\r Completed Beta Android tool for the students to check the scores.\r Interacted with professors obtaining requirements and converting specifications them into functional requirements.\r Responsible for Coding Unit testing and integration testing of the application for enhancements.\r Involved in TDD (Test Driven Development).\r Worked on Eclipse ME IDE as application development environment.\r Used SQLite for managing records.\rEnvironment: CORE JAVA Eclipse CSS SQLite\rSoftware Developer\rWipro Technologies - Chennai Tamil Nadu - January 2007 to May 2008\rWorked on Employee assessment team to create coding procedures to evaluate employee before they are hired and also evaluate current employee level\rResponsibilities:\r Developed an IDE for code review using JAVA EJB Technology.\r Developed front-end screens and GUI.\r Follow the UI Interaction flow in the product specified by the clients. Created Reports to report flow of the product.\rEnvironment: Windows XP Java 1.6 Case logic UML\rEDUCATION\rBachelors in Information technology\rAnna University - Chennai Tamil Nadu\rMasters in Information technology and Management\rIllinois Institute of Technology\rADDITIONAL INFORMATION\rTECHNICAL SKILLS:\rLanguages: JAVA/J2EE (JDBC JSP Servlets EJBs Threading JMS)\rJDK [...] UML C++.\rFront-end Technologies: D3 JavaScript CSS HTML 5\rFrameworks: Struts Hibernate Spring AJAX.\rDesign Patterns: MVC Singleton DAO EAO factory service locator.\rJava Technologies: Core Java - JDK1.5 EJB JSP Servlets Struts Spring Hibernate Java Beans Multithreading Junit Security Encryption.\rWeb Technologies: XML/ XSL/ XSLT and APATH HTML CSS and JavaScript AngularJS JQuery Ajax C# and JSON.\rDatabases: Oracle MySQL SQL Server 2005\rOperating Systems: Android SDK Unix / Linux Windows MAC OS X.\rProtocols: TCP/IP SOAP SMTP and SSL.\rIDEs: NetBeans Eclipse MyEclipse.\rSoftware Testing: Mockito Selenium JUnit.\r\"\r\nresume_108,flagged,\"Tam Tran\rShelburne VT - Email me on Indeed: indeed.com/r/Tam-Tran/ec7c604961ececfc\rCreative data engineer with extensive experiences in database marketing and proven success in previous fast paced environments. Enthusiastically seeking a senior data engineer position where I can leverage existing skills and continue to learn new ones.\rWilling to relocate to: Boston MA - New York NY\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rData Engineer\rCareer Break - Shelburne VT - May 2015 to Present\rIndependent studies in machine learning and predictive modeling. Maintain and improve programming skills in SQL and Python. Re-define my passions.\rRe-focus my career paths.\rData Engineer and Analyst\rKeurig Green Mountain - Burlington VT - February 2013 to April 2015\rDesign build and maintain the internal ETL solution for the marketing data feeds.\rData profiling and data wrangling in SQL Python and shell scripts for Keurig's Marketing Database solution with more than 20 sources 15 millions records and hundreds of attributes.\rAssessed data and issues and directed concerns to business unit leadership when appropriate. Collaborated well cross-functionally with data scientists software engineers and business managers.\rDatabase Developer\rMerkle - Marlborough MA - June 2012 to January 2013\rDesign data model using Kimball/star schema modeling techniques for facts dimensions and aggregates under a tech lead guidance.\rDevelop reusable scripts for data wrangling.\rDevelop and unit test T-SQL stored procedures and SQL functions for ETL processing including staging and target processing from 8 sources.\rBusiness Intelligence Specialist\rEq2 - Burlington VT - June 2011 to June 2012\rDevelop a BI solution for a clinical equipment management system.\rGather requirements from the clients.\rDesign and develop web-based reports (SSRS).\rOptimize existing store procedures to reduce running time to less than 30 seconds from 5 minutes.\rEDUCATION\rBachelor of Science in Computer Science\rSaint Michael's College - Colchester VT 2012\r \rSKILLS\rSQL (5 years) ETL (5 years) Python (2 years) Shell scripting (2 years) SSRS (2 years) SSIS (4 years)\rCERTIFICATIONS/LICENSES\rMicrosoft SQL Server 2008 Business Intelligence Development and Maintenance\r2011 to Present\rPRO: Designing a Business Intelligence Infrastructure Using Microsoft SQL Server 2008\r2011\rADDITIONAL INFORMATION\rData visualization: D3 (in progress)\rWorking knowledge of other programming languages: R C# Javascripts (React and Flux design)\r\"\r\nresume_109,not_flagged,\"Theresa Petzoldt Phlebotomist - North Country Hospital\rWestfield VT - Email me on Indeed: indeed.com/r/Theresa-Petzoldt/7d899e87c1d9e7fd\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rPhlebotomist\rNorth Country Hospital - Newport VT - 2015 to Present\rDraw blood samples from inpatients and outpatients with attention to proper phlebotomy technique and a focus on reducing patient anxiety as well as following protocols for patient identification and correct sample type\r Support laboratory technicians as needed with laboratory tasks including filing paper results maintaining computer logs billing patient tests and cleaning laboratory equipment\rOrganize instructor\rNorth Country Hospital - Jay VT - 2015 to Present\rJay VT 2015 - Present\r Explain adult and child programs offered by the School both in person and via telephone with attention to matching the correct program and ability level to the expectations communicated by the guest\r Organize instructor schedules for private lessons in conjunction with supervisors covering instructor\rlineup\r Perform customer service roles such as selling lift tickets and rental vouchers creating lesson reservations applying discounts and giving directions to guests\rContact: Hailey Jewett (hjewett@jaypeakresort.com or 802 988 2737)\rWaitstaff Bartender and Host\rNorth Country Hospital - Jay VT - 2006 to Present\r Promote a positive dining experience at the resort as a team member with other employees\r Perform duties outside job description as necessary to facilitate service including dishwashing assisting the cooks bartending and cleaning the restaurant\r Maintain a positive friendly and professional attitude towards both customers and coworkers and resolve customer complaints calmly and with understanding\r Operate Sirius point-of-sales software accurately and efficiently and assume responsibility for the cash box\rContacts: Christina Fletcher (cfletcher@jaypeakresort.com) Oral Kelly (okelly@jaypeakresort.com)\rCindy Mead (cmead@jaypeakresort.com)\rLaboratory Assistant\rNorth Country Hospital - Bozeman MT - 2014 to 2015\rRegistered outpatient lab work and received inpatient specimens with a high degree of accuracy and attention to detail regarding patient identifiers specimen requirements test coding and delivery to correct\rdepartment within the lab\r Fielded customer service questions in person and via telephone with kindness and respect using available resources to answer questions as completely as possible and being mindful of regulations and procedures\r \rprotecting patient privacy\r Prepared specimens for transport to other clinical and research laboratories including checking orders for accuracy checking that the the appropriate specimen type transport container and transport temperature were used and documenting the transaction in MediTech\r Performed other tasks as necessary including checking orders for accuracy using the fax machine checking pending lists tracking specimen volume data entering new providers into the electronic\rdatabase and storing and discarding specimens\rContact: Esther Vance (evance@bdh-boz.org or 406 414 3141)\rStudent Library Assistant\rNorth Country Hospital - Waterville ME - 2013 to 2014\r Represented the library to incoming patrons and practiced good customer service\r Facilitated the function of the library by checking books in and out to patrons answering questions if possible and referring questions to the appropriate librarian when necessary and shelf reading\r Troubleshot problems with printers microfilm readers and student computers and referred to a more experienced professional when necessary\rResearch Assistant\rNorth Country Hospital - Waterville ME - 2012 to 2014\rDesigned and performed watershed level research in collaboration with a student partner and an overseeing professor in the Belgrade Lakes Region ME including studying key variables while\rminimizing variation in the controls and writing lab protocols as necessary to achieve that goal and enable study replication\r Familiar with Lachat auto-sampling machinery and able to troubleshoot mechanical issues therein and proficient with basic sampling and lab etiquette and techniques\r Presented results orally to a range of audiences from scientists at national level conferences to stakeholders in the local area with little scientific background as well as communicating results in writing\rContact: Denise Bruesewitz (dabruese@colby.edu)\rAcademic Tutor and Teaching Assistant\rNorth Country Hospital - Waterville ME - 2011 to 2013\rAssisted students in Single-Variable Calculus Multi-Variable Calculus Introductory Biology and\rGeneral Chemistry to develop methods of relating to and understanding material focusing on successful homework completion exam preparation and study habits and time management\r Graded student homework assignments in Introductory Statistics in a timely fashion with attention to detail and fair distribution of partial credit where applicable\rEDUCATION\rBachelor of Arts in Environmental Studies\rColby College - Waterville ME 2014\rDiploma\rNorth Country Union High School - Newport VT 2010\r\"\r\nresume_110,not_flagged,\"Victoria Copeland Senior Research Biologist\rBradford VT - Email me on Indeed: indeed.com/r/Victoria-Copeland/117d777942b0ca8f\rWORK EXPERIENCE\rSenior Scientist\rGlycoFi Inc - Lebanon NH - June 2006 to April 2013\rUnder the supervision of Dr. Robert Davidson Principal Scientist/Research Fellow\rResponsibilities/Accomplishments:\ro Contributed to the development of Glycoengineered Yeast a unique yeast-based antibody and therapeutic protein expression platform.\ro Supported efforts focused on subunit vaccine development utilizing glycoengineered yeast for the evaluation optimization and production of novel vaccine candidates.\ro Involved in the development of a bioinformatics infrastrastructure to optimize GlycoFi's yeast-based production system that including: 1) development and standardization protocols to support RNA profiling and next generation sequencing; 2) follow up of identified mutations using reverse genetics.\ro Responsible for hands-on expression of monoclonal antibodies using glycoengineered yeast host including optimization of critical quality attributes to support lead ID development programs.\ro Executing molecular biology work to improve the host strains for optimal therapeutic protein production that includes primer design PCR cloning sequence analysis DNA isolation as well as standard yeast microbiology (e.g. transformation screening and isolation of yeast strains dot/western blot assays) and immunohistochemistry techniques.\rSenior Research Associate\rGlycoFi Inc - Lebanon NH - November 2004 to June 2006\rAssociate to Dr. Robert Davidson\ro Contributed to one of the six original company specific milestones\ro Participated in external collaboration efforts to produce and optimize drug discovery candidate proteins in Glycoengineered Yeast strains.\ro Supported independent research efforts including development of key tools such as inducible and constitutive expression modules optimization of yeast strain metabolism expression of mAbs and exploration of alternative yeast expression systems.\rResearch Assistant I\rDunlap Lab Dartmouth Medical School - May 2004 to November 2004\rParticipated in a large scale project to knockout 10000 genes in Neurospora crassa adapting PCR yeast sub-cloning and neurospora protocols to create high-throughput methods that were efficient and consistent on a liquid handling robot.\r \rResearch Assistant I\rBeverly Rothermel - August 2002 to May 2004 PhD UT Southwestern Medical Center\r Carried out various molecular biology and biochemical techniques to central experiments in the lab such as DNA and RNA purification PCR Northern blotting Southern blotting Western blotting RT-PCR preparation of protein basic tissue culture immunohistochemistry and subcloning\r Researched developed and facilitated new techniques\r Supervised summer undergraduate students in lab procedures and safety protocols\r Provided assistance to graduate students fellows and post-docs working in the lab\r Maintained records for supplies ordered protocols freezer contents and lab reagents Supervised mouse colony\rAquarist\rDallas Aquarium - Dallas TX - March 2002 to August 2002\rAquarist part time\rMaritime Aquarium - Norwalk CT - June 2000 to February 2001\rEDUCATION\rB.S. in Biology\rSouthern Connecticut State University May 2001\rB.A. in History\rUniversity of Connecticut December 1994\rSKILLS\rStrong Molecular biology techniques in animal and yeast models\rADDITIONAL INFORMATION\rResearch associate with over 10 years progressive academic and industrial experience in molecular research and discovery. Advanced experience in creating new research protocols supporting ongoing studies and compiling data for presentation. Highly responsible and dedicated team player with excellent leadership and mulit-tasking skills.\r\"\r\nresume_111,not_flagged,\"William Glen\rWater Division Chief - Town of Middlebury\rNew Haven VT - Email me on Indeed: indeed.com/r/William-Glen/56bccaa54a80c0ed\rWORK EXPERIENCE\rWater Division Chief\rTown of Middlebury - Middlebury VT - July 2005 to Present\rResponsible for the operation of a public water system with 2200 service connections. Duties include the supervision of employees and the maintenance of wells distribution system and related infrastructure. Other duties include water system improvements monitoring and management of water quality backflow prevention leak detection and upgrading of metering technology. Responsible for professional and effective interface with regulatory agencies developers engineers contractors the highway department and the public. Function as public works liaison with engineering firms interpreting and facilitating complex engineering projects.\rWater Treatment Plant Operator\rCity of Saint Albans St. Albans - Saint Albans VT - January 2004 to July 2005\rOperated and maintained both conventional and direct filtration water treatment plants. Performed daily water analyses and record keeping duties; maintained and repaired equipment including pumps valves filters and laboratory instruments. Monitored water treatment process for quality and compliance with state and federal regulations. Identified treatment process problems and made process changes to insure optimum finished water quality.\rEngineering Process Technician\rIBM - Essex Junction VT - October 2000 to July 2003\rResponsible for improving and maintaining semiconductor manufacturing process controls including chemical vapor deposition and reactive ion etch. Deployed defect reduction protocols; developed monitor rework processes; and qualified reduced flow deposition chamber cleans (C2F6 and NF3) to decrease PFC emissions and process chemical consumption. Other duties included providing data to maintenance personnel to facilitate tool repairs; responding to manufacturing inquiries; and working with design engineers to develop processes for new technologies with an emphasis on manufacturability.\rStaff Scientist\rClancy Environmental Consultants Inc - Saint Albans VT - November 1995 to October 2000\rPerformed analyses supporting research for the detection of Giardia lamblia and Cryptosporidium parvum in drinking water. Assisted in challenge studies of various water filtration systems and inactivation devices both in the laboratory and in the field. Conducted microscopic particulate analyses to evaluate filtration plant performance and surface water influences on groundwater. Performed other analyses as needed including those for total and fecal coliform heterotrophic plate counts and zebra mussels. ICR-approved (Information Collection Rule) analyst for Giardia lamblia and Cryptosporidium parvum. Served as liaison to utilities to assure their compliance with EPA regulations regarding protozoan and virus monitoring. Maintained an ICR QC database and submitted it to the EPA on a monthly basis. Trained utility personnel on EPA protozoan monitoring methods as well as experimental protozoan detection and viability assays.\rStaff Scientist/Quality Control Officer\rAnalytical Services Inc - Williston VT - October 1994 to November 1995\r \rProvided assistance to laboratory personnel for analyses of Giardia lamblia Cryptosporidium parvum and total coliform bacteria. Served as company liaison to regulatory/certifying agencies. Reorganized and maintained laboratory quality control/quality assurance program for total coliform analysis certification. Provided customer service including information on proper sampling equipment and techniques to maintain EPA compliance.\rMicrobiologist\rAnalytical Services Inc - Middlebury VT - June 1995 to August 1995\rSeasonal temporary position. Performed E.coli bacteria and total phosphorus analyses on river water samples for seasonal river watch group. Maintained QC and provided instruction and equipment to volunteers.\rTemporary Lab Technician\rState of Vermont Dept. of Health Laboratory - Burlington VT - June 1994 to October 1994\rProvided support to scientists in the chemistry and microbiology laboratories. Other responsibilities included media and glassware preparation disposal of bio-hazardous materials and providing information to the public.\rEngineering Technician/Manufacturing Group Leader\rBio-Tek Instruments Inc - Winooski VT - August 1984 to October 1991\rAssisted R&D engineers with fabrication and testing of biomedical equipment. Trained and supervised groups of production personnel. Delegated work to meet production deadlines and quality control standards.\rEDUCATION\rBachelor of Science in Biology\rTrinity College of Vermont - Burlington VT May 1994\rLiberal arts\rUniversity of Vermont - Burlington VT June 1989 to August 1992\rMount Abraham Union High School - Bristol VT 1981\rADDITIONAL INFORMATION QUALIFICATIONS AND ABILITIES\r Results-driven public works professional with established experience in water treatment and distribution as well as research and testing\r Extensive experience complying with regulatory agencies\r Solid track record in troubleshooting and solving complex problems within challenging and changing circumstances\r Skilled in streamlining processes reducing costs and meeting deadlines\r Extremely strong mechanical and technical aptitude coupled with creative problem-solving Demonstrated interpersonal skills; ability to communicate effectively with diverse individuals groups and agencies\r Proven ability to take on new responsibilities and learn new content areas with ease\r Seasoned leadership abilities in training and supervising employees and volunteers\r Meticulous laboratory technique; excellent quality assurance and quality control skills\r Bachelor of Science Biology\r Class 4C Water Operator's License\r Management Certificate Vermont Local Roads Management Academy Commercial Driver's License Class B\r High degree of computer literacy from Microsoft Office to GIS Mapping\r\"\r\nresume_112,not_flagged,\"William Lauten Principal Scientist\rSouth Royalton VT - Email me on Indeed: indeed.com/r/William-Lauten/0237475ed188ce01\rWORK EXPERIENCE\rPrincipal Scientist\rNew England Research Inc - White River Junction VT - September 2000 to March 2011\rOversaw measurements of physical properties of rocks for government and industry research projects. Wrote and implemented research proposals.\rSaudi Arabian Oil Co - September 1982 to July 2000 Saudi Aramco) Dhahran Saudi Arabia\rLaboratory Research Scientist\rLaboratory Research and Development Dept - 1994 to 2000\rcreated and supervised Rock Mechanics Laboratory for the measurement of physical properties of rocks.\rGeophysical Research Scientist\rGeophysical Research Dept - 1982 to 1994\rdeveloped state of the art algorithms for processing seismic data.\rPhysicist\rPhillips Petroleum Research Corp - Bartlesville OK - September 1980 to September 1982 field and laboratory seismic applications; development of migration and inversion algorithms.\rResearch Scientist\rMichelin Research Corporation - Greenville SC - June 1979 to August 1980 performed research on tire noise reduction and tire membrane modeling.\rAssistant Professor of Physics\rSweet Briar College - Amherst VA - January 1977 to May 1979\rPhysical Science teacher\rClear Creek School District - Clear Lake TX - September 1968 to May 1970\r EDUCATION\rPh.D. in Physics\rClemson University - August 1976\rM.S. in Physics\rClemson University - May 1974\rB.S. in Physics\rGuilford College -\rClemson SC\rClemson SC\rGreensboro NC\r\"\r\nresume_113,flagged,\"William Sribney\rStatistical Software Developer and Statistical Consultant\rDorset VT - Email me on Indeed: indeed.com/r/William-Sribney/6330add2ea5acf0c\rSeeking a position developing software for statistical or other scientific applications. Extensive experience implementing statistical procedures in several languages (C Objective-C C# Java) for professional software.\rWORK EXPERIENCE\rStatistical Consultant and Software Developer\rThird Way Statistics - 2003 to Present\rSenior Statistician\rData Description Inc. - Ithaca NY - 2010 to 2013 and 1999-2000.\rSenior Scientist\rKynen Inc. - Reston VA - 2010 to 2010\rSenior Statistician\rUniversity of Medicine and Dentistry of New Jersey - 2001 to 2005\rResearch Associate\rDepartment of Genetics Rutgers University - 2001 to 2003\rStatistical Genetics Consultant\rself-employed - 1990 to 2000\rSenior Statistician\rStata Corporation - College Station TX - 1994 to 1999\rFree-Lance Scientific Editor\rself-employed - 1987 to 1990\rAcquisitions editor\rAcademic Press - Cambridge MA - 1985 to 1987\r EDUCATION\rA.B.D. in Biostatistics\rUniversity of North Carolina at Chapel Hill - 1992 to 1994\rM.S. in Biostatistics\rUniversity of North Carolina at Chapel Hill - 1989 to 1992\rMathematics\rQueen's University - Kingston ON 1982 to 1983\rChapel Hill NC\rChapel Hill NC\rPhysics\rPrinceton University - Princeton NJ 1979 to 1981\rB.S. in Mathematical Physics\rQueen's University - Kingston ON 1975 to 1979\rSKILLS\rLanguages: C/C++ Objective-C C# Java Perl and FORTRAN. Development of algorithms to solve complex analysis problems.\rADDITIONAL INFORMATION\rProfessional developer of statistical software since 1994.\rLead programmer for the development of statistical software for the iPad.\rDesigned and implemented in Java a GUI prototype for interactive graphics.\rPI of an NIH-NIAAA SBIR for the development of software for longitudinal analysis of complex survey data. Developed Statas suite of commands for the analysis of complex survey data.\rDeveloped a new optimization method for maximum likelihood estimators that are not globally concave.\rAt Stata implemented procedures for the sandwich (linearization) variance estimator random- and fixed- effects models ML estimators nonparametric statistics bootstrap numerical derivatives and various other statistical and numerical procedures.\rDesigned the GUI for the first Windows version of the Stata statistical software package.\r\"\r\nresume_114,not_flagged,\"\nBennington VT - Email me on Indeed: indeed.com/r//6ad19d71eb320c5f\rWORK EXPERIENCE\rLead Research Scientist\rKeene State College - Keene NH - January 2010 to May 2011\rMy research focused on the Fungal Bioremediation of Pyrene a Polycyclic Aromatic Hydrocarbon (PAH):\r_ Revised application and development of microbiological techniques such as those responsible for growing and maintaining cell cultures of different fungal species.\r_ 2 years of training and experience utilizing sensitive analytical chemistry techniques and instrumentation. (Listed above in qualifications/skills.)\r_ Presented multiple posters and dissertations at the Northeastern Undergraduate Research and Development Symposium (N.U.R.D.S. 2010 and 2011) and the Keene State College Academic Excellence Conference (A.E.C.) centered on my research.\r_ Developed an assertive research grant proposal.\r_ Award recipient of a Keene State College Undergraduate Research Grant (2010).\rMath Tutor\rUniversity System of New Hampshire - Keene NH - December 2008 to March 2011\rConsistently provided patient assistance to people of all ages confused with math and science. Responsible for providing students with a positive responsive learning environment. Coordinated with other tutors to make sure students were fully supported.\rSeasonal Teller\rCitizens Bank - Bennington VT - June 2010 to August 2010\r_ Provided attentive customer service over the phone and in person.\r_ Introduced customers to innovative products and services they could benefit from using. _ Energetic with coworkers and enthusiastic to divide the workload.\rEDUCATION\rBachelor of Science in Chemistry\rKeene State College - Keene NH January 2007 to January 2011\rSKILLS\rExperimental Design Leading a research project writing research grant proposals managing a research budget Microbiological Techniques Sterile Technique High Pressure Liquid Chromatography (HPLC) Capillary Electrophoresis (CE) Data Entry Data Manipulation Data Intrepretation Typing Microsoft Excel Microsoft Word Microsoft Powerpoint Igor Graphing Software\rAWARDS\rKeene State College Undergraduate Research Grant\r2010\r \rI was awarded an undergraduate research grant to fund my research at Keene State College.\rGROUPS\rAmerican Chemical Society American Society of Microbiology\r\"\r\nresume_115,flagged,\" Ph.D.\rSouth Royalton VT - Email me on Indeed: indeed.com/r//e910c60b19bcf799\rExperienced Algorithm Researcher and Developer in estimation and filtering hybrid system modeling multisensor data fusion automated reasoning and uncertainty management for target tracking and machinery diagnostics and prognostics\rHardware and Software Developer with three years of experience in developing embedded systems for signal acquisition and signal processing\rWORK EXPERIENCE\rSenior Research Scientist\rSentient Corporation - March 2006 to June 2012\rLead investigator in SBIR programs sponsored by NASA NAVAIR and ARMY to develop reasoning and uncertainty management algorithms for condition-based maintenance systems\r Developed a model updating architecture in online aircraft prognosis systems\r Developed an automatic reasoner prototype for using operational usage and maintenance data to determine remaining useful life and required actions\r Developed decision and data mining algorithms for an automated intelligent maintainer support system for LAV-25\r Developed the uncertainty management algorithms for aircraft prognosis systems\r Investigated and developed uncertainty quantification methods for the fatigue crack initiation prediction tool for rotorcraft spiral bevel gear\r Developed vibration based diagnostic algorithms in C/C++ for diagnostics for Black Hawk hanger bearings Extensive experience in writing successful SBIR and STTR proposals\r Prepared and delivered technical presentations to clients and international conferences\rGraduate Research Assistant\rUniversity of New Orleans - 2000 to 2006\rDeveloped the optimal dynamics model set designs for maneuver target tracking\r Developed a semi-parametric modeling scheme for linear regression model selection\r Developed the best linear unbiased filter for target tracking with nonlinear measurements and sensor fusion Proposed new performance metrics and tests to assess estimation/filtering algorithms\rResearch Internship\rIntelligent Automation Inc - Rockville MD - July 2005 to December 2005 Developed a joint classification and estimation method to E-nose sensor array\r Created ontology for a battlefield information management system Research Assitant\rAutomation Institute - Xi'an Jiaotong University P.R.China - 1997 to 2000\r Developed a signal generator for a testing rig for mechanical fault diagnosis\r Developed the software for a no-contact smart card reading system\r Developed the signal processing hardware of an innovative stereoscopic display system\r \rEDUCATION\rPhD in Stochastic signal processing and data fusion\rUniversity of New Orleans - 2000 to 2006\rMS in System control\rXi'an Jiaotong University - 1997 to 2000\rNew Orleans LA\rXi'an P.R.China\rBS in Electrical Engineering\rXi'an Jiaotong University - Xi'an P.R.China 1993 to 1997\rSKILLS\rC/C++(+5 Years) Matlab(+12) Python(+3) Intel assemble language (1+);\rAWARDS\rThird place in Challenge Problem Competition in International Conference on Prognostics and Health Management 2008 in professional group\rSeptember 2008\rElectrical Engineering Chevron Graduate Student Award\rJune 2005\rADDITIONAL INFORMATION\rSPECIALTIES\rModeling\rLinear systems Time series Maximum entropy modeling Gaussian mixture Hidden markov model Hybrid systems Bayesian network Graphical model\rEstimation/Filtering\rClassical and Bayesian parameter estimation Least-squares estimation Kalman filter Unscented Kalman filter Particle filter Adaptive filter Expectation maximization algorithm\rClassification and Detection\rNeural network Support vector machine Decision tree Neyman-Pearson test Generalized-likelihood ratio test Bayes test Sequential test Minimax test CUSUM\rdetection\rInformation Fusion and Reasoning\rFuzzy logic Dempster-Shafer evidence reasoning Bayesian inference Least-squares fusion\rUncertainty Quantification\rMonte Carlo simulation KarhunenLoeve expansions Stochastic adaptive sparse grid method\rSignal Processing\rDigital filter design Time-frequency analysis Vibration signal analysis Acoustic signal analysis\rControl Theory\rSystem identification Feedback control Modern control Adaptive control Optimal control\rProgramming Skills\rMatlab (12+ years) C/C++ (5+) Python (3+) Assemble language (3+) Mathematica (2+) SAS (1+)\rField Bus RS-232 RS-485 I2C\rMicrocontroller [...] 89c51\r\"\r\nresume_116,flagged,\"\rEssex Jct VT - Email me on Indeed: indeed.com/r//e8832e56b0377a52\rA scientist position in the pharmaceutical industry that allows me to contribute my skills and knowledge for the development of new drugs and assays.\rWORK EXPERIENCE www.sciencedirect.com - 2012 to Present\r1. Song X and Pan ZZ. ER_ agonist DPN counteracts the estrogenic activity of ERԱ agonist PPT in the mammary gland of ovariectomized Sprague Dawley rats. J Steroid Biochem Mol Biol 130:26-35 2012. http:// www.sciencedirect.com/science/article/pii/S0960076011002603\r2. Tan H Zhong Y and Pan ZZ. Autocrine regulation of cell proliferation by ERԱ in ERԱ-positive breast cancer cell lines. BMC Cancer 9:31(1-12) 2009. http://www.biomedcentral.com/1471-2407/9/31\r3. Pan ZZ Bruening W and Godwin AK. Involvement of RHO GTPases and ERK in synuclein-gamma enhanced cancer cell motility. Int J Oncol 29:1201-1205 2006. http://www.spandidos-publications.com/ijo/29/5/1201\rAssistant Professor in Oncology and Animal Science\rUniversity of Vermont - 2005 to Present\rMeyers RA Wiley-VCH Verlag GmbH & Co - 2005 to 2005\r4. Pan ZZ and Godwin AK. ONCOGENES. Encyclopedia of Molecular Cell Biology and Molecular Medicine 2nd Edition edited by Meyers RA Wiley-VCH Verlag GmbH & Co. KgaA Weinheim 9:435-495 2005. http:// onlinelibrary.wiley.com/doi/10.1002/3527600906.mcb.200400064/abstract\r5. Pan ZZ Slater C Vanderveer L and Godwin AK. Effect of Erbitux on cell viability is correlated with the responsive signaling pathway(s) but not EGFR expression levels in ovarian tumor cells. Proc AACR Volume 46 Abstract #5052 2005. http://www.aacrmeetingabstracts.org/cgi/content/abstract/2005/1/1193\r6. Pan ZZ Vanderhyden B and Godwin AK. Gamma-synuclein transgenic mouse model in tumorigenesis Proc AACR Volume 45 Abstract #5108 2004. http://www.aacrmeetingabstracts.org/cgi/content/abstract/2004/1/1178-b\rPostdoctoral Fellow in Oncology\rFox Chase Cancer Center - Philadelphia PA - 2001 to 2005\rwww.molbiolcell.org - 2004 to 2004\r2004. http://www.molbiolcell.org/content/15/7/3106.long\r8. Frolov A. Chahwan S Ochs M Arnoletti JP Pan ZZ Favorova O Fletcher J von Mehren M Eisenberg B and Godwin AK. Response markers and the molecular mechanisms of action of gleevec in gastrointestinal stromal tumors. Mol. Can. Ther. 2:699-709 2003.\rhttp://mct.aacrjournals.org/content/2/8/699.long\r9. Lu Y * Pan ZZ* Devaux Y* and Ray P. PAK4 interacts with KGFR and participates in KGF-mediated inhibition of oxidant-induced cell death. J. Biol. Chem. 278:10374-80 2003. (*equal contribution). http:// www.jbc.org/content/278/12/10374.long\r10. Pan ZZ Bruening W Giasson BI Lee VM and Godwin AK. _-Synuclein promotes cancer cell survival and inhibits stress- and chemotherapy drug-induced apoptosis by modulating MAPK pathways. J. Biol Chem. 277:35050-35060 2002. http://www.jbc.org/content/277/38/35050.long\r \r11. Pan ZZ Kronenberg MS Huang DY Sumoy L Rogina B Lichtler AC and Upholt WB. Msx2 expression in the apical ectoderm ridge is regulated by an Msx2 and Dlx5 binding site. Biochem. Biophys. Res. Commun. 290:955-961 2002.\rhttp://www.sciencedirect.com/science/article/pii/S0006291X01962941\r12. Pan ZZ Parkyn L Ray A and Ray P. Inducible lung-specific expression of RANTES: preferential recruitment of neutrophils. Am. J. Physiol. 279(4):L658-666 2000. http://ajplung.physiology.org/content/279/4/L658.full.pdf+html\rPostdoctoral Associate in Inflammation/Immunology\rYale University - New Haven CT - 1997 to 2000\rRELEVANT EXPERIENCE AND EXPERTISE\r-- A dedicated and motivated biomedical research scientist with extensive knowledge and 10+ yrs hands-on experience in multiple disciplines including Oncology and Inflammation.\r-- Independent investigator and dedicated team player with strong capabilities in experiment planning and designing data collection and analyzing data organizing and interpretation/presentation.\r-- Research Experience and Expertise Areas\r*Oncology *Inflammation/Immunology *Animal models *Molecular biology *Cellular biology *Translational research *Biostatistics *Computer literacy *Supervising/managing\r-- In Vitro Pharmacology\r Cell Proliferation Assays: MTT assay BrdU labeling cell proliferation rate/index.\r Apoptosis Assays: TUNEL assay annexin V staining caspase assay etc.\r Reporter-Based Assays: luciferase assays etc.\r Mammalian Cell Culture: cancer and non-cancerous cell lines primary cells stem cells\r-- In Vivo Animal Models and Pharmacology\r Transgenic Mouse Models: generated several transgenic mouse models for Oncology Inflammation and Developmental Biology.\r Xenograft Tumor Nude Mouse Models: used for the efficacy and action/resistance mechanisms of anti- EGFR drug Erbitux\r Lentivirus Infection Animal Models: used to infect rat mammary gland cells in vivo.\r In Vivo Pharmacological Models: used for the studies of the ER-selective agonists PPT and DPN.\r-- Molecular Biology Cellular Biology and Histopathology\r Cell transfection stable cell lines or pools generation.\r Expertise in recombinant retrovirus and lentivirus production and transduction (in vitro cell lines and in vivo animal models; BSL2).\r siRNA(plasmid and retrovirus) knock-down of genes of interests.\r DNA: extraction cloning site-directed mutagenesis PCR Southern DNA sequencing analysis.\r RNA and gene expression assays: RNA extraction RT-PCR Northern quantitative real-time PCR cDNA microarray assay.\r Protein: protein lysate preparation and immunoprecipitation SDS-PAGE Western blot ELISA proteomics protein kinase assays protein overexpression in bacteria and yeast GST-fusion protein purification.\r Expertise in fluorescence microscopy flow cytometry analysis (FACS) cell uptake/internalization.\r Signal transduction: protein kinase assays protein-protein interaction signaling pathways.\r Histopathology: tissue fixing paraffin block sectioning H&E staining immunofluorescent staining immunohistochemical staining microimaging data capture and analysis.\r-- Others\rComputer Software/Programs: MS WORD Powerpoint Excel; SPSS JMP PASS; Photoshop; MacVector Vector NTI.\r Principal or leading investigator in multiple projects.\r Collaborated successfully with colleagues in multiple projects.\r Demonstrated history of writing manuscripts grant proposals progress reports and communicating research progress in local and national scientific meetings.\rSELECTED ARTICLES (from >17) AND ABSTRACTS\rEDUCATION\rMS in Developmental Biology\rShandong University 1987\rBS in Biology\rShandong Normal University 1984\r\"\r\nresume_117,not_flagged,\" Postdoctoral Associate - University of Vermont\rBurlington VT - Email me on Indeed: indeed.com/r/c7247a717dc20117\r Highly qualified and technically proficient biochemist with 5.5 years professional and teaching experience\r Excellent research and analytical skills\r Strong technical and scientific writing and data analysis abilities\r Deep understanding of biochemistry enzymology inflammation fibrosis cardiovascular and pulmonary diseases and therapeutics\r Demonstrated track record of successfully completing complex and challenging projects Ability to associate successfully with diverse groups of people\rWilling to relocate: Anywhere\rWORK EXPERIENCE\rPostdoctoral Associate\rUniversity of Vermont - Burlington VT - March 2015 to Present\rUSA\r Established the therapeutic efficacy of tauroursodeoxy cholic acid (TUDCA) in allergic asthma lung inflammation and fibrosis using cell culture techniques and mouse models\rResearch Mentor/Supervisor\rUniversity of Mysore Tulane University and University of Vermont - December 2008 to Present Mentored undergraduate and post-graduate students for their research projects\rPostdoctoral Fellow\rTulane University - New Orleans LA - October 2014 to February 2015\rUSA\r Investigated the role of stromal cell-derived factor 1 (SDF-1) / C-X-C chemokine receptor type 4 (CXCR-4) in interleukin 13-induced epithelial-mesenchymal transition using lung-specific gene altered mouse models\rPostdoctoral Fellow\rTulane University - New Orleans LA - February 2012 to September 2014\rUSA\r Project#1: Demonstrated the therapeutic effect of acetylsalicylic acid (aspirin) and docosahexaenoic acid in cardiac fibroblast migration through the induction of reversion-inducing cysteine rich protein with Kazal motifs (RECK) in vitro\r Project#2: Revealed the role of RECK in myocardial hypertrophy and adverse remodeling and collar injury- induced carotid artery neo-intimal hyperplasia using gene altered mouse models\r Project#3: Studied the role of TRAF3 Interacting Protein 2 (TRAF3IP2) in myocardial hypertrophy and adverse remodeling abdominal aortic aneurysm and atherosclerosis using gene altered mouse models\rGuest Lecturer\rYuvaraja's College University - Mysore Karnataka - January 2008 to April 2008 India\r \r Subjects taught: Nutrition and Physiology Enzymology and Laboratory Experimentations\rGuest Faculty\rDepartment of Studies in Biochemistry University of Mysore - Mysore Karnataka - September 2007 to April 2008\rIndia\r Subjects taught: Biochemical Techniques and Laboratory Experimentations\rPart-time Lecturer\rSBRR Mahajana First Grade College - Mysore Karnataka - January 2007 to February 2008\rIndia\r Subjects taught: Biomolecules Enzymology Immunology Metabolism Biochemical Techniques Organic Chemistry and Laboratory Experimentations\rAssistant Professor (Part-time)\rSBRR Mahajana First Grade College - Ponnampet Karnataka - July 2007 to December 2007\rPonnampet Coorg Karnataka India Subject taught: Plant Biochemistry\rTrainee Scientist (Bio-Curator)\rJubilant Biosys Pvt. Ltd - Bangalore Karnataka - July 2005 to December 2006\rBangalore CAS-Bio Project the then Mysore Branch Karnataka India\r Demonstrated strong and efficient record in scientific journal data mining\rEDUCATION\rPh.D. in Biochemistry\rUniversity of Mysore - Mysore Karnataka January 2007 to January 2012\rPh.D. in Thesis\rUniversity of Mysore - Mysore Karnataka July 2003 to June 2005\rM.Sc. in Research Project\rYuvaraja's College University of Mysore - July 2000 to June 2003\rADDITIONAL INFORMATION SOFTWARE SKILLS\rMysore Karnataka\r Basics of computer internet MS word Excel and Power point\r Image processing using adobe photoshop and adobe illustrator\r Statistical analysis using GraphPad Prism software\r Image analysis densitometry and quantification using MetaMorph and ImageJ software\r\"\r\nresume_118,flagged,\"\rSpatial Analyst and UAV Flight Operator - Spatial Analysis Laboratory University of Vermont\rBurlington VT - Email me on Indeed: indeed.com/r/cff8e6fd4ed2a57f Authorized to work in the US for any employer\rWORK EXPERIENCE\rSpatial Analyst and UAV Flight Operator\rSpatial Analysis Laboratory University of Vermont - Burlington VT - 2015 to Present\r Collaborated with a team of GIS analysts to manually correct thousands of square kilometers of digitized land cover and process terabytes of satellite imagery in ArcGIS\r Conducted quality assessment of digitized land cover maps\r Operated three types of Unmanned Aerial Vehicles (UAV) to acquire aerial imagery\r Processed UAV aerial imagery to create orthophoto mosaics and digital terrain models\r Compiled aerial imagery to build 3D models and calculate volume estimates of structures in QuickTerrain Modeler\r Participated in the UAV disaster response efforts after the Amtrak train derailment in Northfield VT and in the aftermath of the February 2016 flooding of Route 2 in Middlesex VT\rGIS Analyst\rChittenden County Regional Planning Commission - Winooski VT - 2015 to Present\r Created managed and updated county-wide and town-wide databases for Chittenden County Developed a custom database for the storage and maintenance of traffic information\r Utilized Python and SQL to expedite the processing of large databases\r Updated the ESRI Community Basemap for Chittenden County\r Cleaned the CCRPC Housing and Commercial Industrial database to extract accurate information Designed numerous county-wide and town-wide maps for town planners\r Managed multiple projects and prioritized tasks to meet deadlines\rSummer Transportation Intern\rSpatial Analysis Laboratory University of Vermont - Winooski VT - 2015 to 2015\r Employed GPS and GIS technologies to conduct inventories of transportation infrastructure such as pavement sidewalks culverts and signs\r Compiled GPS and GIS field data to construct custom databases\r Acted as Quality Control Project Leader for five town-wide inventories and managed the CCRPC online culvert database\r Performed traffic counts and installed Automatic Traffic Recorders (ATR) to measure the volume and flow of traffic\r Refined management skills while collaborating with a team to delegate tasks create schedules and meet project deadlines\rGIS Technician and Research Assistant\rCenter for Remote Sensing Boston University - Boston MA - 2014 to 2015\r Utilized the HiRISE image database to download satellite imagery of Gale Crater on Mars\r Processed satellite imagery using the USGS Integrated Software for Imagers and Spectrometers\r \r Analyzed and georeferenced images in ArcGIS\r Operated the Shared Computing Cluster at Boston University to process images in QGIS and write a Bash script to create a mosaic of Gale Crater on Mars\r Maintained daily contact with Senior Research Scientist Bradley Thomson to assess progress communicate goals and maximize performance\rGIS Technician and Research Assistant\rCenter for Remote Sensing Boston University - Boston MA - 2013 to 2013\r Wrote a proposal to NASA to use the HiRISE camera on-board the Mars Reconnaissance Orbiter to obtain imagery of Martian craters\r Analyzed and georeferenced crater imagery using ArcGIS\r Pioneered a new technique to extrapolate subsurface stratigraphy and take measurements of craters using MATLAB in combination with ArcGIS\r Applied for and received funding from the Undergraduate Research Opportunities Program (UROP)\r Met daily with Senior Research Scientist Bradley Thomson and participated in faculty meetings to communicate progress and goals\rEDUCATION\rB.A. in Geophysics and Planetary Science minor in Astronomy\rBoston University College of Arts and Sciences - Boston MA 2015\rSchool of the Museum of Fine Arts - Boston MA 2011 to 2012\rADDITIONAL INFORMATION\rSKILLS\r Proficient computer skills: ArcGIS QGIS MATLAB ENVI Quick Terrain Modeler eMotion Excel Access Postflight Terra 3D TerraSync PETRAPro TRAXPro\r Programming experience using Python IDL R Bash and Boston University's Shared Computing Cluster\r Independent and collaborative research and excellent written and verbal communication\r\"\r\nresume_119,flagged,\" | Bioinformatics Analyst\rBurlington VT - Email me on Indeed: indeed.com/r/f71006b4afa46565 Authorized to work in the US for any employer\rWORK EXPERIENCE\rBioinformatics Scientist / Research Assistant Professor\rVermont Genetics Network University of Vermont - Burlington VT - September 2016 to Present\rResponsibilities\rPerform wide variety of bioinformatics analyses in collaboration with investigators from Vermont colleges and regional partners.\rAccomplishments\rCompleted 72 bioinformatics projects with faculty from 18 Vermont colleges.\rSkills Used\rbioinformatics analysis metagenomics\rRNA-Seq\rUNIX programming database management\rhigh performance computing\rAdjunct Faculty\rChamplain College - Burlington VT - January 2016 to Present\rResponsibilities\rDeveloped and taught introductory course in cloud computing Internet of Things and realtime HTML5 web applications.\rAccomplishments\rDesigned course developed all materials led students in practical hands on exercises in use of cloud computing.\rBioinformatics Core Director\rVermont Genetics Network University of Vermont - Burlington VT - June 2007 to September 2016\rDirected a small bioinformatics core facility (until September 2016) as Research Assistant Professor in the Department of Biology.\rCollaborate with a wide variety of researchers in life sciences on projects ranging from serum proteome analysis to de novo genome assembly.\rDesign implement administer all infrastructure including compute/storage shared data center LIMS systems project management and others.\rBioinformatics Scientist\rThe National Cancer Institute National Institutes of Health - Bethesda MD - December 1999 to June 2007\r \rDeveloped techniques and tools for gene expression analysis leading to discovery of several new genes.\rGranted two patents on genes for cancer therapy.\rComputationally engineered immunotoxins as cancer therapies to reduce non-specific toxicity.\rEDUCATION\rPhD in Computational Chemistry\rThe Pennsylvania State University - State College PA 1992 to 1999\rBS in Computer Science\rUniversity of Vermont 1987 to 1992\rBS in Chemistry\rUniversity of Vermont 1986 to 1991\rSKILLS\r- Burlington VT\r- Burlington VT\rDatabase Administration High Performance Computing Unix Administration Metagenomics Genomics Proteomics Bioinformatics Data Analysis (10+ years) Data Mining (10+ years) Databases (10+ years)\rAWARDS\rDean's Recognition Award University of Vermont\rMay 1991\rAward for service to the College of Engineering\rAmerican Chemical Society Undergraduate Award in Analytical Chemistry\rMay 1990\rRecognition of outstanding scholarship in analytical chemistry.\rNational Science Foundation Traineeship in High Performance Computing\rSeptember 1994\rNational Science Foundation Traineeship in high performance computing.\rRoberts Graduate Student Award Pennsylvania State University\rMay 1998\rAward for outstanding graduate student service to the department and university.\rCancer Research Training Award National Cancer Institute National Institutes of Health\rJanuary 1999\rNational Cancer Institute cancer research training fellowship to enhance public health efforts to prevent diagnose or treat cancer.\rTechnology Transfer Award National Cancer Institute National Institutes of Health\rJanuary 2001\rNational Cancer Center award for outstanding scientific or technological contributions.\rTechnology Transfer Award National Cancer Institute National Institutes of Health\rJanuary 2002\rNational Cancer Center award for outstanding scientific or technological contributions.\rTechnology Transfer Award National Cancer Institute National Institutes of Health\rJanuary 2003\rNational Cancer Center award for outstanding scientific or technological contributions.\rPATENTS\rGene expressed in prostate cancer and methods of use (#7816087)\rhttp://goo.gl/QW5XBq\rOctober 2010\rDiscovered and developed Novel Gene Expressed in Prostate (NGEP) gene as target for prostate cancer therapies.\rReduction of the nonspecific animal toxicity of immunotoxins by mutating the framework regions of the Fv to lower the isoelectric point (#7521054) http://goo.gl/hywywU\rApril 2009\rDeveloped recombinant immunotoxins that were modified from a parental immunotoxin to lower liver toxicity as cancer therapeutics.\rPUBLICATIONS\rFull publication list available upon request\rSkateBase an elasmobranch genome project and collection of molecular resources for chondrichthyan fishes\rhttp://f1000research.com/articles/3-191/v1\rAugust 2014\rSkateBase (http://skatebase.org) serves as the skate genome project portal linking data research tools and teaching resources for one of the largest projects to characterize Leucoraja erinacea the little skate.\rQuantitative Comparison of CrkL-SH3 Binding Proteins from Embryonic Murine Brain and Liver: Implications for Developmental Signaling and the Quantification of Protein Species Variants in Bottom-Up Proteomics\rhttp://www.ncbi.nlm.nih.gov/pubmed/25982384\rJuly 2015\rComparison of the identification and quantification of CrkL-SH3 binding partners between embryonic murine brain and liver.\rThermal reactionomes reveal divergent responses to thermal extremes in warm and cool-climate ant species\rhttps://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-2466-z\rMarch 2016\rCharacteriztion of thermal reactionomes of two common ant species in the eastern U.S the northern cool-climate Aphaenogaster picea and the southern warm-climate Aphaenogaster carolinensis across 12 temperatures that spanned their entire thermal breadth.\r \"\r\nresume_120,not_flagged,\"Professional\rNewbury VT - Email me on Indeed: indeed.com/r//0d62faeb0635f317\rWORK EXPERIENCE\rResearch Technologist\rDartmouth College - Hanover NH - June 2011 to Present\rREASEARCH TECHNOLOGIST: Perform all technical aspects of the lab work including DNA extraction multiplex PCR and execution of the Illumina GoldenGate genotyping assay. Perform data analysis and client report generation using proprietary software. Reports include interpretations and recommendations specific to each individual project. Develop proprietary data analysis software. Assessing and maintaining quality control on a continuing basis. DartMouse Speed Congenic Core Facility Dartmouth Medical School Departments of Microbiology and of Immunology Hanover New Hampshire. June- .\rSCIENCE TEACHER\rLife Science Physical Science and Reading - Barre VT - August 2006 to February 2011 Barre Town Middle and Elementary School Barre Vermont. August 2006-Feburary2011.\rScience teacher for 7-12 grade students\rLisbon St. Johnsbury Blue Mountain Hartford - Vermont New Hampshire - January 2000 to 2006\r High and middle school science teacher courses taught include Physics Physical Science Natural Resources Earth Science and Life Science. Lisbon Regional School Lisbon New Hampshire. August 2005-2006.\r High school science teacher courses taught include Accelerated Biology Standard Biology Accelerated Chemistry and Standard Biology. St. Johnsbury Academy St. Johnsbury Vermont. August 2003-2005.\r High school science teacher courses taught include Physics Physical Science Earth Science Environmental Science Biology General Science Science Technology Biotechnology and Virtual Psychology. Blue Mountain Union Wells River Vermont. January 2000-June 2003.\r Science teacher for seventh and eighth grade students at Hartford Memorial Middle School. Vermont certification in Science grades 7- 12. Courses taught include general science seventh grade reading course and an Internet course. FAST teacher certified. Committee chairing include Sunshine and Technology committees. White River Junction Vermont. August 1994 to June 1997.\rADJUNCT FACULTY\rNew Hampshire Community Technical College - September 1998 to June 1999 at the Pease Campus. Fall 1998 and Summer 1999.\rPROJECT MANAGER\rCorning Incorporated - May 1998 to August 1998\rPROJECT MANAGER: Project Manager for Assay Specialties responsible for managing the products from research into development followed by scaling up into manufacturing. Corning Incorporated. Maine June 1999- Janurary 2000. Development work focused on both validation of 384 well PCR polypropylene plates and High Density Arrays using poly-lysine and strep-avidin chemistries. Corning Incorporated. New Hampshire. May 1998-August 1998.\rASSOCIATE SCIENTIST\r \rDartmouth College - Hanover NH - November 1996 to August 1997\rASSOCIATE SCIENTIST: Liaison between the medical school and department of biology worked to develop a bio-marker to investigate biomagnification of heavy metal contamination in water sheds. Daphnia were used as an animal model to investigate heat shock protein expression when exposed to different heavy metal concentrations. Responsibilities included: molecular biology techniques acrylamide gels RT-PCR two dimensional gels clean room techniques along with data analysis. Dartmouth College Hanover New Hampshire. November 1996 to August 1997.\rASSOCIATE SCIENTIST\rGenetics Institute - Cambridge MA - May 1990 to August 1992\rASSOCIATE SCIENTIST: Member of immunoregulation laboratory project focused on the identification of proteins regulating the immune responses and cloning the genes for these proteins. Studied several factors made by T- cells that suppressed both T and B cell responses to antigens. Specific responsibilities included cell culture techniques ELISA ADCC assays stem cell progenitor assays proliferation assays FACS analysis maintaining primary immune response cultures establishing and maintaining hybridoma cell lines and T-cell clones. Genetics Institute Cambridge Massachusetts. May 1990-August 1992.\rRESEARCH TECHNICIAN III\rDana Farber Cancer Institute - Boston MA - September 1989 to May 1990\rRESEARCH TECHNICIAN III: Research focused on production of monoclonal antibodies to develop a therapeutic cancer treatment. Specific responsibilities included: immunization and ascites production in animals prepared cell banks cell line characterization sub-cloned cells all aspects of phenotyping ELISA western blots electrophoresis gel column purification techniques for the characterization and purification of antibodies. Computer skills were used to tabulate the data of ongoing clinical trials and to write SOP's for the transfer of cells from research into production. Dana Farber Cancer Institute Boston Massachusetts. September 1989 to May 1990.\rASSOCIATE SCIENTIST\rWistar Institute of Anatomy and Biology Philadelphia Pennsylvania - Billerica MA - August 1988 to September 1989\rPosition demanded proficiency in the iodination and subsequent purification of proteins and steroids by HPLC gel exclusion chromatography ion exchange chromatography and TLC. Other duties included large scale production of buffers and standard solutions and antibody solutions. Cambridge Medical Technology Billerica Massachusetts. August 1988 to September 1989.\rRESEARCH TECHNICIAN I: Research focused on the development of a wildlife oral vaccine for both rabies and population control. Specific responsibilities included cell culture techniques virus isolation ELISA procedures collection and processing tissue samples from wild and captive animals determining attractants for wildlife through behavior testing. Computer skills were needed for tabulation correlation and analysis of data. Project culminated in the production of a Discover episode for Public Broadcasting Service centered on current research in the rabies field. Wistar Institute of Anatomy and Biology Philadelphia Pennsylvania. August 1986 to August 1988.\rEDUCATION\rMaster's of Science in Biochemistry\rUniversity of New Hampshire - Durham NH December 2001\rMaster in Biology\rUniversity of Massachusetts at Amherst - Amherst MA February 1994\rMaster of Science in Animal Science\rArkansas State University August 1985\rBachelor of Science in Animal Science\rUniversity of Massachusetts at Amherst - Amherst MA May 1985\rSKILLS\rMotivated and organized professional. Excellent interpersonal skills - diplomatic and tactful with professionals and non-professionals at all levels. Talent for quickly mastering new technology and adapting existing technology to expand opportunity. Flexible and versatile able to maintain poise and decorum under pressure. Excellent team-building skills.\rLINKS http://www.linkedin.com/home?trk=guest_home_login\rPUBLICATIONS\rGuinea Pig GnRH: Localization and Physiological Activity Reveal That It Not Mammalian GnRH Is the Major Neuroendocrine Form in Guinea Pigs http://www.ncbi.nlm.nih.gov/pubmed/11956141\r Guinea Pig GnRH: Localization and Physiological Activity Reveal That It Not Mammalian GnRH Is the Major Neuroendocrine Form in Guinea Pigs. D. Grove-Strawser S.A. Sower P.M. Ronsheim J. B. Connolly C.G. Bourn B.S. Rubin. Endocrinology 2002 May 143(5):1602-12.\rT Cell Receptor Alpha Chain Plays A Critical Role In Antigen Specific Suppresser Cell Function\rhttp://www.pnas.org/content/88/19/8700\rOctober 1 1991\r T Cell Receptor Alpha Chain Plays A Critical Role In Antigen Specific Suppresser Cell Function. Vijay K. Kuchroo M.C. Byrne Y. Ausaku E. Greenfield J.B. Connolly M.J. Whitters R.M. O'HaraJr. M. Collins and Martin E. Dorf. Proceedings of National Academy of Science 1991 October 1 88(19):8700-8704.\r \"\r\nresume_121,not_flagged,\"\rBrattleboro VT - Email me on Indeed: indeed.com/r//929e08bcc072c324\rSkilled presenter trainer and microscopist with a strong scientific background and experience supporting sales and marketing teams. Personable professional and comfortable in university and laboratory settings and able to form rapport with clients and colleagues. Willing to travel where needed and as often as needed.\rCore Qualifications\rSkilled presenter and public speaker Training and Troubleshooting\rExperience with multiple types of microscopy PCR qPCR and Western Blots SalesForce WebEx and Clearslide Transfection and Transformation\rApple and iMac OS application Analysis and Interpretation\rMicrosoft Word Excel and PowerPoint Managing multiple projects simultaneously Willing to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rSales Scientist\r- March 2014 to Present\rSapling Learning - Remote\r Provide support to the sales team by giving virtual and in-person demonstrations of Sapling Learning's online homework platform to college professors who teach biology courses.\r Conduct research on faculty courses and department prior to scheduled demonstrations\r Form rapport with instructors as a peer while being persuasive with the goal of driving adoptions of Sapling Learning.\r Monitoring opportunities and scheduling post-demonstration reconnections.\r Travel to college campuses across the United States to present the Sapling Learning in person to biology department key decisions makers.\r Conducted on-site visits to professors' offices for the purpose of driving interest in and scheduling of Sapling Learning demonstrations.\r Provided support that was key to the adoption of more thousands of new users at dozend of schools.\r Use SalesForce to document demonstrations and coordinate with members of the sales team to support university adoption of Sapling Learning.\r Provide training and troubleshooting to new adopters and existing users of Sapling Learning.\rManager and Vintner\rRaven Hollow Winery - Westfield MA - October 2013 to Present\rWestfield MA\r Built business from the ground-up implementing procedural protocols to ensure compliance with federal state and local regulations.\r Created wine making and sanitation protocols.\r Trained employees in multiple techniques including wine maintenance and bottling.\r Manage day-to-day operations including reporting record keeping marketing social media\revent scheduling and customer service.\r \r Created and produced 14 varieties of wine from grapes blueberries apples strawberries pineapples and other\rfruit.\r Won 4 medals at national and local wine competitions.\r Expanded sales to 12 local area retail venues and counting.\rProfessor of Biology\rBrandeis University - Waltham MA - August 2013 to December 2014\rInstructed 240 students each semester in a sophomore level undergraduate lecture course and managed all aspects of curriculum development and lab preparation.\r Managed and supervised a team of 22 graduate and undergraduate teaching assistants as well as a technical staff.\rLab Manager/Postdoc\rBrandeis University - Waltham MA - January 2013 to August 2014\r Supervised and coordinated multiple research projects simultaneously.\r Trained new lab members on proper microscope usage technique and maintenance.\r Developed protocols to ensure all aspect of the lab met safety and regulatory standards for a neuro/molecular biology lab.\r Coordinated laboratory equipment microscope maintenance and repair and managed lab inventory.\rAnalyst\rAdvantage Human Resourcing - New York NY - April 1998 to August 2003\r Worked primarily for American Express International Payments but successfully completed multiple assignments in various departments within American Express.\r Analyzed data and generated periodic reports relating to revenue and sales.\r Reviewed prospective customer applications performed background checks and ensured compliance with federal and local regulations.\r Managed customer database.\rEDUCATION\rPh.D. in Molecular Cell Biology\rBrandeis University - Waltham MA 2013\rMaster of Science in Molecular and Cell Biology\rBrandeis University - Waltham MA 2010\rBachelor of Science in Molecular Biology\rState University of New York at New Patlz - New Paltz NY 2006\rAssoc in Theater in Theater arts\rThe American Musical and Dramatic Academy - New York NY 1993 to 1995\rSKILLS\rMicrosoft Office (10+ years) Salesforce (1 year) Apple iOS applications (10+ years) Velocity (7 years) ImageJ (7 years) WebEx and Clearslide (1 year) PCR and qPCR (7 years) Transfection and Transformation of cells (7 years) working with model organisms for scientific research (7 years) Sales support (4 years) Presenting science and science products and protocols (10+ years)\r\"\r\nresume_122,not_flagged,\"\rResearch and Teaching Assistant - University of Vermont\rBurlington VT - Email me on Indeed: indeed.com/r//e409cdd16e30d052\rGraduate student with a multidisciplinary background including biology and environmental chemistry. A driven and goal oriented worker who considers the entire scope of a project while still maintaining a high attention to detail. Highly dedicated individual with a desire to apply science and technology to solve the most pressing environmental issues of our time.\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rResearch and Teaching Assistant\rUniversity of Vermont - 2015 to Present\rResearch: Soil biomass and water quality analysis of bioretention cell performance in mixed use agricultural landscape;\r-Calibration and use of field sampling instruments (ISCO-autosamplers) and lab equipment such as sensor probes and spectrophotometers;\r-Outreach to community students policymakers and scientists;\r-Creation of posters grant proposals technical reports and sampling/management plans.\rTeaching (Landscape Design Fundamentals Ecological Landscape Design): Grading lectures desk critiques GIS base map creation course logistics and QC.\rEcological Restoration Technician\rPizzo and Associates Ltd - 2014 to 2014\rManage natural areas and open space in Chicago Parks District; seeding and installation of plant communities; wetland native species surveys; invasive species removal; trail and sign maintenance.\rNatural Areas Intern\rCommunity Groundworks - 2013 to 2013\rOversight and facilitation of prairie and wetland restorations; facilitate community participation and involvement; led tours hikes and work days; development of management plans.\rResearch Fellow\rUniversity of Wisconsin-Madison - Madison WI - 2012 to 2013\rNative plant survey; development of sampling protocols; creation of plant identification guides; management and upkeep of natural areas (controlled burns invasive species removal strategic herbicide application).\rEDUCATION\rM.S. in Plant and Soil Science\rUniversity of Vermont 2017\rM.S. in Landscape Architecture\rUniversity of Wisconsin - Madison WI\r \r2014\rB.S. in Biology\rState University of New York at Albany - Albany NY 2012\rSKILLS\rMicrosoft Office (5 years) Esri ArcGIS (5 years) MatLab (2 years)\rLINKS https://www.linkedin.com/in/jason-kokkinos-36ba39102\rADDITIONAL INFORMATION\rExperience and Skills\r Environmental field sampling including vapor vegetation soil surface water and groundwater\r Quantitative environmental analysis and instrumentation (Gas Chromatography Flow Injection Analysis) of water quality (stormwater groundwater)\r Work and research experience in industrial settings brownfields and urban areas\r Green infrastructure and groundwater well installation upkeep and maintenance\r Esri ArcGIS Trimble GPS and cartography skills\r Proposal grant and technical report writing\r Data management (Excel Matlab) and statistical analysis (JMP SAS)\r Field site analysis including topography and wetland delineation\r Knowledge of key environmental legislation and stormwater permitting\r Sampling protocol and management plan writing\r Oversight of interns research assistants and volunteers\r Carpentry and construction skills\r \"\r\nresume_123,not_flagged,\"\rMedical Coder - Highly Skilled - Entry Level\rSudbury VT - Email me on Indeed: indeed.com/r//0364f57cab60487c\rJay G. Cooke\r105 Wanee Rd. Sudbury VT 05733 Recycle360@gmail.com (802) [...]\rMarch 2015\rMedical Coding Department\rRe: Application for Coder Position\rDear Madame or Sir\rI am interested in pursuing a career in Medical Coding and would like to be a part of the Coding Department at your organization. I am very excited at the opportunity of joining your excellent team.\rAs a recent graduate of AHIMAs Coding Basics and A&P program I am well trained for this position. I achieved the CCA credential in November 2014 scoring a 363/400 on the ICD-9 examination. However I was also trained in ICD-10 CPT and HCPCS. Therefore I anticipate a smooth and immediate transition on October 1 2015.\rWhile studying for the Coding basics program I worked an internship at Sound Shore Medical Center in New York. During this time I gained familiarity with the HIM department and an understanding of the incredible level of detail and accuracy needed to be a successful coder.\rI am willing to relocate to the area as soon as possible or I am able to work remotely.\rThank you for considering my application. My resume is attached and please let me know if I can provide you with any additional information. I am available by telephone or email and I look forward to hearing from you.\rSincerely\rJay G. Cooke\rAttachment\rWORK EXPERIENCE\rVolunteer\rThe Carving Studio and Sculpture Center - West Rutland VT - September 2013 to Present\rLandscaper in residence responsible for upkeep and beautification of sculpture garden and grounds. _ Docent at Studio Gallery and general organizational support.\r \rSpecialized Catering\rAll American Staffing - New York NY - June 2012 to June 2013\rUniversities and Private Clubs call in specialists when skilled staff is needed for prestigious events.\r_ Dealing with many variables every day being confronted with surprises and adapting accordingly. Medical Records Internship\rSound Shore Medical Center - New Rochelle NY - January 2012 to June 2012\rLearning to assign codes with the 3M encoder and gaining an understanding of the HIM department.\r_ Exposure to EMR's mastering chart filing systems and the ICD-9 CM/PCS and CPT classification systems. I.T. System Replacement Project - Asst. Hardware Tech\rSound Shore Medical Center - New Rochelle NY - June 2011 to January 2012\rJune 2011 -January 2012\r_ Assisting the I.T. Techs in the rollout of the new software and hardware systems Hospital wide.\r_ Assembling Testing Transporting and Assisting in the setup process for all Hospital departments.\rLogistics/Research\rMicroEcologies Inc - New York NY - March 2009 to February 2011\rTransporting supplies equipment personnel and waste materials to / from sites in NYC.\r_ Generating and distributing reports affecting this Indoor Air Quality (IAQ) Company. Staff Scientist\rDelta Environmental Consultants - Armonk NY - October 2007 to March 2008\rWriting reports for remedial groundwater and soil monitoring to state regulatory agencies in NYC and NJ. _ Interpreting chemical and analytical results for groundwater and soil petroleum pollutants.\r_ QA / QC of Excel data logs containing Environmental data for over 100 sites.\r_ Monitoring of gas station contamination in NYC for Hess Corp as it effected Aquifer Water Quality.\rAssistant Project Manager\rInternational Valuation and Inspection (IVI) - White Plains NY - March 2007 to June 2007 Providing technical support to senior project managers and preparing materials for technical reports.\r_ Collecting environmental lead in water samples from locations across New Mexico. Wildlife Management Technician\rLMS Environmental Engineers Inc - Valhalla NY - June 2002 to April 2003\rIn this seasonal position I assisted in the protection of the water quality of New York City's drinking water through:\r_ Operating Boats to patrol NYC's Watershed's Terminal Reservoir at Kensico Valhalla New York.\r_ Gathering and logging waterfowl population data along with ongoing log maintenance.\r_ Management responsibilities to ensure: quality sampling reservoir watershed protection and crew safety. Circulation Department Assistant Manager\rSt. John's University Library - New York NY - March 2001 to May 2002\rPromoted to this supervisory position from the Assistant Manager of the periodicals department and acted as a liaison between the library management and student workers:\r_ Managing schedules of student workers.\r105 Wanee Rd. (802) 345-0987\rSudbury VT 05733 Recycle360@gmail.com\r_ Evaluating and reviewing the quality and accuracy of student worker performance.\r_ Communicating with and training new student workers in the library policies and procedures. _ Providing personalized customer service to all library patrons.\rManaging Sample Custodian\rEnvirodyne Inc - Boca Raton FL - June 1998 to July 1999\rIn this high volume position I managed a staff consisting of three employees and completed the following tasks:\r_ Logging in chains of custody after receiving soil water and air samples and processing and delivering samples to the appropriate labs.\r_ Ordering preserving organizing and cleaning of all sampling materials.\r_ Insuring critical deadlines were met and scheduling tasks accordingly.\r_ Initiating / completing projects such as: generating a sub-contractor sample and procedure list preparing a detailed and comprehensive internal policy and procedure manual and reorganizing the stock room.\rEDUCATION\rB.A. in Environmental Science\rState University of New York at Purchase College May 2006\rItalian Language Study\rJohn Cabot University - Roma Lazio 1999 to 2000\r- Purchase NY\r\"\r\nresume_124,flagged,\"\rWaterbury VT - Email me on Indeed: indeed.com/r//b90bec097e6169fc\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rScience Teacher\rHarwood Union High School - Duxbury VT - 2001 to April 2016 Teach four courses per year.\r2001 to Present - Science Teacher - Harwood Union High School Duxbury Vermont\rTeaching Biology Environmental Science and Earth/Space Science classes at Honors Regular and Topics levels. 2002-16 - Special project was to raise and observe Atlantic Salmon in the classroom as part of the U.S. Fish and Wildlife Service/U.S. Forest Service Adopt-A-Salmon/Salmon-In-The Schools program. Atlantic Salmon were released in an appropriate stream segment so they have a chance of becoming part of a naturally reproducing population. 2012-16 participation with Vermont EPSCoR Research on Adaptation to Climate Change (RACC) program. Role was to involve students in data collection using data loggers (which continuously monitored stream flow and temperature of local streams) and more traditional water quality measurements and have students explore their own research inquiry. 2016 RACC project won Honorable Mention in Governor of Vermonts awards for Environmental Excellence. 2012-13 assist in our schools participation in the Vermont Energy Education Project Whole School Energy Challenge. 2011-13 participated with University of Vermont (UVM) Community-University Partnerships and Service Learning Program by having UVM students teach selected laboratory periods in my classroom for their service learning projects. 2013 Winner of American Chestnut Foundation Learning Box in response to contest to develop a lesson plan for the Learning Box. 2012-13 Initiated and facilitated an agreement between the American Chestnut Foundation and Harwood to plant five highly blight resistant America Chestnut trees in the Harwood forest and become research site for the American Chestnut Foundation. Environmental Science class carried out the planting of the American Chestnut trees. 2008-2009 Participant in National Science Foundation/ University of Michigan/Northwestern University study on comparing professional development techniques for classroom teachers. Summer 2006 Selected for and completed course Biology in Genomic Age Howard Hughes Medical Institute Summer Teachers Workshop at Amherst College. 2002-3 A special project completed in affiliation with the Vermont Institute of Natural Science Community Mapping Program got students to learn about and utilize Global Positioning Systems (GPS) and Geographic Information System (GIS)technology. Computer grade book used during entire period of employment. Test generating software utilized from 2002-16. Test grading software utilized 2009-16. Involved in selecting purchasing maintaining and operating laboratory equipment. Served on faculty committee which recommended and facilitated adoption of Powerschool (online grade book and data management). Served as a peer teacher trainer for adoption of Powerschool. Informal involvement with our schools conversion from oil to wood chip heating. Modeling Dynamic Systems using STELLA course developed approved by school board for one year but not taught. Small grant proposals written and received for the following: Aquarium chiller aquarium to raise Atlantic Salmon in the classroom data logger and phase-contrast microscope. Taught East Coast Swing Dance 2015-16. 2010-2016 Assistant Golf Coach boys and girls teams.\rOperations and Management Section\rVermont Wastewater Management Division - 1997 to 2001\r \rOversight of the operations and management of municipal industrial and private wastewater treatment and pretreatment\rfacilities collections systems and pump stations. Included carrying out inspections\rmonitoring data submittals review of twenty year facility engineering evaluations\rassessments of proposed Operations and Maintenance Manuals engineering design review of proposed facility upgrades technical assistance for wastewater operators follow-up to\rresolve plant operations/design/water quality problems commenting on compliance with environmental health and safety regulations and investigating complaints. Received further\rtraining in wet weather operations; environmental health and safety issues at wastewater\rfacilities; and identification and use of microorganisms as indicators of the status of biological\rtreatment processes. Confined space training. On my own time participated in meetings\rrelated to a federal grant to the Vermont Department of Public Service and the Vermont\rDepartment of Agriculture to promote anaerobic digestion technology on Vermont dairy farms for manure treatment energy recovery and water quality improvement.\rResiduals Management Section\rVermont Wastewater Management Division - 1993 to 1997\rResponsibilities included administrative and technical review of sludge management projects (land application heat drying composting\rlime stabilization biological digestion etc.) writing certifications coordinating public\rinvolvement process. Also involved in design review of new projects/renovations for compliance with pathogen reduction and vector attraction reduction requirements; compliance\rmonitoring; responding to and resolving citizen complaints; enforcement of permit conditions; and responding to technical and administrative questions of the permittees and citizens.\rPrepared Notes on Pathogens and Land Application of Sewage Sludge and Domestic Septage. Participant in Sludge/Septage Advisory Group and Pathogens Subcommittee of the\rSludge /Septage Advisory Group. Quality Assurance Coordinator and Field Sampling Coordinator for dioxin in sewage sludge sampling project. Wrote Data Validation Report to federal Environmental Protection Agency for dioxin in sewage sludge sampling project.\rAssistant Professor\rNorwich University - Northfield VT - 1992 to 1993 Teaching and research in environmental engineering.\r1992-1993 - Assistant Professor - Norwich University - Northfield Vermont.\rTeaching undergraduate courses in the Department of Environmental Engineering Technology. Courses taught include Fluid Mechanics (including laboratory) Water Chemistry/Physical- Chemical Treatment Senior Project (Proposal To Establish A Recycling Program For the Dormitories At Norwich University) Senior Seminar Water and Wastewater Treatment Applied Hydrogeology Water Analysis Laboratory Introductory Biology Laboratory.\rParticipant in National Science Foundation three week seminar on developing teaching materials for learning about Constructed Wetlands For Water Quality Improvement.\rNote: position ended due to a faculty reduction-in-force. One-quarter of the faculty full-time equivalent positions were eliminated at the University. The Department of Environmental Engineering Technology and my position were eliminated.\rAssistant Professor\rUniversity of Nebraska-Lincoln - Omaha NE - 1990 to 1992\r- Omaha Nebraska. Teaching and research in environmental engineering.\r1990-1992 - Assistant Professor - University of Nebraska-Lincoln (Omaha Campus) - Omaha Nebraska.\rTeaching undergraduate and graduate courses in the Department of Civil Engineering and developing externally funded research projects. Courses taught included Biological\rWastewater Treatment (including laboratory) Fluid Mechanics Graduate Seminar Hydraulics\rLaboratory Principles of Environmental Engineering Advanced Biological Processes\rEngineering and Applications of Chemistry to Environmental Engineering (including laboratory).\rWrote proposal received $40000 grant was principal investigator and supervised graduate\rstudent for a two year research project on the feasibility of utilizing constructed wetlands for removal of nitrate from groundwater. Major advisor for two full-time M.S. students. Wrote\rinternal proposals and received approximately $50000 for purchase of teaching and research\rlaboratory equipment. Responsible for purchase of laboratory equipment laboratory\requipment set-up and laboratory equipment maintenance. Equipment purchased included gas chromatograph muffle furnace phase-contrast microscope walk-in environmental chamber\rdissolved oxygen meter and probe refrigerator and an autoclave.\rJeff Robins Ph.D P.E. Resume\rTheses Supervised\r Improving Oxygen Demand Removal At A Secondary Wastewater (Trickling Filter) Treatment Plant. M.S. Okan Nalbant August 1992\r The Feasibility of Utilizing Constructed Wetlands For Removal Of Nitrate From Groundwater. M.S. Jennifer Rock August 1993.\rProject Manager\rHoyle Tanner and Associates - 1990 to 1990\rResponsible for project management and engineering analyses for an evaluation of six potential landfill sites and selection of a finalist site to serve 75000\rpeople in 34 towns for 40 years for the Central Vermont Solid Waste Management District.\rDeveloped proposal including scope of work for the entire project; involved in sub-consultant\rselection; determined work allocation and time deadlines for sub consultants (geologists\rgeotechnical engineers naturalists transportation engineers and landscape architect) and company staff; wrote all contracts; prepared and managed budget; conducted engineering\ranalyses related to landfill capacity permitability cost leachate concerns incremental air\rpollution costs from locating landfill distant from center of waste generation; developed\roverall scoring system and prepared figures. Wrote HTA progress report with exception of sub consultant reports in the appendices and presented report at a public meeting. Wrote\rHTA final report with exception of overall cost analysis and sub consultant reports in appendices.\rEnvironmental Engineer\rHoyle Tanner and Associates - 1988 to 1990\rReviewed existing process performance and participated in the development of the upgraded\rdesign for the Burlington Vermont 5.3 million gallons per day wastewater treatment plant.\rResponsible for sizing designing and writing specifications for upgraded aeration basins to include flexibility for biological phosphorous removal flexibility to receive underflow from\rvortex separator treated stormwater new diffused aeration system anhydrous hydrogen\rchloride gas cleaning system for cleaning air diffusers new blowers and new gates; and for alum alkalinity and sodium hypochlorite chemical feed systems. Wrote proposals for new\rwork.\rEnvironmental Engineer/Solid Waste\rState of Vermont Agency of Natural Resources - Waterbury VT - 1987 to 1988\rWaterbury Vermont. Assessed technical matters related to all aspects of solid waste problems.\r1987-88 - Environm ental Engineer/Solid W aste\rState of Vermont Agency of Natural Resources Division of Solid Waste - Waterbury Vermont.\rEngineer in the Technical Assistance Section. Reviewed designs for improvements to landfills\rleachate treatment and recycling facilities. Monitored landfill operations and related water\rquality problems. Some involvement with waste reduction and recycling. Responsible for addressing public comments on new regulations. Reviewed landfill siting efforts of Central\rVermont Solid Waste Management District. Participated in landfill compaction studies and Jeff Robins Ph.D P.E. Resume\rmonitoring well sampling. Conducted enforcement actions related to landfill operations and illegal dumping.\rTeaching Assistant\rDepartment of Civil Engineering - Amherst MA - 1986 to 1987\rDepartment of Microbiology University of Massachusetts at Amherst - Amherst Massachusetts. Prepared laboratory materials laboratory lectures and quizzes; coordinated preparations with Microbiology Prep room; assisted and answered questions for students; revised laboratory manual; and graded papers for graduate level Microbial Diversity course.\rGraduate Research Assistant\rDepartment of Civil Engineering - Amherst MA - 1984 to 1987\rDeveloped experimental proposal. Designed supervised and participated in construction of experimental equipment. Operated a laboratory scale anaerobic wastewater\rtreatment reactor (chemostat) for 640 consecutive days. Performed biological and chemical\rlaboratory analyses. Operated phase fluorescence and scanning electron microscopes.\rPrinted photographs. Purchased equipment and supplies. Conducted formal statistical analysis. Lab safety coordinator.\rTeaching Associate\rDepartment of Civil Engineering - Amherst MA - 1983 to 1984\rPrepared laboratory lectures problem sets solution sets lab demonstrations paper assignment and lab grades for the Basic Environmental Engineering course.\rEngineer Intern\rState of Connecticut Department of Health Services - Hartford CT - 1981 to 1983\rWater Supplies\rSection - Hartford Connecticut. All aspects of regulating public water supplies including design review and resolving water quality problems.\r1981-83 - Engineer Intern\rState of Connecticut Department of Health Services Water Supplies Section - Hartford\rConnecticut. Reviewed designs for wells treatment storage and pumping facilities. Responded to and resolved water quality complaints for water supply systems. Participated in water\rquality and quantity planning activities. Monitored water quality data for public water supply\rsystems. Author of 1983 Report to the Connecticut Legislature of Organic Chemicals In\rDrinking Water. Participated in design standards review committee.\rApprentice Farmer\rNortheast Organic Farming Association - Cornish NH - 1979 to 1980\rSharon Vermont. Assisted a family in setting up their new farm in 1979. 1980 spent on a different farm participating in operations of a working farm.\r1979-80 - Apprentice Farmer\rNortheast Organic Farmers Association Apprenticeship Program - Cornish New Hampshire;\rSharon Vermont. Assisted a family in setting up their new farm in 1979. 1980 spent on a\rdifferent farm participating in operations of a working farm. Responsibilities included animal and market garden care maple sugaring green-house cultivation of seedlings chemical soil\rtesting construction carpentry cooking and helping in beekeeping pruning and land clearing\ractivities.\rEnvironmental Scientist\rAssociation of New Jersey Environmental Commissions - Mendham NJ - 1978 to 1979\rMendham New Jersey. Working with all levels of government on water quality toxic substances and land-use problems.\rJeff Robins Ph.D P.E. Resume\r1978-79 - Environm ental Scientist\rAssociation of New Jersey Environmental Commissions - Mendham New Jersey/Upper Raritan Watershed Association. Served as technical assistant to the Morris County Toxic Substances Task Force as part of the federal EPA Toxic Substances Public Participation Pilot Program for New Jersey. In a separate capacity helped local environmental commissions with subdivision review pollution monitoring educational activities and establishing water quality testing programs. Served in a watchdog capacity monitoring water polluters. Performed physical chemical and biological water quality tests and analyzed data.\rJeff Robins Ph.D P.E. Resume\rNature Director\rCamp High Sierra - Sonora CA - 1977 to 1977\rTaught nature merit badges\rsupervised two staff purchased supplies and developed program activities at Boy Scout summer camp of 100 to 200 scouts.\rSum m er 1977 - Nature Director\rBoy Scouts of America Camp High Sierra - Sonora California. Responsible for developing\rresources and day to day operation of the Nature Program for a camp of 100 to 250 scouts\rsupervising two assistants teaching nature related merit badges and planning and participating in other campwide events.\rTaxonomic Botanist and Plant Comunity Ecologist\rJeff Robins - Lee Vining CA - 1976 to 1977\rOne of twelve undergraduates on a\rNational Science Foundation grant who researched the ecological effects of water diversions by Los Angeles at and around Mono Lake. One of three authors of the botanical chapter:\rprincipal author of the plant list for the study area in An Ecological Study of Mono Lake\rCalifornia (approximately 200 plant specimens from the voucher collection were accepted into the permanent collection of the Dudley Herbarium at the California Academy of Sciences in\rResearch Assistant\rDr. Paul Ehrlich's - Stanford CA - March 1976 to May 1976\rCarried out an experiment on the non- use of an available larval food plant.\rEDUCATION\rPhD in Civil (Environmental) Engineering\rUniversity of Massachusetts at Amherst - Amherst MA 1983 to 1988\rMS in Civil (Environmental Engineering and Science) Engineering\rStanford University - Stanford CA 1980 to 1981\rBA in Human Biology (concentrations in botany ecology and water studies)\rStanford University - Stanford CA 1974 to 1978\rSKILLS\rMicrosoft Office (10+ years)\rCERTIFICATIONS/LICENSES\rProfessional Engineer\rJuly 2018\rCertified 7-12 Math and Science teacher\r2021\rProfessional Engineer\rJuly 2018\r\"\r\nresume_125,not_flagged,\"\rResearch and Development Scientist - Burlington Laboratories\rWinooski VT - Email me on Indeed: indeed.com/r//3be6e7810559cda4\rWilling to relocate: Anywhere\rAuthorized to work in the US for any employer\rWORK EXPERIENCE\rResearch and Development Scientist\rBurlington Laboratories - Burlington VT - May 2015 to Present\rResearch illicit and prescription drugs such as amphetamines benzodiazepines methadone gabapentin opiates and related metabolites and develop urine testing assays intended for LC-MS/MS analysis.\r_ Develop and validate in-house drug assays to be more time efficient and cost effective.\r_ Prepare working solutions and reagents.\r_ Perform solid phase extraction on a daily basis.\r_ Analyze data from validation studies.\r_ Prepare and present data in tables graphs and reports.\r_ Train and oversee new employees.\r_ Troubleshoot chromatography issues and perform routine maintenance on LC-MS/MS systems. _ Locate client sample information on LabDAQ (laboratory information management system).\rGraduate Assistant\rThe Center for Forensic Science Research - Willow Grove PA - 2013 to May 2015\rPerformed literature research on designer drugs to create drug monographs for the Society of Forensic Toxicologists website. Drug monographs include: 5-MeO-DALT 5-MeO-DIPT AM-1248 B-22 cathinone and methcathinone.\r_ Researched synthetic cannabinoid toxicity by reviewing and collecting pertinent information from autopsy reports of individuals who have died with synthetic cannabinoids in their system.\r_ Gathered information on synthetic cannabinoid use by collecting articles and entering them into a database. Biologist\rPort Gamble S'klallam Tribe - Kingston WA - 2009 to 2012\rManaged three of the Tribes grant programs in their Natural Resources department.\r_ Managed funds hired environmental science consultants coordinated contamination investigations wrote and maintained grants.\r_ Assisted with finfish and shellfish research.\rINSTRUMENT EXPERIENCE:\r_ LC-MS/MS (Agilent Waters Shimadzu) _ GC-FID (Agilent)\r_ GC-MS (Agilent)\r_ LC-MS (Agilent)\r_ HPLC (Agilent)\r \rEDUCATION\rM.S. in Forensic Science\rArcadia University - Glenside PA May 2015\rB.S. in Fisheries and Wildlife Science\rOregon State University - Corvallis OR 2005\r\""
},
{
"path": "Natural_Language_processing/Sentiment-analysis/README.md",
"content": "# Sentiment Analysis Web App\n\nThe notebook and Python files provided here, once completed, result in a simple web app which interacts with a deployed recurrent neural network performing sentiment analysis on movie reviews. This project assumes some familiarity with SageMaker, the mini-project, Sentiment Analysis using XGBoost, should provide enough background.\n\nPlease see the [README](https://github.com/udacity/sagemaker-deployment/tree/master/README.md) in the root directory for instructions on setting up a SageMaker notebook and downloading the project files (as well as the other notebooks).\n"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/SageMaker Project.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Creating a Sentiment Analysis Web App\\n\",\n \"## Using PyTorch and SageMaker\\n\",\n \"\\n\",\n \"_Deep Learning Nanodegree Program | Deployment_\\n\",\n \"\\n\",\n \"---\\n\",\n \"\\n\",\n \"Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can use to enter a movie review. The web page will then send the review off to our deployed model which will predict the sentiment of the entered review.\\n\",\n \"\\n\",\n \"## Instructions\\n\",\n \"\\n\",\n \"Some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this notebook. You will not need to modify the included code beyond what is requested. Sections that begin with '**TODO**' in the header indicate that you need to complete or implement some portion within them. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `# TODO: ...` comment. Please be sure to read the instructions carefully!\\n\",\n \"\\n\",\n \"In addition to implementing code, there will be questions for you to answer which relate to the task and your implementation. Each section where you will answer a question is preceded by a '**Question:**' header. Carefully read each question and provide your answer below the '**Answer:**' header by editing the Markdown cell.\\n\",\n \"\\n\",\n \"> **Note**: Code and Markdown cells can be executed using the **Shift+Enter** keyboard shortcut. In addition, a cell can be edited by typically clicking it (double-click for Markdown cells) or by pressing **Enter** while it is highlighted.\\n\",\n \"\\n\",\n \"## General Outline\\n\",\n \"\\n\",\n \"Recall the general outline for SageMaker projects using a notebook instance.\\n\",\n \"\\n\",\n \"1. Download or otherwise retrieve the data.\\n\",\n \"2. Process / Prepare the data.\\n\",\n \"3. Upload the processed data to S3.\\n\",\n \"4. Train a chosen model.\\n\",\n \"5. Test the trained model (typically using a batch transform job).\\n\",\n \"6. Deploy the trained model.\\n\",\n \"7. Use the deployed model.\\n\",\n \"\\n\",\n \"For this project, you will be following the steps in the general outline with some modifications. \\n\",\n \"\\n\",\n \"First, you will not be testing the model in its own step. You will still be testing the model, however, you will do it by deploying your model and then using the deployed model by sending the test data to it. One of the reasons for doing this is so that you can make sure that your deployed model is working correctly before moving forward.\\n\",\n \"\\n\",\n \"In addition, you will deploy and use your trained model a second time. In the second iteration you will customize the way that your trained model is deployed by including some of your own code. In addition, your newly deployed model will be used in the sentiment analysis web app.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: sagemaker==1.72.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (1.72.0)\\n\",\n \"Requirement already satisfied: importlib-metadata>=1.4.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (3.7.0)\\n\",\n \"Requirement already satisfied: numpy>=1.9.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (1.19.5)\\n\",\n \"Requirement already satisfied: packaging>=20.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (20.9)\\n\",\n \"Requirement already satisfied: scipy>=0.19.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (1.5.3)\\n\",\n \"Requirement already satisfied: protobuf3-to-dict>=0.1.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (0.1.5)\\n\",\n \"Requirement already satisfied: protobuf>=3.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (3.15.2)\\n\",\n \"Requirement already satisfied: boto3>=1.14.12 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (1.17.75)\\n\",\n \"Requirement already satisfied: smdebug-rulesconfig==0.1.4 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker==1.72.0) (0.1.4)\\n\",\n \"Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from boto3>=1.14.12->sagemaker==1.72.0) (0.10.0)\\n\",\n \"Requirement already satisfied: s3transfer<0.5.0,>=0.4.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from boto3>=1.14.12->sagemaker==1.72.0) (0.4.2)\\n\",\n \"Requirement already satisfied: botocore<1.21.0,>=1.20.75 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from boto3>=1.14.12->sagemaker==1.72.0) (1.20.75)\\n\",\n \"Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from botocore<1.21.0,>=1.20.75->boto3>=1.14.12->sagemaker==1.72.0) (2.8.1)\\n\",\n \"Requirement already satisfied: urllib3<1.27,>=1.25.4 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from botocore<1.21.0,>=1.20.75->boto3>=1.14.12->sagemaker==1.72.0) (1.26.4)\\n\",\n \"Requirement already satisfied: typing-extensions>=3.6.4 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from importlib-metadata>=1.4.0->sagemaker==1.72.0) (3.7.4.3)\\n\",\n \"Requirement already satisfied: zipp>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from importlib-metadata>=1.4.0->sagemaker==1.72.0) (3.4.0)\\n\",\n \"Requirement already satisfied: pyparsing>=2.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from packaging>=20.0->sagemaker==1.72.0) (2.4.7)\\n\",\n \"Requirement already satisfied: six>=1.9 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from protobuf>=3.1->sagemaker==1.72.0) (1.15.0)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Make sure that we use SageMaker 1.x\\n\",\n \"!pip install sagemaker==1.72.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Downloading the data\\n\",\n \"\\n\",\n \"As in the XGBoost in SageMaker notebook, we will be using the [IMDb dataset](http://ai.stanford.edu/~amaas/data/sentiment/)\\n\",\n \"\\n\",\n \"> Maas, Andrew L., et al. [Learning Word Vectors for Sentiment Analysis](http://ai.stanford.edu/~amaas/data/sentiment/). In _Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies_. Association for Computational Linguistics, 2011.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"mkdir: cannot create directory ‘../data’: File exists\\n\",\n \"--2021-05-26 10:00:22-- http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\n\",\n \"Resolving ai.stanford.edu (ai.stanford.edu)... 171.64.68.10\\n\",\n \"Connecting to ai.stanford.edu (ai.stanford.edu)|171.64.68.10|:80... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 84125825 (80M) [application/x-gzip]\\n\",\n \"Saving to: ‘../data/aclImdb_v1.tar.gz’\\n\",\n \"\\n\",\n \"../data/aclImdb_v1. 100%[===================>] 80.23M 24.5MB/s in 3.5s \\n\",\n \"\\n\",\n \"2021-05-26 10:00:26 (23.2 MB/s) - ‘../data/aclImdb_v1.tar.gz’ saved [84125825/84125825]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%mkdir ../data\\n\",\n \"!wget -O ../data/aclImdb_v1.tar.gz http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\n\",\n \"!tar -zxf ../data/aclImdb_v1.tar.gz -C ../data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Preparing and Processing the data\\n\",\n \"\\n\",\n \"Also, as in the XGBoost notebook, we will be doing some initial data processing. The first few steps are the same as in the XGBoost example. To begin with, we will read in each of the reviews and combine them into a single input structure. Then, we will split the dataset into a training set and a testing set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import glob\\n\",\n \"\\n\",\n \"def read_imdb_data(data_dir='../data/aclImdb'):\\n\",\n \" data = {}\\n\",\n \" labels = {}\\n\",\n \" \\n\",\n \" for data_type in ['train', 'test']:\\n\",\n \" data[data_type] = {}\\n\",\n \" labels[data_type] = {}\\n\",\n \" \\n\",\n \" for sentiment in ['pos', 'neg']:\\n\",\n \" data[data_type][sentiment] = []\\n\",\n \" labels[data_type][sentiment] = []\\n\",\n \" \\n\",\n \" path = os.path.join(data_dir, data_type, sentiment, '*.txt')\\n\",\n \" files = glob.glob(path)\\n\",\n \" \\n\",\n \" for f in files:\\n\",\n \" with open(f) as review:\\n\",\n \" data[data_type][sentiment].append(review.read())\\n\",\n \" # Here we represent a positive review by '1' and a negative review by '0'\\n\",\n \" labels[data_type][sentiment].append(1 if sentiment == 'pos' else 0)\\n\",\n \" \\n\",\n \" assert len(data[data_type][sentiment]) == len(labels[data_type][sentiment]), \\\\\\n\",\n \" \\\"{}/{} data size does not match labels size\\\".format(data_type, sentiment)\\n\",\n \" \\n\",\n \" return data, labels\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"IMDB reviews: train = 12500 pos / 12500 neg, test = 12500 pos / 12500 neg\\n\"\n ]\n }\n ],\n \"source\": [\n \"data, labels = read_imdb_data()\\n\",\n \"print(\\\"IMDB reviews: train = {} pos / {} neg, test = {} pos / {} neg\\\".format(\\n\",\n \" len(data['train']['pos']), len(data['train']['neg']),\\n\",\n \" len(data['test']['pos']), len(data['test']['neg'])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we've read the raw training and testing data from the downloaded dataset, we will combine the positive and negative reviews and shuffle the resulting records.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.utils import shuffle\\n\",\n \"\\n\",\n \"def prepare_imdb_data(data, labels):\\n\",\n \" \\\"\\\"\\\"Prepare training and test sets from IMDb movie reviews.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" #Combine positive and negative reviews and labels\\n\",\n \" data_train = data['train']['pos'] + data['train']['neg']\\n\",\n \" data_test = data['test']['pos'] + data['test']['neg']\\n\",\n \" labels_train = labels['train']['pos'] + labels['train']['neg']\\n\",\n \" labels_test = labels['test']['pos'] + labels['test']['neg']\\n\",\n \" \\n\",\n \" #Shuffle reviews and corresponding labels within training and test sets\\n\",\n \" data_train, labels_train = shuffle(data_train, labels_train)\\n\",\n \" data_test, labels_test = shuffle(data_test, labels_test)\\n\",\n \" \\n\",\n \" # Return a unified training data, test data, training labels, test labets\\n\",\n \" return data_train, data_test, labels_train, labels_test\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"IMDb reviews (combined): train = 25000, test = 25000\\n\"\n ]\n }\n ],\n \"source\": [\n \"train_X, test_X, train_y, test_y = prepare_imdb_data(data, labels)\\n\",\n \"print(\\\"IMDb reviews (combined): train = {}, test = {}\\\".format(len(train_X), len(test_X)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have our training and testing sets unified and prepared, we should do a quick check and see an example of the data our model will be trained on. This is generally a good idea as it allows you to see how each of the further processing steps affects the reviews and it also ensures that the data has been loaded correctly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Some amusing humor, some that falls flat, some decent acting, some that is quite atrocious. This movie is simply hit and miss, guaranteed to amuse 12 year old boys more than any other niche.
The child actors in the movie are just unfunny. When you are making a family comedy, that does tend to be a problem. Beverly D'Angelo rises above the material to give a funny, and dare I say it, human performance in the midst of this mediocrity.\\n\",\n \"0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(train_X[100])\\n\",\n \"print(train_y[100])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first step in processing the reviews is to make sure that any html tags that appear should be removed. In addition we wish to tokenize our input, that way words such as *entertained* and *entertaining* are considered the same with regard to sentiment analysis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import nltk\\n\",\n \"from nltk.corpus import stopwords\\n\",\n \"from nltk.stem.porter import *\\n\",\n \"\\n\",\n \"import re\\n\",\n \"from bs4 import BeautifulSoup\\n\",\n \"\\n\",\n \"def review_to_words(review):\\n\",\n \" nltk.download(\\\"stopwords\\\", quiet=True)\\n\",\n \" stemmer = PorterStemmer()\\n\",\n \" \\n\",\n \" text = BeautifulSoup(review, \\\"html.parser\\\").get_text() # Remove HTML tags\\n\",\n \" text = re.sub(r\\\"[^a-zA-Z0-9]\\\", \\\" \\\", text.lower()) # Convert to lower case\\n\",\n \" words = text.split() # Split string into words\\n\",\n \" words = [w for w in words if w not in stopwords.words(\\\"english\\\")] # Remove stopwords\\n\",\n \" words = [PorterStemmer().stem(w) for w in words] # stem\\n\",\n \" \\n\",\n \" return words\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `review_to_words` method defined above uses `BeautifulSoup` to remove any html tags that appear and uses the `nltk` package to tokenize the reviews. As a check to ensure we know how everything is working, try applying `review_to_words` to one of the reviews in the training set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\\"Some amusing humor, some that falls flat, some decent acting, some that is quite atrocious. This movie is simply hit and miss, guaranteed to amuse 12 year old boys more than any other niche.
The child actors in the movie are just unfunny. When you are making a family comedy, that does tend to be a problem. Beverly D'Angelo rises above the material to give a funny, and dare I say it, human performance in the midst of this mediocrity.\\\"\"\n ]\n },\n \"execution_count\": 79,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_X[100]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['amus',\\n\",\n \" 'humor',\\n\",\n \" 'fall',\\n\",\n \" 'flat',\\n\",\n \" 'decent',\\n\",\n \" 'act',\\n\",\n \" 'quit',\\n\",\n \" 'atroci',\\n\",\n \" 'movi',\\n\",\n \" 'simpli',\\n\",\n \" 'hit',\\n\",\n \" 'miss',\\n\",\n \" 'guarante',\\n\",\n \" 'amus',\\n\",\n \" '12',\\n\",\n \" 'year',\\n\",\n \" 'old',\\n\",\n \" 'boy',\\n\",\n \" 'nich',\\n\",\n \" 'child',\\n\",\n \" 'actor',\\n\",\n \" 'movi',\\n\",\n \" 'unfunni',\\n\",\n \" 'make',\\n\",\n \" 'famili',\\n\",\n \" 'comedi',\\n\",\n \" 'tend',\\n\",\n \" 'problem',\\n\",\n \" 'beverli',\\n\",\n \" 'angelo',\\n\",\n \" 'rise',\\n\",\n \" 'materi',\\n\",\n \" 'give',\\n\",\n \" 'funni',\\n\",\n \" 'dare',\\n\",\n \" 'say',\\n\",\n \" 'human',\\n\",\n \" 'perform',\\n\",\n \" 'midst',\\n\",\n \" 'mediocr']\"\n ]\n },\n \"execution_count\": 80,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# TODO: Apply review_to_words to a review (train_X[100] or any other review)\\n\",\n \"review_to_words(train_X[100])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Question:** Above we mentioned that `review_to_words` method removes html formatting and allows us to tokenize the words found in a review, for example, converting *entertained* and *entertaining* into *entertain* so that they are treated as though they are the same word. What else, if anything, does this method do to the input?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Answer:**\\n\",\n \"It remove the captalization from all the word. Remove the puncatiation, remove the prepositions and the articles.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The method below applies the `review_to_words` method to each of the reviews in the training and testing datasets. In addition it caches the results. This is because performing this processing step can take a long time. This way if you are unable to complete the notebook in the current session, you can come back without needing to process the data a second time.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pickle\\n\",\n \"\\n\",\n \"cache_dir = os.path.join(\\\"../cache\\\", \\\"sentiment_analysis\\\") # where to store cache files\\n\",\n \"os.makedirs(cache_dir, exist_ok=True) # ensure cache directory exists\\n\",\n \"\\n\",\n \"def preprocess_data(data_train, data_test, labels_train, labels_test,\\n\",\n \" cache_dir=cache_dir, cache_file=\\\"preprocessed_data.pkl\\\"):\\n\",\n \" \\\"\\\"\\\"Convert each review to words; read from cache if available.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # If cache_file is not None, try to read from it first\\n\",\n \" cache_data = None\\n\",\n \" if cache_file is not None:\\n\",\n \" try:\\n\",\n \" with open(os.path.join(cache_dir, cache_file), \\\"rb\\\") as f:\\n\",\n \" cache_data = pickle.load(f)\\n\",\n \" print(\\\"Read preprocessed data from cache file:\\\", cache_file)\\n\",\n \" except:\\n\",\n \" pass # unable to read from cache, but that's okay\\n\",\n \" \\n\",\n \" # If cache is missing, then do the heavy lifting\\n\",\n \" if cache_data is None:\\n\",\n \" # Preprocess training and test data to obtain words for each review\\n\",\n \" #words_train = list(map(review_to_words, data_train))\\n\",\n \" #words_test = list(map(review_to_words, data_test))\\n\",\n \" words_train = [review_to_words(review) for review in data_train]\\n\",\n \" words_test = [review_to_words(review) for review in data_test]\\n\",\n \" \\n\",\n \" # Write to cache file for future runs\\n\",\n \" if cache_file is not None:\\n\",\n \" cache_data = dict(words_train=words_train, words_test=words_test,\\n\",\n \" labels_train=labels_train, labels_test=labels_test)\\n\",\n \" with open(os.path.join(cache_dir, cache_file), \\\"wb\\\") as f:\\n\",\n \" pickle.dump(cache_data, f)\\n\",\n \" print(\\\"Wrote preprocessed data to cache file:\\\", cache_file)\\n\",\n \" else:\\n\",\n \" # Unpack data loaded from cache file\\n\",\n \" words_train, words_test, labels_train, labels_test = (cache_data['words_train'],\\n\",\n \" cache_data['words_test'], cache_data['labels_train'], cache_data['labels_test'])\\n\",\n \" \\n\",\n \" return words_train, words_test, labels_train, labels_test\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Read preprocessed data from cache file: preprocessed_data.pkl\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Preprocess data\\n\",\n \"train_X, test_X, train_y, test_y = preprocess_data(train_X, test_X, train_y, test_y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Transform the data\\n\",\n \"\\n\",\n \"In the XGBoost notebook we transformed the data from its word representation to a bag-of-words feature representation. For the model we are going to construct in this notebook we will construct a feature representation which is very similar. To start, we will represent each word as an integer. Of course, some of the words that appear in the reviews occur very infrequently and so likely don't contain much information for the purposes of sentiment analysis. The way we will deal with this problem is that we will fix the size of our working vocabulary and we will only include the words that appear most frequently. We will then combine all of the infrequent words into a single category and, in our case, we will label it as `1`.\\n\",\n \"\\n\",\n \"Since we will be using a recurrent neural network, it will be convenient if the length of each review is the same. To do this, we will fix a size for our reviews and then pad short reviews with the category 'no word' (which we will label `0`) and truncate long reviews.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### (TODO) Create a word dictionary\\n\",\n \"\\n\",\n \"To begin with, we need to construct a way to map words that appear in the reviews to integers. Here we fix the size of our vocabulary (including the 'no word' and 'infrequent' categories) to be `5000` but you may wish to change this to see how it affects the model.\\n\",\n \"\\n\",\n \"> **TODO:** Complete the implementation for the `build_dict()` method below. Note that even though the vocab_size is set to `5000`, we only want to construct a mapping for the most frequently appearing `4998` words. This is because we want to reserve the special labels `0` for 'no word' and `1` for 'infrequent word'.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import collections\\n\",\n \"\\n\",\n \"def build_dict(data, vocab_size = 5000):\\n\",\n \" \\\"\\\"\\\"Construct and return a dictionary mapping each of the most frequently appearing words to a unique integer.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # TODO: Determine how often each word appears in `data`. Note that `data` is a list of sentences and that a\\n\",\n \" # sentence is a list of words.\\n\",\n \" \\n\",\n \" word_count = {} # A dict storing the words that appear in the reviews along with how often they occur\\n\",\n \" word_count = collections.Counter(sum(train_X,[]))\\n\",\n \" \\n\",\n \" # TODO: Sort the words found in `data` so that sorted_words[0] is the most frequently appearing word and\\n\",\n \" # sorted_words[-1] is the least frequently appearing word.\\n\",\n \" \\n\",\n \" sorted_words = dict( sorted(word_count.items(),\\n\",\n \" key=lambda item: item[1],\\n\",\n \" reverse=True))\\n\",\n \" sorted_words = list(sorted_words.keys()) \\n\",\n \" word_dict = {} # This is what we are building, a dictionary that translates words into integers\\n\",\n \" for idx, word in enumerate(sorted_words[:vocab_size - 2]): # The -2 is so that we save room for the 'no word'\\n\",\n \" word_dict[word] = idx + 2 # 'infrequent' labels\\n\",\n \" \\n\",\n \" return word_dict\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Question:** What are the five most frequently appearing (tokenized) words in the training set? Does it makes sense that these words appear frequently in the training set?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Answer:** movi, film, one, like,time \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# TODO: Use this space to determine the five most frequently appearing words in the training set.\\n\",\n \"word_dict= build_dict(train_X)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Save `word_dict`\\n\",\n \"\\n\",\n \"Later on when we construct an endpoint which processes a submitted review we will need to make use of the `word_dict` which we have created. As such, we will save it to a file now for future use.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data_dir = '../data/pytorch' # The folder we will use for storing data\\n\",\n \"if not os.path.exists(data_dir): # Make sure that the folder exists\\n\",\n \" os.makedirs(data_dir)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open(os.path.join(data_dir, 'word_dict.pkl'), \\\"wb\\\") as f:\\n\",\n \" pickle.dump(word_dict, f)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Transform the reviews\\n\",\n \"\\n\",\n \"Now that we have our word dictionary which allows us to transform the words appearing in the reviews into integers, it is time to make use of it and convert our reviews to their integer sequence representation, making sure to pad or truncate to a fixed length, which in our case is `500`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def convert_and_pad(word_dict, sentence, pad=500):\\n\",\n \" NOWORD = 0 # We will use 0 to represent the 'no word' category\\n\",\n \" INFREQ = 1 # and we use 1 to represent the infrequent words, i.e., words not appearing in word_dict\\n\",\n \" \\n\",\n \" working_sentence = [NOWORD] * pad\\n\",\n \" \\n\",\n \" for word_index, word in enumerate(sentence[:pad]):\\n\",\n \" if word in word_dict:\\n\",\n \" working_sentence[word_index] = word_dict[word]\\n\",\n \" else:\\n\",\n \" working_sentence[word_index] = INFREQ\\n\",\n \" \\n\",\n \" return working_sentence, min(len(sentence), pad)\\n\",\n \"\\n\",\n \"def convert_and_pad_data(word_dict, data, pad=500):\\n\",\n \" result = []\\n\",\n \" lengths = []\\n\",\n \" \\n\",\n \" for sentence in data:\\n\",\n \" converted, leng = convert_and_pad(word_dict, sentence, pad)\\n\",\n \" result.append(converted)\\n\",\n \" lengths.append(leng)\\n\",\n \" \\n\",\n \" return np.array(result), np.array(lengths)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_X, train_X_len = convert_and_pad_data(word_dict, train_X)\\n\",\n \"test_X, test_X_len = convert_and_pad_data(word_dict, test_X)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As a quick check to make sure that things are working as intended, check to see what one of the reviews in the training set looks like after having been processeed. Does this look reasonable? What is the length of a review in the training set?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Use this cell to examine one of the processed reviews to make sure everything is working as intended.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Question:** In the cells above we use the `preprocess_data` and `convert_and_pad_data` methods to process both the training and testing set. Why or why not might this be a problem?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Answer:** it is important to apply it also to the test data as the model will be already trained on this, so the test data has to have similar features as the train data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Upload the data to S3\\n\",\n \"\\n\",\n \"As in the XGBoost notebook, we will need to upload the training dataset to S3 in order for our training code to access it. For now we will save it locally and we will upload to S3 later on.\\n\",\n \"\\n\",\n \"### Save the processed training dataset locally\\n\",\n \"\\n\",\n \"It is important to note the format of the data that we are saving as we will need to know it when we write the training code. In our case, each row of the dataset has the form `label`, `length`, `review[500]` where `review[500]` is a sequence of `500` integers representing the words in the review.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \" \\n\",\n \"pd.concat([pd.DataFrame(train_y), pd.DataFrame(train_X_len), pd.DataFrame(train_X)], axis=1) \\\\\\n\",\n \" .to_csv(os.path.join(data_dir, 'train.csv'), header=False, index=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Uploading the training data\\n\",\n \"\\n\",\n \"\\n\",\n \"Next, we need to upload the training data to the SageMaker default S3 bucket so that we can provide access to it while training our model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sagemaker\\n\",\n \"\\n\",\n \"sagemaker_session = sagemaker.Session()\\n\",\n \"\\n\",\n \"bucket = sagemaker_session.default_bucket()\\n\",\n \"prefix = 'sagemaker/sentiment_rnn'\\n\",\n \"\\n\",\n \"role = sagemaker.get_execution_role()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_data = sagemaker_session.upload_data(path=data_dir, bucket=bucket, key_prefix=prefix)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**NOTE:** The cell above uploads the entire contents of our data directory. This includes the `word_dict.pkl` file. This is fortunate as we will need this later on when we create an endpoint that accepts an arbitrary review. For now, we will just take note of the fact that it resides in the data directory (and so also in the S3 training bucket) and that we will need to make sure it gets saved in the model directory.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Build and Train the PyTorch Model\\n\",\n \"\\n\",\n \"In the XGBoost notebook we discussed what a model is in the SageMaker framework. In particular, a model comprises three objects\\n\",\n \"\\n\",\n \" - Model Artifacts,\\n\",\n \" - Training Code, and\\n\",\n \" - Inference Code,\\n\",\n \" \\n\",\n \"each of which interact with one another. In the XGBoost example we used training and inference code that was provided by Amazon. Here we will still be using containers provided by Amazon with the added benefit of being able to include our own custom code.\\n\",\n \"\\n\",\n \"We will start by implementing our own neural network in PyTorch along with a training script. For the purposes of this project we have provided the necessary model object in the `model.py` file, inside of the `train` folder. You can see the provided implementation by running the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mtorch\\u001b[39;49;00m\\u001b[04m\\u001b[36m.\\u001b[39;49;00m\\u001b[04m\\u001b[36mnn\\u001b[39;49;00m \\u001b[34mas\\u001b[39;49;00m \\u001b[04m\\u001b[36mnn\\u001b[39;49;00m\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[34mclass\\u001b[39;49;00m \\u001b[04m\\u001b[32mLSTMClassifier\\u001b[39;49;00m(nn.Module):\\r\\n\",\n \" \\u001b[33m\\\"\\\"\\\"\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[33m This is the simple RNN model we will be using to perform Sentiment Analysis.\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[33m \\\"\\\"\\\"\\u001b[39;49;00m\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[34mdef\\u001b[39;49;00m \\u001b[32m__init__\\u001b[39;49;00m(\\u001b[36mself\\u001b[39;49;00m, embedding_dim, hidden_dim, vocab_size):\\r\\n\",\n \" \\u001b[33m\\\"\\\"\\\"\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[33m Initialize the model by settingg up the various layers.\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[33m \\\"\\\"\\\"\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[36msuper\\u001b[39;49;00m(LSTMClassifier, \\u001b[36mself\\u001b[39;49;00m).\\u001b[32m__init__\\u001b[39;49;00m()\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[36mself\\u001b[39;49;00m.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=\\u001b[34m0\\u001b[39;49;00m)\\r\\n\",\n \" \\u001b[36mself\\u001b[39;49;00m.lstm = nn.LSTM(embedding_dim, hidden_dim)\\r\\n\",\n \" \\u001b[36mself\\u001b[39;49;00m.dense = nn.Linear(in_features=hidden_dim, out_features=\\u001b[34m1\\u001b[39;49;00m)\\r\\n\",\n \" \\u001b[36mself\\u001b[39;49;00m.sig = nn.Sigmoid()\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[36mself\\u001b[39;49;00m.word_dict = \\u001b[34mNone\\u001b[39;49;00m\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[34mdef\\u001b[39;49;00m \\u001b[32mforward\\u001b[39;49;00m(\\u001b[36mself\\u001b[39;49;00m, x):\\r\\n\",\n \" \\u001b[33m\\\"\\\"\\\"\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[33m Perform a forward pass of our model on some input.\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[33m \\\"\\\"\\\"\\u001b[39;49;00m\\r\\n\",\n \" x = x.t()\\r\\n\",\n \" lengths = x[\\u001b[34m0\\u001b[39;49;00m,:]\\r\\n\",\n \" reviews = x[\\u001b[34m1\\u001b[39;49;00m:,:]\\r\\n\",\n \" embeds = \\u001b[36mself\\u001b[39;49;00m.embedding(reviews)\\r\\n\",\n \" lstm_out, _ = \\u001b[36mself\\u001b[39;49;00m.lstm(embeds)\\r\\n\",\n \" out = \\u001b[36mself\\u001b[39;49;00m.dense(lstm_out)\\r\\n\",\n \" out = out[lengths - \\u001b[34m1\\u001b[39;49;00m, \\u001b[36mrange\\u001b[39;49;00m(\\u001b[36mlen\\u001b[39;49;00m(lengths))]\\r\\n\",\n \" \\u001b[34mreturn\\u001b[39;49;00m \\u001b[36mself\\u001b[39;49;00m.sig(out.squeeze())\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pygmentize train/model.py\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The important takeaway from the implementation provided is that there are three parameters that we may wish to tweak to improve the performance of our model. These are the embedding dimension, the hidden dimension and the size of the vocabulary. We will likely want to make these parameters configurable in the training script so that if we wish to modify them we do not need to modify the script itself. We will see how to do this later on. To start we will write some of the training code in the notebook so that we can more easily diagnose any issues that arise.\\n\",\n \"\\n\",\n \"First we will load a small portion of the training data set to use as a sample. It would be very time consuming to try and train the model completely in the notebook as we do not have access to a gpu and the compute instance that we are using is not particularly powerful. However, we can work on a small bit of the data to get a feel for how our training script is behaving.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 94,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"import torch.utils.data\\n\",\n \"\\n\",\n \"# Read in only the first 250 rows\\n\",\n \"train_sample = pd.read_csv(os.path.join(data_dir, 'train.csv'), header=None, names=None, nrows=250)\\n\",\n \"\\n\",\n \"# Turn the input pandas dataframe into tensors\\n\",\n \"train_sample_y = torch.from_numpy(train_sample[[0]].values).float().squeeze()\\n\",\n \"train_sample_X = torch.from_numpy(train_sample.drop([0], axis=1).values).long()\\n\",\n \"\\n\",\n \"# Build the dataset\\n\",\n \"train_sample_ds = torch.utils.data.TensorDataset(train_sample_X, train_sample_y)\\n\",\n \"# Build the dataloader\\n\",\n \"train_sample_dl = torch.utils.data.DataLoader(train_sample_ds, batch_size=50)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### (TODO) Writing the training method\\n\",\n \"\\n\",\n \"Next we need to write the training code itself. This should be very similar to training methods that you have written before to train PyTorch models. We will leave any difficult aspects such as model saving / loading and parameter loading until a little later.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 95,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def train(model, train_loader, epochs, optimizer, loss_fn, device):\\n\",\n \" for epoch in range(1, epochs + 1):\\n\",\n \" model.train()\\n\",\n \" total_loss = 0\\n\",\n \" for batch in train_loader: \\n\",\n \" batch_X, batch_y = batch\\n\",\n \" \\n\",\n \" batch_X = batch_X.to(device)\\n\",\n \" batch_y = batch_y.to(device)\\n\",\n \" \\n\",\n \" # TODO: Complete this train method to train the model provided.\\n\",\n \" \\n\",\n \" output = model(batch_X)\\n\",\n \" loss = loss_fn(output, batch_y)\\n\",\n \" optimizer.zero_grad()\\n\",\n \" loss.backward()\\n\",\n \" optimizer.step()\\n\",\n \"\\n\",\n \" total_loss += loss.data.item()\\n\",\n \" print(\\\"Epoch: {}, BCELoss: {}\\\".format(epoch, total_loss / len(train_loader)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Supposing we have the training method above, we will test that it is working by writing a bit of code in the notebook that executes our training method on the small sample training set that we loaded earlier. The reason for doing this in the notebook is so that we have an opportunity to fix any errors that arise early when they are easier to diagnose.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 96,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch: 1, BCELoss: 0.6948487043380738\\n\",\n \"Epoch: 2, BCELoss: 0.6850907325744628\\n\",\n \"Epoch: 3, BCELoss: 0.6767431974411011\\n\",\n \"Epoch: 4, BCELoss: 0.667607057094574\\n\",\n \"Epoch: 5, BCELoss: 0.6565715670585632\\n\"\n ]\n }\n ],\n \"source\": [\n \"import torch.optim as optim\\n\",\n \"from train.model import LSTMClassifier\\n\",\n \"\\n\",\n \"device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n \"model = LSTMClassifier(32, 100, 5000).to(device)\\n\",\n \"optimizer = optim.Adam(model.parameters())\\n\",\n \"loss_fn = torch.nn.BCELoss()\\n\",\n \"\\n\",\n \"train(model, train_sample_dl, 5, optimizer, loss_fn, device)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In order to construct a PyTorch model using SageMaker we must provide SageMaker with a training script. We may optionally include a directory which will be copied to the container and from which our training code will be run. When the training container is executed it will check the uploaded directory (if there is one) for a `requirements.txt` file and install any required Python libraries, after which the training script will be run.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### (TODO) Training the model\\n\",\n \"\\n\",\n \"When a PyTorch model is constructed in SageMaker, an entry point must be specified. This is the Python file which will be executed when the model is trained. Inside of the `train` directory is a file called `train.py` which has been provided and which contains most of the necessary code to train our model. The only thing that is missing is the implementation of the `train()` method which you wrote earlier in this notebook.\\n\",\n \"\\n\",\n \"**TODO**: Copy the `train()` method written above and paste it into the `train/train.py` file where required.\\n\",\n \"\\n\",\n \"The way that SageMaker passes hyperparameters to the training script is by way of arguments. These arguments can then be parsed and used in the training script. To see how this is done take a look at the provided `train/train.py` file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 97,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sagemaker.pytorch import PyTorch\\n\",\n \"\\n\",\n \"estimator = PyTorch(entry_point=\\\"train.py\\\",\\n\",\n \" source_dir=\\\"train\\\",\\n\",\n \" role=role,\\n\",\n \" framework_version='0.4.0',\\n\",\n \" train_instance_count=1,\\n\",\n \" train_instance_type='ml.p2.xlarge',\\n\",\n \" hyperparameters={\\n\",\n \" 'epochs': 10,\\n\",\n \" 'hidden_dim': 200,\\n\",\n \" })\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 98,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"'create_image_uri' will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\\n\",\n \"'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\\n\",\n \"'create_image_uri' will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2021-05-26 10:25:51 Starting - Starting the training job...\\n\",\n \"2021-05-26 10:25:52 Starting - Launching requested ML instances.........\\n\",\n \"2021-05-26 10:27:22 Starting - Preparing the instances for training.........\\n\",\n \"2021-05-26 10:28:58 Downloading - Downloading input data...\\n\",\n \"2021-05-26 10:29:44 Training - Downloading the training image......\\n\",\n \"2021-05-26 10:30:50 Training - Training image download completed. Training in progress..\\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\\u001b[0m\\n\",\n \"\\u001b[34mbash: no job control in this shell\\u001b[0m\\n\",\n \"\\u001b[34m2021-05-26 10:30:50,917 sagemaker-containers INFO Imported framework sagemaker_pytorch_container.training\\u001b[0m\\n\",\n \"\\u001b[34m2021-05-26 10:30:50,942 sagemaker_pytorch_container.training INFO Block until all host DNS lookups succeed.\\u001b[0m\\n\",\n \"\\u001b[34m2021-05-26 10:30:57,195 sagemaker_pytorch_container.training INFO Invoking user training script.\\u001b[0m\\n\",\n \"\\u001b[34m2021-05-26 10:30:57,459 sagemaker-containers INFO Module train does not provide a setup.py. \\u001b[0m\\n\",\n \"\\u001b[34mGenerating setup.py\\u001b[0m\\n\",\n \"\\u001b[34m2021-05-26 10:30:57,459 sagemaker-containers INFO Generating setup.cfg\\u001b[0m\\n\",\n \"\\u001b[34m2021-05-26 10:30:57,460 sagemaker-containers INFO Generating MANIFEST.in\\u001b[0m\\n\",\n \"\\u001b[34m2021-05-26 10:30:57,460 sagemaker-containers INFO Installing module with the following command:\\u001b[0m\\n\",\n \"\\u001b[34m/usr/bin/python -m pip install -U . -r requirements.txt\\u001b[0m\\n\",\n \"\\u001b[34mProcessing /opt/ml/code\\u001b[0m\\n\",\n \"\\u001b[34mCollecting pandas (from -r requirements.txt (line 1))\\n\",\n \" Downloading https://files.pythonhosted.org/packages/74/24/0cdbf8907e1e3bc5a8da03345c23cbed7044330bb8f73bb12e711a640a00/pandas-0.24.2-cp35-cp35m-manylinux1_x86_64.whl (10.0MB)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting numpy (from -r requirements.txt (line 2))\\u001b[0m\\n\",\n \"\\u001b[34m Downloading https://files.pythonhosted.org/packages/b5/36/88723426b4ff576809fec7d73594fe17a35c27f8d01f93637637a29ae25b/numpy-1.18.5-cp35-cp35m-manylinux1_x86_64.whl (19.9MB)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting nltk (from -r requirements.txt (line 3))\\n\",\n \" Downloading https://files.pythonhosted.org/packages/5e/37/9532ddd4b1bbb619333d5708aaad9bf1742f051a664c3c6fa6632a105fd8/nltk-3.6.2-py3-none-any.whl (1.5MB)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting beautifulsoup4 (from -r requirements.txt (line 4))\\n\",\n \" Downloading https://files.pythonhosted.org/packages/d1/41/e6495bd7d3781cee623ce23ea6ac73282a373088fcd0ddc809a047b18eae/beautifulsoup4-4.9.3-py3-none-any.whl (115kB)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting html5lib (from -r requirements.txt (line 5))\\n\",\n \" Downloading https://files.pythonhosted.org/packages/6c/dd/a834df6482147d48e225a49515aabc28974ad5a4ca3215c18a882565b028/html5lib-1.1-py2.py3-none-any.whl (112kB)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting pytz>=2011k (from pandas->-r requirements.txt (line 1))\\n\",\n \" Downloading https://files.pythonhosted.org/packages/70/94/784178ca5dd892a98f113cdd923372024dc04b8d40abe77ca76b5fb90ca6/pytz-2021.1-py2.py3-none-any.whl (510kB)\\u001b[0m\\n\",\n \"\\u001b[34mRequirement already satisfied, skipping upgrade: python-dateutil>=2.5.0 in /usr/local/lib/python3.5/dist-packages (from pandas->-r requirements.txt (line 1)) (2.7.5)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting tqdm (from nltk->-r requirements.txt (line 3))\\n\",\n \" Downloading https://files.pythonhosted.org/packages/42/d7/f357d98e9b50346bcb6095fe3ad205d8db3174eb5edb03edfe7c4099576d/tqdm-4.61.0-py2.py3-none-any.whl (75kB)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting joblib (from nltk->-r requirements.txt (line 3))\\n\",\n \" Downloading https://files.pythonhosted.org/packages/28/5c/cf6a2b65a321c4a209efcdf64c2689efae2cb62661f8f6f4bb28547cf1bf/joblib-0.14.1-py2.py3-none-any.whl (294kB)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting regex (from nltk->-r requirements.txt (line 3))\\u001b[0m\\n\",\n \"\\u001b[34m Downloading https://files.pythonhosted.org/packages/38/3f/4c42a98c9ad7d08c16e7d23b2194a0e4f3b2914662da8bc88986e4e6de1f/regex-2021.4.4.tar.gz (693kB)\\u001b[0m\\n\",\n \"\\u001b[34mRequirement already satisfied, skipping upgrade: click in /usr/local/lib/python3.5/dist-packages (from nltk->-r requirements.txt (line 3)) (7.0)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting soupsieve>1.2; python_version >= \\\"3.0\\\" (from beautifulsoup4->-r requirements.txt (line 4))\\n\",\n \" Downloading https://files.pythonhosted.org/packages/02/fb/1c65691a9aeb7bd6ac2aa505b84cb8b49ac29c976411c6ab3659425e045f/soupsieve-2.1-py3-none-any.whl\\u001b[0m\\n\",\n \"\\u001b[34mRequirement already satisfied, skipping upgrade: six>=1.9 in /usr/local/lib/python3.5/dist-packages (from html5lib->-r requirements.txt (line 5)) (1.11.0)\\u001b[0m\\n\",\n \"\\u001b[34mCollecting webencodings (from html5lib->-r requirements.txt (line 5))\\n\",\n \" Downloading https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl\\u001b[0m\\n\",\n \"\\u001b[34mBuilding wheels for collected packages: train, regex\\n\",\n \" Running setup.py bdist_wheel for train: started\\n\",\n \" Running setup.py bdist_wheel for train: finished with status 'done'\\n\",\n \" Stored in directory: /tmp/pip-ephem-wheel-cache-cjr7cki6/wheels/35/24/16/37574d11bf9bde50616c67372a334f94fa8356bc7164af8ca3\\n\",\n \" Running setup.py bdist_wheel for regex: started\\u001b[0m\\n\",\n \"\\u001b[34m Running setup.py bdist_wheel for regex: finished with status 'done'\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/c9/05/a8/b85fa0bd7850b99f9b4f106972975f2e3c46412e12f9949b58\\u001b[0m\\n\",\n \"\\u001b[34mSuccessfully built train regex\\u001b[0m\\n\",\n \"\\u001b[34mInstalling collected packages: pytz, numpy, pandas, tqdm, joblib, regex, nltk, soupsieve, beautifulsoup4, webencodings, html5lib, train\\u001b[0m\\n\",\n \"\\u001b[34m Found existing installation: numpy 1.15.4\\n\",\n \" Uninstalling numpy-1.15.4:\\u001b[0m\\n\",\n \"\\u001b[34m Successfully uninstalled numpy-1.15.4\\u001b[0m\\n\",\n \"\\u001b[34mSuccessfully installed beautifulsoup4-4.9.3 html5lib-1.1 joblib-0.14.1 nltk-3.6.2 numpy-1.18.5 pandas-0.24.2 pytz-2021.1 regex-2021.4.4 soupsieve-2.1 tqdm-4.61.0 train-1.0.0 webencodings-0.5.1\\u001b[0m\\n\",\n \"\\u001b[34mYou are using pip version 18.1, however version 20.3.4 is available.\\u001b[0m\\n\",\n \"\\u001b[34mYou should consider upgrading via the 'pip install --upgrade pip' command.\\u001b[0m\\n\",\n \"\\u001b[34m2021-05-26 10:31:20,626 sagemaker-containers INFO Invoking user script\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34mTraining Env:\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m{\\n\",\n \" \\\"log_level\\\": 20,\\n\",\n \" \\\"additional_framework_parameters\\\": {},\\n\",\n \" \\\"output_data_dir\\\": \\\"/opt/ml/output/data\\\",\\n\",\n \" \\\"input_data_config\\\": {\\n\",\n \" \\\"training\\\": {\\n\",\n \" \\\"RecordWrapperType\\\": \\\"None\\\",\\n\",\n \" \\\"TrainingInputMode\\\": \\\"File\\\",\\n\",\n \" \\\"S3DistributionType\\\": \\\"FullyReplicated\\\"\\n\",\n \" }\\n\",\n \" },\\n\",\n \" \\\"resource_config\\\": {\\n\",\n \" \\\"network_interface_name\\\": \\\"eth0\\\",\\n\",\n \" \\\"hosts\\\": [\\n\",\n \" \\\"algo-1\\\"\\n\",\n \" ],\\n\",\n \" \\\"current_host\\\": \\\"algo-1\\\"\\n\",\n \" },\\n\",\n \" \\\"output_dir\\\": \\\"/opt/ml/output\\\",\\n\",\n \" \\\"current_host\\\": \\\"algo-1\\\",\\n\",\n \" \\\"hyperparameters\\\": {\\n\",\n \" \\\"epochs\\\": 10,\\n\",\n \" \\\"hidden_dim\\\": 200\\n\",\n \" },\\n\",\n \" \\\"num_cpus\\\": 4,\\n\",\n \" \\\"user_entry_point\\\": \\\"train.py\\\",\\n\",\n \" \\\"input_config_dir\\\": \\\"/opt/ml/input/config\\\",\\n\",\n \" \\\"hosts\\\": [\\n\",\n \" \\\"algo-1\\\"\\n\",\n \" ],\\n\",\n \" \\\"input_dir\\\": \\\"/opt/ml/input\\\",\\n\",\n \" \\\"output_intermediate_dir\\\": \\\"/opt/ml/output/intermediate\\\",\\n\",\n \" \\\"module_name\\\": \\\"train\\\",\\n\",\n \" \\\"module_dir\\\": \\\"s3://sagemaker-us-east-1-274709254325/sagemaker-pytorch-2021-05-26-10-25-50-719/source/sourcedir.tar.gz\\\",\\n\",\n \" \\\"num_gpus\\\": 1,\\n\",\n \" \\\"framework_module\\\": \\\"sagemaker_pytorch_container.training:main\\\",\\n\",\n \" \\\"model_dir\\\": \\\"/opt/ml/model\\\",\\n\",\n \" \\\"network_interface_name\\\": \\\"eth0\\\",\\n\",\n \" \\\"channel_input_dirs\\\": {\\n\",\n \" \\\"training\\\": \\\"/opt/ml/input/data/training\\\"\\n\",\n \" },\\n\",\n \" \\\"job_name\\\": \\\"sagemaker-pytorch-2021-05-26-10-25-50-719\\\"\\u001b[0m\\n\",\n \"\\u001b[34m}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34mEnvironment variables:\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\\u001b[0m\\n\",\n \"\\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-east-1-274709254325/sagemaker-pytorch-2021-05-26-10-25-50-719/source/sourcedir.tar.gz\\u001b[0m\\n\",\n \"\\u001b[34mSM_MODULE_NAME=train\\u001b[0m\\n\",\n \"\\u001b[34mSM_CHANNEL_TRAINING=/opt/ml/input/data/training\\u001b[0m\\n\",\n \"\\u001b[34mSM_HP_EPOCHS=10\\u001b[0m\\n\",\n \"\\u001b[34mSM_LOG_LEVEL=20\\u001b[0m\\n\",\n \"\\u001b[34mSM_INPUT_DIR=/opt/ml/input\\u001b[0m\\n\",\n \"\\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\\u001b[0m\\n\",\n \"\\u001b[34mSM_RESOURCE_CONFIG={\\\"current_host\\\":\\\"algo-1\\\",\\\"hosts\\\":[\\\"algo-1\\\"],\\\"network_interface_name\\\":\\\"eth0\\\"}\\u001b[0m\\n\",\n \"\\u001b[34mSM_NUM_GPUS=1\\u001b[0m\\n\",\n \"\\u001b[34mSM_USER_ARGS=[\\\"--epochs\\\",\\\"10\\\",\\\"--hidden_dim\\\",\\\"200\\\"]\\u001b[0m\\n\",\n \"\\u001b[34mSM_CHANNELS=[\\\"training\\\"]\\u001b[0m\\n\",\n \"\\u001b[34mSM_CURRENT_HOST=algo-1\\u001b[0m\\n\",\n \"\\u001b[34mSM_FRAMEWORK_PARAMS={}\\u001b[0m\\n\",\n \"\\u001b[34mSM_USER_ENTRY_POINT=train.py\\u001b[0m\\n\",\n \"\\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\\u001b[0m\\n\",\n \"\\u001b[34mSM_INPUT_DATA_CONFIG={\\\"training\\\":{\\\"RecordWrapperType\\\":\\\"None\\\",\\\"S3DistributionType\\\":\\\"FullyReplicated\\\",\\\"TrainingInputMode\\\":\\\"File\\\"}}\\u001b[0m\\n\",\n \"\\u001b[34mSM_HOSTS=[\\\"algo-1\\\"]\\u001b[0m\\n\",\n \"\\u001b[34mSM_NUM_CPUS=4\\u001b[0m\\n\",\n \"\\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\\u001b[0m\\n\",\n \"\\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\\u001b[0m\\n\",\n \"\\u001b[34mPYTHONPATH=/usr/local/bin:/usr/lib/python35.zip:/usr/lib/python3.5:/usr/lib/python3.5/plat-x86_64-linux-gnu:/usr/lib/python3.5/lib-dynload:/usr/local/lib/python3.5/dist-packages:/usr/lib/python3/dist-packages\\u001b[0m\\n\",\n \"\\u001b[34mSM_HP_HIDDEN_DIM=200\\u001b[0m\\n\",\n \"\\u001b[34mSM_HPS={\\\"epochs\\\":10,\\\"hidden_dim\\\":200}\\u001b[0m\\n\",\n \"\\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\\u001b[0m\\n\",\n \"\\u001b[34mSM_TRAINING_ENV={\\\"additional_framework_parameters\\\":{},\\\"channel_input_dirs\\\":{\\\"training\\\":\\\"/opt/ml/input/data/training\\\"},\\\"current_host\\\":\\\"algo-1\\\",\\\"framework_module\\\":\\\"sagemaker_pytorch_container.training:main\\\",\\\"hosts\\\":[\\\"algo-1\\\"],\\\"hyperparameters\\\":{\\\"epochs\\\":10,\\\"hidden_dim\\\":200},\\\"input_config_dir\\\":\\\"/opt/ml/input/config\\\",\\\"input_data_config\\\":{\\\"training\\\":{\\\"RecordWrapperType\\\":\\\"None\\\",\\\"S3DistributionType\\\":\\\"FullyReplicated\\\",\\\"TrainingInputMode\\\":\\\"File\\\"}},\\\"input_dir\\\":\\\"/opt/ml/input\\\",\\\"job_name\\\":\\\"sagemaker-pytorch-2021-05-26-10-25-50-719\\\",\\\"log_level\\\":20,\\\"model_dir\\\":\\\"/opt/ml/model\\\",\\\"module_dir\\\":\\\"s3://sagemaker-us-east-1-274709254325/sagemaker-pytorch-2021-05-26-10-25-50-719/source/sourcedir.tar.gz\\\",\\\"module_name\\\":\\\"train\\\",\\\"network_interface_name\\\":\\\"eth0\\\",\\\"num_cpus\\\":4,\\\"num_gpus\\\":1,\\\"output_data_dir\\\":\\\"/opt/ml/output/data\\\",\\\"output_dir\\\":\\\"/opt/ml/output\\\",\\\"output_intermediate_dir\\\":\\\"/opt/ml/output/intermediate\\\",\\\"resource_config\\\":{\\\"current_host\\\":\\\"algo-1\\\",\\\"hosts\\\":[\\\"algo-1\\\"],\\\"network_interface_name\\\":\\\"eth0\\\"},\\\"user_entry_point\\\":\\\"train.py\\\"}\\u001b[0m\\n\",\n \"\\u001b[34mSM_MODEL_DIR=/opt/ml/model\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34mInvoking script with the following command:\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m/usr/bin/python -m train --epochs 10 --hidden_dim 200\\n\",\n \"\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34mUsing device cuda.\\u001b[0m\\n\",\n \"\\u001b[34mGet train data loader.\\u001b[0m\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[34mModel loaded with embedding_dim 32, hidden_dim 200, vocab_size 5000.\\u001b[0m\\n\",\n \"\\u001b[34mEpoch: 1, BCELoss: 0.6766362932263589\\u001b[0m\\n\",\n \"\\u001b[34mEpoch: 2, BCELoss: 0.6226622827199041\\u001b[0m\\n\",\n \"\\u001b[34mEpoch: 3, BCELoss: 0.5091627757160031\\u001b[0m\\n\",\n \"\\u001b[34mEpoch: 4, BCELoss: 0.44011006854018386\\u001b[0m\\n\",\n \"\\u001b[34mEpoch: 5, BCELoss: 0.39196165970393587\\u001b[0m\\n\",\n \"\\u001b[34mEpoch: 6, BCELoss: 0.34930189227571296\\u001b[0m\\n\",\n \"\\u001b[34mEpoch: 7, BCELoss: 0.3431228691217851\\u001b[0m\\n\",\n \"\\u001b[34mEpoch: 8, BCELoss: 0.3094689365552396\\u001b[0m\\n\",\n \"\\u001b[34mEpoch: 9, BCELoss: 0.28768396377563477\\u001b[0m\\n\",\n \"\\n\",\n \"2021-05-26 10:34:20 Uploading - Uploading generated training model\\u001b[34mEpoch: 10, BCELoss: 0.26991305789169\\u001b[0m\\n\",\n \"\\u001b[34m2021-05-26 10:34:17,101 sagemaker-containers INFO Reporting training SUCCESS\\u001b[0m\\n\",\n \"\\n\",\n \"2021-05-26 10:34:27 Completed - Training job completed\\n\",\n \"Training seconds: 329\\n\",\n \"Billable seconds: 329\\n\"\n ]\n }\n ],\n \"source\": [\n \"estimator.fit({'training': input_data})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 5: Testing the model\\n\",\n \"\\n\",\n \"As mentioned at the top of this notebook, we will be testing this model by first deploying it and then sending the testing data to the deployed endpoint. We will do this so that we can make sure that the deployed model is working correctly.\\n\",\n \"\\n\",\n \"## Step 6: Deploy the model for testing\\n\",\n \"\\n\",\n \"Now that we have trained our model, we would like to test it to see how it performs. Currently our model takes input of the form `review_length, review[500]` where `review[500]` is a sequence of `500` integers which describe the words present in the review, encoded using `word_dict`. Fortunately for us, SageMaker provides built-in inference code for models with simple inputs such as this.\\n\",\n \"\\n\",\n \"There is one thing that we need to provide, however, and that is a function which loads the saved model. This function must be called `model_fn()` and takes as its only parameter a path to the directory where the model artifacts are stored. This function must also be present in the python file which we specified as the entry point. In our case the model loading function has been provided and so no changes need to be made.\\n\",\n \"\\n\",\n \"**NOTE**: When the built-in inference code is run it must import the `model_fn()` method from the `train.py` file. This is why the training code is wrapped in a main guard ( ie, `if __name__ == '__main__':` )\\n\",\n \"\\n\",\n \"Since we don't need to change anything in the code that was uploaded during training, we can simply deploy the current model as-is.\\n\",\n \"\\n\",\n \"**NOTE:** When deploying a model you are asking SageMaker to launch an compute instance that will wait for data to be sent to it. As a result, this compute instance will continue to run until *you* shut it down. This is important to know since the cost of a deployed endpoint depends on how long it has been running for.\\n\",\n \"\\n\",\n \"In other words **If you are no longer using a deployed endpoint, shut it down!**\\n\",\n \"\\n\",\n \"**TODO:** Deploy the trained model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 120,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Parameter image will be renamed to image_uri in SageMaker Python SDK v2.\\n\",\n \"'create_image_uri' will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\\n\",\n \"Using already existing model: sagemaker-pytorch-2021-05-26-10-25-50-719\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-----------------!\"\n ]\n }\n ],\n \"source\": [\n \"# TODO: Deploy the trained model\\n\",\n \"lstm_estimator = estimator.deploy(initial_instance_count=1, instance_type='ml.p2.xlarge')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 7 - Use the model for testing\\n\",\n \"\\n\",\n \"Once deployed, we can read in the test data and send it off to our deployed model to get some results. Once we collect all of the results we can determine how accurate our model is.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 100,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"test_X = pd.concat([pd.DataFrame(test_X_len), pd.DataFrame(test_X)], axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We split the data into chunks and send each chunk seperately, accumulating the results.\\n\",\n \"\\n\",\n \"def predict(data, rows=512):\\n\",\n \" split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))\\n\",\n \" predictions = np.array([])\\n\",\n \" for array in split_array:\\n\",\n \" predictions = np.append(predictions, lstm_estimator.predict(array))\\n\",\n \" \\n\",\n \" return predictions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 117,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(25000, 501)\"\n ]\n },\n \"execution_count\": 117,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.shape(test_X.values)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 104,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"predictions = predict(test_X.values)\\n\",\n \"predictions = [round(num) for num in predictions]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 105,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.8362\"\n ]\n },\n \"execution_count\": 105,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import accuracy_score\\n\",\n \"accuracy_score(test_y, predictions)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Question:** How does this model compare to the XGBoost model you created earlier? Why might these two models perform differently on this dataset? Which do *you* think is better for sentiment analysis \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Answer:**\\n\",\n \"\\n\",\n \"The model gives approximatlely the same accuarcy, although i think that the RNN model should preform better, due to the fact that it can handle the complex sentences better.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### (TODO) More testing\\n\",\n \"\\n\",\n \"We now have a trained model which has been deployed and which we can send processed reviews to and which returns the predicted sentiment. However, ultimately we would like to be able to send our model an unprocessed review. That is, we would like to send the review itself as a string. For example, suppose we wish to send the following review to our model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 106,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"test_review = 'The simplest pleasures in life are the best, and this film is one of them. Combining a rather basic storyline of love and adventure this movie transcends the usual weekend fair with wit and unmitigated charm.'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The question we now need to answer is, how do we send this review to our model?\\n\",\n \"\\n\",\n \"Recall in the first section of this notebook we did a bunch of data processing to the IMDb dataset. In particular, we did two specific things to the provided reviews.\\n\",\n \" - Removed any html tags and stemmed the input\\n\",\n \" - Encoded the review as a sequence of integers using `word_dict`\\n\",\n \" \\n\",\n \"In order process the review we will need to repeat these two steps.\\n\",\n \"\\n\",\n \"**TODO**: Using the `review_to_words` and `convert_and_pad` methods from section one, convert `test_review` into a numpy array `test_data` suitable to send to our model. Remember that our model expects input of the form `review_length, review[500]`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 125,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# TODO: Convert test_review into a form usable by the model and save the results in test_data\\n\",\n \"words = review_to_words(test_review)\\n\",\n \"test_data, test_data_len = convert_and_pad(word_dict, words)\\n\",\n \"test_data = [np.array(test_data)]\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have processed the review, we can send the resulting array to our model to predict the sentiment of the review.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 127,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array(0.58940214, dtype=float32)\"\n ]\n },\n \"execution_count\": 127,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lstm_estimator.predict(test_data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since the return value of our model is close to `1`, we can be certain that the review we submitted is positive.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Delete the endpoint\\n\",\n \"\\n\",\n \"Of course, just like in the XGBoost notebook, once we've deployed an endpoint it continues to run until we tell it to shut down. Since we are done using our endpoint for now, we can delete it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 128,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"estimator.delete_endpoint() will be deprecated in SageMaker Python SDK v2. Please use the delete_endpoint() function on your predictor instead.\\n\"\n ]\n }\n ],\n \"source\": [\n \"estimator.delete_endpoint()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 6 (again) - Deploy the model for the web app\\n\",\n \"\\n\",\n \"Now that we know that our model is working, it's time to create some custom inference code so that we can send the model a review which has not been processed and have it determine the sentiment of the review.\\n\",\n \"\\n\",\n \"As we saw above, by default the estimator which we created, when deployed, will use the entry script and directory which we provided when creating the model. However, since we now wish to accept a string as input and our model expects a processed review, we need to write some custom inference code.\\n\",\n \"\\n\",\n \"We will store the code that we write in the `serve` directory. Provided in this directory is the `model.py` file that we used to construct our model, a `utils.py` file which contains the `review_to_words` and `convert_and_pad` pre-processing functions which we used during the initial data processing, and `predict.py`, the file which will contain our custom inference code. Note also that `requirements.txt` is present which will tell SageMaker what Python libraries are required by our custom inference code.\\n\",\n \"\\n\",\n \"When deploying a PyTorch model in SageMaker, you are expected to provide four functions which the SageMaker inference container will use.\\n\",\n \" - `model_fn`: This function is the same function that we used in the training script and it tells SageMaker how to load our model.\\n\",\n \" - `input_fn`: This function receives the raw serialized input that has been sent to the model's endpoint and its job is to de-serialize and make the input available for the inference code.\\n\",\n \" - `output_fn`: This function takes the output of the inference code and its job is to serialize this output and return it to the caller of the model's endpoint.\\n\",\n \" - `predict_fn`: The heart of the inference script, this is where the actual prediction is done and is the function which you will need to complete.\\n\",\n \"\\n\",\n \"For the simple website that we are constructing during this project, the `input_fn` and `output_fn` methods are relatively straightforward. We only require being able to accept a string as input and we expect to return a single value as output. You might imagine though that in a more complex application the input or output may be image data or some other binary data which would require some effort to serialize.\\n\",\n \"\\n\",\n \"### (TODO) Writing inference code\\n\",\n \"\\n\",\n \"Before writing our custom inference code, we will begin by taking a look at the code which has been provided.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 129,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36margparse\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mjson\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mos\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mpickle\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36msys\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36msagemaker_containers\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mpandas\\u001b[39;49;00m \\u001b[34mas\\u001b[39;49;00m \\u001b[04m\\u001b[36mpd\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mnumpy\\u001b[39;49;00m \\u001b[34mas\\u001b[39;49;00m \\u001b[04m\\u001b[36mnp\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mtorch\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mtorch\\u001b[39;49;00m\\u001b[04m\\u001b[36m.\\u001b[39;49;00m\\u001b[04m\\u001b[36mnn\\u001b[39;49;00m \\u001b[34mas\\u001b[39;49;00m \\u001b[04m\\u001b[36mnn\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mtorch\\u001b[39;49;00m\\u001b[04m\\u001b[36m.\\u001b[39;49;00m\\u001b[04m\\u001b[36moptim\\u001b[39;49;00m \\u001b[34mas\\u001b[39;49;00m \\u001b[04m\\u001b[36moptim\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mtorch\\u001b[39;49;00m\\u001b[04m\\u001b[36m.\\u001b[39;49;00m\\u001b[04m\\u001b[36mutils\\u001b[39;49;00m\\u001b[04m\\u001b[36m.\\u001b[39;49;00m\\u001b[04m\\u001b[36mdata\\u001b[39;49;00m\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[34mfrom\\u001b[39;49;00m \\u001b[04m\\u001b[36mmodel\\u001b[39;49;00m \\u001b[34mimport\\u001b[39;49;00m LSTMClassifier\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[34mfrom\\u001b[39;49;00m \\u001b[04m\\u001b[36mutils\\u001b[39;49;00m \\u001b[34mimport\\u001b[39;49;00m review_to_words, convert_and_pad\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[34mdef\\u001b[39;49;00m \\u001b[32mmodel_fn\\u001b[39;49;00m(model_dir):\\r\\n\",\n \" \\u001b[33m\\\"\\\"\\\"Load the PyTorch model from the `model_dir` directory.\\\"\\\"\\\"\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[36mprint\\u001b[39;49;00m(\\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mLoading model.\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m)\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# First, load the parameters used to create the model.\\u001b[39;49;00m\\r\\n\",\n \" model_info = {}\\r\\n\",\n \" model_info_path = os.path.join(model_dir, \\u001b[33m'\\u001b[39;49;00m\\u001b[33mmodel_info.pth\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m)\\r\\n\",\n \" \\u001b[34mwith\\u001b[39;49;00m \\u001b[36mopen\\u001b[39;49;00m(model_info_path, \\u001b[33m'\\u001b[39;49;00m\\u001b[33mrb\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m) \\u001b[34mas\\u001b[39;49;00m f:\\r\\n\",\n \" model_info = torch.load(f)\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[36mprint\\u001b[39;49;00m(\\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mmodel_info: \\u001b[39;49;00m\\u001b[33m{}\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m.format(model_info))\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# Determine the device and construct the model.\\u001b[39;49;00m\\r\\n\",\n \" device = torch.device(\\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mcuda\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m \\u001b[34mif\\u001b[39;49;00m torch.cuda.is_available() \\u001b[34melse\\u001b[39;49;00m \\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mcpu\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m)\\r\\n\",\n \" model = LSTMClassifier(model_info[\\u001b[33m'\\u001b[39;49;00m\\u001b[33membedding_dim\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m], model_info[\\u001b[33m'\\u001b[39;49;00m\\u001b[33mhidden_dim\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m], model_info[\\u001b[33m'\\u001b[39;49;00m\\u001b[33mvocab_size\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m])\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# Load the store model parameters.\\u001b[39;49;00m\\r\\n\",\n \" model_path = os.path.join(model_dir, \\u001b[33m'\\u001b[39;49;00m\\u001b[33mmodel.pth\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m)\\r\\n\",\n \" \\u001b[34mwith\\u001b[39;49;00m \\u001b[36mopen\\u001b[39;49;00m(model_path, \\u001b[33m'\\u001b[39;49;00m\\u001b[33mrb\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m) \\u001b[34mas\\u001b[39;49;00m f:\\r\\n\",\n \" model.load_state_dict(torch.load(f))\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# Load the saved word_dict.\\u001b[39;49;00m\\r\\n\",\n \" word_dict_path = os.path.join(model_dir, \\u001b[33m'\\u001b[39;49;00m\\u001b[33mword_dict.pkl\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m)\\r\\n\",\n \" \\u001b[34mwith\\u001b[39;49;00m \\u001b[36mopen\\u001b[39;49;00m(word_dict_path, \\u001b[33m'\\u001b[39;49;00m\\u001b[33mrb\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m) \\u001b[34mas\\u001b[39;49;00m f:\\r\\n\",\n \" model.word_dict = pickle.load(f)\\r\\n\",\n \"\\r\\n\",\n \" model.to(device).eval()\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[36mprint\\u001b[39;49;00m(\\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mDone loading model.\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m)\\r\\n\",\n \" \\u001b[34mreturn\\u001b[39;49;00m model\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[34mdef\\u001b[39;49;00m \\u001b[32minput_fn\\u001b[39;49;00m(serialized_input_data, content_type):\\r\\n\",\n \" \\u001b[36mprint\\u001b[39;49;00m(\\u001b[33m'\\u001b[39;49;00m\\u001b[33mDeserializing the input data.\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m)\\r\\n\",\n \" \\u001b[34mif\\u001b[39;49;00m content_type == \\u001b[33m'\\u001b[39;49;00m\\u001b[33mtext/plain\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m:\\r\\n\",\n \" data = serialized_input_data.decode(\\u001b[33m'\\u001b[39;49;00m\\u001b[33mutf-8\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m)\\r\\n\",\n \" \\u001b[34mreturn\\u001b[39;49;00m data\\r\\n\",\n \" \\u001b[34mraise\\u001b[39;49;00m \\u001b[36mException\\u001b[39;49;00m(\\u001b[33m'\\u001b[39;49;00m\\u001b[33mRequested unsupported ContentType in content_type: \\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m + content_type)\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[34mdef\\u001b[39;49;00m \\u001b[32moutput_fn\\u001b[39;49;00m(prediction_output, accept):\\r\\n\",\n \" \\u001b[36mprint\\u001b[39;49;00m(\\u001b[33m'\\u001b[39;49;00m\\u001b[33mSerializing the generated output.\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m)\\r\\n\",\n \" \\u001b[34mreturn\\u001b[39;49;00m \\u001b[36mstr\\u001b[39;49;00m(prediction_output)\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[34mdef\\u001b[39;49;00m \\u001b[32mpredict_fn\\u001b[39;49;00m(input_data, model):\\r\\n\",\n \" \\u001b[36mprint\\u001b[39;49;00m(\\u001b[33m'\\u001b[39;49;00m\\u001b[33mInferring sentiment of input data.\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m)\\r\\n\",\n \"\\r\\n\",\n \" device = torch.device(\\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mcuda\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m \\u001b[34mif\\u001b[39;49;00m torch.cuda.is_available() \\u001b[34melse\\u001b[39;49;00m \\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mcpu\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m)\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[34mif\\u001b[39;49;00m model.word_dict \\u001b[35mis\\u001b[39;49;00m \\u001b[34mNone\\u001b[39;49;00m:\\r\\n\",\n \" \\u001b[34mraise\\u001b[39;49;00m \\u001b[36mException\\u001b[39;49;00m(\\u001b[33m'\\u001b[39;49;00m\\u001b[33mModel has not been loaded properly, no word_dict.\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m)\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[37m# TODO: Process input_data so that it is ready to be sent to our model.\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[37m# You should produce two variables:\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[37m# data_X - A sequence of length 500 which represents the converted review\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[37m# data_len - The length of the review\\u001b[39;49;00m\\r\\n\",\n \" \\r\\n\",\n \" words = review_to_words(input_data)\\r\\n\",\n \" data_X, data_len = convert_and_pad(model.word_dict, words)\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# Using data_X and data_len we construct an appropriate input tensor. Remember\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[37m# that our model expects input data of the form 'len, review[500]'.\\u001b[39;49;00m\\r\\n\",\n \" data_pack = np.hstack((data_len, data_X))\\r\\n\",\n \" data_pack = data_pack.reshape(\\u001b[34m1\\u001b[39;49;00m, -\\u001b[34m1\\u001b[39;49;00m)\\r\\n\",\n \" \\r\\n\",\n \" data = torch.from_numpy(data_pack)\\r\\n\",\n \" data = data.to(device)\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# Make sure to put the model into evaluation mode\\u001b[39;49;00m\\r\\n\",\n \" model.eval()\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# TODO: Compute the result of applying the model to the input data. The variable `result` should\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[37m# be a numpy array which contains a single integer which is either 1 or 0\\u001b[39;49;00m\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[34mwith\\u001b[39;49;00m torch.no_grad():\\r\\n\",\n \" output = model.forward(data)\\r\\n\",\n \" \\r\\n\",\n \" result = \\u001b[36mint\\u001b[39;49;00m(np.round(output.numpy()))\\r\\n\",\n \" \\u001b[34mreturn\\u001b[39;49;00m result\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pygmentize serve/predict.py\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As mentioned earlier, the `model_fn` method is the same as the one provided in the training code and the `input_fn` and `output_fn` methods are very simple and your task will be to complete the `predict_fn` method. Make sure that you save the completed file as `predict.py` in the `serve` directory.\\n\",\n \"\\n\",\n \"**TODO**: Complete the `predict_fn()` method in the `serve/predict.py` file.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Deploying the model\\n\",\n \"\\n\",\n \"Now that the custom inference code has been written, we will create and deploy our model. To begin with, we need to construct a new PyTorchModel object which points to the model artifacts created during training and also points to the inference code that we wish to use. Then we can call the deploy method to launch the deployment container.\\n\",\n \"\\n\",\n \"**NOTE**: The default behaviour for a deployed PyTorch model is to assume that any input passed to the predictor is a `numpy` array. In our case we want to send a string so we need to construct a simple wrapper around the `RealTimePredictor` class to accomodate simple strings. In a more complicated situation you may want to provide a serialization object, for example if you wanted to sent image data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 130,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Parameter image will be renamed to image_uri in SageMaker Python SDK v2.\\n\",\n \"'create_image_uri' will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-------------------!\"\n ]\n }\n ],\n \"source\": [\n \"from sagemaker.predictor import RealTimePredictor\\n\",\n \"from sagemaker.pytorch import PyTorchModel\\n\",\n \"\\n\",\n \"class StringPredictor(RealTimePredictor):\\n\",\n \" def __init__(self, endpoint_name, sagemaker_session):\\n\",\n \" super(StringPredictor, self).__init__(endpoint_name, sagemaker_session, content_type='text/plain')\\n\",\n \"\\n\",\n \"model = PyTorchModel(model_data=estimator.model_data,\\n\",\n \" role = role,\\n\",\n \" framework_version='0.4.0',\\n\",\n \" entry_point='predict.py',\\n\",\n \" source_dir='serve',\\n\",\n \" predictor_cls=StringPredictor)\\n\",\n \"predictor = model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Testing the model\\n\",\n \"\\n\",\n \"Now that we have deployed our model with the custom inference code, we should test to see if everything is working. Here we test our model by loading the first `250` positive and negative reviews and send them to the endpoint, then collect the results. The reason for only sending some of the data is that the amount of time it takes for our model to process the input and then perform inference is quite long and so testing the entire data set would be prohibitive.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 131,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import glob\\n\",\n \"\\n\",\n \"def test_reviews(data_dir='../data/aclImdb', stop=250):\\n\",\n \" \\n\",\n \" results = []\\n\",\n \" ground = []\\n\",\n \" \\n\",\n \" # We make sure to test both positive and negative reviews \\n\",\n \" for sentiment in ['pos', 'neg']:\\n\",\n \" \\n\",\n \" path = os.path.join(data_dir, 'test', sentiment, '*.txt')\\n\",\n \" files = glob.glob(path)\\n\",\n \" \\n\",\n \" files_read = 0\\n\",\n \" \\n\",\n \" print('Starting ', sentiment, ' files')\\n\",\n \" \\n\",\n \" # Iterate through the files and send them to the predictor\\n\",\n \" for f in files:\\n\",\n \" with open(f) as review:\\n\",\n \" # First, we store the ground truth (was the review positive or negative)\\n\",\n \" if sentiment == 'pos':\\n\",\n \" ground.append(1)\\n\",\n \" else:\\n\",\n \" ground.append(0)\\n\",\n \" # Read in the review and convert to 'utf-8' for transmission via HTTP\\n\",\n \" review_input = review.read().encode('utf-8')\\n\",\n \" # Send the review to the predictor and store the results\\n\",\n \" results.append(float(predictor.predict(review_input)))\\n\",\n \" \\n\",\n \" # Sending reviews to our endpoint one at a time takes a while so we\\n\",\n \" # only send a small number of reviews\\n\",\n \" files_read += 1\\n\",\n \" if files_read == stop:\\n\",\n \" break\\n\",\n \" \\n\",\n \" return ground, results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 132,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Starting pos files\\n\",\n \"Starting neg files\\n\"\n ]\n }\n ],\n \"source\": [\n \"ground, results = test_reviews()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 133,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.848\"\n ]\n },\n \"execution_count\": 133,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import accuracy_score\\n\",\n \"accuracy_score(ground, results)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As an additional test, we can try sending the `test_review` that we looked at earlier.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 134,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"b'1'\"\n ]\n },\n \"execution_count\": 134,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predictor.predict(test_review)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we know our endpoint is working as expected, we can set up the web page that will interact with it. If you don't have time to finish the project now, make sure to skip down to the end of this notebook and shut down your endpoint. You can deploy it again when you come back.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 7 (again): Use the model for the web app\\n\",\n \"\\n\",\n \"> **TODO:** This entire section and the next contain tasks for you to complete, mostly using the AWS console.\\n\",\n \"\\n\",\n \"So far we have been accessing our model endpoint by constructing a predictor object which uses the endpoint and then just using the predictor object to perform inference. What if we wanted to create a web app which accessed our model? The way things are set up currently makes that not possible since in order to access a SageMaker endpoint the app would first have to authenticate with AWS using an IAM role which included access to SageMaker endpoints. However, there is an easier way! We just need to use some additional AWS services.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"The diagram above gives an overview of how the various services will work together. On the far right is the model which we trained above and which is deployed using SageMaker. On the far left is our web app that collects a user's movie review, sends it off and expects a positive or negative sentiment in return.\\n\",\n \"\\n\",\n \"In the middle is where some of the magic happens. We will construct a Lambda function, which you can think of as a straightforward Python function that can be executed whenever a specified event occurs. We will give this function permission to send and recieve data from a SageMaker endpoint.\\n\",\n \"\\n\",\n \"Lastly, the method we will use to execute the Lambda function is a new endpoint that we will create using API Gateway. This endpoint will be a url that listens for data to be sent to it. Once it gets some data it will pass that data on to the Lambda function and then return whatever the Lambda function returns. Essentially it will act as an interface that lets our web app communicate with the Lambda function.\\n\",\n \"\\n\",\n \"### Setting up a Lambda function\\n\",\n \"\\n\",\n \"The first thing we are going to do is set up a Lambda function. This Lambda function will be executed whenever our public API has data sent to it. When it is executed it will receive the data, perform any sort of processing that is required, send the data (the review) to the SageMaker endpoint we've created and then return the result.\\n\",\n \"\\n\",\n \"#### Part A: Create an IAM Role for the Lambda function\\n\",\n \"\\n\",\n \"Since we want the Lambda function to call a SageMaker endpoint, we need to make sure that it has permission to do so. To do this, we will construct a role that we can later give the Lambda function.\\n\",\n \"\\n\",\n \"Using the AWS Console, navigate to the **IAM** page and click on **Roles**. Then, click on **Create role**. Make sure that the **AWS service** is the type of trusted entity selected and choose **Lambda** as the service that will use this role, then click **Next: Permissions**.\\n\",\n \"\\n\",\n \"In the search box type `sagemaker` and select the check box next to the **AmazonSageMakerFullAccess** policy. Then, click on **Next: Review**.\\n\",\n \"\\n\",\n \"Lastly, give this role a name. Make sure you use a name that you will remember later on, for example `LambdaSageMakerRole`. Then, click on **Create role**.\\n\",\n \"\\n\",\n \"#### Part B: Create a Lambda function\\n\",\n \"\\n\",\n \"Now it is time to actually create the Lambda function.\\n\",\n \"\\n\",\n \"Using the AWS Console, navigate to the AWS Lambda page and click on **Create a function**. When you get to the next page, make sure that **Author from scratch** is selected. Now, name your Lambda function, using a name that you will remember later on, for example `sentiment_analysis_func`. Make sure that the **Python 3.6** runtime is selected and then choose the role that you created in the previous part. Then, click on **Create Function**.\\n\",\n \"\\n\",\n \"On the next page you will see some information about the Lambda function you've just created. If you scroll down you should see an editor in which you can write the code that will be executed when your Lambda function is triggered. In our example, we will use the code below. \\n\",\n \"\\n\",\n \"```python\\n\",\n \"# We need to use the low-level library to interact with SageMaker since the SageMaker API\\n\",\n \"# is not available natively through Lambda.\\n\",\n \"import boto3\\n\",\n \"\\n\",\n \"def lambda_handler(event, context):\\n\",\n \"\\n\",\n \" # The SageMaker runtime is what allows us to invoke the endpoint that we've created.\\n\",\n \" runtime = boto3.Session().client('sagemaker-runtime')\\n\",\n \"\\n\",\n \" # Now we use the SageMaker runtime to invoke our endpoint, sending the review we were given\\n\",\n \" response = runtime.invoke_endpoint(EndpointName = '**ENDPOINT NAME HERE**', # The name of the endpoint we created\\n\",\n \" ContentType = 'text/plain', # The data format that is expected\\n\",\n \" Body = event['body']) # The actual review\\n\",\n \"\\n\",\n \" # The response is an HTTP response whose body contains the result of our inference\\n\",\n \" result = response['Body'].read().decode('utf-8')\\n\",\n \"\\n\",\n \" return {\\n\",\n \" 'statusCode' : 200,\\n\",\n \" 'headers' : { 'Content-Type' : 'text/plain', 'Access-Control-Allow-Origin' : '*' },\\n\",\n \" 'body' : result\\n\",\n \" }\\n\",\n \"```\\n\",\n \"\\n\",\n \"Once you have copy and pasted the code above into the Lambda code editor, replace the `**ENDPOINT NAME HERE**` portion with the name of the endpoint that we deployed earlier. You can determine the name of the endpoint using the code cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 135,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sagemaker-pytorch-2021-05-26-12-08-55-597'\"\n ]\n },\n \"execution_count\": 135,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predictor.endpoint\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once you have added the endpoint name to the Lambda function, click on **Save**. Your Lambda function is now up and running. Next we need to create a way for our web app to execute the Lambda function.\\n\",\n \"\\n\",\n \"### Setting up API Gateway\\n\",\n \"\\n\",\n \"Now that our Lambda function is set up, it is time to create a new API using API Gateway that will trigger the Lambda function we have just created.\\n\",\n \"\\n\",\n \"Using AWS Console, navigate to **Amazon API Gateway** and then click on **Get started**.\\n\",\n \"\\n\",\n \"On the next page, make sure that **New API** is selected and give the new api a name, for example, `sentiment_analysis_api`. Then, click on **Create API**.\\n\",\n \"\\n\",\n \"Now we have created an API, however it doesn't currently do anything. What we want it to do is to trigger the Lambda function that we created earlier.\\n\",\n \"\\n\",\n \"Select the **Actions** dropdown menu and click **Create Method**. A new blank method will be created, select its dropdown menu and select **POST**, then click on the check mark beside it.\\n\",\n \"\\n\",\n \"For the integration point, make sure that **Lambda Function** is selected and click on the **Use Lambda Proxy integration**. This option makes sure that the data that is sent to the API is then sent directly to the Lambda function with no processing. It also means that the return value must be a proper response object as it will also not be processed by API Gateway.\\n\",\n \"\\n\",\n \"Type the name of the Lambda function you created earlier into the **Lambda Function** text entry box and then click on **Save**. Click on **OK** in the pop-up box that then appears, giving permission to API Gateway to invoke the Lambda function you created.\\n\",\n \"\\n\",\n \"The last step in creating the API Gateway is to select the **Actions** dropdown and click on **Deploy API**. You will need to create a new Deployment stage and name it anything you like, for example `prod`.\\n\",\n \"\\n\",\n \"You have now successfully set up a public API to access your SageMaker model. Make sure to copy or write down the URL provided to invoke your newly created public API as this will be needed in the next step. This URL can be found at the top of the page, highlighted in blue next to the text **Invoke URL**.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Deploying our web app\\n\",\n \"\\n\",\n \"Now that we have a publicly available API, we can start using it in a web app. For our purposes, we have provided a simple static html file which can make use of the public api you created earlier.\\n\",\n \"\\n\",\n \"In the `website` folder there should be a file called `index.html`. Download the file to your computer and open that file up in a text editor of your choice. There should be a line which contains **\\\\*\\\\*REPLACE WITH PUBLIC API URL\\\\*\\\\***. Replace this string with the url that you wrote down in the last step and then save the file.\\n\",\n \"\\n\",\n \"Now, if you open `index.html` on your local computer, your browser will behave as a local web server and you can use the provided site to interact with your SageMaker model.\\n\",\n \"\\n\",\n \"If you'd like to go further, you can host this html file anywhere you'd like, for example using github or hosting a static site on Amazon's S3. Once you have done this you can share the link with anyone you'd like and have them play with it too!\\n\",\n \"\\n\",\n \"> **Important Note** In order for the web app to communicate with the SageMaker endpoint, the endpoint has to actually be deployed and running. This means that you are paying for it. Make sure that the endpoint is running when you want to use the web app but that you shut it down when you don't need it, otherwise you will end up with a surprisingly large AWS bill.\\n\",\n \"\\n\",\n \"**TODO:** Make sure that you include the edited `index.html` file in your project submission.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that your web app is working, trying playing around with it and see how well it works.\\n\",\n \"\\n\",\n \"**Question**: Give an example of a review that you entered into your web app. What was the predicted sentiment of your example review?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Answer:** \\n\",\n \"\\\"The movie was so good, i actually recommend it to everyone.\\\"\\n\",\n \"The web app classified it as a postive review. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Delete the endpoint\\n\",\n \"\\n\",\n \"Remember to always shut down your endpoint if you are no longer using it. You are charged for the length of time that the endpoint is running so if you forget and leave it on you could end up with an unexpectedly large bill.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 136,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"predictor.delete_endpoint()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda_pytorch_p36\",\n \"language\": \"python\",\n \"name\": \"conda_pytorch_p36\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/sevre/model.py",
"content": "import torch.nn as nn\n\nclass LSTMClassifier(nn.Module):\n \"\"\"\n This is the simple RNN model we will be using to perform Sentiment Analysis.\n \"\"\"\n\n def __init__(self, embedding_dim, hidden_dim, vocab_size):\n \"\"\"\n Initialize the model by settingg up the various layers.\n \"\"\"\n super(LSTMClassifier, self).__init__()\n\n self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)\n self.lstm = nn.LSTM(embedding_dim, hidden_dim)\n self.dense = nn.Linear(in_features=hidden_dim, out_features=1)\n self.sig = nn.Sigmoid()\n \n self.word_dict = None\n\n def forward(self, x):\n \"\"\"\n Perform a forward pass of our model on some input.\n \"\"\"\n x = x.t()\n lengths = x[0,:]\n reviews = x[1:,:]\n embeds = self.embedding(reviews)\n lstm_out, _ = self.lstm(embeds)\n out = self.dense(lstm_out)\n out = out[lengths - 1, range(len(lengths))]\n return self.sig(out.squeeze())"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/sevre/predict.py",
"content": "import argparse\nimport json\nimport os\nimport pickle\nimport sys\nimport sagemaker_containers\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.utils.data\n\nfrom model import LSTMClassifier\n\nfrom utils import review_to_words, convert_and_pad\n\ndef model_fn(model_dir):\n \"\"\"Load the PyTorch model from the `model_dir` directory.\"\"\"\n print(\"Loading model.\")\n\n # First, load the parameters used to create the model.\n model_info = {}\n model_info_path = os.path.join(model_dir, 'model_info.pth')\n with open(model_info_path, 'rb') as f:\n model_info = torch.load(f)\n\n print(\"model_info: {}\".format(model_info))\n\n # Determine the device and construct the model.\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = LSTMClassifier(model_info['embedding_dim'], model_info['hidden_dim'], model_info['vocab_size'])\n\n # Load the store model parameters.\n model_path = os.path.join(model_dir, 'model.pth')\n with open(model_path, 'rb') as f:\n model.load_state_dict(torch.load(f))\n\n # Load the saved word_dict.\n word_dict_path = os.path.join(model_dir, 'word_dict.pkl')\n with open(word_dict_path, 'rb') as f:\n model.word_dict = pickle.load(f)\n\n model.to(device).eval()\n\n print(\"Done loading model.\")\n return model\n\ndef input_fn(serialized_input_data, content_type):\n print('Deserializing the input data.')\n if content_type == 'text/plain':\n data = serialized_input_data.decode('utf-8')\n return data\n raise Exception('Requested unsupported ContentType in content_type: ' + content_type)\n\ndef output_fn(prediction_output, accept):\n print('Serializing the generated output.')\n return str(prediction_output)\n\ndef predict_fn(input_data, model):\n print('Inferring sentiment of input data.')\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n \n if model.word_dict is None:\n raise Exception('Model has not been loaded properly, no word_dict.')\n \n # TODO: Process input_data so that it is ready to be sent to our model.\n # You should produce two variables:\n # data_X - A sequence of length 500 which represents the converted review\n # data_len - The length of the review\n \n words = review_to_words(input_data)\n data_X, data_len = convert_and_pad(model.word_dict, words)\n\n # Using data_X and data_len we construct an appropriate input tensor. Remember\n # that our model expects input data of the form 'len, review[500]'.\n data_pack = np.hstack((data_len, data_X))\n data_pack = data_pack.reshape(1, -1)\n \n data = torch.from_numpy(data_pack)\n data = data.to(device)\n\n # Make sure to put the model into evaluation mode\n model.eval()\n\n # TODO: Compute the result of applying the model to the input data. The variable `result` should\n # be a numpy array which contains a single integer which is either 1 or 0\n\n with torch.no_grad():\n output = model.forward(data)\n \n result = int(np.round(output.numpy()))\n return result\n"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/sevre/requirements.txt",
"content": "pandas\nnumpy\nnltk\nbeautifulsoup4\nhtml5lib"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/sevre/utils.py",
"content": "import nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import *\n\nimport re\nfrom bs4 import BeautifulSoup\n\nimport pickle\n\nimport os\nimport glob\n\ndef review_to_words(review):\n nltk.download(\"stopwords\", quiet=True)\n stemmer = PorterStemmer()\n \n text = BeautifulSoup(review, \"html.parser\").get_text() # Remove HTML tags\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower()) # Convert to lower case\n words = text.split() # Split string into words\n words = [w for w in words if w not in stopwords.words(\"english\")] # Remove stopwords\n words = [PorterStemmer().stem(w) for w in words] # stem\n \n return words\n\ndef convert_and_pad(word_dict, sentence, pad=500):\n NOWORD = 0 # We will use 0 to represent the 'no word' category\n INFREQ = 1 # and we use 1 to represent the infrequent words, i.e., words not appearing in word_dict\n \n working_sentence = [NOWORD] * pad\n \n for word_index, word in enumerate(sentence[:pad]):\n if word in word_dict:\n working_sentence[word_index] = word_dict[word]\n else:\n working_sentence[word_index] = INFREQ\n \n return working_sentence, min(len(sentence), pad)"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/train/model.py",
"content": "import torch.nn as nn\n\nclass LSTMClassifier(nn.Module):\n \"\"\"\n This is the simple RNN model we will be using to perform Sentiment Analysis.\n \"\"\"\n\n def __init__(self, embedding_dim, hidden_dim, vocab_size):\n \"\"\"\n Initialize the model by settingg up the various layers.\n \"\"\"\n super(LSTMClassifier, self).__init__()\n\n self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)\n self.lstm = nn.LSTM(embedding_dim, hidden_dim)\n self.dense = nn.Linear(in_features=hidden_dim, out_features=1)\n self.sig = nn.Sigmoid()\n \n self.word_dict = None\n\n def forward(self, x):\n \"\"\"\n Perform a forward pass of our model on some input.\n \"\"\"\n x = x.t()\n lengths = x[0,:]\n reviews = x[1:,:]\n embeds = self.embedding(reviews)\n lstm_out, _ = self.lstm(embeds)\n out = self.dense(lstm_out)\n out = out[lengths - 1, range(len(lengths))]\n return self.sig(out.squeeze())"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/train/requirements.txt",
"content": "pandas\nnumpy\nnltk\nbeautifulsoup4\nhtml5lib"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/train/train.py",
"content": "import argparse\nimport json\nimport os\nimport pickle\nimport sys\nimport sagemaker_containers\nimport pandas as pd\nimport torch\nimport torch.optim as optim\nimport torch.utils.data\n\nfrom model import LSTMClassifier\n\ndef model_fn(model_dir):\n \"\"\"Load the PyTorch model from the `model_dir` directory.\"\"\"\n print(\"Loading model.\")\n\n # First, load the parameters used to create the model.\n model_info = {}\n model_info_path = os.path.join(model_dir, 'model_info.pth')\n with open(model_info_path, 'rb') as f:\n model_info = torch.load(f)\n\n print(\"model_info: {}\".format(model_info))\n\n # Determine the device and construct the model.\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = LSTMClassifier(model_info['embedding_dim'], model_info['hidden_dim'], model_info['vocab_size'])\n\n # Load the stored model parameters.\n model_path = os.path.join(model_dir, 'model.pth')\n with open(model_path, 'rb') as f:\n model.load_state_dict(torch.load(f))\n\n # Load the saved word_dict.\n word_dict_path = os.path.join(model_dir, 'word_dict.pkl')\n with open(word_dict_path, 'rb') as f:\n model.word_dict = pickle.load(f)\n\n model.to(device).eval()\n\n print(\"Done loading model.\")\n return model\n\ndef _get_train_data_loader(batch_size, training_dir):\n print(\"Get train data loader.\")\n\n train_data = pd.read_csv(os.path.join(training_dir, \"train.csv\"), header=None, names=None)\n\n train_y = torch.from_numpy(train_data[[0]].values).float().squeeze()\n train_X = torch.from_numpy(train_data.drop([0], axis=1).values).long()\n\n train_ds = torch.utils.data.TensorDataset(train_X, train_y)\n\n return torch.utils.data.DataLoader(train_ds, batch_size=batch_size)\n\n\ndef train(model, train_loader, epochs, optimizer, loss_fn, device):\n \"\"\"\n This is the training method that is called by the PyTorch training script. The parameters\n passed are as follows:\n model - The PyTorch model that we wish to train.\n train_loader - The PyTorch DataLoader that should be used during training.\n epochs - The total number of epochs to train for.\n optimizer - The optimizer to use during training.\n loss_fn - The loss function used for training.\n device - Where the model and data should be loaded (gpu or cpu).\n \"\"\"\n \n # TODO: Paste the train() method developed in the notebook here.\n\n for epoch in range(1, epochs + 1):\n model.train()\n total_loss = 0\n for batch in train_loader: \n batch_X, batch_y = batch\n \n batch_X = batch_X.to(device)\n batch_y = batch_y.to(device)\n \n # TODO: Complete this train method to train the model provided.\n \n output = model(batch_X)\n loss = loss_fn(output, batch_y)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.data.item()\n print(\"Epoch: {}, BCELoss: {}\".format(epoch, total_loss / len(train_loader)))\n\n\nif __name__ == '__main__':\n # All of the model parameters and training parameters are sent as arguments when the script\n # is executed. Here we set up an argument parser to easily access the parameters.\n\n parser = argparse.ArgumentParser()\n\n # Training Parameters\n parser.add_argument('--batch-size', type=int, default=512, metavar='N',\n help='input batch size for training (default: 512)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n\n # Model Parameters\n parser.add_argument('--embedding_dim', type=int, default=32, metavar='N',\n help='size of the word embeddings (default: 32)')\n parser.add_argument('--hidden_dim', type=int, default=100, metavar='N',\n help='size of the hidden dimension (default: 100)')\n parser.add_argument('--vocab_size', type=int, default=5000, metavar='N',\n help='size of the vocabulary (default: 5000)')\n\n # SageMaker Parameters\n parser.add_argument('--hosts', type=list, default=json.loads(os.environ['SM_HOSTS']))\n parser.add_argument('--current-host', type=str, default=os.environ['SM_CURRENT_HOST'])\n parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])\n parser.add_argument('--data-dir', type=str, default=os.environ['SM_CHANNEL_TRAINING'])\n parser.add_argument('--num-gpus', type=int, default=os.environ['SM_NUM_GPUS'])\n\n args = parser.parse_args()\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n print(\"Using device {}.\".format(device))\n\n torch.manual_seed(args.seed)\n\n # Load the training data.\n train_loader = _get_train_data_loader(args.batch_size, args.data_dir)\n\n # Build the model.\n model = LSTMClassifier(args.embedding_dim, args.hidden_dim, args.vocab_size).to(device)\n\n with open(os.path.join(args.data_dir, \"word_dict.pkl\"), \"rb\") as f:\n model.word_dict = pickle.load(f)\n\n print(\"Model loaded with embedding_dim {}, hidden_dim {}, vocab_size {}.\".format(\n args.embedding_dim, args.hidden_dim, args.vocab_size\n ))\n\n # Train the model.\n optimizer = optim.Adam(model.parameters())\n loss_fn = torch.nn.BCELoss()\n\n train(model, train_loader, args.epochs, optimizer, loss_fn, device)\n\n # Save the parameters used to construct the model\n model_info_path = os.path.join(args.model_dir, 'model_info.pth')\n with open(model_info_path, 'wb') as f:\n model_info = {\n 'embedding_dim': args.embedding_dim,\n 'hidden_dim': args.hidden_dim,\n 'vocab_size': args.vocab_size,\n }\n torch.save(model_info, f)\n\n\t# Save the word_dict\n word_dict_path = os.path.join(args.model_dir, 'word_dict.pkl')\n with open(word_dict_path, 'wb') as f:\n pickle.dump(model.word_dict, f)\n\n\t# Save the model parameters\n model_path = os.path.join(args.model_dir, 'model.pth')\n with open(model_path, 'wb') as f:\n torch.save(model.cpu().state_dict(), f)\n"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/website/index.html",
"content": "\n\n \n Sentiment Analysis Web App\n \n \n \n \n \n\n \n\n \n \n\n
\n
Is your review positive, or negative?
\n
Enter your review below and click submit to find out...
\n \n \n
\n \n\n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/1_Data_Exploration.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Plagiarism Text Data\\n\",\n \"\\n\",\n \"In this project, you will be tasked with building a plagiarism detector that examines a text file and performs binary classification; labeling that file as either plagiarized or not, depending on how similar the text file is when compared to a provided source text. \\n\",\n \"\\n\",\n \"The first step in working with any dataset is loading the data in and noting what information is included in the dataset. This is an important step in eventually working with this data, and knowing what kinds of features you have to work with as you transform and group the data!\\n\",\n \"\\n\",\n \"So, this notebook is all about exploring the data and noting patterns about the features you are given and the distribution of data. \\n\",\n \"\\n\",\n \"> There are not any exercises or questions in this notebook, it is only meant for exploration. This notebook will note be required in your final project submission.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Read in the Data\\n\",\n \"\\n\",\n \"The cell below will download the necessary data and extract the files into the folder `data/`.\\n\",\n \"\\n\",\n \"This data is a slightly modified version of a dataset created by Paul Clough (Information Studies) and Mark Stevenson (Computer Science), at the University of Sheffield. You can read all about the data collection and corpus, at [their university webpage](https://ir.shef.ac.uk/cloughie/resources/plagiarism_corpus.html). \\n\",\n \"\\n\",\n \"> **Citation for data**: Clough, P. and Stevenson, M. Developing A Corpus of Plagiarised Short Answers, Language Resources and Evaluation: Special Issue on Plagiarism and Authorship Analysis, In Press. [Download]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"!wget https://s3.amazonaws.com/video.udacity-data.com/topher/2019/January/5c4147f9_data/data.zip\\n\",\n \"!unzip data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# import libraries\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This plagiarism dataset is made of multiple text files; each of these files has characteristics that are is summarized in a `.csv` file named `file_information.csv`, which we can read in using `pandas`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"csv_file = 'data/file_information.csv'\\n\",\n \"plagiarism_df = pd.read_csv(csv_file)\\n\",\n \"\\n\",\n \"# print out the first few rows of data info\\n\",\n \"plagiarism_df.head(10)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Types of Plagiarism\\n\",\n \"\\n\",\n \"Each text file is associated with one **Task** (task A-E) and one **Category** of plagiarism, which you can see in the above DataFrame.\\n\",\n \"\\n\",\n \"### Five task types, A-E\\n\",\n \"\\n\",\n \"Each text file contains an answer to one short question; these questions are labeled as tasks A-E.\\n\",\n \"* Each task, A-E, is about a topic that might be included in the Computer Science curriculum that was created by the authors of this dataset. \\n\",\n \" * For example, Task A asks the question: \\\"What is inheritance in object oriented programming?\\\"\\n\",\n \"\\n\",\n \"### Four categories of plagiarism \\n\",\n \"\\n\",\n \"Each text file has an associated plagiarism label/category:\\n\",\n \"\\n\",\n \"1. `cut`: An answer is plagiarized; it is copy-pasted directly from the relevant Wikipedia source text.\\n\",\n \"2. `light`: An answer is plagiarized; it is based on the Wikipedia source text and includes some copying and paraphrasing.\\n\",\n \"3. `heavy`: An answer is plagiarized; it is based on the Wikipedia source text but expressed using different words and structure. Since this doesn't copy directly from a source text, this will likely be the most challenging kind of plagiarism to detect.\\n\",\n \"4. `non`: An answer is not plagiarized; the Wikipedia source text is not used to create this answer.\\n\",\n \"5. `orig`: This is a specific category for the original, Wikipedia source text. We will use these files only for comparison purposes.\\n\",\n \"\\n\",\n \"> So, out of the submitted files, the only category that does not contain any plagiarism is `non`.\\n\",\n \"\\n\",\n \"In the next cell, print out some statistics about the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# print out some stats about the data\\n\",\n \"print('Number of files: ', plagiarism_df.shape[0]) # .shape[0] gives the rows \\n\",\n \"# .unique() gives unique items in a specified column\\n\",\n \"print('Number of unique tasks/question types (A-E): ', (len(plagiarism_df['Task'].unique())))\\n\",\n \"print('Unique plagiarism categories: ', (plagiarism_df['Category'].unique()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You should see the number of text files in the dataset as well as some characteristics about the `Task` and `Category` columns. **Note that the file count of 100 *includes* the 5 _original_ wikipedia files for tasks A-E.** If you take a look at the files in the `data` directory, you'll notice that the original, source texts start with the filename `orig_` as opposed to `g` for \\\"group.\\\" \\n\",\n \"\\n\",\n \"> So, in total there are 100 files, 95 of which are answers (submitted by people) and 5 of which are the original, Wikipedia source texts.\\n\",\n \"\\n\",\n \"Your end goal will be to use this information to classify any given answer text into one of two categories, plagiarized or not-plagiarized.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Distribution of Data\\n\",\n \"\\n\",\n \"Next, let's look at the distribution of data. In this course, we've talked about traits like class imbalance that can inform how you develop an algorithm. So, here, we'll ask: **How evenly is our data distributed among different tasks and plagiarism levels?**\\n\",\n \"\\n\",\n \"Below, you should notice two things:\\n\",\n \"* Our dataset is quite small, especially with respect to examples of varying plagiarism levels.\\n\",\n \"* The data is distributed fairly evenly across task and plagiarism types.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Show counts by different tasks and amounts of plagiarism\\n\",\n \"\\n\",\n \"# group and count by task\\n\",\n \"counts_per_task=plagiarism_df.groupby(['Task']).size().reset_index(name=\\\"Counts\\\")\\n\",\n \"print(\\\"\\\\nTask:\\\")\\n\",\n \"display(counts_per_task)\\n\",\n \"\\n\",\n \"# group by plagiarism level\\n\",\n \"counts_per_category=plagiarism_df.groupby(['Category']).size().reset_index(name=\\\"Counts\\\")\\n\",\n \"print(\\\"\\\\nPlagiarism Levels:\\\")\\n\",\n \"display(counts_per_category)\\n\",\n \"\\n\",\n \"# group by task AND plagiarism level\\n\",\n \"counts_task_and_plagiarism=plagiarism_df.groupby(['Task', 'Category']).size().reset_index(name=\\\"Counts\\\")\\n\",\n \"print(\\\"\\\\nTask & Plagiarism Level Combos :\\\")\\n\",\n \"display(counts_task_and_plagiarism)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It may also be helpful to look at this last DataFrame, graphically.\\n\",\n \"\\n\",\n \"Below, you can see that the counts follow a pattern broken down by task. Each task has one source text (original) and the highest number on `non` plagiarized cases.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"# counts\\n\",\n \"group = ['Task', 'Category']\\n\",\n \"counts = plagiarism_df.groupby(group).size().reset_index(name=\\\"Counts\\\")\\n\",\n \"\\n\",\n \"plt.figure(figsize=(8,5))\\n\",\n \"plt.bar(range(len(counts)), counts['Counts'], color = 'blue')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Up Next\\n\",\n \"\\n\",\n \"This notebook is just about data loading and exploration, and you do not need to include it in your final project submission. \\n\",\n \"\\n\",\n \"In the next few notebooks, you'll use this data to train a complete plagiarism classifier. You'll be tasked with extracting meaningful features from the text data, reading in answers to different tasks and comparing them to the original Wikipedia source text. You'll engineer similarity features that will help identify cases of plagiarism. Then, you'll use these features to train and deploy a classification model in a SageMaker notebook instance. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda_amazonei_mxnet_p36\",\n \"language\": \"python\",\n \"name\": \"conda_amazonei_mxnet_p36\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/2_Plagiarism_Feature_Engineering.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Plagiarism Detection, Feature Engineering\\n\",\n \"\\n\",\n \"In this project, you will be tasked with building a plagiarism detector that examines an answer text file and performs binary classification; labeling that file as either plagiarized or not, depending on how similar that text file is to a provided, source text. \\n\",\n \"\\n\",\n \"Your first task will be to create some features that can then be used to train a classification model. This task will be broken down into a few discrete steps:\\n\",\n \"\\n\",\n \"* Clean and pre-process the data.\\n\",\n \"* Define features for comparing the similarity of an answer text and a source text, and extract similarity features.\\n\",\n \"* Select \\\"good\\\" features, by analyzing the correlations between different features.\\n\",\n \"* Create train/test `.csv` files that hold the relevant features and class labels for train/test data points.\\n\",\n \"\\n\",\n \"In the _next_ notebook, Notebook 3, you'll use the features and `.csv` files you create in _this_ notebook to train a binary classification model in a SageMaker notebook instance.\\n\",\n \"\\n\",\n \"You'll be defining a few different similarity features, as outlined in [this paper](https://s3.amazonaws.com/video.udacity-data.com/topher/2019/January/5c412841_developing-a-corpus-of-plagiarised-short-answers/developing-a-corpus-of-plagiarised-short-answers.pdf), which should help you build a robust plagiarism detector!\\n\",\n \"\\n\",\n \"To complete this notebook, you'll have to complete all given exercises and answer all the questions in this notebook.\\n\",\n \"> All your tasks will be clearly labeled **EXERCISE** and questions as **QUESTION**.\\n\",\n \"\\n\",\n \"It will be up to you to decide on the features to include in your final training and test data.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Read in the Data\\n\",\n \"\\n\",\n \"The cell below will download the necessary, project data and extract the files into the folder `data/`.\\n\",\n \"\\n\",\n \"This data is a slightly modified version of a dataset created by Paul Clough (Information Studies) and Mark Stevenson (Computer Science), at the University of Sheffield. You can read all about the data collection and corpus, at [their university webpage](https://ir.shef.ac.uk/cloughie/resources/plagiarism_corpus.html). \\n\",\n \"\\n\",\n \"> **Citation for data**: Clough, P. and Stevenson, M. Developing A Corpus of Plagiarised Short Answers, Language Resources and Evaluation: Special Issue on Plagiarism and Authorship Analysis, In Press. [Download]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# NOTE:\\n\",\n \"# you only need to run this cell if you have not yet downloaded the data\\n\",\n \"# otherwise you may skip this cell or comment it out\\n\",\n \"\\n\",\n \"!wget https://s3.amazonaws.com/video.udacity-data.com/topher/2019/January/5c4147f9_data/data.zip\\n\",\n \"!unzip data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 258,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# import libraries\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This plagiarism dataset is made of multiple text files; each of these files has characteristics that are is summarized in a `.csv` file named `file_information.csv`, which we can read in using `pandas`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 259,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
File
\\n\",\n \"
Task
\\n\",\n \"
Category
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
g0pA_taska.txt
\\n\",\n \"
a
\\n\",\n \"
non
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
g0pA_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
cut
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
g0pA_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
light
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
g0pA_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
heavy
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
g0pA_taske.txt
\\n\",\n \"
e
\\n\",\n \"
non
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" File Task Category\\n\",\n \"0 g0pA_taska.txt a non\\n\",\n \"1 g0pA_taskb.txt b cut\\n\",\n \"2 g0pA_taskc.txt c light\\n\",\n \"3 g0pA_taskd.txt d heavy\\n\",\n \"4 g0pA_taske.txt e non\"\n ]\n },\n \"execution_count\": 259,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"csv_file = 'data/file_information.csv'\\n\",\n \"plagiarism_df = pd.read_csv(csv_file)\\n\",\n \"\\n\",\n \"# print out the first few rows of data info\\n\",\n \"plagiarism_df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Types of Plagiarism\\n\",\n \"\\n\",\n \"Each text file is associated with one **Task** (task A-E) and one **Category** of plagiarism, which you can see in the above DataFrame.\\n\",\n \"\\n\",\n \"### Tasks, A-E\\n\",\n \"\\n\",\n \"Each text file contains an answer to one short question; these questions are labeled as tasks A-E. For example, Task A asks the question: \\\"What is inheritance in object oriented programming?\\\"\\n\",\n \"\\n\",\n \"### Categories of plagiarism \\n\",\n \"\\n\",\n \"Each text file has an associated plagiarism label/category:\\n\",\n \"\\n\",\n \"**1. Plagiarized categories: `cut`, `light`, and `heavy`.**\\n\",\n \"* These categories represent different levels of plagiarized answer texts. `cut` answers copy directly from a source text, `light` answers are based on the source text but include some light rephrasing, and `heavy` answers are based on the source text, but *heavily* rephrased (and will likely be the most challenging kind of plagiarism to detect).\\n\",\n \" \\n\",\n \"**2. Non-plagiarized category: `non`.** \\n\",\n \"* `non` indicates that an answer is not plagiarized; the Wikipedia source text is not used to create this answer.\\n\",\n \" \\n\",\n \"**3. Special, source text category: `orig`.**\\n\",\n \"* This is a specific category for the original, Wikipedia source text. We will use these files only for comparison purposes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"## Pre-Process the Data\\n\",\n \"\\n\",\n \"In the next few cells, you'll be tasked with creating a new DataFrame of desired information about all of the files in the `data/` directory. This will prepare the data for feature extraction and for training a binary, plagiarism classifier.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### EXERCISE: Convert categorical to numerical data\\n\",\n \"\\n\",\n \"You'll notice that the `Category` column in the data, contains string or categorical values, and to prepare these for feature extraction, we'll want to convert these into numerical values. Additionally, our goal is to create a binary classifier and so we'll need a binary class label that indicates whether an answer text is plagiarized (1) or not (0). Complete the below function `numerical_dataframe` that reads in a `file_information.csv` file by name, and returns a *new* DataFrame with a numerical `Category` column and a new `Class` column that labels each answer as plagiarized or not. \\n\",\n \"\\n\",\n \"Your function should return a new DataFrame with the following properties:\\n\",\n \"\\n\",\n \"* 4 columns: `File`, `Task`, `Category`, `Class`. The `File` and `Task` columns can remain unchanged from the original `.csv` file.\\n\",\n \"* Convert all `Category` labels to numerical labels according to the following rules (a higher value indicates a higher degree of plagiarism):\\n\",\n \" * 0 = `non`\\n\",\n \" * 1 = `heavy`\\n\",\n \" * 2 = `light`\\n\",\n \" * 3 = `cut`\\n\",\n \" * -1 = `orig`, this is a special value that indicates an original file.\\n\",\n \"* For the new `Class` column\\n\",\n \" * Any answer text that is not plagiarized (`non`) should have the class label `0`. \\n\",\n \" * Any plagiarized answer texts should have the class label `1`. \\n\",\n \" * And any `orig` texts will have a special label `-1`. \\n\",\n \"\\n\",\n \"### Expected output\\n\",\n \"\\n\",\n \"After running your function, you should get a DataFrame with rows that looks like the following: \\n\",\n \"```\\n\",\n \"\\n\",\n \" File\\t Task Category Class\\n\",\n \"0\\tg0pA_taska.txt\\ta\\t 0 \\t0\\n\",\n \"1\\tg0pA_taskb.txt\\tb\\t 3 \\t1\\n\",\n \"2\\tg0pA_taskc.txt\\tc\\t 2 \\t1\\n\",\n \"3\\tg0pA_taskd.txt\\td\\t 1 \\t1\\n\",\n \"4\\tg0pA_taske.txt\\te\\t 0\\t 0\\n\",\n \"...\\n\",\n \"...\\n\",\n \"99 orig_taske.txt e -1 -1\\n\",\n \"\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 260,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"palgerism_df = pd.read_csv(csv_file)\\n\",\n \"palgerism_df['Category'].replace(\\n\",\n \" to_replace=['non', 'heavy', 'light', 'cut', 'orig'], \\n\",\n \" value=[0, 1, 2, 3, -1], inplace=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 261,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
File
\\n\",\n \"
Task
\\n\",\n \"
Category
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
g0pA_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
g0pA_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
5
\\n\",\n \"
g0pB_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
6
\\n\",\n \"
g0pB_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
11
\\n\",\n \"
g0pC_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
12
\\n\",\n \"
g0pC_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
18
\\n\",\n \"
g0pD_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
19
\\n\",\n \"
g0pD_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
22
\\n\",\n \"
g0pE_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
23
\\n\",\n \"
g0pE_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
25
\\n\",\n \"
g1pA_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
29
\\n\",\n \"
g1pA_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
30
\\n\",\n \"
g1pB_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
31
\\n\",\n \"
g1pB_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
37
\\n\",\n \"
g1pD_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
38
\\n\",\n \"
g1pD_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
40
\\n\",\n \"
g2pA_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
44
\\n\",\n \"
g2pA_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
45
\\n\",\n \"
g2pB_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
46
\\n\",\n \"
g2pB_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
51
\\n\",\n \"
g2pC_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
52
\\n\",\n \"
g2pC_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
58
\\n\",\n \"
g2pE_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
59
\\n\",\n \"
g2pE_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
60
\\n\",\n \"
g3pA_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
64
\\n\",\n \"
g3pA_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
65
\\n\",\n \"
g3pB_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
66
\\n\",\n \"
g3pB_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
71
\\n\",\n \"
g3pC_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
72
\\n\",\n \"
g3pC_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
75
\\n\",\n \"
g4pB_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
76
\\n\",\n \"
g4pB_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
81
\\n\",\n \"
g4pC_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
82
\\n\",\n \"
g4pC_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
87
\\n\",\n \"
g4pD_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
88
\\n\",\n \"
g4pD_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
93
\\n\",\n \"
g4pE_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
94
\\n\",\n \"
g4pE_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" File Task Category\\n\",\n \"0 g0pA_taska.txt a 0\\n\",\n \"4 g0pA_taske.txt e 0\\n\",\n \"5 g0pB_taska.txt a 0\\n\",\n \"6 g0pB_taskb.txt b 0\\n\",\n \"11 g0pC_taskb.txt b 0\\n\",\n \"12 g0pC_taskc.txt c 0\\n\",\n \"18 g0pD_taskd.txt d 0\\n\",\n \"19 g0pD_taske.txt e 0\\n\",\n \"22 g0pE_taskc.txt c 0\\n\",\n \"23 g0pE_taskd.txt d 0\\n\",\n \"25 g1pA_taska.txt a 0\\n\",\n \"29 g1pA_taske.txt e 0\\n\",\n \"30 g1pB_taska.txt a 0\\n\",\n \"31 g1pB_taskb.txt b 0\\n\",\n \"37 g1pD_taskc.txt c 0\\n\",\n \"38 g1pD_taskd.txt d 0\\n\",\n \"40 g2pA_taska.txt a 0\\n\",\n \"44 g2pA_taske.txt e 0\\n\",\n \"45 g2pB_taska.txt a 0\\n\",\n \"46 g2pB_taskb.txt b 0\\n\",\n \"51 g2pC_taskb.txt b 0\\n\",\n \"52 g2pC_taskc.txt c 0\\n\",\n \"58 g2pE_taskd.txt d 0\\n\",\n \"59 g2pE_taske.txt e 0\\n\",\n \"60 g3pA_taska.txt a 0\\n\",\n \"64 g3pA_taske.txt e 0\\n\",\n \"65 g3pB_taska.txt a 0\\n\",\n \"66 g3pB_taskb.txt b 0\\n\",\n \"71 g3pC_taskb.txt b 0\\n\",\n \"72 g3pC_taskc.txt c 0\\n\",\n \"75 g4pB_taska.txt a 0\\n\",\n \"76 g4pB_taskb.txt b 0\\n\",\n \"81 g4pC_taskb.txt b 0\\n\",\n \"82 g4pC_taskc.txt c 0\\n\",\n \"87 g4pD_taskc.txt c 0\\n\",\n \"88 g4pD_taskd.txt d 0\\n\",\n \"93 g4pE_taskd.txt d 0\\n\",\n \"94 g4pE_taske.txt e 0\"\n ]\n },\n \"execution_count\": 261,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"palgerism_df[palgerism_df['Category'] == 0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 262,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"palgerism_df.loc[non_index.index,'Class'] = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 263,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Read in a csv file and return a transformed dataframe\\n\",\n \"def numerical_dataframe(csv_file='data/file_information.csv'):\\n\",\n \" '''Reads in a csv file which is assumed to have `File`, `Category` and `Task` columns.\\n\",\n \" This function does two things: \\n\",\n \" 1) converts `Category` column values to numerical values \\n\",\n \" 2) Adds a new, numerical `Class` label column.\\n\",\n \" The `Class` column will label plagiarized answers as 1 and non-plagiarized as 0.\\n\",\n \" Source texts have a special label, -1.\\n\",\n \" :param csv_file: The directory for the file_information.csv file\\n\",\n \" :return: A dataframe with numerical categories and a new `Class` label column'''\\n\",\n \" \\n\",\n \" # your code here\\n\",\n \" palgerism_df = pd.read_csv(csv_file)\\n\",\n \" palgerism_df['Category'].replace(\\n\",\n \" to_replace=['non', 'heavy', 'light', 'cut', 'orig'], \\n\",\n \" value=[0, 1, 2, 3, -1], inplace=True)\\n\",\n \" palgerism_df['Class'] = 1\\n\",\n \" non_index = palgerism_df[palgerism_df['Category'] == 0].index\\n\",\n \" palgerism_df.loc[non_index, 'Class'] = 0\\n\",\n \" orig_index= palgerism_df[palgerism_df['Category'] == -1].index\\n\",\n \" palgerism_df.loc[orig_index, 'Class'] = -1\\n\",\n \" \\n\",\n \" return palgerism_df\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test cells\\n\",\n \"\\n\",\n \"Below are a couple of test cells. The first is an informal test where you can check that your code is working as expected by calling your function and printing out the returned result.\\n\",\n \"\\n\",\n \"The **second** cell below is a more rigorous test cell. The goal of a cell like this is to ensure that your code is working as expected, and to form any variables that might be used in _later_ tests/code, in this case, the data frame, `transformed_df`.\\n\",\n \"\\n\",\n \"> The cells in this notebook should be run in chronological order (the order they appear in the notebook). This is especially important for test cells.\\n\",\n \"\\n\",\n \"Often, later cells rely on the functions, imports, or variables defined in earlier cells. For example, some tests rely on previous tests to work.\\n\",\n \"\\n\",\n \"These tests do not test all cases, but they are a great way to check that you are on the right track!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 264,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
File
\\n\",\n \"
Task
\\n\",\n \"
Category
\\n\",\n \"
Class
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
g0pA_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
g0pA_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
3
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
g0pA_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
g0pA_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
g0pA_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
5
\\n\",\n \"
g0pB_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
6
\\n\",\n \"
g0pB_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
7
\\n\",\n \"
g0pB_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
3
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
8
\\n\",\n \"
g0pB_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
9
\\n\",\n \"
g0pB_taske.txt
\\n\",\n \"
e
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" File Task Category Class\\n\",\n \"0 g0pA_taska.txt a 0 0\\n\",\n \"1 g0pA_taskb.txt b 3 1\\n\",\n \"2 g0pA_taskc.txt c 2 1\\n\",\n \"3 g0pA_taskd.txt d 1 1\\n\",\n \"4 g0pA_taske.txt e 0 0\\n\",\n \"5 g0pB_taska.txt a 0 0\\n\",\n \"6 g0pB_taskb.txt b 0 0\\n\",\n \"7 g0pB_taskc.txt c 3 1\\n\",\n \"8 g0pB_taskd.txt d 2 1\\n\",\n \"9 g0pB_taske.txt e 1 1\"\n ]\n },\n \"execution_count\": 264,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# informal testing, print out the results of a called function\\n\",\n \"# create new `transformed_df`\\n\",\n \"transformed_df = numerical_dataframe(csv_file ='data/file_information.csv')\\n\",\n \"\\n\",\n \"# check work\\n\",\n \"# check that all categories of plagiarism have a class label = 1\\n\",\n \"transformed_df.head(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 265,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed!\\n\",\n \"\\n\",\n \"Example data: \\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
File
\\n\",\n \"
Task
\\n\",\n \"
Category
\\n\",\n \"
Class
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
g0pA_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
g0pA_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
3
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
g0pA_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
g0pA_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
g0pA_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" File Task Category Class\\n\",\n \"0 g0pA_taska.txt a 0 0\\n\",\n \"1 g0pA_taskb.txt b 3 1\\n\",\n \"2 g0pA_taskc.txt c 2 1\\n\",\n \"3 g0pA_taskd.txt d 1 1\\n\",\n \"4 g0pA_taske.txt e 0 0\"\n ]\n },\n \"execution_count\": 265,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# test cell that creates `transformed_df`, if tests are passed\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"# importing tests\\n\",\n \"import problem_unittests as tests\\n\",\n \"\\n\",\n \"# test numerical_dataframe function\\n\",\n \"tests.test_numerical_df(numerical_dataframe)\\n\",\n \"\\n\",\n \"# if above test is passed, create NEW `transformed_df`\\n\",\n \"transformed_df = numerical_dataframe(csv_file ='data/file_information.csv')\\n\",\n \"\\n\",\n \"# check work\\n\",\n \"print('\\\\nExample data: ')\\n\",\n \"transformed_df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Text Processing & Splitting Data\\n\",\n \"\\n\",\n \"Recall that the goal of this project is to build a plagiarism classifier. At it's heart, this task is a comparison text; one that looks at a given answer and a source text, compares them and predicts whether an answer has plagiarized from the source. To effectively do this comparison, and train a classifier we'll need to do a few more things: pre-process all of our text data and prepare the text files (in this case, the 95 answer files and 5 original source files) to be easily compared, and split our data into a `train` and `test` set that can be used to train a classifier and evaluate it, respectively. \\n\",\n \"\\n\",\n \"To this end, you've been provided code that adds additional information to your `transformed_df` from above. The next two cells need not be changed; they add two additional columns to the `transformed_df`:\\n\",\n \"\\n\",\n \"1. A `Text` column; this holds all the lowercase text for a `File`, with extraneous punctuation removed.\\n\",\n \"2. A `Datatype` column; this is a string value `train`, `test`, or `orig` that labels a data point as part of our train or test set\\n\",\n \"\\n\",\n \"The details of how these additional columns are created can be found in the `helpers.py` file in the project directory. You're encouraged to read through that file to see exactly how text is processed and how data is split.\\n\",\n \"\\n\",\n \"Run the cells below to get a `complete_df` that has all the information you need to proceed with plagiarism detection and feature engineering.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 266,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
File
\\n\",\n \"
Task
\\n\",\n \"
Category
\\n\",\n \"
Class
\\n\",\n \"
Text
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
g0pA_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
inheritance is a basic concept of object orien...
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
g0pA_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
3
\\n\",\n \"
1
\\n\",\n \"
pagerank is a link analysis algorithm used by ...
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
g0pA_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
the vector space model also called term vector...
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
g0pA_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
bayes theorem was names after rev thomas bayes...
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
g0pA_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
dynamic programming is an algorithm design tec...
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" File Task Category Class \\\\\\n\",\n \"0 g0pA_taska.txt a 0 0 \\n\",\n \"1 g0pA_taskb.txt b 3 1 \\n\",\n \"2 g0pA_taskc.txt c 2 1 \\n\",\n \"3 g0pA_taskd.txt d 1 1 \\n\",\n \"4 g0pA_taske.txt e 0 0 \\n\",\n \"\\n\",\n \" Text \\n\",\n \"0 inheritance is a basic concept of object orien... \\n\",\n \"1 pagerank is a link analysis algorithm used by ... \\n\",\n \"2 the vector space model also called term vector... \\n\",\n \"3 bayes theorem was names after rev thomas bayes... \\n\",\n \"4 dynamic programming is an algorithm design tec... \"\n ]\n },\n \"execution_count\": 266,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"import helpers \\n\",\n \"\\n\",\n \"# create a text column \\n\",\n \"text_df = helpers.create_text_column(transformed_df)\\n\",\n \"text_df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 267,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Sample processed text:\\n\",\n \"\\n\",\n \" inheritance is a basic concept of object oriented programming where the basic idea is to create new classes that add extra detail to existing classes this is done by allowing the new classes to reuse the methods and variables of the existing classes and new methods and classes are added to specialise the new class inheritance models the is kind of relationship between entities or objects for example postgraduates and undergraduates are both kinds of student this kind of relationship can be visualised as a tree structure where student would be the more general root node and both postgraduate and undergraduate would be more specialised extensions of the student node or the child nodes in this relationship student would be known as the superclass or parent class whereas postgraduate would be known as the subclass or child class because the postgraduate class extends the student class inheritance can occur on several layers where if visualised would display a larger tree structure for example we could further extend the postgraduate node by adding two extra extended classes to it called msc student and phd student as both these types of student are kinds of postgraduate student this would mean that both the msc student and phd student classes would inherit methods and variables from both the postgraduate and student classes \\n\"\n ]\n }\n ],\n \"source\": [\n \"# after running the cell above\\n\",\n \"# check out the processed text for a single file, by row index\\n\",\n \"row_idx = 0 # feel free to change this index\\n\",\n \"\\n\",\n \"sample_text = text_df.iloc[0]['Text']\\n\",\n \"\\n\",\n \"print('Sample processed text:\\\\n\\\\n', sample_text)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Split data into training and test sets\\n\",\n \"\\n\",\n \"The next cell will add a `Datatype` column to a given DataFrame to indicate if the record is: \\n\",\n \"* `train` - Training data, for model training.\\n\",\n \"* `test` - Testing data, for model evaluation.\\n\",\n \"* `orig` - The task's original answer from wikipedia.\\n\",\n \"\\n\",\n \"### Stratified sampling\\n\",\n \"\\n\",\n \"The given code uses a helper function which you can view in the `helpers.py` file in the main project directory. This implements [stratified random sampling](https://en.wikipedia.org/wiki/Stratified_sampling) to randomly split data by task & plagiarism amount. Stratified sampling ensures that we get training and test data that is fairly evenly distributed across task & plagiarism combinations. Approximately 26% of the data is held out for testing and 74% of the data is used for training.\\n\",\n \"\\n\",\n \"The function **train_test_dataframe** takes in a DataFrame that it assumes has `Task` and `Category` columns, and, returns a modified frame that indicates which `Datatype` (train, test, or orig) a file falls into. This sampling will change slightly based on a passed in *random_seed*. Due to a small sample size, this stratified random sampling will provide more stable results for a binary plagiarism classifier. Stability here is smaller *variance* in the accuracy of classifier, given a random seed.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 268,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
File
\\n\",\n \"
Task
\\n\",\n \"
Category
\\n\",\n \"
Class
\\n\",\n \"
Text
\\n\",\n \"
Datatype
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
g0pA_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
inheritance is a basic concept of object orien...
\\n\",\n \"
train
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
g0pA_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
3
\\n\",\n \"
1
\\n\",\n \"
pagerank is a link analysis algorithm used by ...
\\n\",\n \"
test
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
g0pA_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
the vector space model also called term vector...
\\n\",\n \"
train
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
g0pA_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
bayes theorem was names after rev thomas bayes...
\\n\",\n \"
train
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
g0pA_taske.txt
\\n\",\n \"
e
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
dynamic programming is an algorithm design tec...
\\n\",\n \"
train
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
5
\\n\",\n \"
g0pB_taska.txt
\\n\",\n \"
a
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
inheritance is a basic concept in object orien...
\\n\",\n \"
train
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
6
\\n\",\n \"
g0pB_taskb.txt
\\n\",\n \"
b
\\n\",\n \"
0
\\n\",\n \"
0
\\n\",\n \"
pagerank pr refers to both the concept and the...
\\n\",\n \"
train
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
7
\\n\",\n \"
g0pB_taskc.txt
\\n\",\n \"
c
\\n\",\n \"
3
\\n\",\n \"
1
\\n\",\n \"
vector space model is an algebraic model for r...
\\n\",\n \"
test
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
8
\\n\",\n \"
g0pB_taskd.txt
\\n\",\n \"
d
\\n\",\n \"
2
\\n\",\n \"
1
\\n\",\n \"
bayes theorem relates the conditional and marg...
\\n\",\n \"
train
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
9
\\n\",\n \"
g0pB_taske.txt
\\n\",\n \"
e
\\n\",\n \"
1
\\n\",\n \"
1
\\n\",\n \"
dynamic programming is a method for solving ma...
\\n\",\n \"
test
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" File Task Category Class \\\\\\n\",\n \"0 g0pA_taska.txt a 0 0 \\n\",\n \"1 g0pA_taskb.txt b 3 1 \\n\",\n \"2 g0pA_taskc.txt c 2 1 \\n\",\n \"3 g0pA_taskd.txt d 1 1 \\n\",\n \"4 g0pA_taske.txt e 0 0 \\n\",\n \"5 g0pB_taska.txt a 0 0 \\n\",\n \"6 g0pB_taskb.txt b 0 0 \\n\",\n \"7 g0pB_taskc.txt c 3 1 \\n\",\n \"8 g0pB_taskd.txt d 2 1 \\n\",\n \"9 g0pB_taske.txt e 1 1 \\n\",\n \"\\n\",\n \" Text Datatype \\n\",\n \"0 inheritance is a basic concept of object orien... train \\n\",\n \"1 pagerank is a link analysis algorithm used by ... test \\n\",\n \"2 the vector space model also called term vector... train \\n\",\n \"3 bayes theorem was names after rev thomas bayes... train \\n\",\n \"4 dynamic programming is an algorithm design tec... train \\n\",\n \"5 inheritance is a basic concept in object orien... train \\n\",\n \"6 pagerank pr refers to both the concept and the... train \\n\",\n \"7 vector space model is an algebraic model for r... test \\n\",\n \"8 bayes theorem relates the conditional and marg... train \\n\",\n \"9 dynamic programming is a method for solving ma... test \"\n ]\n },\n \"execution_count\": 268,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"random_seed = 1 # can change; set for reproducibility\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"import helpers\\n\",\n \"\\n\",\n \"# create new df with Datatype (train, test, orig) column\\n\",\n \"# pass in `text_df` from above to create a complete dataframe, with all the information you need\\n\",\n \"complete_df = helpers.train_test_dataframe(text_df, random_seed=random_seed)\\n\",\n \"\\n\",\n \"# check results\\n\",\n \"complete_df.head(10)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Determining Plagiarism\\n\",\n \"\\n\",\n \"Now that you've prepared this data and created a `complete_df` of information, including the text and class associated with each file, you can move on to the task of extracting similarity features that will be useful for plagiarism classification. \\n\",\n \"\\n\",\n \"> Note: The following code exercises, assume that the `complete_df` as it exists now, will **not** have its existing columns modified. \\n\",\n \"\\n\",\n \"The `complete_df` should always include the columns: `['File', 'Task', 'Category', 'Class', 'Text', 'Datatype']`. You can add additional columns, and you can create any new DataFrames you need by copying the parts of the `complete_df` as long as you do not modify the existing values, directly.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"# Similarity Features \\n\",\n \"\\n\",\n \"One of the ways we might go about detecting plagiarism, is by computing **similarity features** that measure how similar a given answer text is as compared to the original wikipedia source text (for a specific task, a-e). The similarity features you will use are informed by [this paper on plagiarism detection](https://s3.amazonaws.com/video.udacity-data.com/topher/2019/January/5c412841_developing-a-corpus-of-plagiarised-short-answers/developing-a-corpus-of-plagiarised-short-answers.pdf). \\n\",\n \"> In this paper, researchers created features called **containment** and **longest common subsequence**. \\n\",\n \"\\n\",\n \"Using these features as input, you will train a model to distinguish between plagiarized and not-plagiarized text files.\\n\",\n \"\\n\",\n \"## Feature Engineering\\n\",\n \"\\n\",\n \"Let's talk a bit more about the features we want to include in a plagiarism detection model and how to calculate such features. In the following explanations, I'll refer to a submitted text file as a **Student Answer Text (A)** and the original, wikipedia source file (that we want to compare that answer to) as the **Wikipedia Source Text (S)**.\\n\",\n \"\\n\",\n \"### Containment\\n\",\n \"\\n\",\n \"Your first task will be to create **containment features**. To understand containment, let's first revisit a definition of [n-grams](https://en.wikipedia.org/wiki/N-gram). An *n-gram* is a sequential word grouping. For example, in a line like \\\"bayes rule gives us a way to combine prior knowledge with new information,\\\" a 1-gram is just one word, like \\\"bayes.\\\" A 2-gram might be \\\"bayes rule\\\" and a 3-gram might be \\\"combine prior knowledge.\\\"\\n\",\n \"\\n\",\n \"> Containment is defined as the **intersection** of the n-gram word count of the Wikipedia Source Text (S) with the n-gram word count of the Student Answer Text (S) *divided* by the n-gram word count of the Student Answer Text.\\n\",\n \"\\n\",\n \"$$ \\\\frac{\\\\sum{count(\\\\text{ngram}_{A}) \\\\cap count(\\\\text{ngram}_{S})}}{\\\\sum{count(\\\\text{ngram}_{A})}} $$\\n\",\n \"\\n\",\n \"If the two texts have no n-grams in common, the containment will be 0, but if _all_ their n-grams intersect then the containment will be 1. Intuitively, you can see how having longer n-gram's in common, might be an indication of cut-and-paste plagiarism. In this project, it will be up to you to decide on the appropriate `n` or several `n`'s to use in your final model.\\n\",\n \"\\n\",\n \"### EXERCISE: Create containment features\\n\",\n \"\\n\",\n \"Given the `complete_df` that you've created, you should have all the information you need to compare any Student Answer Text (A) with its appropriate Wikipedia Source Text (S). An answer for task A should be compared to the source text for task A, just as answers to tasks B, C, D, and E should be compared to the corresponding original source text.\\n\",\n \"\\n\",\n \"In this exercise, you'll complete the function, `calculate_containment` which calculates containment based upon the following parameters:\\n\",\n \"* A given DataFrame, `df` (which is assumed to be the `complete_df` from above)\\n\",\n \"* An `answer_filename`, such as 'g0pB_taskd.txt' \\n\",\n \"* An n-gram length, `n`\\n\",\n \"\\n\",\n \"### Containment calculation\\n\",\n \"\\n\",\n \"The general steps to complete this function are as follows:\\n\",\n \"1. From *all* of the text files in a given `df`, create an array of n-gram counts; it is suggested that you use a [CountVectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html) for this purpose.\\n\",\n \"2. Get the processed answer and source texts for the given `answer_filename`.\\n\",\n \"3. Calculate the containment between an answer and source text according to the following equation.\\n\",\n \"\\n\",\n \" >$$ \\\\frac{\\\\sum{count(\\\\text{ngram}_{A}) \\\\cap count(\\\\text{ngram}_{S})}}{\\\\sum{count(\\\\text{ngram}_{A})}} $$\\n\",\n \" \\n\",\n \"4. Return that containment value.\\n\",\n \"\\n\",\n \"You are encouraged to write any helper functions that you need to complete the function below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 269,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Calculate the ngram containment for one answer file/source file pair in a df\\n\",\n \"from sklearn.feature_extraction.text import CountVectorizer\\n\",\n \"\\n\",\n \"def calculate_containment(df, n, answer_filename):\\n\",\n \" '''Calculates the containment between a given answer text and its associated source text.\\n\",\n \" This function creates a count of ngrams (of a size, n) for each text file in our data.\\n\",\n \" Then calculates the containment by finding the ngram count for a given answer text, \\n\",\n \" and its associated source text, and calculating the normalized intersection of those counts.\\n\",\n \" :param df: A dataframe with columns,\\n\",\n \" 'File', 'Task', 'Category', 'Class', 'Text', and 'Datatype'\\n\",\n \" :param n: An integer that defines the ngram size\\n\",\n \" :param answer_filename: A filename for an answer text in the df, ex. 'g0pB_taskd.txt'\\n\",\n \" :return: A single containment value that represents the similarity\\n\",\n \" between an answer text and its source text.\\n\",\n \" '''\\n\",\n \" \\n\",\n \" # your code here\\n\",\n \" \\n\",\n \" answer = df[df['File'] == answer_filename]['Text'].values\\n\",\n \" source = df[df['File'] == 'orig_'+answer_filename[5:]]['Text'].values\\n\",\n \" \\n\",\n \" counts = CountVectorizer(analyzer='word', ngram_range=(n,n))\\n\",\n \" ngrams = counts.fit_transform([answer[0], source[0]])\\n\",\n \" ngram_array = ngrams.toarray()\\n\",\n \" inter_voc = []\\n\",\n \" for i in range(np.shape(ngram_array)[1]):\\n\",\n \" inter_voc.append(min(ngram_array[0][i], ngram_array[1][i]))\\n\",\n \"\\n\",\n \" contaiment = sum(inter_voc)/sum(ngram_array[0])\\n\",\n \"\\n\",\n \" return contaiment\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test cells\\n\",\n \"\\n\",\n \"After you've implemented the containment function, you can test out its behavior. \\n\",\n \"\\n\",\n \"The cell below iterates through the first few files, and calculates the original category _and_ containment values for a specified n and file.\\n\",\n \"\\n\",\n \">If you've implemented this correctly, you should see that the non-plagiarized have low or close to 0 containment values and that plagiarized examples have higher containment values, closer to 1.\\n\",\n \"\\n\",\n \"Note what happens when you change the value of n. I recommend applying your code to multiple files and comparing the resultant containment values. You should see that the highest containment values correspond to files with the highest category (`cut`) of plagiarism level.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 270,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Original category values: \\n\",\n \" [0, 3, 2, 1, 0]\\n\",\n \"\\n\",\n \"2-gram containment values: \\n\",\n \" [0.07906976744186046, 0.9846938775510204, 0.7194570135746606, 0.26881720430107525, 0.11578947368421053]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# select a value for n\\n\",\n \"n = 2\\n\",\n \"\\n\",\n \"# indices for first few files\\n\",\n \"test_indices = range(5)\\n\",\n \"\\n\",\n \"# iterate through files and calculate containment\\n\",\n \"category_vals = []\\n\",\n \"containment_vals = []\\n\",\n \"for i in test_indices:\\n\",\n \" # get level of plagiarism for a given file index\\n\",\n \" category_vals.append(complete_df.loc[i, 'Category'])\\n\",\n \" # calculate containment for given file and n\\n\",\n \" filename = complete_df.loc[i, 'File']\\n\",\n \" c = calculate_containment(complete_df, n, filename)\\n\",\n \" containment_vals.append(c)\\n\",\n \"\\n\",\n \"# print out result, does it make sense?\\n\",\n \"print('Original category values: \\\\n', category_vals)\\n\",\n \"print()\\n\",\n \"print(str(n)+'-gram containment values: \\\\n', containment_vals)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 271,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed!\\n\"\n ]\n }\n ],\n \"source\": [\n \"# run this test cell\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# test containment calculation\\n\",\n \"# params: complete_df from before, and containment function\\n\",\n \"tests.test_containment(complete_df, calculate_containment)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### QUESTION 1: Why can we calculate containment features across *all* data (training & test), prior to splitting the DataFrame for modeling? That is, what about the containment calculation means that the test and training data do not influence each other?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Answer:** Because the calculation of the contaiment features depend only on the data point and the source that we are compairng it to, so there will be no chnage in the calculcated values whether it was calcualted before splitting or after.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"## Longest Common Subsequence\\n\",\n \"\\n\",\n \"Containment a good way to find overlap in word usage between two documents; it may help identify cases of cut-and-paste as well as paraphrased levels of plagiarism. Since plagiarism is a fairly complex task with varying levels, it's often useful to include other measures of similarity. The paper also discusses a feature called **longest common subsequence**.\\n\",\n \"\\n\",\n \"> The longest common subsequence is the longest string of words (or letters) that are *the same* between the Wikipedia Source Text (S) and the Student Answer Text (A). This value is also normalized by dividing by the total number of words (or letters) in the Student Answer Text. \\n\",\n \"\\n\",\n \"In this exercise, we'll ask you to calculate the longest common subsequence of words between two texts.\\n\",\n \"\\n\",\n \"### EXERCISE: Calculate the longest common subsequence\\n\",\n \"\\n\",\n \"Complete the function `lcs_norm_word`; this should calculate the *longest common subsequence* of words between a Student Answer Text and corresponding Wikipedia Source Text. \\n\",\n \"\\n\",\n \"It may be helpful to think of this in a concrete example. A Longest Common Subsequence (LCS) problem may look as follows:\\n\",\n \"* Given two texts: text A (answer text) of length n, and string S (original source text) of length m. Our goal is to produce their longest common subsequence of words: the longest sequence of words that appear left-to-right in both texts (though the words don't have to be in continuous order).\\n\",\n \"* Consider:\\n\",\n \" * A = \\\"i think pagerank is a link analysis algorithm used by google that uses a system of weights attached to each element of a hyperlinked set of documents\\\"\\n\",\n \" * S = \\\"pagerank is a link analysis algorithm used by the google internet search engine that assigns a numerical weighting to each element of a hyperlinked set of documents\\\"\\n\",\n \"\\n\",\n \"* In this case, we can see that the start of each sentence of fairly similar, having overlap in the sequence of words, \\\"pagerank is a link analysis algorithm used by\\\" before diverging slightly. Then we **continue moving left -to-right along both texts** until we see the next common sequence; in this case it is only one word, \\\"google\\\". Next we find \\\"that\\\" and \\\"a\\\" and finally the same ending \\\"to each element of a hyperlinked set of documents\\\".\\n\",\n \"* Below, is a clear visual of how these sequences were found, sequentially, in each text.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"* Now, those words appear in left-to-right order in each document, sequentially, and even though there are some words in between, we count this as the longest common subsequence between the two texts. \\n\",\n \"* If I count up each word that I found in common I get the value 20. **So, LCS has length 20**. \\n\",\n \"* Next, to normalize this value, divide by the total length of the student answer; in this example that length is only 27. **So, the function `lcs_norm_word` should return the value `20/27` or about `0.7408`.**\\n\",\n \"\\n\",\n \"In this way, LCS is a great indicator of cut-and-paste plagiarism or if someone has referenced the same source text multiple times in an answer.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### LCS, dynamic programming\\n\",\n \"\\n\",\n \"If you read through the scenario above, you can see that this algorithm depends on looking at two texts and comparing them word by word. You can solve this problem in multiple ways. First, it may be useful to `.split()` each text into lists of comma separated words to compare. Then, you can iterate through each word in the texts and compare them, adding to your value for LCS as you go. \\n\",\n \"\\n\",\n \"The method I recommend for implementing an efficient LCS algorithm is: using a matrix and dynamic programming. **Dynamic programming** is all about breaking a larger problem into a smaller set of subproblems, and building up a complete result without having to repeat any subproblems. \\n\",\n \"\\n\",\n \"This approach assumes that you can split up a large LCS task into a combination of smaller LCS tasks. Let's look at a simple example that compares letters:\\n\",\n \"\\n\",\n \"* A = \\\"ABCD\\\"\\n\",\n \"* S = \\\"BD\\\"\\n\",\n \"\\n\",\n \"We can see right away that the longest subsequence of _letters_ here is 2 (B and D are in sequence in both strings). And we can calculate this by looking at relationships between each letter in the two strings, A and S.\\n\",\n \"\\n\",\n \"Here, I have a matrix with the letters of A on top and the letters of S on the left side:\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"This starts out as a matrix that has as many columns and rows as letters in the strings S and O **+1** additional row and column, filled with zeros on the top and left sides. So, in this case, instead of a 2x4 matrix it is a 3x5.\\n\",\n \"\\n\",\n \"Now, we can fill this matrix up by breaking it into smaller LCS problems. For example, let's first look at the shortest substrings: the starting letter of A and S. We'll first ask, what is the Longest Common Subsequence between these two letters \\\"A\\\" and \\\"B\\\"? \\n\",\n \"\\n\",\n \"**Here, the answer is zero and we fill in the corresponding grid cell with that value.**\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Then, we ask the next question, what is the LCS between \\\"AB\\\" and \\\"B\\\"?\\n\",\n \"\\n\",\n \"**Here, we have a match, and can fill in the appropriate value 1**.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"If we continue, we get to a final matrix that looks as follows, with a **2** in the bottom right corner.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"The final LCS will be that value **2** *normalized* by the number of n-grams in A. So, our normalized value is 2/4 = **0.5**.\\n\",\n \"\\n\",\n \"### The matrix rules\\n\",\n \"\\n\",\n \"One thing to notice here is that, you can efficiently fill up this matrix one cell at a time. Each grid cell only depends on the values in the grid cells that are directly on top and to the left of it, or on the diagonal/top-left. The rules are as follows:\\n\",\n \"* Start with a matrix that has one extra row and column of zeros.\\n\",\n \"* As you traverse your string:\\n\",\n \" * If there is a match, fill that grid cell with the value to the top-left of that cell *plus* one. So, in our case, when we found a matching B-B, we added +1 to the value in the top-left of the matching cell, 0.\\n\",\n \" * If there is not a match, take the *maximum* value from either directly to the left or the top cell, and carry that value over to the non-match cell.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"After completely filling the matrix, **the bottom-right cell will hold the non-normalized LCS value**.\\n\",\n \"\\n\",\n \"This matrix treatment can be applied to a set of words instead of letters. Your function should apply this to the words in two texts and return the normalized LCS value.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 272,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0., 0., 0.],\\n\",\n \" [0., 0., 0.]])\"\n ]\n },\n \"execution_count\": 272,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.zeros((2,3))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 273,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compute the normalized LCS given an answer text and a source text\\n\",\n \"def lcs_norm_word(answer_text, source_text):\\n\",\n \" '''Computes the longest common subsequence of words in two texts; returns a normalized value.\\n\",\n \" :param answer_text: The pre-processed text for an answer text\\n\",\n \" :param source_text: The pre-processed text for an answer's associated source text\\n\",\n \" :return: A normalized LCS value'''\\n\",\n \" \\n\",\n \" # your code here\\n\",\n \" answer_words = answer_text.split()\\n\",\n \" source_words = source_text.split()\\n\",\n \" lsc_matrix = np.empty((len(source_words)+1, len(answer_words)+1))\\n\",\n \" lsc_matrix[0,:] = 0\\n\",\n \" lsc_matrix[:,0] = 0\\n\",\n \" \\n\",\n \" for i in range(len(source_words)):\\n\",\n \" for j in range(len(answer_words)):\\n\",\n \" if answer_words[j] == source_words[i]:\\n\",\n \" lsc_matrix[i+1,j+1] = lsc_matrix[i,j] + 1\\n\",\n \" else : \\n\",\n \" lsc_matrix[i+1,j+1] = max(lsc_matrix[i,j+1], lsc_matrix[i+1,j])\\n\",\n \" \\n\",\n \" lsc_norm = lsc_matrix[i+1,j+1] / len(answer_words)\\n\",\n \" \\n\",\n \" return lsc_norm\\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test cells\\n\",\n \"\\n\",\n \"Let's start by testing out your code on the example given in the initial description.\\n\",\n \"\\n\",\n \"In the below cell, we have specified strings A (answer text) and S (original source text). We know that these texts have 20 words in common and the submitted answer is 27 words long, so the normalized, longest common subsequence should be 20/27.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 274,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"LCS = 0.7407407407407407\\n\",\n \"Test passed!\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Run the test scenario from above\\n\",\n \"# does your function return the expected value?\\n\",\n \"\\n\",\n \"A = \\\"i think pagerank is a link analysis algorithm used by google that uses a system of weights attached to each element of a hyperlinked set of documents\\\"\\n\",\n \"S = \\\"pagerank is a link analysis algorithm used by the google internet search engine that assigns a numerical weighting to each element of a hyperlinked set of documents\\\"\\n\",\n \"\\n\",\n \"# calculate LCS\\n\",\n \"lcs = lcs_norm_word(A, S)\\n\",\n \"print('LCS = ', lcs)\\n\",\n \"\\n\",\n \"\\n\",\n \"# expected value test\\n\",\n \"assert lcs==20/27., \\\"Incorrect LCS value, expected about 0.7408, got \\\"+str(lcs)\\n\",\n \"\\n\",\n \"print('Test passed!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This next cell runs a more rigorous test.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 275,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed!\\n\"\n ]\n }\n ],\n \"source\": [\n \"# run test cell\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# test lcs implementation\\n\",\n \"# params: complete_df from before, and lcs_norm_word function\\n\",\n \"tests.test_lcs(complete_df, lcs_norm_word)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, take a look at a few resultant values for `lcs_norm_word`. Just like before, you should see that higher values correspond to higher levels of plagiarism.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 276,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Original category values: \\n\",\n \" [0, 3, 2, 1, 0]\\n\",\n \"\\n\",\n \"Normalized LCS values: \\n\",\n \" [0.1917808219178082, 0.8207547169811321, 0.8464912280701754, 0.3160621761658031, 0.24257425742574257]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# test on your own\\n\",\n \"test_indices = range(5) # look at first few files\\n\",\n \"\\n\",\n \"category_vals = []\\n\",\n \"lcs_norm_vals = []\\n\",\n \"# iterate through first few docs and calculate LCS\\n\",\n \"for i in test_indices:\\n\",\n \" category_vals.append(complete_df.loc[i, 'Category'])\\n\",\n \" # get texts to compare\\n\",\n \" answer_text = complete_df.loc[i, 'Text'] \\n\",\n \" task = complete_df.loc[i, 'Task']\\n\",\n \" # we know that source texts have Class = -1\\n\",\n \" orig_rows = complete_df[(complete_df['Class'] == -1)]\\n\",\n \" orig_row = orig_rows[(orig_rows['Task'] == task)]\\n\",\n \" source_text = orig_row['Text'].values[0]\\n\",\n \" \\n\",\n \" # calculate lcs\\n\",\n \" lcs_val = lcs_norm_word(answer_text, source_text)\\n\",\n \" lcs_norm_vals.append(lcs_val)\\n\",\n \"\\n\",\n \"# print out result, does it make sense?\\n\",\n \"print('Original category values: \\\\n', category_vals)\\n\",\n \"print()\\n\",\n \"print('Normalized LCS values: \\\\n', lcs_norm_vals)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Create All Features\\n\",\n \"\\n\",\n \"Now that you've completed the feature calculation functions, it's time to actually create multiple features and decide on which ones to use in your final model! In the below cells, you're provided two helper functions to help you create multiple features and store those in a DataFrame, `features_df`.\\n\",\n \"\\n\",\n \"### Creating multiple containment features\\n\",\n \"\\n\",\n \"Your completed `calculate_containment` function will be called in the next cell, which defines the helper function `create_containment_features`. \\n\",\n \"\\n\",\n \"> This function returns a list of containment features, calculated for a given `n` and for *all* files in a df (assumed to the the `complete_df`).\\n\",\n \"\\n\",\n \"For our original files, the containment value is set to a special value, -1.\\n\",\n \"\\n\",\n \"This function gives you the ability to easily create several containment features, of different n-gram lengths, for each of our text files.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 277,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# Function returns a list of containment features, calculated for a given n \\n\",\n \"# Should return a list of length 100 for all files in a complete_df\\n\",\n \"def create_containment_features(df, n, column_name=None):\\n\",\n \" \\n\",\n \" containment_values = []\\n\",\n \" \\n\",\n \" if(column_name==None):\\n\",\n \" column_name = 'c_'+str(n) # c_1, c_2, .. c_n\\n\",\n \" \\n\",\n \" # iterates through dataframe rows\\n\",\n \" for i in df.index:\\n\",\n \" file = df.loc[i, 'File']\\n\",\n \" # Computes features using calculate_containment function\\n\",\n \" if df.loc[i,'Category'] > -1:\\n\",\n \" c = calculate_containment(df, n, file)\\n\",\n \" containment_values.append(c)\\n\",\n \" # Sets value to -1 for original tasks \\n\",\n \" else:\\n\",\n \" containment_values.append(-1)\\n\",\n \" \\n\",\n \" print(str(n)+'-gram containment features created!')\\n\",\n \" return containment_values\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Creating LCS features\\n\",\n \"\\n\",\n \"Below, your complete `lcs_norm_word` function is used to create a list of LCS features for all the answer files in a given DataFrame (again, this assumes you are passing in the `complete_df`. It assigns a special value for our original, source files, -1.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 278,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# Function creates lcs feature and add it to the dataframe\\n\",\n \"def create_lcs_features(df, column_name='lcs_word'):\\n\",\n \" \\n\",\n \" lcs_values = []\\n\",\n \" \\n\",\n \" # iterate through files in dataframe\\n\",\n \" for i in df.index:\\n\",\n \" # Computes LCS_norm words feature using function above for answer tasks\\n\",\n \" if df.loc[i,'Category'] > -1:\\n\",\n \" # get texts to compare\\n\",\n \" answer_text = df.loc[i, 'Text'] \\n\",\n \" task = df.loc[i, 'Task']\\n\",\n \" # we know that source texts have Class = -1\\n\",\n \" orig_rows = df[(df['Class'] == -1)]\\n\",\n \" orig_row = orig_rows[(orig_rows['Task'] == task)]\\n\",\n \" source_text = orig_row['Text'].values[0]\\n\",\n \"\\n\",\n \" # calculate lcs\\n\",\n \" lcs = lcs_norm_word(answer_text, source_text)\\n\",\n \" lcs_values.append(lcs)\\n\",\n \" # Sets to -1 for original tasks \\n\",\n \" else:\\n\",\n \" lcs_values.append(-1)\\n\",\n \"\\n\",\n \" print('LCS features created!')\\n\",\n \" return lcs_values\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## EXERCISE: Create a features DataFrame by selecting an `ngram_range`\\n\",\n \"\\n\",\n \"The paper suggests calculating the following features: containment *1-gram to 5-gram* and *longest common subsequence*. \\n\",\n \"> In this exercise, you can choose to create even more features, for example from *1-gram to 7-gram* containment features and *longest common subsequence*. \\n\",\n \"\\n\",\n \"You'll want to create at least 6 features to choose from as you think about which to give to your final, classification model. Defining and comparing at least 6 different features allows you to discard any features that seem redundant, and choose to use the best features for your final model!\\n\",\n \"\\n\",\n \"In the below cell **define an n-gram range**; these will be the n's you use to create n-gram containment features. The rest of the feature creation code is provided.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 279,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1-gram containment features created!\\n\",\n \"2-gram containment features created!\\n\",\n \"3-gram containment features created!\\n\",\n \"4-gram containment features created!\\n\",\n \"5-gram containment features created!\\n\",\n \"6-gram containment features created!\\n\",\n \"LCS features created!\\n\",\n \"\\n\",\n \"Features: ['c_1', 'c_2', 'c_3', 'c_4', 'c_5', 'c_6', 'lcs_word']\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Define an ngram range\\n\",\n \"ngram_range = range(1,7)\\n\",\n \"\\n\",\n \"\\n\",\n \"# The following code may take a minute to run, depending on your ngram_range\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"features_list = []\\n\",\n \"\\n\",\n \"# Create features in a features_df\\n\",\n \"all_features = np.zeros((len(ngram_range)+1, len(complete_df)))\\n\",\n \"\\n\",\n \"# Calculate features for containment for ngrams in range\\n\",\n \"i=0\\n\",\n \"for n in ngram_range:\\n\",\n \" column_name = 'c_'+str(n)\\n\",\n \" features_list.append(column_name)\\n\",\n \" # create containment features\\n\",\n \" all_features[i]=np.squeeze(create_containment_features(complete_df, n))\\n\",\n \" i+=1\\n\",\n \"\\n\",\n \"# Calculate features for LCS_Norm Words \\n\",\n \"features_list.append('lcs_word')\\n\",\n \"all_features[i]= np.squeeze(create_lcs_features(complete_df))\\n\",\n \"\\n\",\n \"# create a features dataframe\\n\",\n \"features_df = pd.DataFrame(np.transpose(all_features), columns=features_list)\\n\",\n \"\\n\",\n \"# Print all features/columns\\n\",\n \"print()\\n\",\n \"print('Features: ', features_list)\\n\",\n \"print()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 280,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
c_1
\\n\",\n \"
c_2
\\n\",\n \"
c_3
\\n\",\n \"
c_4
\\n\",\n \"
c_5
\\n\",\n \"
c_6
\\n\",\n \"
lcs_word
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
0.398148
\\n\",\n \"
0.079070
\\n\",\n \"
0.009346
\\n\",\n \"
0.000000
\\n\",\n \"
0.000000
\\n\",\n \"
0.000000
\\n\",\n \"
0.191781
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
1.000000
\\n\",\n \"
0.984694
\\n\",\n \"
0.964103
\\n\",\n \"
0.943299
\\n\",\n \"
0.922280
\\n\",\n \"
0.901042
\\n\",\n \"
0.820755
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
0.869369
\\n\",\n \"
0.719457
\\n\",\n \"
0.613636
\\n\",\n \"
0.515982
\\n\",\n \"
0.449541
\\n\",\n \"
0.382488
\\n\",\n \"
0.846491
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
0.593583
\\n\",\n \"
0.268817
\\n\",\n \"
0.156757
\\n\",\n \"
0.108696
\\n\",\n \"
0.081967
\\n\",\n \"
0.060440
\\n\",\n \"
0.316062
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
0.544503
\\n\",\n \"
0.115789
\\n\",\n \"
0.031746
\\n\",\n \"
0.005319
\\n\",\n \"
0.000000
\\n\",\n \"
0.000000
\\n\",\n \"
0.242574
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
5
\\n\",\n \"
0.329502
\\n\",\n \"
0.053846
\\n\",\n \"
0.007722
\\n\",\n \"
0.003876
\\n\",\n \"
0.000000
\\n\",\n \"
0.000000
\\n\",\n \"
0.161172
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
6
\\n\",\n \"
0.590308
\\n\",\n \"
0.150442
\\n\",\n \"
0.035556
\\n\",\n \"
0.004464
\\n\",\n \"
0.000000
\\n\",\n \"
0.000000
\\n\",\n \"
0.301653
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
7
\\n\",\n \"
0.765306
\\n\",\n \"
0.709898
\\n\",\n \"
0.664384
\\n\",\n \"
0.625430
\\n\",\n \"
0.589655
\\n\",\n \"
0.553633
\\n\",\n \"
0.621711
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
8
\\n\",\n \"
0.759777
\\n\",\n \"
0.505618
\\n\",\n \"
0.395480
\\n\",\n \"
0.306818
\\n\",\n \"
0.245714
\\n\",\n \"
0.195402
\\n\",\n \"
0.484305
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
9
\\n\",\n \"
0.884444
\\n\",\n \"
0.526786
\\n\",\n \"
0.340807
\\n\",\n \"
0.247748
\\n\",\n \"
0.180995
\\n\",\n \"
0.150000
\\n\",\n \"
0.597458
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" c_1 c_2 c_3 c_4 c_5 c_6 lcs_word\\n\",\n \"0 0.398148 0.079070 0.009346 0.000000 0.000000 0.000000 0.191781\\n\",\n \"1 1.000000 0.984694 0.964103 0.943299 0.922280 0.901042 0.820755\\n\",\n \"2 0.869369 0.719457 0.613636 0.515982 0.449541 0.382488 0.846491\\n\",\n \"3 0.593583 0.268817 0.156757 0.108696 0.081967 0.060440 0.316062\\n\",\n \"4 0.544503 0.115789 0.031746 0.005319 0.000000 0.000000 0.242574\\n\",\n \"5 0.329502 0.053846 0.007722 0.003876 0.000000 0.000000 0.161172\\n\",\n \"6 0.590308 0.150442 0.035556 0.004464 0.000000 0.000000 0.301653\\n\",\n \"7 0.765306 0.709898 0.664384 0.625430 0.589655 0.553633 0.621711\\n\",\n \"8 0.759777 0.505618 0.395480 0.306818 0.245714 0.195402 0.484305\\n\",\n \"9 0.884444 0.526786 0.340807 0.247748 0.180995 0.150000 0.597458\"\n ]\n },\n \"execution_count\": 280,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# print some results \\n\",\n \"features_df.head(10)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Correlated Features\\n\",\n \"\\n\",\n \"You should use feature correlation across the *entire* dataset to determine which features are ***too*** **highly-correlated** with each other to include both features in a single model. For this analysis, you can use the *entire* dataset due to the small sample size we have. \\n\",\n \"\\n\",\n \"All of our features try to measure the similarity between two texts. Since our features are designed to measure similarity, it is expected that these features will be highly-correlated. Many classification models, for example a Naive Bayes classifier, rely on the assumption that features are *not* highly correlated; highly-correlated features may over-inflate the importance of a single feature. \\n\",\n \"\\n\",\n \"So, you'll want to choose your features based on which pairings have the lowest correlation. These correlation values range between 0 and 1; from low to high correlation, and are displayed in a [correlation matrix](https://www.displayr.com/what-is-a-correlation-matrix/), below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 281,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
c_1
\\n\",\n \"
c_2
\\n\",\n \"
c_3
\\n\",\n \"
c_4
\\n\",\n \"
c_5
\\n\",\n \"
c_6
\\n\",\n \"
lcs_word
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
c_1
\\n\",\n \"
1.00
\\n\",\n \"
0.94
\\n\",\n \"
0.90
\\n\",\n \"
0.89
\\n\",\n \"
0.88
\\n\",\n \"
0.87
\\n\",\n \"
0.97
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
c_2
\\n\",\n \"
0.94
\\n\",\n \"
1.00
\\n\",\n \"
0.99
\\n\",\n \"
0.98
\\n\",\n \"
0.97
\\n\",\n \"
0.96
\\n\",\n \"
0.98
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
c_3
\\n\",\n \"
0.90
\\n\",\n \"
0.99
\\n\",\n \"
1.00
\\n\",\n \"
1.00
\\n\",\n \"
0.99
\\n\",\n \"
0.98
\\n\",\n \"
0.97
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
c_4
\\n\",\n \"
0.89
\\n\",\n \"
0.98
\\n\",\n \"
1.00
\\n\",\n \"
1.00
\\n\",\n \"
1.00
\\n\",\n \"
0.99
\\n\",\n \"
0.95
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
c_5
\\n\",\n \"
0.88
\\n\",\n \"
0.97
\\n\",\n \"
0.99
\\n\",\n \"
1.00
\\n\",\n \"
1.00
\\n\",\n \"
1.00
\\n\",\n \"
0.95
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
c_6
\\n\",\n \"
0.87
\\n\",\n \"
0.96
\\n\",\n \"
0.98
\\n\",\n \"
0.99
\\n\",\n \"
1.00
\\n\",\n \"
1.00
\\n\",\n \"
0.94
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
lcs_word
\\n\",\n \"
0.97
\\n\",\n \"
0.98
\\n\",\n \"
0.97
\\n\",\n \"
0.95
\\n\",\n \"
0.95
\\n\",\n \"
0.94
\\n\",\n \"
1.00
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" c_1 c_2 c_3 c_4 c_5 c_6 lcs_word\\n\",\n \"c_1 1.00 0.94 0.90 0.89 0.88 0.87 0.97\\n\",\n \"c_2 0.94 1.00 0.99 0.98 0.97 0.96 0.98\\n\",\n \"c_3 0.90 0.99 1.00 1.00 0.99 0.98 0.97\\n\",\n \"c_4 0.89 0.98 1.00 1.00 1.00 0.99 0.95\\n\",\n \"c_5 0.88 0.97 0.99 1.00 1.00 1.00 0.95\\n\",\n \"c_6 0.87 0.96 0.98 0.99 1.00 1.00 0.94\\n\",\n \"lcs_word 0.97 0.98 0.97 0.95 0.95 0.94 1.00\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# Create correlation matrix for just Features to determine different models to test\\n\",\n \"corr_matrix = features_df.corr().abs().round(2)\\n\",\n \"\\n\",\n \"# display shows all of a dataframe\\n\",\n \"display(corr_matrix)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## EXERCISE: Create selected train/test data\\n\",\n \"\\n\",\n \"Complete the `train_test_data` function below. This function should take in the following parameters:\\n\",\n \"* `complete_df`: A DataFrame that contains all of our processed text data, file info, datatypes, and class labels\\n\",\n \"* `features_df`: A DataFrame of all calculated features, such as containment for ngrams, n= 1-5, and lcs values for each text file listed in the `complete_df` (this was created in the above cells)\\n\",\n \"* `selected_features`: A list of feature column names, ex. `['c_1', 'lcs_word']`, which will be used to select the final features in creating train/test sets of data.\\n\",\n \"\\n\",\n \"It should return two tuples:\\n\",\n \"* `(train_x, train_y)`, selected training features and their corresponding class labels (0/1)\\n\",\n \"* `(test_x, test_y)`, selected training features and their corresponding class labels (0/1)\\n\",\n \"\\n\",\n \"** Note: x and y should be arrays of feature values and numerical class labels, respectively; not DataFrames.**\\n\",\n \"\\n\",\n \"Looking at the above correlation matrix, you should decide on a **cutoff** correlation value, less than 1.0, to determine which sets of features are *too* highly-correlated to be included in the final training and test data. If you cannot find features that are less correlated than some cutoff value, it is suggested that you increase the number of features (longer n-grams) to choose from or use *only one or two* features in your final model to avoid introducing highly-correlated features.\\n\",\n \"\\n\",\n \"Recall that the `complete_df` has a `Datatype` column that indicates whether data should be `train` or `test` data; this should help you split the data appropriately.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 309,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Takes in dataframes and a list of selected features (column names) \\n\",\n \"# and returns (train_x, train_y), (test_x, test_y)\\n\",\n \"def train_test_data(complete_df, features_df, selected_features):\\n\",\n \" '''Gets selected training and test features from given dataframes, and \\n\",\n \" returns tuples for training and test features and their corresponding class labels.\\n\",\n \" :param complete_df: A dataframe with all of our processed text data, datatypes, and labels\\n\",\n \" :param features_df: A dataframe of all computed, similarity features\\n\",\n \" :param selected_features: An array of selected features that correspond to certain columns in `features_df`\\n\",\n \" :return: training and test features and labels: (train_x, train_y), (test_x, test_y)'''\\n\",\n \" \\n\",\n \" selected_features_df = features_df.loc[:,selected_features]\\n\",\n \" train_index = complete_df[complete_df['Datatype']== 'train'].index\\n\",\n \" train_x = selected_features_df.loc[train_index, :]\\n\",\n \" train_x = train_x.to_numpy()\\n\",\n \" train_y = complete_df.loc[train_index, 'Class'].values\\n\",\n \" \\n\",\n \" test_index = complete_df[complete_df['Datatype']== 'test'].index\\n\",\n \" test_x = selected_features_df.loc[test_index, :]\\n\",\n \" test_x = test_x.to_numpy()\\n\",\n \" test_y = complete_df.loc[test_index, 'Class'].values\\n\",\n \" \\n\",\n \" #train_test_df = pd.concat([complete_df, selected_features_df]) \\n\",\n \" # get the training features\\n\",\n \" #train_x = train_test_df[train_test_df['Datatype'] == 'train'].copy()\\n\",\n \" #train_x = train_x[selected_features]\\n\",\n \" #train_x.drop(columns='Class', inplace = True)\\n\",\n \" #train_x = train_x.to_numpy()\\n\",\n \" #z And training class labels (0 or 1)\\n\",\n \" #train_y = train_test_df[train_test_df['Datatype'] == 'train']['Class'].values\\n\",\n \" \\n\",\n \" # get the test features and labels\\n\",\n \" #test_x = train_test_df[train_test_df['Datatype'] == 'test'].copy()\\n\",\n \" #test_x.drop(columns='Class', inplace = True)\\n\",\n \" #test_x = test_x[selected_features]\\n\",\n \" #test_x = test_x.to_numpy()\\n\",\n \" #test_y = train_test_df[train_test_df['Datatype'] == 'test']['Class'].values\\n\",\n \" \\n\",\n \" return (train_x, train_y), (test_x, test_y)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test cells\\n\",\n \"\\n\",\n \"Below, test out your implementation and create the final train/test data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 310,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed!\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"test_selection = list(features_df)[:2] # first couple columns as a test\\n\",\n \"# test that the correct train/test data is created\\n\",\n \"(train_x, train_y), (test_x, test_y) = train_test_data(complete_df, features_df, test_selection)\\n\",\n \"\\n\",\n \"# params: generated train/test data\\n\",\n \"tests.test_data_split(train_x, train_y, test_x, test_y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## EXERCISE: Select \\\"good\\\" features\\n\",\n \"\\n\",\n \"If you passed the test above, you can create your own train/test data, below. \\n\",\n \"\\n\",\n \"Define a list of features you'd like to include in your final mode, `selected_features`; this is a list of the features names you want to include.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 312,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training size: 70\\n\",\n \"Test size: 25\\n\",\n \"\\n\",\n \"Training df sample: \\n\",\n \" [[0.39814815 0. 0.19178082]\\n\",\n \" [0.86936937 0.44954128 0.84649123]\\n\",\n \" [0.59358289 0.08196721 0.31606218]\\n\",\n \" [0.54450262 0. 0.24257426]\\n\",\n \" [0.32950192 0. 0.16117216]\\n\",\n \" [0.59030837 0. 0.30165289]\\n\",\n \" [0.75977654 0.24571429 0.48430493]\\n\",\n \" [0.51612903 0. 0.27083333]\\n\",\n \" [0.44086022 0. 0.22395833]\\n\",\n \" [0.97945205 0.78873239 0.9 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Select your list of features, this should be column names from features_df\\n\",\n \"# ex. ['c_1', 'lcs_word']\\n\",\n \"selected_features = ['c_1', 'c_5', 'lcs_word']\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"(train_x, train_y), (test_x, test_y) = train_test_data(complete_df, features_df, selected_features)\\n\",\n \"\\n\",\n \"# check that division of samples seems correct\\n\",\n \"# these should add up to 95 (100 - 5 original files)\\n\",\n \"print('Training size: ', len(train_x))\\n\",\n \"print('Test size: ', len(test_x))\\n\",\n \"print()\\n\",\n \"print('Training df sample: \\\\n', train_x[:10])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Question 2: How did you decide on which features to include in your final model? \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Answer:** Based on the correlation between the features, so the correlation between the C_1 and C_5 was small and also the importance of the feature, as LSC is an important feature in detection of the palarigized text.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"## Creating Final Data Files\\n\",\n \"\\n\",\n \"Now, you are almost ready to move on to training a model in SageMaker!\\n\",\n \"\\n\",\n \"You'll want to access your train and test data in SageMaker and upload it to S3. In this project, SageMaker will expect the following format for your train/test data:\\n\",\n \"* Training and test data should be saved in one `.csv` file each, ex `train.csv` and `test.csv`\\n\",\n \"* These files should have class labels in the first column and features in the rest of the columns\\n\",\n \"\\n\",\n \"This format follows the practice, outlined in the [SageMaker documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html), which reads: \\\"Amazon SageMaker requires that a CSV file doesn't have a header record and that the target variable [class label] is in the first column.\\\"\\n\",\n \"\\n\",\n \"## EXERCISE: Create csv files\\n\",\n \"\\n\",\n \"Define a function that takes in x (features) and y (labels) and saves them to one `.csv` file at the path `data_dir/filename`.\\n\",\n \"\\n\",\n \"It may be useful to use pandas to merge your features and labels into one DataFrame and then convert that into a csv file. You can make sure to get rid of any incomplete rows, in a DataFrame, by using `dropna`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 343,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def make_csv(x, y, filename, data_dir):\\n\",\n \" '''Merges features and labels and converts them into one csv file with labels in the first column.\\n\",\n \" :param x: Data features\\n\",\n \" :param y: Data labels\\n\",\n \" :param file_name: Name of csv file, ex. 'train.csv'\\n\",\n \" :param data_dir: The directory where files will be saved\\n\",\n \" '''\\n\",\n \" # make data dir, if it does not exist\\n\",\n \" if not os.path.exists(data_dir):\\n\",\n \" os.makedirs(data_dir)\\n\",\n \" \\n\",\n \" \\n\",\n \" # your code here\\n\",\n \" data = pd.DataFrame(x)\\n\",\n \" data.insert(0,'Class',y)\\n\",\n \" data.dropna(inplace=True)\\n\",\n \" data.to_csv(str(data_dir)+'/'+str(filename), header=False, index=False)\\n\",\n \" \\n\",\n \" # nothing is returned, but a print statement indicates that the function has run\\n\",\n \" print('Path created: '+str(data_dir)+'/'+str(filename))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test cells\\n\",\n \"\\n\",\n \"Test that your code produces the correct format for a `.csv` file, given some text features and labels.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 344,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
2
\\n\",\n \"
3
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
0.398148
\\n\",\n \"
0.000100
\\n\",\n \"
0.191781
\\n\",\n \"
0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
1
\\n\",\n \"
0.869369
\\n\",\n \"
0.449541
\\n\",\n \"
0.846491
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
0.440860
\\n\",\n \"
0.000000
\\n\",\n \"
0.223958
\\n\",\n \"
1
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" 0 1 2 3\\n\",\n \"0 0.398148 0.000100 0.191781 0\\n\",\n \"1 0.869369 0.449541 0.846491 1\\n\",\n \"2 0.440860 0.000000 0.223958 1\"\n ]\n },\n \"execution_count\": 344,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fake_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 345,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Path created: test_csv/to_delete.csv\\n\",\n \"Tests passed!\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"fake_x = [ [0.39814815, 0.0001, 0.19178082], \\n\",\n \" [0.86936937, 0.44954128, 0.84649123], \\n\",\n \" [0.44086022, 0., 0.22395833] ]\\n\",\n \"\\n\",\n \"fake_y = [0, 1, 1]\\n\",\n \"\\n\",\n \"make_csv(fake_x, fake_y, filename='to_delete.csv', data_dir='test_csv')\\n\",\n \"\\n\",\n \"# read in and test dimensions\\n\",\n \"fake_df = pd.read_csv('test_csv/to_delete.csv', header=None)\\n\",\n \"\\n\",\n \"# check shape\\n\",\n \"assert fake_df.shape==(3, 4), \\\\\\n\",\n \" 'The file should have as many rows as data_points and as many columns as features+1 (for indices).'\\n\",\n \"# check that first column = labels\\n\",\n \"assert np.all(fake_df.iloc[:,0].values==fake_y), 'First column is not equal to the labels, fake_y.'\\n\",\n \"print('Tests passed!')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 346,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# delete the test csv file, generated above\\n\",\n \"! rm -rf test_csv\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you've passed the tests above, run the following cell to create `train.csv` and `test.csv` files in a directory that you specify! This will save the data in a local directory. Remember the name of this directory because you will reference it again when uploading this data to S3.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 347,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Path created: plagiarism_data/train.csv\\n\",\n \"Path created: plagiarism_data/test.csv\\n\"\n ]\n }\n ],\n \"source\": [\n \"# can change directory, if you want\\n\",\n \"data_dir = 'plagiarism_data'\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"make_csv(train_x, train_y, filename='train.csv', data_dir=data_dir)\\n\",\n \"make_csv(test_x, test_y, filename='test.csv', data_dir=data_dir)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Up Next\\n\",\n \"\\n\",\n \"Now that you've done some feature engineering and created some training and test data, you are ready to train and deploy a plagiarism classification model. The next notebook will utilize SageMaker resources to train and test a model that you design.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda_amazonei_mxnet_p36\",\n \"language\": \"python\",\n \"name\": \"conda_amazonei_mxnet_p36\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/3_Training_a_Model.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Plagiarism Detection Model\\n\",\n \"\\n\",\n \"Now that you've created training and test data, you are ready to define and train a model. Your goal in this notebook, will be to train a binary classification model that learns to label an answer file as either plagiarized or not, based on the features you provide the model.\\n\",\n \"\\n\",\n \"This task will be broken down into a few discrete steps:\\n\",\n \"\\n\",\n \"* Upload your data to S3.\\n\",\n \"* Define a binary classification model and a training script.\\n\",\n \"* Train your model and deploy it.\\n\",\n \"* Evaluate your deployed classifier and answer some questions about your approach.\\n\",\n \"\\n\",\n \"To complete this notebook, you'll have to complete all given exercises and answer all the questions in this notebook.\\n\",\n \"> All your tasks will be clearly labeled **EXERCISE** and questions as **QUESTION**.\\n\",\n \"\\n\",\n \"It will be up to you to explore different classification models and decide on a model that gives you the best performance for this dataset.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Load Data to S3\\n\",\n \"\\n\",\n \"In the last notebook, you should have created two files: a `training.csv` and `test.csv` file with the features and class labels for the given corpus of plagiarized/non-plagiarized text data. \\n\",\n \"\\n\",\n \">The below cells load in some AWS SageMaker libraries and creates a default bucket. After creating this bucket, you can upload your locally stored data to S3.\\n\",\n \"\\n\",\n \"Save your train and test `.csv` feature files, locally. To do this you can run the second notebook \\\"2_Plagiarism_Feature_Engineering\\\" in SageMaker or you can manually upload your files to this notebook using the upload icon in Jupyter Lab. Then you can upload local files to S3 by using `sagemaker_session.upload_data` and pointing directly to where the training data is saved.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: sagemaker in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (2.45.0)\\n\",\n \"Requirement already satisfied: importlib-metadata>=1.4.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (3.7.0)\\n\",\n \"Requirement already satisfied: numpy>=1.9.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (1.19.5)\\n\",\n \"Requirement already satisfied: attrs in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (20.3.0)\\n\",\n \"Requirement already satisfied: protobuf>=3.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (3.15.2)\\n\",\n \"Requirement already satisfied: google-pasta in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (0.2.0)\\n\",\n \"Requirement already satisfied: protobuf3-to-dict>=0.1.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (0.1.5)\\n\",\n \"Requirement already satisfied: pandas in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (1.1.5)\\n\",\n \"Requirement already satisfied: boto3>=1.16.32 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (1.17.79)\\n\",\n \"Requirement already satisfied: packaging>=20.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (20.9)\\n\",\n \"Requirement already satisfied: smdebug-rulesconfig==1.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (1.0.1)\\n\",\n \"Requirement already satisfied: pathos in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sagemaker) (0.2.7)\\n\",\n \"Requirement already satisfied: s3transfer<0.5.0,>=0.4.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from boto3>=1.16.32->sagemaker) (0.4.2)\\n\",\n \"Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from boto3>=1.16.32->sagemaker) (0.10.0)\\n\",\n \"Requirement already satisfied: botocore<1.21.0,>=1.20.79 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from boto3>=1.16.32->sagemaker) (1.20.79)\\n\",\n \"Requirement already satisfied: urllib3<1.27,>=1.25.4 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from botocore<1.21.0,>=1.20.79->boto3>=1.16.32->sagemaker) (1.26.4)\\n\",\n \"Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from botocore<1.21.0,>=1.20.79->boto3>=1.16.32->sagemaker) (2.8.1)\\n\",\n \"Requirement already satisfied: typing-extensions>=3.6.4 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from importlib-metadata>=1.4.0->sagemaker) (3.7.4.3)\\n\",\n \"Requirement already satisfied: zipp>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from importlib-metadata>=1.4.0->sagemaker) (3.4.0)\\n\",\n \"Requirement already satisfied: pyparsing>=2.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from packaging>=20.0->sagemaker) (2.4.7)\\n\",\n \"Requirement already satisfied: six>=1.9 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from protobuf>=3.1->sagemaker) (1.15.0)\\n\",\n \"Requirement already satisfied: pytz>=2017.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pandas->sagemaker) (2021.1)\\n\",\n \"Requirement already satisfied: multiprocess>=0.70.11 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pathos->sagemaker) (0.70.11.1)\\n\",\n \"Requirement already satisfied: ppft>=1.6.6.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pathos->sagemaker) (1.6.6.3)\\n\",\n \"Requirement already satisfied: dill>=0.3.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pathos->sagemaker) (0.3.3)\\n\",\n \"Requirement already satisfied: pox>=0.2.9 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pathos->sagemaker) (0.2.9)\\n\",\n \"Note: you may need to restart the kernel to use updated packages.\\n\"\n ]\n }\n ],\n \"source\": [\n \"pip install -U sagemaker\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import boto3\\n\",\n \"import sagemaker\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# session and role\\n\",\n \"sagemaker_session = sagemaker.Session()\\n\",\n \"role = sagemaker.get_execution_role()\\n\",\n \"\\n\",\n \"# create an S3 bucket\\n\",\n \"bucket = sagemaker_session.default_bucket()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## EXERCISE: Upload your training data to S3\\n\",\n \"\\n\",\n \"Specify the `data_dir` where you've saved your `train.csv` file. Decide on a descriptive `prefix` that defines where your data will be uploaded in the default S3 bucket. Finally, create a pointer to your training data by calling `sagemaker_session.upload_data` and passing in the required parameters. It may help to look at the [Session documentation](https://sagemaker.readthedocs.io/en/stable/session.html#sagemaker.session.Session.upload_data) or previous SageMaker code examples.\\n\",\n \"\\n\",\n \"You are expected to upload your entire directory. Later, the training script will only access the `train.csv` file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# should be the name of directory you created to save your features data\\n\",\n \"data_dir = 'plagiarism_data'\\n\",\n \"\\n\",\n \"# set prefix, a descriptive name for a directory \\n\",\n \"prefix = 'plagiarism_detection'\\n\",\n \"\\n\",\n \"# upload all data to S3\\n\",\n \"input_data= sagemaker_session.upload_data(path=data_dir, bucket=bucket, key_prefix=prefix)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test cell\\n\",\n \"\\n\",\n \"Test that your data has been successfully uploaded. The below cell prints out the items in your S3 bucket and will throw an error if it is empty. You should see the contents of your `data_dir` and perhaps some checkpoints. If you see any other files listed, then you may have some old model files that you can delete via the S3 console (though, additional files shouldn't affect the performance of model developed in this notebook).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/output/model.tar.gz\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/profiler-output/framework/training_job_end.ts\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/profiler-output/system/incremental/2021060313/1622727720.algo-1.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/profiler-output/system/incremental/2021060313/1622727780.algo-1.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/profiler-output/system/incremental/2021060313/1622727840.algo-1.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/profiler-output/system/incremental/2021060313/1622727900.algo-1.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/profiler-output/system/training_job_end.ts\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-report.html\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-report.ipynb\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/BatchSize.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/CPUBottleneck.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/Dataloader.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/GPUMemoryIncrease.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/IOBottleneck.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/LoadBalancing.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/LowGPUUtilization.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/MaxInitializationTime.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/OverallFrameworkMetrics.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/OverallSystemUsage.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-13-39-50-428/rule-output/ProfilerReport-1622727590/profiler-output/profiler-reports/StepOutlier.json\\n\",\n \"deepar-energy-consumption/output/forecasting-deepar-2021-06-03-14-36-28-776/output/model.tar.gz\\n\",\n \"deepar-energy-consumption/test/test.json\\n\",\n \"deepar-energy-consumption/train/train.json\\n\",\n \"energy_consumption/forecasting-deepar-2021-06-03-12-35-13-980/profiler-output/system/incremental/2021060312/1622723880.algo-1.json\\n\",\n \"energy_consumption/forecasting-deepar-2021-06-03-12-35-13-980/profiler-output/system/incremental/2021060312/1622723940.algo-1.json\\n\",\n \"energy_consumption/forecasting-deepar-2021-06-03-12-35-13-980/profiler-output/system/incremental/2021060312/1622724000.algo-1.json\\n\",\n \"energy_consumption/forecasting-deepar-2021-06-03-12-35-13-980/profiler-output/system/incremental/2021060312/1622724060.algo-1.json\\n\",\n \"energy_consumption/forecasting-deepar-2021-06-03-12-35-13-980/profiler-output/system/incremental/2021060312/1622724120.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-12-48-49-242/profiler-output/system/incremental/2021060312/1622724660.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-12-48-49-242/profiler-output/system/incremental/2021060312/1622724720.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-12-48-49-242/profiler-output/system/incremental/2021060312/1622724780.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-12-48-49-242/profiler-output/system/incremental/2021060312/1622724840.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-12-48-49-242/profiler-output/system/incremental/2021060312/1622724900.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-12-54-47-154/profiler-output/system/incremental/2021060312/1622725020.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-12-54-47-154/profiler-output/system/incremental/2021060312/1622725080.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/output/model.tar.gz\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/profiler-output/framework/training_job_end.ts\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/profiler-output/system/incremental/2021060313/1622725380.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/profiler-output/system/incremental/2021060313/1622725440.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/profiler-output/system/incremental/2021060313/1622725500.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/profiler-output/system/incremental/2021060313/1622725560.algo-1.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/profiler-output/system/training_job_end.ts\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-report.html\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-report.ipynb\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/BatchSize.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/CPUBottleneck.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/Dataloader.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/GPUMemoryIncrease.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/IOBottleneck.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/LoadBalancing.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/LowGPUUtilization.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/MaxInitializationTime.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/OverallFrameworkMetrics.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/OverallSystemUsage.json\\n\",\n \"energy_consumption/output/forecasting-deepar-2021-06-03-13-00-35-591/rule-output/ProfilerReport-1622725235/profiler-output/profiler-reports/StepOutlier.json\\n\",\n \"energy_consumption/test/test.json\\n\",\n \"energy_consumption/train/train.json\\n\",\n \"plagiarism_detection/test.csv\\n\",\n \"plagiarism_detection/train.csv\\n\",\n \"s3://energy_consumption/train/train.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-17-15-32-123/profiler-output/system/incremental/2021060817/1623172680.algo-1.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-17-15-32-123/profiler-output/system/incremental/2021060817/1623172740.algo-1.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-17-15-32-123/source/sourcedir.tar.gz\\n\",\n \"sagemaker-scikit-learn-2021-06-08-17-33-27-715/profiler-output/system/incremental/2021060817/1623173700.algo-1.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-17-33-27-715/profiler-output/system/incremental/2021060817/1623173760.algo-1.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-17-33-27-715/source/sourcedir.tar.gz\\n\",\n \"sagemaker-scikit-learn-2021-06-08-17-41-11-982/profiler-output/system/incremental/2021060817/1623174240.algo-1.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-17-41-11-982/source/sourcedir.tar.gz\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/profiler-output/system/incremental/2021060818/1623175920.algo-1.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/profiler-output/system/incremental/2021060818/1623175980.algo-1.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-report.html\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-report.ipynb\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/BatchSize.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/CPUBottleneck.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/Dataloader.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/GPUMemoryIncrease.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/IOBottleneck.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/LoadBalancing.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/LowGPUUtilization.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/MaxInitializationTime.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/OverallFrameworkMetrics.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/OverallSystemUsage.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/rule-output/ProfilerReport-1623175774/profiler-output/profiler-reports/StepOutlier.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-09-34-000/source/sourcedir.tar.gz\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-24-37-647/profiler-output/system/incremental/2021060818/1623176820.algo-1.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-24-37-647/profiler-output/system/incremental/2021060818/1623176880.algo-1.json\\n\",\n \"sagemaker-scikit-learn-2021-06-08-18-24-37-647/source/sourcedir.tar.gz\\n\",\n \"Test passed!\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# confirm that data is in S3 bucket\\n\",\n \"empty_check = []\\n\",\n \"for obj in boto3.resource('s3').Bucket(bucket).objects.all():\\n\",\n \" empty_check.append(obj.key)\\n\",\n \" print(obj.key)\\n\",\n \"\\n\",\n \"assert len(empty_check) !=0, 'S3 bucket is empty.'\\n\",\n \"print('Test passed!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"\\n\",\n \"# Modeling\\n\",\n \"\\n\",\n \"Now that you've uploaded your training data, it's time to define and train a model!\\n\",\n \"\\n\",\n \"The type of model you create is up to you. For a binary classification task, you can choose to go one of three routes:\\n\",\n \"* Use a built-in classification algorithm, like LinearLearner.\\n\",\n \"* Define a custom Scikit-learn classifier, a comparison of models can be found [here](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html).\\n\",\n \"* Define a custom PyTorch neural network classifier. \\n\",\n \"\\n\",\n \"It will be up to you to test out a variety of models and choose the best one. Your project will be graded on the accuracy of your final model. \\n\",\n \" \\n\",\n \"---\\n\",\n \"\\n\",\n \"## EXERCISE: Complete a training script \\n\",\n \"\\n\",\n \"To implement a custom classifier, you'll need to complete a `train.py` script. You've been given the folders `source_sklearn` and `source_pytorch` which hold starting code for a custom Scikit-learn model and a PyTorch model, respectively. Each directory has a `train.py` training script. To complete this project **you only need to complete one of these scripts**; the script that is responsible for training your final model.\\n\",\n \"\\n\",\n \"A typical training script:\\n\",\n \"* Loads training data from a specified directory\\n\",\n \"* Parses any training & model hyperparameters (ex. nodes in a neural network, training epochs, etc.)\\n\",\n \"* Instantiates a model of your design, with any specified hyperparams\\n\",\n \"* Trains that model \\n\",\n \"* Finally, saves the model so that it can be hosted/deployed, later\\n\",\n \"\\n\",\n \"### Defining and training a model\\n\",\n \"Much of the training script code is provided for you. Almost all of your work will be done in the `if __name__ == '__main__':` section. To complete a `train.py` file, you will:\\n\",\n \"1. Import any extra libraries you need\\n\",\n \"2. Define any additional model training hyperparameters using `parser.add_argument`\\n\",\n \"2. Define a model in the `if __name__ == '__main__':` section\\n\",\n \"3. Train the model in that same section\\n\",\n \"\\n\",\n \"Below, you can use `!pygmentize` to display an existing `train.py` file. Read through the code; all of your tasks are marked with `TODO` comments. \\n\",\n \"\\n\",\n \"**Note: If you choose to create a custom PyTorch model, you will be responsible for defining the model in the `model.py` file,** and a `predict.py` file is provided. If you choose to use Scikit-learn, you only need a `train.py` file; you may import a classifier from the `sklearn` library.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[34mfrom\\u001b[39;49;00m \\u001b[04m\\u001b[36m__future__\\u001b[39;49;00m \\u001b[34mimport\\u001b[39;49;00m print_function\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36margparse\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mos\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mpandas\\u001b[39;49;00m \\u001b[34mas\\u001b[39;49;00m \\u001b[04m\\u001b[36mpd\\u001b[39;49;00m\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[37m# sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. \\u001b[39;49;00m\\r\\n\",\n \"\\u001b[37m# from sklearn.externals import joblib\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[37m# Import joblib package directly\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mimport\\u001b[39;49;00m \\u001b[04m\\u001b[36mjoblib\\u001b[39;49;00m\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[37m## TODO: Import any additional libraries you need to define a model\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mfrom\\u001b[39;49;00m \\u001b[04m\\u001b[36msklearn\\u001b[39;49;00m\\u001b[04m\\u001b[36m.\\u001b[39;49;00m\\u001b[04m\\u001b[36mensemble\\u001b[39;49;00m \\u001b[34mimport\\u001b[39;49;00m RandomForestClassifier\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[37m# Provided model load function\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mdef\\u001b[39;49;00m \\u001b[32mmodel_fn\\u001b[39;49;00m(model_dir):\\r\\n\",\n \" \\u001b[33m\\\"\\\"\\\"Load model from the model_dir. This is the same model that is saved\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[33m in the main if statement.\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[33m \\\"\\\"\\\"\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[36mprint\\u001b[39;49;00m(\\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mLoading model.\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m)\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[37m# load using joblib\\u001b[39;49;00m\\r\\n\",\n \" model = joblib.load(os.path.join(model_dir, \\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mmodel.joblib\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m))\\r\\n\",\n \" \\u001b[36mprint\\u001b[39;49;00m(\\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mDone loading model.\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m)\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[34mreturn\\u001b[39;49;00m model\\r\\n\",\n \"\\r\\n\",\n \"\\r\\n\",\n \"\\u001b[37m## TODO: Complete the main code\\u001b[39;49;00m\\r\\n\",\n \"\\u001b[34mif\\u001b[39;49;00m \\u001b[31m__name__\\u001b[39;49;00m == \\u001b[33m'\\u001b[39;49;00m\\u001b[33m__main__\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m:\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[37m# All of the model parameters and training parameters are sent as arguments\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[37m# when this script is executed, during a training job\\u001b[39;49;00m\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[37m# Here we set up an argument parser to easily access the parameters\\u001b[39;49;00m\\r\\n\",\n \" parser = argparse.ArgumentParser()\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# SageMaker parameters, like the directories for training data and saving models; set automatically\\u001b[39;49;00m\\r\\n\",\n \" \\u001b[37m# Do not need to change\\u001b[39;49;00m\\r\\n\",\n \" parser.add_argument(\\u001b[33m'\\u001b[39;49;00m\\u001b[33m--output-data-dir\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m, \\u001b[36mtype\\u001b[39;49;00m=\\u001b[36mstr\\u001b[39;49;00m, default=os.environ[\\u001b[33m'\\u001b[39;49;00m\\u001b[33mSM_OUTPUT_DATA_DIR\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m])\\r\\n\",\n \" parser.add_argument(\\u001b[33m'\\u001b[39;49;00m\\u001b[33m--model-dir\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m, \\u001b[36mtype\\u001b[39;49;00m=\\u001b[36mstr\\u001b[39;49;00m, default=os.environ[\\u001b[33m'\\u001b[39;49;00m\\u001b[33mSM_MODEL_DIR\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m])\\r\\n\",\n \" parser.add_argument(\\u001b[33m'\\u001b[39;49;00m\\u001b[33m--data-dir\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m, \\u001b[36mtype\\u001b[39;49;00m=\\u001b[36mstr\\u001b[39;49;00m, default=os.environ[\\u001b[33m'\\u001b[39;49;00m\\u001b[33mSM_CHANNEL_TRAIN\\u001b[39;49;00m\\u001b[33m'\\u001b[39;49;00m])\\r\\n\",\n \" \\u001b[37m#parser.add_argument('--max_depth', type=int, default= 10)\\u001b[39;49;00m\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m## TODO: Add any additional arguments that you will need to pass into your model\\u001b[39;49;00m\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[37m# args holds all passed-in arguments\\u001b[39;49;00m\\r\\n\",\n \" args = parser.parse_args()\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# Read in csv training file\\u001b[39;49;00m\\r\\n\",\n \" training_dir = args.data_dir\\r\\n\",\n \" train_data = pd.read_csv(os.path.join(training_dir, \\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mtrain.csv\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m), header=\\u001b[34mNone\\u001b[39;49;00m, names=\\u001b[34mNone\\u001b[39;49;00m)\\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# Labels are in the first column\\u001b[39;49;00m\\r\\n\",\n \" train_y = train_data.iloc[:,\\u001b[34m0\\u001b[39;49;00m]\\r\\n\",\n \" train_x = train_data.iloc[:,\\u001b[34m1\\u001b[39;49;00m:]\\r\\n\",\n \" \\r\\n\",\n \" \\r\\n\",\n \" \\u001b[37m## --- Your code here --- ##\\u001b[39;49;00m\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[37m## TODO: Define a model \\u001b[39;49;00m\\r\\n\",\n \" model = RandomForestClassifier()\\r\\n\",\n \" \\r\\n\",\n \" \\u001b[37m## TODO: Train the model\\u001b[39;49;00m\\r\\n\",\n \" model.fit(train_x, train_y)\\r\\n\",\n \" \\r\\n\",\n \" \\r\\n\",\n \" \\u001b[37m## --- End of your code --- ##\\u001b[39;49;00m\\r\\n\",\n \" \\r\\n\",\n \"\\r\\n\",\n \" \\u001b[37m# Save the trained model\\u001b[39;49;00m\\r\\n\",\n \" joblib.dump(model, os.path.join(args.model_dir, \\u001b[33m\\\"\\u001b[39;49;00m\\u001b[33mmodel.joblib\\u001b[39;49;00m\\u001b[33m\\\"\\u001b[39;49;00m))\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"# directory can be changed to: source_sklearn or source_pytorch\\n\",\n \"!pygmentize source_sklearn/train.py\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Provided code\\n\",\n \"\\n\",\n \"If you read the code above, you can see that the starter code includes a few things:\\n\",\n \"* Model loading (`model_fn`) and saving code\\n\",\n \"* Getting SageMaker's default hyperparameters\\n\",\n \"* Loading the training data by name, `train.csv` and extracting the features and labels, `train_x`, and `train_y`\\n\",\n \"\\n\",\n \"If you'd like to read more about model saving with [joblib for sklearn](https://scikit-learn.org/stable/modules/model_persistence.html) or with [torch.save](https://pytorch.org/tutorials/beginner/saving_loading_models.html), click on the provided links.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Create an Estimator\\n\",\n \"\\n\",\n \"When a custom model is constructed in SageMaker, an entry point must be specified. This is the Python file which will be executed when the model is trained; the `train.py` function you specified above. To run a custom training script in SageMaker, construct an estimator, and fill in the appropriate constructor arguments:\\n\",\n \"\\n\",\n \"* **entry_point**: The path to the Python script SageMaker runs for training and prediction.\\n\",\n \"* **source_dir**: The path to the training script directory `source_sklearn` OR `source_pytorch`.\\n\",\n \"* **role**: Role ARN, which was specified, above.\\n\",\n \"* **train_instance_count**: The number of training instances (should be left at 1).\\n\",\n \"* **train_instance_type**: The type of SageMaker instance for training. Note: Because Scikit-learn does not natively support GPU training, Sagemaker Scikit-learn does not currently support training on GPU instance types.\\n\",\n \"* **sagemaker_session**: The session used to train on Sagemaker.\\n\",\n \"* **hyperparameters** (optional): A dictionary `{'name':value, ..}` passed to the train function as hyperparameters.\\n\",\n \"\\n\",\n \"Note: For a PyTorch model, there is another optional argument **framework_version**, which you can set to the latest version of PyTorch, `1.0`.\\n\",\n \"\\n\",\n \"## EXERCISE: Define a Scikit-learn or PyTorch estimator\\n\",\n \"\\n\",\n \"To import your desired estimator, use one of the following lines:\\n\",\n \"```\\n\",\n \"from sagemaker.sklearn.estimator import SKLearn\\n\",\n \"```\\n\",\n \"```\\n\",\n \"from sagemaker.pytorch import PyTorch\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"train_instance_type has been renamed in sagemaker>=2.\\n\",\n \"See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\\n\",\n \"train_instance_count has been renamed in sagemaker>=2.\\n\",\n \"See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\\n\",\n \"train_instance_count has been renamed in sagemaker>=2.\\n\",\n \"See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\\n\",\n \"train_instance_type has been renamed in sagemaker>=2.\\n\",\n \"See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sagemaker.sklearn.estimator import SKLearn\\n\",\n \"# your import and estimator code, here\\n\",\n \"output_path = 's3://{}/{}'.format(bucket, prefix)\\n\",\n \"\\n\",\n \"SKlearn_estimator = SKLearn(entry_point='train.py',\\n\",\n \" source_dir='source_sklearn',\\n\",\n \" role=role,\\n\",\n \" framework_version='0.20.0',\\n\",\n \" py_version='py3', \\n\",\n \" train_instance_count=1,\\n\",\n \" train_instance_type='ml.c4.xlarge',\\n\",\n \" output_path=output_path,\\n\",\n \" sagemaker_session=sagemaker_session \\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## EXERCISE: Train the estimator\\n\",\n \"\\n\",\n \"Train your estimator on the training data stored in S3. This should create a training job that you can monitor in your SageMaker console.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2021-06-08 22:50:06 Starting - Starting the training job...\\n\",\n \"2021-06-08 22:50:26 Starting - Launching requested ML instancesProfilerReport-1623192606: InProgress\\n\",\n \".........\\n\",\n \"2021-06-08 22:51:47 Starting - Preparing the instances for training......\\n\",\n \"2021-06-08 22:53:07 Downloading - Downloading input data\\n\",\n \"2021-06-08 22:53:07 Training - Downloading the training image...\\n\",\n \"2021-06-08 22:53:28 Training - Training image download completed. Training in progress..\\u001b[34m2021-06-08 22:53:29,502 sagemaker-containers INFO Imported framework sagemaker_sklearn_container.training\\u001b[0m\\n\",\n \"\\u001b[34m2021-06-08 22:53:29,504 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\\u001b[0m\\n\",\n \"\\u001b[34m2021-06-08 22:53:29,515 sagemaker_sklearn_container.training INFO Invoking user training script.\\u001b[0m\\n\",\n \"\\u001b[34m2021-06-08 22:53:29,926 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\\u001b[0m\\n\",\n \"\\u001b[34m2021-06-08 22:53:32,952 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\\u001b[0m\\n\",\n \"\\u001b[34m2021-06-08 22:53:32,966 sagemaker-training-toolkit INFO No GPUs detected (normal if no gpus installed)\\u001b[0m\\n\",\n \"\\u001b[34m2021-06-08 22:53:32,976 sagemaker-training-toolkit INFO Invoking user script\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34mTraining Env:\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m{\\n\",\n \" \\\"additional_framework_parameters\\\": {},\\n\",\n \" \\\"channel_input_dirs\\\": {\\n\",\n \" \\\"train\\\": \\\"/opt/ml/input/data/train\\\"\\n\",\n \" },\\n\",\n \" \\\"current_host\\\": \\\"algo-1\\\",\\n\",\n \" \\\"framework_module\\\": \\\"sagemaker_sklearn_container.training:main\\\",\\n\",\n \" \\\"hosts\\\": [\\n\",\n \" \\\"algo-1\\\"\\n\",\n \" ],\\n\",\n \" \\\"hyperparameters\\\": {},\\n\",\n \" \\\"input_config_dir\\\": \\\"/opt/ml/input/config\\\",\\n\",\n \" \\\"input_data_config\\\": {\\n\",\n \" \\\"train\\\": {\\n\",\n \" \\\"TrainingInputMode\\\": \\\"File\\\",\\n\",\n \" \\\"S3DistributionType\\\": \\\"FullyReplicated\\\",\\n\",\n \" \\\"RecordWrapperType\\\": \\\"None\\\"\\n\",\n \" }\\n\",\n \" },\\n\",\n \" \\\"input_dir\\\": \\\"/opt/ml/input\\\",\\n\",\n \" \\\"is_master\\\": true,\\n\",\n \" \\\"job_name\\\": \\\"sagemaker-scikit-learn-2021-06-08-22-50-06-020\\\",\\n\",\n \" \\\"log_level\\\": 20,\\n\",\n \" \\\"master_hostname\\\": \\\"algo-1\\\",\\n\",\n \" \\\"model_dir\\\": \\\"/opt/ml/model\\\",\\n\",\n \" \\\"module_dir\\\": \\\"s3://sagemaker-us-east-1-274709254325/sagemaker-scikit-learn-2021-06-08-22-50-06-020/source/sourcedir.tar.gz\\\",\\n\",\n \" \\\"module_name\\\": \\\"train\\\",\\n\",\n \" \\\"network_interface_name\\\": \\\"eth0\\\",\\n\",\n \" \\\"num_cpus\\\": 4,\\n\",\n \" \\\"num_gpus\\\": 0,\\n\",\n \" \\\"output_data_dir\\\": \\\"/opt/ml/output/data\\\",\\n\",\n \" \\\"output_dir\\\": \\\"/opt/ml/output\\\",\\n\",\n \" \\\"output_intermediate_dir\\\": \\\"/opt/ml/output/intermediate\\\",\\n\",\n \" \\\"resource_config\\\": {\\n\",\n \" \\\"current_host\\\": \\\"algo-1\\\",\\n\",\n \" \\\"hosts\\\": [\\n\",\n \" \\\"algo-1\\\"\\n\",\n \" ],\\n\",\n \" \\\"network_interface_name\\\": \\\"eth0\\\"\\n\",\n \" },\\n\",\n \" \\\"user_entry_point\\\": \\\"train.py\\\"\\u001b[0m\\n\",\n \"\\u001b[34m}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34mEnvironment variables:\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34mSM_HOSTS=[\\\"algo-1\\\"]\\u001b[0m\\n\",\n \"\\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\\u001b[0m\\n\",\n \"\\u001b[34mSM_HPS={}\\u001b[0m\\n\",\n \"\\u001b[34mSM_USER_ENTRY_POINT=train.py\\u001b[0m\\n\",\n \"\\u001b[34mSM_FRAMEWORK_PARAMS={}\\u001b[0m\\n\",\n \"\\u001b[34mSM_RESOURCE_CONFIG={\\\"current_host\\\":\\\"algo-1\\\",\\\"hosts\\\":[\\\"algo-1\\\"],\\\"network_interface_name\\\":\\\"eth0\\\"}\\u001b[0m\\n\",\n \"\\u001b[34mSM_INPUT_DATA_CONFIG={\\\"train\\\":{\\\"RecordWrapperType\\\":\\\"None\\\",\\\"S3DistributionType\\\":\\\"FullyReplicated\\\",\\\"TrainingInputMode\\\":\\\"File\\\"}}\\u001b[0m\\n\",\n \"\\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\\u001b[0m\\n\",\n \"\\u001b[34mSM_CHANNELS=[\\\"train\\\"]\\u001b[0m\\n\",\n \"\\u001b[34mSM_CURRENT_HOST=algo-1\\u001b[0m\\n\",\n \"\\u001b[34mSM_MODULE_NAME=train\\u001b[0m\\n\",\n \"\\u001b[34mSM_LOG_LEVEL=20\\u001b[0m\\n\",\n \"\\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_sklearn_container.training:main\\u001b[0m\\n\",\n \"\\u001b[34mSM_INPUT_DIR=/opt/ml/input\\u001b[0m\\n\",\n \"\\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\\u001b[0m\\n\",\n \"\\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\\u001b[0m\\n\",\n \"\\u001b[34mSM_NUM_CPUS=4\\u001b[0m\\n\",\n \"\\u001b[34mSM_NUM_GPUS=0\\u001b[0m\\n\",\n \"\\u001b[34mSM_MODEL_DIR=/opt/ml/model\\u001b[0m\\n\",\n \"\\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-east-1-274709254325/sagemaker-scikit-learn-2021-06-08-22-50-06-020/source/sourcedir.tar.gz\\u001b[0m\\n\",\n \"\\u001b[34mSM_TRAINING_ENV={\\\"additional_framework_parameters\\\":{},\\\"channel_input_dirs\\\":{\\\"train\\\":\\\"/opt/ml/input/data/train\\\"},\\\"current_host\\\":\\\"algo-1\\\",\\\"framework_module\\\":\\\"sagemaker_sklearn_container.training:main\\\",\\\"hosts\\\":[\\\"algo-1\\\"],\\\"hyperparameters\\\":{},\\\"input_config_dir\\\":\\\"/opt/ml/input/config\\\",\\\"input_data_config\\\":{\\\"train\\\":{\\\"RecordWrapperType\\\":\\\"None\\\",\\\"S3DistributionType\\\":\\\"FullyReplicated\\\",\\\"TrainingInputMode\\\":\\\"File\\\"}},\\\"input_dir\\\":\\\"/opt/ml/input\\\",\\\"is_master\\\":true,\\\"job_name\\\":\\\"sagemaker-scikit-learn-2021-06-08-22-50-06-020\\\",\\\"log_level\\\":20,\\\"master_hostname\\\":\\\"algo-1\\\",\\\"model_dir\\\":\\\"/opt/ml/model\\\",\\\"module_dir\\\":\\\"s3://sagemaker-us-east-1-274709254325/sagemaker-scikit-learn-2021-06-08-22-50-06-020/source/sourcedir.tar.gz\\\",\\\"module_name\\\":\\\"train\\\",\\\"network_interface_name\\\":\\\"eth0\\\",\\\"num_cpus\\\":4,\\\"num_gpus\\\":0,\\\"output_data_dir\\\":\\\"/opt/ml/output/data\\\",\\\"output_dir\\\":\\\"/opt/ml/output\\\",\\\"output_intermediate_dir\\\":\\\"/opt/ml/output/intermediate\\\",\\\"resource_config\\\":{\\\"current_host\\\":\\\"algo-1\\\",\\\"hosts\\\":[\\\"algo-1\\\"],\\\"network_interface_name\\\":\\\"eth0\\\"},\\\"user_entry_point\\\":\\\"train.py\\\"}\\u001b[0m\\n\",\n \"\\u001b[34mSM_USER_ARGS=[]\\u001b[0m\\n\",\n \"\\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\\u001b[0m\\n\",\n \"\\u001b[34mSM_CHANNEL_TRAIN=/opt/ml/input/data/train\\u001b[0m\\n\",\n \"\\u001b[34mPYTHONPATH=/opt/ml/code:/miniconda3/bin:/miniconda3/lib/python37.zip:/miniconda3/lib/python3.7:/miniconda3/lib/python3.7/lib-dynload:/miniconda3/lib/python3.7/site-packages\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34mInvoking script with the following command:\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/bin/python train.py\\n\",\n \"\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\\n\",\n \" import imp\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py:34: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" from ._gradient_boosting import predict_stages\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py:34: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" from ._gradient_boosting import predict_stages\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\\n\",\n \" \\\"10 in version 0.20 to 100 in 0.22.\\\", FutureWarning)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:489: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_store_unique_indices = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/tree/tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\u001b[0m\\n\",\n \"\\u001b[34mDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n \" y_encoded = np.zeros(y.shape, dtype=np.int)\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m/miniconda3/lib/python3.7/site-packages/sklearn/externals/joblib/numpy_pickle.py:104: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\\n\",\n \" pickler.file_handle.write(chunk.tostring('C'))\\u001b[0m\\n\",\n \"\\u001b[34m2021-06-08 22:53:34,485 sagemaker-containers INFO Reporting training SUCCESS\\u001b[0m\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"2021-06-08 22:54:08 Uploading - Uploading generated training model\\n\",\n \"2021-06-08 22:54:08 Completed - Training job completed\\n\",\n \"Training seconds: 60\\n\",\n \"Billable seconds: 60\\n\",\n \"CPU times: user 575 ms, sys: 10.7 ms, total: 586 ms\\n\",\n \"Wall time: 4min 12s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# Train your estimator on S3 training data\\n\",\n \"SKlearn_estimator.fit({'train': input_data})\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## EXERCISE: Deploy the trained model\\n\",\n \"\\n\",\n \"After training, deploy your model to create a `predictor`. If you're using a PyTorch model, you'll need to create a trained `PyTorchModel` that accepts the trained `.model_data` as an input parameter and points to the provided `source_pytorch/predict.py` file as an entry point. \\n\",\n \"\\n\",\n \"To deploy a trained model, you'll use `.deploy`, which takes in two arguments:\\n\",\n \"* **initial_instance_count**: The number of deployed instances (1).\\n\",\n \"* **instance_type**: The type of SageMaker instance for deployment.\\n\",\n \"\\n\",\n \"Note: If you run into an instance error, it may be because you chose the wrong training or deployment instance_type. It may help to refer to your previous exercise code to see which types of instances we used.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"---------------------!CPU times: user 361 ms, sys: 8.69 ms, total: 370 ms\\n\",\n \"Wall time: 10min 33s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# uncomment, if needed\\n\",\n \"# from sagemaker.pytorch import PyTorchModel\\n\",\n \"\\n\",\n \"\\n\",\n \"# deploy your model to create a predictor\\n\",\n \"predictor = SKlearn_estimator.deploy(initial_instance_count=1, instance_type='ml.t2.medium')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Evaluating Your Model\\n\",\n \"\\n\",\n \"Once your model is deployed, you can see how it performs when applied to our test data.\\n\",\n \"\\n\",\n \"The provided cell below, reads in the test data, assuming it is stored locally in `data_dir` and named `test.csv`. The labels and features are extracted from the `.csv` file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"import os\\n\",\n \"\\n\",\n \"# read in test data, assuming it is stored locally\\n\",\n \"test_data = pd.read_csv(os.path.join(data_dir, \\\"test.csv\\\"), header=None, names=None)\\n\",\n \"\\n\",\n \"# labels are in the first column\\n\",\n \"test_y = test_data.iloc[:,0]\\n\",\n \"test_x = test_data.iloc[:,1:]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## EXERCISE: Determine the accuracy of your model\\n\",\n \"\\n\",\n \"Use your deployed `predictor` to generate predicted, class labels for the test data. Compare those to the *true* labels, `test_y`, and calculate the accuracy as a value between 0 and 1.0 that indicates the fraction of test data that your model classified correctly. You may use [sklearn.metrics](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics) for this calculation.\\n\",\n \"\\n\",\n \"**To pass this project, your model should get at least 90% test accuracy.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Test passed!\\n\"\n ]\n }\n ],\n \"source\": [\n \"# First: generate predicted, class labels\\n\",\n \"test_y_preds = predictor.predict(test_x)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# test that your model generates the correct number of labels\\n\",\n \"assert len(test_y_preds)==len(test_y), 'Unexpected number of predictions.'\\n\",\n \"print('Test passed!')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.96\\n\",\n \"\\n\",\n \"Predicted class labels: \\n\",\n \"[1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 1 1 0 0]\\n\",\n \"\\n\",\n \"True class labels: \\n\",\n \"[1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 0]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import accuracy_score\\n\",\n \"\\n\",\n \"# Second: calculate the test accuracy\\n\",\n \"accuracy = accuracy_score(test_y, test_y_preds )\\n\",\n \"\\n\",\n \"print(accuracy)\\n\",\n \"\\n\",\n \"\\n\",\n \"## print out the array of predicted and true labels, if you want\\n\",\n \"print('\\\\nPredicted class labels: ')\\n\",\n \"print(test_y_preds)\\n\",\n \"print('\\\\nTrue class labels: ')\\n\",\n \"print(test_y.values)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[10, 0],\\n\",\n \" [ 1, 14]])\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"confusion_matrix(test_y, test_y_preds)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1 0.619792\\n\",\n \"2 0.026596\\n\",\n \"3 0.341584\\n\",\n \"Name: 12, dtype: float64\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test_x.iloc[12,:]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Question 1: How many false positives and false negatives did your model produce, if any? And why do you think this is?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"** Answer**: Just one false negaitve and no false postive. I think this because this data entry has small values of three features, which means that the answer was smarlty palgrised not just copy paste and a more complex model is needed to detect it. Another possible reason is that the answer was short compared to the answer, therefore the values of the features are small. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Question 2: How did you decide on the type of model to use? \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"** Answer**: I decided to use the Random Forest model, as this problem is based on numerical features that and can be used to make conditions to decide whether the text is palagrized or not. I used radnom forest instead of decison trees to provide more complex decesion conditions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"----\\n\",\n \"## EXERCISE: Clean up Resources\\n\",\n \"\\n\",\n \"After you're done evaluating your model, **delete your model endpoint**. You can do this with a call to `.delete_endpoint()`. You need to show, in this notebook, that the endpoint was deleted. Any other resources, you may delete from the AWS console, and you will find more instructions on cleaning up all your resources, below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# uncomment and fill in the line below!\\n\",\n \"predictor.delete_endpoint()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Deleting S3 bucket\\n\",\n \"\\n\",\n \"When you are *completely* done with training and testing models, you can also delete your entire S3 bucket. If you do this before you are done training your model, you'll have to recreate your S3 bucket and upload your training data again.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# deleting bucket, uncomment lines below\\n\",\n \"\\n\",\n \"# bucket_to_delete = boto3.resource('s3').Bucket(bucket)\\n\",\n \"# bucket_to_delete.objects.all().delete()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Deleting all your models and instances\\n\",\n \"\\n\",\n \"When you are _completely_ done with this project and do **not** ever want to revisit this notebook, you can choose to delete all of your SageMaker notebook instances and models by following [these instructions](https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-cleanup.html). Before you delete this notebook instance, I recommend at least downloading a copy and saving it, locally.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"## Further Directions\\n\",\n \"\\n\",\n \"There are many ways to improve or add on to this project to expand your learning or make this more of a unique project for you. A few ideas are listed below:\\n\",\n \"* Train a classifier to predict the *category* (1-3) of plagiarism and not just plagiarized (1) or not (0).\\n\",\n \"* Utilize a different and larger dataset to see if this model can be extended to other types of plagiarism.\\n\",\n \"* Use language or character-level analysis to find different (and more) similarity features.\\n\",\n \"* Write a complete pipeline function that accepts a source text and submitted text file, and classifies the submitted text as plagiarized or not.\\n\",\n \"* Use API Gateway and a lambda function to deploy your model to a web application.\\n\",\n \"\\n\",\n \"These are all just options for extending your work. If you've completed all the exercises in this notebook, you've completed a real-world application, and can proceed to submit your project. Great job!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda_pytorch_p36\",\n \"language\": \"python\",\n \"name\": \"conda_pytorch_p36\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/README.md",
"content": "# Plagiarism Detector Web App\nThis repository contains code and associated files for deploying a plagiarism detector using AWS SageMaker.\n\n## Project Overview\n\nIn this project, i build a plagiarism detector that examines a text file and performs binary classification; labeling that file as either *plagiarized* or *not*, depending on how similar that text file is to a provided source text. Detecting plagiarism is an active area of research; the task is non-trivial and the differences between paraphrased answers and original work are often not so obvious.\n\nThis project is broken down into three main notebooks:\n\n**Notebook 1: Data Exploration**\n* Load in the corpus of plagiarism text data.\n* Explore the existing data features and the data distribution.\n* This first notebook is **not** required in your final project submission.\n\n**Notebook 2: Feature Engineering**\n\n* Clean and pre-process the text data.\n* Define features for comparing the similarity of an answer text and a source text, and extract similarity features.\n* Select \"good\" features, by analyzing the correlations between different features.\n* Create train/test `.csv` files that hold the relevant features and class labels for train/test data points.\n\n**Notebook 3: Train and Deploy Your Model in SageMaker**\n\n* Upload your train/test feature data to S3.\n* Define a binary classification model and a training script.\n* Train your model and deploy it using SageMaker.\n* Evaluate your deployed classifier.\n\n---\n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/helpers.py",
"content": "import re\nimport pandas as pd\nimport operator \n\n# Add 'datatype' column that indicates if the record is original wiki answer as 0, training data 1, test data 2, onto \n# the dataframe - uses stratified random sampling (with seed) to sample by task & plagiarism amount \n\n# Use function to label datatype for training 1 or test 2 \ndef create_datatype(df, train_value, test_value, datatype_var, compare_dfcolumn, operator_of_compare, value_of_compare,\n sampling_number, sampling_seed):\n # Subsets dataframe by condition relating to statement built from:\n # 'compare_dfcolumn' 'operator_of_compare' 'value_of_compare'\n df_subset = df[operator_of_compare(df[compare_dfcolumn], value_of_compare)]\n df_subset = df_subset.drop(columns = [datatype_var])\n \n # Prints counts by task and compare_dfcolumn for subset df\n #print(\"\\nCounts by Task & \" + compare_dfcolumn + \":\\n\", df_subset.groupby(['Task', compare_dfcolumn]).size().reset_index(name=\"Counts\") )\n \n # Sets all datatype to value for training for df_subset\n df_subset.loc[:, datatype_var] = train_value\n \n # Performs stratified random sample of subset dataframe to create new df with subset values \n df_sampled = df_subset.groupby(['Task', compare_dfcolumn], group_keys=False).apply(lambda x: x.sample(min(len(x), sampling_number), random_state = sampling_seed))\n df_sampled = df_sampled.drop(columns = [datatype_var])\n # Sets all datatype to value for test_value for df_sampled\n df_sampled.loc[:, datatype_var] = test_value\n \n # Prints counts by compare_dfcolumn for selected sample\n #print(\"\\nCounts by \"+ compare_dfcolumn + \":\\n\", df_sampled.groupby([compare_dfcolumn]).size().reset_index(name=\"Counts\") )\n #print(\"\\nSampled DF:\\n\",df_sampled)\n \n # Labels all datatype_var column as train_value which will be overwritten to \n # test_value in next for loop for all test cases chosen with stratified sample\n for index in df_sampled.index: \n # Labels all datatype_var columns with test_value for straified test sample\n df_subset.loc[index, datatype_var] = test_value\n\n #print(\"\\nSubset DF:\\n\",df_subset)\n # Adds test_value and train_value for all relevant data in main dataframe\n for index in df_subset.index:\n # Labels all datatype_var columns in df with train_value/test_value based upon \n # stratified test sample and subset of df\n df.loc[index, datatype_var] = df_subset.loc[index, datatype_var]\n\n # returns nothing because dataframe df already altered \n \ndef train_test_dataframe(clean_df, random_seed=100):\n \n new_df = clean_df.copy()\n\n # Initialize datatype as 0 initially for all records - after function 0 will remain only for original wiki answers\n new_df.loc[:,'Datatype'] = 0\n\n # Creates test & training datatypes for plagiarized answers (1,2,3)\n create_datatype(new_df, 1, 2, 'Datatype', 'Category', operator.gt, 0, 1, random_seed)\n\n # Creates test & training datatypes for NON-plagiarized answers (0)\n create_datatype(new_df, 1, 2, 'Datatype', 'Category', operator.eq, 0, 2, random_seed)\n \n # creating a dictionary of categorical:numerical mappings for plagiarsm categories\n mapping = {0:'orig', 1:'train', 2:'test'} \n\n # traversing through dataframe and replacing categorical data\n new_df.Datatype = [mapping[item] for item in new_df.Datatype] \n\n return new_df\n\n\n# helper function for pre-processing text given a file\ndef process_file(file):\n # put text in all lower case letters \n all_text = file.read().lower()\n\n # remove all non-alphanumeric chars\n all_text = re.sub(r\"[^a-zA-Z0-9]\", \" \", all_text)\n # remove newlines/tabs, etc. so it's easier to match phrases, later\n all_text = re.sub(r\"\\t\", \" \", all_text)\n all_text = re.sub(r\"\\n\", \" \", all_text)\n all_text = re.sub(\" \", \" \", all_text)\n all_text = re.sub(\" \", \" \", all_text)\n \n return all_text\n\n\ndef create_text_column(df, file_directory='data/'):\n '''Reads in the files, listed in a df and returns that df with an additional column, `Text`. \n :param df: A dataframe of file information including a column for `File`\n :param file_directory: the main directory where files are stored\n :return: A dataframe with processed text '''\n \n # create copy to modify\n text_df = df.copy()\n \n # store processed text\n text = []\n \n # for each file (row) in the df, read in the file \n for row_i in df.index:\n filename = df.iloc[row_i]['File']\n #print(filename)\n file_path = file_directory + filename\n with open(file_path, 'r', encoding='utf-8', errors='ignore') as file:\n\n # standardize text using helper function\n file_text = process_file(file)\n # append processed text to list\n text.append(file_text)\n \n # add column to the copied dataframe\n text_df['Text'] = text\n \n return text_df\n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/palagrism_data/test.csv",
"content": "1,1.0,0.9222797927461139,0.8207547169811321\n1,0.7653061224489796,0.5896551724137931,0.6217105263157895\n1,0.8844444444444445,0.18099547511312217,0.597457627118644\n1,0.6190476190476191,0.043243243243243246,0.42783505154639173\n1,0.92,0.39436619718309857,0.775\n1,0.9926739926739927,0.9739776951672863,0.9930555555555556\n0,0.4126984126984127,0.0,0.3466666666666667\n0,0.4626865671641791,0.0,0.18932038834951456\n0,0.581151832460733,0.0,0.24742268041237114\n0,0.5842105263157895,0.0,0.29441624365482233\n0,0.5663716814159292,0.0,0.25833333333333336\n0,0.48148148148148145,0.022900763358778626,0.2789115646258503\n1,0.6197916666666666,0.026595744680851064,0.3415841584158416\n1,0.9217391304347826,0.6548672566371682,0.9294117647058824\n1,1.0,0.9224806201550387,1.0\n1,0.8615384615384616,0.06282722513089005,0.5047169811320755\n1,0.6261682242990654,0.22397476340694006,0.5585585585585585\n1,1.0,0.9688715953307393,0.9966996699669967\n0,0.3838383838383838,0.010309278350515464,0.178743961352657\n1,1.0,0.9446494464944649,0.8546712802768166\n0,0.6139240506329114,0.0,0.2983425414364641\n1,0.9727626459143969,0.8300395256916996,0.9270833333333334\n1,0.9628099173553719,0.6890756302521008,0.9098039215686274\n0,0.4152542372881356,0.0,0.1774193548387097\n0,0.5321888412017167,0.017467248908296942,0.24583333333333332\n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/palagrism_data/train.csv",
"content": "0,0.39814814814814814,0.0,0.1917808219178082\n1,0.8693693693693694,0.44954128440366975,0.8464912280701754\n1,0.5935828877005348,0.08196721311475409,0.3160621761658031\n0,0.5445026178010471,0.0,0.24257425742574257\n0,0.32950191570881227,0.0,0.16117216117216118\n0,0.5903083700440529,0.0,0.30165289256198347\n1,0.7597765363128491,0.24571428571428572,0.484304932735426\n0,0.5161290322580645,0.0,0.2708333333333333\n0,0.44086021505376344,0.0,0.22395833333333334\n1,0.9794520547945206,0.7887323943661971,0.9\n1,0.9513888888888888,0.5214285714285715,0.8940397350993378\n1,0.9764705882352941,0.5783132530120482,0.8232044198895028\n1,0.8117647058823529,0.28313253012048195,0.45977011494252873\n0,0.4411764705882353,0.0,0.3055555555555556\n0,0.4888888888888889,0.0,0.2826086956521739\n1,0.813953488372093,0.6341463414634146,0.7888888888888889\n0,0.6111111111111112,0.0,0.3246753246753247\n1,1.0,0.9659090909090909,1.0\n1,0.634020618556701,0.005263157894736842,0.36893203883495146\n1,0.5829383886255924,0.08695652173913043,0.4166666666666667\n1,0.6379310344827587,0.30701754385964913,0.4898785425101215\n0,0.42038216560509556,0.0,0.21875\n1,0.6877637130801688,0.07725321888412018,0.5163934426229508\n1,0.6766467065868264,0.11042944785276074,0.4725274725274725\n1,0.7692307692307693,0.45084745762711864,0.6064516129032258\n1,0.7122641509433962,0.08653846153846154,0.536697247706422\n1,0.6299212598425197,0.28,0.39436619718309857\n1,0.7157360406091371,0.0051813471502590676,0.3431372549019608\n0,0.3320610687022901,0.0,0.15302491103202848\n1,0.7172131147540983,0.07916666666666666,0.4559386973180077\n1,0.8782608695652174,0.47345132743362833,0.82\n1,0.5298013245033113,0.31543624161073824,0.45\n0,0.5721153846153846,0.0,0.22935779816513763\n0,0.319672131147541,0.0,0.16535433070866143\n0,0.53,0.0,0.26046511627906976\n1,0.78,0.6071428571428571,0.6699029126213593\n0,0.6526946107784432,0.0,0.3551912568306011\n0,0.4439461883408072,0.0,0.23376623376623376\n1,0.6650246305418719,0.18090452261306533,0.3492647058823529\n1,0.7281553398058253,0.034653465346534656,0.3476190476190476\n1,0.7620481927710844,0.2896341463414634,0.5677233429394812\n1,0.9470198675496688,0.2857142857142857,0.774390243902439\n1,0.3684210526315789,0.0,0.19298245614035087\n0,0.5328947368421053,0.0,0.21818181818181817\n0,0.6184971098265896,0.005917159763313609,0.26666666666666666\n0,0.5103092783505154,0.010526315789473684,0.22110552763819097\n0,0.5798319327731093,0.0,0.2289156626506024\n0,0.40703517587939697,0.0,0.1722488038277512\n0,0.5154639175257731,0.0,0.23684210526315788\n1,0.5845410628019324,0.04926108374384237,0.29493087557603687\n1,0.6171875,0.1693548387096774,0.5037593984962406\n1,1.0,0.84251968503937,0.9117647058823529\n1,0.9916666666666667,0.8879310344827587,0.9923076923076923\n0,0.550561797752809,0.0,0.2833333333333333\n0,0.41935483870967744,0.0,0.2616822429906542\n1,0.8351648351648352,0.034482758620689655,0.6470588235294118\n1,0.9270833333333334,0.29347826086956524,0.85\n0,0.4928909952606635,0.0,0.2350230414746544\n1,0.7087378640776699,0.3217821782178218,0.6619718309859155\n1,0.8633879781420765,0.30726256983240224,0.7911111111111111\n1,0.9606060606060606,0.8650306748466258,0.9298245614035088\n0,0.4380165289256198,0.0,0.2230769230769231\n1,0.7336683417085427,0.07179487179487179,0.4900990099009901\n1,0.5138888888888888,0.0,0.25203252032520324\n0,0.4861111111111111,0.0,0.22767857142857142\n1,0.8451882845188284,0.3021276595744681,0.6437246963562753\n1,0.485,0.0,0.24271844660194175\n1,0.9506726457399103,0.7808219178082192,0.8395061728395061\n1,0.551219512195122,0.23383084577114427,0.2830188679245283\n0,0.3612565445026178,0.0,0.16176470588235295\n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/problem_unittests.py",
"content": "from unittest.mock import MagicMock, patch\nimport sklearn.naive_bayes\nimport numpy as np\nimport pandas as pd\nimport re\n\n# test csv file\nTEST_CSV = 'data/test_info.csv'\n\nclass AssertTest(object):\n '''Defines general test behavior.'''\n def __init__(self, params):\n self.assert_param_message = '\\n'.join([str(k) + ': ' + str(v) + '' for k, v in params.items()])\n \n def test(self, assert_condition, assert_message):\n assert assert_condition, assert_message + '\\n\\nUnit Test Function Parameters\\n' + self.assert_param_message\n\ndef _print_success_message():\n print('Tests Passed!')\n\n# test clean_dataframe\ndef test_numerical_df(numerical_dataframe):\n \n # test result\n transformed_df = numerical_dataframe(TEST_CSV)\n \n # Check type is a DataFrame\n assert isinstance(transformed_df, pd.DataFrame), 'Returned type is {}.'.format(type(transformed_df))\n \n # check columns\n column_names = list(transformed_df)\n assert 'File' in column_names, 'No File column, found.'\n assert 'Task' in column_names, 'No Task column, found.'\n assert 'Category' in column_names, 'No Category column, found.'\n assert 'Class' in column_names, 'No Class column, found.'\n \n # check conversion values\n assert transformed_df.loc[0, 'Category'] == 1, '`heavy` plagiarism mapping test, failed.'\n assert transformed_df.loc[2, 'Category'] == 0, '`non` plagiarism mapping test, failed.'\n assert transformed_df.loc[30, 'Category'] == 3, '`cut` plagiarism mapping test, failed.'\n assert transformed_df.loc[5, 'Category'] == 2, '`light` plagiarism mapping test, failed.'\n assert transformed_df.loc[37, 'Category'] == -1, 'original file mapping test, failed; should have a Category = -1.'\n assert transformed_df.loc[41, 'Category'] == -1, 'original file mapping test, failed; should have a Category = -1.'\n \n _print_success_message()\n\n\ndef test_containment(complete_df, containment_fn):\n \n # check basic format and value \n # for n = 1 and just the fifth file\n test_val = containment_fn(complete_df, 1, 'g0pA_taske.txt')\n \n assert isinstance(test_val, float), 'Returned type is {}.'.format(type(test_val))\n assert test_val<=1.0, 'It appears that the value is not normalized; expected a value <=1, got: '+str(test_val)\n \n # known vals for first few files\n filenames = ['g0pA_taska.txt', 'g0pA_taskb.txt', 'g0pA_taskc.txt', 'g0pA_taskd.txt']\n ngram_1 = [0.39814814814814814, 1.0, 0.86936936936936937, 0.5935828877005348]\n ngram_3 = [0.0093457943925233638, 0.96410256410256412, 0.61363636363636365, 0.15675675675675677]\n \n # results for comparison\n results_1gram = []\n results_3gram = []\n \n for i in range(4):\n val_1 = containment_fn(complete_df, 1, filenames[i])\n val_3 = containment_fn(complete_df, 3, filenames[i])\n results_1gram.append(val_1)\n results_3gram.append(val_3)\n \n # check correct results\n assert all(np.isclose(results_1gram, ngram_1, rtol=1e-04)), \\\n 'n=1 calculations are incorrect. Double check the intersection calculation.'\n # check correct results\n assert all(np.isclose(results_3gram, ngram_3, rtol=1e-04)), \\\n 'n=3 calculations are incorrect.'\n \n _print_success_message()\n \ndef test_lcs(df, lcs_word):\n \n test_index = 10 # file 10\n \n # get answer file text\n answer_text = df.loc[test_index, 'Text'] \n \n # get text for orig file\n # find the associated task type (one character, a-e)\n task = df.loc[test_index, 'Task']\n # we know that source texts have Class = -1\n orig_rows = df[(df['Class'] == -1)]\n orig_row = orig_rows[(orig_rows['Task'] == task)]\n source_text = orig_row['Text'].values[0]\n \n # calculate LCS\n test_val = lcs_word(answer_text, source_text)\n \n # check type\n assert isinstance(test_val, float), 'Returned type is {}.'.format(type(test_val))\n assert test_val<=1.0, 'It appears that the value is not normalized; expected a value <=1, got: '+str(test_val)\n \n # known vals for first few files\n lcs_vals = [0.1917808219178082, 0.8207547169811321, 0.8464912280701754, 0.3160621761658031, 0.24257425742574257]\n \n # results for comparison\n results = []\n \n for i in range(5):\n # get answer and source text\n answer_text = df.loc[i, 'Text'] \n task = df.loc[i, 'Task']\n # we know that source texts have Class = -1\n orig_rows = df[(df['Class'] == -1)]\n orig_row = orig_rows[(orig_rows['Task'] == task)]\n source_text = orig_row['Text'].values[0]\n # calc lcs\n val = lcs_word(answer_text, source_text)\n results.append(val)\n \n # check correct results\n assert all(np.isclose(results, lcs_vals, rtol=1e-05)), 'LCS calculations are incorrect.'\n \n _print_success_message()\n \ndef test_data_split(train_x, train_y, test_x, test_y):\n \n # check types\n assert isinstance(train_x, np.ndarray),\\\n 'train_x is not an array, instead got type: {}'.format(type(train_x))\n assert isinstance(train_y, np.ndarray),\\\n 'train_y is not an array, instead got type: {}'.format(type(train_y))\n assert isinstance(test_x, np.ndarray),\\\n 'test_x is not an array, instead got type: {}'.format(type(test_x))\n assert isinstance(test_y, np.ndarray),\\\n 'test_y is not an array, instead got type: {}'.format(type(test_y))\n \n # should hold all 95 submission files\n assert len(train_x) + len(test_x) == 95, \\\n 'Unexpected amount of train + test data. Expecting 95 answer text files, got ' +str(len(train_x) + len(test_x))\n assert len(test_x) > 1, \\\n 'Unexpected amount of test data. There should be multiple test files.'\n \n # check shape\n assert train_x.shape[1]==2, \\\n 'train_x should have as many columns as selected features, got: {}'.format(train_x.shape[1])\n assert len(train_y.shape)==1, \\\n 'train_y should be a 1D array, got shape: {}'.format(train_y.shape)\n \n _print_success_message()\n \n \n "
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/source_pytorch/model.py",
"content": "# torch imports\nimport torch.nn.functional as F\nimport torch.nn as nn\n\n\n## TODO: Complete this classifier\nclass BinaryClassifier(nn.Module):\n \"\"\"\n Define a neural network that performs binary classification.\n The network should accept your number of features as input, and produce \n a single sigmoid value, that can be rounded to a label: 0 or 1, as output.\n \n Notes on training:\n To train a binary classifier in PyTorch, use BCELoss.\n BCELoss is binary cross entropy loss, documentation: https://pytorch.org/docs/stable/nn.html#torch.nn.BCELoss\n \"\"\"\n\n ## TODO: Define the init function, the input params are required (for loading code in train.py to work)\n def __init__(self, input_features, hidden_dim, output_dim):\n \"\"\"\n Initialize the model by setting up linear layers.\n Use the input parameters to help define the layers of your model.\n :param input_features: the number of input features in your training/test data\n :param hidden_dim: helps define the number of nodes in the hidden layer(s)\n :param output_dim: the number of outputs you want to produce\n \"\"\"\n super(BinaryClassifier, self).__init__()\n\n # define any initial layers, here\n \n\n \n ## TODO: Define the feedforward behavior of the network\n def forward(self, x):\n \"\"\"\n Perform a forward pass of our model on input features, x.\n :param x: A batch of input features of size (batch_size, input_features)\n :return: A single, sigmoid-activated value as output\n \"\"\"\n \n # define the feedforward behavior\n \n return x\n \n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/source_pytorch/predict.py",
"content": "# import libraries\nimport os\nimport numpy as np\nimport torch\nfrom six import BytesIO\n\n# import model from model.py, by name\nfrom model import BinaryClassifier\n\n# default content type is numpy array\nNP_CONTENT_TYPE = 'application/x-npy'\n\n\n# Provided model load function\ndef model_fn(model_dir):\n \"\"\"Load the PyTorch model from the `model_dir` directory.\"\"\"\n print(\"Loading model.\")\n\n # First, load the parameters used to create the model.\n model_info = {}\n model_info_path = os.path.join(model_dir, 'model_info.pth')\n with open(model_info_path, 'rb') as f:\n model_info = torch.load(f)\n\n print(\"model_info: {}\".format(model_info))\n\n # Determine the device and construct the model.\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = BinaryClassifier(model_info['input_features'], model_info['hidden_dim'], model_info['output_dim'])\n\n # Load the store model parameters.\n model_path = os.path.join(model_dir, 'model.pth')\n with open(model_path, 'rb') as f:\n model.load_state_dict(torch.load(f))\n\n # Prep for testing\n model.to(device).eval()\n\n print(\"Done loading model.\")\n return model\n\n\n# Provided input data loading\ndef input_fn(serialized_input_data, content_type):\n print('Deserializing the input data.')\n if content_type == NP_CONTENT_TYPE:\n stream = BytesIO(serialized_input_data)\n return np.load(stream)\n raise Exception('Requested unsupported ContentType in content_type: ' + content_type)\n\n# Provided output data handling\ndef output_fn(prediction_output, accept):\n print('Serializing the generated output.')\n if accept == NP_CONTENT_TYPE:\n stream = BytesIO()\n np.save(stream, prediction_output)\n return stream.getvalue(), accept\n raise Exception('Requested unsupported ContentType in Accept: ' + accept)\n\n\n# Provided predict function\ndef predict_fn(input_data, model):\n print('Predicting class labels for the input data...')\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n \n # Process input_data so that it is ready to be sent to our model.\n data = torch.from_numpy(input_data.astype('float32'))\n data = data.to(device)\n\n # Put the model into evaluation mode\n model.eval()\n\n # Compute the result of applying the model to the input data\n # The variable `out_label` should be a rounded value, either 1 or 0\n out = model(data)\n out_np = out.cpu().detach().numpy()\n out_label = out_np.round()\n\n return out_label"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/source_pytorch/train.py",
"content": "import argparse\nimport json\nimport os\nimport pandas as pd\nimport torch\nimport torch.optim as optim\nimport torch.utils.data\n\n# imports the model in model.py by name\nfrom model import BinaryClassifier\n\ndef model_fn(model_dir):\n \"\"\"Load the PyTorch model from the `model_dir` directory.\"\"\"\n print(\"Loading model.\")\n\n # First, load the parameters used to create the model.\n model_info = {}\n model_info_path = os.path.join(model_dir, 'model_info.pth')\n with open(model_info_path, 'rb') as f:\n model_info = torch.load(f)\n\n print(\"model_info: {}\".format(model_info))\n\n # Determine the device and construct the model.\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = BinaryClassifier(model_info['input_features'], model_info['hidden_dim'], model_info['output_dim'])\n\n # Load the stored model parameters.\n model_path = os.path.join(model_dir, 'model.pth')\n with open(model_path, 'rb') as f:\n model.load_state_dict(torch.load(f))\n\n # set to eval mode, could use no_grad\n model.to(device).eval()\n\n print(\"Done loading model.\")\n return model\n\n# Gets training data in batches from the train.csv file\ndef _get_train_data_loader(batch_size, training_dir):\n print(\"Get train data loader.\")\n\n train_data = pd.read_csv(os.path.join(training_dir, \"train.csv\"), header=None, names=None)\n\n train_y = torch.from_numpy(train_data[[0]].values).float().squeeze()\n train_x = torch.from_numpy(train_data.drop([0], axis=1).values).float()\n\n train_ds = torch.utils.data.TensorDataset(train_x, train_y)\n\n return torch.utils.data.DataLoader(train_ds, batch_size=batch_size)\n\n\n# Provided training function\ndef train(model, train_loader, epochs, criterion, optimizer, device):\n \"\"\"\n This is the training method that is called by the PyTorch training script. The parameters\n passed are as follows:\n model - The PyTorch model that we wish to train.\n train_loader - The PyTorch DataLoader that should be used during training.\n epochs - The total number of epochs to train for.\n criterion - The loss function used for training. \n optimizer - The optimizer to use during training.\n device - Where the model and data should be loaded (gpu or cpu).\n \"\"\"\n \n # training loop is provided\n for epoch in range(1, epochs + 1):\n model.train() # Make sure that the model is in training mode.\n\n total_loss = 0\n\n for batch in train_loader:\n # get data\n batch_x, batch_y = batch\n\n batch_x = batch_x.to(device)\n batch_y = batch_y.to(device)\n\n optimizer.zero_grad()\n\n # get predictions from model\n y_pred = model(batch_x)\n \n # perform backprop\n loss = criterion(y_pred, batch_y)\n loss.backward()\n optimizer.step()\n \n total_loss += loss.data.item()\n\n print(\"Epoch: {}, Loss: {}\".format(epoch, total_loss / len(train_loader)))\n\n\n## TODO: Complete the main code\nif __name__ == '__main__':\n \n # All of the model parameters and training parameters are sent as arguments\n # when this script is executed, during a training job\n \n # Here we set up an argument parser to easily access the parameters\n parser = argparse.ArgumentParser()\n\n # SageMaker parameters, like the directories for training data and saving models; set automatically\n # Do not need to change\n parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])\n parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])\n parser.add_argument('--data-dir', type=str, default=os.environ['SM_CHANNEL_TRAIN'])\n \n # Training Parameters, given\n parser.add_argument('--batch-size', type=int, default=10, metavar='N',\n help='input batch size for training (default: 10)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n \n ## TODO: Add args for the three model parameters: input_features, hidden_dim, output_dim\n # Model Parameters\n \n \n # args holds all passed-in arguments\n args = parser.parse_args()\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n print(\"Using device {}.\".format(device))\n\n torch.manual_seed(args.seed)\n\n # Load the training data.\n train_loader = _get_train_data_loader(args.batch_size, args.data_dir)\n\n\n ## --- Your code here --- ##\n \n ## TODO: Build the model by passing in the input params\n # To get params from the parser, call args.argument_name, ex. args.epochs or ards.hidden_dim\n # Don't forget to move your model .to(device) to move to GPU , if appropriate\n model = None\n\n ## TODO: Define an optimizer and loss function for training\n optimizer = None\n criterion = None\n\n # Trains the model (given line of code, which calls the above training function)\n train(model, train_loader, args.epochs, criterion, optimizer, device)\n\n ## TODO: complete in the model_info by adding three argument names, the first is given\n # Keep the keys of this dictionary as they are \n model_info_path = os.path.join(args.model_dir, 'model_info.pth')\n with open(model_info_path, 'wb') as f:\n model_info = {\n 'input_features': args.input_features,\n 'hidden_dim': ,\n 'output_dim': ,\n }\n torch.save(model_info, f)\n \n ## --- End of your code --- ##\n \n\n\t# Save the model parameters\n model_path = os.path.join(args.model_dir, 'model.pth')\n with open(model_path, 'wb') as f:\n torch.save(model.cpu().state_dict(), f)\n"
},
{
"path": "Natural_Language_processing/plagiarism-detector-web-app/source_sklearn/train.py",
"content": "from __future__ import print_function\n\nimport argparse\nimport os\nimport pandas as pd\n\n# sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. \nfrom sklearn.externals import joblib\n# Import joblib package directly\n\n#import joblib\n\n## TODO: Import any additional libraries you need to define a model\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Provided model load function\ndef model_fn(model_dir):\n \"\"\"Load model from the model_dir. This is the same model that is saved\n in the main if statement.\n \"\"\"\n print(\"Loading model.\")\n \n # load using joblib\n model = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n print(\"Done loading model.\")\n \n return model\n\n\n## TODO: Complete the main code\nif __name__ == '__main__':\n \n # All of the model parameters and training parameters are sent as arguments\n # when this script is executed, during a training job\n \n # Here we set up an argument parser to easily access the parameters\n parser = argparse.ArgumentParser()\n\n # SageMaker parameters, like the directories for training data and saving models; set automatically\n # Do not need to change\n parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])\n parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])\n parser.add_argument('--data-dir', type=str, default=os.environ['SM_CHANNEL_TRAIN'])\n #parser.add_argument('--max_depth', type=int, default= 10)\n\n ## TODO: Add any additional arguments that you will need to pass into your model\n \n # args holds all passed-in arguments\n args = parser.parse_args()\n\n # Read in csv training file\n training_dir = args.data_dir\n train_data = pd.read_csv(os.path.join(training_dir, \"train.csv\"), header=None, names=None)\n\n # Labels are in the first column\n train_y = train_data.iloc[:,0]\n train_x = train_data.iloc[:,1:]\n \n \n ## --- Your code here --- ##\n \n ## TODO: Define a model \n model = RandomForestClassifier()\n \n ## TODO: Train the model\n model.fit(train_x, train_y)\n \n \n ## --- End of your code --- ##\n \n\n # Save the trained model\n \n joblib.dump(model, os.path.join(args.model_dir, \"model.joblib\"))\n\n \n"
},
{
"path": "Readme.md",
"content": "# Data Science Portfolio #\n\n[](https://youssefh.substack.com/)\n[](https://medium.com/@yousefhosni)\n[](https://www.kaggle.com/youssef19)\n[](https://www.youtube.com/channel/UCeEcSgRzYFuVt-2Yk1ULdhQ)\n\n\n## End to End Projects ##\n\n### [Respiratory Modulation of Cardiovascular Brain Pulsations in Alzheimer’s Disease](https://github.com/youssefHosni/Respiratory-modulation-of-cardiovascular-pulsation)\n* Utilized Python programming language and Bash scripting to preprocess and extract features from 4D complex brain imaging data amounting to 0.25 TB, applying a 3D multiresolution optical flow technique.\n* Employed signal processing techniques with Python programming language to modulate respiratory signals with calculated brain signal speed.\n* Conducted A/B testing to identify significant differences in the modulation of brain cardiovascular pulse with respiration between control subjects and individuals with Alzheimer's disease.\n* Visualized and presented research findings to neurological researchers, contributing to a greater understanding of Alzheimer's disease. Additionally, authored and submitted a paper to JCBFM.\n* **Paper**: [Elabasy, A., Suhonen, M., Rajna, Z. et al. Respiratory brain impulse propagation in focal epilepsy. Sci Rep 13, 5222 (2023). https://doi.org/10.1038/s41598-023-32271-7](https://www.nature.com/articles/s41598-023-32271-7#citeas)\n### [Alzhimers CV-BOLD Classification](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Machine%20Learning/Classification/Alzhimers%20CV-BOLD%20Classification) ###\n* Trained multiple classifiers that improved the classification performance of imbalanced Alzheimer’s fMRI images by more than 12% compared to state-of-the-art on the same data.\n* Analyzed, visualized, and discussed the results with a team of neurological researchers to have a better understanding of the results and Alzheimer’s disease. \n* Analyzed, visualized, and reported the results and submitted a research paper to the ISPr 2023 scientific conference.\n\n### __[Real Time Sign Language Interpretation Web Application](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Computer%20Vision/Real%20Time%20Sign%20Language%20Interpretation%20App)__ ### \n* Developed a real-time sign language interpretation application using React.js, TensorFlow, and tensorflow.js and deployed it on IBM cloud servers.\n\n### __[Stable Diffusion Web Application](https://github.com/youssefHosni/Stable-Diffusion-Crash-Course/tree/main/Stable%20Diffusion%20Web%20Application)__ ### \n* Build a stable diffusion web application using Hugging Face and React and deploy it on fast API. \n\n### [Building Movie Recommendation System using Pyspark]() ###\n* Building a recommendation engine using Alternating Least Squares in PySpark and using the popular MovieLens dataset and the Million Songs dataset.\n\n### [Real Time Car Plate Detection Mobile Application]() ###\n* Building a real-time car plate detection mobile application using TensorFlow and EasyOCR. \n\n----\n\n# Skill Based Projects #\n\n## Generative AI ##\n\n### Fine Tuning LLMs ###\n* [Finetune Falcon-7b with LoRA](https://www.kaggle.com/code/youssef19/finetune-falcon-7b-with-lora-a-step-by-step-guide):\n* [Instruction Fine Tuning T5 LLM for Summarization]()\n\n### Reterival Augmented Generation (RAG) Application ###\n* [RAG using Gemma & Faiss Vector DB](https://www.kaggle.com/code/youssef19/building-rag-using-gemma-faiss-vector-db)\n\n### Prompt Engineering ###\n\n \n## Machine Learning:\n### Regression\n* __[Automobile price prediction](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Machine%20Learning/Regression/Automobile%20price%20prediction)__: Utilize Python to implement end to end data science pipeline to predict the price of old Automobile based on the given features.\n\n### Classification \n* __[Sensor Activity Recogniation](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Machine%20Learning/Classification/Sensor-activity-recognition)__: Classifying the output of eight sensors into five activities and studied the effect of changing window\nsizes and axel combination.\n* __[Alzhimers CV-BOLD Classification](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Machine%20Learning/Classification/Alzhimers%20CV-BOLD%20Classification)__: Utilized Python to develop supervised machine learning techniques to classify imbalanced Alzheimer’s CVBOLD data, which enhanced the classification performance by 10%. \n\n### Clustering \n* __[Find the best location to open a new Gym](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Machine%20Learning/Clustering/Finding-the-best-Tornoto-neighborhood-to-open-a-new-gym)__: Utilized python to implement unsupervised techniques to helping the business owner to increase his revenue by finding the best neighborhood to open a new gym. \n* __[Customer identification for mail order products](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Machine%20Learning/Clustering/Customer%20identification%20for%20mail%20order%20products)__: Utilized python to implement unsupervised techniques to helping the business owner to increase his revenue by finding the best neighborhood to open a new gym.\n---\n\n## Deep Learning \n\n### Classification\n\n* __[Melenoma Classification](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Deep%20Learning/Classification/Melenoma_Classification)__: Classifying malignant Melanoma using skin lesion images using CNN-based classifiers.\n---\n\n### Computer Vision \n* __[Object Tracking using Particle Filter](https://github.com/youssefHosni/Practical-Computer-Vision-In-Python/tree/main/Tracking%20Objects%20in%20Video%20with%20Particle%20Filters)__: Implemented particle filter to track walking object in video\n*\t__[Pose Estimation and Squat counter](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Computer%20Vision/Pose%20Estimation%20%26%20Squat%20Counter)__: Utilize python to develop a real-time pose estimation and squat counter using MovingNet lightning.\n\n* __[Object Detection Deployed on FastAPI](https://github.com/youssefHosni/Practical-Machine-Learning/tree/main/Deploying-Yolo3-Model-on-FastAPI)__:\n---\n\n### Natural Language Processing \n\n* __[Sentiment Analysis web app](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Natural_Language_processing/Sentiment-analysis)__: Web application for classification of reviews, using deep learning model implemented in PyTorch and deployed on Amazon SageMaker. \n* __[Plagirasm Detector web app](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Natural_Language_processing/plagiarism-detector-web-app)__: Creating plagiarism detector trained on LSC and containments features and deployed on AWS SageMaker.\n* __[Data Science Resume Selector](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Natural_Language_processing/Data-Science-Resume-Selector)__: Selecting the resume that are eligbile to data scientist postions, the dataset used contains 125 resumes, in the resumetext column. Resumes were queried from Indeed. \n---\n\n### Time-series Analysis\n* __[Power consumption prediction](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/time-series-analysis/Power-consumption-forecasting)__: Real time prediction for power consumption using DeepAR on AWS.\n---\n\n### Data Visulization:\n* __[Immigrants to Canada data visualization](https://nbviewer.jupyter.org/github/youssefHosni/Data-Science-Portofolio/blob/main/Data%20Visualization/Python/Immigration_to_Canda_Data_Visualization.ipynb)__: Visualizing the data of the immigrants to Canada using different visualizing libraries in Python.\n* __[Geospatial visualization of San Francisco Police Department Incidents](https://dfm.io/nbview/?url=https%3A%2F%2Fgithub.com%2FyoussefHosni%2FData-Science-Portofolio%2Fblob%2Fmain%2FData%2520Visualization%2FPython%2FSpatial%2520visualization%2520of%2520San%2520Francisco%2520incidents.ipynb)__: Visualizaing the geospatial data of the San Francisco police department incidents for the year 2016.\n---\n\n### Spark \n* __[San Diego Rainforest Fire Predicition](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Spark/San%20Diego%20Rainforest%20Fire%20Predicition)__: Predicting the occurrence of rainforest fire in san Diego using weather data collected by san Diego weather center. \n* __[Cluster Analysis of the San Diego Weather Data](https://github.com/youssefHosni/Data-Science-Portofolio/tree/main/Spark/Cluster%20Analysis%20of%20the%20San%20Diego%20Weather%20Data)__: Utilizing pyspark to implement unsupervised learning model to cluster the san Diego weather data so as to better understand the occurrence of the rainforest fire.\n---\n\n### Data Modeling \n* __[Songs App User Activity Data Modeling](https://github.com/youssefHosni/Data-Engineering-Nanodegree/tree/main/Data%20Modeling/Project:%20Data%20Modeling%20with%20Postgres)__: Modeling user activity data for a music streaming app called Sparkify to optimize queries for understanding what songs users are listening to by creating a Postgres relational database and ETL pipeline to build up Fact and Dimension tables and insert data into new tables.\n* __[Songs App data modeling using Apache Casandra](https://github.com/youssefHosni/Data-Engineering-Nanodegree/tree/main/Data%20Modeling/Project:%20Data%20Modeling%20With%20Apache%20Cassandra)__: Create an Apache Cassandra database which can create queries on song play data to answer analysis questions.\n \n---\n\n### Certificates \n* [Machine Learning Specialization](https://coursera.org/share/fc5c10d3f96939edf29145069b48a6a3) \n* [Big Data Specialization](https://coursera.org/share/be7f65fa09309f4cc3e2c67bcb071c3f) \n* [Intro to Machine Learning with TensorFlow Nanodegree](https://confirm.udacity.com/EAATQCZY)\n* [Machine Learning Engineer Nanodegree](https://confirm.udacity.com/KCFRE3KD)\n* [AWS Fundamentals Specialization](https://coursera.org/share/e34358a0200a916eebb07b64f22d055e)\n* [Data Science Professional Certificate](https://coursera.org/share/696a589922de676e872971acf146f4ec) \n* [AI for Medicine](https://coursera.org/share/e12fc21c24eb1ebfe70121c66b7ee8ad)\n* [Data Visualization with Seborn](https://www.kaggle.com/learn/certification/youssef19/data-visualization)\n* [Time Series Forecasting](https://www.kaggle.com/learn/certification/youssef19/time-series)\n* [Data-Visualization-with-Plotly-in-Python](https://www.datacamp.com/statement-of-accomplishment/course/49b8afff93d6cbd07813b058ada618ddbd85fbab)\n* [Machine Learning Explanality-Kaggle](https://www.kaggle.com/learn/certification/youssef19/machine-learning-explainability)\n---\n\n### Course Work\n* [Deep Learning](https://github.com/youssefHosni/Deep-learning-Specilization) \n* [Data Science](https://github.com/youssefHosni/IBM-data-science-proffesional-certificate) \n* [Machine learning engineering](https://github.com/youssefHosni/Machine-Learning-Engineer-Udacity-Nanodegree)\n* [Intro to machine learning with tesnorflow](https://github.com/youssefHosni/Intro-to-machine-learning-nanodegree)\n* [Data Engineering](https://github.com/youssefHosni/Data-Engineering-Nanodegree)\n* [Time Series Kaggle Course](https://github.com/youssefHosni/Time-Series-Kaggle-Course)\n* [Data Visualization Kaggle Course](https://github.com/youssefHosni/Data-Visualization-Kaggle-Course) \n* [Data-Visualization-with-Plotly-in-Python](https://github.com/youssefHosni/Data-Visualization-with-Plotly-in-Python)\n* [Machine Learning Explanality-Kaggle](https://github.com/youssefHosni/Machine-Learning-Explanality-Course-Kaggle)\n"
},
{
"path": "Spark/Cluster Analysis of the San Diego Weather Data/Cluster Analysis of the San Diego Weather Data.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Cluster Analysis of the San Diego Weather Data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This activity guides you through the process of performing cluster analysis on a dataset using\\n\",\n \"k-means. In this activity, we will perform cluster analysis on the minute-weather.csv dataset using\\n\",\n \"the k-means algorithm. Recall that this dataset contains weather measurements such as\\n\",\n \"temperature, relative humidity, etc., from a weather station in San Diego, California, collected at\\n\",\n \"one-minute intervals. The goal of cluster analysis on this data is to identify different weather patterns\\n\",\n \"for this weather station.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Objectives \\n\",\n \"* Scale all features so that each feature is zero-normalized\\n\",\n \"* Create an \\\"elbow\\\" plot, the number of clusters vs. within-cluster sum-of-squared errors, to determine a value for k, the number of clusters in k-means\\n\",\n \"* Perform cluster analysis on a dataset using k-means\\n\",\n \"* Create parallel coordinates plots to analyze cluster centers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Import the important libraries \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import findspark\\n\",\n \"findspark.init()\\n\",\n \"findspark.add_packages('mysql:mysql-connector-java:8.0.11')\\n\",\n \"\\n\",\n \"import pyspark # only run after findspark.init()\\n\",\n \"from pyspark.sql import SparkSession\\n\",\n \"from pyspark.sql import SQLContext\\n\",\n \"\\n\",\n \"spark = SparkSession.builder.getOrCreate()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pyspark.ml.feature import VectorAssembler, StandardScaler\\n\",\n \"from pyspark import SparkContext\\n\",\n \"from pyspark.ml.clustering import KMeans\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"sc =SparkContext.getOrCreate()\\n\",\n \"sqlContext = SQLContext(sc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Upload the data \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The file minute_weather.cs is a comma-separated file that contains weather data. This data comes from a weather station located in San Diego, California. The weather station is equipped with sensors that capture weather-related measurements such as air temperature, air pressure, and relative humidity. Data was collected for a period of three years, from September 2011 to September 2014, to ensure that sufficient data for different seasons and weather conditions is captured. Sensor measurements from the weather station were captured at one-minute intervals.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = spark.read.csv('minute_weather.csv', \\n\",\n \" header='true',\\n\",\n \" inferSchema='true'\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1587257\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.count()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Data Preprocessing \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"158726\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# using only 10% of the data \\n\",\n \"filteredDF = df.filter((df.rowID % 10) == 0)\\n\",\n \"filteredDF.count()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
2
\\n\",\n \"
3
\\n\",\n \"
4
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
summary
\\n\",\n \"
count
\\n\",\n \"
mean
\\n\",\n \"
stddev
\\n\",\n \"
min
\\n\",\n \"
max
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
rowID
\\n\",\n \"
158726
\\n\",\n \"
793625.0
\\n\",\n \"
458203.9375103623
\\n\",\n \"
0
\\n\",\n \"
1587250
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
hpwren_timestamp
\\n\",\n \"
158726
\\n\",\n \"
None
\\n\",\n \"
None
\\n\",\n \"
2011-09-10 00:00:49
\\n\",\n \"
2014-09-10 23:53:29
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
air_pressure
\\n\",\n \"
158726
\\n\",\n \"
916.8301614102413
\\n\",\n \"
3.051716552830777
\\n\",\n \"
905.0
\\n\",\n \"
929.5
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
air_temp
\\n\",\n \"
158726
\\n\",\n \"
61.851589153636084
\\n\",\n \"
11.833569210641707
\\n\",\n \"
31.64
\\n\",\n \"
99.5
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
avg_wind_direction
\\n\",\n \"
158680
\\n\",\n \"
162.15610032770354
\\n\",\n \"
95.27820101905918
\\n\",\n \"
0.0
\\n\",\n \"
359.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
avg_wind_speed
\\n\",\n \"
158680
\\n\",\n \"
2.7752148979077367
\\n\",\n \"
2.057623969742644
\\n\",\n \"
0.0
\\n\",\n \"
31.9
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
max_wind_direction
\\n\",\n \"
158680
\\n\",\n \"
163.46214393748426
\\n\",\n \"
92.45213853838698
\\n\",\n \"
0.0
\\n\",\n \"
359.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
max_wind_speed
\\n\",\n \"
158680
\\n\",\n \"
3.400557726241551
\\n\",\n \"
2.4188016208098855
\\n\",\n \"
0.1
\\n\",\n \"
36.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
min_wind_direction
\\n\",\n \"
158680
\\n\",\n \"
166.77401688933702
\\n\",\n \"
97.44110914784568
\\n\",\n \"
0.0
\\n\",\n \"
359.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
min_wind_speed
\\n\",\n \"
158680
\\n\",\n \"
2.134664103856835
\\n\",\n \"
1.742112505242438
\\n\",\n \"
0.0
\\n\",\n \"
31.6
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
rain_accumulation
\\n\",\n \"
158725
\\n\",\n \"
3.1784532997328256E-4
\\n\",\n \"
0.01123597908603979
\\n\",\n \"
0.0
\\n\",\n \"
3.12
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
rain_duration
\\n\",\n \"
158725
\\n\",\n \"
0.4096267128681682
\\n\",\n \"
8.66552269347979
\\n\",\n \"
0.0
\\n\",\n \"
2960.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
relative_humidity
\\n\",\n \"
158726
\\n\",\n \"
47.60946977810815
\\n\",\n \"
26.214408535062063
\\n\",\n \"
0.9
\\n\",\n \"
93.0
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" 0 1 2 \\\\\\n\",\n \"summary count mean stddev \\n\",\n \"rowID 158726 793625.0 458203.9375103623 \\n\",\n \"hpwren_timestamp 158726 None None \\n\",\n \"air_pressure 158726 916.8301614102413 3.051716552830777 \\n\",\n \"air_temp 158726 61.851589153636084 11.833569210641707 \\n\",\n \"avg_wind_direction 158680 162.15610032770354 95.27820101905918 \\n\",\n \"avg_wind_speed 158680 2.7752148979077367 2.057623969742644 \\n\",\n \"max_wind_direction 158680 163.46214393748426 92.45213853838698 \\n\",\n \"max_wind_speed 158680 3.400557726241551 2.4188016208098855 \\n\",\n \"min_wind_direction 158680 166.77401688933702 97.44110914784568 \\n\",\n \"min_wind_speed 158680 2.134664103856835 1.742112505242438 \\n\",\n \"rain_accumulation 158725 3.1784532997328256E-4 0.01123597908603979 \\n\",\n \"rain_duration 158725 0.4096267128681682 8.66552269347979 \\n\",\n \"relative_humidity 158726 47.60946977810815 26.214408535062063 \\n\",\n \"\\n\",\n \" 3 4 \\n\",\n \"summary min max \\n\",\n \"rowID 0 1587250 \\n\",\n \"hpwren_timestamp 2011-09-10 00:00:49 2014-09-10 23:53:29 \\n\",\n \"air_pressure 905.0 929.5 \\n\",\n \"air_temp 31.64 99.5 \\n\",\n \"avg_wind_direction 0.0 359.0 \\n\",\n \"avg_wind_speed 0.0 31.9 \\n\",\n \"max_wind_direction 0.0 359.0 \\n\",\n \"max_wind_speed 0.1 36.0 \\n\",\n \"min_wind_direction 0.0 359.0 \\n\",\n \"min_wind_speed 0.0 31.6 \\n\",\n \"rain_accumulation 0.0 3.12 \\n\",\n \"rain_duration 0.0 2960.0 \\n\",\n \"relative_humidity 0.9 93.0 \"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"filteredDF.describe().toPandas().transpose()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"157812\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# count how many values of rain accumulation is 0\\n\",\n \"filteredDF.filter(filteredDF.rain_accumulation == 0.0).count() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"157237\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# count how many values of rain duration is 0\\n\",\n \"filteredDF.filter(filteredDF.rain_duration == 0.0).count() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Drop the rain accomulation, rain duration and hpwren timestamp \\n\",\n \"workingDF = filteredDF.drop('rain_accumulation').drop('rain_duration').drop('hpwren_timestamp')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"46\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Droping missing values \\n\",\n \"before = workingDF.count()\\n\",\n \"workingDF = workingDF.na.drop()\\n\",\n \"after = workingDF.count()\\n\",\n \"before - after\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['rowID',\\n\",\n \" 'air_pressure',\\n\",\n \" 'air_temp',\\n\",\n \" 'avg_wind_direction',\\n\",\n \" 'avg_wind_speed',\\n\",\n \" 'max_wind_direction',\\n\",\n \" 'max_wind_speed',\\n\",\n \" 'min_wind_direction',\\n\",\n \" 'min_wind_speed',\\n\",\n \" 'relative_humidity']\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"workingDF.columns\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We do not want to include rowID since it is the row number. The minimum wind measurements have\\n\",\n \"a high correlation to the average wind measurements, so we will not include them either\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create an array of the columns we want to combine\\n\",\n \"featuresUsed = ['air_pressure', 'air_temp', 'avg_wind_direction', 'avg_wind_speed', 'max_wind_direction', \\n\",\n \" 'max_wind_speed','relative_humidity']\\n\",\n \"assembler = VectorAssembler(inputCols=featuresUsed, outputCol=\\\"features_unscaled\\\")\\n\",\n \"assembled = assembler.transform(workingDF)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# StandardScaler to scale the data\\n\",\n \"scaler = StandardScaler(inputCol=\\\"features_unscaled\\\", outputCol=\\\"features\\\", withStd=True, withMean=True)\\n\",\n \"scalerModel = scaler.fit(assembled)\\n\",\n \"scaledData = scalerModel.transform(assembled)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DataFrame[features: vector]\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create the elbow diagram\\n\",\n \"scaledData = scaledData.select(\\\"features\\\", \\\"rowID\\\")\\n\",\n \"elbowset = scaledData.filter((scaledData.rowID % 3) == 0).select(\\\"features\\\")\\n\",\n \"elbowset.persist()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The k-means algorithm requires that the value of k, the number of\\n\",\n \"clusters, to be specified. To determine a good value for k, we will use the “elbow” method. This\\n\",\n \"method involves applying k-means, using different values for k, and calculating the within-cluster\\n\",\n \"sum-of-squared error (WSSE). Since this means applying k-means multiple times, this process can\\n\",\n \"be very compute-intensive. To speed up the process, we will use only a subset of the dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training for cluster size 2 \\n\",\n \"......................WSSE = 114993.08947326531 \\n\",\n \"Training for cluster size 3 \\n\",\n \"......................WSSE = 103421.37370631342 \\n\",\n \"Training for cluster size 4 \\n\",\n \"......................WSSE = 95150.55982034055 \\n\",\n \"Training for cluster size 5 \\n\",\n \"......................WSSE = 87993.46098416099 \\n\",\n \"Training for cluster size 6 \\n\",\n \"......................WSSE = 84411.7705174048 \\n\",\n \"Training for cluster size 7 \\n\",\n \"......................WSSE = 81557.0210698038 \\n\",\n \"Training for cluster size 8 \\n\",\n \"......................WSSE = 80697.55284547339 \\n\",\n \"Training for cluster size 9 \\n\",\n \"......................WSSE = 75920.5265957006 \\n\",\n \"Training for cluster size 10 \\n\",\n \"......................WSSE = 75490.86918816812 \\n\",\n \"Training for cluster size 11 \\n\",\n \"......................WSSE = 72394.56946876278 \\n\",\n \"Training for cluster size 12 \\n\",\n \"......................WSSE = 70712.64866728337 \\n\",\n \"Training for cluster size 13 \\n\",\n \"......................WSSE = 68595.21340259537 \\n\",\n \"Training for cluster size 14 \\n\",\n \"......................WSSE = 67617.99648048694 \\n\",\n \"Training for cluster size 15 \\n\",\n \"......................WSSE = 66596.97107802847 \\n\",\n \"Training for cluster size 16 \\n\",\n \"......................WSSE = 65426.73749265432 \\n\",\n \"Training for cluster size 17 \\n\",\n \"......................WSSE = 64233.31578544854 \\n\",\n \"Training for cluster size 18 \\n\",\n \"......................WSSE = 63252.03183467636 \\n\",\n \"Training for cluster size 19 \\n\",\n \"......................WSSE = 63328.00891485271 \\n\",\n \"Training for cluster size 20 \\n\",\n \"......................WSSE = 61698.78274079937 \\n\",\n \"Training for cluster size 21 \\n\",\n \"......................WSSE = 62097.93367510601 \\n\",\n \"Training for cluster size 22 \\n\",\n \"......................WSSE = 61454.346088191494 \\n\",\n \"Training for cluster size 23 \\n\",\n \"......................WSSE = 60482.037592034445 \\n\",\n \"Training for cluster size 24 \\n\",\n \"......................WSSE = 59724.15264364756 \\n\",\n \"Training for cluster size 25 \\n\",\n \"......................WSSE = 59020.246257137864 \\n\",\n \"Training for cluster size 26 \\n\",\n \"......................WSSE = 58571.39852608297 \\n\",\n \"Training for cluster size 27 \\n\",\n \"......................WSSE = 58437.013246819384 \\n\",\n \"Training for cluster size 28 \\n\",\n \"......................WSSE = 57549.55297045781 \\n\",\n \"Training for cluster size 29 \\n\",\n \"......................WSSE = 56986.12432051836 \\n\",\n \"Training for cluster size 30 \\n\",\n \"......................WSSE = 56735.241778289936 \\n\"\n ]\n }\n ],\n \"source\": [\n \"# compute the k-means clusters for k = 2 to 30 \\n\",\n \"import utils\\n\",\n \"from utils import elbow\\n\",\n \"clusters = range(2,31)\\n\",\n \"wsseList = utils.elbow(elbowset, clusters)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"utils.elbow_plot(wsseList, clusters)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DataFrame[features: vector]\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"scaledDataFeat = scaledData.select(\\\"features\\\")\\n\",\n \"scaledDataFeat.persist()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The values for k are plotted against the WSSE values, and the elbow, or bend in the curve, provides\\n\",\n \"a good estimate for the value for k. In this plot, we see that the elbow in the curve is between 10 and\\n\",\n \"15, so let's choose k = 12. We will use this value to set the number of clusters for k-means.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Cluster using selected k.\\n\",\n \"kmeans = KMeans(k=12, seed=1)\\n\",\n \"model = kmeans.fit(scaledDataFeat)\\n\",\n \"transformed = model.transform(scaledDataFeat)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[array([-1.25780361, -1.34777313, 0.38113428, 1.54798342, 0.48870872,\\n\",\n \" 1.52672966, 1.44243757]),\\n\",\n \" array([-0.17864337, 0.69588949, 0.28443268, -0.40546746, 0.44128038,\\n\",\n \" -0.42242637, -0.3822184 ]),\\n\",\n \" array([-0.62583922, 0.29446191, -1.12160785, -0.62184343, -0.9872638 ,\\n\",\n \" -0.64942549, 0.06781479]),\\n\",\n \" array([ 1.18225683, -0.3553033 , -1.14849104, 2.71267382, -1.05705926,\\n\",\n \" 2.83527612, -1.1340859 ]),\\n\",\n \" array([ 0.23435387, 0.31444308, 1.88825651, -0.65088048, -1.54944593,\\n\",\n \" -0.57517102, -0.27958085]),\\n\",\n \" array([ 0.2984676 , 0.68246496, 1.35060228, -0.64221491, 1.61707318,\\n\",\n \" -0.59397374, -0.688285 ]),\\n\",\n \" array([ 1.20905817, -0.09280855, -1.12215411, 0.9891463 , -1.01080655,\\n\",\n \" 1.06493824, -1.0591806 ]),\\n\",\n \" array([-0.48491474, 0.24260748, 0.43414506, 1.12438181, 0.52966081,\\n\",\n \" 1.05878316, 0.05945657]),\\n\",\n \" array([ 1.45777164, -0.20709285, -0.93507121, -0.47426982, -0.7646462 ,\\n\",\n \" -0.46973205, -0.89457066]),\\n\",\n \" array([-0.18807571, -1.04672147, 0.52593288, -0.29946899, 0.6810938 ,\\n\",\n \" -0.29513355, 1.24208698]),\\n\",\n \" array([ 0.24732381, -1.03045549, -1.20105704, -0.56694075, -1.03757887,\\n\",\n \" -0.575128 , 1.02229527]),\\n\",\n \" array([ 0.09233888, 1.00625083, -1.34698388, -0.55248747, -1.19724351,\\n\",\n \" -0.5611887 , -0.87529728])]\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# determine the center measurement of each cluster\\n\",\n \"centers = model.clusterCenters()\\n\",\n \"centers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Create parallel plots of clusters and analysis\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A parallel coordinates plot is a great way to\\n\",\n \"visualize multi-dimensional data. Each line plots the centroid of a cluster, and all of the features are\\n\",\n \"plotted together. Recall that the feature values were scaled to have mean = 0 and standard deviation\\n\",\n \"= 1. So the values on the y-axis of these parallel coordinates plots show the number of standard\\n\",\n \"deviations from the mean.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#use the pd_centers()function in the utils.py library to create the Pandas DataFrame\\n\",\n \"P = utils.pd_centers(featuresUsed, centers)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Dry Days\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's show clusters for \\\"Dry Days\\\", i.e., weather samples with low relative humidity\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"utils.parallel_plot(P[P['relative_humidity'] < -0.5], P)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first argument to parallel_plot selects the clusters whose relative humidities are centered less\\n\",\n \"than 0.5 from the mean value. All clusters in this plot have relative_humidity < -0.5, but they differ in\\n\",\n \"values for other features, meaning that there are several weather patterns that include low humidity.\\n\",\n \"\\n\",\n \"Note in particular cluster 4. This cluster has samples with lower-than-average wind direction values.\\n\",\n \"Recall that wind direction values are in degrees, and 0 means wind coming from the North and\\n\",\n \"increasing clockwise. So samples in this cluster have wind coming from the N and NE directions,\\n\",\n \"with very high wind speeds, and low relative humidity. These are characteristic weather patterns for\\n\",\n \"Santa Ana conditions, which greatly increase the dangers of wildfires.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Warm Days\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Let's show clusters for \\\"Warm Days\\\", i.e., weather samples with high air temperature\\n\",\n \"utils.parallel_plot(P[P['air_temp'] > 0.5], P)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All clusters in this plot have air_temp > 0.5, but they differ in values for other features.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Cool Days\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Let's show clusters for \\\"Cool Days\\\", i.e., weather samples with high relative humidity and low air temperature\\n\",\n \"utils.parallel_plot(P[(P['relative_humidity'] > 0.5) & (P['air_temp'] < 0.5)], P)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All clusters in this plot have relative_humidity > 0.5 and air_temp < 0.5. These clusters represent\\n\",\n \"cool temperature with high humidity and possibly rainy weather patterns. For cluster 5, note that the\\n\",\n \"wind speed values are high, suggesting stormy weather patterns with rain and wind.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"## Other Days\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"So far, we've seen all the clusters except 2 since it did not fall into any of the other categories. Let's\\n\",\n \"plot this cluster.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"utils.parallel_plot(P.iloc[[2]], P)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Cluster 2 captures days with mild weather.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
},
{
"path": "Spark/Cluster Analysis of the San Diego Weather Data/readme.md",
"content": "\n"
},
{
"path": "Spark/San Diego Rainforest Fire Predicition/Readme.md",
"content": "\n"
},
{
"path": "Spark/San Diego Rainforest Fire Predicition/San Diego Rainforest Fire Prediction.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# San Diego Rainforest Fire Prediction \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Objectives\\n\",\n \"* Read CSV files into Spark Dataframes.\\n\",\n \"* Generate summary statistics.\\n\",\n \"* Compute correlation coefficients between two columns.\\n\",\n \"* Generate a categorical variable from a numeric variable\\n\",\n \"* Aggregate the features into one single column\\n\",\n \"* Randomly split the data into training and test sets\\n\",\n \"* Create a decision tree classifier to predict days with low humidity\\n\",\n \"* Determine the accuracy of a classifier model\\n\",\n \"* Display the confusion matrix for a classifier model\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Import important libraries\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import findspark\\n\",\n \"findspark.init()\\n\",\n \"findspark.add_packages('mysql:mysql-connector-java:8.0.11')\\n\",\n \"\\n\",\n \"import pyspark # only run after findspark.init()\\n\",\n \"from pyspark.sql import SparkSession\\n\",\n \"from pyspark.sql import SQLContext\\n\",\n \"\\n\",\n \"spark = SparkSession.builder.getOrCreate()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pyspark.ml.classification import DecisionTreeClassifier\\n\",\n \"from pyspark.ml.feature import Binarizer\\n\",\n \"from pyspark.ml.feature import VectorAssembler, StringIndexer, VectorIndexer\\n\",\n \"from pyspark import SparkContext\\n\",\n \"from pyspark.ml import Pipeline\\n\",\n \"from pyspark.ml.evaluation import MulticlassClassificationEvaluator\\n\",\n \"from pyspark.mllib.evaluation import MulticlassMetrics\\n\",\n \"\\n\",\n \"sc =SparkContext.getOrCreate()\\n\",\n \"sqlContext = SQLContext(sc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sqlContext = SQLContext(sc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Upload data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The file daily_weather.csv is a comma-separated file that contains weather data. This data comes\\n\",\n \"from a weather station located in San Diego, California. The weather station is equipped with\\n\",\n \"sensors that capture weather-related measurements such as air temperature, air pressure, and\\n\",\n \"relative humidity. Data was collected for a period of three years, from September 2011 to September\\n\",\n \"2014, to ensure that sufficient data for different seasons and weather conditions is captured. The dataset can be downladed from __[here](https://www.kaggle.com/youssef19/san-diago-weather-data)__.\\n\",\n \"\\n\",\n \"Sensor measurements from the weather station were captured at one-minute intervals. These measurements were then processed to generate values to describe daily weather. Since this dataset was created to classify low-humidity days vs. non-low-humidity days (that is, days with normal or high humidity), the variables included are weather measurements in the morning, with one measurement, namely relatively humidity, in the afternoon. __The idea is to use the morning weather values to predict whether the day will be low-humidity or not based on the afternoon measurement of relatively humidity__. This will help in prediciting the occurance of rainforest fire.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = spark.read.csv('daily_weather.csv', \\n\",\n \" header='true',inferSchema='true')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Data Exploration\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['number',\\n\",\n \" 'air_pressure_9am',\\n\",\n \" 'air_temp_9am',\\n\",\n \" 'avg_wind_direction_9am',\\n\",\n \" 'avg_wind_speed_9am',\\n\",\n \" 'max_wind_direction_9am',\\n\",\n \" 'max_wind_speed_9am',\\n\",\n \" 'rain_accumulation_9am',\\n\",\n \" 'rain_duration_9am',\\n\",\n \" 'relative_humidity_9am',\\n\",\n \" 'relative_humidity_3pm']\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Look at data columns and types\\n\",\n \"df.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"root\\n\",\n \" |-- number: integer (nullable = true)\\n\",\n \" |-- air_pressure_9am: double (nullable = true)\\n\",\n \" |-- air_temp_9am: double (nullable = true)\\n\",\n \" |-- avg_wind_direction_9am: double (nullable = true)\\n\",\n \" |-- avg_wind_speed_9am: double (nullable = true)\\n\",\n \" |-- max_wind_direction_9am: double (nullable = true)\\n\",\n \" |-- max_wind_speed_9am: double (nullable = true)\\n\",\n \" |-- rain_accumulation_9am: double (nullable = true)\\n\",\n \" |-- rain_duration_9am: double (nullable = true)\\n\",\n \" |-- relative_humidity_9am: double (nullable = true)\\n\",\n \" |-- relative_humidity_3pm: double (nullable = true)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.printSchema()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
0
\\n\",\n \"
1
\\n\",\n \"
2
\\n\",\n \"
3
\\n\",\n \"
4
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
summary
\\n\",\n \"
count
\\n\",\n \"
mean
\\n\",\n \"
stddev
\\n\",\n \"
min
\\n\",\n \"
max
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
number
\\n\",\n \"
1095
\\n\",\n \"
547.0
\\n\",\n \"
316.24357700987383
\\n\",\n \"
0
\\n\",\n \"
1094
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
air_pressure_9am
\\n\",\n \"
1092
\\n\",\n \"
918.8825513138094
\\n\",\n \"
3.184161180386833
\\n\",\n \"
907.9900000000024
\\n\",\n \"
929.3200000000012
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
air_temp_9am
\\n\",\n \"
1090
\\n\",\n \"
64.93300141287072
\\n\",\n \"
11.175514003175877
\\n\",\n \"
36.752000000000685
\\n\",\n \"
98.90599999999992
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
avg_wind_direction_9am
\\n\",\n \"
1091
\\n\",\n \"
142.2355107005759
\\n\",\n \"
69.13785928889189
\\n\",\n \"
15.500000000000046
\\n\",\n \"
343.4
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
avg_wind_speed_9am
\\n\",\n \"
1092
\\n\",\n \"
5.50828424225493
\\n\",\n \"
4.5528134655317185
\\n\",\n \"
0.69345139999974
\\n\",\n \"
23.554978199999763
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
max_wind_direction_9am
\\n\",\n \"
1092
\\n\",\n \"
148.95351796516923
\\n\",\n \"
67.23801294602953
\\n\",\n \"
28.89999999999991
\\n\",\n \"
312.19999999999993
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
max_wind_speed_9am
\\n\",\n \"
1091
\\n\",\n \"
7.019513529175272
\\n\",\n \"
5.598209170780958
\\n\",\n \"
1.1855782000000479
\\n\",\n \"
29.84077959999996
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
rain_accumulation_9am
\\n\",\n \"
1089
\\n\",\n \"
0.20307895225211126
\\n\",\n \"
1.5939521253574893
\\n\",\n \"
0.0
\\n\",\n \"
24.01999999999907
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
rain_duration_9am
\\n\",\n \"
1092
\\n\",\n \"
294.1080522756142
\\n\",\n \"
1598.0787786601481
\\n\",\n \"
0.0
\\n\",\n \"
17704.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
relative_humidity_9am
\\n\",\n \"
1095
\\n\",\n \"
34.24140205923536
\\n\",\n \"
25.472066802250055
\\n\",\n \"
6.090000000001012
\\n\",\n \"
92.6200000000002
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
relative_humidity_3pm
\\n\",\n \"
1095
\\n\",\n \"
35.34472714825898
\\n\",\n \"
22.524079453587273
\\n\",\n \"
5.3000000000006855
\\n\",\n \"
92.2500000000003
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" 0 1 2 \\\\\\n\",\n \"summary count mean stddev \\n\",\n \"number 1095 547.0 316.24357700987383 \\n\",\n \"air_pressure_9am 1092 918.8825513138094 3.184161180386833 \\n\",\n \"air_temp_9am 1090 64.93300141287072 11.175514003175877 \\n\",\n \"avg_wind_direction_9am 1091 142.2355107005759 69.13785928889189 \\n\",\n \"avg_wind_speed_9am 1092 5.50828424225493 4.5528134655317185 \\n\",\n \"max_wind_direction_9am 1092 148.95351796516923 67.23801294602953 \\n\",\n \"max_wind_speed_9am 1091 7.019513529175272 5.598209170780958 \\n\",\n \"rain_accumulation_9am 1089 0.20307895225211126 1.5939521253574893 \\n\",\n \"rain_duration_9am 1092 294.1080522756142 1598.0787786601481 \\n\",\n \"relative_humidity_9am 1095 34.24140205923536 25.472066802250055 \\n\",\n \"relative_humidity_3pm 1095 35.34472714825898 22.524079453587273 \\n\",\n \"\\n\",\n \" 3 4 \\n\",\n \"summary min max \\n\",\n \"number 0 1094 \\n\",\n \"air_pressure_9am 907.9900000000024 929.3200000000012 \\n\",\n \"air_temp_9am 36.752000000000685 98.90599999999992 \\n\",\n \"avg_wind_direction_9am 15.500000000000046 343.4 \\n\",\n \"avg_wind_speed_9am 0.69345139999974 23.554978199999763 \\n\",\n \"max_wind_direction_9am 28.89999999999991 312.19999999999993 \\n\",\n \"max_wind_speed_9am 1.1855782000000479 29.84077959999996 \\n\",\n \"rain_accumulation_9am 0.0 24.01999999999907 \\n\",\n \"rain_duration_9am 0.0 17704.0 \\n\",\n \"relative_humidity_9am 6.090000000001012 92.6200000000002 \\n\",\n \"relative_humidity_3pm 5.3000000000006855 92.2500000000003 \"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Print summary statistics\\n\",\n \"df.describe().toPandas().transpose()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"+-------+-----------------+\\n\",\n \"|summary| air_pressure_9am|\\n\",\n \"+-------+-----------------+\\n\",\n \"| count| 1092|\\n\",\n \"| mean|918.8825513138094|\\n\",\n \"| stddev|3.184161180386833|\\n\",\n \"| min|907.9900000000024|\\n\",\n \"| max|929.3200000000012|\\n\",\n \"+-------+-----------------+\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.describe('air_pressure_9am').show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.7298253479609021\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Compute correlation between two columns\\n\",\n \"df2.stat.corr(\\\"rain_accumulation_9am\\\", \\\"rain_duration_9am\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Data Preprocessing \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Drop rows with missing values.\\n\",\n \"df2 = df.na.drop(subset=['air_pressure_9am'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1092\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df2.count()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Select features used for classification\\n\",\n \"featureColumns = ['air_pressure_9am','air_temp_9am','avg_wind_direction_9am','avg_wind_speed_9am',\\n\",\n \" 'max_wind_direction_9am','max_wind_speed_9am','rain_accumulation_9am',\\n\",\n \" 'rain_duration_9am']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Drop unused and missing data\\n\",\n \"df = df.drop('number')\\n\",\n \"df = df.na.drop() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1064, 10)\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.count(), len(df.columns)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create categorical variable for the relative humidity\\n\",\n \"binarizer = Binarizer(threshold=24.99999, inputCol=\\\"relative_humidity_3pm\\\", outputCol=\\\"label\\\")\\n\",\n \"binarizedDF = binarizer.transform(df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"+---------------------+-----+\\n\",\n \"|relative_humidity_3pm|label|\\n\",\n \"+---------------------+-----+\\n\",\n \"| 36.160000000000494| 1.0|\\n\",\n \"| 19.4265967985621| 0.0|\\n\",\n \"| 14.460000000000045| 0.0|\\n\",\n \"| 12.742547353761848| 0.0|\\n\",\n \"+---------------------+-----+\\n\",\n \"only showing top 4 rows\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"binarizedDF.select(\\\"relative_humidity_3pm\\\",\\\"label\\\").show(4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"+---------------------+-----+\\n\",\n \"|relative_humidity_3pm|label|\\n\",\n \"+---------------------+-----+\\n\",\n \"| 36.160000000000494| 1.0|\\n\",\n \"| 19.4265967985621| 0.0|\\n\",\n \"| 14.460000000000045| 0.0|\\n\",\n \"| 12.742547353761848| 0.0|\\n\",\n \"+---------------------+-----+\\n\",\n \"only showing top 4 rows\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"binarizedDF.select(\\\"relative_humidity_3pm\\\",\\\"label\\\").show(4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# aggregate the features we will use to make predictions into a single column\\n\",\n \"assembler = VectorAssembler(inputCols=featureColumns, outputCol=\\\"features\\\")\\n\",\n \"assembled = assembler.transform(binarizedDF)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Splitting the data into train and test data \\n\",\n \"trainingData, testData) = assembled.randomSplit([0.8,0.2], seed = 13234 )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(846, 218)\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"trainingData.count(), testData.count()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Decision tree classification \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create decision tree\\n\",\n \"dt = DecisionTreeClassifier(labelCol=\\\"label\\\", featuresCol=\\\"features\\\", maxDepth=5,\\n\",\n \" minInstancesPerNode=20, impurity=\\\"gini\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# train decision tree \\n\",\n \"pipeline = Pipeline(stages=[dt])\\n\",\n \"model = pipeline.fit(trainingData)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# make predictions using our test data set\\n\",\n \"predictions = model.transform(testData)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"predictions = predictions.select(\\\"prediction\\\", \\\"label\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Evaluation \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compute accuracy\\n\",\n \"evaluator = MulticlassClassificationEvaluator(\\n\",\n \" labelCol=\\\"label\\\", predictionCol=\\\"prediction\\\", metricName=\\\"accuracy\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy = 0.784404 \\n\"\n ]\n }\n ],\n \"source\": [\n \"accuracy = evaluator.evaluate(predictions)\\n\",\n \"print(\\\"Accuracy = %g \\\" % (accuracy))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[Row(prediction=1.0, label=1.0), Row(prediction=1.0, label=1.0)]\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predictions.rdd.take(2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[(1.0, 1.0), (1.0, 1.0)]\"\n ]\n },\n \"execution_count\": 57,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predictions.rdd.map(tuple).take(2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"metrics = MulticlassMetrics(predictions.rdd.map(tuple))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[87., 28.],\\n\",\n \" [19., 84.]])\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Display confusion matrix\\n\",\n \"metrics.confusionMatrix().toArray().transpose()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
},
{
"path": "time-series-analysis/Power-consumption-forecasting/Energy_Consumption_Solution.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Time Series Forecasting \\n\",\n \"\\n\",\n \"A time series is data collected periodically, over time. Time series forecasting is the task of predicting future data points, given some historical data. It is commonly used in a variety of tasks from weather forecasting, retail and sales forecasting, stock market prediction, and in behavior prediction (such as predicting the flow of car traffic over a day). There is a lot of time series data out there, and recognizing patterns in that data is an active area of machine learning research!\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"In this notebook, we'll focus on one method for finding time-based patterns: using SageMaker's supervised learning model, [DeepAR](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html).\\n\",\n \"\\n\",\n \"\\n\",\n \"### DeepAR\\n\",\n \"\\n\",\n \"DeepAR utilizes a recurrent neural network (RNN), which is designed to accept some sequence of data points as historical input and produce a predicted sequence of points. So, how does this model learn?\\n\",\n \"\\n\",\n \"During training, you'll provide a training dataset (made of several time series) to a DeepAR estimator. The estimator looks at *all* the training time series and tries to identify similarities across them. It trains by randomly sampling **training examples** from the training time series. \\n\",\n \"* Each training example consists of a pair of adjacent **context** and **prediction** windows of fixed, predefined lengths. \\n\",\n \" * The `context_length` parameter controls how far in the *past* the model can see.\\n\",\n \" * The `prediction_length` parameter controls how far in the *future* predictions can be made.\\n\",\n \" * You can find more details, in [this documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar_how-it-works.html).\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"> Since DeepAR trains on several time series, it is well suited for data that exhibit **recurring patterns**.\\n\",\n \"\\n\",\n \"In any forecasting task, you should choose the context window to provide enough, **relevant** information to a model so that it can produce accurate predictions. In general, data closest to the prediction time frame will contain the information that is most influential in defining that prediction. In many forecasting applications, like forecasting sales month-to-month, the context and prediction windows will be the same size, but sometimes it will be useful to have a larger context window to notice longer-term patterns in data.\\n\",\n \"\\n\",\n \"### Energy Consumption Data\\n\",\n \"\\n\",\n \"The data we'll be working with in this notebook is data about household electric power consumption, over the globe. The dataset is originally taken from [Kaggle](https://www.kaggle.com/uciml/electric-power-consumption-data-set), and represents power consumption collected over several years from 2006 to 2010. With such a large dataset, we can aim to predict over long periods of time, over days, weeks or months of time. Predicting energy consumption can be a useful task for a variety of reasons including determining seasonal prices for power consumption and efficiently delivering power to people, according to their predicted usage. \\n\",\n \"\\n\",\n \"**Interesting read**: An inversely-related project, recently done by Google and DeepMind, uses machine learning to predict the *generation* of power by wind turbines and efficiently deliver power to the grid. You can read about that research, [in this post](https://deepmind.com/blog/machine-learning-can-boost-value-wind-energy/).\\n\",\n \"\\n\",\n \"### Machine Learning Workflow\\n\",\n \"\\n\",\n \"This notebook approaches time series forecasting in a number of steps:\\n\",\n \"* Loading and exploring the data\\n\",\n \"* Creating training and test sets of time series\\n\",\n \"* Formatting data as JSON files and uploading to S3\\n\",\n \"* Instantiating and training a DeepAR estimator\\n\",\n \"* Deploying a model and creating a predictor\\n\",\n \"* Evaluating the predictor \\n\",\n \"\\n\",\n \"---\\n\",\n \"\\n\",\n \"Let's start by loading in the usual resources.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Load and Explore the Data\\n\",\n \"\\n\",\n \"We'll be loading in some data about global energy consumption, collected over a few years. The below cell downloads and unzips this data, giving you one text file of data, `household_power_consumption.txt`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ! wget https://s3.amazonaws.com/video.udacity-data.com/topher/2019/March/5c88a3f1_household-electric-power-consumption/household-electric-power-consumption.zip\\n\",\n \"# ! unzip household-electric-power-consumption\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Read in the `.txt` File\\n\",\n \"\\n\",\n \"The next cell displays the first few lines in the text file, so we can see how it is formatted.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Date;Time;Global_active_power;Global_reactive_power;Voltage;Global_intensity;Sub_metering_1;Sub_metering_2;Sub_metering_3\\\\n',\\n\",\n \" '16/12/2006;17:24:00;4.216;0.418;234.840;18.400;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:25:00;5.360;0.436;233.630;23.000;0.000;1.000;16.000\\\\n',\\n\",\n \" '16/12/2006;17:26:00;5.374;0.498;233.290;23.000;0.000;2.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:27:00;5.388;0.502;233.740;23.000;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:28:00;3.666;0.528;235.680;15.800;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:29:00;3.520;0.522;235.020;15.000;0.000;2.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:30:00;3.702;0.520;235.090;15.800;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:31:00;3.700;0.520;235.220;15.800;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:32:00;3.668;0.510;233.990;15.800;0.000;1.000;17.000\\\\n']\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# display first ten lines of text data\\n\",\n \"n_lines = 10\\n\",\n \"\\n\",\n \"with open('household_power_consumption.txt') as file:\\n\",\n \" head = [next(file) for line in range(n_lines)]\\n\",\n \" \\n\",\n \"display(head)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Pre-Process the Data\\n\",\n \"\\n\",\n \"The 'household_power_consumption.txt' file has the following attributes:\\n\",\n \" * Each data point has a date and time (hour:minute:second) of recording\\n\",\n \" * The various data features are separated by semicolons (;)\\n\",\n \" * Some values are 'nan' or '?', and we'll treat these both as `NaN` values\\n\",\n \"\\n\",\n \"### Managing `NaN` values\\n\",\n \"\\n\",\n \"This DataFrame does include some data points that have missing values. So far, we've mainly been dropping these values, but there are other ways to handle `NaN` values, as well. One technique is to just fill the missing column values with the **mean** value from that column; this way the added value is likely to be realistic.\\n\",\n \"\\n\",\n \"I've provided some helper functions in `txt_preprocessing.py` that will help to load in the original text file as a DataFrame *and* fill in any `NaN` values, per column, with the mean feature value. This technique will be fine for long-term forecasting; if I wanted to do an hourly analysis and prediction, I'd consider dropping the `NaN` values or taking an average over a small, sliding window rather than an entire column of data.\\n\",\n \"\\n\",\n \"**Below, I'm reading the file in as a DataFrame and filling `NaN` values with feature-level averages.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Data shape: (2075259, 7)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Global_active_power
\\n\",\n \"
Global_reactive_power
\\n\",\n \"
Voltage
\\n\",\n \"
Global_intensity
\\n\",\n \"
Sub_metering_1
\\n\",\n \"
Sub_metering_2
\\n\",\n \"
Sub_metering_3
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
Date-Time
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
\\n\",\n \" \\n\",\n \" \\n\",\n \"
\\n\",\n \"
2006-12-16 17:24:00
\\n\",\n \"
4.216
\\n\",\n \"
0.418
\\n\",\n \"
234.84
\\n\",\n \"
18.4
\\n\",\n \"
0.0
\\n\",\n \"
1.0
\\n\",\n \"
17.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2006-12-16 17:25:00
\\n\",\n \"
5.360
\\n\",\n \"
0.436
\\n\",\n \"
233.63
\\n\",\n \"
23.0
\\n\",\n \"
0.0
\\n\",\n \"
1.0
\\n\",\n \"
16.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2006-12-16 17:26:00
\\n\",\n \"
5.374
\\n\",\n \"
0.498
\\n\",\n \"
233.29
\\n\",\n \"
23.0
\\n\",\n \"
0.0
\\n\",\n \"
2.0
\\n\",\n \"
17.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2006-12-16 17:27:00
\\n\",\n \"
5.388
\\n\",\n \"
0.502
\\n\",\n \"
233.74
\\n\",\n \"
23.0
\\n\",\n \"
0.0
\\n\",\n \"
1.0
\\n\",\n \"
17.0
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2006-12-16 17:28:00
\\n\",\n \"
3.666
\\n\",\n \"
0.528
\\n\",\n \"
235.68
\\n\",\n \"
15.8
\\n\",\n \"
0.0
\\n\",\n \"
1.0
\\n\",\n \"
17.0
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Global_active_power Global_reactive_power Voltage \\\\\\n\",\n \"Date-Time \\n\",\n \"2006-12-16 17:24:00 4.216 0.418 234.84 \\n\",\n \"2006-12-16 17:25:00 5.360 0.436 233.63 \\n\",\n \"2006-12-16 17:26:00 5.374 0.498 233.29 \\n\",\n \"2006-12-16 17:27:00 5.388 0.502 233.74 \\n\",\n \"2006-12-16 17:28:00 3.666 0.528 235.68 \\n\",\n \"\\n\",\n \" Global_intensity Sub_metering_1 Sub_metering_2 \\\\\\n\",\n \"Date-Time \\n\",\n \"2006-12-16 17:24:00 18.4 0.0 1.0 \\n\",\n \"2006-12-16 17:25:00 23.0 0.0 1.0 \\n\",\n \"2006-12-16 17:26:00 23.0 0.0 2.0 \\n\",\n \"2006-12-16 17:27:00 23.0 0.0 1.0 \\n\",\n \"2006-12-16 17:28:00 15.8 0.0 1.0 \\n\",\n \"\\n\",\n \" Sub_metering_3 \\n\",\n \"Date-Time \\n\",\n \"2006-12-16 17:24:00 17.0 \\n\",\n \"2006-12-16 17:25:00 16.0 \\n\",\n \"2006-12-16 17:26:00 17.0 \\n\",\n \"2006-12-16 17:27:00 17.0 \\n\",\n \"2006-12-16 17:28:00 17.0 \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import txt_preprocessing as pprocess\\n\",\n \"\\n\",\n \"# create df from text file\\n\",\n \"initial_df = pprocess.create_df('household_power_consumption.txt', sep=';')\\n\",\n \"\\n\",\n \"# fill NaN column values with *average* column value\\n\",\n \"df = pprocess.fill_nan_with_mean(initial_df)\\n\",\n \"\\n\",\n \"# print some stats about the data\\n\",\n \"print('Data shape: ', df.shape)\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Global Active Power \\n\",\n \"\\n\",\n \"In this example, we'll want to predict the global active power, which is the household minute-averaged active power (kilowatt), measured across the globe. So, below, I am getting just that column of data and displaying the resultant plot.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(2075259,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Select Global active power data\\n\",\n \"power_df = df['Global_active_power'].copy()\\n\",\n \"print(power_df.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAr8AAAGPCAYAAABVi7jeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABIrklEQVR4nO3debgcVbX38d+CACozAioIoggIKDJERBBEQEQQcUIG4WWQCwooF0FFBmVSRBQBETWCCjKJYQpchkCYZUxCICBzEkggJCEkIQlkPOv9o7o9fU66Tw817Kqu7+d5ztN9uqt2re7eXbVq9669zd0FAAAAlMFSoQMAAAAAskLyCwAAgNIg+QUAAEBpkPwCAACgNEh+AQAAUBokvwAAACgNkl8AXcvM/m5mZ7a4rJvZRzvczgQz26WTddvcztNmtmPa2wGAbkbyC6CwzGxfM3vEzOaa2dTK/SPNzELHFle9xN3dN3X3ewKFBABdgeQXQCGZ2XGSzpd0jqT3S3qfpO9K2k7SsgFDQwxmNih0DAC6G8kvgMIxs5UlnS7pSHcf6u6zPfK4u3/b3ec3WO9/zOxFM3vTzIaZ2Vr9FtndzMaZ2Rtmdo6ZLVVZb30zu8vMpleeu8LMVmkx1j3M7HEze8vMJprZqf2e/6yZPWhmMyvPH2xmh0v6tqQfm9kcM7upsuwEM9vFzNYys3fMbLWacraoxLZM5f9DzewZM5thZreb2YcaxLdepcvH4Wb2mplNrpxYVJ9fzszOqzz3WuX+cpXn7jWzb9S8Djez3Sv/72JmY2rKaRhPZb2jzOwFSS+08r4CQKdIfgEU0WckLSfpxlZXMLOdJJ0l6VuSPiDpZUlX91vsa5IGS9pS0l6SDq2uXll3LUkbS1pH0qktbnqupP8naRVJe0j6npl9tRLTupJulfR7SWtI2lzSGHcfIukKSb929xXcfc/aAt39NUkPSfpGzcP7Sxrq7gsr5Z8o6euVcu+XdFWTOD8vaQNJu0o6oaYP80mStqnE9klJW0s6ufLcvZJ2rNzfQdI4SZ+r+f/eyutsJZ6vSvq0pE2axAkAsZD8Aiii1SW94e6Lqg/UtJ6+Y2Y71Fnn25L+6u6jKy3DP5X0GTNbr2aZs939TXd/RdJ5kvaTJHd/0d3vcPf57j5N0rnqTfIG5O73uPtYd+9x9ycVJX3Vdb8t6U53v8rdF7r7dHcf0+J7cGU1vkof530rj0nSEZLOcvdnKu/RLyVt3qj1t+I0d5/r7mMl/a1adiXG0919auW1nybpwMpz96pvsntWzf+fqzzfajxnVd77d1p8/QDQEZJfAEU0XdLqtf1D3X1bd1+l8ly9fdtailp7q8vPqSy7ds0yE2vuv1xZR2a2ppldbWavmtlbki5XlIA3ZWafNrO7zWyamc1S1C+5uu46kl5qpZw6hipK3tdSlHi6ohZVSfqQpPMrJwMzJb2pqPV67XoFVdR97er3vvV77iFJG5rZ+xS1DF8maR0zW11RC/F9bcRTu30ASA3JL4AiekjSfEVdE1r1mqIkTJJkZstLeq+kV2uWWafm/rqVdaSoRdMlbebuK0k6QFHy1oorJQ2TtI67ryzpTzXrTpS0foP1fKBC3X2mpOGKunHsL+kqd6+uM1HSEe6+Ss3fu939wQGKbPTa+7xvtc+5+9uSRkk6RtJT7r5A0oOSfijpJXd/o414Bny9AJAUkl8AhVNJ/E6TdJGZfdPMVjCzpcxsc0nLN1jtSkmHmNnmlQu2finpEXefULPMj8xsVTNbR1FC98/K4ytKmiNpppmtLelHbYS7oqQ33X2emW2tKFGtukLSLmb2LTMbZGbvrbwGSZoi6SNNyr5SUX/ib6i3y4MUJdg/NbNNpegCQTPbu0lZp5jZeyrrHKLe136VpJPNbI1Ki+7PFLV8V90r6Wj1dnG4p9//ncYDAKkg+QVQSO7+a0UtjD+WNFVRsvhnST9R1PrYf/kRkk6RdK2kyYpaXPftt9iNiloyx0j6P0mXVB4/TdFFcLMqj1/XRqhHSjrdzGYrShyvqYnpFUm7SzpOUVeAMYouKlNl25tUugrc0KDsYYouUpvi7k/UlHu9pLMlXV3ppvGUpC81ifNeSS9KGiHpN+4+vPL4mZJGSnpS0lhJoyuP1a63onq7OPT/v9N4ACAV1vsrGQCgbCoX/I2XtEztBYQA0K1o+QUAAEBpkPwCAACgNOj2AAAAgNKg5RcAAAClQfILAACA0hjUfJHkrL766r7eeutluUkAAACU0KhRo95w9zX6P55p8rveeutp5MiRWW4SAAAAJWRmL9d7nG4PAAAAKA2SXwAAAJQGyS8AAABKg+QXAAAApUHyCwAAgNIg+QUAAEBpkPwCAACgNEh+AQAAUBokvwAAACgNkl8AAACUBskvAAAASoPkFwAAAKVB8gsAaNmQIdLOO4eOAgA6Nyh0AACA4jjiiNARAEA8tPwCAACgNEh+AQAAUBokvwAAACgNkl8AAACUBskvAAAASoPkFwAAAKVB8gsAAIDSIPkFAABAaZD8AgAAoDRIfgEAAFAapU1+H3lEOvHE0FEAAAAgS6VNfrfZRjrrrNBRAAAAIEulTX4BAABQPiS/AAAAKA2SXwAAAJQGyS8AAABKg+QXAAAApUHyCwAAgNIg+QWAnHGXenpCRwEA3YnkFwBy5rDDpKWXDh0FAHQnkl8AiOmNN6S//S258v761+TKAgD0RfILADHts4906KHSCy+EjgQA0AzJLwDE9Prr0e2CBWHjAAA0R/ILpKSnh4uWAADIG5JfICXbbstFSwAA5E3T5NfM/mpmU83sqZrHVjOzO8zshcrtqumGCRTPI4+EjgAAAPTXSsvv3yXt1u+xEySNcPcNJI2o/A8AAADkWtPk193vk/Rmv4f3knRp5f6lkr6abFgAAABA8jrt8/s+d58sSZXbNZMLCQAAAEhH6he8mdnhZjbSzEZOmzYt7c0BAAAADXWa/E4xsw9IUuV2aqMF3X2Iuw9298FrrLFGh5sDgPxyDx0BAKBVnSa/wyQdVLl/kKQbkwkHAIrLLHQEAIBmWhnq7CpJD0nayMwmmdl3JP1K0hfM7AVJX6j8DwAAAOTaoGYLuPt+DZ7aOeFYAAAAgFQxwxsAAABKg+QXAAC05L77pHvvDR0FUN/jj0tz5zZfjuQXaOD556UZM0JHAQD58bnPSTvuGDoKYElvvSVtuaW0X6POujVIfoEGNtpI2mKLztadPz/ZWAAAQGPz5kW3Dz/cfFmSX2AAL7/c2XoPPZRsHAAAIBkkv0ATN9wgnXNO6CiA5FG3AZRR06HOgLL72tei2x/9KGwcyK+izvBG3QZQRrT8AgAAoDRKn/yOHx86AiB9o0ZJH/+4NHt26Ei6E9MaA0BxlD75ve220BEA6TvhBOnpp1u7ChYAgG5WiuR35kxp+eWlu+8OHQkAAABCKkXyO2qU9Pbb0plnho4EAAAAIZUi+UU5jBsnnXZaca+8BwAAnWnn2J/r5HfOnKjLAtCKL39ZOvXUziemAAAAxdbKBci5Tn7XWUdaddXQUaAo3nknuqXlFwAANJLr5JdWX2Rl3LjobPHpp0NHgiLihAsAiiPXyS+QlaFDo9tLLw0bB4qN8X4BIP+CJ79Dh0rz5oXbPi02AADk04IF/AqM5AVNfu+7T9p7b+n440NGAQAA8uD556Xrr+/9f6+9uPYHrSnMaA/Vs7k//CEahxdAOviFA0ARbLSR9PWv9/7PLKxoV6FGe5g2LXQEyJPdd5d+97vQUXQf+qQCAMouN8lvmqpDYKE4br1V+uEPQ0fROVpaAQDIp1Ikv3vuGToCFAVJ68DGjpUmTAgdBQAAfbVz/B6UXhhAGJ0ksNXuACS/A9tss+iW9wkAkEeF6vObBfo7drc4ny91A3G0cjIweXL6cQAAmgua/GadcNBaVQ777hs6ApRVo33aNddIa60l3XNPpuEAAOooVctvPSTE3eexxzpfd/Hi5OIAqh58MLodMyZoGAAAkfwCfZx3XugIgGSdfXbUIt3TEzqS/HjwwWgyBQDlRPILpKD/pC2TJkkzZoSJBdnJ4y9JJ50U3ZL89tpuu2gyBTR39dXSiy+GjmJgixdLF18sLVoUOhIURZDk98EHpa98hZ+Yi6ynJxqH95VXQkeST2+91ff/ddaR1l8/TCxSPpOybsIFkyiSLbeUVlmltWX32693lJe8uugi6X/+J7oFWhEk+d17b+mmm6TXX+99rPbgfPDBSx5MRo6UTjwxk/DQgocfjmZgO+CA0JEkI07yMnx4tP6bbw68XNyW36eekjbffMnEuh0kaQAef1yaNav15fM+UdT06dFts30wUJXL0R4uvXTJxz71Kemss9KNB62rnqx0S+t9nKTw7LOj28cfTyaWRk45RXriCWnEiHS3AwBAN8tl8guk6dOflrbdNnQUQDquvz76VQYAyoQZ3oABPPpo82W6tY9st76uohjo/d9mm6gf5m23xdvG178e3R577MDLvfFGvO0AQB4xwxu6xqJF0nXXDZw8JPlLwu9/n1xZecCvLOlqdlLRyvv/yCPS7bcnE08rnn46u22hu/X0tNaoAKSpncad0ie/tIQVw9lnS9/4RvSTbhZuvTWb7aC7cJKBMjrnnKg72X33hY4EoOW3JSS/xVAdUm3atLBxAEAZDRnS+Lknn4xuJ07MJpYimzdPeuGF0FGgVMkvrTJohLqBbseJPuI44ojQEbRu7tz8JuL77y9tuGGUBCOcrh/tYebM9LeB4otTF++6a8nHSDSQF5zYoWw+/3lp3XVDR1HfHXdEtwsXho2j7Lq+5Xfq1N77JCRIU1pJhpl02GHxyqDuI6068Prr6Y9xDbTjscfSK/vBB6OplFFsXZ/8Aml64IFstnPJJcmUU03QZ8+WXnut9/FFi6S3305mG2gsxElIo5OypGLZeONoulygDLbbLppKGcUWNPmt3flmcVCg9QtJ23770BG07447pJVWktZeu/exXXeVll8+XEzdrpu7HtC1DEDRBEl+Qx0IGNcyOZxIFNeuuy752N13Zx9H0T30ULQvGz8+dCQAgEKO89tqQhwn6Vq8uPN1JWnOHGnkyHhldJsHHyQRRjlVu6KMGMF3AIijp0eaMiV0FOgWuR3nt3qgWCoHqXc7B62995Y+9amovyR6DR0aOoJInF8Ukvw14rnnpPnz21tn9GjplFPa39ZLL0kPP9z+ekhHN3dvQLFNndo70kDenH669P73S5MmdbY+J5/l8cILyVzQOCjOymZ2rKTDJLmksZIOcfeWR68r2oHikUei20suiZKObpsCt1OtnAyYST/8ofTb33a2jbR3bknVxTfekD72MWnZZdtbb+uto18mTjut+Ulh7Xvx0Y8u+Rjyq5XP6f7709v+t74Vjc7ATFzls9NOUde/uL+ApuHmm6Pb11+PV07Rcgq0b8MNo9u4x7yO217NbG1JP5A02N0/LmlpSfvGC0caNy5uCek79ljpwgtDR1E8554bv4ykd26TJ0v/+lcyZZlJb70V3V+woL11qwekgV4fO/biauezSysxdY/qeprJNfKres0L+xEgfreHQZLebWaDJL1H0mtNlh/Qf/4jrb/+wMu8+mqcLSyJFrNy22mnqDWs/zBft90WJh4gaSQ7SMoFF4Td/ty50vTpYWNICrlHWB0nv+7+qqTfSHpF0mRJs9x9eJxgJkyo/3jtznuXXdors90df0+P9KEPSZdf3t56iNx9d9R/tSheeSW6ZUeEpDz5ZOM+aXmqZ3mKpWry5OQbOJCcY44Ju/1NNpFWXz1sDHFxMpoPcbo9rCppL0kflrSWpOXN7IA6yx1uZiPNbOS0adM6j7QiTp+gVirdvHlRQnT44Z1vp8x22knaaqvQUfS6++7oc290YpWFL30p3LaRLXfpk5+M+nDX4oDXmrXWkj74wdBRIK+qjRVAPVkNdbaLpPHuPs3dF0q6TtK2SwbjQ9x9sLsPXmONNWJsrrGbbopaDNJw3HHSD34Q3c9jS0kZTZ8eJRPDhjVftjocVTszsSV9QUgeulBQd9NVhOS2lTowe3Z0EWX14l4AKJq0hzp7RdI2ZvYeMzNJO0t6ppUVq9OqJnHA6OmRvvIV6XOfi19WPeeey6gOeTN2bHSbxAV0VbV18aqrkis3b4qQpCFZ7XzmDz0UjWRTHXbPvb0Tpzlzon1y1fe+J511Vuvrt2r69PYvKkV+cXKOrMXp8/uIpKGSRisa5mwpSUMGWmfWLOn442vLqH+/vTii25de6mx9xNdtO652x+gFmsnj8FKt+OxnWx+Pfe5cacUVpRNO6H3sT3+STjwx+bhWX13aZ5/kyy2K6dOL3Tp/xBH1RzXh5BxxZDbDm7v/3N0/5u4fd/cD3X3AtOHFFzsf5zUt3Za4oT1F3NkuWhRmu++8E2a7RdBsP3LeeZmEkbgHH2x92ep435ddlk4s/d1wQzbbyaPtt5e22SZ0FJ0bMiS9X2uL5JJLpN12Cx1F92nluB5rkosQ4iQrJLrJyeN7GTqRNUv3famWvc8+0tprS9ttl9626tl2iR795Vbvsw5dB1EOz7TUwRB5d9hhoSMorxxMMNyemTPbW37EiFTCQEFdc03Y7VcnwYjr7ruTKacdY8Zkv808qya/7SS8tQnzzTdLG2/c24c9a3k8gQWALARNfpNsJam9yKLWccclt40kzJ8vjR8fOoriidsnvOrPf44fSxwrrzzw85/4RHTREYqj0/3YYYdJzz4rbbZZdJu2pJPd669PtryyuvFGac01o2E2s8BJD9BFyW8jtTN3xf2ZMomdxqGHSh/5SHRxCNrXaZ1ptl5eDghPP933olCUw2ux5sYcWFr72b33rv/4lCnSpEnpbLMbHXusNG1aunVAoktO3uTlmFNkL7wQzf1w+eWN90eNFK7bQ9KyroC33hrddjKiwNlnd1erYE+PtN9+0uabt7deO5/ZQMtee23v/aQODHnfoeU9PiRnoM867YsX11kn3fKBouIkJDkbbih94APSgQdKQ4e2t26uWn5DHJiT6oPZqhkzOl/3hBO666Kjgw6Srr5aeuKJztZvZydSb9lvfrO3Bb7oO6T//Gfg57N8fX/4Q/s7ojKaMiXd8i+9tPFzK6yQ7raB/m6/vfF+Ku6xn5N6tKuwLb9m0k9/Gr+cn/88fhnd7sc/TmdYncsvT77MrMyalf2VugMlsJtuOvC6WR4cjj56yZ+g3n67bxekUGbPLs84zs8/3/i5RtdIAGnZbbdoPzXQvijuSXrRGzGQnVwlv+0eoH/1q+xbbsvonHMGHlC9jGfdv/pV79TJVUXY8YaKcYUVpOWXD7PtWiutJG21Vego2nPPPVHftiL7xS+kUaNCRwF0h7PPlg44IHQUxZar5LcTq63W+rLtHPgZ0L879B+eK6kLDRsl/GU8EWhFnt6Xp59Ovswkf7Y9+eS+z33+81Hftrj6x5jlZ3LyydLgwa0vP3eudOed6cUDFNkJJ0hXXBE6ivzJbIa3JCW1Ix4/Xjr99HwdbLPy9ttR61q3DUF0553SxRf3/t/OZ1sdDL564tOoBbuM9QXJy+OoIkX4RaK/ww6TvvAFpq1HY4sXS8OHh44CedTKPi83yW9S9twz6sebxli6eU+QXn45SoBPPDH9bWV5QL366tZiKMuIDXHssUfoCNAt+n9PktwnVC+MmjOn9XUOOEA699zkYkB+3XabtMMO0he/KN10U+hoUERdl/xWuyskNSlCJ8aNi18G8mfXXUNH0KvTeprVQPqor9Hn1umIJ0lsu1tccUX+JjVqRTufywMPdOckSdX34JVXWlv+S1+SHnwwup/2+MgojkJ2e0ijJfF3v0u2vFbe2Ntvl9Zfv3FrZbcIeSDtZNvNBt1vZSSCO+7ovb9wYfsxJCnt93/BgnTLz7tbbpFOOWXgZZL8DGrrVlKq+9Qidnsoo1Y+p+23jyZJ6jYTJ0a3X/tavHIuuig/9d1M2n330FGUUym7PdQaMqT9deIe0J58MroNcWXzr34V3XZ7C09VOzu5H/944OfPOKO9baf582roz+/OO6XlluttWSmjPfaQzjxz4GWqn1NeDrb9pR1X6HqKzoT+3Optf/HizsurHQkl7bGz21Wd1Kqe0J9D2RUy+a0meQN56inp2Wf7PpanypbGgemyy6Lb2td5ySXSvfcmv624Fi3qbL0sEo00RgNIwhtvZLOd6kUkDzyQzfaKLok6mad9U6fyehKASN4+n6TiyVvC20zePoeyytUMb61qZXKLr361s7K7zWGHSTvumO42OjlwV1vIs7Tccq0td955nZWf9k7t/vvTLR/xZZnETp0a1emBxuAeSDck3Mi/xYuls84KHQXQVyGT36zcdlvf/5M8WNxzTzRrWui+o3n38svJlVX2fqxIT4jv8d13R3U6qS44N9wQXT2flCTiKkOC3u2vcejQbEYgStPcudLkyaGjQJJy1e0h1E7g1FOjRLz/9tPst3vYYVGLTZLJXVU37UzXWy90BO1J4r1P46Qwr3Vi0aJoXO6kJh8JZcKE1paL8zmsv366U0THvdiovzgjL4T4ZSi0vDcGdaqVEWbSeu31vm+rry4df3x75XzmM9JaayUTE/IhV8lvKKedFt32/6LUtuZMmdLemJNITlEOCu75TTLz6h//iMbl/tnPQkeSjFbr6qxZ7Zc9bly++qPHqesTJkQz13XyPqCcnnoqmXKmT5d++9v21hk7NpltI12FGeosjaRmoDF2m70x//533/+vu673/jXXdB4TklXvc5w2re9VvyHk8cLCNL35ZvwRIaqtQmm2aObNs89Kq6yS/XbzdGJ22mlR16/afWxZVT+XBx+MfgVBfZ/4ROgIUBSFGuosDzvm11/ve4bX0xMulm631Vbxy6it4HmYgKJsfYp32UXabrvQUXTm+OOlu+4Ks+3q7GW10tj/ddM4v7NmST/5SXdfI/Htb0e/gnS7enU9bv3PQ/6AYgma/PavsGlX4FYOAkOHJrOtTl/Lyy9HY9KSePdq5XPrP6xdt6tXv7KuM48/nl7ZCxdKxxyT3vBuv/2ttPPOyZc7bVpn66WZ/Ka5jYG2l6QTTpB+/WvpyivT2waSM31679CbjXTDSRmKKzctv92i+oVut09R1T77SOeck25iUXR5PcsPvTO/7z7pn/+UHnoobBxJuO466YILpGOPDR1Jezq9aCzNOt2oXubte9R/dJ1a8+dHt3EmQyiKCROaz0iZhieekIYNS6asffeVDjqo9a5oofedIeTt+1c2g0JuvIwVvqr62ge6yC5u2d2i0QEvb6+zk53ZqqtGLZztltNolqR99+08lv6mTImGqzr22Ozf6+pnXrRkp9OWX/ROcVt2H/5wdJt1crT55n23+5OfSB/6UGdlVYcFq560FM0rr0TdILfeOvmy83bcyoMnn5Q++lHpPe/JbptBk98yK/oXoHbHnPZO+vLLe+8X/X3rb+bM3tFG8qY6TuvOO0uf/GQ225wyRXr/+6VvfSub7dXTaveRTup9yNae97638XOHHJJdHBMmZDM0XDtGj5bGjJEOPTSb7YXU6nv661+nV3beVZP+bnk9efbWW9Hx5etfl669NrvtBk1+33kn5NaXFCKxSuPLlfcvbLtTGxflQrK8v++d6nQq6k6MGRPdhhxd5aqr2ls+y/1GvTr20kutrTvQr0p//3tH4XSk2rLZjrS/W9ULcLNIfqdPj/qyb7RR+tuqlWY9zWujhHt+Y0OkOtJP/9G20ha0z+9PfhJy6+1Jeufb7AtZ/fl05syoD3DeEqs48Xz0o43LPPZY6bHHOi87tJCfUzdfCZ+FsWOl4cNbH3u2+lm3O0FH0le7jxoVf8i5TmW93W5IZDbdVPrYx0JH0VjcfdisWfkajxr5M2qU9IMfhD1eBk1+Z84MufX68rJz/dKXotsjj4xGf7jzzrDx9Pf6660td9tt0sMP932s0ax2CxZI550nffazjctjIonI4sXS8sv3faze+3LTTVGdbvXzqlfObbc1H3VhwQLp8MOLPQXoZpu1N71v9WKeo45KJ5521JuxKot9WVGHugtpypS+/zfbnz38sDR1av3nsvxVplWHH973/5DH1GOOYeSkPPr856Xf/16aPTvZcgszyUWtvLT2NXrzavudZumtt6LbTi4ceOWVZGPpxJe+FE0N2Y5Wuznk5USlqtUv3owZ0bTNo0d3vq0zz2xtYog//CG6jTMU3MknS7vtNvAyN94o/eUv0dl8WVS/m+147bXk42ikG04SL744OoFrR7MhtormM59pfOFVu110stAoUa9KY5zfRi68UHr++XTK7lQeG/26RbUeFWqSi+pV6nE0e8FxvmBJJOdZHozMOr9SN8+eeioaziuPWv187747av0+88ww2+/Ec8+lV3aZXHhh/cfT+OzOOGPg55PeZhoH9Ysukr7ylfZiveii5ONIUyuvbaBfy/JsoNFamh2vn3giXgNBq9tBMVVnB62nlc88V6M9dENLRbuK9prnzm3vJ/SknX9+uG03Y1a8zzMJZXzN3e6JJ9pfZ6BxepNCItOezTaLrgV45pkw248zbXl16DWgnrhdJnLT8tstWt05t7pcmt1Bpk7tvbq+VXvu2fiCtZD6v58nnVS8MWKral9LOxeO7LFH8rGUUbcl850kjM1+ukYxjB07cJenLPrDtvJ94qSmvELtb0uV/LbyBav9IPLQUb6Tg1CrO5KPf1zaYov2yr777iUfy2Oy8MtfSnfcke0203gfttwy+TIHMmeO9JvfZLe9Zu9Ztww/2Kz8VrfZSWxZvYetbGegZfK4H+l2abeuplmvQ/jFL5IrqyivOStZ7+tLlfy2K82fihrN8NbIQMvddVff/i+tltnts1Fl3fKb1M6stpys+/Q1Gmtxzpy+/w8fnux2i9Tyc8op0fTLSeJAOLB23p9HHkkvjryJ+70ZOzaZOELKct9x8snplDtihHTrremUjfpIfvvJUyvJQN56Kzog3HdfNAPX976XTFwDKVKCIkXjI2cpj8OwtTpmbbu+8pV0ys3b+1fPmWdK3/hG58O6pfEai/bdrJo1K5rsodULm4r6OkPL8nuVt5kPQyeV8+dHv0QO1JCxyy7S7rtnF1PeXX11NOVxmnKV/BbhwJcHEydKK68cTT97xBHRY1nO0JSGRx5pfzi36oFw9uz6swXee2/8uNKQZT3vP8YykjNjRugI0pXFmM333RcNRfWzn5HYpmnEiN77zfY/cfdPzfa7m24ar/z+mjU6ZN39rb/zz4+uQcnzxdp5s99+0ZTHacpV8hta3J3vv/+d/KDN9UyYEN0ef3y88Vvj6HQH2SjebbaJJvRoR/XzSuunqLRxsG8u9HuUtzFCt9km/YNC1VprZbOdqm7vhtVIFifDCxYk/13ad9/Oyqx+p2q7pYX+nqep2mWsXgNNN7nnnmhc/1a6G2aRJzWTq6HOimzmzIFnJusvyT6//VVnnsqjjTdu/FynYzpmvVNJqytBVR4utEzLnDnSCitEF3KutlroaOqr/a5ttFHYX6TqtS63+3Ngo8k48vZL20knNX4ub7HmRcikMYnx1k8+WTr11PjlILy9945mAn3zTWmNNVpbJ05Xwbj7BFp+E9LuT/bt/qQYoluDe/r9borogAPqPz5vnvTqq/HL7+YLdrbdNkrG3vc+6X//t/fxsic3jV5/qycI3dxyVqvR68zjTGehzJ4tjRvX97Ek9kudcB+4bv7iF9I112QXD/IriX1YIac3xsDuvz/7bf7979n9xBpHlgf+ffaRbr65/nMXXCDtumt2sQxk9dVDR1Df2LG9P3ldf33vZ/fww+GuPF+8WLr00t7/Q5x8FCX5P/vsdMqNk5yNHy/tv39ysRTdDjtI66/f97E8t64uWhRv/bKc+HU7d+nOO5MZ4ahQ0xvnRV6/SHEPjmbtD92Wdatv/+G08qgorRTTp4eOILJgQWs7szFjotmoQvjOd6SDD+79/4orllymp0eaMiWzkBIzcWIy5ZhFfXJPOy2Z8vprZ7KdkSP7/t9ut6ek3pO8qr6XeTiW5SEG5Fs1t3n9dekLX5CWWy6b7ZL81kj7i9puAtto+U7jTHJs1trYkmq16vSgVLQdbJ5b+TqJ7Y03Gq//3vdKK60UL6a01bb6NnLGGdL735/Mz8dJff61Xa0afQfWXTeZbUnSSy8lV1ZVJ9c8dDLxz6hR0vbbR12TWr36f86cvuOn51Ea+765c6Xzzku+3IGkuQ9vVPbEiWF+Ue3Eeef1fpfnzg0aSqZOO23JLjxJyVXym+ekIC1lfM1Juf326LaoyW/R4m5kzTUbX/wyZ077/eFr5eU9uumm6DaL4b9a9aMfhY6gryw+q063ceSR0gMPSE880fo6K64YXfCYtrwdA376U+nYY7PdZoj3YN11oy4i992X/bal9l7zscf2Ng7ttls68eTRqadKe+6ZTtm5Sn5Dy9tOqFZefsauauUgdMMNqYdRaGknC2l2I+mf0Nab9rqZPHzfQl0I1F+778Wee3bWAor2vPJK6Aiyl+T41Vl8x+PuRz/3ucbPJRF/0u/BAw8kW16a/v3v6POJ02jwn/+0viwXvDXQ7I0ZNkx6++30tv/oo0s+5h7NZnLWWUs+d9RR6cUSVys7nKFD048Djf3619lvs5XvT15acyXpgx9sbbm0hw9s9wDZ6KLLNLcZUnXa7f/3/6SXXw4bS9LifA5pfJc6iad6fcjVVycbS2jXXx+/jG77pa8d1Yk9BupeEmrGy1jj/JrZKpIulvRxSS7pUHd/KE6ZIV12WTrlnnxyVHajM+r99otuf/rTvo//8Y/1l99+++RiS8u999a/cCgNZdyptCLLvmFvvhndhp5KNC2NxsrtRF6SznYvXmsWdyffw1bfi/Hje+/ndebGIos7tnh1pJZu+/5X92tJ6PZJLrIWepzf8yXd5u4fk/RJSW2OJ5CtUEnSL36RjyuMR43KbluMDxzeueeGjqC5vCSCrbrtttARJKeoU18Xrc7kQe2xr//798or0tJLd172kCEDP5/251WUxo96v+4inI6TXzNbSdIOki6RJHdf4O4zE4qrcI4/Pply0vwi/+Mf6ZUdUlF2fmXT7Er5oiQxtb8InXJKOttI6r1I69crKTqhTfozS6K8JGYaK4NG+8kPfSheuUcc0Xs/xOyUzYYg5PiQXyGPAXFafj8iaZqkv5nZ42Z2sZkt338hMzvczEaa2cgli+gr6ytMk3T55aEjyFaeEpei7dzy9N6l6f/+r/7jRfu8Djoo2fLuuSfZ8qTe9zTN5DevP9uefnroCPKvne/cpEnJbTeLfV2zX2OKtr8pqjwc17K64G2QpC0l/dHdt5A0V9IJSwbjQ9x9sLsPblZgt13I0IkQFag641Z/re408lDpiyiPO+W8fZZm0kUXNU6ki+hPf0q+zDzWpW702mvd3+c46bFvm9XNvI2v347XXktmRrKsLVwYf2a9RvKwL3rxxb7XCdQTJ/mdJGmSu1cnAx2qKBnOrbwd2KV8xJRGf+Rf/jL5MhvJw5etHYcfHjqCYjnqKOnLX17yu/LOO9Itt4SJKWl52A80k5cYQ8bxyU9KO+6YbJmtDgNV73Xn5TNpJKt9c5LvQ6snNwsWSGuvLR1yyMDL1V7oHmL4zzPOWHI/ucIK0jrrpL/tkPWzOpBAIx0nv+7+uqSJZlYdBnxnSW2MyAYp/zuvdtS+ltdfz267s2Zlt60kVOMNNbj6QPIYU1V12Jyqo4+W9tijvYkLUHwh95m1sxmmzb1vH/O4w9t107EmLY8+2vfk5uWXG4/4sHBhdNssoa0ddedrX4sTXWd+9rNoP1lrwYJsj9EDnQSlVS+blRt3tIfvS7rCzJ6UtLmkDNv7wiviziRPM1QlpagX8jW7UCML/Yfw+tWvstt2K9+f2p1mdTilqurYu0U7+aknxIVCnSjiPq+/oryG8eOlM8/s/b/epDWttqya9f7MncY01XnWTutz/33ymWdK668/8Dpvv9289beeotTDdrXzuop6wZvcfUylP+9m7v5Vd09wbpj8u/DC+GU89VT8Mtqxzz71H3/f+6TNN880FOTAqqtmv8211mr83EA7w8WL+/5fTdzvuit+TKEVYdixNPpudnLw69akob9WXueCBdKWW7Y3w+KECR2H1LZWT3Dz/JnOnNl8mb//vfFzeX5tScq6+2HocX5L7Zk2RzWud+ae9fi/jQbrnzp1yZ+Py/KlLbM0WxyTrj+XXio99ljv/9X62u5kDeiM+8CfaScHv+ee6zyegTz0ULbjmmeh3rjd48dLjz8uffe72ccDDGT//cPO8vroowMf30h+0dC4cY2fIzFGCEWa1z5L1cQz5Pfye99rf53nn29/nWuvbb7MtttKgxuML5Snfdd/2rhKZmTTwULzqZXXWLSLltHcVVf13s/q850/v+//A/2iRvJbUv/6V/Nlhg1LPw4AKKOhQ6VNN81mWwPN8Jamf/5TOvvs7LZXdLNm5Xc87bQkWR/7t/QOVDbJb4paubDgz39OP456fvazMNsFqvLUAgdkrf8FnGkK1bLa6kgsedkXhG6BXmUVaZNNwsaQpTife7MZRJsh+U3R7rs3X2agrgVFEXLH9fbb4bYN5E1ekghk45HKKPvPPy+dd17j5aZOzSScXEoroW31u9budzLLCxKzksZQZ6NHxyuL5DeGZl+qTs5MQp95Fs3yS0yojbxg/N1sTZoUvzUExVI70smxxzZervan9LydIM2dm+5xr5WyO5lBsogzuyXlqKPymauQ/KItcfsY5fFLgPBaGb6rbGOOpmmddaJRDlAMnVzsVzStHBu++93WhhNL09VXt7/OmmsmH0dRXHTRko9lfVIVN28pVfKbtzPevDjooGTK4f1FrWZzq0v1h2+q1crBk5Mv3oOkbLKJdMwxzZernbK2EfeBh1rqJOFC+1r5blx+eZTQ7bln6+X2H3e8jK6/Pnp/a4dQzWpfdMAB8dYvVfKb9NllKzvAdixenP7YlPV+in766XS3iXxrd7zqVjVr1W13hrdG65D4ZfcedPsJ7jPPSBdc0Hy5VhoMtthCWnrp3v8nT25tmDaEMVC3kTQk8V264or4ZcRRnYGwOttmkZQq+U1a7Th29SxY0F4F/+IX48XTDhIGVBX96mLqMtqRRNLRytTk/RsadtpJ+uY342+7nW2WxUB93fuP/Zq1RvVt+PD4ZVdbP+fPl+65J355eTV9evJlkvym6PXXpd//vvXlR4xIL5Y0laHvGsJoNG3ozTdnGkbuvflm6AjQTBZX8edtivqsfikYqPtUXrsnvP56cmUdd5z0+c9LTz6ZXJlJijvaw223tbYdLnjLkWatw0VVe3VsluNVArfd1l7fvDLI6jtYxG4P06f39knsP8V8o+ne41i0SPrRj5IvF91l1qyBn3/xxdaH8qx2XUyjhbS/adPaXyePv86R/KIjtX2N3KOfXeJ2QEe5dJpIlXnM0pCaHazzavXVpWefje7vskvf51ZeOfntXX+99JvfJF9uERXxZCkrZ5zR+Dl3aYMNpK9/Pbt4WhVilIvnnkv+BJ/kF4m45ZbubeVGvuWxVaEbVS9uwcBJ3aJF2cXRCRLS+vI4bu/tt4eOoH3urV9EfeONrS13xhnSZpt1HlM9JL8AUHBZnAB0S9LU7uv461+757UDaandBz3wQGvrHHqodPHFycVw1FGtL0vyW1JJHixrZxkCskbLL+9BO04/vb3lv/OdgacOLpqBxh5OWh7q5fDhYacMvvzycNvOyssvS2+80dm6v/hFcnG0MrFSFckv6mp37N889k1C93GPpvFFGN3QAvrqq+2v88MfSrvtFm+77SaCaSWOe+2VTrn15CH5laQddggdQeeKcI3Dfvv13j/ssGwuvItrUOgAul0eDxYzZkQdyAfy8Y9nEwsa6/aLZup9N1r5vjAzFkIoYv/L0O67L3QEkSIkY43su2/zZV58se//hx0WTeo1dGgqIf1Xdbzr2n7uw4ZF1wDVystJUC2S3xIaPDi/Yx+i1y9/GTqCYsjjjjVrvAf1nXRS6AiAeFrpTtD/17BLLkknlv6qv6I060oTqhFwoFlGSX5LaNy40BEAnTn/fGnixL6P5TXxq857/9Wvho4kGe75fa8bSesEMouDed5HjUBj/fdR7frd75KJo+wGmoac5BdAYdQ7qOQ1Iav2g88iUVq4MP1tdKvZs0NHUB/Jb3HtuGO89Y87rvf+zJmtr9d/X/jb38aLI2+S3NdzwRsAoLTmzw8dAbpNklMXt9OK3P9E+/jjk4ujHXltkKhF8puyaodwAH3l8WJQNFaEA1pS0pjwoEzvX97wywj6I/lNWcjxBYGi+ctfQkeAgZTlhGW55UJHgCRlnfyW5XvSSBFeP8kvgNy44Yb216FFLRtFOKB1E+o1ukUe6zLJLwCgJSTAEd4HhFCU7htFuFiT5BfIqRkzQkeQrqRaA/LYqtCNzEj6sjRtWugIkDfLLpvdtnp6pFNP7Xza4lp53G+Q/AIIIqkdIskv4shra9rJJ4eOAO0wk0aPDh1FckaMkE47TTriiNCRpIPkFwBQWscem8122j1J46SueIYPj2674bOrnhS+807YONJC8gsAKKQ//jF+GY8/Hr8M5N+3vhU6gvTlNemm2wMAAAk5/fT4ZXRyYO6/zl//Kt1/f/xYkJ5//St0BMgTpjcGEMSbbyZTTl5bO7pNHt/nJGfSiuM734lu3aXp08PGgvBCtXTmsYVVyue+g5ZfLOHuu0NHALQujztWlFe39pEEugnJL/p45RVpp51CRwG0juQ3G3ltVYrrhRfaX2f06OR+uQC6XR73HSS/6GP27NARAED+7b57e8sz2kN4b78dOoLk/elPoSNo7uST85cAk/wCANCmRx6RXn01vfLvvTe9sstq+eVDR5C8732v7/9J9TlPMll95hlpwoTkyksCyS+AQqOFLBs9PaEjyJ8PfjB0BMiT0PuiKVOk1VdPpqxq8pvka3rttfDvURXJLwAAQEyvvBL9hbroMY3RT265RZo1K5my8jSmNskv+sjLWRkAAEVy0UXSdtuF235ax+8RI9IpNySSXwAAgARMmhQ6ArSC5BdAofFrBfKkUX2knqLMGO0BABJEUoGQGO8XRTV5cugIwiH5BVBoJL8I6Z//DB0B0Jm11godQTgkv+iDRAIAkscEQiiSNLop5Cm/iJ38mtnSZva4md2cREAIa9y40BEAAIBuM3p06Ah6JdHye4ykZxIoBznw5S+HjgBoT55aEwAglEcfDR3BwE45JXQEvWIlv2b2QUl7SLo4mXAAoD0kvwAgXXllcmUl3e2h20Z7OE/SjyUx8SUAoHRqD+rPPx8uDiAteUtck9Bx8mtmX5Y01d1HNVnucDMbaWYjO90WAAB5t9FGoSMAkldNfqdP77yMkTnLAOO0/G4n6StmNkHS1ZJ2MrPL+y/k7kPcfbC7D46xLQCoi24PyBPqI7pVnKHR9t03uTiS0HHy6+4/dfcPuvt6kvaVdJe7H5BYZECJTZsWOgIAAHotWBA6guQwzi+QQ1/8YugIAABl1Y39fGslkvy6+z3uziBZQEJeeCF0BMXBz8wIqX+SQH1EHpV5KuN6aPmN6bnnQkcAlBvJBgAMbM6c0BHky6DQARTdxz7W/T8PAHlG8gsAzU2aJL35Zugo8oHkF8ghEjqgu7zrXdK8eaGjQJmts07ry3Z7ox7dHgAAAFBXNybCJL8AAHSIC96A4iH5BVBoJBsoAuopQorTekvLLwDkDEkFAKAdJL8AAAD4r25s7a1F8gsAQIeefTZ0BEBz/ELWF8kvAAAd+ve/Q0cAoF0kv0AOcZbeOt4rAEA7SH4BFBrJLwAMrNv78LaL5BdAoZH8IqRWkwrqKYqktl53Y+JM8gvkEAfK1vFeIU+oj0D+kfwCOdSNZ9pAN2r1u8p3GsgPkl8AADo0f37oCIDk0e0BANCxP/4xdARI03PPhY4AaI7uOH2R/AI5xI6qexx5ZOgIAJRdN7bexkHyCwBAyjihBfKD5DcBU6aEjgAoL5IK5An1Ecg/kt8EDB8eOgKgvEg2ACA9N9wQOoLkkfwmgL40SBoJXet4r5An1Ed0g9q8Zvz4cHGkheQ3ASS/SBp1qnUkGwCAdpD8JmD06NARAAAAJINxftHUBReEjgDdhtZMoLvwnQbyg+QXAAAApUHyC6DQaFFDEXTjT8coh26suyS/AAqN5BcABnbFFZ2vS/ILIBMkdACApJx+eugI8oXkF0ChLV4s/fCH0quvho4EALoPLb8AkDPjx0u/+5108MGhIwEa/2rDrzkoKpJfAMipRYtCRwAA3aE24e3pCRdHWkh+AQAA8F/d2Npbi+QXAAAApUHyCwBAQn7wg9ARAMnqxlZgkl8gh7g4pn28ZwCAVpQu+Z08OXQEAAAAxUDLbxeYPz90BAAAAMVA8tsFpk8PHQEAoGy6MYEAiqp0ye/s2aEjAJAG+vwCQPK68cStdMkvB0gAQNa6MYFA96qtr91Yd0uX/C5VulcMAADQuv7J79FHh4slDaVLBWn5RRFQT4HuwncaRfaHP4SOIFkkvwC6At9tAGhPoy4N77zTfJkiK13yS7cHAEBWXn01uu3GBALF16hFd/HibOPI2qBOVzSzdSRdJun9knokDXH385MKLC0kvwCArOy1l/Te9/ZtSQPy4oknpIULpWWWif5/8MGw8WQlTiq4SNJx7r6xpG0kHWVmmyQTVnpIflEE/IQPdIdRo6Thw0NHAdR38cXSsstK3/9+9P9220V/jPbQgLtPdvfRlfuzJT0jae2kAksLSQWKoBt3NgCAfLrwwr7zIBxxRO/9bjweJdIOambrSdpC0iNJlJcmkl8AAIC+Vl45dATZiZ38mtkKkq6V9L/u/lad5w83s5FmNjLutpJAtwcAAIC+GrXw9vRkG0cWYqWCZraMosT3Cne/rt4y7j7E3Qe7++A420oKLb8oAuopACAP6PZQw8xM0iWSnnH3c5MLKV0kFUB34rsNAGhFnJbf7SQdKGknMxtT+ds9obhSQ7cHAACA1nRjy2/H4/y6+wOSCtfWQusQiuCNN0JHAABAd6IdFAAAAHV1Y8tv6ZJfWn6B7sR3GwDQitIlvwAAAGgNLb9dgNYhAACA1rz9dugIkkfyCwAAgLqmTg0dQfJKl/zOmSNNnhw6CgBJ48QWANCKjoc6K6qtt5ZWXTV0FAAAAAihdC2/kjRjRugIAAAAEEIpk18AAACUE8kvgK5An18AQCtIfgEAAFAaJL8AukKzgdi7caB2AED7SH4BAABQGiS/AEqBll8AgETyC6BLcMEbAKAVJL8AusLttw/8PC2/AACJ5BdAFxk1qv7jzz4rDSrdfJYAgHpIfgF0jXnzokR3woS+j996a5BwAAA5RFsIgK5hJm28cXS/tpsDXR4AAFW0/ALoGo0ueiP5BQBUkfwC6BqTJ/fed5dWW006/3zp6afDxQQAyBfzDJtEzAa7NDKz7QEorzFjpM03Dx0FACAcG+Xug/s/SssvgK5E4gsAqIfkFwAAAKVB8gsAAIDSIPkFAABAaZD8AgAAoDRIfgEAAFAaJL8AAAAoDZJfAAAAlAbJLwAAAEqD5BcAAAClQfILAACA0iD5BQAAQGmQ/AIAAKA0SH4BAABQGiS/AAAAKA2SXwAAAJQGyS8AAABKg+QXAAAApUHyCwAAgNIg+QUA5MY3vyn19NR/7uabs40FQHci+QVStNde0he+EDqKyIYbSpddFt3fa6+wsXSbDTZofdlbb5XuuqvvY3fdJa288pLL3n9/vLjmzYu3fiuuv1768587X7+nR3rnHWmrraTzz5euuEIyk4YPj56/8kpp8mRpwQJpjz2k//mfvusPGyZ97GOdb3/LLTtfF5H/+7/O1tt//yUfW2aZJR9bf33p5Zfrl7Hddn3/N2u+3U7jTcJWW0lz5kju0V+ZfOc79R+/4QbpoIP6Hpf23186/PDe/z/3Oem886L7Tzwh/exn0XvZMXfP7E/ayns/cncz9w9/OLp/wAHu77zjPmOG91mm0d8nPuE+Z477ffe5P/64+8UXu++1l/vf/uZ+zz3uSy/t/swz7nff7X7ggQOXdeqp7j/8ofuuu0b/b7xx/eUuuKD3/mGHtRYnf/H/Dj20+ft9+OHuo0a5T5vm/v3vR4+deKL7wQe7f+Yz0f/rruu+/fbR/WWWcf/Pf9yHDHF/97ujx+65p2+ZzzwT1Ud398mT3WfPju7PmOH+2c9GywwfHtW5t992f+yxqB7Nnu1+4YX+X4sWuf/mN73lfuhD7qut5r7DDtH/t9ziPmKE+xZbDPwax47tvX/iie4rrxzd/8533L/73d7nrrzSfYMNovvXXOMdu//+8J99nv5eeMF97tzm79vEiUuu6x7Vldde612upyfapwwb1ltPatc58MBomQ98oO/jxxwTLXvKKQPHe+mlUblbbRX9v9FG0e1RR/UuUy1jl13cH3yw9/F1141ud965d/3q39Zbuy+1VN/X5h59Bz7xCfdHH60fz7Bh0e1uu0Xfu733dn/22c7rZ38rrdQ3psWLo/eg0f5cct99d/dttw1ft9L4O/LI6L3+1rei/w86yP0f/4g+nwULouNt9fPfbDP344+PjoXHHBMdVxctij6fv/wl2gc//rj7jTe6v/lmVI933z16jwcyZUrfmA45JLnPO0szZrhPmhTd7+lJtuzQ9STLvxEjkn3v3KPPZd686P7vfte7rU99yv30090ljXSvk4/WezCtv6222ir5V54Dr73WeWX4xjeal3/HHeErbRp/yLdZs8LXkbz8DR0a+tNAMz/4QfRZffnLSz5X7zMdMiR6bt99w9ev/n8nneT+wAP1nzvkEPav3aRR/mDWfr350peWfGzFFePXx9tv7/1+xfl78sns399Ukl9Ju0l6TtKLkk5otny3Jr/9W2za+Wt25lzr8suT27nm4Q/F1NOzZOvHwoVRi9KoUUsu20nd+Otfo/WjM3f3/faLkpp3vztqfY1b91ZaKToo/Pa30f+bbBK1hLlH38l586IW/B/9KHpdKIabb67fMrdwYfQ5VxOBBQt6n1u8uHE9eeEF95/8pO9jQ4Y0r18///nA+71589zPOaf3sRVWiH7Zefhh9+OO643tmWeWXL+nJ2pFP+OMxttYYYVU32bkzKJFfet0PdXW/kWLov9nzXIfNy5qyZ89O6pXCxcuuVw7enrc3/WuvnVxjz3cl102+vU2hEbJr0XPtc/Mlpb0vKQvSJok6TFJ+7n7fxqtM3jwYB85cmRH2yuKhQuliROlffaR3vUu6aSTpN12k666Spo9O+rz8s47UZ+V/n2VWnHhhdL3v7/k44sWSdOmSe97X9Tn6dVXpVVWkebOlRYvlj7wgd5l582LYpgwQVptNend75b+9S/p6KPbi+Uf/4j6sx54YNQf6/nnpSlTpKFDo744J58c9dU67DDpuuuks86K1rvmGumjH5W22KL914/iWWmlqJ5ce23yZbfSv6+eDnd7AIACMbNR7j54icdjJL+fkXSqu3+x8v9PJcndz2q0ThmSXwDZ6emJLoRadtnopG7QIGmppaSll46eN4tODN2jE9P3vCdsvACA7DRKfgfFKHNtSRNr/p8k6dN1Nny4pMMlad11142xOQDoa6mlol9YpMaJ7aDKXq7eVeQAgPKJM9RZvR8cl2hGdvch7j7Y3QevscYaMTYHAAAAxBMn+Z0kaZ2a/z8o6bV44QAAAADpiZP8PiZpAzP7sJktK2lfScOSCQsAAABIXsd9ft19kZkdLel2SUtL+qu7P51YZAAAAEDC4lzwJne/RdItCcUCAAAApCpOtwcAAACgUEh+AQAAUBokvwAAACgNkl8AAACUBskvAAAASoPkFwAAAKVB8gsAAIDSMHfPbmNmsyU91+Dp1SW90UGxK0ualcE6cdctynohtlmU9aRs6ynr5WubRVlP6u79aRnqTIhtZr1ep3W0020W5X2hriW77kbuvuISj7p7Zn+SRnbyXJMyh2SxTtx1i7JekWIN9N5kVk9ZL1/bLMp6lXW7dn9ahjpTpFhjrNdRHe10mwV6X6hrCa7bqJ51Q7eHmzJaJ+66RVkvxDaLsl4cRXmNRVkvxDaLsl4cRdiflqHOhNgm9bSc64XYZohY+8i628NIdx/c7nNAXlBPUQTUU+QddRRZaFTPsm75HdLhc0BeUE9RBNRT5B11FFmoW88ybfkFAAAAQuqGPr+QZGZzmjx/j5nxExOCop4i76ijKALqaTyZJ7/NPjAgNOooioK6iryjjiKPaPntIma2o5ndXPP/hWZ2cMCQgCVQT5F31FEUAfW0c0GSXzNbwcxGmNloMxtrZntVHl/PzJ4xs7+Y2dNmNtzM3h0iRpQbdRRFQV1F3lFHkTehWn7nSfqau28p6fOSfmtmVnluA0l/cPdNJc2U9I0wIaLkqKMoCuoq8o46ilwZFGi7JumXZraDpB5Ja0t6X+W58e4+pnJ/lKT1Mo+uuBap7wnNu0IF0gWoo+mhniaLupo86miyqKPpoJ52KFTL77clrSFpK3ffXNIU9X5o82uWW6xwCXoRvSxpEzNbzsxWlrRz6IAKjDqaHuppsqiryaOOJos6mg7qaYdCVbKVJU1194Vm9nlJHwoUR1cws0GS5rv7RDO7RtKTkl6Q9HjYyAqNOpow6mlqqKsJoY6mhjqaIOppfJkmv9UPTNIVkm4ys5GSxkh6Nss4utCmkl6SJHf/saQf91/A3XfMOKZCoo6minqaIOpqKqijCaKOpoZ6GlOmM7yZ2Scl/cXdt85so13OzL4r6QeS/tfdh4eOp+ioo+mgniaPupos6mjyqKPJo54mI7Pklw8MeUcdRVFQV5F31FHkWaYtvwAAAEBIzPAGAACA0kgt+TWzdczs7srsLU+b2TGVx1czszvM7IXK7ao16/zUzF40s+fM7IuVx1Y0szE1f2+Y2XlpxY1ySaqeVh7frzJ70ZNmdpuZrR7iNaH7JFxP96nU0afN7NchXg+6T7t11MzeW1l+jpld2K+srSr70hfN7IKaCTGARKTW7cHMPiDpA+4+2sxWVDR49VclHSzpTXf/lZmdIGlVd/+JmW0i6SpJW0taS9KdkjZ098X9yh0l6Vh3vy+VwFEqSdVTRYO4vyZpE3d/o5JUvO3up2b9mtB9EqynqygaDmkrd59mZpdKuszdR2T9mtBdOqijy0vaQtLHJX3c3Y+uKetRScdIeljSLZIucPdbM31B6Gqptfy6+2R3H125P1vSM4pmddlL0qWVxS5V9OVQ5fGr3X2+u4+X9KKiHfd/mdkGktaUdH9acaNcEqynVvlbvtJKsZKiZBiILcF6+hFJz7v7tMpyd4rpZJGAduuou8919wcUTX38X5UkeiV3f8ij1rnL1FuvgURk0ufXzNZTdIb3iKT3uftkKfqyKEpmpehLMrFmtUmVx2rtJ+mfzlV6SEGceuruCyV9T9JYVVqAJV2STeQok5j70xclfczM1quMwfpVSetkEznKosU62sjaiuprVb1cAIgl9eTXzFaQdK2i4U7eGmjROo/1T3L3VfRTHpCouPXUzJZRlPxuoehn5icl/TTxQFFqceupu89QVE//qegXtAmSFiUdJ8qrjTrasIg6j9HghUSlmvxWEoJrJV3h7tdVHp5S+Vmj+vPG1Mrjk9S3BeKDqvnZuDJY9iB3H5VmzCifhOrp5pLk7i9Vfpm4RtK26UePskhqf+ruN7n7p939M5KeUzQtKhBbm3W0kUmK6mtVn1wASEKaoz2Yop99n3H3c2ueGibpoMr9gyTdWPP4vma2nJl9WNIGkh6tWW8/0eqLhCVYT1+VtImZrVFZ7guK+rwBsSW5PzWzNSu3q0o6UtLF6b8CdLsO6mhdla4Rs81sm0qZ/6/ZOkC70hzt4bOKflYbK6mn8vCJivoAXSNpXUmvSNrb3d+srHOSpEMV/Qz3v7VXd5rZOEm7uztzgiMxSdZTi2Y0OkbSQkkvSzrY3adn92rQrRKup1dJ+mSljNPd/eqsXge6V4d1dIKii4OXlTRT0q7u/h8zGyzp75LeLelWSd/nWh8kiRneAAAAUBrM8AYAAIDSIPkFAABAaZD8AgAAoDRIfgEAAFAaJL8AAAAojUGhAwCAIjOzxYqGd1pG0bBil0o6z917BlhnPUnbuvuVLW7jEEXD6EnR1NnPSVos6TZJCyTd5+53dvoaAKBMGOoMAGIwsznuvkLl/pqSrpT0b3f/+QDr7CjpeHf/cgfbmyBpsLu/0VHAAFBydHsAgIS4+1RJh0s62iLrmdn9Zja68led8vpXkrY3szFmdqyZLW1m55jZY2b2pJkd0eo2zezvZvbNyv0JZvZLM3vIzEaa2ZZmdruZvVSZhKW6zo9qtnVaku8BAOQd3R4AIEHuPs7MlpK0pqSpkr7g7vPMbANFU7QPlnSCalp+zexwSbPc/VNmtpykf5vZcHcf30EIE939M2b2O0WzZG0n6V2Snpb0JzPbVdF0x1tLMknDzGwHd78vzusGgKIg+QWA5FnldhlJF5rZ5or66G7YYPldJW1WbcGVtLKiBLWT5HdY5XaspBXcfbak2WY2z8xWqWxrV0mPV5ZbobItkl8ApUDyCwAJMrOPKEp0p0r6uaQpkj6pqJvZvEarSfq+u9/er6xfSNpDktx98xZDmF+57am5X/1/UGVbZ7n7n1ssDwC6Cn1+ASAhZraGpD9JutCjq4lXljS5MvLDgZKWriw6W9KKNaveLul7ZrZMpZwNzWx5dz/J3TdvI/Ftxe2SDjWz6kV6a1cu1AOAUqDlFwDiebeZjVHvUGf/kHRu5bmLJF1rZntLulvS3MrjT0paZGZPKOqXe76k9SSNNjOTNE3SV9MI1t2Hm9nGkh6KNqU5kg5Q1FINAF2Poc4AAABQGnR7AAAAQGmQ/AIAAKA0SH4BAABQGiS/AAAAKA2SXwAAAJQGyS8AAABKg+QXAAAApUHyCwAAgNL4/wZnuxGVo1PEAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# display the data \\n\",\n \"plt.figure(figsize=(12,6))\\n\",\n \"# all data points\\n\",\n \"power_df.plot(title='Global active power', color='blue') \\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since the data is recorded each minute, the above plot contains *a lot* of values. So, I'm also showing just a slice of data, below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# can plot a slice of hourly data\\n\",\n \"end_mins = 1440 # 1440 mins = 1 day\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,6))\\n\",\n \"power_df[0:end_mins].plot(title='Global active power, over one day', color='blue') \\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Hourly vs Daily\\n\",\n \"\\n\",\n \"There is a lot of data, collected every minute, and so I could go one of two ways with my analysis:\\n\",\n \"1. Create many, short time series, say a week or so long, in which I record energy consumption every hour, and try to predict the energy consumption over the following hours or days.\\n\",\n \"2. Create fewer, long time series with data recorded daily that I could use to predict usage in the following weeks or months.\\n\",\n \"\\n\",\n \"Both tasks are interesting! It depends on whether you want to predict time patterns over a day/week or over a longer time period, like a month. With the amount of data I have, I think it would be interesting to see longer, *recurring* trends that happen over several months or over a year. So, I will resample the 'Global active power' values, recording **daily** data points as averages over 24-hr periods.\\n\",\n \"\\n\",\n \"> I can resample according to a specified frequency, by utilizing pandas [time series tools](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html), which allow me to sample at points like every hour ('H') or day ('D'), etc.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# resample over day (D)\\n\",\n \"freq = 'D'\\n\",\n \"# calculate the mean active power for a day\\n\",\n \"mean_power_df = power_df.resample(freq).mean()\\n\",\n \"\\n\",\n \"# display the mean values\\n\",\n \"plt.figure(figsize=(15,8))\\n\",\n \"mean_power_df.plot(title='Global active power, mean per day', color='blue') \\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this plot, we can see that there are some interesting trends that occur over each year. It seems that there are spikes of energy consumption around the end/beginning of each year, which correspond with heat and light usage being higher in winter months. We also see a dip in usage around August, when global temperatures are typically higher.\\n\",\n \"\\n\",\n \"The data is still not very smooth, but it shows noticeable trends, and so, makes for a good use case for machine learning models that may be able to recognize these patterns.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"## Create Time Series \\n\",\n \"\\n\",\n \"My goal will be to take full years of data, from 2007-2009, and see if I can use it to accurately predict the average Global active power usage for the next several months in 2010!\\n\",\n \"\\n\",\n \"Next, let's make one time series for each complete year of data. This is just a design decision, and I am deciding to use full years of data, starting in January of 2017 because there are not that many data points in 2006 and this split will make it easier to handle leap years; I could have also decided to construct time series starting at the first collected data point, just by changing `t_start` and `t_end` in the function below.\\n\",\n \"\\n\",\n \"The function `make_time_series` will create pandas `Series` for each of the passed in list of years `['2007', '2008', '2009']`.\\n\",\n \"* All of the time series will start at the same time point `t_start` (or t0). \\n\",\n \" * When preparing data, it's important to use a consistent start point for each time series; DeepAR uses this time-point as a frame of reference, which enables it to learn recurrent patterns e.g. that weekdays behave differently from weekends or that Summer is different than Winter.\\n\",\n \" * You can change the start and end indices to define any time series you create.\\n\",\n \"* We should account for leap years, like 2008, in the creation of time series.\\n\",\n \"* Generally, we create `Series` by getting the relevant global consumption data (from the DataFrame) and date indices.\\n\",\n \"\\n\",\n \"```\\n\",\n \"# get global consumption data\\n\",\n \"data = mean_power_df[start_idx:end_idx]\\n\",\n \"\\n\",\n \"# create time series for the year\\n\",\n \"index = pd.DatetimeIndex(start=t_start, end=t_end, freq='D')\\n\",\n \"time_series.append(pd.Series(data=data, index=index))\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def make_time_series(mean_power_df, years, freq='D', start_idx=16):\\n\",\n \" '''Creates as many time series as there are complete years. This code\\n\",\n \" accounts for the leap year, 2008.\\n\",\n \" :param mean_power_df: A dataframe of global power consumption, averaged by day.\\n\",\n \" This dataframe should also be indexed by a datetime.\\n\",\n \" :param years: A list of years to make time series out of, ex. ['2007', '2008'].\\n\",\n \" :param freq: The frequency of data recording (D = daily)\\n\",\n \" :param start_idx: The starting dataframe index of the first point in the first time series.\\n\",\n \" The default, 16, points to '2017-01-01'. \\n\",\n \" :return: A list of pd.Series(), time series data.\\n\",\n \" '''\\n\",\n \" \\n\",\n \" # store time series\\n\",\n \" time_series = []\\n\",\n \" \\n\",\n \" # store leap year in this dataset\\n\",\n \" leap = '2008'\\n\",\n \"\\n\",\n \" # create time series for each year in years\\n\",\n \" for i in range(len(years)):\\n\",\n \"\\n\",\n \" year = years[i]\\n\",\n \" if(year == leap):\\n\",\n \" end_idx = start_idx+366\\n\",\n \" else:\\n\",\n \" end_idx = start_idx+365\\n\",\n \"\\n\",\n \" # create start and end datetimes\\n\",\n \" t_start = year + '-01-01' # Jan 1st of each year = t_start\\n\",\n \" t_end = year + '-12-31' # Dec 31st = t_end\\n\",\n \"\\n\",\n \" # get global consumption data\\n\",\n \" data = mean_power_df[start_idx:end_idx]\\n\",\n \"\\n\",\n \" # create time series for the year\\n\",\n \" index = pd.DatetimeIndex(pd.date_range(start=t_start, end=t_end, freq=freq))\\n\",\n \" time_series.append(pd.Series(data=data, index=index))\\n\",\n \" \\n\",\n \" start_idx = end_idx\\n\",\n \" \\n\",\n \" # return list of time series\\n\",\n \" return time_series\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Test the results\\n\",\n \"\\n\",\n \"Below, let's construct one time series for each complete year of data, and display the results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# test out the code above\\n\",\n \"\\n\",\n \"# yearly time series for our three complete years\\n\",\n \"full_years = ['2007', '2008', '2009']\\n\",\n \"freq='D' # daily recordings\\n\",\n \"\\n\",\n \"# make time series\\n\",\n \"time_series = make_time_series(mean_power_df, full_years, freq=freq)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# display first time series\\n\",\n \"time_series_idx = 0\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,6))\\n\",\n \"time_series[time_series_idx].plot()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Splitting in Time\\n\",\n \"\\n\",\n \"We'll evaluate our model on a test set of data. For machine learning tasks like classification, we typically create train/test data by randomly splitting examples into different sets. For forecasting it's important to do this train/test split in **time** rather than by individual data points. \\n\",\n \"> In general, we can create training data by taking each of our *complete* time series and leaving off the last `prediction_length` data points to create *training* time series. \\n\",\n \"\\n\",\n \"### EXERCISE: Create training time series\\n\",\n \"\\n\",\n \"Complete the `create_training_series` function, which should take in our list of complete time series data and return a list of truncated, training time series.\\n\",\n \"\\n\",\n \"* In this example, we want to predict about a month's worth of data, and we'll set `prediction_length` to 30 (days).\\n\",\n \"* To create a training set of data, we'll leave out the last 30 points of *each* of the time series we just generated, so we'll use only the first part as training data. \\n\",\n \"* The **test set contains the complete range** of each time series.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create truncated, training time series\\n\",\n \"def create_training_series(complete_time_series, prediction_length):\\n\",\n \" '''Given a complete list of time series data, create training time series.\\n\",\n \" :param complete_time_series: A list of all complete time series.\\n\",\n \" :param prediction_length: The number of points we want to predict.\\n\",\n \" :return: A list of training time series.\\n\",\n \" '''\\n\",\n \" # get training series\\n\",\n \" time_series_training = []\\n\",\n \" \\n\",\n \" for ts in complete_time_series:\\n\",\n \" # truncate trailing 30 pts\\n\",\n \" time_series_training.append(ts[:-prediction_length])\\n\",\n \" \\n\",\n \" return time_series_training\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# test your code!\\n\",\n \"\\n\",\n \"# set prediction length\\n\",\n \"prediction_length = 30 # 30 days ~ a month\\n\",\n \"\\n\",\n \"time_series_training = create_training_series(time_series, prediction_length)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training and Test Series\\n\",\n \"\\n\",\n \"We can visualize what these series look like, by plotting the train/test series on the same axis. We should see that the test series contains all of our data in a year, and a training series contains all but the last `prediction_length` points.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAA2oAAAHdCAYAAABse4rvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd5hcZfXA8e8tU7bvZje9kAoEAkkgAUINvYqggkoTLAgCCgoI/ARFQQEFFaUKiggoSpcOAgJCCAmEBAKkkF53N9t3Z+be+76/P2Z25s7s7O5Msi3hfJ7Hh5m5c+99p2y8Z877nmNorRFCCCGEEEIIMXCY/T0AIYQQQgghhBDpJFATQgghhBBCiAFGAjUhhBBCCCGEGGAkUBNCCCGEEEKIAUYCNSGEEEIIIYQYYOz+OnFVVZUeO3Zsf51eCCGEEEIIIfrV/Pnza7TWg7Nt67dAbezYscybN6+/Ti+EEEIIIYQQ/cowjFWdbZOpj0IIIYQQQggxwEigJoQQQgghhBADjARqQgghhBBCCDHA9NsaNSGEEEIIIcTnm+M4rF27lkgk0t9D6VXhcJhRo0YRCARy3kcCNSGEEEIIIUS/WLt2LSUlJYwdOxbDMPp7OL1Ca01tbS1r165l3LhxOe8nUx+FEEIIIYQQ/SISiVBZWbnDBmkAhmFQWVmZd9ZQAjUhhBBCCCFEv9mRg7R2W/MaJVATQgghhBBCiAFGAjUhhBBCCCHE51J9fT233377Vu37u9/9jtbW1h4eUYoEakIIIYQQQojPpYEcqEnVRyGEEEIIIcTn0hVXXMHy5cuZNm0aRx55JEOGDOGf//wn0WiUk08+mWuvvZaWlhZOPfVU1q5di+d5XH311WzatIn169dz6KGHUlVVxauvvtrjY5NATQghhBBCCNHvxl7xTK8de+UNx2d9/IYbbuDDDz9kwYIFvPjiizzyyCPMnTsXrTUnnngir7/+OtXV1YwYMYJnnomPr6GhgbKyMm655RZeffVVqqqqemXMMvVRCCGEEEII8bn34osv8uKLLzJ9+nT22msvPvnkE5YuXcoee+zByy+/zI9//GPeeOMNysrK+mQ8klETQgghhBBCfO5prbnyyiv57ne/22Hb/PnzefbZZ7nyyis56qijuOaaa3p9PBKoCSGEEEIIIfpdZ9MTe1NJSQlNTU0AHH300Vx99dWcfvrpFBcXs27dOgKBAK7rMmjQIM444wyKi4u577770vbtramPEqgJIYQQQgghPpcqKys54IADmDJlCsceeyynnXYas2bNAqC4uJgHHniAZcuWcdlll2GaJoFAgDvuuAOAc889l2OPPZbhw4f3SjERQ2vd4wfNxYwZM/S8efP65dzbm8XrG2mMOOw7btDnonO7EEIIIYT4fPj444+ZPHlyfw+jT2R7rYZhzNdaz8j2fCkmMsAtXt/Icbe+wdfunsPTCzf093CEEEIIIYQQfUACtQHup099mLx90d/f78eRCCGEEEIIIfqKrFEb4A5qfp7fhR5AaZN/eYcAfb/IUgghhBBCCNG3JFAb4MrNNkYatWBAmWrp7+EIIYQQQggh+oBMfRzgglbqdgGR/huIEEIIIYQQos9IoDbArS9KVYY52FrUjyMRQgghhBBC9BUJ1AY4J5RqoBfTMlNVCCGEEEKInlJfX8/tt9+e937HHXcc9fX1PT8gHwnUBjgjVAKApw000kNNCCGEEEKIntJZoOZ5Xpf7Pfvss5SXl/fSqOIkRTPAuUVD2DXyFyIEAYOV/T0gIYQQQgghdhBXXHEFy5cvZ9q0aQQCAYqLixk+fDgLFixg8eLFnHTSSaxZs4ZIJMIPfvADzj33XADGjh3LvHnzaG5u5thjj+XAAw/krbfeYuTIkTz55JMUFBRs89gkozbAhQM2EUKQyKYppft3QEIIIYQQQvSWV38FPyuL/+/VX3Xc/sL/pba/9YeO25/6fmr7vL90e7obbriBCRMmsGDBAn79618zd+5crr/+ehYvXgzAn//8Z+bPn8+8efO49dZbqa2t7XCMpUuXcsEFF/DRRx9RXl7Oo48+mvfLzkYyagPcPmv/woLQ/SgM7nBPpNU5muKQfGxCCCGEEEL0tH322Ydx48Yl79966608/vjjAKxZs4alS5dSWVmZts+4ceOYNm0aAHvvvTcrV67skbHIFf8AZ3kRyo14/7QQDi1RVwI1IYQQQgghekFRUVHy9muvvcbLL7/M22+/TWFhIbNnzyYS6dguKxQKJW9blkVbW1uPjEWu+Ac6rZI3hxp1tLS2Qmm4HwckhBBCCCFELzn0yvj/OnP09fH/debEW+P/y1FJSQlNTU1ZtzU0NFBRUUFhYSGffPIJc+bMyfm4PUHWqA1wb448h7U6XqL/TPtlvI0f9fOIhBBCCCGE2DFUVlZywAEHMGXKFC677LK0bccccwyu67Lnnnty9dVXs99++/Xp2CSjNsA5Rph1uopRRg0AsZbGfh6REEIIIYQQO46HHnoo6+OhUIjnnnsu67b2dWhVVVV8+OGHyccvvfTSHhuXBGoDnFKaJl1Aky6ghTCRmNPfQxJCCCGEEEL0MgnUBjhPa77tXEp7ef7fV0xj7/4dkhBCCCGEEKKXyRq1AU5raA/SAFqiXXdJF0IIIYQQYnui9Y7fJ3hrXqMEagPcMatvZmnoTD4NncXp1su0RN3+HpIQQgghhBA9IhwOU1tbu0MHa1pramtrCYfzq9wuUx8HOEN7BIx4Fs1A0yyBmhBCCCGE2EGMGjWKtWvXUl1d3d9D6VXhcJhRo0bltY8EagOdr4/aTsYm2prXAzv323Bi0QjBkPRxE0IIIYQQ2y4QCDBu3Lj+HsaAJFMfB7gnRvyQnzlnAfAd+1n2XXNvv43l7fuvhl+O5J0/nNVvYxBCCCGEEOLzQAK1Ac7VJvW6OHnfclr6bSwTP/sbQcNl39onaayv7bdxCCGEEEIIsaOTqY8DnNLQQBHVuoxmHaae0n4bi6m9ZAHKWKQVqOy3sQghhBBCCLEjk0BtgNNa86qazszoHQDsVzKII/ppLMqXgNVK2gQIIYQQQgjRWyRQG+i8KCFiKExczH7to3a9OgvTi+FhckWgpN/GIYQQQgghxI5OArUB7vQNN3BL+BUAvh+7kA+jR/bbWJ72ZuGqeI+LS62CfhuHEEIIIYQQOzoJ1AY6X3l+hdGvfdR+Zd2BNg08TDzv4H4bhxBCCCGEEDs6CdQGOA+LmLYIGh67mGuojy2GflilppXiFOv15P3lO27zeCGEEEIIIfqdlOcf4O4dehWTo/cBcJH9BPdzDVqprnfqBZ6XyuR52sCTQE0IIYQQQoheI4HaAOepeFatTQcBMA1Na0tjn4/DVVCviwCwDI1Zv6rPxyCEEEIIIcTnhUx9HOC0jqeuPtWjCGmXFsLs1NpGUUl5n45DGSbrdBXlRqLhdqShT88vhBBCCCHE54kEagOcSgRqJ8WuSz72qlXK4D4eh6c0ni8BqzzpoyaEEEIIIURvkUBtgAt5TQyiEQ+TFsK42LT0Q+VHpeAG9+uU0IqHxUUlY/p8DEIIIYQQQnxeSKA2wF1QfR13ht8D4MzYFbyh9uyXEv2e1rylpiTvfzdQ2udjEEIIIYQQ4vNCArUBziC9jxrQPxm11nruCPwWD5MGXYyn9uvzMQghhBBCCPF5IYHaANdqFLJFFzPIaGaq8Rm2qVD1Q4GhfToOHWvhWOtdADbrcpYpqc8vhBBCCCFEb5FAbYD7TcU1zK3fwo323VweeBiAuevCwIw+HUdaHzVMXAnUhBBCCCGE6DXSR22Aay/P30I4+ZiKNvX5ONxQBcvVcACGG1soqn6vz8cghBBCCCHE54Vk1AY4L5G5WqGHMV9NokWHaTKr+nwcrlXAKj2UCWwAwIrU9fkYhBBCCCGE+LyQQG2Aa59h+DfvKP7mHQXAuWXjOb6Px+FpjYeVGpfX9wVNhBBCCCGE+LyQQG2Aq3Q3M9qoQ2mTasqJEeiX8vxKaX7nfpm/ekfhYfLtQdP7fAxCCCGEEEJ8XkigNsBd0Xgdk0LLAPhC9DoW6fH9Up7f05qP9FhIZPhOD1b0+RiEEEIIIYT4vJBAbYAzSFVX7M8+albdCu4P/AqFyQo9DE9N6/MxCCGEEEII8XnRbaBmGMZo4H5gGKCAu7XWv894zmzgSWBF4qHHtNY/79GRfk7VGhWE1GDKjBb2NT9hlK5mbMNgYGbfDiTSwMHWIgAqVQNLpTy/EEIIIYQQvSaXjJoL/Ehr/Z5hGCXAfMMwXtJaL8543hta6xN6foifb1cXXcOS5mamGct4InQNAEsbJwEX9uk4lPRRE0IIIYQQos9020dNa71Ba/1e4nYT8DEwsrcHJuLa46FmXx+1kGrt83G0lY7nbW83AKaZnzF8/ct9PgYhhBBCCCE+L/JqeG0YxlhgOvBOls2zDMP4wDCM5wzD2L2T/c81DGOeYRjzqqur8x/t55BKNLyu0yW87E3nJW9v3mO3Ph9HLFDKCj00eT8Yre3zMQghhBBCCPF5kXMxEcMwioFHgYu11o0Zm98DdtJaNxuGcRzwBDAp8xha67uBuwFmzJghc+dyoBIptVrK+LZzGQBDC0KctBXHWvfZR6yZ8xhjD/waw8Z0+Hi65Kn0Pmoo6aMmhBBCCCFEb8kpUDMMI0A8SHtQa/1Y5nZ/4Ka1ftYwjNsNw6jSWtf03FA/n0Z7awgbLSgMVujhONh4Kv/jaKVQD5zCfmodSz57imE/eTev/ZXW3O0dz2PeQXiYfH3IAeyb/zCEEEIIIYQQOcil6qMB3At8rLW+pZPnDAM2aa21YRj7EJ9SKXPjesCN0V8wIrQZgAOjv2OtHpKcDpmPtpZ6Rqt1AOzsLsl7f1dp1uihrCE+/fFEW/qoCSGEEEII0VtyyagdAJwJLDIMY0HisauAMQBa6zuBrwDnG4bhAm3A17TeimhCdGDqVPpM63gfNW8rKi7Gog6FidsNupCyPPcv3jyfx4LX4GEyX+2Mp6T7ghBCCCGEEL2l20BNa/0mJDotd/6cPwJ/7KlBiZSVxijqvAJCOBxjzWWLLiWoAsBReR0nagS50vkWIRw8TK5VGtPs8mNNY0XqmGYuA6BRF/GpxOFCCCGEEEL0mpyLiYj+8X37aja3RSmmlQ/D3wagRYeBX+R1nJgR4u/e4cn712iN2XX8nUYrL3lbYSaLnAghhBBCCCF6Xl7l+UXfa1+P5vk+KpP8q4lEXS/tfr7TJ2sr9+Zx7wAAjrTms/vaf+Q9BiGEEEIIIURuJKM2wLXHUy42T3r7ozBwjSCn5HmcqKv4of1PCohhofBih0CgOOf9I4EyVqjhtFfoL4jV5TkCsbU++/Ad9OPn0RgaxpSLnyAQDPX3kIQQQgghRC+TQG2Aa8+oOdj8wLkQAMs0tipQO9t6gVKjDYBGJwrkHqjF+6ilsnpa+qj1mbKnzqbSWw+tn7Hk6ZvZ+UtX9feQhBBCCCFEL5NAbYCboj6hzXDRGHygJ+BhbVXVR6vmk2SQBqBcJ6/9PaX5h3coL6m98TD5wrA9mJX3KMTWcHUqQG72rC6eKYQQQgghdhQSqA1wd+pfUhyKB1hTIvfQnCiyr/Ks2mg0bUi771r5TZ/zlKaWMmp1vLB/i5Vvgf/+t+6zj9jy8IW0FQxjrwv+ih0I9veQcrKwaH9GbZmDiWJLeHR/D0cIIYQQQvQBCdQGOH/hEOWbeqjyrNqoYpHk7f9409nDKshrHMPXv8TzwVtQmLzgzaBR/Siv/QeER77JHtFPIAqfPn8bu3zhkv4eUU7+Nei7vLjxJAB+Uj65fwcjhBBCCCH6hARqA9wCPYmgjmGi+JL1BhAP3jzvCOw8smK1JZP5fuwCQobDJl3BjXn2QQtE69jVXAPAB2o89dthef6RrZ8kb5eseRXYPgI1/1sd8/Kv+CmEEEIIIbY/EqgNcGd7P0lenH8S+gZhI762rM25HvKo/tcQHMxT6oDkfdfLM9DSqfL+Hibedtjw+lcj/8iaFUvwsDh+92M4sb8HlCPle6+jjgRqQgghhBCfBxKoDXD+i3R/1UXPy6/qYsxNv8BXeQZaK4YcxXsLF3K+/W/OsP/D62sHA3fldYz+tjSwK6+oQQAcHB7ez6PJ3b5NL3NB8FFCOGxaexJwbX8PSQghhBBC9DIJ1AY4f+bqcQ7FcB08TL5IftX/oq7iYvsRhlCHhYamXaFyQs77t9hlrNTDkvfDbmNe5x8IHN+0wa2pnNlfJre9x97mUgDc5qX9PBohhBBCCNEXJFAbwLTW+BNfNxnfpNGNZ9K+YIXzOlbMVZxgzmGiuR6AVZH6vPZXSqN8xUsMvf1NwdteA7WDW19M3h7V9EE/jkQIIYQQQvQVCdTypDwP0+qbXlZaaWabC1AYKEw+MvdKjSPPqYtj1j+TDNIAVJ5TJz2tedbbl7e83fEwOXTSWGbmdYT+Vx7byE7GFlxtod1ofw8nZ+vsUYx01wLw0uBv8PV+Ho8QQgghhOh9EqjlKBaNsPw3hzHEWcvGI+9g9wOO7/Vzep7DfcGbgHjT4/3Mf6a25RmolTWvSN5erwcRLRjWxbOzjEVpWiighXhZ/2azJK/9B4I7a86GRP2VD5aeDofc3q/jydXC4F5siBViolhrj+nv4QghhBBCiD4ggVqO3nv4evZzPgKg4MVz4IDNvX5OpVKVFhUGpmH4tuV3LO2lMkj3u0dxfLA8r/2nrPk7b4buw9Mm93tHsUF9M78BDDCF0d7//HrKvSXnM29LHQBH20P7eTRCCCGEEKIvSKCWo2j1suTtQqNvps1pDa96U7FQaMPiZP0yWPVYKFRkBpTlvk5tfsVx3LtqKEEcPtWjOSbPjFzQbWSUUQMGlKpW1m5Ha7yy8bajr75/mmtm9U4hhBBCCLFj2n6uVvvZ7YUX8M3qrydK5Bus7INzemaQc5wfA1AQsHjFvZDhgWoA1rf9CMg9u7I+MIaXfdf4Xp4pOcOX3fO0SVd9l1fXtjK4JERBsG/W8uXqjILbsBpW42Jy2Jh92bW/B5Qjf8u7qARqQgghhBCfCxKo5aghqvDyLIm/rfyZFMs00IYJiYfyLQaS2Si5q0Arm3eGfZ0nV8CvAvfyw8AjzNu0EXimw/Meemc1Vz2+iMElIV6/7NABFayt0CNYpyoAmB4c0c+jyd1YZxk3BH9NCIfNNROAZ/t7SEIIIYQQopdJoJajxjanz8/pn11oGPCMfQSqrR6FybGB/Ip5xDzF96wn2NVcg4kiWFsB4/bPef9Wq5gNujJ5P6giWZ/33IcbAKhuijJ/VR0HTqrKa5y9yV+e392Opm5OiS1ksrkGgKAzcAJfIYQQQgjReyRQy1FTJL8MVk9QvmDCNAz+Hj6Vz5paADgyXJHXsaKOxwHmRxxgxQuifNhWnfdY4tM+4wztZX3ehJYFDDJXYqGwmkcBAydQ8wdn+bY36E/far03eXuUWtePIxFCCCGEEH1FArUc7RL7ENPQNFLEBj2oT86pnDaON+egMAgYhXxszEpuy3fq4sk1dySDNNiKPmoK5qjdmBq5G4XJtOGD+VuW553Y/E/2Cs4D4IO66cDU/Abaiya5y2gyFA42ntv3Ze61Usx76g5UtJlpX7yIULgwp/02msMYoeKZystD13BTbw5SCCGEEEIMCBKo5eiR0M+Tt1t0GPhqr59TRxq4LXgrALWqjNPN1FRFL8+pe6MjqaqVD7mHslPZLnntr7TGwaaBYgCiOpT1eWPcVL82M9aS1zl628PGFck+aq9u+DZwc5+e//2XHmTmgqsAmGsF2OcrP8xpv/nWVJY6gzFRLNXbz9o6IYQQQgix9SRQ2wrNRhFFfXAe7avMqDDT+6jlOXXP1rHk7ce8gzgvNDiv/Y9Y8wd+EPo3HiY3ul/jM31y1udVqdrkbdNpzuscfak41vd91Jy370re3nPRLyHHQO23ofOTU14HBYO9MjYhhBBCCDGwSKCWg0g0So2uivcRA+rMijwK4289zwzwtLcvJho3UMKxsec5xl6LhcJsGg+U5Xys2wrPp6Z6IyFiLNGj8i6mEfBaqDDigVcQt9P9PzPHMl6tBKCufM+8ztGblKd8K+xA98MatZe86exrLQLg795hnJPjftJHTQghhBDi80cCtRzURRQHRm9N3h8zqJDX++C8XngQFzo/AGBEYZj7olews70EgE+bzwEm53ysT9mJJSpVtTHfjJy/eIiHiepkkdxyaxx1bgAbj5hVkNc5epOjNQdGbmOMsRmFyT5Vu7BPH4/hn85BvOBOR2mTFqMg50DN831WUTd7ERchhBBCCDFwVTdFef7DDRw4aTDjqnKbmyeBWg62tMTS7rv5VvLYSv6qj4ZhoH05Ia3yu2DPzMTku8btiWHf53ebpvJQ8JfcELiH5Q3/ARZ0eN4vQxezIjFN79aivi/Y0RnH01RTQbWOV8scZw/v8zE0UkSjjv9hloRy/9NTnua/wYsJGzFCOChvDaYlZfqFEEIIIbYXl/7rA/67pJqhpSHe/PFhBCyz230kUMtBh0Ctj3pwZTa8/k/hMTxTOwUPkyML8ws0oq7iW9YzzDIXY6EIbv4B8OWc948QZosuTd43dfZg1fWvqxtAvcoyg+v+HltRMPc/vf29uexkptbURaJthAuLe2NYQgghhBCihyml+e+SeGusTY1RVm9pZcLg7q/lJFDLQf8FaqnbpgGvFR3D3M1bADiwIL9ALeYqdjdXcYT1PgDvtmzMa39Pp/dRM8keqO3pLOJgayUmisKGYmBkXufpLbGMQK0/Gl5/wXyLsBGjSReyLjgz5/0ud+9Kux+NRCRQE0IIIYTYTtRmxBL+AoFdGXCBWk1zlJaoy06VfVFXMTd602KONucCBmt1FTGvb/qo6bZ6TrVeRWMQ9ipZZsxObst3jdm97lVMs5akjt1Jw+rOeEqzTI9gUuR+XEzGVpXwapbnHeO+yhcC8S3v1k0ADsnrPL3Fjbayv/khjrZpJYynhvX5GL5vP84kM96w+gLrtpz38wfFR0Zv4kE7t/5rQgghhBCi/21oaEu7n2vNgQEVqK3Z0srhN/+XmKe49xszOHxyX9RW7N7Qtc9zV/Ce5P1WHQJO6/XzWs0buSnwJwBWxUZxlTk7uS3fQG03Pkve/m7sYo4ftF9e+yut0Zg4iaxaZ2vcZuhFyduGF83rHL1JN27goeAvk/df3XwiZG3Z3XvagzSAb0fuA87Iab//GjMIu02YaDbqQUTz61UuhBBCCCH60fr69EAt1yreAypQe+Lhe7nHfBjPNHnlgekcfv3v+3tIAJhtW9LuW51M++tpyrcOTGf0UcunGIhWHkEjdXX/gprJweH8guBvrf8Zvw29hcLkIuciPlYHZn3ecGqSt1uCAyPQBnBj6SnnCqfv+6it14MYYSS+S2buf3rXch4NjpO8H5US/UIIIYQQ24119ZG0+7leyw2oQC3Uso6DE32m1ur8GjL3phXGGNq8qcy2PgCgmjJG9cF5HbuUh93ZWIbCDQ3l0JbnONT+GBNFQV0pMCSn40RdzcnRXxHEIYALGHlXfbS0QygR7BnoTvdv0WGKjPiXccOgGXmdozc5RiDtvpHn1M9t5XqKXzqn88fgHwBQVijnfTOzp9JLTQghhBBi+7FhR8ioBc1UxkiR2yK7vvBk4FjedvaBVFKDz5TGNHt3jLGiYfzYPReA3QpL+WXbdUyz5wCwoOnk3I+jNB/rndIeyzdQ8wc2QVxCqjXr897WUwiqKDYeLgOnhHxb0WimRO5htFGNi8Vuw0fQl/na5qjLS2pv9o7cgYfJIeNHsXeO+2ZWqJReakIIIYQQ24/1O8IatYXF+/ONmhIMFOt1Fd/o7wEl1LXGOjzmKp0WWPYGfyLFNEEbqaqLSuWeVYk6HZ+bb6D226qfs2TpEt4Kf5/bgreyxS0FvtDhed91f5Q89k+NYF7n6E0xT9FMYTJgrTIqu9mjZzVFXKIEiRJ/T9oI57yv0nBP4Nfsa35CEIfl6x6AMcf21lCFEEIIIUQPypz6uF1m1JrDw/mv6r75W1/LLM8P+Qc6W8PzRWqmYTCn9FiebhiPh8ns0kk5H6e9NP1p1n/4gvk2luHRtOEs4MKcj+Fqk1ZS0/WylefXOn1KZF+8R7lyMsrze3kWY9lWjREn7X4+xWBO4pVkWwUAL9bWxbOFEEIIIcRAkjn1cbtcozZWreLd0CWEifGZHg4c399DQmudNaPmKEVBL0/tUxmB2qKSA3jWmwDAjKLROR8n6sTTq2OMzcyyFgPwdtuGvMbiKY2HRVTbKMxkZijzOfsaHzPVXIaJpqreBcbndZ7e4nrpgVFfB5Gx6s/4nvUETRSyUg/DU4fnvO/PzXvS7nuxSCfPFEIIIYQQA0nU9djcFM14bDsM1Cw7wGCjAYBi3TNZg+bGOj585naKRk5hj4O/mPf+Tc2NfJUXqTNLCODygZ6AhYfnOBAOdH+AbWA1beCb1nMoDAqjo1htHJncllegUbOE14M/YIxZnXpsK/qoNVHILtH7ASgMWizOeI6rNIda73Oe/TQAc+qGAPm/573BaN7IkeY8HCyqdQWeKu/b82/+mMsD/wTgP950/qpzD9T82ctdI3/h5iH7Ma2nByiEEEIIIXrcpoaO7aq2y6mPBAqSN8NGxyzW1lj0958wa8MDqE8MVg19jZ12mZbX/k3Va7ku8JcOj9e0HAslY3pkjJ0JNK3mmkC819fHbbtxp3lUcls+U+e8SDPjfUHaqdGrOaZqX2blMRZDRQng4mGiMHCzBIquE+N4853k/XybavemwuoF/Cl4S/L+/Pq9gVf67PxOa33y9uHW+wzedCXwn27301rzL+8QLBSmoYkQJOpJ1UchhBBCiO1BZiER2E4zapNb57FaDaaFMA94R3J9Dxxz1oYHADANzacv3M1Ou9ye1/7NWzZlfdxzeyaQ7Ir2FQzRhoWV1kct9+O4vjVN89TOzNWT2S+UX4+zn2+5nF3CnwLwpejPWKh26fAcL9LMaF9AuLF4Sl7n6E3KTV8jVq62dPLM3rExNJYP1HimmvHG46bOrWu10nBFovJnO+mjJoQQQgixfchsdg3baUatwq1JTs8boup65JhvertzoPURAO/oyRzVzfMzbVGFPOgeToXRxHHWXFaoobjYFPVBsigaHsyf3WPiOazScezT+Dz72G9joRhU/U3IsZtbXfkUDor+lhBOsmS+l0fVSEiffudhZs2oeU4qtVurS1hdNjOvc/Sm1uAg1qjByUDS1H0b7KwKTuIh9+v8PRj/+cEgty9Qtsyp9FETQgghhNg+ZAvUtsvy/Lav2r3SJqoHepWd7fwYy1FYKAa3lXJ1nvuvs0bxf+634nd8SZlXi4Zv07hy0Voyjp+7ZwGwf0kll7Tcykz7NQDeaTks5+NEtM0anZ5By3f2nKdNHG0RMDwqjCaG6i0oT/HMhxtZVdvCmbPG4ho2T3v7YePRQrjPKyt2ZWP5Xnwr9huGGltQ2mR4ZTmP9OH5myIu76jJ7BK5D4XJXkMH8XAO+6WvRdTYeMSkmIgQQgghxHYhszQ/bKcZtYVDTuTm5SMw0ayniu+6HoXBbRuii037JLN1jU6Xz81mS0vHBYAAbh+sE/JnUyzTQJu+1gUq95Reti9DPmvcAC4u+Q2fbGxiWegM7gv+GoBF647lor/Hy8a3OR6n7bsTFzrfT+5z0QAK1FyliRFIBqwBCvv0/I0RN61apqNza0PR/hZeYj/CRdbjmIZmzorzYPaNvTVUIYQQQoheobXGMHq3D/FAs2FHWaPWEBzKPL0roAni0hp1tjlQKw3bNEbioVq26Xrd2dKSPbjbmmPly5+RMgyDBRXH8NimoXiYHFi+d87Haf8ynGi+xbftZ7BQbFh3AvDLnI/RHth5mNiJaZBbPnyZJ4M3EsRlw0d74c24I3380kctqSmjj5qX4+k9N8q3rGc503oR04jvpL3eXx8phBBCCNFTPKU55753+WRDI7/72jT2n1DV30PqMzvMGjWl4aXgZYw31mMZmvW1c6GkY9GKfAwrC1MaXcFIahhlVKO8YzCt3PufNbRlvyjO7MvVG3RaHzVYWzqNf3gVAOxeODbn47QHapVGA3uaKwBojmUvktKZ9sC0llICOp4dMiL1yeIYMWcYrlLsbXzKQdYiTBRD6w4Bds3rPL3Fyeyj1gefn9/M6sfZxV5Nky7gaW8WSpXltJ8Xa+PqwAPJ+462UN7AqaYphBBCCNGdZxdt4PUl8ToBp/3pHVbe0P+9kvvK+ixTH7fLNWpKaww0ViJz4LQ1b/Mxi23Fo8ErKDHi0WzNpvOpGjE25/3H1s/lTGsRCpN1upJqXU4ADxXdE8jtYntrhRtWcJH1GB4mxa27srnymOS2fOKM0SsfZV7oZqqMxtSDeRbTUIlAbf/oH5OP3WGkKieaysVVmr3NJVxsPwbAnIZC4Jt5nae3lDR8yhfNN3GxWapH0qAn9un59216iV3tjwH4UI+j1RuZ245eqjpkky5gj+i9nD18LAf0xiCFEEIIIXrBipqW/h5Cv2iMODRHO1b63j4zakrTlljDE9MWsWjrNh/zpvofJYM0gJr1y/MK1Pauf57vBl7q8PjHNbvChNHbPL6uhBuW8aNAvOTF+y3787R5bHKbymNaoRFrSgZp76qd+ZlzNgcN3pV98xhLkWqgnAgKkyYK0JisLpzCidFf4GIxYcgILmyp5RTr9dROfVxZsSujN7/K74N3Ju+viQ0DPu2z84dV6keHh4K/ZEXjWOCDbvdzjQD3usdioogSb7Ce668wQgghhBADwR6tc3gp+CscbF5VU9H6uM/FWrUNWbJpsJ2uUdtvw/2U0ManahS3uydyWulu23xM09d0eZkawaZWI6/JeEYnTZuVm1sfLL/aTWvZsPQ9Ju93HJadw1vvD3QME38BzHzWWGlfz7f5amc+0mOZYg3OeX+Ae9suZVg4nrI+MPp71urBlDQu4XDrPVxt0RwDq6GSiea65D5LSvZjv7zO0otU+udVTN/+svOg8QWmevM5wYo3BM+5PH+giF+4Z6Y9Jn3UhBBCCLE9KfYamZS4RvxEj6ahzaG8MNjPo+p9zdHstS62y4xaSayasWZ87VSF0Uybs+2ZAw8TV5vYhuIC5/ucrHfikDz2/7hgbxbXmVgopprL0Ri4WGgrnNc42lqaMO/Yjyk08fbis5h17h+63ae5eBx/cE/CRFFYvDu7173MHwPPYKIwN58CTMjp3POGnsJFiycTxKGVEJB/MQ3TF1h4iYqFgxsWcpr9OACPRxWelwqBF6gJLC8ZOH3UNoXH86o3lUOteBbLyjFQ6ikPOYfwijuOY8x349/JHP/0slXnlEBNCCGEENsT7aYySzFts66+7XMRqLXFsl+zxXKsHj+gAjXtyyApDCI9EKh9t+i3fFbdnj3RrKvrWHmlK2+WHMvz7vQOj/+lYo+8jrPprQcYSxMAs9bfD3QfqDUUT+Bm91QAji8dzhkt9zHLmgPA2y25V31s00FqMtbT5TN1EqCJIoI6SrnRwkijmgKimE5qampMW0SDFTzszsY2PFarIQOqj9pH5YdyjzOGQU4TLhbBYIB3++jcntK0xDyWM5KJ0XhhkHFVRbya474ABooQDkEcrEhDL45WCCGEEKJnLRt8JFdFiwnh0kAhP6uPsPuI3q31MBD4Y5nikJ1crxZ1tsNA7bUhZ3HtupmYaDbqCqb3QKCWHpAYrK3Lb91bZ2X486362Fi+e/L2SnMMY3PYx59NMU0DTF+1SpV7ViVbBibfIOoU67dsaYnxZuj7PBL6OQAPWj/hZucrBAyXDeYejCnaiR+75yb3+foASvw4nkZjUpsIWEMqtz5mPaE50nGabK6tC9q/Awebi/hrMN47beHmGcChPTY+IYQQQoje1GIUs0yPSt7P1ltsR+SfHVhWEEgFattjRq3BruRjPYYQDmFixFq3vepjZkCyLksvg674gyXbNJKBm5dHoATQXDSaM2JX4mJRXlHJnd3vgn/opgHLqo7goc8K8TDZp/IAZuV47vZ5sLPNBVxt/w0LxcqN+wP35Tz+9sDC0yYk1srVGuWcZP0PG48t0eW0qNMzxj9wMmod+qj1YY+3xkjH+cm5nl+31vN96zH2MT9OPmap7E3YhRBCCCEGosypfus6KbKxo/Fn1EoLAsk4JJpjMmpABWpKwTX23/im/TwAc1b+CA64ZpuPaaCYYSxhpFHDTnV1aHUQhplbRsV/QR20TdxY/I3N7MvVnahZxJsqPl1ynFGU97lNw6CuZGf+nfiejw+Py/3ciSqBhUSYYG4AYItTm/P+kMpMrtdVoMDFIuap5PFsZbBUKfY0lvMF620sFOEtM4A98zpPb8kM1PqiYXm7SPUKbrDvpolCVujhPOQdnnXtWVZtW/hhovInQKMuJJJYZyiEEEIIsT3ILJ7xecmoRdIyaqmwa7tco+bpVHl+AO1se3n+Qq+BUmL8KzFdD6Ch7lrKKofmtP8+zf9hN2slLiZrrAnUGWAbLoGWEcCInMfhDxQyg4bOlNUt5Ar7IRQmRU0zaBp8XHJbPlMXT1l9HdeGXqPASFV/7KyaZWfaz/d15yfJx75I6nXY2sX1NJOMdXzHfhaAd5sHztzH8Q1zOc36GAeLOWoya/RQlNLxKaW9zNmymq/ZrwGwRI3keW8mJV5uvyRpX7XKz9QwDovdwm6DSnm2NwYqhBBCCNELMgOTzsrW72givrVoZQWB5O3tco2a0po2HSKqbdoIEVPbfhH9d+diqsL1aY9Vr12Wc6A2u/k5dg/EKwVu0pUMDcUzUXM3FQEdi4x0RjlRSmnGxUa7ub3tZY1LOc9+On6+Zpd5ZqqLe84ZGcBSkWSQdrPzFf6tZrHX6NF5jB6Gq01E0HiYbGQQYLBBlXF49Ne4WBQVFnNFwwq+Yb+Q3McYQH3UZtY/w3mB9PIdMWcTwVB+1Tu3RqwlVfxjZ3Md74XPo9YtA77c7b5usJxb3ZMw0dTrYkD6qAkhhBBi+zJr1V2cGXqCmA7wR+8k5jQc099D6hOZa9TabZcZtS9u+APjrf+yQVfyS/c0Rg0+Na9S+tlYvqzPHDWZD9R4JkeDTMxxf8O3v2MEoT0+Uvn1Uata8zwLw5cD8IJ7AHB0t/uotD5qBpavMWA+VRtNL5VJ+1jvxEo9nLFmRc77AzxnXULQjn/Zdo78lRgB9ml9jQprMQ4Wr6sDKaprZU9zRXKfd0uPZEZeZ+k9puq4Tkx5+ffC2xobC8ZzlfMtxhibkoG3SW5/oE64klsSlT/b5frHLYQQQggxEIScekYYW8CAQi/CxoYIntJYfTCzqT9FOgnUtss1akVuHaPNeFPlAqI90ketkUJM7WKiOS92MfWUcE2sioNz3P/10Gxebx2LiWJ4oce6SBmutogGKvMahxWtT94+Wv8vp31qSnfnBudrmGiGlO3N+NrX+HPgfiwUzZuOBnJrCP7byp8yd9kGgri0tfdRy3ONlj/gdYlXn5wanc+RiSl9q9VOaDU2+Zwnvf35sHCfvM7RmxaFZ/BxQ5DT7FeSj7lu9iaEPW2zOYSHvMMppZmvW6/gYdJkFJNLqJztc8o1XS6EEEIIMRAYXqoQWowArtLUNEcZWtr7M5v60w6VUfNnrxRmj/RRO0HfSlM0PXOST+XH58PHssCtB2CPkjIWNcansf2sMrcgqZ2n8//FYEvJLtzpnQjA6WVj2LXtSfazFgDwTtvOOR+nzTVoI4z/VeczdVIrxXpdhYlipFHLrsbqeBNuryl1DmXRULATf3aPwcZjkR6Xd6+23vRS4XG84e7Dte5ZeJh4mCywi/vk3E2J8vyNFDM1eg8AJSGbRTns2/45GSgG0UQIhwpnx/71SQghhBA7lscHn88lG44iaLjU6hIA1te37fCBmn+NWqk/o+YqtNYYRtfXdAMqUHu46iIurjkRC0WNLuPA2LYHatmq+2Xra9UZf0ATslOVIjOPu2HVp0Sa6xm3+75Zj7NkxInM+Oh6AKI6kFPdPpVR9dHw91HLY/1Xtl4N+WTUPA0HxX4PwKPBn/Js6CoA7tVf5hVnMgFcPtJjmFW6Gz93z0rud/QAKs/f3vcu6itW01cNubOW58/x3O3fvzAx5ofPB+INzOGsLvYSQgghhBg4GnUR6xicWkIEbGiI5FUvYXvkTzoVBW0s08BTGq3jsUTA2o4CtUaznPW6hRJaKaGNcNvGbT5mtgviXKsuQnpj61Age6C26tMFjHpoNpaheX/dbUw/6owOx4moACdFf46DhTIsnsvh3P4g0TIN1g05mHMWeihMplfuQfaQsKP2ebBTjWXcFfwtFh6ra3cFXsppf/972D7tEeANtQe/sm8lgMt5/Jun3SPS9xtAM/SyfeZunr3wtlbTNjS8NhvXcKX9IP4/4yAOWqmcW0wIIYQQQvSnqNvxmmt9nr2Nt0f+QK0gaBGyTVoTiaiYqwhYXV/LDahAzdOaGcYSHg79AoDF1XsAx3e9Uzfas1I7GRuZbX5AAJfBDbsC03LbPy2jlgpSXN+Ff+j5H2IZ8edNf+sCyBKouRoW6EQJE01OpeH91/KGAW1Fo3hVxX97GBEandP4AbQbxUBh4zHMqAOg1jdtsTv+eGaVGkoJbbiY1Hs2lXYDQSPxhYtF2cVYzTesF7DQqPrJMEDKiThZAqM+itOYuf4B9gkspEkX8nfvMBbrsTlPPbWbN/Jd+5nk/fV6EDEdYKTnEjCDXewphBBCCDEwZPvBfP3noES/f41aOGAS9AVqUVdR1M0UuwEVqOmMPmoBte0f4HC9CYXBoeYCfha4H4D36w8Avp/T/l9se4Lj7drEmrlD0eZqAriUNlvAJABWVs1mWN18AN6sPIUDsxwns0G2oxQh/1TGbGOveZtf2I+iMCioPww19ITktnymLt7Xcj7Dw9Vpj5nkPq3Un1G73P1u2jbXtgkmjuXEogw3ajnNjpfBX9jWkvM5etvslufZx6pBYfCMtx8NFOH2UdXH8c3vMdWaC8ASPYomVYCd4+enfVNc31U7c0rsZwB8pEwCnewjhBBCCDGQZDa8hs9H0+tIWqBmEfRl0LK9J5kGVKDmKU0LYep0MW0E2WKUbtPxlKd4I3Rxh8dNnXu1vxNizzHaXg/AvKZGfhx8GYC3N50DxHtAfFZ5MH/+KJ6xmli2Z9ZAzYo2MJxaXCyaKMD1NKFu3v1BTZ9wnB0/35yWIaw2v5Dcls+0woDv9Z4Y/QUbdCXjhw/i4Rz391yHycYqPEwcbFbo4clth0ZvRmHiYHF5/adcYD/p23PgzH08OfIEYwOrAbg68CAA6+rfgkG79/q5A17qH6JfBO5L3tbqnG6nL7YVjuRXztcx0WzS5cnHc/kVRgghhBBiILii5gp2Ci3BweZbsUtZqCd8LqY+tvmKiYQDVtoyqlz64g6oQO286uuYGHwPF5NLnfNYVTCT3ArZZ+cpF/9l8F/dI4kRQId3YWqOx/BnnpSVujI2fH3U6kKjeFHNBODk0Misx5m65m+8Hf4LEG867XpfyPq8NL65eTqzj1o+VRt9t1frIdRTQhW5VzzUbQ08F7oSgHpdxLTonwD4gfUow4wtuFjc4Z7I0PpPmGkuAaBOF/Nc8cnsmfNZepelO2bP+qqP2qOFX+Xe5gMwDcWvA3enzq8UVjeBWqRwGHdl+a7k8iuMEEIIIcRAUOg1UW6kz7Ra37DjT33090sr2N4zaoVeE5VGfO2UjbfN5fk912VToqy8wuSn7jkA7BMaxLk5HuPv1hehrQ7b8BhbPIlXatbhYdEW2in5HP+822xVJgHwNVz+UeARapt/AYUjujz3qop9eXpJMyaKnQcdwLiat3k4+BssFNWbZwG35vQaDvLuJOJ4BPBwEsVA8imd758i6C8mcrQ1j93MVQD83TsM5aVe44Pe4SwIzsz5HL3t39YR2JFazk2s92rVIbw+qnYyP7AXC9R4AC6yHsc0NJ42GaE8rG7+BDtbR5fLrzBCCCGEEAOB7ZvdFUss3qhuiu7wTa/bMqY++utdZCuwkmlABWr+Pmoe5jY3vFZWkAOjHYOZbOXqO/OodQwbvHjE/+2h47hnaTwXd/agsZyYeI52HUppwcIj6GTPVEWMorT7nhPN+jy/zUW78oAXj7wvLJ3Irs5/mWF+AsD8WPbMXSatdSJiN3B8H3c+pemVho/VaCwUlUYjM41PsAzFMKM2+ZwYNktDU/jEPQkbj7lq1z4rf5+Lv5pfZJMb5Zfu6cnHni+f0Cfn9mc/D060OQD4xLC7XWfmfw+HUUuRESGEQ6ytBSjqfEchhBBCiAHi/IJfs6G2niAOdZQkH3c8hdVNzYbtWSQzo2b7pz5uZ4HaLRXX8MHKTZhoGijCcbycmsF1prOCG04e08b8x0gvz586xk61b7Aw/GMA3t94AHBIh+O8OvQsJq17lFFGTXx/J9btuf3DNw3S+qgZOrcg1lWabG9DPsVI3HAlx8ZuBOC2wO/4V+jnADzlzeIttTsBXDbrcj4KTeIZd3ByvxkDqOF1ZjEXSG+90Js6e69zmb7qf849wZuZYq4EYFntVBg1pEfGJ4QQQgjRm5q8AA1Zlt10OhNtB9EWS6/6GLK34zVqzUYhtZQxghpGG5spIEo05hAObV0Z8s6mjeXTR81/kR20rKyPT938ePL29Nbsq+ocT3NG7EoMwMHibwVDuz+37yLdMAzqB+/LV6NX42Gw2+jx7JXD+P3zX0cZm3kueCUWis0tg4GPcjhC+mtVvlV/L3t78037WXYx1nKN/Td+3nI3UJB67gDKqGX7zPMJVrdFZ+fJ5fyFWz7mevtePMxkkAbgRnf8BbhCCCGE2DF0lj1yB1LT3V4QcdOLifgzatvdGjWduLB/NnRlcsFhQ/OphEPdBzXZ+LNepTRzhvUyQcMlGCklW9YrG3+w5M+o+TM0pdHuG3O7nmKlr1qif61XZ3RGw2u3sIp39GQAyu3c3pNYzKGSBqIECOJSYsQv8EO6+6mX7fwB13I9gnfVzihMtlBCCJcCI54ddJ0oOxkN/Mj+FyaaSNNo4ICcz9ObsgZqfRRIntdyJ6HAZjxMrnfOYAOVQG593IJNazjd/k/y/jI1ghgBHL3jThMQQgghxI4l5sseWaaR/LF6R86oeUongzHDgJBt9vwaNcMwRgP3A8OI11u/W2v9+4znGMDvgeOAVuBsrfV7Ob+ShPYPrY0Q5cQDtbbWZsoqty5Q89wYuxqr8TAZbNRzeeCfAGyMVSWG270fqPvBjuFhUhH9Ll8w38LGY6fmyZCoHflZ8V4MjqwE4K/lF/CNLMfJ/CJmm4qXaVL1S9wSeA6NQWjLyVjDU33Ucs1WOY0bmR8+v8PjZh6l8/2Zn9+5X+F3fCV1fF/A6ToxymjhROttAJbGanI+R2+7iH/g2CYhHO5xj8NEo5y+qTa0l7eAMdY6AJ7y9qdMt2Ch8NwYdLNKzR+sv+DN4LvODwH4S/mUXhuvEEIIIURPcr1UoFYYsGiKuonHd9xAzT+1MWxbGIaRNvWxpzJqLvAjrfV7hmGUAPMNw3hJa73Y95xjiXd/ngTsC9yR+G9e2uOB1XoIrTpEGyFKnG0ood5Sw/OhKxLHTq1zC5B7H7VT9UsU2fEL+ncaZvKH4B8BmL/lUEiEZG9Wfplb1+9CAI+S4G5ZA7XiyHomGmtxsdisK9KyfZ0Z3Pwps6w3AXi7dU8ivqo4uU7b80+RW6urODZ6Ay4mg4oLcm59oKKtzDA+wcOihTBL9Ojktq/HfoLCwMXm7NjHXGb/O7ktn2CwN2mluMB6Inn//MQYF296CCYc3+vnN31Nq+8O/jZ5u6b1RCjtuiBIQ+ku/MQ5BxPFGp1akxZ1BsZ7K4QQQgjRnUXmaaiQSQybIwL305SY2JXPcqTtTeb6NCBjjVoPBGpa6w3AhsTtJsMwPgZGAv5A7YvA/Tr+8/8cwzDKDcMYntg3Z7+q+yHjQ8vxMDk1djUf6vE8HRqWzyHS+PtkNVLEQ+5hONjE7GIuy/EYlq+PmhkIp277+qhtsEfzPxUPomaaFVmPc/Lm2/lF6A0AzotdjKsO6/bc/oIhhmHhr6mSa0Yt5jhs0cUEcWk1CmmiEICI7q7eYIrZuJJHEgVElqiRHBX7NQB/CNyKxsDB4sfOuewZe4+DrA8BeMvbjaeLvsovcz5L73GcGNlWOfZVH7XfBM6lrbkBG8VNgbsoMuL/OukcgvWWolE84B3Z4fHYDvwPmxBCCCF2HJ7rYBkaCw9LKwKBIBBPJPRVvYD+0JZR8RHo3TVqhmGMBaYD72RsGgms8d1fm3gsLVAzDONciLcwGzNmTIfjB7RDyEjPdm1LLzWFySeJsvL1ZgU3uV8DIGyYOQdqv1JnYCkXE8WhZWN52tsPB4tIaA+mJ57j/5J1NtfW9PVRuzP4OxZvOAjGHNHluT8adCQPry7FRLNf1QFM2vIBzwSvxESxsXZ34OFux99cPJbZ0XiT5eFlYYjEs4P5rM/Sbiqg8ZJTHTXHm+9gGvHjXOach+F7jU+oA1hgT2cgcDzN751TCRguF9uPsUUX42GS3g6998w1prJexd/3M9TLVBhNKEwq6b6aaWf/gEW3sXWFEEIIIURfiEUjyVJzMWxsO3sV9R1NxEkvJAJkTH3swaqPhmEUA48CF2utGzM3Z9mlwxWm1vpu4G6AGTNmdNhu+HbRiYvobemlFi0cxjGJsvIjywsgEo/eM9eHaa35YG0DlmGwx6iytG33u0fSHtPsO3QGFzrfB+DQksF8PfEc5UYZRi22oajopOx+rTU47b5yWrsd/7qCXXhcxXNBk0t2xvY+ZPdEg2nlZs/cZfKnVQuC2atWdsc1w8xTO2OhsPCYbS4ghJMM0jxtoDB5Xu/Hh84QLBQfqnED5lcSF5vbvJOA+Bq7dn8Z3DcNuf1B8enuT5JTfN8sqOp2X5XRR22IUU8IB6u5Chjd+Y5CCCGEEANAzAgzOfIgQVwqwlDmW8qTS82G7VUko9k10Dt91AzDCBAP0h7UWj+W5SlrSb9qHAWsz+XYfj8o/g1LNzVioogmJqv553fmK60Hmu+N8ZRO64T+v2W1nHFvPEn49+/sx6wJ8ap8Wmv8iadg2i8AqQ1DIiuYE74IgOWN44GTO4zlntILCTSs5GBrEQDK7X6dnP8i3TQMDDP1cRk6t18g/GnVwkSgZqDSsl/daSsbz1diPwPgGvt+7gveBMC7amf+5h6FjUsQh/+6U2jyUkUuxg+Qv73Opgn2VaUh//cwYJnJP8xcfkTyP+e79tOcY78AwJz1l7IVy0CFEEIIIfpU1PMAgxgBPDuIbaZfk++o0gO19jVqqaRJj0x9TFR0vBf4WGt9SydPewq40DCMfxC/emzId30aQFTbRAkyjFoGG+spIIpqGkm84GT+0gId0yBom8k3xd8JvWnOn3ks+DABXDb976sw4Sog/ctjGhDw/QLgr1JzUvXdydsTvM+yjsVVikuc72E5CheL31Tt0+34tU4/f2TwFI6PXo/CZFzlEG7v9gjpFWfKbZeV4dOA9jVquVVl9L8Pnm+64IveDI625nK8NZdbuJMLnB/wjC946Kvy993pLK3u9VG6Pa0Xn50K1HJ5fwbVzOW3gXtQGBxmLkg+rt3uG6YLIYQQQvQ3f0AStEwClj+jtuNOfUxboxbsvYzaAcCZwCLDMBYkHrsKGAOgtb4TeJZ4af5lxMvzn5PDcTtoD6x+aD/CqfZ/AZi7zgambc3h0i6QLcPgIusxQrQQwCUWOZBwID7NsTBSzV7mMgCaI5uS+/gzLrZpJjNw8W2pN7fS7b6PmuNpaklNq4x1U5Yd0i/kTdOAYDEf6XEAhMzybvcH8NoaGWVsJqYDlNqpkhr5VGT0B7zL9Ehe86biYbJGD2EqqcDU0h5VNHBj4G5MFJFIBXBozufpLY7bWcPpvjn/zepG7EAUhclPzEtpSqzzy+VXpKLmVRxqpepzLlTjiBKg0RrUa+MVQgghhOgpaYGanXk9PTB+1O8NaWvU7CzFRHK4EM2l6uObZF+D5n+OBi7o9mzdniv+3zZfjT4V634tV6fHizazt/EpCpOhuoozjOeosJsAqG1tgpJ44OT4gybflEDltHFz4A48beKYIcKxiXzVepUALmWtQ4H9Aai2hjLMjffJujz8U27KMpbMXwxyWTw5s/oxDg78F4VJoPZbmCOPSb22HLNV5evf4M3QxQC8X5dqPq1zKGSRHKsve/iwdygPe6ng62jrXQBi2gI0IWIcbr0PwEav+zVYfcFr2shP7b/GK35i8w/vUGw8iDYCw7vdf1vtoz+i2Iqvj9zdXM1QI4aFQsdmAMVd76xSv8Y85B7KVe53APhe5QSO7q0BCyGEEEL0EMdxKKINB5ugZWBbvqVEO/AaNX9GLRzsWEwkl8JweVV97G2GimHhsU5X8aEaSyshGq3yrT6evWUZj4auBWBZy4S0gMx1osnbH5Qfwe2fDcbB5tDBezAr8bgXi/JlK15Sv1kXsL51PTcG/gTA0paJwJUA/LnsAlasWYeFR2vBxKxj2Sm2jIDRiIPNMj0ipy/m8Lal7GPNB+CdtmPTfoHIdVqh9lJT5JQVYlzkgWShlhVaYxjdB2xmWy2HmB/gYVKnS/hIj01u+6FzPpc43wMMzrae52eBv6b2GyB91GipTq7tArjQfhKAeRtuAHbp9dP734drvFsZGYpnbT+rPwhGdd3Mff2gfbg49j0sFCt0agpwLulyIYQQQojeppXi0/deRXsuu848EsNMr6ptbVrER+FvAfHr8Z+Wphbv7NhVH9MbXkMvZNT60t/aLmREOH4Re3D0t6zWQ/l++USO2srjKX92zLB4IPBlIq3NxAjwLSuVyWgxwqzWQ3GwaDBKU/v4+mwpw8SyU4GeRWrbWnMEC3S88OgIleq15ndF668ZE4pn3Q6P/jq3Obm+bIphmpi+oCrXaXuOEWStriKAS5tdimGYycylpzS21X2gVlD7EX8NxqtnvuFN4UznKkZQww2BP+Fgs1ZX8VP3HA43U33U/uwew9zQftyZ2zB7letkL5zSV33UvutdDsrBQnFN+OFkPVStuv8lpbFwDE+oAzs8nssCVCGEEEKI3rb47efY/aV4DYQPWu9h6qGnpG33J0c8I4Blfj4yapG0NWodi4lEne0sUDN9lQxVD5Tn96wC5qtJWChq7Z14OnACnzW1AHCGVZh83jFr/8A14WcBeLT2cmAvAFwrzKXOdzFRhINBvlFYxT/c2bhYOKHhjE/s7y8t2tlcW0ungoL/hC7jnVUtMOOSLsf/5qAvcc/mXTDRfGHwfuza+BmvBS/BRLG5aRTwWrfvweqhh/P16BAATh05CmvzOpTXXlZf5/QF0L7m3u2fS4nRmqxg+akaBUDASH1WL6m9WWpMYSBoKxjCtc6Z2HicZ/+bNkK42sIxswfVPe1/3uRkSf7TrXnUuUE8TMpyOL9/HVs4YCbnO0dz6L0hhBBCCNHbQu/9KXm78r1bISNQczyPZh0miINrBtKL8+3Qa9S6zqhFt7eMmoeJq01MNJ7e9kCtuWwXvhqLT32cOaKCYCQVcMR8BSYMnTpHfK1VYjxWiEe8QwCosoJ8o2wEV7jnAjA+UJSsmKI9lwnGOmw8yr3sRUKWGmMZqVOFSsihat/a8CReVPGA8oii0VhsZKwZP4buJHOXyf8HYCUKorQHlrlmm2OBMv7r7YmFRxshjjfnsKe5PLndSXyN7nWP5RljX2w8VqhhA6bkalugkr94xwLwJ++E5OPXD5vCAZ3t1EO01vjfhltLf8SidQ0APFE2vpO9Uvzv4Qi7maHeCoK4DG5uAqb29HCFEEIIIfKyvngKE2tfBWBt4e6MytheO2gvvhD9MwAHja6k0PJXUd9xZwi1xTr2Mk5fo7adBWonB25nc1M07TH/i8yXfx2XlSjP384/LzRiFFCjSwngEtGpQiZpVSNNA9vfoM8X5QTcZv4TugyABlUEfLvDWC41L+eC2N3JtVLay6GPWkZ7AMNIjT/XPmr+lHLAMig2ogRwMFG4ngtYne+cUFexJ+c6VwBwrvVvbgveCsDHajS/c7+CwqCCRt5UU2gjFUCWDpBArbNfa/oikPSfwjBIX2eYw/n9SxFnWZ9wffA3ALxXfTDwtZ4aphBCCCHEVtlYMJF/uLPxsLALp7Bfxnb/co1QwErro+b0w7Wi1ppF6xoYXVFIRVGw+x22UsTtuuH1drdGrf2zKqOZXY01FBgRhjXWsbWZg8xAK+CrMuNfI/Z+4f4M3jKfAC7jWhdk399Ir1Lj+QKgn7Zcl7xdRkvWsbhKcaP7NW52T8HB5tKRu3f4Imfyf3ct08ArH8vs6M14mAwrL+Ff3ewfP2/6e/CKeT6l4XglzYa2w6BgcLfH6KyP2ltqClPMFVxkPwHAb5xT+KOXavY9QNqodfqH0BeBmn+RrG0aaYGayuENGrP5Ze4O/AOFSYH2rZFU0kdNCCGEEP1vWem+3O3GryfPLNuJr2Zs91+HBW0zrT5CX/W0baeU5of/XMATC9YzpCTE65cfmgyielpbrGOg5s+oxXJYxtJvgdonG5q49F8f8JtTUkFY+4Xr3uYS/pzIHHxQuy9wxladI71htcEJkX/zJftTgjgEasphbHzim+21sau5BoBGpzo1Ht93x+yQUUsde4janLzdoIt83dJSXE8TIZS8H1Nmlmel81/IG4aBFQixUifKyRsF3e4PYLfVsLOxBheLUjUoucYMci+m4c9MfqZH8Iy3Dx4WH6mdGG2k3q+A4REixl+DN2KiUARgABSR72yhap9k1GIRngj+BIVJhDC/M36T1/lLW1YxK1H5c6ExhTlqMjFtUxuYKBMfhRBCCNHv/MmPbFUcMxte+4uJOH1cTOSWl5bwxIL1AGxuivLJxiamjS7vlXNF0zJq7cVEer7hda8oVg2MrH4Tf7asPTBp8wU0thfZ6nNYbTUcnCgrv5M7lr2jc9nTngfAB01nJZ/n+Nalmb6iH7ppPXcGfouLSZszBNvbi29Zz2LjEXSDwBEAeL7pg19yr+c/WcaSmdXJpcrN0TV/5WuBd1GY2LWXYY4+Mrkt1x8gdln3OC+GbgNgzqazaKUAS3soDFyVY6DmCyheVdN5VU1P3j/X+jdNugAXi5i2MdDsZ34MQER339S7LxRWf8BN9l24WKzVVfxH7YWNItBWBXS/TmxbeG6MaWa8KXirDjHBXUrYXIuJwmydBFR2fQDf+smlwd34UfNJAOxbNIiTO9lFCCGEEKKvTNryXy6z38bFhuajgT3TtutoM0OoI4ZNoeng+Yqp9WXVx0fmr+WPry5LeyynKuxbyZ9RK0hm1FIxQy4VvPstUBtlVHNY4+P4+2QXqSYULo62eEftSpsOUWvtzO5beY6S6gXcnygrv6B+P5SZChyUr1Toh4HdOSZ6Aw4Wk4eP4o/tGyKNHJNo6LxajcTWLlcHHgCgURcC8bVaFwV+SmNTMzYea6nIOpZZ6n0c08LVFnP1rjn1jRgZXc5e1kcAvB/dkvf6psQLTd02A5wUuDO5DnBOuPtpjwDhlrUcZ87Bw4z3uNOp4OZu7wvc7X0BgP+zH+A3gbtSpxsgfdRCTSs51f5v8v7l/BOAtzdcAOzbq+f2fL+muIbF6U33MiW4AIBFW6YAO3e5/5LKI/jzkgJMFGVDJsOW+OO9+Q+LEEIIIUSuJjS8zb72UwD8s2Vsh+2jVj/J3PAvAXhn00k8NerS5La+mvoYcxXXPbO4w+NOL7Y7iviKhWRbozagM2oApk6fm/mCvoDicBsAUyL30EwhuwRL+PJWHl/5+1QZJv8rP4F/NUwmhs0xpalGx0G3mdZEyfYtuijr/goTO5B6u2zf2FfroVTrct9+GtO/Fsnzkn3IACZG7sfrpLeXn79giDbtrWp43WKVsUSNxEIRCVZs1TEGVb/L7YkCIo96B/Ij53vsb37Ij+x/4WDzlrc7t3pf4mjzXcaY8amQlznnspKROa2j6226swqbOfQx21ZeoJCToj/HRFEctrmMx1LjyuH8W8JjeEnFA+vDC4cA8Wm2uSxAFUIIIYTobYMjK5O3Jze/3WG7dlPJEW0FM2pG9E1GrbYlSn1rx2vv3rye8leuL8i6Rm0AB2pbdAnvh2ayh+8xw5eB6Yk+apFABa97e2CiaAhNZEnp/jzpjQVg/9Dw5PMu3HIDk0PxzNVPIjcC8ZL8kcJhnBe7GAvFoIoK/i8Q5l73WFxMXCOYzAVmlhZ1lSboX8/mRH2TOWFZ+CzmrP46dNMO+smKb/Cb+oOxUHyzajqj22p4N3Q+JoompwT4uNv3YN7Qr/Ctj+NTFS8bvQvm+tXJbSrnrJwvYE20Taiigb3NpQBs1uXggWWk3oe3vN1ZR24Zu962sWwqlzvfIYDHQeYixhob8TBpsbNnP3uSh8UCPRGASjPIytDO1LU6eJiUBMu73d+/TtG/2FUaXgshhBBiIKgOjGQ8CwEYFl3ZYXsUm826nCAOnl2UljTIZYZZT+isFH5vTr3091ELJdaopWfUBnAxkXUM5unCkznL91gTRbjawkCjiH+IkW0I1Goq9+L7zpUAHD94OIVW9ijWvy4t6luv5tqlPK/2AWD3UCmBYIhfuGfG9zHwBWqaKcZnBHGxUHjukWCnyn06rsc73h7JBtEAhuo+o7Y6MJ7/qWIAvlE4GMtwGWTEe3B1X4qkfWy+NgKWkVZpJ9cmg03h4Tzr7YOFotko4lTrVU61UlMJ2/uo/dw5iyLasAzFFkqAjtnF/lAfHs0/vUMBeNA7Ivn4hUMmckRnO/WQzMqjjw/6Nq9sjmfF7invviG4f/8SK8oR5nyCOFREimj/QUEIIYQQor8sLDqAfRueA2BdcGyHn+nfH/oVvrEoXpPi+2MnYqcVH+mbjFpnmbOeXErSGnMpDKZCq0iWjFqwk1ikM/069TEzij7Yvb1j0Y1t+ABVRnn9QFrvgtS2WrOS1WowtuHR4mtYndmHzR9vKJ0KQlyl+XvwekqM+LTNpug5EB6Ueg1miLOcKzlDvcR1gb/gapNcAnh/NsU0DCwr9XFZOa7/ymx4XUETERqw8fCcKFDU+c4J6wbty9VO/HmnhuZwkxWfBrlWV/ET55u4WIwyqpmvdqYmo+al0hqT/g3UYp282X3xj0PH71B+U0/9Q6w0mrgheDMAG6NVwBU9Nk4hhBBCiK2x0R7On91jcLEww7syLWN7WtVH20y7tumrYiKdBUU9NfXxwofe49lFG/jhkTtz4WGTgOxr1EK+2VEDfo1a5mfTfuFq43KY+T4WirBnAUd23DmX42dkM/xRrH/x4D2hb3Bt5CeYaM5vuws4O7G/StvfMAwClpGcT9s+xfFl84JkkAag3PRsWfvzH/IO50HvcDQmXx02mv3zGL9hgFFYzszI7SgMQqEQb+XwHmQ2vP5921XsFI63IlhZPwWGD+ps19Tr8Y/DNCFxd4GaSDnN/C54OwBPePtzsXNh+mvQut+b9XXW9T6XPmbbSmW0iPB9BXOaejp102M8GHgahUFLc+rvwCa3ip1CCCGEEL1pjT2We934HLkDCiv5dsb2zEDNvy6ts2u0ntZZUNQTa+Q2NkR4euEGAH7z4hK+N3sipmlkXaO2XWXUMiu9tF84h3C4O/hbIF7SHH66lcdPD9SmNrzCbwPPEcTB3ngKcG5yHKMTRTBcFfbtnzqWlciEWKY/UFMEMRlObfJ589UkxmZ85u1pVX8PMyeHObn+OMIyDSzToppyAApUbs35ytpWsZexBAebIndURh+13KaV+t/HTdZw/hk5BA+TRXp8ctojQCARPDwS/BmFRDFQqOghYBfndJ7e4k9rh2wz+cfaF7/i6IY1vBy8FBeLLc5QHjBvSm7LJaNW0baa/RKVP9/S+/KyN50YAdqs4q0usiOEEEII0VP8M5SyXVulNby2TGJW30997Gw9WE8Eis3R9B/PV29pZWxVUdrUx/aMWsAyMIz4Nb6rdLdV3PstUKuigdltLwMHAaC1TgYmrq8vWa5T/LIJt6zmWPMdFCajIhGGRZYxy/ofAG837ZV8XtTXfNr2rVcL1XzE/YFfoTCpa94F2J9zzX9j2a3YeDiRA/HMIiwjPnClDb4cu5Z3gunT/7J9aXMJEr6x5VYuDX6Mh4m95Sassak1SblWbDx40wNcGnoWgHc3X0uDWcZGrwIPE0/nNiXRn3laFtiZy5tHJ+/PNt9nky7HwaZex9elTTZWUWTEK/y05NirLV8frKmnJeoya0IlhtH16xi38QVuCzyJg80iawrveyOw8Shvs4DdemV87XSsjYlmvLHiGuUxNvopx5qfYKEINg8GRnRzgNQfuRcs5dvOZQAUYEmgJoQQQoh+N7vhcQ6yl+FisTB2MjArbXs4spmxxgYcbRM2okSs1DKjPluj1mlGbdsDtZaMQG3xhkbGVhVlzagZRnyGX3vSoLusWr8FasONWr4SeQy4FohPAxtlVKMSwcOL3t64WLjYnLiV5xi6+S3uCP4egHfqTkQVpyo94qVKtm/Qgzgw+jtcbVFSXMxLicfNti3JAiAfuvFxnWM8RYXdBEBd9CbcgiL2i9xGAA/LiH8gmV86N9LAseY7uFg06iLe0ZNzqnIzwl3F7ubyxPlb8McjOsdAzfQVLTHsAFeX/YpF6+IFSZ4s77qHV7uyxk85xXoNT5u0sDNrGZbc9pqazr7R+NTHuwM3c1fglmSQBuDlmLXLxwdr6vnibfGA+4+nTeeEPbsOdgY1L2GWNReA/fmEIaF4M7J3Np0EHNPj4/PTvkBVGRaH1z/C3sF4S/R5NSOBfbrcf27lydy6diImmhOGHQiL45+dlOcXQgghxECwV+v/mGIvAOAap+PCniPX38FVoRcBmLvpOhZWHZ/c1ld9YTtfo7btgWJLLCNQW9/IcXsMT6s0GQ6mkkL+2V0DNlADMPFlC9wYb4Z+AICjLSZF/5bcdsLWVg7092kzTFYPmc0/l5nECDCtYr9kvD/aXUMtUVzDot6rSu3uu8jWiSmDni/b57kOroLq9ibXic/ay/jQjYa1yYBxra7iuOgvsZ2mbodv+IuJmHZy+iXk3vB6c2AE76uJ2Lg44UFp72OuWblRNW9ySuBuAB5WX+YFvszZ1vN81XoVB5u/eUfyL+8QjrLmJ/c5Pno9Hhb/sAtzOkc+3lmRmmr67oot3QZqhu9zjBnB5Odk6J4PIjO1Fe/EEdGbsFCMqSrluzye3JZLH7Xq0BjeSnzWRxaPwjAa0Dr++XtKp5W4FUIIIYToa4Pc6uTto9qeBS5K2276kiNmIIRt5n89u616s+pjSzT9em7xhkY8pZPnNIz0tWlB24LEcqHuSvT3W6BWo8t43jo0sUosvbm0JnvRjnw1hEfynDcTC0W0cBJNZbvyeOILMTQ4Lvm837vXUhmKZypmq7uTjzdW7M6ZsSuw8Bg3dDR7AH+3TsSNtOBgcaZVQCDLFywzWxavrhg3yqhhYfhcPti0DzC7y/HfWXoRa9ZvxEDzk8rJWAYsDp2DhcJAoVVNvLhHF/5d8Q2eW3c0ALeP2Atr+WfJbVvTR80w44HqUKOOyWa8KEmV14iJb36yNvlIx99fZeS2li4fbbHU+5tLsPl++dH8bXUlNi5TS5qZ2vY2HibVgZE9PrZMnhlkmR4FgGWXsjY0meq6ejxMisLDu9k7/fWZZnq63PEUltnz768QQgghRK5eD8/may0PAjDV+aDD9nqzghVqKEHDhVApdj80vO68j9q2B2qtGRm1j9Y3dCjN71+mE0rrpTZAM2obqOSv1pd9gZpira7CQONiZy3aka/Vgw7gOide1fCbQ8YxNq08f+oN9FfQi6jUG9kWqOANtScAVsEQAB4OnszalniFx69ZRckPeJqxjBKjFQsPFdkLSBXQiFpFPOfN5DBzASEjPhXRzGHt1hprNO8n1n3pUCmGaRImhplYE+cphdVNoOZmFFSxfc/PdV7w5sKJ/Ms9GNNQtFjlnGM9x/n2v5PbHSw08K3Yj7BRmL51hb3xS8n0lX9iQegBHGzmbjoD0tqmd7Q2NJ5nVPyrvrKkjJ83xKc7fqlyJMd3tWMPyCxo81bVV/jnyv0AuGFQ1+OGji0mjrfnYuoWgrhEIwcTDpT0/KCFEEIIIXL0RuBAvkY8UKszy8m8Mvlbxfd4bfMpAPx59AzsxlQCo6+qPnaWUWuf+ri8upmFa+s5ardhFIXyC48yi4lsaoyyrj5VDT4cSP9RfbsI1CC9SIWyCzgwGu/PVRS0CJgGEdozB1t3sZ9+kQyBtPL8qW1L9RiKdDM2ilbPyrp/+5RBO6ObupsoRPKrwJ+SGabPGo6E0alsSWvxTpzvXMJ+5mLuDfwaD4uIEep2/P4Yp73/loeZnDLqeR6W3fVHmNnwulLXMMHYEM+ARZuBym7HsbziIG5146/nm6G1/DRwOQANupBvxS4lYHjsbKxlhR7OZzp9GmJvlMDfd/3fCBmtABy/4Tbgl10+31+4xd+/IueM4jbIDJStPKeepvfSg59wD4MCjQDUtFwCJRKoCSGEEJ8HtZvWEggVUFre/bVbNsrz0Fp3e+2Yry2U8gf3JFxtoQqr+FHG9rTy/JaVllHrs6mPXRQTaYm6nHTb/2iKuHx9nzp+9aXsP6RrrdnUGGVoaSgtQ5ZZTATgvVV1ydsFGYFa0M69RH8/N7xOfThpQZFhcIw5B6xmLBRe9EAoKMt2iC5lmzbWzh9Zn+5dw1vWuQRwmRv4Lso7CdOy0i6S29eH+b9crtLohrV8FDonrYCG8jL7qMXPNUftxu7RvwAwY1gFj3Qz/sweXAAzvXtpc+Ol/hflMK0ws+H1BfW/YUpoAQCLqofA5J26PUba++ir1LNEj6KNEI8E/w+AD9VYToilB029EahZOtb9k3z884/9fyx9UWlIddHwOpdA8fBNf+G04H9RmLRUX4Lr+5N1Y5GeHawQQgghBqSP3nqWnV84Aweb9ee8wYixu+S1/6a1y/HuPRZTezhnPMHoid3P6snVFl3Kze6pAIwww10HarZJwEpdCzn9Hai5imWbm2mKxIOteSu3dHqMyx5ZyCPz1/Kl6SO55avTko9nrlEDmO8L1EKB9Nlv6Rm1AbpGDdKDM38VQ8OAH+s/UxWoB6Cm9ftQnn+gljltbEjDB9wduJkgLk2b9wJ+B4CroMRqJZio2hh1ooSswvQgx8qSUfM0nhNNC9Je9aYyzEovoJEtI5jLFzM9mxI/b8QIE6W9L1v3dmr7GG1uxMUi7E5GGamPXOfcRy11uzk4mL+4R+NhskYPSWulYCcyfX8PXMcIoyaetWt4Hsom5nSeXD01+Dy+tPmPALxbeiQzu3m+/70OB/r2V5yCTe/xVuhCFCarGnbnhRGpQDaX8w+KrWOaGV9XONdp4HV7f4xoA1Ftc2AOWVkhhBBCbP9K3/wFAcMjgMfS/97FiLG35LV/2+M/YKzeBMCap34AP3ylx8bmr82Q7UfwtD5qtpk2u6jvGl6nrnkLgxatsVSldv/4smXHIB5TPPbeWgAee38d15+8BwVBq9N93lvdeUYtZKfuD9iM2hDqOMt7DDgSSJ/mZ5kGytfbzM3IUOVqUOMnnGK9jqsthrZpiow6DkpUJny/rQiIB4ieiq+LCyYCDdeJEQoXUrXxDR4L3oSHyebaA4G9ODn2byx7DTYeRsNOeFbqw/lMDeMc58c8WjI2bRzZvoS5fDH/r+k6BgfX4GFiNf4V2Dvvyo+nN9zN5GC8YfLixj2otgezXA1HYeKYuV3o+wPGxoIRXOt+I3l/jLGJz9QwXCxW6/g6vpFGNWMSDcTXu/llv3LxfPHJ/HB1vPzr8ZOGdxuoHbX5L3w5sAAHm5WRL9FmNmKiGN06Fti7x8eXxmllhBH/daZB1TOq9WNOseZioCltCgDjutzd0KnviWma3FV4LkubmwF4MVjRa8MWQgghxMCxyR7JaBYDUBvufjZUpvKmpcnbptPcY+MC+E7bn7HsOhws7vHO6LB9ZHQ5ntFEjABBYltVL2Fb+QOiopCdDNRinkrblrnerF3E9dJilU2NEcZWxWOJzPL8AMurW5K3M9eoBbeHNWpDjTq+rR8H7gDAc2LsYqxGYVJEAa9aB2BGG3CxOGgrMwfjal7jtMA9ALxd52GVpS7K2/uLtX9B9o/eioeFg8U7ZjwjZrfVsJe5DIB5TvyPYnbsv+xifwrAJy3n0TZyBrtH7o0HbonKh5nNrO3G1ZxivYajbdbqKubpXXNqeD3c28A4cx0AnyWKj/jL6+fQig3TV4LetGz+NvhHvFyzGYC7q3ILUkY2zOds6x08TIqdg4CC5LbVeiiHxW6hkgZuC97KA4Hrk0FafIw9XwI/4vp/uen+TRgdXcI0K16F6N2WYXwn+AwAC+r2Azr+g9KTtOfvo2ayZ/3LnBv4BwBzaoqBo7rc/+lBZ3Nd9cGYKL4z9EACK+qT27r7FUYIIYQQO4Z6ilmjBlNktNFC/q2Pbhj3F/638BPKaWHGxF35aQ+Obbb7FsPs+LXfX9WXO2y/qvkGRofWA7CqdR8CVqqeQZ9l1HznKQpatF+pOq5Kz6jF4uv4/GvQACIZVSM3+gO1LFMf/bbbNWqWr4+a0VrLC6ErAKhR5ZxS/FdWtMSj0VdCW5k5yOijFqmayrmxS4gRYOSQMUwFXMdhP3Mxro73V1uoJxBrD5n9LQPM+FvlLzevXAetocUXuEDHTFdx7Qf8OtGHbK7ahYtjF1DutnY7fMM3udG04ue1DY2Ni4nGyyFIWWJPojpqETA8ysNl6Wukclw/tuuWV/lGIL6i7slICTCLG+27mWV+hIPN1e45rNZD2M/8OLnPIdFbUBjcVzwqp3PkIxLz9d/LJVj1Nf1Wdjh5uy/6qG0ZPJNZkT9gGR57jRjKaerp1MZc+qgFRrJAx/+g3XAlQbsxuU2aXgshhBCfD38pPY//JSonfq9sAl/Ic//N0QBr9FDWAMNVz87ICZFaAnSxehA4M2275auubgcKsBx/Yb7+yai1czIyap7SRF3VIQuWuZZsY0OqTkBn0yXbhbtcozZAA7XNuoK71Be5OHHf85WrV2TMX93KD3Fj4c484h2MhUeoeBKVxUN4UcUnys00419SJ9LMP4LXAdCkC9gjem8yS7N28EHcEP0ZFop9Bu/CTODFoi/wSPV0PCxOKBpFIEtmLHO82k0FCvuYn/JW+Pusax0KnNLl+H9S8BM2bWnARHF7WTyj9x/9HQaFE1X/mj+E4tFdHuP2gu/ycX38+c9U7oxtLet0nJ3yT7+z4l+ZIUZdMnMWwkmuTwNYoYaySg8DSFsT11PaHH+g1n2w8nDJ2dzSeCgBPI4cNJLopiV4mNQHd2Zqj48unUOQDVSChp2Dg9loT+YR72A8bVJU1P3avczy/vn8CiOEEEKIHUOzL2uzNf//X9eaWoqSbaretrjFPJtfqHjl9mPNtzts/4zR1KkCgriUhQsJpC1v6qM+ap0Fakp3eD+bo26HQC1bRs3//HZ7jipj4dqGtOd2NfUx1k29iH4L1DZRwe/dk5OBmjIsPlGjMVE0WRVpRTu2tmv4xxWHcndiyuIVQ3ZlWNobk+hF5qS+uO2FMdq/NC2BQbyndwZgUuEYAN4tPpw3N9YAcFhoKEWJC+npxlKGGHXYKKzm0cDg5HEbwiN4xDuYMcYm9jHj0yatHLI564xhrEj0USMQz9opXz85nUNGJr08v5mWUcu1mMbyomks3tiCjUcwNIQLrcc51Eo1NHSw2agH8fXY/2GhiOnU16o3fij5Zf1l7BGOz9O+peVaYJ8un7/EmsC7Kt5Pb7fBk7h8YbzVwKziSk7u+eGl8TIKwiypOpLbPxwPwKUVO+e9/8zYPCZbSwniYNYPI5f2CkIIIYTYvlW2ruQ0az5BHEbVTQd2y2v/htZU0qC5m6l6+fq3OoBfEA/UlDY6dD6+0LiC+lj8/O+VDcOONCW35bKEpSf4g7Fif6Dmqg5xRkvUpao4fdmVv4E1pGfUWn0zvb43eyIXPvReWjKkyz5qnTTibtevUx+1jldRMU0Dt2AIx8RuBGBkeQEVVv4BRSYvo+pjWnn+xAfmanhH7YqFokWHMVHEEunNzKqRQHofLKVxXQ/QfM9+kiOt9wBYUDsZ2DP5vE1l07nKOY/B1PNs6EocLGrNCoZ1M/5s7QE8LFxt4mHmtP4rs4/XYHcjexrLsVDYkdHAyG6PsbD0UP7hxrM/VxTbXBq4MjE+g6NiN1JJE7sZq4hpm/k6vVxsb1RW3MNbnLx9Rt3tkAz3s/NX3fT/seTSx2xbpX2HzMzvT/f764w+ake1/pupgXcAWFB3CL1eDEUIIYQQ/W5SdBFXBu4FYG5tDfDNvPaPtTZygjmXIiPCoKYi4IAeG5urDW5wv4aLhYvF1Z7CynLNDfFskm0N3KmPkL2gSFeBmn/q48QhRRy/53CeXLA++VjmGrXCYOr8nRUvadevgRrEL5ZNjA79pg503maGtSa+jq1xPFCe/7G7mDbWHj07wXK+GruGp4NXsZe5lM/CZ7Ci5mUYMrPD/kB67wdPUVQzh5Xh9Lm4WmXvo1ZNOTOj8eIpFYUB3u9m/NnK858c+hPrE1+ON4uGZ93Pz/9LRcA0Oa72L/w09CIA72608QeUnUl7H3x91NZTyVo9mJfD8QbYUR1gl+hfO30NvcHN4Svs/6Wkr8vz+4NB28zIaObw3pxR/VuuCL6Ph0lzzY14ZjC5TTnSR00IIYT4PNjJXZm8balo50/MQnkeb6ozIXEJUR0ZBFzbY2NzPMWd3onJ+1cojeWLTdIbXpvpVR/7aOpjekYtvadu5pp/f4asXeZaMv/UR/9U0qKQzXcOGp8WqG1pTa+AXlqQupZujAz0QE1pAlZmw2s4qe1xdg3EMyeLG08EJm/VsdtZpkFB22YeDFxPwHCJtFQAhyS/IAYa00ifDpktUMvMqCk3PSh72tuX8tDgtMeyTd3M5Yvpzwa3X9/nW/Vx39i77Ga24WJi61notD5quc1R9gcUXricP7pfxMOiQRdl9FGLH+++wI3sbq7AQlGz+R8w8qCczpOrH+pLuJFbCRgeHwX3YEQ3z08P1HwZtT4I1IaseZ6Fof/Dw+SjzYfy3rBUnaVcGl4Pcjcz0Yz/sX/gtbG4eF8WNYRxsJkQ7vlCLUIIIYQYWLRSnMZzyfuLw9Pzmk/T1FiHvxuxv/hHdzatXc6qR36CN2gi+51xLYaZObGx4/WUP0umlE67H7CMtIza1i5vype/GEiRL6MVc7cuo7bJH6j5ppIWhWyGl6UXGRxWGk67X+YP1Nq6bkHWb4HaMLbwE/tveLFDIFCctpbJNA08X0ChtrIX19jGd/mGFc9GVLQWEDBHc4AV7ym2yYuv7XES0Y6TCDiUNvAS55u4/nFeCP4ZD4t1m78E/IyDmp9jtj0fG4/imvNRvuzZK940LnR+wO/L00tUZEvrOjlEWX+KXkZZqA6Ngd36Mgwalx4o5pCRudK9ncpgPQA1sW9QHxzOQjUOhUlboLzb/SGjMXmojN+4X/Vt1SxSY3GxE++hptxoZrARL2BS7fVsHzWtNU86+/CY+hsAUweXJzrxde6qlhspDNbgaJtI23UcY87FxqMqUkVPpv6zMdwIpUa8wmdYtzGs5RO+Yb2EhWJ4wwHALl3v71vLaJgm8ypP5NFVewFwU3HX+wohhBBi+xeNRmi/1I9pi9cKj8moq9i15rrqtEDtX94hfDNLCfpsGv75PfZpnAP1sGzO3kzc/6S07VopbrNuxrVMPCy+71yI50tGxByHA81FxLSNZ4UxDCMto9YXP5pDeqXsDlMfs6xRy5RZTGRzUxRPaUwjPaNWmEgIPHr+/nz9T3MwDfjitPSUQmk4df7GyAAN1AYb9Xzbfo6GWASKitGxFvYylqAwqVIVvFd4AO+2xhspTw13t5oruz3qXuZbgXg59HfqRmMHU1X22rM/7V+QU2I/i6/7wuRflfFAKxytZRcz3oW8wakFYNe2BcywXwVgXtNxbJpwMmMjD2LjYXbSR62ybiFnW6/gYLNQjWeRHp9TRq1Kb2FIolny5sTfUr4Nr01fNUYrEOC14efw0OrDAfjFkCnJbVprqpuiDMmI+gH2aHiNUdZiFCaDo5nbDb4Q+yVTjM/4ZeBeHg/+lGnm8tRxe3iRqOPptNedS9XHnb2ljDDjvePeb1vNncHfAbC8dRzdrW/bVv6CL9qwGF0/l1MD8emhc7Yo4Otd7n9X+SV8snoDFoqfDdmb4JbUAty++hVKCCGEEP2nta2Nj9VEComgMLst6Z6pJjCcwyL3UUYLBUaUVXoYp2cpQZ9Nceu65O3IukXASWnbPc/laGseAK42+T4XpSUjYm1NPBD8FRCvrg4X9vsatbRiIl7Hqo/ZArXM8vye0tQ0RykJ27TnTcIBEzuxNm/vnSqYc+XhKK07FCZJm/rYNsCnPqrE9Du7YSWPhX4GwIq2nbi26h5e2xgv//7n4vw7sANpZeUN0yJQVMnpsStxtI0RKuRhwGut4whzPi4W9bqYBXoiTvsH5i/WYca/zP6pg8pzEgGXkbZWKjPTNar2TU4J3A/A494B3OZ+kYD20EplTSEnT+nvo2bEn1dAG6U0Y6FQXtdROMDr7EWBF3/+jGBBWqDXPvVOa81pf3qHtz+r5eIjJnHxEenVCPdqeoW9Aq/Hj9e6NzCS54JXMMSow8Hmy9GfUWq0sqe5AoCP1RjOjF2JwuCuyu7XwOWjLSP1nEvAa+vUH4EZSjWJNHXvBzorhx/Ht98ZgoXihMk7cbzzQmpjDuevsQazTCe+e8FiQnaq072U5xdCCCF2fC0UcHLs58n7M938qjbWtzlECbKZIImcQtYS9NksCezCCHcNAG1WaYftrhNLXgHbhuI3gTvxWvaC4ngdBSfalnquEQ9QAmbHmhHZtBcc7An5FRPp+P5mTn2EeEERw0glMPwBIMCgomDmLgCUhv1r1AZoRm2jHsR1zimcmyiOoHwflCY9Leps5ULDJYV78XGNg4ViZOkEAqEQ/1N7ABD24se36lZwT/BmABaqcZwYux4nEcB8MPRL/GzJWCwUXxw+jVnAexXH8ljNSFwsDi6fjsqS0emQ6fIFVCdb/+Nk63+J1/VtAmb2DxHgdPMmmlojmIbmieIqAP7Y+mPGhVcBsGLLizB83y7fgyu87yWDm8UFJVn7061viPD2Z/GM4b/mre0QqBm+gMJKrA4tN5qpNOLZHYVJwJe5q9Zl1CSS7F4P91GLOh1/0ejOJeaPibW1EDBcLiufwHPeTDxMWgPDGdejo+vIMWwaKQbADRRTG57Mfe5RaAwKi6eyXzf7+79epmmkFbORQE0IIYTY8WX2Pcv3///rWzsuQ8lWgj6bvxd8jZvrZxMlwDlV+zMzY7tr2Hw3dgl3BX8LwFes11nf1ggkAjUFb3q7EzRcYlYxBwJWDpXdn1u0gSseW8S+4wZx15l75zRNsyvpfdRSAarjZS/P39X+7TY2RtKyY/5qjl0pLfBNfRyoa9SqKece73i+bcUzHJ4V4j01ERNNXXBUWh+1rZ2/+m7pETzpxvtM/LZqalp5/vbgz7/+LdVHLf5htNjlfKrj/dOODg8BYHXZ3vzdixcL2TM8msLEcaYay5horMcyPIoawsCY5HHXFu3Ofe5RBHE5zX4ldb5YjECg80BtMxXU4YAGM1FtUePLiHXTJA86FkSxzI4ZNf+vBNmaIL5beDBvNQzCQrNzyWiusO9neGJKJsT7qL2nJvGF6HXYeDSTWkSZS8GMfMRqV/JB6NuUGa242uQy5+fAIV3us0iNo1G7oEFXjud85xIAxpYWcmqPjq4jlVYkx2Bz1b5c68Z7451dNrbb/b2MaqjjWhfxPes/BHAZUnskMKGnhyyEEEKIAaQl6lJOE1+xXieEQ2FLBXBgzvvXt3YMBrorC99ulRrKJzp+rd5EUYftrjZ5Qc1klRrCTollJp7v2joSqOAM5/8AGDOokNeBQFqv5OzXife8uYKGNocXF29i8YZGdh9RlvV5udrWqY+dZdRGlqeueYtCOQZq4e2gmEi79vLxkbLxfCmR1t1jcBk79UBFGC/jItkyDQwj3r/NU/G1TjG7mP940wkRY7UeQgmtuLFIYmy+0uqJ8dgZGSntRCmija9ar3JaYu3aOzUVwOHJ5y0uPZC/uPEKfdPM5QRxcLAY6bmk14Xpavzx/7aahTR6hfH1dLr7XxecjPL8lbH1zDI/wkJR2FoCjMeLRZgTuoBCopieAjalHWNO0WG84sbXs91ePpHz7H8nt+0b+SOT7M1YKkIAl7fU7kRJBZ893ass1tZIWaI4h20oLo38Abioy33Smg7afdtHLe07ZBp5NxxXGX3UxjfN47TAwwDM2VJOd2vchBBCCLF9a4l6DDKa+EngQQDWREYAN+W8f1NTExYeB5kLGWtsoogITs0oGDG12339S04yC2pA6jrnZvcUiowILhbnhwYlt/sLdbS3ybJ9iZPOGl7XNKcqUzZ1U8I+F9Gupj56mVMfuy8mAvGMmj+o85f978p2V57f/1+IX5BObX2HMdZ7BPAorguSS2PmTJm92YxE0+v2D8vxFE2lk/iWcxl/DPye0+x4sDV/7c0w9dsdsiGQ8eXyNKPXP85H4V+knVer9DfdH2geF/tV8vb7ViFd8ccR7XN0ryr/NQvXNgDwxKCuWxZ4SiePYRjxY0yvfpLvBePr5d7edAFwMKq5hmFGXWo/18Wy7bTjtLN9jzvaYhODeNy+lhHE1xMeGP09a3WqPUFPrxF1I61p93NZZ+amNbz2VRrqg94dad9r00iba51LoPh/ddcwIvQZCpPmLX+lzkoFwdrLr4+KEEIIIbY/bv069jaXJO8HdH4VtWcsu5Xl4X+mPfZBzZFA94HaL1t/jh1sJaoDfNB2c8exJa6lnlKpKtrftEuStzN7qEFGq6tOrsX8maZ8i6dk4w/GOmbU0seQa0ZtU0MkredarlMfS0J2MnE04Btet0fimeX5pzX/l5mBeM+Id+um0hI9lF+/8CmGAT8+ZtecFkCmTftLBFp/ta6nxGwkgEu0ZVYyiPL3A2vvjZY+bTD+38yMGhlB2cPubIoLxqe/xk6+hN2V6M/W8DqfjIzrxjjFeg1Pm3hmCDg+WRQlfoL4l8to3pC2X2tLIyVlqV9D/OcJBILc6HwNDwMv8Z55hp1cnGrjckfgt8w2P8BE8cnaO2HnU7ocZz62FO/C8dHr+VPwZlxt8aG9G921/fb/WhMK9G1Gbcqqv7I89HtcLOat+zqrhlye3JbLtNByVceIxDTTJVpRWzGVO5Z+gRgBKotnMqvXRi6EEEKIgaBk/Zv8OnB38v7fzeO4JI/97Whdh8fctsac9p2iP6XMjBcym5slYMqWEfNf9/qDrEAio+Zfb5/tWlhrnZZp6ok1+f4aB91n1LpveA2woSGSFmhlFhPpjGkalITsbrNp0I+B2ghquN7+E0bjJBi8c4egRJv+6oouD8xZxX1vrQTib8SPjuq+h9T0xleZYMVL/he3VgHD2cVYRUWix1dttBXXi6cfIzpIRAdwsGn/fh249k+cHXoKT5us3Hgh8AN2q3+FPwSewkJhbPwSLgYtOoSNx4PeEfzcPYvLKtLH1tn82+4qFr5mnEsw5KAwMd2PIVSKv/iN6ibQcKNtyT/sVh0Cfk5jwSjmqMm42qQ5GA9x/E27F6pxDDMKKPEdxx+oBW2bO3zd5wE+tnZlVXQQDjYxHSCIS4ER/7UnM7u4rdqUxUd6HPtH/whAVTDEcV08X3mK5wKX42ERwyagD+Ar1n8xURS4IeCIHh1fJu25WIbGwsU0NIOaP+V71hNYKCrrpwNdV8U08P1dWCZ1g/fhRjc+R/yMojGd7SaEEEKIHYSKpFrz/M09gj9bJ+QVqCk3itIGpqFZq6t4ytuf8aHcriHKSFWb3n/tPZDxE7FuWM8DgevjPYd1JVe530kLvlTTZo423yWKzVA1BjggrWBgtmvhlpiXdu2ZWRp/a6T3UUsvJhLLOH7OGbWMqY+FwdymPkJ8+uOADtQqjUZOs1/ls7Z4lG+01XGw+QEeJqPcESwr3Y95mw0cLCaW7MK7L/+LS+xPiWmb/726W06B2n7NLzEt8A4AHzTNBvbF8b1kJxbBVfH7V7jncoV7LgDXD5/CfkCBW88oowYM2ODFp9wNafuMWdYcAN5umcrCiedz+oJUPzLomOmaXP8a37M+xMHmFTWd5To+jbO7QK2UVkJGPIiKJDJh2YqBdMb1BWCuEd//k2Ff5LcfxqdMXjR4IscALQXDucH5GjYem6jgwox5uIc1/5uD7VW4WBS0jMIyjeRrtE2DW4p/yLjWl7nU/if3BW9kZzPVc8PfR6wnZP6hdNdHzXVjyV54MW3R4kX4TeAuAOpVMXB9j46vA1/DagyLQY2fcHkgPv3g3cZm4MIud7+q6FpWb67HMhR/GrQLAV+ZW6n6KIQQQuz46swK3vJ2o9CIsEoPJZpn7YZfFl3Bwi3nUkoLbYSIEuQnoYnd7qeU5kH3CM60XwYg4DR1fE6kiQOtjwBYruIJAP91cLjmw2RFyEXN04GzOtZ7yGi+3ZBRYCOaZX1YvjovJqI6JFSyFdbrbI2aP6OWazERaC8o0tbt8/p96mN7H7Xwlo+5P3gjAB817cG/drqL+9x48fSri3fjsOA/Oc17EoBfOrkVUPCXlceKv9Qrw1exsSFKjAD3hKoINC3lRPN/uFis1YNZqCek+qhl9GEDwJfpQ7lZG/VlPrZX48vJPmRFboTXvGnYuLjRmUDn69QMfx+1xPnLdCMjqME0FNrp+gN2tcm/3IOxDAV2iC+RmsIJqT+k1vAw7vRlyb4ZSw+GDmx7hcn2YgAWt32dYiPKe6FzcIiXnv+W9QDlRjMTzPgUyse9A7jK+RYKk98P36fLMearQx+17oJVJ1XaxMPCtFKfn79PXW+ZO/KbnPHJ/lgozh8zkQOjrye3Gbr7ILbOKGMDFmgwAiGCdmpd2ta2rRBCCCHE9mNR2Wxuc0b7HlEdgpuuNLTFZ2fV++ZLtWSZ3pcp4nq8q3blTOKBmpllbbzyUuvlJpgbuD3wOwI15TA2vmbNc1L7qERLKtM0MI3UsidP6bQm2JmVEDOnJubL9VTyXKaRXlgu56qPWbJ6rTGPTY2R5P1cpz5Ceon+rvRboLZOV3GlczqnFwyLP5CWeTHT5q96SlESULS36nJyHPacgkP4X/NwTBR7l8RTvCsDk1iuWxLHsSirns+twdsAeNLbnx84FyYv/p8f8h1+tP5QLDwuHLkv+wKrBh/K35fauJhMH7Rf1qxYZpbH8E3/u9h+jIvtxwBYUXcQjBjS6fhnxu5Ca4WJZl6iPP/F9b9iSngBAIs23Q+7jOp0f88u4jL3PAAGh9sDNV8xjcTrzJxf3JoRqPkLdpimRdj0EtP5HGLEsC0Dyxf0tOgwbcQbAHq684beWyMzUOtunZ5jhDgqemO8KE3Q5M/BMI94B+NpE8cKc0aPjq4jT2s8LDwsDDtIc3hnbndPxMOkqHgaM7rbP2OdZXvFJJCMmhBCCPF5kC2oinmKkJ3bVLu6bH3UsmSNMkUcxWtqKodHf02UIHuNGcPeGc9pKxzFGbEreSAYL5Z3nDWXRS2p6uGtgQpe9PYmiEtzeJdk+RLbMpPXMa7S+F9Kx4zats3O8q8vC9omZqJdVfs1VuZ7ke397iyrt7w6NTW0MMeqj5Beor8r/RaobaGUv3uH85VECc9YoJTXvT2wUNQHJ6UFFI6n+bRkFiuaLUYYW3IO1F4rOJK33b0AeLA83m8q4EspRV2F8jWjbi8o0p6paDZLkhUMVSjejb2pdGeeSgSVw0PjKEoEObsZK9nbXIKNx/D6/YFdk8d9t+Ag3m6sIoDL2dYLBIz4/v4+E9k06FS2zUqMW5Maf3frvxx/EZBEmjlbRs2fmbFxiUTTfzF5Nnw8T9ZNxURxTMlIrjZ/lxoXHgHT5Clvf95Wu2GhaPSNu7t1dPkat/xBFoVupcRo42M1hjv1ycAxnT7fw2SJjv8KVW4FsIIFXOrEg9cQZp8Eau0sw6C5Yjducr8GwPHFw/lmN/tnFtkpa13JpfbDBHEJb9kZ2KvnBy2EEEKIAaM9w3Oe9RSlRitBHKJtBxIq6b63mFK6Q+ADufVRizgejRTRqONr48eqjn3UYnYRb6o9eNWbyqHWB/Fz+rJsm8v25GLnRwCcOGQEJyQet02D9mdlzo7KzKhta9VH/w/b7cFtwPIFatHMQC1bw+tU8NZesRFg2ebm5OP5ZdQGeKDWrv1NaqzYnbOdKwE4eNBgpmZ0LV8/aB9u3Hw1AcPjZPMNXOcm7C6aRfuPDam1XSE7PQBsDo/gKW8WxbSxVlcxjFqMaLz8vZel6mJaSVGlMWJNDKaOE6w5fM9+CoA5W1zgtOTzXi84nDfd+G8IVUYD440NuFgUGp2PX2cEOO3p7SargvV6EAoTr4v9IdW4G1Jd4Esj6znCnI+FoqolBuyG62n+GLiVo813CRgeC1bdATunxv9y8FA+8uIFWI4uGcnBxnvJbacHfsdU72MqzFUE8HhH7Uo1FcntPR2ombFmSoz4lM/J5mpu1n8Aft7p8/3vgW2aHT6/3ubP1psZfdRyqfqYWWSntG0dF9rxKcALWzZ1tpsQQgghdhDtGZ9v289SlSiIV9PSCDkEak2tEYbqWuooYaRRw9HmPIqMNso37wbs0eW+mbOYshX1aJ9Fdrd3Ao97B+FicmrpbsntsYxsVru0dWoZUxszA8ttnUGUrZdbwDKT684yM2gtMbfD1FJ/jYTRFYWs3hKvXdH+X4CiHMvzw3aQUWvXPu0urWeYATs1vsdl9rPYeAyuOZLPjGnUU8RgGrEMzebN6xgyclyXx/Z0x0DNn1GLuYp1FTO42gnzA+tRLgk8yg/sx5mz5lvAXun9w6z2/dMbcc9afy+XhB9KP3EXfdQucS5I3n6ktPOFnNmaXQP8afAVvLol3rPs3iFdT5xz0zJq8dc9tvYN7gnG+2C8U/0l4GSKaxdwSKJACoAbbU47TnovMJJl+QE8O8yXm+5hz+A8AM6OXc5rqhwTjYXC83q26qN20vuo2YZCK4Vhmlnna2c2nLZ827tb39Yj3AghYihMbENnzWh25c+t32dwqAaFQUvTq9h2Kji3VH59VIQQQgix/RndMJ8jzI3JIA3AjXVfiAKgaeNy5oQv6vD4gobNwBVd7qs2f8LLwUtpI8inegx/dX7c4Tnts7LeVrsnHzshlOqn6y984r8G99/OXHOfWQ2xJzNq7b3c/OdvzZj6qHQ8SPX3RfMXE9llWElagNauKJ+pjwN9jVq79uVR6YGJwajmRXwlkaF6u6ESp3gqG3V8muRmXYFZV9d9oJalYfWFTb9lbPADAobLlg234er4FEX/dErtdeyjlsqoZazxygjK/uIeTbhoKvv5HnMzMnvZphxm8l/D+7NA+WSEdHMt51jP4WATUkOA2amiKJAsllLYsDxtPzeWPvUxs3H4H8wziUYjeJg4xYUolfpVIIDLbwJ38RUrXjTj3TXXwd4d/4HYWq8MPYfvLp/F/cEbaNEF1FHCsVrz9IJ1/PSpjzhi8lB+c0qqgaN/DaFtxRtO+1PWSum0JtRdiUUjvP/E7zDsMDNOugjT6v4Pcvaa27ki/DAAc9b9kMbB301uyyXbWKRbKTXi/xi0GuBVjOM3zinEsAmVjWX3bvYXQgghxPbtpLq/MDkYL+r2X29P3lB7cIbZcRpiNq2NNVkfD7gtWR/381rr2dVcD4Cr7Kwl6rNdi/oTFOnTDlPX0F1dz3ZYo7aN5fn9+4ey9HLLtiatOepmBGqp50weVsJLizvOasqn6mPZQJ/6ONrYzC2BPxCuLYJJs7vso2YoD8dVfDF2HSqxRutORjC5m3Mc2/okR9kbcDEJt00AKhik6hhrxt/cmmgTrpVYj0aYGl2Ki0XUiBfCOH3DDfwi9D88TFZs/BVwBiPq3uHewG0EcGncdABRo4BqXYaNx03uV/m7dzhfLx2Dvy6lP6VbGLBoSsx9TWUTNTe98Ckrqlu48rhd2amyCOW08VnodBQGEYLApuR70667C32jaR0/DfwNgOXRccBVtBaN5GVvOgqTlnB83Z72rdN72J1N69AT0gLNzIIWz9iHs6k1HsztahewwtydTU0ODhabdTlK+wIfr2fL87d6Jo0Uc1LsuuRjn2r40xufUd/q8Mj8tfzg8EmMHpRYJ1e3gjdD38fVFptjI4DDOMt6CUu7WHi47lEEg7n9scx/9i/MWHwTAcPj/ZIqph/V/Qo3I6M8f0nzCi63/4GJItiwMzCzy/39lSlNy0aXDeWP3skA7GGX8aOcRi6EEEKI7VVQpbJnN7lf4yM9li9bJV3skdLS0sImXU45zWwwBvO8uzctOowuGdftj71uNJU1mm4u4+6mC4AP055TuGk+jwevwcXkPTWJX7mnp/1IXlz/CSebbxAjwIgYkDhrekYtPWPWoerjNmbUMouJZJ4/W1XJlqiHv6mw/xi7DCvNep78y/N3r98CtXKaOdF6mwUtGwEItGzgWPMdFCZjohPZPGIvbnJOxcNieOksDqh5kpn2Mhxs/uUdklYOszOHR15moh3PFi2NfBvYFc9MvTHKieEa8S/T/d7R3O8dDcDZw8YyGwh7zVQY8WmAaxIFQIqi1cy03gdgXmQoj479Gd9YdXTaeTOrPh7X8jhH2ZtxsXkucByfRONBRPsXed6qOu54LT7OiqIgv/rSHijlYRo6PoXQV3Ux/ReIrl+/5+ujpoz4R71pyMFc4cTXkJ1aMYqTgY1lU7naOZsAHkv1SPbLqPp4VuQhsLegMAlEdidoaELEcLAJWCb/qTydUZuaOdt+gWnB5YwyalDawOuFAviZ86UhHkg2+dLkDW0O7UVsldPGmEQvPEPHpw1eaT1AuL0/nftryDFQa9q8IvFDgYf32euQQykSF4uIDsQbpFs2Ba1rk2sZF7Z2V/MRTrFvpa65FRPNCyXDCLalXn/mP2xCCCGE2PHMN6awwiun2IhQp4uB3IOXVcXTODl6O6DZY1ghizb+P3vnHS5HWbbx3zttd8+eXnLSK+kQAqEk9N6kd0SaCiiCCIgKfogooChVkaIiICAWRATpvYYESKEkkIT0dnrdNu39/pjd2dk9e0ogJEH3vi4uTmbfKTszO/Pc7/M89+2RvrFE+X4/6zZVTON08woeSis6lsmePmoi2cZ0ZRmALzoSVBMf2fgypxief+3stm7gKwA5cvxbVkzEI2hGsBelAPIFRYIZtYmDC5PkTepR29YzahlkfNQqWhZwp3EbAPM69uGD7X/LHY73Jc6MjuKkVfewg+YRpLfcqQMiakogm6GkM3SPDLqYSz45AUtq/LhuD6pXfcBp6qvYqHzkjmaRHO0z66CPGWnDaKEFTqx0ejRAQs8b7tDUs4zW1gDQqoylWjHQcRCxkcAg4ivmcpX2AEl0nPXTgR1wAze5JHszV7ktbCfWouKipMYCQ3r9/imjinvtQ9FwIDqM8eQSvcxxNofH8IBziL98xzyidpD9CsM0L6O31voJ27OUO8Oe8MvHHZO5e9BdVIgYg4VnXv4r6xTucI4B4Nph27N7r0e46SiUdrddyZGpp6nSVmNgQ9swGDYdAMfO/tDcdG+dG1DODJqC94cWt8w3IJfJzn5Ge/jPkIs4fU36XIzanh1T7/mf5fj89YIYYTrTxytUFUPL3ltFef4iiiiiiCKK+O/HrziLZiu3L32g5KXdl+YX1FeV+0StkLJhPuIyxIduts3IoGfMFKzKOlCdz5/5Be7G84AzvYUBBUiCffY5iYe8jFryiyNqmYxakCgWQr4qZjD+rC01qC01aO7OvSab1KMW3sZ71NbIQXzXPIfjKjxlGBnwUZMiV53PciSqzJ4wS2psHABRe9Q4CrerAQ2Xo0vrAYiF61mZJnBJaTCy5TVO0e8B4Fb7eBbZo33ydVfNFcxpXY+K5NdD9wKgo25XvmFeho3GdhXbFazNzV+mBY79LPsRxhorAZjXOATYgUj7Er6hPQ3AyzHvB+RqJYxJPoiCpCKkkNFZPL7pLq4PvQjAuxtugEABaL6YRrxkGNfYZwGwW1k1Z5D7w8ioDm6Sj5qqEVKy/3YVzRPpCJBaKyA2kq9e+XmRKuD74TiSy+27/bv545aTgekAxMvHslfqNjRsxtVXcA/wN3EI0rZwUDhZDvxHtU5Ws8odRANVrJKD+yla9ODm9SemKsbwK+tkXBQiVWN9vaWk5fDA7FWEdYWv7j7Kv049fNTy7CWKKKKIIoooooj/bhSS0h/oZG1bPEt6hldF/L8HYnidsBw6KWFW8rck0ZFqmAV5Y5qqd+G41DVcr9/DZGU1+6gfMCe2zv98Q2Q7/uXsiYGFVjbOX64rvYuJbP4etb5LHwuhR0YtT+J/dE20B1H7r5Lnb6eUx909OTjsGV7Hw4N42tkVFZd4ZFIPw+snw0fy79j2DBPNVItOKpveIROM94an9INY7njNkkeXego0hpZXExuYCbCkdzoyJYkxwrTh1aEKPW3gHB3Mi65n96frg4ikA+lxYh2HKO+h4lDZMQXYyd/uw9oxEG9FFzb7RFaBuxLA93CTAeWeRFqYw5WeZ5oD2CIgdCICPmoy2+P2vb8tYM7yVn5xwg7sP9Ez0c4XMQn+HyDzu/B+IBIdhwhJZLI95zzerZ6Gm+xAw+XckmrOT93nf1bptqGpCr+xj+Me+3B04eT4qG1uCfxLN1zOXaEP0YXD3+z9mCfHY6f2yB0UyHaZ6L4XXqVeCcBvlDNpS2fSTtDCA973W0zndvNWACaJMk4awDr518AqG8kdzrEA7B6u5uL0Z4/OW8d1Ty0GYHBFhIOneBML+RYTISfG1dr9GNgoZgQ4YMDHX0QRRRRRRBFFfLnguNJXHDxJfYWpYiUGFnpTCYzfv9/1g6RnWGWAqBWQoM9H0nKQKGygJn0wPUXYElo58+V4ZrtTmKysBnKzbO+X7899lpeV+8ngrGx/TuljvupjIpckfW55/oI+an0TtSA5llL2KJ8cVRPl3VVtOeuU/DeWPmYC0ebqnbnEugSAY2qGsneAaduO5FV9Txy3nKdDV3Amz7OqZThwbp/bdguoNuY2D0rWl0zmYXt/ykScGGHGiXVEkwowvYfaIfSsqS1LNTBabOAAZT4/1P8KwPzOvYBL/XGPiINpcDzxjSHhZ0kmE565tu6pWK4vncq99qFUiS4a8UhWb6qPXXoty9yhOCiYqlcL/PHGLv69wFPl+f2rywsStcxxRxMbOEZ5AxWXkd0TgJ2wHclxyhvcYtwJwLtrDgKy5OdJZV+a0sd/fqScCFlVyDvrrmJU6hMOUd5FEw4L3XFsUAb5X6APYcvPBNVN+Ybhp2ivcAqv0Jj4Ts6Y9tLsjE2OxUIBspqfTewLeryBcmy6iNDYlep/BfIUM4XIsVoIfrasoQ0VxzPobujyiVowISmEwMDmHO1ZANrd0gEfexFFFFFEEUUU8eVDLFBJdIAyn8PVdwCY13HMgNZX21cyUjTQLqNUl2hcbPybcreTKAnisb2JlhYWxoDC7SYp2yViZKuRMiTrb85+vOlOxUbjkJq9fVG6YDasVx+1vFisZ0ZtM/qoqT1VHwshmHHMz8gpimBMbUnOeENVcr5ff9jmSx8zyJCJ4DVShaC2azE/1e5Dw0Ft3YmP7H1plFkj5Wqnpd9tF/JRC55E03ZZUbEvf7JHcrgyJ9sj17g3cHiPsjPwTJP9Y3ckJ7XexvWht3L2m6P0R+5MwbM1Z/BSkyc+ckO1V/jWSakffG9ILQVu66GCmcHTg7/NP9Yd5a1fn14/cEO3xbNp2FyzZ28b1R0fcptxBwDz2/cCvsHYxuc4V/9T9rvaud4c+YTXDWT1dAWmtz/PtwyPpF5rnc56ZRi4KRRcFHtgPh8DhSZ7eoc5tslV7nmoThIVh12jY/3Pcs6B2pOobQJP4/buS6kLt+FKwazYbzFtt98fpWF1UkMHDgoaFqpSONt46Opb+Un4UQBeWnM58H8AvM2ZhEMpj5hbn0IomwE05MD764oooogiiiiiiC8f4p1tnKq+REyG2UX5xF/uWv23AAEcs/4Wrgp55G5h593sqzxLrdIOQHNnW59EbeKKB3gzdD9JafCAczD3OYeRtJwcopaJZT6RI/lEjgRgt1BWPyHoPxbWs+tpgcRJDzGR/B41q/9g7Y2lzSxp6OKkXYZTlqeoWKhHbVNKH4OENZxef1RNrj1CySb0p4EnPKKI3MRMIWx1oub6mZfskQohKI+v4WztOQDmxSwssTdtlLFW1tImS2mQVcyMJygtiRTcLni9SxlkgvN9G/7Mt0N/xcBi0crzWVV2ApDro6a4PX3U/Ixa3gyAEug/+8QdznPuLoQiE3OKMoPqfJHATZqpyY3JkL8slM5WuY5XhuiioAfELwqpPqpty7lCewgLjc74aGAfb1vty/iO+hgOCqXJKcBuiIDtQcZHrbbrY0pENkOUkrm3RT7hfbDkDFpaWnBRCIWGIN3seB2Hy9S/c7b6BABvr7kY+BmbC98M3cS61i6u0+5BFzYpqbOXGuGvzv7++dwhcPy5htdKzv+9zwfO1EplDAQoQtJJCc3dKYZW9n7/AZyy8UZuCL8KwHsbb0LUneB/lpNtDPjxmW72+BRcVCE9KwFVRdPD/Mw6AxMNU4T41YCPvogiiiiiiCI+O15f2sTrS5v52u6jGFlT0v8KRWwWpNrW80v9j/6/f2qdSQqdvcq3H9D6ITur1BiKVpMUYUjHH4lYOzCq13W1VBvDRAsIKHfiCFySlg1kRUGsAnFUMO7NITl64YxacLzluD20ElL9qFyvbolz5p/m4Epo6ExyxRG5Bl6Fsnr9EjUzSNR6ks0xtblEbVMUHwEURVAe0WmP9z3pvtWI2ijRwB36r1EbzgVOzxGdUBVAzbJh4dpYuIBgr9Rv/OUvxhxK+3hWnGc9CFo3LgqaOQMoISRTDBLt3natuB/cdxNhtVuHhUar6tXiXtPyfbYLfYyDysrmv8LYAynrXMpfjZ+j4dDWNpY2pZqVbj2acLjaPpu33SnsXVLLeYHjCDZJ5qaLvQvf7RqslbUkpUGHKKMaoLuBxeGvA9DoVAMrAHLqgjMEKty6iPO1J72FJmSIUaRtCZfrfwdgXmwf4GLMkiH829kDB4VUaHt2AoSbvUmut05jXv2ZPBI4/vzM4nuhmSx0OwA41iinWZ3Mo85e2FLlYzmCwWKp/xAIisRsDiRsFwuNH9hZ4+iXQtU5hCyY4i5f9xrzQ9/FRmFJ227APzneeQbUFlThImPToKr/F07KTNFCOcPxjCMPV+bSumEKQyv7cfMLCLEIRSUcW89PtftQcZHdQ4E9Aag0N/rjamJL/b8VAtlMRUXXdf7kHO4v++UmGHYXUUQRRRRRxGdBZ9Li/AfeI246LGno4r5zdtvah/Q/g1Qs23f/gTua+5zDAJgcGtHbKjloFZWslbUY2GiRMv4VOYH2jnbihDlDVPa9sp3N2l2qP8Kl+iOsaX4DKnfwl49c+x+eM36HjcYTzizudI7OqSSb2vEKNeoakhiUWyOAoUBuK1EwzsyX5gdIFSjBDGLRhk4/M7VoQ09V7kLy/JuSUcsxzNYzGbXc2HFThEQyKA9vw0StnBgHqfN9ZZiSzhWcqL6KKwWjEtNJjprET60zsVGpKpvABU1/QmrdWGjcZJ9MJ1EaOpKMq+u9T+dI9yVqtXYAmtM9VkLNzgLgmL4k6NvuFPYxvdLHPSpqOADQpIUhHMBBpINh3YkzU/FEHz6xBNfU/4a3WnLLMPMFNK7kHmxNYKOyRvuRvzxDLrplKIeArpASN6iCGZDnz+lxyuynl/S3DBAwmRYkidfuwMXWhQDsW1bHacDCqoP5+5oKNOHwvjsOJW8m42p5F0K3cKSC4u5DRKSooQMLjbCw+bT+EIYufo7D1bkcrL5HCzV0ygguCo4YWLPkQJEwe/5YTcfN6eUK/iCllfC98NZKz7jxZPsJRuheT9+axPeA/h92nSnYK/Ub/qT/igPUBdxs3MX8NTvA5L6JWlJEaJFlHjFTwxhmm58p/tTMlmh+GN2Did1zvWMOXPsp1v3gOihIFulhr09NVXwyajouYWXT0u1FFFFEEUUUsSloXLeCC92HSKoGyfWDgSJR21LoFiX81d6PUpH0xdFg4AIbP4lcydIuLw56rn57Xi+Hua2tABwt+u51f77ubL6/aiavhC719QGsZDxnjJ5qZYLixfJz3EnemECWbf+Ox5iqvw/AB/E9gWlAz1aiDPL706DndzVtlw/WdTB5SBklhpaTtStUJllY9bE/ef7sNnMyamkxkrKwniPRv6mljwDlkf5p2FYvfZTpkq9BLXO5UfcM8ea0HU2i/CZ/1mBvo5Zz3F9QrXks+Tf28QD9SvQHJeOF6p3Aj0Z+lYuWTMNC46uDJzFh45t8U/0QG5XX3Gksl0P9FGzQh01VvVOlBnzUVGn3UKqB3HI76bqcoT7n//tu9wwOVt5FwyHaaQBjmdbwONdrc0lh8JizJ0nLxXVd4jKEgosVMOmuthqYLpah4BJK1AKjaauYQhDSdRGKQnvJWG63j0HDobRsOjPIL530jnN1aAKPutnepzF5Mxdf4Q0iqncjxnHZP/Uyfw3/FoA5DUfz7qCrKREpyoTXj/Zb41v8qWNnAC4dNoG9epyhz45kgQdT0nI5XJnD3sr7hIRN2cZTgDOA7P0FINNeeK5Q/Iyfa/eUvC2ETL10Q6BP0mxf3+96f6z9AS81N3p/D9+FSmeF/1nQRy1FgNAG1JI89U8VB1DSDzVDyyNqepGoFVFEEUUU8cXB7VjPBdrjACy2xwHXbd0D+h9CS2gEP7LP67F8oAIbwSojXVVyvL7681LrcCOso45FchQ7iuUAWKlcohaMWc7RnmUHZQUt648ArgFAc7OtNVoom4UKEqVg6WNnsucx5X/XKx79gH/OW8vE+jKe+d7eJIJErYCUf46YyOftUQvEXKMCEv2fNaPWH7YaUVsl6/mG+S0OzijDBAU4hJJzAh1XohHwUUsfdiGiFjdtWrpNRlSXcBtfBSuOisuFobSLeLiCJrxgOyF1dup4nhn6SwB81/xOmqh5UfzFZTezdGM7Kg7/qPVqge2qcZxm/hhLqtTW1hWszQ1m1Bw72/1mS4Vprc9yvnEfALMbU8DBjO5+j120lwFY6I4jYTnYpcOZkroXgBHVEV5Pb2OPpr9xacgrZ3x742XA7rRGx3KTdSIJQiQx+InjYigKLWUTuNE+BYCTqobzNQoTtXyyGc/zKgsSXlXTPFPpDBQ97aMWuH6BstXNKc/vOC5huxMIYaJBOtOYSsR8IRiAtzuz1gjr6vfjG8m70HA5YNxQdgaeMg7H6m7CReErocoB7TuTiv9YjmSOO4kGWYUl6/o1887vc5SRIfzUOhMHhVD5oLRkCDRp9bzo7EQKnVhoPLvhycEGM4WZS2doChnhTavopVZEEUUUUcQXjGAWJSE3b6VMEX0jPybLYMBELS+bFA0Qilgv284gQ1C+av4YGxUTjYerpuWM+aj+KH6yaCjnqk9ykvYauyhLmJPMjnld35N3k8MJC5Ptywb7ywvFo1A4oxb8rg2dSf45by0AnzR0sbYtkVNtVei8BLNsIfXzErXseqNroryXlujf1B412MaJWidRXnRnsFvIq1Vtj4ziEWcfFFyITmFYULTDkfzcPQfNSVJDJ5PEampEJ5F1bcB2/rjWmMnBN79Ka9zkxhN35BF5AN2Od6IvDnss3shj8CKQcbHTRs0ZgQlbKpjogI6qpTNqkXJmu1MB2E6UUpK+uQbTwunai2g4aN1DyMjbWy781DoHHRtdEewfyI5lBCQUJyDkgU7CcnpVfUQEsidpcms7kt86x/uLL7ckhp4vz6/02Famx82byZCUEyOCSa2Ze5P/0D4PFQcFl+tVg2O7/+Z/VmuuRVMVvmd9hx9YNioOY6rrAe+hvjkNr5PxLt4PZ2eVrrbOQsNBtFfmjJNOVhnSkprvhZdK+6g9ETmGRe1edvagUO2A9p2Z4bnfOZT7HU+18xR1BCf0tRK5EvyKIiBa7WeKx2lRn6h9FNqJWyzvt3BY6WBOIlcJSBH4XieXyT9TordgYGF17wilwwf0HYoooogiiijis6ArNJhfWycTFiYtSi07b+0D+h9C0M9rb+V9jlJmExIWyoaDCVpB9YYcoqYqOZmfQkbaQWQISoxIj2UZxNRKlsgRLJTjOInXvIWB2Pov6jEstz1P4xdqRvvLg6qPVr9ELbvPJxbmVjMlLCcno1aoJDSYUQulM2KG1l/pY4CoBbYZzKiNDvSp/deWPmbIxLrqmfzM8gLqs2tHMypIqFyXR+29cFzJUcpb/D30cwDmrd8b+Jo/7g9PvIwWa0RSzQ//+X4OW87I6+fL8y+IzGReR5QSkkQwmSY+ZViqHNg7RxGwkAeX40qGmitwRAeTldVcpD0GwLJk1sfLQuFB52AASjWNGdGlPO/sjI2KFfKUdp6PHsk7HeUMEu2UiTjJWCcYWTWZILnqCg1mgTsWF4VuvTZ9DnNvyoTlUIGekynLHH8o1cxX1Rc9Y+74UGAWlisxsH0SZEoVOAvwiNa/nGzx4g2axhpjDFVJr775vaGnUZdYzsnqK+g4LJHDSegjyRA1ZzMStVSim6DGzjX6/QDMa5uZM+6Tir2Zlf475xyk74feZnH6QqK9kXFiHZ2yhE6ipDBo7OpfGjf/GgSNJYO71uxuyol590XajNtxXEpI4iJQAgR9fzmHoWoDAOviHUCRqBVRRBFFFPHFoUOv41j1TQwsFCGBm7b2If3PIB7olZqgrONkzVOSfrtr2IDWH+Msp0ZILDQMFXZKzGaa9jSlIkl47Qmwe++exMkCZYTJvB6wjDDeC87OfCqHYkmVXWum+hVHvZUN6jkJmUDpYz89ao/nEzXTye1RK0TU8sgq5PbI+cekiqwieyDbGBQzCQV4xD4T6rjp+SUAzBiVbY0ZKLbpjFoGGUGM/AxSJLaOX2t3oQkHq2so893jANgoq/1x0VRTzrYmL7mbOeEX2SCr+al1Fi/LbGFaJjgPkjfLdnk9ejAv29MZJ9bxYuhyAFbHhwFn52Y0MusHLqzluPwo/itGh1bnHEewty0YqOuqYMWQw/nlAs+h/bzqsRwNLNCm83/KHUxVVnGc+iZLm45FDN0xcD6y25439FTuXLIrAJcPmshB5KpKQjZNXsjwuiS2huv1ewD4JDYR+CEHND/IkfoCf6whHCwzhW6Ecs6B8LM6AR81XAZ3LODn+n0A/NXej2fE7tTTioqLZmYbXz8vUskEnTJCGDMt8uIhicH3zAvQhY0lNQaFs0Q5V56/ANkeIJGsWvU0L6YnCB629+cK+9wBmV5X2I2MFM04UkF1TVSRnZUKksRvNf6cO8LvAnBD7FpgJq6dZFFa+TMldcAzt7SF5vfY2ebAjLe3Fdz16qf87Z01XLj/dpwwo0gwiyiiiCK+DEg6gvFpwQjwLIQUtdgfvSVQ0TiXc9Q3iBFmot7ov/+F29NXthAelD8mHPLIT0KewsjUEmal221mt0/tc90fNvyAkaFlJDH4pnkZH8qxPXrAMnHWRmrY6Hqq6SO1bIljb9koNccqqf/SRyklK5pjvL+2I+ezuOnkyPn3S9T66FGrKjH82C5oeB38DqHAd9hxRCV/P38WrTGTgyYP6rG9/lAe+RIQNbsAUVMVzyj4JM1LoS5LjQHSRI0q5riT6JQlNBujmRjY1kT7E1BgiGilXZb2MGoGGNHwIh+FLkfH5sO1e3FL1Y+BXB81Ne2NpjopdGzPrDgd26t5cqJqwEftUWcvlrrDUcsG8f3M98sxXFYK+kakbIcEWS81KxlDsy3qaMdFUBb4jkHSlgn0axtnc632dyw03nankLC8DFhd81x+qD2BhUpt9/7AVBQ14BeX7j0bnVjETuq7/vIVbj3V8RgVRqig6fd/qs7gt6v2xkFhj4qpDE/O9cfowuGw5H+4L+x5fry97nQ2lzpULFzPrJRHMs9Tn2CYaMZBZXBoOI+5WeWiswM/qJysaAHD64Fm1NxE9sHQiZfqbujsnyRd3PYLJocWAbCo9W+otfsU3HfQjy/leA8P18kucwPKn/dHzqK1rQ0TnQvDm48If9GImzY3P7cE03G56blPikStiCKKKOJLgqTtkpI6IeEF0WYqQbikb8XAIjYPRja9wsn6wwA8ox3I5fHzMKXGmPId++2Tl66boytghCKIUPa6CbO7z/WjbheVIgbEUJBESWDmqT7mm1VDbnzTa0ZNDWbUAvL8ycJy9abj8u8FPUXcEpbdr5hIQR+1AqWPQaLW3avhde4ExW5jqvmsqNiWidoYsYH7jJ/TseE44IcEvewUIVD1rIx+MIhdI+s5xfwJAHXhEKell3cnUjTLCobLJsKYJOq251dtv8VVFS+bIby+IE1ViKbNnYVr+WQpIQ0WuyOw0GjXahkGPJj4DoPDXtZuffdcqJ2IkWrlCeNKNFziqVJWqCPotHRUXG60TmY9tYzVoj5Ry1HbUUQOg8/cmEnLZZk7FAOLBCE0DKpal/JO+AIAlsdGA18BsmQJsuS2svNjDtNeBOAcnuXDxiNg6G4Mal/I0ZpnPD27qxw4GzdSx1/s/XFQsSLDGU/W4BvgHPNyXnZ3Yo5SQgUFeqyAlZGpPO96ZZe7ldQRN7bjIftATDQWuOPYSwn8SGTPmY3PimC6/ffOUf7fPwsNA9r9fwfP+fgVf2FJ6EZsNOZtOBG4g4OTz3CwthwVB729Huj/R9blGnzqDqFcxJgiVnGi+iqDku3Y1j5ogXs1H0rg+yuqhpZq40b9LhRcbKscOAAANXANpifmAN/BcRxiMoSKSwrDrxB/N7InC1s84vhN5cvzouxK2v61aS8wY1ZEEUUUUcS2iaTlcoR5vScoIXWeQSfc/2pFbAYIK+b/vSYymX90eRoIR+pD/OVLG7r4wT/fZ3RNlBtP2tGfkLYdh0/kKAxpoQmXsZpGS/2e/HhxJ90yzLjyXchtHsmFLrMT0o+HrgLg7ZVXwMys1dSea/7AmaF/40iF3zrH8Yizrx/fStflXPcfJFWdJAZh9TB/vaCPWnBSvVDpI3iZsvyyR4CE6ZLchIxapnTRKJBRqyzJEqccH7VexEQ+L7bpHrVSEsxUFjM74ZXx1XZ8yJnqi7goDIvthSybxeXWedhSJRQKcYd+KxYanZRylXU2AF0B1v3++i4usS7gW9oTjBdrOcn8N8erb/ifyzTBUbRs5kp1Lf9maqKKw80bvGMxQrwLKAElw0yKXxWwg7ISgDbK+Ip2P+tjub1KOfL8nRu5Rf8dtlRJuPWo6lWBcdmMWlB69a7SyVS6y3scO+QaXmcyhtLJvall51pgtxxpeqF4l9qtGMGVtlePPClUxteBR8u+xh2de6Lj8JE7GsBPI9upOL/Xb8JBwRRh4HAqZTsjRAOW1IiQoqtuOpOVVUwXn3K6qvCufQgbZRUugoSS69z+eZDoxfAwv7E1+IMUbgpDOBg4aOkM4l7JV9leWwDAh12nArv0u+9XK47lPNNrn3439C32Vj8EoKnxCuqGjel1vRalmtVuHYqQCD2C6qQ4UfUyxZ6RuYc/lH6bm9s8f7sdzIUAuHoZO6SVP8vCGh+kx+b3WX5ZkIp3sYfyIUlpkHDK+l+hiCKKKKKIbQK1G1/na+oLpDB4y51CvqVpKhmntWENQ0ZNLLyBIj4zFoV2YpGdIkqSjtJx4Dn+5Lz/r378I+avbmf+6naO2nEIB0yqB8CSgiPN6wGI6CqLAbtuKg+l48ajlKF97vss7UbaOzv4uX4fR6pvA54/bRBhq43hohkERB0vHs4kQSzL5BLtkfSxqGha1jO4Nx+1zkRhgZNPNnaxojnWY3nctHuIiUgpczQBCsnzF+pRCxK1uOnguhJFETnkL6RtvpLfL0WPmkj3cw1vfYuT0uIQs9slasnh/MPZD4ARdge/VG8FoJlKrra9/rGk5WI5LrqqsGBNOxUixte1ZwAYnWr19+FI4dfCxobvzdTkPZjo7Dp0EAd0/oc91Q1YqDzi7EsjVdkbDA1Lql6vVcZHTc8SPU3a/aZ83UQbx6lvArDKGc6HyfUco7yBLhxGdE4FpnFR/HcIvZukNPiFfRoJy8ZVFZpkBQousYAhYWVqI3sqH6DiUh7XgYmsrJzJLG73x9jptPSq8hm8bp2CisOoSq/8sFDZ38faROa4uSV0mT43x0pxiPoeAN3Sy+kc2fpnfhX6NwBzNl5BW83ZqLgoQmLg8Hb1MXy10fMxO3vIaPbvcYY+GwqZXYN3H+wklnK29iwGFqnGGYDX4ycD5YMyrbjpBoQ5guWFfSFYM90kK6kVnmpke+OaPonadaVX8lGnN/aJ2u1R1Hb/MyVge5CU2Z+iJr26896UP4NELb8/cVuG0/wpfzGuDyz55lY7liKKKKKIIgaO6vYP+Ir2LACWreYEvslEjLYbdmQITcyZ8mN2P/kHW+sw/yvxZnhvXrA9Anxa7UhY7ukiZMjDxtVLuXrNN3hM3Yt/OXuyrLHbJ2pBMpcpNQzKyPfro+botFHOCjkYVwqSGD3j3oAuwzX6/ZyqvsyGph2BB0kmYmRqjlIYQcfYnFagYEatUI8awPr2RMHl+aqP4BGzIKEqJCZSqPQxrKuUGKqfrIiZNmVhvVd5/s+LyhKdqhKdVX2M2WpEbYUcwqnmxexROz3to5a98EKoObKdrmWSVs7HRqM0pPly6d1Jm6qowYLV7WlvLQ+atLjU/BaqcNGE4BeZ5XrIlxm1XMkhiafZTv8UgNfcaTTKKr+v7Cj1DlpjXtD8brmXYtbCZRyVuhYHBaGFChK14A3n2AHzYjQGtc/nNuMOb5utBwFnsZczl9p0AH+jfRIJ0yU+dBIHp+4EYIfaCp5Ib2Ni8zOcbfwOgNkNZwKHsCEygXPMyxkmmonLEIeVej/oVaU7cqfjkbzLa7xlwUxvRkij0HfIkKIg0XFF+uYO1DsL1UBXRa7X2hfko2YmuhhMCwlCJAilrRMg0rWCf4Wu9sfNi2cLMt4ZdiZnLd4VFYfzRo5nFvBG6WE81TUeG5UDS0cPaN9BA8ZX5HQ+dkbQKCvZ3oowvo/18n3URLiCy63zvHLccJRfpz9rl1GecGaSwiBu1HEmuUInQYIdLJ81ncLkdVtEfl27a9so2lafKyqiiCKKKKI/2NkgOSmNnFKw9S/exVi8NpHdF10HFIna5kSwV6o6mo2vMn1XK166h1nKWn6o/JWZyiJeaM2aJ+SKaHiBdPQzyPPfbJ/ITfZJgOCC+nHsHRjz1KDzuHTdARyozuNn+v1MVlZjW97kf9KF++xjCWMR0rW0nriHHHn+AfSotcUKi6fETafHRH7KziVqqQJiIoVKH/W0fUGGqHWnMkStsCDK58WMUdXM/8khiKt7H9NvlCSE+BNwJNAopdy+wOf7Af8GVqQXPSql/Fl/2+0mwtvuFCama2w3lE7mfvtgFCTlZVMYEwhMWynj2+bF6NhUlUXZx/0ExWygTMTpbt+JypIhLFjTTkKWc711GiY6pl7Bo64n0h7WFZ+o5ZeNqYGZACfNBjM3TCEhDU3X+ECOBUB3BeE0qYuS4Pva31FxEVYEOAiARHgwl5jfRhMOVVU1HKJlf2QZDzdDmhnvZlIYPX3UAudCBLJBmWyk6UhedrMmz7tq3kxKjpBJehvBzEymdDIzrppOKkU3EUys7olANbZWwvnmJSi4RMIhbgYG21nlp1IZI6EqHGlej8BFx+HU0rHAGu8cbkZ5/so1L/B22FPmTEiD39rHoeIwuC2XKqkBJSRbCt8LT+gegXuvdH9edDylo91LBiZoEayZ/kflN1je5KXfr5f17NnHevlETQ2V+JniCqn7RK2FSi6yvgtAbSjEmfSeUftK1yN8VX8XHZvIxh/ApCMH9B22NhLk9vJZtkmoSNSKKKKIIrZ5LCrbk+csSRiLOe4kjgrEF01lkxmb/nuDOoQhhTdRxGdEUNFwFBu4Xb+NEBapthFIOZOa1c/5n//T2ZvOtuykaKpAb1Y04PfVV0ZNSulnqmRA7Ttfnr9LlLKOOj6V2TJKJR3fJmWEm+2TARhWGsklagMwvA7K5bfGCxO4fHl+SBtcB5ooC5UuBvefgaEpVEezgiIt3SZDKiJfWEZtIBhIlHQfcDvw5z7GvC6l/EzRYiaQX1G5B7+1PWnLS2omsFfgBCYI87TradtsFyrl993fZayxEoBlzSfSgM3B8SdZILbjHucIHFQMFEhneYICHEGVGdOR/Fs/DBFrQMdhlGigUnSjSwc4PDfIVgvJ80tmKh+QElAm4pyTLgtol9lSxaRewb9cb+5hp5JK9ivt4jFnD2w0zPA0ZgA/cr7NKLmO8cpadlcWE+6owpXZvqngvdQdGcqbzlQcFDpDHskIEjLIZsMKGV7rVjfnqv9BxcUwy4D9/R/BDfofODhd5jh/fTlsvx2uYvCsm+4jVLyyT+vAa2h6/Gw61GpGHXYR8RWr+Zb6OBoODVSh6xP8/bqbMaPmprIPn4gw+YHuGW/P6T42Z9y/Sk8jY25QiKzmln8OrMdrWOxDLGHRSQkTaqf6RK0/LzUnT81UFCDK4Mn4DyGe9lHzvDhc26GONiQKlQGzybHmJ8xIX6d3YxsGdPzbAlpLx3OG+SNcBLbU+JNUA1qnRRRRRBFFbKtYFprKwcqd7KAs59s8zsaNu8JgT2G6TR/Cr6yTsdDQS4cV82mbGcGsV61ucoA6B4BlZgfz17RzevzHHKe+yfX6PUxU1lC14S4yatt2spvdxWIsVCqEJ51f7rRzi/47oiShswxy8mNZmLaDkG4OSYOe3mqZ/rKF7jiOSl2LjcrY2iH8Lm9sPsHJERPpxUetrjTE+g4vzuoto9Zb6WPudykgz6/1JFyGqlBTmp1Ubu72CFuqF4uBLYF+iZqU8jUhxOgv6gCcXuT5NTvB7/Rb0XEw0bjQuhjw0pJJtTTDwUjFOuhcv4Tr9D8B8KozjbOsH+VcpGBg7qU6JToOworzmHYYK22PACwPnZ42cgTHvrxgRk1RBFFDJZYmQ39UfkEolDsjoQZESOwc1UeFWN1OfM/yRCMOLB3Eqa7kKXsGF6qrOV59g+PVN5i9vg136gx/vWA2ZeXgQ7n2vZEAnFM7miPpWbqYuWELGV5rVgc/1v8CwEarFriRi7tvoURv8AUyAJykR0TyiQbAhJ33w5m2jFpFQSgK0cQcfqT/FYB33Am8wSmME+tQkJSZvSsibiqSrspGWUWEFBUiS9o2asM5z7wEDYd2SrHVSf5nhciqlmOxMLB9XxO/ntpQOwB3VP2HZ9LL+5PoH2WvJCTiOCgoroWqBHzUAuf25viPGR7eCMDB9q3ewu4NvBP+jrcfu4aMRYWrZs+ptL48PmoJ0+V1d5r/b1tu2Vmp/wa4jsNHbz5OqLSaCTvvu7UPp4giivgfQcpyqBAx6tL92evMbClki1rNHc6xAExUy4pEbTPj2PijJNUkKXQqQsf4y3Vp8s/31pIgzBPOLK7X7+E72uOkUnrW565tBX9Le8CuTI0EzqREw9dOaLYre91vsrud5eGvkZIazVSwZ+q33vI8UpSJlbsp8SvOwkplj7H5BCfH1zgjjidlTqtJXVmWqLXGeyt9tHsQtVQfInOhPnzUDE2htjQ7hdzSbfb4HqECBO+LxOaqO5olhFgIrAe+L6X8qNAgIcR5wHkAxuDtgCyZCAbMQgg0Fb6iev5cMZk9aYYqWFqyE8tbS+mUEUaJctRV//E//0iO7rHfIFEria1mZfh0ANbF6jk5dJf/mYVGCI/JW1aKctlBCImLQiC2pzSs+URNC5Cyn1unY6Ij1RDXZrYZJEuqyCEJlit9lh4sCxNWHMw4o8UGXBRq8srnMshkZKY1Ps7N+hvYUuWfzj4kTO/cbt/yLNdosz2vsc7TgdEFfdQm24sZqWYlTxe5o4ilc8aFyCqAGihZUwI9aTo2U9pf5ZKQV/36zsbDgIPZHFg06AjOTHkPgSOV2UxRVmGjEAtN47mAj9qOgZvJtUx0bGyUguWf9gAzamUy5penDh8yGPBeVo2d2YxaR8JCVwUlgUbd65LXMTTkSTSti++LWl3hfxY8t1qgBDfhCKSUuIH+s+CM1pzaE/lj01RSaBxTtTu7DugbbH3kP0gHeu6LyGLek39gl3k/BGCZ+iTb7bjXF7q/5xc18OfZKzl995Ectn2xoKmIIv5XkbQdTBnoPw8QtWB/kDXQ2c8iBowznH9RpXsxx4fl3+BC8yJS6ITLanjrQ2+Ct4sS2mQpVaKbkLBo2riaumFjsM3sZK4tvOsXLi33l0Vk71VBZsKbsA8Jm7A0KSNOGBM9kWvMUOiaB+2nMsgnaoXE7bpTtv93RFdz+umCGbXaUoPmNIlKmC4JM/cY8iX6C/moFepRM1SFmmiAqMW889ebafeWwOYgavOAUVLKbiHEEcBjUFhfQUr5e+D3AFOGlsi/GVeyYeM+wG+RecIJmp59IATJkK4qvFT7Df7d4BGLm0Ojsdxa4s50qkQ381xv1+V0c6N+Nw4KCVkBHOJty8heAF1aOcHiXHciunCwpMp02+ZN9VtoWkYB8hgyp+tG99eMNJZTJuI0U8FKdzAaDvc4nteZIZQsUcsxXFZySidtx/VvnnWyjjnuJBIyRJs+gvFNC3gldBkAH3XsgNcmmHdjp8/Z8O732S1tRXCy9ir/WZcCfsyYrnfZTXsegHdiXr+eEirnD/YROCg4RjnfyTu/+6RuYbWs56rqKexLnilzgXpeAFk2lAftA0lhsFoOYm+1Zx/d5kDwZfAfdxb/SfcgHqTWAw3+Z1bgB3XQmtv4v/A/AXh77Q+BK5nV9Ry7a++g4lDZ8k1gWJ/7TaZSLJDbUS7jREWSsUPq2F48w+HqXPZa+TG29SYvLWnl/Affo7Y0xDMX701NekYm10dNRZEWd+q3eOIrQgEOB6BZVDJYes3YZ6nPYllfxZXQJCsQSDqVMgant9NYPpVn08R0f/3LY3idPwtXSMSmiL7hrHzL/7tzwePwBRO1K//1AU1dKT5a38khUwb3+gwooogi/ruRtFwutC5CsSQpNH5Tk23PSCYSaNheS0eRqG12BHUMIpWD/dinwtTpSHhkRVcFL0UOobk7xRo5iBO6oQ5P92COOwkDm7bwaLYDSkrK+J55AXFCxAnzQJ6UfQZBAbAa0cUHYU+pecHGWcCB/mfnbryGX4Xew0blEusCXnen+e930byEH2l/IYWBYk0E9vDXC/aIZcjekoYuf1l9eSgne9WaQ9RCWaJm2STM3Mq2fOui4H2Z2WYwcZKBoSmUBHr4mgtm1L5kRE1K2Rn4+ykhxB1CiFopZXNf65WQYgdlJSnLy/6MbJ/DBepbuAjquxw0fTTfNi/GQWGCWMv9+i8x0eiMTWb+kG/72+lK2jxvfIU3LK+H7RrtXg5T32GEaGR35WMAmmSVPz4c8fybLKliCTWnPPAM60r/7/lKBE0ElQyzp6qeVkYpXpbk2NTPWCC3I3iPB8lNtHE+f9BvwkQl1r0TmvpD/zPbkf5swzPubjxjejXFx5UPY7wbFOvM3qjBbFDmvhMy9wat6/a+d9DImnTWS4mUc539NQCqDJ3vAD9Rv0cy3omOTaOsBMje9J3reEi/DgeFLquejEFzDiqGcqj6jl8S8bq4ik/dIbgotGu1Pcd/RuQH+hnku9AHf5DBcyPS13BCfB67ai8AMLfzQPpDpyk51fT872qiBu8MKeNPoZsZhGcB8eGcZzjvP94Pt6krxfOLGjh1N688daUYRptTgopDhR5GFYLD1XcAzzYig7OUX/CafTpRkeJc7Sm6kzHM0mHslVb+HF1TwivpscFZoC+Tj1qofRkHKe+RxGCVHFScef0M6ApYdaS+4NJR13G5NfkTHF3BNDUs9wBCypZ9QRVRRBHbBg5tfZDDtBUkMbjXOYyUm33+zFhxB8vCD5KSGneYX6NgnFDEZ8bdzlGESRHC4sjSrAdpUHSjOmrw0oiLePIDr299RlxnJ6CzfDu+av4EgJnDqzkA0HSdp5V9/KxT0nKJGD2f7V0lwxidfIgQFgco87nTuA0A1c3NwoXcOJXCy75lFMAzbT9a2zK+pXlVbwsSs4CsUXaw9DATi8/+tMVfttuY6hxPtbZ4LlEDj9TFzZ49avkZtYI9ar2UPlaX9OxR29bFRPqEEGIw0CCllEKI3fBYRUs/q2XXTyvDjGt/i9PT4hBvdwxDVY/mGbk7UkJKMfi++g8APrAFywIGcV1JK+finaC+TqnIvYncANGJlNcyPvlnLDQ0RfAj40EczcJB5Q55At2Ot+2UZdMiyzx/MCTlgSxRSo2SUagvFQmQHkPPkC7Hlb7Znt693hfomJcKoZrNfE19Hg2Hsvhg7PY6/qz/ghQ6a2Ud19hnETdtHCXECrceFZd2NWuMXJbawKHKOyi4DO3uBnbg9YqjSbau93vM1HTP3dvlh/J4y1B0HPas9gQ7C6WaFzCeZje39tdXGUp1s6fqVbKudlspBE1RfDNpgPVDDuKMBZMBOLJ2SFr/8vOjN6KWtBzqaOd6/Y+eElKsCvD6d1zpkXJdOIg0WZUB5Uzc/jN+wQdFRURHUVVW1O7PoGYvU7fqzb8Dp/ljgo2/F6pX0Zye8ZpbNiinZFQV0rOlEALblaTQieI9FMxkAjfgOBIk6MEG2C/T7OWIDc/xRyNbary2Yy+omtDHGkXko5soHbKEOGGSbv9GmZ8Hlp3yf/umVDEdSWhzFcsXUUQRXyrsmJjDJG0xAI87s3J7fpJeyBcSNhc79wO3bY1D/K+E7bj8xvb604WAU0qyfe5TxQr+ZvycNlnGMqbwdvUN/merW7zS1FwftWzsEA1ppGwvNomZdkGi5sW0ghQGjbKSLhkhiU43JTnjlMCE+H3Gr1jt1tHWPQh4Cydgju2oufJhao6PWpqoLc/Sh5lja3jlkyb/322xLDGtDQh+dCQs8gt0ekzgF/BR602eP1dMxExvbxsufRRCPAzsB9QKIdYCV4MXQUop7wJOBL4thLCBBHCqlP1rsi+TwzgqdTnTRo5hBnklciLjGu7JcgZ9u1yhUxbOHnZX0qY9INkZ9FL7kfVNOmSU8tIomdvX0FUvu+RIbFdyqnyGUs0jdg9wPN3pw0jakpmpu/3jWBY49r/WX8qFS5uIyQjtRAFPKMRWpH+zOa5EUwWOE/BRU3RK4xu4Vr8XgKWx7bBih7KP+oH3b9crwUtYLm21O3OMeQsAe4+q9SXghzS/zd2Gt/yd1sOBU1hqTOFf9jfYxVlCghDDyyeyC/BReGeedjy51J0rvYA4WLqUubELmSZniJobIDIuhWcRdNUzQUxIAxUHNWBB4G5GeX4jtp6xYj0JGSJpVNJmej+WSbH3uMG4nbGKV6vd6GSJ7T8GX8pp608BJDeO2pHdgIXVh/J4Uz0uCrMqdyywp1wEZ63KIt53K5lxKv98qoGnnd3ZxV7CzfodzHan0CQrqU6VQ1qsOL/HTygq37G+iy0VXBTudiWKKrAdl0fcfQljkkLnKLReLRqCD5f8WaNtGTLwwAZwAiqeRQwMT5SdzPfW7QfAOXWbbibvuJL/vL8eV0qOmjY0x8emx1grO3njoPZQly2iiCL+d6AFJnOTGLnVHE62D0qh/3e+60rmrW5jbF0p1dHNJzj234gcgqCpOaWANaKTUpGkVCTpEB2MqMoSqDVpif5CIhoAJYZKq5cEI55yIFus4SM4Of6RNpkdUvcAsH1tOf8JjLuh8qd8sKqBsWIDj4Z+ykilCS1dMdRcMp4brFMJC5Oqih2YEVhPz1N9TNkO761q85fNHFuTk2ELTkzXlWVJX2sBNchUnoVAIR+1gYmJpFUft2UxESnlaf18fjuefP8mIUGID+RY6lXP82tZ2a68t8FEwWVUxRTv4BQFy3GY727HWeYP0bGZMGw0450NnKS+QjkxhjbuQGtsjL/dGzkLy7JISZ1n3V1JYTBKz2X/JYbmB996oD9L07IPjOBFze/LSJSOZJXMa4pUBdfKe9BlChUH2zoATY3QWLkz55mXoOMwsXoiXwmQGFXa2IGG3GQ6g5I0naD/d042hWDpUZrcWo7LGlnPGumdy72FV26Yq3iYlqYXPTNqmQCsnBgjRCMlpKjosoCpJKPDOd28AhWXuuoqbqInNFVhVip7C9wcyQpmbE69iEM33MWPQy8C8KTYj5VaOSouY5Mxn6RBriF3trRV+EHpqvIZPOh4vV2TSrL3Tm8Imi+WpycJpux+MGe/IIjHOrlFv4MykeD4dJ/g2xtSZORu833UAJ6Vs7KEHi8FbbuS6+3T/bEHK1HcwIxQ8LpNaX2BB/UHMYRN+7qjgf/r9ztsC2gwRuT827W3vGLlC4sauPG5TzhihyF898C+rMq3TQSfS/kGnwPBC4sbuPivCwDvJXXktKG9jrWEwVfNKz2FXCS//hJNChRRRBGbF/cap2F3NhDGZJ2sxQy8n+6rvZydO18CvJL+/vINv3t5GTc9v4TysMbsKw7MEYwoIhc5/l+6J4p2j/5rIqTYQ12UHWdUMaI6QNRaPaIWnIgP+ghHA6JnsXSrS+O6FYQipVRUe/FRsJywMmKw0fKSGvk+anFCtFPGBpklTFo6y9YUHs2dztEAfLV2ZM56WlCzwZUsXNPhb3tUTQlDKyOEeikzDJKp1u6eRK0veX7fR61Aj1ooL6OWVX3chjNqXzQyWYNFZXvykO0Fcj+v9syIM42GLVTwqutlPiKlQzkgNodf678HYF7jXpzgjqdVKaNdVPB69CDWtObO3Kt5TZJRQ/WJ2i/tUzGwUXHYKbycCUobGg5mbIde1y9LP1RCmOyjvI+Nii7KOFq8ToniBZ8xKwXhCF2hQTyX9iGLlg1HRCP8xT4ACxUzOpQZJSM40/whNXSyvbKSI5S3GRyrwpVjs/sPEMVkdCjPOTM8kZTQRHalZ0Ys6cvzF/AQw+ES7REUXFQEcJgvi7q/Mp/bjDsAeHfjgcCxOFoJb7reuZiiZ5WCgjCcOJdqf09bKegYWtZ8e3MaXqtO9rruIJbyFc0z3l5v1/vL18tqfsTFvumfU4CsBh8OzgAELazmleyvzKdDRhmZ/oGriuCQqYNJvfsCZSL3fpMBAlKIqHnEO5t51VUYK1dhCgUHhZVyMKbj4poJxogNOCjUBh6AFVYjM9MlabMTa/s9/m0Fc8oP5Q+pEgwsLFRuKB29xY/hpueX8PHGLpY0dHHabiNzZuW+DPhO0zVcYyzHROfxzquAaf2uE8RH6zr8vxet7+yTqNlS4VrtTwwXTRjCoaHzFCjf7rMeehFFFPElxityBmeIezhTfZ6rtAd4b8XlsLvX199hKVxtnYWFhoXGr/vZ1kufeD3+nUmbhWvb2WPc5utlLwTpunS0NlJZO7j/wdsYgiV8IU1BCMEeykdEhBcT7Jr8HSUixbFTRnJsqeQC9d8MF43UNtjAf1C71rC/Mh8TjWG2Bemc1nnWnxmkLyYqksgNt/HB0hamvOhdw3Vnvc6wsZMxkwlKiZPEoLJEZ2NnhqgVFgZrooIDU7/GQqO0JMpT5BGcPBGOHB81V/J2oOxx1ljP881QC5OiIFHrKmDaHTxvritzEheZTF6hjJquidyMWiyFlDLPD+5/jKhlTl4wXs5ksG4Ut1Cqd6IJh3PNS+mkFF0VaNFKf2xlci3X6l4mo4VKTlbv77GP/IxYmeGpQkZJ8ZBzEGY6k/WyeQVjDE/E4/32rKR8vnt5aTqrUkMnfzBuBqDBqcEO9D7Ztnfj5MrzK1A+lCttTzlntFbCJBHlNXdHxou13KLdyTd4mlWx4XzifjV7/IHdtw2aycWWJ416VNVQjqenzHm8D8NrVcDF2qPe52kxgn8oV+IaCjsp2QLPDCmyCxCNfOiuyXe1x7zjk6XMc69kmvgUFZf6ZALYpeB6m4oOUc4Ktx4Nly6tElyPqM0WO/JoagY6DhtlNcuV0f46VgGyqhTIKvaF8nWvcq/hvXrmdBwNHAbA4dsP5ptzZ5EwQ36T7avONLr17MtgulxESqQtHtLdeooCmUSuKyXSdXlaz4rMjE4+hGm7hFqX8HJa+XNZbBwZHzWhZR8iwvky+ag5OfYZlrLlSdJP269ANVIkpEFb6w7UlY3sf6VtCHX2RsYpXrP4P22rn9E9oSaamC6WoeJQGjeASb2OtV3Pb9IQ6eeJuen7K6KIIv47kLIdNBxKhPfOkXZ28jBuudzvHOr/+5eu7DVeAPhO6y/ZMbQQDYc16+6Accf0OvbzQroui365D5NTH/L2hEuYefrVX9i+Pg8Wre/kxuc+YZfRVVywX3ZCzOpo5Mfag6TQsWQ9cBCm0IngnX8TnSZZhVI9mqHVpXxf+zuKkLiOwEwlqd74lh+/zG07AjgWgAnOp+yQ1jX4oKuRia98C1VIVCzcJ78PFz1Jzaqn+TDsxSavJ/fnDM4FembUMkkBG41PpdfCU5m2cuhLhEPLU0EPljnOTBO1XjNq/UyyBksfg9k1I012oTd5fpWwrlIa0uhO2ViOpDNh52zvSycm8nnhpEmGGwiYM4H0DBZTq7YD+P5mhqqgVI/hH/Y+dFGCocJYsRKALqUco4BsZn5G7LeJHzEhvASAY1I/Y6HcDl0VOCJ7OpxUgpGiAReFsMj1jCgN6entZm9AR6jcwDdIpixsFK5OB6FWjuG1yDX4c6R/8RMEvOLcFEqqk6liJQ4KNYHJiyDJyJyzk9v+yFn6cixUbrZPImF5fXNHtj/EsfpyLKlS3lUL1OWoV2rCxXUcdlSW+8sWuaOIE2KDMoydKJwRyocatDzAprb1PR4PeSqJ77fuitfG+PlxZ/n3mNPiCZqcXbuGpxrew0FhkT6FN93ATL/jZsVcrC4q6MZBQU8LnuTP4vQHmcjWTLuhbFZx1rgayqJRno7tzujkX/CyZIJvVo7hK+kx9yrXYaQN0ZPyfKBn+altW75siEeeBSnbwQgQcEl2nY1DDuBr7wtMqbFLzfbM7PcbbBvo4aO2FXqetneXElW8WcH5hb0zt2lE3Jj/d2lyYx8jC2NC4zNcHPJMS9/eeAp9eRzarsQKFDE5W6FUtYgiitg2kLRcf1IbgMDzIJbKfbZbjovah0Js2OmmTnjZ/VV5vcubG+vef4mp5gcgYObSm/FkFrY93Pz8El76uJGXPm7kkCn1bDfIU3d0ujZyrvYUACudEcCvuVK9jLaETUrqvudtdVQnFC6hQVRTTwuKkDSuWZZT4SPVbEmfpWbLJO1kF7PLD2ffjscAaNdqGQG4ZraPXNFD1NNKWJhU5s3ZFZrw9n3U+shEBWOxmOkwb3Vufxr03g8WFBMphCA5C5KsUCAG17XC8vyZ7WeE4Zq6U7nf48smz/9ZMV6s5d/GZWxongQ8liuckD53TiBIyKgK6qqCPngyl9vfAmCE04CipqgW3ciSIf5JHi02cJ32JxwU2pOjgH38bVlKVjWnRKRAesx+sTGVld3l2GhUWyavhS4BoI1y4FR/ne1bnmVO6AbqRTsALzs7kgzV8Cz70ex6P4ofK94DLSj/r6tKHklw/YvfKSO87OxInBBdWg0TGt/iyZBXVjCvZW9Ih/6FVBsnpT5goupJ8u+jfMB7sWnAfkxLvsvkdIncItM7X4qqcqt9PI70yuy+a6bI0FBTqhxh/gKAnUsqOYo8olmgnhfACJdws30iltSwVYOjRPC22nylj8GHwfLSnXltvVcqawgFyA36LUdiaILzm67ld+F3AVjYdA9wIlPaXuIm/Sk0XPTG44Bxfe63URnEK86OlIsYydJR/nJdVfjNaTtx31sr6UhYzF3hkchgTbma0wPpnZdblZuI6DFUJG5yFk4ozDJ3BJOVNWjC5XvaI9A2GEdorHDrUZC0GLW+OaFVOow30uWow5UvTylHj1m4LeyjJl2XMIHgwt3q81SbjN/rp3ON6XWKHt3xEHDZJq1/+Lrf+n9Pa3mqz7G243K4+UtcFGwUninv+3dSRBFF/PciaTnc5h7P7faxmGhcMHSSP0kYTrVQjkmMMA4qpuP2WR6WcFXfFyyYmRso4qbN395Zw/CqEg6eUt/n2KC/VjeRQpoZ2wQ2dGQJ6/KmmE/UrFRWxdwS3qT4QmMn1sZyCW512qT5ybKTWdUaZ42s45tmlG6jnpedHTGw6Ypk45dXBp3BLUv2IibDnF0+g7cjYR5uGk0nJexatws74MV/3TJMhBS6pjEnfCEAptSAb/jburvz2wwNbcBG5UjzOj6Vw/xKr3ENz/Jz7TWSGAyKnUDQZjlYrfbuylY/dhpTG2VwhReZGr0StYFn1FJOoHw0kA0LZvQyyMS5NaUhVrZ4RLWlO5Xro/a/klELYzJO2YCZVujbof0lRmvzcFGo7tSBkVwb+h6tXXGOUOZyt3EzMRmhtfUrlIUv9bezRtZzpe2lYw8bMpjTGu7jfP1jdlGWMChNpJbauTd0SiujI1VCjDAiTSQ0RfDnim/zTpvH6O8OZ02QnTy1w4gufJL2mLMH37MuZLuqUrRk9oGQIRWjN/yHh/S/YqHR3XoMmnKhP8YOZNQ6KeUcy0sxR1B5MF3WBxA0aQsmtTLkNiiNWiYSTHc/Si/P3liKmp0Ju909yQ+Sz3MVTkpdi46NEiA7mfJJfdVrPG1cRTcR1iVnEjQrzCAUCrNmh4v41/x1nLnbKJzQet53x+Ci0KAN38Qumt5hBQL74CxLIS8x03ExNCXn3GTOQX18KbPSwh+zu/s/urejB/Kw5T1cfj5u+5zP9tyulj23q+Wvc1cHiJp37qSUvCsnokoXFZfp6WzmriyiUu0GoN1KYYXLONy8gT/rv2Af9QO+pz3KwravEBtzAIenlT93HlzJo+l9Bh9cKSebkX584XoSlsOJM4YXrL3e2pjc9RYVSiNJDOa4k3MmMbYELEdylnUl9bRRIzrYI9YJ9N6jtS1imRsg5p/BTN4SOrr0pkPb9cF5Isu5kG2r+ZvxM2w0VstBWM4+fYwuoogi/lthWyZ3qr8mpRrEZJgf2uf57x6Au+Pfoy7sxU67J2/H6kN4yEwlOVi8Q0yGaJBVrBu0Dzv1Orow7nj5U25/2WvVePrivZk8pHD/PEBndDRXWt8gjElMLeeGXkduXRwR+xf7qV0kMehoGwl4z/pYaBDXWqcTwqKsbDDjycYAobRKNAiqol588+GIU3m0yYsfD+pS6ajamxssTxzk/KFj040b0FIxldddjwweRykfu8OZ53o0drDrZbPerz+O0+dPBiTnbzeaXeY+iSIkhrBxbNu3G9KkjS4cdBw/Xs6834d1zGP3tG/tnESuynZQdTgeEMfaeWTW+7g3c+nSkEZYV3pMAGcQ7FErJM2f/7e/LH1uawJqpE3dqT577b5obPUpZSG9Lz+l60120Z4H4J2u3YFD+FCfxgo3xg5iBacrntrfbGtnykKF/YOqojpTrQ/YQZ2fs1yK3Itx34hreXzheqIkuEp7gCOUOZiijBfUrJG2abmsdutQhKRLqSDY6qpFsg+FUjwSqCki1xMifZOWxtYxKyP8YM5AlynOU59Aw8FwdOrWruER405SUud5dwb3OYeRsBxSaimL3FEIXFoDPU+RRAPHKa+jCpeh8bHALvw+8nXM1rW+EEhEelmDhyOn0d26Hg2HMyqzMymKIvymwJQDHwSESzLIlKm5nRuZrKwBQDi99/Pccsp0rjxiMnVlId5cNpjTzesA2KOihkN7XWvT4ARKAQulw+/Wb6ZGdHpiFd27QmgQCSK0yygqLiKj6qlkb/uMj19fKKT6mI/gDEtmVsiVcEraaFIIWJGevQnaHDiunb0WZB8MrpXM7dsMkPXgwyXzQnx1SRPf+9uC9Fg4Zddtr/fqhK4HGW8s9f89f8ODMP6oLbb/hO1ysvoKx6lvAvDOmu1h5957tLZFtNkhNshq4jLEBnUQPX+5feODyO7sHPcmKV6oO4Mz+xjrml1MT5dFl7sxYkV5/iKK+J9EMhHjYHUeADEZ4of2eTnBb0Qm/QzZjfpd2N0HQOmwQpuiu6OFaiAqUlTSjfUZwtAMSQP469zVXHPM9r2O7dSq+YtzoPcPB37hyh66BdsCzkw9TJnuZXCeWDcd8Kpmuo1a/uh4FVV7VtTwLbLk5T/GjxkpGmijjLj1b6C2h0R/kFTkqD4GlDZjppPj/5rxjs22KwhChsFa6pAupNAZnkpQonlEL5gUeCV0GS2yDA0H6a5DsbMZQUXPVrOB1w5UCIPKs9my3kofI7pKiaGRtApnZIP3ZyGzayhc+pjZX7AHbmNH9jsYqrLF75+tRtSWyOEckPo/RlfV8icgWCInlKyPGuTKraMavphHPipLDFwlS+LutQ/lFXc6Q6vq+EVgXDTk3bglJDlVewXwhEheUb/jj2nXatjH9AQihlVGeDOwfnLkPuz+4u10EyGe7i3TVJFT1uiX6QWNpBUdDYcr9YcBiMsQ78eGsYuyxD8nGayumsVX02WIRw8a6ptGV3R+zC3GnQAs7NgN+Bbzle1Z5o4iZRokCBGXIf7qSuZoM1jseJmgM0oHZU9hIOjvzUTa91FLZlXiHKPvooGMgt6minUMFMPMlUjRgYNKVKnzl39NfZ4T1Vf9oBKgMen18vyi4irmdbQD8MjQ3QFYUbs///hUxUZl56o9++3x6gz4qJVHCk8SlNltHKi8RwiLkZ1jgJ1yRF6CKf4r1UvpTqRwULhNL8dNk/pnnF1ZJodiorNDZCSRYN9m0Ect8HDJ1GF/EFDze29V2zZJ1HQ314hepLq26P6TlkNcZvtN3VT3Ft3/5sAyexCzbM8KY1AoxNxNXP83Nf/HnNb1hDE5uKRvmuda2fveRtvipapFFFHEtoFUIuaXDCbTE4qZ4Fe6Lp2U+JPWe6sfsj7RCRQmarHOVjJOp12yBMvetOeKdF2u1u5nraxjmRxGTUnfJdnJPBuTmGlTFi78Ht+aKCPbDzZm/ZOQFu7I6a9Kk64MmagWnYSEzWDaaK70slDDKrNkqKEjyZDK7DsvOMlbEjC4jqdsugIVYZnJ6VSeEMjR6u983+J5hPyKjOP1O2nuiqNh81HoG9QI791u2RZvlh3CE831hDGZWZubUetN96AyEGcVImpRQ0VRBJE+ymuDLSiFPNSgcOljRmWyNpBRW9uWrcrb0mWPsBWJWgqD5XIoEeFlp94r3ZeXWqpRcdmpYqJ3cOmb6h/Ovsx2p6ALh8MG78IsRXCW8QqlTjtlIs5t9vEkCFNdYvBC1Wnc3TETE52F7jiaqGTXUFXOvkvS/hFBDzUbFSNAtII3aP7NFI2W0+A/ajxoisJF5j2U6hu9vqSO7aB2InOrjuI3y4cQJcmhQ/ZgJz178TUcZGC2IRVo1A3ObgR3LwINuplspNdHJnjG3c3/LGE5efL8wZsz8D0DN3CpIZhsLSYiUlSYEjiID6oO4ZpUGWUkOGjYVHanfwTP1+Y0vL4ydgOjQl5277Hu7zNJ+wANh/3VBQwXzTljM3XdTgHly7byiTya/tpDI/333eQQtV4e8LWdH3GP4fUOLWzbDfh6jodckLwuUKexMU1aHNVApI/xn+4+fqvdjSVjGRo4d0FyXdW+iH8ZP8HAorFpEvB3Qu3LuFm/g8G0Yq4YBfy13++1pfGmsguj3TX+v6WzZdU8EqZDExWslbXEZJiY/HJJ80spc2YGP4uPWsKGBOH0f303Y8fLxnBc6hrCwsSVChf3MnNZRBFF/HcjoZRwrnkp4bTSYAgTN+0Bm3Ike6R+y4vGZb4irW31LjzUZgzluOSdlIsYGg5nbaLZakvjWs7RngWgS0b4V7RPq98cEQjwhE+2RaL2kRzDVLECAGllSVuuGIcXw5wfu4uhxoc+IQKoqPZ69YKZsoTlDNBHzeFfyW/ihARdsoQfxb1YJjiRH9HVdHbO6vGZJfGtGUw0wukxtmXxoT6NFxyvKmxGTW4Fi9ZLi0ZlSfb6FOpRK0l/xyDZzEeql4xasJSyUOljJssWzKita88StS0tzQ/bQOljJpB+P7oXTzpe0Hx7VZqopQP+jdSwUdaAhH3SYg7fUR5hkOL1BKm4fOSOZogYxPtlO/GUm9tc2oNopS9uB1F+aJ2Ljk1JtJTx5seUKB+jYxPqiva6fiajV0sHeykf4KBQZY9gF2cho9XVAKxIdQKwgVpmu54v3AHVE9CNML+3v4KNio1GuPowblxcRUhYTBarOEl9hTAmdjzbOxPMptglg/mXsycuCsnQZKZBwV6fhOkU9BADuEj8DV3rRsHFjO/sLy8PKfxD+Zl3XaRAulfQ5ERYlJZU36dqYAbBwdO1OTNqwfR6vbmGY9MP6yCec2Zwu30st4S9YtUcewRF5PwfBkYkd+56haGKSQdRKvXdCo5RjewslprOogY95IL3UP75kVaCGeITbFQShFgiR3gPllQn24vluCjUu1myGCLFlLSVgppW+LSTMd9we0Uiqwy4LeEWTufO1H5E8LKJ36/bc5N7Ez4PEpbDrfaJ3Gp7KqTfrR7PAVtw/58XqUSM+aHzSKHTKUs4wrpxk7eRDLywnH4CJFMJc4n2CPuoHwDw/roxMP6ETd4nwMrmGJf8fQFVJQa3f3Unf7KsiCKK2PaRlAbPu7twjPJG1mt13UHAP/3qm1/ZpxIhhYXGxaHefdE6U5JmKmiWFQB99rMVQsOn7/utKGUiwci1TwDf7XV8wnS5Xf8N48VaoiJJqvExqNih1/FbA1JKrrLO4nrtHppkBevEBDJHWCijNsxdx7R0BdH55vdYYUzgubT6dm18Cb/Rf0sEk2TjeDaqB3GUsgATnSqzgox42nYdb/KI8RuiJNm4ci8GCy+eRrTwk85rgENREi3U0UYKg4gqc7JJQaIWrLbYJ3UrDgoWKm8qRp9G0b0J1FVEspOIhXrUStNELTJQouYUzqgVVH1UMz1qWaKWk1HrpRTzi8RWf1tmLnAwoM9kHy6M3U69sQQNh8ut81ksR/kXNq5EwfVurIx06YLUNAx1So995BOtMs1mMC1ERZJXnWlspIaxRpRfdd7GLsZLADzZNhTYLn08udvLlE6OV9Zya/qh9VFsGk7AR81NexwFM2PRkIqqqvzCOZ1MDH++Us17ciJI+Llxrz8jdXfsMH+9YDYmUT2ZSyyvRHNWtIbTyVVmzCBpOVi9lN6dxPNUaR6RXNy8mJeMS7FRaXKHYkkVXTioQpIykznp8LJeSk7zodndzFI+QsFlqFkH7Dmg9frDSmU4nZaOiourZ8swX3N24Gb7JDRs1sk6NlDjl2fklB+m751CvYR94bupu6kyvPPVrBSevROlg3jR2ckLorUJbA84ZooDFE8gR1HCZPzXgsTbdUHpXM8/Q9cAsModxL7mraRsh9LGefwn9H8AvN8+g4z/iaZnHyAZsZTh7e/4y8a4q/r9TlsDCdOhWWZLVk2lLymLLwAbFvKicRlJDBa6Y1meun7L7v9zIpWIUSW8cs0IKWzXy7D1popVCKFUK3sqH6HhMizWRsb8tBBsV2IHlHdd57P7qP159irmr25Hw+bWRzu48tSD+l+piCKK2CaQCcrtQMiopCsiYukY51l3V/+zb6llvW6rK2nxfe1vnKM+g4bD/JXfhT0HLpn/Saqa7aRGSHj7DSUb+xxfu/5lDlHf9v+9pKutj9FbB6bjMs+dwGGmJ3UymhJfZ7yi4W2u1x4ihY4e2wuYjqNkiYxEYAX6AaNOJ0erswFYlDQZ1BTnXONxAOa0RYG9ACiTMb/txuzO9o4DjHc9EviVdbdxddjTh3in8QbCWja+DhKwYBzVolT78bzt5vmo5b2rei19DGTUCpUaZjJpfZc+9i8mUkh0zRcTCcj/r2vLZjj/pzNqufL83sUb6axmsvIpkK3fzZzYVyMH81RHMxdoj/vrhcvrMDp7nnglz0dtWtMTvB32+r8esA/iKvvraKpABh9CZhdTxEpsVIbklTlmxEyMQO+cKzTuK/kGjS3NOKh8L+r9cGIBopaZBdAVxWf4QSKXDJQiRbpWs5vowkGh1hSAV9sbLKnNnLNfWb9A0xPYaFxkXUQXJcRNhx8kb0fTO3FQMczpgJd9CapY2skuJiueH1NIKswV2+M4LglC7J5M0f0ZiFpJ53IeNjwxkaXd4wnKuH4e/ED7ERtiXsngzwYpPL/SwkZlmRzGArldzthMJq3abmQYMSw0tHSp66aUZkrXpUx2+43SZZWFZwrdusl8w7ocgO3D5ZwKuMlO/mR4WY92SslIqedYLEiJdIK9QN5DwLRdpAz6qAUESGoncnzqp6TQGVRWy73A6O6sgM6vrZO5yHK2ygOlN0gpc7I5UHiC4YuEE2/zJ0I6ZJQPPkPp4NaEmcrO6lWIOLuJxSQS+2GURftYKxeTUwu5Jm1+6pXofq3XsbYjMdExpYqFhvM5rtfzb83hVv3v7K8s4JNFI4AiUSuiiC8LMkGvmY6RUlIjE5vn+2NCbgYjH51JCw2XaAHj7IFgYVc5a5yjuFj7l7egH3/Hss5Pco8t3tHLyK2HfOXC5u7sOSnt+ISDNC+B8HbCI8BP1J3PNUsOJ4XOGlnHhACx0UPZ94HmpnxCDSACk7xqODvZnbBhp+RdzA97Nk5lIuGpOjrZ1hwtFGG40owiNhLGxIxvD3itS7obx0BgoRLWdGLpd6sVsKCCQhm1/ksfQwXGZMo2B1r6GCRtuT1qvWfUgvL/nYE4eEubXcNWJGqTxGqeC11Aa7wOeLegj5ob8OPShAMye2FfrDmN11qaaJBVjBEbqBLdTK8dQWijd0J3ER/zf/pDOCg0dOwEge4qJZS9QUuEdyNqisKa8PZsaO3ybjbb4qm0j9mn8THAyf46YU0wL3Qe1enZ7WedXegOb88Hxs4scNsBuED3buCgEWSmdlhTBZkYMUjUXnR3YrEcRUIaDO1cyN9DDwEwt+VI4Aggt1cpc85myI8oU70g7hHxUyw05MY7meW+5xuGNwdk6u9RTyGVjOOgcJTM/iAcVC41fkJDp/fgmy3CdAUUD3tT28xHoT66zYFgej1WMYH7+ngZZGZQboz/H8PDHhFd2z0dqGJE61vcrf8JFYdEw37A1F63E0umeMrZmwoRI6qY7BUunAUKpuczpQqOEyDyAaJ1deomqo31KLgoHX/BFhrvueOZoSxlnLKBK7WHqG88FGtQDR+6o1FxadGH+OvrkTLmyQkAJNLkW3OyMz7z5HgaO1OMrNnCGas+YDmyRxnslhansFPZc5QgRDzgr/NlQNyoYXrybhaEPeP0v4d+TmPnCVA2ZsDbuCb1a//vWqtvw2zLdfmWdYn/79/W79RH/q1vTBs7jKPXzkYRkhksobVxHdWDCosNFFFEEdsWMkTiBXdnRicfAgR7j6zlASDR3ckYsYG4DNFNhBiRPifhuhIWViBTzwCUl4NY1tTNSns/XnV2xETj8NqdmdXXCgFD7b/b+1JVviO9a0RuHeSLunWnbBKmQ8RQkVZAhEv1yEN7yRgWyOzEfnWgTI+acXzXvJAEBqXRwcwKrafBacPAJlqaFawzh+3OCamriRPGEVW0Uc6Z5g/plhE6KeGRlE03JTTKSsKkEKFSLovdzJSQVwr/UcN42M5rRZqrfAM97H2HmerDxNLc0HYkZ3f9AaG3kpQGUWsSUOkfQyGiBFAZLH0sQIwyVW19lj4GyG9uj1p2e0IIdFUU7OPrzVB7S0vzw1Ykajo29aId6XrB/z4dj7OX9jESQXlXBTCYB6suYPHqBi7RHuGX2h9oo4zO5suAr/nZnfudrPj7/PoxzHj9NvbXH2df9X1/+QKrJmffSkkVG2UVMRmmJV0nramC9wYdz0MrdgHg0nBWEc4l98IIRUEV2Qv7Q+tcdqneDi2enbnIBKUXt1xDqdFATIapTt0F1OTcnMGM2812lgxeK4J6btkbq5DhtRYQRZmorAXgw1hLjtmyomUv9eP6YaxLmyXOKp/CAakb0XAYV19Bia1B2hQ4bjpcsPEqrjZW0EUEmfgd0Le5JAChMmY7U3AQdBgj2K7/NQaEYKDf36xG5oepBb3kNO9eK0tuZHfVM8Gemxjec+UAOkz4ge0FxvXlIeb0Mi7448/M5DhC4yVnOioulhb1cwjj3JWMSlserDLjJMrHc4J5DRepj3KZ/gjnaU8yu30QLVN+xPmmV553UG2930+VUSWCbFbqSeMwnolNpFQkWCMH0dCV3KaIWiLezTnq0yQI0SlLeMqdmSN2syWwoWoXfpy6jvFiHSouVZ1LYIt2yX0+pBxop4wVbj1jlAZvWbzzM2/vY2NqL7psHsrWv8XzxpXYqMx2p2C7O/Yxum+IaC3vyfHsKpawkWo+eX8BBxxUJGpFFPFlQGj9XP6iX0sSg3fdCdzhHOu/57T17/JyyKsWme1M4TTr//okalOX3MGu6uN0ygi32ccTHXp230QrD8sau2mgjnXpMvqZSlWf4z8o35+/mWHCwuRDdzTfcLY9j1Gzo4GfaH8micFGWcWfnUNp7k4xorqE5eW78R/r64SwmFi7FzPxyEQ5nhhLO6VURwMZqLJaHnc9v9uRsoRE2S48s9rrrb9jcFaTwCir9dpugLRgJ68FnvGdSZffll/G3FavxejhETNRlDv8z23Tm/iUrosuAuJ7WgjIirntab3FELUJgHXkZk8LqS5CXkatADHKiIlE9N4pTDCr25vqI3jJH8vpmXGriOhoiugxofw/pfqYgZImE9Pjs5mmeX02C+Nes//G8FgWyDLaKWWk0sRImnjX9YQS8v2sFOFJp9ea65gVIGkAUuRe6NjoQ5j5UiXjxDou0f7JLfrviCfGsVT9VnaMo7HYHYmKQ2NoMPkyGglKqCBGQhpESfbqozbaXs6wdFC1Jn2BT1FfokLbQDVdDNsAlxnLSKFzg32qLzzSKCuY605EQdIazpIJI9XM19TnUXApSwwG9uTr9o9QpcWDRtaEwEnG+DHfQZoJNByujVRkz1XgPou7GsulJ1wSNSopVbM3ZWfCosbayOj08S/rY/YiCKdqLKdZXm/VxOoyvjKgtfpH8OFf6Md7lfYAeygfYWDRsf4GGH8MjaIa25Xo2Gi6J1Obk/Fz+y5/64hnM4oVvUjzQ76PWrqeP1TJ160fADCkJOwTtWB2zXUd/0EQVP3ESSELZJkhtwE2Q0ifknuy0smSjqDvx7YAs7uVq/UH/H83yj+zdPWFMOvyLXYM3W6IStHt95V+2DIdOGWL7R+gNWbyx9eXM7o2ysm7jNikdTOzrsvkcGKuZw1SuYlJwTZZRlVaKey+0nM5sI+xqtnBeMUzTl0tB9HxOQzKo6kmHrIP4k6O5k13e/bfMPhLJeRSRBH/yxDdDeyhLgLS7ykn++6xklnxqlnqIu7jBiINP4bxhR1URaoDTbiUk0CiYLoD96TqTFp+xU8GmYDcdSUrWmKMrY0iApVHq/QxPOZm3/ndqW2v5N3u3MjXtWf8f6cwaG8Yw4jqKawNj+ehNLm8vMYjViFN4Qz1eS7X/44jBW+1fZNMe0wwy5SwnFwhjUAZYVAdshA6k1ZOWWvEUGkxhrI4PoIUBqm0NZXjOJgyhIaNAO6zLmNEaC0aDhtaXsGQWXIWyqtI0gqIiYQ0JadEslAPdulASh+tXnrUChA1AkmNTNWeEIKaUqPH/RYsidxS2GpE7WM5kt2SP6c8GsbzLM+eyEwgnZHuDPqoKWnT4nx51YqI7hElLXsSX3e251b7BKaNHJUzb55Jm9bSyZHpJtNFVgcrAtHwCmUUh5u/BGDnwZXp9sssvl1+O+83mjjpbNtOvfioRWQ27R4p9cjS6fIpRmueOuSGZB1DFG+2oUZLkZlweFXZnd+Y3o/yrPpRvpt8OLaBa/V7AVga3w4pr2C240meHpG6nihJ4oS4qHJnXnYqSaSJyC+NrJdGro9a9rzrikJpKHsTt8cthrjZh3BJad8zVxnk+KhtRnn+nd0PSQhP4CCkZosXrtbuZy/lQz+oBJifbAfgHOU6WhLeSX2n3JOIbaqbyfnmJTgoTKqeQrYFuic6EgMkasLhaOUtQsKkxFKBg3ALCOQA3Bi9jPXNbTgo/LJslJ9ZetedyE3WiaTQGVy6B/WBScngJEDwYZt5AOW/fBo6ty2ilornepYNEu0st7asOmW+j5oRKBfdUrjr1U/5/Wteo/bUoeVMHVrRzxpZZH6r51qX+cseiQyc7Ekp2dm8GyklISym9GNPkBFEArBQByS80xtOa7qF6YbX4P5N8zJeWxohuY31URZRRBGF4ZoBufg8H7WEK3Ky/PupC5kX613gQzWzVQBdRKjYhMqK9Yvncq9+A0vlcN51J/Ccu6t/HGfdO5fXlzZz2m4j+MXx0/x18nvoglVMQXQkLF75pJFZY2sYVB4uOOaLghXoPwa4Qf8D89dMh8lT8lQfvXd/SFepTk+4qUKiR7J9acFnatJ0eiUpQZIjcJEI/GZ8vIn6ZJ6P2qNDLuWJxvUA3Fo5HQAbhSmpe9PbFzyv/oCw8N4djm3xc/lNdLuLEBaXl+XZWhUgasFsWvA7B1GS8UIeYI9aY1eWbOVbLAWVJ4XILcesiYZyiJoQcOasUb3u84vCViNqFhqNVGGle6ReiB7Fo11TUZAcVuHJh2ZO2LXW17jdPhYdm0sGe0ny8alFXKY9Thlx5rqT+LjEmxteOfgw7l5a7jdZrpWDGBwekrPvTLpUE7liIHrghggKHxRSplEi5Thk1YM0RXBy5/2cr3veXqHGa2D8IZzuXE3IjhEVCf5Q4aXqY1qFT8jqZIv/29BCJf7yeOBhEpwdCmaDFNyc2tqMjD54/iK9yfMHv0+wyVJTBZO0jYSVBVSJLuwNJZzC9SipTsqI8+favssE/eMq0Ee3OfB78QtCIe8B8Eb3JH6t3YUqXF+WPggn7eMSzMJlfpBm2XBfoarUGNpj3SA2haj9xvCMiONuCPhlTso8eP5XGWP5UHovK0eJYNvetZ4nJzDP8XrPTisZQV0woxYkam6C54zL0bFxHRVYTHfK4kDlPc7VnmIQbaxfdBjsfVuf321LIi7C3Gsf6vvfADkiKlsCCcuhgyhL3WHECNEgRjBhix4ByFVvs6+ygYQ0WL1u3CYRtZTdcyY4vgmCKKbjptVmBSkM+lPFXl+3Fz9NXUuZiJOUBid8DoNyxQ2SPo246TD70xb2nzToM2+ziCKK2DJYXT2TW80riGASI0w1nZRY3m96ZdWenGbewu36b/yJb9mHwMcfqr/POQ0nUUaCLiKcsAlErXPlPPZXF7I/CxnmNHlEzXHpSFi8vtTzUn18wfocopbKE+rojaid9+d3mbOilQn1pTx98T69KhJ+EejUB/Fz62tcpT/oLzM7POIbFOMIpUnYbk2PcpD2tL9cLc2KnAWVEHtk1HrxUTtdfZGfaH+mixL+4hzITfbJdCatXCEQTc1RbfSVQINxjqL4yQsA27Z40t7Fj1OviuRl1AqUPgb706Bvef6+JvqCBHVJQ/bdNb6+NGdcUNDEUJWceDs/fj17j9HMGJVLNrcEtnrpY+YivxOaxex0kHpgmUcIMhm19dSyXqZvxBLvJI1MfsxJ2mMAnM1zXK9VA/uRKBvNK27uTLmap/qYyagtcUdwkXkhGg5D60YwNL6E09RXPCPq2I5k+rEK/WCjIY3txFpmKoux0BiRms5oaxk7pj2HFiRbsR2XxZZHEgUQCXuzNPMqD+XZdRNppYzF7kjihAlhsX2p5Mz4s0QwWZ6YxlI85p5DfEpqedA+EAcFJzyEU3vxQkr0kOfP3oxnmX8lpG1AEw7x7iv85bqqcGD3E8w0/gHA7HXlrE7NQEpvtqa0ZGA9TzmqiptRMCLYcxe12zlJe63HmJusE3nK3Z0La/ZmFwobXquBc2H1c3zKxg84S32WDhlluNgZesm/hcJZH7UQJtJ1c/YdvAd7ZBxjLeyjLMRGpVWW87EcScpy0RNN7C4W4yCotxzAqy+PRsvYTqxHSfdJJlMpkpZLuRJnprIYgLbu1X1+ry2Nbr2Ga+yzuNU+gVISWGh8fcgOm9Sb8HmRMF2Wy6EcbHqCGsNKIhQuzvnicGzbn5hqeKXZr7WOASb1vUIApate4JPQhaTQecGdwaXWBZtE1PKVxfoTc0koUY5T3/BLct5e00Vfwjt9oV1Ussat85456Uzeix83FIlaEUV8CdCuVPOmuwOjxEZeDV0KwLpYPXCsT3z+aB/B484sTDROrupddqgj6dBNCd1E0HFwN0H10Wn82P/7K+pcpohLWL1uFknrbn95zHSwHdd/3ycsh+OV1/ie9k9KRYLFnx5DU9ct/N9jHxA1NK4/fgd0VWHuSq8Xa0lDN0saupg8pHzAx/V50WXUco9zBCYaByrzaaYCRXgVQIUyalE3m5W8yz6KieOP9f+tq4K7jVuIyjgRYfJC13GMVtuwpEbYmgx4mg0luuQp4wpKSPrtLTV0cZH2GMepb7B22fcZnAqjCoeEDBHRRW62Lk3UnDyv2h9W3sgH67uwUflH+UQsx6ukEKKnwXShjFpFXkatoOH1gFQfs+/GJQ3Zip6J9bnWETlELW9fEweX8fHGLMm7/NCJve7vi8RWJ2p9yfMf3fxHvmu8jIbNL+3TeNbdzT+pSiR3JvoI81ngkoIXNZ9olWiCMWIDJSRZJ2uZJydwYHQQe3U8whn6PQD8I3EKD3JMwfUBykIauymf+GWIc9qPQgZUKqVj+xKl4M1eZJj64iHH8pfVPQPpr4t/c7T+NwB+Y8HzaaIWvLdl+VD+z/bk7ieESjmhl3Kk7pTte7UJkfsd9rJmM1ZbCcAbG6YzJ/QAFhqftu2GrBrpj7O6mvxtRHS1Vxf5fKh2nIOVd1GQlNqlwP4DWq8vuI7LXHcSqnDT/WbZsq1l7lC+ZX0PHYe1ss6zJ0h7dFlu7kMECpcO9obyjW9xjX4/AG93nQScVHCcphs87uyBiUZK6pzsOJBs52jlLWwUSt1aYD8gj6i5kpLm9/mz4fmnvOpM4yzrR6Qcl5qGt/lb6OcAvNtyEHC0t76q0iG8HkmApnUr+IX2B3ZMG2ACRFJ9e8tsaWQe6h2U0oE3oxUXkb5W2ezYecXdvB16hIQ0uNM5mufMQ7bo/gF0N1uSmhSbVuvumjFCwiaETSidek9YA29SS1kOURLsqXyIhkNVsgLYu9fxjitz1dk+Rwb0txWX8U5bG1ESjBBNTBUrsBqS4Nu6FlFEEdsqMs9vM6ASrUvveZBIxzkL5HaQft0erucKuAXRlbQ5SX2FX+u/B2DumiOAhwd0HP9WD+UBs5rvaP9mqrKKMUoD7VYTdssq3gpdSErqrJb1xFKHUFHiveePa7qbo9KTzwC62c5Dc1bx7EceOZk1roaDp9RnYx2SfPjxx0westuAjmlzIJk+hw84h/CA472XztBGcTywZ9PD7K0vJCUN6rsuAEaAli3NVHCpKs+SDyEEe4oPKVW8csrBsfsZpqd1BpJHAmMBMHSD7cRaDNFzsm+4aGZtdyP32HdRHvISHx3OYTkibpmqs5yEgKpgayUk0++nmBkwu9bUnGwVeO02+agaQOljdBNKH23H5dPGLFEbn0fUgmQxn0gePKWefy9Yj6Ep3H/Obj5B3NLY6kTNLkjUvP9XOK1+31G5yPVRMwdNY62sZbjw0t2WUQkUZt/5RCuqWr5KUUIaTE7d512sgNxpmd3KLuJjXBSG2j37QEoNlRICfUCKylPVZ3JT+z7YqHyjeifq88yuM6guKSz7KYxsnfEYZyX7KfORKNQmNcAzGlTzgnynu4X/GFdiodEiy/hm2ssrFk/wF/1abFRMEYKApIcbEFdRzU7qRTsATW4nHdXjeXHFTrRRRoOTrcUdqIcagJZs5Q/GzQBsSNUB3x/wur3BAb6aFijRFMHDVaO43DoPW6o0UMUymVuWaaV/pOPd5VhCxUYlo8GRK/zRN1GT6V43ABmu7HPsFeJin5wf4wpEx1q/HHJFcjRwMdDTx80NyPhnfNRSlgtBH7U8QZzLwj9neYdLh4xyV0eS07SX/c8OSv2KUMlQnuzzaLcs8uWHgS2u+miYbQwWbSCgxEkR3wpN5cvFSFw3QYWIYW3i13fN7PNmR2U5+ykLUNqrgIGVJCdTJpPFKn5v3ALAmuRQ+vpt2o5LTEZok6Xp58jA7DkKIVP6srOylAcMr/f3g9adgBM+8zaLKKKILYOMMEMKnZgMYaLTIUoZBDkT0hmYffSzdiYtbBlo4XD7nwCSUvLi4kaeXhemw51Jwg5xb9oPUnFNrFSM0aIVBIyhgfUtq6ko8VpoJqRyxeVUK8bqlmzV1Zq2BB2dnZykvsKF6mOMEE3Mn7cf7P9Yv8e1uZAsUNbe3O2Vj46JLWQn9S0A5pme0N6q4Udz9aIhpNBpk6X8Jy+mTIoQpWkpx1KZJSlaQKtACEFChDHSE767J2/nIu1ffE3zDK5loo0I2RLWUKSEGtnKDPEJYWES6QwB43BsmzrasVApF1F0JVta2J3KXttCSt2FkiA9Sx9791EbSOnjqta4X/45uDzco4XF6COjduS0oYyrKyVqaFtVRXurEbUpYhWvhTJGyBsJVuH4Fy/Qj6Wny94yfUaifgrnmZeyn7KQKtFFbc0sdiV7UQ9X5nCp9ggOCmuaDgZu8bcVjpTiSoEiJBFhouCiKQrt0Yk8bO+PjUq1Jnkk9DMAFnbsBpyYc/zHNt/JLD07C7SudBrrQxN53fXS5afpVTm10EGFnapoYaLWXD6VPzccTAKDHZXl3Jd+EM1uPhPSRVpKTpAPtplge2Wlv+xJcQURUqxZeZCv0hTPEw34R8kptLc24aBwoJolh66i0zXmCC562yvXHJeKQvpHvClETQnIxytsnmA8KGSgKgK3dDD/cPbrdbzpuLiOy5PGlf4yqXj3W1XXUv6s/4KQsOhoHgf8pdftfGpMYql9sOejVjWlz2MM6ar/0kpZDtIN9kBmHwAXdt7McGMRCi6Jxjvo1it4zdmBCcpaDlTnc528B9G2HYna6cxxJ6Hg0hzKnSxoLJ3I8nbPuLOlrdVfvtQdxjI5nFCngpSyxwzW1kLC7Hkf5PuqfdFQAn46CUKYjotpuwUnd74odMowk9LWDDUt84AjB7zuJ/VH8rXkMK7T/8SJ6mvcZ/yKt9c7wB4DWl82L/WfaZBbSlwIliP5jXM8v3GOB+C7g8ezz4CPNhd2etbVDmToFLnliXIRRRSx6chkT1opZ2paOCJqqHwEhLtWMlWsJEaIRllFnLA/UVoIdqLL72OypEp/GkW243LBQ/N4blGDv+wddyKHpX6JicbEwcO4LE+MI9XZDHhETXezZOMM80eEqvZmUMdSvqG+TJXoZtC6nUnVTPczfAAjuhYiXRfRi3z85kah92NTWgBDCxy/ZnhVKDJaxxKZjQmq8zy/rgtdTGOXSUKGOFh9jxo6MYTFjJLc/qoL9GtZ1+0Sk2GaqeB39rE87BxIByXsZQwHdw7DRBNRxaQmEmV685N8K+SpJs/ecCZwEG7HWt4JXwDARruWS5SH/O2bHRu5V7+BJAadYhCQW8WiD0BMRFMVVEXkxAvZjFrvcWlmEn5JoHQxvz/NO4beiRqwRUtge8NWI2oqDmUigSsFUkpO7HqQ0/Q1OFIh1DUMqObVwV/nxxv25o/6jXxf+xtfV5/G7XoQmEF5WGeRHM0iZzQAPxjh1Y7Wt83jH8bP2FVZ4u+rw27O2beiqnzMSGxXECOMgYWmChoH7cXVtqdseDLv85W0HmV+NgOAQPbrNvt4uuoOR+vO/qAc18VZN5+3QhcSkxFWmZPJlL4FPS+CaB68F79b7JGk76iP+f1GIkDO8jNqtpVb3z1VWQVAazJrZuuI3JtvbmQv5jvtAEQr6/np0rHowuGA8SM4NEAiq9ve5/HQtXRRwhJzmn/8/UHoJTzv7IxEkNAq0wWknw92Tr+dKFjbHIRpu9i2SebbWFL10+xhkkxP9xJ+Yvb9lnjH2I1/217G4qZRfftI5XupWVo5TzgzUXFJhYelXxswyGnwM8UfWjGaq2ZwoXUFs5SPeNi4jtO1F/ko3sT7dRdyhemV+J4yaASHB/YVnBVanizlCusblJKgm4i//86E3aPee2sh1PwhF6iPkcJgkRzFbHdqjhDOlsDf677DRRsPY1flEyw0DlbeJRHfB6O8rP+VNxMSTqA8uo+G+0JIOZIUBg2yMrsNM977CnkwU7ljZ4ud+sxnjV/zd94M3Y0jFR52DsR2LuljdN+wbO9ad8kIH7sjsFHZqA79jB1vRRRRxJbErmvu4wjjeZIY3GMfzvPuLn6WYo/19/O90FMA/MA6l787+/fqoyZdlzflWQgd2mWUGam7OGjQUHbvY98Llq7ig0WLUKjCRaEsrHHktEk8PNdrH6mWlXSWjWO9rGao8CYtk93t/vq3GOdidjYTwmShO44JpsKZ7Q+wj/4qAO83NZHsyPV0bHNLMBsaGD4kV4jui0LNxtf5hfYPkhi86W7PC+4MP6P2z5KTuadzd0LC4uQar6c5JFwmi1W0yjK61QqieSWAH4VnsKTDy6TNs7OSWW9W5H6fjeGxrOzKqi9voIYN0itbnduk89d0FdOUwaU8BaAHMnLp91dQHdgRKpriTdDr2DjdzeyvLgRgjdtTuE0I0YOEFYpZDFXxFcwhm/gYSI9asD9tQn3Pd32QLOoDbO/Z0tiKpY/eyVGExHFdZpjvMEH1yNUSqx2AeKSepTJBCp1Roptq0c1Kxbug+RmeqnTqN+zGmB4gad6uep78M4ybmRR7h6+rT/Nb/Xa62vcmMebr/uctToR33QmouDTpPUsfRagMU6p0UYItlTTrz+7HdiRWvM17cAgwM2IowBC3gSu0h6imi5O011jlDiKFztuBDN1qOYiXnR1RkCQi2RJE1ezifPUJVFw0s5xU5EqOTF2Lgc3x6ut+2tpyJF81r0TDoTSkkbUpzCV7MVengWqQkDBqc2YzStxuolqKKCkaRa68el8Q0WrOtbySqtpQaLMQtXxRELXATNfX1Oc5V30SQ9gsW3kazswfs8gdhYaNFCqZNlAtlE1hB2fbCmGgqo/Qk6glykZwkfVdAKaXVXJc+jMZuB+lkxUdSQXq/1XXyi0Hzvu6wWP5tDvEP52ejlgbO5PbDFEra57PD/S/+//ulmHeX3008IctdgztToiN1HCF/hf/hb6x43QqtiBRa3VLaKASC43kJvboZcpHV8rBzHUnkpAhuvTBA17fsm1aZBlhTD6UY/iFdm6fRE0zOxkmPFXaCjdG6+fIgA6zVqCIGAlCHGVeh4XG9PpK31uwiCKK2HZRkVjNdOVTAB4TewJejOG6Es3OBvq/0v/AieprdK07Bbisx3Zi3R2UpkWwdGnjoPY7YecufoLZ4auwpcJzJV9h9+/cw5q2hE/UTMclKVW+b32LKEm6iXBBZJy//lx3Eg2B93x3ymaf1Kv+v4ckP2VpymalW89opYHXnB0407qCm9Y7DN8yPI3y9o84NN2+sK+7kMFOK4O6E8D+zFd3YKHrxaCnVHqEssJp4umQJwTXSDVCHJ2zvUgvJYH5PVilfXiprWjJXtehVV5iIhUdynvueJLSoEXziJftSppkORouMaWU77f8hAfCcwB4e33Wm9gShSvJ8olafukjeO0qQZuFTCYt0gdRM20XKWWO4mO+kAiQo72Qf362FWw1orZYjmL75C+xUXnfzS2DEelANiP+EPRRU9MiEvk3WIaoqQGRiQZZyVnmjzhkyMQeMzZRQ2V4vMln+3NTQ1gZuEhv2ZN40fopAEcMGky+7MCK8Wdz6odZBcALFZHjv+C4EiuRVeYxtWwGrkaJcb6W7SAapXjCDx9Fs7MVT7h78ETaXf679eN9HzXN6uSKNKHbaNfSxdV8KL3m0JX2YB52DiBBiNGDhvNWizerVafllj4GyyeDvUOaqlAdyKiVkZ2BtwPH3x/UL0Ce30olOFh5FxsVXUTRlOwVfVC/jrHKBj/4BlhpdWApBkeYngl4aUjjw/RnomoMZ5k/JIVOWUVNDlWwHZdfPv0xMdPhR4dNyiVq/ZCeA+QcbHU9ISzszkk4StZ3LliL/afqS/hgZQMugp/WTsdKeOdojfQkelPohEqHMboXHzbIJWrr2gr5kUka2mNMHLzlSEhfyM/8lIokmrNlvd4y93pchn1LjFSss481Ni+klPzWPJLbpFfu+J36cZsks5PpE/mHs59f9nt2+egBF0+2VGzPEamsOlqV1k9ZcqB010LtV3inL1ybuI6hIa90aZ/ULayW9Vu89LWIIor4bFCcLNFJBnr5TcelQaljsTuSyYpHnHZTPmF2Yn3B7XR3tqNKg4gw6SIt+NVPr7LT5pWKa8KluqqKmtJcbyvTdknZLm+5WW/V02R2MjZf7TZm5gow/TV0ImVlM/mqmWmP8Z5L765q5YQZA+v//bwQVvZdOFbZyLWKV16aiN2UY9zsqz6mkxkAXWol+dq5vfVu5Zf2ZQhPKXEShHKk9YOh2/Aqb1KxacThnPW2R9COqxjG0UCybDT7pu4CYEJ9Kb+yfumv16zU8nXz+4QxGVJbw1UFjklXBMG6sPzSR+jZp5aJ//MJqRBerOS40msNcmWf0vzQd4/atoKtRtQcFLrTP1RHwoPhrxJrb0AgOafcmz3IMN1TzasICQsdm/srvc/KQho/1h6kjDilIklVxKuLTdXtyEmpn2Ci0U4pq+Rg9gj1nHUuMTTUQP+UVLScFGhQ2jM/SAaIhnNZv6YK9m15mFOM51Bx6Vh/AauGHsBFyduIiiT7jB3mm26XVtcXPCfl0Z43EeRJu+f1fwU9Mlopp1V69bSGGQW8G1TLa9jUcnzUcn3GqiI6X1Hepkp0UU0X2yf/SCkJjpw4PC0O3z/yVQ03B2Q8K1DS6FbTETuM3+m3ouGyl/pRj/HCMXNlYwPXNhQt51XXK2Mc6uYaW/7jvbX88Y0V3mcV4U3KqJ1q/pMJejor3H4qTmWl/1nwGrYaQ1mazp7ZSgQnrQTYRCX3OEcAsJ1SyshCfZtpHND+Ty4M3U8FMf7SfDxvcxQAF6r/4iT1VepFG+8v/glM+m6fx7ylsLZkCr+zj+Zs9VmiIv2SdQeuWLg5kFEnWyjH0uRUEiPEMGfLGS6blskx4nUSIkScEJYzdpPWt80kGna6z8u7HxKfR56/n5ns9+pP5qolY6kTHSSlwe6pRj6rPL8qA4I5aSGB/gK0IoooYtvAo1XncH3z3oSERbssZbhowsDCtCzujX6TuY3Hc4H6b36QVq3GKSy536HVMjN1Hzo2kbQYW3/Pgc6EQ5Msp050Iio9VepgQG3abg+vtO5k9nmTb3jdFU/xT2cvqumiUsT4m3MAJyaCgibes3Xuila2FD4q34t/WxDG5Gr9AX95W9O6nAmyDAGrFtkJxpHOmh7b6y3TlE94Mr1eTxtXMEJpIi5DHGL+irWyLmfc0EovTgoSo3ia8Aavn6oouIqGIwU2Kh1uhJdcr7d/Zmlh/zEvzs9eo8oCcVZPgllY9bFEV5Fk/UW7kzYrmrMT2fmKj5Bb2SbCHwABAABJREFU+ljMqOUhKHJguy5v67vxseMRi7MjXo2sng5OM6V5AEZatcbQVc7VnvK3sZwWoBY1WsU7MtebqNC5j4ZUXnamc470jIN3rpvGhPhqvqE+iYHDCjmYZ6Qnz5pPdMAjijOVRUwTn2KjMTRmU2NtZGdlGQBzks10OSrrqAMJu5ZlZe8r6oZxg3UqbZSiIHnTnUpUsfg57XxLfZyISNEsK3yZ1uDulVA5d9lHIhE4Rjl79xJsdQUeVPn9XMd0/42v6h+h4vJm91lAdfp7KoQNjV/rd1OSDqb/5BzGRmqgbOAlVoqy+TNqTsBrxUXFcOJ8RZ3bY9zZ5g/4xB3B4YMmMDavry2DoIFivurjh4sXI3CRKHzc0MWJsb/Srnqy8hX6nn0eox1I7dtmIsdDLki08olsqHMVhytzsFFZJetZIkdg2i4l3WvYX5mPRDAoBZCdMYxqLkPSGUQl2eEvLxVJP0Prtq/r83i3JD4tmcaddpTf2McTxsRG5YjBo9hyAsjZF/Zl1gX+sr+Eh/U2fLMj2d3JLcadAHTKEm62N60oeN+Vt/GD8D9xpOBq+2wedA7uEYT0uf+8sf35qMWUKLsqn/CLtGXJ3PVH8lmtNpaL4bQ6JRjYpPBexMWMWhFFfDmwjnrmSQUkvBc6nxrhxWotXYf7Afvj7iwWmOOwpMY+NdMLemR2Jj1C5OAZI1fQTdjqOwz9c+hkzk8dSAiT30/xpotLEht53bgYQ1h0xKpYbOdqHHdl9uNKTNslhMkboYuJkkRIyWTrPn9sCTbt8Z7Kk1XN77Fh/TiGDO3Z+rK5sdKYwMOOV/k0TDQTJUkTFeyXEjnP7QzR2m7WscybfR/18U/oOOQ28mXOTux8gIuNtwiTok2W8YI7AxMNXTk8Z9zp7XdzXeh5X/m7RKR4wvgxNgp1opOvmVewXtYwvNyLPYLEKEOGgs9xXRXcV3cNj7+/AYCjw0MBL7vaW5YvP74uVLmUb3qd6VHLJ6QRQ8VxpX9sizd2+u+5YZWRgqWe/YmJbAvYekQt8LeXpuwZ1O688e+8GboXHYd77cO40zm612a/2jIvO1foRCsFiNZo0QCiBYlgvrsdI0snsVv3e1yle5m5Z51deMbdrdf1S8Ma+yvz/RLGtzuqkEpAKMC16Q6oPgZvkEgkyp/EsTkkIaqqlJnN/Ej/KwDvu2OyRC2wfxEp55f2VwGoDhnMCpARReCrZ2YeiNDT/X2CvYSdVK+GeHRrNz8MrcRG5YONXwV+Tacop4QmAKpEF92yhNJNUH1Upc0xyhuouOiuBpvBVtjG4HlnBho2KaOaqVr2eDplhMNSN6AJhw2yBguNLhnJ7WsLnIOgPH/wIShdl++tOJ/LQybvuhO4v+UHfNP5O4bujUmGf9rnMc4v2YN3WoaQwmAnowa1ax2nqi/hoFBjjgFmAj0Nr4c0vcGdxm0A3G8fzNX2OaRshxFNr3FvOov4dvMpBC0W1JJsWeVpygtU6+3EZIQRanYWUMZyRXS2JpK+vLNBKi3xkpJb9vHz2/bvUBtqIoHBSebVrJb1W1SiP5XI9nnG06qTmwKRLj9ShfTVEzfF8DpluwhcjlHeQhc2upTgF1X3hOejlr1GYgAy2r3hW/wfHaZFCJOpYiXbsZ5BVhTY9zNvs4giitgyCL4nTbKBtGUm/WfoWjmItdIrwpuiFa4ayhCoaWI5j4V+AsCStgn0FSOsb/cybykMhtR5k8q6pjJU8WIUVcLglY+xMHQtKQwec/akK3W1d9zxLv5pXE1SGtSJ7ISmiuOX+cVNh8au3DL8EpLcadxK5O5fM3vs19nt9J+hal/c+yo44XatfYb/9yS7nJ+aN4KeIoVO2NkdKEFRVXa+/AkACk011rsb2SmdNADYQ12ELRVU9daccVGR9ElaBlUBPYIHDa91ZIn7b2BUDjHKVHMEhd5URaAFYvBgDBzWeiFqeYmEygL2Vb1lAvNLH8O6mq4U8e6zD9dlr3lvbSBFotYXBIBEQeIEBBUgm0EKyYTXzA6UpX3Ughd1/qzfUDHnZhrHHMPMQd6sRyHPBbVA6eJpXX9i59BrAFxgfhdN3QVFyz6ARopG9lMW4KAwxLSB6TnrR3WF2sAPH1XnvfqT+dWaKdionDxoVq/y/EIIqqMGG/6fvfMMjKM6v/7vTt2mVZdc5W6DjY2NsTHNmN5b6C20EEJCIAmBFEIgIYQAgRBCgEBCSCBAQui9d9wwxoB7r7Ks3rZMfT/Mand2tbIlWRjyf3W+aLU7M3t3dufOfZ7nPOc0ZyaHgCqjBzN9YJOktRwnzcZBUBIPAKM7fRbbcdFqPuUt7UdYyCyWxvLDxCUAlCQ38Yh2NxYy9cmh+BUbLSXD347YzRQIT9pWSzGFPwwcSLKtiUYixF2vglkQ6L4oheQY/FHz5Evirgbc1O19u0IyWM4lptecPLIwzEORgVxmXImDRCtBtlCWrrqCV463kzGmiWVYyETIGKQHuqiobVm3nMF4PlvTpWX8ot5Im0EmXZVAKD81tQNvFZ/OmzVeNeuBQBUVNe/xO/WvAHzWtjfgVXL8uQbHyfZR67h5GFa2j1quIE7zyGPZb34JzYS5QnkqnTB4OngyhzadTY1bzIyCEXmzml8F8vqoObuW+hZy2omKGFFiafpdbr/Cl4mYJXjNnsowUUMSlXBsMz0xfLYdxxMuEg77SktocUNUtQ4A9u7W/k6skSqxjTu1jLSQ69zapQS1aTu0uiE2uWVYrkyLVJh3u+6gwzOvUjTylH4DAFsSlcBlvT5mP/rRj12DhO8+udUtwUbCcBVUy86bLOrKR60lnqLL+Xqh/LToXDiOy+amjPT+wEJvPaL6/MBUTDDaKRQxIMa3lRd5c+1g4DaSsVamSis7HTdMnBYy93O3ZgnjRBtNboQJY0Yxcc1fKU/RC/dd+2dm/wv2Pf+3XY5zZ+G/PwZUKU1Tr2szOMFZSET2zkFLHi+yfHDkzkJVBmqnBb+jZtacN5ln85Q4jPnyxUgi+/srGeSJs0REggOlzwiRpLI9CuyHk4wxTGzFRqbUlVClTECUFah1MfbcQsKOqI+KJNIUxVx5/qAqkxSZ3+rnmzMU0Xz9adBPfdwuxrjrWRQ4B4C61s+yGhc7aJHCV6FS0j5qmRM55cjz4cjz8Xd6aKkerovll7hYeQnblVm99RzgV1nvb/vEMcIigSIJzOgw/mYdjYnCNGkZD2m3AjCv4XjI0S4sMms4Rf4g/X9TdDdalCF84nrBztFKCe2xTCAWziN+4g/UdEVCiVZyn3UcCTQull/mbu1PAMypA/BU/aRcoY5ECyMlT4pfE/CW9iNCIukZ+6awOqcX6NPyE3m4dgwxAhynfcLQVGkaybtAnin/Du831Xkc9NRPpGc+aplt5T7yUctWfRRIgQgvO12L+hq2g9O8mSdSvlGbzQHAhYB3Yf5b+zUhEp7wh3kYiqqxauUSdDdKmWghKuK84H6fP9onEyJJRBOctYMxZhtp27g+OVm/0uPZdXdxvf4+Mg4bNv+W+sBgXranESbBRcorFItWZFuiWT+Bt+zJSLjEfMqfAOFoqRecAhGf8bodHcbqRi/HVtOya8U6tod8vVS7Wp5f95l3xlNVvZ5UpHYW7WoxL9n7pAMlo+YBtlfRysU/yn7MWTXnso9Yyr/1GzlR/ohlTbsD3+vW/lUbnuZd/fas5xzHyaugCl4P26vONF5NeqJJx5QNoLO2aPdgpq7fLKPbfh+1fvTjfwJ+QYuTjYwX42uhQexhPM9gIRFHZ5lbhYPUZd9ZorWBEloAQburYyETI5B3W4D6diPdoxUNKOmEsRop4YDknRiuiqzq/MFcmrVfNOYJm/hZDElXYa/kX4ih4yJxuvw2o8UWimnl4OZFlOle4n3ZHk8QG3YkNR++y1PmvtS7UaLGl5v09AdqQ4tDrNzmjbu2NZlOoAMEAt0zXZ4/8BxuqZ6Ci+AM+W3i6CiKwnk5231SdRFXrD+QGDoxApQWBDnJ/APbEjK/UB+hXDRTLpoZXu6tKQqSNTyseWIhG9sHAT8iWLOAd/UfAbC4eU8eHfTn9PEntrzPVdqTJFyNhpbDIK3UkEEuBT6f5L6/ABPWlXSMEFAlhMgIn+RSIZdsyRRTxlbsuKKm9lfUsuGvcVm2xfcT96Gr9TgItPh4IMKKoady1eKRfBi4kvPk1zleno3qK+fmQ8Co5yXtZ4xP+YkhYJPT2WuoKTSc+Q1jibkBat1CBssCs2QMN6bKzufKr6czMfl81EKRTHa52Q1RV74PSnMm82M7DketvZkr9bdpJ8C6hmuBEenXS3JMrwOqjF5YkaY1hklkevB8xt9SbjXG72Eh6Yxy13Qaq0P2+OtKpvKc41HnPjAmc51zDgoWlw2bwAwypee71bs4VPqENoKsbLwTOKPTsfNBVlSesffDQcJG4bRu7bV95Das5mZhcmFYDq7v3Fgiu6I5UaxN9+G1J2IoqsbLbWM4P3kv61IJhDLRwn/tmWx0KxldEtlxoOav1JkOsUAlj1uzkHFwghPocGGLOC1phcpNVozVJQdzm1mJisVK+ZucLH+I6crcX3Ynt5neBHnZgFEc43svv7DJY/bBzHfGERFxxg+ZAeu9c9VhmPl1wJT6FxmtrCDharzgzGCdOyCrkrgrMNO+B2Em2E1sYG9pBUEMtHoFqNrhvn2BhGmnEx/gCd70BB0CR61ksqWaE+9q884wswP3x6yDOdm2u6T0HLXxD/xYfwkThZvMc2izT+3ReLPeOnX9JtBY4IzBQqZNLqb7na/96Ec/dgVaEyZLq1uZUlWUXsTe1PJzCrQGEmj8gB+zxvDWD4Zpc6/7W1Tdm5vGJP653UBt+JrH+CTgLeTvtk7k99YZjCgI83YXY6nZuoW9xAo2u2WUFWV6xTRNTdMsFVuwcMBpPLOkOc1gUSwv0ImpJZySvJ6gMHCBdoJMEOvYU1rNb1O9t7kIFlWw2z5H8PagA7j3nx/SQpiD5PK82/YVjql7iOPVNSRcjYXh81iZmuMb2pNcYv4YHYOAMPmjml/iPhfx6DA+SVUqF1pjACgL6J0CNSlSxjYyLRIFAYVGdRhbE3EuN68EYERZmLdTvwM9lAl2dNe7nzi2fw2qoGMSpR0Zm0pjHftIywCYY44jH/zMM8jWr0i/l29t5feME0IQVOV0wjWgylltVJsaM/fHQUX57XD8wVl/RS0HbipUs12BY1vsY3/CINmTb95seyfX1aJsTQldBIWB6jYhy/l5rh1QFTkTpHUgj4/aJ1UX0LI+yQnyR1wiv0RbfTHa6MxSvNot4V17EhIOLYHhnfYPFRTT7IZoJUSLG0YRZPuoOS6a2UyxaKOYNmqU7KzBkdZb/FJ7mLEp4+P5ib0Iqq+nX1/qVPGCPQOBgxrOvL/sWvxI+Q8yDgKZutJfckjy9yjYTBkS4ZbaTHb9xOSvUbAZPqAUfx7dn3VIODKJlPqmmyqDl6SaOQuIIwuXQmLoetdZr1xIssoPzMsBTy61LwK13IbVXBVEgBnSEu5W70LDZFX1nhjiNuY7Y1GxaVYH4q9JGUIllKqwGIk44YIi5q1rAAQLnDHpIH2wqGejW7lDxUfo7KPWHN2Nn1rfBuDI4spMmOv3UXPstPqeiYzjCiThogob28pMYLkf1y9h+4U7Mm3R8KeqPeHDhQDUtydxXTfvxLerMaX1bSYpHwPwY54A4LOaqcBbu+T9Hcel1ZSBMAfIn3OF8gwAs6tN+qKHsjuIGw7NhFnjDMBApUEu2/FOPnTQYRrdAt61JxFDp0kdnMUo2B5iIsAmtwwdk79ZR3OffQIn0PV8qtqxFJ0IFGH3WqXRtm32EUs8iX9UTjE8dkNJWOOTXh2xH/3ox5cB23E5969zWbSpmSMnVPKX8zxa9VBnMxWSl1yMqCodRZ72eAxVZKpBT2g3oGGzdetU4O+dju8mMxWOdtdbOG9vXjFWvZOmSi809qWjhUORRLqSYjkuMVviDXsq57lltLlBqgYMZwpe68UCd1xWW8RB0qdZnp4dWO4MoVi0ESny5uVwMEgL3pqoLfnlJhXHJz5hN3kJAEKaQVjeRFS0U7hlN/6RUqgOqFKXNPVc5PNRy9cWlGuUXaArKfGNTIAz2Bfg6JFiPrQnEEOnRS7iFMASKuucShRh0yoXc8TWv/DLgKe10GhG0/u6Sv41ZFs32g+0nIqaH/5ALajKaZo9ZLe2lEXyB7mqb3HV36OWg7VSFSMSt+Mi8U5kCAFfP45IVZAUWWR5qJkoOxywmhNQHJj8A+cP3rNT2TqsyRSJmky0b9RklUDfdKbypjMVgAsHDPfJOHjQdJ097AfTZfnfK3InHzXVZwSpBKNZ+4+xV6WDNIAo7QRVGVUWmLbLU85MnnJmAnB9RUbTR8JJLzINV+EN6WbWuJ6vxfBIOQdvup24qxFHpznFwZb0jPCE99nzn8UOO4SOipomMpkSPVKUd5988AcVrkvfBAvttZwkfYCFTLE1CEXK6AUu0C9FwU4vKgF0p51EZBinGTcAMKEsmiVb8CPlWmrbbZKo/EOJUN+WZE2t933dZp2BwGWLW8qWlFF5dwK1cfGFXCZ/hC4MCuuS2ENnpF/zVwCfrfw+V2w7ARuJn1bui9Xc8dsX/MS6BMv1FrRVPjpEbp9lZjwuEeJpq4vSiEZEV2hLmgTtdlrakxRGuh9kf1nI55km70J5fv+EHXMz50MY3Tdy31nEDIsPnYkcYngCMQcWlnF4D/bvqKhVU8r55k8BKA/oO6z0dmB22alctHRq1nPbk+iXfOIhtivtUM6/K5jJOI9rvwG8Xs9xyX+k3rtfnr8f/fg64aPFq/hiUwMg8+rimvTzfvqdFgh1OP/Q0h7jPXsiYZFgqrSSyZLH6DGMQXmPn7AFDW6EKLE0M2C7gVrDhszjUMZ9WgiBKkvp9VdrwqKOQt53JgHgWB7jKZ8qrl8so8GNcIt1Fh86E9IVupVl3tg7BCugc9Wnr6H4DLmHUsPp6iMAzGval44+5lzlw+0hn8JiviAkpCkESaS+jxAFeim507w/UAsWFHOOeS0AqiM4Bagt24dvpDzoDh9VyUXxTID+oj2DF+1p6BgcPGBaXvqov+0pXzCZ+3woN1DTZEgttYNq136fuSy2DmSJichffVI7H75CMRGBi3eCLMflFuVSErFWJFyuDXfI80sk0JiUuB8Nm6IAvLGDw2rBKMcmf0sShSQqG91KLL2o03ZeE6Kvh0hSu+zDyidGAl72oT4lG69Igj1qnuMV7e8o2GzcdAo3RX/JiqZqwiLOnUOyf6JuMNtTwpI0JEkwtDjEGp/vA2T3pcm+/i8JJ2uS01SFzdLgTmpyuTTBkJ7/gu9oqhzOZi6WX+IjZwL/sg7lLWcKTw2alHeffBBCZClQOi7s7O9fbVyV7u1Z2j4B2TmHf6o3I+GkpYL9UByTdr88f05Je7W+O+vbvMAuYUt8tiXT0zfHyRW77V6gtnvbXC5MqXbOqR9CzeBMD51fuTOmlbA5ZSZuyAEsJ/N9dxgZA1zguzfkKo+GVIm5+ncppQVFOIxN/AMDlQJd5V/yDeymr0AXFus3j6Vw3OQdjv3Lxkv60bwYG89B8iKmS8u9J91dt1D337DXuAN5y55MjAAJbcR29upbyA0rOVd+nbirs8YdiGnn95XpClqykSgxkqgpiXtBYid81ADM7Qi6/KviKi7fdgIjpK0kXZXhiXVA132hXcE0jXQXil9EYEf2AP3oRz92LfQ3f8kc/UOes/fj7/aRmLaDKkucat+EbMUJYDCo3Gas2IyGSUt7FVeZPwNglvRpuq9f6kIh9smii7hww9F0CMnB9pNFNUaAz5wRDBL1uIXZMvl6TqDmR8f/ub3RAocV7hCesg8gTILH7IN5x8n0TUV0Jb1w9yt1f9kVtfu0b2I21xAQJseUjYNUDl8zM2IYXYlx5EM+H7V8tL6wLnOI9Cl/1u4CYEHTTO4f4ClmSjhIOAwu9lXUFCm9tjNt1xNty1LXFjhyiBY3iIXMWqeC2Y7nvblP4egdjjuffL73vvmpj5Dd0xbUZBJW53uiEPnVJCGH+thfUesatuPykTSFmlRW4TrNqwR59DaRVudx5R3zc1VFZrE7POu5fD5oYV3mbvtIXrWnoQiLkwfMZIxq8235eaIihorFzdY5vnF0xonKRxQq67BcicJYIUG7md0kz3ywzmigyZDSJtTBcHZFrW7wYfx4tcBwVTa5ZQwZvDt3AceEFhNWZlMlanjEPpzZzoSsRbosq9xunpq6hCRG2X5KoERQkzHiOYFaTpQ0tPUzHlDvIEyCT9wx/N46HRDpgG5ociUnpzI6L9j78JQzk4Jw95pYOyAJkeYK247b5TnsLvw8aEfIKAJmyp932m5a4h6SKIwaWMrP/XTJnPfPpina1C56lWmijs/ckQSCYZrjJgdInzNL+pQWN0yxdTC5yp+5cBU989hKZk1g/q/APxTHcalo/pxT5fmYrswX7ghWu15fWmHLco6XFnjy/nEVGJveT0gSqnBQUmIthbRTSxFhXcaQQU9Vq9rqt+xw3LsCr0oHstKewj32Ccg42EhMrijh6V30/nHDTJlFK7zmTOM1xxPI+EZ4MGXLt7FsaytnTavK6+HSVyjYtoDfqF628T/WQTxuTevR/re2XkNVwLuDH5q8jdXuYGKm3e2KdTLPDWx7XmYJNAaLOh7TPNXWZc27090+VT8s22WusxsKNqbfa7A/UOtHP742SMTaGN/4NhER52LlZZ6196M9aVEYVFllZnq0ftV2F5P1OQC8XH0HpDpNP3HGcFLy15gojK4czB/zvEdLKoASuJTSjIpNod313PWSciivGp6H158mTs567TnxQ8r1ejRMftb2eNZrranAyp+gu1l5gDPkd5CEy1XGd3gyxVjyw5+QDUsGP1f+RYQ4elwAhwBQ15akOKTt9JrGj9nuJDan+o2PGVTJgwuOooUQW6RMZbInFbXhDR/xinYTI0Q1urC4zTwdyR4CZH/mgY0fp4M0AFuNcHbT/fwl4BmXP2fvixP+Q/p1IQQhTUkHrjEjmxKvyBLzh1/COatmdRpTdwLNrmygdkR9TD/WZGJG5/cp2c731U993A6ET07Edlz8RaCO8za05g0W6T9DxuFVZxq3yT/c8XGFJ93prypJeRYxRW4LI8RWQilDwGRkMAUBlZ+rjwFgujI3W2cD+fuhAA5zPmI/xZuwPmk7CMOnUsl2fNQAGDCR//rEFMp0j2J3oLOAfRTPH+MzZxSzmZC1sBeS4E/2N9L/3+z7nKosqCjQaY5nZ7NyA9UCp5mZstcdsh9LuFx5FsOVWVj9a+By9GhmUi7Gown0RPUR4Az5bSRhIOHiWIdCF/zk7iKhlfKMvR8KDkZgDKOV7AX1non7UbGpS8nwtztqjgBJbqCWLfwxef2DnKt/StJV+PvwO/jd0jKmScv5lvIyALMTRcC52x3jtqK9uG9dLQlUKiOTKWlawcXyizhIVMSm0KF45B+L7brsXv8aF6Y48zea57La9gK1CQ1v8EPN8/WbU28BR2S9X5tUQInjZdz+pd3E5+4ICsR0NmklYEK7q9Pe2rTdMe8qdNwwXSSsjkr6LlR9tGpWsCrwTSxXYqlbxfGGJ7W8eEsLz3y6GceFjQ0xbjq5+3L5PYVrZCqncbQeq15qToZ+5DeNNmynWzfxjoraSdIHFIp2L3CNTYVofkkP0874tcH2ZbS3B1MJc4bheSYVBxVmSguQcdBcBzh6+zv3ox/92CWY+/E8dkcnQpxtbhE1bjHt8UQWjU6TJRxfwjwRz7QbtBDmU9ermoREdrtFBzp81IIYzA94/fQxVwe+mXd7vzT/oOLsZHGIBBHhUeov2vobbta/IInGtebFvJ44EIDCjW/ysvY74mjs5ROiC4v8Ikz+3u9IQOPbKdsbI+X5+fi8Dfz86c8ZVR7h5SsP7MTU6S38AWV04Ch+bXnno0rU8IR2A0lXpcGsAg7u1vGCIpkuGgBcrf6HZYndgeuzttN1Pet/R4/if+YEeTaL7TXA7plja3J6bRs37E4VtXyFEeja8NqPritqvkAtj8l1+rEqo+W5F3ZFewSYMizzW92rKv/v9qvGVxeoCZcgCRQcbMvE9RFVOypIMm667yhIslNlqCtoSnagli/Qqmz9gn9qtwDwlj2ZOukM9ECIuKt5wiXC5jT5XVrcEOUJBdit0zHKydDldDvG8kHH8ssvKrCROLJid9rrMpNBOIdumPvD0VM/YrdkBB0iPNcoj9NAAYXtBZCSwsilFQ5d/zRz9TswUVix7Vi2FZ/Lym1tHCYt4FfqQ7S4ITY1TQcyPV1qsLNMqSZs5FRvoFY+mr9bR9LoFrDGHYgqiy65w13hWukfhGSvQhqzboTtSPB2By3R0WmBklnF5Zyo6pxv/AQLCRs53Y/XAcNykGJ1zJI+xUKmyq4CH0M6kGN6XWx7vzNdWAyvLIalXt9gB0SwaIdj3FaxP7+zPDrbxaERHNn4atpAfV5LPXAxkGOx4LgIx9+Hmfmd+FUR3TyCOD8vuYO5m5J8rl/MWGkzY9lMLKDx4vCfc/aCrcQJ8KuCCb5v/qtDPh+13opT9AZmwks4KMLBbxGzvCZDm/1odf2XOoatgRH8157JJLEaCZfhiWXAAd3ev40gLW4IHYMDpc8xXJWgSJJoPxC9MLrD/aOx9YwT1Vk+apvavwddaC9ajkM7AVY4g7FQqJEGMabbo83A9N3IA6rMP91b0v+7zrXpnuR+9KMfXx0eXlvA28k/sTpwHhWiibmBy1lXsxvJ4OT0Nroq0awOYIUz2BNEsjLrGE3JUBG79lHzAjXTt/T06xDkosPsGmBIcbZqnynUtEiIbrehCRuNOHdpd7PJfRzLWILUXsvu0oas/WKunmUbVE4ju0sbaHIjDNNHZT6rHsR0ZVRhowmLZCLGT5/yWDwrt7XxwmfVnDQln910z+G/P1ZEM2ulQtqZJq0AYJXTfbsdWQ93es6W8viThTL3jc+d4cwZ/SNmbMxWwyweNCrr/wPlL7ClGkIiSaJlEnKsgQliHSYyxXYARc4vg9+dilputawDfuuj3G38XmpBVc67Vt1eoDZrbDkPXzwdx4WZY3om8LWr8JUFakPsTXwRuAiA5duexvYHaqmFrOSTjVawuy2dqSkS19kPc6r8LjYSS7f8BLgiaxs1kPkxhUWCxlQw94h8Ii1Jl4uUV7hNvR+A2fUx8inDGaGB0Oz12xQM3wurpYwVrsejbpaLSCSb6TAiyOej5kcg9eMSww7gziVr+K78LJqwuU29n/mNg/GXrGVJ4KQmQmG0pp3lNzttnJV8gtv1x9J9W4NFPXEruw/HqdyDS4wfEUPneuWfaVETIXsXcmTgaH5jnUsRbV6DaVjtsRiI66+Y2jvvl+SvviiShCRJvOvs2eX2huUQrP0szZn/rGUacHb69Utb/8zvtAXowqR+851sphLhtBMUSYaUFfFv7eq00Mzd1omMr9hrh2Pcro+ar9p6VPV9XKM/hYTLFxuuYn1wdzbZB6Jgc7r8LgdIX6BhUefumVb+VMKde6nUcAkOWwmkRF9sVxAMFRAqKidOE+DRNL4OyOejtj3aXV/DNBJps+gEmWvvGGkOo8QW1roD+bh+HHHDzsvv7wusDk/lBaudt/QfM1bazKzYEuBb3d7/ZPf3aUrPXP17VKa8Emtav09hNwK102v/xG/0j7Oes638vSQAluWwyh3CEcZtAIwpinQzn5tzHD81RpEwk3JaKc6yTNQv6Xz3ox/96B4a2g3eWV6LjcynzigmS6sBSMaas3p+dEXm5cGX85/qkwA43ZU4UppDjABGYABz2zxBDrMLQYey+FriSLQRpNaNYqBiugrDHKeTomHcsGlo91gEqiwoj2RXf66I3M7K2jgGCn8N38NoVqdfGyLqaG5twjEyFb9HrYP5hXUxTorRMYg6vq28wAXKa+ltFrTOgpR0nJAk7hDn0WoKWt0g1xsOhbTRTgALhZZE13NnT+C6blZFzf85Az4RF1PK/vzbgzFwGkcmf0e5aOZQ6RPaCRAqGcmEnO2UyvFMSjxADB0LhV8EVFaN+ibL1qylWLQxUDQwZXD22uNy4++M1NYBsKrhFAZWz+ZF/UYA5tUdz2dDMz571yv/YKq0ggQaTtN1wJDtjrtbPWr5xER8j/MFaqVdKD6CV/w4cMyXa7+ws/gKe9T8VQWb251bUdUENhKyuR+g0TR4Fmckr+Pf+o0cKn3CkPhPgLk7PPKDznVMVjIGiIrb+YJSCit5z57oZYzdoQxPVev+EzmPlbE2NGHx/ZS6Il1IolaedTezn70VdeAE9h4/HWWeT6EoGWepeg4JRaWJCLqyNmvfkrCGwEkFNCK9yC8dszd3Ph9jiKjjVPk9ANycjLMXyHqLXMfKXMiOrFEYUDuJa/iDBIBAYQWvO57s7hHGbZ4FADZ/GuopwpWENKrENt7WrwJgnTMEWJz3HHSFp8QhYBnYSJy8HQnw7iK3vC6ER0ntarFvWlZWX1uuF16ZU88oqRqA2kQLv1B+xOY2rwL63vCpjBDr0tv+1TqGWwdmq+XlQ1bfm+nQGB7N36yjkXAIRvZKyzCobjJdKRZ2kvnR43nC9KbQZ7RfcIS8AICfKhfyuOkZIv+iMkM96EBhUMVFcK7xMyLEKdYsbpYkyn2T0tchUHMdh1+495FQNBJo3Gmd4lHfttOb0NeoLZrM0clHULAYFU5yvPURIZHkFvWB9DZXm99mVfURTBxW+aWMIZ7jo5ZvXtoe/AsmQwqks8l+U9ftwa8sttwZwgfORGaqkS63/1nt1YzTv8BC5nzjp2xLzRk9hZlFz5Z4390THAcbmQNthy+vK7Af/ehHdzBnTX26Z7TVDXq0eYIkkiZW3Vrm698hicYWZzDPKRlD40HNn3KrdicA74r9mYtHZ+yKLfGQeQ3BgLdmGZ94MG12vcIBLWeZVV29meOk2Wx2y3CiQzsJaiXVQlpT68gr3atpTcRZoH+HaOre2t7axJKyI/n1Z4UEMGiVChG4/FB5gka3gGLRlhWkAZ2E554LnMjmhLcu+LEl8YZ+NeWimZir86bzKjB8B2d2xzCMBA8rNxFHp12E0JRjKdAVWpMWy9wqTk3+El2YjC6v4FfdPKYWLmK5W8VyFz5wPDr/YYUVXJSzXSiopy0IAKIBFUUu4kfWBQAMLAwwW8sOEA0pSEdB0ky04Tp+5o9CgCQDqEcRNntLy5korQNgkbvjiuCgovzMq/EDM4WVCYOyk5L+HrWAKuftMysNdz/I/TriK/VRi7k6FhKO47KP+wUR2bsgWlK/AkkN0J5izMrCRaN7C5tyGrL+l/JQa+SK3VjhDmGGtJSBop5E/TzgZKKpZtKVzhBetKej4ODmqWYAlA0YStmlf8oc0zeRGDGvdyggTIIYnSpSxSGZtYFMz9MHW88H7mJIcQghYK6zG5YrIeNQHs425P2x/BgqcSRcPi29mh8vHeNRNQfvxv58Cuu87eY4u3ODeT77jRqOP8zIdX53kTCQUFJmikFNplTOXFRWDzI5HbhdupCmVLb+RCW/0WBP4KdPyamg2gvUMtL2ZUqc9+TvomESswMsVv/Ku/YkZGyaA6Px199sOfOZHCOeldEK6gq1cjlhx+N4DxQN3fRR8/W9WQ7VhXtyo+Xtd36pz8XN/3t0sznehm/Z6vgMivNVNAuDKg5SeiIelLKmKPNl5Gpbe2aq/GXAMBKcJWcsTS9L9WBuiVVClo33l4eO79dCYWAwwZ/suzttc5t6P/M+qYJh3//SxpBwdb5whmOg0CyXMnDHuwFeoqKjp00ImKftw7xYHXFXZ2+3e7TialGB7AwhgMGPzMtY7I7gBb3rTKLkWigpwRoH0aXs8Q7HHmvmEOkTLGSKnAq+z09oT30fnwt1J0nR/ehHP3YWTbHM2uo882d0JNLvK5xMNLGJwcJbz1gE0XxetnYykyQqFu28pl2NikVLWykwO+s9jGSCoPDuR5Yr4ahBMFPKj46DRvYCu33dx9yteeurJfZE4JSs1/0L8takhYnCWca12Mi0EeQBtZxmBEtS4nKDIgGKmmu4UskvYfW5M5xYJHut5a/wtCYshgrPBy4kkhS5zfQFEu3tHCB7ifCWlM3OBerrROwtRIlxh3UatW4Rerii28fMq/qY10ctOwQoCChZcvX5TKKXBvdiRVMRMVdnpFRIXI6yxBmGjE2bVs6Y6heYE/hNp/1kPf868KaT9+Dap78gpMn88LCxebc5fPwAbjt1Eo7rcuzE7Lvm6IpMsnFUeZja1s7J6e1RH/8X8JUFatXKEMYnfw/Ao2V7Ifk4w1KqgqVKAs3HX7ZF93KvltDS2eYzktdxxtDjyNVXC+syw0QNe6Si/YW2148UTYlmPOfsx3POfgD8tLJzf1o++JsojXhb2rw4TjAlcZGBrmZ/lrDrTXgBVWZANMATzbN4ImXueGf55Kxtz+Q1ChQvqL3NupoaSsAFK1CMGH40+3wUppaidIl/YiDb02RHPmoApUFoMCIUECcpd+Y77wh+SwM/rbW3CDav5hz5DSxkBicmAnuhSIJy6vgwcCWWK1EnitMm1pprUls8hStSflPHlg/M8sJ7acBlXLP4WJJoXFW2PzEj02gc0mT+VPlz3lsXo9otJYnWLTXAktg6rlL+g45JsGEcySHnpV/zZwPfH3Ipl64/BAeJKwZNxNya6WX8g3VqSoJdodHJ0ATytWcWhjRULAppx0IirHsTVmlER+BQTBtKSxzoXSWkr5CItZMv1O+tOEWvxuALxAORKHRRhHK29qxy3BPEDE/s5riUkElRSOXTbu6bzKIfSTxadCkLmj3q4xNa92gbtwV/wLLm7Gr79pQXJTfznhYy1nak/LcHqWktD2reXL8qMYp3pJvTr+1K+ms/+tGP/LBatzFU1NDuBmkllO4ha0/amD5/UlPoWQv+dWYRr9p7EyJBMjCCw4xPAdjqdKa6t7W2UOsMoUDEEEJClWUSpncPMC0XctbS8br16cftwc6+bP5WmI4k1mI3k1RvM7Pn/dKITrwlM/9tFpX8zTiCRjdCA1Hedfbk2lG7Z9G7/doCTa3ZN41IfDN9ASOROa6ROgknO68zUlkHwCP24V6g1gOdgHyG1/lahwKqxEipGtsVtLlBCjSJScNKKAqpNMVMjpvUOZX4UvlFvF7reezdFxzGhopyLjC8RPQlQ0ZweCJTpZzj7M5vzbMJYHBtxR55x3rOPsOYPLSIgYXBLgMqWRKctvfQvK+ds08VsaRFNKgyc0w5Czc0ddpme9TH/wV8hWIimceW4/Jd58e4toXA4V7NyyrIkuATdwyjE/9ExWLasCL+2Y1j/7LgV6ysacNApZEIZ6qdc7YhTUH1+ah19MNF81ROulKxycWgmnf4QL8eGYcFzdMZmXyEIEkmlGv8dwf7Bt3MYr2qJER1s7+akr2t4xOWMH0LOEWSGFBR4QVu/vHnrPK79FHzfc6xai1PxA+ihFY+rzifX+fdo2uIHMGMnUVR/UJuUh8EYH7zUcD53C/9lgm6x0tXhJP25QOvp9HKOjfZ5yAWGszq1EK0zVGzPKaCqoxVMZF1azNU1u5U1AqTG9N02UWt25jrZiqmWYGrEkqLn5hC8VUFSXuOAAy0AoD3O8gniDOj7kl+FPgdAH+3juS5wJUADLC3sEo/D1m4bGmoBE7d4di/TCRchd+ZFxPEoFQ08y35JSxkEmLXTZ7+HrlwQTEvbN6HmBugiQgLnDH8JUXfcVurv7Qx7N7wFpfIK4mj844zmUYrv4hHPiSSBsNFNQlXQ1PCBNXMNR7rppdaPkGX7ZlO/6jg96yqrmcvaSUBYbCHtRg4rNtj7oBtZjKctsjO2PZL9PejH189xqx/jPf1vwJwp/UN7rS8e0a7YdFUPo5piXvQhcHEwUVMdhqYIlaiYbEkUcbz9o8AuHBAgMNavGpVPoGQFsIcaXg941UlIVSfKnY+P8fNViGv2nszSNQRL+osY+QFjC4KdtquyI/WhJnFlCkLq6xB4V/WoZSJZlqiY3jCOT7d9wvZqo+Q3Q+1udXmZXsaR8vzvSecvkk0xuQCzjN+SgCD0kiA3wEJpYCO9rSoaAe3e6qJHeiu4bUQgrvVuxkvvNaclW3PEA0czHvXHEx1U4KxlZ2p8X5GVsyws+ZwWZJwtAhb3WIsZD5zRvKZ64mRqOGufUMnDMotZXQfBQGVHx0xLv2/nke0pJ/62Ev4l5224/KRMxEjdbGKlKmzV+ERWMjehbidfgo/mrUBbEmJKUB+ef6QJnOddQGFVjsKDtdUeFWHqcbHTFTeJEqMl5zpvONMybt/PqiuyRDhSTYGTU9IJE4AuwvFwOujN7K2ro12N8Dx4w5J60oerS7gLPVFholtPGofgiSmZO13tzgb00hiI2E7vp4XWVBZoHfq3co1vNZkiX9otxClnXLRxDHJ3xInkBXMHGm/wx7KpwDUixO69fn98Cdv+mIt5voVEFPUwQmsoVhkslGm0JiY/DtxR8ZC5ma763Pgz061tMXZV1pM3NUxlDCSJBhcFOQP6p9pdwM0E6ZQP3SHY5TVTGlfcYzsCcwXLOf6qI1v+ZAyeRU2Mu85e7LR9Sok443POUjeiI1EYXuYXD68X72zSLSlaRpFZQORU9KGhU7TDsf9ZSPuajxme+evIKBwW+JMoGcVpZ2FmWgjShsJdCLhMN+3rqSj0FtIG6clf8lydwi6Wsr8L2kM05tfZk91HgAXGT/mfbv7VBajaQvvpHpGt7klXKs9kX4tbnRvwZDP8Hp7gZLtgo6R9lFrcULAld0ecwcMOcxb9mQUbNpCI5DNzAWwKy0a+tGPfuSHSGYqTe1kZPDbkhZxR6aWInBheKCMPWuf5VLd61O7xzqBW/Hmc6WgnMOTt2IiowdCvJrzHn7xjWhQocTYQqloQ8XCMpKQw7tYGJjOP02vX/j6seNzHMDgyvob+Yf+AZJwucL4Hi87+6RUk735pTVhZSWnpsor+bvuBZWfOKN5ouJbFLutOYFadvLwyMSrnKzOJUKc1vXf5m1nMlvdEixkxgbzV3h6irir8r4zCYCxQW+NOzt6DM9VT6CFMGsdL6HXk4pawI3znnYlVVItAPdaxxNuPwCyGkA8dARpACHFm4+jAZXogPzJ6U6BWpaXr6Bm4NGc+WHnStyXJdKVi3yVw37qY2/hW6zajps2R4ZM9SDa+AUr9fNQhc1nzgj+IN/frUPnZg7yVSNUWWIPZTNBu42QSCJSVbyxyS+YkfLO2miW8w5Tum1s2BFgAji+bEtXSjbri2fw3jbvQjrBF8yNk7ewr/wR4JlI5r7/0/KR1NleuuVEJ3MxabKEIksMiAay/Edy9xdCMEVaSRSP0vBZ4NsALGt4CkZ6C+qkWkSKRUiZlE2X6g6+4bwGcjMCByexNxTuXCdKY2g4j1qHIOMgR/ZkOmD7REr2S9xFccUgLMvGStEu/JUGNaeq6M+6JFrr04vRRqLAtxhaIDhB/hAAw5VRtR1f6E7xCG43TyWJihodzqiGBVwpv4qFzJDWg+nwIsnyUXNgVssL7Kl6Ijnfc69hY9IL1I603+V09S0A5jUNgBQVNv2ZIqXpxyfLH+LEngT2oSBaTNJVSaBS5xYyoL2dcLjn9NW+gj+rWaArtKZMT+1duEgfs/YRPgt4i4vZNd/kSe24tBdMMxHmu6k0SWuShnbjS5nYFTtTJY+jY9put82qzWTmejaElnWzjOeplOVDhxjJsdIchomtKDgoTQOA0rzbm7aDlSV+0jv11paCMVxkXgPAfsWlHLjtfQypyTM+T0zf6bmhH/3YVajbuhFFUSkq6341/H8BbW6QTW4ZYRKogTDlbU2e11hrIQkzkyAPqBL4fNT8ugFFkRArXY+uH7Y7L8o75n2AAl3lDut6Bure+mdL86FQki0S4WdB5PbVg9dOIKUSkndpfwa8+f2A5J1scitoS1ocv+l2vqvNIY7GVvvE9L5hEhSHVIpDKht8kga5FbWx5lKmyx8A8GTjGv5jZ4iRdxaMoy/gT6B1VMIWVxzDU5s2c5L0AQ9qvyeJSnXDccCkbh0zEAylgzTw+sLntOfvEfs4ehh7t7xBNeVU7L7/Do8dVDP3hHiniprIYkz40R1WUl9Az1NN7Kc+9hISDuU0IWPjGLH88vxSRsZZwenyB5CL3MxDV9TF38p/oVj2gpCFjqdWRCBTgv2W8hJb3DKK4hG6o+7TNPAADkj+EcuViPuyQ131hPllWP3BnFY+ClJJjouVl3m3/Yfgkx3wV/iOq/4zN+nPYyHzRfVVwFUMLgpyVOuTXKc+QosbZEHN+UDGuwggQSAdqHVAVjI/5hVlh/NG4wAaiTCqpGsZ/K5wnv00g9RtAGyJXQXsnJJedeEUbrC883V+2TBOBa5WfkZTewILL+NXJAfQlIwwiNa8hmOlOVjIDEqa+Ce5gE/4I97ekn6cSH1vQ4OZG5As3E7SwfkgioalzcgnKFEOaHqeb6hPAjC7JUKHPUCWj5rrInwLYFnR0gEyjk2azSE6Tz7mqMO55u1LuDWlXFhle1RNIUkcpv+LjS3ejfG9hOArjNOyA7WACilabz66y5cG02dwqgYJ+Uw7c7F8ayv7jsofvGwPjuPyxZZmxlYW5KWevKvO5KPYEGZKnzFKbCEoJTHMw9G7kQQwTJt1TiW6MGmSixlnLOE8eR5BkgRqBTuSPQaYZH5Gm5C5Qf0H5amm+EXNR0FKasgyDRa9/i+0SAkTZ56I5biYKCx0RmMiY6D1wPUtA//3rMgSP7IeYLDm9Thsbj8X+HpLI/ejHwDL5r7GiJfOxkZi+fGPM27vQ77qIfUZni46nxc3HgHAdQXvMN/6LgBzN5zKVp/cuq7KJAMD+NQZRRKVjW6GFTC0JFOJM/Mk4To81MCrqFk+HzTb6iwAkbA6BzB+OJI3b3Z4nXXgde0aYugsX3kJQaOW4ZI31zTrOklXpQ2PJVMS1igKaRwlzcNGosmNUKztk/0eaubGmWjLFg/pK9q2PyDt+JzRgBfUVIpGxkter17Cru28cxdQVQ3DldF858WV899n9vjOP1j4wTMMnTQTVdsxRXCYuZqz5LcJkaSoth1XLmGaWOYxf6wSZMkLuAdSj4lCHYWMqyzYZVWtnvqo/S/gKwvUyswa5ge8yWBB9Z24vgu+I66S1czJlXoQqGmyxF/UOzhEWoiDxJLqu2CPszttlyQAeIGaaqcUJytncNOyWq5VH6VQxLhdu495jZXAQTt8X6GH2ZSirekYRIh5/hVd9ISdOb2K15bUUBLWOGx8JpAJj9wP5mW2i7asBDITiL8io9gxIsJb+CrCm9iuTNzD/upz3r4iTtDNDsgAbgxew+amBP/UfkcAAxeRFag5ux/Pvcu8xtw/DuuemIoffh81tw+43Lk8aIAVylg2+3r7VDk7mzNw2wf8WbsLgLmNpwInpV+bUvs0s/X70DFYunVfZtvjCYoEca2MgcDwAaW0uCGiIsZ6ZSQjuzHGQJaPmpNlZI3PImGfTX9npf4XZBzmrb+Aj/UZLGwrRsHhYPUTTlefRhMWrW6Ix61ZyDiURYZ3er+ikE6ETJXGf1MpjobZ2OLdWGrbklSVhjrtv6uQ8N2IIoHMediVtDfDEbS4IQIkEVqQsK5wTfwfFNHKWncgb6sHsSrhUUmXb23pVaB2/XOLeXjOesZVFvDylQd2kpN+RjmCFVYbZ+kX8Rv17wC0JS7vVqDWGhnOScYfAJhUXsjP2/7BDPU/AMypHcSO1DNdx+Gf8o3kOmX4KcWfPPdnpn9+AwDLQk+hWEkkHE4xbkj3f6ztZgXQjyxqjCRwfIOwzL7xIupHP75sbPj8fXZLeVaOe+FknCkNSPKuoXN92fAnrdRgQVpsSZgxkv6KjyJTPeQYzp/n0f7OkN/mNPkdqt1ShhZkWjQM2+nEFrCaNjNZrKKVIBVqIdXyQBKGwEQh4Eg8uWATby/fxncOGsUegwuzAph84hj/HfpzTl9wAQ4SH+rfp5wmNGETFIantB1vRLEz6wOnbBzjPv9H+v+fhDSOMl7jrFR/MkCddFrWe6yuPIonNpfSRgBhDmMg9bQQ8rzUttPf2xP4bVfSgVqq+qT7KpZC6Rnz4ATxR4YYa9lDWovhqowo2S/vdoFQhClHnJv3tXwY2zqb81XPFHt2HQx1Xb6t/wuAOduuIDb8cgAuU57jm8rrfOaMYEnlldCJvPrlIDdQE6Kzb/H/Gr4W1EfXTPK09kucVD+aEJ4+n106lqvNb3Obej+7SRu5oOZm4In8x/PhooY72F/uMHa1u+wxm6PtgxNvIubqTE31F1kDp/CA7TJWbOI0xfMxE3nk/fPBX7k7S36LG1RP+uSj2jOAzrTNqcOKmXftoaiSlLWoGzhsNG/YUzhMXkhqAFn7ZX0eXzAgpQyrpXAJvha9Tj5qAOtCE/m8sZk9kg+mn3ujYnz68Sl7DaGhzUBVJI6Z2F0R8QxeUI/EjTXiIDhG3bEZ747gD9Q6aIy5IimKLBGQvSBZx8ySt+/kJYfJQOFxHhqdIJebvwBgz4pCngWKS8v5ZN+bsZe9QtnhP+zWGLPl+W3WR/dirtWAgs2g6OT0a5IgkwF0bF4JHce8Gm8SvVP6d1qq90bzHP5me9fCHeWdq5qFQZXn7P1Y5IwkIhIcPGhyOpz3S/R/1V5qytaF/FG9m7irkTQnsYbxKNgojoObx+j0y8BrFRfyzTUerffGkeM5ZOt/+a7yXPp1feTh3L0EymimYf1i2D+/Jcf2MPTzu/mdUk24IcHmDfcwdPiorNc7Koumb9o1kzv2loEc1UpFxpUzgbdrtO9w/2QyniWDf491AhYSU0KZPouhyx9KP25Z/gGPJR+iIuBdIzMSf2IrpViO24lGvCPk+qjNV6eyKF6FhcREeeetO/rRj12Bl519mOmqBISJ4wo+eOdlZh56XK+ONeeR6yla9yrmzJ8xceaJO97hS0ZWoBYpoW5blHY3QAthqtY8xhL99yRQ+azuZBpG/zS97Y+V/6Sr8zXaaaiySFfTcueKivUv8IzuJZvmNJzO9QW/Ymm1x2b5l1PEP596llZH48/NW7j3suMY1TofVaomjkbEGQtk000VVUsnkPZPejL+F8svcp3qBQ2S0cofotewZtMWApjcMHQ6p8n3MFJU0+hGqHILKLOz/W2jxdl9wy1lU3jS8eaoi9re5r6Ax155zDoYy/ljT09zXoQ3fcBT2k3EXZ3atn2A6Wn18X/Zh/Kmsxc6BicN3psZPThugzqQZYkS3nA8xsRVJfml73sKoWUSwpIZw5V8lEZZRXOSjBKb+abyOgCTpLVIw8r65L27g9xArTikdbt96euKryxQc5CpdcNYyJhI7C158uimm1nsqnL28PIFHPmg+9zcAegi6/XZxGt58MO1lIY1Phzh1Uw6Ss7z3XFggSQcSvNUM/LB/2MI+yodQu26nOxf3HegMKjyurM3690BHkVUVOCXE7nMehhdqUMSDo8Fv8UVDaehYPHLoZ4JgVQ0FDaD7Qr+bc+idfDZ7JvzHvk4337BjYAq8/1DOystdRf/1k9hTYu3gDwiUNzr43TAdjr3MMqSQMFCx8RCRhUOjxrfpyqwBYAX7Mt40Z6Oio0Zyl40C58SqOujxfkbXvc66gI46oJuj9Hf95Y0HdYW7M19lvfZry718dn9NEbXzs7MKXqa+qj7lLPyJRuiQZU6CqlzC8GFfUsy71HqK/V/1YGa3LKBEzt6Lg2H+frd6eqvZZ2B0g26xfaQiLcTa22ipGJwl9vEDV9WWFM4I5mtwzqzSuWC1d+hTLSwbM3uQM8XYLPsjxireDSV1S01QE6glhrDPGc3wsQxUNmjm0lZf6CmqxL10Uk8sv5Q4uhEg+M7Xd+5SBomnzq7o2NiI3Gr5QkA3B3J1IrrRDEDUyaMtcERjPb9/jp61SzbpQfiYwCoLRs4XvoIE4UR5m48EL6UpS3eAu3F0P+tXp9+/N+FVTCYB+2jmSRWc7N1Dk3zQ7w50+6RGh9AbfUGZqy6E4DY25fC1yBQa/cFag1VR7H3Ui9RNTNazneNFwiJJCGSBISV1gBQsdJBmuMKSiuH8opyHFGlHRULM74UNZJpJXETmRYDVy9EczL3tOZNy3hW/TkAK7cNB47j5OZ/spu2BIClsWlA9npEy7Oue9KeyRvOVNrcIIeWjqFmW4J1rpdoDkdLOFr+mEOkTwBYaB6AGchOImt6dtXKzwDRrFY6bE7PUt7mpYZPgWHsLKT2GvZKrX8/tryxVhkr+ZXyd6IixhfOCP5mH8Mxka7vb/mQK96RT/WxN4gV78Zj1sHE0ZGDkyl3GpjnjEPFJhGooKzhU97Ur05v3+4GGLv34X3y3t1B7pr6f532CF9hoNaoVTIteRsAvy0dk15o+CVWZVkg4ZJ0FXRhZdHHtocO7jLAL8wLOWJg/pLrz47ZjVnjyhk/KJqebDsaHv9jH8x/Uo4auT5mXSFXWbDd1QmLJEIr6GKPrvFvX9PqdYHsBd8sezZDFE9G/FX7PFrxji9pXuYnMeEMRi4Ylz6XV+cJlMJ5BE5yK1Q7gyw5+j5gCJQ3fMIl8tvYyAxpPxjYDUUS7Cst4WHNk6j/vHEvz2svFdMt0qfygHkgAN8bMCrLR6166HHMWFBBEhUlkGmWDnXRT9gd6G6SXyr/RMdEMWVWOb9Kv+avtn4+7DzOXToNG4lvDRmFvTbTzTyn8Cj+2zgKw1XY4KcD58kIBVSZUjVJwGyhULQTUTOBWkVYpoJGykUzVm2Ivrih9Ba24aNnSjo2MgodgZq5U4FaY201/Hk6EbedRbP+wp4Hn5Z3O3+gE9RkDDlEB6tklRjGiLGTKHvHW0gMMdf1qtLX7gbSTAEj3lmAp0Od8VLzR+nn3le7J0uc9PVr6IpMzcBDuHGBF+RcEBq+4/1FgDON6zo976clfiT2YoUdppxmGtQBGKgkXQUFO6Wm5vWbBXP5kztAUe3H/EnzDMY/bjqcD9Qf5H3/fvTjy0Bta5JXFm/loDHlO0UBb09a3G6dho2nRk1TnIdnr+eSmd0hxmewqb413ZXZ5gb56kjpGQyMLccRNm1ukMqCjN9Ve9LCtjNzmRsoTKvqqVjcZx1PpWigULE4RNUoE80U4iVom43shHm9KGKRM5ICYtjhShQjM7/658uE663BVCdz31ADnZus8wUeTRTQ5HrroSZDZPVHBzWJUp8wWqCwgg3iWL67OkIjBSQKhpNrhR3xta0YZIthlDQuAnY+yLaTmdYUR/YCxXK7liNSFakICf5mH9Mj1UfoTBftq0CtbeC+/MzyxnlsYCCFIZVHV08H4DeD96BSLE1vu8wZyjNDruan+q4TjMo9T6X9gVrvIXzcx6Qrc3LyV0g46Irg0dTzqiT4t31wKmhxuXDYMKZ349ivDLmCS7aehIFCEo1jlPwfU5UlZo7NbmSPBjtvm2+RnA8FDZ+zUP82MjaLnRFMSP4dgcMNo8b1qGQNcPWR47jt1eUEVIlT9srOpDhCSgcjlumjLKQm0EGlRVkBbz4xlXwVtXyypr2Fv7ro9IHh9dDG2Zyuer+MOc0h4BR+EbuFmdqHmfcRMpbwFpcGKoav/yU3iJaDUbZ2qN352Gf5uPDdha5IXKS84h3SVbnVF6j5z4eQ1XSFwnayG69bwyP4wOk8qcld0Hdflq+iQvYCvVd9fYwHNj3N1QHPZHjuum9Alo3nrkVN4WR+aFyGJixGluzBoOaFqK6JiUzU3rn+xZqXf8dutICAsjm/gy4CtawbtirzRcGBfNxWRowA6wYfxy0DhtLshhE4mCjI8RjBcPfsQDrwN+dYIs6BxNwAZwSqsl5zHYcrnEeIyRpxNO63jwMERjezGE5bHePFOpKolIkgIS0TxMe6Ic+fT5ofsmmJ95tHU2969NDvaVUc4txDzLDZRyxliuT5JpmJA7IEl7oD184s2BxJzUoI9fuo9ePLxpWPL+Sj1fUMLQnyzo8P7jUNqi1pZSkNA7y+tKbHgVpNPPP+AXrHdrjv3dU8Mmc93zt4NGdNr9rxDjvAH4wbKNK9xrQ5eqZBvj1p8XzFhVy0eiZFtHHVqD0ZZLewv/Q5Ghav23uxwB3HpMpCDgFMXzBjGtm07rcKTuRJw6Ph3Tp6Euonm9KvtZoSS5xh6BhspJSJwHxpT1YYpQQxGB7pTJ3TZZcgCW9eQiFG9n2zLWll9UcHVJlHtBN5tX0DJaKVwypGIAXCvOR438H4UOcWDb8Q3IP20VSKBi5VXvSe6CMftdWls7glKREUSfYfMJ7pgBbOJNejwgt886kZbg+ZSq83x/ZVoBbyjSNmWFlCeIokQA2z2hmIicJidxhVe+7atUfu5/xfV3yEr4nhtenAQtcra4d8tDBFEmiYKNjIOCh5aIL54OrRtKEwdD/QAojKFr9R/kZUxFCw+a75g24bXsuCtK9XKCXw4SIRCva8D+PSmSOZUlXEiLJwJ2+Pv+vn0dbSgONKVAczi6aOcQ4qyn6/fDem45oe5XLtFUaIan5vnc4T9kH00XUMZBte232xGPNNim6KEjvQqcnaxJY0flp6FwtSzvTTKQG8ICb3O/TTVYaKGsaKTcQIMJjenwRNz5z3gDCxbZ+huu985Ko+HhV/iVlyPRYSSems9GuHSx9TJWpwkIi0l+FX/uxATIqA433GaEcHOKBEM+I0SqK+15+pL9CgD+Zpx6tsnlc0jKOle2lOKYB9Ku9cPrlRZH7/S4NT6IocEoxvZQCNxNEJyrCg4hSe2OgtFM4dWIWQJA7g72lPnc9kna6u2qRld6JXuI7DR+ZYDLwkwVFSUdbrhpHgUuV577Erc799vPfY6l6gVrb5TV7SfwnA/Pqj2KzekX4t3kUQ5oe/Yd2PjmvTsh0aYpmAKmk66SDqT9qfqBBNANTGLoCingVqzYFBPG/P8Cpz4XEott9HbRcqf/bj/0t8urEJgI0Ncerbk1QU9C67P7B9GRNEO+0E2OyWY6JkUQa7i80xmRvNc0igk5BC3N7D/ROmzR2vr8CwHH770lLOnDa0xwI/friOQ8iNp9kAZaWlgEfFazcsGmMGSTRqKCFcVE5R68f8S7sZgDnO7pxpXEdl1DunF2m3Ud1iYqDwsp5tcJzloxZQGexUM1lsRMViMzO4zrg5/foax+Uu6Ty2pPrMPyjpHIzuu+VhfpiyXPmvPZMbzXNJoqaVm9uSVpZyZFCVKd/3LG57dTn7jizl4uGjqVvfmH59QB6bkPLEOu5V/0CYBOvcAbzlTMG0vBXp0IKJ3TvBO0CjKOQTdyy4MDHqsaeUyjHcYH6TFjfEFrwgtacVtZ+33sT0gJfIXuUMYm3r7fQFsybXRy1X1TdWMoGjjcyv+qNxu1bVt5/6+CXBX1HwL2Jls5UVgfMBaHFD3Ce/3a3jdcdHrSsUhIOcq7wJeD1eAqfbhteSr6dOwad014WP2vagyBL7jcrfgDk3cABLGj2aVnlSBixAoKY+dy6tsSnWWVmtiGZ2kzYCcK36KNeqj9JiHQb0TYn6WONljlE2e75nbSOB3jvPA6wP78nn1rEeLz46AfAqaB04OfkrBow+EM33Wf2qUUpOtdA/6R0qLUwLv8xpPAU4uldjlBWFm+zziDsKSVT2rH+LnylzcZAY0Ho64PH+s33UXE4ynqVK3QzA3dLx6ddOk9/lCHkBAAtbZ+BX/uzAIGdr+nGJlKFQBIoGUudGqXMLqXZKOu23K5ErJuFvMM8n49wTNDoRFjkjKaKNWtG1UuOV9b9mbGAFAMtanqK8IBPSja30qDL+So/ZRQD17Keb+cmTnzFpSBGPXTIj/V2apsEnge94j12ZZ5KLAPjr+2tYvKWFb+1dzITUMRI+6w6zuxU1nzCOowSy+g+6Y3jtp34eIn3CVGkFCjZF9acDVTS0G8wUizhXeQMdg7YtB/Og7fEADN9twjJ7XgHYWDidX6e8mC6oHM6UdW8xUl7vJeBah9KVj1s/+rGzcB2Hn7kPEFd04mjEkwdBzzsRALim9XcM1r359uDk7ax1B5Loptm8H1vb7LRIFDbc6rg9WqO0J610gieeSNDYlqCkoPeiPEnDYKk7gogbJyBMCiNhhooaIiSoSLg0xvZKb1scUlESmTVCh4/agFSg1qyWU5uy/cnNH7VmBWoK32y+jz11zz/0r02/5RvSBgpEnBBJEolZnVgQneBTqT5Vfo9T5ffY7JamhUVi8SSvWt8C3SXuagRYyPcOHs2Z04ZSEtYQQjB1WDHfmDKYzzY3c9msUZ3eIiIlOVqe7312p5VfWhfyjjMZgB9G+kacI+kXikr1uYdKhvCQfRS/U+5nhrSUJCrNDTdBl6nIztBF5rc5WtpCU3xjn4w3mOPh6U/EK5JgcFEm+SpE58LBlw2/VgBASXjneuC/DvjKAjUZm+GiGhkHKZ6JuP3zlapmBz7d9lGTpbSgiI3UoxqJomq0uwHCIoEsXC6TnyMcH0Su4lA+WOW7MznxF2zkdE8H5O8H2xn4G1z/aP2a/QKLMVyZ5dsegnEnAHCT8jfOSQWcL6y5DPhd1jFctTPnW1X6LvNwRPJ1xirewnhF20V0mD33Fsuj+/L3VKPtdeWeOuVdhdewYks9NhKb3TKGyFI6WAUYHv+C3eTVWK5MWSKAX9zBX1EL+egnrrpzFZ7HpONpS9FRT2x5jANSNIm5rZPT28jCQcdAwkGyE0g+HzVN1yD125XI3OmEyP8rXnrQPQx59yo2hsYzadqR6efV0bPY+4X7ABgphXshjdF38PdXaYqUtSixdtJL7b3IUTxueCHQfqFSzupiO9XJfMdaIMJZ06v4aHU9BQGFU/byPMj880tXlLwHP1xHwnSYt7aB+esamDHSCzKMZJyOq8dAIWbYLK1u4Tcvenz9hsZGxptnEhBJpohVHCnNQ8PCaR0DFO3wc7aLSJoaFNPLKTJquFR+nqBIEm4cCkzb7v5Way2HSgtIoPFt5UVmSN645jTtAZxEbVuSAaKBw1OJgbmxKhzXC9TmOruxilZMFHbL6dPoDvzfsSIJjm9/iomq19D/eeMBdNfEtR87h49W1fHfBZs4Y9pQ9hn5/0dwnEzGOU9+w3vsKqwx7+r1sQI+K5i39atIuCoN7SXAih4dp2DrXA6UtlLjFrPerSRh2j1aI8RNmz+qdzNDWkI5zaxZ8yIle+7YqLgrtFuCkw3PK604pPKRbPO+7ikdJ22V42NPprctCmmIRCkf2hMwUFjleoFDRzXKP4fmsgVa4pnAIRpUMUXmMxuGwS/URyhJMZIa2n6Z01/WOVBzlQCJlApnByIkmKd/lwhxYu1BylJMAATYQe/eXupTRBZCcMcZk7s8N3o4k2D2C8QB2H3kA5ovIO2Q5x8oGtI+cItE54T79mDL2Ul3aTuidj1BxG3jYvklgiQJt0ZpCU5jlrQaC4WgWcWAwsFcd9x4PlhZy48O7xtT8J4gt4WnrJ/62HsUmPW8o18FwFs1V9JRLfDTFP2+XjIOushP38mFpkj8R/s1e0prAFjR8CwMn9Xtsf1BvZhfWF5J/Rr1PyxqPZwOU9jtQVFUmihAx2A/aTFfOCNoI0A4zySzMyjwTepyqnKnCTurotcRpAEMiK/qdIwVVafzqzXjeEX/qSebjkq4G35O3YW/R85xuve9bQ9+0YEOGmOtNpiVbjDref9FOjPxDqepLwMwp7EAODT9WlHTEj7Tv4WOiS5M3rSnECJJW3j4To1TVyQ6RBZd2zex+hSqxmx6muUB78Y4b/NxPC0fhZKoR8ahKrGCp7QH0TEZLOr4m3U0Eg7jC4aSD3sefBruQadQKnU9OflVH2taEmxqjLFXVfFO0WV6An/VSJNFVr/gzopJ+KvFye3QCBuIEnBLCJJEC0YYUhLime9lL262t8jogNmwCYUAFgob6mPpQM00DBrcCBoWcXTaDYuN2+qJ0k4bQeZtTvKu7SVRHtdu5C8p754v6vcBus7Mzv7L96nY9j4fFV3CX1PUoO9WjeJ4Yz0/Ux8DYFnb7sCvMY0kX7zzBEWDxzBiQnb1Va39gr9peUhWjnf+2jd9waXy8+mnJTtBOY3YyNxgXkBrSvLg9UDPaSz+qqkiS1mVcMfp91HbFXAclx/8+1O2tSaZv76B96/5v2PWvD0k2tvSHBFdWFh1a2Dg5F4da5lbRbHbwnCxlZBIEhAmmtvzCvNhNX/jCu1zAM42fk68h4FaIhFjglhHZSoIaavdAPQ+UPNL84d1hUAwhO0KZOGiCxO7ZSsyQWxkisMqLdIYzjGv5TT5HQbQwGnyOwxTvUR21hyawxbYLTafQsmixQ1ToO7Pcn0on7aNxESh3goQI0BJir6fiLVl9dUG8rS9rB1xNmd9OpFRYjOPaTehY9AglTDC9SpHAddI0zmTroreC887vXQo3zWuoI0glaKREaKaFjdEAwV91l+b9TlTgVqBriAEBESGjq5oPatM/XfwT7nv872oEE3IOBxZOmHHO3UDYaed69RHAKhOlrO1YRk/SWkFLGwcCEzk4gNGcPEBPbe46Qt0rqj1B2o7AV9DbbKe17SrcZBodQqBIwBQtSB/sY7lUuVFdGGyV/VjwK93eOS9qh9LB2kAUjd90DrwQeQo3qt/j5myN5kKuXunSRICHYP71Ts4SP4M8PpRWgKLgL6jnxUE/IFa5iKXlUy2e+74X7DPkt8AUH70z/IcZBDL3EaGJx5NP7W2jzIuAG+GjuKF+ok4CI4I9dyHLRdWHnn+3L4zRRJEJINiWtCwCDgZfykhZ1cCVE0lKjyKxnJnCBebnpzstVU7V/nzUyo/1A/gg8ZiZBz2LM4oafl9+YTj8Ih0PDWWd8N/UN2clup92Z7GjdZ5ADxU3HVmKp86YVFIQxLguNCSsEhaNs31NZz1l49YEwty1eFjd8p+oScYU/0896sveqpZzacxSBSiimYUbCwzATuhexZra+ZA6TMCGFTGioD8pp6XyTewLead47nlnSkuAGVyGzZNqMJKNcJnjyuZiPGS/W1sXbCVEh5veDHzmlrIz83vcKL8ESESjNz4NEWNAT4LeP58j1kH8zMuAcBwM9evY+ZYifiwfuNGija/Q4Wo5fzaP/BXPN+egCpnqaCpqcXigsd/w4w1d2G5ElsvnseAqsz3aycz1YCEq3KXdTI2MmNSJrViyyeMlDxaV4Mb4aXQicwPfA+AmKszPukZdPeGqpobqC8N7c2yNs+aZYTeL8+/K9DW2sgLxsW0akHq2gpx3Tm7LFHzVSLuKtn16qb1wOQeH8eyHc5JevfRKO18FvCuZb0XgVrUrEs/rnGLsyj63ULNUkZLW9L/xhq3bmfjHcMfqEV0BSFJrBZDcRyXdgK8KX0bAl77iaatJmF695tT5ffYR1oGwBfO/sBUtO3Q2m9I/p6o5t2Tm6RTeKnyOzxT7SWvZlkJNrtlbHOLWOsOZPcknCO/QcLVSMohJOlYctHR4rLaHcz05D0ATBoc5em645GF9957Je5DwaYs4PJSL85NOFLIS47HLJijf48Bwutpm5H4U58Fagdv+jPnaO+TQKOl8SpgOJIkiOgKlyeuICzi6JjcMmByj44rBQt4zckwLQ4tHN4n49VDGf2HAAmELxEvpJ4zLvoa/T1qfQhHyKx1KrGRSaAzVvJ6dOrIyKdKkkDzefkgd++EyzmBmeimrH8HogGV15y9We0OQsZhQrh7vGBF8iiXJSLjF7JgxLfZd0D+akhvsV/8HQ5VX6GAOA9aR3Ga80tUbJ6ozHDJp558JYvKhhIqGcyY3TtXA3NVHxVJ9OmN+93QUczb5olcHBTc+cWYn2bQ0eMkS4IQCcLEsZAJCoMzau/gzoAna/uhswf/cQ5CETZFkezsjqpnFroBn+9ePopFT+BXZpot7cUi21MEu73QFwDKCklXwUHCQs6uFvoESXQy1Ybu9kmm30ISlIT1dDVtS1OCLx67mVfsf/GaujcPvX3CLgvUSttWMiNFqZudmMHvk39gmO4Jeaxv2Bsqe5/ECMS2cH/KnmF9+1DgyrzbxbP6APJ/x3fGr2VEYAMAaxsmwoDscdVXb2AQIAuXwdQT3fAWHZRew3IYIbam/eLmtI7BUTKN20kfZfBjZxzerVehTOv6s29ug+liE4pwCJOggBithNAVCblwIH+xjvU0z/RBXAUM2egZeCvCof7jpxhQ9ZP0sdrkQt60pxDAYIE7hnvskwD4carPwm7Zlt72SXsmq51McsXCTwntOd2nrOlzzpQ/wkKmIiHxTvFpvLLFW1zeG9311Jj/HxFvbaRSNFEhmoi4cRzXE7/6v44YOm/YUzhMXgiAlWjbwR750e4LphJyhMmJv5BAw5G1HhEfXddltr07p0mevc6vlYewGg6Eku4nCM1kJgH5qTOS9wuO7iI91T20JbIDNYDz9Tupbk4QIcYXgW8BXvtJIBhCSyV9KsgIcRRUeGucG1p+yVj9C1QsVm95HIZ6SXfXcQi7sXR+PhwtRpUzYmD7xd5MB31zrPFUJuEm9UEAmgkD13cadz4Vw4CqcIS4l60JlRg6bmruEr20gAmpMkKA60KUTA/495VnUOra2NmWDoDCxJa0XsDHbua7vUZ+nFJ1E1Ha+bH5HdRgz5orvyx5/mBBEX+3jiSGTrsoYIwq0dQWR8YmFNp1xtZdIVd0pSzS36PWa8S0Ug42bgHg0qIAh2z9G5BNmQMwhUaDG0HFBrV7pV+hZL6YR6xDmVaxx3a27oxoUOERO2PQ91jx6G7tFwkotIoI5xo/5xHtt7QNO5x9L/jdjnfsIYaaa9lXngPAx85YXCQMJFQ1sxhUVI09Dzmzy2PkUi360kMNslU97T6Q5x/f+DY/VBZhujLFbUGgCkUSnCa/y6/UfwAwd+s3cHzB/PP2DB63PYrPbRXZfTBy6XAmJe4niZYllpDPtqAnuNh8nLC6AR2Tx+LfgpT6qL8va1PVSZw+xwscTx44GKs5s0iOVU7lG8kbvOZhn3JpbySldxtQwIpVW7lIeYUNT73FXo0vogmb4+S5vGR0Fib5siB88uxC0XB859u2d476Nja2MP1YdxNdbpfYUWM64Ph6Juw8ohn1ddsY5Pv/wG2PAF7VybBt2n1CPJLZnvWeFyivcZ91PFsp5S77G3RoDf25oOsbva0EWO4OZYJYjyRc9pDWMtuZ4FXUCiu42ToHgEEEuApYq4xiiLkOgJicfVOvjk7iavNqctGR9V6u7sYC6wTKaWaBM5a4YVHrFiLjeStltu95oDa6/k3OVf8FwOwmjdnKNzLH65fn3yVItDWnH7e5QYocp1NC8/8i4obNc/b+LHJGEUNn32Dv6Fh+dcfisMa21gJcF7A9Qaj66nVs+PePMcMD2OdbdyF1QbNrSVhcbXyL3bQ1TJTWsb+8mFWttfRkwZ80HTa7pQRJemJRTfEd77QdmM3V7Cd9QbsboErx2AYd98EIcRrdCIW00yIKCJHpAbrfPo4qZxsVopEjBnrnVcMkLLy50z+HtsfjzHamEBUxgsJikqZnCXwJKzN3x12N1tZMsjtJ/oV2PjshXZVIBCppT2Sfk97a7kiSIKwpxJIGq9xBTBJrAa+1ZG5LUa+OmQvZ1z8taxkWx34sZJTsvV+p1dKpUrQjfFmBWiAQ5lfW+en/942UMnubpyz9cMWUPnmPnUHu5+yvqPURGqUiDk/eioxDaUTnX77X7hTn8tukJxFwy/CJ3fIj2zj8FM5ZMCYl6CF4o4e/z2ggu3zb3UVySVjj0pmjePbTzXy872NcOGt8z964u/D5GBWITJZH7UGwlRuQqD00990RsnzU+qDndo+W99g71Xf3cds04BAuaLiTg9QM/cyVVCyh0ehGMFAwfRSz3EBU1zRafIFQB3Y2UJtuL2Cs7OVYnzZOIV+glqv66JcoDxeVe1K9OehpRQ3ge7u1M3XjFWjChi2w3B0CAurdAlYUHdjj4/UWs4tP4MEtVWiYHF6+P9KatxGWVwV13O797mo2rWbDvBdo1StpGngAh+xWQVFI48f2X9PbfMpuHJVnX9N20gGJLIkur5NmqZhqqwTLlcnXorZGHcVlyTv5QP8BAKVmdfq1pOUw2xnPFcb3iBFgRHQCUtl4IqueTVNZ/67dyrvOnixxhvOcs196bF2hPWnzuj2LEqeVdjeAjsEeYg0Rt4qAmql4dVQL10hVSPYEkqgIJbtSl+ii566jQvaJGM+zVlH6+WFWkGnJewGYJpZxrDQHFQvaRtFjGrfPK0/IKmrW779fnn9XoDFYxemJu4mIOBIuz9gufaxx9bVEwrTT1xrAGG1Ir47TntPHpcfNdG9RwrTZ+Myv2LvlDcxmmc8+PJLJM0/Ie5xtLV5AEvcrvyba827bFWqK9+KklLIhwPTmrhNU3UFg80c8qv0WgAXNs4Dj0pW1rZQyJXk/Eg5TKxWeADTJ4QhpPq1uiKVuFXcr53JKoTcnOD7qm21lEnQtpsQl5o8BqIzqzCV7vbLBKuJjMZYABlspoTIh+Jd1KAGRRAQKyaR2fOOWbMpoRsPEQmYbxWiylFdluysWRXcQ1mXakhInGDdxrvw6v1E9Gjhu3/ioPRi9nBWNJxIkydUDMwnUhBxJJ/SiIpZWhOwuPHaPi46JwO0zn1xJEgRVOX3faU127Vf7VUCRBMUhlcaYSYGuUBzqD9R6DT/NLulIrHS9CXSwnF01K5HaCJP0VB+l7mVfVc2jFXWgp4vc/eNvcZT6AlER4zHrYGSp+8SCnx69Gz89ercevV9PUTdwFlcss2klxFo3QyvMlaDfHspal/Gh/n0Gi3o2OuV8S7qxT8fY14bXkutf7HnfbYHTkrWNpYR5e9AlnLOx800ydwLxT3oHSYuI0k4MnQJ3DPn8yroLS2QmBdfMZPWyDK/9HnOuy+U8hq242MhUFWaawk+T32EgDTgIArEqeipjvs++B7HprQFUOR6t+B/2kXxsjqVKbCO5Cy/9jeoIXnW8G/iBRaO4qehXLEp5Gz1VuGP6ZXNDLQUP7Ms0keQVexo/MgvYY3CUJy6akvY6M1yZa7g8b6CWSCaZINaSQAM10iXF9+bS3zB/nUfn+U9R5yTLlqYE1W4pi51hbHbLWO9Wcp5hEdAUzEQMB8FcZ3fi6LiiigFJi61uJqjZXdrI7tJGnrdnpBeP2/NRixkW/7Q9Jc+71D9xrez1k35cfwtBNZOB71gwPqZ+g2WmxwS4IWf8fgloPzr6LGpbsyuI/oXpD5Qn2V9eDMDn9TOAns1vG0LjWWfNQhU2BZHRyMm+E5PpR/fQbnrfY4GIoeBgJU4HvXjHO/6PI57zu4/1tB+s4zjNtcySPqXNDVCqDKFBLU5fd0nLQcS9ioIqbJrru+4Zq2nxrrN7rRN4jDbi6Hwz3LMqXyLnM1U371xFzY5n2k1s1Uss5jJuHCS0Au/3oksO92t/ADyRjqOjR6S3u6/yet5eXoeJwj3lmaCj1Uev7EiEV5hb2E/6Ag2Ld609eNjNiH1VWBH+bF0MwO5l0byBWln9x3wcuAyAareEU5I3UCRFaA123IM75haxUy0Nv3DvZ6C2ljBx7rZO5lbzDExkKgv2zmOY03NsdktY5nr3JLUgc49/reRc/rxhMy2EPcXfHlbUpmz9L+sCGW+61ckF7KxNUgdCmi9Q8323fc3M6g2EENxwwgT+8dE6ztt3WK8N7r9O+OoCNd9j/2IlNyB/nh9SHPAW5K/aH3br2FrOj6WnUf5ga1O6p2aOtPvX7ot2ynfjOcfLohUQI0gCCwWlB8MMqDKDhXdzGSrV8i/3p9CluHnPcXD7SxyiLEXCQW8qAnbO9HBe6CDeaypFxmFKoUdFdX3qcZcb32fYsHMJdbEIz62i+LNLVyhPMVVaCcDS2PZV+HaElwrP5IEtmz0fNeVzDlbex0Yi2hymIwCU/YbXjssl4jkUJXXTL7wXRRJYjsuZ8tvpcS2LnQ70zGBTkmW2jv8WNZ89xv3Wcbzh7IWLxAp3KOW9XLD0BtliElJORWXHC/V1i95hzxSd5ih5PpjwxeYW1tY0sNmeSqFow0WQ7GLRn2zexov6tQDUUQScnXc7v2JZvkpXdXMcG5ljfcasBzclGF0RQdu6MC1pPc8Zxx3GH2lLWjxr78enzigOkj5LBztxN5NNz1VG80OrXcwZ8tvEXJ1JIiOOJOvBrAxx3LRxXTdrUZpbQfMrYh6gLOUw5qJgE6ydBexOXVuSIaKWW5T70YXJNqOE73KFdy58twnH7rl4wsLoITxmeQH5TQP2YNzSt7hIXoCCRaRJBfq2h7cfndGWtDhSnp+WP280dq4K878CdetCblfvJe5qfOaOJG72ridS2voZD2m3AvBF+2QWKb9EIomOQcIwUaxMsNRidS2osDVVUXvbyVDETpKKejSWLH9QLOTmjTi23SXdckdolqLMtscTFnFaQ961OFSqZy+xgrBIsMIZQg0l6cqEpmUo3rowGVCQmc8cNUIspdzopzX7za47xNCm1j/H5drDANxinsnn7ggmiTUERBK59mg6DO+6qiTJvnEMFA18FLiCj+sO5909bmLeunoulV/gZ+pjtLkBlrRMhV7JicA4dy1jJY8ls8Ut5cWUuMgpod5VZ3PhD7z96pabSvbl2uqT0bBIuBq6czh0QQPNB0XNriT1RvimKwQ1GVKF4Dpfki+fOudXgRMnD+bEyd33nPu64ysL1CTXZrxYh4xNNOFAqjdNzllo276eNa0H8vwltKT3l0QP6TXBTNbhB8pTLI1fS3e8jnYV/NTMN/UfU5GS6a1NLgI6+6Plgx6OZv1v9fFPYWr8I/ZU5gHwaeupO328+aGZvGl5mfy/Fnt/n6y8ku/XfQMbiSYifEeSshQxj5DmM0jUYyETjg/EXylTZAlFEriORRmZ/g010JkO2RMsK5jBG47Xc3aKexuHKl4P1aeJUzLvjUEJLcg4BMwAiu/3qakaldEAm5viWYqenTIY3cSkE77PAcsmUmcYFBDjA/0KApgkLQ3YssP9+wL+RIwqS9nG0t3oeWqRinnSPpDDpY9Jkrn5LG2UuMq8yrelg+u6nSpmRjxDLTJE1zc6ZTvS0gDVeShGGxtjjK6IYPt6LAzX81FrS1i86UwH4C1nCvs5iwml2AHfkN5DExaRRhkY1um4AGU1H3CL+kD6//mORw1yg6XIkuBK9RlCbjtBDJKJg7k2dhtCTRBHY3P8t1nHijYu5gRpHglUDlaWcRavATC31cuS17YmKcJKB5Nrncr0vvOccbSnxE8q1Z77b/nV31RJYnLr20xXn/Xev2EkHSq//fjy0Jawsub4ne0N/V+B0ryOU+T3AXjebmdZLwyqAcx4hr1hKiEei1/JiIAnALGx7j0W6NP4oi1CUBjUia4FFcTGOZwuz2GbW8xSp4oaSjpVyHaEjoTMq9o1jBGbkYRLbe3BlA+o6sUngy+iB3GX6XXfXjl8DIcCpzX/nb11T5TrKuM7POnMTAdqkizzsj0NGwkDlfJI5nfVVbIry+w65RGGT4lZxeIoaR7nptobHm2qglSjS1f9ZbIWps6NUuYTb3NknVP2KObYD36aFuiIiESW+XNPYcghOjTtwiKRLtTtrAdoB7L94jLnLxpUKKfJW/cKSOqBfLt3CUnLVi1Weimokg+nuK+jK5sIksR1BJ9Ko9nkljGo8P8P249dja8sUAtYzbyo/xyAV5tP5t+cBnSmKSopkq7tCvRuGv5pksTr+tWUCq+kXxP7BEq6vwBvGHwIrLwjc7xELdA9QZFdAb/hteJTxfTL8+8IeulwzjKu5S/qH0iisE0u74ald/fhr3b1RZNaljx/aqGf0ErYQiaTqcoii498tvwWs+RFACxq2w/IqGICfKxeQlEqw/yePRELmVEFO6da5Kcn+AMtvyR/xdb3+SQlff7pthn8zjwTGQdV2PxAkviZ9E/Ga7MZKW1lgTOG2c54Do74ZSy6j4Cm8p2DRvGbF5dioFCY6mkUfUBH7S6yKmqK1GMfta3hcVxtXtbp+eU1rZ2eS1pOp36EhGmz2BlGAIMWtZyuzqS/Ep9vXIH6xQwCaijGThnab2zwzqfpyGxyy9CwaKSA9qTFYKmBclpoJ8gqdzCrbC8D+zPlX9yhPgHAnLow5CVsAkYmwPyDeQp/tE9B4PCvwd4C5mL5RaKptGZzrJUZ7kKisjeeP1rZi78xNS9znub5rm0RFRlWkGNhGkmuNu5FUzJzSYGIM0JUYyLzhD2Le+wiAO4p7Hnfrb8HU5EFrk+0xbX7ps+jH9tHu2FxnXkBKjYmMtcrO5eQ+l+Bk8z0cB8vz8GqfoGeUncBWojwnj2RsEjQEBxBYWJL+hoyEnGeUY9lkXUAAGeKrivEQza9xCmqZyB9o3kuf7OP6UTP3CHaahgtNjFQ1COlZOgbt6zrdaCWT/XRVjMJ39HSZnTHoDiUWV+8ZO/DvtJiatwSxrMO8GTgu/KitGtXcYL0ES2EGCl5zJDWYBUf2hMwUdjgVhARmXu57VO27CpQswdMYu/kfZwofcAPlCfRhUlSK2L4oAqWaRH8guG55s89wdNll3Lf2iWUiybirkYpzdRT2Gfy/Emfj5p//VCoS1nFCX8lszuoG3E8l8ypIUgSXZj8JNJ3JvfHWm8wVsnWO427GoFwftXlfuwcvhbtxAON9XygX4HrCrbGhwDvpF9bKY1kmrMIWbgUtG8A9t7h8UoaFqSDNAC5h/L8UtkYljtDGCd5EuKS8rU4TWn4q0YJNOKuhoKN3AMftFAoyGxnApOSnhjDqPIwb+5gn57gg4Kjea55NDYShxTsvAy8P3vV4Z+WS0lVZInigKCcRopEO+Ol9enXJLlzEGv7gsmrzMuopYg5pfmrG92FXxr2IfsI3nD2QsLhmOIMndLvy+c6DveljJB1ReIHwAClNe1p9bh9ME/YsziooPdl/IsPGMHAwiBBVeA8LpCEiyYsbNtG7iVdpic4pe5eLlSXYaJQ0PwrBritTBAbUbAQibHsiBbbVV/Jsq3eNX6I9AkR4gSEQTI+k4CarXjYEhrKSSm64p4VhTzbxfuUuk2MEptRsXHjo4DKrNdvaL6O0kAztivYN3k32yhOB2o1pdO5KHkTt6n3U0Irv2q/kYGxBkYFPMriccnf8IXrWTX4qYSu1TUdZYM+mvXWLGbJn/Jd5Vm+Ib/PC84MdMVbECbQ04FaIt5GpU8+urB1FR2LJwDhoyx+qk7h/vZKLGQGR6cyonYLZytvpV8/NflLhks1vK171colzjCOSZ2/3qg+ZlXUZIn10aks3tqOhczASL88/67A6JUPcpJ6P+0EedA6Clvq/cL1fwkbCyYjO2PSFPLBzQt3sEd+rCvYi+tNz0ft3KFVDGueD463ODWNBMKIsZdYQVTEKGuoBSblPY4azyj81rheNbunFbXdN/6HN/S/+Y5TRFNTQw8/UQb+ftSOJHBrqIqFtaOZIq3iMuV5LlOeZ3bdTwAvuT5DWsrZytsAvG1leuw0RQAuKjaWzyMyuuV97tLuBmBuy0nACawafDy3fu7dF/cWy5jMKj5xRrPAGUuLEeAS+QXi6JRZk/DPZZn38u61zzoH8KzhzYnfHjaSA4HmsacSW3wrbzh78R97FlWDZ+akabuPhuh4wuITrlc9mua/rVn8xPo2dh/11z6UuBJdixNHJ+S8RYd/Z0FQZ1LifnRMoqrNmz1k1QR1jdedzHr5FzvJFvLDkDorsG+TKxn2NRAT+b+IrywCcSWFxc4wLGSaRSGTRB0IsN3sH4BKZhKTlO4FIkpO5kH0MNCKBhUetQ+l3GlCweHIUN9lIvoCUdp4QL2dAhFjlTOYb6ZuIEvCRd0+Rm6WSu0jRaAOfBE5gFdsrwq5T3jnudxZXmOpyUCRBeU0ERFxTGSCThWD2temjXoB/m4diYLNHpHOYzDRsF1BAs1TtKMvfNQy5/EdXx/CkQW+91d0GtwINhJtIkNP6AhAzfAgUsxdymkCsu0OegohBMdO8mif0+0HaLYUkqgssV1Cu4BSPsxYwYQUpe4Lp41zG+9hsj4bgIVbS2HS9gN5f6B2ovQBFyqvUikaeXbzkbzH8dyiPkC58OirdW3fh2h2oObv6die+tdp9fdyi+6lKxZsuQWmZjLvyUSM0hRFVhYu06VlDBJ1VK0bCFyPYTu4CA5P9ba2OGFaRGYcbWTmtdXSSJ6yD8B0FfRg1599YfgAHrOGcbQzl3u1PzJMbGOyuzrds/G49g3a29tJoPFNJ8AieypHpN5/fN3L+HtO12ujqbVnoGOyMHwAD7V4MtynBIfQUreZitR2S52hfOzuhulk5kyTzDnrjfjHHi3vMkBeiYlCNFHBF2WH8JclwwH4afTLFV7qhweRbCYq4kSJE8Dos2rA1x012hDetw5nquYFapLVO+GNthzVx+vL7mD2umZA8GjRHoxK/oPb9V8D8HndFOCCvMeZJyax1nY5Rf6An6mPcrrzDsbG0yHVD9otmJmEzG3m6fzZPonr9fFM7/nHAjp/NoAlw7/JHStncJf6J06QvblaiWSEkQYIX2BYkGkpOLH6j9wc+C8Ab634MTfUfoMhxUH28FFHHc1ru/ArTf9UfZy9U31gvzXPZg+jjhtUTzxpXuvRwEWdxp1Xnj8VvO125CVM+2Q47al5d3pL72+gEV0hQKbCd4byDhYyonE3oLNHbU8x2K0hJHmJtDYtwwgaGfuMO9V7iIoYn0oTgPO7OEJ+5K7xcv3FdgZzokfycvU4ykQzlaIRgYtcMLgLEn8/dhZfWaBmqIXppvwTS+qZmfCyM7k+ak1yEZvsMlQsJL1zFJ8PshrAcGUMVBa7wxkX6ZmQRWFQ5R8ptTWAIyOV29l61yMS0NMLwjY3E5T2JNiScyRW+1qtp69VH49se5rDlI3YyIRiQwGvT+dy5WnOVzwu/ZytPyE09Zj0PuucyrTfxxMlozod87zwfaysT+CXttlpef7mV5mlvoKOyeP2wbyS6lHyU3pbBh7AXsn7AZhYVghNXgDQ0SO1ZczZHL5uD6rdEtpS2bW+ErRJqoUkLY9CnDAddoVyreJkKMuyGsDIor7tmM4c8/WVDAwYTHZWA7CfOYcTpNJ0kAZg+KhOHcjyUNvO95stLZ09rm21tdQ7IxkkGqgQTdytefLYqxpGAdeTtBxiPh+1oJtgPYPQ3RjlooXb1L8ggNOMXzI3eCBPN3sZ4ksLR3JSF+NpT3rjXuRkfrvjpI00pa7Vl0InsqzFqyoeawd53ckEan7vOoD3C47heXMyALOi5VBdC3iV6hq3mIfMCykXzTS7HuXJQmatU4kqbLa5GXXA3lTU9mt5iT3Vjn7V/ZClyenXrF4crx89h2RkjJ7bCf5/Y4uQMGw+d0dwo3kOcQIUh8d3g5PTGVlVJ01B1XQ67htJ06HOyiTcAlbXptr/NA9li7k/G5xKfqg+yWC5njltPQuxWqQoK53BBEWSbane+Xz9s93FkOaPOUzaShtBioSXOOkI2NrcIDVuEUW0ofvaAh6wj+UdZ08GiAZ2q5iQOZiPpfLhimoestcB8O2yQursGURpxyz05jO/wFeAzHyVQMMxYpCajt0uEvT5Ao+O5woLC73eslRiqXQnvLTCukKNW8g8ZxzTpeWA56O2KNaygz13DNdxsj57IJihnBaKNvaSPwUgJqK5u+4QnXzU+jAZv7DkaF7cVJ313Dkjq/q7jb8kfIXy/JnHaxjC/ok/IgmHkRWF/MO33a2Rn7Kk2rsgnhvQveK1W7kHY5MPp///rIeL3E4+ajtTzvgSECkownE9CltEJJBwcJDSFZnuIqx7gZrAQenjz5hleN0H2dv94+8wTvEmyWXJCwE4svp+DkwFaeB5NEWKKqhzozS5ETa5mQA937mRVQ3IUMJUWex0ZXGAsY595Y8BmONkJNS78lHz8/g7xlg4YCQr3WwqS18FagFVokPNuaeUm97invB3qK+tQcXix6VjadAGsbjVq6Ynu9ErU1E3l3PlT4i7OoPDLqRYzZOktWk6DcB/rIPY2+1McY13w+waoF0rY7UzEBOFpJTdiL3ZjHCm8RsAphTFeDrxLW9stkdRNSwHE4VvGz+knQAxN8AiRuO4goX6t5mWyhbfo/6Rl9UzeS4lbGNaXV8bHQHqFkr5qfktFjvDWeZW8WEqutZ9n6Wh3WCTW87r9l4k0Yhp47LIQv7v2i+9bdkum60oj9iHZ733YncEBxueBPdksYrT5bdRsQk3a0DPemEkJ7PIlRQVxfH1Av5/Utn5qvHfsu9yycYjiJCgjSCn/X9iixA3bVa7g1lte9TxA5Te9SDn+qgFfEFC0rI52norne/7QowmX53ccVy2pRTy4j5RJH+FrDt4tfgcnlx7UNZzW3bC9Pqkxr+zu7YEgCWxicAoIro3t/zc+laq18vl2VEZm6LSCYfy8OfVFIVUPtzbN9PI3udKukqWsvf9dXsAewBw43Dvr1+86WNnLA1uAQFh0EyYJU4VD1jHECRJuDD/uk+ToUrUoGGi4LDMrcrq8XriO/tx2n0f4bhw3oze13oiusx9zoE8bRzIgdJnPKz9zvuofeCjlrQcZibvJiAMCiSDF31KjbqvglkoevYbAQiqggBJgiQJSA5SH6qXh/LcR4cUh/Js2Y++wNei+SpmS2ymHFwoVLIzBweMKWNJdQtlEZ1xAwq6OEI2cjMtPQ20oorJP9TfERUxFCwkaXaP9v+yIckyl3M19YZKqxvExVvkd+UP1RX+a/+A4QGvD+9G+ybggD4bY19X1PyTopLqN1PJrhrYepRo6QBGJ+/rtH8+i4aAKhMgyZny28TQMdRC4JhO2/VsoD7pYnyVpCwftczmqtnMjcpDWMiYFAGHM6jIqxx/R36OYtGKi4QaH0+HXPHOwB+o7KpAbSkjWeF45LprQsW8OOC7PF19IgB3lO65w/3H1b3GeerzALwSvIRT637JVkq4SnmCk2XPsuNn5sU8Zh/KC3rn6rnbUs00sYw4GgPoOrP61uDLeHij9/3/umICfktwv1fRoCEjeX3FNDY7xWx0y7miPYEUq2Oc2MBKdwgNbgHNPjP1rW4JxSnRmqPl+SxTDqdDgdSwu/4OOipqIJD2voDWVXV8e9JAKgq831jQR7NtjBnMdiYw2/Gy20dEKjnddyy/PH+BP1BzHOrati/bfKL8IRcqrwIwt64UOHy72+diTmB/5rVXomIxsWAISlNm3H2RxOnHjtFqwJXK04wUW7x+5oYxMHDKjnf8H0dnH7XeLa7LGxdylLSKdgKUMjiLQp0wbE7ljfT/v7W/ycl5jlHfnqGcvuFMZb1RSQKdySWTU/qG3UO+eXtnKmoBOxME6Kn2iZCWuzQUFIcz97bfnLQH+4wsYdrwkqzEz+zh3+XMtUcBgkOlBRwofUadW8gqd3C6Nzea6oMrNGo4QpqPgs1z9n584mb6uDdSyccplecrBuQXctPcBO+lLFEADkveSsSXRJo8tIj3rjkY03KpKu19EDG+6V1e0P5EhDifuGP4pXk+NjLRkmGp0LN3WFPbxo/+s4htFIMLUTX7nGuD9uAi48e0uCGiZYN5sIfHj7StYVngQt8z5+zEaLORj3k0pLh7jLd+9BxfoY+aX6Lbp+iXE2z85KjdOHhcBbsNKOi24Z+WG6j1MJNQEI5woPR5WlFps/v1o4l8EphBdTLGABopowVJ7nlpf7i7Kf34h003A5f32fj2bX6J/ZW5yMKhqPYSYOf61P6rn4TVvBUVm5MKUrp9PprFzeZZjB1yLIrsSfT7TRghP7VTVyRKaOUG9Z8AbKMEuG6nxrl2wJE8tKaAJCqnyO9xp3Q3NjLB9qGARyHz/8Y1q43zFO8mv9X2goxBRd4N8Qz5bUZINQBsNK/eqXF1IKDKyNgEMEgkjR3v0AfwX9+aImVdj93peZJ8GWe1dBgfb/BEOeY7u6FiU0QrG1wvEExanRcxZZvf5Andq4bNbTyRrgKNrhTLIHshNLAoyC1F17Fqmxd8ndyUZOSmZ3hVvwuAv1jHcrOVuSn+0foG92l3pv9X9MyiYXsVtX1bXmac3EA7Ac6ccBm/PTnbRy+QU1HzI5kzfv/ibqS9lt+rf0XGxmoYz5KizM38EfUmSkUrGiYnG7+mhTCWr0etO1TVXLwSOIYFlmck/kTpGAZtfIfvyy+iCIcB9QcBfScoMveJ23G3LWX0yddRNqi/Y6ID7YbFVGk5kyVP3GZFsnkHe/QM9727moc+XMd3DhrJBfv3zMD5y8S06sfYX51HHJ1HrMOIGb3rKdqv9t9crnky/wtaBhCUJxEh5lmdtNanLVYMV6bBIK9NSE1LZg5Z4w5ijevdx8qlnglF+a9lBYtKGilq2Ajs1/VO28F8MYE1djEREWdI1Ks4FokY+0ufE05VYD9y9qAonGErFIc1vrnv8E7HUhSVjtLi79S/pmnpf7S+wR8sz6ang7E0oPHjtHH20/b+1FtRTpHfI0SSDW4F/0y1n+hdsCA0LTsweEO/hnm1v8LvNzqwcOeDh4iIs4e0DoAF9pj0uKZpvTeM39gQ48S7P6TVV6k9ckK27vboYUOJDz+chesauH6/nqvthnwVub5GsFMgD4P7A7UvDV+hj5rFdLEUWTgoZgkbUhnm3MlNlgT7juqZmIcmSwwTW3EQOK5ET8lskiyDyCygnPa6Hsn77woUBFTammPMDnwfgBZCkLI46A38Mv99gVGxz5imvAvAvPadZy6/oRzEKju1MC7wegZnD/0Wl647CBuJJBq/TwVjxSGN1oTFefJrRIlhI6GbE4Hsam1IcagUjen/k2LnldBihaN5NdWTdbXybyaklCdXGhk+u+IYDBU1SLhUWk3p561U4FkYVAmqco68f99cqn9ou4bdA0sBWFb9FAw5tE+Ouz34gx5NlrJ6E7pDfftc34tljRASSSoHjEESNo4Lj9qH8qidPf6E2Tmp4voCPVfp+maiyvmTRwDVTdmB2tDiYDpQ29QYo9DXE2aQTb98xZnOWca1DBG1BEkyKBDmXPl1NCwGNo2nK4W4E2JPMlz1vIDW2WcAI7NenxV/k0OVjwkKg+D6/XhSe5K4q7HEHc6b5veztt2n9U1Gyo20uwGGWJUcI78HwGdxg/faMmMfJ21KL650DCDMImcU/7EOwkImEuy5TUmWPL8kGNy6iFNVT3BgTpNOV8ILPcWaz2ezz2JP0GHxv2so++HzfXLc/wtoS1ppSwkAx+p5wN0VTMtm+RsPcYjTTv1rCdx970Z8TdTfhrZ/wV7yfADesqfweS9ZBKqVEZNQgwWcvOZubgk8BcD7667kbutEghhIONiOS7thp6XuO9C+4VOulJ9kKyUsc4ayyPWupZ7K83dsr2GyVL8AWbhYhoRtXdwji54O/Na9gCbT+z18UuZVpEpjq/iX5mkIfOyMZZ41MasS3xXUVJJcwsnqHb5SeYp7rBNIohENeseRlExyWcVikKjnCuUZwGsb6AiIuqKrK6rGereCYSKjpCmpfa9mqgQzTJYImfvAztC2P1mxnmJjMxEUEnKIbx02mUtnZs/vQggevWQf2pIWBYGef6/RyirmFR3DlMZXWTDiOz2q2u4IY2OfcL3yDBcqr/K5M5y/WMczpPCgHe/Yj17hKwvUFDvGf/QbAXjLms77/ADom14cTRa8q/8o/b8jzu3xMT6OHsbeLW+wRhrOiMG98yf5MhEJKFmKmHYvvso7yn7Fidvuw0RhS+Fk+tKq0BW+G7Wz8xQ7P0Wqo5fLVcNZFLOOikhRSGVDA1wkv5ypSBmdq4VX1N/EFN2jzrW4IeYG9qVrB5zuwZ/9k3yBluST5A83LeH9FGVjrTOQ68wLULAJFhRxDd4E/ZB2K1W2J/jwrL0fU0N9kx1zfAGf5fOq+TJhbM9HrRuiBq/rh/GR5dG0Hh40lfKCRdS05Kfr5auoNUvFzHfGEsQgFhyYZy8PXZm1AkRq5jFVNLHVLWFQgcLQkkxVbGNDHFmKsswZioZJnVtIiAQjxFZaCdLshtOURIDb5dV8R/07AJ80HQR8F4C3l2/j2qc+Z/qIEv5wxmQCToZuqYc6014nJD9hWqoaO78xlJYgVxyHl3IqaqfGHmOY6lXQ55CpzkquyaGb7+FApYY6Ciklk1DYS1rJOncAHztjecHZF4AfFo6lp8iV50/6rgXcvqPfikWPpR9PaH6vz477fwHJRJybzbOIiDg2MlcUdBZX6i1akzY3S/cSkL3Ffjz2O4KRnosffBmQ7cw1dLP6V96NfQ4c3OPjfK6MZ0tMIkyc4sKBuEomIEgkTX5vnZG1fUvc7BSosWk+P0x5qD3LLK5MeIFaogv7ka4wILaCSaKJBBoxAhQQRxEONVs3UDmkZ9+r67pZPmrhVG+aHsp8f+PFegYHzW61VnTMoToGz9r7caL8Ufq1nyqPMdfZnajsybmY4YG8bk/FQGahM5q4mxENCfh6x7sUgBKCI527uMJ9lIOkReiYuMHeV7m6QmLw/vzePI1WQmxwK9AxSKL1SgG3AwM2vsR7KZXQecXHMf3gU/NuJ4ToVZDWgek/eIxErI0Zob4tNAxKrOQbKTr8RGkdv1fvQy/4dZ++Rz8y2OHqXgjxIHAcsM113U6UXOFdvX/Ea+6JARe4rvvJjt86c9FPcZfyqX4JDoI1TROA17o7/rzQpOxFitQLr6g9L3+UxR+/yYhJ+39tsoN+FAS8Zt1qtwQFizZRQE+nqBWFB3LoJq/t+ciKyj4N1BaWHsN/awfjIDGzuLcOJhlk+6ilsna5Pmqp/4fpbThiTTpIA5Dz+Kg5cubG8AvzQqoHHkf+6bL78PdH3mKdSRHtKMLm0miG3iL5gqU2N8jDtldxHKcXcE3q+ZDi0hGHP2vvx97Bop0cmQdbCuC4gjgaltl3WfXt4R7zOhTNwEBBtWZQalanq+l6WxEwfLv7++X5Q5pCZTRATUsSCQcJh6liJSOkagIYyA1F5PqfLSo+gnsMbwFz9bBxXf7OC+1GJoo1qFiE2oPgkwQ4Zdvd/ET31CaXm0+ztjCTvKltS1Jb/g0uWTqZnyn/4iT5Q36lZiSRPnbGcqpxQ/p/3WcfInyKmPe9s5otzQme+XQL3zpwJO9wGBGrgTAJDstjxO74+iFVoyn9eIa0lBmxt4H9088FnUxVMVaxF1cv/jaWK1NcUsX5TfcwTPEqdz80LmO5O5TDpQX8JUXXfMHeh8tNz8i0O4F1LvxBrypLNBRN5E/WSViuTElknz7L9G4oP4h1yz8lKAw+k/fg23103P8LeLDlO5RqDbQR5JjkzRhq3wVSbQkLmxCBlH1FW0vD1yZQeyJ8Fv9q2ZNb1QcoEHFmOXN7dZyHlNNZYXoV9JcHTsRVw7S5ARJotFnefWeyWEWlaKBQtNPeOB6Ksimgbv2a9ONkeDAdxZmeVtQub76dkfo6wKNa1rhFVLulKA1NVPawwyBpOenKkCZL6fYSLVpOnRulTLQQEkn+4V4LnLjD4+nCJkwcFYtfmBdhITNUbGO6tJwLlVe5kFfZJi4AoL1yby4xr0LG5gTpIyZJq1ngjOFlezrTpOX8QPkvcVenPFFIVwJGmixxa+JMbuVMAP42pDeanjv4TNEyzlbeZFDKkmC/xF1soWznhJB8DAy3F20rPUGgj4M0AKGFs/7fJlVQ9TVcJ/9fQXfKMA8BdwP/7OL1o/FWNGOAfYB7U3+3D0lmrrMbtisRlEymCC8bXO32XN0mF6qms0SbyHjjc77QJ7NHLxQNVU1nwn47KSzxJeKU9n/zA+0tYq7OrdY3+SJ6EB/28Bj+hlClj33UNkQm8x/bqwLtGRq+08fzZ6/kFEVNkQQjxRaCJLGQCTgej/v8lgfYW/cqDZvdUp639+WkUFGnY5pKmBY3SBIVF5GXd91T+PuG/D5q3/EFWkINstEpx0ai2id97q8mJ4IDIQmtbpAICfpKsOneQTfx8pJaQHBP2V5M7pvDbhcT3NUZnxhFYkbdk/xA96ofs7f+AH9AkQ9+AYCwLvM98yH21N+gnCYuN6/gSHk+J6Uytx/Xje90PP9CaHs+anvUPs8luqciOaf6PPCFdKWp6iZA8cCRDPlsCVfKTxIUBsXbprO0xKNgjhTVTJFWZR23nWy6pV00jEesQz2KZGA8Hb+S2tZMFnlba4I/miemq1HLo0Wdxru87AierykliYpUvBuPtkzkVvUBAM6OPwr8Ir3ts8ykwGoiIhJEKsbxROq4U9Viwk5rerslgcksjxUw1te/6u9RM3ohp39i4jkMuQkTBc2cREPJFG63vMTJWZG+YytsKNyb68yfpP//pmlv9/v+/wlBYqjCppg2L0nThyIurUmTN+0ZBDBoJcRhptiBhf2uw+diHIvt0vR1ESSB6zg9Tr76q04RXeGtkd/h7JVeZW5WsByo5RfqI2kvsCU1h8OI7EDtLXcqc6w4o8VmoiUVPNzyWwLCoLW+Cniy22PR3Mw8cUn4Lt5t8O4hDyhDeixuke2hlrlWAqVVXG5eweOa19sbkwu7dbyxW59nceAGAB63ZnGVeRkAS/ULCAovOAlHvfF2rDlCJPmDdi/g3e9OsX/FdGkZP1A8aunC+L50JXSmKTL42ja6q2PQExQEFKJk1qXXq//0rqH2csiSnOoay+a+RutHfyUw9RwmzjyRhAiywSlHFTZJ5euR1OgJWiumcu+i4zlEWkgcnTVF+/VQC7gfPcEOV6au674nhBi+nU1OBP7puq4LzBFCFAkhBrquW72dfbCVMGcYvwTgQHkxD6s3ee9H31xow694kUXzX2Ps9KP65HhfN1S4tenG8HLRnNVf012EfBOz2ofSrfD/2DvPALnKuov/nlun7Wyv2U2vJCEJgSQk9N6rdBAQEUREUZRiV1QQEelFkaIUQaX3XhMSSAIpkN6TzfY67bb3w52dubM7W5JsEuDd82nK7fU5/3JOpm+Y3Q+Dgu8b/wClHRMFzdgH8CNLEr9VHmC/pJnyosZSYBimnpuScP+7eQwPWEdzaqBrvvH5IVdz5pa0KfBR/TCoG6o2c6t6B8PEFqLoqWvcezyMwrEcnri1y7zeczh/zA/41jsn05r0Uft5P9knaJpGRzZ7V6k+evsfVd0H3uym3XtWL61+CAFVIU8xKEv2Fl6uPE2RpxfCNrqqn8X6KM8vvJFNT8QzljCYY42hXNRTKpooKa2kLPI0JyTLmOY2WSwMu4M2r5cawHq7GE3TuNu5hXLRQNTRqS59kp+bFwEw01+YsnIdHZlPjpBpJkhtSzRF0hRJZPXAqS7Yh38lgyHDjCCGnd43zfZEbB2HP8ZPp+M2fDKcvhdMy+YG5wJUsxU/CUR+PkRMWgiwxB6CgsUWp9Az/bbfy6cYz1Khun0km4wrUeV02Wh/+nl1LiGrbo4xtCjYzdT/f+DYNqpjpopY+ttHrS1mpvwqAaby5Rl4xhIWJgrXGhcRdXSi6NxqWvi0bSRq8Uyi5q2caIy4z7AWJ31dJ9rSvc8deKl1GBtMVzDiHyM09t/gCmmsTmybqNMKBtNg+/CTcElPQ8d2bLs4VLS5jjPlN2lz/AilBJJOWCFdwXIkPrOHkU8bLXrfvGS9z1BVdBwzh8etg8kRUcIiwuHBDsNr94Ls7KHW+TdZ6/4e1jqNe3S1/7M6QU3mbXsSYSJMllZyZNJ+Z4NR0fdlvPwDhttb2frmp1izjuPzsuM5f4FbsXHJ4OEc0O9bvXNhFo7jRvMsbsQdP501dDCn7OZt+jqjP3rUBgEbPN83Jn/rQtSEEN8BtyKlrHIoHYVns60xTLLuQ8Zm74pi7uuHjQqEcpl08PaLa3zZYWvpnpUwke3KiAU1BXDwE09Fu/oL3uxQf0hwH22/Q77isq8Gx30xTtr0GDOTJA1cHzWAWGgwS2uG0OiEqHXygOw+ap2jbztqdg1QVZLPnsnsTsTRAQcQGX1Z3fVhen/PL6qglbrU9/7yQPF59jmb8EZ/w7YdTk78Fh0DFYvHVZ0W3yDm2mMwHZkWtfcBwOnx/9Auu4bSAXk/7JxyqHf/6xBrecw8mBgapb6uSn9RzwDe38MALe4r5jN7GAYKjZ7taolZfNdwewqLQhofKwqSR3FMsmIpwZTbzZN4yDyCdnxsdopoJcApwwP8Ze1Jqenf1tLHvaMs0LFt7rF/Q8dD8e2lPwRcI9yAJmftD/F3Un10HD/PWDOJORpRNS8l0RE37RRJ0xQp4xowLIenjempDMuUQAho4k17L95MuCXLY8R6LpRfQsGiqnFvYNsUyBSvtYamInt7FPvRzyvSiahtbooOEDUgbjnsEX8ADYMgUQyULmI5OwJp08fco95Cq+NngTOK1tg+vc+0g5i7poHWmMEhY0t67J3qyKY/5hEdihoOvm2oNnMcVxykA0FdycjU5rSt4afKK8yS3HfRk+YBFEp5mduRsNjY6PbLSQKGlKVzjt4MWV/wA+fHtCSrDE4pGARrNwHQtB1ELV6/jhvUvwOw2hwKXAG4fpufMJYTEm4A/YyhVX0qURaqj3ZHd70onY6DLFJEPi+gsjB5/3cIj5hIPG3NxIdBW7L64L/W/ixwRuEjzvT8od2ub6S0mWLRgIrJEmdoVhPsHUV+UONH9pUkLJshojqlfyDR90Bnhb0FWThUUU17wsAwM8vBv2roPFYakObfuegPopbtKZn1LeA4zn3g8rBxEyc7HW2+JkpKFCIq77hX1P8HrKw6jT+tHUUrfmqcfCq2YyB/zIabudb3JAALtxwO/Kffti8jo9YfPmqeh6KcVIvqvMtCdyN1q0eezwVfZA4Wssnz+1SJyWIlM6UlRNApN/aFHSwGzCsspZkgubQTEHHKaKCaQryVNt7tHiE2cZnyDJYjE4+OoqNsryLPzy+Uf+IjgYWEZMwkNYrfAfg8EcddkVEzbJsljlsCpMoCIUl8XnEqtyx1JZS/XzSS3nLel9hP4FNdch5Rf0vNqLO4eLmfv2l/AeALu4przYsBuDY0tsv8Za2LOFDaSNTRyHG6b7bfWHUs53/sEr2zCgdzdPL3zoM0gHjhOG41Tybm6AQC4ymIrGYvsZYECiudQbSQJgj5hSWwNr2enED6pdZB8KKRjtypC6O9qcs6O8M7WGyOGkCIHxiuaE6OrKSIWudsgPdeiJlWiqTJkugqgADsLS3nV+o/AfiosQ24KOv2dIdHxHHIRgsaFt/w56HK/aOc1hlRwyJAjEpRS4Woo2VTGEb2rTTp64yO83+Z8gwzpSUoWBjVP4Px/RMDF01rOSqprKhbBi2drFH6G5+sa+T0e11v0xtPncgZ+3RfdJWt/ytqWNvUzx2PxzhLvEq75KNdCqEpx2bce/nRdVymPAvA69YUfmJeyq/1TMuJVbVtdLwGhxQGCRRVcV7iGqKOhh7M55Ft2B5vgK3CIz/fFOl7z/FHj/+RkV/cTUJNy2fF5fQzSwhBUE/b3OQH+8Zs64Ycw/j3ythXWkKlqOVgaQFL7KGuVxhpaX4APdHEydJ7KMLiJWsar9jTUv89Y+9HhxbXs0XdK83+Nv4nhurrAfiJ8R18OyE3FdAUrjl6LE98vIHTJlZx1ZuXYDoyWjCPP/Vhfss0kZMq4rYjMGyB4RVH245qqN2NAaK2a9EfRG0jZIjlVQKbe52rm2tT6qcSr687nPyhfO40MEjUkS9aKdkOafmKtnQ2anLTa/25eezZ8DJ3qC8j4SDXnElnafFtxe/tC5GsGDI21/rcF4rwqMf90zyMCSWuzHl+sKtwSDbDa12RmS59zk/VfwMwp9UAzt6h7RRCcGf4RyyoFXxfeYrfqQ9gIaMkpkByKO7NnJWJBk6V3wdgcSJdwlee52NP+R3Cwq2Nb3b6Z/DjVxxyiOAjgR1r6pdl9oTO0vyQ+WLqbaBumSY+4Q5AbEfg84XIL65gqT2E+82jyROt1Hr6/Dr7hwEcW/8g12ifAPBpyzCgK5mD7lUf2z1Ep8MI1ioaxy2mm7HfTy3ix7W/5We6ex4vTfyQlz2Djhy/yqKDH8D86O+IKefi09ODnkQys9HSHskgauG2VVyl/JuI48MUFUBXGwVfDxlg73Fo79SH4otWc5f6V1QsWtsL+BGuj5pflbmg+S5+p81GFwbXGhfxtj0llakGEPa2X4f/cI6j1XTnO8sfJq91Edcoj6Fg4mucAKkuvR1DUdOnvKz9jMGS2084e3UrHDhA1DrO/wixmWnSMgA+jtX32/KtaFoptM3x48R2rkjRox+tT32++r+LeiRqtyR+h6XaRNH5oXEZMfQumdfe0N5cz/VJldZGcoCf4xcmJTTiEwnKjI10OHLEksG05mjmfbKqti31eWRJCH8gh/ds932Va/Vd1c+07FSfqBBQEXIYJraQTytavUF3z7bO2PvzG5GFQ6HRzHvWBJoJYueOZJxnmoOUJUhSLSERo1Trm9dbxzP0G/I7qffa/ebRLHMqGSs28LFyXGraQHRLqjdtqT2EVxLTuEZ5jDDt+EWca4yLiaP1WK5uivSxu0m9j42x82EndEh+a79hfGu/YVQ3x/jda64MfUkfA6eGaTDHGo8qTAQOQ2yni8DSVw2dlTgHiNrORX8QtWeBy4UQj+OKiDT31p8GIDk2B0kLULBpx5eSr+4Pef7/DwjpCnuIdfxP/zUAy1rHAids0zLajr6N4icOA2D+vnew49qMaZRFVzJDdhW2ZrfvuK7bU/b+qRfUz1X3Ablq8Gmcs2AcFhIOEs93yPP73YHwNcpj2AhMJBTpyC7LDIo4JSJ9qTo91MJvC+oGHca8mk2Mk9ZRLNxBTJ2HaIU0iUpRQy7tqXIZAMdj4F2R68fCSzr65744sPohrvG5jfVz1l5Efw2Su0OGNHuyLEXJMLzuufwyEo/zD/MU/MQJyBbnyhJluT42UczvzPO6TJ8tS6hY6QyOqge6/N8BrRui5h3YBZMvKO+LKmpYSJ6esESnx2pIV5i4/ylwoJvBWLVuPZfIz6Fh4IvkAfvT7IQ4LnY3haKZJifEDH8LtyrXAbDCHAX8usv2DmpdxM3q3fhIMFVaQaOTQxSNB80jedaahWU7yJIg3rCJy+Wnkv1zVSh2CcfIcwFYZ6VLPP2aTL7dyNCkWmoObs3DBmUIDySOxERGC+7DtC5b0jO85Y2KLMiJrOdSxfU4W9DW0t1s24xJW59JkTQAuWVjD1P//0FHVsQrCsN2GJd3h5Xh6Swwj+dAaSGDxVYatnwI7Dyz8bBfYZTYyCxpMdOlz4lFD8Lnz/7sns4iVNm9fy3D3f/oNhK1WHs6gBYVfvKBQfUfMtf3PcDtQ/2TcTp+kWCF7coutnQiq4FPH+JW9T1W2hXkhU7Gp6afu9ui+hjzBGD8qsyY5vd5S/8xAPM3HwCc1Osy2lubCXp8Ys8zrgUEx5aWZ4wgrrLvZ4jm3kMvi76NLTpIRzHpY3aa8k5KjMMS6UyjrKaJTkcf8xnyW+QLl9T+1jiPOFqPgkAb1SGMtFal1+/buYTBG2Tsa0uHgcI5xs9S3z+0HPyRLYwT60igELT73uv2ZUHIamatLx3Urg6t72HqAewo+iLP/xhwEFAkhNgI/Ipk/MhxnHuAF3Gl+VfiyvNf2JcVCyvOg9pNAHxmD0vVQg/wtL4hx6eiifTg35K23Wtj2B77sOHc94m1NbHX5P6NPDtS+uEq+sFHrbNpLoCsqJieS7jjIZoXUAEnNRh0t+eBLsucUPMc05W3U98b8ift8HYCDEv2xXgNqyXP8QhrDu/rP0x9/1HiUmRhU1EylInJ3/yaTB06IWI0ESKU008N+l5DULOr8EZ/I3tGzUuIen7ZRSyZW0zXNKHIp3MuUJrTffY4W0btC3k0tTEJn0iQGyrMMhfJ7fIaXnv6yJo2cZj0Ce34qJLc0klvlDeasNgkV2LbDWgYNDkhxoj1hIjShp98JTOb7LNauVZ1VS83J0qBm2mOGtSRS53jqqs1tlXTMa5OSNkHH7nGVg5JRq2BlMDKc8L1PEuYNn5NxmpYw1WqW+L8hTEOWTkpNU8Fddyn3kwUnRanHMtjWbGftJhNThEt6iB+E3F7TI4LlpOWjegbMqw1ZIHkydBJ/ZQpBpA8nlkAK5yKbSaVX0dEIu3k0sa95vE8ah6KhcT5hbPoLyHzLaIUHB/jJLdVfXbNbOCsnmfaARQGNe5Rb2GE5AbZFn/yBhP260okEvE4mki/e+5Sb8VPDLbcAJV9L5FrsxQeMQ8lKKIIfwEnArKevic3OCXcZZ2UMU9LNJOoFdZ8yOHyhyDDPGkvdEVCCHAc9z7tCKr0hmh7GwdLC4ihISm56Dllqf90o6lP+9PQFuMR81iKRRNSsoca6GJoPcROBzoKRd8CKh1E7W17MrXkUkwzgwMJwnE3kzvNmJuaVgrk85Q1C9OR2YIrihRFS5WlBojTSM9KvfcU/JTIhjaKRDM6BoN9/S9F70VGkLGvRM1ymCRWEhKubYEZ3YfpW/7FFbrbajKn5qfs7IBpfyMgMvsqS/J27nH//46+qD72+MRNqj1+b1tX7G0A3lNawwr9PGwkPquZBTyzrYv7f4ccn0LCUVhll6Ng0SR3PwDtCVUjJ/Y+0XZgWdHhPLI2FwuJWYX775BXkm07eJ+JHS80uVM5Y0d5Y54O08QXqd9NR8pa+ig8pOXf5kHEy/unvn1YsUvUrjS+h04CCYcbfGmi5fMHiTpaSsDlZXsaEXwcEcoU1mg+9XFWvvcP8vY+nbFqP3mtaEFaHT8xVGJsO7nfVthNG3hLu5IEKrVWGXAYuYmtHCQtQMWipC0OjO92/oxsVlKlNOxX8KkSppGgiGYqtAhTrEXoJCiv34vOYhd3aheyssmN0r5WMo7uELBamCEtRcVkUHsjMBUA35aP+bt2MwDzWw4ATs0YPMQMiwdCFzOvrpGDpIX8Xr0/NWgFmN94C3iKimTPdackhXGaOw3s1piF/Nk+jaCIESwYnvUIyXr2LMKZ8lvoGMSjs/BreRiR9CArIQcQoSIuS1yR8jj6hep2yKyxh/B4xS1cVXMUl8jPc7byJmcob/OIczoLkpH6zkbgvcFxnMysqiQRyxvJDcaZmMiEikbTP+ERWK6OZYvVjp8Ed5vH02hN5Zx+WvaXCdXNMR6du57pwwqYNbKrv15n6Bve5VPfJQC8Zu3FxcZVnKb2nzFwW9zE9hTuSonWHqbecQyueStF0gBaP38TshC1qOlwbvwXBEScB7U/cbicLH9u29pl2p7QrBTzs6RK69SKfJeo+cLUOrnEHI063OBKMY3MlJYQFhEq6waD58oujK5Jfc4bMhEhBD5FTmXTYobVbS+qF0ZzNQ8kA9xb7GLa8h5hnV1CIyE2MKiHJ2kaNQmNP5hd74zO6/88OJ1x7W5VTHlZeR+WDJpkUkwjr1h785I1jS0U8ugslaFvumXikZLJqWmlcDlXGt9jiKhmP2kxp0rv8p61J4ucYVyp/IfLlGepIxe/3L1BuabIXGb8MPV9Sc72jYP6CnkbqkE6YFg2N6v3MFJyO4LWNR+ToXYsdrKP2s5AUcUI1klVDLE3sCC4H1O2w6t4AH1Hf5Q+bh+EzFvWJExkJkhrKRcNgJWRhRhA9yiMb+QP6v34RILNTiH3Fv+Mmbt7ozxoCI3h+aRc+Bj/jpXBWB4xEkUSKZKvSIKpYhkKNgYyqjMDCJEvJ3hC/11qntvt07gyy3IdX5itTh4xR6OREIX95LnUkVF7x55EgBgRfNykZWaBVktDEFaCJieIToIIvi5NxSP2nMmIPfv3rK4dfg5nLXR7I84oquKgfl16Vxix9pTxuD95HqvqP0hl0+fWHQc9CPt6+6s6slhCCP6j/oo9pOVIwuHf6kmcYTwNwLzGVuC7Gcvwljr1FJ0tbPws5Rv0acM+wLkAWPH0wNNS3AGp327jWuURfCRQo37+nRwI6yQySBqA4s/MhqrBPO41jyWBiqmGuZKuEfgNTil3WCcDcHxxBd/Msr1G0Xh+nLiUGBomEuudUl7Sr2WMtJFrpMepbb8WcvNo0Cq4wzyRIDH8oTEcqod40XZDJx3+lQCGpBP3l7HOiVGLxzfJkwHbVpVG0zT4hfJPDGQMoSFJx2LkDuUeyx1Yz/L138DqhcBJvG+k/ZYCzTEcx+lRFfCriN89v5QXFm1BVyRmX3soBb0IPRjR9PUbTwZn+tVHLWay1h7KA+aRtOJH9+3TByPV7Udxa7pcvNrJ5+HYfuybZbqYBXOdceDAy9Y+KcETK9aWZerukdnjmexRrdibfeJ3Z0w3WtrIrdpdACxpnAS4JYkJ0+aq+MWMYAMjxSbOGuk+f++Tb6RA1OMjQbx5CsGS3kvgDM+2JyQfgao9mZX4KwClPp3j+7A/dW3ZVSY7CwnlnX47Hz37B0TlVKaN61tuOty0jHnJktCOSqmpMw/lU/NeYnXrmXxs+tnckX2bJFbxe/UfADxr7cu/zMP4jfIg5yhvAGCq93a7vs62JTtD9dELFZO71VtQsBEI6FUKyz3/hmeobRoJmuVCPrer0DAxfXk7b4N3EoQkkff9t/l0weuMmXZ07zMMYIew+4iaonNh0pz0WGkOd2q3ub9/zV6qOwtBn05FckDoILbLR21nwisnv6NjAjMe4071r1jIJISOW2kLknC4V7uFomRZRnX8OKCQnLxCbEcgJevw/84JWYlaw+BjOOO9dOnInf1geA0wNF/nCe03DBNbyKWdcfEHu5S1/Dj3L3xRnRl5zpb16294VR+3pTdie2F5fM06Gr8ljwhMb2Wx3m30Rnwn2MtSbXsTxOrU75LVdRCS4aPWgwCHpKTL/mQnTZyapEJet6YQJE5r0C1j9EsWlygvANBo5fBwUnWys4/aMrsSNZzZ3K74w/wxGdHOkRX32qxbxuHSxzQ7QdY5pdSQh4N7roLdbLOUV8l/7cws8Hq7ONWnlYi5pYA12mD+bJ4BwDdKKznK86xY45RxSeJKfMQZVFCaGuhsdoqYa49BwaLZY1VgbOPNbMZjXKS8BEA0Kdet7DR5frPTd4vmqEFeoH8i1i98toWVNW2cP3NIvy1ze7B4s9v/Ezdt1tS1URAs6HH6RMKkxQkQJEq745bsWdthXN4d2uIGnzhj+MR0+48OUUq4pN+W3hXC01/3oHkkr24J0BY3uxANb4DmPvNYnrL2I4LO6blTtqkfu9VD1DrKA31Z/LpanHSGW7fShGpdfTvzrFHMYxSD8vx8OycPgFGsp0xyLVi2RPuWhYzaCm9bk/CLOO1aBTMC6SBKX1UfvURtP2kRk8QqJksrabR+CYxO/Vc+ZAzl33+oT8vsgLfvTMUk16+iKzKTDjmzy7QdgUmfxxoo5mhopFUSE46C1kM1iZeYyZLYLpuibYEsSRydJPym07d1mdEWikUTAC2On4TQeanwfJ5a7+oD/KWyv2oKdi1y84uyntcB9D92H1Hz4EV7GqNiDyNhc/TI8n4Vtfi6IugxrQ0T+dIpB/Wn4bVpxDg2KX7Q7qQHwpXVr6dIGoCSlO2XFYX37PGYyDQRwjayRxA7m2P2h48aQNDvY6hUSzHutlWK2i5qprn+bMqUO59sZ/qo7Xyi1hYaxsHxm1ExGVES5m4gFqzgbWsSBjJt+kh6cl1yaj7nWuURouhI5h6QzBsvyT2Q8c3vALCleD/mrBtEHBXZv2eyYDGNaB8Nr51AAXPscSQchUZlFBOSv68IT+cmIw+AS6tGcATgC6Rr8nUnkRK6WWCP5Oj4H2nHR4sToIkcXhmU+UTzDi465qvY+DJ/09IOkoYjMzr+EA5SSmmyM7Lty1P2/oTtduJoHJ7sbWv3EJigJmcEDZrI4RXbPQMH5xQzMblt/7YO5t+WW3J0csjm8vqnUIWFv2UIbEPnV8JI0NHN0yFmsS2qn9uCaBZfwE1N0X4hVcu3tvK9R+cDrlDEL47bNi+5/sRp7Y+zp+qW2Dkbrochx/Q4/dKiIzk7PhhwUJI2J/153E/eejtnqWtow89fzVNojfVfWWU2LM47mKc3+FExmW+PwnIc5q1p4OCxJRnTee/7+c7olGnQESJvm9bXWTUVMjPzJ0rvM0VaSVhEWGuX8p49kRZlEB2i8itr0qRtREn6uZGQ9JQEfSLa3qdtafZXcUEywD2tooCDVRlNkUiYNnHTJpqwegxGAdS1ponRv7Q/pj7P3vgs0H2ZYV8ga35qnVwSKNQ5uRSFur/3OsYsq+1yHjEPxSfifOKMxkHwS+N8/CTwq/DDHtY3yNrEvtLn6BisV3aegE0HFCX93laEjWPbiN4CrM0b08Fkp4D20BAMa216OV+ysdsAvnzYbUTNOyR1kDCS0WPHE9UeQPcI5hZydPyPtBKgxfFz4JfsZs8wvN5BHzXbTEcKTY8yIlLm5ett8P6u9MsM/6hs0BWZ8+VXKBLNRB2dsDkUKOlxnr6iVqukJNFA3FEpo7ELCctLRkL3lz7jPPk1DGRizQewoz5uvSGjtyqL8EZ/I4HKGsftbwjpeQA0ls3iCsMtITwut5yTe5hfaViRylwtiNUAPwGg+PRbmffktZi5Q5Bn/JDfrfoYgAN8xRnRfMe2Odp6i4ikE0XDp3Y/qDWKJ3Bm4hcATA7kcWLy90gnogPg84f4o3EWUTSi6EyILaRCipFwFBY6I0l4+v9Cvszr1BtUSVi2W54Xb8qcRliEiNFKIDU47IxsUf0O4RWAWYo7YO5cutVdUMevuYO+zhhEbUqMZGn7eG58+Uzq2+L89KixFIV6fl6bKPzOOBcVE03T+BGd+jz6kTB0BB4qRS0jxSYqRD3N6/OgwvUmbIubGKbdZ08oL5ZuTgeEFm9q7mHKnQvbdhhureYAeREAn7T0KrCcUn28QH6F0+R3ULDYsvFC4Kp+2abhsaWMlpcD8HfzGFqi/ScQkw3r/eN43MpUb/1wVV2PRM2LbZXnD9Z8wnfkV2jHzxBzFjApI9gyS1rC6YobNPqpcTFPWAcTNpVU4/4KD1Eb5SFqN+VczeqaNqJo3Oav7NO2ePfJp8kIIcgPqGxtcYORjZEEfq1n5cPSjS9xufwZdeRmZOBDow/q0zb0BFE4kjMTP+dY6SPqyGWWthK6KbBXhcW58msoWKx0KnjATJfQPWy5Ks0lAb1HonZU/cNcp70KwLMcSEe5+s6CkCQuM36I4UiYyPzNduit2tLyBIo7zOa9vb7al6waagBfPuw+ojbgo7ZDUBSFVrWQQqOaXNFOkbVzo5jbipG1b/CA+ggyNm1bjwWu3e5lJSQ/lyWuQMEioGvckPxdklXijoqJxNv2JPYLpI9BUJd7JWo+VeI0+R0mSGsBWGH030P+1aFXkbvsR2x0irlceQpZ/Cjj/w4LgSpRyxHJJvePYmVdltPf8MkWpTTgEwnC0Tjbkh3ZHmSoPmaV5+/FR83Tk2Eq6cFZyaBhlPzwccAdpHUg3mlwFo9HuVm9x/3sqMjSz7tdl7ffwatU6B3YBZKlT5Is86A4MaUyOc+4jGKtCYDpsTvYSrocrXNJliwJZElg2Q6O48o8r5eraLOmcJi8IDXdn9V7WO5UMih6EpBpoAs9l3ECxE13uzsbdivYPKTegJI0tjjLcI+JT5XZc+szzNNvQ8fgEetQbjTPQvaUHsXjce5+25XDLg37+PERXbfLC0Pycb/lkuMSRedHQCBSze+Uf6Bg4bSV0WHyvqM4JPoqLbLbExcWbtnnR2uLYcYslm5u4dz7P6ItZnL/BXuz/6ht81pqb66jgBaaCNHQnuh9hp2E1ubGlLUCuGVVvaGDqBeLJsZL6wBojDf02zb57HQ2qBU/0Z3so5ZN0GbO6q77o2z+hOe064iiM98exQ2mKye+rSXfxTUfcl1SpXVOmwN8A10RDBNb0DGYLn2emjbmuIGL1riJbTtIksjIqI30ELVq/yiWOK5Sa8Tu21Aso4w7GagZr9UyXNpAHm20NOxBRd6wHpcxuu41zlTfdfdn9E+pqV6AkTOI6bOO63G+vkBVBBPEGn6kuoqGn8QPAr6dfVpJpPzpDEfmAetoTpPfZqa0hABxHrYOZ6PWc7ej34mkPp/AOzu8/X3B62JGqhLCdHofRCckP69ZU1EwWe+UMMyyMy1LdkHLwwC+2th9pY+Ow3HSbGQsTBReSDa3DxC1vuNodT4/k9xB6Nz6Y4Ejdu8GeZAb38QM+VMA5kQm9DJ1z7AkLSV+UOqpgW+qPJgx8XQN/VJPZKpCjfAD5QEMZOqdXODYLsv1CZPxSZIGoPr7T2I2UD6WyuV1VIo6LEd0KY+oUFqYKpZxpDQv/aO082/H/OalfOS7HIDljaPpScijP5DIYuy5LRLHWwJj+KNxFgERozhvatYyyZ6yhPFIW6prLCa0Hi1KVcUjz2+mtysS7+qj1rHeDqKmOkaqTKCzj1q2HrMrlP+h2zFUTBLxA3nVdwyvG1OpMOqIoPNT5d+crbzJkXzMR7HsSpU+O8bd6i34SXCQ/Clr7VKiaFxsXMVGpzi1bUOrX+UnyjzaHR+V8W8gy8M5UP4svSDDlej2qzK6Y1Is3IxRELe/MOYv4w7zRExkNjlplcENDelBUnfIZuyqGs2cp7wOwJr40F6X0VdcYT1IrppZQuY0bSDS1sy3b3uaBtxtf+7TzdtM1EasfJD5PlfwYHnLEOCznmfYSWhr3OqVeWFZ7gG9Cnd0lL5m+KjZ/Uemfim+j5qo4Urlv3xTfhU5ppDNoL2/0KEimkcrN6p/Q8fAbgwAL2dMZ7fXMTn5fG910kGejt61vgrNCI+YEFoOAD7ZTvmXAfza+CZ+EixxhiSXDW0Jk7BP5bj1N3Gauo52/JTKvwdcg27vc6uv5DFbGfdViTvZQ3MzrIs3TYXhPRM1n4ekhwZPZMLZP+th6m2DKksUiXTG2fR3r0rqLSNUhQU4TBarOFn+AID37InUKT0Ho1p95dB/Vox9giIJOkI1fakIaAsN5WIjfa3cb9mURVcwVVRjIuNzulciHsAAYHdm1ByLO7TbAWhygrwe3wsLaUD1cRsQUhySLQc42+GjtlPh8Q3D2bFzmuHD5CE8nSNR3u8lepyzlTcBWGtnyt53IBTJNMTVwv1T9ggwJD/dS2ci0/l1M635RX6g3wnAaruMm83TmVQ0eaeqpQGoHkl31c7eu9ef8GbUOsqF1G4yV9mwWR/OvZarZfad0uFZp/GWIXXOqEVNmzetWfhJIFQfXW3P0/BeP16CMajpI06Q1tKOTr5TRcdAy6/KKVn9D+zxhImgCRMLiaOkubThJybnZO1BuFB6kbDkkoqWaCSl+rg5SSZ0T4O9pGcPIPh1NdXYDqSMqjsqjTui78Mb3+cM5RUA5kb3REhHupYVwt3Hx7XraXUCtLcchwilr9uj5bm8Zk9F02amxEhcOO7gON67AIJ3INPRmyYr6deORP/1SfqdeIosz7NHs9Yuo0UahfjH5bysv861xrd5wZ6RkeHoK5Ro2kh7NOuwLQtpN0hSR1rS2eMl9hDqnJxe55HbqimjnmetmbxmTcVA4cSSyTtkmeLFXGMYUbuSe8Ut7KG4GTvLNDPOc3+iQxZdwuFI2S15brS7HgdvNn5faSn/Un+PXySoWXcwZ913KusbItxx9hSmDO65GmWZfzKLzGaCRCkpcAXwNVXPEKz6p3U4VqenfEvUIOxTGZ74gpGym4VeIafv684WH32B0riaY6Q5RNEZbNvAFGJqHh3MId5a19PsADwrHco75jCKaGZq6chep98WqLLEp/YI7jRPoIgW8oo6dwynISSJR6zDUmWEEg4xkQ6l+Ujg66Vq4ONB59G0ZTUaJglf4S4JVXsDjVYfxJAMy2aCWE2FqEfFQm4t4+zGuxmvu8GexY3DgOzvtgEMAHZr6WP6Ys8T7SzzXQDA3C3HAY/sno36iiGh5fNpdDgqFm16djKyu7Ch+BDuWSLjILFP0aQdGhR4ywS8/S2dlRS9ypdjRJqEdQxgO0PV01HWjU4Rgdz+I2qVhTmcmfg5EjYCp8sVLQXSpXEf2eN4wZ7BoPDOf1ir/lDKkmCrXEjPsdcdR/6GN/hIvw4DhRUN+wMP4ovXc5w0GwWL8vZKeiq/jGSR5+8Mv9nC9+X/oQsDPRIG0kqIESmHKw23W2RYbrBHoqbbMQ6VPkHFIjfhp6O34sD6J7lCc/2EFraNBlx5bW/p4fdSXj4OS/VvpQxBGwmTzWbSK9ecSES7+Kg9be3HUnsIQeJMK8qekdZ9gay/X6S8RBs+tPogUIpspjNfStIQ9hL7amIWXCY/yyzZlTufY0ymevCl7DOvmBvUv3GovIB/ajfwfPuv6Ci9fFj9Y6o/qm59CbCCnpAto0aojJ8bF2IhoeUU8Zsel9A3mKbF49bB+InjFwkuN74PCEas2cQb+rMg4E7tNmrjuSBte6llq5UZCGttqiO3cNc/c+ulIv5hXEQu7TQTxB/rvR/stC1/5nqfe/1+O/FjXren0qr0T6m8ZTvJ0mCJNnyEcUtO21oayS3YtqxlX7F//b85VF2ARnrfdadrOer6/Bn8Kn49fhIcKc/j20n10aerK5gdrwfgzrdW8vfze5Izgk/06TxrVgFwyyBXoU9IEiuoxLYhhoqChYXMkdJcBosawiJCpHYE5I/FZ6fvPz2QJpT+7SBqxdXvcFdSIXtO4+nA8TQERzK3tYZGJwfh5BJtb2XTyk8ZNn5GVrL8SHwWraYbEpxf0b9ETZMEdeTytLUfKibnD++5nPJ6LmaG/TEjxSbOlt9gjTqSexLHc478OgdIn1GeyKen0uh4oJzvJLNVE0rCu4aoeQJvRi+BRnCrSi5Vnuc4eQ4AH9cORnbS165XKXMAA8iG3eijJvG8NQMTiZPkD1M/O2KgXrevmG7Np0rUkEOUT4M994rsaiRyB/O2PQWACqVqh5aVLSoPoEgOR0jzMFAwhYoQ6fLGyWa6NGmjU0S2Vm3VH2S1XUYcja1OPjP6SfURYHR5HgvlCcQMm/EV4S7/i9zBzLdH0uwEWe64W9eZeO4MKEUjmB53vX5KfTof7eT1iUQLpUlp4mrHzcDktK1MZdOXtkwkG5HpQDbD687wOxF+nOyJqE5kltpkNN/34pOnmc3cnzS2rkkUANcAoFjpgZbqSw+0vmn+l5CyFr9I8CfzDNY7pYBIkTQAp5vSqn/KpxCPRUkgc5EcoKVTX8979p68lySED5Vkt7EVksT3rR/RZsnkEGGRM5y71b/yLcUtAfu4YQZwEO/6DuGNpnJCIsreRe5z4iNpCm2GyX7SYmbhEjVH9aP4cqglj4inSNRborSvtDT1uciqybpdGWhazw3KfZjIxBJVwIFIgQL+ZR0OQKXw9wtRi5o2vzQv7PL7KmcQ8+zR7CMtZ6E9nCg6I1s/g6zOW93j/tClmPVrCRKjkRB7tMfI3UYLOMdxaImZWRVf+4o6cnnMSpcVntaHfjDVQ9Tbk4XAVj+JuHj7gO8x3cx3C0EuNURGiWZ/YnhkEVOSg96nrFk8Y83CEHqXYFirCLHYcYNfw9RWcFyiVmA3oGBiorBwQ1OvJZAZYjweBdYz5L/Q2EkS/zvqy0wVXwCwpO4MGD2Wa6UfQrSRIDF+U5h+Fx7f8BBXai/iFwnWrvkJTHafg47jEDft7M+rRDT10VHdc/nJ8Eu5e717P12pDGPszdMZaW9i7jvHM+2Kf2XMHjOslLiMLAnyduBazAZVsnk7WRJqOhLvhL7R8/Sy4AQ+TJU73qhdydzYGC5VnuMAeVEyYNY9vOJHei9lkv2FP9h/Jaw2IAsbu20ShHoWgjFMO6Mc3jESrJWH0B43ULDQfTvrThnA1wW7L6MmSVxuXAHAXHscv1IeQsLBEbu+nOSripAUp0C45R0Bp2/yvrsKmTLwO1b6KDWt4yH1BgxkWuOVdGQ6VCvCfdotnimvTn1q9ldBslT+LWsy52VZrpZbxiGJvwCuuM3qfjTL1BSJJy7Zl5cXV3PKXl0f5Ilhh3D6m8GM39RdIc+v9t956QscMx3ptqUO+4S0OIXk9BxJ9gph+LuRqVd96eOokRlZz9Z83x0UT2RT8UTrF8iTWG0FCRBnUDidRdnXnMdYxRUS+Id5FOsp7aKauEEZ6pEVSeNp34msbXcH0OdKAQ6PvkyjpNFCkHftia6XEDYRfN36qAG8r+5Lo5EeLC60RzA26a9oJwd178rTmW+5BO0/xWPd/UsGPB6zDuFDezx+4hxZfiB5ye1fYg8lRBQFCzOY3me3l8RFmwjSa1dney1nKm8DsMIYlbFu6D/C4CXkOT6FSMJKLfs+8ziGag8wWVrNc/rPqWkvgG10+mqMJLjYSKsk/sfO2aZstGU7nHL3hyze1Mz1J03grGmDt2n9HejsldWbYBJAIyGqnXxCRGlN+qj1l3+dd/13WSelPp9t7TyfOWGn1/mCNYO37cmAWxLpzXZ4fdRWByZxXuM1RBydq5Qn+UK/gC1OIb+LnMumpllU5mfPTkPmPnqFgdxnaeb5SCg5qXaEeJvbC/aJMZSI7RK0P4fSg/IwrQxLVnusjTWl9uH0e2ezZHMLN502iRMmZZpg12iVvGBNw08CM+SSUC/ZWr9sAYPtTQBMa3iO1Ys/YviEdEG9VwinIKhl+J32B2RZSZWEKsKmwN/zeE6VJXxWepsUXxBfS7rpzJR82WZLQc8garsmyD/Z+YIy2S0xrfYQ5w7EIm0s/N9NCD2HfU65ErXVNToHN2jcppdym/97LG1y9/P5ooEetQH0jN0oz59+QDxqHcqjySjhNwcP6bfa+a87TDUd3Q/YvTf270pkNEpvoxxyZ9jRppT4wSqrKfV7TsOibudpDQ3nZWsfmpwgH9ujsxK1kK5Qketjc3OMMaU5fWos3xbsWZnHnpV5Wf/LC3SNZMq72PB6V/iorSg9iu/FCtGEyTEVw5gG2IEinremYyITCQxnrGd6IxFn/jO341/1ItHhRzG5PsoeymdE0CmPXgR09crRAmFuM08i7mgklBDe1vhoIk1Ge1NJVPQgb1hTMFCIysGUbcA/lNNYbbiBkNdL05lrU9bp4HN+EQcHdFnizMDfWNVg0I6PSeUVPJZlXd4+vUgsym+l+yA5to07KrowuNc8lj+a53TrowZdB4tv25OpN8PEHI1RQffItnvFUJIDzY4+i/VOaTITCIfkjUhFpe+xTuAe6wQAbigYztXK71GwWGQP5d/WweTTyrv5J/NMD8cTwDbSgzBLZK4b+k+eP+Y5z7l+lXOmD+Fv761mr8F5nL7/d/BXXQF/GQpA2OmbubAXnQlS/TYqPy7Z3MynG5oA+NecddtN1JqjBhPEan6lPkyIKPWbRwP/63Ge67SrWdea+X6w+lCy1RfEq5fzgnYtbfhZZlelspqdM8T9iedzTucfzXujYLLEHpr6PdGZqHmeb1JOGe/VuzeYg+uBVSVqOUqex5rFc6jc/5Bu19fWyd6iAx3E4HrlfjRMomisCk5mcUMxLU6A8Woldqo01EXA8160PSq2TsI9P/PXNzF/fRMA97+3ugtR+zR8EPcb7jPwZ5XuAD/f4xP43NYCjpL24nDZ9fx78733Moia1+y6N2uN7YGQJFY4FeC45d15vRA1RRa8Zk1lo1OMjwSR0BA+29rOT4zv4CPB4IJxXXwxvcg3tnKY9AkaBmXWKNgFo0dbyClPPsvsep0veepGZqx0y1MXFZSTE2ljT2kNAPPtUTTn75Pho5bNEmUAA/BiN5Y+Zv95QPWx73h/yPe4bMuxtOLne4MmcPju3iAPMgiBuYNEzUq/KG3hESJQuo/a1pXsyy8Xp3skbs0yjSwJ7jlvKi8uquaUvQbt0DZuKzrKn86U3+Q4aTYmCrGGs4BRO3W93kxn3LT7rHy2vYg5GjXkgwMJn1uWaOeP5HLjBwCM94U5Ozntp28+QdF7P2e6sxXLEWxesp6gVsYExSXkCxJHZd8nf4i/mKcDoDgig6jRsJpz5NeJOhol1lh6epGrgRwuMlyfNh9Siqh5VR+9pOmN3G/w0KZpOAjGiI2EpCiSHGa9bxq1SSmyUDelRd6Xc3NDpgCALtyXfx4uOeyu5BO69u29bE/jZdvt+bs24JLKbBmBbJLQfk3OMIEX2DhIBCWTM5XnAGh0QqmyxUFmz2VJlu3QFhzMNca3UbDIz6tgLJ181LJIrW8PIkZ6HwOazDVHj+UnR45JrcuxbdY6ZbQ7Oo1OiL1jMXy+nqP1XjRGMolZ4zYSNa+3WIfn1fagJWqgYbKP5PqWLe+lNAzSpXvHSx/yY+VJFGGxauORwF3bvR0dSLTUpCT/VU8WurUPvXPbiyXKeGbbXa1M4oaN19vcS9QKPN55fo9Qzyny+8xZMgS6IWqJeIzf1F/FHGUM71p7UhA4KPVfRzDyeHk2ucIlWteVPsujSbGaX6hDMszmA5qckcFaVHUW162dRNTROLt0EvsC8vKXeF//Jc1OkHdr98FxZmU8nzv7qEFm0C9h2fzFPo3PncHcbx5D87oQ+1W3MLbMLb+Pr/+E65X7qScXU54M7J91v3cEF+fcybp693gsK+xa9u/FKc7rCNFAm+PnDuskjikYx8aV63nScvvFzy7oOaAxbtN/OFX7OwAbGkYA39zxHegFN/h+QF1zG6Yjc4O/ax/mpJV3pz6XLryTtcPPSn03UDBMO7OdYxdU0gzgq43dangtCegcUB0gan3HXuNGctsc14dl+rBsBVa7D4Vty3lMvR6fSNBYOwJ4YruXFQkN4YLET5GxKM8r5vrk746/gAYnhIXMWqkqQ7r9mzOHct97q4kZNhfOGtrtsnvKeu1MdLxch4st7Nch5hA/aKevV5IEI5UaVCuKjwTxRAKfvvOamb3y/CkftSylb40NdVS98yMKhJvt+J15Ho9Yh/GQ8ydIcocOIYzOUGWBEK7aoWk7GSVQ/tpP+b3qyqp/0nYw0LWPKb0cjxqlpzSsPZG9R2V53ixeWD+cCur40OeWcdfYBVyuPZqaJkfvplzTs676dpNHzYMJiwiHSAtTPW5nKG/Thp+A2BcIZl1OT313HfL8kU6DRYA/mDdRoNWgYPHtxI+pphCfKpPbuJQl+oXoGCx1hnBC4vfoPm9JaHqg2J2kuOM4XPbIfN5fUcdRE8p40nIHwfuFXKKu2DH+ot7l+qjZKv1hK2I2buRS+Vmi6Gj2YODADEIoJInTtDupbXWP7Zw4lPWRp8UTcYYYq2kkRCM5xNG2OaNmNW3kaOkjAsSxoxKWedB2qSLuseExTlCfTn3X+1BJ0UHUAyKeElbabDZt87qzIdGelmJvc9KksSW68zJq3SnFxjtZc+y77h5O114his5n5rd4GVc44+TEbzlRep9bNZeohhu6t1pY8cmb7C2+YG/lC05RZ1Oen/bDHC62EBCbUyQNIC83D3CJWkvUyJrN7oATLGVN0ketzXaJpNW6lUrh2rosModR2xqnJJy+UGPeUvDkvV+oJThQ+pQ8WjFQeNGewedmuvLglteWc8+5U13CV72Yc5U3AJhntAE/6Hbftxd3nzOVJz/ZwLETy3vtGzvbfIYqdTMAz9n7MtZZxb3q7fiJs9QZSq16XY/z604s9bkqsWrHN74P+FydwArbPcfZSjPfyj+NQ+sfI4HCiqLDiOoVPGXNQsVkoT2CSsvOUENWs6gCD2AAXuy+jBpwrH8xOfGtFNDCw9bhtBBiILjQdxw4upinLpuJLIndQjZ6go84k2VXeGBZYsf6DuNKTqoPYZovTUjtwtHsFb8PgPJcH7M98xSFdP5z6UyWbmnh2InlO7T+nYGQrjBT/pzjZc9W7wIfNYD/KT8jrLjZmua2M/HpO89o28h4Ibk3tzeC2KEKuLpV4tT4vegkuEx5Fh0DGYv7zKN5SexNkBjHFmVXKBNCoCtSqucubqaJmh1P925acs/Zh84lebbtIESmoIm3fLJjoKSJ9KDUECoBTwYs5Mt+To+PP88pygpUTCKNl3OdebG7rOR+L9QvQRcGFykvEemhZPPS2N/ZV3+TYuFm8N63xnOu8bPkcXC3+xrzbtoVjXZ8BFU3GzbaWUultMXdD5EAx90fFZVgkijqjrtfmh7gT8YZGMhEPSIj3ZU0L9vaykuLqwF48pO0+moHQVeEwyny+wBEnH4KEjSs4hrVNUBfEp8IXNVlkjy/miJqTdEEZbl9Y2ottZt5Sb829f2nxsXkbqkH+q6YF9w6l7u1dF6/ru67FJVte/ljVcv8VAbrdvMkXvMfxbM9TG9aduq+sPBkS+3+KXveFJ7EtfHfkyOizJCW8ivlIcIiQu6G02Dq+d3O11CzCVnVyc3v3merOxieIMrN6l1UiVp0DMzGkZCbLk0OxGoYkbzGNyuxjGV8ZI+j0QnxmT2c+bHxjLGdrGJOLYtfSn3eULAvFZ5M9Pfab2e87mb7n7Zm8qkzkkHhdDCpJWYQr13FS9rVtOOnxhkMHJb63/ss6biXrEhj6rcmQqyqbc8kamZXolZk1fKQdiMAq+xyXkxkVg28smQrP3x8AX88YRRma1oB2cqSDeoP7FER5lcV2QWQOsMSSqqMUMWkWImkLBdky+bDXvqKi47/DTW3vkiO08bKQ+9j4g5ted8gZ3l/efFE+EIu3nwsILiifCSFIZ1fGelzeJVlM9Wcz0gpQQIF1dlvF2z1AL7K2K1E7cf8i6HqegDmO6NYYg9FZ+d7O31dIITo1QNmd8Erfa/toF+XV3DA+5D0RqKyRaUmDMplwqAvp6KSEII71VvJT5bI/cT4DjNKu++T6E/E0SBZVheLtu80dTbolFFL+k4pGT5q7rntGEDH0bjLPCG5jaSUQwFOyB/a7Xp8qpxB1ILJ8X+dbzCPmIfiE3HknJ5f40IINFlKbbNh2zhmgm+KF4nIPqIiiKaklUU7BkpRR+d5awYaBglfEWPtVeRJbl9dla0CXeX1p8VnM1FZAMCjTScCbqlPTjBIU3s0Vf5oOwJfD0bsw+21KZIGrtVJB+KGTSIe4wzpTZBcFTZZfQDIHCA9o/2cz+zh5EZuQQml79sx0kaOkuYSFGO4yzox9buPuCuDbyRwbLuLmXtk/SJ+r9xPCwGW2kN4zp4JpMstvWIySj/5qFkeQm52Q8i9JWKde856QmtjDd4h7Z/UvzFv6xFsS5mV7fH0Amiu2bBdRE0z0ud6jj2OlfGen//tkRgTxWra8POJPZoD43/BQuaAkqFZzeO3Fc2WzhJnGDiwn7SIC5N+fbMbux+oL/v4TYY99w1AsPq0FzN6qPoCb0ZtT7GGUZIr1LA2mtl7KFtpchbSJP6r/YoAcQxkTkhcz5T4vXT0YBxX28ao0q5ebH+JHEtBopADpM8YM/7EjP8sTzblaWsW85S9+ZWn9rIlapJobWZcUtxnZSfhpGyG1+8XnMKDX8gcLX/EHmIdsSXPw4g04R3d9AFBeQNRRydsDwXKCealbWVGSFtQMbEllSPHl/LiIjdgUrT472xd8T4LB/+Yl43zKRTNjCid2WV/dzVe0w8n0VqLiUKDE8YfSJdKBkS8W0uWDuTmF2Fc9wXRSBsT87ZRhnU70ZsYUrth03FdxS0bw7IZLTawh1iHKkwKmhL8xryVfM29lxusc2CnvoUH8FXHbiVq7Uo+JFyi9qj2BwBmb/omcPtu3KoB9AdE4QjOSvyMuKMSyivk4R1YVnfy/IMLAowty+GL6laOHP/l8pHrC9pEiHzHfVjPt0cxOWfHbAz6inVSFXVmiDgaBeaO9whtaIigqxIlOV0zFDPW3sUl+uMkUFhcfRnwM1QzwhnyWyhYBOJB4OAUUQNSJK0zeurV+o54Gr9Sh06CROtkCLo9h+tCk7jBdFnbJeXDOamXfTlG/gjZiaIKEyN+EEakhV+p/wTcCDceMfmOPsytFKQUbMcXh3lhw3EpYZDmZU9CUv7eC9tjUN8eSSuHVRUEaGqP8oPEZQRFnBzZ5NoejJVNKZOUjBIb+YnyOD4MQrXTibadnTqaEeEnnCRLNwV+xKb6Fp7Sf0VYRNlPXsJKYSIXDGN87H7+rf2OCdJa7tH+yhcR15fuYGkBF8ovp3zUAGKxU/EFMomkU7ecc5LlVS9b+6SImqa4966q6VyZ+C4WMpaQubPbves7GrVy7jGPx08MPbQHk7JMk+tPX1fbRNSiMZbZlYyR0tlBPd7YwxxZtk/JzF40tW2f+NND2llYbfuSK9pZaQ9KqVt2Z+0RbdrMc/rPAah28pkRd492s+i5d6iv8PY/tjppki9izdkmB0B9+3q0pHqo9uIPYcLsbqfNhktb78SvVmMiUyTS6zHimQp8/8r/Lj+tOxofCa4YvC8zP3eL5uOOSucm+YUbmroQtdrWOPOqLWAf3mAaC2ZmdoFv1YewINJI1NFodQL4NZlScwvfl/9HWEQIVg8nPiid3UrImcqS/ixErS4mM1JsSlkWzdn4AZAmakc0P8Eeqnv/LW6fDownt6CE96wJVIpahklb+Vy/gI+VKUw+/TVy/UtYNe9VrlUeQ7FtEivf5iHLlcy/eXC2u2TX4vnQqcxqeRAVi5Pl99ALv8vb1iSmSssJEGN46zx669tWNR1V23VeZN7+3mxiSF6l4oRpY1gOh0mf8FPVbf+YXRdHcYzUJTjgozaA3rBbidry8HQWV+dzRlK+GYBdoHw3gJ0PXzDMbNuNqpZZfW/azwZvBNVbniaE4NnL92N1XRtjskRDv+xY7JvCkrZBNDtB4mi7rKn4upzrWb7VjfC/tING6e+vqOPc+z9CkQQv/3B/RpZkngfZjBASbmS7Y3Cmmi3cqP4NgBqzALg+g6jtUR5mxZYGykQ9QeJ84biZh55UG0+w36BScaPHG9oaAJeoecvzevNRA/i19DfyNPfYNEV/QrS9hY6cRYxMUjSx5V3uVP+LnwQvWDP4r30AmiLxRdHhjK17DYBNw0/LGit9P/d4/tsyjgQqUTNt3zC4IMDCDU08Y7vlMMUBnWuzzN+Bl4q/xY0rD0HGZotTwDHSRylPubnNgjbrPP5ofJsgMcI+NdWRslYfxWKnhVV2eao8TPMH0TSV9k77KSsampxgFBszSBpArL21C1GzIg2pz6PFBm5Xb8NAIdEyHZiKJEk8ZadFDG63nR2WCa/1DecG023aP6V4EGdmmaZKaWKqWEaeaMOqCwN9K/nd5BvNZYk/MVps4GrlcRrJoUEel3S56xtWhGdwaexfOMnyw5u0sT2q2XWHD4zRbLYzM3Ft8e692WJtaSLT7qSfw1Y/yfN7RUM+903mdzGZFgJU5UzrVrZnmRjKcD4BoDnuZPW47AkTzUUMkV3S/HMuY1m8iDgqvw5mGibU2GFWOe5zwF9QlZKN14WBgolP96WI5qcbmzht78xA2XsralOf9xqcT44v8xg/X345z2zdnPpeqcoUWltT99/StolsDV3EMfE/ECDGnuWl/NIzf0XDHObqV+IjwYqNewEv0BRN0OKkS/X9Lasz1ql4qlNU3e1bVTWdy6RfcpjxFrdod6MIG93nx6fK/PGUPZnb9m+U1e479DhpNrdwKiAoytn9BEGVBJcoz6f6/BaFr2GRU8BBIsoYsZGmxOZelrDr8d22OyjVvkDBhprbYPCBGf9PaJ9DkdROBB9OogTD0jA8Q21hJZjNRHxWFBWLydqOjY8G8PXHbiVqC6vO56GN61jhDOIy5RkkHCx54KL9OiDDr2sHVR9zqufypPZbLGRqm6cC01L/aYqUUrT6quHfJT/k7cb0YCCbEt/OQKaX2o6dm63v/p1fKguod8LMmysx8rhMZUbh8cgRijswyCx9cwdK8YaNFNNEPWGOKG7g+YbzkYTDSruCwxJ/BjKFPDrDFFqqlM+Mp7MVGT5qfTA0Nz2PRNNMELFVHjCPJEgMOZDLqZ5pixMb2FeeC8Bypwps0GSJopNuZsEjl2GqOex5wk+zrueL3P15bu0IAE5tW89F8mKaCTJGPZCXZQefFSFPtKGoPZfH1YXGMNtOC42sd9JlUJIVp93ReTwp5jEyGEoRtY5r7VLjSnKI4Bdx/pxflZIcf8+eyFqnFBWLEf58FLmdIjtddgewySlEjmf2/gCsCezJIKeQQaKe4VI1w3EJ9NyYS+iEECiSSEWjTdtB20Gi5j3PgW7O85EtT/Ir3e1jm7PRoq+Kdx2Kj8udqpQqaKXt5zvbsH3tCStF0gBq27avHLw5i0hHa8zolqhFEhaL7aEEibLBc230ly2CN6PWnD+B+ze6ZOcY0T0J/ot1BtfHDmATxQgBCyIJ8gLZs+jZIDvpdS7TxjMv5oZSYsJHUyTBb59fiiZLGVnTgK5wjvNbrnfuYoS0haX6hfyr8tf8dpV7D362sWsG8N3l6WfzgWO69nP5Ogll+FUZXyjdQ+0zW2lxdJY6QwEoD2dK7WuyTIloAkC3ksGhiMEWZyiPmIey2imnRh+bUV/0oTyNz+Jl+IgzLpw+n3lBFaXZYoNdTJVUSywvnYXa8xvXsvLGp3nDmsSzVrrcsSi087zu+gpVlvB57EXycnIyVDklrXt/u92FSmsj4yWXQC+Ot3T5/weROynR6gH4a/tQihCMk1wT9M/sYWzwj+Ma44RU2eRK35dvHwfw5cJuJWqFSR+Pv1vH8nfL7f24omoUA62VX330p1+XHKtnr6Qc9Xxj25vPv6zI6zS48pZ17kz0pxn5sMYP2Et5F4D/1I0HMona06Xf56JNx6Fict3gvZiOa2r6b/MgLCTiSogLgaPW/5lrfR9iOYKP/dcjCfclNlLazM3q3bQ5PvzK0d1ux9P+U2hpqieOyllaelAVNbo23/eEt6V9EUYbCUfhQBRalBx+Y7qlR5Ny8zKImvAMIvSk0bamSBRVDKHoJy/0uB7Vc64ntc/mm6rrSDa7XeUD9RGKVbe07lz1oR6X49cyyf0KaRh/Mk4njkpucDz7deMB1ZG9XeEk8xkO+AI5KZW2G820pPRreUO4SrqRUc46ap0wd5onMcfegy+cwbyhdx3ArpWqGOvkMkjUZ/7hEctR5DRR6w/T60gWNbzOcHx56c+RvpcuZiuTbNhG1cfOwis12yHRb1h2qqzqDvVWJoi1hESUli3/hfxpWeepDwzjvMQfuvzeXz5qB665lbO1d2jDz3vKt/mUoUD38vyGZbO6ycJKdv05Dny8tpHD9uh7Zv8PyveItzehYBHNKabDzDBu2jz58Ub+N39Tl3n8mswKbRxtSTsDTVhMHD2cY9fMoVQ0Ura1Cceemeq3tC2Lz5cvA9wKgQNGdb3OdVVihNjEtcqjRNFpMUfiL7yaO80TaHaCxALljPHef50CCKpngK4m++maIwnWO6X8zLwIAKkF/mxaqfvyH/I3WG+4gah3CtMZxPyAxpMNB/GkdRABYvx5UtpE2RcIcecej/DUgszjUrwTfNS2FYos+Kt5Kj4Rx0eCM3Ny+Id5NK9bU/GLOMeVbE/eeefCFunz6Fhdnw0lpJ97R269j6bwGPZN+tq9ZE2nKXwwlr0hNU13ZcsDGEAHditR83qbdEAekOf/WqAzGdgRvy7b8zB0dpEy4q5AXkDjauUxZkifYyJh1v+MjpK9nQm9Hz3u9ERT6vNmo6uEfMxRaMMdkEia+7/sy+Fq081HBCWZC4FgwvUSk4VDbsUoWha684RFhFPl94g4OlIPMsYf5BzJvHp38H2CnC42HFb3Dt+WlxBDoziRC8mBZHe4zXcJ6yPuQOhtJZdIa7rvpfNAq750f763KEaxaKJS1HKK9C4l5higd3EE3eOjphgtqSex5M8jIgXBdvelWIlmmz2FzqSkzj+cu1rda+hArZi9vPLgnu3PFhTwa3LS6sBBcwx0DFoIoMgSJ/E2BbIbPX7e2pe6ZEFnNuXHlqhBo5MugX3amsm71p6MKZqUyoW7Gb20aIufHVOG7QshN3OHMt8eSaOTQ6NckXWabMjmmRZJWMQMq0/ltO70mcRlezJq3mxauWhISe3XtTZ1O4+XMO2vfsFfpL+iYLOqZk/gpW7n6ytC8S2p0tkV/vQ56E6ef0NDpAsxn7u2IUXU1tW3c81/F1Ea1rnptElZRaLmsQe1yRLAyb4Q0AS4Kqf/ev0jLpHfYS9pBQ1ODtcm1VT9qoxfk2mKh6h3cigUrQwZOYE/vnkx4WTZXVNDDXlFbiawesNKXrEvYZlWyevyLMZXHNNlO3yqTLFo5jDZFQVaYrYTKijnJtMtvM1JKFzRgzy/XT6J6bE7iKIzpKyI54AHmi9C0eM0OyHOTPyCOieXdfURRidL+7u7zr0Z1Qg+hlRmvkeOn1TOswvWc496C/VOmFryKAhk96Xcldg/+haGiKBi8bw4kG/pCouc4SxyhgNwbP6wXpaw6/FowfdYunYzJjJX53ft84s6WioraDoywjN+SSBnPDM1WdqpPqYD+Hpgt456s6XeB4ILXw9IkuA/+m/Icdpdv67ogV16WfqKrYXT+Ub8l6jCYnLhiO3q7fgyItevMlxsYYq0EoD5Zmsvc/QPKtnKVLESn0hASyUdioPbg38rx6O1T6RQtPB5omvU2esX0+Gj5h18GclBW5Ptp8EJUSDaCBdXcXruI9TXbGGe7zIAYkKnpwIRvZORdwf2bHqNC9W3APi4fTz0kq/3ZrrcDIbXgyzzcWnkj+AFu5VjpTn8WnXlcua3HgB8q8d1uOtJH4MP7InETY2waKescAyD7C2p/06J/Q84K8sSXPhUOWlMLQBBXkClpkNB07QyStO8A8WLm2/nd9pCZCyuMS5mrjMOXXEHDUu1C1MDjbGxB9wyRc+rQvEYG2fzUmuOGjxsHc6L9jQanDAL7ZHUkcuF4aGpaW4Qt+NX21CwsCLTwbdjmfKKmne5UnmfmKNTFT8OGNNlmsYRJ3LeXLeU9FhfOd/o47L1hs+ZJDbSSA5bnfyU2M2q2jZeXLSFUSU5nDSl5wBLZdNcTpFWEhJRNjlFFNRXAHttwx5C+9bVPKpeTxMhpkorUr/H27sX7vAadRcEZIqTpVp+q727WbYJipleTig3rUDZXUatwwjZi4/WpHsa//H+GmavdjMSR4wv45gs1ipek/SQ55qOmzZTiuHa+sdSvx0sLyTi6ASN1/CrMucb1wCQr8T5pGwwG+QCwkkvusat61JELdrqBkrGSBvRxEdZeyiLza0cLn2S3i7Jl2HH0Ro3aYmlB+mdiZrPF2Arbqlkm+k+D/KdJnzCoFi00J60wlhd25Yial4fNZ8n8LKntIYy+RPyaeVDZwIjijNJ2H4jiymglcOTmZ0GJ4TSi8fZrsA+0feYorhWHcvEWFRZ4gHtJnJox0+cVunl3byFXVGjD+XTpHBOXM4c05iWzWXGD7hVvYMEKs1SLpsCE1hl1qFistypwu85h7uqimYAX23sVqJWqrTzTfkVCkULEcfHvdbxO9xUPoAvD0aLjYSTcuHN0fbtJmoRJZ+PnbHgQEVgW1vPv7wYRE3KMwZAUrL3mfQ3Tm16kL11V5Xv4y15kFUjr294MbEX7dYISkQTY1truvzvlefvICde0RTLdnAch3Pj15KwbFRMPh00krL8NtbW+LkscQVBESMvqPOzHrbDm6Hyltp6JboVLbtptBcZJNJyMqKfnVUnO7IpmqfHwpb61vcxqfktblZfQcPgGWtWqrzyoapp3F90DZfU/R4gZX7dHfZseJk1vrREwePWN7kmWX4aN20Cm97nNvXvtDs+zNgsYG8AiqyalKx5UMTwK3IqsmsIFX+ylFPHQJEFf1PPIRJpJ4FCi8d8O1tGrTlq8J7dlYR4j+2+fEah7BKMukTPWcO+YEjDB5yuPAXAnLYhwPFdpvGWGjdF+166eMSWe/mJ/hEAD5pHsNIZRCEt/PmJGG9Vuz3VY8tzeuyVPaD+P1yhzUl939BQAXy7z9sAEG3YxMykN2WDE+KUxG9ocwL8Kn9at3ewNxuY4/fR4X4jnB3LpHfg5tCP2di8kRBRriibwB+U3xAWEbR2GTioy/T2kme4QXmetU4Z8+1R6MJgRvUXtLfsQTCcz/qGNJHLRuog04z+G5HHuUZ7Ex2D2vU/wimcQkudn7Bwr6ky0QgCmnUdvyfQUpBfgCRLfBw8kHcaa9jq5LOfEaAjf2NE0uQ3LmW3e5jQ+Br7Km5WcqNTxFsFZzBJEuT4lBRRnbjibt7WXqQdH1saLwFGp+bv7KMWi7ThS9pyxB0l5Vm4qjZNhrvLqB3Q/irT1f8CcLv2rS6ZXk2RmFluQyO0On6anBAF7H541W/9kntep0rLCSftYxZJ21ZivCuQKc+fWUIcMSzesqewZ/x+APYfXERl2M9j5ojUNAfHo5wgfYiBDFKQzu0CAxhAZ+zeHjWpjd8mezBsR/C8NQPd+voMxP+/w+vXFY9tnxw1ZDa+q1+jCNSwyGepz5ucQtqKp/Qwdf/BltO9Cbax/YNk23YwI4187nMHnJGoDs7F4CnlMAwDV+VDpMiU3ImotUTNFKHTNJ2ArjEoz08MnRdtVztubKBnVU/vwMSbUftI35d5Lfn4ibNH3pBe90lTvETNxlczn8vkZ4ngo8yYAaTPUcdAa51TyuPmQWjChOCeSSrUMypiy9lXfg+Az+3BvJ7ME+f6VXLVNPGzlJ7JZY6dmYX1e8pa44aN3rSKE5Km6h/F0yWh3gHSA9pNfMBkOgYMCdL/nSm/hWIfxavaoaxrde/hMO0MohafSGC0NwKZmdRT6u7lJLWGZifIfeaxVOP6G2UQdE+po5Wlz2NbIcw0Ie9OgGB7fdR8RnrQfpb8Vsrj7uG6FhZxCnXksqy6tUeipliZ91m+vW3y/gDx1nTmaZE9nLVJdcDWRPfPRLV2KftKy2hwcojmjWGf6rswkRhXVMSj27wFXbE5EUwpKxaWlHGo4mav43b2oFNoy4cc6lV5TmLRghOZeOApHFD7GHvIbdQTprWxEBjRZVrDMzguduqZIK0FoCFajxZv4DHrEGqdPH6uPpKaTg+E+EH7bYzQ5hMQce4P/AI4iPcrv8NTtW7AYoiZx77J6WvCE7gwdjsBEWOvsgJuyrIvQk0Ln71mTWV9rutMF/apKaKmRKpTJaptZL4Dve0BUcOiyVA4MPYgubSzl7SCnyv/YrjYgrN4PBx8F4aR4CLxLFFZIyb8qHLa0zHs8U8c5s8e3Dn9kOn854kDOED6jOacvpu170x8Gj6ENxqKMZFZ73dpcgw9RdT8dBUr2t3win4ZndRTOweu4qZNwnSoFLUcJC1ExaSkJZfvancAUE8ucPVO3+YBfLWxW4lablG6XEQSDh/4fsCc6h+yIxH+AXx5cLXvF2xuThBD5UF1++N3ZjeG1191aDnpY7LCrkQN7BrDzqbAED5pGEXM0YjK22+Y3hozabL9xB0VXRgERJz21iaC4fQyr6q5hgd9C4k7CstrH4SxxyOE4ELlFVQngYLN5sZ9U9MXJyWjK/Iyo9jdKfl1YFbbqxygzEYXCcLV34ZJruzHK+phzDdd6vTfknE9LQKAfY2PmCSvR8OElqHk1ab9b+a0GcA5qWk7ItrzndHMN91I+bmlgzml17UAHrKsiXSZWK5fZUvRDC5ZdyUBYkwonkB2mQgXQs08TmE5zq+Uh/CRQLTlYcfTkuOOmiZ9/ym6nNtWzuIBzR2CjiAtNHCy736ej55HrohwrfoYzfZvUKL1vKr9kjonN5XVAfh4459gSjpLALB3bDZVsiur/Yh1aEqN05tRu0H9Hs3tEUwUfq/uuNnr/MAsPqj14xcJxhbukXWavO30UVtFJQk7Rr5oI6aEGWm5im/fVF7jm8prLLSHs6Dtvz1vnzKZ1dEAJ8iziTsq8+zRzIjG8Pv7rnK8MTiePyWuJY92mjxZzdZY9/uy1+Z/cYn2KgD/s67jv0nz9TayZ4m2Fd7S2sLcMAlHRhMWujCIRdvx+TMDDf62danP1RRRhtub2rb8XTjwFM6JPIymust8dpMEWUT+Hxa/xtHAcGRkJX1PO2aM87beyJ7KPACuTHyXOfYeBKQ4r+t+Cpwmhkhu1n9wwF1HiUei3msR0m5KbKEQHBgZzC50Eg9V8b41nhgaK5zKVCniOeJlcpRVhEWEimhaOVL2ZQabvBk104jTFE0QR6MGjWaCfDuZrVvW7JKVaHsr16puWWeb4wduTM0fGXYE1LplgiX52StXZk0czZyFU9hYDYXH/ybrNLsa1cGxVIj3iKGxD+5z5Y6C61i6pQVT8vNAyZcvcN850OhFezyz5Nf1UXMNr69XHwBgdfvQ1P/m7h2CD+Argt16lYTzCnnEOoxz5NfTP4oBH7WvCzbqI1nuuLLDUWv7CZblKZ/bVV5juwJK4XCesWbS5AT5whnMibto3z6uPJ/7VrvS5Nfkj+Ww7VxOQyQBCDY6RW7pkZNHYWNjBlGTbXcQqQsTWU1H2a+WH02V+bzTmC5q7CBqVbkqlaKWQcmBnKr1LKU+Mr6YaclI/UfNh6Z+j3pULfsi/HBC9CnGdxjKNh0F8bbUf06n0snc6AYeUG/ELxJscgr5sXEZWg/m1F6sLz6I/6ySMVD4wkmTqVy/ilYwhFdsd3BWVdxz5Luu6jCOm+/69ETRObe8jO/UuT1tW4xiXg7fwWMJi6CIMblkVmrY2+wrZ4OTNnCOSx7iqCooUU/5qKqhx2sZrW5iNJnKcXaia6Y85KSP2ZHSx0yVHsVExmo8nY7Sr0+0qakMnSntuCXLx/p0XrfciPzfSrIH+nJ1iZnSYvJooyiaAA7p07Kvsy+hKeFeq78vm8/cTfM5WF5IuXAzXI1ODg29EL9/KqeywmjjCuP7qd/ei9pUbQNfqjUDfGBP7PJ7Wzx7PxiA5jHmVnLSmU9zO3zUXl68hdvfXMkpe1Vy0X7usW7z9KKF/Rq/ly6lIS5oJcBNcQdfp/27zzmFAmMCw0Q1w0eNo3HV//jIHsv62Dhm2LYbIElij+Z3umyDY9tMS0qdA9xd+Xt+t3EvYqicVbw3M9e9lvpvC4VsoZCAIiMkyVNWqFIScoc+JeH0tbe1JZ29aU90LwLSgbpBh/HjD9PH9KLk8+VA60PGK+4z5IfGD/nUrCJAjKsqD8iYX5cFn+nfxk8cVVh82Lo89V8sPIyOZFKZsYE5q+oY6U9nzWJCx0vHJh1+HrOrl4HRzqRvdF8kPuO833b73+5Avl3PBYobSFhmuH2lp596Ove+u5pDxhaTH96+domdiePr7+cK7Q1kLKo3XgNT0j3J8eatnCG/RcTR2erk02ZOx7DsDB81y4FnrX1RMbG0XI7bHTsxgK8UditRE5LErb7vsjWSx2nyOwjhYChfPePiAWSHv5/8uoZteoaXtPsxkNlSdwIko8Jfdejle/AD4/LU92/sMnn+/rFOiGxcxB3qrcy292CxM4zHrUN4ws7D6/zl7YXx+qe5kUR34NSydQ0TxGpqnTxKgq6oxCjjC97XXdevhfZw7tIO6nFbbI//omOmy8xi2yjPb4s0mXSMBKsDE5lvnkCAGCWdFL58wuRg+VMAVtquiqC3dLIntOTtwX9tgcDmRuVvrHXKaCFAjn4UJ00ZxD9nr8N2HE7sRaRCCpawOKmQBqCF0iRZxWC9XMWz9iwARhSnsw+qLLHOKeOg+M34STCyJJjya9IVmSetA/GTQBEWx2o+CpxMwYqNTpGbkXUye/Icx+Ea42LGi1VcoTzNT9V/p/6bE0t7OHmj0mY/SMX3RZ4/x6/yT/WPyEnrByPxW1StZ4lyy3Yy1BarR5zO7ev24ih7Lj9R/s0IaQtlopHcrXPIJmCSbfs6UNMap6qg7x5KzVGvsI2cWmZrNwqLACvFYGJ2KwW0ouaV06G0ua2WCJbtcOm/XCGKJZuXcvrelQQ1hTaP2E5Ql3k7cDjroi4BbzUyi2ITps2LLcOwnWEIAc8fvh9Hf+7mi4fEAlwZS/CKdQDfkF27D8XqGgQwDIOOK850JBLBSpY47nQtBGkhSK2Ti48EEcc9t1KyFLup6lBY9gkqJsNGu3blpeH0+c8gahmy+tmHSV71XEhfdwklh2SLJ1EL1iRLVHWP7xm44x8FC1W457G2sSn1X2HZEP669iyWJ4pZ5VSw7G9zmFoicYR5LH4SqL5AhsSQomrs+60/Zd3OLzM6emEBjGTAZsKgXG4/a9e0AWwPcs16RkpuxUBtIvO56NSt4Eb1bwBsdfK4yqhkz5aN5Etuf+pr1l58ou7FPYYbJBqeFxwgagPoFbs971oY0rmt9RRus9yCoV9X7tFHG9IBfNmhZxC17R+M6bF6xknrAYgYdTu8XV8WeHtmAORdZHjtPS/ZVPv6CqN2NcfJrsjCm9ZkHrcOoaY1s6fgh6GbWFbdjIbJ/8omp37/tzgSyzSwkBi/7hWe1+8C4KPmU4Dp5Feke1MmS6s5NPIi9ND9tbz4cJ7anEfcUZmWOyOVOfL2DPTF8Pqz4L4saC/EQGEPfxlLfGEeMV3y+LuKzACB6k9He30i7aPWF3T0Wl6lPMHpips5aHd8qMpfGZTn54NrDsF2nKzy5F74Ou1TIFzIr41vEkfFVENIHnlwr0KeLAkMlFSfU44/XYarKRK/Ni9IfT9R1ZkhLWWZXUm5aOCP5lk8ZrlZy58WjeFAz/ojCYtXrKmsEOVcoTydubEeoq56rvX+MF/OEFnQsh8zSZZpFCHycfv6WpvqKCjpmQi3RA2c5Obl+JRUxvdlexqqaXK7dgfjpPVEt/4HOL9P29cBb6ldX+AVQDkzOJ9LxL2EiLJ0zSHA41nn+as4lw1JsZa/V+4FuOJF20qOF2/qGJA6BIjT0BpB6DafahfRhp968lDkYwn70s+0lk7KjxsaI3Sc6vKwj8Eekrq1JUZdxOJ64xxCRKl1cqlVy/lRp+0wEZydVAD2yTDd25Np2vzSfx3LW9IZXQUTK2nKPvXUq5j/RgWBgkGMnehaaFSo7Vwkv0CpaCJvSw4dz5gMtVc9+3PDp8icK7/GMdJHRNFJNJ8NjGFB4XE81TyKFifIIjsdRMmWmYsLnQBxTEeisaEeGQsLmbygTt7hV/PqC59jJi/AT2ocPkmWXo8OhnrQgv3qoMVfxa+M8/ETJ79w6FciBOu1CHKszGvciKZ7hktFEze0/5J6c3CqHPcR6zDmiqmA+zzQenm+D2AA8GUgap281L5OPUj/3+EtNdshvy47/TD8Ovmo5fpV7lNvZqioxkRBaX4Aqna++UCG8MYOEGijNd1/0YArpNB58GlYNg4ScTQ0T+nj3cp51MXcae9vT5tD20E36lxUNjhjOcMTy+kJ9flT+LflEqfBWlo05ILEo0RlhygafnFAd7On8E7eKby62W3+vyc4nEhia+q/zj5qWl4ZFyZ+wiRpFUPEVs6VX6MiJvAqu3UHLSkk8KR1IN+WX0QXJqv849kz+b8sCWR6fxb6Okls54RyeNByRUE0IXF4xoAzfe90FuXxEr6QbJBHKzoGTYSQJcHe0nLGSG6p5FqnLDVtrFOmqEOOvMHJrIy4JPFDjipJH/+MjNp2lOF1RgYhV7t/Rnws74WZiNDkhJgRM3tVvmvwyNvnB7QM78960uIhXj/BbOjcuwJQ29o3oYTmqNEls1cY1CiNuuuUjbZu5oTG9vQ8g5xqluoXImNR01YCfN6n9QO8v7KOx7XfsbdYhiJsVm56nkhxJSUiSpgoarIRMexPH/v1DREmV+Wlvq+tS5fuDS0KkuNTCWoy7QmLmGGztq6dJnK41LjSnciEy007U+DHFikF4BxV4QAlk6h1EOJjpDlcoLzCnmI1N5unAaeiajp7HX1hxn6V6gl+kRQdqY6kLSImr7ybT/VHacfHsrqLga79rT5VZoTYnOrZnGO6wYsNJQfx8IqhXabPRtRO1+9mbbOFgcy9655jle8uWpwA85vO5aDTbuKQsaXc/Noynlm4OTWPgtmn6oCvAsrjqwmLLWiYNGp79j7DlwDvVnyL67bsj4XEd8pmZLhmtsiFvGdNYH95MQCqYyA5Xh81JSNoMyDPP4C+YLePegs7eakNmP99fXB+0138Xnsfn0iwfu0fYcw5vc+UBZ+WnMhvl1UiY3N8xST27X2WrwRUWWKI2MropEz6YmPbTXC3B3lWPftLn+EjQVFzKzB+u5azzD+FfyYuJ1+0siY5eK/pRNS88vzeAZe317DaDPC5PZgi0YwUdksIFVXjDuV8LjddVVhL7blXQc+IrKdfhN9ynk6JE8TUm3vdJ9WzjQnLyRhgd/ZR8/uDvGVPYZa0mJPlDzhZ/oA5zXlAV3PczijPc8t81jrl3Gsdx/QSm7Hn/qXX+TrDr8kEiWIiE0fNML5NmHZGD1HIkxk4fOs/uFx/FhWLm8zTaVHPSP3306bfsafPzbx8y7waIU7GcDxm2aSPb+eSvg4y0UyQKxPfpYEw9U4Oi53hHBROm9d+L3oPhdoqFCyU+jthUE+SKb3jrPaHMZQ2IugE7clAdgXGOwqu5tMNTQD81wkzLOtUabTVb+IIaR6NTg55+hAKAun3VbVTwBx7HA1ODjViVLd3kWVZnOs8T0TWUTFZbA+jWDQjbU7QmwH7hoYIx972Hu0Ji9/K93OOto5mJ4ilpoeHWjf+iwnTTvWvyZIgHPCl7B4Up+eeus6o/vwDfMRRhHs/x9oaUX0emwbhZsemDs7ng5X1SNjM/+BVTph0QWqatR65/aFF7rylYR+rkwRu6ZaWLuutb49TnptudPN6qCmyyHimxE2LaML9f39pEdOkZQD8TH0UuDvrfhWUpgNCBU4jtmUjyRJSooVcESGXCOuk7EHGoN3KifIHqe8daqPeezBj+iyZOUfPwcAl2ka76x8XFhF8yWDD4MIAfzl9Mp9vaUGtWczVymOscip4Wb0y6zq+aqiKLGVGskftZaN3Vd4vAyJ6CauS5bYRkVm6XB0YxR+Ny3lJuoaEo9Ig5TNH358P2wehYrLZKUxl6IFeKyYGMAD4EhC1vewljFFeoZAW3rCnIEtdm6UH8NVELq1USW7WZV2860u4r2hT8lnqDAXgcH9XA9SvMjqyFACOuWs8YwY3zuWf2g0AfFx7GHDmdi1nvVPMs/ZMdBIMEnXMkJbi39oCjE1NY5heawUPUfNEEu+3juNniYPdz3ukyxs/zzuYCzaXECTOvmX7ZNF/SyPD8Nqw3R4c20wpKtqOQNd7V27wlqKYlp1BRLr3UUuTIaH03PPUgf1HFvHdg0awtSXGwfveykRP5mFbEI6sZ4nvotT3Zc1PoylSymj81JrbOVfdSDs+CmK/A1wFu4DdxiDhDgxDRDE8EXrL4wXnTx6/e6zj+Y91ACYKX9jpwW3nkr7mpKiGg8RTdmYRu5ecjzBXMVZyMzpfxLo3bO4rjjFep1hxhTNqRPeKdl4vteY+eKk5G+dzn3YLAJ9G98GvTudb8ksUiBYsZM5M/AKAIkvngm6WEW1v4Rfqv7r8/tGmE4CTe1z/S4u3pEoIxyurmSy5ipMvFH+bGWtupw0/IwtLeTrLvE0Z2UAVxZNplOl7hUMs0sbPt/4YXXLPre0I4tEIhn8Ix8f+RogoE8oD3Aecvncl1rt/5iz5TSpr6ti8ZgYVw9znwdDFt/OQOo81Thlh7QJgIvvrK5glL6ZUNNK45kQgU1imtrUTUfOUySqyxNC6d3hLux5dGKzecCDPGW5B4F3WCZwqv4smLObmHdOtcqo/mMMDHE+tEWCrk8917VEKw0EkI539k3zZg0Q5sU0UiHQ2s6ncvd695Z8f6ZcBrkpjUMyGTmqbVfl+Vta4y2hqacV2BJJwEIF0rleWBL/bx2T669cBMMNZyustp8DXIGTpyOljpXVDiL9s6OwD6kUkYdFAmOlxt5Q/pCuM0EN8ajalpimlgYPlhZjI5BpDgFm7YrMH8BXGbidqYxJLmaE8C0CR1Uyz+Y3dvEUD6C9k+HXtgKnt11WeH+CjolOYXvc/NosS9piy3y5Zp+QhLJK1/Vm8hqSZ7ixpMf/Q/gzAZ1v2Bk/3hGa24sfCQEHzkDMvadvcnL42ikLpa0YtGsrbG13SsGdBmvxlg8+TUXt64Sb+88lGxpX42Ns4E5+IE5BtLu5DD6C3JNCwbA5pepIZSg0Rx0fYHIlXHkGWBJosMdveA8uU0DApze297BHcQebVR/W8T32B3tnY1hfApyRSRG1sfBGj5TUArBSeUjtPCfGv1H/yeoNCh0ec5blvvyG5/XOhcYfxzBK3DPTk8WEWL12Knzg5bSaQDq5JG2bzsHoDLQSZa4/hYevI1H/ec24LDzE0d9xHTSd9Hev+7rOv2+qllmhN98QaWh6DVJtfqv9M/XaLeSogaIwksG0HKcvzKRZpJdsW6bHaLL9moq4tQTGNnKO8kSJpAKHiKqqTXlMt8ezly831WzhRep9GctD0KqTwfoyL/SPZA+VnbpZ5HNtmxcJ3KR0yjtxCl9Sv+PgNJiYVE9fZJRyY+Ct/yt+T8oSdEvAYmrQWqSwIckRoDZUx97ite+0uKr5zGwAljQuYKH/GgXzGAvVEAE4wXmKq+gYAH2zawhAlTD1h3rX2ZL4zOmspdQdUSaBjMCzpU1ZtNDDEXE2b0Ig6OksO/jvxtR8z+pjv9XiMHwlfnCJL3263KAzDI0U/4OItJxIkxs+HZA8RqXo6m7LKLsfOdQMY4WQwQGBTTDOScCgVTVhZvCBHloR4a5l7Hfw8di6/4GzCRLhhZKZh/LSZB7PxtSIqRR215DFTLOlxn74q2Fw4k1+vjGCgMLRgZh91WHcvFLn7/lpvbyMk5flNmxwinCW/gYrFVGk5h8gLAVgcmQz0fH0OYAC7nahJHsngQ+UFzN36Fl8XVb//73hn0Hf4cfVhxByd75dM7dEPqid4y12+TobXAFMvuZcl806jao/pyMquuR3tUDnvWROIodGkjmav3mfJig6iVuPkpX4LGvUZ07xgXUqOzyVizeYqOiLmJ5qvIJRqFEweNI4ihruMYo+v0cyRRTyd7M2Y3EvGqaLlU+5Q70TH4HD7E56wDmTOpnE8YB9JDJ1CXePiPuzTmMh8vivPQcEkp95hWuRVhimu99Nq66Iu0/tUiZdi03nJdkvRbu5GGn5nQe8U7df8Ia6WbiOkNuAjQaldQ0ermx5IlwPOGXQhSzc18i3F9V4Kkc4gPFP+Q/ZudY2LJwu3fOyXx48npKuMKAkyPfIuU/WfAjB/6wHAiekNaFzLAbIrTW4gd0vU/pV7Kas3V2M6Elflpft/Nq/5gvamGkZO2g/RB2Ld1tJIKJzPn8yz8TlR/MT5frB75WBvWVpfiFo9ebxm7UWBaKU5NIJwYVnG/4VKjHrTj2U7tMZMcgNdy96ilsQD5pH4iTNC2oyGSa2TyxZGMbmX9SuS4AfK/zhXcclMrZPLtca3uWLQKGAhAK3dyPPHNy/mVs2N7C81JqLIZxBN3n8JJ/tz9KP7LmdG9SNsFiVIP/yInNwClm2qRbWrGCtt4C3b3eLWmElI95bVpp9d9l4XwIfzaHBCfFJtsrdlY5kmpbE1qWuxcKj7jjcCpZAstphlzWVWcjGnye/wnDUTsS4G405NLdtp3MBL2tWYyNSb5cjqueljZUZ4Qb0GcBUh5QPqEQf17mpYGtZTRG1rS4xx5WFaDYlmQjQTQg/mZZ1PCRbwqjWVGBpbnALGJvs8y+Nr+JNyL2WiASmpMBpxdAJZnu8jS0IIbAaJesK0s9QZSjMhcnIy/S2FJNF2/H0knjudQaKeqeXZyyu/ahg++QB+/KF7XP62Z/diUV8mKD3010bimVnBhOX6qIWIcl3SA88Lr8rwAAbQHXY7UbPKp8Ki9Hcx4KP2tYEZKGWD4w4A2+ztv9QyM2pfr+tDUTXGz+y9p6k/kSjfm/OMZBlNoIDTtnM5aaKWzzq7hFry2CCqGOGZxlsWqOvpsqbjEy8xQnEzPS9YM6hNkj1vz+qpe7kS4AFdZtqwnmUfchK1HCzPSX0/XXmHk5z3eTHuEqi+eKgBjG/9gBnqkwDMaRiG7qSzfXqgqzHzreJmCrRa/MQ5J3Fdn1Uf+wtqbgmHxm8i6ugkUHmxcDgHO3OpkF1j3x8lLqWVAEFi/Cw/rXBo+fKod9LEzVHSWVa/nL7fLNzjNijPz82nuyT00zc/Tv0vW5mCGFYk7dvV7AS5RnmMKdIKEo6C2vRzwC1d3uAfzRzbPadxxSWb65cvZNAjByELh4833cTex32nx32f8+B1TFtzF58E9+Nf5qWAQJYEP9K0bucZxmZOlt4jT7SRs6UZeulSmy1N4WHjKgB+NGI0h+o6swddwJSNj7Cg8hwCjQXUN7jXSEMkkSJqX8x7Heu139BasT+5R17Db0xXEdIrq19h+Tivx7WD1d7IU9Z+KaIWR+U9eyI3lKSFL9pi3RC15nQ2MK7mInuCXFY3Ai4zql1hjQqnhjlP/ZkZF/yBh+vH8lniRoppSpVMtkSNDEELb6Zy4sGn87OPVvGf9j2JozF86VYkIfhh/C8cJH3KEcEVnDzSFY5oLdmbRzdWs9XJ53Tl7VQ57iBRz6XKc8yurgTSRM2OtzFO2gDAesdg/aBZHBy/mbijMr4gh7/FXLGQGDqhPr4rSnPSz6WaFjeD19ZDb2oH1LwyvmP8OPX9ieTxyBNtzEoquX5uV3Fh4qdUBB3+l2UZE5UNLNEvIiDifG4P5uiEW5LeWREYYOzeh7LG/yJtdZuYvv9Jfdq3LzumDM7n+e/vR9y02Gtwfu8zfAkwdcvjzNYfRMHii/XnAn9I/VdaN5sL5E+IoPOJPZpVziAiCQuTzPfPY+bBqMLCDozjqyGhMoDdid1O1LRBE7nTPIFDpQXI2JjagI/a1wW+fpLnP3DTfVygvYiJzKatVwCX9cPW/f+Ft0xwR87LDxqux1DjNDg5nJT4LY2EEQKOt2wUWcJxHCLoWI6EhoGqpQdE3tK3o+S56JZB1Fec0WsmS4Jj9+xbT6Kide0/+9QZQQw3Q9cXaX4goyQQK8EDnIRuNBISMU7LLeoy+SjWUyltASAkYrucqPl1nVXOIM93hQahkRTh4zNnOCudSgB+n5Mmmookcbd1tOuXJuKcVzU+1fEiqX7uM49FwULoITJ18kAEClhhDyKKxmapDG8OcVH4QO5OyOTSzkxpccrMFuBTM92nmhmVTl6Dr/4i5XG298c/gV6I2oy1d4KAqZH3KON0qinEr8o9ClKNa5/LhZorLPHR1ghwNq0xg2v/twghBH88ZSIhXWFzU5Rr/reId5enyxM7FB/3vfhWLPNm9lUU9r3lb+wtraJAtNBWXQVFSX+uly4m326AtZ/xysq0U9LgggBfVLviH7VtcRzH6XF7D1v/V67V3aznW9YkLjZ+jIlCYVBDCNe3ThjtGKaF2kkBtI5cnrNmkEcb0dBYxmR412Unav80D+M85XUAVta0MKY9waKkNH9tMusNbkbNL7fjXmiCPI/IiqJqFE4/k/ibKwG4551VDC4IEEPnZXsao/Y5O5UtjY04muvmulnKj5xxjBEb+KnyOMGk6InUVpOxfaanTNZCQfGHUz5lxXGLZXYlPhIkJB+juj2qmSjO4qUW6eQPlw2dgz8dxNUXSgeVJByqKUT3ZffLGzRkTErgZbjYgoSNjZSVqAEMGz896+9fZUwY1DUA9mWGz4mnzO5XdRLyGVP3GuepzwPwsHk4/7X259DY5/iUltRvW5xC7rZOAOCYwjJO34XbPoCvJnY7USsKadxknslNSUGDWysm794NGkC/IVMGfvsbhf2JBoZI7gu7zu5qgjqAbYOXDO2I4fXe5gJyZDebcDPnggmOA/XtCUrDPkzbYUr8PsAlXas8pT+vB47ihcbNjBSbuUJ5miuUp1knqoDtUwaNFU/k+4nLsZEoEY34ibPVSQ+Y+ipnvTFvb+7Z1EYCmcrQRB42QsSTROL8nK4R34TQU6TIR2LXZ9RkkZI41xQJvyrzsP+b1DU2EUPr9hgosiCG7hJZBwilzXgdfy5/MN3zUBX2dyFqVsU+HJ64CYBJgTyO9vy3yS7kHdulbuPUzRnzSR4fNTmLj1qtXJIyS/9P7gX01q1sOlJKhbAp2QXWGyGXvSINSbPahz5cy/OfuWS7Is/HNUeO4acPvcH7W9LEJj+gctAYT39i8lo+J/EEkzS322vhlv1ggkvUIo5Kx9Wypi4tOFEQ1MjRFVrjJobl0BQxyA92nwFUzHRJ6pPWgZgoqLJACMF7+g8od+qQhUNz836pnrIOLNMn8hfjCgAuGzaCgyQJgY2CjWp3Lfs0LZvbzZN5z57IO/YkEnGNNW+tzFSpwySHCHZbDZM3PcFK/TGaCLG48XK8EvbnzhjCPe+uJmHafLaxOUX2gIzgS2k4HbyZY+/BHPZgiT2E4+Q51Dp56Pq0DPnzSM5Qjo7/EQWLISW5XOS53zbE/ByZcE2fK/P9vN/tUc3EHs5qfq78k1LRiLxqHzj0emKxtBBLdxk1nyLzN/XPFItmoo5OTvxBIBdf8RCuNi6mxQlQ6+T2uIzcgiLqyAPHpt3xUUojWyjIIL4D+HLBkT3n0s58f8pmenzyTeU1vqm8huHIqIo73Z3miWz1mIIoX7MKoQHsHOx2olYYylRJkwbk+b82yMzcbD8hEB4fNfE18lHbXfAOZuPm9mXUEvEYOcIlaaYjUVhQTG2NO6isbY1TGvZlNv536i1cUn4qz9duYX/pM45Lliy2Kb25WnUPEa7gOXtmxm/DxWZ+qzxAAoWEMQ7oXaxlQ9EB3Gq6A8nLAyOJm25WQIjM67kDd4R/xNS6Z8gV7RwufUJObD+gpMt0OwtCCH5w2CjufnsV35o1DEWWWBDcj/n1TRnTBTU5Q+hC6SR64Q2qhIgyiFp0YZDvEU/pgPf66eKj5vH6kgKFkKwcXW2XEStKi454r4cO5bSn8i7gl+v3olg0oYkRPRK1WCzGJqeUMBF8JIjhDmx7JeRFI3namkmTEyKuTmZvYPZqt9xusNhK3rznWBI+lgcazudVdSr/so5g6N5H8qPDx2T0T3YgoeWn9tHrKxh00gRrTZsKuMcloMkUh3Vaa91n2tbWWI9ELebItDp+gsRoTyoGXnGImytScFIZyPbWpi5ErdGj+lgQ1FCEwxqf29NlOwJo6jS9QQ35vGrvA4DsWNz//mo6Gst+OngZl9W4ipoLNu6HoeWiCJsiWvB3IiIlYR9nTxvMgx+uBSDHaaeFACNLchhTmq6aKQ13PaYfO2P52HSFdqZZBVzh+S8hdD53XBl3WcvLCDp5VS77WuoMUEkNJyovufvV5M73aPu3ydVb3KvL+ACyyMGosmC8tI6KZLnmluQhCOcV8m/r4IxpQ91k5QB+UvYAH65tZpnvAmb7vk+b4yOobunz9g9g12J55an8aMlITGROKRuToUa8UN+bRY1KRiWBKtLPSKPTkHtAnn8AfcFuH/V2NpHd3oHjAL58GFP7Ch/rv8dPnMVrjwC6SlT3Bc8VX8JPth6GgsUVFQNStjsKnzA4UpqHjzi5MR04aJuX0RQ1uTz+CwpEK2U+g+JcP18kidqSzc18vLaBvYemiZfW6YX0vYNH8s6yWtoTPt6wplAsmmgIDN/+feo0MJslLeJu9a+Ek2Tys0TvCnuQSSC8BsNBTclaorYlMIax0nqmSitAhi9iZ8Au7jr4zpmBbR0AAF7iSURBVAEjuHj/4ant6xi8ClyzcYCCTn6V4zf/l4X6X1GweMQ6lID2p9R/+2z9D9/33QnA49apwEkZ83rJUMTI7I/qMLwG2FB6KGctL6HByaHaKeAfwfT1cELDA1ymvYeCTdOma2Hi2ayP6ixxhoIDFdFMmfbOaDHg0ITri1cpavmLejdRRydiDwYO7nY+qWpvfmhcDsBkKY+LLJsF65u4Tb2dE+TZYEPba8+gCotj5bkUFZcx/ZSfdLu8mrwpPN3Q6vqsKVXsg1uO+DPjW+TSTg4RrIa1XC5/QBSdPGMv8v1BxkhLKRMNtGwogLLuZdZvCv2U+Q1NgMOTl8zgjvJccpLy71EpCHYdUUcj2tbV/qSxPdOsW5LllPy7JBxsy0KS0+fSS+wAviO/wJHyXG43T2ZF7n7MGj8MkpWImtGK4RFCUENdgyyXHjiClz5azKPyrxghbeG8xDVM3fPUjPuoJKfn81zblqn62FlYyuuf6K3m3BZD6EBhunQ4mHxOBJwoujDRaaO5G3EaIUSKpEG6BzeoKUgCHMfGQQAiq9l1B6rKSwivTWef46Lv/XUD2PVwtDBbcFVOYyLz+n1dO5TZ5mRGiU0UihYSKMy1x9KGHxWT9k72E183cbQB7BzsdqImhOB78tOMkDZTSAubGm4CKnf3Zg2gH6BJDkXCHUB4SwK2Fc1SLuuShsr48/phy/5/w29HuDfpDdVo5gC/3uZl1Edt5jrjwIFRoRD7+ZqxpMWU0MQD/1vPF85gdE9ZUueSwHHlYR781j6cerfJRYY7EL541LDtoIwu9E7ZrkpRlyJpAJbc84CwA94IZ5OHqAW6KanzazIa6elkrW/r6W94B78dx+Ib8rv8RHmCRfYwVuccDx7xa1VY5AmXWPtJZA5s1XSWQ6erUIV32g6D4Q54yW1u+TBmf+ExKPYMPovMaiZKawGYF28CyJBib4j07HHW6hHQKKGRU2S30G2ZOabH+XL9acLaFEnwxcZ6IgmTTUq6/zBE+llVeOAlPS5v7dDTuGmZS8y/owznZKA9YfGClY6zf6flHa5KCtTMa29khm2wj+ZG3OeuHwn7dE/U2lMqcoKgrqVIGsAvCm9mzoYIJgpPBEdkiPgANHhULTv660xkcBwsZGQjgSanezsbPMTOT4xvKy9QKFq5X7uZD8eW488ZjelItBKgzdG5OedqFtRdSC7t3DEs0zMPoCzXx1/LX2VEnZsd+qd2A6vGnp8xjV+TucD3DqXmZkpEI78xvklUzsFIip3UdZLnz/BRkyT8sa3M1S9DJ0Gdk5si79tC1HIGjeWPxllsdfKJqoO4x7bRSB+LYA8qogv9M5gcncMaaQhDS6oAkCRBjk/lwPg73KLeSTs+Pm06jO4ClSNLQgREnGonn3zaaJNzkjRgAF9GyJ53hNHFR819Lp1j/Kzb+ceLNcyQlmKiUBmdzq4O7A3gq4fdTtQAvq88hS/p0/JkwxfQo7XtAL4qkD0CD53V4bYF1tfYR213QPcHU599zvb5qHmj9QVBjSPanuRXmusvVevk8qI1jUGijr+KU6l2CsmXupYOTR1SwKMXT+cHjy8kYdp8Y2rVdm0LuP0ihTRTTxgQ6MNmwsa/pf6fn3t40iWsZ3iJmmjdwh+VvxHBR1QMAg7rMr1flXnMOpS37Xo0DA4Nl3WZZlejgyDvKVZTIpo4VF5A0J9ZFio8fRbfVF5jQeM8wG1w9znpe3WaMa/L8n2qYJJYiV8kCBs23uPyg7rfoGpNtDgB4oEbM+bzmpxnira4z/4OoqZiEjZaicTiBHzZDcQ7iFqV2Mrx8uzU76bUM1EuCetJEQ5Y1xBhyxt3Ml//G42OOxh/3ZrC49YhrHdKODv/cy6Y1HO5bKGnbLGD6LTGDHQSlIkGCmlhRHxZ6k1rqUEcxQfJli2reXPnRWbA68sU6pSVkf25mElC0Rbv2nM2pf5FSuV2GpwcClW3b3BP+5/EDPd5+rmUmWVVl7/IXP3n1Ds5fGqP4N/WwZwtv4EmLPY5+jw2NpuMjP8TEAwOBvBHDAwU6sglJxzuvHoAhp14LZG/P0dAxPlC3YOxlRVdprlAepGhiqvkOFJs5n3/wfy17TAMy6E1brKyppXZqxs4cnxpRjm1Igs0VaNINAEQJMZeYjlRdMq3ISFVVDqIe63j3WW2CyKGzfj4w+gY5CsJ5qjdl6aO/t4TLPjwOYbtdViGnUTYrxBIxJCFQ5gouui+9H9kcYh1Thkz4ncCDtMHh/h33zd/ALsYGYbXneX5E92f5+/JTxMUMS6Vn0vZNsxpibK9fdkD+P+DLwVR6yBpAEOUpt23IQPoV7RUHcLUt+8mhsY+VYN4cDuXk/FyHiBqOwyfP5Ty/4k4Omf0ojyXDQ2d+l+EVAbJtopi0cz5ymsAHCYvAGCjUUYHEfBi5ogi3r/aLVXTlb5HwTvDZzbxie+7qe9zpr7Lz9dcyAJ7FF84VexlF9HVBa0rSts/58fKE6hYFDf6OFVx/cRWWp3zFcn1qjKPWoemvh8e7joQ3dU4vPVpvqF+wOHy/NRvwWGZHkXrq04kvOghxialzjWP0Et98XRY6fpvtcpdFdn8Mjyj/xJw+xMd+yepQepoczklkquI9ll+Jmnylr++VXoBv906CwuJbxUfwF62Q0N7nNe0nzBK2gTA5up9CAzNniHr6IV7XLs+Jen+gHkk7cX7Mr6HYxP2qew7vJAPV9XjOOCsfY8CqY0C0cYt5je41Uz7bukH9W6b4e0v6whetERNjpTmcZvmlo9ucQq40zyBAHHy8/Yizyfx2oapVDv5mNJIus+nQXu8e/XBkC99zlqzSPSf2f4vylW3lG+zcDv+VEkmlsySGraN3yMbbrZUUyKaKBFN5In2lLLdvMl/YB/NR44/Tke/WmvMIG6mB6X53YhflFWNZOUp/6Pus1cZeWj2O7BVLYK4ex1OklajOkFa/SoFsQ0UiybO+ksjteTzxLwN/Hp8De9pPyWBwtqmaWi+W1LLUYTN//RfA7CgaRZwbNb1dYZPlQn7FFpiJqbtsLExCgjiaCT07s3TAQKhXKYccW6X3y+3H+MM9YnUd1vrfjkjS0Pk0cpIsYnh0hbq/QM6gF9mKD2op/ZE1L6jPE+u6FRZJA34qA2gd3wpiNriQx9m0BuXU61WMeX47/Y+wwC+EtB8QepxB3qR7FY/fYLVqdxlADsGWVH4nn1VqrzoJNPepuZ7yCyTyg9qEBjJR/ZYap28lDiIF2YPxp47QtA64O/knTVx5BDOtA5PfV++ta3zLFlR3LacY5WnAdgUS2fHElJX+X+AIxoe4VLtefwizl/NU9GU7vujdhUGJ1YxLUnS7jOPZak9hF9PyCxNE2qAOJ4eI186y6pL6fvNFl1fEaqmu0pmwkIRNgkjgZbszwk57SlT40HlFfxD/T4logkDBS3yGOBmrlr8g1jkuIOaqJJDY80G5msXp8oxAdobt0I3RC3etJUJYjUxR0utb549BpF3YK/H59Q9iyhe8wwnyx9wkPRp6nf/hOM6PKQJajInTOqddBd4iFp9B1GLGTSSLpdbZZdzk+mqGv+kfAy+4hCXznXL+w+hpIuqphfF8Q2o6LTj69LPHe6FqOU6LaljEypwhUZ68lKz29O+a2sHHU/NyJkgZPY52HVazPGsryVmZlQ3dCcnDzBy0n6M7CEzuajwKLSNtYyRNgIQ0/I5NfY6oxRXyGeFXcld1gks2tSMGNVCleSSzxa7Hj0YZlrsTuKoHCt/xB/U+919U7Lfr92hNOyjJeY+I7wqnd2VPPeG8fby1OfLElcwbuhJ3dYJFQc13vX9iHDSdP632pHdTDmALwMGV7/KZ/rPUbBYtPEgIE3Iz4r/m4js0I6Px62DiZN+PiQ8w+2nrZk0O0HCuV5zkwEMIDu+FERtwv4nYu17LPnKl2JzBtBPyFCH2wHVx0tqfssN+kIsZLbU3Ua2zMwAtg1hn5oaWDZFDMpyt21AUrXinzyj/Y9GJ4eW6JmEp5/LMW+5g9MXrWmUiwbCop3vyc/QSIg2KXtpVH8ht6CYBYGZTIl8yEfF32B6Th5XHDKS25JeTlcc2jdXJSGnX6ybnELuMI4lQIySwir2yLZepyWVAcqndZfL82eDo6QzWRucYuaFDyevMFO9UZEFpyZ+jZ8EPuI8UjYh9Z+ZU8ktxqkowsKXNyTrfi8UY7Btm6ijMyUWQ9N9JEybUxO/Jle0ky8i3JVfwCHywtQ8q9oa0uvvJM/fXLORER6SVueEac0ikNGB8IbXeD6ZPQF40DyCjU4xo/swsD5yXCHHvPh3/MK9/i9PfJ8C1eAnJxzNk5vmsKq2ne8eNKJHAYgOFGomZ8j/1959x0dWlQ0c/51779Qkk57sZpPtjV22sAsLSG/SpTcBQVEEXxRB7KLYXruiL6gIoqKoWBBE6U1All52FxbYwvaWZJNNn3bP+8edzNxJJtkkJDOT5Pl+PnyYuWXm5O7Mnfvcc87zPEEZrZQ1m8AhqE3P8T3Pr1J/t04FfAGPyYTi1L/Pjj19DwmPhLt42HMteCCqTSzrzLT1hT4LL1GK6CDc2gRMSa7rikS5M34MZaqVUtXOUSGnWEB/vQGPlp7PtW/Pp0y1cvHURZx/dHpw5bNMfJZBOGaj7ThFdit7KMAwvYOaE9bT5smn89P1k1hsrKVCtTCnah8Wbf8bJDKufs5zF6ebz3BN9BPEoqnvp60svJbFrkQhhB26lFftmfgJ0+IbWP3FblUhH2t2dQdqqV6Pgj7S6u9NxFOEa5obVrCkz22VYbDNmkwothqA6Wwd0nuK7DCVTSjRM2bZ6VMHPmb/HZ/H6e1fq2vwEuMM8xl26lJ8RPl+9DwiWPwpfjTtBPh05UCr/YnxLG8iI1OCtDFnuNLzB+MtVCaSkjQgWUGHQ2WRLxmo1beG0y4eByLYsp5FxnoAnuM45tWE+MKJc3lzWwtKncmvX3Pm3vwk5tyNX1xVwj3D1/yMFl/3bxp2bObAGueC9aqjZ9EWjhOJxzln/4ElKGorm8cPo+cQxWINtTwed2a2nVyW+cJPe1J37gNE8JnvvXfwvVpRdRq3bZtKGA9r7FoW1/YevmgZBjEsWrFoJUjAn/o7VNlUfho/C4BjSqr4WIb3+B/PN9mVmFP2vPJTjNOTtFpPAe3UHVOGwQ4qmYDTAzJxWqrOlplW8FrT3piaq/VsfB4fjH6FHwUXsaSPv1F3Nicf3xY7kW/FLgZg0QAChsJQKS+FDmP/1scAKFMtvDv5QoqCfh789OHUt4apKRlYj0yZ1+Z7HmcuZEs0CPwcmjdQkxg2eE/8fXwtluozC3pNJrhqh+1q7TtQ62htTt6P71R+Qj1GExzd+Ge+7P8pAM+tOReOS83JbO6M8+2YMySvssjHi4l9i1QXMdqxsInHYkBqDmBDF2ynnO26HE/51IxtmuCLEo01MdvYwm8TmUI3MhGlTsy4/UBMCPnZSRkP2csA+HTNLFZF29myQXOy6dSom6J2sl2Xs7ZkEZ8J/wQvMQ6bMYWlSuG1DCIxm8ftJTwecT4xl9ZNdaXO2buTYo9zoecJylULa9d/kBATacdPsJ+0+v15tvqD/N9bB9Gig6zVkzh4L6/TGNqHVQ2dzsV9wegqAD3eGGaq91hpV+r9SBifaxrPnd7vpO23Wxfy8/hpacskPb8YCImOxIjxJ4e0aeKRoSWtgPSToTJlTPdwqCzy8daOVqD7YnFwFwdWuCn1uNDprbniCGce1x+f38S9r6UnSeiZnn8kKMOgoibVq+C1DL56aqb+oL6Fy+ZwU/yMXst7zg/q9lbtuXRufIUO/BSrdrxG7m8kNBfP5RE79T25aFKGQC05BE6j0GlDXw+cVsaMygI2N3Vy9tLMAa67t7wzMS/DnfExFHDeP3zeXTz3+C8oXHgq+xam2uFOLBKzNW8XHsAlXb+kSjUnUpr3Thef9jfaBbxpT6GIDnbpkozt6o9xwGX84KFy7rUPYYuu5NpEKQmPaQw4SAMIlVYS1yqRNKKDSDhMvKM5ub5FF6RtH/RZVBR6MZSTTr6hLUIkZmfsie3o6qTJrqZQddFhFNKzT9pbMQWceyX42zanrXMPTS5zzR/7Y/QaJvoT89b2LIXSOZn3Kcw85+wP9uep86d/t/V7nGfTs5ZaeaGP7bUn8Yu1pTTqYj5kPcJL9hyKVActUcVm7Qzj3M/vDE32JQI1t8EO5Z5hb+SgRFBYsPMfrPA7c2tfa1kGPDLov6m5bDFP2KnP+157Z0/6Aafc9jyGgn8esPd6jyJ3GicdzcKuW4lhctjUidySWN4RiXFr9BwKVBdXWvf12q9nDTWQ9PxiYCRQEyMmGG7gLd8l+FWU+s5SYMOQXud7xV/jzc31mMS5ubqve+xiMBZ5tjLBXE4Vzdib4rDP4IaT/qbwo3yz4SjKVAsfm5xe225Cce9MffkwJHAg+poD2VdRYl1QzZHG68ksXvFhmG/3XvkskxlqK5t0NVEsFmQI1Kp3PsUa3xV4VJzH44sJeFM9In6PySPXHEFbJEbIn/kiPC1Ff6K33F3sujgRqE3ZZylT9rmt1/7Ldt7Fud4/YxFn4+YPsaruApoI0aRT4Uhje9+B2vLik7gi4qQNOcN4ml96fkInXuzm04F9+tyv28JDTuTyp7w0tDnvsf+U0r3uk4lhmvzZOIHmqEmTLuKy9k5WFx3KrZEYIdrZoCdwgfkYtaqeDu2nPFyFZdZwesEqSjs3UaWaaNixLzW1U3q9dqtVwfERJ1nGzKpCHu2xvmiCM2xqly6hIZr+nXMHuaUFqX/DuDIhMeLRjqVniuwruHPrNAtxV2yIa0VHhoQzg1EVSu/Nryjw0tIZZYuu5KuxD3O6+V8OMd/gP+a13NJ2T3K77otcn2XSSgwDGztRt2ywQzF1Qao8Q1kkFYhqs++Mj/0JBTwU0EkMkzAegnsZQnnIzAqevO5ILFNRWxoc0nuK7LA8flpwbsCEderftTNuJm/yxTF4n/EGFnG263JetOekzQnuJj1qYiAkUBMjxhcIJDN6+uj7omtv9hCkIdHjk6s6VWPNIV1PcLDnDgCWbytjsPP+1nUVs1rPBA3XVk5OW1cd6v1vNFruHHr6CCj7unANWDoZpMW0gZUHQ7g9xLjf+0UAVuvJTKn+T69tDMNJBgJgEe91YWsYqs8gDdJ7LDoy9aj1sy9AMN7KDMNJE7otsiethlq33W19nzNaXMkz5hqbOMF0ygj8s2nfvnZJY5kGnz9hLl+5ZxXLppVx4PShV666rehK1ibmN50RMdhKBY/aS5PrP2v8hYPNNwFYFXHSwF/JX5nlWQPAW9suzRiotaVlfOz9uaqctT9zu35DFz6C2uQNV/bW3T3KZ3TrUAU02wXEMInb6b1QTW1dGfdxa/WUsz1aRqsO8Pno5bymZ3DStFJuzrj1wEzwx/mC9UeqVRM+opQX3oXluoBt0KHknKBwZ2r+WPdNlb/EP02dbxseFefQ8I1s0VUEvIO7AN5TdxxXva1oJMS+xkY+af6dArqIWUMLmkJ+i195fswh5htEtclbTb8DTu13n6kVBf2uF/nBPWzbnejMXUrjh7Hz+tz/WONl5qmNxDCpbLeAaSPSTjF25P6qQoxZ7npdhh76kDD3BYVkfRweqjCV0dBo2zHo/RvaUhfWPXubJgThm9btzDK2MkNt5ezIDZSq0RFguwPKT5j3Ms/YQJMuYkLs49CrpDAEvAbfjF6IjxheEz6dvab2KbxtFT7lXDSU61ZKi3sncrFU6jt1uLkSBln2Yi4bKDXeJUgYe89UoJTgugd40vttWgiyseNI4MA+91fuOmp2LC1QC9BFuWrF3BOjr2KwrV2poLBW1ScfF8UaB/w3nLN/HafvN+k939V2B/G72yK0dDrHfrLayUR2J4M0AI/fSdPe5q2EmBOotTdkTh7hTs1fmGHobUlhAMtXAOEYHZE4je0RKgqdnjXP1ue4yryfJoqo4RDACRyvKvpJMgPqQ6HUBaK2bR4NX0jY52G3LqLM27t+HsDttf/Lv1duT1sW7Kcg9EBUFBdyhfWv5PMNqplFs6dw6MwKNu5up76thALdRZf2JobZOseie/ispezkTYdjjFdZaU+jRA8umYhn4j78y3aS2Sy353Nr7CRA86GpkzhgCH9TKOChQHU6r63ieAPSSzZWuBPyuEsHdYT7n4d/qfkg09T2ZOkagBda5wGS5VP0TwI1MWJ8vgDzwrfTpb3YGKyz9ZAKVsdcaaSl4PXwsCfsy99XH0q9LiVmLe3nkrq3uK1pdAVqlYXpw65Kiwo5z3wCb+Li6T++a3m58SjgGPJdYcc2vmrdgYcYF1upwWavcU7G7f0+P7+OO/Wair2evAjU5ntSQ7eU0hm36axcnHzcQkGv+U97c0Hb71jkfR6A13bNQ+t5dDRtY6qxE4Au+u/ZenPS2XzpnZnEMDljwnyatjrzJQ9Qb/FX3zcAeGvHPOC0jPu7e9Raao+E7c78oslHDaRaXspwDD1y9z7t7ojQkggiv2Xd7gTBwAv2HF6053B8qVPUfWP5YaxoKWCnLmOGUcfS3i9Lh+sOfabsg0opaksDybmmm3d3JAO14h3Luc7zVwCWdwI4ddTcN7rcF5mdHa0EVZggzn/eYObeHXeK/m59DQseqO7SDt0qS0vwWSZ/+KhzVjrt5h/z+uZmAL7b/F9e9t1BDJPVO88HvkdUpd7/657fAfDC7m9Bn6loeisv7D1cGxQB39BuMNV2rGZxItkSgF8ShIwZqd5ejY6nvqPuHrVMTjRf4EDjrbRlMudeDIQEamLEKMPAtoLYUeeCIByL73WsfibuNNKjZQhdvrOmHcZnHnb+LRbbJVw1iH0b28N0/5OUBj295p8ZpkmjKmMiqZ4O23hvF3PZ4o828RHrwV7LA8WVGbaG6qZXeMb3KQKEWclc4P0j3MK9W3bc+Wx5++eU2HtoO+WWjNt4iBHRJl4VpwvfoAO1uJm6gN2wo4Ev/98zRLeXstI8nU9a9xD39X9hGvOXsT6Rtr5dFfGdXR+m1LebokQvBEAw1tzn/pNbX8VWnbRQwILjP8qKDXPxhyqYvfB9g/xL3rueRa+7AzV3LbU/xo7hHvtQPlA6CYCt08/lB2ucGkqX25Myvm60ZSfz1bu046fCyvwvNLksmArUmjrZb7Iz187oTJVCUMGy5GN3Ehf3sK3mxp109/k0qxDVfYxc6E4S41acYdlgrTjydozlP6Nj7tksKy5LW+d3nV+MaBvlyvl712tnqOaXSn/EK1vauNFzUzJLpOEdXA9WeR/B5lB+rwAKrVQQvNKeStmEvc+bFKNDUeNrrPNdiKk0bzfOARKfuV1v8lXrDtrx85Y9mX/b6ZXzIq75bK/aM3nO3od9SjPXiRTCTQI1MaL8HpOuRKDWFbXpY6pPv25tu4oJvh3EMGlqfRSqBzYPRfStqih1BznT/KD+tGx4lRd9V9CgS1hv7UOm4OSPxZcxa/d/+IC5nJ26hA5PWe8XykOGJ/0D+qnIVZSpFi6bkLnejc9U1CqnUHAJ7Rm3ybbi8moKvrQSrTVzvZl6CoDQRGaHf49FjFnlXh4Y5HtsD87l8T176MTLH98I84ZuAeq4K+7jSvOfFO7Tf8Das45aid2UFqRt02XspITJmXYGPtP2Yyb6nBsBW+0jmHTkWYP8C4bPfPttrjbvp1S1UrrxGC7b9RCf8OzkEPMNduhSVtjTk3Nsuwsou+dx9lVLrWrTA/zb910Antt9BnBYr23qSv1MoJE6VU/7JgMSRbpX+RbzaixMkC7qKlLDR80+6qg1mFUc1vV7SmhjUZXF7X38rUWJuXITaUShaaaQksB7v4xYeORZ0Me/oc81HzItAUp3tklPkChdvG1PpoR2AioMBVWDev+KjD1qfWd73ZtAKHW+8xOl0Dc6blSJvTMNCzMxUsFwZaQ2d69J3uRbYU+jNRZgrtrESeYLvGlPwatifC16CVEsnogvZjvl/KJsfk7+BjG6SKAmRlTAY9KM8+PaOcRaaj4dThanbTHkIzscKnsEatqViGBvOhq2MFM5te3iKnMShvVVx/N/OxfyqegnAbi4dgpHvPdmjzhVVMM3ohcTwWK3LuL+xF3Raysyz3mx/KkhYgE19BIUw83y9H9h2N0LEsMiUDD4YuTP1XyI32/q/S+6bPF+rJn3KAsWZBrM52qfq2enq7MDn46Acgo7z4n8Dls7gdyauJ1xeGKBKyguLB5axsbhMjOymos8fwfg+YZCJkXfpNZ05n1eEPkK77rmS3X30Lhrqe1oyRyo2eHW5GPtyTwU8bj2+7je7wRzL6w/BXCydz7vPZiHY9MB+GVNaghgGXuYRD2mstGRDkgUi97dHiGOSSPFRIsr6EupJ8p0tY07vf/LxEStuJebbgQ+3Oc+75XP1aN2h3E6P+g6DIs4l9Xty8GkArmfxc+ExE/MnbWDGcztlHW42XczE/VO5qjNnBb5Jjt0GYXeoY3g8FdN54LIl2nRQZp1IU8OMeAT+Se95m/qZke8qy35eKHxLnd4v5d8vthYx7Pxefwunj4fTbI+ioGQq14xogKWIkAXfiJ0dXUBA69R1C3trpVHPrLDocBnUeA1aY/EicRt9nRGqX/7OdobtrDgqHP7LUDf1ZxKJtDly3xR1zPz42hJz28WVnB7PL14r8dUyZ6Enkqn7cfhkZ/RZntZMrOG3ono89OU8gIuOmgyT75dz/8cNXPQ+2eqV1Ya9PCjcxcNKOB39+xsbbOZE/4dIdqZWxSh2PTR1JEYPtgRoaoo/bNkx21ejM+hRLVRRAfTi3IbqHmKU4GYt2MnBToVRLbo1BA8pcDvcb4H7hIWu/oI1Foo4k17ipPmPZB56G2gahokpr0EO7Ykl3dn4oT04XvXNn+H+f4VAKza+QeY5Qy7dGeJLO1n2MOcpid53PfFtGW+oqFnzByICtXKdLUNPxHi4RoaE72Tts+5weDLcG7pPs6DsdhYyyTtzLF81Pc5AF6q/wFw+aBfqyRUwgvsS1xrinyWXJCPIbHK+czo+j1xDGZXF/FwYvm2gn34ZvQignRxuLmCA4x30vbLWEdtlPwuitySq14xom7rvIbp/g0ArNv1AEwY/BySM6ybaG7rwMLmsVDNMLdw/Don+DKh+HqqVBPvPNPCkv9+AkvZPFe/noM++JU+93up+P1c1VVGlWriA1NmsH+GbXoWsR0tFyqZfjhLg94+g4/K0hBXn3UMT6+p54oje2eFzGffOn3BkPftTs+/VL3NWeZTvKPr6CxbNuBe2ek7HuJ537cxsXlmx6E8zUW0UEhXcTFl4VgqUGuP9grU2qJxLot+FnCGEr7Z1/DOLPHVLeaW5SezTVcQtubyVvRECnU7IdoJ4+F71q/owEebEUIpJ/FMdaGHy8x/M0E1UdXagraPQPWYF7a89AN8POIMW/zq9HkZBj5Ccc1sdutCNusq1sRrkylc3IkN3Kn9tUoF2DqeGkbYVzr/nqyC9KC4U3sJhPrugRsOZzb+ku/4nCFl10eu4G0Od9qSCPZ9psJDDB8RIniI4Bl0wWuANrMUYjvTlln+oWW0DHhNPnroNO5YvnHUnRdE/0zTJJ7IPOpOdLbVO41fx08CYKNRR1fcS7lqpR0f/4gfxg7d+4aSR5KjiQGQQE2MqKjhg8S86mi4o/+N+xDWFp04F2umKUNIhsu58fuZ51kFwH9ftZMp2/ddewvQd6C2qy3OLkrZpUs5vWpuxm0mFI/OHrVMP5z9XbgCnLW0lrOW1o5Uk/JSd921A423+KD1BADP0wZcPKD9LR2lWjUDYERSQ/wqC334LIN19U6vVGN7GEi/WG51ZXzcW722bCifMp/vxC4EINhs0hFP9WZV0sR51pMAyZ4ggKKAj89YfyOYGC67p7mR4rL0XrP0OmqZz3vV0xexT/hXAJgxxelxG8s00lKFu/dtscrY0lVBXBvEXAV421qa8BIlgqffz7u3qJJ37WpaKOAlew7fjF3Mf+sW97n9cLBdiWuIpYYXd9/8+dCuH3CT35lleV304/wtfsSgC14D/L3yE7yyoYHrPX9gitqJRRwrMPTSA188aR8+d8JcyVQ8xrgTmrnnebqztL4YPIx/7slc2OHD5gNMULuJYlHQNRkY2RsdYvSTQE2MqJjhp1N76cJLNNp/+tq+uNNIW6OkZ2Y06PRVkJg+yM72GCQO7QbP9H6Tq7uTj7jnurlNZgcPeL/APsYmtugKHrT/PEytHlndF39L1Dt8yfNHdusidtlLIHEXXzgqY9s5xVjO5zx3pRZWD3xivLJcAZadOi9UFvkoVB1MU9sppZWOhhqYkX4h466hlildfLZVFfkxDUXc1mlDDn1EON58Kfk87K4lqBSNRjlB7ZRS2L1jQ69ArX0vBa/B6bmpLPJR3xombmu27+mirizIOe130mF10a4DFOr9IZHX8/aqL/Jo4y4Abq1M9YW/b91P+LT/Ptq0n9earwcyJ89Rdcs4KvKTtGUlQ8kQNQidvgrW2xMI46FFp4bOd39XtZV6/1OM59ihywgYhwz6fVor9+OldzdzWuRbyWX3Th7867hJkDb29FXw2v3dLw542N4jSdDpxjMcaq7ibPOp5LK3uy4duYaKMSP3v3JiTPvhhB/wxDtOVrxfl+43pNdwnwwt+eEbNhsqj+S55hC7dCmbdSXa1LToAjoKZr7nQK2y0EetsQmAWtXAvvX/giGVjs0uD1G+Z/2Ks8ynkj2Mr9iFOW5V/pnc8jI3ef8v+fwvsSPYd+ZB/eyRrqHmGA7suok4Jj4iFNFBKwEqi3wcuvU2fur7JwDPb/gSHJh+3uguKA2Z08Vnm2koJoT8bG3uTFte4w/zLX6TfP5X/9lc7Vr/WNHp7GzczXZdxjnxEqaRrm0AgRpAXWkg+Z3c3NRBXVmQs2L/ptRyeip3m9cnt03Ltum6AeYJO4lBClUXwT5qqAGEemR49JpGMpPlSHlh8se4dP3RAHzX+hXf89xKFIs3tn8NuBJteJOlJo40X+dI83Wa9eCTm5QX9D6XDTXroxi7LKXwEXGG28ZS31F3L3ameZ6LjHVpQRqA6cntsG0xOkigJkaU3zWRvTtN/2ApO4oCNIbcoRxGu6Z8gB+snp18/rjtZIebSpD/6We/xpZUdquqPgK1ipqp6QtGSbZOj6mSQ9W6Rf0jmyxhNDJ8qSQZ98UP4nOxj7Nin0Fk2vMWsBMnhfm3rV9zofUYndrLyy3XYwdSqc3t9oZeu0YaN3KS8RwtFFBrZu75ybaakt6BWklZNSTKmcW0wX8KT0wL1FZMOo+7d20F4KDO3t+jSW2r2F81006AIrPvm1x1ZUFe2dQMOEWvmQFB3QWJU2WwMDXk0uxj2FY8FiOmDSxl4wv1ndq+qMdQ0+KgZ8DzEofKZ6WCJb+K4FdR/ETpHk39xLRrOX/zGbztuwSfcnpb/f0Em30pL+x9cd1fgCzGJ0+4kbf9lwKwOxYCTgNg6c6/sshaSSd+dlhnspz0z6A7mUiDDvHb2PGcXpQ5m7AQbnIWEiPKPam7a4jp+VeYF2NaGlsrtKoH5C7ncOirN6ylq/8hqve2X0zUZ1Kvi6n0PZtxG3+wkJfs2exvvEOzLmC3Gh111Dw97nBeGbmaw2r2Y3DJvse+eGgK/4ofSBc+XrNnMKkkMKj5Yu55HlWqCYCAihAsDNEZm8RGu4omimiwe5cO8G17np97fwbAS63HALmrodbt/eYrnGY9ztnmU/hVlJfs2WyJHcB/9BJabS+7dRFFXp22z+TyVLC7rr6t50vy8T0/Y5pvg7O+YzGQOZHSzJDNEvUOtaoe3t1DdL/z+H7sXAoIU2h08TF/6n2sPoZtfd77JdbtaSNEB3+d2nfPaKHPopAOZqmtNFNIqX9Cf4dlWLgzOFqkfkOU6XzefB4L0DxsL8VPFD9hDvUPruA1QHmGWmpDLXgtxi7LNWzbdH0eZ7e9wGLrOQD+Hl/I8UaY482XOMZ4hSftxTTpQj4bvZyItlilp7FOT+LMosHV+xPjk5yFxIhKC9Rigw/U7Hg8VVxSabQhQdpw6e4Nu9B8lFOM51ilp/Lv+EGs6pzVZ1219tZmChOFiYOE8Rb1XYPr7MgNyceXFkzlpOFt/ogwTJMvxC4nYhvEsHjQPoCDJgw9O+JYFZ2whKuiqf6ho6oHNzw0rfAyJu3aR4EKU1g1jU3Vx3Hha858t1OCEzm1x752Z3Pycdw79GQPw2lf/TYHW48ln+9vvIPStVxT8FU27XaSKB3rTQ8eZlWl2r52V+9AzadTyZf8rl6xng7qeoZP+m4A4KUtx9IROYdfx53skkUei8tdwx1L4ruZrTZjEcfonAy40/MrWiigLNT3v6VpKD7oe5YvKack9tqu2cApfW4/HNzp96+OXsV10SvwEOeHdctc6xWfjH4KcBLdrDYGP5d5cmQd//J+iX2NDWyyKzkr8vURH9YpRh/D8tClPcQw6cCfTBHkiae+rzV6B7d4f5d8fpr5LL+Onchf40emvdZoyYYscksCNTGiCs0oFezBryLY7buBKYPaPx6PEUvMP4hqE88QfoBFZlWJFPrLjLc42HyTg3mTjbqa1+Iz6YzGM95Nbqrfjl8rTKXZbZQwcYD/HpH40Ia95sI96mi67FR7S/eS9XE8CnjT/91nTxhcwOSeK3Vl9BpAU0Yrj81cSumO1BBCd9r4bvVGFQ/GD6CIDjqKBl8DbiQYJXWwLX1ZzBui1PAmA7WeF/0zq1IB0doMPWqr9Ay228UU0EVlYd+14gLV05OPCzu3pafm7/EdPqX+13zd928AXth+A7CAuK1p7kwlaCkJ9t8z2h2kAfjU0BJEDUYhbcxX7+InQjOFrNOT6AJMy0nO0rOO2lBqqAGUFAaYamwAYLJRzzO+q/GYF76XposxyBMsZm7YCcJ8lsHbieV/9Z/Fne0HEKCL95XO7XU+yFhHTQI1MQASqIkRdczO3/Bl/x0APLfpf4DBJRQJa5N9w78HoNCrWDXcDRzHKgOKa6y/cZqZGr54uLGCWtVAa9MSgtW9x89vU9UcHv49ZbSyrMbi5/28/hdPnMt3HngLpeDyw6b3s2V+8ZhG2nzKcgnUevF7TL5t/ZqJqpG3dR3Ti64a1P4ljS+z0ncZFnFesWdxYfTLRHxllBQVUN6a6nnPFKitLDiYW6LVAHxu6hyOe29/yrDQkw/hhtc/RBsB2nSABh3i+Nr5HL79NS7z3EeH9mF3Hon7/De1IsjV1t3MUZuY0baNro7n8AdTAe9V0auTNzjeKq3u870LKqeyPD6PVgI0eKexzBWoBXskw3CPSNBxZ7s9nVF0YhRkccAzqIvHiDXyPZpTG5/h374vA/CP+CFcE3Vm0FqJ4bN+06aYNnxEiWIS8AxtOFmoPP1816H8SKoH0VNfWR+fYT/Wxp05s/tXFvFQfH9qVT0eYtwcO42NRl2v13IPAReiLxKoiRGlrVQ6ZR3r7GfLzJo7UhdqRQH52RxOpYVBrjTvTVv2fvNl4GU27v40ZAjU6lvD2Bg0UEyspO+LR4APHzKN2tIgtaUBplYMfnJ/rvS8UM2UwWu8C3hMDjVWMsXYxdG8xrrQJwa1v6UURYkhtN0JIGpLAyil0up4NWYI1NxzKHsmt8iV4ikL+G28OW3ZMaWzmLnjP3zAXA7Ac13p3xefZXK251nqEin6161dwYyFTjr4SMxOBmmmoXr1GrkFqmdxQdSpe1jl93GrK/tcYY9kGG3eSt6xJxHHpCsRZDW3tlJDAy0EKQ3sfS7pE4HjKGjfRIsOsnb65xjpcs6GJ1XWwEeq58+b+J7O3nk/r/udzJZ/ix/Oz72fGdL7FJdP4OPRa/iB9Uu68LHbKKfvfkwxXrnnecZsnZwm0OLqlfZWz+Gy6LVp+5X4PRCLcoP1WyziRLGw7MNAbgeIvZBATYwo2xeiURfRhZfOIZyQmjtSJ7/iPEjFPZYYpkmTKqGaRgAnOYjaA0Bn6+6M+9S3pmrDdA+d7IvXMjh54ejLauUxFfd4v4ICdusiys1UHSrh8MY7qDV2JZ9PmrVoUPu766h1J4ioK3PmcJUVeJmhtlKlmqnsbMWOH4XhKnTf4qqjFsqDOmoANSWBXstCAYvJ8S3J5xXRbb22aQhMpa7DWd60cRUkAjV38dyg1+w3s6K7llxLVxR755v8r3Ub7fiJxeYChybXPz3pMn6z4RgArq+axxFAbNtKnvU787vWRGYBL9Gf31d/nsffcv7tP1s+u99th4MOVrDKnkoYD1t0JSZx4hjJmpruQO5s8yle0icARw76fUzT5OXAISxsc8qIzKku4qHh+APEmKKUStZNBKdXzTSgyXVTuTrk77XfkeYq3u95gJPMF5LLOqVHTQxAfvzKiTFr3fSLuHClM9zn4oopHD3I/fe47lJJz8bwu7vgfLY2d1CvS5ihthJSnbToIIealRm33+WuoVbY+8doLPhs/NcsNtYnn4eL8yNhRT6ZWFHGNlVFjd7F6/4DWBQYXI9ptGohC7puo07twiJOER3UlTqBmt9j8g/f1wjhzO3a3XglZVWpjIetrh61wWSaHEkhv0WB16TdVfQ25Peg5pwIDU5NuPYFH+q135rJ5/KHFQtZq2s42jqQ7hLU7hpqPXvFegp6zeSFY1fUJt74Lh+0HgfgtXD6DRd3b3F3HbWw66ZM2Nx7Uhj3DbO9zWcbDl2TDuasyP8CcI/3etb5LwbgrcZ/wLSjUd70z96l4T9AvwVG+lZe4KOhzbng7jlsVIhuFUYr2o5iEScWj9MRhWjcCdyCXjPjTeXJZmNakAZgeeSaRuydBGpiRPldNXA6h5Cev7W5gRoaaKaQkoB8XIfby1Vn8Ghjd89IqiD1HDPzPI/2pl34iBDGu9cetdHq6Pgzac99Q0j1PdaZloXnY4/w0ksPMuuQM4awv4dWglxkPsIHrScAWN7+JWAeAHtUCaFE1sOWxm1pgdqCpseoMJppoYASs7/S7NmjlKKmJMAaV/bGUMDDomPO53XLg0Kx8Igze+1nzjqOv7/mfNdqGlPnx86WRo43XqADPwGr/15ppRRFfis5+qCttTm5Lm6lf3ZNo3cdtY5wlG26jBAdRDx7vylx8oKJ3PPaVgIek2Pm9j/8eTi4h3260/ObiV7ZlinHcfGTX+D33u8CEDWGfl4qL/TCTudxz0QsQnR73PwUBZYzuqQtfBp7GnZyh+c7NBKiwTsFr3Vkr30Mjw9SA1L4evRivmrlx40mkd/kTCRGlM+VgWsoddRK19/Hs/5vAfB802nAHcPVNAGcuO9EHl29i6DX5KDp5ckhTe7x9m5nbvwGX/e/SIsOsKb9F8DkLLY2O3brIspUCwDfjF7E9TluT76qrJlK5QeuGNK+3fM86lR9clmgMpURdoNvDts7imjQxUzo1Ex17Xtu+x+Y4nWGFG6IncRgM8mOlIvNh/mQ/6bk89Wdj6CMShYddU6f+6RlftzVxrbmTh5YtYNZkTe5xXsjAO+EZwOX9vveR3jeQhnbKVIdbLAP5cvRjxCki9qyBSx1bZepjtq64oM5L+y0+4IFk1iyl7/z2HnV/PfzR1Pot7LSo+kueG2jiCeyzpqWN7HeoF6XcFfsSCe7cHAuQy2oUZGopaawKfKMnky1IrviKvWZjEejtDds5nBzJQBr2IWXGGcZT/EJ615mGNt5JL6Ed43DuTJyNVEstuly1hjT+JpksRYDIIGaGFHpBa8H/8OnO5qSj21f37WExNCctbSWRXXFlBX4+NVT61OBWh9Frwujzny2kOokVDI6ilgP1jeiFxEgTAyTl5gngdoI6J5ftEVXstauoVbVU1KTSrX/p7rruX/lDgB+atSkBQ8FdqrXKhgqz0p7B2KO2pT2vCCw92FNM1yB2obGdk67+b/Ut4Y51FjJ4YndI+bee3Q/GfstM73rAPhB20HcGT8WgMsqp6VtF4o2sJ9ag0mcYEcQmJU2568oOLDeqExz8kaKO93+ByLfBsDA5uHKfQAnkHtLT+bzscsBOKVyIoPv43Vc2HQzP/P/FYDH95wPHDzkdouxq5FiurSHKCb+eIyu5tR83Q6rBK+K8SPvL5PLjjNf4XbrGB6wU1lfg5KaXwyQBGpiRAWNGHVqJ36ilHZ2QXIWxsC0xRTbdRmltKICJSPSxvFuZqLwbiiQnpQgk7CtiCTq2oUqa7PSvmx7yk4lxsjGHJzxqLtn58uxyzCxiWHwxvTUce/u2QCSc4a63WcfQoluJkQHBxXnT6BmF9WAa0rYQILIQp/FxGI/2/d0EY3bVLet5jzzdf7HupfNdiUbdRVt/tnsbYBn2Cyke1SgkwjISX5T0KN227719/Ex3y8AWL79EuCotN7zor3Mh8sFn4qxTK3Gp6JoFM/YC7AxsBIJZnpmxAx4hj63bGbbK8nHR+/+M3DLkF9LjF3nev6P+sR87ed9pWwIzOPGyGcpo5W5ddOY5+t9I8Pwpt8EcfduC9GfAZ2VlVInAD8FTOA2rfV3e6w/ErgXeDex6G6t9TeGr5litKpoXsnTvmsAWN04HzhrUPs/VHwuH137PgC+N3c+Bw13A0XS5Mh6rrH+SogOirYuAfZJWx+3Nad0fROtbYpp54Xq/BhyNpKK8iSr4Fjjad/Oet+FGEqzXZdxqudWgr5UUJweqKUS2ERiNl+POEWITUOxtiB/Er20TjuJz6+N0679NFHIr0sHVs/rqsCDzOt8nBlqKx7iBJQTmN4ZO4Zfxj/AGTWTOGEvr7G2cAlr6wto1QHWdqR64II9Ai9lpI6xsp1e87QsmnmYWTcQ28NffN8EYJcuYVnYqd7oSQRoftOmhgZ8KopCE/AO/bzUeOxPKP/nBwB4+YAfpQ0bFaKbp8dcz+2xIp5M9JZ9bMI0LMvD3fFDma62U0or345dyNTi/YDUuczbT8kNIdz2ehWilDKBm4HjgC3Ai0qpf2qt3+yx6dNa61NGoI1iFLNciRg8dlc/W2bW5E7PHxybWQbzxYTIBk6x/gHAy83hXusb28M401oMjIJyvJ6xH8QU+vLvwnUssDweDOXMkbKIU1eWfge6vDA1bLDBlWm01T1Mz2/1m7Y+24rq9uWuuDMs02sZ+H0DG0Y4w9vMfsZaANbZE5mhtgNwmLGSX8Y/QMEAsg8+M/HD/HWbM2/P06YA59j27FHrDFTzmj2DKCZNXicRiHfPRqar7bToAorzMD+Q15/K6ugn1bvafbEc6NiZLC+w2a7kD577hvxes5ccwRp1H+H2Zpa8Ty5nRGamK61+LG6zuz11jior8KEMgy/qqwi7pnt8pKCGMl7nG57fEsWkU5fjXFYL0b+BXGktA9ZqrdcDKKX+DJwG9AzUhOjFChSzRVfQqX1st6qZufdd0uzpTP0wyzC0keUpSJV39cRae63fscdVQ60oD6/ohsl11l2cbT5Fky7iYfsC4LBcN2nMMc3Ud1mjkqn5u03wRTjMWEE5LUzZVQE4wyJb04pd59eNgtrSVLBZOohzlVExE3un4h/2oWzSVdTpep6KL+C/tjPgsWAAwxHdhb8v5n4WeNbTrv1M7Pg4uFKxbKg5mQ+/7Mxbu7RiKicCp22/ka/6XgTgteZb0rbPB75AkOftuYS1B6+KEaKdKGZy6JjXnzrudUY9kyLr6DkaYDBm7Xf4e22yGOMsVxKQmK1pbE9dp5QXODeZvJZBOJYK1Gpjm/i792tMM5y0otvskc+YKsaGgfzSTQI2u55vAQ7MsN3BSqnXgW3AdVrrN3puoJS6HLgcYPLksZctTvRmVc3m0PDPAJgY8LN8kPu7C15LoDayjMo53Bg7kxZdgBGazsIe67c0dSYfuy9Kx5qrrHsBmKCaaIq9nOPWjE1GQTlnhm+gVLWyWVdxYnF6z9ik6KZkuvU1u2cCnwHS6yrmSw21brWlAY7dp4pHV+/iwgMHPvyuZsnJ6De+wynGcu6a/r/8avdc3tmZSpgykDTx7vmlBxqrOd50ila/Gjs9bTv3vJhooo6aL5Z6L19hKfnG6wtyXuSrAKz0XcYK/8cAaImtB3x4e5TPmNXyPCC9YWLkTKQBVDMWcexIJ7tdgVppQSobqft2Z8gMJ4M0gDhSp08MzEACtUxjS3SP568AU7TWbUqpk4B7gFm9dtL6V8CvAPbff/+eryHGoMqi9Lkmtq0xBjGJdmL7amLKokkXUpJnF2ZjTaBqGjfGzgZgui7gKz3W79m2hhlqK1t1BbWlY7e22AulJ7Os6d8AFB14UY5bMzZ5LIOLrUc4w/wvAC903gAsTq4vKp+UfByKpzK/dm5ZyVXmP2igmIBnAfnU26mU4tYP7U9LZ4ziQdxUqp25L++cdi+t29dw4fGXsvHBdwjteok6VU8Hfqp0KRl+TtO4g9YCUjdUrED6HD7LlWmuOz3/dirw2hMIqQ78RfmTnKWbYSi8lkEkZuMh1aPqSRQL9haW8bvYcVxiPQKA8ozdm0giP3yr45tM920AYF3jAj6y/Ttc4a2nURcxwf4RUI23R1ZHT49kIr8JXMJXs9ReMboNJFDbAtS5ntfi9Jolaa1bXI/vV0r9XClVobVuGJ5mitHK7zEpDnjY0xklGtc0dUQoLxzYsDlt29wW+zJen/Pj3OWVu6QjyX2x19LZOz3/9DW/4THf3QD8t+0LwPxsNS2rpp/3PZ67uwBVPoMDDx9qom/RH9NQaTXUgtUz0taXVtfwbHweDRSzi3Iu0xqlFMa2l7nO46RPf7HrBPZWXyzblFKDCtK6zV5yBHAEAIfOqmDuc09yjvUUAC/sKQHe1+/+1fZOzjOfIEQ7a/Uk/h45nALVxbkVc9O2y1Tw+vPq0+yKOHNslk/Kz++0LxGotVBARIfxEMfypIaYPWfPw44Z+IlQXTonx60VY53tqqNmx2PMiq5mouGcz7b4nQDtRJ7mev9PAFht17HF+j6XRD5PBItWHYDyxVlvtxidBhKovQjMUkpNA7YC5wMfdG+glJoA7NRaa6XUMsAAGoe7sWJ0qg75kkOWdraEBxyodbS3UKASQZr24A8W7mUP8V6457lkSs/vb9+afBwsr8lKm3KhYkIdFZ+4NdfNGNM8hsEL9lw6tI86tYuy2vSL62BBiI+qr9ERcXLOn5PopYq37khuEwtWZLXN2XLgtDKeUKn5oF2xvQ8+qelay/c8zmf2kfgSbrAvBeCisklp2xVEGjnEWImHOBPb24FF6Vkf83TUgs8yaSWWzPgI8G4iUDMNxaPqIB6IOTMyfjphcS6aKMaRerMaI9pOHJMYJsV2S3LsWahiIgAXR/+e3H4fYzMNHi//cZV+WSzFrsUA7TVQ01rHlFJXAQ/hpOe/XWv9hlLqisT6XwJnA1cqpWJAJ3C+1lqGNgoApgfD+NR6qlUTzTtqoGa/ve8EtLS2ssaeTiltxE0f00e4neOd32PgMRXRuCYSs+mKxtMKlm+LhSixq6hRjRRNmNHPKwnRP8NQfD92fvL5mrreQ/sqCn1s2t0BQH1bmOKgh7c983ktdioV7KGkYmDnkdEm6LVoMiuIaQNL2VRMnrvXfbyuREAh1ZF83HN+W/XuF7nT+x0AXm48inDsLLoSmelMQxH05ue8mYONN4kYjfiI8ri9H2GzIC3jp88yicadm3r+91BHTYiB+FHp9by00RmSfUdwFtdGbqBMtVBltPLjkPNdjKn0mx4+n5+09PxS8FoM0IDSZmmt7wfu77Hsl67HNwE3DW/TxFhxWcdtHOB7CIAX3g3BkoFdYO0mxOmRbwEwd0IRD45YCwU4w7Y+472H4uguQqqdtqYl+Kucu4Naa67t+iid0TgGNq/OGFzhciF6+sWFS7hj+UYuOmgKngwXLRWF3mSg1tAWZmZVIS+q+fw7VgbAjdMXZ7O5WXXwWVez7t717Cmawf7L3r/X7a2yydwVO5IWgqzXE5PLe2aMNMzUc2XH07JohvKs3IHbdbFbmOJ1yg8cG/4+W430uXezzB1EVCs+ogSNRZleQohh4x5CvKstwmo9BTRUJVLzA/w3cCQ79/jxqih3xY/ivNIpwIrkfpaZn981kX/yK7+xGJNiBdWwx3kcb9k+4P32uGuo5WEh1rHoZJ6mznKmoG5q3gmJQG13e4TOqDMMrcDnJVQwdtPzi+w4ccFETlwwsc/1mYpe17tqqlWO4RIRMxYcBAueH/D2weoZfD52ee/lPXrIooFKnonPJ45Jk3cGpXuaOMh4kxYdJOgbWIHuXIipVF09H9FeF7m/sL/BBJ8zJf71+LE4U+mFGBnuG0vuc1JZQepz+kDoHJ5rOCb5/NLCEIvUWq627iaGyZ62hcBBWWmvGN0kUBMjLl4yndVbJrNTl1JvD3xeSZOk5s+6TrOQ7sRqnS27k8vdqfknlQby9s67GDvmW1spNp+hghaMTWFYeGYyYIP0QG68655bZhHjds8P6MBPK0F81klp27VVH8Bl0S8DcHRxFfvsfJs/e51RC2siM4FzstrugXrDu5D1bSWUqxaKVCeVRnqdxwmk8pYVdu0EFmS5hWI8SetRa03NJy0vTAVqXiv9JknQiHCvL5Xn8dWonL/EwEigJkZc6z7nc9ErswE41qoe8KVAs6vYdWnQ28+WYrg8FDqb3+7cSasOcL53Et0pHtJrqI3d1PwifyzrfIqrPbcBsHx7EDiThlZ3oCbnhG6FieLfQbo43FwJQAvBXjdUzB511MKtqZsxYSt/kzX9qfRK1jet4Xn/VfzZ+y3q7VKcqfGO9b65TA+/BUB5nWR9FCOrOr6TfdUGLGzamgqSy8tcI018Vvpw7qA3/XxlK7n8FgMjnxQx4qpC/uRj992nvWpYw0HGmzTpQqq8lSPQMtHT6vJjuX+bk1nv/To1D6Rj82scZqxgi65kSvHYzfgo8odRmBqKZ3TU09XZwedjt1BvFbOLMkqDJ/Wz9/hiGopCn0VBOBXIduEn1GM7T486aq1xi+ftuYRoZ7evjnzl8xh4VDz5PN7j0sV33m957qEbCcw5ikXV+ft3iLHhguZfsp/vGQBe27KEK73b2E2IbZ1nAM4cfG+PQC0Q8Kc9/0/5eSzNSmvFaCeBmhhx1aHUXaZdLeF+tkw3Y/Pd/Nl7JwDLmz4FLBvupoke3Om53YkGJr/7F37vdWqoPdf+Gbp/jIQYKbpqPnevOpQGXUzUs5Qp9Vu50HoMgHpKMYwf5biF+eV8z1OUxTfxtl3LHfH3YxZV8Y0e2/Sso7axYBFfijjDsc6bXMfhWWzvYPgtE61hs12JpeLsNkuZ4Fo/afo+TLrylpy1T4wv7t6wiuh2ao0dTGcHy9We5PLFHc9zs/8LzvZaEVHbOC/6NbpskxgWcwrys2ahyD8SqIkR5570X98WJm7rtAuGvhhdTcnHZrC0ny3FcAkFMtdS87dvST72VkzLapvE+GROex/XRp3zxCJdwnEN25IX5y1mKdLHnu5C+z6mWRsBeCU6C0/hwl7b+KJ7ON54EZM41Z3VtHSlei1Dgfy9HPB5DLZSyWGRnwIws6qQR3PcJjF+7fZOZJU9lRgmlSpVQ80oKE9us6j9meRjQ2k8Hi8rjHl0xp2e4QWWlJEQA5O/Z2YxZvgsk4MCWwiFt1OlmtnduJTKyuq97rdFTQR7DiW0YYRkuF02hPypU0JLZypQWxWfSqfdSq2qJzRRaqiJkedOFtLYFmaHruQP0UuoVHsoK5mIfArTdZmF4JREI0QHKkNNtGDrBm7x/gSAd9pmc0/nscl1+VrsGmBG5G3ONF7Fp6K8bM/GMOblukliHHtgwhXcve1UAEK0U6maKaOVK6amsjwqI/37Z1oWHlPR/bMq6fnFQEmgJrLiO+rnTPNuAGDt1rMGFKj92XcOL0ScE9+fZkga22yY2bmCH3tuJ0Q7kU2HAN9Ga803O8+kK3o6AK/POiCnbRTjgztZSENbmG3xYu6IHw/AWTW1XJirhuWpl4qO4bFdM2nRQbboCuZ6e/+8m1YqGDN0PK3XPJTHJVAObHmITyWGXn81egmvmvvmuEViPLNcI4JaKKBFF7AO+ExFan7k+tJDaNy1DQ08zwJuIH2OaKbakUJkIoGayIpWbwV0bQCgrWELrz/+F0yvn30P/UCf+7izPkp6/uyoiO9if9MZsvFSq1NYuLE9QlfUuVVf5Lekpp3IikKfhc8yCMdsuqI2Gxo7kusqiiTjY08vV53JPdu2JZ8v9WX4eQ+U8nB8KVFMOny1lO9+jSONTbToIOVW/g5ptq1UIgY/EemNEDllGpmDrHJXHbWNFUfw81VOPb9Cn8UNwKk8zYneh4hqkz27TwNknprYOwnURFZsL1pIfXucnbqE4lcf4uT2fwDwSvtNLDn+4oz7NEsdtayzClJzAT0xp1aRpOYXuaCU4rzACxR3bKJC7eHVLR9JrquUGmq9hAIeTjBe4KPW/XRoH7vbjqNn0h9dMoXLo58BYKavkB/Vf4trvC8A8FrrRCA/hxQ2FM3noa37U6WaiWFRpXfvfSchRoinjxsF7oLX7qyP3dvfEP8pJBa/2SrZScXASKAmsmLlzCu4afNaAJ5uvTp5snpp5SqWHN97e611eqAWkDvoWTFhIddGrqCFAkotDx23fBJv45vc5DG5Kno1taWBXLdQjCMf5EHmet4E4MWt+wGLASl2nUnI78Gr6tnfeAeA52K979anZX2M2/hiqcLRvsL8Tdi0ceLxLH+jjTu932E/Yy2rWlYCp+a6WWKcKo9u40C1GkvF2KgnsEVXohSUBPsK1Hr3wJndE0qF2AsJ1ERWdKfo9xDDStTDsbXi1oaFHLCpif0mp18kdHZ1coJ+mmajkBajBL9HxnNnQ6C8jrttJ0n3hzsf4ODtvwegyXCK4U4ukx41kT3NZYtgpxOo3cT3OdQ6kgaKmWRMBibltnF5pshvYeKqU+kp6LWN+4IxZmtWqDnsiluEVAeh4gm9ts8XPsvEQ6qOmhQLFrl0YP3dXO37U/L5Fl3BZlWDaZycXObNMB+tnQAFOCNU1lUfh5RmFwMhZzuRFd1Fr6NYHBy+ielqG/uptdRTwm+f3dArUGtp2MbPvDcDsIsylPpE1ts8HrlTdN8Rfz9nm08x39hIqWpjRkGYc/eX4Roie8y5J3DX1u28YM/lW57bOd96EoB3PZ/MbcPy0OToejqMXay2J7NRV9NccxI9UzC5e9TituaH+mJ2RZ3alssnzs5iawfH5zHo1D7esuvwEKPJU7X3nYQYIdpMn4pRqxqIGuk3RubUP8SGRB015/7JHq4t+QnrdrYQxeLiyvdlp7Fi1JNATWRFVVH6UKX1uob12km5/9LKN3ildju07sSq259JU2fT1lyf3LbdKMpqW8ez0qAXv8egK2oTx+TnoWv4yOwuymYs4f5Zi/F5ZciZyJ5ZB57EOQ85F0Vf4w4COAmGiiukN62nqsgWlppPA7AhXk20bFavbax4F2cYT2OpOIGYj79EDkmuy+f0/H7L5Hm9DydEvgfACVUTOCLHbRLjV2ugluftuSxRa/AkRgjtmHIq7nQ8Va2reu23yzuZtboZAMOVIEeI/kigJrKiusDgeOMFqlUTRXRyc/x0ZlQWsK6+nRK7mcDD32MfYxOfePpT3G8fxCxzB1cbB1FMG5FALfmbj2xs8XtMvnHavvztpS28f341l7zvREkjLHKmJOhl7oQi3trRwnXRj1Op9lCp9vCp8r2X9xhvvAXFycdFdNCZoY6aFe/gJ95fANAUK+KO6MGA09MWzLB9viiNbOUi8xF8RNisq7DMvrMFCzHS3phwBj99wykR4SPCZ0qe4tLzr0/bJlNmyLT0/Jb8roqBkUBNZEVlkZ9bvDcCzty05RMv4iOHzeSPd/2BD5sPMUdtBuBdPRGANfEJXBX/FADH11RzTMZXFSPh3P3rZIijyBvLppXx1o5WHrad+n1VRT4+beZvUJErRtUcfhg9hxaCbNJVnJ+xjloq2YHpmvMV8lsolb8p78vb3uFbnt8A8FB8fx40T89tg8S4FrNTiUDCeDnooq/h9aX3kG2bcibPvr0dDXSUzOZKesxbM/L3+ybyiwRqIis8riFzNorTZ3o4cX41VdUrmNy0kXXWTNo95QTLZlHQEKc94lxEKAWnLKzJVbOFEDl24LRy7li+EQAvUSoKQzluUX4KVEzhpvgZyeeX+TL0qHn93B0/lLg28KgY55hP0qKDmN78TSQCYHpT2WZ9RNMKDguRbfvWpHqvLz98OgtrS3ptE66Yx1djHwbggMJSrgTOa/kNX/AuJ4pFe+N1wOTsNFiMahKoiaxZ6VvCgvArtBLkqH1rMS2LZVffCUD3ZcLfcVLzt0fiNLaFCXjMZCISIcT4c8CUEF+w/sSBxmpmqS1cXfj3XDcpLxX5PfzccyNT1E7a8VPQcTNQmbaN6QtybdRJzLRQreOfPme41proTODKLLd44OziOh6JL6VSNbNNl1Eer9/7TkKMkOPnT+AHZy8kErc5/4DMwZY7U3V3qv5TW/6ULE20eeu/gAtGuqliDJBATWRNyTk/4/kHf4xn1tEsqe37TpJSikKfRaFPPp5CjHdVxQWc7HmZOr0NgP2M9cChuW1UHiryW8xUW5ltbAXgXUv32sbdExVSHcnHYatw5Bv4Htjlc/ivPZ8bPHew2FjH8w2lwLG5bpYYpwxDcc5epgfsP6WMyiIf9a1hTtzXmdKxovJUFtbfB0B0v0tHuplijJArYZE1dTMXUHfVb3LdDCHEKKP9JdDpBGrz7Ldz25g85feYFKhw8rnPlVykmzs9/25dxN/ihxOinWhwHvtmpZVD4/cYWK45ddrI3wyVQgAEvCZPffYodrZ0MbXCSd0/+bwfsvyeSszy6SxbelyOWyhGCwnUhBBC5LWu475L7N7TsVHU7vf+XDcnb32p6Ns0NDZS5olwS1Vtr/VKKSxDEbM1b+qpXBe9AoDzJtVxcq+t84fPMqnXxbxiz8QiTpt/Yq6bJMReBbxmMkgDKKmYwMEf/UkOWyRGIwnUhBBC5LXZS46gcdJKdDzO7JopuW5O3vrEmcdx2zPvcuqiGoLBgozbXGA+hqkimMT5Xfx4YlhUFuV3fUSfZXCvfSj3Rpwhr1dNmikDH4UQ44IEakIIIfJeeXXvHiKR7sDp5Rw4vbzfbb5g/IECswuAu+JH0YbF9MrMQV2+8BPm4+Z9+Ihio7DMz+W6SUIIkRUSqAkhhBDjREyl0vabOPWgplXkd6DmM2y+6PkTAK06wB3mF3LcIiGEyA4pjS6EEEKME/eoY/ldzElkcJH5KEcarzGtPJjjVvXP50/VUfMTkTpqQohxQ3rUhBBCiHHiZutDmJ3buMR6hM96/kITIUoKvpzrZvXL6/XzRHwRpaqVdh0gFK0HZuS6WUIIMeIkUBNCCCHGCctQTDW2J5/v9NRSmsP2DIQyDLarKo4yXgfAavwPcFBuGyWEEFkggZoQQggxTpimYpOu4tvRDzJNbae4ajZzc92oAfAZdvKxMqWOmhBifJBATQghhBgnPIbBel3FrfFTAPjsPnNy3KKB2WzW8lRkAR7ixALVuW6OEEJkhQRqQgghxDhxcuwRbHMXFnHujB+b9xkfu93tO4Mb248H4MaaxbltjBBCZIkEakIIIcQ4cUbkX0z3bADgEXvpqAnUzrEfwGNtxUeUwvBEYFKumySEECNOAjUhhBBinIj3qKM2tXx0BGonRB9jlrUWgGcjH81xa4QQIjukjpoQQggxTiyPp1KHHGisJuA1+9k6f8QMb/Kxj0gOWyKEENkjPWpCCCHEOPGfyFw+5P03AEebr+e4NQP33+JTuHf7QsJ4OLV0aq6bI4QQWSE9akIIIcQ4MU3tSD6u99XlsCWDM+O4j/En71m8OflCFs6bl+vmCCFEVkiPmhBCCDFOPGgvY0ekjGlqO1QtyXVzBuyoOVW8/JVjsUy5vyyEGD8kUBNCCCHGicLq6fxrRyUA31+2MMetGRwJ0oQQ442c9YQQQohx4rtnLWR+TYizl9Zy9pLaXDdHCCFEP6RHTQghhBgnFteV8O9PHZbrZgghhBgA6VETQgghhBBCiDwjgZoQQgghhBBC5BkJ1IQQQgghhBAiz0igJoQQQgghhBB5RgI1IYQQQgghhMgzEqgJIYQQQgghRJ6RQE0IIYQQQggh8owEakIIIYQQQgiRZyRQE0IIIYQQQog8I4GaEEIIIYQQQuQZCdSEEEIIIYQQIs9IoCaEEEIIIYQQeUYCNSGEEEIIIYTIMxKoCSGEEEIIIUSekUBNCCGEEEIIIfKMBGpCCCGEEEIIkWckUBNCCCGEEEKIPKO01rl5Y6Vagbffw0sUA3tG8f79vUYF0JCFNuTz/gM5Bvnc/pHcv/vYjORncLTtP9DvzEi9fz7v39+xyXX7c9UG9zHJ9THIx/0H833Kx/aP5P6Zjs1o+xuGe/+hnn+H6/3zef9sXMsMx2tkc3/5DvU2R2tdlHGN1jon/wEvvcf9fzWa9+/vNQZ6bHL9N4zk/gM5Bvnc/pHcv/vYjORncLTtP9TzSb60fyT37+/Y5Lr9uWqD+5jk+hjk4/6D+T7lY/tHcv9Mx2a0/Q0jsP+4vp7L9bVMvh+DgRyT0dT+Edq/z8/JaB76eN8o3z8f2iD7j+/986ENsv/43j8f2iD7y/7vVa7bkOv936tct3+0758PbZD9R0guhz6+pLXePydvnufk2Mgx6I8cm97kmPRNjk1vckz6J8enb3JsepNj0jc5Nr3JMemtv2OSyx61X+XwvfOdHBs5Bv2RY9ObHJO+ybHpTY5J/+T49E2OTW9yTPomx6Y3OSa99XlMctajJoQQQgghhBAis9E8R00IIYQQQgghxiQJ1IQQQgghhBAiz4x4oKaUahvp9xiNlFJxpdRrrv+m9rPtk0qpMTPxUimllVK/dz23lFL1Sql/5bJd+UYpdUbiWM3NdVtyST4vAyPn2r7t7diMtXPs3si5pW9KqS8rpd5QSq1I/DYfmOs25QOlVK1S6l6l1Bql1Dql1E+VUt5+tv+0UiqYzTbmQuJ79CPX8+uUUjfksEk557q+fUMp9bpS6lqllHQMDZEcuNzp1Fovdv23IdcNyqJ2YF+lVCDx/Dhg62BeQCllDXur8s8FwDPA+YPZSSlljkxzcuY9f16EEGmGdG4Z65RSBwOnAEu01guBY4HNuW1V7imlFHA3cI/WehYwGygEvt3Pbp8GxnygBoSBM5VSFbluSB7pvr6dj/N7fRLwtRy3adTKSqCmlCpUSj2mlHpFKbVSKXVaYvlUpdRqpdSticj7YdfF2LijlFqqlPqPUuplpdRDSqmJrtUXKaWeVUqtUkoty1kjh88DwMmJxxcAf+peoZRalvhbX038f05i+aVKqb8qpe4DHs5+k7NHKVUIHAJcRuJiSil1pFLqKaXUP5RSbyqlftl9l0op1aaU+oZS6nng4Ny1fMQM5fPytFJqsWu7/yqlFmaz0dmW+Iz8y/X8JqXUpYnHG5RSX3edh8dVb0p/x2Y86efc0tfn5iSl1FtKqWeUUj8b4z3ZE4EGrXUYQGvdoLXe1tdvc6In9sYx9tucydFAl9b6NwBa6zhwDfARpVSBUuqHiXPKCqXUJ5VSnwJqgCeUUk/ksN3ZEMPJ2HdNzxVKqSmJa98Vif9PVkoVJ87F3b/dQaXUZqWUJ9sNzwat9S7gcuAq5TCVUj9QSr2YOC4f795WKfW5xOfodaXUd3PX6vySrR61LuAMrfUS4CjgR4k7NACzgJsTkXczcFaW2pRrAZUa9viPxJf0/4CztdZLgdtJv1tVoLV+H/CJxLrR7s/A+UopP7AQeN617i3gcK31fsBXgf91rTsYuERrfXTWWpobpwMPaq3fAXYrpZYkli8DPgMsAGYAZyaWFwCrtNYHaq2fyXZjs2Aon5fbgEsBlFKzAZ/WekXWWpyfGhLn4V8A1+W6MSInTifzuaWXxPftFuBErfWhQGV2mpgzDwN1Sql3lFI/V0odMQ5/mzOZD7zsXqC1bgE2AR8FpgH7JXoh79Ra/wzYBhyltT4q243NgZuBC5VSxT2W3wTc0X1cgJ9prfcArwNHJLY5FXhIax3NWmuzTGu9HifeqMK5QbRHa30AcADwMaXUNKXUiTjnpgO11ouA7+eqvfkmW8PHFPC/SqnDARuYBFQn1r2rtX4t8fhlYGqW2pRrnVrrxd1PlFL7AvsCjyRiWBPY7tr+TwBa66eUUiGlVInWujl7zR1eWusVypmXdwFwf4/VxcDvlFKzAA247zQ9orXenZ1W5tQFwI2Jx39OPP838ELipIdS6k/AocDfgDjw9+w3MzuG+Hn5K3C9UuqzwEeA32antXnt7sT/XyYV5Ivxpa9zSyZzgfVa63cTz/+Ec3d8TNJatymllgKH4dxUvgv4FuPot7kPCufcmmn54cAvtdYxgHHy+5xGa92ilLoD+BTQ6Vp1MKnz7O9JBR93AecBT+D0av88S03Npe7OmfcDC5VSZyeeF+N02BwL/EZr3QHj83PUl2wFahfi3IlbqrWOKqU2AP7EurBruzgwXoc+KuANrXVfw9Z6niTHQgG8fwI/BI4Eyl3Lvwk8obU+I3Fx/qRrXXu2GpcrSqlynKEm+yqlNM6FgcYJUPr6HHQlhqOMZYP6vGitO5RSjwCnAecC4yFZRIz0kRL+Huu7z7dxsnf+zxd7OzZjXj/nln+S+dgoxpnEefRJ4Eml1Ergfxh/v809vUGP0U5KqRBQB6xnbP7Ng3Uj8Arwm3626T5O/wS+o5QqA5YCj49s03JLKTUd5zdnF8455ZNa64d6bHMC8jnKKFtDH4uBXYkg7ShgSpbedzR5G6hUzmRmlFIepdR81/rzEssPxek23pODNg6324FvaK1X9lheTCpZxKVZbVF+OBtnuMQUrfVUrXUd8C5O79myxDABA+czMRaHOfZlKJ+X24CfAS+Okzt0G4F5SilfYhjOMbluUB6RY9P3uQUyH5u3gOkqlZX4vOw2N7uUUnMSPfPdFgOrGX+/zT09BgSVUh+CZMKqH+GMUngYuEIlEnwlgg+AVqAo+03NjcTvy19whvZ1e5ZUwp4LSfxea63bgBeAnwL/Gss3WZVSlcAvgZu01hp4CLiye06eUmq2UqoA53P0EZXIFOr6HI17I3pHNfHFDeOMzb1PKfUS8BrOyV+4aK0jia7gnyV+KC2cOzRvJDZpUko9C4RwhnGNelrrLTgnqp6+jzOU7VrG+J2mPlwA9JxI+3fgSmB5Yt0C4CngH9ltWu4M5fOitX5ZKdVC/3c5R73uc63WerNS6i/ACmAN8GpuW5Z7cmzS9HVu+SDORWbasdFadyqlPgE8qJRqwLm4HMsKgf9TSpXg9MCuxRnq+SvG0W9zT1prrZQ6A/i5Uup6nJv89wNfwukpmQ2sUEpFgVtx5mb9CnhAKbV9nMxTAyd4vcr1/FPA7Ynh9/XAh13r7sIZnn9k1lqXPQGl1Gs40xBiOMM+f5xYdxvOFKdXErkq6oHTtdYPKif510tKqQipz9e4p5wAd4ReXKlFwK1a67GaCUmIrFFKHQlcp7U+JcdNGTWUUjU4w5jmaq3tHDdnxMi5tm9ybN4bpVRhYu6WwkmasEZr/ZNctysfKKWexDknv5TrtgghxqYRG/qolLoCZ5LtV0bqPYQQoi+JYTrPA18e40GanGv7IMdmWHwscXf8DZxhxrfktjlCCDF+jGiPmhBCCCGEEEKIwctWMhEhhBBCCCGEEAM0bIGaUqpOKfWEUmq1UuoNpdTVieVlSqlHlFJrEv8vde3zRaXUWqXU20qp4xPLilSqEPRrSqkGpdSNw9VOIYQQQgghhMh3wzb0USk1EZiotX5FKVWEU1D1dJx02bu11t9VSn0BKNVaf14pNQ9n7sAyoAZ4FJjdM02pUupl4Bqt9VPD0lAhhBBCCCGEyHPD1qOmtd6utX4l8bgVp/bIJJxis79LbPY7nOCNxPI/a63DWut3cdLgpmXlStQzqQKeHq52CiGEEEIIIUS+G5E5aonimPvhZFyr1lpvByeYwwm8wAniNrt225JY5nYBcJeWjCdCCCGEEEKIcWTYAzWlVCFOAc1Pa61b+ts0w7KeAdn5OMMjhRBCCCGEEGLcGNZATSnlwQnS7tRa351YvDMxf617HtuuxPItQJ1r91pgm+u1FgGW1vrl4WyjEEIIIYQQQuS74cz6qIBfA6u11j92rfoncEni8SXAva7l5yulfEqpacAs4AXXfhcgvWlCCCGEEEKIcWg4sz4eipP0YyVgJxZ/CWee2l+AycAm4Byt9e7EPl8GPgLEcIZKPuB6vfXASVrrt4algUIIIYQQQggxSgxboCaEEEIIIYQQYniMSNZHIYQQQgghhBBDJ4GaEEIIIYQQQuQZCdSEEEIIIYQQIs9IoCaEEEIIIYQQeUYCNSGEEEIIIYTIMxKoCSGEEEIIIUSekUBNCCGEEEIIIfLM/wMtkgarnGDAMAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# display train/test time series\\n\",\n \"time_series_idx = 0\\n\",\n \"\\n\",\n \"plt.figure(figsize=(15,8))\\n\",\n \"# test data is the whole time series\\n\",\n \"time_series[time_series_idx].plot(label='test', lw=3)\\n\",\n \"# train data is all but the last prediction pts\\n\",\n \"time_series_training[time_series_idx].plot(label='train', ls=':', lw=3)\\n\",\n \"\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Convert to JSON \\n\",\n \"\\n\",\n \"According to the [DeepAR documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html), DeepAR expects to see input training data in a JSON format, with the following fields:\\n\",\n \"\\n\",\n \"* **start**: A string that defines the starting date of the time series, with the format 'YYYY-MM-DD HH:MM:SS'.\\n\",\n \"* **target**: An array of numerical values that represent the time series.\\n\",\n \"* **cat** (optional): A numerical array of categorical features that can be used to encode the groups that the record belongs to. This is useful for finding models per class of item, such as in retail sales, where you might have {'shoes', 'jackets', 'pants'} encoded as categories {0, 1, 2}.\\n\",\n \"\\n\",\n \"The input data should be formatted with one time series per line in a JSON file. Each line looks a bit like a dictionary, for example:\\n\",\n \"```\\n\",\n \"{\\\"start\\\":'2007-01-01 00:00:00', \\\"target\\\": [2.54, 6.3, ...], \\\"cat\\\": [1]}\\n\",\n \"{\\\"start\\\": \\\"2012-01-30 00:00:00\\\", \\\"target\\\": [1.0, -5.0, ...], \\\"cat\\\": [0]} \\n\",\n \"...\\n\",\n \"```\\n\",\n \"In the above example, each time series has one, associated categorical feature and one time series feature.\\n\",\n \"\\n\",\n \"### EXERCISE: Formatting Energy Consumption Data\\n\",\n \"\\n\",\n \"For our data:\\n\",\n \"* The starting date, \\\"start,\\\" will be the index of the first row in a time series, Jan. 1st of that year.\\n\",\n \"* The \\\"target\\\" will be all of the energy consumption values that our time series holds.\\n\",\n \"* We will not use the optional \\\"cat\\\" field.\\n\",\n \"\\n\",\n \"Complete the following utility function, which should convert `pandas.Series` objects into the appropriate JSON strings that DeepAR can consume.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def series_to_json_obj(ts):\\n\",\n \" '''Returns a dictionary of values in DeepAR, JSON format.\\n\",\n \" :param ts: A single time series.\\n\",\n \" :return: A dictionary of values with \\\"start\\\" and \\\"target\\\" keys.\\n\",\n \" '''\\n\",\n \" # get start time and target from the time series, ts\\n\",\n \" json_obj = {\\\"start\\\": str(ts.index[0]), \\\"target\\\": list(ts)}\\n\",\n \" return json_obj\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# test out the code\\n\",\n \"ts = time_series[0]\\n\",\n \"\\n\",\n \"json_obj = series_to_json_obj(ts)\\n\",\n \"\\n\",\n \"#print(json_obj)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Saving Data, Locally\\n\",\n \"\\n\",\n \"The next helper function will write one series to a single JSON line, using the new line character '\\\\n'. The data is also encoded and written to a filename that we specify.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# import json for formatting data\\n\",\n \"import json\\n\",\n \"import os # and os for saving\\n\",\n \"\\n\",\n \"def write_json_dataset(time_series, filename): \\n\",\n \" with open(filename, 'wb') as f:\\n\",\n \" # for each of our times series, there is one JSON line\\n\",\n \" for ts in time_series:\\n\",\n \" json_line = json.dumps(series_to_json_obj(ts)) + '\\\\n'\\n\",\n \" json_line = json_line.encode('utf-8')\\n\",\n \" f.write(json_line)\\n\",\n \" print(filename + ' saved.')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# save this data to a local directory\\n\",\n \"data_dir = 'json_energy_data'\\n\",\n \"\\n\",\n \"# make data dir, if it does not exist\\n\",\n \"if not os.path.exists(data_dir):\\n\",\n \" os.makedirs(data_dir)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"json_energy_data/train.json saved.\\n\",\n \"json_energy_data/test.json saved.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# directories to save train/test data\\n\",\n \"train_key = os.path.join(data_dir, 'train.json')\\n\",\n \"test_key = os.path.join(data_dir, 'test.json')\\n\",\n \"\\n\",\n \"# write train/test JSON files\\n\",\n \"write_json_dataset(time_series_training, train_key) \\n\",\n \"write_json_dataset(time_series, test_key)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"## Uploading Data to S3\\n\",\n \"\\n\",\n \"Next, to make this data accessible to an estimator, I'll upload it to S3.\\n\",\n \"\\n\",\n \"### Sagemaker resources\\n\",\n \"\\n\",\n \"Let's start by specifying:\\n\",\n \"* The sagemaker role and session for training a model.\\n\",\n \"* A default S3 bucket where we can save our training, test, and model data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import boto3\\n\",\n \"import sagemaker\\n\",\n \"from sagemaker import get_execution_role\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# session, role, bucket\\n\",\n \"sagemaker_session = sagemaker.Session()\\n\",\n \"role = get_execution_role()\\n\",\n \"\\n\",\n \"bucket = sagemaker_session.default_bucket()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### EXERCISE: Upoad *both* training and test JSON files to S3\\n\",\n \"\\n\",\n \"Specify *unique* train and test prefixes that define the location of that data in S3.\\n\",\n \"* Upload training data to a location in S3, and save that location to `train_path`\\n\",\n \"* Upload test data to a location in S3, and save that location to `test_path`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# general prefix\\n\",\n \"prefix='deepar-energy-consumption'\\n\",\n \"\\n\",\n \"# *unique* train/test prefixes\\n\",\n \"train_prefix = '{}/{}'.format(prefix, 'train')\\n\",\n \"test_prefix = '{}/{}'.format(prefix, 'test')\\n\",\n \"\\n\",\n \"# uploading data to S3, and saving locations\\n\",\n \"train_path = sagemaker_session.upload_data(train_key, bucket=bucket, key_prefix=train_prefix)\\n\",\n \"test_path = sagemaker_session.upload_data(test_key, bucket=bucket, key_prefix=test_prefix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training data is stored in: s3://sagemaker-us-east-1-274709254325/deepar-energy-consumption/train/train.json\\n\",\n \"Test data is stored in: s3://sagemaker-us-east-1-274709254325/deepar-energy-consumption/test/test.json\\n\"\n ]\n }\n ],\n \"source\": [\n \"# check locations\\n\",\n \"print('Training data is stored in: '+ train_path)\\n\",\n \"print('Test data is stored in: '+ test_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Training a DeepAR Estimator\\n\",\n \"\\n\",\n \"Some estimators have specific, SageMaker constructors, but not all. Instead you can create a base `Estimator` and pass in the specific image (or container) that holds a specific model.\\n\",\n \"\\n\",\n \"Next, we configure the container image to be used for the region that we are running in.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"'get_image_uri' method will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sagemaker.amazon.amazon_estimator import get_image_uri\\n\",\n \"\\n\",\n \"image_name = get_image_uri(boto3.Session().region_name, # get the region\\n\",\n \" 'forecasting-deepar') # specify image\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### EXERCISE: Instantiate an Estimator \\n\",\n \"\\n\",\n \"You can now define the estimator that will launch the training job. A generic Estimator will be defined by the usual constructor arguments and an `image_name`. \\n\",\n \"> You can take a look at the [estimator source code](https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/estimator.py#L595) to view specifics.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Parameter image_name will be renamed to image_uri in SageMaker Python SDK v2.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sagemaker.estimator import Estimator\\n\",\n \"\\n\",\n \"# dir to save model artifactsc\\n\",\n \"s3_output_path = \\\"s3://{}/{}/output\\\".format(bucket, prefix)\\n\",\n \"\\n\",\n \"# instantiate a DeepAR estimator\\n\",\n \"estimator = Estimator(sagemaker_session=sagemaker_session,\\n\",\n \" image_name=image_name,\\n\",\n \" role=role,\\n\",\n \" train_instance_count=1,\\n\",\n \" train_instance_type='ml.c4.xlarge',\\n\",\n \" output_path=s3_output_path\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Setting Hyperparameters\\n\",\n \"\\n\",\n \"Next, we need to define some DeepAR hyperparameters that define the model size and training behavior. Values for the epochs, frequency, prediction length, and context length are required.\\n\",\n \"\\n\",\n \"* **epochs**: The maximum number of times to pass over the data when training.\\n\",\n \"* **time_freq**: The granularity of the time series in the dataset ('D' for daily).\\n\",\n \"* **prediction_length**: A string; the number of time steps (based off the unit of frequency) that the model is trained to predict. \\n\",\n \"* **context_length**: The number of time points that the model gets to see *before* making a prediction. \\n\",\n \"\\n\",\n \"### Context Length\\n\",\n \"\\n\",\n \"Typically, it is recommended that you start with a `context_length`=`prediction_length`. This is because a DeepAR model also receives \\\"lagged\\\" inputs from the target time series, which allow the model to capture long-term dependencies. For example, a daily time series can have yearly seasonality and DeepAR automatically includes a lag of one year. So, the context length can be shorter than a year, and the model will still be able to capture this seasonality. \\n\",\n \"\\n\",\n \"The lag values that the model picks depend on the frequency of the time series. For example, lag values for daily frequency are the previous week, 2 weeks, 3 weeks, 4 weeks, and year. You can read more about this in the [DeepAR \\\"how it works\\\" documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar_how-it-works.html).\\n\",\n \"\\n\",\n \"### Optional Hyperparameters\\n\",\n \"\\n\",\n \"You can also configure optional hyperparameters to further tune your model. These include parameters like the number of layers in our RNN model, the number of cells per layer, the likelihood function, and the training options, such as batch size and learning rate. \\n\",\n \"\\n\",\n \"For an exhaustive list of all the different DeepAR hyperparameters you can refer to the DeepAR [hyperparameter documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar_hyperparameters.html).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"freq='D'\\n\",\n \"context_length=30 # same as prediction_length\\n\",\n \"\\n\",\n \"hyperparameters = {\\n\",\n \" \\\"epochs\\\": \\\"50\\\",\\n\",\n \" \\\"time_freq\\\": freq,\\n\",\n \" \\\"prediction_length\\\": str(prediction_length),\\n\",\n \" \\\"context_length\\\": str(context_length),\\n\",\n \" \\\"num_cells\\\": \\\"50\\\",\\n\",\n \" \\\"num_layers\\\": \\\"2\\\",\\n\",\n \" \\\"mini_batch_size\\\": \\\"128\\\",\\n\",\n \" \\\"learning_rate\\\": \\\"0.001\\\",\\n\",\n \" \\\"early_stopping_patience\\\": \\\"10\\\"\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# set the hyperparams\\n\",\n \"estimator.set_hyperparameters(**hyperparameters)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Training Job\\n\",\n \"\\n\",\n \"Now, we are ready to launch the training job! SageMaker will start an EC2 instance, download the data from S3, start training the model and save the trained model.\\n\",\n \"\\n\",\n \"If you provide the `test` data channel, as we do in this example, DeepAR will also calculate accuracy metrics for the trained model on this test data set. This is done by predicting the last `prediction_length` points of each time series in the test set and comparing this to the *actual* value of the time series. The computed error metrics will be included as part of the log output.\\n\",\n \"\\n\",\n \"The next cell may take a few minutes to complete, depending on data size, model complexity, and training options.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\\n\",\n \"'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2021-06-03 14:36:28 Starting - Starting the training job...\\n\",\n \"2021-06-03 14:36:31 Starting - Launching requested ML instances.........\\n\",\n \"2021-06-03 14:38:04 Starting - Preparing the instances for training......\\n\",\n \"2021-06-03 14:39:09 Downloading - Downloading input data...\\n\",\n \"2021-06-03 14:39:39 Training - Downloading the training image..\\u001b[34mArguments: train\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:12 INFO 140134321161600] Reading default configuration from /opt/amazon/lib/python3.6/site-packages/algorithm/resources/default-input.json: {'_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_tuning_objective_metric': '', 'cardinality': 'auto', 'dropout_rate': '0.10', 'early_stopping_patience': '', 'embedding_dimension': '10', 'learning_rate': '0.001', 'likelihood': 'student-t', 'mini_batch_size': '128', 'num_cells': '40', 'num_dynamic_feat': 'auto', 'num_eval_samples': '100', 'num_layers': '2', 'test_quantiles': '[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]'}\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:12 INFO 140134321161600] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {'prediction_length': '30', 'time_freq': 'D', 'context_length': '30', 'num_layers': '2', 'epochs': '50', 'learning_rate': '0.001', 'early_stopping_patience': '10', 'mini_batch_size': '128', 'num_cells': '50'}\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:12 INFO 140134321161600] Final configuration: {'_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_tuning_objective_metric': '', 'cardinality': 'auto', 'dropout_rate': '0.10', 'early_stopping_patience': '10', 'embedding_dimension': '10', 'learning_rate': '0.001', 'likelihood': 'student-t', 'mini_batch_size': '128', 'num_cells': '50', 'num_dynamic_feat': 'auto', 'num_eval_samples': '100', 'num_layers': '2', 'test_quantiles': '[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]', 'prediction_length': '30', 'time_freq': 'D', 'context_length': '30', 'epochs': '50'}\\u001b[0m\\n\",\n \"\\u001b[34mProcess 1 is a worker.\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:12 INFO 140134321161600] Detected entry point for worker worker\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] Using early stopping with patience 10\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] random_seed is None\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] [cardinality=auto] `cat` field was NOT found in the file `/opt/ml/input/data/train/train.json` and will NOT be used for training.\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] [num_dynamic_feat=auto] `dynamic_feat` field was NOT found in the file `/opt/ml/input/data/train/train.json` and will NOT be used for training.\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] Training set statistics:\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] Real time series\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] number of time series: 3\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] number of observations: 1006\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] mean target length: 335.3333333333333\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] min/mean/max target: 0.17381805181503296/1.05969123006104/2.7984180450439453\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] mean abs(target): 1.05969123006104\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] contains missing values: no\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] Small number of time series. Doing 427 passes over dataset with prob 0.9992193598750976 per epoch.\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] Test set statistics:\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] Real time series\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] number of time series: 3\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] number of observations: 1096\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] mean target length: 365.3333333333333\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] min/mean/max target: 0.17381805181503296/1.0892051362643276/2.7984180450439453\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] mean abs(target): 1.0892051362643276\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] contains missing values: no\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] #memory_usage:: = 4.172706604003906 mb\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] nvidia-smi took: 0.025242328643798828 secs to identify 0 gpus\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] Number of GPUs being used: 0\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] Create Store: local\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731213.0850823, \\\"EndTime\\\": 1622731213.2613766, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"get_graph.time\\\": {\\\"sum\\\": 173.62189292907715, \\\"count\\\": 1, \\\"min\\\": 173.62189292907715, \\\"max\\\": 173.62189292907715}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] Number of GPUs being used: 0\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:13 INFO 140134321161600] #memory_usage:: = 63 mb\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731213.2614877, \\\"EndTime\\\": 1622731213.5248322, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"initialize.time\\\": {\\\"sum\\\": 439.61453437805176, \\\"count\\\": 1, \\\"min\\\": 439.61453437805176, \\\"max\\\": 439.61453437805176}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:14 INFO 140134321161600] Epoch[0] Batch[0] avg_epoch_loss=1.486732\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:14 INFO 140134321161600] #quality_metric: host=algo-1, epoch=0, batch=0 train loss =1.486731767654419\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:15 INFO 140134321161600] Epoch[0] Batch[5] avg_epoch_loss=1.025513\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:15 INFO 140134321161600] #quality_metric: host=algo-1, epoch=0, batch=5 train loss =1.0255134801069896\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:15 INFO 140134321161600] Epoch[0] Batch [5]#011Speed: 734.39 samples/sec#011loss=1.025513\\u001b[0m\\n\",\n \"\\u001b[34m/opt/amazon/python3.6/lib/python3.6/contextlib.py:99: DeprecationWarning: generator 'local_timer' raised StopIteration\\n\",\n \" self.gen.throw(type, value, traceback)\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:15 INFO 140134321161600] processed a total of 1259 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731213.5249054, \\\"EndTime\\\": 1622731215.943424, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"epochs\\\": {\\\"sum\\\": 50.0, \\\"count\\\": 1, \\\"min\\\": 50, \\\"max\\\": 50}, \\\"update.time\\\": {\\\"sum\\\": 2418.4181690216064, \\\"count\\\": 1, \\\"min\\\": 2418.4181690216064, \\\"max\\\": 2418.4181690216064}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:15 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=520.5540890865619 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:15 INFO 140134321161600] #progress_metric: host=algo-1, completed 2.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:15 INFO 140134321161600] #quality_metric: host=algo-1, epoch=0, train loss =0.8806492388248444\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:15 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:15 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_7a6c0809-0d6f-43ff-881f-c410df17b519-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731215.9435368, \\\"EndTime\\\": 1622731215.980954, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 36.4985466003418, \\\"count\\\": 1, \\\"min\\\": 36.4985466003418, \\\"max\\\": 36.4985466003418}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\n\",\n \"2021-06-03 14:40:08 Training - Training image download completed. Training in progress.\\u001b[34m[06/03/2021 14:40:16 INFO 140134321161600] Epoch[1] Batch[0] avg_epoch_loss=0.498699\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:16 INFO 140134321161600] #quality_metric: host=algo-1, epoch=1, batch=0 train loss =0.49869897961616516\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:17 INFO 140134321161600] Epoch[1] Batch[5] avg_epoch_loss=0.456500\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:17 INFO 140134321161600] #quality_metric: host=algo-1, epoch=1, batch=5 train loss =0.45650029679139453\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:17 INFO 140134321161600] Epoch[1] Batch [5]#011Speed: 659.57 samples/sec#011loss=0.456500\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:18 INFO 140134321161600] Epoch[1] Batch[10] avg_epoch_loss=0.478872\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:18 INFO 140134321161600] #quality_metric: host=algo-1, epoch=1, batch=10 train loss =0.505718994140625\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:18 INFO 140134321161600] Epoch[1] Batch [10]#011Speed: 720.60 samples/sec#011loss=0.505719\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:18 INFO 140134321161600] processed a total of 1287 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731215.981058, \\\"EndTime\\\": 1622731218.683639, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2702.347993850708, \\\"count\\\": 1, \\\"min\\\": 2702.347993850708, \\\"max\\\": 2702.347993850708}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:18 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=476.22590850968965 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:18 INFO 140134321161600] #progress_metric: host=algo-1, completed 4.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:18 INFO 140134321161600] #quality_metric: host=algo-1, epoch=1, train loss =0.4788724319501357\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:18 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:18 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_11db3b8e-6fc4-41e9-a7ea-63309c5b3027-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731218.68373, \\\"EndTime\\\": 1622731218.7192335, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 34.9578857421875, \\\"count\\\": 1, \\\"min\\\": 34.9578857421875, \\\"max\\\": 34.9578857421875}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:19 INFO 140134321161600] Epoch[2] Batch[0] avg_epoch_loss=0.367726\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:19 INFO 140134321161600] #quality_metric: host=algo-1, epoch=2, batch=0 train loss =0.36772555112838745\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:20 INFO 140134321161600] Epoch[2] Batch[5] avg_epoch_loss=0.379747\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:20 INFO 140134321161600] #quality_metric: host=algo-1, epoch=2, batch=5 train loss =0.3797471225261688\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:20 INFO 140134321161600] Epoch[2] Batch [5]#011Speed: 732.90 samples/sec#011loss=0.379747\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] Epoch[2] Batch[10] avg_epoch_loss=0.337452\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] #quality_metric: host=algo-1, epoch=2, batch=10 train loss =0.2866967782378197\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] Epoch[2] Batch [10]#011Speed: 712.81 samples/sec#011loss=0.286697\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] processed a total of 1305 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731218.7193117, \\\"EndTime\\\": 1622731221.2175, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2498.114585876465, \\\"count\\\": 1, \\\"min\\\": 2498.114585876465, \\\"max\\\": 2498.114585876465}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=522.3655546608068 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] #progress_metric: host=algo-1, completed 6.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] #quality_metric: host=algo-1, epoch=2, train loss =0.3374515114860101\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_3bc8c772-bd0a-4e4e-b843-ce5afc9042aa-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731221.2175925, \\\"EndTime\\\": 1622731221.2407467, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 22.504806518554688, \\\"count\\\": 1, \\\"min\\\": 22.504806518554688, \\\"max\\\": 22.504806518554688}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] Epoch[3] Batch[0] avg_epoch_loss=0.291028\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:21 INFO 140134321161600] #quality_metric: host=algo-1, epoch=3, batch=0 train loss =0.29102763533592224\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:22 INFO 140134321161600] Epoch[3] Batch[5] avg_epoch_loss=0.279702\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:22 INFO 140134321161600] #quality_metric: host=algo-1, epoch=3, batch=5 train loss =0.279702365398407\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:22 INFO 140134321161600] Epoch[3] Batch [5]#011Speed: 670.59 samples/sec#011loss=0.279702\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:23 INFO 140134321161600] processed a total of 1265 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731221.24083, \\\"EndTime\\\": 1622731223.5649505, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2324.0485191345215, \\\"count\\\": 1, \\\"min\\\": 2324.0485191345215, \\\"max\\\": 2324.0485191345215}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:23 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=544.2752165567833 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:23 INFO 140134321161600] #progress_metric: host=algo-1, completed 8.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:23 INFO 140134321161600] #quality_metric: host=algo-1, epoch=3, train loss =0.2659980162978172\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:23 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:23 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_52239629-0e09-40ee-b0b0-fda9302f53d1-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731223.5650506, \\\"EndTime\\\": 1622731223.5931284, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 27.432680130004883, \\\"count\\\": 1, \\\"min\\\": 27.432680130004883, \\\"max\\\": 27.432680130004883}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:24 INFO 140134321161600] Epoch[4] Batch[0] avg_epoch_loss=0.267057\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:24 INFO 140134321161600] #quality_metric: host=algo-1, epoch=4, batch=0 train loss =0.2670571208000183\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:25 INFO 140134321161600] Epoch[4] Batch[5] avg_epoch_loss=0.239538\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:25 INFO 140134321161600] #quality_metric: host=algo-1, epoch=4, batch=5 train loss =0.23953777551651\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:25 INFO 140134321161600] Epoch[4] Batch [5]#011Speed: 702.85 samples/sec#011loss=0.239538\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] Epoch[4] Batch[10] avg_epoch_loss=0.228438\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] #quality_metric: host=algo-1, epoch=4, batch=10 train loss =0.21511825621128083\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] Epoch[4] Batch [10]#011Speed: 719.58 samples/sec#011loss=0.215118\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] processed a total of 1327 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731223.5932152, \\\"EndTime\\\": 1622731226.0693257, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2476.0148525238037, \\\"count\\\": 1, \\\"min\\\": 2476.0148525238037, \\\"max\\\": 2476.0148525238037}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=535.9132693152603 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] #progress_metric: host=algo-1, completed 10.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] #quality_metric: host=algo-1, epoch=4, train loss =0.22843799401413312\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_9ed82b1e-96c7-4062-b24b-b255dc6593d5-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731226.0694163, \\\"EndTime\\\": 1622731226.103655, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 33.736228942871094, \\\"count\\\": 1, \\\"min\\\": 33.736228942871094, \\\"max\\\": 33.736228942871094}}}\\n\",\n \"\\u001b[0m\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] Epoch[5] Batch[0] avg_epoch_loss=0.159677\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:26 INFO 140134321161600] #quality_metric: host=algo-1, epoch=5, batch=0 train loss =0.15967687964439392\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:27 INFO 140134321161600] Epoch[5] Batch[5] avg_epoch_loss=0.183388\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:27 INFO 140134321161600] #quality_metric: host=algo-1, epoch=5, batch=5 train loss =0.18338842441638312\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:27 INFO 140134321161600] Epoch[5] Batch [5]#011Speed: 724.01 samples/sec#011loss=0.183388\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:28 INFO 140134321161600] processed a total of 1249 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731226.1037436, \\\"EndTime\\\": 1622731228.3292086, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2225.389003753662, \\\"count\\\": 1, \\\"min\\\": 2225.389003753662, \\\"max\\\": 2225.389003753662}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:28 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=561.2070195044798 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:28 INFO 140134321161600] #progress_metric: host=algo-1, completed 12.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:28 INFO 140134321161600] #quality_metric: host=algo-1, epoch=5, train loss =0.16805945932865143\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:28 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:28 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_c171f275-1c1d-4244-8739-a1f5abca1e74-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731228.3293364, \\\"EndTime\\\": 1622731228.3612053, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 31.32915496826172, \\\"count\\\": 1, \\\"min\\\": 31.32915496826172, \\\"max\\\": 31.32915496826172}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:29 INFO 140134321161600] Epoch[6] Batch[0] avg_epoch_loss=0.182080\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:29 INFO 140134321161600] #quality_metric: host=algo-1, epoch=6, batch=0 train loss =0.18208037316799164\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:29 INFO 140134321161600] Epoch[6] Batch[5] avg_epoch_loss=0.167168\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:29 INFO 140134321161600] #quality_metric: host=algo-1, epoch=6, batch=5 train loss =0.16716831177473068\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:29 INFO 140134321161600] Epoch[6] Batch [5]#011Speed: 714.82 samples/sec#011loss=0.167168\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:30 INFO 140134321161600] Epoch[6] Batch[10] avg_epoch_loss=0.143360\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:30 INFO 140134321161600] #quality_metric: host=algo-1, epoch=6, batch=10 train loss =0.11478973068296909\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:30 INFO 140134321161600] Epoch[6] Batch [10]#011Speed: 722.49 samples/sec#011loss=0.114790\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:30 INFO 140134321161600] processed a total of 1282 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731228.361314, \\\"EndTime\\\": 1622731230.8118417, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2450.4570960998535, \\\"count\\\": 1, \\\"min\\\": 2450.4570960998535, \\\"max\\\": 2450.4570960998535}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:30 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=523.13863544535 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:30 INFO 140134321161600] #progress_metric: host=algo-1, completed 14.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:30 INFO 140134321161600] #quality_metric: host=algo-1, epoch=6, train loss =0.14335986582392996\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:30 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:30 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_d8566d33-ad25-4d43-9b8b-9ff66a6556c1-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731230.8119376, \\\"EndTime\\\": 1622731230.8355653, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 22.9799747467041, \\\"count\\\": 1, \\\"min\\\": 22.9799747467041, \\\"max\\\": 22.9799747467041}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:31 INFO 140134321161600] Epoch[7] Batch[0] avg_epoch_loss=0.118735\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:31 INFO 140134321161600] #quality_metric: host=algo-1, epoch=7, batch=0 train loss =0.11873453855514526\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:32 INFO 140134321161600] Epoch[7] Batch[5] avg_epoch_loss=0.126833\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:32 INFO 140134321161600] #quality_metric: host=algo-1, epoch=7, batch=5 train loss =0.12683284282684326\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:32 INFO 140134321161600] Epoch[7] Batch [5]#011Speed: 728.93 samples/sec#011loss=0.126833\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] Epoch[7] Batch[10] avg_epoch_loss=0.120399\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] #quality_metric: host=algo-1, epoch=7, batch=10 train loss =0.11267736218869687\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] Epoch[7] Batch [10]#011Speed: 731.21 samples/sec#011loss=0.112677\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] processed a total of 1281 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731230.8356535, \\\"EndTime\\\": 1622731233.238925, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2403.1898975372314, \\\"count\\\": 1, \\\"min\\\": 2403.1898975372314, \\\"max\\\": 2403.1898975372314}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=533.0095825397485 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] #progress_metric: host=algo-1, completed 16.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] #quality_metric: host=algo-1, epoch=7, train loss =0.12039853344586762\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_4bc98d1f-4989-4167-ad23-fb1af4b29d8e-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731233.2390242, \\\"EndTime\\\": 1622731233.2638626, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 24.199247360229492, \\\"count\\\": 1, \\\"min\\\": 24.199247360229492, \\\"max\\\": 24.199247360229492}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] Epoch[8] Batch[0] avg_epoch_loss=0.081529\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:33 INFO 140134321161600] #quality_metric: host=algo-1, epoch=8, batch=0 train loss =0.08152855932712555\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:34 INFO 140134321161600] Epoch[8] Batch[5] avg_epoch_loss=0.089391\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:34 INFO 140134321161600] #quality_metric: host=algo-1, epoch=8, batch=5 train loss =0.08939058581988017\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:34 INFO 140134321161600] Epoch[8] Batch [5]#011Speed: 681.57 samples/sec#011loss=0.089391\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:35 INFO 140134321161600] processed a total of 1257 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731233.2639608, \\\"EndTime\\\": 1622731235.5657465, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2301.7022609710693, \\\"count\\\": 1, \\\"min\\\": 2301.7022609710693, \\\"max\\\": 2301.7022609710693}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:35 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=546.0859866030659 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:35 INFO 140134321161600] #progress_metric: host=algo-1, completed 18.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:35 INFO 140134321161600] #quality_metric: host=algo-1, epoch=8, train loss =0.09801957979798318\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:35 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:35 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_249d4b96-6bbe-4b9d-8403-0bf0e1a5f834-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731235.5658364, \\\"EndTime\\\": 1622731235.5997417, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 33.2179069519043, \\\"count\\\": 1, \\\"min\\\": 33.2179069519043, \\\"max\\\": 33.2179069519043}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:36 INFO 140134321161600] Epoch[9] Batch[0] avg_epoch_loss=0.125405\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:36 INFO 140134321161600] #quality_metric: host=algo-1, epoch=9, batch=0 train loss =0.12540525197982788\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:37 INFO 140134321161600] Epoch[9] Batch[5] avg_epoch_loss=0.082503\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:37 INFO 140134321161600] #quality_metric: host=algo-1, epoch=9, batch=5 train loss =0.08250306112070878\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:37 INFO 140134321161600] Epoch[9] Batch [5]#011Speed: 739.44 samples/sec#011loss=0.082503\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:37 INFO 140134321161600] processed a total of 1234 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731235.5998292, \\\"EndTime\\\": 1622731237.8597047, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2259.7899436950684, \\\"count\\\": 1, \\\"min\\\": 2259.7899436950684, \\\"max\\\": 2259.7899436950684}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:37 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=545.9914937177609 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:37 INFO 140134321161600] #progress_metric: host=algo-1, completed 20.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:37 INFO 140134321161600] #quality_metric: host=algo-1, epoch=9, train loss =0.0706805419176817\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:37 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:37 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_46c848cc-e68f-4329-bcea-ae814d4a6fce-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731237.8599663, \\\"EndTime\\\": 1622731237.883341, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 22.71747589111328, \\\"count\\\": 1, \\\"min\\\": 22.71747589111328, \\\"max\\\": 22.71747589111328}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:38 INFO 140134321161600] Epoch[10] Batch[0] avg_epoch_loss=0.045247\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:38 INFO 140134321161600] #quality_metric: host=algo-1, epoch=10, batch=0 train loss =0.04524651914834976\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:39 INFO 140134321161600] Epoch[10] Batch[5] avg_epoch_loss=0.056748\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:39 INFO 140134321161600] #quality_metric: host=algo-1, epoch=10, batch=5 train loss =0.05674808472394943\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:39 INFO 140134321161600] Epoch[10] Batch [5]#011Speed: 738.84 samples/sec#011loss=0.056748\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:40 INFO 140134321161600] processed a total of 1277 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731237.8834271, \\\"EndTime\\\": 1622731240.2574086, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2373.901605606079, \\\"count\\\": 1, \\\"min\\\": 2373.901605606079, \\\"max\\\": 2373.901605606079}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:40 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=537.9026877026251 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:40 INFO 140134321161600] #progress_metric: host=algo-1, completed 22.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:40 INFO 140134321161600] #quality_metric: host=algo-1, epoch=10, train loss =0.06861307099461555\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:40 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:40 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_cc00cd31-8380-457c-8eda-18452f967f12-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731240.257499, \\\"EndTime\\\": 1622731240.279858, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 21.496057510375977, \\\"count\\\": 1, \\\"min\\\": 21.496057510375977, \\\"max\\\": 21.496057510375977}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:40 INFO 140134321161600] Epoch[11] Batch[0] avg_epoch_loss=0.069540\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:40 INFO 140134321161600] #quality_metric: host=algo-1, epoch=11, batch=0 train loss =0.06953985244035721\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:41 INFO 140134321161600] Epoch[11] Batch[5] avg_epoch_loss=0.048232\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:41 INFO 140134321161600] #quality_metric: host=algo-1, epoch=11, batch=5 train loss =0.04823200311511755\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:41 INFO 140134321161600] Epoch[11] Batch [5]#011Speed: 727.59 samples/sec#011loss=0.048232\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:42 INFO 140134321161600] Epoch[11] Batch[10] avg_epoch_loss=0.076654\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:42 INFO 140134321161600] #quality_metric: host=algo-1, epoch=11, batch=10 train loss =0.11076128147542477\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:42 INFO 140134321161600] Epoch[11] Batch [10]#011Speed: 713.55 samples/sec#011loss=0.110761\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:42 INFO 140134321161600] processed a total of 1302 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731240.2799537, \\\"EndTime\\\": 1622731242.7080817, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2428.0476570129395, \\\"count\\\": 1, \\\"min\\\": 2428.0476570129395, \\\"max\\\": 2428.0476570129395}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:42 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=536.2028641820923 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:42 INFO 140134321161600] #progress_metric: host=algo-1, completed 24.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:42 INFO 140134321161600] #quality_metric: host=algo-1, epoch=11, train loss =0.07665440236980264\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:42 INFO 140134321161600] loss did not improve\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:43 INFO 140134321161600] Epoch[12] Batch[0] avg_epoch_loss=0.094222\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:43 INFO 140134321161600] #quality_metric: host=algo-1, epoch=12, batch=0 train loss =0.09422154724597931\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:44 INFO 140134321161600] Epoch[12] Batch[5] avg_epoch_loss=0.086095\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:44 INFO 140134321161600] #quality_metric: host=algo-1, epoch=12, batch=5 train loss =0.08609458059072495\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:44 INFO 140134321161600] Epoch[12] Batch [5]#011Speed: 722.50 samples/sec#011loss=0.086095\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] Epoch[12] Batch[10] avg_epoch_loss=0.045945\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] #quality_metric: host=algo-1, epoch=12, batch=10 train loss =-0.0022343707270920275\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] Epoch[12] Batch [10]#011Speed: 693.39 samples/sec#011loss=-0.002234\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] processed a total of 1307 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731242.7081776, \\\"EndTime\\\": 1622731245.187476, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2478.7182807922363, \\\"count\\\": 1, \\\"min\\\": 2478.7182807922363, \\\"max\\\": 2478.7182807922363}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=527.2633365191891 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] #progress_metric: host=algo-1, completed 26.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] #quality_metric: host=algo-1, epoch=12, train loss =0.045945057264444505\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_5756af79-fc53-4a0d-8b73-2cc3d19a9a5a-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731245.187556, \\\"EndTime\\\": 1622731245.2229905, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 34.92617607116699, \\\"count\\\": 1, \\\"min\\\": 34.92617607116699, \\\"max\\\": 34.92617607116699}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] Epoch[13] Batch[0] avg_epoch_loss=0.046778\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:45 INFO 140134321161600] #quality_metric: host=algo-1, epoch=13, batch=0 train loss =0.04677758365869522\\u001b[0m\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[34m[06/03/2021 14:40:46 INFO 140134321161600] Epoch[13] Batch[5] avg_epoch_loss=0.026805\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:46 INFO 140134321161600] #quality_metric: host=algo-1, epoch=13, batch=5 train loss =0.02680507938688\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:46 INFO 140134321161600] Epoch[13] Batch [5]#011Speed: 733.92 samples/sec#011loss=0.026805\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:47 INFO 140134321161600] Epoch[13] Batch[10] avg_epoch_loss=0.018360\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:47 INFO 140134321161600] #quality_metric: host=algo-1, epoch=13, batch=10 train loss =0.00822615884244442\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:47 INFO 140134321161600] Epoch[13] Batch [10]#011Speed: 704.42 samples/sec#011loss=0.008226\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:47 INFO 140134321161600] processed a total of 1331 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731245.2230806, \\\"EndTime\\\": 1622731247.6415298, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2418.368101119995, \\\"count\\\": 1, \\\"min\\\": 2418.368101119995, \\\"max\\\": 2418.368101119995}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:47 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=550.3383657787011 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:47 INFO 140134321161600] #progress_metric: host=algo-1, completed 28.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:47 INFO 140134321161600] #quality_metric: host=algo-1, epoch=13, train loss =0.018360115503045647\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:47 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:47 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_a0eba91e-24a7-4910-b9c5-0abd0dac0db2-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731247.6416306, \\\"EndTime\\\": 1622731247.665032, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 22.740602493286133, \\\"count\\\": 1, \\\"min\\\": 22.740602493286133, \\\"max\\\": 22.740602493286133}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:48 INFO 140134321161600] Epoch[14] Batch[0] avg_epoch_loss=-0.012018\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:48 INFO 140134321161600] #quality_metric: host=algo-1, epoch=14, batch=0 train loss =-0.012017663568258286\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:49 INFO 140134321161600] Epoch[14] Batch[5] avg_epoch_loss=0.013922\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:49 INFO 140134321161600] #quality_metric: host=algo-1, epoch=14, batch=5 train loss =0.013921767744856576\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:49 INFO 140134321161600] Epoch[14] Batch [5]#011Speed: 736.94 samples/sec#011loss=0.013922\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:49 INFO 140134321161600] processed a total of 1187 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731247.6651268, \\\"EndTime\\\": 1622731249.8804832, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2215.2791023254395, \\\"count\\\": 1, \\\"min\\\": 2215.2791023254395, \\\"max\\\": 2215.2791023254395}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:49 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=535.7910317206134 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:49 INFO 140134321161600] #progress_metric: host=algo-1, completed 30.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:49 INFO 140134321161600] #quality_metric: host=algo-1, epoch=14, train loss =0.020618335041217507\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:49 INFO 140134321161600] loss did not improve\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:50 INFO 140134321161600] Epoch[15] Batch[0] avg_epoch_loss=0.015105\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:50 INFO 140134321161600] #quality_metric: host=algo-1, epoch=15, batch=0 train loss =0.015104921534657478\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:51 INFO 140134321161600] Epoch[15] Batch[5] avg_epoch_loss=0.016516\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:51 INFO 140134321161600] #quality_metric: host=algo-1, epoch=15, batch=5 train loss =0.01651586506826182\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:51 INFO 140134321161600] Epoch[15] Batch [5]#011Speed: 734.78 samples/sec#011loss=0.016516\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] Epoch[15] Batch[10] avg_epoch_loss=0.006541\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] #quality_metric: host=algo-1, epoch=15, batch=10 train loss =-0.005429421737790108\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] Epoch[15] Batch [10]#011Speed: 725.67 samples/sec#011loss=-0.005429\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] processed a total of 1300 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731249.8805757, \\\"EndTime\\\": 1622731252.3116972, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2430.500030517578, \\\"count\\\": 1, \\\"min\\\": 2430.500030517578, \\\"max\\\": 2430.500030517578}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=534.8388797192148 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] #progress_metric: host=algo-1, completed 32.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] #quality_metric: host=algo-1, epoch=15, train loss =0.006540734701874581\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_1b336aa2-51bf-4fa3-abb4-38e643160a13-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731252.3117952, \\\"EndTime\\\": 1622731252.3364236, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 24.0175724029541, \\\"count\\\": 1, \\\"min\\\": 24.0175724029541, \\\"max\\\": 24.0175724029541}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] Epoch[16] Batch[0] avg_epoch_loss=0.023324\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:52 INFO 140134321161600] #quality_metric: host=algo-1, epoch=16, batch=0 train loss =0.02332369238138199\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:53 INFO 140134321161600] Epoch[16] Batch[5] avg_epoch_loss=-0.000867\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:53 INFO 140134321161600] #quality_metric: host=algo-1, epoch=16, batch=5 train loss =-0.0008667904767207801\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:53 INFO 140134321161600] Epoch[16] Batch [5]#011Speed: 736.28 samples/sec#011loss=-0.000867\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:54 INFO 140134321161600] Epoch[16] Batch[10] avg_epoch_loss=-0.006566\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:54 INFO 140134321161600] #quality_metric: host=algo-1, epoch=16, batch=10 train loss =-0.013404919765889645\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:54 INFO 140134321161600] Epoch[16] Batch [10]#011Speed: 720.88 samples/sec#011loss=-0.013405\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:54 INFO 140134321161600] processed a total of 1321 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731252.336511, \\\"EndTime\\\": 1622731254.7380464, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2401.416063308716, \\\"count\\\": 1, \\\"min\\\": 2401.416063308716, \\\"max\\\": 2401.416063308716}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:54 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=550.0505393114088 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:54 INFO 140134321161600] #progress_metric: host=algo-1, completed 34.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:54 INFO 140134321161600] #quality_metric: host=algo-1, epoch=16, train loss =-0.006565940153615718\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:54 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:54 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_c0697e5a-ba24-4b5c-b500-3bebd363f3d7-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731254.738143, \\\"EndTime\\\": 1622731254.7666144, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 27.9543399810791, \\\"count\\\": 1, \\\"min\\\": 27.9543399810791, \\\"max\\\": 27.9543399810791}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:55 INFO 140134321161600] Epoch[17] Batch[0] avg_epoch_loss=0.014383\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:55 INFO 140134321161600] #quality_metric: host=algo-1, epoch=17, batch=0 train loss =0.014383395202457905\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:56 INFO 140134321161600] Epoch[17] Batch[5] avg_epoch_loss=-0.005715\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:56 INFO 140134321161600] #quality_metric: host=algo-1, epoch=17, batch=5 train loss =-0.00571489380672574\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:56 INFO 140134321161600] Epoch[17] Batch [5]#011Speed: 729.69 samples/sec#011loss=-0.005715\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] Epoch[17] Batch[10] avg_epoch_loss=0.020989\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] #quality_metric: host=algo-1, epoch=17, batch=10 train loss =0.05303449556231499\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] Epoch[17] Batch [10]#011Speed: 730.93 samples/sec#011loss=0.053034\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] processed a total of 1294 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731254.766695, \\\"EndTime\\\": 1622731257.1731093, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2406.3398838043213, \\\"count\\\": 1, \\\"min\\\": 2406.3398838043213, \\\"max\\\": 2406.3398838043213}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=537.7137013512616 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] #progress_metric: host=algo-1, completed 36.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] #quality_metric: host=algo-1, epoch=17, train loss =0.02098937408829277\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] loss did not improve\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] Epoch[18] Batch[0] avg_epoch_loss=0.005450\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:57 INFO 140134321161600] #quality_metric: host=algo-1, epoch=18, batch=0 train loss =0.005449979566037655\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:58 INFO 140134321161600] Epoch[18] Batch[5] avg_epoch_loss=0.004355\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:58 INFO 140134321161600] #quality_metric: host=algo-1, epoch=18, batch=5 train loss =0.004355373792350292\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:58 INFO 140134321161600] Epoch[18] Batch [5]#011Speed: 737.38 samples/sec#011loss=0.004355\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:59 INFO 140134321161600] processed a total of 1279 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731257.1732109, \\\"EndTime\\\": 1622731259.3990564, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2225.193738937378, \\\"count\\\": 1, \\\"min\\\": 2225.193738937378, \\\"max\\\": 2225.193738937378}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:59 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=574.7436016359121 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:59 INFO 140134321161600] #progress_metric: host=algo-1, completed 38.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:59 INFO 140134321161600] #quality_metric: host=algo-1, epoch=18, train loss =-0.00855096783488989\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:59 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:40:59 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_3c013854-590b-4bba-b1a7-90b1bc29ece3-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731259.3991587, \\\"EndTime\\\": 1622731259.4275527, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 27.691125869750977, \\\"count\\\": 1, \\\"min\\\": 27.691125869750977, \\\"max\\\": 27.691125869750977}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:00 INFO 140134321161600] Epoch[19] Batch[0] avg_epoch_loss=-0.018629\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:00 INFO 140134321161600] #quality_metric: host=algo-1, epoch=19, batch=0 train loss =-0.018628694117069244\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] Epoch[19] Batch[5] avg_epoch_loss=-0.028627\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] #quality_metric: host=algo-1, epoch=19, batch=5 train loss =-0.028626597796877224\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] Epoch[19] Batch [5]#011Speed: 693.44 samples/sec#011loss=-0.028627\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] Epoch[19] Batch[10] avg_epoch_loss=-0.038317\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] #quality_metric: host=algo-1, epoch=19, batch=10 train loss =-0.049945876374840735\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] Epoch[19] Batch [10]#011Speed: 728.90 samples/sec#011loss=-0.049946\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] processed a total of 1290 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731259.427646, \\\"EndTime\\\": 1622731261.9011364, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2473.4127521514893, \\\"count\\\": 1, \\\"min\\\": 2473.4127521514893, \\\"max\\\": 2473.4127521514893}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=521.5190974431102 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] #progress_metric: host=algo-1, completed 40.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] #quality_metric: host=algo-1, epoch=19, train loss =-0.03831717896867882\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:01 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_7a9c6233-05b8-4b3b-b5a8-db2b594c6d28-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731261.9012265, \\\"EndTime\\\": 1622731261.9305649, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 28.80859375, \\\"count\\\": 1, \\\"min\\\": 28.80859375, \\\"max\\\": 28.80859375}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:02 INFO 140134321161600] Epoch[20] Batch[0] avg_epoch_loss=0.012149\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:02 INFO 140134321161600] #quality_metric: host=algo-1, epoch=20, batch=0 train loss =0.012148914858698845\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:03 INFO 140134321161600] Epoch[20] Batch[5] avg_epoch_loss=-0.006967\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:03 INFO 140134321161600] #quality_metric: host=algo-1, epoch=20, batch=5 train loss =-0.00696744800855716\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:03 INFO 140134321161600] Epoch[20] Batch [5]#011Speed: 679.02 samples/sec#011loss=-0.006967\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:04 INFO 140134321161600] Epoch[20] Batch[10] avg_epoch_loss=-0.029709\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:04 INFO 140134321161600] #quality_metric: host=algo-1, epoch=20, batch=10 train loss =-0.05699936933815479\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:04 INFO 140134321161600] Epoch[20] Batch [10]#011Speed: 632.66 samples/sec#011loss=-0.056999\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:04 INFO 140134321161600] processed a total of 1282 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731261.930647, \\\"EndTime\\\": 1622731264.7010138, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2770.1988220214844, \\\"count\\\": 1, \\\"min\\\": 2770.1988220214844, \\\"max\\\": 2770.1988220214844}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:04 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=462.76120019673664 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:04 INFO 140134321161600] #progress_metric: host=algo-1, completed 42.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:04 INFO 140134321161600] #quality_metric: host=algo-1, epoch=20, train loss =-0.02970923043110154\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:04 INFO 140134321161600] loss did not improve\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:05 INFO 140134321161600] Epoch[21] Batch[0] avg_epoch_loss=-0.036076\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:05 INFO 140134321161600] #quality_metric: host=algo-1, epoch=21, batch=0 train loss =-0.03607587888836861\\u001b[0m\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[34m[06/03/2021 14:41:06 INFO 140134321161600] Epoch[21] Batch[5] avg_epoch_loss=-0.027970\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:06 INFO 140134321161600] #quality_metric: host=algo-1, epoch=21, batch=5 train loss =-0.02796976330379645\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:06 INFO 140134321161600] Epoch[21] Batch [5]#011Speed: 572.52 samples/sec#011loss=-0.027970\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:07 INFO 140134321161600] processed a total of 1240 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731264.7011025, \\\"EndTime\\\": 1622731267.400464, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2698.8182067871094, \\\"count\\\": 1, \\\"min\\\": 2698.8182067871094, \\\"max\\\": 2698.8182067871094}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:07 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=459.4382047682861 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:07 INFO 140134321161600] #progress_metric: host=algo-1, completed 44.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:07 INFO 140134321161600] #quality_metric: host=algo-1, epoch=21, train loss =-0.028421435877680777\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:07 INFO 140134321161600] loss did not improve\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:08 INFO 140134321161600] Epoch[22] Batch[0] avg_epoch_loss=-0.048015\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:08 INFO 140134321161600] #quality_metric: host=algo-1, epoch=22, batch=0 train loss =-0.048015423119068146\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:08 INFO 140134321161600] Epoch[22] Batch[5] avg_epoch_loss=-0.043346\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:08 INFO 140134321161600] #quality_metric: host=algo-1, epoch=22, batch=5 train loss =-0.04334574999908606\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:08 INFO 140134321161600] Epoch[22] Batch [5]#011Speed: 718.94 samples/sec#011loss=-0.043346\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:09 INFO 140134321161600] processed a total of 1269 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731267.4005532, \\\"EndTime\\\": 1622731269.6512513, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2249.951124191284, \\\"count\\\": 1, \\\"min\\\": 2249.951124191284, \\\"max\\\": 2249.951124191284}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:09 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=563.9699405161456 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:09 INFO 140134321161600] #progress_metric: host=algo-1, completed 46.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:09 INFO 140134321161600] #quality_metric: host=algo-1, epoch=22, train loss =-0.05009483378380537\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:09 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:09 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_11591b1e-081b-4c21-a17a-8161f50c0ce2-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731269.6513498, \\\"EndTime\\\": 1622731269.685869, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 33.98609161376953, \\\"count\\\": 1, \\\"min\\\": 33.98609161376953, \\\"max\\\": 33.98609161376953}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:10 INFO 140134321161600] Epoch[23] Batch[0] avg_epoch_loss=-0.036292\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:10 INFO 140134321161600] #quality_metric: host=algo-1, epoch=23, batch=0 train loss =-0.03629159927368164\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:11 INFO 140134321161600] Epoch[23] Batch[5] avg_epoch_loss=-0.049868\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:11 INFO 140134321161600] #quality_metric: host=algo-1, epoch=23, batch=5 train loss =-0.04986786935478449\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:11 INFO 140134321161600] Epoch[23] Batch [5]#011Speed: 690.60 samples/sec#011loss=-0.049868\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:12 INFO 140134321161600] processed a total of 1236 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731269.6859488, \\\"EndTime\\\": 1622731272.0548477, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2368.8251972198486, \\\"count\\\": 1, \\\"min\\\": 2368.8251972198486, \\\"max\\\": 2368.8251972198486}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:12 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=521.7491689659389 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:12 INFO 140134321161600] #progress_metric: host=algo-1, completed 48.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:12 INFO 140134321161600] #quality_metric: host=algo-1, epoch=23, train loss =-0.04426752347499132\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:12 INFO 140134321161600] loss did not improve\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:12 INFO 140134321161600] Epoch[24] Batch[0] avg_epoch_loss=-0.063750\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:12 INFO 140134321161600] #quality_metric: host=algo-1, epoch=24, batch=0 train loss =-0.06375045329332352\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:13 INFO 140134321161600] Epoch[24] Batch[5] avg_epoch_loss=-0.064618\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:13 INFO 140134321161600] #quality_metric: host=algo-1, epoch=24, batch=5 train loss =-0.06461834783355395\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:13 INFO 140134321161600] Epoch[24] Batch [5]#011Speed: 724.16 samples/sec#011loss=-0.064618\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:14 INFO 140134321161600] processed a total of 1275 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731272.0549345, \\\"EndTime\\\": 1622731274.3434987, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2288.06734085083, \\\"count\\\": 1, \\\"min\\\": 2288.06734085083, \\\"max\\\": 2288.06734085083}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:14 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=557.2022655947186 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:14 INFO 140134321161600] #progress_metric: host=algo-1, completed 50.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:14 INFO 140134321161600] #quality_metric: host=algo-1, epoch=24, train loss =-0.06733495257794857\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:14 INFO 140134321161600] best epoch loss so far\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:14 INFO 140134321161600] Saved checkpoint to \\\"/opt/ml/model/state_438e69cd-0ecf-4fb1-94f7-a13d3c639458-0000.params\\\"\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731274.3436031, \\\"EndTime\\\": 1622731274.366484, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"state.serialize.time\\\": {\\\"sum\\\": 22.184371948242188, \\\"count\\\": 1, \\\"min\\\": 22.184371948242188, \\\"max\\\": 22.184371948242188}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:15 INFO 140134321161600] Epoch[25] Batch[0] avg_epoch_loss=-0.053199\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:15 INFO 140134321161600] #quality_metric: host=algo-1, epoch=25, batch=0 train loss =-0.053198810666799545\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:15 INFO 140134321161600] Epoch[25] Batch[5] avg_epoch_loss=-0.062585\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:15 INFO 140134321161600] #quality_metric: host=algo-1, epoch=25, batch=5 train loss =-0.06258516013622284\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:15 INFO 140134321161600] Epoch[25] Batch [5]#011Speed: 717.78 samples/sec#011loss=-0.062585\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:16 INFO 140134321161600] Epoch[25] Batch[10] avg_epoch_loss=-0.062134\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:16 INFO 140134321161600] #quality_metric: host=algo-1, epoch=25, batch=10 train loss =-0.061593336192891\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:16 INFO 140134321161600] Epoch[25] Batch [10]#011Speed: 719.75 samples/sec#011loss=-0.061593\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:16 INFO 140134321161600] processed a total of 1283 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731274.3665552, \\\"EndTime\\\": 1622731276.7963164, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2429.6891689300537, \\\"count\\\": 1, \\\"min\\\": 2429.6891689300537, \\\"max\\\": 2429.6891689300537}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:16 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=528.0214988559992 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:16 INFO 140134321161600] #progress_metric: host=algo-1, completed 52.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:16 INFO 140134321161600] #quality_metric: host=algo-1, epoch=25, train loss =-0.062134331071072\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:16 INFO 140134321161600] loss did not improve\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:17 INFO 140134321161600] Epoch[26] Batch[0] avg_epoch_loss=-0.044157\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:17 INFO 140134321161600] #quality_metric: host=algo-1, epoch=26, batch=0 train loss =-0.04415673762559891\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:18 INFO 140134321161600] Epoch[26] Batch[5] avg_epoch_loss=-0.053495\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:18 INFO 140134321161600] #quality_metric: host=algo-1, epoch=26, batch=5 train loss =-0.05349482533832391\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:18 INFO 140134321161600] Epoch[26] Batch [5]#011Speed: 736.06 samples/sec#011loss=-0.053495\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] Epoch[26] Batch[10] avg_epoch_loss=-0.045742\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] #quality_metric: host=algo-1, epoch=26, batch=10 train loss =-0.03643805906176567\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] Epoch[26] Batch [10]#011Speed: 737.65 samples/sec#011loss=-0.036438\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] processed a total of 1290 examples\\u001b[0m\\n\",\n \"\\u001b[34m#metrics {\\\"StartTime\\\": 1622731276.7964106, \\\"EndTime\\\": 1622731279.1924024, \\\"Dimensions\\\": {\\\"Algorithm\\\": \\\"AWS/DeepAR\\\", \\\"Host\\\": \\\"algo-1\\\", \\\"Operation\\\": \\\"training\\\"}, \\\"Metrics\\\": {\\\"update.time\\\": {\\\"sum\\\": 2395.3988552093506, \\\"count\\\": 1, \\\"min\\\": 2395.3988552093506, \\\"max\\\": 2395.3988552093506}}}\\n\",\n \"\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] #throughput_metric: host=algo-1, train throughput=538.5030186373236 records/second\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] #progress_metric: host=algo-1, completed 54.0 % of epochs\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] #quality_metric: host=algo-1, epoch=26, train loss =-0.04574174975807017\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] loss did not improve\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] Epoch[27] Batch[0] avg_epoch_loss=-0.043299\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:19 INFO 140134321161600] #quality_metric: host=algo-1, epoch=27, batch=0 train loss =-0.04329928755760193\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:20 INFO 140134321161600] Epoch[27] Batch[5] avg_epoch_loss=-0.052881\\u001b[0m\\n\",\n \"\\u001b[34m[06/03/2021 14:41:20 INFO 140134321161600] #quality_metric: host=algo-1, epoch=27, batch=5 train loss
![](\\\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod2Fig1-Histogram.JPG\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We can easily graph this distribution by passing `kind=hist` to `plot()`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"![](\\\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig4SplitApplyCombine.png\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"![](\\\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/boxplot_complete.png\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"To make a `box plot`, we can use `kind=box` in `plot` method invoked on a _pandas_ series or dataframe.\\n\",\n \"\\n\",\n \"Let's plot the box plot for the Japanese immigrants between 1980 - 2013.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"![](\\\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig5Subplots_V2.png\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABJ8AAAGHCAYAAAAeMPMgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAADEyUlEQVR4nOzdd3gUZdcG8Ht2N71XQiohDQih9xZ6ExVRxA6Iig0FbCgiKvLaPkQpoi+iqKgvooiiFClC6NJCCZCEEmogpPe65/sjZmWTTbJJNtlA7t91cZHMzjzPmZns7uzZZ86jiIiAiIiIiIiIiIioHqjMHQAREREREREREd26mHwiIiIiIiIiIqJ6w+QTERERERERERHVGyafiIiIiIiIiIio3jD5RERERERERERE9YbJJyIiIiIiIiIiqjdMPlGN9O/fH4899pi5w6jUtm3b0LZtW1hYWKB///7mDsegCRMmYPDgweYOo14UFxfj0UcfhZubGxRFwbZt28wdUo0oioIVK1ZUuU5jfw4YIyEhAYqiYOfOnQ3elyn7zs7Oho+PD/bv31/ntqh67777Lu655x5zh0HU4BrT6/7y5cuh0WjMHUalMjMzcdddd8HR0RGKoiAhIcHcIVWwbds2KIqCS5cumTuUerFq1SoEBQVBrVZjwoQJ5g6nRoy5Rm7szwFjNeTrSvm+TNn3lClT8Oyzz5qkLara+fPn4e7ujsTERHOHUmtMPjUyEyZMgKIoun9OTk7o2bMn1q1bV+99L1++XK/vZs2aYdSoUTh27Fid2tVoNFi+fLlpgqzGU089hU6dOuHs2bNYvXq1wXXefPNNBAcHN0g8hnzyySdYtWqV2fo3ZPDgwSa5QPn555/x/fffY+3atUhMTESvXr3qHpyJxMTE4OGHH4avry+srKwQEBCAu+66C3/99VeN2lm9ejU++uijeoqy8aiv54mfnx8SExPRvXv3Orf1/vvvo0uXLujatatu2dy5c9G3b1/dBx9DHy7Onz+P+++/H15eXrC1tcWgQYNw5MgRvXVyc3Mxbdo0+Pv7w8bGBkFBQZg9ezZKSkr01lu3bh06dOgAKysrtGjRwmR/GxcuXMDkyZMREhICGxsb+Pr6YuLEibh8+XKFdT/44AMEBATA2toaHTt2xJ9//qn3+C+//IIRI0bAy8ur0gTrt99+i86dO8PFxQU2NjZo3bo15s2bBxHRrfP8889j+/btDZK0JGpI1X3gbcjX/Ruvw+zt7dG+fXssW7asTm0+9thjDfaF3JIlS7Bnzx7s2rULiYmJ8PPzq7BOQ34BYkivXr2QmJgIb29vs/RvyIoVK6AoSp3bKSkpwaOPPop7770XFy5cwCeffGKC6EwjNzcX77zzDtq1awdbW1u4urqie/fuWLhwIXJzc41uZ9y4cQbfC2819fk8MdVrWmxsLL766iu8/vrrumVRUVG48847ERAQAEVR8M4771TYrri4GB988AHCwsJgbW2NkJAQLF68uMJ6q1atQufOnWFvbw9PT0+MGTMGp0+f1lsnMTER9957LxwdHeHo6Ij77rsPSUlJdd43APjwww/Rs2dPuLi4wNnZGX369MGGDRsqrLdv3z706tUL1tbWaN68OV599VW968XExEQ8+OCDCA8Ph0ajMfh+c/HiRQwdOhTe3t6wsrKCt7c3HnnkEb3r2ICAANx7772YNWuWSfbPHJh8aoT69u2LxMREJCYmYu/evejUqRNGjx6NM2fO1HvfarVa1/eaNWuQlJSEYcOGISMjo977NoX4+HgMGTIEfn5+cHV1NXc4Bjk5OcHFxaVObWi12gofghuD+Ph4+Pj4oFevXvDy8oKlpWWFdQoLCxs8ro0bN6JLly64cuUKli5dihMnTmDt2rXo0aMHJk+eXKO2XF1d4ejoWE+R3vrUajW8vLxgYWFRp3by8/OxZMmSCuevoKAAd9xxB2bOnGlwu9zcXAwZMgRpaWlYt24dDh48iBYtWmDgwIG4du2abr2XXnoJP/74I7744gucPHkS77//Pj766CN88MEHunUOHDiAO++8E8OHD0d0dDTefPNNvPbaa/jss8/qtG9A6QVdTk4OPv74Yxw/fhz/+9//cOzYMQwfPlzvuf/xxx9j9uzZmDNnDg4fPowhQ4bg9ttvx9GjR3XrZGdno1u3bliyZEml/Xl6emLWrFnYvXs3YmJiMGPGDLzxxhtYsGCBbh1bW1vcf//9+Pjjj+u8f0Q3k4Z+3V+0aBESExNx+PBhjBgxAo899lij+9KqMvHx8QgPD0dERAS8vLygVqvNHVIFlpaW8PLygkpVt49B5rieqU5iYiKys7MxcuRI+Pj4wMnJqcI6IoKioqIGjSszMxO9e/fGwoUL8cwzz2D37t04ePAgXnzxRfz4448VvjSpio2NDZo1a1aP0d76TPWatmDBAowcORJeXl66ZdnZ2WjTpg0++OADveU3mj17Nj788EO89957OHHiBN588028/PLLWLp0qW6dffv24b777sPdd9+No0ePYt26dUhJScFtt92mW0er1WLUqFE4d+4cNm3ahD///BNxcXEYPXq03pdntbV161Y8+uij+Ouvv7Bv3z706NEDo0aNwq5du3TrXLx4EUOGDEFYWBgOHjyIJUuW4PPPP9e7Di0oKICrqyumT59e6RcdGo0Gd999N9auXYv4+Hj8+OOPiIuLw+2336633mOPPYYVK1YgOTm5zvtnFkKNyvjx42XQoEF6yzIzMwWArF69Wm/ZE088Ie7u7mJlZSWdO3eWjRs3iohIfn6+dOjQQe68807d+rm5uRIeHi733ntvpX1/9dVXolar9Zbt3LlTAMiGDRtERCQyMlImTZqke7ywsFBeeeUV8fb2FgsLC2ndurV89913uscDAgIEgN4/EZGMjAyZMGGCNGvWTCwtLcXX11emTZtW5bE5deqUjBw5Uuzs7MTOzk5GjRol8fHxIiLy119/Vejnq6++MtjO7NmzJSgoqMLvK1eulODgYLGxsZE777xTMjIy5Oeff5bQ0FCxt7eXu+++W9LT03XblZ2rBQsWiI+Pj9jZ2cmkSZOksLBQlixZIv7+/uLs7CyPP/64FBQUVNiuTElJibz66qvi7u4udnZ2Mm7cOJk/f77euSiL8X//+5+EhYWJWq2WY8eOycGDB2X48OHi4eEhdnZ20qVLF1m/fr3e/gYEBMisWbPkueeeExcXF/H09JQXXnhBiouLdfGUP3Z//fWXiIjMnTtXAgMDxdLSUtzd3WXo0KGSm5tr8LhGRkbqtREQEKBb/uijj8rrr78uXl5e4u7uLiIie/bskb59+4q1tbU4OzvL/fffL9euXavzeSkvJydHPD09Zfjw4QYfT01N1f0MQBYvXiwPPfSQ2Nvbi6+vr7z//vsV9vPG50DZ72+//bY0a9ZMXFxcZPz48ZKdna1bx5jzVJ5Wq5XHHntMWrZsKdbW1hIYGCivvvqq5OfnVzhGa9askbCwMLG1tZX+/fvL6dOn9dpauXKlBAUFiZWVlfTs2VN+/fVXASA7duyotP/Knid17evcuXMV+n7ttdekVatWYmNjI76+vjJ58uQqz6mIyC+//CK2trZSVFRk8PGy14SLFy/qLd+0aZMAkMTERN2y4uJicXV1lTfeeEO3rH379jJ9+nS9bceMGSOjR4/W/X7//fdLz5499dZ58cUXpUWLFlXGXlsHDhwQAHL06FERKf0b8fb2lldffVVvvS5dusj48eMNtgFAvv32W6P6Gz16tN7+iohs3bpVNBqNZGZm1nwHiBopQ9deN6rN676IyA8//CDt27cXKysrCQgIkGnTplVYpzxDz9Hg4GC57777RMTwtdoff/whnTp1EktLS/Hw8JCnnnpK18/s2bMrvT5aunSptGrVSqysrMTV1VX69u1b4TXzRjW95ouMjDTYTvn3gbLfv/vuOxk6dKjY2NhIWFiYbNu2TS5duiQjRowQW1tbad26tURFRenaKXud/+OPP6RHjx5ibW0tnTp1kuPHj8vx48eld+/eYmNjI127dpWYmJgK2924r5s2bZK2bduKlZWVREREyLZt2/TORVmMK1as0MXzwgsvmOS92tA1bNlr+I4dO6RXr15ib28v9vb20q5dO901eXlfffWVweu5sr+ZrVu3SocOHcTCwkLWrl1b5WeJupwXQ5599lmxtraWs2fPVnhMq9VKWlqaiPz7XPz888/F399fHBwc5I477pCkpCS9/bzxOVD2+86dO6Vjx45iY2MjXbp0kQMHDuj1Ud15MuS7776Tbt26iaOjo7i5ucnIkSMlNja2wjFauXKljBo1SmxsbCQwMFC++eYbvXYSEhJk2LBhYm1tLX5+frJgwYIKryvlVfY8MUVf5X//888/JTIyUlxcXMTR0VH69esn+/btq/LYlJSUiIuLi95rQHkBAQEyZ86cCst9fHzk3Xff1Vv23HPP6T47iIjMnz9fXF1d9db57bffBIDuGnHjxo0CQE6dOqVb5/jx43qfZUytbdu2eteHr776qvj4+EhJSYlu2aJFi8TW1tbg63117zc3WrNmjd7+lvH395clS5bUcg/Mi8mnRqb8H2RBQYHMmzdPrKysJCEhQbf8nnvukYCAANmwYYOcOHFCnnvuObGwsJCTJ0+KiEhsbKzY29vLwoULRUTksccek8DAwCo/0Bm6oDl48KAAkLVr14pIxRerF198UVxdXeXHH3+U2NhYmTt3riiKIps3bxYRkaSkJFGr1fLxxx9LYmKi7gPflClTpF27drJ37145f/687Nq1S/773/9WGltubq74+/vLwIED5cCBA3LgwAHp37+/BAUFSUFBgRQUFEhiYqIAkEWLFkliYmKlSRJDH6ptbW1l5MiRcuTIEdm2bZu4u7vLkCFDZMSIERIdHS1RUVHi6ekpL7/8st65cnR0lEceeUROnDghv/76q1hZWcmIESPk4YcflpiYGFm7dq1YW1vLp59+qrfdjed43rx5YmdnJ998843ExcXJvHnzxMXFpULyycbGRvr16yd79uyR2NhYyczMlL/++kuWL18uMTExEhsbKzNnzhQLCwu9N8aAgABxdnaWd999V+Li4uR///ufqNVq+fLLL0VEJD09Xfr27Sv33nuv7hwVFBTIzz//LA4ODvLbb7/J+fPn5fDhwzJ//vxKj2tKSoq88MIL0qJFC0lMTNRdKERGRoq9vb1MnjxZYmJi5OjRo5KYmCgODg5y//33y9GjR2XHjh0SEREhffr0qfN5Ke+XX36pNtFSBoB4enrKf//7Xzl9+rR88sknAkC2bt2qW8fQG7iTk5NMnTpVTp48KevXrxcnJye9RIYx56m8kpISmTlzpuzdu1fOnTsnv/76q3h5eem1W3aMhg0bJgcOHJDo6Gjp0KGD9OvXT7fOoUOHRFEUmTFjhpw6dUp+/vlnadGiRa2ST6boy1Dyac6cORIVFSXnzp2TzZs3S1hYmDzyyCOVxiYiMnXqVOndu3elj1eWfFq7dq0A0F3olvHy8pL+/fvrfn/mmWekdevWugvlw4cPi6enpyxdulS3jr+/v7z11lt67WzevNlgv6awZcsWASBXrlwREZGzZ88KANm+fbveeq+//rreubuRMcknrVYr+/btEw8PD/nkk0/0HsvOzhZFUWTdunV12BOixqU2yafqXve/+uorcXZ2lm+++UbOnDkj27dvl4iICHnooYeqjMXQczQiIkLuvvtuXbs3Xh8cOXJE1Gq1TJ06VU6cOCHr1q0TPz8/XT9ZWVnywAMPSM+ePXXv8bm5uXLgwAFRq9Xy9ddfS0JCghw9elSWLl1a5WuXMdd89957r/Tt21cSExMlJSXFYDuVfahu2bKl/PLLLxIbGyujR4+W5s2by6BBg2T16tUSGxsrY8aMEV9fXyksLBSRf1/nO3ToIFu2bJGYmBjp0aOHRERESN++fWXz5s1y4sQJ6d27t3Tr1k3Xf/n3h0uXLomNjY1MmjRJYmJiZPPmzdKpUyeDyScfHx/59ttv5cyZM3L27FmTvFcXFBTIokWLdF+MJCYmSnp6uhQXF4uLi4tMmzZN4uLiJC4uTlavXl1poic3N1f+/vtvASC//vqr7nruq6++EkVRpEuXLrJlyxY5c+aMJCUlVftZorbnpbySkhJxdXWtMtFSpuza+r777pNjx47Jrl27xN/fX++awFDySVEU6du3r0RFRcnJkydlyJAh0rJlS90XVMacJ0O+/PJLWbt2rZw+fVoOHTokt99+uwQHB+u+VC47RoGBgbJy5UqJj4+XV155RdRqtcTFxYlI6Xtqx44dpUuXLrJ37145fPiwDB48WBwcHGqVfDJFX+Vf01avXq17Xh8/flwmTZokLi4ukpycXGl80dHRAkA3EMCQypJPbm5uMn/+fL1lr7zyigDQfd7dt2+fqFQqWblypZSUlEhaWpqMHTtW79rvjTfekMDAwArt+/r6Guy3rkpKSiQgIED+85//6Jb169dPJk6cqLfe6dOnK73ONjb5dP36dbn77rulU6dOFR4bO3ZslQNKGjMmnxqZ8ePHi1qt1o3uURRF7OzsZOXKlbp14uPjdd/03Khjx456f/zLly8XKysrmTVrllhYWFSbwS7/Yp6UlCSjRo0SR0dH3YiUG1+scnJyxNLSUhYvXqzXzujRo2XAgAG639VqdYVRSHfccUel38wb8sUXX4iNjY1cv35dt+zq1atibW0tX3/9tW6ZMR+sDH2oVqvVem0//fTTolKp9L5pee6556Rz586638ePHy8eHh56o5pGjhwpbm5uet+k3HHHHbqLxrLtbnzR8fb2ltdff10vxnHjxlVIPimKIufPn69y30RE2rVrJ++8847u94CAALn99tv11hk2bJjuW1QRkUGDBlU4Hx999JGEhIRUejFhSPljK1L6NxMSEqL3jcDrr78uPj4+eseu7E2s7IN0bc9Lee+//74AqPQi+EYAZMqUKXrLwsLCZMaMGXr7U/4NPCIiQm+byZMnS48eParsq/x5MsZHH30kwcHBut/LjtGNx+OHH34QRVEkLy9PREQefPDBCqNzFi5cWKvkkyn6MpR8Km/16tViaWmp9zdT3p133lnlG29lyafr16+Ls7OzPProo5KRkSH5+fkyZ84cASChoaG69QoLC+Wpp54SAKLRaERRFHn77bf12rKwsJDPP/9cb1nZN25///13pbHVRlZWlrRv317vtWTXrl0CoEISs+wbN0Oqeo1MT08XOzs7sbCwEJVKVSGxVsbFxUUWLVpUyz0hanxqk3yq7nU/ICCgwjfT27dvFwB6I27Lu/E5WlRUJEuXLhUAurbKX6s99NBD0rVrV7021qxZI4qi6D7ETZo0qcIopNWrV4ujo6NkZGRUGsuNjL3mM+aDVWUfqm/8MFqWRPm///s/3bJDhw4JADl27JiI/Ps6/8svv+jW+fHHHwWA/PTTT3r7CkCysrL0tit7f3jttdckICBANyJcRGT9+vUGk0/l3wcMqc179bfffqu7O6BMampqjUdwGHqPLRsRdWPSypjPErU9L+Vdu3ZNAMi8efOqjX/8+PHi7u6udx397rvvipeXl97+lE8+AZCDBw/qlu3Zs6fCiJjyyp8nY6SkpAgA2blzp4j8e4xu3LeioiKxs7OTzz77TET+HXF943t1UlKSWFtb1yr5ZIq+qht1VVJSIs7OzrJixYpK1yn7creyL6VFKk8+PfTQQxIYGChHjx4VrVYre/fuFQ8PDwEgu3fv1q3322+/iYuLi2g0GgEg3bt310uIPf744xWuO0VKR4A//fTTlcZVW3PmzBEnJye9a8uQkJAKI9Czs7MFgPz4448V2qjuNfK+++4TGxsbASA9e/bUe90oM23aNOnSpUsd9sR8WPOpEerevTuio6MRHR2NQ4cO4Y033sD48eOxceNGAMCJEycAAP369dPbrl+/foiJidH9Pn78eNx5552YM2cO5syZg27dulXbd0lJCezt7XWF3U6fPo2ffvoJnp6eFdY9ffo0CgsLK8QRGRmpF4chTz/9NH766Se0bdsWzz//PNavXw+tVlvp+jExMWjTpg3c3d11y5o1a4awsLBq+zKGj4+PXtteXl7w8vKCh4eH3rLyBexat26tV9fIy8sLYWFhsLKyqnK7MpmZmbhy5Qp69Oiht7xnz54V1m3WrBn8/f31ll2/fh1PP/00WrVqBWdnZ9jb2yMmJgbnz5/XW69Dhw4V9vfG+jaG3HvvvSgqKkJAQAAmTJiAb7/9FllZWVVuU5nOnTvr1VaIiYlBjx499I5d+/bt4eTkpHc+a3tebiQ1vOe7Nsequm2MPU/lLV26FN27d0ezZs1gb2+PV199tcI23t7eesfDx8cHIqI7JidOnEDv3r31tunTp0+V/VamvvpavXo1+vXrB29vb9jb2+PBBx9EYWEhrl69Wuk2eXl5sLa2rvE+uLu7Y/Xq1di5cyecnZ1hZ2eHvXv3YuTIkXq1ST799FNs2LABP//8Mw4dOoQvv/wSH330Ef773/8a1U9lhWP/85//6F5j7e3tsWPHjmrbysnJwR133AGNRmN04eHaFK51cHBAdHQ0Dhw4gMWLF2P+/Pn44osvKqxnbW2NvLy8GrdPdCup6nX/+vXrOH/+PKZPn673fB8xYgQAVCiYW95jjz0Ge3t7WFtbY9q0aZgxY0al9QljYmIMXoeJiO560ZAhQ4agZcuWCAwMxH333Yf//ve/VdYQqcs1n7Hat2+v+7msVky7du0qLCv/nl/b7cqcOHECXbt21XsPMHQdBsDgtbQp3qsNcXFxwWOPPYZhw4ZhxIgReO+99xAbG1vp+tW5cXIOYz9LAHU/vmXXYca+L7Vu3VrvOtqY6zBFUfTi9PHxAQC97Yw5T+VFR0fjrrvuQmBgIBwcHHTX4VVdZ2s0GjRr1kzX94kTJ+Du7o7Q0FDdOh4eHggLC6uy78rUR1/nzp3Dww8/jODgYF3h7oyMjCqPT9l1wI3nyliffPIJunTpgg4dOsDCwgJjx47FpEmTAED3PDx16hSeeuopTJs2Dfv378fWrVthYWGBu+66y6i6t5X9vV24cEHvdfnJJ580KuZPP/0U//nPf/DTTz/B19fXqL5rcy02f/58HD58GOvXrwcA3HfffRX292a+Drv556m8BdnY2OjNMtWhQwds2bIFc+fOxbBhwyrdTkT0/sizs7Nx6NAhqNVqxMXFGdW3Wq1GdHQ0FEWBp6cnHBwcqt2m/BOrfByGDBs2DBcuXMDGjRuxbds2PPTQQ4iIiMCWLVsqLU5pqE1j+jJG+eLHiqIYXFY+QVbb7crU5A3Zzs6uwrIJEybgwoUL+OCDDxAYGAgbGxvcd999FYpgli/8XVVMZXx8fHDq1Cn89ddf2Lp1K+bMmYNXXnkF+/btMzh7TU1jr2yfb1xe1+MLQPeGe+LECaMSIbU5VtVtY+x5utGqVavwzDPP4L333kNkZCQcHR2xatWqCoW0DfUNQNe/qZ4j9dXXvn37MHbsWLz66qv48MMP4eLigr1792L8+PFVHh8PDw+kpqbWcA9KDRgwALGxsUhLS4NWq4Wbmxu6deuGoKAgAKXFzF9++WV88803GDNmDAAgIiICFy9exNtvv40nnngCANC8efMKCbKyC8DKimw++eSTuPfee3W/l10gVyYjIwO33XYbioqKsHnzZr3isc2bNwcAXL16Ve9C89q1a5X2XxWVSqV772nXrh3S0tLw+uuvV5iOOTU1Ve9DFFFTVNXrftn/n3zyCQYMGFBh2+o+uMydOxd33nkn7OzsdLNUVsWY99Py7O3tceDAAezatQubN2/GZ599hpdffhlbtmxB586dje7LlO8xN76/l7VpaFlV12I12e5G5fehsn0qfz1jqvfqyixduhTPP/88/vzzT2zatAmzZs3CokWLajxZilqtNuoLG0Pns67H18PDAy4uLkYnKQ0dq+q+SFSpVHqfH8rHZOx5ulFubi6GDh2KPn364Msvv9S9r4aHh9foOtuUz5H66mvUqFFwd3fH4sWL4efnB0tLS/Tp06fa6zAASEtLg5ubW436c3V1xY8//ojCwkIkJSXB29tbN1lLYGAggNIv6yIiIvRmdvv+++/h7++Pv/76C4MHD0bz5s2xefPmCu1XdR3k7e2N6Oho3e/GFF7/v//7P8yePRu//fZbhYLhhq4Fy36vzbVY2RftYWFhaN++Pby9vbFp0yYMHz5ct87NfB3GkU83CY1Go5uGNDw8HEDpVJY32rFjh+4xAHjqqaegVquxdetWrFixAv/73/+M6is4OBhBQUHVJp6Cg4NhZWWF7du36y2PiorSi8PS0tJghtrV1RX3338/Pv/8c/zxxx/Yvn17pd/ShYeHIyYmRu9buWvXriEuLk6vr5uNk5MTvL29sWfPHr3le/fuNWr7qKgoPP3007jjjjsQERGB5s2b4+zZszWOo7JzZGVlheHDh+ODDz7AsWPHkJubizVr1tS4/fLCw8OxZ88evTe1I0eOICMjw+Tnc+jQofD09MTcuXMNPp6WlmbS/gypzXmKiopCx44dMX36dHTu3BkhISFISEiocd/h4eF6s3IAqPC7qdSmr507d8Ld3R3vvPMOunfvjtDQUL1pZSvTqVOnOn/b7uLiAjc3N8TGxuLgwYO6RFNRURGKiooqzISkVqv1LoB79+6tG5FaZsOGDQgICKj0w6WrqyuCg4N1/2xsbCqNLzk5WffBddOmTXB2dtZ7vEWLFvD29jYYQ21Ht91Iq9WioKBAb1l8fDwKCgrQpUuXOrdPdKtq1qwZ/Pz8EBsbq/d8L/tXXRKgWbNmCA4ORvPmzav9IBkeHl7hOmz79u1QFAVt2rQBUPl7vFqtRr9+/fD222/j4MGDaN68Ob7//nuD/Rh7zXczatOmDfbv3693jMpfl1XGVO/VZQkFQ+epbdu2mD59OtavX49JkyYZPQK3KsZ+ljAFlUqFBx54AN999x3OnTtX4XERqfdZtWtznk6ePInr169j7ty5GDBgAFq3bo20tLQaj6gPDw/H9evXER8fr1uWnJxs9MCA+u4rJSUFJ06cwIwZMzBs2DC0adMG1tbWVY7KA4COHTtCUZQ6XYtZWlrC19cXKpUKP/zwA/r166dLquTk5Bi8DgP+/fK+d+/eOHfunN7+njx5EhcvXqz0Okij0ei9Hhu6u+dGb7zxBt566y2sW7fO4Ex1vXv3xqZNm/SSrxs2bICtrS06duxoxFGoXFmb5a/Fjh07dtNeh3HkUyN04+0mOTk52LhxIzZu3Ii33noLABAUFISxY8fi6aefxueff46AgAAsWbIEx48f1100rFixAqtWrcLevXvRoUMH/Oc//8HkyZPRvXt3XUa5rmxtbfHcc89h1qxZ8PDwQIcOHbBq1Sr8+uuv2LRpk269wMBA/PXXXxgxYgQsLS3h7u6OmTNnonPnzggPD4dKpcJ3330He3v7CreVlXnggQfw9ttvY9y4cfjwww8hInjxxRfh4+ODcePGmWR/zOWFF17A7Nmz0apVK3Tr1g1//PEH/vzzT6O+uQgLC8N3332HPn36oKSkBG+88YZRQ1HLKztHZ86cgZOTE5ycnPDNN99Aq9WiW7ducHZ2xpYtW5CVlaW7mK2LZ599Fp988gkmTJiA1157Denp6Xj66afRp08f9O3bt87t38jW1hbLly/HXXfdhcGDB+OFF15AWFiY7rm1dOnSOg1jN0ZtzlNYWBiWLVuGX3/9FW3btsXvv/+O1atX17jvadOmoWvXrpg5cybGjx+PmJgYzJs3r7a7YvK+wsLCcP36dSxbtgwDBgzAzp078emnn1bb14gRI/DCCy/g4sWLeiPxLly4gNTUVN1tLSdOnEBycjL8/f3h6uoKAFi+fDlCQkLQvHlzHD58GFOnTkW/fv3wyCOPACi9/WzAgAGYOXMmnJ2dERISgoMHD2LevHl44IEH9Pa3V69emDlzJh5++GH8/fffWLhwIebPn1/jY1deYmIiBg0aBBsbG3z99dfIzc3VfQHh6uoKS0tLKIqCl156Ca+99hpat26NLl26YPny5Thy5IjedMWpqam4cOGC3jGKjo6Gq6ur7jV39uzZ6Nu3L1q2bImioiJERUXh/fffx8SJE/Xi2rZtGwICAm76D5tE5WVnZ+t9Gw6U3trQqlWrWrU3d+5cTJo0Cc7Ozhg9ejQsLCxw8uRJrF+/Hp9//rkJIi710ksvoVOnTpg+fTqeeOIJJCQkYMqUKXjwwQd1z+/AwECsWrUKMTExaNasGRwcHLBhwwacPXtW92Hv4MGDuHjxYqXv8cZe892Mnn76acyfP193i8/Vq1d1I2KquxYz1Xt12bX5b7/9hj59+sDGxgZXr17F0qVLcfvtt8PPzw9XrlzBjh070KlTp5rvZDnGfJYwpblz5yIqKgo9evTAnDlz0L17dzg6OiI6Ohrz58/H9OnTMXr0aJP3W6Y25ykgIABWVlZYuHAhXnjhBSQkJGDGjBk1Hlk0aNAgtG/fHg899BAWLlwIS0tLvPLKK9BoTP8xvDZ9ubi4wMPDA0uXLkVQUBBSUlLw8ssvV/nlGADdqPHt27fr3b6ZnZ2tuwYr+0wbHR0Ne3t73ejq/fv3IyEhAZ06dUJSUhLmzZuH6Oho7Ny5U9fO6NGjMWHCBMyfPx933HEH0tLS8Nprr8Hb2xvdu3cHAAwePBidOnXS7a+I4JlnnkGPHj0QGRlZ6+NYZurUqfj888/xww8/ICwsTPf53MbGRjcS/amnnsKiRYvw+OOPY/r06Thz5gxmzZqFKVOm6I2ULHt/SU1N1Xu/KbuN8ueff0ZOTg46deoEBwcHxMfHY/bs2fD19cXAgQN17WRlZeHgwYOVfqne6DVgfSkyQvlp721sbKRNmzby4Ycf6hXfzcjI0E2PamlpqTc9anx8vDg4OMiCBQt062u1Whk+fLh069at0gLShma7K698gbrqpt0VKS3a2KpVK7G0tNQVU3z77bclPDxc7OzsdFN6Vjcb2alTp2TEiBG6Yuy33XZbhRkWUMuC4+WLZM+ZM0dvuk+R0oKHPj4+ut8NFYwzVNRz8uTJejMzlN+upKREZsyYIW5ubmJnZyfjxo2TuXPnir29fZUxiogcPXpUevbsKdbW1hIQECCLFy+uUDzcULG/8nGeOXNG+vbtK3Z2drrilj///LP07NlTnJ2dxcbGRsLDw+WLL76oEMONKis4bqio4Z49e6Rv375ibW0tTk5Ocv/99+sK21fWljHnpTJHjx6VBx54QJo3by4WFhbi5+cnd911l95MYYb+fsofz+qmqzUUpzHnqbzCwkJ54oknxMXFRTczYFnx7jKGjtGOHTsEgJw7d0637IcffpCWLVuKpaWldOvWTTd1a00LjpuiL0PFUF9//XXx9PQUW1tbGTFihHz//fcV2jWkf//+MnfuXL1l5V9Dy/7dOOnBzJkzdX8H/v7+8vLLL0tOTo5eO0lJSfL444+Lr6+vWFlZScuWLWXGjBkVCmv+/vvv0q5dO7G0tBR/f3+jCqoaw9CU2WX/yhefff/998XPz08sLS2lffv2FabhrqytG//+pk6dKkFBQWJtbS3Ozs7SqVMnWbRokV4BXpHSWV1unOWF6FZQ2etGWFiYiNTudV+ktCBvjx49xMbGRhwcHKR9+/aVFvIvU911jKFrtT/++EM6deoklpaW4u7uLk8++aTeFN8pKSkyYsQIcXR01L0ebt++XQYMGCDu7u5iZWUlwcHB8u6774pWq620b2Ou+epScPzG94WLFy9WeL0rm9V406ZNImJ4YglD70tlxafLrhkNbbdp0yYJDw8XS0tLiYiI0BUcLytcXtlkGaZ8r37++efF09NTFEWR8ePHy5UrV+Suu+4SHx8fsbS0lObNm8tjjz1W5czVlRUcN3R9X9VnicraMua8VCY7O1vefPNNCQ8P173XdOvWTRYtWqR7bzX091O+GLuhguPl9698nMacJ0NWrVolwcHBYmVlJR06dJBt27bpTaRU2d9FUFCQzJ49W/f7uXPnZMiQIWJlZSU+Pj7y8ccfV1v025jnSW37Kv/7tm3bpF27dmJlZSWhoaHy008/VWjXkOXLl0tISIjesrLnV/l/N37u2LFjh+7vwNHRUUaNGiVHjx6t0P5nn30mERERYmtrKx4eHnL77bdXKGx/5coVueeee8Te3l4cHBzk3nvv1fs8UReVXYeVv37fs2eP9OzZU6ysrKRZs2YyY8aMCtdPlbVVZu3atdK9e3dxcnLSXXc++eSTcuHCBb12vvzyS917081IEanh2EEiqnePPvoojhw5goMHD5o7FKJGa8eOHbjvvvsQHx8PW1tbc4dzy/v7779xxx13IC4uzqgaCUREN6uoqChERkbi6NGjiIiIMHc4RI1SUVER2rVrh3fffbdeR65RKa1Wi/bt2+P111+/ae/84W13RGZ25coV/PLLLxgwYADUajXWrl2Lb775BosWLTJ3aESNWt++fTF79mycO3eOt4E1gGvXrmHFihVMPBHRLWfJkiW64r4nTpzAtGnT0L17dyaeiKpgYWGBr7/+Wq/mEtWfy5cvY8KECTdt4gkAOPKJyMyuXbuGcePG4ejRo8jPz0dwcDCmTJmCxx9/3NyhEREREd3yZsyYge+//143S9aQIUPw/vvv13gWLyIiqhyTT0REREREREREVG9U1a9CRERERERERERUO0w+ERERERERERFRvWHyiYiIiIiIiIiI6k2TnO3uypUrFZa5u7sjOTnZDNGQKfD83dx4/m5+PIc3t1vt/Hl7e5s7BKqEoWswU7jV/oZNgcfEMB4Xw3hcDONxMYzHxTAel6qvwTjyiYiIiIiIiIiI6g2TT0REREREREREVG+YfCIiIiIiIiIionrTJGs+ERERERERERHVhoggPz8fWq0WiqIAAK5du4aCggIzR1b/RAQqlQrW1ta6fTcGk09EREREREREREbKz8+HhYUFNJp/UyoajQZqtdqMUTWc4uJi5Ofnw8bGxuhteNsdEREREREREZGRtFqtXuKpqdFoNNBqtTXahsknIiIiIiIiIiIj1eR2s1tVTY8Bk09ERERERERERDeRkJCQGq2/e/duPPLIIwCAP//8E4sWLaqPsCrVdMeJERERERERERE1MUOHDsXQoUMbtE8mn4iIiIiIiIiIbkK7d+/GRx99BBcXF8TGxqJdu3ZYuHAhFEXBX3/9hdmzZ8PV1RURERG6bVauXImjR49i7ty5+PPPP7FgwQIUFhbCxcUFixYtgoeHh8njZPKJiIiIiIiIiKgWtP9bCrl4DlpFgYiYpE3FLxCq+x43ev3jx49j69at8PLywp133on9+/ejXbt2eOmll/Djjz8iMDAQTz75pMFtu3XrhrVr10JRFHz//ff49NNPMXv2bJPsx42YfCIiIiIiIiKiaqXmFUOdW2TuMKicDh06wNvbGwAQHh6OixcvwtbWFv7+/mjZsiUA4O6778aKFSsqbJuYmIinnnoKSUlJKCwshL+/f73EyOQTEREREREREVXrra0X0cwpGa/18TJ3KI1G2QgljUaD4uJis8RgaWmp+1mtVuviMGZGulmzZuGJJ57A0KFDdbfw1QfOdkdEREREREREVbqUUYCE9AIcvZKFEq1pbi+j+hMcHIwLFy4gISEBALBmzRqD62VmZsLLqzSZuGrVqnqLh8knIiIiIiIiIqrS7otZAIC8ohJcyiw0czRUHWtra3zwwQd45JFHMHr0aPj6+hpc74UXXsDkyZNx1113wdXVtd7iUcRUFbFuIleuXKmwzN3dHcnJyWaIhkyB5+/mxvN38+M5vLndauevrOYBNT6GrsFM4Vb7GzYFHhPDeFwM43ExjMdF37R155CRX4KUvGJM6eGFwUHO5g7JbHJzc2Fra6u3zJy33ZmDoWNQ1TUYRz4RERERERERUaWuZhXibFoBbm/lAgcrNeKS880dEt1kWHCciIiIiIiIiCq1559b7nr5OyAmpQhxKXlmjohuNhz5RERERERERESV2nMxC0GuVmhmb4k2Xg44n16A/GKtucOimwiTT0RERERERERkUHJuEWKT89HTzwEAEN7MAVoBzqTy1jsyHpNPRERERERERGTQ3n9uuevpX5p8auNlDwCI5613VANMPhERERERERGRQXsuZMHfyRK+jlYAABdbS3jaWbDoONUIk09EREREREREVEF6fjFOXM/TjXoqE+pujbhkjnwyt6SkJDz11FPo1asX+vfvj4cffhgrVqzAI488YnD9F198EXFxcQ0cZSnOdkdEREREREREFey7mA2tAL38yiWf3Gyw83wW0vKK4WLDtII5iAgmTZqEsWPHYsmSJQCA48ePY9OmTZVu83//938NFV4FHPlERERERERERBXsvpiF5g4WCHC20lse6mYNAIhj3Sez2bVrFywsLPRGObVt2xbdu3dHbm4uHn/8cfTr1w/PPvssRAQAcM899+DIkSMAgJCQELz33nsYPHgwRo0ahevXrwMA/vzzT4waNQpDhw7FuHHjdMvriilKIiIiIiIiItKTVVCCY1dzcGdrVyiKovdYS1drqBQgLjkf3X0dKmmhafjiwDWcS8uHoii6JE9dBbpY47EuzapcJzY2FhEREQYfO378OLZu3QovLy/ceeed2L9/P7p166a3Tm5uLjp16oQZM2bgnXfewXfffYepU6eiW7duWLt2LRRFwffff49PP/0Us2fPrvM+MflERERERERERHr+vpSFEgF6+VdMLllpVGjhbMUZ7xqpDh06wNvbGwAQHh6OixcvVkg+WVpaYsiQIQCAiIgI7NixAwCQmJiIp556CklJSSgsLIS/v79JYmLyiYiIiIiIiIj07LmYBQ9bDYJdrQ0+Hupug6iETGhFoCo3MqopKRuhpNFoUFxc3GD9hoaG4o8//jD4mKWlpe5ntVptMC6NRqMb0XbjOrNmzcITTzyBoUOHYvfu3fjoo49MEi+TT0RERES3GK1WixkzZsDV1RUzZsxAdnY25s+fj+vXr8PDwwPTpk2Dvb09AOCXX37B1q1boVKpMHHiRHTo0AEAcPbsWSxevBiFhYXo2LEjJk6cCEVRUFRUhEWLFuHs2bNwcHDA1KlT4enpaca9JSIiU8stKsHhxFyMCHWucMtdmRA3a2yIT8flzEL4OVkZXIfqT58+ffD+++/ju+++w4MPPggAiI6Oxt69e+vUbmZmJry8vAAAq1atqnOcZVhwnIiIiOgWs27dOvj4+Oh+X7NmDSIiIrBgwQJERERgzZo1AIBLly7pvtWcOXMmli1bBq1WCwBYunQpJk+ejAULFuDq1auIjo4GAGzduhV2dnZYuHAhbrvtNnz33XcNvXtERFTPDlzOQbFWKsxyd6NQdxsAQHxKfkOFRTdQFAVffPEFoqKi0KtXLwwYMADz5s1Ds2ZV14qqzgsvvIDJkyfjrrvugqurq4mi5cgnIiIioltKSkoKDh06hDFjxuD3338HAOzfvx9vvvkmACAyMhJvvvkmHnroIezfvx+9evWChYUFPD094eXlhdOnT8PDwwN5eXkIDQ0FAPTr1w/79+9Hx44dceDAAYwdOxYA0KNHD3z55ZcQkUq/GSciopvP7gtZcLFWo5WHTaXr+DpawkajQlxyHga2dGrA6KiMl5cXPv/88wrLy0ZCAcDcuXN1P//000+6n+Pj43U/jxo1CqNGjQIADBs2DMOGDTN5rEw+EREREd1Cli9fjoceegh5ef8Wgc3IyICLiwsAwMXFBZmZmQCA1NRUhISE6NZzdXVFamoq1Go13NzcdMvd3NyQmpqq26bsMbVaDVtbW2RlZcHR0bFCLJs3b8bmzZsBAO+99x7c3d1NvLelNBpNvbV9s+IxMYzHxTAeF8Oa6nHJLyrB4cQ4jGjtCU8PjwqP33hc2nhdxdmMoiZ3nK5duwaNpmI6xdCyW5WVlVWNznvTOTJEREREt7iDBw/CyckJLVu2RExMTLXrVzYldFVTRRt6rLJRT4MHD8bgwYN1vycnJ1cbU224u7vXW9s3Kx4Tw3hcDONxMaypHpc9F7KQX6xFR08Lg/t/43EJdNLglxMZuHw1CVaaplPVp6CgAGq1Wm9ZQxccN7eCgoIKfx9lM+wZwuQTERER0S0iNjYWBw4cwOHDh1FYWIi8vDwsWLAATk5OSEtLg4uLC9LS0nSjlNzc3JCSkqLbPjU1Fa6urhWWp6Sk6Oo+lD3m5uaGkpIS5Obm6oqXExHRzW/3xSw4WKnR1tO22nVD3axRIsDZtHy09qh+fWq6mk5qkoiIiOgW98ADD+Czzz7D4sWLMXXqVLRt2xbPPfccunTpgu3btwMAtm/fjq5duwIAunTpgt27d6OoqAhJSUlITExEcHAwXFxcYGNjg7i4OIgIoqKi0KVLFwBA586dsW3bNgDA3r17ER4eznpPRES3iKISLQ5czkZ3X3uoVdW/tof8U3Q8LrlpFR2vaoRwU1HTY8CRT0RERES3uNGjR2P+/PnYunUr3N3dMX36dACAn58fevbsienTp0OlUmHSpElQqUq/m3zsscfw6aeforCwEB06dEDHjh0BAAMHDsSiRYswZcoU2NvbY+rUqebaLSIiMrEjV3ORW6RFzypmubuRq40G7rYaxKfkVb/yLUSlUqG4uLhJ1Xi6UXFxse56wVhN80gRERER3eLCw8MRHh4OAHBwcMAbb7xhcL0xY8ZgzJgxFZYHBQVh3rx5FZZbWlrqkldERHRr2X0hC3YWKrT3Mv4WulB3G8SlNK2RT9bW1sjPz0dBQYFu9K+VlRUKCgrMHFn9ExGoVCpYW1vXaDsmn4iIiIiIiIiauGKt4O9LWejqYw8LtfGjWkLcrLH7QhYy8ovhZN00UgyKosDGxkZvWVMtUG8s1nwiIiIiIiIiauKOX8tFVqEWPf2Nu+WuTJhbaRImvomNfqKaYfKJiIiIiIiIqInbczEL1hoFHZvb1Wi7lq7WUClAbHLTqvtENcPkExEREREREVETVqIV7L2Yhc7e9rDS1CxNYGOhgr+TVZOr+0Q1w+QTERERERERURN2KjkP6fklRs9yV16ouzXiU/IgIiaOjG4VTD4RERERERERNWF7LmTBQqWgs0/NbrkrE+Jmg5xCLa5kFZk4MrpVMPlERERERERE1ESJCPZczEJHbzvYWqhr1UaomzUAID6FdZ/IMCafiIiIiIiIiJqo+JR8JOcW1/qWOwDwc7KCtUZBHIuOUyWYfCIiIiIiIiJqovZczIJaAbr52Ne6DbVKQbCrNYuOU6WYfCIiIiIiIiJqgkQEuy9koZ2XHeytanfLXZlQdxucS8tHUYnWRNHRrYTJJyIiIiIiIqImKCG9AFezi9DLv/a33JUJdbNBsRY4m1ZggsjoVsPkExEREREREVETtPtCFlQK0N239rfclQlxLy06zrpPZAiTT0RERERERERN0J6LWWjjaQsna02d23K3tYCrjQbxrPtEBjD5RERERERERNTEXMoowMWMQvSqwyx35YW4WSMuhSOfqCImn4iIiIiIiIiamN0XswAAPfzqfstdmVB3GyRmFSGzoMRkbdKtgcknIiIiIiIioiZmz4UshLnbwM3WwmRthrqV1n06zdFPVA6TT0RERERERERNyNWsQpxNK0Avf9ONegKAYDdrKADikln3ifTVvaoYERERERERETV6IoLsQi22nM0AAPQ0Yb0nALC1UMPPyZJ1n6gCJp+IiIiIiIiIbmJ5RVqk5xcjLa8YafnFSM8rQVpeMdLzi/9ZXoK0/GJk5BejWFu6TbCrNZrZW5o8llB3G+y7lA0RgaIoJm+fbk5MPhERERERERHVUWxyHn45kQoXGzUmd/VqkD6/O3Idv55MRUGJVHhMpQBO1hq4WKvhbK2Bv7MVXKzVcLHRwNlag9aeNvUSU4ibNTafycDV7CI0dzB9cotuTkw+EREREREREdWCiOBwYg5+jknB8aQ8qBRAK8CIUBf4O1nVa98puUX4OSYF4Z626NjcDs42GrjY/JNsstHAwVINtcq0I48kMx1ay6oLlIe6lSa14pLzmHwiHSafiIiIiIiIiGqgRCvYdSELq0+k4FxaAdxsNHi0kyd6+NnjmbXn8EdsGp7qVr+jnzbEp0MrwDPdveDVQEke7aJ3kJqbDXnlAygOjgbXCXC2gqVaQXxKPiIDnRokLmr8mHwiIiIiIiIiMkJBsRZbz2ZgzclUXM0ugq+jJZ7r4YV+LZxgoS4dZRQZ6Ii/zmbg4fYesLdS10schSVabIxPR1df+wZLPEluDpAQjxIR4PP3oZr6FhRNxZSCWqUg2NWaRcdJj8rcARARERERERE1ZtmFJfjpeAoe//UMPtt/DY5WarzazwcLRwViUJCzLvEEAKPCXFBQIvjzTHq9xbMjIRMZBSUYFeZSb31UcOYkIAKbwbcDsccgP35R6aqh7jY4m1qAIgO1qKhp4sgnIiIiIiIiIgNScouw9lQaNsSnI69Yi07N7XB3uBvCPW0qnckt0MUabT1tsC42DXe2cjV93SURrI1NQ4CTFdo1szVp21X2GxcDqDVweGwa8lUayJ+/QOvTAqrI4RXWDXWzxhqtICE9HyFu9VPYnG4uTD4RERERERER3SAltwjLtsRj3YkkaEXQ298BY9q4oaWrtVHbj2rliveiLuPvS9no6e9g0thOJOXhXFoBnunuVWkCrD5IfAzQIhiKlTWUux+BXDkP+eFzSHNfKKFt9dYN0RUdZ/KJSvG2OyIiIiIiIqIbLNh7FRtOJmFIkBM+vb0lXuzjY3TiCQC6+djD084Ca2NTTR7b2tg0OFiqENnCcMHv+iAFBUBCPJTQcACAolJD9fiLgIcXtEveg6Qk6a3vYaeBs7Ua8az7RP9g8omIiIiIiIjoH6l5xTh6NQf3d/bFk9280LwWBb3VKgW3hTkjJikPZ1PzTRZbUnYR9l3KwpBgZ1hpGvDj/NlTQEkJlJB/RzgptvZQPfM6UFIC7aJ3IPn/JpoURUGImw3iUky373RzY/KJiIiIiIiI6B87EjKhFWBYmEed2hnc0hlWagW/x6aZKDJgfXxpWyNDG7DQOP6p96SogODWessVLx+onngJuHwB2q8+gWi1usdC3a1xObMQ2QUlDRorNU5MPhERERERERH9IyohE0GuVghwrVsxb3srNQa2dEJUQiYy8ovrHFd+sRZ/nk5HDz8HeNhZ1Lm9mpD4GMC/JRSbisdEadsJyj0TgEO7Ib+v1C0P/afW02kTjvyimxcLjjdx4eFeSE+/VXKQ3uYOgOqE5+/mV7dz6OysRUzMVRPFQkRERFRzlzMLcTo1HxM71W3UU5lRYS5YH5+OjfHpuDfCvU5tbTuXgexCLW4Pa+BRT0VFwNlYKJEjKl1HGXIncCkBsvYHiG8AlE69EOxWWiMrLjkPHZrbNVS41Egx+dTEpaercPnyFXOHUWfu7u5ITk42dxhUSz4+3rfE32FTZornoI8PE5BERERkXtsTMqAA6BtgmmLevk5W6NjcDuvi03FXGzdYqGs3O52I4PfYNLR0sUJrjwaePe58PFBUqCs2boiiKMDDT0OuXoJ22XyoPJrD3i8Qvo6WiGPRcQJvuyMiIiIiIiKCiGD7uUxENLOFm63pbmsbFeaCtLxi7LmYVes2jlzNxcWMQtzeyrU00dOAJC6m9IfgNlWup1hYQvX0a4CtHbSL50KyMhDqbo24lHyISANESo0Zk09ERERERETU5MWn5ONqdhH6tTDNqKcynbzt4O1gibWnUmvdxu+xqXCyVqNvgIMJIzOOxMcA3v5QHKo/LoqzK1RPzwQy06H97D0EO1siI78ESTlFDRApNWZMPhEREREREVGTtz0hExqVgp7+pk3wqBQFo8JcEJeSj9jkmt+ClphViAOXczAs2BkW6ob9CC8lJcDpk1XecleeEhgCZfwUIC4GoQfXAwDikll0vKlj8omIiIiIiIiatBKtYMf5THT1sYO9pdrk7Q9o6QhbCxV+P5VW423/iE2DSgFGhDZsoXEAwKVzQH4eEGJ88gkAVN0joQy/G/5RP8MCWsTfwnWfRKtFyQczkPPLd+YOpVpnUvORllf3mRdrg8knE2KxXCIiIuPwPZOIiBqTo9dykZFfgsgWTvXSvq2FGoOCnLDrQiZSco2/BS23qASbz2Sgd4AjXG0afr4wiT0OAFBqmHwCAOWuh6Bp2wktMy8i7mKKqUNrPE4dBeJPIG/rH+aOpFKpecVYsCcRL6xPwKoY85wLJp+IiIiIiIioSdt+LgN2Fip09rGrtz5GhbpAK8CG+HSjt9l6NgN5xVrcHmaGUU/4p96ThxcUF7cab6uo1FA9/iJCilNxJqsERdcS6yFC89Nu3wAAKLmUALl+1czR6Css0eKn4yl46rez2J6QgdGtXfFgO3ezxMLkExERERERETVZBcVa7LmYjZ7+DrCsx5pKXg6W6Oprj43x6Sgs0Va7vlYEf8SmIczdGqHuNvUWV2VEqwXiT0AJbVvrNhQbW4T264VClQUSVq00YXSNg6SnAtF7gfbdSn8/fsjMEZUSEew6n4ln1p7Dt0euo0NzWywa1RITOnnCrh5uKzVGg43bS09Px/Lly3HmzBloNBp4enqia9euOHDgAGbMmFFh/c8++wyjRo2Cr69vQ4VIRERERERETczfl7KRX6xFpIlnuTPk9jAX/H0pG1EJmRgc5Fzluoeu5OBKVhFeaOdR73EZlHgRyMkCalBs3JCwls2B42cRfz0XwSnXobiZaX/qgezaDGi1UI19FMrVSyg5fhAYMNKsMZ1Oyceyg9dw4noeAl2sMKeHH9p51d+IPmM1SPJJRPDhhx8iMjISU6dOBQAkJCTgwIEDlW7z5JNPNkRoRERERERE1IRFnc+Eq40G4Z629d5XRDNbBDhZ4ffYNAxq6QRFUSpdd21sGlxtNOhl4tn3jCVxMQBqV+/pRs3sLeBooSDO0R/D92yFMmqcKcIzO9GWQKI2Aq3bQ2nmDctOPZG3ZS2kqBCKhWWDx5OaV4wV0dex9WwGHK3UeKa7Fwa1dIJaVfnfWENqkORTTEwMNBoNhg4dqlvWokUL5OTk4Pjx45g3bx4uXryIli1bYsqUKVAUBW+++SYefvhhBAUF4eGHH8bIkSNx6NAhWFpa4qWXXoKzszMOHDiA1atXo7i4GA4ODpgyZQqcnZ0bYpeIiIiIiIjoJpdVUIJDV7JxW6hLg3xIVxQFo1q5YPG+qziRlIfwZoYTXhczChCdmIMH27tDY67kQXwM4OIOuDerUzOKoiCiuT0O5YejePdnsBg5ForqFqgAdPwQkHodqnsfBQBYde6JvPU/A3ExQHjHBgujoFiL306l4qeYFBRrgbvauOKecDez3V5XmQZJPl24cAGBgYEGHzt37hw++ugjuLi4YNasWYiNjUWrVq301ikoKEBISAjuv/9+rFixAlu2bMHdd9+NVq1aYe7cuVAUBVu2bMFvv/2GRx55pEIfmzdvxubNmwEA7733HtzdKxbY0mg0BpfX1M04e48p9tvcTHX+yHx4/m5uTfk19NbR8Meez3siIjK3XRcyUawFIgPrZ5Y7QyJbOOKbw0lYG5taafLp99g0WKgUDAt2brC4biQikLgYKGERVY7OMlZvfwfsupCFk0U2aBd/AgirfR2pxkIbtRFwcgHadwcAWLbtBFhYQo4dgNIAyScRwa4LWfj6cBKScorR088e4zt6orlD5aOuRMQk57M2Gn6uxnKCg4Ph5lZaOb9FixZISkqqkHzSaDTo3LkzAKBly5Y4evQoACA1NRUff/wx0tLSUFxcDE9PT4N9DB48GIMHD9b9npycXGEdd3d3g8trxhuXL1+pYxsNy8fH2wT7bX6mOX9kPrfG32FT1lRfQ28V5ngNrc/3H29vJjGJiMg4UQmZ8HW0REsXqwbr00qjwtBgZ/xyMhXXsgvRzF4/WZBdUIK/zmagXwtHOFmb6SP79UQgI7XO9Z7KdPaxh6VawR6vjojYtRnKTZ58kpTrwNEDUEbcA0VTeo4UK2sgrC3k2EHgvsfrtf+zqflYeuDGuk7Nq63rJMVF0H72PlS9B0Pp2KNe4zOkQca6+fn54dy5cwYfs7Cw+DcYlQpabcWq/2q1WpedU6lUKCkpAQB8+eWXGD58OObNm4cnnngCRUVF9RA9ERERERER3Wqu5xQhJikPkS0cG3w0yIhQFwDA+rj0Co9tOpOOghLBqDCXBo3pRrp6TyZKPllrVOjsbY+9zTqg5OBuSH6uSdo1F9n5JwCB0m+o3nKlbRcg6Qokqf6+UM0uKMGrmy7gclYhnunuhXnDW1SfeNJqIcsXAEf+huRm11tsVWmQ5FPbtm1RVFSku/UNAE6fPo0TJ07Uqd3c3Fy4uroCALZv316ntoiIiIiIiKjpiErIBAD0bYBZ7srzsLNATz8H/HkmHfnF/w7AKNEK1sWlIdzTBi1drRs8Lp2444C9I+Blutnne/k7IE2xwikbL8iBXSZrt6FJcTFkxyagbWcobvp3XykRnUrXOXao3vrf9M/fzFsD/TA02NmoWmXy89eQfduhjH4Iqt6Dq12/PjRI8klRFLz44os4evQopkyZgunTp2PVqlVwcalbJnfs2LH46KOP8MYbb8DBwTwzABAREREREdHNZ3tCJsLcrauskVOfbg9zQU6hFn+dzdAt+/tyNpJyinF7mKtZYiojcTFAaLhJR4R18bGDhUrBnoCekF1bTNZugzu6H8hIhSpyeIWHFE9vwNMbcvxAvXRdmpxMR7inDQJdjEtOav9cA/nzFygDRkIZObZe4jJGg91A6urqiunTp1dYfmMtpkmTJul+fvPNN3U/f/vtt7qfe/TogR49Su9P7Nq1K7p27VoP0RIREREREdGtKiEtH+fTC/BEl7rN5FYXrTxsEORqjd9j0zA8xBmKouD3U6nwsNWgm6+92eKSlOtAShKUwXeYtF1bCzU6edthr7TFxKP/g+raFSjNbr46jdrtG0pnAWzb2eDjSkRnSNRGSEEBFCvT1hI7cDkbSTlFmNjJw6j1tfu2Q1Z9CXTqBeW+x81WbBxooJFPRERERERERI1FVEImVArQO8B8d9AoioJRYS64lFmII1dzcS4tH8eT8jAyzMWoW6nqi8SX1XsyfVHwXv4OSBELxDkFQHbffKOf5PpV4MRhKH2HQlGrDa6jRHQBigqBuGMm7//32DS422rQ3bf6v1s5EQ356hMgNByqx6ZDURmOt6Ew+WRCnKWJiIjIOHzPJCIic9GKICohEx287OBsrtnk/tE3wAHO1mqsPZWK32PTYKlWMCTI2awxIT4GsLEDfANM3nRXH3toVAr2tBoE2b0Voi0xeR/1SaI2AioVlD5DKl8pNBywtCqd9c6ELqQX4Oi1XIwIrT45KRfOQPvpu4CXD1TPzIRiYZ5bS2/E5BMRERERERE1Gaeu5+F6bjEiAxu+0Hh5FmoVhoc448CVHGw7l4kBgU5wsDLvCBWJiwGCW9fLSBk7SzU6NrfDHodgSHoKcOKIyfuoL1JcBNm1GWjXDYqLW6XrKRaWQKt2kOMHISIm6//32DRYqBQMDXKqOs7rV6H95C3Azh6q59+EYmu+WzhvxOQTERERERERNRnbEzJhpVaMunWpIQwPcYFGBRRrBaNa1W1SrrqSzHTg6iUooeH11kcvfwckF6sR7xl2U916J4f3AlkZBguNl6dEdAauXwWuXTZJ39kFJdh2LgORgY5wrGK0nmSmQ/vxbKCkBKqpb1aZJGtoTD4RERERERFRk1BUIth1PhPdfR1gY2Hej8MiAjkbC6fflmNU4l4MyDsLfyfTFqiusfgTAAAlpP6ST9187KFRAXvaDIUc3gvJya63vkxJtm8A3JsBbTpUu67yTzFyOW6aW+82n01HQYngttDKk5OSnwftwjlAegpUU2ZBae5nkr5Nxbw3uBIRERERERE1kOjEHGQVatGvhXluuRMR4FICZH8UZP9OIPkaoNHgkWY+wMnzkGGtofgFmiU2AJC444ClFRAQVG992Fup0d7LDntS/PBIcRHk7ygoA0bWW3+mIImXgNhjUMY8AkVVfdJScW8GNPcrrfs0+M469V2iFayLS0cbDxu0dLU2HF9xMbSfvw+cPwPVM69BCWpVpz7rA5NPBB+fm296S8Nulf1omm6dv8OmrG7n0NlZa6I4iIiIiAzbnpABBys1OnrbNWi/cvUSZP9OyN9RwNVLgEoFtOkA5fb7oHToAYgW2pcmQrath/Lw0w0am16ccTFAUCsoGot67aeXvwMWXsnB2aCuCNq1GWjsyaeojYBaA6X3IKO3USI6Q7b+DsnPg2JtU+u+D1zJxrXsIozv4GE4NhHINwuB44egPPIslPbdat1XfWLyqYm7VWYbcnd3R3JysrnDoFri+bv58RwSERFRY5dbVIJ9l7IxqKUTNNXMFmYKkpIE2b8Dsn8HcOEsoChASDiUwXdA6dQTioN+4WilSx/Ivu2QsROgWNvWe3wV4s3JBi4nQLn9/nrvq7uvAz5VrmJ32EAErXsfcvk8FB/Tz65nClJYANm9BUrHHlAcja/JpbTtDPlzDRB7DKhDQuiP2DS42WrQ3c9wjTL55RvInr+g3PkAVH2H1rqf+sbkExEREREREd3y9l3MRmGJ1Ostd5KTDdn7V2nC6cyp0oWBoVDGTYLSuU/Vs6T1HwHZsxWydxuU/mYYCXT6JCBSr8XGyzhYqRHhZYc9GWo8pFZDdm2Gcu+keu+3NuTALiA3G4oRhcb1BLcBrGwgxw7UejTShYwCHLmai4fauxtMmGq3rIWs/xlK5HAot42rVR8NhcknIiIiokassLAQKpUKGg0v24iI6iIqIROedhq08qj9LVBVkaJCaP/vNeBSAuAbWFofqEsfKB5exjUQGAr4t4RsWw+JHAFFqf/RWTeS+OOARlMaRwPo7e+AxftykNBhMAL3boOMGQ+lEb7XSdQGoJkPEBZRo+0UCwugdXvIsYMQkVqdz3WxabBQKRga7FzhMe3+nZCVXwAdekB5YHKD/73UFGe7IyIiImpEvvnmG5w+fRoAcOjQIUycOBETJkzAgQMHzBwZEdHNKz2/GNFXc9CvhRNU9fQhXVZ9CVxKgOqZ16Ce/QlUI+4xPvEEQFGU0tE1l8//O2qqAUlcDNAiFIplw8y418PXHioF2NOyD5CVARxvfO9zcukccOYUlH7DapXcUSI6AanXgcSLNd42u7AEW89moG8LRzhZ6yfl5NRRyJcfAUGtoXr8BSgqdY3bb2hMPhERERE1Ijt37oSfX+n0yD/99BOmTJmCl19+GT/88IOZIyMiunntPJ8JrQCR9XTLnRzeC/lrHZQhd5YWEK8lpVskYG0D2b7ehNFVT/LzgAtnGuSWuzKO1hq0bWaL3fn2EEdnaHdtabC+jSXbNwIaCyi9BtZqe6Vt59J2jh2s8bZbzmSgoEQwKky/zpRkZUK75D3A0xuqZ19vsGRhXTH5RERERNSIFBQUwMrKCllZWbh27Rp69OiBdu3asag/EVEdRCVkooWzFfydTf9BXVKvQ7t8ARAQDGXMI3VqS7G2gdJzAOTALkhWpokiNMLZWKCkBEpIwyWfAKCXnwOuZBXhYrfbgGMHIJnpDdp/VSQ/D7L3LyhdekOxr13SUnH1AHwCIMdrlnwq0QrWxaWhjYcNglyt9eNa+wOQnwvVEy9DsbOvVVzmwOQTERERUSPi7e2NHTt2YMOGDWjXrh0AIDMzE5aWlmaOjIjo5pSYVYjY5Px6GfUkJSXQfjEPKCmB6okXoWgs6tymEjkCKC6C7G64kUASHwMoKiC4VYP1CQA9/RygUoDdPp2BkhLI3m0N2n9VZP8OID+v5oXGy1HadgbiT0Dyco3e5tCVHFzNLsJt5Uc9JV6EbF9fehugj3+d4mpoTD4RERERNSKTJk3Cxo0bERMTg3HjSmeuOXLkiC4RRURENROVkAkFQN/6SD79sRKIPwHloSeheHqbpE3FJwAIbgPZvh6i1ZqkzepI3HHAvyUUa9sG6a+Ms40GbTxtsTtNBQSGQnZvgYg0aAyVke0bAJ8AIKh1teteyy5EXlGJwceUiC5ASTFw8ojRff8emwo3Gw16+DnoLdeu+gqwsoFyxwNGt9VYNL5S8kRERERNmLu7O9555x29ZX379kVERPWz7BQWFmL27NkoLi5GSUkJevTogXvvvRfZ2dmYP38+rl+/Dg8PD0ybNg329qVD9X/55Rds3boVKpUKEydORIcOHQAAZ8+exeLFi1FYWIiOHTti4sSJUBQFRUVFWLRoEc6ePQsHBwdMnToVnp6eJj8ORESmICLYnpCJcE8beNjVfVSSXtuxxyG//wil5wCoegwwadtK/xGQL+YBp44AbTqatO3ypKgQOBsHZcDIeu2nMr38HPDfA9dwqetw+P64ADh/GmgRYpZYykhCPHD+NJT7n6i00HhBsRZ7Lmbhz9PpiEnKQ7D7VbwR6V2hODiCWgE2tpDjB6F06llt3xczChB9NRcPtneHRvVv3xJzGDh2AMo9E6E4ONVp/8yBI5+IiIiIGpHnn3/e4PJp06ZVu62FhQVmz56NDz/8EB988AGio6MRFxeHNWvWICIiAgsWLEBERATWrFkDALh06RJ2796Njz76CDNnzsSyZcug/edb9qVLl2Ly5MlYsGABrl69iujoaADA1q1bYWdnh4ULF+K2227Dd999Z5L9JiKqD2fTCnA5sxD9Wpj2w7pkZ5bebufRDMoDk03aNgAonXoB9o7QbmuAwuPn4oHiogYtNn6jnv4OUADsdm0DWFhCGkHhcYnaCFhaQTGQVLyQXoAvDlzDo7+cxvzdiUjJLcbo1q64kJaPmZsvIC2vWG99RaMBWneAHDto1KiuP2LTYKFSMCzY+d94Skqg/XEZ4OEFZeCoOu+fOTD5RERERNSIGLowzc3NhUpV/WWboiiwti4tTFpSUoKSkhIoioL9+/cjMjISABAZGYn9+/cDAPbv349evXrBwsICnp6e8PLywunTp5GWloa8vDyEhoZCURT069dPt82BAwfQv39/AECPHj1w/PjxRnOLBBFReTsSMqFRAb38Hapf2UgiAu3XC4GsDKieeKleblVTLCyg9B4MHPkbkpZi8vZvJPExpT8Et6nXfirjaqNBaw8b7EksgNKxB+Tv7aWjsWpJRPBzTAq+OHANUQmZuJpVWKP3KcnNgfwdBaVrXyi2dgBKRzltPZuBVzaex5Q/zmF9fBo6NLfDnEF+WHJHS0zs5In/u7MNkrKLMHPzBaTkFum1qUR0BtJTgMsJVfadU1iCv85loG8LB70RVLJzE3DlAlR3T4BiYdoRfA2Ft90RERERNQJPPfUUgNJb58p+LpOdnY3evXsb1Y5Wq8Urr7yCq1evYtiwYQgJCUFGRgZcXEqLlrq4uCAzs3QGpdTUVISE/Htrg6urK1JTU6FWq+Hm5qZb7ubmhtTUVN02ZY+p1WrY2toiKysLjo4Va6ls3rwZmzdvBgC89957cHd3N2ofakqj0dRb2zcrHhPDeFwMu5WPS2zqJUQ0d0SgT7Mab1vZccld9zOyovfBfuJzsOvcwxRhGlR8531I2bgaNod2wn7cpHrrJy0hDlr/lnBr0dKo9evj72Vom0J8vP0sMiLvguPfUXA4cwLWfQbXqq3VRxLxTfR1qFUK1samASitLdW6mQPaeDkg3MsBrZs5wLH87XH/yF2/HVkF+XC58z6cFxv8evwq/jyVhOzCEvg52+DZvi0wvFUzuNjqJ4G8NBp8dFdbvPjrCbyx9TIW3B2BZg6lsyuW9BuM5K8XwvbsKdh16Fpp7FsOX0Z+seDBbi3h7l56e7w2JxvJv30Pizbt4TL09kpvA2zsmHwiIiIiagSmTJkCEcG7776LKVOm6D3m7OwMb2/jCtmqVCp8+OGHyMnJwf/93//hwoULla5b2TfBVX1DbOixyi6EBw8ejMGD//3wkJycXGm7deHu7l5vbd+seEwM43Ex7FY9LkUlWsRdz8EdrVxqtX+GjotcPAftVwuAiC7I7TkIefV53CysgfCOyNmwBnn9R0FRq03ehZSUQHvyKJReA40+RvXx99LOtfR9ZFOBI+52dUfGhl+Q3apDjduJuZaLj7dfQBdvO8zo54OLGYWITc5DXEo+4pJzsDchDWXvYt4Olgh1t0aomw1C3a3RwtkaGhWQu24NdoWPxKZ9uYhLOQwLlYKe/g4YFuyMcE8bKIqCktwMJJebvM7d3R2+VkV4a6Av3tx6EU/9GI05g/zQzN4SgAL4BSJ7XxTy+o0wGLtWBD8euoRW7jZwV+cjOTm/dPnPX0My01Fy1yykpNTvKLi6qupahcknIiIiokagTZvS2x2WLVsGKyurOrdnZ2eHNm3aIDo6Gk5OTkhLS4OLiwvS0tJ0o5Tc3Nz0LmRTU1Ph6upaYXlKSgpcXV31tnFzc0NJSQlyc3N1xcuJiBqTs2kFKNYKQt1sTNKeFORD+98PATsHqCY+3yAjUFT9R0C7+D/A0f1Ax3oYZXXhLFCQD4S0NX3bNeBma4FW7jbYfTEb9/QcCFm3CpKaDMXV+BFW13OK8P6Oy/BysMT03t6wUKvQ0tUaLV2tUZbuySkswenUfMT9k5CKTszBtnOlo4EtVApa2AgutRiPPI01fIu0mNTZE/0DneBoZXziL8zdBm8P8sPsrRfx2qYLeGewP5o7WEKJ6ALZ8DMkN0d3O9+NDl3JwdXsIjzU3kO3TK5fhWz+FUrPAVDMXIS9rljziYiIiKgRUavV2Lx5M7744gssWrRI7191MjMzkZOTA6D09r1jx47Bx8cHXbp0wfbt2wEA27dvR9eupUP+u3Tpgt27d6OoqAhJSUlITExEcHAwXFxcYGNjg7i4OIgIoqKi0KVLFwBA586dsW3bNgDA3r17ER4eftPeAkBEt7a45DwAQIi7tUnak/8tBa5dhmrStIabbSyiK+DiXm+FxyX+OABACTFPvacb9fJ3wLm0AiS27w+IQPZsNXrbgmIt/rP9Eoq0gtcifWBnaThZZGepRnsvO4xt646Zkb5YPiYYS+8Mwkt9vDEixBkWmcnonnYK/4n0xKJRgbijlWuNEk9lQtxs8M4gfxSUCF7bdAGXMgugtO0MaLXAyWiD26yNTYOrjQY9b6hPJj9/DahUUO56pMYxNDYc+URERETUiCxatAjnz59H586d4eRUsw83aWlpWLx4MbRaLUQEPXv2ROfOnREaGor58+dj69atcHd3x/Tp0wEAfn5+6NmzJ6ZPnw6VSoVJkybpCps/9thj+PTTT1FYWIgOHTqgY8fSqb4HDhyIRYsWYcqUKbC3t8fUqVNNuv9ERKYSl5IPVxsN3G3rXqBZu38HZOcmKCPHQmnd3gTRGUdRq6H0HQr57XtI0hUonsbdgm0siYsBPL2hOLuatN3a6OXvgC8PJWFPrg3uCg2H7N4CGTm22i84RASL9l3FubQCvN7fF76Oxo8eVhQFnvYW8MhIRM8tnwKnT0AZehdUvnU/Hi1drfHOID+8sfUiXt90AW8PaAEfWzvIsQNQOuvXcbyUUYDoxBw82M4dGlXp/srpE5CDu6Dcfh8UFzdDXdxUmHwiIiIiakSOHDmCRYsWwc6u4pD86gQEBOCDDz6osNzBwQFvvPGGwW3GjBmDMWPGVFgeFBSEefPmVVhuaWmpS14RETVmccl5CDXBqCe5fhXy7WKgZRiU2+83QWQ1o/QdAvn9f5CojVDumWiydkWrBeJjoHTqZbI268LDzgKhbtbYfSETY3oNhiz/BDh9EqhmVNaak6mISsjEg+3d0cWnZreBS0E+5PeVkE1rAGtbKOOnQOk1qA57oa+FizXmDvbHrM0X8PrWy3izbSQCju+BiOgl1f6IS4NGpWBoiHNpXFottCuXAc6uUIZVfI++GfG2OyIiIqJGxN3dHUVFRdWvSERElcrML8bV7KI613uS4mJol/4fAAWqx1+Eomn48RuKsxvQoTtk12ZIUaHpGr5yHsjNAULCTddmHfXyd8CZ1AJcC+sKWFlDdm2ucv1DV7LxTfR19PJ3wNjwmo0OkqP7oZ39LGTDz1B69IdqzhKo+gyBojJtmsTPyQpzhwRAo1Iwy74fzpTYAhfP6h7PKSzB1rOZ6BvgAOd/ZuCTv7cDCfFQ7noEipVpbhs1NyafiIiIiBqRfv364cMPP8TOnTtx/PhxvX9ERGScuJTSmcLqOvJJfv0OOBcH1SPPQHFvZorQakUVOQLIzoIc3G2yNiUuBgCghDau5BMA7EkqgtKlN+TALkhBvsF1E7MK8X+7rsDPyQrP9WhudP1BSU1GyZJ3oV04B7C0guql/0A14XkoDo4m24/yfBwtMXeIP2ysNHiz/eOIO3xC99jWsxnIL9bitjCX0vgKCiCrvwUCgqH06F9vMTU03nZHRERE1Ihs2LABAPDDDz/oLVcUxaii40REBMSl5EGlAMGutR/5VBD9d+momL5DoXTpY8LoaqFVO8DTG7J9PWCqhERcDODqDrh5mqY9E2hmb4lgV2vsvpCFu3oNhuzaAtmxERgwCor638LfuUUl+M/2S1ABmBnpAxuL6sfVSEkJZOvvkF+/B6QEyphHoAy5E4qm7jXBjNHcwRL/GRqI1386jDczAzD7eultoX/EpSHM3QYh/4zSk02/AGnJUD32gslHYZkTk09EREREjcjixYvNHQIR0U0vLjkffk5WRiUlDJHMdGQumAM094My7nETR1dzikoFJXIYZNVXkEsJUHxb1Kk9EYHEx0Bp3b7RzVjay98B30RfR1KfELj7BEBWLoP89gMQ3AZKWFtISFt8ctEGlzIL8eZAPzSzt6y2TTkbC+23nwKXzgERXaC6/wkoHl4NsDf6PO0t8I5TAt5Ibo7ZWy/g9jBXJGYV4YF2HqVxpqdA1v8MdOrVqEakmcKtk0YjIiIiIiKiJk9EEJ+Sh1C32t9yJ2tWQJudBdUTL0GxMn72tPqk9BoEaCwg2zfUvbFrV4DMdKARJjjKbr3bezEbqpfehfLYC1C69gWuJ0J+Wo4fv9+AvZdyMD7rMCKiN0DOxUFKSgy2JbnZ0H63BNr3XgayM6B6cgZUU2aZJfFUxqNdO8yJ/hxuShFWxaTAxUaDnn6l+yy/rAC0JVDdPd5s8dUXjnwiIiIiakRyc3OxatUqnDhxAllZWRAR3WNLliwxY2RERDeHxKwiZBdqEepe+1vuJP4ErDp0Q3EdRxiZkmLvCKVrH8jevyB3j4diXZf9K6v31NZU4ZlMcwdLBLpYYdeFLNzZOgBK90igeyQAYO+pRPzvYAb6SyJGXdoBOXAJAgBWNkBIayihEVDC2gL+QZCDuyArvwCys6AMuh3KnQ9AsbY1674BAAJD4GopeCd7O+YH3I7IFo6wUCuQ82cge7ZCGTIaimdzc0dpckw+ERERETUiX3zxBVJTU3HPPfdg4cKFmDJlCn777Td0797d3KEREd0U4lLyAKDWI58kPxe4dhma/sNRbMrATECJHAHZ8xdk33YokcNr1YYU5EMO7AIcnIBmPiaO0DR6+ztgxZFkXM8pgoddaU2mCxkFmH8kC8Gu1nh6SCQ0mgGQjLTSwulxxyCxxyGrvy5NRmk0QHEx0CIEqqlvQvEPMuv+3EhRqaG06QSn43vx9oSnoKhUEBFof1wG2DlAuW2suUOsF0w+ERERETUiR48exfz58+Hg4ACVSoWuXbsiKCgI77//PkaNGmXu8IiIGr245DxYa1Twc6rl7XIXzgIisAhuBcPzrJlRyzDANxCybT2k37Aa1WsSbUlp4mrNCiA9Fcqo+xpdvacyvfwdseJIMvZezMLtrVyRXVBaYNxao+DVSB9YaUorCClOLlC69gG6lhaEl8w0IC4Gcvok4O0Ppc9gKCp1VV2ZR0Rn4O/twIUzQIsQ4PBeIO44lAeehGJrb+7o6gWTT0RERESNiIjA1rb0tgBra2vk5OTA2dkZV69eNXNkREQ3h7iUfAS7WUOtql1iRRJOAwA0Qa2AYq0pQ6szRVGg9B8BWfEpcDYWCGpl1HZy8gi0P35ZWnA7MBSqyS9DCW5Tv8HWgY+jJQKcrbD7QhZGhrpg3q4ruJ5ThHcG+cPdtvLZ6RRHF6BLH/PPTlgNJbwTRFEgxw4Cvi2g/emr0uL2/YaZO7R6w+QTERERUSMSEBCAEydOICIiAq1atcKyZctgbW2N5s1vvfoPRESmVliixbm0fNzRyrX2jZw/A7i4Q+3sCiQnmy44E1G69yud9W77BijVJJ8k8SK0q74Cjh0A3DyhPP4ilK59G+2Ipxv19nfAD0eTsXjfVRxKzMHT3bzQ2rMR1GwyAcXBEWgRAjl2ALCyBq5fher52VDUjXCUlolwtjsiIiKiRmTy5Mnw8CidcvnRRx+FpaUlcnJy8Oyzz5o5MiKixu9cWgGKtUCoWx2KcZ8/DQQEmzAq01KsbaH07A85sBOSk2VwHclML53l7c0pwOkTUO4eD9WcT6Hq1u+mSDwBpbPeCYAtZzMwLNgZw0KczR2SSSkRXYCEeMjv/wPCO0Jp29ncIdUrjnwiIiIiaiS0Wi22bduGMWPGAAAcHR3x5JNPmjkqIqKbR1zyP8XG3WtZbDyvtNi40qO/CaMyPSVyeGndp11boAwdrVsuhQWQzb9B1v8EFBZAiRwB5fb7S0fa3GT8nKzQyt0GFmoFj3dpZu5wTE6J6Az57XugIB+qsZPMHU69Y/KJiIiIqJFQqVTYuHEjxo69NWe6ISKqb3HJ+XCz0cCtirpAVbpwBgCgtGi8I58AQPENBIJaQbZvgAy5ExCB/B0F+eVbIPU60L4bVPdMgOLla+5Q6+Sdwf7QqHDTjNaqEf8gwMMLSvtuUHz8zR1NvWPyiYiIiKgRiYyMxKZNmzBs2K1bdJSIqL7EpeTVetQT8G+x8cZ8210Zpf8IyLL5kA2rIYd2AwnxgH8QVBOfh9KqnbnDMwkL9S2YdPqHolJBNWcJoGoa1ZCYfCIiIiJqRE6fPo0NGzbgt99+g5ubm963vW+99ZYZIyMiatwy8otxNbsIw4Kda9/I+dOAqwcUByeTxVVflM69ISu/gKz+GnB2gzJxKpQe/aE0kWTGreBWLjBeHpNPRERERI3IoEGDMGjQIHOHQUR004lPyQcAhLrXsdh4I7/lroxiYQnVw89Ckq9CiRwJxcrK3CERVYrJJyIiIqJGpH///uYOgYjophSXkgeVAgS51rLYeG42kJQIpdfN8wWA0qknbt0b0+hWwuQTERERUSOydetWg8stLCzg5uaGkJAQWFjUspAuEdEtLC45H/5OVrCxqOVtZ+fLio2HmDAqIgKYfCIiIiJqVKKiohAXFwcnJye4ubkhJSUFGRkZCAoKQlJSEgDg5ZdfRlBQkJkjJSJqPEQE8Sl56OXvUPs2zpcVG+frK5GpMflERERE1Ij4+vqiW7duGDlypG7Zhg0bcPnyZbz99ttYvXo1vvzyS8ydO9eMURIRNS5XsoqQXahFqFvt6z3h/BnAzROKvaPpAiMiAADL4BMRERE1Irt27cLw4cP1lg0dOhQ7d+6Eoii44447cOnSJTNFR0TUOMUl5wFoOsXGiW42TD4RERERNSJOTk44ePCg3rJDhw7B0bH0m/iioiJoNBy8TkR0o7iUPFhrVPB1tKzV9pKTBVy/CiWAySei+sArFyIiIqJGZOLEifjoo4/g7++vq/l04cIFTJ8+HQAQHx9fYWQUEVFTF5ecj2A3a6hVtZz7razYOJNPRPWCySciIiKiRqR9+/ZYuHAhoqOjkZqaio4dO6JTp05wcHDQPd6+fXszR0lE1HgUlmiRkJ6PO1q51rqNf4uNM/lEVB+YfCIiIiJqZBwdHdGvXz9zh0FEdFM4m1qAYq0J6j15eEGxszdhZERUptbJp8LCQqhUKtYcICIiIqqjuXPnYubMmQCAN954A4pi+LaRt956qyHDIiK6KcSl/FNs3M269o0knIYSGGqiiIioPKMzR9988w169eqF4OBgHDp0CPPmzYOiKJg6dSq6dOlSnzESERER3dIiIyN1Pw8cONCMkRAR3XzikvPgZquBm61FrbaX7EwgJQkYMNLEkRFRGaOTTzt37sS4ceMAAD/99BOmTJkCW1tbfP3110w+EREREdVBnz59dD/379/ffIEQEd2E4lPyEepW+1vudMXG/YNMFBERlWd08qmgoABWVlbIysrCtWvX0KNHDwBAcnJyvQVHRERE1BSdPHkS586dQ35+vt7yMWPGmCkiIqLGKSO/GFezizAsxLnWbUhCfOkPAUw+EdUXo5NP3t7e2LFjB65evYp27doBADIzM2FpaVlvwRERERE1NV9++SX27NmDVq1a6V1nVVYHioioKYtPKU3Sh9Vh5JNcOAN4Nodiy2LjRPXF6OTTpEmTsHz5cqjVajz11FMAgCNHjugSUURERERUdzt27MC8efPg6lr7KcOJiJqK2OQ8qBQgqK7FxoNamS4oIqrA6ORTcHAw3nnnHb1lffv2Rd++fU0eFBEREVFT5e7uDguL2hXNJSJqauJS8hHgbAVrjapW20tWBpB6HRg0ysSREdGNjE4+AcDRo0exa9cuZGRkYMaMGThz5gzy8vLQtm3b+oqPiIiIqEl58skn8fnnn6N3795wcnLSe6xNmzZmioqIqPHRiiA+JQ99/B1r38j50wAAJSDYRFERkSFGJ5/Wr1+PdevWYdCgQdi7dy8AwNLSEl999VWFEVFEREREVDtnz57F4cOHcfLkyQq1NZcsWWKmqIiIGp8rWYXIKdQi1L32t9xJQmnyCZzpjqheGZ18WrduHWbNmgVPT0/8+uuvAAAfHx9cuXKl3oIjIiIiamp++OEHvPLKK6yrSURUjbjk0mLjoXUpNn7+NNDMB4qNranCIiIDjL4xNi8vD+7u7nrLiouLodHU6M49IiIiIqqClZUVb68jIjJCXHIerDUq+DjWYQb282d4yx1RAzA6+dS6dWusWbNGb9n69esRHh5u6piIiIiImqxx48Zh+fLlSE9Ph1ar1ftHRET/ikvJR4ibNdQqpVbbS2YakJYMtGDyiai+GT1s6dFHH8X777+PLVu2ID8/H88//zxsbW3xyiuv1Gd8RERERE1KWV2nTZs2VXhs5cqVDR0OEZFBIgIAUJTaJX7qqqBYi4S0fIxu7Vr7Rs6fAQAoAaz3RFTfjE4+ubi44N1338Xp06eRnJwMNzc3BAcHQ6Wq3ZSWRERERFTRokWLzB0CEVG15M9fIJvXQvXES1BCGv5W4bNp+SgRINS9DvWeEk4DigL4tzRhZERkSI0KNimKgpCQEISEhNRXPERERERNmoeHh7lDICKqkmi1kK2/A+kp0M57HcqDT0LVd2iDxhCf8k+x8bokn8qKjVuz2DhRfasy+TRt2jTMnz8fAPDUU09Vuh6n/SUiIiIyjdzcXKxbtw4JCQnIz8/Xe+z11183U1RERDeIPwGkJkN54ElI9D7IN4ugvXweythHoajVDRJCXHIe3G01cLWpwwRY509DacWZRYkaQpXP1MmTJ+t+njJlSr0HQ0RERNTUffTRR9BqtejWrRssLeswgxMRUT2RvX8BVjZQeg2C0m8Y5KflkM2/QhIvQvXEy1Ds7Os9hriU/LqNekpPBdJTAc50R9Qgqkw+tWrVCgCg1WqxdetWTJ48GRYWFg0SGBEREVFTFB8fj2XLlkGjqcO3+URE9USKCiEHd0Hp1BOKlRUAQBk3CVoff8iKJdD+5wWonn0dSnO/eoshI78Y17KLMCLEufaN6IqNM/lE1BCMqhauUqlw9OhRs81kQERERNRUtGrVCpcvXzZ3GEREhh3dD+TlQunRX2+xqs8QqF58B8jLhfbdlyDHDtRbCHHJpqj3FA8oKsAv0FRhEVEVjP5K7bbbbsOPP/6Ie++9l9/EEREREdWTp59+Gu+++y6Cg4Ph7Oys99g999xjnqCIiP6h3bsNcHIFWkVUeEwJbgPVzI+gXfwOtAvnQLl7ApSho00+iCEuJQ8qBQh2ta51G5JwGmjuC8W69gksIjKe0VmkDRs2ID09HX/88QccHR31HmPBcSIiIiLT+OGHH5CSkgIPDw/k5eXplnMEOhGZm2RnAscOQhk0CorKcGFxxc0Dqlfeh3z1CeSnr4BLCcAjz0CxMF0Nu7jkPAQ4W8FKY9SNPBWICHDhDJQ2HUwWExFVzejkEwuOExEREdW/3bt345NPPoGLi4u5QyEi0iMHdgIlxVB6DKhyPcXKGpj8MvD7Sshv30OuXYbq6degOLvWOQatCOJT8tEnwLH6lSuTngpkpAEBIXWOh4iMY3TyKSMjAz179qywfO/evSYNiIiIiKgpa9asGdQNNFU5EVFNyN5tgE8A4Nui2nUVRYFy+30QH39ol82Hdu4LUD3zGpQWdUv4XMksRE6RFqHutb/lDudPl8YYEFSnWIjIeEaPU/zss88MLv/8889NFgwRERFRU9e3b1988MEH2LlzJ44fP673j4jIXOT6VeDMKSjd+9foNmClUy+oZnwAqNXQfvAqtPu21ymOuJR/io271aXY+Ol/io23rFMsRGS8akc+Xbt2DQCg1WqRlJRUen/sDY9ZWpru3l0iIiKipm7jxo0ASms/3UhRFCxatMgcIRERlY56UhQo3fvVeFvFLxCq1/4P2s/eg3wxD9rriVCNuq9WccQl58FGo4KPY+0/h0rCacDbD4qVVa3bIKKaqTb59Nxzz+l+Ll/3ydnZGWPHjjV9VERERERN1OLFi80dAhGRHhEpTT6FtoXi6lGrNhRHZ6imz4F8+THktx8gvQbVqq24lHyEuFlDrardJAwiApw/DSWiS622J6LaqTb5tHLlSgDA7Nmz8dZbb9V7QERERERERNSIJMQDSVegjLi7Ts0oGgvgroch+3dAdm+FMmpcjbYvKNYiIS0fd7Vxq30QaclAVgbQIrj2bRBRjRldcJyJJyIiIqL688Ybb1RbR4XXY0RkDrJ3G6CxgNKpV53bUjy8gLAIyK7NkJFjoaiMLkOMs2n5KBEg1K0OxcYT/ik27s9i40QNyejkU1JSEn744QckJCQgPz9f77ElS5aYPDAiIiKipmTgwIHmDoGIqAIpLobs3wGlfTcotnYmaVPpMwSy7CMg7jjQqp3R28Ul/1Ns3L0uxcbPACoV4BdY6zaIqOaMTj598sknaNasGR555BFYsTAbERERkUn179/f3CEQEVV04jCQlQGl5wCTNal06gn53g6ycxOUmiSfUvLgYauBi43RH2MrkPPxgHcAFEt+piVqSEY/ay9duoQ5c+ZAVYNhkURERERERGQ87d9RkLU/QPXSf6A4upg7nNJb7uwdgPCOJmtTsbSC0r0fZNcWSG42FFt7o7aLS86v26gnEeD8GSjtu9W6DSKqHaMzSa1bt0ZCQkI9hkJERERERNS0yYGdwNXL0K5YUposMWcsebmQ6H1QuvQtLRZuQkqfIUBRIeTvKKPWT88vRlJOEULd61DvKfU6kJ3JYuNEZmD0yCcPDw/MnTsX3bp1g7Ozs95j48bVbJYCIiIiIiIi0idaLRAfUzrS6PBeyN9RULpHmi+ew3uAokIoPfqbvnH/IMC3BWTnZqD/yGpXj0vOAwCEutV+5JOu2HgAk09EDc3okU8FBQXo3LkzSkpKkJKSovePiIiIiGpv5syZup9XrVplxkiIyKyuXACys6CMGQ8EtYJ8/zkkPdVs4cjebYCHF9AyzORtK4pSOvrp/GnIpXOVrpdfrMW+i1n49VQaVAoQ5Fr7kU9y/jSgVgO+LWrdBhHVjtEjn55++un6jIOIiIioybpy5QoKCwthaWmJ33//HWPHjjV3SERkBhJ3HACgtG4PJSQc2refh3bFp1A9MxOKojRsLGkpwKmjUEaNq7e+le6RkJ++guzcDOW+x3XLr2UX4sDlHBy4nI1j13JRpBXYWqhwb1s3WGlqX4NYzp8GfAKgWFiaInwiqgGjk0/Xrl2r9LFmzZqZJBgiIiKipqhr1654/vnn4enpicLCQsyePdvgem+99VYDR0ZEDUnijgOuHlDcSz9fKWMehqxcBtnzF5ReAxs2lr+jABEo3fvXWx+KvSOUDj1QvHc7TkeOw4Gr+ThwOQfnMwoAAM0dLDAi1BldfezR2sMWFuraJ8F0xcY79TRV+ERUA0Ynn5577rlKH1u5cqVJgiEiIiJqip5++mmcOnUKSUlJOH36NAYMMN2U5kR0cxARIC4GSngn3TJl4O2QQ3sg/1sKad0eiotbw8WzdxsQGAqlmXe9tJ9dWILDV3Kw328kDloNQvbWK1ArQBtPWzwa5IkuPvbwcTThCKXka0BOFsB6T0RmYXTyqXyCKT09HatWrULr1q1NHhQRERFRU9OqVSu0atUKxcXF6N+/v7nDIaKGlngRyMoAwtrqFikqFVQTnoP2reeh/WYRVM+90SC338mlBODSOSj3P2HSdrUi2JGQiT/PZOBEUi60AjhYWqJz9il0Uaej06SJsLdUm7RPnfP/FBvnTHdEZmF08qk8Z2dnTJgwAc8//zz69OljypiIiIiImqyBAwfi+PHjiIqKQlpaGlxcXNCvXz+0bdu22m2Tk5OxePFipKenQ1EUDB48GCNHjkR2djbmz5+P69evw8PDA9OmTYO9vT0A4JdffsHWrVuhUqkwceJEdOjQAQBw9uxZLF68GIWFhejYsSMmTpwIRVFQVFSERYsW4ezZs3BwcMDUqVPh6elZn4eEqEnQ1XsK1X+uK57eUO4eD/nhv5Cdm6D0HVr/sezdBqjVULr2NVmbR6/mYPnhJJxJLYCPoyXGtHFDFx87hLrZQFm7H/LHWqiy7gTcPEzW543k/BlArQG8A+qlfSKqWu2rtaG0OGZBQYGpYiEiIiJq8rZs2YKPP/4Yzs7O6NatG1xcXPDJJ59g8+bN1W6rVqvx8MMPY/78+Zg7dy42btyIS5cuYc2aNYiIiMCCBQsQERGBNWvWAAAuXbqE3bt346OPPsLMmTOxbNkyaLVaAMDSpUsxefJkLFiwAFevXkV0dDQAYOvWrbCzs8PChQtx22234bvvvquvQ0HUtMQeB5zdSmeXK0fpPxIIi4D8uAyScr1ewxCtFrJvOxDeCYqDU53bS0jLx9t/XcSsLReRmV+Cab2aY9GoQDzcwQOtPWyhVilQeg0CRCB7tphgDwyT86cB3xZQLCzqrQ8iqpzRI5/eeEN/iGdBQQEuXryIe+65p14CIyIiImqKfvvtN7z++uto0aKFblmvXr0wb948DB48uMptXVxc4OLiAgCwsbGBj48PUlNTsX//frz55psAgMjISLz55pt46KGHsH//fvTq1QsWFhbw9PSEl5cXTp8+DQ8PD+Tl5SE0NBQA0K9fP+zfvx8dO3bEgQMHdLPx9ejRA19++SVEpMFn4iK6lYgIJO546Sx3Bp5Lutvv3nwO2q8XQDXt7fp7zsUdB9JToNz7aJ2aScktwvdHk7H1bAZsLFSY0NEDt4W5wFJdcfyD4uEFtGoH2bkZMvJeKKo6jZGooLTY+GkoXXjHDpG5GJ18GjhQf3YFa2trBAQEoHnz5iYPioiIiKipysrKgq+vr94yb29vZGdn16idpKQknDt3DsHBwcjIyNAlpVxcXJCZmQkASE1NRUhIiG4bV1dXpKamQq1Ww83t38LGbm5uSE1N1W1T9pharYatrS2ysrLg6OhY850lolLXLgOZ6UBo5bfXKu7NoIydCFnxKSRqI5TI4fUSiuz9C7C2gdK+W622zy0qwc8xqfjtVCq0Atwe5oKxbd3hYFV1LSelzxDIF/OA2GNA6/a16rtS168CuTksNk5kRkYnn1j4koiIiKj+tWrVCt988w0efPBBWFlZIT8/H99//71uFJIx8vPzMW/ePEyYMAG2traVriciNVpe2WOVjcDYvHmz7nbB9957D+7u7lWFXWsajabe2r5Z8ZgY1liPS+6hncgC4Nq9LzRVxCdjHkT6sf0o+ukruPQZCLWJZqIrOy5SUIDrh/bAutdAOHn71KiNohItfj1+FV/tu4D0vGIMCfPAEz0D4O1kbdT2MngUrv/wX1ge2AGnvoNqsxuVyj8VjQwALh26wKIG57+x/r2YG4+LYTwuVTM6+VRcXIzVq1dXKH45ZswYaDS1rltORERERDd4/PHH8fHHH2PChAmwt7dHdnY2QkND8fzzzxu1fXFxMebNm4e+ffuie/fuAAAnJyfd9VtaWppulJKbmxtSUlJ026ampsLV1bXC8pSUFLi6uupt4+bmhpKSEuTm5uqKl5c3ePBgvVsFk5OTa3YwjOTu7l5vbd+seEwMa6zHRXtwL+DkijRLGyjVxCf3Pwl5awqS578F1fQ5JrlFrey4aPfvhOTlorBDD6OPk4hg98UsfBt9HYlZRYhoZotZkb4IdrMGirKRnFyDUZtd+yJ/5yYUjpkAxc7w60ptaI8fBjQapNs6Vnt8b9RY/17MjcfFMB6X0pHalTE6a7RixQqcOXMGjz/+ODw8PHD9+nX8/PPPyM3NxYQJE0wRJxEREVGT5+LigrfeegspKSm6hNGNt8BVRUTw2WefwcfHB6NGjdIt79KlC7Zv347Ro0dj+/bt6Nq1q275ggULMGrUKKSlpSExMRHBwcFQqVSwsbFBXFwcQkJCEBUVheHDS2/x6dy5M7Zt24bQ0FDs3bsX4eHhrPdEVAe6ek+hxj2XFDcPKPdOgny9ELJtHZSBo6rdxuhY9m0rLXoeVv3smgBwIikXyw8nITY5H/5OlpjV3xedve1q/Zqg9BkC2bYO8ncUlAEja9WGIZJwGvANhKJhsXEiczE6+bR37158+OGHcHBwAFCa0QoMDMRLL73E5BMRERGRibm5uRmddCoTGxuLqKgo+Pv746WXXgIA3H///Rg9ejTmz5+PrVu3wt3dHdOnTwcA+Pn5oWfPnpg+fTpUKhUmTZoE1T+jKB577DF8+umnKCwsRIcOHdCxY0cApXVAFy1ahClTpsDe3h5Tp0413U4TNUXXE4H01CrrPZWn9B4MObgb8vPXkLadoHjW/fY7ycoAjh+EMvgOKKqq6zMBwB+xafjvgWtwtdFgSg8vDAh0glpVx0S0f0vANxCyazNgouSTaLXAhTNQuvUzSXtEVDtGJ5+quvefiIiIiMyvVatW+PHHHw0+9sYbbxhcPmbMGIwZM6bC8qCgIMybN6/CcktLS13yiojqTmKPAwCUsAijt1EUBapHnoV29rPQfrUAqpf+U+fb7+TATqCkBEqPAdWuqxXBmpMpaO1hg7cG+sFKY5rZ6RRFKR399L//Qi6eg+IXWPdGr18F8nJZbJzIzIx+lejZsyfef/99REdH49KlS4iOjsaHH36IHj161Gd8REREREREt66444CjM+BVswLfiosblPseB06fgGxZW+cwZO82wLcFFN8W1a579GouknKKMTLUxWSJpzJK936ARgPZuckk7UlCfGm7LUKqWZOI6pPRrxQPPfQQIiIisGzZMsyYMQNffvkl2rZti4cffrg+4yMiIiJqMrRaLY4fP47i4mJzh0JEDUBX7ymkdrXTlJ4DgPbdIL98C7l6qdZxFCdeAs7GQunR36j1N59Jh72lCj38TFcUvIxi7wilY0/I3m2QosI6tSUiQMwhQGMBNPczUYREVBvVJp9OnTqFFStWQKPRYNy4cVi4cCFWrFiBBQsWoKioCGfPnm2IOImIiIhueSqVCh988AFnEiZqKpKvAanJQA1uubuRoihQPfwMYGkF7VefQLQltWonP2ojoChQulZfFymroAR7L2YjsoUjLNWmHfVURuk9GMjNhkTvq3Uboi2BfP85ZM9fUPoMgcLXVSKzqvbV4pdffkGbNm0MPta2bVusXr3a5EERERERNVWtW7dGXFycucMgogYgcf/Ue6pBsfHyFCcXKPc/AZyNhaz9HyQpEZKTZXQiSkSQv20D0KodFFf3atffnpCBIq1gcJBzrWOuVut2gKsHZOfmWm0uRYXQfv5B6WyAw+4qPT5EZFbVpn8TEhLQoUMHg49FRERgyZIlpo6JiIiIqMny8PDAu+++iy5dusDNzU3vVpxx48aZMTIiMrnY44C9I+Bdt1vClG79IId2Q35fCfl95T8LFcDGFrC1L/1nZw/ln/9x4/8F+dBevQxl+D1G9bX5TAaCXK3Q0tW6TjFXuT8qNZRegyB/rISkJEFx8zR6W8nJhnbxO8Dpk1DGPQbV4DvqLU4iMl61yae8vDwUFxfD0tKywmMlJSXIy8url8CIiIiImqLCwkJ07doVAJCammrmaIioPknccSC0ba3qPd1IURSoHn8ROHkEkp0F5GQBudlATnbp7Wtl/6el/PNYDlDyb205xdoWSqee1fZzJjUf59IKMLlrszrFawyl9z/Jp91bodx+n1HbSOp1aD95C0i6AuXxl6Dq2qeeoyQiY1WbfPLx8cGRI0d0F0E3OnLkCHx8ajYrAxERERFV7umnnzZ3CETUACQlCUhJgjJktEnaUzQWQEQXGJPGEhGgsECXpHL19kWayqLa7TadToeFSkG/AMe6B1wNxb0Z0KodZNdmyG33QlFVXTFGLp+H9uM3gYI8qJ5/E0qrdvUeIxEZr9qaT7fddhv++9//Yt++fdBqtQBKZ2LZt28fli5dittuu63egyQiIiJqSi5duoSffvoJy5YtAwBcuXIF58+fN3NURGRKEvtPvaew8AbvW1EUKFbWUFw9oPgGQu3ZvNptCoq1iDqfiZ7+DrC3UjdAlP8UHk9JAmKPVbmexB2H9oMZgAhUL7/LxBNRI1TtyKc+ffogPT0dixcvRlFRERwdHZGZmQlLS0uMHTsWffpwKCMRERGRqezZswdffPEFunfvjl27dmHSpEnIy8vD999/j1mzZpk7PCIylbhjgJ0D4B1g7kiMsvdiFnIKtRgS5NRgfSqdekJs7SA7N0Fp3d7gOnJwN7RfzAPcPaGa+laN6kMRUcMxar7JUaNGYeDAgYiLi0N2djbs7e0RGhoKW1vb+o6PiIiIqEn58ccfMWvWLLRo0QJ79uwBAAQEBCAhIcG8gRGRSUlcDBASXu3tZI3F5jMZaGZvgbbNGu4zoGJhCaV7JGTHJkhONhQ7e73HtX/9Afnhv0DL/2/vvuOsqO4+jn9mtvdeKEvZpfcqgoI0a9QYH8RgLCAmNjRoLGiwxViioQpELGCN9RGjsRABKUpUqtJ7W9pWttc75/ljdR+QXaTs7uze/b5fL17szp3yvWeGy72/e86Z9tjjJmKF1v5wQBE5PSdVfAIIDg6u9q53IiIiIlIzcnJyaNny2J4QlmWd8YTEIlJ/mKx0SD+ENbRhTGFyKK+UHw4Xck23WOw6fi2yzj0f8+WnmO+WYA2paC9jDObDNzCfvgfdz8L+/b1YAQF1mktETk3DKLOLiIiINBLJycksXbr0mGVff/01bdq0cSmRiNQ0s/XH+Z7adXU5yclZuDMHCxiaXHdD7n5itUiBpNaYr74AwJSXY16Zjvn0PaxBF2Lf+oAKTyINwEn3fBIRERGR2jdmzBj++te/smjRIkpKSnjiiSc4cOAAEydOdDuaiNSUrRsgOASa1//5njyOYeHOHHo2CSEu5JfviFcbrHPPx7z1AmbbRpxP34P1q7Auvwbr0qvVK1SkgVDxSURERKQeadasGVOnTmXVqlX07t2bmJgYevfuTWBgoNvRRKSGmC3rf5zvqW7uGncmvj9UQGZhOWN7uTeRt9XvPMx7c3EmPwQeD9Z1t2MPutC1PCJy6lR8EhEREalnAgIC6NChA1lZWURHR6vwJOJFzJFMSDuAdd5Fbkc5KV/syCEswIezmof+8sq1xAoJw+p7LmbV19i3PIjV/SzXsojI6VHxSURERKQeycjIYPr06Wzbto2QkBAKCgpo06YNd955J3FxcW7HE5EzZLb8ON9T+y4uJ/llucXlfJeax8XtovDzcXe6YOva27BGjMEKj3Q1h4icHk04LiIiIlKPzJw5k+TkZObOnctLL73E3LlzSUlJYebMmW5HE5GasHUDBAVDUmu3k/yixbtzKXfg/JRIt6Ng+Qeo8CTSgKn4JCIiIlKP7Ny5k2uvvbZyqF1gYCDXXnstO3fudDmZiNQEs3UdtOlU7+d7MsawYHsObWMCaRmpu8mJyJlR8UlERESkHmnbti3bt28/ZtmOHTto166dS4lEpKaYnGw4tL9BDLnbllnMnpwShqdEuB1FRLyA5nwSERERcdk777xT+XNCQgJPPfUUvXr1IiYmhszMTNasWcO5557rYkIRqQlm6wYArHb1v/i0YEcO/j4WA1uGux1FRLyAik8iIiIiLsvMzDzm9379+gGQm5uLn58fZ511FqWlpW5EE5GatHU9BARBixS3k5xQSbnDsj25nNMijBD/+j08UEQaBhWfRERERFx22223uR1BROqA2bIO2nbE8qnfBZ2v9+ZRWOYwvB5MNC4i3kHFJxEREZF6pqSkhEOHDlFcXHzM8vbt27uUSETOlMnLgYP7sPoPcTvKL1q44whNwvzoHB/kdhQR8RIqPomIiIjUI0uWLGHOnDn4+vri7+9/zGP/+Mc/XEolImesgcz3dDCvlPVpRVzXPQ7LstyOIyJeQsUnERERkXrkjTfe4E9/+hPdunVzO4qI1CCzZR0EBELLNm5HOaEFO3KwLRiSrInGRaTm2G4HEBEREZH/5+vrS6dOndyOISI1zGxdDykdsHzr7/f/HsewcGcOvZqEEBPs53YcEfEiKj6JiIiI1CNXX301r732Grm5uW5HEZEaYvJzYf+eej/kbs3BArKLyhneJtLtKCLiZepv2V1ERESkEWratCnvvvsu8+fPP+6xd955x4VEInLGfprvqX39Lj59seMIEYE+9G0W6nYUEfEyKj6JiIiI1CPPPfccgwYNYsCAAcdNOC4iDZPZuh78/aFVW7ejVOtIUTkrUvO5rEM0vrYmGheRmqXik4iIiEg9kp+fz9VXX627TIl4EbN1PSR3wPKtv/MofbkrB4+B4SkRbkcRES+kOZ9ERERE6pHBgwezdOlSt2OISA0xBfmQurteD7kzxrBgRw7tY4NIighwO46IeCH1fBIRERGpR7Zv387nn3/OBx98QGRk5DGPPfbYY+6EEpHTt20DGFOvJxvfcCiP1NxSxvVLdDuKiHgpFZ9ERERE6pFhw4YxbNgwt2OISA0xW9eDrx+0bud2lGp9vOEwgb4W57QMczuKiHgpFZ9ERERE6pHBgwe7HUFEapDZsh5SOmD51c8bCBSVOSzcmsE5LcIJ9vNxO46IeCkVn0RERETqkUWLFlX72NChQ+swiYicKVNYAPt2YV060u0oVXKMYd6mTIrKPJyvicZFpBap+CQiIiJSjyxbtuyY348cOcKhQ4fo0KGDik8iLtibU8Ln244wpmc8fj6neBfK7RvBOPVyvqdDeaXM+PYQ6w4Xck7raDrEBbkdSUS8mIpPIiIiIvXII488ctyyRYsWsX//fhfSiMi/N2czf/sREkL8+HXH6FPa1mxZD76+kNy+ltKdOo9j+HRrNq+vTcfHtri9XyKj+qWQmZnpdjQR8WK22wFERERE5MQGDx58wuF4IlI7HGP4NjUPgHfWZZBbXH5K25ut66F1Oyz/gNqId8pSc0p48Iu9vLQqjS4JwTx3aWsuaBOJZZ1ijy4RkVOk4pOIiIhIPeI4zjF/iouLWbBgASEhIW5HE2l0tmYUc6TYw8guMRSVO7y1LuOktzVFhbB3R70YcudxDP+7IZPxn+4mNbeEuwY04aHBzYkN9nM7mog0Ehp2JyIiIlKPjBo16rhl0dHR3HzzzS6kEWnc/rsvF18Ml73xILnNBvO505ML588gyeSDnz/4+lX87edXcTc7Pz/wC6j4Oz8XHAerfVdXn8Pu7GKmf3OIHVnF9E8K5ea+iUQF6WOgiNQtveqIiIiI1CMzZsw45veAgADCw8NdSiPSeDmecr7dtJ8uWfsIaZLIqOh8luLhlaTzeShvGZSVVvzJy4HyMkxZKZSV/bi8DMpLITIGkju4kr/MU9Hb6b0NGYT4+XDfwKac00KvJSLiDhWfREREROqRuLg4tyOINHqmpJi9L8/mYMSFXB7vh33VY0TaNr/dlMWc1X58f+V4ejUN/eX9GOPKfErbM4t57puD7D5SwnmtwrmpdzzhgfroJyLu0SuQiIiISD3w2GOPnfBxy7J4+OGH6yiNSONlcrNxpj/Ot7SACOh32flYdsVUuZe0i+Kzbdm8vCqN7okh+NgnLizVdeGp1OPw9g8ZzNuURWSgL38+rxlnNQ+r0wwiIlVR8UlERESkHhg4cGCVy7Oysvjss88oKSmp40QijY85mIoz7VHIy2HFsLG0Cw4k5qhJuf18LEb3jOeppfv5fNsRftU+yr2wP7M3p4Snl+5nf24pw1MiGNMrnlB/H7djiYgAKj6JiIiI1AtDhw495ve8vDzmzZvHwoULGTBgACNGjHApmUjjYLZuwJn5BPj4kHXHE2xb5XBd++N7DfVrHkrXhGDeWpfBea3CCQ1wv8DjGMO05QfJL/Hw6NAkejbR3TFFpH6x3Q4gIiIiIv+vsLCQt99+mzvvvJOcnBz+9re/cfPNNxMTE+N2NBGv5axYhjPlIQiPwH7gWb6zKuZeOzvp+HmdLMvixl7x5Jd4eHd9Rl1HrdKinTlszyrmxt7xKjyJSL2knk8iIiIi9UBpaSmffPIJ//73v+nUqRN/+ctfSEpKcjuWiFczxmD+Mw/z/ivQphP2uD9jhYTx7Q97aR7uT/PwgCq3S44OZFhKBJ9szeaitlE0Dfev2+BHKSj18NradDrEBnFeK93NTkTqJxWfREREROqB22+/HcdxuPzyy0lJSSEnJ4ecnJxj1unSpYtL6US8j/F4MG+/iFn8KVbfgVhj/ojl509+iYf1hwu5omP0Cbe/tnscX+3J45U1aTx4XvM6Sn28d9ZlkFvs4eHBCa7cWU9E5GSo+CQiIiJSD/j7V/Sc+M9//lPl45ZlMWPGjLqMJOK1TEkxzgvPwg8rsC68EuvK6yvvaLfyQD4eA/2STnyXuKggX67qHMPr36fzw6ECuiXW/XC3fTkl/HtLNsNTImgTE1jnxxcROVkqPonX69w5kSNHNL1Z/dfU7QCNXmSkw4YNh9yOIdJozZw50+0IIo2Cyc3Gmf447N2Jdc0t2EMuOebxb1PziQrype1JFHMu7xjF/O3ZvLwqjckXt8LHrrueR8YYXlqVRqCvzbU94ursuCIip0PFJ/F6R47Y7N9/wO0YcgKxsbFkZFRM2NmsWVOdL5c0a6YCoIiIeLfy1N04T94LeTnYtz+I1f2sYx4v9TisPpDP4NYR2CcxhM3fx+aGnvE8+9UBFu7M4YI2kbWU/Hjf7c9n7cECbuodT2SgPtaJSP2m7iAiIiIiIuL1zNYNZD1wM5SWYN/z5HGFJ4DvDxZSXG44+xeG3B3tnBZhdIwL4s3v0yks89Rk5GqVehzmrEojKcKfi9tF1ckxRUTOhIpPIiIiIiLi1ZwVy3CmPIQdEYX9wLNYrdtWud43qXkE+9l0iQ8+6X1blsXY3vEcKfbw/vrMmop8Qh9tyuZQfhk39U7Atw6H+omInC4Vn0RERERExCsZY3Dmz8O88Cy0akf0k7Ox4hKrXNfjGFak5tOnaSh+PqdW0GkbE8Tg1uF8tDmbw/mlNRG9WpmFZby3IYN+zUPp0aTuJzkXETkdKj6JiIiIiIjXMY4H89YLmPfnYvU5F/vuv2CHR1S7/paMInJKPPRLCj2t413XIw7LglfXpJ9u5JPy6pp0PA7c2Cu+Vo8jIlKTVHzyApokWESk/tFrs4hI9Ux5Oc5Hb+G5fyzOon9jnJqdK8mUlOD842nMl59gXfAbrN/fg+Xnf8Jtvk3Nx9e26NX09HoTxQb78T+dYvh6bx6b0gpPax+/ZFNaIUt25/KbTtEkhp34+YiI1CcqPomIiIiISJ0xB1Nxnr4P8/Fb4OePeesFnCfuwezaWjP7zz2CM+nP8P13WKP+gH3VGCz7xB97jDF8sy+P7onBBPv5nPaxr+gUTUyQLy+tSsMx5rT3UxWPY3hh5WFign35n84xNbpvEZHapuKTiIiIiIjUOuM4OAs+wnl8PGQexr5lAvbjs7D+cB/kZuM8dS/O67MwBXmnf4zDB3Cevg9Sd2Pf+gD20EtPars9R0o4lF9Gv+Ynf5e7qgT62lzXI47tWcUs2ZV7Rvv6uYU7c9iZXcLonvEE+upjnIg0LK68al133XWntP6GDRt4+umnAVi5ciUffvhhLaQSEREREZHaYDLTcaY8jHnnJejYHfvRGVi9B2BZFnbfc7H/Mgtr2OWYr/6DM/FWnK8XYk6x55DZsRnn6XuhqBD7T3/F6nn2SW/7bWo+FnBW89Ob7+lo57UOp21MIK+vTae43Dnj/QHkl3h4fW06neKCGNjyzApkIiJuaHAl8z59+nDFFVe4HUNERERERH6BMQbnv1/iPHYH7NqGdf047HETsSKijlnPCgrGvnos9sQpkNAU88o0nGcewKTuPrnjrF6OM2kiBIdiP/AMVkqHU8r5bWoe7WODiAryPaXtqmJbFmN7xZNZVM68jZlnvD+At9ZlkF/q4fd9ErCsU7sTn4hIfXDmr65nYMOGDbz33nuEhYWxb98+kpOTueOOO7Asi7Vr1/LKK68QFhZG69atK7dZvHgxO3bsYOzYsaxcuZIPPviA8vJywsLCuOOOO4iMjHTvCYmIiIiICAAmLwfnjVmw+r/QphP2jeOx4hJPuI2V1Br7vqcxyxdi/vcVnMfHYw2/HOuy32IFBle5jbPgI8y7L0PrdhWFrbDq72hXlfSCMnZklXBDz7hT2u5EOsYHc06LMN7fkElxuWFE5xjCAk5vLqm9R0r4dGs2F7SJJDk6sMYyiojUJVeLTwC7du1i8uTJREVF8dBDD7FlyxaSk5OZPXs2Dz/8MImJiUyZMqXKbTt06MATTzyBZVksXLiQjz76iOuvv/649RYsWMCCBQsAePrpp4mNjT1uHV9f3yqXNxS6qxJA9W3QkM9tY/Dzf386X+45s9cSvQ79XEO5lhv6/4EiUv+Y77/DefU5KCrAGjEa6/xfY9knV3yxbBvr3PMxPfphPngN858PMd8tw/7tTdBrQGXPH+M4mPfmYBZ8BD3Pxh77J6yAgFPO+m1qxRxTZ5/hfE8/d8tZiQT4WvxrUxZfbD/CbzpFc1mH6FOar8kYw4urDhPkZ/O7bnqdFpGGy/XiU5s2bYiJqbhbQ6tWrUhLSyMwMJD4+HiaNGkCwKBBgyqLR0fLyspi6tSpZGdnU15eTnx8fJXHGD58OMOHD6/8PSMj47h1YmNjq1zeMDRl//4Dbodw1YnOX7NmTRvwuW0cjj1/Ol/uOf3Xkob9Glo7GtJrj7edv6ZNVQgVcYspKsS8+zLmqy+geWvsux/Hat7qtPZlhYZjXT8Oc85wnDf/gfP836BzT+xrbobIGJyXp8Dq5VjDLsMaeeNJF7d+7tt9+SRF+NM03P+0tq9OeIAPf+zflCs6xvDm9+m88X0G/96SzdVdYzk/JRI/n18ePvfNvnx+OFTIH/okEB7o+kc3EZHT5vormJ+fX+XPtm3jOCc/Kd+cOXO49NJL6dOnT+UQPhEREZHGbNasWaxevZqIiAgmTZoEQH5+PlOmTCE9PZ24uDjuuusuQkMrJlaeN28eixYtwrZtxowZQ48ePQDYuXMnM2fOpLS0lJ49ezJmzBgsy6KsrIwZM2awc+dOwsLCGD9+fLVfAErjYraux5kzFbIysC4egXX5KCxfv1/c7pdYKR2w/zwZs/hTzIdv4DxyB8QlwsF9WCPHYp//69Ped26Jh/VphVzZKeaMc1anZWQAD57XnE3phby2Jp3ZKw7zr01ZXNMtloGtwrGrmcOppNxhzuo0WkYEcFHbyFrLJyJSF+rlhONNmzYlLS2NQ4cOAfDVV19VuV5hYSHR0dEALFmypM7yiYiIiNRXgwcP5sEHHzxm2YcffkjXrl2ZPn06Xbt2rbxzcGpqKsuXL2fy5Mn8+c9/5uWXX678IvDFF1/k5ptvZvr06Rw6dIi1a9cCsGjRIkJCQnjuuef41a9+xZtvvlmXT0/qIVNejvPeHJy//xlsG/u+p7CvvL5GCk8/sXx8sIddhv34P7B69YesdOyb7zujwhPAyv35OAbOTjrzu9z9ko5xwTx5fgseHtycID+bycsPcvdnu1m1P7/KO/t9uCmLtIIybuoTj4+tScZFpGGrl8Unf39/br75Zp5++mkeeugh4uKqnvzvqquuYvLkyTz88MOEhemWoyIiIiKdOnWq7NX0kxUrVnDeeecBcN5557FixYrK5QMGDMDPz4/4+HgSExPZvn072dnZFBUV0a5dOyzLYtCgQZXbrFy5ksGDBwNw9tlns379+io/OEvjYT5+G/OfD7EGXYj98DSsNh1r7VhWZDT27+/Bnv4WVp9zz3h/36bmERPkS5s6msjbsix6Nwtl8sWt+NM5TSkqc/jL4lT+vGAvm9OLKtdLLyjj/Q2ZDGgRRrfEkDrJJiJSm1wZdvf6668D0LlzZzp37ly5fOzYsZU/9+jRg6lTpx637eDBgyvf8PTt25e+ffvWalYRERGRhi4nJ4eoqIpb20dFRZGbmwtUzJ/Ztm3byvWio6PJysrCx8enck5OgJiYGLKysiq3+ekxHx8fgoODycvLIzw8vK6ejtQjJnU3Zv7/YvUfgn3tbXV23NOd3+loJeUOqw8UMDwlonIS87piWxaDWoXTPymML3Yc4Z11Gdz/nz30ax7Ktd3jeGd9xTx8Y3pqSKuIeAfX53wSEREREXdU12PpRD2Zqnqsug/uJ3PH4ZqgOzYery7axHg8ZD/7ACYkjNhb7sUOj6zV49WEo9tl2c5MSj2GCzo3JzY20rVM1yfEcVXfZN5dc4A3V6Vy5ye7MMCN/ZLo1KpJnWTQv6GqqV2qpnapmtrlxFR88gKN/U53IiL1kV6bpT6JiIggOzubqKgosrOzK3spxcTEkJmZWbleVlYW0dHRxy3PzMysnGfzp8diYmLweDwUFhYeN8zvJydzx+Ga4G13bKwJddEmzsJ/Y7ZuwBp7N1ml5dAAzsHR7fLFhoOE+NkkBZbVi+vnV8lBDGyWzAcbMtmbU8JFrYLqLJf+DVVN7VI1tUvV1C4nvuNwvZzzSURERERqTp8+fSpvzrJkyZLKaQv69OnD8uXLKSsrIy0tjYMHD9KmTRuioqIICgpi69atGGNYunQpffr0AaB3794sXrwYgG+++YbOnTvX+ZAlcZ/JSsfMex0698Tqd57bcU6ZxzF8tz+fPs1C8a1Hk3mHB/gwulc8Dw9JIsBXH9VExHuo55OIiIiIF5k6dSobN24kLy+PW265hZEjR3LFFVcwZcoUFi1aRGxsLHfffTcASUlJ9O/fn7vvvhvbthk7diy2XfGB96abbmLWrFmUlpbSo0cPevbsCcDQoUOZMWMGd9xxB6GhoYwfP96tpyouMcbgvPk8GAf72tsaZPFxU3oReSWeOrnLnYiIqPgkIiIi4lWqKwY9/PDDVS6/8sorufLKK49bnpKSwqRJk45b7u/vX1m8ksbJrPwafliBddWNWLEJbsc5Ld+k5uFnW/RsouKTiEhdUF9OERERERE5KaYgH/P2C9CyDdawy9yOc1qMMXy7L58eTYIJ8tPHIRGRuqCeT9IoNGtW/cRnUl/8/znS+XJHZKTjdgQREannzPtzIT8X+4+PYvn4uB3ntOw+UkJaQRkju8S4HUVEpNFQ8Um8nu44Vf/pzhAiInI6THYmzpN/IjMuEU9oOFZULETFQFQsVlQMRMZAVAyWn7/bUb2C2bIO89UXWBdeidUi2e04p+3bfflYQN/mGnInIlJXVHwSERERkQbL6tIbuyAXDh/AbF4HRQUAmKNXCg3//6LUjwUpomOxUjpCfJMGOWF2XTOlJTivzYS4RKzLRrkd54x8k5pHx7ggIgP1UUhEpK7oFVdEREREGiQrKgbrhjuIOqoHrSkuhOwsyM7AHMmE7MyKn3/6e9dWyMupWBcgJh6rUw/o2AOrYzes0HDXnk99Zj55F9IOYN/1F6yAALfjnLYDOcXsyi7hxl7xbkcREWlUVHwSEREREa9hBQZDk2Bo0pzq+jOZslLIOIzZvA6zcS1m5Vew7D8Yy4IWKVidelQUpFI6Yvn51WX8esmk7sLM/wCr/9CKdmnAlu3MBOAsDbkTEalTKj6JiIiISKNi+flDkySsJkkw5BKMxwO7t2E2rMFsWouZ/wHms/fBPwDadfn/YlTTFo1uiJ5xPBXD7YJDsUbe6HacM7ZsRyYtIwNoEqZ5wERE6pKKTyIiIiLSqFk+PpDSASulA1w+ClNUCFt+7BW1aS3m3ZcrhuhFRGN16o7Vfyh06NYoClHmy09h11asm/7U4Ick5haX8/2BXEZ01l3uRETqmopPIiIiIiJHsYKCoUc/rB79ADCZ6ZhNa2HjWsz3KzD//bKi59TgiyuGogUFu5q3tpjMdMy816FLL6yzBrkd54x9tTcPx8DZSWFuRxERaXRUfBIREREROQErJg7r3PPh3PMxZaWYFcswX36KeesFzAevY/UfjDX4V1jNWrgdtcYYY3De/AcYg/27Wxt8L69Sj8P7GzLp0iSM5KiGO2G6iEhDpeKTiIiIiMhJsvz8sQYMgwHDMLu2Yb78BPPVAsziz6BdF+whl0CPs7F8G/bbbLPyK1i3EmvkWKzYBLfjnLH5246QWVjOIxe1xLLK3Y4jItLoNOz/FUVEREREXGK1bovVejzmqhsxX3+BWfwZzuxnIDIaa9BFWAMvwIqMdjvmKTMFeZi3XoCWbbCGXep2nDNWXF7R66lrQjC9kyLJyMhwO5KISKOj4pOIiIiIyBmwwsKxLvofzAVXwLpVOF9+gvnon5hP3sHqNQBr8MXQtnODGbpm3psLBXnY4x/Dsn3cjnPGPt2SzZFiDxMGxrodRUSk0VLxSURERESkBli2D3Q/C5/uZ2EOH8As/gyzfAFmxTJo3gp79J1YLdu4HfOEzOYfMF8vwLrwSqwWyW7HOWOFZR4+2JhJ76YhdIz3zonhRUQaAtvtACIiIiIi3sZKaIp99VjsZ17Bun4cFObjPDMBs3q529GqZUpLcF6fCXGJWJeNcjtOjfhoczZ5pQ7XdItzO4qISKOm4pOIiIiISC2xAgKwB16A/edJ0Lw1zj+exvnsfYwxbkc7jvn4bUg7iH3tbVgBDf+OcHklHv61KYuzk0JpExPodhwRkUZNxScRERERkVpmhUdh/+mvWH0HYj54DTN3GqaszO1YlcyubZj58yomSe/Uw+04NeLDTVkUlTmM6qq5nkRE3KY5n0RERERE6oDlHwC/vwcSm2M+fguTcQj71gexwsJdzWXKynBemQYRUVgjxriapaYcKS7n481ZDGwZTqso9XoSEXGbej6JiIiISKOyZFcOoz/YzuoD+XV+bMuysC8fhXXTn2DXNpyn7sEcTK3zHEczn74LB/ZiX3cbVnCIq1lqyv9uyKTMMfy2m3o9iYjUByo+iYiIiEij4HEMc1enMXn5QXKKy5m94jBlHseVLHa/87DveQKKi3Ceuhezca0rOczenZjP3sc6ewhWt76uZKhpmYVlfLb1CENaR9As3N/tOCIigopPIiIiItII5JV4+MuX+/hwUxaXtIvkwUHNOZRfxr+3ZLuWyUrpUDEReXQszrRHcZZ8XqfHN+XlFcPtQsKwfntTnR67Nr23PhOD4equMW5HERGRH6n4JCIiIiJebXd2Mfd8vpv1aYXc3i+Rm/sm0rd5KH2ahvDu+kyOFJe7ls2Kice+/2/QqSfmjVk477yEcTx1cmzz+f/Cvl3Yv7sVKySsTo5Z2w7nl/LFjiOcnxJJQqh6PYmI1BcqPomIiIiI11q+N5f7/7OHEo/hieEtuaBNZOVjY3rHU1Lu8Ob36e4FBKygYOxxE7GGXYZZ8BHOjCcwxYW1ekyzfy/m3+9g9R2I1at/rR6rLr2zLhMLi6u6qNeTiEh9ouKTiIiIiHgdxxjeWJvO35YdoEVEAJMuakmHuKBj1mkeHsAl7aP4YnsOO7OKXUpawfLxwf7t77F+dwtsWI3z9P2YzLRaOZbxeCqG2wUFY436Q60cww37c0v5clcOF7eLJCbYz+04IiJyFBWfRERERMSrFJR6eGJxKu9tyGR4SgRPnt+i2mLEb7vEEhrgw8urDmOMqeOkx7MHX4L9x0cgKwPnyXswO7fU+DHMgn/B7m1Y19yMFRZR4/t3y9s/ZOBnW/xPZ/V6EhGpb1R8EhERERGvkZpTwj2f72HNwQJu7pvAuH6J+PlU/5Y3NMCH33WLZX1aEf/dl1eHSatndeqJ/cAzEBCI8+yDOF8vrLF9m0OpmA/fhB5nY/U5t8b267bd2cUs25PLZR2iiQz0dTuOiIj8jIpPIiIiIuIVvkvN457P91BQ6uHxYS24pF0UlmX94nYXtImkZUQAr6xJp9Tj1EHSX2Y1ScJ+4O/QpiPmlWk4r83AlJWe0T6N48F5ZTr4B2Bfe+tJtU1D8da6DIL8bK7oGO12FBERqYKKTyIiIiLSoDnG8M66DJ5Ysp+m4X5MurgVnROCT3p7H9tibJ94DueX8dHm7FpMemqssHDsux7DuuQqzLL/VMwDlX7otPdnFn0COzZj/fb3WBFRNZjUXdszi/lmXz6/7hhNWICP23FERKQKKj6JiIiISINVWOZh4ieb+ecPGQxuFc5T57ckLuTUJ5vunhhCv+ahvLc+k6yi8lpIenos2wf7N9dhj5sI6Ydw/noX5ocVp7wfk3YQM+816NoH6+zBNR/URf/8IZ0wf5vLO3hPQU1ExNuo+CQiIiIiDdLh/FLun7+HZTszubFXPOMHNCHA9/Tf3o7uGU+54/DG2vQaTFkzrO5nYT80BWITcJ57HGfeGxjHc1LbGsfBeW0G+PhiX3ubVw2325RWyKoDBVzZKYZgP/V6EhGpr1R8EhEREZEGKdDXxs/HYvIVXfh1x+gzLqo0Dffn0vbRLNqZw/bM4hpKWXOsuETs+/+Gde75mE/fxZn6KCYv5xe3M0vnw5Z1WFfdiBUdWwdJ686bP2QQGejDJe3V60lEpD5T8UlEREREGqSIQF8mXdSKvi0ia2yfI7vEEB7gw8urDmOMqbH91hTLPwD7hjuwRt8J2zfh/GU8Zsfmatc3mWmY91+Bjt2xzj2/7oLWgR8OFbDucCEjOscQeAY93kREpPbpVVpEREREGqyaHkIW4u/DtT3i2JhexNd782p03zXJPmc49oRnwM8P59kHcBZ+fFyxzBiD89pMwGBfP86rhtsZY3jj+wxign25sG2k23FEROQXqPgkIiIiInKUYckRtI4K4JXVaZSUO27HqZbVIhl74mTo0hvz9ouYF57FFBdWPm6+XgAb12D9z2is2AQXk9a8VQcK2JJRxNVdYvH30UcaEZH6Tq/UIiIiIiJH8bEtbuqdQHphOf/alOV2nBOygkOxb3sQ68obMKuW4zxxD+bAXjyZ6Zh350C7LljnXeR2zBpljOHN79NJDPVjWEqE23FEROQkqPgkIiIiIvIzXRKC6Z8UxvsbMsksLHM7zglZto198f9g3/0XKMjDefIejjx5L3jKsG8Yh2V7z1t+YwzvrMtkZ3YJV3eNxdf2nqGEIiLezHv+JxIRERERqUGje8bhMfDa2nS3o5wUq0M37IenQlJrynduxbriOqz4pm7HqjHF5Q7PfnWAt9ZlMKhVOOe1Cnc7koiInCRftwOIiIiIiNRHiWH+/LpDFP+7MYtftYuiXWyQ25F+kRUZg/2nJ4jIOkxObBO349SYw/mlPLlkP3uOlHBDzzh+0zHaqyZQFxHxdur5JCIiIiJSjRFdYogK9OGlVWnH3U2uvrJ8ffHv1N1rhtv9cKiAP32+h/TCMh4e0pwrO8Wo8CQi0sB4x/9IIiIiIiK1INjPh2t7xLElo4ilu3PdjtOoGGP4eHMWjyzaR0SAD3+/sBW9moa6HUtERE6Dik8iIiIiIicwNDmClOhAXl2bTnG543acRqHU4zD9m4O8tCqNPs1CefailjQN93c7loiInCYVn0RERERETsC2LG7qHU9mYTnzNma6HcfrZRaW8eAXe1m0M5eru8bwwKBmBPv5uB1LRETOgCYcFxERERH5BZ3igzm3ZRgfbMyif1IYraIC3Y7klTanF/H00lSKyh0mDGxG/xZhbkcSEZEaoJ5PIiIiIiInYXTPeIL8bO6dv4eFO464HcfrfLH9CH9esJcAX5tnLmylwpOIiBdR8UlERERE5CTEhfgx9ZLWtIsNYvo3h5i6/IDmgKoB5Y5h9opDzPj2EF3ig/j7Ra1oGRngdiwREalBGnYnIiIiInKSooN8+cvQJN5dn8E76zLZllnMfQObqVhymnKKy3lm2X7WpxVxRcdoru8Rh49tuR1LRERqmIpPIiIiIiKnwMe2GNUtjs7xwUz6+gD3fL6bP/RJYHhKBJalwkmZx1BY5qGwzKGg1KGwzENBmUNBacWywlKHgh8fX3uwgNwSD3cNaMLg1hFuRxcRkVqi4pOIiIiIyGnolhjC1EtaM3n5AWZ8e4h1hwu55ayERnNnNmMM27OKWbo7l7UHC8grqSgylXrML24b6GsT4m8TG+zLA4Oa0yZGE7iLiHgzFZ9ERERERE5TVJAvjw5J4v0Nmby9LuPHYXhNae3Fd8M7kFvK0t25LNmdy4G8Unxti+6JwXSICyLYz4cQf5sQPx+C/WyCf/w5xN8m2K/i5yA/W0PrREQaGRWfRERERETOgI9tcXXXWDrHB/P3rw9w7+d7uKlPPBe2ifSaYXhZReV8tSeXpbtz2ZZZjAV0SQjmN52iGZAURmhA4+jtJSIip0fFJxERERGRGtAlIZipl7RiyvKD/OO7w6w7XMjt/RIb7DC8glIP/92Xx9Lduaw7XIhjICU6gDG94hjYMpyYYD+3I4qISAOh4pOIiIiISA2JDPTlkSHN+WBDFm/+kM6OrGLuO7cZydENYxheqcdh1f4CluzOZeX+fMocQ2KoHyM6xzCoVThJEbqrn4iInDoVn0REREREapBtWYzoEkOn+CD+/tUB7p2/hzG94hiaHFHnvaDKPIb8Ug95JRV/co/6Oa/EQ26Jh7yjlmUUllFcbogI9OHCtpEMahVOu5hArxk+KCIi7lDxSURERESkFnSKrxiGN/W/B3lxZRovrkwjPsSXlpEBtIgIoEVkAK0iA2gW7o+fj31ax/A4hozCMvbnlrI/t5QDeaUcyC0lrWg32QWlFJU71W7rZ1uEB/gQ9uOfFpEBdE8Mpm/zMLolBGtScBERqTEqPomIiIiI1JLwQF8mDm7O2oMF7MwqYc+Rij+rDxTgMRXr+FjQNNyfFhEVxagWkQG0jAwgIdQP27IwxpBT4uHAj8WlowtNB/PKKHdM5fGC/WyahfvTOSGMQKu8srAU5l/x99HFpgAfSz2aRESkTqj4JCIiIiJSi2zLolfTUHo1Da1cVuYxHMgrrSxG7c0pYXtWMV/vzatcJ8DHIiHUj8yicgpK/78Hk68NiaH+NAv3p2+zUJqG+dM03J9mYf5EBPpgWRaxsbFkZGTU6fMUERGpjopPIiIiIiJ1zM/HouWPPZyOVlTmsDenhL0/FqUOF5TROT6YZuEVxaamYf7EhfhpSJyIiDQoKj6JiIiIiNQTQX427WODaB8b5HYUERGRGnN6MxuKiIiIiIiIiIicBBWfRERERERERESk1qj4JCIiIiIiIiIitUbFJxERERERERERqTUqPomIiIiIiIiISK1R8UlERERERERERGqNr9sBRERERKThWbt2LXPnzsVxHIYNG8YVV1zhdiQRERGpp9TzSUREREROieM4vPzyyzz44INMmTKFr7/+mtTUVLdjiYiISD2l4pOIiIiInJLt27eTmJhIQkICvr6+DBgwgBUrVrgdS0REROopDbsTERERkVOSlZVFTExM5e8xMTFs27btuPUWLFjAggULAHj66aeJjY2tlTy+vr61tu+GSm1SNbVL1dQuVVO7VE3tUjW1y4mp+CQiIiIip8QYc9wyy7KOWzZ8+HCGDx9e+XtGRkat5ImNja21fTdUapOqqV2qpnapmtqlamqXqqldoGnTptU+pmF3IiIiInJKYmJiyMzMrPw9MzOTqKgoFxOJiIhIfabik4iIiIickpSUFA4ePEhaWhrl5eUsX76cPn36uB1LRERE6inLVNVvWkRERETkBFavXs2rr76K4zgMGTKEK6+80u1IIiIiUk+p59OPJkyY4HYEOQM6fw2bzl/Dp3PYsOn8yeno1asX06ZN47nnnnO98KRr+Hhqk6qpXaqmdqma2qVqapeqqV1OTMUnERERERERERGpNSo+iYiIiIiIiIhIrVHx6UdH3wZYGh6dv4ZN56/h0zls2HT+pKHTNXw8tUnV1C5VU7tUTe1SNbVL1dQuJ6YJx0VEREREREREpNao55OIiIiIiIiIiNQaX7cDuG3t2rXMnTsXx3EYNmwYV1xxhduRGq1Zs2axevVqIiIimDRpEgD5+flMmTKF9PR04uLiuOuuuwgNDQVg3rx5LFq0CNu2GTNmDD169ABg586dzJw5k9LSUnr27MmYMWOwLIuysjJmzJjBzp07CQsLY/z48cTHx7v1dL1ORkYGM2fO5MiRI1iWxfDhw7nkkkt0DhuI0tJSHnnkEcrLy/F4PJx99tmMHDlS568BchyHCRMmEB0dzYQJE3QOpUGq6j3B7t27efHFFykuLiYuLo4777yT4OBgysvLef7559m1axeO4zBo0CB+85vfANVfyw1VTbXLo48+SnZ2Nv7+/gBMnDiRiIgI157XmTrVdnnhhRfYsWMHtm0zevRoOnfuDOh6qa5dvOl6qYv3qw1RTbZLY75e8vLymDx5Mtu3b2fw4MGMHTu2cl/edL2cNtOIeTweM27cOHPo0CFTVlZm7rnnHrNv3z63YzVaGzZsMDt27DB333135bLXX3/dzJs3zxhjzLx588zrr79ujDFm37595p577jGlpaXm8OHDZty4ccbj8RhjjJkwYYLZsmWLcRzHPPHEE2b16tXGGGM+//xzM3v2bGOMMV999ZWZPHlyHT4775eVlWV27NhhjDGmsLDQ3HnnnWbfvn06hw2E4zimqKjIGGNMWVmZeeCBB8yWLVt0/hqgjz/+2EydOtU89dRTxhi9jkrDVNV7ggkTJpgNGzYYY4xZuHCheeutt4wxxixbtsxMmTLFGGNMcXGxue2228zhw4crt6nqWm6oaqpdHnnkEbN9+/a6DV+LTqVdPvvsMzNz5kxjjDFHjhwx99133y++9jVUNdUu3nS91MX71YaoJtulMV8vRUVFZtOmTWb+/PnmpZdeOmZf3nS9nK5GPexu+/btJCYmkpCQgK+vLwMGDGDFihVux2q0OnXqVFlJ/8mKFSs477zzADjvvPMqz8+KFSsYMGAAfn5+xMfHk5iYyPbt28nOzqaoqIh27dphWRaDBg2q3GblypUMHjwYgLPPPpv169djNOVZjYmKiiI5ORmAoKAgmjVrRlZWls5hA2FZFoGBgQB4PB48Hg+WZen8NTCZmZmsXr2aYcOGVS7TOZSGqKr3BAcOHKBjx44AdOvWjW+//bbyseLiYjweD6Wlpfj6+hIcHHzCa7mhqol28Uan0i6pqal06dIFgIiICEJCQti5c2ejv16qaxdvUxfvVxuimmoXb3Oq7RIYGEiHDh0qe339xNuul9PVqItPWVlZxMTEVP4eExNDVlaWi4nk53JycoiKigIq/vHn5uYCx5+76OhosrKyTnhOj37Mx8eH4OBg8vLy6uqpNCppaWns2rWLNm3a6Bw2II7jcO+993LTTTfRtWtX2rZtq/PXwLzyyitce+21x3Tj1jkUb5GUlMTKlSsB+Oabb8jMzAQqCqGBgYH84Q9/4LbbbuOyyy4jNDS00bzPO9V2+cmsWbO49957ef/9972yiFxdu7Rq1YqVK1fi8XhIS0tj586dZGRkNPrrpbp2+Yk3Xi+19X61oTuTdvlJY71equPN18upaNRzPlX1D6HRjbtsoKp7ETvRi5vOd90oLi5m0qRJjB49+oTfsOoc1j+2bfPss89SUFDA3//+d/bu3Vvtujp/9c+qVauIiIggOTmZDRs2/OL6OofS0Nx6663MnTuX999/nz59+uDrW/E2dvv27di2zezZsykoKODhhx+ma9euXvOB55ecarskJCRw5513Eh0dTVFREZMmTWLp0qWV3+J7i+raZciQIaSmpjJhwgTi4uJo3749Pj4+jf56qa5dAK+8Xmrz/WpDdqbtAo37eqmOt14vp6pRF59iYmIqq/1QMVzhpwqm1A8RERFkZ2cTFRVFdnY24eHhwPHnLisri+jo6CrPaXR09DHbxMTE4PF4KCwsPK4rspyZ8vJyJk2axMCBA+nXrx+gc9gQhYSE0KlTJ9auXavz14Bs2bKFlStXsmbNGkpLSykqKmL69Ok6h+I1mjVrxsSJE4GKoUOrV68G4KuvvqJHjx74+voSERFB+/bt2bFjBx07dqz2WvYmp9ouCQkJle0QFBTEueeey/bt2xv8h8Ofq65dfHx8GD16dOV6EydOpEmTJoSEhDTq66W6dgG87nqp7ferDVVNtAs07uulOt54vZyORj3sLiUlhYMHD5KWlkZ5eTnLly+nT58+bseSo/Tp04clS5YAsGTJEvr27Vu5fPny5ZSVlZGWlsbBgwdp06YNUVFRBAUFsXXrVowxLF26tPKc9u7dm8WLFwMV3Yw7d+6sb+xrkDGG559/nmbNmnHppZdWLtc5bBhyc3MpKCgAKu58t27dOpo1a6bz14Bcc801PP/888ycOZPx48fTpUsX7rzzTp1D8Ro5OTlAxRDhDz74gPPPPx+A2NjYyvnHiouL2bZtG82aNTvhtexNTrVdPB5P5RCR8vJyVq1aRVJSkmv5a0t17VJSUkJxcTEAP/zwAz4+PjRv3rzRXy/VtYu3XS918X61Iaqpdmns10t1vO16OV2WaeR9wFavXs2rr76K4zgMGTKEK6+80u1IjdbUqVPZuHEjeXl5REREMHLkSPr27cuUKVPIyMggNjaWu+++u/Jb9g8++IAvv/yy8nawPXv2BGDHjh3MmjWL0tJSevTowY033ohlWZSWljJjxgx27dpFaGgo48ePJyEhwc2n7FU2b97Mww8/TIsWLSo/jI4aNYq2bdvqHDYAe/bsYebMmTiOgzGG/v37M2LECPLy8nT+GqANGzbw8ccfM2HCBJ1DaZCqek9QXFzM/PnzATjrrLO45pprsCyL4uJiZs2aRWpqKsYYhgwZwuWXXw5Ufy03VDXRLsXFxTzyyCN4PB4cx6Fr167ccMMN2HbD/U76VNolLS2NJ554Atu2iY6O5pZbbiEuLg5o3NdLde3ibddLXbxfbYhqql10vcDtt99OYWEh5eXlhISEMHHiRJo3b+5V18vpavTFJxERERERERERqT0NswQpIiIiIiIiIiINgopPIiIiIiIiIiJSa1R8EhERERERERGRWqPik4iIiIiIiIiI1BoVn0REREREREREpNao+CQiZ2zmzJm8/fbbrhzbGMOsWbMYM2YMDzzwgCsZfrJs2TL++te/uppBRERERESkvlHxScQL3X777fz+97+nuLi4ctnChQt59NFH3QtVSzZv3swPP/zAP/7xD5566qnjHl+8eDEPPfRQnWQZOHAgEydOrJNj/dyGDRu45ZZbXDm2iIiIeJ/p06cza9asY5Zt3LiRG2+8kezsbJdSiUhDpeKTiJfyeDx8+umnbsc4ZY7jnNL66enpxMXFERgYWEuJ6obH43E7goiIiEilMWPGsGbNGn744QcASktLmT17Ntdffz1RUVFnvH+99xFpXHzdDiAitePyyy/nX//6FxdeeCEhISHHPJaWlsa4ceN466238PHxAeDRRx9l4MCBDBs2jMWLF7Nw4UJSUlJYvHgxoaGh3HHHHRw8eJB33nmHsrIyrr32WgYPHly5z9zcXB5//HG2bdtG69atGTduHHFxcQDs37+fOXPmsHPnTsLDw7n66qsZMGAAUDFkz9/fn4yMDDZu3Mi9995Lt27djsmblZXFiy++yObNmwkNDeXXv/41w4cPZ9GiRbz88suUl5dz3XXXcdlllzFy5MgTtsvtt9/OhRdeyNKlSzl8+DADBgxg1KhRzJo1i82bN9O2bVvuuusuQkNDK9vp1ltv5d1336W4uJhRo0aRnJzM888/T0ZGBgMHDmTs2LEAle32+OOPA/D9998zZ84cjhw5wsCBA9m3bx+DBg06ro2XLFnChRdeyODBg5k9ezZ79uzBsiy6d+/O2LFjK8/f0dnT09Pp0aMHt99+O47j8OSTT1a2A8C0adPIysripZde4uDBg/j7+3Puuedyww03nM7lJCIiIo1MWFgYN954I7Nnz2bSpEl88MEHJCQk0LRpUyZOnEhqaipxcXGMHj2azp07A/Dll1/y0UcfkZmZSXh4OL/+9a85//zzgYpe2s899xwXXXQRn3zyCd26deOGG26ofA9mWRZJSUk8+uij2Lb6SIh4GxWfRLxUcnIynTt35uOPP+a3v/3tKW+/bds2hg4dypw5c3j33XeZOnUqvXv3Zvr06WzcuJFJkyZx9tlnV/Y4+uqrr5gwYQJt27bljTfeYPr06Tz++OMUFxfz17/+lZEjR/Lggw+yZ88ennjiCZKSkkhKSqrc9oEHHuD++++nvLz8uCzTpk0jKSmJ2bNnc+DAAR5//HESEhIYOnQotm0fU/A5Gd9++y0TJ07EcRzuu+8+du/ezS233ELz5s158skn+eyzz7jqqquOaYtp06axadMmnnnmGbp3785DDz2Ex+Phvvvuo3///nTq1OmYY+Tm5jJ58mRuu+02+vTpw/z581m4cCGDBg06Zr8DBgzgpZdewuPxkJWVxW9+8xs6duxIUVERkyZN4r333mP06NGV2/z3v//lwQcfxN/fn4ceeojFixdzwQUX8OCDD/Lcc8/x/PPPV647adIkLrnkEgYNGkRxcTF79+496TYSERER6d+/P8uXL2fatGls2bKFv/3tb9x///2MGzeOHj16sH79eiZNmsTUqVMJDw8nIiKC+++/n4SEBDZt2sSTTz5JSkoKycnJABw5coT8/HxmzZqFMYb333+f6OhoXnrpJaDivZFlWW4+ZRGpJSopi3ixkSNH8tlnn5Gbm3vK28bHxzNkyBBs22bAgAFkZmYyYsQI/Pz86N69O76+vhw6dKhy/V69etGpUyf8/PwYNWoUW7duJSMjg9WrVxMXF8eQIUPw8fEhOTmZfv368c0331Ru27dvXzp06IBt2/j7+x+TIyMjg82bN/O73/0Of39/WrVqxbBhw1i6dOlpt8tFF11EZGQk0dHRdOjQgTZt2tC6dWv8/Pw466yz2LVr1zHrjxgxAn9/f7p3705AQADnnnsuERERldv/fH2ANWvW0Lx5c/r164ePjw8XX3wxkZGRx6wTFRXFxRdfjI+PD/7+/iQmJtKtWzf8/PwIDw/nV7/6FRs3bjxmm4svvpjo6GhCQ0Pp3bs3u3fvrvZ5/nSOcnNzCQwMpF27dqfdZiIiItI4jR07lvXr1zNixAi+/vprevbsSa9evbBtm27dupGSksLq1auBiveDiYmJWJZFp06d6NatG5s3b67cl2VZjBw5Ej8/P/z9/fHx8eHIkSNkZGTg6+tLx44dVXwS8VLq+STixVq0aEHv3r358MMPadas2SltGxERUfnzTwWho4sn/v7+x0xoHhMTU/lzYGAgoaGhZGdnk56ezrZt247pvePxeI7pAXT0tj+XnZ1NaGgoQUFBlctiY2PZsWPHKT2fo/38uf3895KSklNa/+h2ODr30c/Lsiyio6OPWSc2NvaY33Nycpg7dy6bNm2iuLgYx3EIDQ09Zp2fn4OsrKxqn+ctt9zCO++8w1133UV8fDwjRoygd+/e1a4vIiIi8nORkZGEh4fTvHlzvvvuO7755htWrVpV+bjH46kcdrdmzRref/99Dhw4gDGGkpISWrRoUblueHj4MV80Xn755bz33nuVdwsePnw4V1xxRd08MRGpUyo+iXi5kSNHcv/993PppZdWLvtpqFxJSQnBwcFARTfoM5GZmVn5c3FxMfn5+URFRRETE0OnTp1OeMe5E33DFRUVRX5+PkVFRZUFqIyMjOMKOfVNZGTkMYUhY8wJC0UA//znPwH4+9//TlhYGN999x1z5sw5qeNV1YZNmjRh/PjxOI7Dd999x+TJk3n55Zcb/OTsIiIi4o6YmBgGDhxY5R12y8rKmDRpEuPGjaNPnz74+vryzDPPHLPOz9+vBAUFcf3113P99dezb98+HnvsMVJSUujatWutPg8RqXsadifi5RITE+nfvz+fffZZ5bLw8HCio6NZtmwZjuOwaNEiDh8+fEbHWbNmDZs3b6a8vJy3336btm3bEhsbS+/evTl48CBLly6lvLyc8vJytm/fTmpq6kntNzY2lvbt2/PPf/6T0tJS9uzZw5dffsnAgQPPKG9t69WrF3v37uW7777D4/Ewf/78XyzwFRUVERgYSEhICFlZWXz88ccnfbyIiAjy8vIoLCysXLZ06VJyc3OxbbuyyKgJPEVEROR0DRw4kFWrVrF27Vocx6G0tJQNGzaQmZlJeXk5ZWVlhIeH4+Pjc8yd8qqzatUqDh06hDGGoKAgbNvWexURL6WeTyKNwIgRI1i2bNkxy26++WZeeukl3nrrLYYOHXrG8wGdc845vPfee2zdupXk5GTuvPNOoOIbrYkTJ/Lqq6/y6quvYoyhZcuWp3TXtT/+8Y+8+OKL3HzzzYSGhnLVVVcdd0e8+iY8PJy7776buXPnMnPmTAYOHEhycjJ+fn7VbnPVVVcxY8YMbrjhBhITExk0aBCffPLJSR2vWbNmnHPOOYwbNw7HcZg8eTJr167ltddeo6SkhLi4OP74xz8eN6eWiIiIyMmKjY3lvvvu44033mDatGnYtk2bNm34/e9/T1BQEGPGjGHKlCmUlZXRu3dv+vTpc8L9HTx4kDlz5pCbm0tISAgXXHBB5RA+EfEuljHGuB1CRMTbOY7Drbfeyh133EGXLl3cjiMiIiIiIlJn1KdRRKSWrF27loKCAsrKypg3bx7GGN1xTkREREREGh0NuxMRqSVbt25l+vTplJeX07x5c+69914NexMRERERkUZHw+5ERERERERERKTWaNidiIiIiIiIiIjUGhWfRERERERERESk1qj4JCIiIiIiIiIitUbFJxERERERERERqTUqPomIiIiIiIiISK1R8UlERERERERERGrN/wFZID00OYwpZwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"![](\\\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig3FeatureScaling.png\\\")
\\n\",\n \"\\n\",\n \"where _`X`_ is an original value, _`X'`_ is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# normalize Brazil data\\n\",\n \"norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\\n\",\n \"\\n\",\n \"# normalize Argentina data\\n\",\n \"norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Step 3: Plot the data. \\n\",\n \"\\n\",\n \"- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \\n\",\n \"- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\\n\",\n \" - multiply weights by 2000 to scale it up on the graph, and,\\n\",\n \" - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 102,\n \"metadata\": {\n \"button\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"![](\\\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/coursera/Images/2000_census_population_density_map_by_state.png\\\")
\\n\",\n \"# display the cloud\\n\",\n \"fig = plt.figure()\\n\",\n \"fig.set_figwidth(14)\\n\",\n \"fig.set_figheight(18)\\n\",\n \"\\n\",\n \"plt.imshow(wordcloud, interpolation='bilinear')\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"# Choropleth Maps \\n\",\n \"\\n\",\n \"A `Choropleth` map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region. Below is a `Choropleth` map of the US depicting the population by square mile per state.\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now, let's create our own `Choropleth` map of the world depicting immigration from various countries to Canada.\\n\",\n \"\\n\",\n \"\\n\",\n \"```\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"In order to create a `Choropleth` map, we need a GeoJSON file that defines the areas/boundaries of the state, county, or country that we are interested in. In our case, since we are endeavoring to create a world map, we want a GeoJSON that defines the boundaries of all world countries. For your convenience, we will be providing you with this file, so let's go ahead and download it. Let's name it **world_countries.json**.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 175,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"GeoJSON file downloaded!\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"'wget' is not recognized as an internal or external command,\\n\",\n \"operable program or batch file.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# download countries geojson file\\n\",\n \"!wget --quiet https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/world_countries.json\\n\",\n \" \\n\",\n \"print('GeoJSON file downloaded!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"Now that we have the GeoJSON file, let's create a world map, centered around **[0, 0]** _latitude_ and _longitude_ values, with an intial zoom level of 2.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 177,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Folium installed and imported!\\n\"\n ]\n }\n ],\n \"source\": [\n \"import folium\\n\",\n \"\\n\",\n \"print('Folium installed and imported!')\\n\",\n \"world_geo = r'world_countries.json' # geojson file\\n\",\n \"\\n\",\n \"# create a plain world map\\n\",\n \"world_map = folium.Map(location=[0, 0], zoom_start=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"source\": [\n \"And now to create a `Choropleth` map, we will use the _choropleth_ method with the following main parameters:\\n\",\n \"\\n\",\n \"1. geo_data, which is the GeoJSON file.\\n\",\n \"2. data, which is the dataframe containing the data.\\n\",\n \"3. columns, which represents the columns in the dataframe that will be used to create the `Choropleth` map.\\n\",\n \"4. key_on, which is the key or variable in the GeoJSON file that contains the name of the variable of interest. To determine that, you will need to open the GeoJSON file using any text editor and note the name of the key or variable that contains the name of the countries, since the countries are our variable of interest. In this case, **name** is the key in the GeoJSON file that contains the name of the countries. Note that this key is case_sensitive, so you need to pass exactly as it exists in the GeoJSON file.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 185,\n \"metadata\": {\n \"button\": false,\n \"new_sheet\": false,\n \"run_control\": {\n \"read_only\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"![](\\\"Web)
\\n\",\n \"\\n\",\n \"The diagram above gives an overview of how the various services will work together. On the far right is the model which we trained above and which is deployed using SageMaker. On the far left is our web app that collects a user's movie review, sends it off and expects a positive or negative sentiment in return.\\n\",\n \"\\n\",\n \"In the middle is where some of the magic happens. We will construct a Lambda function, which you can think of as a straightforward Python function that can be executed whenever a specified event occurs. We will give this function permission to send and recieve data from a SageMaker endpoint.\\n\",\n \"\\n\",\n \"Lastly, the method we will use to execute the Lambda function is a new endpoint that we will create using API Gateway. This endpoint will be a url that listens for data to be sent to it. Once it gets some data it will pass that data on to the Lambda function and then return whatever the Lambda function returns. Essentially it will act as an interface that lets our web app communicate with the Lambda function.\\n\",\n \"\\n\",\n \"### Setting up a Lambda function\\n\",\n \"\\n\",\n \"The first thing we are going to do is set up a Lambda function. This Lambda function will be executed whenever our public API has data sent to it. When it is executed it will receive the data, perform any sort of processing that is required, send the data (the review) to the SageMaker endpoint we've created and then return the result.\\n\",\n \"\\n\",\n \"#### Part A: Create an IAM Role for the Lambda function\\n\",\n \"\\n\",\n \"Since we want the Lambda function to call a SageMaker endpoint, we need to make sure that it has permission to do so. To do this, we will construct a role that we can later give the Lambda function.\\n\",\n \"\\n\",\n \"Using the AWS Console, navigate to the **IAM** page and click on **Roles**. Then, click on **Create role**. Make sure that the **AWS service** is the type of trusted entity selected and choose **Lambda** as the service that will use this role, then click **Next: Permissions**.\\n\",\n \"\\n\",\n \"In the search box type `sagemaker` and select the check box next to the **AmazonSageMakerFullAccess** policy. Then, click on **Next: Review**.\\n\",\n \"\\n\",\n \"Lastly, give this role a name. Make sure you use a name that you will remember later on, for example `LambdaSageMakerRole`. Then, click on **Create role**.\\n\",\n \"\\n\",\n \"#### Part B: Create a Lambda function\\n\",\n \"\\n\",\n \"Now it is time to actually create the Lambda function.\\n\",\n \"\\n\",\n \"Using the AWS Console, navigate to the AWS Lambda page and click on **Create a function**. When you get to the next page, make sure that **Author from scratch** is selected. Now, name your Lambda function, using a name that you will remember later on, for example `sentiment_analysis_func`. Make sure that the **Python 3.6** runtime is selected and then choose the role that you created in the previous part. Then, click on **Create Function**.\\n\",\n \"\\n\",\n \"On the next page you will see some information about the Lambda function you've just created. If you scroll down you should see an editor in which you can write the code that will be executed when your Lambda function is triggered. In our example, we will use the code below. \\n\",\n \"\\n\",\n \"```python\\n\",\n \"# We need to use the low-level library to interact with SageMaker since the SageMaker API\\n\",\n \"# is not available natively through Lambda.\\n\",\n \"import boto3\\n\",\n \"\\n\",\n \"def lambda_handler(event, context):\\n\",\n \"\\n\",\n \" # The SageMaker runtime is what allows us to invoke the endpoint that we've created.\\n\",\n \" runtime = boto3.Session().client('sagemaker-runtime')\\n\",\n \"\\n\",\n \" # Now we use the SageMaker runtime to invoke our endpoint, sending the review we were given\\n\",\n \" response = runtime.invoke_endpoint(EndpointName = '**ENDPOINT NAME HERE**', # The name of the endpoint we created\\n\",\n \" ContentType = 'text/plain', # The data format that is expected\\n\",\n \" Body = event['body']) # The actual review\\n\",\n \"\\n\",\n \" # The response is an HTTP response whose body contains the result of our inference\\n\",\n \" result = response['Body'].read().decode('utf-8')\\n\",\n \"\\n\",\n \" return {\\n\",\n \" 'statusCode' : 200,\\n\",\n \" 'headers' : { 'Content-Type' : 'text/plain', 'Access-Control-Allow-Origin' : '*' },\\n\",\n \" 'body' : result\\n\",\n \" }\\n\",\n \"```\\n\",\n \"\\n\",\n \"Once you have copy and pasted the code above into the Lambda code editor, replace the `**ENDPOINT NAME HERE**` portion with the name of the endpoint that we deployed earlier. You can determine the name of the endpoint using the code cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 135,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sagemaker-pytorch-2021-05-26-12-08-55-597'\"\n ]\n },\n \"execution_count\": 135,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predictor.endpoint\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once you have added the endpoint name to the Lambda function, click on **Save**. Your Lambda function is now up and running. Next we need to create a way for our web app to execute the Lambda function.\\n\",\n \"\\n\",\n \"### Setting up API Gateway\\n\",\n \"\\n\",\n \"Now that our Lambda function is set up, it is time to create a new API using API Gateway that will trigger the Lambda function we have just created.\\n\",\n \"\\n\",\n \"Using AWS Console, navigate to **Amazon API Gateway** and then click on **Get started**.\\n\",\n \"\\n\",\n \"On the next page, make sure that **New API** is selected and give the new api a name, for example, `sentiment_analysis_api`. Then, click on **Create API**.\\n\",\n \"\\n\",\n \"Now we have created an API, however it doesn't currently do anything. What we want it to do is to trigger the Lambda function that we created earlier.\\n\",\n \"\\n\",\n \"Select the **Actions** dropdown menu and click **Create Method**. A new blank method will be created, select its dropdown menu and select **POST**, then click on the check mark beside it.\\n\",\n \"\\n\",\n \"For the integration point, make sure that **Lambda Function** is selected and click on the **Use Lambda Proxy integration**. This option makes sure that the data that is sent to the API is then sent directly to the Lambda function with no processing. It also means that the return value must be a proper response object as it will also not be processed by API Gateway.\\n\",\n \"\\n\",\n \"Type the name of the Lambda function you created earlier into the **Lambda Function** text entry box and then click on **Save**. Click on **OK** in the pop-up box that then appears, giving permission to API Gateway to invoke the Lambda function you created.\\n\",\n \"\\n\",\n \"The last step in creating the API Gateway is to select the **Actions** dropdown and click on **Deploy API**. You will need to create a new Deployment stage and name it anything you like, for example `prod`.\\n\",\n \"\\n\",\n \"You have now successfully set up a public API to access your SageMaker model. Make sure to copy or write down the URL provided to invoke your newly created public API as this will be needed in the next step. This URL can be found at the top of the page, highlighted in blue next to the text **Invoke URL**.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Deploying our web app\\n\",\n \"\\n\",\n \"Now that we have a publicly available API, we can start using it in a web app. For our purposes, we have provided a simple static html file which can make use of the public api you created earlier.\\n\",\n \"\\n\",\n \"In the `website` folder there should be a file called `index.html`. Download the file to your computer and open that file up in a text editor of your choice. There should be a line which contains **\\\\*\\\\*REPLACE WITH PUBLIC API URL\\\\*\\\\***. Replace this string with the url that you wrote down in the last step and then save the file.\\n\",\n \"\\n\",\n \"Now, if you open `index.html` on your local computer, your browser will behave as a local web server and you can use the provided site to interact with your SageMaker model.\\n\",\n \"\\n\",\n \"If you'd like to go further, you can host this html file anywhere you'd like, for example using github or hosting a static site on Amazon's S3. Once you have done this you can share the link with anyone you'd like and have them play with it too!\\n\",\n \"\\n\",\n \"> **Important Note** In order for the web app to communicate with the SageMaker endpoint, the endpoint has to actually be deployed and running. This means that you are paying for it. Make sure that the endpoint is running when you want to use the web app but that you shut it down when you don't need it, otherwise you will end up with a surprisingly large AWS bill.\\n\",\n \"\\n\",\n \"**TODO:** Make sure that you include the edited `index.html` file in your project submission.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that your web app is working, trying playing around with it and see how well it works.\\n\",\n \"\\n\",\n \"**Question**: Give an example of a review that you entered into your web app. What was the predicted sentiment of your example review?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Answer:** \\n\",\n \"\\\"The movie was so good, i actually recommend it to everyone.\\\"\\n\",\n \"The web app classified it as a postive review. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Delete the endpoint\\n\",\n \"\\n\",\n \"Remember to always shut down your endpoint if you are no longer using it. You are charged for the length of time that the endpoint is running so if you forget and leave it on you could end up with an unexpectedly large bill.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 136,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"predictor.delete_endpoint()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda_pytorch_p36\",\n \"language\": \"python\",\n \"name\": \"conda_pytorch_p36\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/sevre/model.py",
"content": "import torch.nn as nn\n\nclass LSTMClassifier(nn.Module):\n \"\"\"\n This is the simple RNN model we will be using to perform Sentiment Analysis.\n \"\"\"\n\n def __init__(self, embedding_dim, hidden_dim, vocab_size):\n \"\"\"\n Initialize the model by settingg up the various layers.\n \"\"\"\n super(LSTMClassifier, self).__init__()\n\n self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)\n self.lstm = nn.LSTM(embedding_dim, hidden_dim)\n self.dense = nn.Linear(in_features=hidden_dim, out_features=1)\n self.sig = nn.Sigmoid()\n \n self.word_dict = None\n\n def forward(self, x):\n \"\"\"\n Perform a forward pass of our model on some input.\n \"\"\"\n x = x.t()\n lengths = x[0,:]\n reviews = x[1:,:]\n embeds = self.embedding(reviews)\n lstm_out, _ = self.lstm(embeds)\n out = self.dense(lstm_out)\n out = out[lengths - 1, range(len(lengths))]\n return self.sig(out.squeeze())"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/sevre/predict.py",
"content": "import argparse\nimport json\nimport os\nimport pickle\nimport sys\nimport sagemaker_containers\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.utils.data\n\nfrom model import LSTMClassifier\n\nfrom utils import review_to_words, convert_and_pad\n\ndef model_fn(model_dir):\n \"\"\"Load the PyTorch model from the `model_dir` directory.\"\"\"\n print(\"Loading model.\")\n\n # First, load the parameters used to create the model.\n model_info = {}\n model_info_path = os.path.join(model_dir, 'model_info.pth')\n with open(model_info_path, 'rb') as f:\n model_info = torch.load(f)\n\n print(\"model_info: {}\".format(model_info))\n\n # Determine the device and construct the model.\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = LSTMClassifier(model_info['embedding_dim'], model_info['hidden_dim'], model_info['vocab_size'])\n\n # Load the store model parameters.\n model_path = os.path.join(model_dir, 'model.pth')\n with open(model_path, 'rb') as f:\n model.load_state_dict(torch.load(f))\n\n # Load the saved word_dict.\n word_dict_path = os.path.join(model_dir, 'word_dict.pkl')\n with open(word_dict_path, 'rb') as f:\n model.word_dict = pickle.load(f)\n\n model.to(device).eval()\n\n print(\"Done loading model.\")\n return model\n\ndef input_fn(serialized_input_data, content_type):\n print('Deserializing the input data.')\n if content_type == 'text/plain':\n data = serialized_input_data.decode('utf-8')\n return data\n raise Exception('Requested unsupported ContentType in content_type: ' + content_type)\n\ndef output_fn(prediction_output, accept):\n print('Serializing the generated output.')\n return str(prediction_output)\n\ndef predict_fn(input_data, model):\n print('Inferring sentiment of input data.')\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n \n if model.word_dict is None:\n raise Exception('Model has not been loaded properly, no word_dict.')\n \n # TODO: Process input_data so that it is ready to be sent to our model.\n # You should produce two variables:\n # data_X - A sequence of length 500 which represents the converted review\n # data_len - The length of the review\n \n words = review_to_words(input_data)\n data_X, data_len = convert_and_pad(model.word_dict, words)\n\n # Using data_X and data_len we construct an appropriate input tensor. Remember\n # that our model expects input data of the form 'len, review[500]'.\n data_pack = np.hstack((data_len, data_X))\n data_pack = data_pack.reshape(1, -1)\n \n data = torch.from_numpy(data_pack)\n data = data.to(device)\n\n # Make sure to put the model into evaluation mode\n model.eval()\n\n # TODO: Compute the result of applying the model to the input data. The variable `result` should\n # be a numpy array which contains a single integer which is either 1 or 0\n\n with torch.no_grad():\n output = model.forward(data)\n \n result = int(np.round(output.numpy()))\n return result\n"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/sevre/requirements.txt",
"content": "pandas\nnumpy\nnltk\nbeautifulsoup4\nhtml5lib"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/sevre/utils.py",
"content": "import nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import *\n\nimport re\nfrom bs4 import BeautifulSoup\n\nimport pickle\n\nimport os\nimport glob\n\ndef review_to_words(review):\n nltk.download(\"stopwords\", quiet=True)\n stemmer = PorterStemmer()\n \n text = BeautifulSoup(review, \"html.parser\").get_text() # Remove HTML tags\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower()) # Convert to lower case\n words = text.split() # Split string into words\n words = [w for w in words if w not in stopwords.words(\"english\")] # Remove stopwords\n words = [PorterStemmer().stem(w) for w in words] # stem\n \n return words\n\ndef convert_and_pad(word_dict, sentence, pad=500):\n NOWORD = 0 # We will use 0 to represent the 'no word' category\n INFREQ = 1 # and we use 1 to represent the infrequent words, i.e., words not appearing in word_dict\n \n working_sentence = [NOWORD] * pad\n \n for word_index, word in enumerate(sentence[:pad]):\n if word in word_dict:\n working_sentence[word_index] = word_dict[word]\n else:\n working_sentence[word_index] = INFREQ\n \n return working_sentence, min(len(sentence), pad)"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/train/model.py",
"content": "import torch.nn as nn\n\nclass LSTMClassifier(nn.Module):\n \"\"\"\n This is the simple RNN model we will be using to perform Sentiment Analysis.\n \"\"\"\n\n def __init__(self, embedding_dim, hidden_dim, vocab_size):\n \"\"\"\n Initialize the model by settingg up the various layers.\n \"\"\"\n super(LSTMClassifier, self).__init__()\n\n self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)\n self.lstm = nn.LSTM(embedding_dim, hidden_dim)\n self.dense = nn.Linear(in_features=hidden_dim, out_features=1)\n self.sig = nn.Sigmoid()\n \n self.word_dict = None\n\n def forward(self, x):\n \"\"\"\n Perform a forward pass of our model on some input.\n \"\"\"\n x = x.t()\n lengths = x[0,:]\n reviews = x[1:,:]\n embeds = self.embedding(reviews)\n lstm_out, _ = self.lstm(embeds)\n out = self.dense(lstm_out)\n out = out[lengths - 1, range(len(lengths))]\n return self.sig(out.squeeze())"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/train/requirements.txt",
"content": "pandas\nnumpy\nnltk\nbeautifulsoup4\nhtml5lib"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/train/train.py",
"content": "import argparse\nimport json\nimport os\nimport pickle\nimport sys\nimport sagemaker_containers\nimport pandas as pd\nimport torch\nimport torch.optim as optim\nimport torch.utils.data\n\nfrom model import LSTMClassifier\n\ndef model_fn(model_dir):\n \"\"\"Load the PyTorch model from the `model_dir` directory.\"\"\"\n print(\"Loading model.\")\n\n # First, load the parameters used to create the model.\n model_info = {}\n model_info_path = os.path.join(model_dir, 'model_info.pth')\n with open(model_info_path, 'rb') as f:\n model_info = torch.load(f)\n\n print(\"model_info: {}\".format(model_info))\n\n # Determine the device and construct the model.\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = LSTMClassifier(model_info['embedding_dim'], model_info['hidden_dim'], model_info['vocab_size'])\n\n # Load the stored model parameters.\n model_path = os.path.join(model_dir, 'model.pth')\n with open(model_path, 'rb') as f:\n model.load_state_dict(torch.load(f))\n\n # Load the saved word_dict.\n word_dict_path = os.path.join(model_dir, 'word_dict.pkl')\n with open(word_dict_path, 'rb') as f:\n model.word_dict = pickle.load(f)\n\n model.to(device).eval()\n\n print(\"Done loading model.\")\n return model\n\ndef _get_train_data_loader(batch_size, training_dir):\n print(\"Get train data loader.\")\n\n train_data = pd.read_csv(os.path.join(training_dir, \"train.csv\"), header=None, names=None)\n\n train_y = torch.from_numpy(train_data[[0]].values).float().squeeze()\n train_X = torch.from_numpy(train_data.drop([0], axis=1).values).long()\n\n train_ds = torch.utils.data.TensorDataset(train_X, train_y)\n\n return torch.utils.data.DataLoader(train_ds, batch_size=batch_size)\n\n\ndef train(model, train_loader, epochs, optimizer, loss_fn, device):\n \"\"\"\n This is the training method that is called by the PyTorch training script. The parameters\n passed are as follows:\n model - The PyTorch model that we wish to train.\n train_loader - The PyTorch DataLoader that should be used during training.\n epochs - The total number of epochs to train for.\n optimizer - The optimizer to use during training.\n loss_fn - The loss function used for training.\n device - Where the model and data should be loaded (gpu or cpu).\n \"\"\"\n \n # TODO: Paste the train() method developed in the notebook here.\n\n for epoch in range(1, epochs + 1):\n model.train()\n total_loss = 0\n for batch in train_loader: \n batch_X, batch_y = batch\n \n batch_X = batch_X.to(device)\n batch_y = batch_y.to(device)\n \n # TODO: Complete this train method to train the model provided.\n \n output = model(batch_X)\n loss = loss_fn(output, batch_y)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.data.item()\n print(\"Epoch: {}, BCELoss: {}\".format(epoch, total_loss / len(train_loader)))\n\n\nif __name__ == '__main__':\n # All of the model parameters and training parameters are sent as arguments when the script\n # is executed. Here we set up an argument parser to easily access the parameters.\n\n parser = argparse.ArgumentParser()\n\n # Training Parameters\n parser.add_argument('--batch-size', type=int, default=512, metavar='N',\n help='input batch size for training (default: 512)')\n parser.add_argument('--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)')\n parser.add_argument('--seed', type=int, default=1, metavar='S',\n help='random seed (default: 1)')\n\n # Model Parameters\n parser.add_argument('--embedding_dim', type=int, default=32, metavar='N',\n help='size of the word embeddings (default: 32)')\n parser.add_argument('--hidden_dim', type=int, default=100, metavar='N',\n help='size of the hidden dimension (default: 100)')\n parser.add_argument('--vocab_size', type=int, default=5000, metavar='N',\n help='size of the vocabulary (default: 5000)')\n\n # SageMaker Parameters\n parser.add_argument('--hosts', type=list, default=json.loads(os.environ['SM_HOSTS']))\n parser.add_argument('--current-host', type=str, default=os.environ['SM_CURRENT_HOST'])\n parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])\n parser.add_argument('--data-dir', type=str, default=os.environ['SM_CHANNEL_TRAINING'])\n parser.add_argument('--num-gpus', type=int, default=os.environ['SM_NUM_GPUS'])\n\n args = parser.parse_args()\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n print(\"Using device {}.\".format(device))\n\n torch.manual_seed(args.seed)\n\n # Load the training data.\n train_loader = _get_train_data_loader(args.batch_size, args.data_dir)\n\n # Build the model.\n model = LSTMClassifier(args.embedding_dim, args.hidden_dim, args.vocab_size).to(device)\n\n with open(os.path.join(args.data_dir, \"word_dict.pkl\"), \"rb\") as f:\n model.word_dict = pickle.load(f)\n\n print(\"Model loaded with embedding_dim {}, hidden_dim {}, vocab_size {}.\".format(\n args.embedding_dim, args.hidden_dim, args.vocab_size\n ))\n\n # Train the model.\n optimizer = optim.Adam(model.parameters())\n loss_fn = torch.nn.BCELoss()\n\n train(model, train_loader, args.epochs, optimizer, loss_fn, device)\n\n # Save the parameters used to construct the model\n model_info_path = os.path.join(args.model_dir, 'model_info.pth')\n with open(model_info_path, 'wb') as f:\n model_info = {\n 'embedding_dim': args.embedding_dim,\n 'hidden_dim': args.hidden_dim,\n 'vocab_size': args.vocab_size,\n }\n torch.save(model_info, f)\n\n\t# Save the word_dict\n word_dict_path = os.path.join(args.model_dir, 'word_dict.pkl')\n with open(word_dict_path, 'wb') as f:\n pickle.dump(model.word_dict, f)\n\n\t# Save the model parameters\n model_path = os.path.join(args.model_dir, 'model.pth')\n with open(model_path, 'wb') as f:\n torch.save(model.cpu().state_dict(), f)\n"
},
{
"path": "Natural_Language_processing/Sentiment-analysis/website/index.html",
"content": "\n\n \n 
\\n\",\n \"\\n\",\n \"* Now, those words appear in left-to-right order in each document, sequentially, and even though there are some words in between, we count this as the longest common subsequence between the two texts. \\n\",\n \"* If I count up each word that I found in common I get the value 20. **So, LCS has length 20**. \\n\",\n \"* Next, to normalize this value, divide by the total length of the student answer; in this example that length is only 27. **So, the function `lcs_norm_word` should return the value `20/27` or about `0.7408`.**\\n\",\n \"\\n\",\n \"In this way, LCS is a great indicator of cut-and-paste plagiarism or if someone has referenced the same source text multiple times in an answer.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### LCS, dynamic programming\\n\",\n \"\\n\",\n \"If you read through the scenario above, you can see that this algorithm depends on looking at two texts and comparing them word by word. You can solve this problem in multiple ways. First, it may be useful to `.split()` each text into lists of comma separated words to compare. Then, you can iterate through each word in the texts and compare them, adding to your value for LCS as you go. \\n\",\n \"\\n\",\n \"The method I recommend for implementing an efficient LCS algorithm is: using a matrix and dynamic programming. **Dynamic programming** is all about breaking a larger problem into a smaller set of subproblems, and building up a complete result without having to repeat any subproblems. \\n\",\n \"\\n\",\n \"This approach assumes that you can split up a large LCS task into a combination of smaller LCS tasks. Let's look at a simple example that compares letters:\\n\",\n \"\\n\",\n \"* A = \\\"ABCD\\\"\\n\",\n \"* S = \\\"BD\\\"\\n\",\n \"\\n\",\n \"We can see right away that the longest subsequence of _letters_ here is 2 (B and D are in sequence in both strings). And we can calculate this by looking at relationships between each letter in the two strings, A and S.\\n\",\n \"\\n\",\n \"Here, I have a matrix with the letters of A on top and the letters of S on the left side:\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"This starts out as a matrix that has as many columns and rows as letters in the strings S and O **+1** additional row and column, filled with zeros on the top and left sides. So, in this case, instead of a 2x4 matrix it is a 3x5.\\n\",\n \"\\n\",\n \"Now, we can fill this matrix up by breaking it into smaller LCS problems. For example, let's first look at the shortest substrings: the starting letter of A and S. We'll first ask, what is the Longest Common Subsequence between these two letters \\\"A\\\" and \\\"B\\\"? \\n\",\n \"\\n\",\n \"**Here, the answer is zero and we fill in the corresponding grid cell with that value.**\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Then, we ask the next question, what is the LCS between \\\"AB\\\" and \\\"B\\\"?\\n\",\n \"\\n\",\n \"**Here, we have a match, and can fill in the appropriate value 1**.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"If we continue, we get to a final matrix that looks as follows, with a **2** in the bottom right corner.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"The final LCS will be that value **2** *normalized* by the number of n-grams in A. So, our normalized value is 2/4 = **0.5**.\\n\",\n \"\\n\",\n \"### The matrix rules\\n\",\n \"\\n\",\n \"One thing to notice here is that, you can efficiently fill up this matrix one cell at a time. Each grid cell only depends on the values in the grid cells that are directly on top and to the left of it, or on the diagonal/top-left. The rules are as follows:\\n\",\n \"* Start with a matrix that has one extra row and column of zeros.\\n\",\n \"* As you traverse your string:\\n\",\n \" * If there is a match, fill that grid cell with the value to the top-left of that cell *plus* one. So, in our case, when we found a matching B-B, we added +1 to the value in the top-left of the matching cell, 0.\\n\",\n \" * If there is not a match, take the *maximum* value from either directly to the left or the top cell, and carry that value over to the non-match cell.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"After completely filling the matrix, **the bottom-right cell will hold the non-normalized LCS value**.\\n\",\n \"\\n\",\n \"This matrix treatment can be applied to a set of words instead of letters. Your function should apply this to the words in two texts and return the normalized LCS value.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 272,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0., 0., 0.],\\n\",\n \" [0., 0., 0.]])\"\n ]\n },\n \"execution_count\": 272,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.zeros((2,3))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 273,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compute the normalized LCS given an answer text and a source text\\n\",\n \"def lcs_norm_word(answer_text, source_text):\\n\",\n \" '''Computes the longest common subsequence of words in two texts; returns a normalized value.\\n\",\n \" :param answer_text: The pre-processed text for an answer text\\n\",\n \" :param source_text: The pre-processed text for an answer's associated source text\\n\",\n \" :return: A normalized LCS value'''\\n\",\n \" \\n\",\n \" # your code here\\n\",\n \" answer_words = answer_text.split()\\n\",\n \" source_words = source_text.split()\\n\",\n \" lsc_matrix = np.empty((len(source_words)+1, len(answer_words)+1))\\n\",\n \" lsc_matrix[0,:] = 0\\n\",\n \" lsc_matrix[:,0] = 0\\n\",\n \" \\n\",\n \" for i in range(len(source_words)):\\n\",\n \" for j in range(len(answer_words)):\\n\",\n \" if answer_words[j] == source_words[i]:\\n\",\n \" lsc_matrix[i+1,j+1] = lsc_matrix[i,j] + 1\\n\",\n \" else : \\n\",\n \" lsc_matrix[i+1,j+1] = max(lsc_matrix[i,j+1], lsc_matrix[i+1,j])\\n\",\n \" \\n\",\n \" lsc_norm = lsc_matrix[i+1,j+1] / len(answer_words)\\n\",\n \" \\n\",\n \" return lsc_norm\\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test cells\\n\",\n \"\\n\",\n \"Let's start by testing out your code on the example given in the initial description.\\n\",\n \"\\n\",\n \"In the below cell, we have specified strings A (answer text) and S (original source text). We know that these texts have 20 words in common and the submitted answer is 27 words long, so the normalized, longest common subsequence should be 20/27.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 274,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"LCS = 0.7407407407407407\\n\",\n \"Test passed!\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Run the test scenario from above\\n\",\n \"# does your function return the expected value?\\n\",\n \"\\n\",\n \"A = \\\"i think pagerank is a link analysis algorithm used by google that uses a system of weights attached to each element of a hyperlinked set of documents\\\"\\n\",\n \"S = \\\"pagerank is a link analysis algorithm used by the google internet search engine that assigns a numerical weighting to each element of a hyperlinked set of documents\\\"\\n\",\n \"\\n\",\n \"# calculate LCS\\n\",\n \"lcs = lcs_norm_word(A, S)\\n\",\n \"print('LCS = ', lcs)\\n\",\n \"\\n\",\n \"\\n\",\n \"# expected value test\\n\",\n \"assert lcs==20/27., \\\"Incorrect LCS value, expected about 0.7408, got \\\"+str(lcs)\\n\",\n \"\\n\",\n \"print('Test passed!')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This next cell runs a more rigorous test.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 275,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed!\\n\"\n ]\n }\n ],\n \"source\": [\n \"# run test cell\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# test lcs implementation\\n\",\n \"# params: complete_df from before, and lcs_norm_word function\\n\",\n \"tests.test_lcs(complete_df, lcs_norm_word)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, take a look at a few resultant values for `lcs_norm_word`. Just like before, you should see that higher values correspond to higher levels of plagiarism.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 276,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Original category values: \\n\",\n \" [0, 3, 2, 1, 0]\\n\",\n \"\\n\",\n \"Normalized LCS values: \\n\",\n \" [0.1917808219178082, 0.8207547169811321, 0.8464912280701754, 0.3160621761658031, 0.24257425742574257]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# test on your own\\n\",\n \"test_indices = range(5) # look at first few files\\n\",\n \"\\n\",\n \"category_vals = []\\n\",\n \"lcs_norm_vals = []\\n\",\n \"# iterate through first few docs and calculate LCS\\n\",\n \"for i in test_indices:\\n\",\n \" category_vals.append(complete_df.loc[i, 'Category'])\\n\",\n \" # get texts to compare\\n\",\n \" answer_text = complete_df.loc[i, 'Text'] \\n\",\n \" task = complete_df.loc[i, 'Task']\\n\",\n \" # we know that source texts have Class = -1\\n\",\n \" orig_rows = complete_df[(complete_df['Class'] == -1)]\\n\",\n \" orig_row = orig_rows[(orig_rows['Task'] == task)]\\n\",\n \" source_text = orig_row['Text'].values[0]\\n\",\n \" \\n\",\n \" # calculate lcs\\n\",\n \" lcs_val = lcs_norm_word(answer_text, source_text)\\n\",\n \" lcs_norm_vals.append(lcs_val)\\n\",\n \"\\n\",\n \"# print out result, does it make sense?\\n\",\n \"print('Original category values: \\\\n', category_vals)\\n\",\n \"print()\\n\",\n \"print('Normalized LCS values: \\\\n', lcs_norm_vals)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Create All Features\\n\",\n \"\\n\",\n \"Now that you've completed the feature calculation functions, it's time to actually create multiple features and decide on which ones to use in your final model! In the below cells, you're provided two helper functions to help you create multiple features and store those in a DataFrame, `features_df`.\\n\",\n \"\\n\",\n \"### Creating multiple containment features\\n\",\n \"\\n\",\n \"Your completed `calculate_containment` function will be called in the next cell, which defines the helper function `create_containment_features`. \\n\",\n \"\\n\",\n \"> This function returns a list of containment features, calculated for a given `n` and for *all* files in a df (assumed to the the `complete_df`).\\n\",\n \"\\n\",\n \"For our original files, the containment value is set to a special value, -1.\\n\",\n \"\\n\",\n \"This function gives you the ability to easily create several containment features, of different n-gram lengths, for each of our text files.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 277,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# Function returns a list of containment features, calculated for a given n \\n\",\n \"# Should return a list of length 100 for all files in a complete_df\\n\",\n \"def create_containment_features(df, n, column_name=None):\\n\",\n \" \\n\",\n \" containment_values = []\\n\",\n \" \\n\",\n \" if(column_name==None):\\n\",\n \" column_name = 'c_'+str(n) # c_1, c_2, .. c_n\\n\",\n \" \\n\",\n \" # iterates through dataframe rows\\n\",\n \" for i in df.index:\\n\",\n \" file = df.loc[i, 'File']\\n\",\n \" # Computes features using calculate_containment function\\n\",\n \" if df.loc[i,'Category'] > -1:\\n\",\n \" c = calculate_containment(df, n, file)\\n\",\n \" containment_values.append(c)\\n\",\n \" # Sets value to -1 for original tasks \\n\",\n \" else:\\n\",\n \" containment_values.append(-1)\\n\",\n \" \\n\",\n \" print(str(n)+'-gram containment features created!')\\n\",\n \" return containment_values\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Creating LCS features\\n\",\n \"\\n\",\n \"Below, your complete `lcs_norm_word` function is used to create a list of LCS features for all the answer files in a given DataFrame (again, this assumes you are passing in the `complete_df`. It assigns a special value for our original, source files, -1.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 278,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# Function creates lcs feature and add it to the dataframe\\n\",\n \"def create_lcs_features(df, column_name='lcs_word'):\\n\",\n \" \\n\",\n \" lcs_values = []\\n\",\n \" \\n\",\n \" # iterate through files in dataframe\\n\",\n \" for i in df.index:\\n\",\n \" # Computes LCS_norm words feature using function above for answer tasks\\n\",\n \" if df.loc[i,'Category'] > -1:\\n\",\n \" # get texts to compare\\n\",\n \" answer_text = df.loc[i, 'Text'] \\n\",\n \" task = df.loc[i, 'Task']\\n\",\n \" # we know that source texts have Class = -1\\n\",\n \" orig_rows = df[(df['Class'] == -1)]\\n\",\n \" orig_row = orig_rows[(orig_rows['Task'] == task)]\\n\",\n \" source_text = orig_row['Text'].values[0]\\n\",\n \"\\n\",\n \" # calculate lcs\\n\",\n \" lcs = lcs_norm_word(answer_text, source_text)\\n\",\n \" lcs_values.append(lcs)\\n\",\n \" # Sets to -1 for original tasks \\n\",\n \" else:\\n\",\n \" lcs_values.append(-1)\\n\",\n \"\\n\",\n \" print('LCS features created!')\\n\",\n \" return lcs_values\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## EXERCISE: Create a features DataFrame by selecting an `ngram_range`\\n\",\n \"\\n\",\n \"The paper suggests calculating the following features: containment *1-gram to 5-gram* and *longest common subsequence*. \\n\",\n \"> In this exercise, you can choose to create even more features, for example from *1-gram to 7-gram* containment features and *longest common subsequence*. \\n\",\n \"\\n\",\n \"You'll want to create at least 6 features to choose from as you think about which to give to your final, classification model. Defining and comparing at least 6 different features allows you to discard any features that seem redundant, and choose to use the best features for your final model!\\n\",\n \"\\n\",\n \"In the below cell **define an n-gram range**; these will be the n's you use to create n-gram containment features. The rest of the feature creation code is provided.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 279,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1-gram containment features created!\\n\",\n \"2-gram containment features created!\\n\",\n \"3-gram containment features created!\\n\",\n \"4-gram containment features created!\\n\",\n \"5-gram containment features created!\\n\",\n \"6-gram containment features created!\\n\",\n \"LCS features created!\\n\",\n \"\\n\",\n \"Features: ['c_1', 'c_2', 'c_3', 'c_4', 'c_5', 'c_6', 'lcs_word']\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Define an ngram range\\n\",\n \"ngram_range = range(1,7)\\n\",\n \"\\n\",\n \"\\n\",\n \"# The following code may take a minute to run, depending on your ngram_range\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"features_list = []\\n\",\n \"\\n\",\n \"# Create features in a features_df\\n\",\n \"all_features = np.zeros((len(ngram_range)+1, len(complete_df)))\\n\",\n \"\\n\",\n \"# Calculate features for containment for ngrams in range\\n\",\n \"i=0\\n\",\n \"for n in ngram_range:\\n\",\n \" column_name = 'c_'+str(n)\\n\",\n \" features_list.append(column_name)\\n\",\n \" # create containment features\\n\",\n \" all_features[i]=np.squeeze(create_containment_features(complete_df, n))\\n\",\n \" i+=1\\n\",\n \"\\n\",\n \"# Calculate features for LCS_Norm Words \\n\",\n \"features_list.append('lcs_word')\\n\",\n \"all_features[i]= np.squeeze(create_lcs_features(complete_df))\\n\",\n \"\\n\",\n \"# create a features dataframe\\n\",\n \"features_df = pd.DataFrame(np.transpose(all_features), columns=features_list)\\n\",\n \"\\n\",\n \"# Print all features/columns\\n\",\n \"print()\\n\",\n \"print('Features: ', features_list)\\n\",\n \"print()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 280,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"In this notebook, we'll focus on one method for finding time-based patterns: using SageMaker's supervised learning model, [DeepAR](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html).\\n\",\n \"\\n\",\n \"\\n\",\n \"### DeepAR\\n\",\n \"\\n\",\n \"DeepAR utilizes a recurrent neural network (RNN), which is designed to accept some sequence of data points as historical input and produce a predicted sequence of points. So, how does this model learn?\\n\",\n \"\\n\",\n \"During training, you'll provide a training dataset (made of several time series) to a DeepAR estimator. The estimator looks at *all* the training time series and tries to identify similarities across them. It trains by randomly sampling **training examples** from the training time series. \\n\",\n \"* Each training example consists of a pair of adjacent **context** and **prediction** windows of fixed, predefined lengths. \\n\",\n \" * The `context_length` parameter controls how far in the *past* the model can see.\\n\",\n \" * The `prediction_length` parameter controls how far in the *future* predictions can be made.\\n\",\n \" * You can find more details, in [this documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar_how-it-works.html).\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"> Since DeepAR trains on several time series, it is well suited for data that exhibit **recurring patterns**.\\n\",\n \"\\n\",\n \"In any forecasting task, you should choose the context window to provide enough, **relevant** information to a model so that it can produce accurate predictions. In general, data closest to the prediction time frame will contain the information that is most influential in defining that prediction. In many forecasting applications, like forecasting sales month-to-month, the context and prediction windows will be the same size, but sometimes it will be useful to have a larger context window to notice longer-term patterns in data.\\n\",\n \"\\n\",\n \"### Energy Consumption Data\\n\",\n \"\\n\",\n \"The data we'll be working with in this notebook is data about household electric power consumption, over the globe. The dataset is originally taken from [Kaggle](https://www.kaggle.com/uciml/electric-power-consumption-data-set), and represents power consumption collected over several years from 2006 to 2010. With such a large dataset, we can aim to predict over long periods of time, over days, weeks or months of time. Predicting energy consumption can be a useful task for a variety of reasons including determining seasonal prices for power consumption and efficiently delivering power to people, according to their predicted usage. \\n\",\n \"\\n\",\n \"**Interesting read**: An inversely-related project, recently done by Google and DeepMind, uses machine learning to predict the *generation* of power by wind turbines and efficiently deliver power to the grid. You can read about that research, [in this post](https://deepmind.com/blog/machine-learning-can-boost-value-wind-energy/).\\n\",\n \"\\n\",\n \"### Machine Learning Workflow\\n\",\n \"\\n\",\n \"This notebook approaches time series forecasting in a number of steps:\\n\",\n \"* Loading and exploring the data\\n\",\n \"* Creating training and test sets of time series\\n\",\n \"* Formatting data as JSON files and uploading to S3\\n\",\n \"* Instantiating and training a DeepAR estimator\\n\",\n \"* Deploying a model and creating a predictor\\n\",\n \"* Evaluating the predictor \\n\",\n \"\\n\",\n \"---\\n\",\n \"\\n\",\n \"Let's start by loading in the usual resources.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Load and Explore the Data\\n\",\n \"\\n\",\n \"We'll be loading in some data about global energy consumption, collected over a few years. The below cell downloads and unzips this data, giving you one text file of data, `household_power_consumption.txt`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ! wget https://s3.amazonaws.com/video.udacity-data.com/topher/2019/March/5c88a3f1_household-electric-power-consumption/household-electric-power-consumption.zip\\n\",\n \"# ! unzip household-electric-power-consumption\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Read in the `.txt` File\\n\",\n \"\\n\",\n \"The next cell displays the first few lines in the text file, so we can see how it is formatted.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Date;Time;Global_active_power;Global_reactive_power;Voltage;Global_intensity;Sub_metering_1;Sub_metering_2;Sub_metering_3\\\\n',\\n\",\n \" '16/12/2006;17:24:00;4.216;0.418;234.840;18.400;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:25:00;5.360;0.436;233.630;23.000;0.000;1.000;16.000\\\\n',\\n\",\n \" '16/12/2006;17:26:00;5.374;0.498;233.290;23.000;0.000;2.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:27:00;5.388;0.502;233.740;23.000;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:28:00;3.666;0.528;235.680;15.800;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:29:00;3.520;0.522;235.020;15.000;0.000;2.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:30:00;3.702;0.520;235.090;15.800;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:31:00;3.700;0.520;235.220;15.800;0.000;1.000;17.000\\\\n',\\n\",\n \" '16/12/2006;17:32:00;3.668;0.510;233.990;15.800;0.000;1.000;17.000\\\\n']\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# display first ten lines of text data\\n\",\n \"n_lines = 10\\n\",\n \"\\n\",\n \"with open('household_power_consumption.txt') as file:\\n\",\n \" head = [next(file) for line in range(n_lines)]\\n\",\n \" \\n\",\n \"display(head)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Pre-Process the Data\\n\",\n \"\\n\",\n \"The 'household_power_consumption.txt' file has the following attributes:\\n\",\n \" * Each data point has a date and time (hour:minute:second) of recording\\n\",\n \" * The various data features are separated by semicolons (;)\\n\",\n \" * Some values are 'nan' or '?', and we'll treat these both as `NaN` values\\n\",\n \"\\n\",\n \"### Managing `NaN` values\\n\",\n \"\\n\",\n \"This DataFrame does include some data points that have missing values. So far, we've mainly been dropping these values, but there are other ways to handle `NaN` values, as well. One technique is to just fill the missing column values with the **mean** value from that column; this way the added value is likely to be realistic.\\n\",\n \"\\n\",\n \"I've provided some helper functions in `txt_preprocessing.py` that will help to load in the original text file as a DataFrame *and* fill in any `NaN` values, per column, with the mean feature value. This technique will be fine for long-term forecasting; if I wanted to do an hourly analysis and prediction, I'd consider dropping the `NaN` values or taking an average over a small, sliding window rather than an entire column of data.\\n\",\n \"\\n\",\n \"**Below, I'm reading the file in as a DataFrame and filling `NaN` values with feature-level averages.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Data shape: (2075259, 7)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"