Merge branch 'master' of mit.cs.uchicago.edu:ajfriedman/election_district_drawing

README.txt

 algorithms directory:
     algorithms.py contains all the functions for constructing district plans by utilizing
         the efficiency gap
+    drawing.py (must be run using Python2) creates a basemap of the United States and maps color-
+        coded tracts (the color corresponding to their district) onto the basemap using Networkx     
+
 
 reading_shapes directory:
     get_combined.py contains functions to convert shapefiles of census tracts, determine which tracts
         are connected to each other, and analyze demographic data to estimate the number of Republicans and Democrats in each district
     location_api.py uses the GPS coordinates of census tracts to determine which state they are in 
-        and sorts them accordingly 
+        and sorts them accordingly, using the LocationIQ API 
     state_set_json_files contains the partially completed tracts as dictionaries created by
         location_api.py    
     state_tracts_full_json_files contains the completed tract dictionaries created by get_combined.py

reading_shapes/get_combined.py

@@ -3,8 +3,6 @@ import json
 import ast
 from util import create_json_file, recover_json
 
-
-
 shapefile_path = "Census_tract/Tract_2010Census_DP1.shp"
 
 def get_shapeRecs(shapefile_path):
@@ -46,7 +44,7 @@ def get_connects_tracts(sf_connect):
     dict_tracts = {}
     for sh_num1, sh_num2 in sf_connect:
         if sh_num1 not in dict_tracts:
-            dict_traics[sh_num1] = [sh_num2]
+            dict_tracts[sh_num1] = [sh_num2]
             count += 1
         if sh_num1 in dict_tracts:
             if sh_num2 not in dict_tracts[sh_num1]:
@@ -149,6 +147,8 @@ def gender_RD(record):
     2012 exit poll data, found here: http://www.cnn.com/election/2012/results/race/president/
     '''
     total_population = record[6]
+
+
     weight_R = 0
     weight_D = 0 
     
@@ -160,18 +160,22 @@ def gender_RD(record):
     weight_D += pop_female * -0.12
 
     total_population_above_16 = pop_female + pop_male
+
+
     if not total_population_above_16:
         return 0.0 
+
+
     population_change = 1 - (total_population_above_16/total_population) + 1
     weight_D = weight_D * population_change
     weight_R = weight_R * population_change
     add_weights = weight_R + weight_D
     if not add_weights:
         return 0.0
-    percent_RD =((add_weights)/(abs(weight_D)+weight_R))
 
-    return percent_RD
+    percent_RD =( (add_weights)/(abs(weight_D)+weight_R))
 
+    return percent_RD
 
 
 def get_populations_RD(shapeRecs, connect_tracts, state_set):
@@ -219,7 +223,6 @@ def get_populations_RD(shapeRecs, connect_tracts, state_set):
 
 
 
-
 def create_state_tracts(shapeRecs):
     '''
     Recover the json files created in location_api.py that have sorted tracts by state
@@ -261,4 +264,5 @@ def state_tracts_hard_coded(Master_tract_dicts):
                 Master_tract_dicts[state][new_connection]["connections"].append(tract)
         single_state_dict = Master_tract_dicts[state]
         create_json_file(single_state_dict, "state_tracts_full_json_files/" + state + "tract_dict")
-    return Master_tract_dicts
\ No newline at end of file
+    return Master_tract_dicts
+    

reading_shapes/location_api.py

@@ -68,8 +68,7 @@ def hard_code(output=False):
     "11001009700": "District of Columbia",
     "15009990200": "Hawaii",
     "23005990000": "Maine",
-    "26001990000":"Michigan",
-
+    "26001990000": "Michigan",
     "26009990000": "Michigan",
     "26157990000": "Michigan",
     "28047990000": "Mississippi",

reading_shapes/util.py

@@ -35,3 +35,4 @@ def color_csv(path, file_name):
             count += 1
 
         return colors_dict
+