one last merge

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/util.py

@@ -4,7 +4,10 @@ import csv
 
 
 def create_json_file(dictionary, path_file_name):
-    tract_str = str(dictionary)
+    if type(dictionary) != str:
+        tract_str = str(dictionary)
+    if type(dictionary) == str:
+        tract_str = dictionary
     with open(path_file_name + ".json", 'w') as json_file:
         json.dump(tract_str, json_file)
 
@@ -22,4 +25,14 @@ def read_csv(path, file_name):
             num_districts_states[row['state']] = row['num']
         return num_districts_states
 
+def color_csv(path, file_name):
+    with open(path + file_name) as csvfile:
+        colors_dict = {}
+        reader = csv.DictReader(csvfile)
+        count = 1
+        for row in reader:
+            colors_dict[count] = row['colors']
+            count += 1
+
+        return colors_dict