Updating README

README.md

+Description of Project:
+    In our project, we seek to apply the concept of the efficiency gap to
+    create an algorithm that will not only be able to analyze existing 
+    districting plans but develop new, fairer districts. We plan to write a 
+    program that can create a set of hypothetical district boundaries and map
+    the one with the lowest efficiency gap to a map of the state.   
+
+Efficency Gap:
+	In their paper “Partisan Gerrymandering and the Efficiency Gap,”
+	Nicholas Stephanopoulos and Eric McGhee analyze the efficiency gap as
+	a new measure of the severity of gerrymandering. Simply put, the
+	efficiency gap measures the difference in “wasted votes” by Republicans
+	and Democrats in all the districts in a state. The efficiency gap can
+	also be understood as the difference between the percentage of votes
+	that a party receives and the percentage of seats it receives in
+	the legislature.
+
+Notes about Commits:
+    The commits that say Anne Rogers were students committing from their
+    Virutal Machines.
+
+Before Preceeding Please run:
+    git fetch - all
+    sudo pip3 install pyshp
+    Use the following link to download the census tract data and place it in a directory in read_shpaes called Census_tract
+        http://www2.census.gov/geo/tiger/TIGER2010DP1/Tract_2010Census_DP1.zip
+
+
+algorithms directory:
+    algorithms.py contains all the functions for constructing district plans by utilizing
+        the efficiency gap
+        __name__=="__main__" Function works if no specialization is needed
+        if specialization is needed 
+            RUN using python 3 [1] completed_result(state, initial_percent_of_people=60, goal=5,
+            special_name='', better=False)
+
+    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    
+        Run the function in the terminal as follows:
+        python2 state_name
+        ie.
+        Python 2 
+    Colors.csv contains numbers and colors for drawing.py
+    ./demo.sh contains basic shell script
+    FOR  EASY RUN USE ./demo.sh State
+        if State is two words it should be first_second
+
+
+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
+        RUN: [1] get_shapeRecs(shapefile_path), [2] create_state_tracts(shapeRecs)
+    location_api.py uses the GPS coordinates of census tracts to determine which state they are in 
+        and sorts them accordingly, using the LocationIQ API 
+        RUN: [1] get_shapeRecs(shapefile_path), [2] create_state_tracts(shapeRecs)
+    state_set_json_files contains the sets of all items in a state
+    state_tracts_full_json_files contains the completed tract dictionaries created by get_combined.py
+    state_num.csv contains the state and number of districts
+    tracts1.json JSON file for helping location_api.py fun
+    tract.py example of tract dict
+    util.py contains helper functions for reading and writing CSVs, JSON, and pickle files
+    state_runs contains the partially completed tracts as dictionaries created by
+        location_api.py  
+    Census_tract contains Census tract files
+    test_files contains files orginally used for testing