%matplotlib inline

import matplotlib
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt

from mpl_toolkits.basemap import Basemap
import numpy as np
import pandas as pd

#List of Franklin County Zip Codes
ziplist = [43146, 43202,43201,43204,43203,43206,43205,43209,43207,43211,43002,43210,43213,43004,43212,43215,43214,
           43217,43216,43219,43221,43220,43223,43016,43222,43224,43017,43228,43227,43230,43229,43232,43026,43231,
           43235,43236,43268,43054,43068,43081,43085,43109,43110,43119,43125,43123,43126,43137]

#Reding in poverty data
df = pd.read_csv('ohio/ACS_15_5YR_S1702_with_ann.csv')

#Removing bogus columns - Probably a better way
df = df.drop(df.columns[6:], axis=1)
df = df.drop(df.columns[2:5], axis=1)
df = df.drop(df.columns[0], axis=1)

#Renaming the keepers
df.rename(columns={'GEO.id2': 'zipcode','HC02_EST_VC01':'Poverty_Pct'}, inplace=True)

#Removing all rows which zipcode key is not in 'ziplist'
df = df[df['zipcode'].isin(ziplist)]

#Converting Pverty_Pct to float
df['Poverty_Pct'] = df['Poverty_Pct'].astype(float)

df.head()

#Make map with 4 corners set in Lat/Lon w/ Mercator projection type
m = Basemap(resolution='c', # c, l, i, h, f or None
            projection='tmerc',
            llcrnrlon = -83.26, llcrnrlat = 39.79,
            urcrnrlon = -82.76, urcrnrlat = 40.175,
            lat_0 = 40, lon_0 = -83)

m.readshapefile('cb_2015_us_zcta510_500k','frankzips', zorder=2, color = 'none')
#I get a blank plot. Should this happen?

#determine header name with zipcode for matching - 'ZCTA5CE10'
print(m.frankzips_info[1])

#Pull shapes matched with zipcodes into dataframe convering zipcode to int.
df_poly = pd.DataFrame({
        'shapes': [Polygon(np.array(shape), True) for shape in m.frankzips],
        'zipcode': [int(zipcode['ZCTA5CE10']) for zipcode in m.frankzips_info]
    })

df_poly.head()

#Create newframe which merges df and df_poly on zipcode w/ left merge  (keeping only those that match with df zips)
newframe = pd.merge(df, df_poly, on='zipcode', how='left')
newframe.head()

#Add bins to dataframe based on poverty %
num_colors = 7
values = newframe['Poverty_Pct']
cmap = plt.get_cmap('Oranges')
scheme = [cmap(i / num_colors) for i in range(num_colors)]
bins = np.linspace(values.min(), values.max(), num_colors)
newframe['bin'] = np.digitize(values,bins) -1
newframe.head()

#this is where things go to hell
plt.clf()
fig = plt.figure() #figsize=(22, 12)
ax = fig.add_subplot(111, frame_on=False)

patches = []
colors = []
for bin, shape in zip(newframe['bin'], newframe['shapes']):
    colors.append(scheme[bin])
    patches.append(shape)


pc = PatchCollection(patches)
pc.set_facecolor(colors)
pc.set_edgecolor('black')

ax.add_collection(pc)
plt.show()