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from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals


    

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import grequests
import uscensus
from uscensus import SqlAlchemyCache


    

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from configparser import RawConfigParser


    

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cp = RawConfigParser()


    

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cp.read('.census')


    

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import requests
s = requests.Session()


    

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# Work around for some Windows TLS issues

import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.poolmanager import PoolManager
import ssl

class MyAdapter(HTTPAdapter):
    def init_poolmanager(self, connections, maxsize, block=False):
        self.poolmanager = PoolManager(num_pools=connections,
                                       maxsize=maxsize,
                                       block=block,
                                       ssl_version=ssl.PROTOCOL_TLSv1_2)
s.mount('https://', MyAdapter())


    

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cache = SqlAlchemyCache('sqlite://')


    

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%%time
with cache:
    cl = uscensus.DiscoveryInterface(
        cp.get('census','api_key'),
        cache,
        session=s,
    )


    

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acs = cl.search('"acs 5-year detailed tables"')
acs


    

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api = next(iter(acs))
[api.endpoint for api in acs]


    

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api.endpoint


    

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api.geographies


    

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api.groups


    

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vars = api.searchVariables('"median gross rent"')
vars


    

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med_rent_by_county = api(['B25064_001E'], geo_for={'county': '*'})
med_rent_by_county.set_index(['state', 'county'], inplace=True)
med_rent_by_county.sort_index(inplace=True)
med_rent_by_county


    

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api.vintage


    

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%matplotlib inline


    

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import matplotlib.pyplot as plt
import fiona
import geopandas as gpd
import shapely


    

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%%time
# Generated in GetCountyShapes.ipynb
counties_df = gpd.GeoDataFrame.from_file('counties.geojson',
                                         driver='GeoJSON')
counties_df.crs = fiona.crs.from_epsg(4326)


    

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continental = shapely.geometry.box(-125.0011, 24.9493, -66.9326, 49.5904)
counties_df[counties_df.geometry.within(continental)].simplify(0.1).plot(
    figsize=((15,15)),
    linewidth=0.5)


    

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%time
counties_df.set_index(['STATE', 'COUNTY'], inplace=True)
counties_df.sort_index(inplace=True)


    

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%time
counties_df.index.names=['state','county']
joined = counties_df.join(med_rent_by_county)


    

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#joined.total_bounds
continental = shapely.geometry.box(-125.0011, 24.9493, -66.9326, 49.5904)
joined_cont = joined[joined.intersects(continental)]


    

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axes=joined_cont.plot(column='B25064_001E', 
                      figsize=(15,15),
                      linewidth=0.5)
axes.figure.dpi=190
axes


    

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joined_cont.head()


    

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import matplotlib as mpl
import numpy as np
inc_min=joined_cont['B25064_001E'].min()
inc_max=joined_cont['B25064_001E'].max()
X = [[0, 0], [.9, .9]]
plt.gca().imshow(X, interpolation='bicubic', cmap=mpl.cm.get_cmap(),
          extent=(0, .1, 0, 1), alpha=1)