import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.cm as cm
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from matplotlib.colors import Normalize
import neighborhoodize
print "where"
hood_map = neighborhoodize.NeighborhoodMap(neighborhoodize.zillow.ILLINOIS)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
print "where"
mymap = Basemap(llcrnrlon=-87.9,
llcrnrlat=41.3,
urcrnrlon=-86.91,
urcrnrlat=42.7,
projection='lcc',
lat_1=32,
lat_2=45,
lon_0=-95,
resolution='h')
print "where"
mymap.drawmapboundary(fill_color='#46bcec')
mymap.fillcontinents(color='#f2f2f2', lake_color='#46bcec')
mymap.drawcoastlines()
print "where"
for hood in hood_map.neighborhoods:
print hood.name
def test_nyc_zillow():
params = neighborhoodize.zillow.NEW_YORK
nyc_map = neighborhoodize.NeighborhoodMap(params)
for (lat, lng), answer in known_zillow_nyc_pairs.iteritems():
assert nyc_map.get_neighborhoods(lat, lng) == answer
def test_chicago_zillow():
params = neighborhoodize.zillow.ILLINOIS
chicago_map = neighborhoodize.NeighborhoodMap(params)
for (lat, lng), answer in known_chicago_pairs.iteritems():
assert chicago_map.get_neighborhoods(lat, lng) == answer
def test_nyc_city():
params = neighborhoodize.nyc.NEIGHBORHOOD_TABULATION_AREAS
nyc_map = neighborhoodize.NeighborhoodMap(params)
for (lat, lng), answer in known_city_nyc_pairs.iteritems():
assert nyc_map.get_neighborhoods(lat, lng) == answer
def test_chicago_city():
params = neighborhoodize.city_of_chicago.NEIGHBORHOODS
chicago_map = neighborhoodize.NeighborhoodMap(params)
for (lat, lng), answer in known_chicago_pairs.iteritems():
assert chicago_map.get_neighborhoods(lat, lng) == answer
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import zipfile
import requests
import StringIO
import os
from datetime import datetime
import neighborhoodize
hood_map = neighborhoodize.NeighborhoodMap(neighborhoodize.zillow.NEW_YORK)
def get_neighbor(df):
neighbor_list = []
for i in range(df.shape[0]):
neighbor = hood_map.get_neighborhoods(df['Lat'][i], df['Lon'][i])
neighbor_list.append(neighbor)
neighbors = pd.DataFrame(neighbor_list)
return neighbors
def get_time_piece(df):
date_time = pd.DataFrame(
df['Date/Time'].map(lambda x: x.split(' ')).tolist(),