shapefile.py

import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from descartes import PolygonPatch
import fiona
from shapely.geometry import Polygon, MultiPolygon, shape

# We can extract the London Borough boundaries by filtering on the AREA_CODE key
mp = MultiPolygon(
    [shape(pol['geometry']) for pol in fiona.open('data/boroughs/boroughs.shp')
    if pol['properties']['AREA_CODE'] == 'LBO'])

# We can now do GIS-ish operations on each borough polygon!
# we could randomize this by dumping the polygons into a list and shuffling it
# or we could define a random colour using fc=np.random.rand(3,)
# available colour maps are here: http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
cm = plt.get_cmap('RdBu')
num_colours = len(mp)
 
fig = plt.figure()
ax = fig.add_subplot(111)
minx, miny, maxx, maxy = mp.bounds
w, h = maxx - minx, maxy - miny
ax.set_xlim(minx - 0.2 * w, maxx + 0.2 * w)
ax.set_ylim(miny - 0.2 * h, maxy + 0.2 * h)
ax.set_aspect(1)

patches = []
for idx, p in enumerate(mp):
    colour = cm(1. * idx / num_colours)
    patches.append(PolygonPatch(p, fc=colour, ec='#555555', alpha=1., zorder=1))
ax.add_collection(PatchCollection(patches, match_original=True))
ax.set_xticks([])
ax.set_yticks([])
plt.title("Shapefile polygons rendered using Shapely")
plt.savefig('data/london_from_shp.png', alpha=True, dpi=300)
plt.show()