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How to plot Shapely Points using Matplotlib, Basemap, and Descartes
"""
required packages:
numpy
matplotlib
basemap: http://matplotlib.org/basemap/users/installing.html
shapely: https://pypi.python.org/pypi/Shapely
descartes: https://pypi.python.org/pypi/descartes
random
numpy and random are only required to generate random points for this example
"""
from random import shuffle, randint
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from mpl_toolkits.basemap import Basemap
from shapely.geometry import Point, MultiPoint, MultiPolygon
from descartes import PolygonPatch
# lower left minx miny , upper right maxx maxy
bounds = [-6.108398, 49.61071, 1.669922, 58.972667]
minx, miny, maxx, maxy = bounds
w, h = maxx - minx, maxy - miny
# generate random points within the bounds
lon = np.linspace(minx, maxx).tolist()
lat = np.linspace(miny, maxy).tolist()
random.shuffle(lon)
random.shuffle(lat)
# create a new matplotlib figure and axes instance
fig = plt.figure()
ax = fig.add_subplot(111)
# add a basemap and a small additional extent
m = Basemap(
projection='merc',
ellps = 'WGS84',
llcrnrlon=minx - 0.2 * w,
llcrnrlat=miny - 0.2 * h,
urcrnrlon=maxx + 0.2 * w,
urcrnrlat=maxy + 0.2 * h,
lat_ts=0,
resolution='h')
m.drawcoastlines(linewidth=0.3)
m.drawmapboundary()
# a shapefile can be added like so if needed
# m.readshapefile('london_shp', 'london', color='#555555')
# set axes limits to basemap's coordinate reference system
min_x, min_y = m(minx, miny)
max_x, max_y = m(maxx, maxy)
corr_w, corr_h = max_x - min_x, max_y - min_y
ax.set_xlim(min_x - 0.2 * corr_w, max_x + 0.2 * corr_w)
ax.set_ylim(min_y - 0.2 * corr_h, max_y + 0.2 * corr_h)
# square up axes and basemap
ax.set_aspect(1)
# buffer units are translated to metres by Basemap
# we're randomly varying between 7.5k and 15k metres
patches = [PolygonPatch(Point(m(lon, lat)).buffer(1.0 * randint(7500, 15000)),
fc='#cc00cc', ec='#555555', alpha=0.5, zorder=4)
for lon, lat in zip(lon, lat)]
ax.add_collection(PatchCollection(patches, match_original=True))
plt.savefig('data/uk.png', dpi=300)
plt.show()
# We can extract the London Borough boundaries by filtering on the AREA_CODE key
# Get maps from EDINA http://digimap.edina.ac.uk/digimap/home
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', lw=0.2, 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.tight_layout()
plt.savefig('data/london_from_shp.png', alpha=True, dpi=300)
plt.show()
@Isaquedanielre

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@Isaquedanielre Isaquedanielre commented Jun 7, 2016

Hi, I tried to plot my shape and my points using your code, but I can't add the points on the fiona python object (I must be use this because I need to use a selection inside the original shapefile for different combinations of municipalities in my Thesis).
thank you!
My data is here, is a pickle object:

agents

https://drive.google.com/open?id=0Bxs2IRsacPU4VjVvUE9va0FSbVU

shapefile

https://drive.google.com/open?id=0Bxs2IRsacPU4Q0JvYzZndHRodDA

my code is constructed over your's:

-- coding: utf-8 --

author = 'B2046470858'

source https://gist.github.com/urschrei/6436526

import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from mpl_toolkits.basemap import Basemap
from shapely.geometry import Point, MultiPoint, MultiPolygon, shape
from descartes import PolygonPatch
import fiona
import pickle
import pandas as pd

load the agents data

with open('my_agents_data.agents', 'rb') as stored_agents_file:
my_agents, my_houses, my_families, my_firms, my_regions = pickle.load(stored_agents_file)

create a data firms location

lat = pd.DataFrame(columns=["lat"])
lon = pd.DataFrame(columns=["lon"])
for firm in my_firms:
lat = pd.concat([lat, pd.DataFrame([firm.address.GetY()],columns=["lat"])],axis=0)
lon = pd.concat([lon, pd.DataFrame([firm.address.GetX()],columns=["lon"])],axis=0)
coords_firms = pd.concat([lat, lon], axis=1)

lower left minx miny , upper right maxx maxy

mp = MultiPolygon([shape(pol['geometry']) for pol in fiona.open('URBAN_IBGE_ACPs.shp') if pol['properties']['ACP'] == 'Brasília'])
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 - 0.02 * w, maxx + 0.02 * w)
ax.set_ylim(miny - 0.02 * h, maxy + 0.02 * h)
ax.set_aspect(1)

add the points

patches = [PolygonPatch(Point(mp(coords_firms.iloc[index,:].values)), fc='#cc00cc', ec='#555555', alpha=0.5, zorder=4)
for index in range(coords_firms.shape[0])]

ax.add_collection(PatchCollection(patches, match_original=True))
ax.set_xticks()
ax.set_yticks()
plt.title("Shapefile polygons rendered using Shapely")
plt.tight_layout()
plt.savefig('london_from_shp.png', alpha=True, dpi=300)
plt.show()

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