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# urschrei/basemap_descartes.py

Last active November 6, 2020 02:49
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How to plot Shapely Points using Matplotlib, Basemap, and Descartes
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 """ 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()
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 # 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 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:

# shapefile

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

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:
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()
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)