Fill countries in python basemap

Question

Hi I am trying to plot a map using pythons basemap with some countries filled in a certain colour.

Is there a quick and easy solution out there??

Answer 1

Updating @pelson answer for Python 3:

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cartopy.io.shapereader as shpreader
import itertools
import numpy as np

shapename = 'admin_0_countries'
countries_shp = shpreader.natural_earth(resolution='110m',
                                        category='cultural', name=shapename)

print(countries_shp)

# some nice "earthy" colors
earth_colors = np.array([(199, 233, 192),
                         (161, 217, 155),
                         (116, 196, 118),
                         (65, 171, 93),
                         (35, 139, 69),
                        ]) / 255
earth_colors = itertools.cycle(earth_colors)

ax = plt.axes(projection=ccrs.PlateCarree())

for country in shpreader.Reader(countries_shp).records():
    print(country.attributes['NAME_LONG'], next(earth_colors))
    ax.add_geometries(country.geometry, ccrs.PlateCarree(),
                      facecolor=next(earth_colors),
                      label=country.attributes['NAME_LONG'])

plt.show()

Answer 2

Inspired by the answer from pelson, I post the solution I have. I will leave it up to you which works best, so I will not accept any answer at the moment.

#! /usr/bin/env python

import sys
import os
from pylab import *
from mpl_toolkits.basemap import Basemap
import matplotlib as mp

from shapelib import ShapeFile
import dbflib
from matplotlib.collections import LineCollection
from matplotlib import cm

def get_shapeData(shp,dbf):
  for npoly in range(shp.info()[0]):
    shpsegs = []
    shpinfo = []

    shp_object = shp.read_object(npoly)
    verts = shp_object.vertices()
    rings = len(verts)
    for ring in range(rings):
        if ring == 0:
            shapedict = dbf.read_record(npoly)
        name = shapedict["name_long"]
        continent = shapedict["continent"]
        lons, lats = zip(*verts[ring])
        if max(lons) > 721. or min(lons) < -721. or max(lats) > 91. or min(lats) < -91:
            raise ValueError,msg
        x, y = m(lons, lats)
        shpsegs.append(zip(x,y))
        shapedict['RINGNUM'] = ring+1
        shapedict['SHAPENUM'] = npoly+1
        shpinfo.append(shapedict)

    lines = LineCollection(shpsegs,antialiaseds=(1,))
    lines.set_facecolors(cm.jet(np.random.rand(1)))
    lines.set_edgecolors('k')
    lines.set_linewidth(0.3)
    ax.add_collection(lines)


if __name__=='__main__':

  f=figure(figsize=(10,10))
  ax = plt.subplot(111)
  m = Basemap(projection='merc',llcrnrlat=30,urcrnrlat=72,\
            llcrnrlon=-40,urcrnrlon=50,resolution='c')
  m.drawcountries(linewidth=0.1,color='w')

  sfile = 'ne_10m_admin_0_countries'

  shp = ShapeFile(sfile)
  dbf = dbflib.open(sfile)
  get_shapeData(shp,dbf)

  show()
  sys.exit(0)

This is the result

Here is my example how to fill in Albania in the correct colour (not very elegant I know ;)).

  #HACK for Albania
  shpsegs = []
  shpinfo = []

  shp_object = shp.read_object(9)
  verts = shp_object.vertices()
  rings = len(verts)
  for ring in range(rings):
      if ring == 0:
          shapedict = dbf.read_record(9)
      name = shapedict["name_long"]
      continent = shapedict["continent"]
      lons, lats = zip(*verts[ring])
      if max(lons) > 721. or min(lons) < -721. or max(lats) > 91. or min(lats) < -91:
          raise ValueError,msg
      x, y = m(lons, lats)
      shpsegs.append(zip(x,y))
      shapedict['RINGNUM'] = ring+1
      shapedict['SHAPENUM'] = npoly+1
      shpinfo.append(shapedict)
  lines = LineCollection(shpsegs,antialiaseds=(1,))
  if name == 'Albania':
    lines.set_facecolors('w')
  lines.set_edgecolors('k')
  lines.set_linewidth(0.3)
  ax.add_collection(lines)

It is important that you do this after you have done all the other shapes. Perhaps you can get rid of some part of this code, but as I said it was sufficient for me.

For my application I coloured contries by name or continent, therefore these lines:

    name = shapedict["name_long"]
    continent = shapedict["continent"]

The data used I got from this website: http://www.naturalearthdata.com/

Answer 3

As has already been said by @unutbu, Thomas' post here is exactly what you are after.

Should you want to do this with Cartopy, the corresponding code (in v0.7) can be adapted from http://scitools.org.uk/cartopy/docs/latest/tutorials/using_the_shapereader.html slightly:

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cartopy.io.shapereader as shpreader
import itertools
import numpy as np

shapename = 'admin_0_countries'
countries_shp = shpreader.natural_earth(resolution='110m',
                                        category='cultural', name=shapename)

# some nice "earthy" colors
earth_colors = np.array([(199, 233, 192),
                                (161, 217, 155),
                                (116, 196, 118),
                                (65, 171, 93),
                                (35, 139, 69),
                                ]) / 255.
earth_colors = itertools.cycle(earth_colors)



ax = plt.axes(projection=ccrs.PlateCarree())
for country in shpreader.Reader(countries_shp).records():
    print country.attributes['name_long'], earth_colors.next()
    ax.add_geometries(country.geometry, ccrs.PlateCarree(),
                      facecolor=earth_colors.next(),
                      label=country.attributes['name_long'])

plt.show()