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Python cartopy map, clip area outside country (polygon)

I create a Stamen terrain map with the country border from NaturalEarth. Now I want to remove all the data (terrain in this case) from outside the country border. How would I do that?

My example with the terrain visible inside and outside of Switzerland:

from cartopy.io import shapereader
import cartopy.io.img_tiles as cimgt
import cartopy.crs as ccrs
import geopandas
import matplotlib.pyplot as plt


resolution = '10m'
category = 'cultural'
name = 'admin_0_countries'

shpfilename = shapereader.natural_earth(resolution, category, name)

df = geopandas.read_file(shpfilename)

poly = [df.loc[df['ADMIN'] == 'Switzerland']['geometry'].values[0]]

stamen_terrain = cimgt.Stamen('terrain-background')

fig = plt.figure(figsize=(8,6))

ax = fig.add_subplot(1, 1, 1, projection=stamen_terrain.crs)
ax.add_geometries(poly, crs=ccrs.PlateCarree(), facecolor='none', edgecolor='r')
exts = [poly[0].bounds[0], poly[0].bounds[2], poly[0].bounds[1], poly[0].bounds[3]]
ax.set_extent(exts, crs=ccrs.Geodetic())

ax.add_image(stamen_terrain, 8)
fig.tight_layout()
plt.show()

enter image description here

How can I show only the terrain within the border, and the rest outside as white/transparent?

Tried to play with this direction, but without success so far: https://scitools.org.uk/cartopy/docs/latest/gallery/logo.html

Solution

  • You need a mask to hide the un-wanted part of the image. Here is a runnable code that demonstrates all the steps to get the intended plot.

    from shapely.geometry import Polygon
from cartopy.io import shapereader
import cartopy.io.img_tiles as cimgt
import cartopy.crs as ccrs
import geopandas
import matplotlib.pyplot as plt

def rect_from_bound(xmin, xmax, ymin, ymax):
    """Returns list of (x,y)'s for a rectangle"""
    xs = [xmax, xmin, xmin, xmax, xmax]
    ys = [ymax, ymax, ymin, ymin, ymax]
    return [(x, y) for x, y in zip(xs, ys)]

# request data for use by geopandas
resolution = '10m'
category = 'cultural'
name = 'admin_0_countries'

shpfilename = shapereader.natural_earth(resolution, category, name)
df = geopandas.read_file(shpfilename)

# get geometry of a country
poly = [df.loc[df['ADMIN'] == 'Switzerland']['geometry'].values[0]]

stamen_terrain = cimgt.Stamen('terrain-background')

# projections that involved
st_proj = stamen_terrain.crs  #projection used by Stamen images
ll_proj = ccrs.PlateCarree()  #CRS for raw long/lat

# create fig and axes using intended projection
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(1, 1, 1, projection=st_proj)
ax.add_geometries(poly, crs=ll_proj, facecolor='none', edgecolor='black')

pad1 = .1  #padding, degrees unit
exts = [poly[0].bounds[0] - pad1, poly[0].bounds[2] + pad1, poly[0].bounds[1] - pad1, poly[0].bounds[3] + pad1];
ax.set_extent(exts, crs=ll_proj)

# make a mask polygon by polygon's difference operation
# base polygon is a rectangle, another polygon is simplified switzerland
msk = Polygon(rect_from_bound(*exts)).difference( poly[0].simplify(0.01) )
msk_stm  = st_proj.project_geometry (msk, ll_proj)  # project geometry to the projection used by stamen

# get and plot Stamen images
ax.add_image(stamen_terrain, 8) # this requests image, and plot

# plot the mask using semi-transparency (alpha=0.65) on the masked-out portion
ax.add_geometries( msk_stm, st_proj, zorder=12, facecolor='white', edgecolor='none', alpha=0.65)

ax.gridlines(draw_labels=True)

plt.show()

    The resulting plot:

    enter image description here

    and the mask:

    enter image description here

    Edit1

    The code above is good for a single country. If multiple contiguous countries are our new target, we need to select all of them and dissolve into a single geometry. Only a few lines of code need to be modified.

    Example: new target countries: ['Norway','Sweden', 'Finland']

    The line of code to be replaced:

    poly = [df.loc[df['ADMIN'] == 'Switzerland']['geometry'].values[0]]

    New lines of code to replace:

    scan3 = df[ df['ADMIN'].isin(['Norway','Sweden', 'Finland']) ]
scan3_dissolved = scan3.dissolve(by='LEVEL')
poly = [scan3_dissolved['geometry'].values[0]]

    The sample output map:

    enter image description here