Note

This page was generated from an Jupyter notebook that can be accessed from github.

Plotting

Every region has two plotting functions, which draw the outlines of all regions:

plot: draws the region polygons on a cartopy GeoAxes (map)
plot_regions: draws the the region polygons only

Import regionmask and check the version:

import regionmask

regionmask.__version__

'0.13.0.post1.dev13+g9b22414'

We use the srex regions to illustrate the plotting:

srex = regionmask.defined_regions.srex
srex

<regionmask.Regions 'SREX'>
Source:   Seneviratne et al., 2012 (https://www.ipcc.ch/site/assets/uploads/2...
overlap:  False

Regions:
 1 ALA       Alaska/N.W. Canada
 2 CGI     Canada/Greenl./Icel.
 3 WNA         W. North America
 4 CNA         C. North America
 5 ENA         E. North America
..  ..                      ...
22 EAS                  E. Asia
23 SAS                  S. Asia
24 SEA                S.E. Asia
25 NAU             N. Australia
26 SAU S. Australia/New Zealand

[26 regions]

Plot all regions

Calling plot() on any region without any arguments draws the default map with a PlateCarree() projection and includes the coastlines:

srex.plot();

../_images/4cd7da3ed51e001044e21db28ae9667a70dd9f8be5133310e12ed152f62b59ed.png

Plot options

The plot method has a large number of arguments to adjust the layout of the axes. For example, you can pass a custom projection, the labels can display the abbreviation instead of the region number, the ocean can be colored, etc.. This example also shows how to use matplotlib.patheffects to ensure the labels are easily readable without covering too much of the map (compare to the map above):

import cartopy.crs as ccrs
import matplotlib.patheffects as pe

text_kws = dict(
    bbox=dict(color="none"),
    path_effects=[pe.withStroke(linewidth=2, foreground="w")],
    color="#67000d",
    fontsize=8,
)

ax = srex.plot(
    projection=ccrs.Robinson(), label="abbrev", add_ocean=True, text_kws=text_kws
)

ax.set_global()

../_images/d9419c47427f73d58ab6c010d6a382c11aea802134548a34d8316c4e18638857.png

Plot only a Subset of Regions

To plot a selection of regions subset them using indexing:

# regions can be selected by number, abbreviation or long name
regions = [11, "CEU", "S. Europe/Mediterranean"]

# choose a good projection for regional maps
projection = ccrs.LambertConformal(central_longitude=15)

ax = srex[regions].plot(
    add_ocean=True,
    resolution="50m",
    projection=projection,
    label="abbrev",
    text_kws=text_kws,
)

# fine tune the extent
ax.set_extent([-15, 45, 28, 76], crs=ccrs.PlateCarree())

../_images/b019b4484ca06dcc94df3f7c0b320a5da9f382159672e97df08b7d4aa56b3dd1.png

Plotting the region polygons only (no map)

srex.plot_regions();

../_images/65bddf1b4ef35ce4ba2fa2a9ebe6a31d49533fe4a2c189eec0ef102e140a6d22.png

Note

This does not create a cartopy GeoAxes.

To achieve this, you need to explicitly create the axes:

import matplotlib.pyplot as plt

f, ax = plt.subplots(subplot_kw=dict(projection=ccrs.Robinson()))

srex.plot_regions(ax=ax, line_kws=dict(lw=1), text_kws=text_kws)

ax.coastlines()
ax.set_global()

../_images/7d67da020f6f43f252769fbae076962972c7ecf2a0f8592954d79fe2161913fb.png