This tutorial was generated from an IPython notebook that can be downloaded here.

Edge behavior and interiors

This notebook illustrates the edge behavior (when a grid point falls on the edge of a polygon) and how polygon interiors are treated.


Import regionmask and check the version:

import regionmask


Other imports

import xarray as xr
import numpy as np

import as ccrs
import matplotlib.pyplot as plt

from matplotlib import colors as mplc
from shapely.geometry import Polygon

Define some colors:

color1 = "#9ecae1"
color2 = "#fc9272"
color3 = "#cab2d6"

cmap1 = mplc.ListedColormap([color1])
cmap2 = mplc.ListedColormap([color2])
cmap3 = mplc.ListedColormap([color3])


Regionmask offers three “methods”* to rasterize regions

  1. rasterize: fastest but only for equally-spaced grid, uses rasterio.features.rasterize internally.

  2. shapely: for irregular grids, uses shapely.vectorized.contains internally.

  3. pygeos: a faster alternative for irregular grids. This is method is preferred over (2) if the optional dependency pygeos is installed. Uses pygeos.STRtree internally.

All methods use the lon and lat coordinates to determine if a grid cell is in a region. lon and lat are assumed to indicate the center of the grid cell. Methods (1) and (2) have the same edge behavior and consider ‘holes’ in the regions. regionmask automatically determines which method to use.

  1. and (3) subtract a tiny offset from lon and lat to achieve a edge behaviour consistent with (1). Due to mapbox/rasterio#1844 this is unfortunately also necessary for (1).

*Note that all “methods” yield the same results.

Edge behavior

The edge behavior determines how points that fall on the outline of a region are treated. It’s easiest to see the edge behaviour in an example.


Define a region and a lon/ lat grid, such that some gridpoints lie exactly on the border:

outline = np.array([[-80.0, 50.0], [-80.0, 28.0], [-100.0, 28.0], [-100.0, 50.0]])

region = regionmask.Regions([outline])
ds_US = regionmask.core.utils.create_lon_lat_dataarray_from_bounds(
    *(-161, -29, 2), *(75, 13, -2)

Dimensions:   (lon: 65, lat: 30, lon_bnds: 66, lat_bnds: 31)
  * lon       (lon) float64 -160.0 -158.0 -156.0 -154.0 ... -36.0 -34.0 -32.0
  * lat       (lat) float64 74.0 72.0 70.0 68.0 66.0 ... 22.0 20.0 18.0 16.0
  * lon_bnds  (lon_bnds) int64 -161 -159 -157 -155 -153 ... -39 -37 -35 -33 -31
  * lat_bnds  (lat_bnds) int64 75 73 71 69 67 65 63 61 ... 27 25 23 21 19 17 15
    LON       (lat, lon) float64 -160.0 -158.0 -156.0 ... -36.0 -34.0 -32.0
    LAT       (lat, lon) float64 74.0 74.0 74.0 74.0 ... 16.0 16.0 16.0 16.0
Data variables:

Let’s create a mask with each of these methods:

mask_rasterize = region.mask(ds_US, method="rasterize")
mask_shapely = region.mask(ds_US, method="shapely")
mask_pygeos = region.mask(ds_US, method="pygeos")


regionmask automatically detects which method to use, so there is no need to specify the method keyword.

Plot the masked regions:

f, axes = plt.subplots(1, 3, subplot_kw=dict(projection=ccrs.PlateCarree()))

opt = dict(add_colorbar=False, ec="0.5", lw=0.5, transform=ccrs.PlateCarree())

mask_rasterize.plot(ax=axes[0], cmap=cmap1, **opt)
mask_shapely.plot(ax=axes[1], cmap=cmap2, **opt)
mask_pygeos.plot(ax=axes[2], cmap=cmap3, **opt)

for ax in axes:
    ax = region.plot_regions(ax=ax, add_label=False)
    ax.set_extent([-105, -75, 25, 55], ccrs.PlateCarree())

        ds_US.LON,, "*", color="0.5", ms=0.5, transform=ccrs.PlateCarree()


Points indicate the grid cell centers (lon and lat), lines the grid cell borders, colored grid cells are selected to be part of the region. The top and right grid cells now belong to the region while the left and bottom grid cells do not. This choice is arbitrary but follows what rasterio.features.rasterize does. This avoids spurious columns of unassigned grid points as the following example shows.

SREX regions

Create a global dataset:

ds_GLOB = regionmask.core.utils.create_lon_lat_dataarray_from_bounds(
    *(-180, 181, 2), *(90, -91, -2)
srex = regionmask.defined_regions.srex

srex_new = srex.mask(ds_GLOB)
f, ax = plt.subplots(1, 1, subplot_kw=dict(projection=ccrs.PlateCarree()))

opt = dict(add_colorbar=False, cmap="viridis_r")

srex_new.plot(ax=ax, ec="0.7", lw=0.25, **opt)

srex.plot_regions(ax=ax, add_label=False, line_kws=dict(lw=1))
ax.set_extent([-135, -50, 24, 51], ccrs.PlateCarree())
ax.coastlines(resolution="50m", lw=0.25)

    ds_GLOB.LON,, "*", color="0.5", ms=0.5, transform=ccrs.PlateCarree()

sel = ((ds_GLOB.LON == -105) | (ds_GLOB.LON == -85)) & (ds_GLOB.LAT > 28)


ax.set_title("edge points are assigned to the left polygon", fontsize=9);

Not assigning the grid cells falling exactly on the border of a region (red points) would leave vertical stripes of unassigned cells.

Points at -180°E (0°E) and -90°N

The described edge behaviour leads to a consistent treatment of points on the border. However, gridpoints at -180°E (or 0°E) and -90°N would never fall in any region.


From version 0.8 this applies only if wrap_lon is not set to False. If wrap_lon is set to False regionmask assumes the coordinates are not lat and lon coordinates.

We exemplify this with a region spanning the whole globe and a coarse longitude/ latidude grid:

outline_global = np.array(
    [[-180.0, 90.0], [-180.0, -90.0], [180.0, -90.0], [180.0, 90.0]]
region_global = regionmask.Regions([outline_global])

lon = np.arange(-180, 180, 30)
lat = np.arange(90, -91, -30)

LON, LAT = np.meshgrid(lon, lat)

Create the masks:

# setting `wrap_lon=False` turns this feature off
mask_global_nontreat = region_global.mask(lon, lat, wrap_lon=False)

mask_global = region_global.mask(lon, lat)

And illustrate the issue:

f, axes = plt.subplots(1, 2, subplot_kw=dict(projection=ccrs.PlateCarree()))

opt = dict(add_colorbar=False, ec="0.3", lw=0.5, transform=ccrs.PlateCarree())

ax = axes[0]
# work around for SciTools/cartopy/issues/1845
mask_global_nontreat = mask_global_nontreat.fillna(1)
mask_global_nontreat.plot(ax=ax, colors=[color1, "none"], levels=2, **opt)

# only for the gridlines
mask_global.plot(ax=ax, colors=["none"], levels=1, **opt)

ax.set_title("Not treating points at -180°E and -90°N", size=6)
ax.set_title("(a)", loc="left", size=6)

ax = axes[1]
mask_global.plot(ax=ax, cmap=cmap1, **opt)

ax.set_title("Treating points at -180°E and -90°N", size=6)
ax.set_title("(b)", loc="left", size=6)

for ax in axes:

    ax = region_global.plot(
        line_kws=dict(lw=2, color="#b15928"),
    ax.plot(LON, LAT, "o", color="0.3", ms=1, transform=ccrs.PlateCarree(), zorder=5)


In the example the region spans the whole globe and there are gridpoints at -180°E and -90°N. Just applying the approach above leads to gridpoints that are not assigned to any region even though the region is global (as shown in a). Therefore, points at -180°E (or 0°E) and -90°N are treated specially (b):

Points at -180°E (0°E) are mapped to 180°E (360°E). Points at -90°N are slightly shifted northwards (by 1 * 10 ** -10). Then it is tested if the shifted points belong to any region

This means that (i) a point at -180°E is part of the region that is present at 180°E and not the one at -180°E (this is consistent with assigning points to the polygon left from it) and (ii) only the points at -90°N get assigned to the region above.

This is illustrated in the figure below:

outline_global1 = np.array([[-180.0, 60.0], [-180.0, -60.0], [0.0, -60.0], [0.0, 60.0]])
outline_global2 = np.array([[0.0, 60.0], [0.0, -60.0], [180.0, -60.0], [180.0, 60.0]])

region_global_2 = regionmask.Regions([outline_global1, outline_global2])

mask_global_2regions = region_global_2.mask(lon, lat)
ax = region_global_2.plot(
    line_kws=dict(color="#b15928", zorder=3, lw=1),

ax.plot(LON, LAT, "o", color="0.3", ms=2, transform=ccrs.PlateCarree(), zorder=5)

# work around for SciTools/cartopy/issues/1845
mask_global_2regions = mask_global_2regions.fillna(2)
mask_global_2regions.plot(ax=ax, colors=[color1, color2, "none"], levels=3, **opt)
# only for the gridlines
mask_global.plot(ax=ax, colors=["none"], levels=1, **opt)

ax.set_title("Points at -180°E are mapped to 180°E", size=6)



This only applies if the border of the region falls exactly on the point. One way to avoid the problem is to calculate the fractional overlap of each gridpoint with the regions (which is not yet implemented).

Polygon interiors

Polygons can have interior boundaries (‘holes’). regionmask unmasks these regions.


Let’s test this on an example and define a region_with_hole:

interior = np.array(
        [-86.0, 44.0],
        [-86.0, 34.0],
        [-94.0, 34.0],
        [-94.0, 44.0],
        [-86.0, 44.0],

poly = Polygon(outline, holes=[interior])

region_with_hole = regionmask.Regions([poly])
mask_hole_rasterize = region_with_hole.mask(ds_US, method="rasterize")
mask_hole_shapely = region_with_hole.mask(ds_US, method="shapely")
mask_hole_pygeos = region_with_hole.mask(ds_US, method="pygeos")
f, axes = plt.subplots(1, 3, subplot_kw=dict(projection=ccrs.PlateCarree()))

opt = dict(add_colorbar=False, ec="0.5", lw=0.5)

mask_hole_rasterize.plot(ax=axes[0], cmap=cmap1, **opt)
mask_hole_shapely.plot(ax=axes[1], cmap=cmap2, **opt)
mask_hole_pygeos.plot(ax=axes[2], cmap=cmap3, **opt)

for ax in axes:
    region_with_hole.plot_regions(ax=ax, add_label=False, line_kws=dict(lw=1))

    ax.set_extent([-105, -75, 25, 55], ccrs.PlateCarree())

        ds_US.LON,, "o", color="0.5", ms=0.5, transform=ccrs.PlateCarree()


Note how the edge behavior of the interior is inverse to the edge behavior of the outerior.

Caspian Sea

The Caspian Sea is defined as polygon interior.

land110 = regionmask.defined_regions.natural_earth.land_110

mask_land110 = land110.mask(ds_GLOB)
f, ax = plt.subplots(1, 1, subplot_kw=dict(projection=ccrs.PlateCarree()))

mask_land110.plot(ax=ax, cmap=cmap2, add_colorbar=False)

ax.set_extent([15, 75, 25, 50], ccrs.PlateCarree())
ax.coastlines(resolution="50m", lw=0.5)

    ds_GLOB.LON,, ".", color="0.5", ms=0.5, transform=ccrs.PlateCarree()

ax.text(52, 43.5, "Caspian Sea", transform=ccrs.PlateCarree())

ax.set_title("Polygon interiors are unmasked");