Note

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

In this tutorial we will show how to create a mask for arbitrary latitude and longitude grids using xarray. It is very similar to the tutorial Create Mask (numpy).

Import regionmask and check the version:

import regionmask

'0.4.0'


Load xarray and the tutorial data:

import xarray as xr
import numpy as np

airtemps = xr.tutorial.load_dataset('air_temperature')


The example data is a temperature field over North America. Let’s plot the first time step:

# load plotting libraries
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

# choose a good projection for regional maps
proj=ccrs.LambertConformal(central_longitude=-100)

ax = plt.subplot(111, projection=proj)

airtemps.isel(time=1).air.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree())

ax.coastlines();


Conviniently we can directly pass an xarray object to the mask function. It gets the longitude and latitude from the DataArray/ Dataset and creates the mask. If the longituda and latitude in the xarray object are not called lon and lat, respectively; their name can be given via the lon_name and lat_name keyword. Here we use the Giorgi regions.

mask = regionmask.defined_regions.giorgi.mask(airtemps)

All elements of mask are NaN. Try to set 'wrap_lon=True'.
All NaN?  True


This didn’t work - all elements are NaNs! The reason is that airtemps has its longitude from 0 to 360 while the Giorgi regions are defined as -180 to 180. Thus we can provide the wrap_lon keyword:

mask = regionmask.defined_regions.giorgi.mask(airtemps, wrap_lon=True)

All NaN?  False


This is better. Let’s plot the regions:

proj=ccrs.LambertConformal(central_longitude=-100)
ax = plt.subplot(111, projection=proj)

levels = np.arange(low - 0.5, high + 1)

ax.coastlines()

# fine tune the extent
ax.set_extent([200, 330, 10, 75], crs=ccrs.PlateCarree());


We want to select the region ‘Central North America’. Thus we first need to find out which number this is:

regionmask.defined_regions.giorgi.map_keys('Central North America')

6


## Select using where¶

xarray provides the handy where function:

airtemps_CNA = airtemps.where(mask == 6)


Check everything went well by repeating the first plot with the selected region:

# choose a good projection for regional maps
proj=ccrs.LambertConformal(central_longitude=-100)

ax = plt.subplot(111, projection=proj)

airtemps_CNA.isel(time=1).air.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree())

ax.coastlines();


Looks good - let’s take the area average and plot the time series. (Note: you should use cos(lat) weights to correctly calculate an area average. Unfortunately this is not yet (as of version 0.10) implemented in xarray.)

ts_airtemps_CNA = airtemps_CNA.mean(dim=('lat', 'lon')) - 273.15
ts_airtemps = airtemps.mean(dim=('lat', 'lon')) - 273.15

# and the line plot
ts_airtemps_CNA.air.plot.line(label='Central North America')
ts_airtemps.air.plot(label='Entire Domain')

plt.legend(ncol=2);


## Select using groupby¶

# xarray version > 0.8 is required
xr.__version__

'0.10.1'

# you can group over all integer values of the mask
# you have to take the mean over stacked_lat_lon
airtemps_all

<xarray.Dataset>
Dimensions:  (region: 6, time: 2920)
Coordinates:
* time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...
* region   (region) float64 4.0 5.0 6.0 7.0 8.0 9.0
Data variables:
air      (region, time) float32 293.60544 292.2063 291.432 293.64203 ...


we can add the abbreviations and names of the regions to the DataArray

# extract the abbreviations and the names of the regions from regionmask

airtemps_all.coords['abbrevs'] = ('region', abbrevs)
airtemps_all.coords['names'] = ('region', names)
airtemps_all

<xarray.Dataset>
Dimensions:  (region: 6, time: 2920)
Coordinates:
* time     (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...
* region   (region) float64 4.0 5.0 6.0 7.0 8.0 9.0
abbrevs  (region) <U3 'CAM' 'WNA' 'CNA' 'ENA' 'ALA' 'GRL'
names    (region) <U21 'Central America' 'Western North America' ...
Data variables:
air      (region, time) float32 293.60544 292.2063 291.432 293.64203 ...


now we can select the regions in many ways

f, axes = plt.subplots(3, 1)

# as before, by the index of the region
ax = axes[0]
airtemps_all.sel(region=6).air.plot(ax=ax)

# with the abbreviation
ax = axes[1]
airtemps_all.isel(region=(airtemps_all.abbrevs == 'WNA')).air.plot(ax=ax)

# with the long name
ax = axes[2]
airtemps_all.isel(region=(airtemps_all.names == 'Eastern North America')).air.plot(ax=ax)

f.tight_layout()