plot_cartopy_basemap.rst

.. testsetup::

   import numpy as np
   lons = np.zeros(1000)
   lats = np.arange(-80, -90, -0.01)
   tb37v = np.arange(1000)
   from pyresample import SwathDefinition
   swath_def = SwathDefinition(lons, lats)
   if plt is not None:
       import matplotlib
       matplotlib.use('agg')

Getting a Cartopy CRS

To make more advanced plots than the preconfigured quicklooks Cartopy can be used to work with mapped data alongside matplotlib. The below code is based on this Cartopy gallery example. Pyresample allows any AreaDefinition to be converted to a Cartopy CRS as long as Cartopy can represent the projection. Once an AreaDefinition is converted to a CRS object it can be used like any other Cartopy CRS object.

>>> import matplotlib.pyplot as plt
>>> from pyresample.kd_tree import resample_nearest
>>> from pyresample.geometry import AreaDefinition
>>> area_id = 'alaska'
>>> description = 'Alaska Lambert Equal Area grid'
>>> proj_id = 'alaska'
>>> projection = {'proj': 'stere', 'lat_0': 62., 'lon_0': -152.5, 'ellps': 'WGS84', 'units': 'm'}
>>> width = 2019
>>> height = 1463
>>> area_extent = (-757214.993104, -485904.321517, 757214.993104, 611533.818622)
>>> area_def = AreaDefinition(area_id, description, proj_id, projection,
...                           width, height, area_extent)
>>> result = resample_nearest(swath_def, tb37v, area_def, radius_of_influence=20000, fill_value=None)
>>> crs = area_def.to_cartopy_crs()
>>> fig, ax = plt.subplots(subplot_kw=dict(projection=crs))
>>> coastlines = ax.coastlines()  # doctest: +SKIP
>>> ax.set_global()
>>> img = plt.imshow(result, transform=crs, extent=crs.bounds, origin='upper')
>>> cbar = plt.colorbar()
>>> fig.savefig('amsr2_tb37v_cartopy.png')

Assuming lons, lats, and i04_data are initialized with real data the result might look something like this:

Getting a Basemap object

Warning

Basemap is no longer maintained. Cartopy (see above) should be used instead. Basemap does not support Matplotlib 3.0+ either.

In order to make more advanced plots than the preconfigured quicklooks a Basemap object can be generated from an AreaDefinition using the :func:`area_def2basemap <pyresample.plot.area_def2basemap>` function.

Example usage:

>>> import matplotlib.pyplot as plt
>>> from pyresample.kd_tree import resample_nearest
>>> from pyresample.geometry import AreaDefinition
>>> area_id = 'ease_sh'
>>> description = 'Antarctic EASE grid'
>>> proj_id = 'ease_sh'
>>> projection = {'proj': 'laea', 'lat_0': -90, 'lon_0': 0, 'a': 6371228.0, 'units': 'm'}
>>> width = 425
>>> height = 425
>>> area_extent = (-5326849.0625, -5326849.0625, 5326849.0625, 5326849.0625)
>>> area_def = AreaDefinition(area_id, description, proj_id, projection,
...                           width, height, area_extent)
>>> from pyresample import area_def2basemap
>>> result = resample_nearest(swath_def, tb37v, area_def,
...                           radius_of_influence=20000, fill_value=None)
>>> bmap = area_def2basemap(area_def) # doctest: +SKIP
>>> bmng = bmap.bluemarble() # doctest: +SKIP
>>> col = bmap.imshow(result, origin='upper', cmap='RdBu_r') # doctest: +SKIP
>>> plt.savefig('tb37v_bmng.png', bbox_inches='tight') # doctest: +SKIP

Assuming lons, lats and tb37v are initialized with real data as in the previous examples the result might look something like this:

Any keyword arguments (not concerning the projection) passed to plot.area_def2basemap will be passed directly to the Basemap initialization.

For more information on how to plot with Basemap please refer to the Basemap and matplotlib documentation.

Adding background maps with Cartopy

As mentioned in the above warning Cartopy should be used rather than Basemap as the latter is not maintained anymore.

The above image can be generated using Cartopy instead by utilizing the method to_cartopy_crs of the AreaDefinition object.

Example usage:

>>> from pyresample.kd_tree import resample_nearest
>>> import matplotlib.pyplot as plt
>>> result = resample_nearest(swath_def, tb37v, area_def,
...                           radius_of_influence=20000, fill_value=None)
>>> crs = area_def.to_cartopy_crs()
>>> ax = plt.axes(projection=crs)
>>> ax.background_img(name='BM')  # doctest: +SKIP
>>> plt.imshow(result, transform=crs, extent=crs.bounds, origin='upper', cmap='RdBu_r')  # doctest: +SKIP
>>> plt.savefig('tb37v_bmng.png', bbox_inches='tight')  # doctest: +SKIP

The above provides you have the Bluemarble background data available in the Cartopy standard place or in a directory pointed to by the environment parameter CARTOPY_USER_BACKGROUNDS.

With real data (same AMSR-2 as above) this might look like this: