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.. _plot:
Plotting with pyresample and Cartopy
====================================
Pyresample supports basic integration with Cartopy_.
Displaying data quickly
-----------------------
Pyresample has some convenience functions for displaying data from a single
channel. The :func:`show_quicklook <pyresample.plot.show_quicklook>` function
shows a Cartopy generated image of a dataset for a specified AreaDefinition.
The function :func:`save_quicklook <pyresample.plot.save_quicklook>` saves the Cartopy image directly to file.
**Example usage:**
In this simple example below we use GCOM-W1 AMSR-2 data loaded using Satpy_.
Satpy simplifies the reading of this data, but is not necessary for using
pyresample or plotting data.
First we read in the data with Satpy_:
>>> from satpy.scene import Scene
>>> from glob import glob
>>> SCENE_FILES = glob("./GW1AM2_20191122????_156*h5")
>>> scn = Scene(reader='amsr2_l1b', filenames=SCENE_FILES)
>>> scn.load(["btemp_36.5v"])
>>> lons, lats = scn["btemp_36.5v"].area.get_lonlats()
>>> tb37v = scn["btemp_36.5v"].data.compute()
Data for this example can be downloaded from zenodo_.
If you have your own data, or just want to see that the example code here runs, you can
set the three arrays :code:`lons`, :code:`lats` and :code:`tb37v` accordingly, e.g.:
.. doctest::
>>> 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)
But here we go on with the loaded AMSR-2 data. Make sure you have an :code:`areas.yaml`
file that defines the :code:`ease_sh` area, or see
:ref:`the area definition section<area-definitions>` on how to define one.
.. testsetup::
import numpy as np
lons = np.repeat(np.linspace(-95, -70, 100)[np.newaxis], 10, axis=0).ravel()
lats = np.linspace(-80, -90, 1000)
tb37v = np.arange(1000)
if plt is not None:
import matplotlib
matplotlib.use('agg')
.. doctest::
:skipif: plt is None
>>> from pyresample import load_area, save_quicklook, SwathDefinition
>>> from pyresample.kd_tree import resample_nearest
>>> swath_def = SwathDefinition(lons, lats)
>>> result = resample_nearest(swath_def, tb37v, area_def,
... radius_of_influence=20000, fill_value=None)
>>> save_quicklook('tb37v_quick.png', area_def, result, label='Tb 37v (K)')
Assuming **lons**, **lats** and **tb37v** are initialized with real data (as in
the above Satpy_ example) the result might look something like this:
.. image:: /_static/images/tb37v_quick.png
The data passed to the functions is a 2D array matching the AreaDefinition.
.. include:: plot_projections.rst
.. include:: plot_cartopy_basemap.rst
.. _Satpy: http://www.github.com/pytroll/satpy
.. _zenodo: https://doi.org/10.5281/zenodo.3553696
.. _`Cartopy gallery example`: http://scitools.org.uk/cartopy/docs/v0.16/gallery/geostationary.html
.. _Cartopy: http://scitools.org.uk/cartopy/
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