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More advanced mapping with cartopy and matplotlib
=================================================
From the outset, cartopy's purpose has been to simplify and improve the quality of
mapping visualisations available for scientific data.
Contour plots
-------------
.. plot::
:include-source:
import os
import matplotlib.pyplot as plt
from scipy.io import netcdf
from cartopy import config
import cartopy.crs as ccrs
# get the path of the file. It can be found in the repo data directory.
fname = os.path.join(config["repo_data_dir"],
'netcdf', 'HadISST1_SST_update.nc'
)
dataset = netcdf.netcdf_file(fname, maskandscale=True, mmap=False)
sst = dataset.variables['sst'][0, :, :]
lats = dataset.variables['lat'][:]
lons = dataset.variables['lon'][:]
ax = plt.axes(projection=ccrs.PlateCarree())
plt.contourf(lons, lats, sst, 60,
transform=ccrs.PlateCarree())
ax.coastlines()
plt.show()
Images
------
.. plot::
:include-source:
import os
import matplotlib.pyplot as plt
from cartopy import config
import cartopy.crs as ccrs
fig = plt.figure(figsize=(8, 12))
# get the path of the file. It can be found in the repo data directory.
fname = os.path.join(config["repo_data_dir"],
'raster', 'sample', 'Miriam.A2012270.2050.2km.jpg'
)
img_extent = (-120.67660000000001, -106.32104523100001, 13.2301484511245, 30.766899999999502)
img = plt.imread(fname)
ax = plt.axes(projection=ccrs.PlateCarree())
plt.title('Hurricane Miriam from the Aqua/MODIS satellite\n'
'2012 09/26/2012 20:50 UTC')
ax.use_sticky_edges = False
# set a margin around the data
ax.set_xmargin(0.05)
ax.set_ymargin(0.10)
# add the image. Because this image was a tif, the "origin" of the image is in the
# upper left corner
ax.imshow(img, origin='upper', extent=img_extent, transform=ccrs.PlateCarree())
ax.coastlines(resolution='50m', color='black', linewidth=1)
# mark a known place to help us geo-locate ourselves
ax.plot(-117.1625, 32.715, 'bo', markersize=7, transform=ccrs.Geodetic())
ax.text(-117, 33, 'San Diego', transform=ccrs.Geodetic())
plt.show()
.. _vector_plotting:
Vector plotting
---------------
Cartopy comes with powerful vector field plotting functionality. There are 3 distinct options for
visualising vector fields:
:meth:`quivers <cartopy.mpl.geoaxes.GeoAxes.quiver>` (:ref:`example <sphx_glr_gallery_vector_data_arrows.py>`),
:meth:`barbs <cartopy.mpl.geoaxes.GeoAxes.barbs>` (:ref:`example <sphx_glr_gallery_vector_data_barbs.py>`) and
:meth:`streamplots <cartopy.mpl.geoaxes.GeoAxes.streamplot>` (:ref:`example <sphx_glr_gallery_vector_data_streamplot.py>`)
each with their own benefits for displaying certain vector field forms.
.. figure:: ../gallery/vector_data/images/sphx_glr_arrows_001.png
:target: ../gallery/vector_data/arrows.html
:align: center
:scale: 50
Since both :meth:`~cartopy.mpl.geoaxes.GeoAxes.quiver` and :meth:`~cartopy.mpl.geoaxes.GeoAxes.barbs`
are visualisations which draw every vector supplied, there is an additional option to "regrid" the
vector field into a regular grid on the target projection (done via
:func:`cartopy.vector_transform.vector_scalar_to_grid`). This is enabled with the ``regrid_shape``
keyword and can have a massive impact on the effectiveness of the visualisation:
.. figure:: ../gallery/vector_data/images/sphx_glr_regridding_arrows_001.png
:target: ../gallery/vector_data/regridding_arrows.html
:align: center
:scale: 50
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