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 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 = 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 = 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
