Using cartopy with matplotlib
=============================

.. toctree::
    :hidden:

    advanced_plotting.rst
    gridliner.rst


Beautifully simple maps
-----------------------

Cartopy has exposed an interface to enable easy map creation using matplotlib.
Creating a basic map is as simple as telling Matplotlib to use a specific map projection,
and then adding some coastlines to the axes:

.. plot::
    :include-source:

    import cartopy.crs as ccrs
    import matplotlib.pyplot as plt

    ax = plt.axes(projection=ccrs.PlateCarree())
    ax.coastlines()

    # Save the plot by calling plt.savefig() BEFORE plt.show()
    plt.savefig('coastlines.pdf')
    plt.savefig('coastlines.png')

    plt.show()


A list of the available projections to be used with Matplotlib can be
found on the :ref:`cartopy_projections` page.

The line ``plt.axes(projection=ccrs.PlateCarree())`` sets up a 
:class:`~cartopy.mpl.geoaxes.GeoAxes` instance
which exposes a variety of other map related methods, in the case of the 
previous example, we used the 
:meth:`~cartopy.mpl.geoaxes.GeoAxes.coastlines` method
to add coastlines to the map.

To save the figure, use Matplotlib's :func:`~matplotlib.pyplot.savefig`
function.

Lets create another map in a different projection, and make use of the
:meth:`~cartopy.mpl.geoaxes.GeoAxes.stock_img` method to add an underlay
image to the map:

.. plot::
    :include-source:

    import cartopy.crs as ccrs
    import matplotlib.pyplot as plt

    ax = plt.axes(projection=ccrs.Mollweide())
    ax.stock_img()
    plt.show()


At this point, have a go at :ref:`picking your own projection <cartopy_projections>` and creating a map
with an image underlay with coastlines over the top.


Adding data to the map
----------------------

Once you have the map just the way you want it, data can be added to it in exactly the same way as
with normal Matplotlib axes. By default, the coordinate system of any data added to a GeoAxes is
the same as the coordinate system of the GeoAxes itself, to control which coordinate system 
that the given data is in, you can add the ``transform`` keyword with an appropriate 
:class:`cartopy.crs.CRS` instance:


.. plot::
    :include-source:

    import cartopy.crs as ccrs
    import matplotlib.pyplot as plt

    ax = plt.axes(projection=ccrs.PlateCarree())
    ax.stock_img()

    ny_lon, ny_lat = -75, 43
    delhi_lon, delhi_lat = 77.23, 28.61

    plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
             color='blue', linewidth=2, marker='o',
             transform=ccrs.Geodetic(),
             )

    plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
             color='gray', linestyle='--',
             transform=ccrs.PlateCarree(),
             )

    plt.text(ny_lon - 3, ny_lat - 12, 'New York',
             horizontalalignment='right',
             transform=ccrs.Geodetic())

    plt.text(delhi_lon + 3, delhi_lat - 12, 'Delhi',
             horizontalalignment='left',
             transform=ccrs.Geodetic())

    plt.show()


Notice how the line in blue between New York and Delhi is not straight on a flat
:class:`~cartopy.crs.PlateCarree` map, this is because the
:class:`~cartopy.crs.Geodetic` coordinate system is a truly spherical coordinate
system, where a line between two points is defined as the shortest path between
those points *on the globe* rather than 2d Cartesian space.

.. note::

    By default, Matplotlib automatically sets the limits of your Axes based on the data
    that you plot. Because cartopy implements a :class:`~cartopy.mpl.geoaxes.GeoAxes`
    class, this equates to the limits of the resulting map. Sometimes this autoscaling
    is a desirable feature and other times it is not.

    To set the extents of a cartopy GeoAxes, there are several convenient options:

       * For "global" plots, use the :meth:`~cartopy.mpl.geoaxes.GeoAxes.set_global` method.
       * To set the extents of the map based on a bounding box, in any coordinate system,
         use the :meth:`~cartopy.mpl.geoaxes.GeoAxes.set_extent` method.
       * Alternatively, the standard limit setting methods can be used in the GeoAxes's
         native coordinate system (e.g. :meth:`~matplotlib.axes.Axes.set_xlim` and
         :meth:`~matplotlib.axes.Axes.set_ylim`).


In the :doc:`next section <advanced_plotting>`, examples of contouring and adding
geo-located images are provided for more advanced map based visualisations.