.. currentmodule:: geopandas

.. ipython:: python
   :suppress:

   import geopandas
   import matplotlib
   orig = matplotlib.rcParams['figure.figsize']
   matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]]
   import matplotlib.pyplot as plt
   plt.close('all')


Mapping Tools
=========================================


*geopandas* provides a high-level interface to the ``matplotlib`` library for making maps. Mapping shapes is as easy as using the ``plot()`` method on a ``GeoSeries`` or ``GeoDataFrame``.

Loading some example data:

.. ipython:: python

    world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
    cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))

We can now plot those GeoDataFrames:

.. ipython:: python

    # Examine country GeoDataFrame
    world.head()

    # Basic plot, random colors
    @savefig world_randomcolors.png
    world.plot();

Note that in general, any options one can pass to `pyplot <http://matplotlib.org/api/pyplot_api.html>`_ in ``matplotlib`` (or `style options that work for lines <http://matplotlib.org/api/lines_api.html>`_) can be passed to the ``plot()`` method.


Choropleth Maps
-----------------

*geopandas* makes it easy to create Choropleth maps (maps where the color of each shape is based on the value of an associated variable). Simply use the plot command with the ``column`` argument set to the column whose values you want used to assign colors.

.. ipython:: python

    # Plot by GDP per capta
    world = world[(world.pop_est>0) & (world.name!="Antarctica")]
    world['gdp_per_cap'] = world.gdp_md_est / world.pop_est
    @savefig world_gdp_per_cap.png
    world.plot(column='gdp_per_cap');


Creating a legend
~~~~~~~~~~~~~~~~~

When plotting a map, one can enable a legend using the ``legend`` argument:

.. ipython:: python

    # Plot population estimates with an accurate legend
    import matplotlib.pyplot as plt
    fig, ax = plt.subplots(1, 1)
    @savefig world_pop_est.png
    world.plot(column='pop_est', ax=ax, legend=True)

However, the default appearance of the legend and plot axes may not be desirable. One can define the plot axes (with ``ax``) and the legend axes (with ``cax``) and then pass those in to the ``plot`` call. The following example uses ``mpl_toolkits`` to vertically align the plot axes and the legend axes:

.. ipython:: python

    # Plot population estimates with an accurate legend
    from mpl_toolkits.axes_grid1 import make_axes_locatable
    fig, ax = plt.subplots(1, 1)
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.1)
    @savefig world_pop_est_fixed_legend_height.png
    world.plot(column='pop_est', ax=ax, legend=True, cax=cax)


Choosing colors
~~~~~~~~~~~~~~~~

One can also modify the colors used by ``plot`` with the ``cmap`` option (for a full list of colormaps, see the `matplotlib website <http://matplotlib.org/users/colormaps.html>`_):

.. ipython:: python

    @savefig world_gdp_per_cap_red.png
    world.plot(column='gdp_per_cap', cmap='OrRd');


The way color maps are scaled can also be manipulated with the ``scheme`` option (if you have ``mapclassify`` installed, which can be accomplished via ``conda install -c conda-forge mapclassify``). The ``scheme`` option can be set to any scheme provided by mapclassify (e.g. 'box_plot', 'equal_interval',
'fisher_jenks', 'fisher_jenks_sampled', 'headtail_breaks', 'jenks_caspall', 'jenks_caspall_forced', 'jenks_caspall_sampled', 'max_p_classifier', 'maximum_breaks', 'natural_breaks', 'quantiles', 'percentiles', 'std_mean' or 'user_defined'). Arguments can be passed in classification_kwds dict. See the `mapclassify documentation <https://mapclassify.readthedocs.io>`_ for further details about these map classification schemes.

.. ipython:: python

    @savefig world_gdp_per_cap_quantiles.png
    world.plot(column='gdp_per_cap', cmap='OrRd', scheme='quantiles');


Maps with Layers
-----------------

There are two strategies for making a map with multiple layers -- one more succinct, and one that is a little more flexible.

Before combining maps, however, remember to always ensure they share a common CRS (so they will align).

.. ipython:: python

    # Look at capitals
    # Note use of standard `pyplot` line style options
    @savefig capitals.png
    cities.plot(marker='*', color='green', markersize=5);

    # Check crs
    cities = cities.to_crs(world.crs)

    # Now we can overlay over country outlines
    # And yes, there are lots of island capitals
    # apparently in the middle of the ocean!

**Method 1**

.. ipython:: python

    base = world.plot(color='white', edgecolor='black')
    @savefig capitals_over_countries_1.png
    cities.plot(ax=base, marker='o', color='red', markersize=5);

**Method 2: Using matplotlib objects**

.. ipython:: python

    import matplotlib.pyplot as plt
    fig, ax = plt.subplots()

    # set aspect to equal. This is done automatically
    # when using *geopandas* plot on it's own, but not when
    # working with pyplot directly.
    ax.set_aspect('equal')

    world.plot(ax=ax, color='white', edgecolor='black')
    cities.plot(ax=ax, marker='o', color='red', markersize=5)
    @savefig capitals_over_countries_2.png
    plt.show();


Other Resources
-----------------
Links to jupyter Notebooks for different mapping tasks:

`Making Heat Maps <http://nbviewer.jupyter.org/gist/perrygeo/c426355e40037c452434>`_


.. ipython:: python
    :suppress:

    matplotlib.rcParams['figure.figsize'] = orig


.. ipython:: python
    :suppress:

    import matplotlib.pyplot as plt
    plt.close('all')