.. _example_customization:

====================================
Customize the appearance of the plot
====================================

-  use ``m.set_plot_specs()`` to set the general appearance of the plot
-  after creating the plot, you can access individual objects via ``m.figure.<...>`` … most importantly:

   -  ``f`` : the matplotlib figure
   -  ``ax``, ``ax_cb``, ``ax_cb_plot`` : the axes used for plotting the map, colorbar and histogram
   -  ``gridspec``, ``cb_gridspec`` : the matplotlib GridSpec instances for the plot and the colorbar
   -  ``coll`` : the collection representing the data on the map

.. image:: /_static/example_images/example_customization.png
  :width: 75%
  :align: center


.. code-block::

    # EOmaps example: Customize the appearance of the plot

    from eomaps import Maps
    import pandas as pd
    import numpy as np

    # ----------- create some example-data
    lon, lat = np.meshgrid(np.arange(-30, 60, 0.25), np.arange(30, 60, 0.3))
    data = pd.DataFrame(
        dict(lon=lon.flat, lat=lat.flat, data_variable=np.sqrt(lon**2 + lat**2).flat)
    )
    data = data.sample(3000)  # take 3000 random datapoints from the dataset
    # ------------------------------------

    m = Maps(crs=3857, figsize=(9, 5))
    m.set_frame(rounded=0.2, lw=1.5, ec="midnightblue", fc="ivory")
    m.text(0.5, 0.97, "What a nice figure", fontsize=12)

    m.add_feature.preset.ocean(fc="lightsteelblue")
    m.add_feature.preset.coastline(lw=0.25)

    m.set_data(data=data, x="lon", y="lat", crs=4326)
    m.set_shape.geod_circles(radius=30000)  # plot geodesic-circles with 30 km radius
    m.set_classify_specs(
        scheme="UserDefined", bins=[35, 36, 37, 38, 45, 46, 47, 48, 55, 56, 57, 58]
    )
    m.plot_map(
        edgecolor="k",  # give shapes a black edgecolor
        linewidth=0.5,  # with a linewidth of 0.5
        cmap="RdYlBu",  # use a red-yellow-blue colormap
        vmin=35,  # map colors to values between 35 and 60
        vmax=60,
        alpha=0.75,  # add some transparency
    )

    # add a colorbar
    m.add_colorbar(
        label="some parameter",
        hist_bins="bins",
        hist_size=1,
        hist_kwargs=dict(density=True),
    )

    # add a y-label to the histogram
    m.colorbar.ax_cb_plot.set_ylabel("The Y label")

    # add a logo to the plot
    m.add_logo()

    m.apply_layout(
        {
            "figsize": [9.0, 5.0],
            "0_map": [0.10154, 0.2475, 0.79692, 0.6975],
            "1_cb": [0.20125, 0.0675, 0.6625, 0.135],
            "1_cb_histogram_size": 1,
            "2_logo": [0.87501, 0.09, 0.09999, 0.07425],
        }
    )
    m.show()