.. _example_inset_maps:

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Inset-maps - get a zoomed-in view on selected areas
===================================================

Quickly create nice inset-maps to show details for specific regions.

- the location and extent of the inset can be defined in any given crs

  - (or as a geodesic circle with a radius defined in meters)

- the inset-map can have a different crs than the "parent" map

(requires EOmaps >= v4.1)


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


.. code-block:: python

    # EOmaps example: How to create inset-maps

    from eomaps import Maps
    import numpy as np

    m = Maps(Maps.CRS.Orthographic())
    m.add_feature.preset.coastline()  # add some coastlines

    # ---------- create a new inset-map
    #            showing a 15 degree rectangle around the xy-point
    mi1 = m.new_inset_map(
        xy=(5, 45),
        xy_crs=4326,
        shape="rectangles",
        radius=15,
        plot_position=(0.75, 0.4),
        plot_size=0.5,
        inset_crs=4326,
        boundary=dict(ec="r", lw=1),
        indicate_extent=dict(fc=(1, 0, 0, 0.25)),
    )

    mi1.add_indicator_line(m, marker="o")

    # populate the inset with some more detailed features
    mi1.add_feature.preset("coastline", "ocean", "land", "countries", "urban_areas")

    # ---------- create another inset-map
    #            showing a 400km circle around the xy-point
    mi2 = m.new_inset_map(
        xy=(5, 45),
        xy_crs=4326,
        shape="geod_circles",
        radius=400000,
        plot_position=(0.25, 0.4),
        plot_size=0.5,
        inset_crs=3035,
        boundary=dict(ec="g", lw=2),
        indicate_extent=dict(fc=(0, 1, 0, 0.25)),
    )
    mi2.add_indicator_line(m, marker="o")

    # populate the inset with some features
    mi2.add_feature.preset("ocean", "land")
    mi2.add_feature.preset.urban_areas(zorder=1)

    # print some data on all of the maps

    x, y = np.meshgrid(np.linspace(-50, 50, 100), np.linspace(-30, 70, 100))
    data = x + y

    m.set_data(data, x, y, crs=4326)
    m.set_classify.Quantiles(k=4)
    m.plot_map(alpha=0.5, ec="none", set_extent=False)

    # use the same data and classification for the inset-maps
    for m_i in [mi1, mi2]:
        m_i.inherit_data(m)
        m_i.inherit_classification(m)

    mi1.set_shape.ellipses(np.mean(m.shape.radius) / 2)
    mi1.plot_map(alpha=0.75, ec="k", lw=0.5, set_extent=False)

    mi2.set_shape.ellipses(np.mean(m.shape.radius) / 2)
    mi2.plot_map(alpha=1, ec="k", lw=0.5, set_extent=False)


    # add an annotation for the second datapoint to the inset-map
    mi2.add_annotation(ID=1, xytext=(-120, 80))

    # indicate the extent of the second inset on the first inset
    mi2.add_extent_indicator(mi1, ec="g", lw=2, fc="g", alpha=0.5, zorder=0)
    mi2.add_indicator_line(mi1, marker="o")

    # add some additional text to the inset-maps
    for m_i, txt, color in zip([mi1, mi2], ["epsg: 4326", "epsg: 3035"], ["r", "g"]):
        txt = m_i.ax.text(
            0.5,
            0,
            txt,
            transform=m_i.ax.transAxes,
            horizontalalignment="center",
            bbox=dict(facecolor=color),
        )
        # add the text-objects as artists to the blit-manager
        m_i.BM.add_artist(txt)

    mi2.add_colorbar(hist_bins=20, margin=dict(bottom=-0.2), label="some parameter")
    # move the inset map (and the colorbar) to a different location
    mi2.set_inset_position(x=0.3)

    # share pick events
    for mi in [m, mi1, mi2]:
        mi.cb.pick.attach.annotate(text=lambda ID, val, **kwargs: f"ID={ID}\nval={val:.2f}")
    m.cb.pick.share_events(mi1, mi2)

    m.apply_layout(
        {
            "figsize": [6.4, 4.8],
            "0_map": [0.1625, 0.09, 0.675, 0.9],
            "1_inset_map": [0.5625, 0.15, 0.375, 0.5],
            "2_inset_map": [0.0875, 0.33338, 0.325, 0.43225],
            "3_cb": [0.0875, 0.12, 0.4375, 0.12987],
            "3_cb_histogram_size": 0.8,
        }
    )
    m.show()