.. _example_timeseries:

=========================
Timeseries and histograms
=========================


Callback-functions can be used to trigger updates on other plots.
This example shows how to use EOmaps to analyze a database that is associated with a map.

- create a grid of ``Maps`` objects and ordinary matplotlib axes via ``MapsGrid``
- define a custom callback to update the plots if you click on the map


.. image:: /_static/example_images/example_timeseries.gif
    :width: 75%
    :align: center

.. code-block:: python

    # EOmaps example:  Data analysis widgets - Interacting with a database

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

    # ============== create a random database =============
    length, Nlon, Nlat = 1000, 100, 50
    lonmin, lonmax, latmin, latmax = -70, 175, 0, 75

    database = np.full((Nlon * Nlat, length), np.nan)
    for i in range(Nlon * Nlat):
        size = np.random.randint(1, length)
        x = np.random.normal(loc=np.random.rand(), scale=np.random.rand(), size=size)
        np.put(database, range(i * length, i * length + size), x)
    lon, lat = np.meshgrid(
        np.linspace(lonmin, lonmax, Nlon), np.linspace(latmin, latmax, Nlat)
    )

    IDs = [f"point_{i}" for i in range(Nlon * Nlat)]
    database = pd.DataFrame(database, index=IDs)
    coords = pd.DataFrame(dict(lon=lon.flat, lat=lat.flat), index=IDs)

    # -------- calculate the number of values in each dataset
    #          (e.g. the data actually shown on the map)
    data = pd.DataFrame(dict(count=database.count(axis=1), **coords))
    # =====================================================


    # initialize a map on top
    m = Maps(ax=211)
    m.add_feature.preset.ocean()
    m.add_feature.preset.coastline()

    # initialize 2 matplotlib plot-axes below the map
    ax_left = m.f.add_subplot(223)
    ax_left.set_ylabel("data-values")
    ax_left.set_xlabel("data-index")

    ax_right = m.f.add_subplot(224)
    ax_right.set_ylabel("data-values")
    ax_right.set_xlabel("histogram count")

    ax_left.sharey(ax_right)

    # -------- assign data to the map and plot it
    m.set_data(data=data, x="lon", y="lat", crs=4326)
    m.set_classify_specs(
        scheme=Maps.CLASSIFIERS.UserDefined,
        bins=[50, 100, 200, 400, 800],
    )
    m.set_shape.ellipses(radius=0.5)
    m.plot_map()


    # -------- define a custom callback function to update the plots
    def update_plots(ID, **kwargs):
        # get the data
        x = database.loc[ID].dropna()

        # plot the lines and histograms
        (l,) = ax_left.plot(x, lw=0.5, marker=".", c="C0")
        cnt, val, art = ax_right.hist(x.values, bins=50, orientation="horizontal", fc="C0")

        # re-compute axis limits based on the new artists
        ax_left.relim()
        ax_right.relim()
        ax_left.autoscale()
        ax_right.autoscale()

        # add all artists as "temporary pick artists" so that they
        # are removed when the next datapoint is selected
        for a in [l, *art]:
            m.cb.pick.add_temporary_artist(a)


    # attach the custom callback (and some pre-defined callbacks)
    m.cb.pick.attach(update_plots)
    m.cb.pick.attach.annotate()
    m.cb.pick.attach.mark(permanent=False, buffer=1, fc="none", ec="r")
    m.cb.pick.attach.mark(permanent=False, buffer=2, fc="none", ec="r", ls=":")

    # add a colorbar
    m.add_colorbar(0.25, label="Number of observations")
    m.colorbar.ax_cb_plot.tick_params(labelsize=6)

    # add a logo
    m.add_logo()

    m.apply_layout(
        {
            "figsize": [6.4, 4.8],
            "0_map": [0.05625, 0.60894, 0.8875, 0.36594],
            "1_": [0.12326, 0.11123, 0.35, 0.31667],
            "2_": [0.58674, 0.11123, 0.35, 0.31667],
            "3_cb": [0.12, 0.51667, 0.82, 0.06166],
            "3_cb_histogram_size": 0.8,
            "4_logo": [0.8125, 0.62333, 0.1212, 0.06667],
        }
    )
    m.show()