.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "python_module/10_tracking_diagnostics/pet_groups.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_python_module_10_tracking_diagnostics_pet_groups.py>`
        to download the full example code. or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_python_module_10_tracking_diagnostics_pet_groups.py:


Groups distribution
===================

.. GENERATED FROM PYTHON SOURCE LINES 6-12

.. code-block:: Python

    from matplotlib import pyplot as plt
    from numpy import arange, ones, percentile
    import py_eddy_tracker_sample

    from py_eddy_tracker.observations.tracking import TrackEddiesObservations








.. GENERATED FROM PYTHON SOURCE LINES 13-14

Load an experimental med atlas over a period of 26 years (1993-2019)

.. GENERATED FROM PYTHON SOURCE LINES 14-20

.. code-block:: Python

    a = TrackEddiesObservations.load_file(
        py_eddy_tracker_sample.get_demo_path(
            "eddies_med_adt_allsat_dt2018/Anticyclonic.zarr"
        )
    )








.. GENERATED FROM PYTHON SOURCE LINES 21-22

Group distribution

.. GENERATED FROM PYTHON SOURCE LINES 22-31

.. code-block:: Python

    groups = dict()
    bins_time = [10, 20, 30, 60, 90, 180, 360, 100000]
    for t0, t1 in zip(bins_time[:-1], bins_time[1:]):
        groups[f"lifetime_{t0}_{t1}"] = lambda dataset, t0=t0, t1=t1: (
            dataset.lifetime >= t0
        ) * (dataset.lifetime < t1)
    bins_percentile = arange(0, 100.0001, 5)









.. GENERATED FROM PYTHON SOURCE LINES 32-33

Function to build stats

.. GENERATED FROM PYTHON SOURCE LINES 33-61

.. code-block:: Python

    def stats_compilation(dataset, groups, field, bins, filter=None):
        datas = dict(ref=dataset.bins_stat(field, bins=bins, mask=filter)[1], y=dict())
        for k, index in groups.items():
            i = dataset.merge_filters(filter, index)
            x, datas["y"][k] = dataset.bins_stat(field, bins=bins, mask=i)
        datas["x"], datas["bins"] = x, bins
        return datas


    def plot_stats(ax, bins, x, y, ref, box=False, cmap=None, percentiles=None, **kw):
        base, ref = ones(x.shape) * 100.0, ref / 100.0
        x = arange(bins.shape[0]).repeat(2)[1:-1] if box else x
        y0 = base
        if cmap is not None:
            cmap, nb_groups = plt.get_cmap(cmap), len(y)
        keys = tuple(y.keys())
        for i, k in enumerate(keys[::-1]):
            y1 = y0 - y[k] / ref
            args = (y0.repeat(2), y1.repeat(2)) if box else (y0, y1)
            if cmap is not None:
                kw["color"] = cmap(1 - i / (nb_groups - 1))
            ax.fill_between(x, *args, label=k, **kw)
            y0 = y1
        if percentiles:
            for b in bins:
                ax.axvline(b, **percentiles)









.. GENERATED FROM PYTHON SOURCE LINES 62-63

Speed radius by track period

.. GENERATED FROM PYTHON SOURCE LINES 63-72

.. code-block:: Python

    stats = stats_compilation(
        a, groups, "radius_s", percentile(a.radius_s, bins_percentile)
    )
    fig = plt.figure()
    ax = fig.add_subplot(111)
    plot_stats(ax, **stats, cmap="magma", percentiles=dict(color="gray", ls="-.", lw=0.4))
    ax.set_xlabel("Speed radius (m)"), ax.set_ylabel("% of class"), ax.set_ylim(0, 100)
    ax.grid(), ax.legend()




.. image-sg:: /python_module/10_tracking_diagnostics/images/sphx_glr_pet_groups_001.png
   :alt: pet groups
   :srcset: /python_module/10_tracking_diagnostics/images/sphx_glr_pet_groups_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    (None, <matplotlib.legend.Legend object at 0x749027705400>)



.. GENERATED FROM PYTHON SOURCE LINES 73-74

Amplitude by track period

.. GENERATED FROM PYTHON SOURCE LINES 74-82

.. code-block:: Python

    stats = stats_compilation(
        a, groups, "amplitude", percentile(a.amplitude, bins_percentile)
    )
    fig = plt.figure()
    ax = fig.add_subplot(111)
    plot_stats(ax, **stats, cmap="magma")
    ax.set_xlabel("Amplitude (m)"), ax.set_ylabel("% of class"), ax.set_ylim(0, 100)
    ax.grid(), ax.legend()



.. image-sg:: /python_module/10_tracking_diagnostics/images/sphx_glr_pet_groups_002.png
   :alt: pet groups
   :srcset: /python_module/10_tracking_diagnostics/images/sphx_glr_pet_groups_002.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    (None, <matplotlib.legend.Legend object at 0x749027774a70>)




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.999 seconds)


.. _sphx_glr_download_python_module_10_tracking_diagnostics_pet_groups.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/AntSimi/py-eddy-tracker/master?urlpath=lab/tree/notebooks/python_module/10_tracking_diagnostics/pet_groups.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: pet_groups.ipynb <pet_groups.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: pet_groups.py <pet_groups.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: pet_groups.zip <pet_groups.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_