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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_resample_plot_group_by_region.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_resample_plot_group_by_region.py:


===============
Group by Region
===============

Example of aggregating (grouping) data on regions defined by a series with integers denoting different states.

In the figure below we are running a Steady State Detection algorithm that produces a series with two states:
0 - transient region, 1 - steady region. This binary series is used to identify the state of interest. Then we run the
group_by_region calculation, specifying the state we are interested on, the type of aggregation and where the result
should be placed on (timestamp).

.. GENERATED FROM PYTHON SOURCE LINES 14-70



.. image-sg:: /auto_examples/resample/images/sphx_glr_plot_group_by_region_001.png
   :alt: Data grouped by region according to steady state
   :srcset: /auto_examples/resample/images/sphx_glr_plot_group_by_region_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    import os

    import matplotlib.pyplot as plt
    import pandas as pd

    from indsl.detect import ssd_cpd
    from indsl.resample import group_by_region


    # Import a dataset with process data
    base_path = "" if __name__ == "__main__" else os.path.dirname(__file__)
    data = pd.read_csv(os.path.join(base_path, "../../datasets/data/suct_pressure_barg.csv"), index_col=0)
    data = data.squeeze()
    data.index = pd.to_datetime(data.index)

    # Steady state detection parameters
    min_distance = 60
    var_threshold = 5.0
    slope_threshold = -8.8

    # Evaluate the Steady State Conditions
    ss_map = ssd_cpd(data, min_distance, var_threshold, slope_threshold)

    # Group the process data for the regions where steady state is present
    aggregated_result = group_by_region(data, ss_map)

    # Plot the process data
    fig, ax1 = plt.subplots(figsize=(9, 7))
    ax1.margins(x=0)
    ax2 = ax1.twinx()
    ax1.plot(data.index, data.values, label="Process data")
    # Plot the aggregated result
    ax1.plot(aggregated_result.index, aggregated_result.values, "ko", ms=10, label="Grouped data")
    ax1.set_ylabel("Pressure (barg)")

    # Plot the Steady State regions
    ln2 = ax2.fill_between(ss_map.index, ss_map.values, color="orange", alpha=0.2)
    ax2.margins(y=0)
    ax2.set_yticks([])

    # create legend below the plot
    plt.legend(
        (
            plt.Line2D(data.index, data.values),
            plt.Line2D(aggregated_result.index, aggregated_result.values, color="black", marker="o", ms=10, ls=""),
            ln2,
        ),
        ("Process data", "Grouped data", "Steady State Regions"),
        loc="upper center",
        bbox_to_anchor=(0.5, -0.05),
        fancybox=True,
        ncol=3,
    )
    plt.title("Data grouped by region according to steady state")
    plt.show()


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

   **Total running time of the script:** (1 minutes 1.280 seconds)


.. _sphx_glr_download_auto_examples_resample_plot_group_by_region.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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