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.. "auto_examples/detect/plot_ssd_cpd.py"
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.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_detect_plot_ssd_cpd.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_detect_plot_ssd_cpd.py:


====================================
Steady State Detection: Change Point
====================================

Example of steady state detection (SSD) based on the ED-PELT change point detection (CPD) algorithm. We use data from a
compressor suction pressure sensor (in barg). The dataset contains 4 days of process of process data (sampled using 1m
granularity).

In the figure below it can be observed how the CPD algorithm identifies change point segments in which steady state
conditions are evaluated. The input parameters used by the algorithms in this example are:

    - min_distance = 60 → specifies the minimum size of segments to be detected (CPD and SSD algorithms)
    - var_threshold = 5.0 → specify the maximum variance allowed for each region (SSD algorithm)
    - slope_threshold = -8.8 → specify the maximum slope (10^-8.8) allowed for each region (SSD algorithm)

.. GENERATED FROM PYTHON SOURCE LINES 19-71



.. image-sg:: /auto_examples/detect/images/sphx_glr_plot_ssd_cpd_001.png
   :alt: Steady State Detection based on ED-PELT Change Point Detection algorithm
   :srcset: /auto_examples/detect/images/sphx_glr_plot_ssd_cpd_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 cpd_ed_pelt, ssd_cpd


    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)
    # TODO: Create load_pressure_data method from above

    # Plot the process data
    fig, ax1 = plt.subplots(figsize=(9, 7))
    ax1.margins(x=0)
    ax2 = ax1.twinx()
    ax1.plot(data.index, data.values)
    ax1.set_ylabel("Pressure (barg)")

    # Change point and steady state detection parameters
    min_distance = 60
    var_threshold = 5.0
    slope_threshold = -8.8

    # Detect the Change Points
    cp_ts = cpd_ed_pelt(data, min_distance)

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

    # Plot the Steady State regions
    ln2 = ax2.fill_between(ss_map.index, ss_map.values, color="orange", alpha=0.2)

    # Plot the Change Points
    ax2.plot(cp_ts.index, cp_ts.values, color="k", linestyle=":")
    ax2.set_ylim([0, 1])
    ax2.set_yticks([])

    # create legend below the plot
    plt.legend(
        (plt.Line2D(data.index, data.values), plt.Line2D(cp_ts.index, cp_ts.values, color="k", linestyle=":"), ln2),
        ("Sensor Readings", "Change Points", "Steady State Regions"),
        loc="upper center",
        bbox_to_anchor=(0.5, -0.05),
        fancybox=True,
        ncol=3,
    )
    plt.title("Steady State Detection based on ED-PELT Change Point Detection algorithm")
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  6.295 seconds)


.. _sphx_glr_download_auto_examples_detect_plot_ssd_cpd.py:


.. only :: html

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



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

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



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


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

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