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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_reindex_plot_pearson_correlation.py:


=======================================================
Re-indexing and compute Pearson correlation coefficient
=======================================================

When working with multiple time series it is often important that time stamps are aligned.
Even simple operations, like addition and subtraction of time series, require time stamp alignment. In this example, we demonstrate how re-indexing can be used to align time stamps.


The aim of this example is to compute the Pearson correlation of two compressor suction pressure sensors. The pressure sensors have non-uniform sampling frequency,
and hence the time stamps do not align. Therefore, the data is first re-indexed and then a rolling median is used to remove outliers
(smooth the data). Finally, the Pearson corelation coefficient is estimated.
The figure shows data from a pressure sensor randomly split into two time series to simulate two pressure sensors (Sensor 1 and Sensor 2).

.. GENERATED FROM PYTHON SOURCE LINES 16-80



.. image-sg:: /auto_examples/reindex/images/sphx_glr_plot_pearson_correlation_001.png
   :alt: RAW Data, Re-indexed data, Rolling window (median) data
   :srcset: /auto_examples/reindex/images/sphx_glr_plot_pearson_correlation_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    import os

    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd

    from indsl.resample.reindex import reindex


    # Load the pressure sensor 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)

    # Reduce the initial dataset
    msk = np.random.rand(len(data)) < 0.05
    data = data[msk]

    # We simulate the two pressure sensors by split the input data randomly into two sets
    msk = np.random.rand(len(data)) < 0.7
    raw_sensor1 = data[msk]
    raw_sensor2 = data[~msk]

    # Computing the correlation at now would result in only NaNs, because of non-matching indices.
    # Therefore, we need to re-index our time-series to a common index.
    re_sensor1, re_sensor2 = reindex(raw_sensor1, raw_sensor2, method="slinear")

    # We can now check that both time-series have the same index
    assert (re_sensor1.index == re_sensor2.index).all()

    # Compute the rolling correlation
    window = 10
    rolling_correlation = re_sensor1.rolling(window).corr(re_sensor2)


    # Plot the results
    fsize = 10
    f, ax = plt.subplots(4, 1, figsize=(9, 7), sharex=True)
    ax[0].plot(raw_sensor1, ".", label="Sensor 1")
    ax[0].plot(raw_sensor2, ".", label="Sensor 2")
    ax[0].set_ylabel("Pressure (barg)", fontsize=fsize)
    ax[0].legend(loc=1, fontsize=fsize)
    ax[0].set_title("RAW Data", fontsize=fsize)

    ax[1].plot(re_sensor1, ".", label="Sensor 1")
    ax[1].plot(re_sensor2, ".", label="Sensor 2")
    ax[1].set_ylabel("Pressure (barg)", fontsize=fsize)
    ax[1].set_title("Re-indexed data", fontsize=fsize)
    ax[1].legend(loc=1, fontsize=fsize)

    re_sensor1.rolling(window=window).median().plot(ax=ax[2], label="Sensor 1")
    re_sensor2.rolling(window=window).median().plot(ax=ax[2], label="Sensor 2")
    ax[2].set_ylabel("Pressure (barg)", fontsize=fsize)
    ax[2].set_title("Rolling window (median) data", fontsize=fsize)
    ax[2].legend(loc=1, fontsize=fsize)

    rolling_correlation.plot(ax=ax[3], color="g", label="Pearson r")
    ax[3].set_xlabel("Rolling window correlation", fontsize=fsize)
    ax[3].set_ylabel("Correlation", fontsize=fsize)
    ax[3].legend(loc=1, fontsize=fsize)
    f.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.371 seconds)


.. _sphx_glr_download_auto_examples_reindex_plot_pearson_correlation.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_pearson_correlation.py <plot_pearson_correlation.py>`



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

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


.. only:: html

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

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