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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_numerical_calculus_plot_sliding_window_integration.py:


==========================
Sliding window integration
==========================

In this example a synthetic time series is generated with a certain skewness (to make it more interesting) and a use
the sliding window integration with  a integrand rate of 1 hour. In other words, carry out a sliding window
integration of the data over 1 hour periods.

.. GENERATED FROM PYTHON SOURCE LINES 12-38



.. image-sg:: /auto_examples/numerical_calculus/images/sphx_glr_plot_sliding_window_integration_001.png
   :alt: Sliding window integration with matching integrand rate and window
   :srcset: /auto_examples/numerical_calculus/images/sphx_glr_plot_sliding_window_integration_001.png
   :class: sphx-glr-single-img





.. code-block:: default


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

    from indsl.ts_utils.numerical_calculus import sliding_window_integration


    np.random.seed(1337)
    datapoints = 5000
    x = np.random.randn(datapoints)
    y = np.zeros(len(x))
    y[0] = x[0] + 100  # initial synthetic start
    for i in range(1, len(x)):
        y[i] = y[i - 1] + (x[i] + 0.0025)  # and skew it upwards

    series = pd.Series(y, index=pd.date_range(start="2000", periods=datapoints, freq="10s"))
    result = sliding_window_integration(series, pd.Timedelta("1h"))

    plt.figure(1, figsize=[9, 7])
    plt.plot(result, label="Cumulative moving window result, with matching units to raw time series data")
    plt.plot(series, alpha=0.6, label="Raw timeseries data")
    plt.legend()
    plt.ylabel("[-]/h")
    plt.title("Sliding window integration with matching integrand rate and window")
    _ = plt.show()


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

   **Total running time of the script:** ( 0 minutes  4.154 seconds)


.. _sphx_glr_download_auto_examples_numerical_calculus_plot_sliding_window_integration.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_sliding_window_integration.py <plot_sliding_window_integration.py>`



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

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


.. only:: html

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

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