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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_data_quality_plot_rolling_stddev_timedelta.py:


====================================================
Rolling standard deviation of data points time delta
====================================================

Example of visualizing rolling standard deviation of time delta of time series data to identify dispersion in the
ingestion of data.

.. GENERATED FROM PYTHON SOURCE LINES 10-51



.. image-sg:: /auto_examples/data_quality/images/sphx_glr_plot_rolling_stddev_timedelta_001.png
   :alt: Standard deviation of time delta
   :srcset: /auto_examples/data_quality/images/sphx_glr_plot_rolling_stddev_timedelta_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    from datetime import datetime, timedelta

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

    from indsl.data_quality.rolling_stddev import rolling_stddev_timedelta
    from indsl.smooth import sma


    data = pd.Series(1, index=pd.date_range(start="2022-01-01 10:00:00", end="2022-01-01 11:00:00", freq="1s")).sample(60)

    data = data.sort_index()


    time_window = pd.Timedelta(minutes=5)

    # Calculate rolling standard deviation of time delta
    stddev = rolling_stddev_timedelta(data, time_window=time_window).values

    # Calculate rolling mean of time delta
    mean = sma(data.index.to_series().diff().astype("timedelta64[s]").fillna(0), time_window="5min", min_periods=1)

    # Plot standard deviation and mean
    fig, ax = plt.subplots(figsize=(15, 5))
    ax.title.set_text("Standard deviation of time delta")
    ax.plot(data.index, stddev, label="Standard deviation", marker=".")
    ax.plot(data.index, mean, label="Mean", marker=".")

    values = np.arange(data.index[0], data.index[-1], timedelta(minutes=4)).astype(datetime)

    ax.set_xticks(values)
    ax.set_xticklabels([ts.strftime("%d-%m-%Y \n %H:%M:%S") for ts in values], fontsize=8)

    legend = ax.legend(loc="upper right")

    plt.xlabel("Timestamp")
    plt.ylabel("Standard deviation")

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  2.000 seconds)


.. _sphx_glr_download_auto_examples_data_quality_plot_rolling_stddev_timedelta.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_rolling_stddev_timedelta.py <plot_rolling_stddev_timedelta.py>`



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

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


.. only:: html

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

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