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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_smooth_plot_ma.py:


=============================================
Data smoothing with Moving Averages
=============================================

Example of noise removal from time series data using the Simple Moving Average (SMA), Linear Weighted Moving Average
(LWMA) and Exponential Weighted Moving Average smoother (EWMA).
We use data from volumetric flow rate (m3/h) sensor with non-uniform sampling frequency measuring flow into
a compressor.
In the figure below it can be observed that using SMA produces a less noisy time series, but changes in the trend
are seen with a greater delay than LWMA or EWMA.
Increasing the window size results in a stronger smoothing of the data.

.. GENERATED FROM PYTHON SOURCE LINES 15-71



.. image-sg:: /auto_examples/smooth/images/sphx_glr_plot_ma_001.png
   :alt: MOVING AVERAGES, Noise removal effect from window size, Noise removal effect from window size
   :srcset: /auto_examples/smooth/images/sphx_glr_plot_ma_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    import os

    import matplotlib.pyplot as plt
    import pandas as pd

    from indsl.smooth import ewma, lwma, sma


    base_path = "" if __name__ == "__main__" else os.path.dirname(__file__)
    data = pd.read_csv(os.path.join(base_path, "../../datasets/data/vol_flow_rate_m3h.csv"), index_col=0)
    data = data.squeeze()
    data.index = pd.to_datetime(data.index)

    _, axs = plt.subplots(1, 2, figsize=(15, 7))

    # Calculate the different moving averages for a time window of 20min and plot the results
    axs[0].plot(data, ".", markersize=2, label="RAW")
    time_window = "20min"
    axs[0].plot(
        sma(data, time_window=time_window, min_periods=1), color="mediumblue", linewidth=2, label=f"SMA win={time_window}"
    )
    axs[0].plot(
        lwma(data, time_window=time_window, min_periods=1), color="darkorange", linewidth=2, label=f"LWMA win={time_window}"
    )
    axs[0].plot(
        ewma(data, time_window=time_window, min_periods=1),
        color="forestgreen",
        linewidth=2,
        label=f"EWMA win={time_window}",
    )
    axs[0].set_ylabel("Vol. Flow (m3/h)")
    axs[0].set_title("Noise removal effect from window size")
    _ = axs[0].legend(loc=2)

    # Calculate the different moving averages for a time window of 50min and plot the results
    axs[1].plot(data, ".", markersize=2, label="RAW")
    time_window = "50min"
    axs[1].plot(
        sma(data, time_window=time_window, min_periods=1), color="mediumblue", linewidth=2, label=f"SMA win={time_window}"
    )
    axs[1].plot(
        lwma(data, time_window=time_window, min_periods=1), color="darkorange", linewidth=2, label=f"LWMA win={time_window}"
    )
    axs[1].plot(
        ewma(data, time_window=time_window, min_periods=1),
        color="forestgreen",
        linewidth=2,
        label=f"EWMA win={time_window}",
    )
    axs[1].set_ylabel("Vol. Flow (m3/h)")
    axs[1].set_title("Noise removal effect from window size")
    _ = axs[1].legend(loc=2)

    _ = plt.suptitle("MOVING AVERAGES")
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  1.561 seconds)


.. _sphx_glr_download_auto_examples_smooth_plot_ma.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_ma.py <plot_ma.py>`



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

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


.. only:: html

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

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