.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/smooth/plot_sg_smooth.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_sg_smooth.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_smooth_plot_sg_smooth.py:


=============================================
Data smoothing with the Savitzky-Golay filter
=============================================

Example of noise removal from time series data using the Savitzky-Golay smoother.
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 how a small window size (win=21) and linear fit (order=1) exposes the trend
and some of the noise (data fluctuations). Increasing the window size while keeping a linear fit results in a stronger
smoothing of the data. However, increasing the order of the fit to 5 (non-linear fit) produced a nice and smooth
trend while allowing the larger fluctuation through.

.. GENERATED FROM PYTHON SOURCE LINES 16-44



.. image-sg:: /auto_examples/smooth/images/sphx_glr_plot_sg_smooth_001.png
   :alt: Noise removal effect from window size and polynomial order
   :srcset: /auto_examples/smooth/images/sphx_glr_plot_sg_smooth_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 sg


    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)
    # TODO: Create load_flowrate_data method from above

    # Apply the smoother with different settings and plot the results
    plt.figure(1, figsize=[9, 7])
    plt.plot(data, ".", markersize=2, label="RAW")
    # Short window, order =1 (linear)
    plt.plot(sg(data, window_length=21, polyorder=1), color="mediumblue", linewidth=1, label="win=21, Order=1")
    # Long window, order =1 (linear)
    plt.plot(sg(data, window_length=155, polyorder=1), color="k", linewidth=2, ls="--", label="win=155, Order=1")
    # Long window, order =5 (non-linear)
    plt.plot(sg(data, window_length=155, polyorder=5), color="forestgreen", linewidth=2, ls="-", label="win=155, Order=5")

    plt.ylabel("Vol. Flow (m3/h)")
    plt.title("Noise removal effect from window size and polynomial order")
    _ = plt.legend(loc=2)


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

   **Total running time of the script:** ( 0 minutes  0.156 seconds)


.. _sphx_glr_download_auto_examples_smooth_plot_sg_smooth.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_sg_smooth.py <plot_sg_smooth.py>`



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

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


.. only:: html

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

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