.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/data_transform/_05_gaussian_smoothing.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_data_transform__05_gaussian_smoothing.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_data_transform__05_gaussian_smoothing.py:


Gaussian Smoothing
==================
A common low-pass filtering technique is Gaussian smoothing, which applies a Gaussian window to the data with a moving
average.
We provide a class based implementation of Gaussian smoothing in the :class:`~mobgap.data_transform.GaussianFilter`
class.

.. GENERATED FROM PYTHON SOURCE LINES 9-14

.. code-block:: default


    import matplotlib.pyplot as plt
    from mobgap.data import LabExampleDataset
    from mobgap.data_transform import GaussianFilter








.. GENERATED FROM PYTHON SOURCE LINES 15-17

Loading some example data
-------------------------

.. GENERATED FROM PYTHON SOURCE LINES 17-26

.. code-block:: default

    example_data = LabExampleDataset()
    ha_example_data = example_data.get_subset(cohort="HA")
    single_test = ha_example_data.get_subset(
        participant_id="002", test="Test5", trial="Trial2"
    )
    data = single_test.data_ss

    data.head()






.. raw:: html

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          <th>gyr_x</th>
          <th>gyr_y</th>
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          <th>2020-08-21 10:30:50.479000092+00:00</th>
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    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 27-33

Applying the Gaussian filter
----------------------------
We need to specify the standard deviation of the Gaussian window.
Note, that we need to specify the standard deviation in seconds to make the parameters of the filter independent of
the sampling rate of the data.
It will be converted to samples internally.

.. GENERATED FROM PYTHON SOURCE LINES 33-39

.. code-block:: default

    gaussian_filter = GaussianFilter(sigma_s=0.1)
    gaussian_filter.filter(data, sampling_rate_hz=single_test.sampling_rate_hz)
    filtered_data = gaussian_filter.filtered_data_

    filtered_data.head()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
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        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>acc_x</th>
          <th>acc_y</th>
          <th>acc_z</th>
          <th>gyr_x</th>
          <th>gyr_y</th>
          <th>gyr_z</th>
        </tr>
        <tr>
          <th>time</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
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          <th>2020-08-21 10:30:50.479000092+00:00</th>
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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 40-42

We can also access the standard deviation in samples that was calculated from the provided sigma_s and the
sampling rate.

.. GENERATED FROM PYTHON SOURCE LINES 42-44

.. code-block:: default

    gaussian_filter.sigma_samples_





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    10.0



.. GENERATED FROM PYTHON SOURCE LINES 45-46

We will plot the filtered data together with the original data.

.. GENERATED FROM PYTHON SOURCE LINES 46-55

.. code-block:: default

    fig, axs = plt.subplots(3, 1, sharex=True)
    for ax, col in zip(axs, ["gyr_x", "gyr_y", "gyr_z"]):
        ax.set_title(col)
        data[col].plot(ax=ax, label="Original")
        filtered_data[col].plot(ax=ax, label="CWT-filtered")

    axs[0].set_xlim(data.index[300], data.index[500])
    axs[0].legend()
    fig.show()



.. image-sg:: /auto_examples/data_transform/images/sphx_glr__05_gaussian_smoothing_001.png
   :alt: gyr_x, gyr_y, gyr_z
   :srcset: /auto_examples/data_transform/images/sphx_glr__05_gaussian_smoothing_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.384 seconds)

**Estimated memory usage:**  9 MB


.. _sphx_glr_download_auto_examples_data_transform__05_gaussian_smoothing.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: _05_gaussian_smoothing.py <_05_gaussian_smoothing.py>`

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

      :download:`Download Jupyter notebook: _05_gaussian_smoothing.ipynb <_05_gaussian_smoothing.ipynb>`


.. only:: html

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

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