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

.. only:: html

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

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

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

.. _sphx_glr_csi_transform_tutorial.py:


CSI Preprocessing.transform Tutorial
==============================================================

.. GENERATED FROM PYTHON SOURCE LINES 7-10

.. code-block:: Python


    # !pip install pysensing








.. GENERATED FROM PYTHON SOURCE LINES 11-13

In this tutorial, we will be implementing a simple csi.preprocessing.transform tutorial using the pysensing library.


.. GENERATED FROM PYTHON SOURCE LINES 13-18

.. code-block:: Python

    import sys
    sys.path.append('../..')
    import pysensing.csi.preprocessing.transform as transform
    import numpy as np








.. GENERATED FROM PYTHON SOURCE LINES 19-21

remove_nan
-----------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 21-32

.. code-block:: Python


    # remove_nan is a function that removes NaN values from the input data. It can replace NaN values with zero, mean, or median values.

    test_data = [1, None, 3, np.inf, None, 6, -np.inf, 8, 9]
    test_data1 = transform.remove_nan(test_data, interpolation_method='zero')
    print(test_data1)
    test_data2 = transform.remove_nan(test_data, interpolation_method='mean')
    print(test_data2)
    test_data3 = transform.remove_nan(test_data, interpolation_method='median')
    print(test_data3)





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

 .. code-block:: none

    [1, 0, 3, 0, 0, 6, 0, 8, 9]
    [1, 5.4, 3, 5.4, 5.4, 6, 5.4, 8, 9]
    [1, 6, 3, 6, 6, 6, 6, 8, 9]




.. GENERATED FROM PYTHON SOURCE LINES 33-36

normalization
------------------------
normalization is a function that normalizes the input data.

.. GENERATED FROM PYTHON SOURCE LINES 36-41

.. code-block:: Python


    test_data4 = transform.normalization(test_data1)
    print(test_data4)






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

 .. code-block:: none

    [0.11111111 0.         0.33333333 0.         0.         0.66666667
     0.         0.88888889 1.        ]




.. GENERATED FROM PYTHON SOURCE LINES 42-43

And that's it. We're done with our CSI augmentation.normalization tutorials. Thanks for reading.


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

   **Total running time of the script:** (0 minutes 0.765 seconds)


.. _sphx_glr_download_csi_transform_tutorial.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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