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

    Click :ref:`here <sphx_glr_download_auto_examples_preprocessing_plot_transformers.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_preprocessing_plot_transformers.py:


============
Transformers
============

Some algorithms make assumptions on the distribution of the data.
Therefore it can be useful to transform time series so that they
approximatively follow a given distribution.
Two transformers are made available:

* :class:`pyts.preprocessing.PowerTransformer`
* :class:`pyts.preprocessing.QuantileTransformer`.

This example illustrates the transformation from both algorithms.



.. image:: /auto_examples/preprocessing/images/sphx_glr_plot_transformers_001.png
    :class: sphx-glr-single-img






.. code-block:: default


    # Author: Johann Faouzi <johann.faouzi@gmail.com>
    # License: BSD-3-Clause

    import matplotlib.pyplot as plt
    from pyts.datasets import load_gunpoint
    from pyts.preprocessing import PowerTransformer, QuantileTransformer

    X, _, _, _ = load_gunpoint(return_X_y=True)
    n_timestamps = X.shape[1]

    # Transform the data with different transformation algorithms
    X_power = PowerTransformer().transform(X)
    X_quantile = QuantileTransformer(n_quantiles=n_timestamps).transform(X)

    # Show the results for the first time series
    plt.figure(figsize=(6, 4))
    plt.plot(X[0], '--', label='Original')
    plt.plot(X_power[0], '--', label='PowerTransformer')
    plt.plot(X_quantile[0], '--', label='QuantileTransformer')
    plt.legend(loc='best', fontsize=8)
    plt.title('Transforming time series', fontsize=16)
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.379 seconds)


.. _sphx_glr_download_auto_examples_preprocessing_plot_transformers.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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