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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_preprocessing_plot_scalers.py:


=======
Scalers
=======

Scaling data is a usual requirement for many algorithms and allows to have
identical scales for time series with originally different scales.
For time series, scaling is performed sample-wise instead of feature-wise.
This example illustrates several scaling algorithms made available in
:mod:`pyts.preprocessing`.

.. GENERATED FROM PYTHON SOURCE LINES 12-56



.. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_scalers_001.png
   :alt: Scaling time series with different strategies, Original time series, Scaled time series
   :srcset: /auto_examples/preprocessing/images/sphx_glr_plot_scalers_001.png
   :class: sphx-glr-single-img





.. code-block:: default


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

    import numpy as np
    import matplotlib.pyplot as plt
    from pyts.preprocessing import (StandardScaler, MinMaxScaler,
                                    MaxAbsScaler, RobustScaler)

    # Parameters
    n_samples, n_timestamps = 100, 48
    marker_size = 5

    # Toy dataset
    rng = np.random.RandomState(41)
    X = rng.randn(n_samples, n_timestamps)

    # Scale the data with different scaling algorithms
    X_standard = StandardScaler().transform(X)
    X_minmax = MinMaxScaler(sample_range=(0, 1)).transform(X)
    X_maxabs = MaxAbsScaler().transform(X)
    X_robust = RobustScaler(quantile_range=(25.0, 75.0)).transform(X)

    # Show the results for the first time series
    plt.figure(figsize=(16, 6))

    ax1 = plt.subplot(121)
    ax1.plot(X[0], 'o-', ms=marker_size, label='Original')
    ax1.set_title('Original time series', fontsize=16)
    ax1.legend(loc='best', fontsize=12)

    ax2 = plt.subplot(122)
    ax2.plot(X_standard[0], 'o--', ms=marker_size, color='C1',
             label='StandardScaler')
    ax2.plot(X_minmax[0], 'o--', ms=marker_size, color='C2', label='MinMaxScaler')
    ax2.plot(X_maxabs[0], 'o--', ms=marker_size, color='C3', label='MaxAbsScaler')
    ax2.plot(X_robust[0], 'o--', ms=marker_size, color='C4', label='RobustScaler')
    ax2.set_title('Scaled time series', fontsize=16)
    ax2.legend(loc='best', fontsize=12)

    plt.suptitle('Scaling time series with different strategies', fontsize=20)
    plt.tight_layout()
    plt.subplots_adjust(top=0.85)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.423 seconds)


.. _sphx_glr_download_auto_examples_preprocessing_plot_scalers.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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