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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_approximation_plot_sax.py:


================================
Symbolic Aggregate approXimation
================================

Binning continuous data into intervals can be seen as an approximation that
reduces noise and captures the trend of a time series. The Symbolic Aggregate
approXimation (SAX) algorithm bins continuous time series into intervals,
transforming independently each time series (a sequence of floats) into a
sequence of symbols, usually letters. This example illustrates the
transformation.
It is implemented as
:class:`pyts.approximation.SymbolicAggregateApproximation`.

.. GENERATED FROM PYTHON SOURCE LINES 15-57



.. image-sg:: /auto_examples/approximation/images/sphx_glr_plot_sax_001.png
   :alt: Symbolic Aggregate approXimation
   :srcset: /auto_examples/approximation/images/sphx_glr_plot_sax_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.lines as mlines
    import matplotlib.pyplot as plt
    from scipy.stats import norm
    from pyts.approximation import SymbolicAggregateApproximation

    # Parameters
    n_samples, n_timestamps = 100, 24

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

    # SAX transformation
    n_bins = 3
    sax = SymbolicAggregateApproximation(n_bins=n_bins, strategy='normal')
    X_sax = sax.fit_transform(X)

    # Compute gaussian bins
    bins = norm.ppf(np.linspace(0, 1, n_bins + 1)[1:-1])

    # Show the results for the first time series
    bottom_bool = np.r_[True, X_sax[0, 1:] > X_sax[0, :-1]]

    plt.figure(figsize=(6, 4))
    plt.plot(X[0], 'o--', label='Original')
    for x, y, s, bottom in zip(range(n_timestamps), X[0], X_sax[0], bottom_bool):
        va = 'bottom' if bottom else 'top'
        plt.text(x, y, s, ha='center', va=va, fontsize=14, color='#ff7f0e')
    plt.hlines(bins, 0, n_timestamps, color='g', linestyles='--', linewidth=0.5)
    sax_legend = mlines.Line2D([], [], color='#ff7f0e', marker='*',
                               label='SAX - {0} bins'.format(n_bins))
    first_legend = plt.legend(handles=[sax_legend], fontsize=8, loc=(0.76, 0.86))
    ax = plt.gca().add_artist(first_legend)
    plt.legend(loc=(0.81, 0.93), fontsize=8)
    plt.xlabel('Time', fontsize=14)
    plt.title('Symbolic Aggregate approXimation', fontsize=16)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.689 seconds)


.. _sphx_glr_download_auto_examples_approximation_plot_sax.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_sax.py <plot_sax.py>`

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

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


.. only:: html

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

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