.. _sphx_glr_auto_examples_plot_sax.py:


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

This example shows how you can quantize a time series (i.e. transform a
sequence of real numbers into a sequence of letters) using
:class:`pyts.quantization.SAX`.




.. image:: /auto_examples/images/sphx_glr_plot_sax_001.png
    :align: center





.. code-block:: python


    import numpy as np
    import matplotlib.lines as mlines
    import matplotlib.pyplot as plt
    from scipy.stats import norm
    from pyts.quantization import SAX

    # Parameters
    n_samples, n_features = 100, 12

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

    # SAX transformation
    n_bins = 3
    quantiles = 'gaussian'
    sax = SAX(n_bins=n_bins, quantiles=quantiles)
    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
    plt.figure(figsize=(12, 8))
    plt.plot(X[0], 'o-', label='Original')
    for x, y, s in zip(range(n_features), X[0], X_sax[0]):
        plt.text(x, y, s, ha='center', va='bottom', fontsize=24, color='#ff7f0e')
    plt.hlines(bins, 0, n_features, 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=14, loc=4)
    ax = plt.gca().add_artist(first_legend)
    plt.legend(loc='best', fontsize=14)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.028 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

     :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.readthedocs.io>`_