.. _sphx_glr_auto_examples_plot_fastdtw.py:


=========================
Fast Dynamic Time Warping
=========================

This example shows how to compute and visualize the optimal path when computing
the Fast Dynamic Time Warping distance between two time series. It is
implemented as :func:`pyts.utils.fast_dtw`.




.. image:: /auto_examples/images/sphx_glr_plot_fastdtw_001.png
    :align: center





.. code-block:: python


    import numpy as np
    import matplotlib.pyplot as plt
    from pyts.utils import fast_dtw

    # Parameters
    n_samples, n_features = 2, 48

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

    # Dynamic Time Warping
    region, D, path = fast_dtw(x, y, dist='absolute', window_size=6,
                               approximation=False, return_path=True)

    # Visualize the result
    timestamps = np.arange(n_features + 1)
    matrix = np.zeros([n_features + 1, n_features + 1])
    for i in range(n_features):
        for j in region[i]:
            matrix[j, i] = 0.5
    for i in range(len(path)):
        matrix[path[i][0], path[i][1]] = 1

    plt.figure(figsize=(8, 8))
    plt.pcolor(timestamps, timestamps, matrix, edgecolors='k', cmap='Greys')
    plt.xlabel('x', fontsize=20)
    plt.ylabel('y', fontsize=20)
    plt.title("Path along the constrained cost matrix", fontsize=16)
    plt.suptitle("Fast Dynamic Time Warping", fontsize=22)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.137 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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