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

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

.. _sphx_glr_auto_examples_image_plot_mtf.py:


=======================
Markov Transition Field
=======================

A Markov Transition Field is an image obtained from a time series, representing
a field of transition probabilities for a discretized time series. Different
strategies can be used to bin time series. his example illustrates the
transformation on the first sample of the *GunPoint* dataset.
It is implemented as :class:`pyts.image.MarkovTransitionField`.



.. image:: /auto_examples/image/images/sphx_glr_plot_mtf_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.image import MarkovTransitionField
    from pyts.datasets import load_gunpoint

    X, _, _, _ = load_gunpoint(return_X_y=True)

    # MTF transformation
    mtf = MarkovTransitionField(image_size=24)
    X_mtf = mtf.fit_transform(X)

    # Show the image for the first time series
    plt.figure(figsize=(5, 5))
    plt.imshow(X_mtf[0], cmap='rainbow', origin='lower')
    plt.title('Markov Transition Field', fontsize=18)
    plt.colorbar(fraction=0.0457, pad=0.04)
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  1.800 seconds)


.. _sphx_glr_download_auto_examples_image_plot_mtf.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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