.. _sphx_glr_auto_examples_plot_rp.py:


================
Recurrence Plots
================

This example shows how you can transform a time series into a Recurrence Plot
using :class:`pyts.image.RecurrencePlots`.




.. image:: /auto_examples/images/sphx_glr_plot_rp_001.png
    :align: center





.. code-block:: python


    import numpy as np
    import matplotlib.pyplot as plt
    from pyts.image import RecurrencePlots

    # Parameters
    n_samples, n_features = 100, 144

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

    # Recurrence plot transformation
    rp = RecurrencePlots(dimension=1,
                         epsilon='percentage_points',
                         percentage=30)
    X_rp = rp.fit_transform(X)

    # Show the results for the first time series
    plt.figure(figsize=(8, 8))
    plt.imshow(X_rp[0], cmap='binary', origin='lower')
    plt.show()

**Total running time of the script:** ( 0 minutes  0.099 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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