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

    Click :ref:`here <sphx_glr_download_auto_examples_plot_5_4_2_hough_noisy_chirp.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_5_4_2_hough_noisy_chirp.py:


=================================
Wigner-Hough Transform of a Chirp
=================================

This example demonstrates the use of the Hough transform to extract the
estimates of a chirp signal from its Wigner Ville distribution.

Figure 5.4 from the tutorial.



.. image:: /auto_examples/images/sphx_glr_plot_5_4_2_hough_noisy_chirp_001.png
    :class: sphx-glr-single-img





.. code-block:: default


    import numpy as np
    from tftb.generators import noisecg, sigmerge, fmlin
    from tftb.processing.cohen import WignerVilleDistribution
    from tftb.processing.postprocessing import hough_transform
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D

    N = 64
    sig = sigmerge(fmlin(N, 0, 0.3)[0], noisecg(N), 1)
    tfr, _, _ = WignerVilleDistribution(sig).run()

    ht, rho, theta = hough_transform(tfr, N, N)
    theta, rho = np.meshgrid(theta, rho)
    fig = plt.figure()
    ax = fig.gca(projection='3d')
    ax.plot_wireframe(theta, rho, ht)
    ax.set_xlabel('Theta')
    ax.set_ylabel('Rho')
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  2.303 seconds)


.. _sphx_glr_download_auto_examples_plot_5_4_2_hough_noisy_chirp.py:


.. only :: html

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



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_5_4_2_hough_noisy_chirp.py <plot_5_4_2_hough_noisy_chirp.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_5_4_2_hough_noisy_chirp.ipynb <plot_5_4_2_hough_noisy_chirp.ipynb>`


.. only:: html

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

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