.. _sphx_glr_gallery_lines_bars_and_markers_csd_demo.py:


========
CSD Demo
========

Compute the cross spectral density of two signals




.. image:: /gallery/lines_bars_and_markers/images/sphx_glr_csd_demo_001.png
    :align: center





.. code-block:: python

    import numpy as np
    import matplotlib.pyplot as plt


    fig, (ax1, ax2) = plt.subplots(2, 1)
    # make a little extra space between the subplots
    fig.subplots_adjust(hspace=0.5)

    dt = 0.01
    t = np.arange(0, 30, dt)

    # Fixing random state for reproducibility
    np.random.seed(19680801)


    nse1 = np.random.randn(len(t))                 # white noise 1
    nse2 = np.random.randn(len(t))                 # white noise 2
    r = np.exp(-t / 0.05)

    cnse1 = np.convolve(nse1, r, mode='same') * dt   # colored noise 1
    cnse2 = np.convolve(nse2, r, mode='same') * dt   # colored noise 2

    # two signals with a coherent part and a random part
    s1 = 0.01 * np.sin(2 * np.pi * 10 * t) + cnse1
    s2 = 0.01 * np.sin(2 * np.pi * 10 * t) + cnse2

    ax1.plot(t, s1, t, s2)
    ax1.set_xlim(0, 5)
    ax1.set_xlabel('time')
    ax1.set_ylabel('s1 and s2')
    ax1.grid(True)

    cxy, f = ax2.csd(s1, s2, 256, 1. / dt)
    ax2.set_ylabel('CSD (db)')
    plt.show()

**Total running time of the script:** ( 0 minutes  0.046 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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