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# Csd Demo

Compute the cross spectral density of two signals

    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 0.051 seconds)

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