Showing content from http://matplotlib.org/api/../mpl_examples/pylab_examples/csd_demo.py below:
""" 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) 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()
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