import matplotlib.pyplot as plt import numpy as np
# Fixing random state for reproducibility np.random.seed(19680801)
# create some data to use for the plot dt = 0.001 t = np.arange(0.0, 10.0, dt) r = np.exp(-t[:1000] / 0.05) # impulse response x = np.random.randn(len(t)) s = np.convolve(x, r)[:len(x)] * dt # colored noise
# the main axes is subplot(111) by default plt.plot(t, s) plt.axis([0, 1, 1.1 * np.min(s), 2 * np.max(s)]) plt.xlabel('time (s)') plt.ylabel('current (nA)') plt.title('Gaussian colored noise')
# this is an inset axes over the main axes a = plt.axes([.65, .6, .2, .2], facecolor='k') n, bins, patches = plt.hist(s, 400, density=True) plt.title('Probability') plt.xticks([]) plt.yticks([])
# this is another inset axes over the main axes a = plt.axes([0.2, 0.6, .2, .2], facecolor='k') plt.plot(t[:len(r)], r) plt.title('Impulse response') plt.xlim(0, 0.2) plt.xticks([]) plt.yticks([])