artist-layer.py

import matplotlib.pyplot as plt
import numpy as np

# set up a seed to make the random result reproducible
np.random.seed(seed=666)

# 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

# set up subplot axes
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax2 = fig.add_axes([0.2, 0.6, .2, .2], facecolor='y')
ax3 = fig.add_axes([.65, .6, .2, .2], facecolor='y')

# the main axes 
ax1.plot(t, s)
ax1.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
ax1.set_xlabel('time (s)')
ax1.set_ylabel('current (nA)')
ax1.set_title('Gaussian colored noise')

# this is another inset axes over the main axes
ax2.plot(t[:len(r)], r)
ax2.set_title('Impulse response')
ax2.set_xlim(0, 0.2)
ax2.set_xticks([])
ax2.set_yticks([])

# this is an inset axes over the main axes
n, bins, patches = ax3.hist(s, 400, density=1)
ax3.set_title('Probability')
ax3.set_xticks([])
ax3.set_yticks([])

fig.canvas.draw()