Edit: Nowadays, it is easier and better to use matplotlib.animation:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
def animate(frameno):
x = mu + sigma * np.random.randn(10000)
n, _ = np.histogram(x, bins, normed=True)
for rect, h in zip(patches, n):
rect.set_height(h)
return patches
mu, sigma = 100, 15
fig, ax = plt.subplots()
x = mu + sigma * np.random.randn(10000)
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
ani = animation.FuncAnimation(fig, animate, blit=True, interval=10,
repeat=True)
plt.show()
There is an example of making an animated graph here.
Building on this example, you might try something like:
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
mu, sigma = 100, 15
fig = plt.figure()
x = mu + sigma*np.random.randn(10000)
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
for i in range(50):
x = mu + sigma*np.random.randn(10000)
n, bins = np.histogram(x, bins, normed=True)
for rect,h in zip(patches,n):
rect.set_height(h)
fig.canvas.draw()
I can get about 14 frames per second this way, compared to 4 frames per second using the code I first posted. The trick is to avoid asking matplotlib to draw complete figures. Instead call plt.hist once, then manipulate the existing matplotlib.patches.Rectangles in patches to update the histogram, and call
fig.canvas.draw() to make the updates visible.
