normed =直方圖的True參數使直方圖繪制分布的密度.從the documentation開始：

normed : boolean, optional

If True, the first element of the return tuple will be the counts normalized to form a probability density, i.e., n/(len(x)`dbin), i.e., the integral of the histogram will sum to 1. If stacked is also True, the sum of the histograms is normalized to 1.

Default is False

這意味著直方圖條的高度取決于箱寬度.如果僅繪制一個數據點,則動畫開始時的情況下,條形高度將為1./binwidth.如果箱寬度小于零,則桿高度可能變得非常大.

因此,在整個動畫過程中修復分檔并使用它們是個好主意.

清除軸也是合理的,這樣就不會繪制100個不同的直方圖.

import numpy as np

from matplotlib.pylab import *

import matplotlib.animation as animation

# generate 4 random variables from the random, gamma, exponential, and uniform distribution

x1 = np.random.normal(-2.5, 1, 10000)

x2 = np.random.gamma(2, 1.5, 10000)

x3 = np.random.exponential(2, 10000)+7

x4 = np.random.uniform(14,20, 10000)

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)

def updateData(curr):

if curr <=2: return

for ax in (ax1, ax2, ax3, ax4):

ax.clear()

ax1.hist(x1[:curr], normed=True, bins=np.linspace(-6,1, num=21), alpha=0.5)

ax2.hist(x2[:curr], normed=True, bins=np.linspace(0,15,num=21), alpha=0.5)

ax3.hist(x3[:curr], normed=True, bins=np.linspace(7,20,num=21), alpha=0.5)

ax4.hist(x4[:curr], normed=True, bins=np.linspace(14,20,num=21), alpha=0.5)

simulation = animation.FuncAnimation(fig, updateData, interval=50, repeat=False)

plt.show()

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