如何更改此代码以输出颜色取决于高度的直方图，而不是我选择的默认“酷”值

Question

This code generates a probability distribution psi_0_x_squared. Then performs a markov chain simulation according to this probability. This probability psi_0_x_squared is actually the probability to be at position x for energy level n=0. After moving x 1000 times according to this probability I want to generate a histogram of position x. (The position frequency)

''' Markov-chain Monte Carlo algorithm for a particle in a Gaussian potential,
using the Metropolis algorithm. '''
import math, matplotlib.pyplot as plt, random 

def probability(x):

    #wavefunction n=0 evaluated at position x
    psi_0_x=math.exp(-x ** 2 / 2.0) / math.pi ** 0.25

    #probability n=0 to be at position x
    psi_0_x_squared= psi_0_x**2

    return psi_0_x_squared

data_x=[0]

x = 0.0        #starts at position 0
delta = 0.5    #stepsize

for k in range(1000):    #for this number of trials
    x_new = x + random.uniform(-delta, delta) #I displace it a distance delta


    if random.uniform(0.0, 1.0) < probability(x_new)/probability(x):
        x = x_new 
    data_x.append(x)

#histogram
cm = plt.cm.get_cmap('cool') 
n, bins, patches= plt.hist(data_x, bins=100, normed=True, color='k')
bin_centers = 0.5 * (bins[:-1] + bins[1:])
col = bin_centers - min(bin_centers)
col /= max(col)
for c, p in zip(col, patches):
    plt.setp(p, 'facecolor', cm(c))

plt.show()

Answer 1

The variable n contains the height of your bars. Therefore this should do the trick:

for height, p in zip(n, patches):
    plt.setp(p, 'facecolor', cm(height))

like so:

a = np.random.normal(size=(1000,))

cm = plt.cm.get_cmap('cool')
n, bins, patches= plt.hist(a, bins=100, normed=True, color='k')
for c, p in zip(n, patches):
    plt.setp(p, 'facecolor', cm(c))