在与@EOL进行了决定性的讨论之后，我想使用随机高斯样本作为摘要发布我的解决方案(左上角)：
import numpy as np
import matplotlib.pyplot as plt
from math import ceil, floor, sqrt
def pdf(x, mu=0, sigma=1):
"""
Calculates the normal distribution's probability density
function (PDF).
"""
term1 = 1.0 / ( sqrt(2*np.pi) * sigma )
term2 = np.exp( -0.5 * ( (x-mu)/sigma )**2 )
return term1 * term2
# Drawing sample date poi
##################################################
# Random Gaussian data (mean=0, stdev=5)
data1 = np.random.normal(loc=0, scale=5.0, size=30)
data2 = np.random.normal(loc=2, scale=7.0, size=30)
data1.sort(), data2.sort()
min_val = floor(min(data1+data2))
max_val = ceil(max(data1+data2))
##################################################
fig = plt.gcf()
fig.set_size_inches(12,11)
# Cumulative distributions, stepwise:
plt.subplot(2,2,1)
plt.step(np.concatenate([data1, data1[[-1]]]), np.arange(data1.size+1), label='$\mu=0, \sigma=5$')
plt.step(np.concatenate([data2, data2[[-1]]]), np.arange(data2.size+1), label='$\mu=2, \sigma=7$')
plt.title('30 samples from a random Gaussian distribution (cumulative)')
plt.ylabel('Count')
plt.xlabel('X-value')
plt.legend(loc='upper left')
plt.xlim([min_val, max_val])
plt.ylim([0, data1.size+1])
plt.grid()
# Cumulative distributions, smooth:
plt.subplot(2,2,2)
plt.plot(np.concatenate([data1, data1[[-1]]]), np.arange(data1.size+1), label='$\mu=0, \sigma=5$')
plt.plot(np.concatenate([data2, data2[[-1]]]), np.arange(data2.size+1), label='$\mu=2, \sigma=7$')
plt.title('30 samples from a random Gaussian (cumulative)')
plt.ylabel('Count')
plt.xlabel('X-value')
plt.legend(loc='upper left')
plt.xlim([min_val, max_val])
plt.ylim([0, data1.size+1])
plt.grid()
# Probability densities of the sample points function
plt.subplot(2,2,3)
pdf1 = pdf(data1, mu=0, sigma=5)
pdf2 = pdf(data2, mu=2, sigma=7)
plt.plot(data1, pdf1, label='$\mu=0, \sigma=5$')
plt.plot(data2, pdf2, label='$\mu=2, \sigma=7$')
plt.title('30 samples from a random Gaussian')
plt.legend(loc='upper left')
plt.xlabel('X-value')
plt.ylabel('probability density')
plt.xlim([min_val, max_val])
plt.grid()
# Probability density function
plt.subplot(2,2,4)
x = np.arange(min_val, max_val, 0.05)
pdf1 = pdf(x, mu=0, sigma=5)
pdf2 = pdf(x, mu=2, sigma=7)
plt.plot(x, pdf1, label='$\mu=0, \sigma=5$')
plt.plot(x, pdf2, label='$\mu=2, \sigma=7$')
plt.title('PDFs of Gaussian distributions')
plt.legend(loc='upper left')
plt.xlabel('X-value')
plt.ylabel('probability density')
plt.xlim([min_val, max_val])
plt.grid()
plt.show()
