import numpy as np import matplotlib.pyplot as plt from time import time import math def normfun(x,mu,sigma):     pdf = np.exp(-((x - mu)**2)/(2*sigma**2)) / (sigma * np.sqrt(2*np.pi))     return pdf def raylifun(x,mu,sigma):     pdf = x*np.exp(-((x - mu)**2)/(2*sigma**2))/sigma**2     return pdf startTime = time() xaxis=np.linspace(-10,10,200) miu=0 delta=1 np.random.seed(1) N=10000 rand1=np.random.rand(N) rand2=np.random.rand(N) y1=np.arange(10000,dtype=float) y2=np.arange(10000,dtype=float) for index,value in enumerate(rand1):     a=(-2 * math.log(value))     y1[index] = pow(a, 0.5)     y1[index] = miu+delta*y1[index]     for index,value in enumerate(rand2):         y2[index]=y1[index]*math.sin(2*math.pi*value)         y2[index] = miu + delta * y2[index] # print(y1) # print("#####################") # print(y2) # patches=plt.hist(y2,bins=100) # plt.show() patches2=plt.hist(y1,bins=30) plt.subplot(221) patches=plt.hist(y1,bins=100) plt.plot(patches[1][:-1],patches[0]) plt.xlabel("values") plt.ylabel("frequency")

plt.subplot(222) patches1=plt.hist(y2,bins=100) plt.plot(patches1[1][:-1],patches1[0]) plt.xlabel("values") plt.ylabel("frequency")

plt.subplot(223) plt.scatter(patches2[1][:-1],patches2[0]/1400) plt.plot(patches2[1][:-1],patches2[0]/1400) plt.xlabel("values") plt.ylabel("probability") c=np.arange(0,5,0.1) d=raylifun(c,0,1) plt.plot(c,d,'r--')

plt.subplot(224) plt.scatter(patches1[1][:-1],patches1[0]/800) plt.plot(patches1[1][:-1],patches1[0]/800) plt.xlabel("values") plt.ylabel("probability") b=np.arange(-5,5,0.1) a=normfun(b,0,1) plt.plot(b,a,'r--') duratrion = time()-startTime print(duratrion) plt.show()