#!/usr/bin/env python# -*- coding:utf-8 -*-from matplotlib import pyplot as pltimport numpy as npimport pylabimport os,sys,time,math,random# 图1-给已有的图加上刻度file=r‘D:\jmeter\jmeter3.2\data\Oracle数据库基础.png‘arr=np.array(file.getdata()).reshape(file.size[1],file.size[0],3)plt.gray()plt.imshow(arr)plt.colorbar()plt.show()# 图2-随机柱状图SAMPLE_SIZE=100random.seed()real_rand_vars=[]real_rand_vars=[random.random() for val in range(SAMPLE_SIZE)]pylab.hist(real_rand_vars,10)pylab.xlabel("number range")pylab.ylabel("count")pylab.show()# 图3-正太分布图duration=100# 中值mean_inc=0.6# 标准差std_dev_inc=1.2x=range(duration)y=[]price_today=0for i in x: next_delta=random.normalvariate(mean_inc,std_dev_inc) price_today+=next_delta y.append(price_today)pylab.plot(x,y)pylab.title(‘test‘)pylab.xlabel(‘time‘)pylab.ylabel(‘value‘)pylab.show()# 图4SAMPLE_SIZE=1000buckes=100plt.figure()plt.rcParams.update({‘font.size‘:7})# 子图1-随机分布 0~1plt.subplot(621)plt.xlabel(‘random1‘)res=[random.random() for _ in range(1,SAMPLE_SIZE)]plt.hist(res,buckes)# 子图2-均匀分布plt.subplot(622)plt.xlabel(‘random2‘)a=1b=SAMPLE_SIZEres=[random.uniform(a,b) for _ in range(1,SAMPLE_SIZE)]plt.hist(res,buckes)# 子图3-三角形分布plt.subplot(623)plt.xlabel(‘random3‘)low=1high=SAMPLE_SIZEres=[random.triangular(a,b) for _ in range(1,SAMPLE_SIZE)]plt.hist(res,buckes)# 子图4-beta分布图plt.subplot(624)plt.xlabel(‘random4‘)alpha=1beta=10res = [random.betavariate(alpha,beta) for _ in range(1,SAMPLE_SIZE)]plt.hist(res,buckes)# 子图5-指数分布图plt.subplot(625)plt.xlabel(‘random5‘)lambd=1.0/((SAMPLE_SIZE+1)/2)res=[random.expovariate(lambd) for _ in range(1,SAMPLE_SIZE)]plt.hist(res,buckes)# 子图6-gamma分布图plt.subplot(626)plt.xlabel(‘random6‘)alpha=1beta=10res = [random.gammavariate(alpha,beta) for _ in range(1,SAMPLE_SIZE)]plt.hist(res,buckes)# 子图7-对数正太分布图plt.subplot(627)plt.xlabel(‘random7‘)# 中值mu=1# 标准差sigma=0.5res = [random.lognormvariate(mu,sigma) for _ in range(1,SAMPLE_SIZE)]plt.hist(res,buckes)# 子图8-正太分布图plt.subplot(628)plt.xlabel(‘random8‘)# 中值mu=1# 标准差sigma=0.5res = [random.normalvariate(mu,sigma) for _ in range(1,SAMPLE_SIZE)]plt.hist(res,buckes)# 子图9-帕累托分布图plt.subplot(629)plt.xlabel(‘random9‘)# 形状参数alpha=1res = [random.paretovariate(alpha) for _ in range(1,SAMPLE_SIZE)]plt.hist(res,buckes)plt.tight_layout()plt.show()