Monte Carlo计算Pi，python实现

Monte Carlo

 import random
 import matplotlib.pyplot as plt
 import numpy as np

 6 

 # 函数模拟点的随机掉落，并分为两组
 def check(tmp):
     global ans, ansX, ansY
     global inX, inY
     global outX, outY

     tmpX = random.random()
     tmpY = random.random()
     if (tmpX ** 2 + tmpY ** 2) <= 1:
         ans = ans + 1
         inX = np.append(inX, tmpX)
         inY = np.append(inY, tmpY)
     else:
         outX = np.append(outX, tmpX)
         outY = np.append(outY, tmpY)
     ansX = np.append(ansX, tmp)
     ansY = np.append(ansY, float(ans / tmp * 4))

 # 变量声明
 ans = 0;
 inX = np.array([])
 inY = np.array([])
 outX = np.array([])
 outY = np.array([])
 ansX = np.array([])
 ansY = np.array([])
 circleX = np.linspace(0, 1, 10000)
 circleY = (1 - circleX ** 2) ** (0.5)

 # 主体
 N = int(input("循环次数: "))
 # N = 1000
 for i in range(N):
     check(i + 1)

 fig = plt.figure()
 p1 = fig.add_subplot(121)
 p2 = fig.add_subplot(122)
 # i行j列，一维顺序下的第k个

 p1.axis("square")
 p1.axis([0.0, 1.0, 0.0, 1.0])
 p1.scatter(inX, inY, c="r", marker=".")
 p1.scatter(outX, outY, c="b", marker=".")
 p1.plot(circleX, circleY, 'r')

 p2.plot(ansX, ansY)

 # 结果输出
 plt.show()
 print(format(float(ans / N * 4), "0.6f"))