Question

It should be close to 0.3

$ cat monte.py 
import random,math
density=int(1e6)
x = [random.uniform(0,1)*7*math.pi for _ in range(density)]
y = [random.uniform(0,1) for _ in range(density)]
i = [math.sin(xx)*math.cos(xx) > yy for (xx,yy) in zip(x,y)]

print sum(i)/(float(density)*10.0)*7*math.pi

$ python monte.py 
0.350184850795

I am trying to rewrite the below but for some reason the python code is not even close.

x = rand(1, 1000000)*7pi;
y = rand(1, 1000000);
i = sin(x).* cos(x) >y;
Area3 = (sum(i) / 10000000)*7pi;

Answer 1

I’m getting identical results between your matlab and python versions… Are you sure that the matlab version is giving you ~2, and not ~0.35?

For example:

MATLAB:

x = rand(1, 1000000)*7*pi;
y = rand(1, 1000000);
i = sin(x).* cos(x) >y;
Area3 = (sum(i) / 10000000)*7*pi

This yields: 0.3511

Your pure-python version:

import random,math
density=int(1e6)
x = [random.uniform(0,1)*7*math.pi for _ in range(density)]
y = [random.uniform(0,1) for _ in range(density)]
i = [math.sin(xx)*math.cos(xx) > yy for (xx,yy) in zip(x,y)]

print sum(i)/(float(density)*10.0)*7*math.pi

This yields: 0.347935156296

Numpy-based:

import numpy as np
x = np.random.random(1e6) * 7 * np.pi
y = np.random.random(x.size)
i = np.sin(x) * np.cos(x) > y
print 7 * np.pi * i.sum() / (10 * x.size)

This yields: 0.350475133957