monte_carlo.py

import random

import matplotlib.pyplot as plt

# Normally distributed random variable with expected value 0 and variance 1

for _ in range(10):
	if(random.random() < .5):
		print "head"
	else:
		print "tail"


class Coin(object):
	'''fair coin, can be pseudorandomly flipped'''

	sides = ('heads', 'tails')
	last_result = None

	def flip(self):
		self.last_result = result = random.choice(self.sides)
		return result


	# Manipulate the coins:
	def create_coins(number):
		'''create list of number of coin objects'''
		return [Coin() for _ in xrange(number)]


	def flip_coins(coins):
		'''side effect function, modifies object in place, returns None'''
		for coin in coins:
			coin.flip()


	def count_heads(flipped_coins):
		return sum(coin.last_result == 'heads' for coin in flipped_coins)


	def count_tails(flipped_coins):
		return sum(coin.last_result == 'tails' for coin in flipped_coins)


	def main():
		coins = create_coins(1000)
		for i in xrange(100):
			flip_coins(coins)
			print count_heads(coins)


	if __name__ == '__main__':
		main()


# Normal variable. 
from numpy.random import normal

s = normal(size=(1042*32,))
hist(s, bins=50)

for _ in range(10):
	print np.var(normal(size=(1024*32,)))