A simple tool to perform numerical integration using Monte Carlo techniques.

## Project description

========================

Monte Carlo integrator

========================

This package provides a Monte Carlo integrator which can be used to evaluate

multi-dimensional integrals. The results are numerical approximations which are

dependent on the use of random number generation.

Example 1 (computing :math:`\int_0^1 x^2 dx`)::

import mcint

import random

def integrand(x): # Describe the function being integrated

return (x**2)

def sampler(): # Describe how Monte Carlo samples are taken

while True:

yield random.random()

result, error = mcint.integrate(integrand, sampler, measure=1.0, n=100)

print "The integral of x**2 between 0 and 1 is approximately", result

Example 2 (computing :math:`\int_0^1 \int_0^\sqrt{1-y^2} x^2+y^2 dx dy`)::

import mcint

import random

import math

def integrand(x):

return (x[0]**2 + x[1]**2)

def sampler():

while True:

y = random.random()

x = random.random()

if x**2+y**2 <= 1:

yield (x,y)

result, error = mcint.integrate(integrand, sampler, measure=math.pi/4)

Monte Carlo integrator

========================

This package provides a Monte Carlo integrator which can be used to evaluate

multi-dimensional integrals. The results are numerical approximations which are

dependent on the use of random number generation.

Example 1 (computing :math:`\int_0^1 x^2 dx`)::

import mcint

import random

def integrand(x): # Describe the function being integrated

return (x**2)

def sampler(): # Describe how Monte Carlo samples are taken

while True:

yield random.random()

result, error = mcint.integrate(integrand, sampler, measure=1.0, n=100)

print "The integral of x**2 between 0 and 1 is approximately", result

Example 2 (computing :math:`\int_0^1 \int_0^\sqrt{1-y^2} x^2+y^2 dx dy`)::

import mcint

import random

import math

def integrand(x):

return (x[0]**2 + x[1]**2)

def sampler():

while True:

y = random.random()

x = random.random()

if x**2+y**2 <= 1:

yield (x,y)

result, error = mcint.integrate(integrand, sampler, measure=math.pi/4)

## Project details

## Release history Release notifications | RSS feed

## Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

### Source Distribution

mcint-0.1dev3.zip
(2.9 kB
view hashes)