Skip to main content
Help us improve PyPI by participating in user testing. All experience levels needed!

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

In this example we compute :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

The second argument to the integrate() function should be an iterable
expression, in this case it is a generator. We could do away with this sampler
using the following::

result, error = mcint.integrate(integrand, iter(random.random, -1), measure=1.0, n=100)

This creates an iterable object from the random.random() function which will
continuously call random.random() until it returns -1 (which it will never do as
it returns values between 0.0 and 1.0.

Example 2

In this example we compute :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

This version
History Node


History Node


History Node


History Node


History Node


Download files

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

Filename, size & hash SHA256 hash help File type Python version Upload date (3.3 kB) Copy SHA256 hash SHA256 Source None Dec 29, 2011

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page