scikit-monaco

scikit-monaco is a library for Monte Carlo integration in python. The core is written in Cython, with process-level parallelism to squeeze the last bits of speed out of the python interpreter.

A code snippet is worth a thousand words. Let’s look at integrating sqrt(x**2 + y**2 + z**2) in the unit square:

>>> from skmonaco import mcquad
>>> from math import sqrt
>>> result, error = mcquad(
...     lambda xs: sqrt(xs[0]**2+xs[1]**2+xs[2]**2),
...     npoints=1e6, xl=[0.,0.,0.], xu=[1.,1.,1.])
>>> print "{} +/- {}".format(result,error)
0.960695982212 +/- 0.000277843266684

Installation

From Pypi

The easiest way to download and install scikit-monaco is from the Python package index (pypi). Just run:

$ python easy_install scikit-monaco

Or, if you have pip:

$ pip install scikit-monaco

From source

Clone the repository using:

$ git clone https://github.com/scikit-monaco/scikit-monaco.git

And run:

$ python setup.py install

in the project’s root directory.

Testing

After the installation, run $ python runtests.py in the package’s root directory.

Issue reporting and contributing

Report issues using the github issue tracker.

Read the CONTRIBUTING guide to learn how to contribute.

Metadata

Release files for scikit-monaco 0.2.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for scikit-monaco 0.2.1
File	Size	Uploaded
scikit-monaco-0.2.1.tar.gz	597.9 kB	Nov 14, 2014	Details

Release files / scikit-monaco-0.2.1.tar.gz

Download URL	scikit-monaco-0.2.1.tar.gz
Size	597.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`bb3d4e2a7830b6af68c82fb47ce12afe6dc332bf762f1ee823ef1e6c5ac17ba3`
BLAKE2b-256 checksum How to use checksums	`5609d70ffb523e8eb7b53144fbd776b759c141eda88f0dc143d81673bcb5414b`
Upload date	Nov 14, 2014
Uploaded using Trusted Publishing? What is trusted publishing?	No