GitLocker: The Coding Marketplace

Description:

scikitmonaco 0.2.1

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

Links

Home page: https://pypi.python.org/pypi/scikit-monaco
Documentation: http://scikit-monaco.readthedocs.org/en/latest/
Source code: https://github.com/scikit-monaco/scikit-monaco
Issues: https://github.com/scikit-monaco/scikit-monaco/issues

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.