Chaospy is a numerical tool for performing uncertainty quantification using polynomial chaos expansions and advanced Monte Carlo methods implemented in Python 2 and 3.
A article in Elsevier Journal of Computational Science has been published introducing the software: here. If you are to use this software in work that is published, please cite this paper.
Installation should be straight forward:
pip install chaospy
Alternativly, to get the most current version, the code can be installed from github as follows:
git clone git@github.com:jonathf/chaospy.git
cd chaospy
pip install -r requirements.txt
python setup.py install
The last command might need sudo
prefix, depending on your python setup.
Optionally, to support more regression methods, install the Scikit-learn package:
pip install scikit-learn
chaospy
is created to be simple and modular. A simple script to implement point collocation method will look as follows:
>>> import chaospy as cp
>>> import numpy as np
>>> def foo(coord, prm): # your code wrapper goes here
... return prm[0] * np.e ** (-prm[1] * np.linspace(0, 10, 100))
>>> distribution = cp.J(
... cp.Uniform(1, 2),
... cp.Uniform(0.1, 0.2)
... )
>>> polynomial_expansion = cp.orth_ttr(8, distribution)
>>> foo_approx = cp.fit_regression(
... polynomial_expansion, samples, evals)
>>> expected = cp.E(foo_approx, distribution)
>>> deviation = cp.Std(foo_approx, distribution)
For a more extensive description of what going on, see the tutorial. For a collection of reciepies, see the cookbook.
To test the build locally:
pip install -r requirements-dev.txt
python setup.py test
It will run pytest-runner
and execute all tests.
For any problems and questions you might have related to chaospy
, please feel free to file an issue.