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
And you should be ready to go.
Alternatively, to get the most current experimental 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 ... """Function to do uncertainty quantification on.""" ... 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 recipes, see the cookbook.
For any problems and questions you might have related to chaospy, please
feel free to file an issue.