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Chaospy is a numerical tool for performing uncertainty quantification using polynomial chaos expansions and advanced Monte Carlo methods implemented in Python 2 and 3.
If you are using this software in work that will be published, please cite the journal article: Chaospy: An open source tool for designing methods of uncertainty quantification
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 email@example.com:jonathf/chaospy.git cd chaospy python setup.py install
To install tools for doing development add the extra step:
pip install -r requirements.txt
chaospy is created to be simple and modular. A simple script to implement point collocation method will look as follows:
import chaospy import numpy # your code wrapper goes here def foo(coord, prm): """Function to do uncertainty quantification on.""" return prm * numpy.e ** (-prm * numpy.linspace(0, 10, 100)) # bi-variate probability distribution distribution = choaspy.J(chaospy.Uniform(1, 2), chaospy.Uniform(0.1, 0.2)) # polynomial chaos expansion polynomial_expansion = chaospy.orth_ttr(8, distribution) # samples: samples = distribution.sample(1000) # evaluations: evals = [foo(sample) for sample in samples.T] # polynomial approximation foo_approx = chaospy.fit_regression( polynomial_expansion, samples, evals) # statistical metrics expected = chaospy.E(foo_approx, distribution) deviation = chaospy.Std(foo_approx, distribution)
For a more extensive description of what going on, see the tutorial.
For a collection of recipes, see the cookbook.
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