Numerical tool for perfroming uncertainty quantification
Chaospy is a numerical tool for performing uncertainty quantification using polynomial chaos expansions and advanced Monte Carlo methods implemented in Python.
Installation should be straight forward:
pip install chaospy
And you should be ready to go.
chaospy is created to be simple and modular. A simple script to implement point collocation method will look as follows:
import numpy import chaospy # your code wrapper goes here coordinates = numpy.linspace(0, 10, 100) def foo(coordinates, params): """Function to do uncertainty quantification on.""" param_init, param_rate = params return param_init*numpy.e**(-param_rate*coordinates) # bi-variate probability distribution distribution = chaospy.J(chaospy.Uniform(1, 2), chaospy.Uniform(0.1, 0.2)) # polynomial chaos expansion polynomial_expansion = chaospy.generate_expansion(8, distribution) # samples: samples = distribution.sample(1000) # evaluations: evals = numpy.array([foo(coordinates, 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 guides on what is going on, see the tutorial collection.
Please feel free to file an issue for:
- bug reporting
- asking questions related to usage
- requesting new features
- wanting to contribute with code
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
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