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Numerical tool for perfroming uncertainty quantification

Project description


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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.


Installation should be straight forward:

pip install chaospy

And you should be ready to go.

Example Usage

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.

Questions and Contributions

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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