chaospy 3.3.1

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.

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

Table of Contents:

• Installation

• Example Usage

• Related Projects

• Development

• Questions and Contributions

Installation

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 [e-mail]:jonathf/chaospy.git    # first time only
cd chaospy/
git pull                                        # after the first time
pip install .

Example Usage

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
coordinates = numpy.linspace(0, 10, 100)
def foo(coordinates, params):
    """Function to do uncertainty quantification on."""
    return params[0] * numpy.e**(-params[1]*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 = [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 description of what going on, see the collection of tutorials.

Related Projects

Chaospy is being used in other related projects that requires uncertainty quantification components chaospy provides.

easyVVUQ	Library designed to facilitate verification, validation and uncertainty quantification.
STARFiSh	Shell-based, scientific simulation program for blood flow in mammals.
Profit	Probabilistic response model fitting via interactive tools.
UncertainPy	Uncertainty quantification and sensitivity analysis, tailored towards computational neuroscience.
SparseSpACE	Spatially adaptive combination technique targeted to solve high dimensional numerical integration.

Also a few shout-outs:

OpenTURNS	Thanks to Régis Lebrun for both proposing a collaboration and creating an initial implementation of both Chaospy Compatibility in OpenTURNS and OpenTURNS Compatibility in chaospy.
orthopy quadpy	Thanks to Nico Schlömer for providing the implementation for several of the quadrature integration methods.
UQRF	Thanks to Florian Künzner for providing the implementation for sample distribution.

Development

Development is done using Poetry manager. Inside the repository directory, install and create a virtual environment with:

poetry install

To run tests:

poetry run pytest chaospy/ tests/ doc/ --doctest-modules

To build documentation, run:

cd doc/
make html

The documentation will be generated into the folder doc/.build/html.

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