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Numerical tool for performing uncertainty quantification

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Chaospy is a numerical toolbox designed for performing uncertainty quantification through polynomial chaos expansions and advanced Monte Carlo methods implemented in Python. It includes a comprehensive suite of tools for low-discrepancy sampling, quadrature creation, polynomial manipulations, and much more.

The philosophy behind chaospy is not to serve as a single solution for all uncertainty quantification challenges, but rather to provide specific tools that empower users to solve problems themselves. This approach accommodates well-established problems but also serves as a foundry for experimenting with new, emerging problems. Emphasis is placed on the following:

  • Focus on an easy-to-use interface that embraces the pythonic code style <>.

  • Ensure the code is “composable,” meaning it’s designed so that users can easily and effectively modify parts of the code with their own solutions.

  • Strive to support a broad range of methods for uncertainty quantification where it makes sense to use chaospy.

  • Ensure that chaospy integrates well with a wide array of other projects, including numpy <>, scipy <>, scikit-learn <>, statsmodels <>, openturns <>, and gstools <>, among others.

  • Contribute all code as open source to the community.


Installation is straightforward via pip:

pip install chaospy

Alternatively, if you prefer Conda:

conda install -c conda-forge chaospy

After installation, visit the documentation to learn how to use the toolbox.


To install chaospy and its dependencies in developer mode:

pip install -e .[dev]


To run tests on your local system:

pytest --doctest-modules chaospy/ tests/ README.rst


Ensure that pandoc is installed and available in your path to build the documentation.

From the docs/ directory, build the documentation locally using:

cd docs/
make html

Run make without arguments to view other build targets. The HTML documentation will be output to doc/.build/html.

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