chaospy 4.3.21

• Documentation

• Interactive tutorials with Binder

• Code of conduct

• Contribution guideline

• Changelog

• License

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 <https://docs.python-guide.org/writing/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 <https://numpy.org/>, scipy <https://scipy.org/>, scikit-learn <https://scikit-learn.org>, statsmodels <https://statsmodels.org/>, openturns <https://openturns.org/>, and gstools <https://geostat-framework.org/>, among others.

• Contribute all code as open source to the community.

pip install chaospy

conda install -c conda-forge chaospy

pip install -e .[dev]

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

cd docs/
make html