Chaospy is a numerical toolbox for performing uncertainty quantification using polynomial chaos expansions, advanced Monte Carlo methods implemented in Python. It also includes a full suite of tools for doing low-discrepancy sampling, quadrature creation, polynomial manipulations, and a lot more.

The philosophy behind chaospy is not to be a single tool that solves every uncertainty quantification problem, but instead be a specific tools to aid to let the user solve problems themselves. This includes both well established problems, but also to be a foundry for experimenting with new problems, that are not so well established. To do this, emphasis is put on the following:

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

  • Make sure the code is “composable”, such a way that changing one part of the code with something user defined should be easy and encouraged.

  • Try to support a broad width of the various methods for doing uncertainty quantification where that makes sense to involve chaospy.

  • Make sure that chaospy plays nice with a large set of other similar projects. This includes numpy, scipy, scikit-learn, statsmodels, openturns, and gstools to mention a few.

  • Contribute all code to the community open source.


Installation should be straight forward from pip:

pip install chaospy

Or if Conda is more to your liking:

conda install -c conda-forge chaospy

For developer installation, go to the chaospy repository. Otherwise, check out the user guide to see how to use the toolbox.