Chaospy is a numerical toolbox for performing uncertainty quantification using polynomial chaos expansions, advanced Monte Carlo methods implemented in Python. It also include 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
Contribute all code to the community open source.