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.
Installation should be straight forward from pip:
pip install chaospy
Or if Conda is more to your liking:
conda install -c conda-forge chaospy