Chaospy ======= .. toctree:: :hidden: user_guide/index reference/index about_us 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: Installation ------------ Installation should be straight forward from `pip `_: .. code-block:: bash pip install chaospy Or if `Conda `_ is more to your liking: .. code-block:: bash 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.