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.