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https://github.com/jonathf/chaospy

Chaospy - Toolbox for performing uncertainty quantification.
https://github.com/jonathf/chaospy

gaussian-quadrature sensitivity-analysis sparse-grids uncertainty-quantification variance-reduction

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Chaospy - Toolbox for performing uncertainty quantification.

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README 

        
.. image:: https://github.com/jonathf/chaospy/raw/master/docs/_static/chaospy_logo.svg

   :height: 200 px

   :width: 200 px

   :align: center



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.. |circleci| image:: https://img.shields.io/circleci/build/github/jonathf/chaospy/master

    :target: https://circleci.com/gh/jonathf/chaospy/tree/master

.. |codecov| image:: https://img.shields.io/codecov/c/github/jonathf/chaospy

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.. |readthedocs| image:: https://img.shields.io/readthedocs/chaospy

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.. |downloads| image:: https://img.shields.io/pypi/dm/chaospy

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.. |pypi| image:: https://img.shields.io/pypi/v/chaospy

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* `Documentation `_

* `Interactive tutorials with Binder `_

* `Code of conduct `_

* `Contribution guideline `_

* `Changelog `_

* `License `_



Chaospy is a numerical toolbox designed for performing uncertainty

quantification through polynomial chaos expansions and advanced Monte

Carlo methods implemented in Python. It includes a comprehensive suite

of tools for low-discrepancy sampling, quadrature creation, polynomial

manipulations, and much more.



The philosophy behind ``chaospy`` is not to serve as a single solution

for all uncertainty quantification challenges, but rather to provide

specific tools that empower users to solve problems themselves. This

approach accommodates well-established problems but also serves as a

foundry for experimenting with new, emerging problems. Emphasis is

placed on the following:



* Focus on an easy-to-use interface that embraces the `pythonic code

  style `.

* Ensure the code is "composable," meaning it's designed so that users

  can easily and effectively modify parts of the code with their own

  solutions.

* Strive to support a broad range of methods for uncertainty

  quantification where it makes sense to use ``chaospy``.

* Ensure that ``chaospy`` integrates well with a wide array of other

  projects, including `numpy `, `scipy

  `, `scikit-learn `,

  `statsmodels `, `openturns

  `, and `gstools

  `, among others.

* Contribute all code as open source to the community.



Installation

============



Installation is straightforward via `pip `_:



.. code-block:: bash



    pip install chaospy



Alternatively, if you prefer `Conda `_:



.. code-block:: bash



    conda install -c conda-forge chaospy



After installation, visit the `documentation

`_ to learn how to use the

toolbox.



Development

===========



To install ``chaospy`` and its dependencies in developer mode:



.. code-block:: bash



    pip install -e .[dev]



Testing

-------



To run tests on your local system:



.. code-block:: bash



    pytest --doctest-modules chaospy/ tests/ README.rst



Documentation

-------------



Ensure that ``pandoc`` is installed and available in your path to

build the documentation.



From the ``docs/`` directory, build the documentation locally using:



.. code-block:: bash



    cd docs/

    make html



Run ``make`` without arguments to view other build targets.

The HTML documentation will be output to ``doc/.build/html``.