Metadata-Version: 2.1
Name: chaospy
Version: 3.0.7
Summary: UNKNOWN
Home-page: https://github.com/jonathf/chaospy
Author: Jonathan Feinberg
Author-email: jonathf@gmail.com
License: MIT
Description: Chaospy
        =======
        
        |travis| |codecov| |pypi| |readthedocs|
        
        |logo|
        
        Chaospy is a numerical tool for performing uncertainty quantification using
        polynomial chaos expansions and advanced Monte Carlo methods implemented in
        Python 2 and 3.
        
        A article in Elsevier Journal of Computational Science has been published
        introducing the software: `here
        <http://dx.doi.org/10.1016/j.jocs.2015.08.008>`_.  If you are using this
        software in work that will be published, please cite this paper.
        
        Installation
        ------------
        
        Installation should be straight forward::
        
            pip install chaospy
        
        And you should be ready to go.
        
        Alternatively, to get the most current experimental version, the code can be
        installed from Github as follows::
        
            git clone git@github.com:jonathf/chaospy.git
            cd chaospy
            pip install -r requirements.txt
            python setup.py install
        
        The last command might need ``sudo`` prefix, depending on your python setup.
        
        Optionally, to support more regression methods, install the Scikit-learn
        package::
        
            pip install scikit-learn
        
        Example Usage
        -------------
        
        ``chaospy`` is created to be simple and modular. A simple script to implement
        point collocation method will look as follows:
        
        .. code-block:: python
        
            import chaospy
            import numpy
        
            # your code wrapper goes here
            def foo(coord, prm):
                """Function to do uncertainty quantification on."""
                return prm[0] * numpy.e ** (-prm[1] * numpy.linspace(0, 10, 100))
        
            # bi-variate probability distribution
            distribution = choaspy.J(chaospy.Uniform(1, 2), chaospy.Uniform(0.1, 0.2))
        
            # polynomial chaos expansion
            polynomial_expansion = chaospy.orth_ttr(8, distribution)
        
            # samples:
            samples = distribution.sample(1000)
        
            # evaluations:
            evals = [foo(sample) for sample in samples.T]
        
            # polynomial approximation
            foo_approx = chaospy.fit_regression(
                polynomial_expansion, samples, evals)
        
            # statistical metrics
            expected = chaospy.E(foo_approx, distribution)
            deviation = chaospy.Std(foo_approx, distribution)
        
        For a more extensive description of what going on, see the `tutorial
        <https://chaospy.readthedocs.io/en/master/tutorial.html>`_.
        
        For a collection of recipes, see the `cookbook
        <https://chaospy.readthedocs.io/en/master/cookbook.html>`_.
        
        Questions & Troubleshooting
        ---------------------------
        
        For any problems and questions you might have related to ``chaospy``, please
        feel free to file an `issue <https://github.com/jonathf/chaospy/issues>`_.
        
        
        .. |travis| image:: https://travis-ci.org/jonathf/chaospy.svg?branch=master
            :target: https://travis-ci.org/jonathf/chaospy
        .. |codecov| image:: https://codecov.io/gh/jonathf/chaospy/branch/master/graph/badge.svg
            :target: https://codecov.io/gh/jonathf/chaospy
        .. |pypi| image:: https://img.shields.io/pypi/v/chaospy.svg
            :target: https://pypi.python.org/pypi/chaospy
        .. |readthedocs| image:: https://readthedocs.org/projects/chaospy/badge/?version=master
            :target: http://chaospy.readthedocs.io/en/master/?badge=master
        .. |logo| image:: logo.jpg
            :target: https://gihub.com/jonathf/chaospy
        
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Description-Content-Type: text/x-rst
