# -*- coding: utf-8 -*-
from setuptools import setup

packages = \
['chaospy',
 'chaospy.bertran',
 'chaospy.chol',
 'chaospy.descriptives',
 'chaospy.descriptives.correlation',
 'chaospy.descriptives.sensitivity',
 'chaospy.distributions',
 'chaospy.distributions.collection',
 'chaospy.distributions.copulas',
 'chaospy.distributions.evaluation',
 'chaospy.distributions.operators',
 'chaospy.distributions.sampler',
 'chaospy.distributions.sampler.sequences',
 'chaospy.external',
 'chaospy.orthogonal',
 'chaospy.poly',
 'chaospy.quadrature',
 'chaospy.quadrature.genz_keister',
 'chaospy.quadrature.recurrence']

package_data = \
{'': ['*']}

install_requires = \
['numpoly>=0.1.16', 'scipy']

extras_require = \
{':python_version >= "2.7" and python_version < "3.0"': ['functools32']}

setup_kwargs = {
    'name': 'chaospy',
    'version': '3.2.10',
    'description': 'Numerical tool for perfroming uncertainty quantification',
    'long_description': '.. image:: doc/.static/chaospy_logo.svg\n   :height: 300 px\n   :width: 300 px\n   :align: center\n\n|circleci| |codecov| |pypi| |readthedocs|\n\n.. |circleci| image:: https://circleci.com/gh/jonathf/chaospy/tree/master.svg?style=shield\n    :target: https://circleci.com/gh/jonathf/chaospy/tree/master\n.. |codecov| image:: https://codecov.io/gh/jonathf/chaospy/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/jonathf/chaospy\n.. |pypi| image:: https://badge.fury.io/py/chaospy.svg\n    :target: https://badge.fury.io/py/chaospy\n.. |readthedocs| image:: https://readthedocs.org/projects/chaospy/badge/?version=master\n    :target: http://chaospy.readthedocs.io/en/master/?badge=master\n\nChaospy is a numerical tool for performing uncertainty quantification using\npolynomial chaos expansions and advanced Monte Carlo methods implemented in\nPython.\n\nIf you are using this software in work that will be published, please cite the\njournal article: `Chaospy: An open source tool for designing methods of\nuncertainty quantification <http://dx.doi.org/10.1016/j.jocs.2015.08.008>`_\n\n.. contents:: Table of Contents:\n\nInstallation\n------------\n\nInstallation should be straight forward::\n\n    pip install chaospy\n\nAnd you should be ready to go.\n\nAlternatively, to get the most current experimental version, the code can be\ninstalled from Github as follows::\n\n    git clone git@github.com:jonathf/chaospy.git    # first time only\n    cd chaospy/\n    git pull                                        # after the first time\n    pip install .\n\nExample Usage\n-------------\n\n``chaospy`` is created to be simple and modular. A simple script to implement\npoint collocation method will look as follows:\n\n.. code-block:: python\n\n    import chaospy\n    import numpy\n\n    # your code wrapper goes here\n    coordinates = numpy.linspace(0, 10, 100)\n    def foo(coordinates, params):\n        """Function to do uncertainty quantification on."""\n        return params[0] * numpy.e**(-params[1]*coordinates)\n\n    # bi-variate probability distribution\n    distribution = chaospy.J(chaospy.Uniform(1, 2), chaospy.Uniform(0.1, 0.2))\n\n    # polynomial chaos expansion\n    polynomial_expansion = chaospy.generate_expansion(8, distribution)\n\n    # samples:\n    samples = distribution.sample(1000)\n\n    # evaluations:\n    evals = [foo(coordinates, sample) for sample in samples.T]\n\n    # polynomial approximation\n    foo_approx = chaospy.fit_regression(\n        polynomial_expansion, samples, evals)\n\n    # statistical metrics\n    expected = chaospy.E(foo_approx, distribution)\n    deviation = chaospy.Std(foo_approx, distribution)\n\nFor a more extensive description of what going on, see the `tutorial\n<https://chaospy.readthedocs.io/en/master/tutorial.html>`_.\n\nFor a collection of recipes, see the `cookbook\n<https://chaospy.readthedocs.io/en/master/cookbook.html>`_.\n\nRelated Projects\n----------------\n\nChaospy is being used in other related projects that requires uncertainty\nquantification components ``chaospy`` provides.\n\n+-----------------+-----------------------------------------------------------+\n| `easyVVUQ`_     | Library designed to facilitate verification, validation   |\n|                 | and uncertainty quantification.                           |\n+-----------------+-----------------------------------------------------------+\n| `STARFiSh`_     | Shell-based, scientific simulation program                |\n|                 | for blood flow in mammals.                                |\n+-----------------+-----------------------------------------------------------+\n| `Profit`_       | Probabilistic response model fitting via interactive      |\n|                 | tools.                                                    |\n+-----------------+-----------------------------------------------------------+\n| `UncertainPy`_  | Uncertainty quantification and sensitivity analysis,      |\n|                 | tailored towards computational neuroscience.              |\n+-----------------+-----------------------------------------------------------+\n| `SparseSpACE`_  | Spatially adaptive combination technique targeted to      |\n|                 | solve high dimensional numerical integration.             |\n+-----------------+-----------------------------------------------------------+\n\n.. _easyVVUQ: https://github.com/UCL-CCS/EasyVVUQ\n.. _STARFiSh: https://www.ntnu.no/starfish\n.. _Profit: https://github.com/redmod-team/profit\n.. _UncertainPy: https://github.com/simetenn/uncertainpy\n.. _SparseSpACE: https://github.com/obersteiner/sparseSpACE\n\nAlso a few shout-outs:\n\n+--------------+--------------------------------------------------------------+\n| `OpenTURNS`_ | Thanks to `Régis Lebrun`_ for both proposing a collaboration |\n|              | and creating an initial implementation of both               |\n|              | `Chaospy Compatibility`_ in `OpenTURNS`_ and                 |\n|              | `OpenTURNS Compatibility`_ in ``chaospy``.                   |\n+--------------+--------------------------------------------------------------+\n| `orthopy`_   | Thanks to `Nico Schlömer`_ for providing the implementation  |\n| `quadpy`_    | for several of the quadrature integration methods.           |\n+--------------+--------------------------------------------------------------+\n| ``UQRF``     | Thanks to `Florian Künzner`_ for providing the               |\n|              | implementation for `sample distribution`_.                   |\n+--------------+--------------------------------------------------------------+\n\n.. _OpenTURNS: http://openturns.github.io/openturns/latest\n.. _Régis Lebrun: https://github.com/regislebrun\n.. _Chaospy Compatibility: http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.ChaospyDistribution.html\n.. _OpenTURNS Compatibility: https://chaospy.readthedocs.io/en/master/recipes/external.html#module-chaospy.external.openturns_\n.. _orthopy: https://github.com/nschloe/orthopy\n.. _quadpy: https://github.com/nschloe/quadpy\n.. _Nico Schlömer: https://github.com/nschloe\n.. _Florian Künzner: https://github.com/flo2k\n.. _sample distribution: https://chaospy.readthedocs.io/en/master/recipes/external.html#module-chaospy.external.samples\n\nQuestions & Troubleshooting\n---------------------------\n\nFor any problems and questions you might have related to ``chaospy``, please\nfeel free to file an `issue <https://github.com/jonathf/chaospy/issues>`_.\n',
    'author': 'Jonathan Feinberg',
    'author_email': None,
    'maintainer': None,
    'maintainer_email': None,
    'url': 'https://github.com/jonathf/chaospy',
    'packages': packages,
    'package_data': package_data,
    'install_requires': install_requires,
    'extras_require': extras_require,
}


setup(**setup_kwargs)
