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    "info": {
        "author": "Mark Mikofski",
        "author_email": "mark.mikofski@sunpowercorp.com",
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        "description": ".. image:: https://travis-ci.org/SunPower/Carousel.svg?branch=master\n    :target: https://travis-ci.org/SunPower/Carousel\n\nCarousel - Model Simulation Framework\n=====================================\nCarousel ia a framework for simulating mathematical models that decouples\nthe models from the simulation implementation. It takes care of boilerplate\nroutines such as loading data from various sources into a key store that can be\nused from any calculation, determining the correct order of calculations,\nstepping through dynamic simulations and generating output reports and\nvisualizations, so that you can focus on developing models and don't have to\nworry about how to add new models or how to integrate changes.\n\nRequirements\n------------\n* `Pint <http://pint.readthedocs.org/en/latest/>`_\n* `NumPy <http://www.numpy.org/>`_\n* `h5py <http://www.h5py.org/>`_\n* `xlrd <http://www.python-excel.org/>`_\n* `UncertaintyWrapper <http://sunpower.github.io/UncertaintyWrapper/>`_\n\nInstallation\n------------\nCarousel `releases are on PyPI <https://pypi.python.org/pypi/Carousel>`_ and on\n`GitHub <https://github.com/SunPower/Carousel/releases>`_. You can use either\n``pip``, ``conda``, or ``distutils`` to install Carousel.\n\n`pip <https://pip.pypa.io/en/stable/>`_ ::\n\n    $ pip install Carousel\n\nExtract the archive to use `disutils <https://docs.python.org/2/install/>`_ ::\n\n    $ python setup.py install\n\n`SunPower conda channel <https://anaconda.org/sunpower/carousel>`_ ::\n\n    $ conda install -c sunpower Carousel\n\nDocumentation\n-------------\nCarousel `documentation <https://sunpower.github.io/Carousel>`_ is\nonline. It's also included in the distribution and can be built using\n`Sphinx <http://www.sphinx-doc.org/en/stable/>`_ by running the ``Makefile``\nfound in the ``docs`` folder of the Carousel package. Once built documentation\nwill be found in the ``_build`` folder under the tree corresponding to the type\nof documentation built. *EG*: HTML documentation is in ``docs/_build/html``.\n\nContributions\n-------------\nCarousel `source code <https://github.com/SunPower/Carousel>`_ is\nonline. Fork it and report\n`issues <https://github.com/SunPower/Carousel/issues>`_, make suggestions or\ncreate pull requests. Discuss the roadmap or download presentations on the\n`wiki <https://github.com/SunPower/Carousel/wiki>`_\n\nHistory\n-------\nThe\n`change log for all releases <https://github.com/SunPower/Carousel/releases>`_\nis on GitHub.\n\nQuickstart Example\n------------------\nDefine data, outputs, formulas, calculations, simulations and model::\n\n    #! python\n\n    from carousel.core.data_sources import DataSource, DataParameter\n    from carousel.core.outputs import Output, OutputParameter\n    from carousel.core.formulas import Formula, FormulaParameter\n    from carousel.core.calculations import Calc, CalcParameter\n    from carousel.core.simulations import Simulation, SimParameter\n    from carousel.core.models import Model, ModelParameter\n    from carousel.contrib.readers import ArgumentReader\n    from carousel.core import UREG\n    import numpy as np\n    import os\n\n    DATA = {'PythagoreanData': {'adjacent_side': 3.0, 'opposite_side': 4.0}}\n\n\n    class PythagoreanData(DataSource):\n        adjacent_side = DataParameter(units='cm', uncertainty=1.0)\n        opposite_side = DataParameter(units='cm', uncertainty=1.0)\n\n        def __prepare_data__(self):\n            for k, v in self.parameters.iteritems():\n                self.uncertainty[k] = {k: v['uncertainty'] * UREG.percent}\n\n        class Meta:\n            data_cache_enabled = False\n            data_reader = ArgumentReader\n\n\n    class PythagoreanOutput(Output):\n        hypotenuse = OutputParameter(units='cm')\n\n\n    def f_pythagorean(a, b):\n        a, b = np.atleast_1d(a), np.atleast_1d(b)\n        return np.sqrt(a * a + b * b).reshape(1, -1)\n\n\n    class PythagoreanFormula(Formula):\n        f_pythagorean = FormulaParameter(\n            units=[('=A', ), ('=A', '=A')],\n            isconstant=[]\n        )\n\n        class Meta:\n            module = __name__\n\n\n    class PythagoreanCalc(Calc):\n        pythagorean_thm = CalcParameter(\n            formula='f_pythagorean',\n            args={'data': {'a': 'adjacent_side', 'b': 'opposite_side'}},\n            returns=['hypotenuse']\n        )\n\n\n    class PythagoreanSim(Simulation):\n        settings = SimParameter(\n            ID='Pythagorean Theorem',\n            commands=['start', 'load', 'run'],\n            sim_length=[0, 'hour'],\n            write_fields={\n                'data': ['adjacent_side', 'opposite_side'],\n                'outputs': ['hypotenuse']\n            }\n        )\n\n\n    class PythagoreanModel(Model):\n        data = ModelParameter(sources=[PythagoreanData])\n        outputs = ModelParameter(sources=[PythagoreanOutput])\n        formulas = ModelParameter(sources=[PythagoreanFormula])\n        calculations = ModelParameter(sources=[PythagoreanCalc])\n        simulations = ModelParameter(sources=[PythagoreanSim])\n\n        class Meta:\n            modelpath = os.path.dirname(__file__)\n\n\n    if __name__ == '__main__':\n        m = PythagoreanModel()\n        m.command('run', data=DATA)\n        out_reg = m.registries['outputs']\n        fmt = {\n            'output': out_reg['hypotenuse'],\n            'uncertainty': out_reg.uncertainty['hypotenuse']['hypotenuse']\n        }\n        print 'hypotenuse = %(output)s +/- %(uncertainty)s' % fmt\n\nThis is the `MCVE <https://stackoverflow.com/help/mcve>`_ of a Carousel model.\n\n\n",
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        "license": "BSD 3-clause",
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        "name": "Carousel",
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