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        "author_email": "david.pugh@maths.ox.ac.uk",
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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering"
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        "description": "pyAM\n====\n\n|Build Status| |Coverage Status| |Codacy Badge| |GitHub License| |Latest Version| |Downloads| |DOI|\n\n.. |Build Status| image:: https://travis-ci.org/davidrpugh/pyAM.svg?branch=master\n   :target: https://travis-ci.org/davidrpugh/pyAM\n.. |Coverage Status| image:: https://coveralls.io/repos/davidrpugh/pyAM/badge.svg?branch=master\n   :target: https://coveralls.io/github/davidrpugh/pyAM?branch=master\n.. |Codacy Badge| image:: https://www.codacy.com/project/badge/f051d7b5ccce47cfa3d6907c9a1bd6bf\n   :target: https://www.codacy.com/app/drobert-pugh/pyAM\n.. |GitHub license| image:: https://img.shields.io/github/license/davidrpugh/pyAM.svg\n   :target: https://img.shields.io/github/license/davidrpugh/pyAM.svg\n.. |Latest Version| image:: https://img.shields.io/pypi/v/pyAM.svg\n   :target: https://pypi.python.org/pypi/pyAM/\n.. |Downloads| image:: https://img.shields.io/pypi/dm/pyAM.svg\n   :target: https://pypi.python.org/pypi/pyAM/\n.. |DOI| image:: https://zenodo.org/badge/doi/10.5281/zenodo.22396.svg   \n   :target: http://dx.doi.org/10.5281/zenodo.22396\n\nPython package for solving assortative matching models with two-sided heterogeneity.  The theoretical framework behind the class of models solved by pyAM is described in `Eeckhout and Kircher (2012)`_.\n\n.. _`Eeckhout and Kircher (2012)`: http://homepages.econ.ed.ac.uk/~pkircher/Papers/Sorting-and-Factor-Intensity.pdf\n\nInstallation\n------------\n\nAssuming you have `pip`_ on your computer (as will be the case if you've `installed Anaconda`_) you can install the latest stable release of ``pyam`` by typing\n    \n.. code:: bash\n\n    $ pip install pyam\n\nat a terminal prompt.\n\n.. _pip: https://pypi.python.org/pypi/pip\n.. _`installed Anaconda`: http://quant-econ.net/getting_started.html#installing-anaconda\n\nContributing\n------------\nIf you wish to contribute to the project you will likely want to install from source. First your will need to fork and then clone the source repository.\n\n.. code:: bash\n\n    $ git clone https://github.com/YOUR-USERNAME/pyAM.git \n\nNext create a new `conda` development environment \n\n.. code:: bash\n    \n    $ conda create -n pyam-dev python anaconda\n\nactivate the newly created development environment\n\n.. code:: bash\n\n    $ source activate pyam-dev\n\nand install additional dependencies not available within Anaconda.\n\n.. code:: bash\n\n    $ pip install pycollocation\n    $ pip install seaborn\n\nFinally, change into your local clone of the `pyam` source directory and install the package in development mode.\n\n.. code:: bash\n\n    $ pip install -e .\n\n\nExample notebooks\n-----------------\nAt the moment there are two example notebooks, one for `positive assortative matching`_ and one for `negative assortative matching`_ in the `examples` directory.  The positive assortative matching works fine; the negative assortative matching, however, does not yet work (I suspect because of a poor algorithm for the initial guess).\n\n.. _`positive assortative matching`: https://github.com/davidrpugh/pyAM/blob/master/examples/positive-assortative-matching.ipynb\n.. _`negative assortative matching`: https://github.com/davidrpugh/pyAM/blob/master/examples/negative-assortative-matching.ipynb",
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