https://github.com/davidrpugh/pyam

Python package for solving assortative matching models with two-sided heterogeneity.
https://github.com/davidrpugh/pyam

Last synced: 9 days ago
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Python package for solving assortative matching models with two-sided heterogeneity.

• Host: GitHub
• URL: https://github.com/davidrpugh/pyam
• Owner: davidrpugh
• License: mit
• Created: 2015-07-14T03:53:33.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2015-10-09T16:06:28.000Z (about 9 years ago)
• Last Synced: 2024-11-17T13:24:00.368Z (about 1 month ago)
• Language: Python
• Size: 2.59 MB
• Stars: 0
• Watchers: 5
• Forks: 4
• Open Issues: 10
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
pyAM

====

|Build Status| |Coverage Status| |Codacy Badge| |GitHub License| |Latest Version| |Downloads| |DOI|

.. |Build Status| image:: https://travis-ci.org/davidrpugh/pyAM.svg?branch=master

   :target: https://travis-ci.org/davidrpugh/pyAM

.. |Coverage Status| image:: https://coveralls.io/repos/davidrpugh/pyAM/badge.svg?branch=master

   :target: https://coveralls.io/github/davidrpugh/pyAM?branch=master

.. |Codacy Badge| image:: https://www.codacy.com/project/badge/f051d7b5ccce47cfa3d6907c9a1bd6bf

   :target: https://www.codacy.com/app/drobert-pugh/pyAM

.. |GitHub license| image:: https://img.shields.io/github/license/davidrpugh/pyAM.svg

   :target: https://img.shields.io/github/license/davidrpugh/pyAM.svg

.. |Latest Version| image:: https://img.shields.io/pypi/v/pyAM.svg

   :target: https://pypi.python.org/pypi/pyAM/

.. |Downloads| image:: https://img.shields.io/pypi/dm/pyAM.svg

   :target: https://pypi.python.org/pypi/pyAM/

.. |DOI| image:: https://zenodo.org/badge/doi/10.5281/zenodo.22396.svg   

   :target: http://dx.doi.org/10.5281/zenodo.22396

Python 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)`_.

.. _`Eeckhout and Kircher (2012)`: http://homepages.econ.ed.ac.uk/~pkircher/Papers/Sorting-and-Factor-Intensity.pdf

Installation

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

Assuming 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

    

.. code:: bash

    $ pip install pyam

at a terminal prompt.

.. _pip: https://pypi.python.org/pypi/pip

.. _`installed Anaconda`: http://quant-econ.net/getting_started.html#installing-anaconda

Contributing

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

If 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.

.. code:: bash

    $ git clone https://github.com/YOUR-USERNAME/pyAM.git 

Next create a new `conda` development environment 

.. code:: bash

    

    $ conda create -n pyam-dev python anaconda

activate the newly created development environment

.. code:: bash

    $ source activate pyam-dev

and install additional dependencies not available within Anaconda.

.. code:: bash

    $ pip install pycollocation

    $ pip install seaborn

Finally, change into your local clone of the `pyam` source directory and install the package in development mode.

.. code:: bash

    $ pip install -e .

Example notebooks

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

At 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).

.. _`positive assortative matching`: https://github.com/davidrpugh/pyAM/blob/master/examples/positive-assortative-matching.ipynb

.. _`negative assortative matching`: https://github.com/davidrpugh/pyAM/blob/master/examples/negative-assortative-matching.ipynb