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        "author": "Pierre Rust (Orange)",
        "author_email": "pierre.rust@orange.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.5",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# Dcop python\n\n[![Documentation Status](https://readthedocs.org/projects/pydcop/badge/?version=latest)](http://pydcop.readthedocs.io/en/latest/?badge=latest)\n[![Build Status](https://travis-ci.org/Orange-OpenSource/pyDcop.svg?branch=master)](https://travis-ci.org/Orange-OpenSource/pyDcop)\n\npyDCOP is a python library for Distributed Constraints Optimization.\nIt contains implementations of several standard DCOP algorithms (MaxSum, DSA,\nDPOP, etc.) and allows you to develop your own algorithms.\n\npyDCOP runs on python >= 3.5.\n\n## Installation\n\nUsing pip is recommended, on ubuntu :\n\n    sudo apt-get install python3-setuptools\n    sudo apt-get install python3-pip\n\n\nI also recommend installing pyDCOP in a virtualenv, in order to avoid any\nconflict with other applications you might have:\n\n     python3 -m venv ~/.pydcop\n     source ~/.pydcop/bin/activate\n\nFor now, installation is only from source :\n\n    cd pydcop\n    pip install .\n\nOr without pip, simply use :\n\n    python3 setup.py install\n\nWhen developing on DCOP-python, one would rather use the following command,\nwhich installs pydcop in development mode and test dependencies:\n\n    pip install -e .[test]\n\nTo generate documentation, you need to install the corresponding dependencies:\n\n    pip install -e .[doc]\n\n\n\n",
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