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    "info": {
        "author": "Iacopo Garizio",
        "author_email": "info@iacopogarizio.com",
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        "description": ".. image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg\n  :target: https://github.com/igarizio/py-school-match/blob/master/LICENSE\n\n===================\n**Py-school-match**\n===================\n\n\n**Py-school-match** is an open-source Python package that implements multiple matching algorithms in order to assign\nstudents to schools.\n\nIt provides multiple algorithms ready to use:\n\n- Top Trading Cycles (TTC)\n- Deferred acceptance with multiple tie-breaking (DAMTB)\n- Deferred acceptance with single tie-breaking (DASTB)\n- Stable improvement cycles (SIC)\n- Deferred Acceptance with multiple tie-breaking, plus stable cycles (MSIC)\n- Deferred Acceptance with single tie-breaking, plus non-stable cycles (NSIC)\n\n**Py-school-match** is designed specifically for the student-to-school assignation problem. Because of this,\nyou can focus on evaluating different settings and algorithms, without the need to adapt or develop a\ncomplete solution.\n\nSample code\n===========\n\n.. code-block:: python\n\n    import py_school_match as psm\n\n    # Creating three students.\n    st0 = psm.Student()\n    st1 = psm.Student()\n    st2 = psm.Student()\n\n    # Creating a criteria. This means 'vulnerable' is now a boolean.\n    vulnerable = psm.Criteria('vulnerable', bool)\n\n    # Assigning st1 as vulnerable\n    student_vulnerable = psm.Characteristic(vulnerable, True)\n    st1.add_characteristic(student_vulnerable)\n\n    # Creating three schools, each with one seat available.\n    sc0 = psm.School(1)\n    sc1 = psm.School(1)\n    sc2 = psm.School(1)\n\n    # Defining preferences (from most desired to least desired)\n    st0.preferences = [sc0, sc1, sc2]\n    st1.preferences = [sc0, sc2, sc1]\n    st2.preferences = [sc2, sc1, sc0]\n\n    # Creating a lists with the students and schools defined above.\n    schools = [sc0, sc1, sc2]\n    students = [st0, st1, st2]\n\n    # Defining a ruleset\n    ruleset = psm.RuleSet()\n\n    # Defining a new rule from the criteria above.\n    rule_vulnerable = psm.Rule(vulnerable)\n\n    # Adding the rule to the ruleset. This means that a 'vulnerable' student has a higher priority.\n    # Note that rules are added in order (from higher priority to lower priority)\n    ruleset.add_rule(rule_vulnerable)\n\n    # Creating a social planner using the objects above.\n    planner = psm.SocialPlanner(students, schools, ruleset)\n\n    # Selecting an algorithm\n    algorithm = psm.SIC()\n\n    # Running the algorithm.\n    planner.run_matching(algorithm)\n\n    # inspecting the obtained assignation\n    for student in students:\n        print(\"Student {} was assigned to School {}\".format(student.id, student.assigned_school.id))\n\n\n\nInstallation\n============\n\nDependencies\n------------\n\n* graph-tool (>= 2.27)\n\nUser installation\n-----------------\n\n.. code-block:: shell\n\n  pip install py-school-match\n\nOr you can clone the repo and install it:\n\n.. code-block:: shell\n\n  git clone https://github.com/igarizio/py-school-match\n  cd py-school-match\n  python setup.py install\n\nRemember to first install `graph-tool <https://graph-tool.skewed.de>`_.\n\nDevelopment\n-----------\n\n| Contributions are more than welcome. Feel free to open an issue or contact me!\n| Remember that this package does not provide ANY WARRANTY OF ANY KIND.\n\nHow to cite?\n============\n\nInsert citation here.\n\n\n",
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        "license": "GLP v3",
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        "name": "py-school-match",
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