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        "description": "IBM\u00ae OPL connector for Python (DOopl)\n-------------------------------------\n\nWelcome to the IBM\u00ae OPL connector for Python.\nLicensed under the Apache License v2.0.\n\nWith this library, you can quickly and easily add the power of optimization to\nyour Python application. You can model your problems by using the OPL language and IDE, and integrate it in Python via Python/pandas/sql alchemy inputs/outputs.\n\nSolving with CPLEX requires that IBM\u00ae ILOG CPLEX Optimization Studio V12.8.0 is installed on your machine.\n\nInstall the library\n~~~~~~~~~~~~~~~~~~~\n\n::\n\n    pip install doopl\n\n\nGet the examples\n~~~~~~~~~~~~~~~~\n\n* `Examples <https://github.com/IBMDecisionOptimization/doopl-examples>`_.\n\nGet your IBM\u00ae ILOG CPLEX Optimization Studio edition\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n- You can get a free `Community Edition <http://www-01.ibm.com/software/websphere/products/optimization/cplex-studio-community-edition>`_\n  of CPLEX Optimization Studio, with limited solving capabilities in term of problem size.\n\n- Faculty members, research professionals at accredited institutions can get access to an unlimited version of CPLEX through the\n  `IBM\u00ae Academic Initiative <https://ibm.onthehub.com/WebStore/ProductSearchOfferingList.aspx?srch=cplex>`_.\n\nLicense\n~~~~~~~\n\nThis library is delivered under the  Apache License Version 2.0, January 2004 (see LICENSE.txt).\n\nStarting point\n~~~~~~~~~~~~~~\n\nThe API is very compact and simple.\nYou must have the OPL binaries in your PATH/LD_LIBRARY_PATH or\nDYLD_LIBRARY_PATH, depending on your platform. They are located in\n``<cplex_studio_dir>/opl/bin/<platform>`` where:\n\n   * ``cplex_studio_dir`` is the installation directory of CPLEX 12.8\n   * ``platform`` is your plaform (OPL nomenclature, that is ``x64_win64``, ``x86-64_linux`` or ``x86-64_osx``)\n\nHere is small sumup of the capabilities:\n\n   * Inputs can be tuple lists, panda's dataframe, sql alchemy fetch statements.\n   * Generate, solve and get output tuplesets as panda's dataframe\n   * Get the CPLEX problem statistics and quality metrics for the solution\n   * Convert all integer variables to floating point variables and vice-versa.\n   * Run the conflict/relaxation mechanism.\n   * Call the 'RunSeed' diagnosis for CPLEX/CPO based problems.\n\nEach of these features are demonstrated with simple examples.\n\nHere is a small example to start working with the API::\n\n    from doopl.factory import *\n\n    # Create an OPL model from a .mod file\n    with create_opl_model(model=\"file.mod\") as opl:\n        # tuple can be a list of tuples, a pandas dataframe...\n        opl.set_input(\"TupleSet1\", tuples)\n\n        # Generate the problem and solve it.\n        opl.run()\n\n        # Get the names of post processing tables\n        print(\"Table names are: \"+ str(opl.output_table_names))\n\n        # Get all the post processing tables as dataframes.\n        for name, table in iteritems(opl.report):\n            print(\"Table : \" + name)\n            for t in table.itertuples(index=False):\n                print(t)\n\n",
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