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        "author": "Marco Favorito",
        "author_email": "marco.favorito@gmail.com",
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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
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        "description": "========\nPythogic\n========\n\n.. image:: https://img.shields.io/pypi/v/pythogic.svg\n        :target: https://pypi.python.org/pypi/pythogic\n\n.. image:: https://img.shields.io/pypi/pyversions/pythogic.svg\n        :target: https://pypi.python.org/pypi/pythogic\n\n.. image:: https://img.shields.io/travis/MarcoFavorito/pythogic.svg\n        :target: https://travis-ci.org/MarcoFavorito/pythogic\n\n.. image:: https://readthedocs.org/projects/pythogic/badge/?version=latest\n        :target: https://pythogic.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n.. image:: https://coveralls.io/repos/github/MarcoFavorito/pythogic/badge.svg?branch=master&service=github\n        :target: https://coveralls.io/github/MarcoFavorito/pythogic?branch=master\n\n.. image:: https://img.shields.io/badge/License-MIT-yellow.svg\n        :alt: MIT License\n        :target: https://opensource.org/licenses/MIT\n\n.. image:: https://codecov.io/gh/MarcoFavorito/pythogic/branch/master/graph/badge.svg\n        :alt: Codecov coverage\n        :target: https://codecov.io/gh/MarcoFavorito/pythogic/branch/master/graph/badge.svg\n\n.. image:: https://img.shields.io/badge/status-development-orange.svg\n        :alt: Status: Development\n        :target: https://codecov.io/gh/MarcoFavorito/pythogic/branch/master/graph/badge.svg\n\n\n\nPython package for deal with logical formulas and formal systems.\n\n\n* Free software: MIT license\n* Documentation: https://pythogic.readthedocs.io.\n\nUsage\n--------\n\nFirst of all, create symbols and an alphabet\n\n.. code:: python\n\n    from pythogic.base.Alphabet import Alphabet\n    from pythogic.base.Symbol import Symbol\n\n    a_sym = Symbol(\"a\")\n    b_sym = Symbol(\"b\")\n    c_sym = Symbol(\"c\")\n    alphabet = Alphabet({a_sym, b_sym, c_sym})\n    # you can also write:\n    alphabet = Alphabet.fromStrings({\"a\", \"b\", \"c\"})\n\nCreate some formulas:\n\n.. code:: python\n\n    from pythogic.base.Formula import AtomicFormula, TrueFormula, FalseFormula, Not, And, Or\n\n    # Propositions\n    a = AtomicFormula(a_sym)\n    b = AtomicFormula(b_sym)\n    c = AtomicFormula(c_sym)\n\n    # Elementary formulas\n    not_a = Not(a)\n    not_a_and_b = And(Not(a), b)\n    not_a_or_c = Or(not_a, c)\n    true = TrueFormula()\n    false = FalseFormula()\n\nUsing Propositional Calculus:\n\n.. code:: python\n\n    from pythogic.pl.PL import PL\n    from pythogic.pl.semantics.PLInterpretation import PLInterpretation\n\n    # A dictionary which assign each symbol to a truth value\n    symbol2truth = {\n            a_sym: True,\n            b_sym: False,\n            c_sym: True\n        }\n\n    # The propositional interpretation\n    I = PLInterpretation(alphabet, symbol2truth)\n\n    # main class which contains useful methods\n    PL = PL(alphabet)\n\n    PL.truth(a, I)              # returns true\n    PL.truth(b, I)              # returns false\n    PL.truth(c, I)              # returns true\n    PL.truth(not_a, I)          # returns false\n    PL.truth(not_a_and_b, I)    # returns false\n    PL.truth(not_a_or_c, I)     # returns true\n    PL.truth(true, I)           # returns true\n    PL.truth(false, I)          # returns false\n\n\nFeatures\n--------\n\n- Compose logical formula by common syntax rules;\n- Implementation of several semantics (FOL Interpretation, finite trace, etc.);\n- Support for several logical formal systems: Propositional Logic, First-order Logic, REf, LTLf, LDLf;\n\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\nMany thanks to PySimpleAutomata_ for the automata support.\n.. _PySimpleAutomata: https://github.com/Oneiroe/PySimpleAutomata\n\n\n=======\nHistory\n=======\n\n0.1.0 (2018-02-20)\n------------------\n\n* First release on PyPI.\n\n0.2.0 (2018-02-23)\n------------------\n\n* First-Order logic support (Formulas, Interpretations, Assignment, Truth of the formulas).\n\n0.2.1 (2018-02-23)\n------------------\n\n* Fix on the repo.\n\n0.2.2 (2018-02-25)\n------------------\n\n* Refactoring of the formulas and formal systems functionalities.\n* Implemented LDLf.\n\n0.2.3 (2018-02-25)\n------------------\n\n* \"To negative normal form\" procedure for LDLf formulas.\n\n0.2.4 (2018-02-06)\n------------------\n\n* Support for LDLf for Empty Traces.\n\n0.2.6 (2018-03-09)\n------------------\n\n* Non-deterministic state automata conversion procedure for LDLf_EmptyTraces formulas.\n\n0.2.7 (2018-03-10)\n------------------\n\n* Fix bug in delta function for NFA computation from LDLf_EmptyTraces formulas\n* Fix bug in rewriting automata for PySimpleAutomata package\n\n0.2.8 (2018-03-10)\n------------------\n\n* Implemented DFA conversion for NFAs from LDLf_EmptyTraces formulas\n\n0.2.9 (2018-03-16)\n------------------\n\n* Migrating from PySimpleAutomata to `Pythomata <https://github.com/MarcoFavorito/pythomata>`_",
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