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        "author": "Jan Verbeek",
        "author_email": "jan.verbeek@posteo.nl",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: ISC License (ISCL)",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "[![Build Status](https://travis-ci.org/blyxxyz/python-nnf.svg?branch=master)](https://travis-ci.org/blyxxyz/python-nnf)\n[![Readthedocs](https://readthedocs.org/projects/python-nnf/badge/)](https://python-nnf.readthedocs.io)\n![Python Versions](https://img.shields.io/pypi/pyversions/nnf.svg)\n[![PyPI](https://img.shields.io/pypi/v/nnf.svg)](https://pypi.org/project/nnf/)\n![License](https://img.shields.io/pypi/l/nnf.svg)\n\n`nnf` is a Python package for creating and manipulating logical sentences\nwritten in the\n[negation normal form](https://en.wikipedia.org/wiki/Negation_normal_form)\n(NNF).\n\nNNF sentences make statements about any number of variables. Here's an example:\n\n```pycon\n>>> from nnf import Var\n>>> a, b = Var('a'), Var('b')\n>>> sentence = (a & b) | (a & ~b)\n>>> sentence\nOr({And({a, b}), And({a, ~b})})\n```\n\nThis sentence says that either a is true and b is true, or a is true and b\nis false.\n\nYou can do a number of things with such a sentence. For example, you can ask\n whether a particular set of values for the variables makes the sentence true:\n\n```pycon\n>>> sentence.satisfied_by({'a': True, 'b': False})\nTrue\n>>> sentence.satisfied_by({'a': False, 'b': False})\nFalse\n```\n\nYou can also fill in a value for some of the variables:\n\n```pycon\n>>> sentence.condition({'b': True})\nOr({And({a, true}), And({a, false})})\n```\n\nAnd then reduce the sentence:\n\n```pycon\n>>> _.simplify()\na\n```\n\nThis package takes much of its data model and terminology from\n[*A Knowledge Compilation Map*](https://jair.org/index.php/jair/article/view/10311).\n\nComplete documentation can be found at [readthedocs](https://python-nnf.readthedocs.io).\n\n# Installing\n\n```sh\npip install nnf\n```\n\nAt least Python 3.4 is required.\n\n# Serialization\n\nA parser and serializer for the\n[DIMACS sat format](https://www.cs.ubc.ca/~hoos/SATLIB/benchm.html) are\nimplemented in `nnf.dimacs`, with a standard `load`/`loads`/`dump`/`dumps`\ninterface.\n\n# DSHARP interoperability\n\n[DSHARP](https://bitbucket.org/haz/dsharp) is a program that compiles CNF\nsentences to (s)d-DNNF sentences. The `nnf.dsharp` module contains tools for\nparsing its output format and for invoking the compiler.\n\n# Algebraic Model Counting\n\n`nnf.amc` has a basic implementation of\n[Algebraic Model Counting](https://arxiv.org/abs/1211.4475).\n\n# Command line interface\n\nSome functionality is available through a command line tool, `pynnf`, including a\n(slow) SAT solver and a sentence visualizer. For more information, see\n[the documentation](https://python-nnf.readthedocs.io/en/stable/cli.html).\n\n\n",
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