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        "author": "Jonathan Eunice",
        "author_email": "jonathan.eunice@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.5",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Intensional (rule-defined) sets for Python.\n\nOverview\n========\n\nThere are two ways of defining a set: intensional and extensional. Extensional\nsets like ``set([1,3,5,'daisy'])`` enumerate every member of the set explicitly.\n\nIntensional sets, in contrast, are defined by rules. For example \"the set of all\nprime numbers\" or \"every word beginning with ``'a'`` and ending with ``'t'``.\nIntensional sets are often infinite. It may be possible to generate a list of\ntheir members, but it's not as simple as a \"give me everything you've got!\"\n``for`` loop.\n\nOnce you know what you're looking for, intensional sets are *everywhere*.\nPython doesn't represent them directly, but regular expressions, many list\ncomprehensions, and all manner of testing and filtering operations are really\nfaces of the intensional set concept. Many functions test whether something\n'qualifies'. ``os.path.isdir(d)`` for example, tests whether ``d`` is in the set\nof legitimate directories, and ``isinstance(s, str)`` tests whether ``s`` is a\nmember of the set of ``str`` objects. Even the core ``if`` conditional can be\nconstrued as testing for membership in an intensional set--the set of all items\nthat pass the test.\n\nMany such tests have a temporal aspect--they determine whether a value is a\nmember *right now*. The answer may change in the future, if\nconditions change. Others tests are invariant over time. ``%%734`` will never be a\nvalid Python identifier, no matter how many times it's tested--unless the rules\nof the overall Python universe change, that is.\n\nIntensional sets are part and parcel of all programming, even if they're not\nexplicitly represented or called by that name.``intensional`` helps Python\nprograms represent intensional sets directly.\n\nUsage\n=====\n\n``intensional`` defines several set ``IntensionalSet`` subclasses\nsuch as ``Any``, ``Every``, ``ButNot``,\nand ``EitherOr``. These correspond roughly to set operations union, intersection,\ndifference, and symmetric difference (aka xor). Of these, ``Any`` is the most useful::\n\n    from intensional import *\n    \n    name = 'Stephen'\n    if name in Any('Steve', 'Steven', 'Stephen'):\n        print 'Good name!'\n\nSo far, there's nothing here you couldn't do with standard Python ``set`` data types.\nSo let's broaden out to more generic intensional sets::\n\n    if name in Test(\"x.startswith('S')\"):\n        print \"Your name starts with S!\"\n\n``Test`` takes a lambda expression or string in its constructor. If it's a string, ``Test`` assumes\nthe interesting variable name is ``x`` and compiles the string expression with an automatically provided\n``lambda x:`` prefix. This makes code a bit terser and cleaner. Now the sets start getting more\ninteresting.::\n\n    starts_with_S =  Test(\"x.startswith('S')\")\n    ends_with_n   =  Test(\"x.endswith('n')\")\n    \n    if name in Every(starts_with_S, ends_with_n):\n        ...  # Stephen and Steven pass, but Steve does not\n        \nOf course, this could also be rendered as::\n    \n    if name in Test(\"x.startswith('S') and x.endswith('n')\"):\n        ...  # Stephen and Steven pass, but Steve does not\n        \nOr even::\n\n    S_something_n = starts_with_S & ends_with_n\n    if name in S_something_n:\n        ...\n\nString Search\n=============\n\n``intensional`` defines sets for regular expression (``Re``) and glob (``Glob``) string matching.\nFor example::\n\n    name = 'Stephen'\n    if name in Re(r'\\b(s\\w*)\\b', Re.I):\n        print 'Good name, {}'.format(Re._[1])\n\nNote that this enables a form of (or alternative to) *en passant* assignment\nthat shortens regular expression conditionals by at least one line\ncompared to the standard ``re`` module. A spiffed-up version of the\n``re.MatchObject`` is available at ``Re._``. This object can be\nindexed to get regexp match groups. For named groups (e.g. ``(?P<firstname>\\w+)``),\nthe matched value can be retrieved by attribute: ``Re._.firstname``. All of the\nother ``re.MatchObject`` methods and properties can also be accessed this way,\nsuch as ``Re._.start(1)`` and ``Re._.span(1)``.\n\nFor simple matching, the ``Glob`` class (which plays by the rules\nof Unix glob expressions) may be simpler:\n\n    if name in Glob('S*'):\n        ...\n    \nType Membership\n===============\n\n::\n\n    if x in Instances(int):\n        ...\n\nis identical to::\n\n    if isinstance(x, int):\n       ...\n       \nAn alias ``IsInstance`` exists for ``Instances`` for cases where the singular construction is more linguistically natural.\nA second alias ``Type`` is also available.\n\nSet Operations\n==============\n\n``intensional`` supports some, but not all, of Python's classic ``set`` operations.\nThere are two primary rules:\n\n *  ``IntensionalSet`` attempts to supports all of the ``collections.Set`` methods like\n    ``union()`` and ``intersection()``. But ``IntensionalSet`` objects are immutable, so they do not support\n    self-mutating operations like ``add()``, ``pop()``, and ``|=`` defined by\n    ``collections.MutableSet``.\n    \n *  Because they are defined by rules rather than explicit lists of members, it is\n    not (in general) possible to determine the cardinality (i.e. ``len()``) of an ``IntensionalSet``,\n    nor to iterate through all of its members, nor to test equality. ``IntensionalSet`` objects are\n    used primarily for determining membership.\n\nBecause of an implementation detail, ``IntensionalSet`` classes are parallel to,\nbut are not true subclasses of, ``collections.Set``.\n\nExtensions\n==========\n\nIt's easy to define new ``IntensionalSet`` subclasses that define other kinds\nof logical tests in generalized, linguistically \"clean\" ways that make code\nmore readable. As an example, the ``Instances`` intensional set is defined like this::\n\n    class Instances(with_metaclass(MementoMetaclass, IntensionalSet)):\n        \"\"\"\n        An object is in an IsInstance if it is an instance of the given types.\n        \"\"\"\n        def __init__(self, *args):\n            self.types = tuple(args)\n            \n        def __contains__(self, item):\n            return isinstance(item, self.types)\n\n``__init__()`` simply remembers what arguments the set is constructed with,\nwhile ``__contains__()`` implements the test, answering: Does the given item belong in a set\nconstructed with these arguments?\n\nThe only complexity here is the ``with_metaclass(MementoMetaclass, IntensionalSet)`` phrase,\nwhich is simply a compatibility mechanism to be able to define a class in either\nPython 2 or Python 3 with a given metaclass.\n\n``MementoMetaclass`` is used so that\nonce constructed, a set object is fetched from cache rather than\nredundantly reconstructed if any subsequent mentions are made. This is a useful performance\ntweak. For regular expressions, for example, it allows the ``Re.__init__()`` set constructor\nto compile the regular expression just once, even if a program contains many mentions of\n``Re(<some regular exprssion>)``. Even\nhigher-performance is to assign constructed sets to a name/variable and refer to them\nvia that name. This::\n\n    integers = Instances(int)\n    \n    if x in integers:\n        ...\n\nrequires less work than::\n\n    if x in Instances(int):\n        ...\n\nand is preferred if the test is to be executed frequently. But this pre-naming is just a tweak, and\nnot a requirement.\n\nNotes\n=====\n\n * Commenced automated multi-version testing with\n   `pytest <http://pypi.python.org/pypi/pytest>`_\n   and `tox <http://pypi.python.org/pypi/tox>`_.\n   \n * Now\n   successfully packaged for, and tested against, all late-model versions\n   of Python: 2.6, 2.7, 3.2, and 3.3\n   plus one (2.5) that isn't so very recent,\n   and one (PyPy 1.9, based on Python 2.7.2) that is differently implemented.\n   \n * ``intensional`` is just one facet of a larger project to rethink how items\n   are tested for membership and/or chosen from collections. Stay tuned!\n \n * The author, `Jonathan Eunice <mailto:jonathan.eunice@gmail.com>`_ or\n   `@jeunice on Twitter <http://twitter.com/jeunice>`_\n   welcomes your comments and suggestions.\n\nInstallation\n============\n\nTo install the latest version::\n\n    pip install -U intensional\n\nTo ``easy_install`` under a specific Python version (3.3 in this example)::\n\n    python3.3 -m easy_install --upgrade intensional\n    \n(You may need to prefix these with \"sudo \" to authorize installation. If they're\nalready installed, the ``--upgrade`` flag will be helpful; add it right before the\npackage name.)",
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