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        "author": "Corbin Simpson",
        "author_email": "cds@corbinsimpson.com",
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        "description": "=========\nBlackjack\n=========\n\nBlackjack is a simple implementation of the classic red-black tree as a\nstandard Python data structure. A set and a dictionary are included::\n\n    >>> from blackjack import BJ, Deck\n    >>> bj = BJ()\n    >>> bj\n    BJ([])\n    >>> bj.add(42)\n    >>> 42 in bj\n    True\n    >>> d = Deck()\n    >>> d[1] = 2\n    >>> d[3] = 4\n    >>> d.keys()\n    [1, 3]\n    >>> d.values()\n    [2, 4]\n\nUsage\n=====\n\nBlackjacks and decks behave just like normal Python sets and dictionaries, but\nhave different performance characteristics and different requirements for\nkeys. All keys must be comparable, but need not be hashable::\n\n    >>> bj = BJ()\n    >>> bj.add([1])\n    >>> bj.add([1,2])\n    >>> bj.add([1,2,3])\n    >>> bj\n    BJ([[1], [1, 2], [1, 2, 3]])\n\nThis does impact heterogeneity somewhat, but shouldn't be a problem for most\ncommon uses. On the other hand, the average and worst-case times for access,\nmembership testing, insertion, and deletion are all logarithmic, which makes\nblackjacks ideal for storing mappings of data with untrusted keys::\n\n    $ python -mtimeit \\\n    > -s 'l = [(x*(2**64 - 1), hash(x*(2**64 - 1))) for x in xrange(10000)]' \\\n    > 'dict(l)'\n    10 loops, best of 3: 4.11 sec per loop\n    $ python -mtimeit \\\n    -s 'l = [(x*(2**64 - 1), hash(x*(2**64 - 1))) for x in xrange(10000)]\n    from blackjack import Deck' \\\n    'Deck(l)'\n    10 loops, best of 3: 2.07 sec per loop\n\nEven on small- to medium-sized sets of data, blackjacks quickly become more\neffective than dictionaries in the face of untrusted input.\n\nThis package only contains the ``blackjack`` module; tests are in the module\nand may be run with any standard test runner::\n\n    $ trial blackjack | tail -n1\n    PASSED (successes=17)\n\nTechnical information\n=====================\n\nThe specific trees used are left-leaning red-black trees. Red children are\nopportunistically reduced during balancing if nodes will be recreated anyway;\nthis tends to shorten overall tree height by reducing the number of red\nchildren. Complexities are as follows:\n\n==========  ========== ========\nOperation   Time       Space\n==========  ========== ========\nLookup      O(log n)   O(1)\nMembership  O(log n)   O(1)\nInsertion   O(log n)   O(log n)\nDeletion    O(log n)   O(log n)\nUpdate      O(log n)   O(log n)\nSort        O(1)       O(1)\nLength      O(1)       O(1)\n==========  ========== ========\n\nSorting according to the provided key function is constant because the tree's\ntraversal order is presorted. Length is recorded and updated on mutation.\nNodes are persistent and altering the tree generally requires a logarithmic\nspace commitment to create new nodes.",
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