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        "author": "Enrico Bacis",
        "author_email": "enrico.bacis@gmail.com",
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        "description": "Description\n-----------\n\n*memoize* and *keyed-memoize* decorators.\n\n- ``memo``: The classical *memoize* decorator. It keeps a cache\n  ``args -> result`` so you don't continue to perform the same computations.\n\n- ``keymemo(key)``: This decorator factory act as ``memo`` but it permits to\n  specify a ``key`` function that takes the ``args`` of the decorated function\n  and computes a ``key`` value to use as key in the cache dictionary. This way\n  you can for example use a single value of a dictionary as key of the cache,\n  or apply a function before passing something to the cache.\n\n- ``instancememo``: The classical *memoize* decorator that can be applied to\n  class functions. It keeps a cache ``args -> result`` so you don't continue\n  to perform the same computations. The cache is kept in the class namespace.\n\n- ``instancekeymemo(key)``: This decorator factory works like a combination of\n  ``instancememo`` and ``keymemo``, so it allows to specify a function that\n  generate the cache key based on the function arguments and can be applied\n  to class functions.\n\n\nUsage\n-----\n\n.. code:: python\n\n    from memo import memo\n\n    @memo\n    def fibonacci(n):\n        if n <= 2:\n            return 1\n        return fibonacci(n-1) + fibonacci(n-2)\n\n.. code:: python\n\n    from memo import keymemo\n\n    @keymemo(lambda tup: tup[0])\n    def function(tup):\n        # build a cache based on the first value of a tuple\n        ...\n\nInstallation\n------------\n\nThe package has been uploaded to `PyPI`_, so you can install it with pip:\n\n    pip install python-memo\n\n\n.. _PyPI: https://pypi.python.org/pypi/python-memo\n",
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        "keywords": "memo memoize dynamic programming cache decorator instance",
        "license": "MIT",
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        "summary": "Decorators to memoize function results (also for classes)",
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