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        "author": "Eugene Eeo",
        "author_email": "packwolf58@gmail.com",
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        "description": "promises\n========\n\n.. image:: https://travis-ci.org/eugene-eeo/promises.png?branch=master\n    :target: https://travis-ci.org/eugene-eeo/promises\n\n.. image:: http://pypip.in/v/Promises/badge.png\n    :target: https://pypy.python.org/pypi/Promises\n\n.. image:: https://pypip.in/d/Promises/badge.png\n    :target: https://pypi.python.org/pypi/Promises/\n\nPromises is a little Python toolkit for\nmaintaining some sanity in dynamically\ntyped languages. You can easily declare\nthe signature of your functions using\ndecorators::\n\n    from promises import accepts, returns\n\n    # implement f(x) = x + 1\n    @accepts(int)\n    @returns(int)\n    def f(x):\n        return x+1\n\n--------\nLike it?\n--------\n\nYou can install the library via pip\nor just clone the github repository\nin order to use it in your project::\n\n    $ pip install Promises\n\n-------------\nDocumentation\n-------------\n\nThe new ``promises`` library exposes\nvery few functions which are deemed\nnecessary, and I will add more in the\nfuture should the demand for them be\npresent.\n\n~~~~~~~\naccepts\n~~~~~~~\n\nThe decorator takes an arbitrary\nnumber of positional and keyword\narguments, which types will be used\nto test against the passed-in objects.\nVariable-name mapping is done\nautomatically, so no worries.\n\n.. code-block:: python\n\n    @accepts(list, int)\n    def inc_last(array, inc=1):\n        if len(array) == 0:\n            array.append(0)\n        array[-1] += inc\n\nNote that you can now use traits\nas parts of the accepted types,\nso you do not need separate\ndecorators in order to use the\ntraits system:\n\n.. code-block:: python\n\n    @accepts(list, Each(int))\n    def append_integers(array, *nums):\n        for item in nums:\n            array.append(item)\n\n~~~~~~~\nreturns\n~~~~~~~\n\nDeclares that the decorated function\nwill return a certain type, for\nexample:\n\n.. code-block:: python\n\n    @returns(int, float)\n    def div(x,y):\n        return x/y\n\nStarting from 0.6.18 ``returns``\nwill start to support the usage\nof traits. Note, to support return\nfunctions that iterate through\ntuples, you can do the following:\n\n.. code-block:: python\n\n    from promises.trait.spec import Sequence\n\n    @returns(Sequence(int, bool))\n    def is_zero(x):\n        x = int(x)\n        return x, x == 0\n\n~~~~~~~\nrejects\n~~~~~~~\n\nLogical complement of the ``accepts``\nfunction, will raise a TypeError if\nthe passed in objects correspond to\nthe required types. For example, to\nimplement a grouping function that\nforces the user to cover all possible\ncases:\n\n.. code-block:: python\n\n    @rejects(defaultdict)\n    def group(g, datum):\n        registry = defaultdict(list)\n        for item in datum:\n            for group, match in g.items():\n                if match(item):\n                    registry[group].append(item)\n        return registry\n\n~~~~~~\nkwonly\n~~~~~~\n\nDeclares that the function will require\nthe given keyword arguments when calling,\nif and only if they were captured by the\nkeyword arguments, meaning you'll have\nto define some defaults.\n\n.. code-block:: python\n\n    @requires('config')\n    def lint(config=\"filename\"):\n        # do something here\n\nNote: If you are using Python 3, the better\nway would be to use the \"*\" symbol, like\nthe following:\n\n.. code-block:: python\n\n    def lint(*, config=\"filename\"):\n        # do something here\n\nAs it will provide the same functionality\nas the requires decorator. However you\nreally want to force the use of keyword\narguments, you can use the ``requires``\ndecorator.\n\n~~~~~~~~\nrequires\n~~~~~~~~\n\nDeclares that the function will require\none or more keyword arguments when invoked\nregardless if they were captured. This is\na forced variant of the ``kwonly`` decorator.\nFor example:\n\n.. code-block:: python\n\n    class CombineTrait(Trait):\n        combine = Method(\"combine\")\n\n    @accepts(CombineTrait)\n    @requires(\"x\", \"y\")\n    def combine(x, y):\n        return x.combine(y)\n\nAnother captured-variable variant of the\ndecorator is the ``kwonly`` decorator. It\nis recommended over this if you want to\nset default variables but only check captured\nones.\n\n~~~~~~\nthrows\n~~~~~~\n\nDeclares that the function can only throw\nthe specified exceptions, for example:\n\n.. code-block:: python\n\n    @accepts(float, float)\n    @throws(ZeroDivisionError)\n    def divide(x,y):\n        return x/y\n\nThis is good for debugging or development\nwhen you want to make sure that your\nfunction throws the given exceptions.\n\n-----------------------\nSingle dispatch methods\n-----------------------\n\nIn Python 3, the ``functools`` library\nincludes the ``singledispatch`` method,\nwhich accepts an argspec and then makes\ncallables which, different ones can be\ncalled based on their type. Using that\nit's possible to build PEP443_-style\ngeneric dispatched functions. For\nexample:\n\n.. code-block:: python\n\n    from promises.trait.spec import Number\n    from functools import singledispatch\n\n    @singledispatch\n    def method(x):\n        pass\n\n    @method.register(float)\n    @method.register(int)\n    def _(x):\n        return x*2\n\nKeep in mind that single-dispatch\ngeneric functions do come at a cost,\nespecially if they are done so at\nruntime, unless you use a JIT like\nPyPy. Also, they do not work with\nthe traits in ``promises`` since\nthe functions do not use ``isinstance``\nas a means of type checking.\n\n.. _PEP443: https://www.python.org/dev/peps/pep-0443\n\nIf you need traits when dispatching\nfunctions you can use the following\npattern:\n\n.. code-block:: python\n\n    from promises.trait.spec import Number\n    from promises.dispatch import singledispatch\n\n    @singledispatch(\"x\")\n    def f(x, y):\n        pass\n\n    @f.register(Number)\n    def _(x, y):\n        return x+y\n\n    @f.register(str)\n    def _(x, y):\n        return str(x) + y\n\nThe semantics are almost the same as\nthe standard library dispatch function\nexcept for the fact that it can dispatch\naccording to a given argument instead of\nthe first argument, reducing the need\nfor arg-swapping helper functions.\n\n-----------------\nRunning the tests\n-----------------\n\nYou can also run the test suite for\nthe current version of the promises\nlibrary by running the command below::\n\n    $ git clone ssh://git@github.com/eugene-eeo/promises\n    $ python promises/tests.py",
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