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    "info": {
        "author": "John-Paul Jorissen",
        "author_email": "jjorissen52@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: Microsoft :: Windows :: Windows 10",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 3.6",
            "Topic :: Office/Business"
        ],
        "description": "# python-pipeline\n\nThis library allows users to easily wrap functions with a series of decorators to form \nan execution pipeline. \nThis is useful in scenarios where input needs to be cleaned and output needs to be processed in a systematic way.\n\n## Installation\n    pip install execution-pipeline\n\n\n## Usage\nA pipeline consists of four optional segments\n### Pre\nThe `pre` execution segment allows you to modify any input parameters passed to the decorated function. Any functions \npassed to the `pre` segment will always be executed first.\n\n    from pipeline import execution_pipeline\n\n    def do_thing_before(params):\n        params['arg1'] = 'okay'\n        return params\n\n\n    @execution_pipeline(pre=[do_thing_before])\n    def do_thing(arg1=5):\n        return arg1*10\n     \n    \n    \n    do_thing()\n    # okayokayokayokayokayokayokayokayokayokay\n\n\n### Post\nThe `post` execution segment allows you to modify or handle the result after the decorated function has already run.\n\n    def do_thing_after(response):\n        response['added'] = 'yup'\n        return response\n        \n        \n    @execution_pipeline(post=[do_thing_after])\n    def do_thing(**kwargs):\n        return {**kwargs}  # just make a new dictionary using the passed keyword arguments\n        \n        \n    do_thing(apples=2, oranges=3, bananas=0)\n     # {'apples': 2, 'oranges': 3, 'bananas': 0, 'added': 'yup'}\n     \nJust like the other segments, you can use as many post processing functions as you need; they will be executed in the order\nthat they are passed.\n\n\n    def do_another_thing_after(response):\n        assert response['added'] == 'yup' # the one that is first in the pipeline happens first.\n        response['also_added'] = 'also yup'\n        return response\n    \n    \n    @execution_pipeline(post=[do_thing_after, do_another_thing_after])\n    def do_thing(**kwargs):\n        return {**kwargs}\n        \n     \n    do_thing()\n    # {'apples': 2, 'oranges': 3, 'bananas': 0, 'added': 'yup', 'also_added': 'also yup'}\n        \n### Error\nThe `error` execution segment allows you to pass error handling to log, modify, absorb, etc. any exceptions that are \nraised by the wrapped function.\n\n\n    class MyException(BaseException):\n        pass\n        \n    \n    def handle_this_error(e, response=None):\n        print(f\"oh no, Bob! {e}\")\n        return \"Don't worry, we handled a TypeError.\"\n    \n    \n    def handle_that_error(e, response=None):\n        print(f\"oh no, Bob! {e}\")\n        return \"Don't worry, we handled MyException.\"\n        \n    def handle_other_errors(e, response=None):\n        print(f\"? {e}\")\n        return \"Other errors?\"\n        \n    error_handlers = [\n        {\"exception_class\": TypeError, \"handler\": handle_this_error},\n        {\"exception_class\": MyException, \"handler\": handle_that_error},\n        {\"exception_classes\": (Exception, BaseException), \"handler\": handle_other_errors},\n    ]\n    \n    \n    @execution_pipeline(pre=[do_thing_before], post=[do_thing_after], error=error_handlers)\n    def fun_boys(arg1, arg4, arg2, arg3, thing=None):\n        raise MyException('Something went wrong!')\n        \n    \n    result = fun_boys(1, 2, 3, 4, 5)\n    # oh no, Bob! Something went wrong!\n    \n    print(result) \n    # Don't worry, we handled MyException.\n    \nYou can also use class instances instead of dictionaries to define your error handlers if you prefer.\n\n```\nclass ErrorHandler:\n    def __init__(self, exception_class, handler):\n        self.exception_class = exception_class\n        self.handler = handler\n        \n\nerror_handlers = [\n        ErrorHandler(TypeError, handle_this_error),\n        ErrorHandler(MyException, handle_that_error),\n    ]\n```\n    \n### Cache\nThe `cache` execution segment will record all arguments (before and after the `pre` segment) and store the result \n(after the `post` and `error` segments). \n\n    from pipeline.cache.mock import MockCache\n    # MockCache is basically just a dict() with some expiration convenience methods.\n    mock_cache = MockCache()\n    \n    changing_value = 0\n    \n    @execution_pipeline(cache=mock_cache)\n    def fun_boys(arg1, arg4, arg2, arg3, thing=None):\n        return changing_value\n        \n    \n        \n    fun_boys(1, 2, 3, 4, 5)\n    # 0\n    \n    changing_value = 100\n    \n    \n    fun_boys(1, 2, 3, 4, 5)\n    # 0 # ignores the changes ( \u00af\\_(\u30c4)_/\u00af that's caching! ) \n        \n#### Supported Cache Backends\nNote: if the appropriate backend is not installed, they will be replaced with a `MockCache` instance at runtime. This \nis intended to improve portability of pipeline code. \n\n##### Redis\n```\npip install redis\n```\nAnd then same as above except with\n\n    from pipeline.cache.redis import RedisCache\n    redis = RedisCache(host='localhost', port=6379) # defaults\n    \n    \n##### MemCached\n```\npip install memcached\n```\nAnd then the same as above except with\n\n    from pipeline.cache.mem_cache import MemCache \n    mem_cache = MemCache(host='localhost', port=11211) # defaults\n\n",
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