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        "description": "# argcheck\r\n\r\nA decorator based implementation of argument checks, whose code is largely referenced from [zipline/utils/input_validation](https://github.com/quantopian/zipline/blob/master/zipline/utils/input_validation.py), provides various functionality in argument validation.\r\n* *expect_kinds*: decorator to check argument dtype kinds\r\n* *expect_types*: decorator to check argument types\r\n* *optional*: helper of *expect_types* to deal with default argument\r\n* *expect_element*: decorator to check if argument takes a value in expected set of elements\r\n* *expect_bounded* and *expect_strictly_bounded*: decorators to check argument lies inclusively or exclusively within the bounds\r\n* *expect_dimensions*: decorator to check if argument takes in a numpy array with a specific dimensionality\r\n* *coerce* and *coerce_types*: decorators that deal with type coercions\r\n\r\n\r\n### Usage\r\n``` python\r\nfrom argcheck import *\r\n```\r\n\r\n### Install\r\n\r\n``` python\r\npip install argcheck\r\n```\r\n\r\n### Example\r\n\r\n\r\n\r\n##### *expect_kinds*: decorator that verifies inputs have expected dtype kinds\r\n``` python\r\nfrom numpy import int64, int32, float32\r\n\r\n@expect_kinds(x='i')\r\ndef foo(x):\r\n    return x\r\n\r\nfoo(int64(2))  # 2\r\nfoo(int32(2))  # 2\r\nfoo(float32(2))\r\n# Traceback (most recent call last):\r\n#       ...\r\n# TypeError: ...foo() expected a numpy object of kind 'i' for argument 'x',\r\n# but got 'f' instead.\r\n\r\n```\r\n\r\n##### *expect_types*: decorator that verifies inputs have expected types\r\n\r\n``` python\r\n@expect_types(x=int, y=str)\r\ndef foo(x, y):\r\n    return x, y\r\n\r\n\r\nfoo(2, '3')  # (2, '3')\r\n\r\n\r\n# foo(2.0, '3')\r\n# Traceback (most recent call last):\r\n# ...\r\n# TypeError: ...foo() expected a value of type int for argument 'x',\r\n# but got float instead.\r\n\r\n\r\n```\r\n\r\nworks on class member functions with default argument as well\r\n\r\n``` python\r\nclass test(object):\r\n    @expect_types(y=(int, str))\r\n    def __init__(self, x, y=3):\r\n        pass\r\n\r\n\r\ntest(x=3)  # OK\r\ntest(x=3, y=5) # OK\r\ntest(x=1, y=[3])\r\n# Traceback (most recent call last):\r\n# ...\r\n# TypeError: __init__() expected a value of type int or str for argument 'y',\r\n# but got list instead\r\n\r\n```\r\n\r\n\r\n##### *optional*: helper for use with *expect_types* when an input can be `type_` or `None`.\r\n\r\n``` python\r\nisinstance({}, optional(dict))  # True\r\nisinstance(None, optional(dict))  # True\r\nisinstance(1, optional(dict))  # False\r\nisinstance(1, optional(dict, int))  # True\r\n```\r\n``` python\r\n\r\n# used with expect_types\r\nclass test2(object):\r\n    @expect_types(y=optional(int, str))\r\n    def __init__(self, x, y=None):\r\n        pass\r\n\r\n\r\ntest2(3)  # OK\r\ntest2(3, [2])\r\n# TypeError: __init__() expected a value of type int or str or NoneType for argument 'y',\r\n# but got list instead.\r\n\r\n```\r\n\r\n\r\n##### *expect_element*: decorator that verifies inputs are elements of some expected collection\r\n\r\n``` python\r\n@expect_element(x=('a', 'b'))\r\ndef foo(x):\r\n    return x.upper()\r\n\r\nfoo('a')  # 'A'\r\nfoo('c')\r\n# ValueError: foo() expected a value in ('a', 'b') for argument 'x',\r\n# but got 'c' instead.\r\n```\r\n\r\n\r\n##### *expect_bounded*: decorator verifying that inputs fall INCLUSIVELY between bounds\r\n* Bounds should be passed as a pair of ``(min_value, max_value)``.\r\n    ``None`` may be passed as ``min_value`` or ``max_value`` to signify that\r\n    the input is only bounded above or below.\r\n\r\n``` python\r\n@expect_bounded(x=(1, 5))\r\ndef foo(x):\r\n    return x + 1\r\n\r\n\r\nfoo(3)  # 4\r\nfoo(6)\r\n# ValueError: foo() expected a value inclusively between 1 and 5 for argument 'x',\r\n# but got 6 instead\r\n\r\n\r\n```\r\n\r\n\r\n``` python\r\n@expect_bounded(x=(1, None))\r\ndef foo(x):\r\n    return x + 1\r\n\r\n\r\nfoo(3)  # 4\r\nfoo(0)\r\n# ValueError: foo() expected a value greater than or equal to 1 for argument 'x',\r\n# but got 0 instead.\r\n\r\n\r\n```\r\n\r\n\r\n##### *expect_strictly_bounded*: decorator verifying that inputs fall EXCLUSIVELY between bounds\r\n\r\n``` python\r\n\r\n@expect_strictly_bounded(x=(1, 5))\r\ndef foo(x):\r\n    return x + 1\r\n\r\nfoo(5)\r\n# ValueError: foo() expected a value exclusively between 1 and 5 for argument 'x',\r\n# but got 5 instead.\r\n```\r\n\r\n\r\n##### *expect_dimensions*: decorator that verifies inputs are numpy arrays with a specific dimensionality\r\n\r\n``` python\r\n\r\nfrom numpy import array\r\n\r\n@expect_dimensions(x=1, y=2)\r\ndef foo(x, y):\r\n    return x[0] + y[0, 0]\r\n\r\nfoo(array([1, 1]), array([[1, 1], [2, 2], [3, 4]])) #ok\r\nfoo(array([1, 1]), array([[1, 1], [2, 2]]))  # ok\r\nfoo(array([1, 1]), array([1, 1]))\r\n\r\n# ValueError: foo() expected a 2-D array for argument 'y',\r\n# but got a 1-D array instead.\r\n\r\n```\r\n\r\n##### *coerce*: decorator that coerces inputs of a given type by passing them to a callable\r\n\r\n``` python\r\n@preprocess(x=coerce(str, int, base=2), y=coerce(str, int, base=2))\r\ndef add_binary_strings(x, y):\r\n    return bin(x + y)[2:]\r\n\r\nprint add_binary_strings('101', '001')  # 110\r\n\r\n\r\n```\r\n\r\n\r\n##### *coerce_types*: decorator that applies type coercions\r\n* input param: dict[str -> (type, callable)]\r\n    * Keyword arguments mapping function parameter names to pairs of\r\n         (from_type, to_type)\r\n\r\n``` python\r\n@coerce_types(x=(float, int), y=(int, str))\r\ndef func(x, y):\r\n    return (x, y)\r\n\r\nfunc(1.0, 3)   # (1, '3')\r\n\r\n```\r\n\r\nPlease see [example](/argcheck/example.py) for details",
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