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        "author": "Shezad Khan",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# required: Easy multi-field validation\n\n[![PyPI](https://img.shields.io/pypi/v/required.svg)]()\n[![Build Status](https://travis-ci.org/shezadkhan137/required.svg?branch=master)](https://travis-ci.org/shezadkhan137/required)\n[![Coverage Status](https://coveralls.io/repos/github/shezadkhan137/required/badge.svg?branch=master)](https://coveralls.io/github/shezadkhan137/required?branch=master)\n\nRequired is a simple library which allows you to validate dependencies\nacross multiple fields. The goal is to make writing things like forms, seralizers and functions much easier by providing a declarative way to encode validation logic. It aims to:\n\n-  Have a declarative way to encode validation logic\n-  Allow you to maintain validation logic easily\n-  Allow you to reuse your validation logic easily\n-  Be flexible with what you want to validate\n\nIf this all sounds good. Read On!\n\n## Installation \n\nInstall using `pip`\n\n```\npip install required\n```\n\n## Quickstart\n\nYou can use required in a number of ways. The easiest way is to use the `validate` decorator to validate inputs to function calls. \n\n```python\nfrom required import validate\n\n@validate\ndef calculate_sum(positive_number, negative_number):\n    \"\"\"\n    positive_number -> positive_number > 0\n    negative_number -> negative_number < 0\n    \"\"\"\n    return positive_number + negative_number\n\n# the following will raise a validation exception\ncalculate_sum(1, 1)\n\n# this will pass validation\ncalculate_sum(1, -1) # 0\n```\n\nIf you want to have other information in the docstring, the validation rules can be wrapped inside of `Requires { }` as shown below:\n\n```python\n\n@validate\ndef calculate_sum(positive_number, negative_number):\n    \"\"\"\n    Other documentation relating to calculate_sum\n\n    Requires {\n        positive_number -> positive_number > 0\n        negative_number -> negative_number < 0\n    }\n\n    You can also put information after the requires rules\n    \"\"\"\n    return positive_number + negative_number\n```\n\nValidation rules are written in the doc string of the function. They look like: \n\n`[param] -> [expression_1] [comparator] [expression_2]`\n\nWhen `param` is present, it requires `expression_1 [comparator] expression_2` to evaluate to true. \n\nThe most simple expressions are just variables passed into the function to validate, however they can be more complex. See cookbook for more examples.\n\nThe comparator can be one of the standard python comparator operations; `==`, `!=`, `in`, `>=` `<=`, `>`, `<`.\n\n\n\n## Cookbook\n\nThe following shows some examples for writing validation rules\n\n```\n\n# Arithmetic on the objects follow normal maths rules.\n# you need to put brackets to define expressions\nx -> (x + 1) < 1\nx -> (x - y) == 1\n\n# A value `x` needs to be in an array\nx -> x in arr\n\n# The length of x must be 10\n# see section on registering functions\nx -> len(x) == 10\n\n# The length of x and y must be the same\nx -> len(x) == len(y)\n\n# when x is present y must not be present\n# TODO: not implemented in DSL yet\nx -> x == <empty>\n\n# x must be equal to the return value of a function\nx -> x == func(x)\n\n# Partial dependencies can be also specified\n\n# when x == 1 then y must be 2\nx == 1 -> y == 2\n\n# when x == 1 then y must be set\nx == 1 -> y\n```\n\n## Registering callables\n\nYou can register callables into the validation scope. This is useful when you want to call normal python builtins or custom callables. \n\n```python\nfrom required import validate\n\nscoped_validate = validate.register_callables({\n \"len\": len,\n \"abs\": abs\n})\n\n@scoped_validate\ndef return_first_element(arr):\n    \"\"\"\n    arr -> len(arr) >= 1\n    \"\"\"\n    return arr[0]\n\n\n# validation scoped callables can also be nested\n# len, abs and my_func are available in the second_scoped_validate decorator\n\nsecond_scoped_validate = scoped_validate.register_callables({\n    \"my_func\": my_func\n})\n\n# or inserted at function level\n@validate(callable_dict={\"new_func\": new_func})\ndef other_function(var):\n    \"\"\"\n    var -> new_func(var) >= 1\n    \"\"\"\n    return var\n```\n\n\n## Contributing \n\nIf you want to contribute you are most welcome! This project is distributed under the [MIT](https://choosealicense.com/licenses/mit/) licence. It is tested using [tox](https://pypi.python.org/pypi/tox) against Python 2.7 and 3.4+\n\n\n",
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