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        "author": "Tim Heap",
        "author_email": "tim@timheap.me",
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            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
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        "description": "===========\nvaledictory\n===========\n\n.. image:: https://badge.fury.io/py/valedictory.svg\n    :target: https://pypi.org/project/valedictory/\n.. image:: https://readthedocs.org/projects/valedictory/badge/?version=latest\n    :target: https://valedictory.readthedocs.io/en/latest/\n\nValedictory validates the data in ``dict``\\s.\nIt is designed for use in API validation,\nand other situations where you are receiving structured JSON data\nas opposed to key-value POST form data.\nIt takes in a dict of data (probably obtained from a JSON POST request),\nand validates that data against some fields.\n\nValidators are defined as classes.\nDeclare fields on a Validator class.\nOnce constructed, Validator instances are immutable.\n\n.. code-block:: python\n\n    from valedictory import Validator, fields, InvalidDataException\n\n    class PersonValidator(Validator):\n        name = fields.CharField()\n        height = fields.IntegerField()\n        date_of_birth = fields.DateField()\n\n    person_validator = PersonValidator()\n\nA Python dict can be checked to see if it conforms to this validator.\nThe dict can come from a JSON POST request, or a configuration file,\nor any other external data source that needs validation and cleaning.\nThe cleaned data will be returned.\nValidator classes will return a dict of cleaned data.\nEach field type may transform the data as part of cleaning it.\nFor example, the ``DateField`` will transform the data\ninto a ``datetime.date`` instance.\n\n.. code-block:: python\n\n    input_data = json.loads(request.body)\n\n    try:\n        # cleaned_data will be a dict of cleaned, validated data\n        cleaned_data = person_validator.clean(input_data)\n\n        # Do something with the returned data\n        Person.objects.create(**cleaned_data)\n\n    except InvalidDataException as errors:\n        # The data did not pass validation\n        for path, message in errors.flatten():\n            # This will print something like 'name: This field is required'\n            print(\"{0}: {1}\".format('.'.join(path), message))\n\nValidators can be nested, allowing dicts of arbitrary complexity:\n\n.. code-block:: python\n\n    class ArticleValidator(Validator):\n        content = fields.CharField()\n        published = fields.DateTimeField()\n        author = fields.NestedValidator(PersonValidator())\n        tags = fields.ListField(fields.CharField())\n\n    # Some example data that would pass validation:\n    data = {\n        \"content\": \"An interesting article\",\n        \"published\": \"2018-06-13T1:44:00+10:00\",\n        \"author\": {\n            \"name\": \"Alex Smith\",\n            \"height\": 175,\n            \"date_of_birth\": \"1990-03-26\",\n        },\n        \"tags\": [\"humour\", \"interesting\", \"clickbait\"],\n    }\n\n`Read the documentation for more information <https://valedictory.readthedocs.io>`_.",
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