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        "author_email": "chris@freelancer.com",
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        "description": "*****************************************************\npavlova: simplified deserialization using dataclasses\n*****************************************************\n\n**pavlova** is a library that assists in mapping an unknown input into a\ndataclass.\n\n.. code-block:: python\n\n    from datetime import datetime\n    from dataclasses import dataclass\n\n    from pavlova import Pavlova\n\n\n    @dataclass\n    class Input:\n        id: int\n        name: str\n        date: datetime\n\n\n    Pavlova().from_mapping({\n        'id': 10,\n        'name': 100\n        'date': '2018-08-10',\n    }, Input)\n    # Input(id=10, name='100', date=datetime.datetime(2018, 8, 10, 0, 0))\n\n\nPavlova was born out of frustration with the lack of typing support for\nexisting deserialization libraries. With the introduction of dataclasses in\nPython 3.7, they seemed like the perfect use for defining a deserialization\nschema.\n\n\nSupported functionality\n#######################\n\nParsing of booleans, datetimes, floats, ints, strings, decimals, dictionaries,\nenums, lists are currently supported.\n\nThere are more parsers to come, however to implement your own custom parser,\nsimply implement `PavlovaParser` in `pavlova.parsers`, and register it with the\nPavlova object with the `register_parser` method.\n\nInstallation\n############\n\n.. code-block:: shell\n\n    pip install pavlova\n\nUsage with Flask\n################\n\n.. code-block:: python\n\n    from dataclasses import dataclass, asdict\n\n    from flask import Flask, jsonify\n    from pavlova.flask import FlaskPavlova\n\n    pavlova = FlaskPavlova()\n    app = Flask(__name__)\n\n    @dataclass\n    class SampleInput:\n        id: int\n        name: str\n\n    @app.route('/post', methods=['POST'])\n    @pavlova.use(SampleInput)\n    def data(data: SampleInput):\n        data.id = data.id * len(data.name)\n        return jsonify(asdict(data))\n\n\n    app.run()\n\nAdding Custom Types\n###################\n\nThere are a couple of different ways to implement new types for parsing in\npavlova. In general, the process is to add a parser a specific type. For\nvalidation you should raise a TypeError or ValueError.\n\nThe first one, is creating a new type that extends an existing base type. Here\nis an example on how to implement an Email type, which is a string but performs\nvalidation.\n\n.. code-block:: python\n\n    from pavlova import Pavlova\n    from pavlova.parsers import GenericParser\n\n    class Email(str):\n        def __new__(cls, input_value: typing.Any) -> str:\n            if isinstance(input_value, str):\n                if '@' in input_value:\n                    return str(input_value)\n                raise ValueError()\n            raise TypeError()\n\n    pavlova = Pavlova()\n    pavlova.register_parser(Email, GenericParser(pavlova, Email))\n\nAnother way, is to implement your own pavlova parser, rather than using your\nthe built in `GenericParser` parser.\n\n.. code-block:: python\n\n    import datetime\n    from typing import Any, Tuple\n\n    import dateparser\n    from pavlova import Pavlova\n    from pavlova.parsers import PavlovaParser\n\n    class DatetimeParser(PavlovaParser[datetime.datetime]):\n        \"Parses a datetime\"\n\n        def parse_input(self,\n                        input_value: Any,\n                        field_type: Type,\n                        path: Tuple[str, ...]) -> datetime.datetime:\n            return dateparser.parse(input_value)\n\n    pavlova = Pavlova()\n    pavlova.register_parser(datetime.DateTime, DatetimeParser(pavlova))\n\nRequirements\n############\n\nPavlova is only supported on Python 3.6 and higher. With Python 3.6, it will\ninstall the `dataclasses <https://github.com/ericvsmith/dataclasses>`__ module.\nWith Python 3.7 and higher, it will use the built-in dataclasses module.\n\nLicense\n~~~~~~~\n\nGNU LGPLv3. Please see `LICENSE <LICENSE>`__ and\n`COPYING.LESSER <COPYING.LESSER>`__.\n\n\n",
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