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        "author": "Simon Knibbs",
        "author_email": "simon.knibbs@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3.9",
            "Topic :: Software Development :: Libraries"
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        "description": "Dataclasses JSON Schema\n=======================\n\n.. image:: https://github.com/s-knibbs/dataclasses-jsonschema/workflows/Tox%20tests/badge.svg?branch=master\n    :target: https://github.com/s-knibbs/dataclasses-jsonschema/actions\n\n.. image:: https://badge.fury.io/py/dataclasses-jsonschema.svg\n    :target: https://badge.fury.io/py/dataclasses-jsonschema\n\n.. image:: https://img.shields.io/lgtm/grade/python/g/s-knibbs/dataclasses-jsonschema.svg?logo=lgtm&logoWidth=18\n    :target: https://lgtm.com/projects/g/s-knibbs/dataclasses-jsonschema/context:python\n    :alt:    Language grade: Python\n\n**Please Note:** This project is in maintenance mode. I'm currently only making urgent bugfixes.\n\nA library to generate JSON Schema from python 3.7 dataclasses. Python 3.6 is supported through the `dataclasses backport <https://github.com/ericvsmith/dataclasses>`_. Aims to be a more lightweight alternative to similar projects such as `marshmallow <https://github.com/marshmallow-code/marshmallow>`_ & `pydantic <https://github.com/samuelcolvin/pydantic>`_.\n\nFeature Overview\n----------------\n\n* Support for draft-04, draft-06, Swagger 2.0 & OpenAPI 3 schema types\n* Serialisation and deserialisation\n* Data validation against the generated schema\n* `APISpec <https://github.com/marshmallow-code/apispec>`_ support. Example below_:\n\nInstallation\n------------\n\n.. code:: bash\n\n    ~$ pip install dataclasses-jsonschema\n\nFor improved validation performance using `fastjsonschema <https://github.com/horejsek/python-fastjsonschema>`_, install with:\n\n.. code:: bash\n\n    ~$ pip install dataclasses-jsonschema[fast-validation]\n\nExamples\n--------\n\n.. code:: python\n\n    from dataclasses import dataclass\n\n    from dataclasses_jsonschema import JsonSchemaMixin\n\n\n    @dataclass\n    class Point(JsonSchemaMixin):\n        \"A 2D point\"\n        x: float\n        y: float\n\n\nSchema Generation\n^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n    >>> pprint(Point.json_schema())\n    {\n        'description': 'A 2D point',\n        'type': 'object',\n        'properties': {\n            'x': {'format': 'float', 'type': 'number'},\n            'y': {'format': 'float', 'type': 'number'}\n        },\n        'required': ['x', 'y']\n    }\n\nData Serialisation\n^^^^^^^^^^^^^^^^^^\n.. code:: python\n\n    >>> Point(x=3.5, y=10.1).to_dict()\n    {'x': 3.5, 'y': 10.1}\n\nDeserialisation\n^^^^^^^^^^^^^^^\n\n.. code:: python\n\n    >>> Point.from_dict({'x': 3.14, 'y': 1.5})\n    Point(x=3.14, y=1.5)\n    >>> Point.from_dict({'x': 3.14, y: 'wrong'})\n    dataclasses_jsonschema.ValidationError: 'wrong' is not of type 'number'\n\nGenerating multiple schemas\n^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n    from dataclasses_jsonschema import JsonSchemaMixin, SchemaType\n\n    @dataclass\n    class Address(JsonSchemaMixin):\n        \"\"\"Postal Address\"\"\"\n        building: str\n        street: str\n        city: str\n\n    @dataclass\n    class Company(JsonSchemaMixin):\n        \"\"\"Company Details\"\"\"\n        name: str\n        address: Address\n\n    >>> pprint(JsonSchemaMixin.all_json_schemas(schema_type=SchemaType.SWAGGER_V3))\n    {'Address': {'description': 'Postal Address',\n                 'properties': {'building': {'type': 'string'},\n                                'city': {'type': 'string'},\n                                'street': {'type': 'string'}},\n                 'required': ['building', 'street', 'city'],\n                 'type': 'object'},\n     'Company': {'description': 'Company Details',\n                 'properties': {'address': {'$ref': '#/components/schemas/Address'},\n                                'name': {'type': 'string'}},\n                 'required': ['name', 'address'],\n                 'type': 'object'}}\n\n\nCustom validation using `NewType <https://docs.python.org/3/library/typing.html#newtype>`_\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n    from dataclasses_jsonschema import JsonSchemaMixin, FieldEncoder\n\n    PhoneNumber = NewType('PhoneNumber', str)\n\n    class PhoneNumberField(FieldEncoder):\n\n        @property\n        def json_schema(self):\n            return {'type': 'string', 'pattern': r'^(\\([0-9]{3}\\))?[0-9]{3}-[0-9]{4}$'}\n\n    JsonSchemaMixin.register_field_encoders({PhoneNumber: PhoneNumberField()})\n\n    @dataclass\n    class Person(JsonSchemaMixin):\n        name: str\n        phone_number: PhoneNumber\n\nFor more examples `see the tests <https://github.com/s-knibbs/dataclasses-jsonschema/blob/master/tests/conftest.py>`_\n\n.. _below:\n\nAPISpec Plugin\n--------------\n**New in v2.5.0**\n\nOpenAPI & Swagger specs can be generated using the apispec plugin:\n\n.. code:: python\n\n    from typing import Optional, List\n    from dataclasses import dataclass\n\n    from apispec import APISpec\n    from apispec_webframeworks.flask import FlaskPlugin\n    from flask import Flask, jsonify\n    import pytest\n\n    from dataclasses_jsonschema.apispec import DataclassesPlugin\n    from dataclasses_jsonschema import JsonSchemaMixin\n\n\n    # Create an APISpec\n    spec = APISpec(\n        title=\"Swagger Petstore\",\n        version=\"1.0.0\",\n        openapi_version=\"3.0.2\",\n        plugins=[FlaskPlugin(), DataclassesPlugin()],\n    )\n\n\n    @dataclass\n    class Category(JsonSchemaMixin):\n        \"\"\"Pet category\"\"\"\n        name: str\n        id: Optional[int]\n\n    @dataclass\n    class Pet(JsonSchemaMixin):\n        \"\"\"A pet\"\"\"\n        categories: List[Category]\n        name: str\n\n\n    app = Flask(__name__)\n\n\n    @app.route(\"/random\")\n    def random_pet():\n        \"\"\"A cute furry animal endpoint.\n        ---\n        get:\n          description: Get a random pet\n          responses:\n            200:\n              content:\n                application/json:\n                  schema: Pet\n        \"\"\"\n        pet = get_random_pet()\n        return jsonify(pet.to_dict())\n\n    # Dependant schemas (e.g. 'Category') are added automatically\n    spec.components.schema(\"Pet\", schema=Pet)\n    with app.test_request_context():\n        spec.path(view=random_pet)\n\nTODO\n----\n\n* Add benchmarks against alternatives such as `pydantic <https://github.com/samuelcolvin/pydantic>`_ and `marshmallow <https://github.com/marshmallow-code/marshmallow>`_\n\n\n",
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