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        "author": "Guillaume Chorn",
        "author_email": "guillaume.chorn@gmail.com",
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        "description": "# golden-marshmallows\nA better integration between SQLAlchemy and Marshmallow. A little (SQL)alchemy to turn `marshmallow`s into gold.\n\n# Installation\nSimply install with `pip`:\n```\n$ pip install golden-marshmallows\n```\n# Usage\n## Serialization\nTake these SQLAlchemy models as examples:\n```python\nclass WizardCollege(Base):\n    __tablename__ = 'wizard_college'\n    id = Column(Integer, primary_key=True)\n    name = Column(String)\n    alchemists = relationship('Alchemist')\n\n    def __repr__(self):\n        return '<WizardCollege(name={self.name!r})>'.format(self=self)\n\nclass Alchemist(Base):\n    __tablename__ = 'alchemists'\n    id = Column(Integer, primary_key=True)\n    name = Column(String)\n    school_id = Column(Integer, ForeignKey('wizard_college.id'))\n    formulae = relationship('Formula')\n    \n    def __repr__(self):\n        return '<Alchemist(name={self.name!r})>'.format(self=self)\n\nclass Formula(Base):\n    __tablename__ = 'forumulae'\n    id = Column(Integer, primary_key=True)\n    title = Column(String)\n    author_id = Column(Integer, ForeignKey('alchemists.id'))\n\n    def __repr__(self):\n        return '<Formula(title={self.title!r})>'.format(self=self)\n```\nThe `GoldenSchema` class allows quick and easy generation of `marshmallow` schemas that can be used for SQLAlchemy object serialization/deserialization. Simply pass the model class on initialization and you're ready to go:\n```python\nimport json\nfrom golden_marshmallow.schema import GoldenSchema\nfrom models import Alchemist, Formula, WizardCollege\n\nalchemist = Alchemist(name='Albertus Magnus', school_id=1)\nsession.add(alchemist)\nsession.flush()\n\nschema = GoldenSchema(Alchemist)\n\nserialized = schema.dump(alchemist).data\n\nprint(json.dump(serialized, indent=4))\n# {   \n#     \"id\": 1,\n#     \"name\": \"Albertus Magnus\",\n#     \"school_id\": 1\n# }\n```\nThat's it! No need to define your own `Schema` subclass, unless you really want to (more on that below).\n\n## Nested objects\nBut what about this alchemist's formulae? Nested objects can easily be added to the mix by passing in a dictionary mapping each field that contains a nested object (or objects) to the relevant SQLAlchemy class:\n```python\nnested_map = {\n    'formulae': {\n        'class': Formula,\n        'many': True\n    }\n}\n\nformula = Formula(title='transmutation')\nalchemist.formulae.append(formula)\nsession.commit()\n\nschema = GoldenSchema(Alchemist, nested_map=nested_map)\n\nserialized = schema.dump(alchemist).data\n\nprint(json.dump(serialized, indent=4))\n# {\n#     \"id\": 1,\n#     \"name\": \"Albertus Magnus\",\n#     \"school_id\": 1,\n#     \"formulae\" : [\n#         {\n#             \"title\": \"transmutation\"\n#         }\n#     ]\n# }\n```\nIn fact, the `GoldenSchema` class supports arbitrary nesting in this fashion, simply adjust the map as necessary:\n```python\nnested_map = {\n    'alchemists': {\n        'class': Alchemist,\n        'many': True,\n        'nested_map': {\n            'formulae': {\n                'class': Formula,\n                'many': True\n            }\n        }\n    }\n}\n\ncollege = WizardCollege(name='Bogwarts')\ncollege.alchemists.append(alchemist)\nsession.add(college)\nsession.flush()\n\nschema = GoldenSchema(WizardCollege, nested_map=nested_map)\n\nserialized = schema.dump(college).data\n\nprint(json.dump(serialized, indent=4))\n# {\n#     \"id\": 1,\n#     \"name\": \"Bogwarts\",\n#     \"alchemists\": [\n#         {\n#             \"id\": 1,\n#             \"school_id\": 1,\n#             \"name\": \"Albertus Magnus\",\n#             \"formulae\": [\n#                 {\n#                     \"title\": \"transmutation\",\n#                     \"author_id\": 1,\n#                     \"id\": 1\n#                 }\n#             ]\n#         }\n#     ]\n# }\n```\n## Deserialization\nOf course, you can deserialize data into SQLAlchemy objects just as easily:\n```python\n# Start at the end of the last example and work backwards\ndata = {\n    \"id\": 1,\n    \"name\": \"Bogwarts\",\n    \"alchemists\": [\n        {\n            \"formulae\": [\n                {\n                    \"title\": \"transmutation\",\n                    \"author_id\": 1,\n                    \"id\": 1\n                }\n            ],\n            \"school_id\": 1,\n            \"id\": 1,\n            \"name\": \"Albertus Magnus\"\n        }\n    ]\n}\n\ncollege = schema.load(data).data\nprint(college)\n# <WizardCollege(name='Bogwarts')>\nprint(college.alchemists)\n# [<Alchemist(name='Albertus Magnus')>]\nprint(college.alchemists[0].formulae)\n# [<Formula(title='transmutation')>]\n```\n# Extra Features\n## camelCasing/snake_casing\nThe `snake_to_camel` flag allows serde to/from camelCase, for example when serializing Python data into JSON to send as an API Response:\n```python\n# `Formula.author_id` is easily converted to camelCase\nschema = GoldenSchema(Formula, snake_to_camel=True)\n\nserialized = schema.dump(formula).data\n\nprint(json.dumps(serialized, indent=4))\n# Notice `author_id` has become `authorId`\n# {\n#     \"title\": \"transmutation\",\n#     \"authorId\": 1,\n#     \"id\": 1\n# }\n```\nThe same `GoldenSchema` instance, when used to `load` (deserialize) data, will expect camelCased attributes and load them as snake_cased attributes:\n```python\ndata = {\n    \"title\": \"transmutation\",\n    \"authorId\": 1,\n    \"id\": 1\n}\nformula = schema.load(data).data\n\nprint(formula.author_id)\n# 1\n```\nA flag with the opposite behavior, `camel_to_snake`, is also included.\n\nThis feature also works for manually declared fields; that is, fields you yourself declare when subclassing `GoldenSchema` like so:\n```python\nclass MySchema(GoldenSchema):\n    manually_declared = fields.Function(lambda obj: 'my special value')\n    \nmy_schema = MySchema(Formula, snake_to_camel=True)\n\nserialized = schema.dump(formula).data\nprint(json.dumps(serialized, indent=4))\n# `manually_declared` has become camelCase\n# {\n#     \"title\": \"transmutation\",\n#     \"authorId\": 1,\n#     \"id\": 1,\n#     \"manuallyDeclared\": \"my special value\"\n# }\n```\nIn fact, you can use this feature without involving SQLAlchemy at all; just use `CaseChangingSchema`, the parent class of `GoldenSchema`:\n```python\nfrom golden_marshmallows.schema import CaseChangingSchema\n\nclass SnakeSchema(CaseChangingSchema):\n    attr_one = fields.String()\n    attr_two = fields.Integer()\n    \nclass SnakeObj:\n    def __init__(self, attr_one, attr_two):\n        self.attr_one = attr_one\n        self.attr_two = attr_two\n        \nschema = SnakeSchema(snake_to_camel=True)\nobj = SnakeObj('field1', 2)\n\nserialized = schema.dump(obj).data\nprint(json.dumps(serialized, indent=4))\n# {\n#     'attrOne': 'field1',\n#     'attrTwo': 2\n# }\n```\n\n## Copying objects\nAs a minor convenience, you can pass the `new_obj` flag on initialization to indicate that any fields named `id` should be ignored during deserialization:\n```python\nschema = GoldenSchema(Formula, snake_to_camel=True, new_obj=True)\n\ndata = {\n    \"title\": \"transmutation\",\n    \"authorId\": 1,\n    \"id\": 1\n}\n\nnew_formula = schema.load(data).data\nprint(new_formula.title)\n# 'transmutation'\nprint(new_formula.id)  # None\n```\nThis allows you to quickly deserialize data representations of existing objects into new copies.",
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