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        "description": "[![Build Status](https://travis-ci.org/marcopaz/pyjo.svg?branch=master)](https://travis-ci.org/marcopaz/pyjo)\n\n# pyjo\n\n> Python JSON Objects\n\nEasily specify and (de)serialize data models in Python.\n\n## Install\n\n```\npip install pyjo\n```\n\n## How to use\n\nFirst you need to create a specification of your models and attributes by using the `Model` and the `Field` classes:\n\n```python\nfrom pyjo import Model, Field, RangeField, EnumField\n\nclass Gender(Enum):\n    female = 0\n    male = 1\n\nclass Address(Model):\n    city = Field(type=str)\n    postal_code = Field(type=int)\n    address = Field()\n\nclass User(Model):\n    name = Field(type=str, repr=True, required=True)\n    age = RangeField(min=18, max=120)\n    #  equivalent to: Field(type=int, validator=lambda x: 18 <= x <= 120)\n    gender = EnumField(enum=Gender)\n    address = Field(type=Address)\n```\n\nBy default any field is considered optional unless specified with `required` attribute. If required, its presence will be checked during initialization.\n\n```python\nu = User()\n# ...\n# pyjo.exceptions.RequiredField: Field 'name' is required\n```\n\n```python\nUser(name='john', age=18, address=Address(city='NYC'))\n# <User(name=john)>\n```\n\nWhen the `type` argument is provided, the type of the value assigned to a field will be checked during initialization and assignment:\n\n```python\nUser(name=123)\n# ...\n# pyjo.exceptions.InvalidType: The value of the field 'name' is not of type str, given 123\n```\n\n```python\nu.address.city = 1\n# ...\n# pyjo.exceptions.InvalidType: The value of the field 'city' is not of type str, given 1\n```\n\nIn order to serialize a model you need to call `to_dict`:\n\n```python\nu = User(name='john', age=18, address=Address(city='NYC'))\n\nprint(u.to_dict())\n# {\n#     \"name\": \"john\",\n#     \"age\": 18,\n#     \"address\": {\n#         \"city\": \"NYC\"\n#     }\n# }\n```\n\nTo create a model starting from its python dictionary representation, use `from_dict`:\n\n```python\nu = User.from_dict({\n        \"name\": \"john\",\n        \"gender\": \"male\",\n        \"age\": 18,\n        \"address\": {\n            \"city\": \"NYC\"\n        }\n    })\n\nprint(u)\n# <User(name=john)>\n\nprint(u.gender)\n# Gender.male\n\nprint(u.address.city)\n# NYC\n```\nNote that `from_dict` will also recreate all the nested pyjo objects.\n\n\n# API Documentation\n\n## Field\nThe `Field` object has several _optional_ arguments:\n\n* `type` specifies the type of the field. If specified, the type will be checked during initialization and assignment\n* `default` specifies the default value for the field. The value of a default can be a function and it will be computed and assigned during the initialization of the object\n* `required` boolean flag to indicate if the field must be specified and can't be `None`, even during assignment (`False` by default)\n* `repr` boolean flag to indicate if the field/value should be shown in the Python representation of the object, when printed (`False` by default)\n* `to_dict`, `from_dict` (functions) to add ad-hoc serialization/deserialization for the field\n* `validator` (function) function that gets called to validate the input (after cast) of a field\n* `cast` (function) cast value of the field (if not empty) before (validation and) assignment\n\n## Model\n\n* `to_dict()`, `from_dict()` serialize/deserialize to/from python dictionaries\n* `to_json()`, `from_json()` shortcuts for `json.dumps(model.to_dict())` and `Model.from_dict(json.loads(<dict>))`\n\n## Field subclasses\n\nYou can easily create subclasses of `Field` to handle specific types of objects. Several of them are already integrated in pyjo and more are coming (feel free to create a PR to add more):\n\n* `ListField` for fields containing a list of elements\n* `RegexField` for fields containing a string that matches a given regex\n* `RangeField` for fields containing a int with optional minimum/maximum value\n* `DatetimeField` for fields containing a UTC datetime\n\n# Extensions\n\n* [pyjo_mongo](https://github.com/marcopaz/pyjo_mongo) easily interact with MongoDB documents, a lightweight replacement of the `mongoengine` library",
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