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    "info": {
        "author": "Avi SZYCHTER",
        "author_email": "xentsc2@gmail.com",
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        "description": "EasyJSONParser, a lightweight library for parsing and validating your\nJSON models\n=================================================================================\n\n[![Build Status](https://travis-ci.com/xatavian/easyjsonparser.svg?branch=master)](https://travis-ci.com/xatavian/easyjsonparser)\n\n\nModern Web frameworks all use ORM systems in order to achieve the\nserver-side actions on the database: for instance, Doctrine for Symfony3\n(PHP), Mongoose for MongoDB databases.\n\nORMs have the ability to read data and convert it into a collections of\nin-memory objects. They can also take a special object and convert it\nto the underlying storage format.\n\n**Motivation behind this project**\n\nWhile ORM-like systems are pretty common in Web development, they are\ninexistant for all other purposes. For instance, parsing a configuration\nfile (yeah, that's why I designed this library !) and validating its\ncontent, or outputting the configuration in a file.\n\n# Dependencies\n\nEasyJSONParser only uses the Python's standard library, especially the\n[`json`](https://docs.python.org/fr/3/library/json.html) module which\nserialize and unserialize the source/target data.\n\nEasyJSONParser is built for Python 3.\n\n# Installation\n\nGet the library from Pypi:\n\n```bash\n> pip install easyjsonparser\n```\n\n# Basic examples\n\nIn this section, we will define dummy data structure `User` which follows the following schema:\n\n```json\n{\n    \"id\": \"xxx047AD_\",\n    \"username\": \"jsonisnice\",\n    \"age\": 16,\n    \"isAdmin\": false\n}\n```\n\n`EasyJSONParser` can extract parse the data string into an object with the following code:\n\n```python\nimport easyjsonparser as ejp\nfrom easyjsonparser.document import JSONObjectDocument\n\nclass User(JSONObjectDocument):\n    id = ejp.String()\n    username = ejp.String()\n    age = ejp.Integer()\n    isAdmin = ejp.Boolean()\n\n# In this example, the data string is directly\n# written in the code but it can have\n# various source such as reading a file...\ndata_string = \"\"\"\n...\n\"\"\"\n\nuser = User.loads(data_string)\n```\n\nWhat if you already have loaded your data into a dictionary ? Then, just use `load` instead of `loads`:\n\n```python\nuser = User.load({\n    \"id\": \"xxx047AD_\",\n    \"username\": \"jsonisnice\",\n    \"age\": 16,\n    \"isAdmin\": False\n})\n```\n\nWe can get a representation of our user instance by just printing it:\n```python\n>>> print(user)\n<JSON UserInstance: {\"id\": <JSON Value StringInstance: \"xxx047AD_\">,\n \"username\": <JSON Value StringInstance: \"jsonisnice\">, \"age\": <JSON\n  Value IntegerInstance: 16>, \"isAdmin\": <JSON Value BoolInstance: False>}>\n```\n\nWhat if we now want to parse a list of `User` ?\n\n```python\nfrom easyjsonparser.document import JSONAArrayDocument\n\nclass UserList(JSONArrayDocument);\n    schema = User()\n\nusers = UserList.load([\n    { ... },\n    { ... }\n])\n```\n\nDocument-type objects (ie. the ones that inherit from either `JSONObjectDocument` or `JSONArrayDocument`) can be mixed into one another.\n\nThey also are a variety of types but but the top-level data structure must **always** be either a `JSONObjectDocument` or `JSONArrayDocument`.\n\nWell, now what if we want to turn it back to a JSON string ? Let's use `to_json()` !\n\n```python\n>>>  user.to_json()\n'{\"id\": \"xxx047AD_\", \"username\": \"jsonisnice\", \"age\": 16, \"isAdmin\": False}'\n```\n# Documents\n\nDocuments are classes that inherits from either `JSONObjectDocument` or `JSONArrayDocument`. They are special kinds of `Object` and `Array` because they contain class methods used for parsing the data. They behave like normal `Object` and `Array` but have the capacity of being a top-level data structure.\n\nThe definition of a `JSONArrayDocument` is a little bit different than using a regular `ejp.Array()`: the schema must be defined as a class attribute instead of a constructor argument.\n\n```python\nclass StringList(JSONArrayDocument):\n    schema = String()\n```\n\nHere are the following special methods:\n\n**`JSONObjectDocument.load(obj=None)` (@classmethod)**\n\nCreates a `JSONObjectDocument` with input source `obj`. If no input is given, then an empty data structure is computed. If an input is given but is not a dictionnary, an exception is raised.\n\n**`JSONObjectDocument.loads(obj_string)` (@classmethod)**\n\nCreates a `JSONObjectDocument` from a string. The data is parsed to a dictionary using `json.loads()` from Python's standard library. If  `obj_string` does not repressent a valid JSON object, an exception is raised.\n\n**`JSONArrayDocument.load(*values)` (@classmethod)**\n\nCreates a `JSONArrayDocument` from a list of values. `values` can be a variadic array but it is also possible to pass a single input which must be a list or tuple.\n\n**`JSONArrayDocument.loads(array_string)` (@classmethod)**\n\nCreates a `JSONArrayDocument` from a string. The data is parsed to an array using `json.loads()` from Python's standard library. If `array_string` does not represent a valid JSON array, an exception is raised.\n\n# Available types\n\n## `ejp.String()`\n\nRepresents a JSON string.\n\n## `ejp.Integer()`\n\nRepresents a JSON integer.\n\n*Warning: boolean and floats are accepted as an input but they will be converted to integers with `int()`. A warning is raised when a conversion happens.\n\n*TODO: add an option to disable/enable this behaviour*\n\n## `ejp.Float()`\n\nRepresents a floating-point number. Conversion from integers and boolean work the way as `ejp.Integer()`\n\n## `ejp.Boolean()`\n\nRepresents a boolean. Conversion from floating-point numbers and boolean work the way as `ejp.Integer()`\n\n## `ejp.Null()`\n\nRepresents a `null` value. Every input that is not `None` (in Python) or `null`(as a JSON string) will raise a parsing exception.\n\n## `ejp.Object()`\n\nRepresents a JSON object.\n\nProperties cannot be defined in the constructor: they must be defined as class attributes as follows:\n\n```python\nclass MyObject(ejp.Object):\n    property1 = ejp.String()\n    property2 = ejp.Boolean()\n    ...\n```\n\n*Note: remember that you need to use `JSONObjectDocument` to define a top-level object*.\n\n## `ejp.Array()`\n\nRepresents a JSON array. An array must follow a schema which must be passed to the constructor of `ejp.Array()`. If the schema is invalid, an exception is raised.\n\n```python\nclass MyObject(ejp.Object):\n    array = ejp.Array(schema=String())\n```\n\n*Note: remember that you need to use `JSONArrayDocument` to define a top-level array*.\n\n## `ejp.Empty()`\n\nRepresents an empty value. Used for optional properties/values: if you want to clear the content of an optional parameter, set its value to `ejp.Empty()`.\n\n# License\n\nThis software is distributed under the [MIT license](LICENSE)",
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