ashang / jsonmodels

jsonmodels is library to make it easier for you to deal with structures that are converted to, or read from JSON.

JSON models

jsonmodels is library to make it easier for you to deal with structures that are converted to, or read from JSON.

• Free software: BSD license
• Documentation: http://jsonmodels.rtfd.org
• Source: https://github.com/beregond/jsonmodels

Features

• Fully tested with Python 2.6, 2.7, 3.2, 3.3, 3.4, 3.5.
• Support for PyPy (see implementation notes in docs for more details).

• Create Django-like models:

  from jsonmodels import models, fields, errors, validators
  
  
  class Cat(models.Base):
  
      name = fields.StringField(required=True)
      breed = fields.StringField()
  
  
  class Dog(models.Base):
  
      name = fields.StringField(required=True)
      age = fields.IntField()
  
  
  class Car(models.Base):
  
      registration_number = fields.StringField(required=True)
      engine_capacity = fields.FloatField()
      color = fields.StringField()
  
  
  class Person(models.Base):
  
      name = fields.StringField(required=True)
      surname = fields.StringField(required=True)
      car = fields.EmbeddedField(Car)
      pets = fields.ListField([Cat, Dog])

• Access to values through attributes:

  >>> cat = Cat()
  >>> cat.populate(name='Garfield')
  >>> cat.name
  'Garfield'
  >>> cat.breed = 'mongrel'
  >>> cat.breed
  'mongrel'

• Validate models:

  >>> person = Person(name='Chuck', surname='Norris')
  >>> person.validate()
  None
  
  >>> dog = Dog()
  >>> dog.validate()
  *** ValidationError: Field "name" is required!

• Cast models to python struct and JSON:

  >>> cat = Cat(name='Garfield')
  >>> dog = Dog(name='Dogmeat', age=9)
  >>> car = Car(registration_number='ASDF 777', color='red')
  >>> person = Person(name='Johny', surname='Bravo', pets=[cat, dog])
  >>> person.car = car
  >>> person.to_struct()
  {
      'car': {
          'color': 'red',
          'registration_number': 'ASDF 777'
      },
      'surname': 'Bravo',
      'name': 'Johny',
      'pets': [
          {'name': 'Garfield'},
          {'age': 9, 'name': 'Dogmeat'}
      ]
  }
  
  >>> import json
  >>> person_json = json.dumps(person.to_struct())

• You don't like to write JSON Schema? Let jsonmodels do it for you:

  >>> person = Person()
  >>> person.to_json_schema()
  {
      'additionalProperties': False,
      'required': ['surname', 'name'],
      'type': 'object',
      'properties': {
          'car': {
              'additionalProperties': False,
              'required': ['registration_number'],
              'type': 'object',
              'properties': {
                  'color': {'type': 'string'},
                  'engine_capacity': {'type': ''},
                  'registration_number': {'type': 'string'}
              }
          },
          'surname': {'type': 'string'},
          'name': {'type': 'string'},
          'pets': {
              'items': {
                  'oneOf': [
                      {
                          'additionalProperties': False,
                          'required': ['name'],
                          'type': 'object',
                          'properties': {
                              'breed': {'type': 'string'},
                              'name': {'type': 'string'}
                          }
                      },
                      {
                          'additionalProperties': False,
                          'required': ['name'],
                          'type': 'object',
                          'properties': {
                              'age': {'type': 'number'},
                              'name': {'type': 'string'}
                          }
                      }
                  ]
              },
              'type': 'array'
          }
      }
  }

• Validate models and use validators, that affect generated schema:

  >>> class Person(models.Base):
  ...
  ...     name = fields.StringField(
  ...         required=True,
  ...         validators=[
  ...             validators.Regex('^[A-Za-z]+$'),
  ...             validators.Length(3, 25),
  ...         ],
  ...     )
  ...     age = fields.IntField(
  ...         required=True,
  ...         validators=[
  ...             validators.Min(18),
  ...             validators.Max(101),
  ...         ]
  ...     )
  
  >>> person = Person()
  >>> person.age = 11
  >>> person.validate()
  *** ValidationError: '11' is lower than minimum ('18').
  
  >>> person.age = 19
  >>> person.name = 'Scott_'
  >>> person.validate()
  *** ValidationError: Value "Scott_" did not match pattern "^[A-Za-z]+$".
  
  >>> person.name = 'Scott'
  >>> person.validate()
  None
  
  >>> person.to_json_schema()
  {
      "additionalProperties": false,
      "properties": {
          "age": {
              "maximum": 101,
              "minimum": 18,
              "type": "number"
          },
          "name": {
              "maxLength": 25,
              "minLength": 3,
              "pattern": "/^[A-Za-z]+$/",
              "type": "string"
          }
      },
      "required": [
          "age",
          "name"
      ],
      "type": "object"
  }

  For more information, please see topic about validation in documentation.

• Lazy loading, best for circular references:

  >>> class Primary(models.Base):
  ...
  ...     name = fields.StringField()
  ...     secondary = fields.EmbeddedField('Secondary')
  
  >>> class Secondary(models.Base):
  ...
  ...    data = fields.IntField()
  ...    first = fields.EmbeddedField('Primary')

  You can use either Model, full path path.to.Model or relative imports .Model or ...Model.

• Using definitions to generate schema for circular references:

  >>> class File(models.Base):
  ...
  ...     name = fields.StringField()
  ...     size = fields.FloatField()
  
  >>> class Directory(models.Base):
  ...
  ...     name = fields.StringField()
  ...     children = fields.ListField(['Directory', File])
  
  >>> class Filesystem(models.Base):
  ...
  ...     name = fields.StringField()
  ...     children = fields.ListField([Directory, File])
  
  >>> Filesystem.to_json_schema()
  {
      "type": "object",
      "properties": {
          "name": {"type": "string"}
          "children": {
              "items": {
                  "oneOf": [
                      "#/definitions/directory",
                      "#/definitions/file"
                  ]
              },
              "type": "array"
          }
      },
      "additionalProperties": false,
      "definitions": {
          "directory": {
              "additionalProperties": false,
              "properties": {
                  "children": {
                      "items": {
                          "oneOf": [
                              "#/definitions/directory",
                              "#/definitions/file"
                          ]
                      },
                      "type": "array"
                  },
                  "name": {"type": "string"}
              },
              "type": "object"
          },
          "file": {
              "additionalProperties": false,
              "properties": {
                  "name": {"type": "string"},
                  "size": {"type": "number"}
              },
              "type": "object"
          }
      }
  }

• Compare JSON schemas:

  >>> from jsonmodels.utils import compare_schemas
  >>> schema1 = {'type': 'object'}
  >>> schema2 = {'type': 'array'}
  >>> compare_schemas(schema1, schema1)
  True
  >>> compare_schemas(schema1, schema2)
  False

More

For more examples and better description see full documentation: http://jsonmodels.rtfd.org.