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Models to make easier to deal with structures that are converted to, or read from JSON.

Project description

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

Features

  • Fully tested with Python 2.7, 3.3, 3.4, 3.5, 3.6.

  • 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()
        love_humans = fields.IntField(nullable=True)
    
    
    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)
        nickname = fields.StringField(nullable=True)
        car = fields.EmbeddedField(Car)
        pets = fields.ListField([Cat, Dog], nullable=True)
    
  • 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',
        'nickname': None,
        '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'},
            'nickname': {'type': ['string', 'null']}
            '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': 'null'
                        }
                    ]
                },
                '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(
    ...         nullable=True,
    ...         validators=[
    ...             validators.Min(18),
    ...             validators.Max(101),
    ...         ]
    ...     )
    ...     nickname = fields.StringField(
    ...         required=True,
    ...         nullable=True
    ...     )
    ...
    
    >>> person = Person()
    >>> person.age = 11
    >>> person.validate()
    *** ValidationError: '11' is lower than minimum ('18').
    >>> person.age = None
    >>> person.validate()
    None
    
    >>> 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.nickname = None
    >>> person.validate()
    *** ValidationError: Field is required!
    
    >>> person.to_json_schema()
    {
        "additionalProperties": false,
        "properties": {
            "age": {
                "maximum": 101,
                "minimum": 18,
                "type": ["number", "null"]
            },
            "name": {
                "maxLength": 25,
                "minLength": 3,
                "pattern": "/^[A-Za-z]+$/",
                "type": "string"
            },
            "nickname": {,
                "type": ["string", "null"]
            }
        },
        "required": [
            "nickname",
            "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.

History

2.3 (2018-02-04)

  • Added name mapping for fields.
  • Added value parsing to IntField.
  • Fixed bug with ECMA regex flags recognition.

2.2 (2017-08-21)

  • Fixed time fields, when value is not required.
  • Dropped support for python 2.6
  • Added support for python 3.6
  • Added nullable param for fields.
  • Improved model representation.

2.1.5 (2017-02-01)

  • Fixed DateTimefield error when value is None.
  • Fixed comparing models without required values.

2.1.4 (2017-01-24)

  • Allow to compare models based on their type and fields (rather than their reference).

2.1.3 (2017-01-16)

  • Fixed generated schema.
  • Improved JSON serialization.

2.1.2 (2016-01-06)

  • Fixed memory leak.

2.1.1 (2015-11-15)

  • Added support for Python 2.6, 3.2 and 3.5.

2.1 (2015-11-02)

  • Added lazy loading of types.
  • Added schema generation for circular models.
  • Improved readability of validation error.
  • Fixed structure generation for list field.

2.0.1 (2014-11-15)

  • Fixed schema generation for primitives.

2.0 (2014-11-14)

  • Fields now are descriptors.
  • Empty required fields are still validated only during explicite validations.

Backward compatibility breaks

  • Renamed _types to types in fields.
  • Renamed _items_types to items_types in ListField.
  • Removed data transformers.
  • Renamed module error to errors.
  • Removed explicit validation - validation occurs at assign time.
  • Renamed get_value_replacement to get_default_value.
  • Renamed modules utils to utilities.

1.4 (2014-07-22)

  • Allowed validators to modify generated schema.
  • Added validator for maximum value.
  • Added utilities to convert regular expressions between Python and ECMA formats.
  • Added validator for regex.
  • Added validator for minimum value.
  • By default “validators” property of field is an empty list.

1.3.1 (2014-07-13)

  • Fixed generation of schema for BoolField.

1.3 (2014-07-13)

  • Added new fields (BoolField, TimeField, DateField and DateTimeField).
  • ListField is always not required.
  • Schema can be now generated from class itself (not from an instance).

1.2 (2014-06-18)

  • Fixed values population, when value is not dictionary.
  • Added custom validators.
  • Added tool for schema comparison.

1.1.1 (2014-06-07)

  • Added possibility to populate already initialized data to EmbeddedField.
  • Added compare_schemas utility.

1.1 (2014-05-19)

  • Added docs.
  • Added json schema generation.
  • Added tests for PEP8 and complexity.
  • Moved to Python 3.4.
  • Added PEP257 compatibility.
  • Added help text to fields.

1.0.5 (2014-04-14)

  • Added data transformers.

1.0.4 (2014-04-13)

  • List field now supports simple types.

1.0.3 (2014-04-10)

  • Fixed compatibility with Python 3.
  • Fixed str and repr methods.

1.0.2 (2014-04-03)

  • Added deep data initialization.

1.0.1 (2014-04-03)

  • Added populate method.

1.0 (2014-04-02)

  • First stable release on PyPI.

0.1.0 (2014-03-17)

  • First release on PyPI.

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