jsonmodels

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

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===========

JSON models

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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 3.6+.

  • Support for PyPy (see implementation notes in docs for more details).

  • Create Django-like models:

    .. code-block:: python

    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:

    .. code-block:: python

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

  • Validate models:

    .. code-block:: python

    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:

    .. code-block:: python

    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:

    .. code-block:: python

    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:

    .. code-block:: python

    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" }

    You can also validate scalars, when needed:

    .. code-block:: python

    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 ... ) ...

    def only_odd_numbers(item): ... if item % 2 != 1: ... raise validators.ValidationError("Only odd numbers are accepted") ... class Person(models.Base): ... lucky_numbers = fields.ListField(int, item_validators=[only_odd_numbers]) ... item_validator_str = fields.ListField( ... str, ... item_validators=[validators.Length(10, 20), validators.Regex(r"\w+")], ... validators=[validators.Length(1, 2)], ... ) ... Person.to_json_schema() { "type": "object", "additionalProperties": false, "properties": { "item_validator_str": { "type": "array", "items": { "type": "string", "minLength": 10, "maxLength": 20, "pattern": "/\w+/" }, "minItems": 1, "maxItems": 2 }, "lucky_numbers": { "type": "array", "items": { "type": "number" } } } }

(Note that only_odd_numbers did not modify schema, since only class based validators are able to do that, though it will still work as expected in python. Use class based validators that can be expressed in json schema if you want to be 100% correct on schema side.)

  • Lazy loading, best for circular references:

    .. code-block:: python

    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:

    .. code-block:: python

    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" } } }

  • Dealing with schemaless data

(Plese note that using schemaless fields can cause your models to get out of control - especially if you are the one responsible for data schema. On the other hand there is usually the case when incomming data are with no schema defined and schemaless fields are the way to go.)

.. code-block:: python

>>> class Event(models.Base):
...
...     name = fields.StringField()
...     size = fields.FloatField()
...     extra = fields.DictField()

>>> Event.to_json_schema()
{
    "type": "object",
    "additionalProperties": false,
    "properties": {
        "extra": {
            "type": "object"
        },
        "name": {
            "type": "string"
        },
        "size": {
            "type": "float"
        }
    }
}

DictField allow to pass any dict of values ("type": "object"), but note, that it will not make any validation on values except for the dict type.

  • Compare JSON schemas:

    .. code-block:: python

    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.

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