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
Fully tested with Python 2.7, 3.2, 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
For more examples and better description see full documentation: http://jsonmodels.rtfd.org.