GithubHelp home page GithubHelp logo

isabella232 / jsonmodels Goto Github PK

View Code? Open in Web Editor NEW

This project forked from jazzband/jsonmodels

0.0 0.0 0.0 352 KB

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

Home Page: http://jsonmodels.readthedocs.org/en/latest/

License: BSD 3-Clause "New" or "Revised" License

Python 98.89% Makefile 1.11%

jsonmodels's Introduction

JSON models

Jazzband Tests PyPI Coverage

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:

    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"
}

    You can also validate scalars, when needed:

    >>> 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:

    >>> 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"
        }
    }
}

  • 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.)

>>> 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:

    >>> 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.

jsonmodels's People

Contributors

avrahamshukron avatar beregond avatar danielschiavini avatar dimakuz avatar hugovk avatar jazzband-bot avatar jezdez avatar johncalls avatar kazemat avatar omeranson avatar pre-commit-ci[bot] avatar robertofd1995 avatar vorren avatar xlevus avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.