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Define classes that conform to a JSON Schema, with built-in validation and schema generation.

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jsch

Define classes that conform to a JSON schema, with built-in validation and schema generation.

Creating a schema object

A Schema object represents a validated JSON schema. Any recognised JSON schema field can be passed as a keyword argument when initialising a Schema object:

>>> import jsch
>>>
>>> jsch.Schema()
<jsch.schema.Schema object at 0x...>
>>>
>>> schema = jsch.Schema(title='First name', type='string', max_length=32)
>>> schema.max_length
32
>>> schema.title
'First name'
>>>

Keywords are provided in the underscored format rather than the camel case format used by the JSON schema definition, i.e. max_length rather than maxLength. This is done to conform to the PEP8 Style Guide.

A SchemaValidationError will be raised on initialisation if any JSON schema validation rules are breached:

>>> import jsch
>>>
>>> jsch.Schema(title='Luggage', type='array', max_items=0.5)
Traceback (most recent call last):
  ...
jsch.schema.SchemaValidationError: 'max_items' must be an int
>>>
>>> jsch.Schema(title='Height', type='object', required=[])
Traceback (most recent call last):
  ...
jsch.schema.SchemaValidationError: 'required' list must not be empty
>>>

The schema validation rules in the JSON schema specification go a long way towards ensuring a schema is valid, but there are still some gaps, especially around inter-keyword validation. For more strict validation when creating a schema object, use the SchemaStrict class instead:

>>> # not yet implemented
>>> 

Accessing the JSON schema

The JSON schema can be accessed as either a dict or a JSON string:

>>> import jsch
>>>
>>> schema = jsch.Schema(
...     title='Approximate Age',
...     type='integer',
...     minimum=0,
...     multiple_of=10
... )
>>>
>>> dict = schema.asdict()
>>> import pprint
>>> pprint.pprint(dict)
{'minimum': 0, 'multipleOf': 10, 'title': 'Approximate Age', 'type': 'integer'}
>>>
>>> json = schema.asjson()
>>> print(json)
{"minimum":0,"multipleOf":10,"title":"Approximate Age","type":"integer"}
>>>
>>> pretty_json = schema.asjson(pretty=True)
>>> print(pretty_json)
{
    "minimum": 0,
    "multipleOf": 10,
    "title": "Approximate Age",
    "type": "integer"
}
>>>

If the schema is intended to be a root schema, specify the root flag with an optional $schema string when converting to a dict or a JSON string:

>>> import jsch
>>>
>>> schema = jsch.Schema(title='Height', type='number')
>>>
>>> json = schema.asjson(pretty=True, root=True)
>>> print(json)
{
    "$schema": "http://json-schema.org/draft-04/schema#",
    "title": "Height",
    "type": "number"
}
>>>
>>> dict = schema.asdict(root=True, schema='http://jsch.org/custom-schema#')
>>> import pprint
>>> pprint.pprint(dict)
{'$schema': 'http://jsch.org/custom-schema#',
 'title': 'Height',
 'type': 'number'}
>>>

Simplifying schema object creation

For convenience, a class is provided for each of the primitive JSON schema types, to save specifying the type keyword:

>>> import jsch
>>>
>>> schema = jsch.String(pattern='^[0-9]{4}$')
>>> json = schema.asjson(pretty=True)
>>> print(json)
{
    "pattern": "^[0-9]{4}$",
    "type": "string"
}
>>>
>>> schema = jsch.Array(items=jsch.Integer(), min_items=1)
>>> json = schema.asjson(pretty=True)
>>> print(json)
{
    "items": {
        "type": "integer"
    },
    "minItems": 1,
    "type": "array"
}
>>>

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