Schema Module ¶

The classes utilized to construct schemas for object definitions.

Contents

Schema Module

Usage ¶

The schema_type module contains the class Schema and a set of functions to create and validate schema. Schema are used to validate ontic.ontic_type.OnticType derived instances.

Creating Schema ¶

A Schema is defined as a dictionary with the key entry being the name of the property. The value portion of the dictionary is a ontic.meta_type.PropertySchema instance:

>>> a_schema = Schema([
...     property.OnticProperty(name='property_name', type= 'str')
... ])

While the example above give a strict definition of a schema, creation of a schema can omit the use of the PropertySchema, as the Schema instantiation will convert a dict value to a PropertySchema object. The above example can be simplified to be:

>>> a_schema = Schema([
...     {'name': 'property_name', 'type': 'str'}
... ])

The Schema also supports the dict style of instantiation via parameter naming:

>>> a_schema = Schema(property_name={'type': 'str'})

Dynamic Schema ¶

In cases where necessary, a Schema can be created dynamically:

>>> a_schema = Schema()
>>> a_schema.add(property.OnticProperty({'name': 'a', 'type': 'str'}))

To aid in the handling of dynamic models, utilize the perfect_schema() and validate_schema().

>>> perfect_schema(a_schema)
>>> errors = validate_schema(a_schema)

Classes ¶

SchemaType ¶

class ontic.schema.Schema(*args, **kwargs)¶

The type definition for a schema object.

The Schema contains a dictionary of property field names and the corresponding OnticProperty definition.

Example Schema representation:

Schema({
  'some_property': Schema({
        'type': 'str',
        'required': True
    })
})

For a complete list of OnticProperty settings, see Available Property Schema Settings.

__init__(*args, **kwargs)¶

add(property_type: OnticProperty) → NoReturn¶

Add a property definition to a schema.

Parameters: property_type –

perfect() → NoReturn¶

Method to clean and perfect a given schema.

The perfect will fill in any missing schema settings for each of the OnticProperty. This function should be used to ensure property schema completeness.

validate(raise_validation_exception: bool = True)¶

Validate a given Schema.

This method will iterate through all of the OnticProperty and validate that each definition is valid. The method will collect all of the errors and return those as a list of strings or raise a ontic.validation_exception.ValidationException. The switch in behavior is determined by the raise_validation_exception

Parameters

raise_validation_exception (bool) – If True, then validate_schema will throw a ValidationException upon validation failure. If False, then a list of validation errors is returned. Defaults to True.

Returns

List of errors found. Empty of no errors found.

Return type

list<str>

Raises

ValueError – candidate_schema is None, or not of type Schema.
ValidationException – A property of candidate_schema does not meet schema requirements.

Functions ¶

perfect_schema ¶

ontic.schema.perfect_schema(ontic_schema: OnticProperty) → NoReturn¶

Method to clean and perfect a given schema.

The perfect_schema will fill in any missing schema setting for each of the OnticProperty. This function should be used to ensure property schema completeness.

Parameters: ontic_schema – The schema that is to be perfected.

validate_schema ¶

ontic.schema.validate_schema(ontic_schema: OnticProperty, raise_validation_exception: bool = True)¶