Schemas and predicates

Describe a shape with Python values

A dict maps field names to schemas. A one-element list describes each item in a list. A Python type requires that exact type. PEP 604 unions combine types. Optional wraps an optional field.

Predicates receive the original value and accept it when they return a truthy result. Accepted values are returned unchanged. A predicate should check its own input type before comparing or indexing it.

from zodify import validate

schema = {"users": [{"name": str}], "enabled": bool}
data = {"users": [{"name": "Ada"}], "enabled": True}
print(validate(schema, data))

# A truthy predicate accepts and preserves the original value.
port_schema = {"port": lambda value: type(value) is int and 1 <= value <= 65535}
print(validate(port_schema, {"port": 8080}))

{'users': [{'name': 'Ada'}], 'enabled': True}
{'port': 8080}

Supported schema shapes

Pass a dict as data and either a dict schema or supported Schema declaration as the schema. Lists with zero or multiple schema items are invalid. Malformed schemas may raise TypeError. Validation has a max_depth option, defaulting to 32; cyclic models and arbitrary typing expressions are not promised.

Dict-shaped and list-shaped validation creates result containers, but leaf objects and defaults can remain shared.

• Ownership and defaults