Schemas
BespokeFit uses Pydantic to validate input provided by users. Pydantic helps BespokeFit provide clear error messages when it is configured improperly, and also provides inspection and documentation tools that BespokeFit uses extensively.
Models and fields
Not every class in BespokeFit uses Pydantic; those that do are found mostly in
the schema module and can be identified in documentation as a model,
which appears in green before their path and name.
Models are characterized by their fields, which are like attributes in Python data classes. Fields can be validated at runtime, both according to their type annotations and via custom validator functions. Attempting to set a field to an invalid value will immediately raise a clear error, rather than causing a failure later on when the value is used.
A model’s fields, validation machinery, and other Pydantic configuration forms
its schema. A schema describes every possible configuration of a model and is
useful for investigating what can be done with a class. Schemas are included in
a model’s API reference documentation, and can be accessed programmatically
with the schema() method.
For example, BespokeWorkflowFactory is a model. Its schema is available
on its API page, but can also be accessed on the class itself or any instance:
from openff.bespokefit.workflows.bespoke import BespokeWorkflowFactory
BespokeWorkflowFactory.schema()
factory = BespokeWorkflowFactory()
factory.schema()
Introspection
Models provide a number of methods that are useful for inspecting objects in an
interactive environment, such as a Jupyter notebook. These methods are implemented
on pydantic.BaseModel, documented below. Models in BespokeFit inherit these methods,
though they are not explicitly documented in order to reduce clutter.