Classes

Adapters to linkML classes

class ClassAdapter(*, TYPE: T, cls: TI, parent: ClassAdapter | None = None)

Abstract adapter to class-like things in linkml, holds methods common to both DatasetAdapter and GroupAdapter

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

TYPE: T

The type that this adapter class handles

cls: TI
parent: ClassAdapter | None
classmethod cast_from_string(value: str | TI) → TI

Cast from YAML string to desired class

abstract build() → BuildResult

Make this abstract so it can’t be instantiated directly.

Subclasses call build_base() to get the basics true of both groups and datasets

build_base(extra_attrs: List[SlotDefinition] | None = None) → BuildResult

Build the basic class and attributes before adding any specific modifications for groups or datasets.

The main distinction in behavior for this method is whether this class has a parent class - ie this is one of the anonymous nested child datasets or groups within another group.

If the class has no parent, then…

  • Its name is inferred from its neurodata_type_def, fixed name, or neurodata_type_inc in that order

  • It is just built as normal class!

  • It will be indicated as a tree_root (which will primarily be used to invert the translation for write operations)

If the class has a parent, then…

  • If it has a neurodata_type_def or inc, that will be used as its name, otherwise concatenate parent__child, eg. TimeSeries__TimeSeriesData

  • A slot will also be made and returned with the BuildResult, which the parent will then have as one of its attributes.

build_attrs(cls: Dataset | Group) → List[SlotDefinition]

Pack the class attributes into a list of SlotDefinitions

Parameters:

cls – (Dataset | Group): Class to pack

Returns:

list[SlotDefinition]

build_name_slot() → SlotDefinition

If a class has a name, then that name should be a slot with a fixed value.

If a class does not have a name, then name should be a required attribute

References

https://github.com/NeurodataWithoutBorders/nwb-schema/issues/552#issuecomment-1700319001

Returns:

build_self_slot() → SlotDefinition

If we are a child class, we make a slot so our parent can refer to us

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'TYPE': FieldInfo(annotation=TypeVar, required=True), 'cls': FieldInfo(annotation=TypeVar, required=True), 'parent': FieldInfo(annotation=Union[ClassAdapter, NoneType], required=False, default=None)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_post_init(context: Any, /) → None

We need to both initialize private attributes and call the user-defined model_post_init method.