Group¶

Adapter for NWB groups to linkml Classes

class GroupAdapter(*, TYPE: ~typing.Type = <class 'nwb_schema_language.datamodel.nwb_schema_pydantic.Group'>, cls: ~nwb_schema_language.datamodel.nwb_schema_pydantic.Group, parent: ~nwb_linkml.adapters.classes.ClassAdapter | None = None)¶

Adapt NWB Groups to LinkML Classes

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: Type¶: The type that this adapter class handles

cls: Group¶

build() → BuildResult¶: Do the translation, yielding the BuildResult

handle_container_group(cls: Group) → BuildResult¶

Make a special LinkML children slot that can have any number of the objects that are of neurodata_type_inc class

Examples

- name: templates
  groups:
  - neurodata_type_inc: TimeSeries
    doc: TimeSeries objects containing template data of presented stimuli.
    quantity: '*'
  - neurodata_type_inc: Images
    doc: Images objects containing images of presented stimuli.
    quantity: '*'

Parameters:: children (List[Group]) – Child groups

handle_container_slot(cls: Group) → BuildResult¶

Handle subgroups that contain arbitrarily numbered classes,

eg. each of the groups in

Examples

name: trials neurodata_type_inc: TimeIntervals doc: Repeated experimental events that have a logical grouping. quantity: ‘?’
name: invalid_times neurodata_type_inc: TimeIntervals doc: Time intervals that should be removed from analysis. quantity: ‘?’
neurodata_type_inc: TimeIntervals doc: Optional additional table(s) for describing other experimental time intervals. quantity: ‘*’

build_subclasses() → BuildResult¶

Build nested groups and datasets

Create ClassDefinitions for each, but then also create SlotDefinitions that will be used as attributes linking the main class to the subclasses

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=Type, required=False, default=<class 'nwb_schema_language.datamodel.nwb_schema_pydantic.Group'>), 'cls': FieldInfo(annotation=Group, 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.