Group

Adapter for NWB groups to linkml Classes

pydantic model 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.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:

• cls (nwb_schema_language.datamodel.nwb_schema_pydantic.Group)

field cls: Group [Required]

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_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

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: ‘*’