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neuralset.extractors.meta.ToStatic

pydantic model neuralset.extractors.meta.ToStatic

Crop a extractor by a given offset and duration.

Parameters:

extractor (BaseExtractor) – The extractor to crop.

Fields:

• aggregation (Literal['trigger'])

• event_types (str | tuple[str, ...])

• extractor (neuralset.extractors.base.BaseExtractor)

• frequency (Annotated[float, annotated_types.Gt(gt=0)])

field extractor: BaseExtractor [Required]

field event_types: str | tuple[str, ...] = 'Event'

field frequency: Annotated[float, Gt(gt=0)] = 0.0

field aggregation: Literal['trigger'] = 'trigger'

prepare(obj: Any) → None

Pre-compute and cache extractor data for a collection of events.

This method triggers _get_data on every matching event so that expensive computation (e.g. model inference) is done once and cached. It then calls the extractor on a single event to populate the output shape, which is needed when allow_missing=True.

Call prepare before using the extractor in a dataloader.

Parameters:

obj (DataFrame or sequence of Event or sequence of Segment) – The structure containing the events. When calling prepare on several objects, prefer passing a list of events or segments over a DataFrame to avoid redundant conversion overhead.

requirements: tp.ClassVar[tuple[str, ...]] = ()

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