io_models

Input and output specification models for analyses.

Inputs pydantic-model

Bases: BaseModel

Represents the inputs of the analysis.

Fields:

• recording (RecordingInput | None)
• events (list[EventType] | None)
• metadata (list[MetadataType] | None)

Validators:

• validate_at_least_one_input

events = None pydantic-field

The list of typed event that the analysis requires in the input.

metadata = None pydantic-field

Optional list of metadata type input for the analysis, e.g., 'age'.Can be None if the analysis does not require metadata.

recording pydantic-field

The recording input for the analysis. Can be None if the analysis does not require a recording input.

validate_at_least_one_input(data) pydantic-validator

Validate that at least one of 'recording', 'events' and 'metadata' is set.

Outputs pydantic-model

Bases: BaseModel

Defines the output of the analysis.

Fields:

• supports_full_probabilistic_output (bool)
• events (list[EventType])

Validators:

• validate_probabilistic_consistency

events pydantic-field

The list of typed event that the analysis outputs.

supports_full_probabilistic_output = False pydantic-field

Whether the analysis supports outputting full probabilistic distributions (all class probabilities per prediction, not just argmax). When true, users can request overlapping events with probability scores for all classes.

validate_probabilistic_consistency() pydantic-validator

Validate probabilistic output consistency.

If supports_full_probabilistic_output is True, all event types must be probabilistic.