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neuralset.events.study.StudyInfo

pydantic model neuralset.events.study.StudyInfo

Records expected dataset characteristics for testing and validation.

Provides a baseline for automatic unit tests to verify that a study’s data loading logic and file parsing behave correctly and completely.

num_timelines

The total number of timelines (e.g. subject sessions) expected.

Type:

int

num_subjects

The expected number of unique subjects.

Type:

int

query

A query applied during tests to subsample the data (default: "timeline_index < 1").

Type:

str

num_events_in_query

The expected number of events after applying the query.

Type:

int

event_types_in_query

The expected set of event types present in the queried data.

Type:

set of str

data_shape

The expected shape of the primary data arrays.

Type:

tuple of int

frequency

The expected sampling frequency of the data, in Hz.

Type:

float

fmri_spaces

(fMRI only) The expected spatial reference spaces for the data.

Type:

tuple of str

Fields:

• data_shape (tuple[int, ...])

• event_types_in_query (set[str])

• fmri_spaces (tuple[str, ...] | set[str])

• frequency (float)

• num_events_in_query (int)

• num_subjects (int)

• num_timelines (int)

• query (str)

field num_timelines: int = 0

field num_subjects: int = 0

field query: str = 'timeline_index < 1'

field num_events_in_query: int = 0

field event_types_in_query: set[str] = {}

field data_shape: tuple[int, ...] = ()

field frequency: float = 0.0

field fmri_spaces: tuple[str, ...] | set[str] = ()

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