physioex.data.MultiDataset

class physioex.data.MultiDataset(datasets, memmap_cache_size=1000)

Bases: Dataset

Wraps multiple BasePhysioDataset instances into a single unified dataset.

Provides ConcatDataset-like flat indexing across all datasets, with: - Unified __getitem__ returning the same dict format as BasePhysioDataset - Cross-dataset split coordination via split(fold) - dataset_idx tracking in the returned dict’s subject metadata - Proportional memmap cache distribution across constituent datasets

Parameters:

• datasets (List[BasePhysioDataset])

• memmap_cache_size (int)

__init__(datasets, memmap_cache_size=1000)

Parameters:

• datasets (List[BasePhysioDataset])

• memmap_cache_size (int)

Return type:

None

Methods

__init__(datasets[, memmap_cache_size])
available_channels()	Return union of available channels across all datasets.
close()	Close all memmap caches in constituent datasets.
get_n_subjects()	Return total number of subjects across all datasets.
get_subjects()	Return all subject IDs across all datasets.
get_subjects_with_dataset_idx()	Return (dataset_idx, subject_id) pairs for every subject.
memmap_cache_stats()	Aggregate memmap cache statistics across all constituent datasets.
split([fold])	Return train/valid/test indices coordinated across all datasets.

Attributes

datasets

Return the wrapped dataset list (read-only reference).

available_channels()

Return union of available channels across all datasets.

Return type:

Dict[str, int]

close()

Close all memmap caches in constituent datasets.

Return type:

None

property datasets: List[BasePhysioDataset]

Return the wrapped dataset list (read-only reference).

get_n_subjects()

Return total number of subjects across all datasets.

Return type:

int

get_subjects()

Return all subject IDs across all datasets.

Note: IDs are not guaranteed to be unique across datasets. If disambiguation is needed, use get_subjects_with_dataset_idx.

Return type:

List[str]

get_subjects_with_dataset_idx()

Return (dataset_idx, subject_id) pairs for every subject.

Return type:

List[Tuple[int, str]]

memmap_cache_stats()

Aggregate memmap cache statistics across all constituent datasets.

Returns:

Dict with aggregated stats (total_hits, total_misses, hit_rate, etc.) and per-dataset breakdown.

Return type:

Dict[str, Any]

split(fold=0)

Return train/valid/test indices coordinated across all datasets.

Training: flat integer indices into this MultiDataset’s __getitem__. Valid/Test: list of (dataset_idx, subject_id) tuples, where dataset_idx is the position of the originating dataset in the datasets list passed to the constructor.

Parameters:

fold (int)

Return type:

Tuple[ndarray, List[Tuple[int, str]], List[Tuple[int, str]]]