physioex 2.0.3

CCSHS dataset, opt-in utilities for multi-dataset training, and three data-layer fixes. STAGES and Alzheimers label
caches are recomputed on first use, and event counts per type change (see "artifact event type").

Added — CCSHS dataset (physioex.data.datasets.ccshs)

get_dataset("ccshs"): Cleveland Children's Sleep and Health Study (NSRR ccshs-trec, 515 recordings). EEG
(C3, A2) / (C4, A1) and EOG (LOC, ROC) at 128 Hz, chin EMG at 256 Hz; stage 3 and 4 merged to N3,
Movement/unscored to -1, labels indexed by event onset, unknown stage vocabulary raises. Eleven recordings are
excluded by default (CCSHSDataset.EXCLUDED_SUBJECTS, exclude_subjects=[] keeps them): nine whose stage minutes
disagree with the NSRR summary table and two without a row in it. Metadata is read from
datasets/ccshs-trec-dataset-*.csv whatever its version. Tests in tests/test_ccshs.py.

Added — site-balanced sampling (physioex.data.sampling)

site_balanced_weights / make_site_sampler (also exported from physioex.data): sampling weights that give every
site the same mass and, inside a site, every subject the same mass, for training on collections of datasets of very
different sizes with MultiDataset. Tests in tests/test_sampling.py.

Added — opt-in channel-accuracy tracking during training (ChannelMixer)

ChannelMixer.track_train_acc and acc_momentum update the per-channel accuracy that drives the accuracy-weighted
modality dropout from the training step too (EMA of the clean per-channel logits). Off by default, so the released
models and existing training runs behave exactly as before. Tests in tests/test_channel_acc_tracking.py.

Fixed — subjects without any requested channel (BasePhysioDataset)

A subject for which none of the requested channels can be resolved was returned as a block of zeros with the wrong
shape ((L,) instead of (L, F, T)), which made dict_collate_fn fail on any batch containing it. Its zeros now have
the per-epoch shape of the pipeline output, like the zero-filled channels of subjects that miss only some channels, and
a warning names the subject. Subjects are still never dropped.

Fixed — stale cached labels (BasePhysioDataset.LABELS_REVISION)

Cached labels had no version, so after a fix of a label reader the old caches kept serving the wrong labels without any
warning. Datasets now declare LABELS_REVISION (default 1); the revision is stored with the cached labels and a cache
with a lower revision is recomputed (with a warning), or used with a warning when the source files are not available.
Caches without the key count as revision 1, so existing caches of unchanged readers stay valid. STAGESDataset and
AlzheimersDataset are at revision 2 (timestamp-based alignment of STAGES labels, onset-indexed Alzheimer labels):
caches written before those fixes are recomputed on first use. Cached EDF headers that predate the start_sec field
(STAGES labels are aligned to the EDF start time with it) are probed again, or used with a warning when the EDF is not
available.

Changed — artifact event type

"SpO2 artifact" intervals (an unreliable oximeter signal, not a physiological event) were counted as respiratory by
the NSRR parser and as desaturation by the STAGES parser. They now have their own type artifact; the concept text is
unchanged. Event counts per type change accordingly, for example in CCSHS respiratory drops from 51 915 to 15 040
and artifact has 36 875.