Add command to extract mean timeseries activity from ABA regions#95
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The tform is already inverted in a way that wrap function requires
…mprehensive tests
Returning a lazy HDF5 dataset caused per-frame random I/O during parallel warping. With the real-world chunk layout (1407, 5, 9) each frame access required decompressing ~448 chunks from disk, resulting in ~53 min for a 45 000-frame recording. Changes: - load_deltaf: load /F and /timestamps eagerly with [:] and use a context manager so the file is closed immediately after loading - load_deltaf (NWB path): wrap lazy HDMF arrays with np.array() for the same reason - landmarks_affine: add a defensive np.asarray() guard so the function is safe even when a lazy array is passed in directly Measured on a 45 000 × 140 × 142 float32 recording (3.6 GB): before: ~3178 s (lazy I/O × 10 threads fighting HDF5 cache) after: ~14 s (10 s load + 4 s parallel warp) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Duplicate load_deltaf implementations existed in both mesoscopy.process and mesoscopy.register, with a third caller in mesoscopy.export reaching across into mesoscopy.process. This made it difficult to optimise the loading path in one place and led to subtle behavioural differences between modules (e.g. the process version left the h5py file handle open and did not eagerly load arrays into RAM, while the register version used a context manager and sliced with [:]). Changes: - Add a single canonical load_deltaf(path, nwb=False) to io.py, using the register-style implementation (context manager + eager [:] load) - Remove local load_deltaf definitions from process/__init__.py and register/__init__.py; redirect all call sites to io.load_deltaf() - Update export/__init__.py to call io.load_deltaf() directly, removing the now-unused mesoscopy.process import - Move load_deltaf tests from test_registration.py to test_io.py where all other io function tests live Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
…f-to-io refactor: consolidate load_deltaf into io.py
- Load atlas and annotations once upfront instead of once per region call - Use ThreadPoolExecutor to process all regions concurrently; NumPy releases the GIL during masked-array operations so threads run in parallel without serialising the large deltaf_series array - Fix mutation of module-level DEFAULT_EXCLUDE list: copy it with list() before extending with caller-supplied excludes - Fix region filter to use the computed excluded_regions set instead of the hard-coded DEFAULT_EXCLUDE constant Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
19 tests across two classes covering extract_region_activity and extract_all_regions. Resources are mocked with a minimal synthetic 6×6 atlas (three regions, including one from DEFAULT_EXCLUDE) so tests run without file I/O and stay deterministic. TestExtractRegionActivity: - Output shape for left / right / both hemispheres - Output values verified against manual mask means - Hemisphere argument is case-insensitive - Invalid hemisphere raises ValueError - Different regions yield different signals TestExtractAllRegions: - Returns dict with L_/R_-prefixed keys - Each value has shape (n_frames,) - DEFAULT_EXCLUDE regions absent by default - ignore_default_exclude=True re-includes them - Custom exclude list removes specified regions - Custom exclude is additive with DEFAULT_EXCLUDE - DEFAULT_EXCLUDE is not mutated by repeated calls - Results are numerically consistent with extract_region_activity Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
…which was useless
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Closes #28