[codex] Refine dataset type detection confidence#238
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Summary
This refactors dataset type detection so evidence confidence and class specificity are handled separately. Existing
CandidateStatusvalidators continue to work, while newDetectionResultvalues allow validators to distinguish generic structural/header matches from format-specific markers.The FLAMINGO regression was caused by
ArepoSimulationtreating a generic multi-HDF5NumFilesPerSnapshot == 1layout as certain Arepo evidence. That now reports generic-header confidence, while SWIFT markers report format-marker confidence, so FLAMINGO reduced snapshots resolve toSwiftSnapshotinstead ofArepoSimulation.Validation
uv run pytest tests/unit/test_discovertypes.py -qSCIDA_TESTDATA_PATH=/newdata/data/public/testdata-astrodask uv run pytest tests/external/swift/test_flamingo.py tests/external/test_type_detection.py -m external -rsSCIDA_TESTDATA_PATH=/newdata/data/public/testdata-astrodask uv run pytest tests/external -m external -rsFull external astrodask run result:
365 passed, 2 failed, 3 skipped, 9 deselected. The two failures are pre-existing/unrelated issues observed in CI too:test_load_cachefail[TNG50-4_snapshot]and thetest_areposimulation_lazy_message[TNG50-4]timing assertion.