Add remove_corrupt_h5 utility to clean up interrupted runs - #35
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The generate_*/convert_* scripts resume by skipping input files whose output .h5 already exists, checking only for existence and not integrity. A run interrupted mid-write can leave a truncated .h5 that is then treated as "done" and silently kept in the library. This adds src/openmc_data/other/remove_corrupt_h5.py, a small utility that scans a directory of .h5 files, opens each with h5py (an optional --deep pass fully loads neutron data with openmc), and removes any that are unreadable so a rerun regenerates them. A --dry-run flag reports without deleting. Wired up as the remove_corrupt_h5 console entry point. Closes #33
The develop branch of openmc now requires Python >=3.12, so installing it under the workflow's Python 3.9 failed with "Package 'openmc' requires a different Python". Bump to 3.12 to match the test_package and test_processing workflows, and update the deprecated checkout@v2 / setup-python@v2 actions to v4 / v5 as used by the other workflows.
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Problem: interrupted runs can leave silently-corrupt HDF5 files
The
generate_*andconvert_*scripts support resuming: on a rerun they skip any input (ENDF/ACE) file whose output.h5already exists in the destination. The resume check tests only for existence, not integrity. Because processing runs in amultiprocessing.Pool, several.h5files are written concurrently, so an interrupt (Ctrl-C, crash, OOM, kill) during a write can leave files truncated / corrupt. On the next run those files look "already processed" and are skipped, so the corrupt file survives intocross_sections.xmland the error only surfaces later in OpenMC.What this adds
src/openmc_data/other/remove_corrupt_h5.py, a small cleanup utility to run before rerunning:Opening each file with
h5pyreliably catches truncated writes; the optional--deeppass additionally loads each file withopenmc.data.IncidentNeutron.from_hdf5for a stronger check.Wired up as the
remove_corrupt_h5console entry point inpyproject.toml, alongside the otherother/scripts.Closes #33