check_arrays
Inspects HDF5 array datasets for data quality issues, flagging NaN/Inf, dtype drift, fill values, and out-of-range values while summarizing per-dataset statistics.
Instructions
HDF5 / scientific-array EDA. Source must be loaded as hdf5 modality.
Walks the h5py tree and aggregates leaf datasets by their leaf name (so
all `.../energies` arrays across groups become one schema). Reports
per-leaf shape examples, dtype consistency, finite/NaN/Inf %, range,
units, valid_range. Flags NaN/Inf >1%, dtype drift across groups, fill
values (~9.97e36 / ±9999), and out-of-range values vs declared valid_range.
`max_groups` caps the walk depth for very large files. When the cap is
hit, the result's `cap_hit=true` and a critical finding is surfaced —
raise `max_groups` and re-run to cover the full file. Result also
reports `n_groups_total` (true total) vs `n_groups_walked` (visited).
Output size: scales with distinct leaf names; ~0.5–2KB per schema.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| source_id | Yes | ||
| max_groups | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||