HDF5 Chunked + gzip + shuffle Filters (.h5)
One array stored twice in the same file: chunked with the shuffle and gzip filters applied, and contiguous with no filters at all. Comparing the two proves a reader ran the HDF5 filter pipeline rather than reading raw chunk bytes.
| Dataset | Layout | Filters | Values |
|---|---|---|---|
| /compressed | chunked (16, 64) | shuffle + gzip level 6 | identical to /contiguous |
| /contiguous | contiguous | none | identical to /compressed |
Specifications
- Shape
- 64 x 64
- Chunks
- 16 x 64
- Compression
- gzip level 6
- Shuffle
- true
- Contiguous Twin In Same File
- true
- Elements
- 4096
Testing contract
Expected to pass- Scenario
- Read both datasets and assert they are element-wise equal, then report the storage layout and filter list of each.
- Expected result
- The arrays match exactly, /compressed reports chunks of (16, 64) with shuffle and deflate in its filter pipeline, and /contiguous reports no filters.
What is a .h5 file?
HDF5 (.h5) is a binary container format for large, heterogeneous scientific data. It stores multidimensional arrays (datasets) in a hierarchical group structure with attributes and chunked, compressed storage, and is standard in ML, physics, and geoscience.
How to use this file
Use an example .h5 file to test HDF5 readers (h5py, PyTables), group and dataset traversal, and attribute extraction.
How to use this file for testing
“HDF5 Chunked + gzip + shuffle Filters (.h5)” is a deterministic Novus Examples fixture for Scientific data, Serialization testing, Conversion testing. Citation catalogs (BibTeX, RIS), chemistry structures (MDL Molfile, PDB), and gridded binary data (NetCDF, FITS) — for testing reference managers, molecule viewers, and scientific-data loaders.
Documented properties for this file: H5 · 43,708 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Scientific fixtures are small, valid, and fully synthetic — no real organism, patient, sample, or observation. Point your parser or loader at the file and check it reads the documented records, variables, or headers; binary formats ship a readable twin or metadata listing for comparison.
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- zipNumPy .npz-style Bundle — Five Arrays, Deflated (.zip)The identical five arrays bundled with deflate instead of stored, which is the only difference between NumPy's savez and savez_compressed. Loading both and comparing proves a reader ran the decompressor rather than trusting stored-size shortcuts.
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