NumPy .npy — float32 3-D Cube (.npy)
The identical temperature field carried by the CF NetCDF and HDF5 fixtures, stored as a bare float32 .npy. Comparing the three shows exactly what a plain array container loses: the numbers survive, the units and axes do not.
| Field | Value |
|---|---|
| dtype | float32 |
| descr in header | <f4 |
| shape | (4, 5, 6) |
| fortran_order | False |
| elements | 120 |
| Same values as | the CF NetCDF grid and the HDF5 dimension-scale file |
| Units | kelvin — but .npy stores NO units, which is the point |
| value[0,0,0] | 270.000 |
Specifications
- Dtype
- float32
- Shape
- 4 x 5 x 6
- Elements
- 120
- Byte Order
- little-endian
- Same Values As
- sci-nc-cf-grid
- Units
- K (documented here, not stored — .npy carries no metadata)
Testing contract
Reference control- Scenario
- Load this array and the tas variable from the paired CF NetCDF file and compare them element by element.
- Expected result
- Both are (4, 5, 6) float32 and every element matches exactly, while only the NetCDF file can tell you the values are kelvin on a lat/lon/time grid.
What is a .npy file?
NPY is NumPy's native binary format for a single array. A short header records the dtype, shape, and memory order, followed by the raw array bytes, so an array round-trips exactly without any text parsing. It is the standard way to persist embeddings, tensors, and numeric matrices in the Python data stack.
How to use this file
Use an example .npy to test array loaders (numpy.load), tensor and embedding pipelines, and converters between .npy, JSON, and columnar formats like Parquet.
How to use this file for testing
“NumPy .npy — float32 3-D Cube (.npy)” is a deterministic Novus Examples fixture for Scientific data, Serialization 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: NPY · 608 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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