NumPy .npy — Fortran Order (Column-Major) (.npy)
The same 4x6 matrix stored column-major with fortran_order set to True in the header. Ignoring that one boolean does not produce an error, it produces a transposed matrix — the quietest failure in the whole .npy format.
| Field | Value |
|---|---|
| dtype | float64 |
| descr in header | <f8 |
| shape | (4, 6) |
| fortran_order | True |
| elements | 24 |
| fortran_order in header | True |
| Bytes on disk start | 0.0, 6.0, 12.0, 18.0 — the first COLUMN |
| Consequence of ignoring it | the matrix comes back transposed, not corrupt |
Specifications
- Shape
- 4 x 6
- Fortran Order
- true
- First Four Values
- 0, 6, 12, 18
- Dtype
- float64
- Elements
- 24
- Stored Sequence
- column by column
Testing contract
Expected to pass- Scenario
- Load the file honouring fortran_order and compare the result against the C-order twin.
- Expected result
- The two matrices compare equal; a loader that ignores fortran_order returns the transpose and disagrees at element [0][1], where it reads 6.0 instead of 1.0.
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 — Fortran Order (Column-Major) (.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 · 320 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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