NumPy .npy: C Order (Row-Major) (.npy)
A 4x6 matrix of 0..23 stored row-major, so the byte sequence begins with the first row. It is one half of a memory-order pair that is indistinguishable from its twin unless the header's fortran_order flag is honoured.
| Field | Value |
|---|---|
| dtype | float64 |
| descr in header | <f8 |
| shape | (4, 6) |
| fortran_order | False |
| elements | 24 |
| fortran_order in header | False |
| Bytes on disk start | 0.0, 1.0, 2.0, 3.0: the first ROW |
| Twin | the Fortran-order file holds the same MATRIX, different byte sequence |
Specifications
- Shape
- 4 x 6
- Fortran Order
- false
- First Four Values
- 0, 1, 2, 3
- Dtype
- float64
- Elements
- 24
- Stored Sequence
- 0..23 in reading order
Testing contract
Expected to pass- Scenario
- Load both order twins and compare the matrices, then compare the first four values in the raw data section.
- Expected result
- The matrices are element-wise equal, but this file's data starts 0, 1, 2, 3 while the Fortran twin starts 0, 6, 12, 18.
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: C Order (Row-Major) (.npy)” is a deterministic Testaroo 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.
Generated by generation/scientific.py. Free for any use, no attribution required, license.
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