NumPy .npy: int16 Big-Endian (.npy)
The same nine int16 values written big-endian, so the header descriptor reads '>i2'. A loader that ignores the descriptor and assumes native little-endian order returns 13330 where the file says 4660, without any error.
| Field | Value |
|---|---|
| dtype | >i2 |
| descr in header | >i2 |
| shape | (9,) |
| fortran_order | False |
| elements | 9 |
| Header descr | '>i2' |
| 4660 encodes as | 12 34 (high byte first) |
| -1 encodes as | ff ff (identical either way) |
| Trap | a reader that hardcodes native order returns 13330 instead of 4660 |
Specifications
- Dtype
- >i2 (big-endian int16)
- Elements
- 9
- Descr
- >i2
- Bytes Per Element
- 2
- Value4660 Bytes
- 12 34
Testing contract
Expected to pass- Scenario
- Load this file on a little-endian machine and check the value that should decode as 4660.
- Expected result
- The array decodes to 4660 because the loader honoured the '>i2' descriptor; a byte-order-blind loader returns 13330 and silently disagrees with the little-endian twin.
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: int16 Big-Endian (.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 · 146 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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