NumPy .npy: int16 Little-Endian (.npy)
Nine int16 values including both type extremes, stored little-endian so the descriptor in the header reads '<i2'. It is one half of an endian pair whose values are identical and whose bytes are not.
| Field | Value |
|---|---|
| dtype | int16 |
| descr in header | <i2 |
| shape | (9,) |
| fortran_order | False |
| elements | 9 |
| Header descr | '<i2' |
| 4660 encodes as | 34 12 (low byte first) |
| -1 encodes as | ff ff |
| Twin | the big-endian file holds identical VALUES, different BYTES |
Specifications
- Dtype
- <i2 (little-endian int16)
- Elements
- 9
- Descr
- <i2
- Bytes Per Element
- 2
- Value4660 Bytes
- 34 12
Testing contract
Expected to pass- Scenario
- Load both endian twins and compare their values, then compare the raw bytes that follow each header.
- Expected result
- The decoded arrays are equal element for element, but 4660 appears as the byte pair 34 12 here and 12 34 in the big-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 Little-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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