NumPy .npy Format Version 3.0 — UTF-8 Field Names (.npy)
A structured array whose field names use Greek and CJK characters, which is the only reason format version 3.0 exists: its header is UTF-8 where 1.0 and 2.0 are latin-1. A parser that decodes the header as latin-1 mangles all three names.
| Field | Script | Units | First value |
|---|---|---|---|
| Ω_resistance_ohm | Greek capital omega | ohm | 10.0 |
| θ_angle_rad | Greek theta | rad | 0.0 |
| 温度_K | CJK | K | 273.15 |
Specifications
- Npy Version
- 3.0
- Fields
- 3
- Records
- 5
- Header Encoding
- UTF-8 (1.0 and 2.0 use latin-1)
- Field Names
- Ω_resistance_ohm, θ_angle_rad, 温度_K
- Reason
- field names outside latin-1 require version 3.0
Testing contract
Expected to pass- Scenario
- Read the version bytes, decode the header as UTF-8, and list the field names of the structured dtype.
- Expected result
- The version reads 3.0 and the three field names decode as 'Ω_resistance_ohm', 'θ_angle_rad' and '温度_K'; a latin-1 decode produces mojibake such as 'Ω_resistance_ohm'.
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 Format Version 3.0 — UTF-8 Field Names (.npy)” is a deterministic Novus Examples fixture for Scientific data, Serialization testing, Encoding detection. 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: 5 records · 3 fields. 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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