NumPy .npy — float64 Big-Endian (.npy)
Seven doubles written big-endian, including negative zero and values at both ends of the exponent range. Byte-swapping a double is unrecoverable by inspection — swap pi and you get 3.2e-192, which looks like a plausible tiny number rather than an error.
| Field | Value |
|---|---|
| dtype | >f8 |
| descr in header | >f8 |
| shape | (7,) |
| fortran_order | False |
| elements | 7 |
| Header descr | '>f8' |
| pi encodes as | 40 09 21 fb 54 44 2d 18 |
| Contains -0.0 | yes — byte 0 is 0x80, every other byte is 0x00 |
| Extremes | 1e-300 and 1e300 |
Specifications
- Dtype
- >f8 (big-endian float64)
- Elements
- 7
- Descr
- >f8
- Includes Negative Zero
- true
- Includes Subnormal Range
- false
- Pi Bytes
- 40 09 21 fb 54 44 2d 18
Testing contract
Expected to pass- Scenario
- Load the array honouring the '>f8' descriptor and check the value of pi and the sign bit of the second element.
- Expected result
- Element 3 equals 3.141592653589793 exactly and element 1 is negative zero with copysign returning -1.0; a byte-order-blind load turns pi into roughly 3.207e-192.
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 — float64 Big-Endian (.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 · 184 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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