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npy146 B

NumPy .npy — float16 at Its Limits (.npy)

Every structurally interesting float16 value in one array: one plus epsilon, the largest finite value, the smallest normal and subnormal, negative zero, infinity and a NaN. It is the compact half-precision counterpart to the float32 and float64 boundary fixtures.

Preview — schema + first 7 rowsnpy
IndexValueWhy it is here
21.00097656251 + eps for half precision (eps = 2^-10)
365504.0largest finite float16
46.103515625e-05smallest normal float16
55.960464477539063e-08smallest subnormal float16
6-0.0negative zero
7+Infanything above 65504 overflows to this
8NaNquiet NaN
Nine values, two bytes each — every interesting corner of IEEE-754 binary16.

Specifications

Dtype
float16
Elements
9
Bytes Per Element
2
Max Finite
65504
Smallest Normal
0.00006103515625
Smallest Subnormal
5.960464477539064e-8
Includes Na N
true
Includes Inf
true
Includes Negative Zero
true

Testing contract

Expected to pass
Scenario
Load the array and classify each element as normal, subnormal, zero, infinite or NaN without widening to float32 first.
Expected result
Element 4 is the smallest normal and element 5 the smallest subnormal, element 6 is negative zero with a set sign bit, and exactly one element each is +Inf and NaN.

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 — float16 at Its Limits (.npy)” is a deterministic Novus Examples fixture for Scientific data, Serialization testing, Error handling. 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.