NumPy .npy — complex128 Spectrum (.npy)
The real FFT of a 64-sample sine stored as complex128, where each element is an interleaved pair of doubles rather than two separate planes. Readers without a complex type usually flatten it, which doubles the reported length and shifts the peak bin.
| Field | Value |
|---|---|
| dtype | complex128 |
| descr in header | <c16 |
| shape | (33,) |
| fortran_order | False |
| elements | 33 |
| Bytes per element | 16 (two float64s, real then imaginary) |
| Layout | interleaved, NOT two separate planes |
| Peak bin | 4 |
| |peak| | 31.531319 |
Specifications
- Dtype
- complex128
- Elements
- 33
- Bytes Per Element
- 16
- Layout
- interleaved real, imaginary
- Source
- rfft of a 64-sample sine
- Descr
- <c16
Testing contract
Expected to pass- Scenario
- Load the array and locate the bin with the largest magnitude, checking the reported element count.
- Expected result
- The array holds 33 complex elements of 16 bytes each and the magnitude peaks at bin 4; a flattening reader reports 66 real elements instead.
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 — complex128 Spectrum (.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 · 656 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.
Related files
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- npyNumPy .npy — float64 2-D Grid (.npy)The baseline .npy fixture: a 12x8 float64 array in version 1.0 format with a little-endian descriptor and the header padded to the mandatory 64-byte alignment. Every other array in this family varies exactly one property away from it.
- npyNumPy .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.
- npyNumPy .npy — Fortran Order (Column-Major) (.npy)The same 4x6 matrix stored column-major with fortran_order set to True in the header. Ignoring that one boolean does not produce an error, it produces a transposed matrix — the quietest failure in the whole .npy format.
- npyNumPy .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.
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