NumPy .npz-style Bundle — Five Arrays, Deflated (.zip)
The identical five arrays bundled with deflate instead of stored, which is the only difference between NumPy's savez and savez_compressed. Loading both and comparing proves a reader ran the decompressor rather than trusting stored-size shortcuts.
- temperature_K.npy
- lat_degrees_north.npy
- lon_degrees_east.npy
- time_hours.npy
- uncertainty_K.npy
Specifications
- Members
- 5
- Compression
- deflate
- Member Format
- .npy version 1.0
- Same Arrays As
- the stored bundle
- Why Both Exist
- savez and savez_compressed differ only in this flag
Testing contract
Expected to pass- Scenario
- Load both bundles and compare every array element-wise, then compare the compressed and uncompressed member sizes.
- Expected result
- All five arrays are identical between the two bundles, while this file's members report a compressed size smaller than their uncompressed size and the stored bundle's do not.
What is a .zip file?
ZIP is a widely supported archive format that bundles multiple files and directories into one container, typically with per-file DEFLATE compression and a central directory index. It supports random access to individual entries without decompressing the whole archive. It underlies many document formats such as DOCX and EPUB.
How to use this file
Use an example ZIP to test archive extraction, central-directory parsing, per-entry decompression, and protection against path-traversal (zip-slip) during unpacking.
How to use this file for testing
“NumPy .npz-style Bundle — Five Arrays, Deflated (.zip)” is a deterministic Novus Examples fixture for Scientific data, Serialization testing, Conversion 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: ZIP · 1,112 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.
Code examples
unzip -l npz-style-bundle-deflated.zip # list members
unzip npz-style-bundle-deflated.zip -d out/ # extractRelated files
- fitsFITS with World Coordinate System — Tangent Plane (.fits)A 32-bit integer image carrying a complete tangent-plane WCS: 1-based CRPIX reference pixels, a deliberately negative CDELT1 so right ascension runs the other way, and an ICRS frame. It targets the two mistakes every WCS implementation makes first, the 1-based origin and the flipped axis.
- h5HDF5 Chunked + gzip + shuffle Filters (.h5)One array stored twice in the same file: chunked with the shuffle and gzip filters applied, and contiguous with no filters at all. Comparing the two proves a reader ran the HDF5 filter pipeline rather than reading raw chunk bytes.
- h5MATLAB v7.3 (.mat) Variable Store — the HDF5 File It Actually Is (.h5)A MATLAB v7.3 variable store, which is an HDF5 file with a 512-byte MATLAB user block and MATLAB_class attributes — the bytes are the same whichever extension you give it. Because MATLAB is column-major, A appears in HDF5 as its own transpose, which is the conversion bug this fixture is built around.
- csvfloat64 2-D Grid — Exact-Repr CSV Twin (.csv)Every element of the float64 .npy array written as its shortest round-tripping decimal representation, one row per element in C order. Parsing this text back to float64 reproduces the binary array bit for bit, so it is a usable oracle rather than a lossy printout.
- npyNumPy .npy — Boolean Mask (.npy)A 6x10 boolean mask stored as one byte per element, because .npy does not bit-pack booleans however tempting that assumption is. The True cells follow a simple divisible-by-three rule so a mis-decode is obvious rather than plausible.
- npyNumPy .npy — C Order (Row-Major) (.npy)A 4x6 matrix of 0..23 stored row-major, so the byte sequence begins with the first row. It is one half of a memory-order pair that is indistinguishable from its twin unless the header's fortran_order flag is honoured.
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