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 Testaroo 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/ # extractGenerated by generation/scientific.py. Free for any use, no attribution required, license.
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