Skip to content
Novus Examples
fits8.4 KB

FITS Image — float64 Double Precision (.fits)

The top rung of the BITPIX ladder, carrying the identical synthetic scene as IEEE-754 doubles with DATAMIN and DATAMAX declared. It is the reference against which the 8-, 16- and 32-bit members of the family are scored for precision loss.

Preview — schema + first 6 rowsfits
FieldValue
BITPIX-64 (IEEE-754 double, big-endian)
Bytes per pixel8
DATAMIN120.023489
DATAMAX1539.700000
Same scene asthe BITPIX 8, 16 and -32 images in this family
Pixel [5,6]1027.413551
The reference rung: the only image in the ladder that loses nothing.

Specifications

Bitpix
-64
Naxis1
24
Naxis2
16
Datamin
120.023489
Datamax
1539.7
Byte Order
big-endian IEEE-754
Bytes Per Pixel
8

Testing contract

Reference control
Scenario
Read this image and the float32 image from the same family and compare them pixel by pixel over the finite pixels.
Expected result
Values agree to within float32 rounding — roughly 1e-4 relative — and DATAMIN/DATAMAX bracket every pixel in this file exactly.

What is a .fits file?

FITS (.fits, Flexible Image Transport System) is the standard binary format in astronomy. It pairs human-readable 80-character header cards (in 2880-byte blocks) with binary image or table data, storing pixel arrays, coordinates, and instrument metadata. It has been the astronomical archive format for decades.

How to use this file

Use an example .fits file to test FITS readers (astropy, CFITSIO, DS9), header-card parsing, and image or table extraction, or to verify block-alignment handling.

How to use this file for testing

“FITS Image — float64 Double Precision (.fits)” 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: FITS · 8,640 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.