zigfitsio

Read and write FITS files with a NumPy-first, astropy.io.fits-style API — no C compiler, no CFITSIO required.

Python bindings for zigfitsio, a pure-Zig FITS 4.0 I/O library. The native code is a Zig-built shared library loaded via ctypes, in two layers:

High-level (zigfitsio) — modeled on astropy.io.fits: open, HDUList, the HDU classes, Column, Header, and getdata/getheader/writeto/verify.
Low-level (zigfitsio.lowlevel) — a 1:1 ctypes binding over the C ABI, for power users.

Install

pip install zigfitsio

Prebuilt wheels need no compiler. Building from source requires a Zig 0.16 toolchain (supplied automatically by the ziglang build dependency, or a system zig on PATH).

Quickstart

import numpy as np
import zigfitsio as zf

# Write an image
zf.writeto("image.fits", np.arange(12, dtype="f4").reshape(3, 4), overwrite=True)

# Read it back (NumPy array, shape (NAXIS2, NAXIS1), C-order — like astropy)
with zf.open("image.fits") as hdul:
    data = hdul[0].data
    print(data.shape, hdul[0].header["NAXIS1"])

Tables

cols = [
    zf.Column("INDEX", "J", np.array([10, 20, 30], dtype="i4")),
    zf.Column("FLUX",  "E", np.array([1.5, 2.5, 3.5], dtype="f4"), unit="Jy"),
    zf.Column("NAME",  "8A", np.array(["alpha", "beta", "gamma"])),
]
zf.HDUList([zf.PrimaryHDU(), zf.BinTableHDU.from_columns(cols, name="EVENTS")]).writeto(
    "table.fits", overwrite=True
)

Compressed images

ramp = np.arange(256, dtype="i4").reshape(16, 16)
zf.HDUList([zf.PrimaryHDU(), zf.CompImageHDU(ramp, compression="RICE_1")]).writeto(
    "comp.fits", overwrite=True
)

Headers (dict-like)

with zf.open("image.fits", mode="update") as hdul:
    h = hdul[0].header
    h["OBSERVER"] = ("Hubble", "the observer")  # value + comment
    print(h["OBSERVER"], "/", h.comment_of("OBSERVER"))
    print("BITPIX" in h, list(h.keys()))

Setters remain eager by default. Use Header.edit() to validate related changes together and commit them with one Zig call/header write:

with zf.open("image.fits", mode="update") as hdul:
    with hdul[0].header.edit() as h:
        h["OBSERVER"] = ("Hubble", "who")
        h["ESO DET CHIP ID"] = 42  # HIERARCH is serialized by the Zig core
        h.add_history("calibrated")

Callback, validation, and revision-conflict failures leave the file unchanged. Device failures use best-effort rollback and are not a crash-safe/on-disk journaling transaction.

WCS (celestial)

Pixel pairs are one-based and ordered (longitude-axis pixel, latitude-axis pixel); the celestial axes need not be FITS Axes 1 and 2.

with zf.open("wcs.fits") as hdul:
    lon, lat = hdul[0].pix2world(40.0, 30.0)   # 1-based pixel (FITS CRPIX convention)
    px, py = hdul[0].world2pix(lon, lat)

Validation

for finding in zf.verify("image.fits"):   # fitsverify-style structural checks
    print(finding)

Low-level (ctypes)

import ctypes as c
import zigfitsio.lowlevel as ll

h = c.c_void_p()
ll.check(ll.lib.zf_create_memory(None, c.byref(h)))
ll.check(ll.lib.zf_create_img(h, -32, 2, (c.c_long * 2)(4, 3)))
ll.lib.zf_close(h)

Conventions

Image and table data is exchanged as native-endian NumPy arrays; non-native (byte-swapped) input is coerced automatically before writing. Image shape is the reversed FITS axis order ((NAXIS2, NAXIS1)), C-contiguous — identical memory layout to astropy.io.fits.
BSCALE/BZERO and TSCAL/TZERO scaling and the unsigned-integer convention are applied automatically on read (images and table columns) and honored on write; the output dtype is widened to float when real scaling is present, or to u2/u4/u8 for the unsigned convention.
Errors are raised as typed FitsError subclasses (KeywordNotFound is also a KeyError).

Known limitations

Not a CFITSIO drop-in — the ABI is purpose-built zf_* symbols, not fits_*.
Integer BLANK/TNULLn values are not auto-masked (no numpy.ma); float nulls surface as NaN.
In-place update of compressed images, VLA or scaled columns, or a changed row count raises — use writeto() to a new file instead.
Tables with duplicate effective column names can be inspected as metadata or copied verbatim, but high-level data access/reconstruction raises FitsTableError (status 219); use low-level indexed column reads when duplicates must be addressed.
writeto() of a scanned quantized-float compressed image re-quantizes at the default level (the FITS header does not record the level).

The full list lives in CAVEATS.md.

Development

zig build capi                    # build the shared library into zig-out/lib
pip install -e .[test]            # editable install (builds the lib via the hook)
pytest bindings/python/tests -q   # run the suite (incl. astropy cross-checks)

License

MIT — see LICENSE.

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zigfitsio 0.1.6

zigfitsio

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SHA256	`fb53177725bf4f7fe5fabd33b86af145d4bb13db903d5207b98c62bb7a5679f0`
MD5	`9b9106716e98b18befb5765d9d25caeb`
BLAKE2b-256	`8bac46902ec94d4e9cfb67bb660d3424733a8182efc88d5036b35c77de7d7ded`

Algorithm	Hash digest
SHA256	`d49f563783d940a14bc92c63b930b555c5d14621422e7e2eae9b560bb76eb239`
MD5	`1d1c40e5a7cc3bbe305193751e625003`
BLAKE2b-256	`5df3c9d00383712bcc5e7be30c89bb730557680b6dfbec51f1bfd11b782674bc`

Algorithm	Hash digest
SHA256	`500dd33cd9dc03e8f8505a49b2bca80f492eb469b5aff9f1292cae4bdcedc4b2`
MD5	`ada07bbe5cfe6e462b74c0d23bf0066b`
BLAKE2b-256	`4b9378a77f623a86e1ecaf38f5c1dac5bdaddfc200a08f66f86d06506bf4d497`

Algorithm	Hash digest
SHA256	`d64e425792c5e6c2ad39b3ce9ff9e3e1d5a8bd1395cadea3d2195f89e2832d78`
MD5	`62d20eb97f98597029fdecb01e226fa2`
BLAKE2b-256	`7b53a6226b8bef864578417523ec6d9e13ccaa7ede9b3443d36d3f5597a2225b`

Algorithm	Hash digest
SHA256	`d29ba018b61a13930261c3200a2a9063c9e163538783ccc551628af11f308357`
MD5	`edd2b9ec2ed455ff109e42d179fa1d74`
BLAKE2b-256	`530bc9f384bc9ed760fc715e2c9d8852b7296a89625952c237c7e9a4024a85b9`

Algorithm	Hash digest
SHA256	`2be636e4e99da7559de0b14ad0dc064a7b1c75c34d753a42b38435b5422d3758`
MD5	`ebcef9a173bb5f862505307c51eff821`
BLAKE2b-256	`9be3d964b7e51ad10fd93d56b5178dd773a657c74c9afdf60e6a78234e5139be`

Algorithm	Hash digest
SHA256	`8345daf5b2d2de4dcce67b6c282e7bfb09ae4227fdb6c6ad0165f6e88a29c5a6`
MD5	`3a28793c57b5305f3f687f191050cda6`
BLAKE2b-256	`40d3bca3822857a5da62fd6bdc929a300d0a9bac0f584ff98dde06de922cb316`

Algorithm	Hash digest
SHA256	`ba8d98191ee8d0c9639ea35170659365cf6b55b72f4b143521cca9f883bfceda`
MD5	`91f441f769bcb067d0de8ddcefc6546b`
BLAKE2b-256	`e1c69436f958000aafd8e1b2044656929f71faffb946342f2334c32a51a8b90e`

Algorithm	Hash digest
SHA256	`ccec4f69ed2b7f4e74d2323eed7020c708d9d429755816cae05c29921cf04ab4`
MD5	`f3cc9e45d46f1154e340e333e88fb74c`
BLAKE2b-256	`d8faa75ab506de4c6724ab78261b347c69dc4346222a0083674e2fb76571ee38`

Algorithm	Hash digest
SHA256	`99278344efa7d25304e328b290dc82c3342bc53b711ad977d7a12bd378b7d370`
MD5	`981f7a71885afe74150ef364f9bd4d7f`
BLAKE2b-256	`79319a770bf8f0ca6400f8d6103f87f97558dc5d6953249668cc6a7efe39b021`