rustfits

A Python FITS library with the heavy lifting written in Rust.

What it does

rustfits reads and writes FITS files — the astronomical data format — from Python. The Python surface mirrors fitsio conventions: open a file with FITS(path), index into HDUs with fits[i] or fits["sci"], call hdu.read() for the data, and slice with hdu[a:b, c:d].

What's there today:

Images — read, slice, write, in-place edits, append rows (extend), BSCALE/BZERO and BLANK/MaskedArray support, unsigned-int trick (u2/u4/u8/i1).
Tables — read/write structured arrays, dict, and list+names inputs; row / slice / fancy / column / cell / multi-column writes; append and schema edits (insert_column / delete_column); variable-length columns including string PA; bit-packed X / PX / QX.
Tile-compressed images (ZIMAGE) — all five algorithms (GZIP_1, GZIP_2, RICE_1, HCOMPRESS_1, PLIO_1), quantized and unquantized floats, mutation (__setitem__ + extend) without compounding quantization loss, repack() to reclaim orphans.
Tile-compressed tables (ZTABLE) — full surface including VLA columns and the dual-descriptor heap.
Headers — case-insensitive lookup, CONTINUE chains, HIERARCH long keys, batched updates via FITSHeaderEdit, in-place header grow, CHECKSUM / DATASUM / ZHECKSUM / ZDATASUM.
Cross-tool interop — files written by rustfits round-trip bit-exactly through astropy.io.fits and fitsio. Files written by those tools (or by cfitsio / fpack) read back in rustfits unchanged.

Quick examples

import rustfits

# Read an image — auto-picks the first HDU with data.
img = rustfits.read("image.fits")

# Slice an existing image without loading the full array.
with rustfits.FITS("image.fits") as fits:
    image = fits["sci"].read()
    image = fits["sci"][:, :]

    stamp = fits["sci"][100:200, 50:150]

# Read a table; subset columns and rows.
with rustfits.FITS("catalog.fits") as fits:
    hdu = fits[1]

    # using numpy-style slicing
    tab = hdu[:]
    tabsub = hdu[0:100]
    ra = hdu['ra'][20:30]
    radec = hdu[['ra', 'dec']][[3, 5, 25]]

    # using the read() function
    tab = hdu.read()  # same as hdu[:]
    tabsub = hdu.read(columns=["ra", "dec"], rows=[3, 5, 25])

# Write an image or table to a new file (auto-detects).
import numpy as np
rustfits.write("out.fits", np.zeros((1024, 1024), dtype="f4"))

cat = np.zeros(100, dtype=[("ra", "f8"), ("dec", "f8")])
rustfits.write("cat.fits", cat)

# Update the data with numpy-style setitem
with rustfits.FITS(fname, 'r+') as fits:
    hdu = fits["table"]
    hdu[10:20] = updated_rows
    hdu['ra'] = new_values
    hdu[['ra', 'dec']][10:50] = new_radec

See the tutorial for a guided tour covering images, tables, compression, headers, the error model, and known limitations.

Documentation

Full documentation is hosted at rustfits.readthedocs.io. The latest build tracks main; stable tracks the most recent release tag.

To build the docs locally instead:

sphinx-build docs docs/_build/html
xdg-open docs/_build/html/index.html

Sphinx and the Furo theme are listed in docs/requirements.txt if you need them in your env.

Building from source

There's no PyPI or conda-forge release yet; install from source.

The build needs:

A Python environment with numpy and maturin>=1.0,<2.0.
A Rust toolchain installed via rustup. Don't use conda's rust package — it interacts badly with PyO3's libpython linking.

The simplest setup uses conda for Python and rustup for Rust:

# 1. Clone.
git clone https://github.com/esheldon/rustfits
cd rustfits

# 2. Create / activate a Python env, then install the runtime
#    + build deps.
conda install --file conda-requirements.txt

# 3. Install the Rust toolchain (skip if you already have rustup).
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# 4. Build and install the editable wheel into the active env.
maturin develop          # debug build, for iteration
# OR
maturin develop --release  # optimized build, for actual use

After the build finishes, import rustfits works inside the active env. Re-run the same command after any change to the Rust code.

Debug vs release. maturin develop (no flag) produces an unoptimized debug build: it compiles fast and is what you want during a tight edit-build-test loop. maturin develop --release turns on the full optimizer (LLVM -O3-equivalent, inlining, SIMD, etc.) — slower to compile but the resulting .so runs 5–50× faster depending on workload. Use --release for anything other than development — benchmarks, real data reductions, scripts that touch lots of bytes. The debug build is roughly 100× slower at decompressing tiles, for example, which makes ZIMAGE reads feel broken if you forget the flag.

The release profile keeps line-table debug info (debug = "line-tables-only" in Cargo.toml), so backtraces still resolve to source lines without paying the dead-code- elimination penalty of a true debug build.

Running tests

To run the test suite or contribute, install the dev requirements on top of the build env:

conda install --file conda-test-requirements.txt

Then:

pytest                 # Python tests
tools/cargo-test.sh    # Rust unit tests (wrapped to find libpython)

The wrapper tools/cargo-test.sh prepends the conda env's lib directory to LD_LIBRARY_PATH so the PyO3-linked test binary can find libpython.X.so — bare cargo test fails without it.

Contributing

Contributions and bug reports are welcome. Please feel free to open pull requests or issues at https://github.com/esheldon/rustfits

rustfits is mostly written in rust. If you don't know rust, you can still make contributions. The extensive CLAUDE.md file can be used by claude code or other agents to help you fix bugs or add features. Just start the agent, ask it to load from CLAUDE.md, and start working.

License

Dual-licensed under MIT or Apache-2.0, at your option.

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rustfits 0.1.8

rustfits

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SHA256	`8e7c4aaf0070caaea49cdac7f25b15fc33c284b40b1efbcf3c81dcff934fb8f7`
MD5	`4e81bdbd75d04d3ff85517b9d5293b1b`
BLAKE2b-256	`4500f0b4f5516bee2aac665e66e987216f68107de883049f4bb7dcec47272b95`

Algorithm	Hash digest
SHA256	`58264afc77bdfddb82473d95fbe4925effa13b3019e84bbe2c6426fe7483dff7`
MD5	`70016941638f2b0da5ff3f288639ab0b`
BLAKE2b-256	`41369275477597af93e30181ecd4330562761663a095005376c0a23f810039e2`

Algorithm	Hash digest
SHA256	`4a865f1151c52e97af54198cd0cef9169306705e954f4652944d30587f86ff29`
MD5	`10042272492e590015cf2c8cd2568210`
BLAKE2b-256	`1cbab4037810934731b045901b0625c0b1716e0cdde37f064c5559bd2963e969`

Algorithm	Hash digest
SHA256	`36400a43c7320ea0abc5c8257ca7c1ea1edc284069e3a512d268c940928f7170`
MD5	`c7f4c871a31a2a3c1d02b82a79a2e287`
BLAKE2b-256	`09e7c8a2c31993be13830fa8a039ae81d4fba9ddb7f6d4f17fe210812bc6f949`

Algorithm	Hash digest
SHA256	`2c7423f458b62409e3301effeaaf826c08aa8471234af4a15bc2fb2938ef75c1`
MD5	`54b5c7f5967e0922adf797f2390e6956`
BLAKE2b-256	`8abd7cb2106fb320a72919af0122d7fdfcfeb08070d4ffe00e214de2db560ff9`

Algorithm	Hash digest
SHA256	`ef95547b8a9d5cccdfd019e50000cf82d0b0d047eb324de4ad5798bd6c462c61`
MD5	`029789ebc72de3dfae25a0a05acc1525`
BLAKE2b-256	`ee179be94965c0ede44856172998f92057272c1aef87b1c2dd1fb767a7e1beb2`