casacure and python-casacure

This repository contains two user-facing packages that share one engine — a pure-Rust implementation of the CASA table system that reads and writes real casacore Measurement Sets and tables without needing the C++ casacore library:

"python-casacure" is the name of the Python package. Its published PyPI name stays casacure (module casacure.tables) — the Python package is not renamed. The Python package is built from the internal crates/casacure-python crate, which is not published to crates.io and is not intended for direct use.

Both packages expose the same capabilities — the StandardStMan, IncrementalStMan and TiledColumnStMan on-disk formats, table metadata, keywords and subtable linkage, and a TaQL subset — without building or linking the C++ casacore library. Install is fast (a wheel or the crate), there is no multi-hour C++ dependency build, and it works everywhere a Rust cdylib can be built, including ARM (aarch64).

The Python binding's casacure.tables is an interface-compatible replacement for casacore.tables, so existing tooling — most notably dask-ms — can run on casacure unchanged (CPython 3.9–3.14; wheels for Linux, macOS and Windows).

Status is tracked in ARE_WE_CURED.md; the outline is in CASACORE_TO_CASA_RS.md.

Why choose casacure?

• Drop-in replacement for casacore.tables. casacure.tables mirrors the python-casacore interface, so existing packages — most notably dask-ms — run unchanged. The full dask-ms 0.2.32 test suite passes against it (219/219 on Python 3.13 and 3.14), reading, writing and updating real Measurement Sets.
• No C++ casacore library. The table engine is pure Rust. There is no casacore build, no multi-hour C++ dependency compile, and no Fortran/C system library (wcs, measures, images, …) to install. pip install casacure is the entire setup; building from source needs only a Rust toolchain plus maturin — still no C++ compiler.
• Real on-disk interop. casacure is byte-compatible with the casacore table formats it implements (StandardStMan, IncrementalStMan, TiledColumnStMan): tables it writes open in real casacore and vice-versa, verified against casacore 3.8.1 — including full Measurement Sets with their subtable tree and multi-manager tables.
• Endian-correct anywhere. The engine reads and writes both big- and little-endian tables, so it behaves identically on any host endianness.
• Self-contained wheels. Prebuilt wheels for Linux, macOS and Windows on CPython 3.9–3.14 — including aarch64, the machines where casacore is most painful to build (see "Platforms and architectures").
• One engine, two packages. The same Rust core ships as the casacure crate on crates.io (Rust) and as the casacure Python package on PyPI.
• Memory-safe by construction. A Rust implementation removes the segfault and memory-management warts of the C++ python-casacore bindings: for example, putcol accepts numpy object arrays directly — behaviours that crash the C++ build are absent by construction.
• TaQL included. A real TaQL subset ships in the same package: SELECT with WHERE / ORDERBY / GROUPBY / HAVING / UNIQUE / subqueries, a casacore-style function library (numeric/string/date-time/stats families, g* aggregates, LIKE / IN), and the write statements (UPDATE, DELETE, INSERT INTO, ALTER TABLE, DROPTABLE, SHOW/CALC) — all reachable through taql() / table.taql(), so no separate query layer is needed.

Using casacure in place of casacore

casacure provides a python-casacore-compatible surface. The whole dask-ms 0.2.32 test suite passes against it (219/219 on both Python 3.13 and 3.14), including reading, writing and updating real Measurement Sets.

Direct API

casacure.tables mirrors casacore.tables:

import numpy as np
import casacure.tables as ct   # drop-in for `import casacore.tables`

t = ct.table("test.tab",
             {"TIME": {"valueType": "double", "option": 0, "comment": "",
                       "keywords": {}},
              "DATA": {"valueType": "dcomplex", "option": 0, "comment": "",
                       "ndim": 2, "shape": [8, 4], "keywords": {}}},
             nrow=3)
t.putcol("TIME", np.array([0.0, 1.5, 3.0]))
t.putcol("DATA", np.random.rand(3, 8, 4).astype(np.complex128))
t.flush()

r = ct.table("test.tab", ack=False)          # read-only view
print(r.getcol("DATA").shape)                # (3, 8, 4)

q = ct.taql("SELECT * FROM $1 WHERE TIME > 1.0", tables=[r])
print(q.getcol("TIME"))

ms = ct.default_ms("observation.ms")         # full MS + 17 subtables

Also available: taql CREATE TABLE, getcell/putcell, getvarcol/ putvarcol, getcolslice/putcolslice, getkeywords/putkeywords, addcols, addrows, required_ms_desc/complete_ms_desc, default_ms_subtable, tablefromascii.

Units and quantities

casacure.quanta mirrors casacore.quanta — the unit system and Quantity (a value with a unit), with SI conversion, arithmetic, the time/angle value classes, and the physical-constant / unit / prefix tables:

import casacure.quanta as qa        # drop-in for `import casacore.quanta`

q = qa.quantity(1.5, "Jy")          # Quantity
q.get_value("mJy")                  # 1500.0
q.canonical()                       # '1.5e-26 kg.s-2'
qa.quantity(3, "km") + qa.quantity(500, "m")   # 3.5 km
qa.quantity(45, "deg").formatted()  # '+045.00.00'
qa.quantity(51544, "d").to_unix_time()  # 946684800.0 (2000-01-01T00:00Z)

The full casacore.measures engine (direction/epoch/uvw transforms, backed by the casacore ephemeris data) is not implemented.

Driving dask-ms on casacure

dask-ms imports casacore.tables; point it at casacure in one of two ways:

1. A drop-in casacore shim — a two-file package that re-exports casacure, placed on PYTHONPATH ahead of any real python-casacore (this is exactly what the project's own tests/daskms_smoke.py runs):

casacore/__init__.py          # (empty)
casacore/tables.py            # from casacure.tables import *

2. Backend selection (the cleaner upstream path) — an env-gated DASK_MS_BACKEND=casacure that aliases casacore.tables → casacure.tables in-process. It is a ~10-line sys.modules swap in dask-ms's __init__, validated against the full dask-ms suite; it is the prototype for the upstream store-dispatch change.

Either way, the dask-ms workflow is unchanged:

import dask
import dask.array as da
import numpy as np
import xarray as xr
from daskms import xds_from_ms, xds_to_table
import casacure.tables as ct

n = 4
data = (np.random.rand(n, 8, 4) + 1j * np.random.rand(n, 8, 4)).astype(np.complex64)
ds = xr.Dataset({
    "TIME":     (("row",), da.from_array(np.array([0.0, 1.0, 2.0, 3.0]), chunks=n)),
    "ANTENNA1": (("row",), da.from_array(np.array([0, 1, 0, 1], dtype=np.int32), chunks=n)),
    "DATA":     (("row", "chan", "corr"), da.from_array(data, chunks=(n, 8, 4))),
}, coords={"row": np.arange(n)})

# Create a Measurement Set from datasets (partitioned, chunked writes).
dask.compute(xds_to_table(ds, "example.ms", descriptor="ms"))

# Read it back as lazily-chunked dask arrays.
xds = xds_from_ms("example.ms", columns=["TIME", "ANTENNA1", "DATA"])[0]

# Modify in xarray and write back in place.
xds2 = xds.assign(DATA=(xds.DATA.dims, xds.DATA.data * 2))
dask.compute(xds_to_table(xds2, "example.ms", ["DATA"]))

# The result is a real MS: open it with casacure (or real casacore).
t = ct.table("example.ms", ack=False)
print(t.getcol("DATA")[0, 0, :2])   # doubled visibilities

Because casacure is implemented in Rust, none of this needs a C++ toolchain: the entire dependency stack for reading and writing Measurement Sets is the published wheel.

Install

pip install casacure          # prebuilt wheels once published
# or build from source:
pip install .                # maturin builds the cdylib for your interpreter

The Rust core is published to crates.io as the casacure crate; add casacure = "3.8" to Cargo.toml (the crate tracks the casacore interface: 3.8 == casacore 3.8.x) if you want the table engine in Rust directly.

Versions are <casacore-major>.<casacore-minor>.<casacure-patch> (e.g. 3.8.2): the first two digits are the casacore interface level the package implements (3.8 == casacore 3.8.x), and the last digit is casacure's own patch counter — incremented on every fix/feature release and reset to 1 when the mirrored interface moves (3.8 → 3.9 …). The crates.io casacure crate and the PyPI wheel share the same three-part version, so a release publishes both together. casacure.__version__ reports the same number.

pip install casacure on Python 3.9–3.14 installs a self-contained package — no casacore C++ library, no wcs/measures harness — which is the point: MS support on machines (including aarch64) where casacore is painful to build.

Dependencies

Rust core (casacure crate). A single small runtime dependency: thiserror (error-type plumbing). serde / serde_json are dev-only dependencies, used by the casacore-comparison fixture tests (crates/casacure/tests/compat_fixtures.rs). The core is pure Rust and compiles for any Rust target; there is no unsafe system linkage.

Python bindings (casacure wheel). Built from crates/casacure-python, which links the two standard Rust↔Python bridge crates — pyo3 (CPython binding, 0.27) and numpy (0.27) — against the core crate. The installed Python package has exactly one runtime dependency — numpy — declared in pyproject.toml (dependencies = ["numpy"]): getcol/putcol/getvarcol return and accept the same numpy-typed values as python-casacore (which also hard-depends on numpy), so pip install casacure pulls it automatically and the casacure-test / casacure-bench console scripts run out of the box. Everything else — including the C++ casacore library — is absent.

Explicitly not needed — the point of the project:

• the C++ casacore library or its build system (no wcs, measures, images harnesses);
• any system C / Fortran library or header — the only native code is Rust;
• a C++ toolchain to install or to build wheels.

Building from source adds only the Rust toolchain plus maturin (the pyproject [build-system]): pip install . compiles the cdylib for your interpreter without invoking a C++ compiler.

Platforms and architectures

The pure-Rust core is portable to any Rust target and handles both byte orders, so it runs on any architecture where a Rust toolchain exists. On top of that, the Publish Python package workflow (maturin) builds prebuilt wheels for the following matrix:

Notes:

• Linux wheels are built from the manylinux image, whose --find-interpreter pass covers every supported CPython (3.9–3.14) in a single build per target; the source distribution is produced from the x86_64 Linux job.
• Intel-macOS (x86_64) wheels are deliberately not built: GitHub no longer hosts Intel-mac runners. Intel Mac users install the source distribution, which still needs only Rust + maturin — no C++.
• The Rust casacure crate itself is endian- and target-agnostic: cargo builds it for any supported target (the CI gate runs on Linux, but the engine's portability is what makes the cross-compiled wheel matrix above possible).

Development

• TODO.md — the live task list; CHANGELOG.md records completed steps.
• ARE_WE_CURED.md — progress / test counts.
• CASACORE_TO_CASA_RS.md — the implementation outline (the upstream casacore/ submodule is the reference C++ source and is never modified).
• tests/daskms_smoke.py — end-to-end dask-ms MS create/read/write smoke; tests/test_types_compat.py — type-system comparison vs real casacore.

Rust work is linted with clippy and rustfmt as we go — CI runs these as gates (.github/workflows/ci.yml), so run them locally before committing:

cargo fmt
cargo clippy --workspace --all-targets -- -D warnings

clippy is run with -D warnings (warnings are errors); cargo fmt keeps the formatting canonical. Everything merged must pass both.