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:
| Package | Kind | Install | Entry point |
|---|---|---|---|
| casacure | Rust crate | cargo add casacure (crates.io) |
use casacure; |
| python-casacure | Python package | pip install casacure (PyPI) |
import casacure.tables |
"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).
| On-disk formats | StandardStMan, IncrementalStMan, TiledColumnStMan — read and write |
| Table surface | columns (scalar / fixed / variable-shape arrays, strings, records), keywords, dminfo, subtable linkage (::SUBTABLE) |
| TaQL | SELECT (WHERE / ORDERBY / GROUPBY / UNIQUE) and CREATE TABLE |
| Measurement Sets | full MS + 17 standard subtable schemas, default_ms, writable in place |
| Interop | tables written by casacure open in real casacore and vice-versa |
Status is tracked in ARE_WE_CURED.md; the outline is in CASACORE_TO_CASA_RS.md.
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.
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 = "0.2" to Cargo.toml if you want the table engine in Rust
directly.
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.
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.
1 maintainer
Release files for casacure 0.2.3
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Built distributions (wheels)
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