Codex Africanus presents radio astronomy algorithms to the user as modular functions accepting NumPy inputs and producing NumPy outputs. Internally, it uses Numba to accelerate these codes and Dask to parallelize and distribute them. The library contains functions for plotting convolution filters and tapers associated with convolution filters and can compute the discretised direct Fourier transform (DFT) for an ideal interferometer. Codex Africanus has routines for gridding or degridding complex visibilities onto or from an image, includes deconvolution algorithms and coordinate transforms, and many other functions.

dask-ms constructs xarray datasets from CASA tables, thus providing a data access layer for Measurement Set v2.0 data. It supports the CASA Data Table System, Zarr and Apache Arrow formats, but abstracts them away from the developer at the xarray dataset level. It therefore serves as a basis for writing distributed PyData Radio Astronomy applications and supports writing variables back to the respective column in the Table. The intention behind dask-ms is to support the Measurement Set as a data source and sink for the purposes of writing parallel, distributed radio astronomy algorithms.

Stimela2 develops data reduction workflows and is a significant update of Stimela (ascl:2305.007). Though designed for radio astronomy data, it can be adapted for other data processing applications. Stimela2 represents workflows by linear, concise and intuitive YAML-format "recipes". Atomic data reduction tasks (binary executables, Python functions and code, and CASA tasks) are described by YAML-format "cab definitions" detailing each task's "schema" (inputs and outputs). Stimela2 provides a rich syntax for chaining tasks together, and encourages a high degree of modularity: recipes may be nested into other recipes, and configuration is cleanly separated from recipe logic. Tasks can be executed natively or in isolated environments using containerization technologies such as Apptainer. Stimela2 facilitates the deployment of scalable, distributed workflows by interfacing with the Slurm scheduler and the Kubernetes API, the latter allowing workflows to be readily deployed in the cloud.

QuartiCal is the successor to CubiCal (ascl:1805.031). It implements a suite of fast radio interferometric calibration routines exploiting complex optimization. Unlike CubiCal, QuartiCal allows for any available Jones terms to be combined. It can also be deployed on a cluster.

katdal interacts with the chunk stores and HDF5 files produced by the MeerKAT radio telescope and its predecessors (KAT-7 and Fringe Finder), which are collectively known as MeerKAT Visibility Format (MVF) data sets. The library uses memory carefully, allowing data sets to be inspected and partially loaded into memory. Data sets may be concatenated and split via a flexible selection mechanism. In addition, katdal provides a script to convert these data sets to CASA MeasurementSets.

CARACal (Containerized Automated Radio Astronomy Calibration, formerly MeerKATHI) reduces radio-interferometric data. Developed originally as an end-to-end continuum- and line imaging pipeline for MeerKAT, it can also be used with other radio telescopes. CARACal reduces large data sets and produces high-dynamic-range continuum images and spectroscopic data cubes. The pipeline is platform-independent and delivers imaging quality metrics to efficiently assess the data quality.

Montblanc, written in Python, is a GPU implementation of the Radio interferometer measurement equation (RIME) in support of the Bayesian inference for radio observations (BIRO) technique. The parameter space that BIRO explores results in tens of thousands of computationally expensive RIME evaluations before reduction to a single X² value. The RIME is calculated over four dimensions, time, baseline, channel and source and the values in this 4D space can be independently calculated; therefore, the RIME is particularly amenable to a parallel implementation accelerated by Graphics Programming Units (GPUs). Montblanc is implemented for NVIDIA's CUDA architecture and outperforms MeqTrees (ascl:1209.010) and OSKAR.