DDFacet provides a wideband wide-field spectral imaging and deconvolution framework that accounts for generic direction-dependent effects (DDEs). It implements a wide-field coplanar faceting scheme and uses nontrivial facet-dependent w-kernels to correct for noncoplanarity within the facets. In the imaging and deconvolution steps, DDFacet can handle generic, spatially discrete, time-frequency-baseline-direction-dependent full polarization Jones matrices, and computes a direction dependent PSF for use in the minor cycle of deconvolution for time-frequency-baseline dependent Mueller matrices. The code also allows for the effects of time and bandwidth averaging to be explicitly incorporated into deconvolution. DDFacet has been successfully tested with data diverse telescopes such as LOFAR, VLA, MeerKAT AR1, and ATCA.

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.

smops interpolates input sub-band model FITS images, such as those produced by WSClean (ascl:1408.023), into more finely channelized sub-band model FITS images, thus generating model images at a higher frequency resolution. It is a Python-based command line tool. For example, given input model FITS images initially created from sub-dividing a given bandwidth into four, smops can subdivide that bandwidth further, resulting in more finely channelized model images, to a specified frequency resolution. This smooths out the stepwise behavior of models across frequency, which can improve the results of self-calibration with such models.