Athanaseus Ramaila

Radio Astronomy Reduction Pipelines Using Python and Various Container Technologies

Modern-era radio interferometry software packages have improved enough to allow astronomers to start to address some of the calibration and imaging issues that are posed by the next generation radio interferometers, particularly the MeerKAT, LOFAR, ASKAP and Square Kilometre Array (SKA). I will present a framework that uses a platform-independent scripting tool called Stimela that allows the easy creation of astronomical data reduction pipelines using python and any of the supported (Docker, Podman, Singularity and uDocker) container technology of choice. In this framework, radio interferometry related tasks such as data synthesis, calibration and imaging are executed in containers. In fact, within this framework, the packages that perform these tasks are mostly Python modules. The primary aims are to provide the following services to the Radio Astronomy community: a) A user-friendly environment that gives general users easy access to novel radio interferometry packages. b) Simplified installation and production deployment. I will also discuss other great benefits to this deployment model such as providing a fixed production environment and reproducibility of scientific results.