At the Inter-University Institute for Data Intensive Astronomy (IDIA, www.idia.ac.za), we are focusing on several important use-cases related to the delivery of science data products from large radio telescopes, such as MeerKAT. The requirements for the hardcore processing and analysis of raw radio data has to be counter-balanced with our essential need to collaborate on our science projects.
We have thus adopted the Jupyter Hub/Notebooks as the principle means of running radio astronomy workflows, pipelines and calibration scripts. We have found this to be an enormously useful and powerful medium to prototype technical recipes, and to share lessons learned. This allows us to shorten the amount of time taken to develop a complex astronomical workflow, and shifts the focus on the data and the processes involved in a single, comprehensive framework.
The usage of Jupyter Notebooks is become quite popular in radio astronomy, and marks a distinct paradigm shift in the way that astronomers all over the world are collaborating on large science projects.
In my talk I will focus on our usage of Jupyter Hub/Notebooks within an astronomical context, and some highlights related to the development of our astronomical computing software stack in python.