In radio interferometry, the goal of calibration is to mitigate instrumentation and atmospheric effects on observed visibilities, in an attempt to recover the “true” sky. The first step towards calibration is to generate solutions calculated from the observation of sources known as calibrators. These solutions are then applied to the calibrators’ visibilities to gauge their quality. However, inspecting the generated calibration solutions and the effectiveness of their application to the calibrator’s visibilites is paramount to determining the quality of the calibration process. Inspection can be done visually by plotting the solutions or the calibrated visibilities, before applying the solutions to the target source’s visibility data and imaging, to save time and computing resources. The existence of interactive tools for easier identification of faulty data points is advantageous. While there are tools available that offer this functionality, they do not necessarily provide interactive capabilities independent of Graphical User Interfaces (GUIs). Additionally, they may limit the maximum amount of data points that can be plotted due to RAM concerns or protract plot generation for plots involving large amounts of data, as is synonymous with radio-interferometric data presently.
RaGaVi is a python based tool that generates self-contained interactive plots of calibration solutions and visibilities. It has two aspects which perform these tasks, namely ragavi-gains and ragavi-vis, respectively. Ragavi-gains provides interactivity by displaying useful identification information for each data point in calibration solutions (such as scan number, antenna e.t.c.) in addition to other interactive actions. Interactivity is helpful to determine the origin of outliers or problematic solutions quickly. On the other hand, ragavi-vis not only provides some level of interactivity but also presents the capability of processing large volumes of data relatively fast, and with a reduced strain on RAM.