I'm the primary developer of SMILI, a python-interfaced library for interferometric imaging using sparse sampling techniques and other regularization methods. SMILI is mainly designed for very long baseline interferometry, and has been under the active development primarily for the Event Horizon Telescope (EHT).
SMILI is one of three imaging software packages used to create the first-ever picture of a black hole (EHT Collaboration et al. 2019, Paper IV, Figure 14)
This is an example movie reconstruction from simulated observations of an evolved star with the next generation Very Large Array (ngVLA) presented in Akiyama & Matthews (2019).
EHT analysis toolkit (eat) AIPS module
I wrote a python module eat.aips inside the EHT analysis toolkit (eat), which enables to use AIPS from arbitrary python environments using ParselTongue. With this modeule, you can run AIPS from your python interpretator, for instance, ipython or jupyter. eat.aips was used to create a data calibration pipeline using AIPS mainly written by me for the first EHT M87 observations providing the first-ever images of a black hole (EHT Collaboration et al. 2019, Paper III).