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eht-imaging v1.1.0: imaging VLBI data with regularized maximum likelihood

Andrew Chael; Katie Bouman; Michael Johnson; Chi-kwan Chan; Maciek Wielgus; Joseph Rachid Farah; Daniel Palumbo; Lindy Blackburn; Dominic Pesce

Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. This version is an early release so please submit a pull request or email achael@cfa.harvard.edu if you have trouble or need help for your application.

The package contains several primary classes for loading, simulating, and manipulating VLBI data. The main classes are the ImageArrayObsdataImager, and Caltable classes, which provide tools for loading images and data, producing simulated data from realistic u-v tracks, calibrating, inspecting, and plotting data, and producing images from data sets in various polariazations using various data terms and regularizers.

This version represents an incremental update to v0.1.2: However, since the original, very early eht-imaging released on zenodo with Chael et. al 2018 (10.5281/zenodo.1173414) was tagged as v1.0 before the software was tagged & released consistently in github, we have jumped forward to v1.1.0 for this release. Apologies for the inconsistency!

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