There is a newer version of this record available.

Software Open Access

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

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

!Minor Python 3 bug alert! -- this static version on zenodo is  missing parentheses in a print statement on line 51 of parloop.py. If you plan to run this version with python 3, you will need to add in these parentheses. This bug is fixed in the next release on github (v1.1.1). Our apologies for the error!

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. 

Files (16.8 MB)
Name Size
achael/eht-imaging-v1.1.0.zip
md5:80511f52a2dbff3b63f993d5a951ad85
16.8 MB Download
1,209
44
views
downloads
All versions This version
Views 1,209587
Downloads 4434
Data volume 740.1 MB571.3 MB
Unique views 961507
Unique downloads 3829

Share

Cite as