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 email@example.com 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
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 polarizations using various data terms and regularizers.
pip install ehtim
Incremental updates are developed on the dev branch. To use the very latest (unstable) code, checkout dev, change to the main eht-imaging directory, and run:
pip install .
conda install -c conda-forge pynfft
Alternatively, first install NFFT manually following the instructions on the readme, making sure to use the
--enable-openmp flag in compilation. Then install pynfft, with pip, following the readme instructions to link the installation to where you installed NFFT. Finally, reinstall ehtim.
Certain eht-imaging functions require other external packages that are not automatically installed. In addition to pynfft, these include networkx (for image comparison functions), requests (for dynamical imaging), and scikit-image (for Hough transforms). However, the vast majority of the code will work without these dependencies.
Documentation is here .
A full tutorial is in progress, but here are some ways to learn to use the code:
- Start with the script examples/example.py, which contains a series of sample commands to load an image and array, generate data, and produce an image with various imaging algorithms.
- Slides from the EHT2016 data generation and imaging workshop contain a tutorial on generating data with the VLBI imaging website, loading into the library, and producing an image.
Some publications that use ehtim
Let us know if you use ehtim in your publication and we'll list it here!
- High-Resolution Linear Polarimetric Imaging for the Event Horizon Telescope, Chael et al. 2016
- Computational Imaging for VLBI Image Reconstruction, Bouman et al. 2016
- Stochastic Optics: A Scattering Mitigation Framework for Radio Interferometric Imaging, Johnson 2016
- Quantifying Intrinsic Variability of Sgr A* using Closure Phase Measurements of the Event Horizon Telescope, Roelofs et al. 2017
- Reconstructing Video from Interferometric Measurements of Time-Varying Sources, Bouman et al. 2017
- Dynamical Imaging with Interferometry, Johnson et al. 2017
- Interferometric Imaging Directly with Closure Phases and Closure Amplitudes, Chael et al. 2018
- A Model for Anisotropic Interstellar Scattering and its Application to Sgr A*, Psaltis et al. 2018
- The Currrent Ability to Test Theories of Gravity with Black Hole Shadows, Mizuno et al. 2018
- The Scattering and Intrinsic Structure of Sagittarius A* at Radio Wavelengths, Johnson et al. 2018
- How to tell an accreting boson star from a black hole, Olivares et al. 2018
- Testing General Relativity with the Black Hole Shadow Size and Asymmetry of Sagittarius A*: Limitations from Interstellar Scattering, Zhu et al. 2018
- The Size, Shape, and Scattering of Sagittarius A* at 86 GHz: First VLBI with ALMA, Issaoun et al. 2019
- First M87 Event Horizon Telescope Results IV: Imaging the Central Supermassive Black Hole, The Event Horizon Telescope Collaboration 2019
The oifits_new code used for reading/writing .oifits files is a slightly modified version of Paul Boley's package at http://astro.ins.urfu.ru/pages/~pboley/oifits. The oifits read/write functionality is still being developed and may not work with all versions of python or astropy.
The documentation is styled after dfm's projects
ehtim is licensed under GPLv3. See LICENSE.txt for more details.