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README.rst

ehtim (eht-imaging)

Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. This version is an early release so please raise an issue, submit a pull request, or email achael@princeton.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 Image, Movie, Array, Obsdata, Imager, 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 polarizations using various data terms and regularizing functions.

Installation

The latest stable version (1.2.2) is available on PyPi. Simply install pip and run

pip install ehtim

Incremental updates are developed on the dev branch. To use the very latest (unstable) code, checkout the dev branch, change to the main eht-imaging directory, and run:

pip install .

Installing with pip will update most of the required libraries automatically (numpy, scipy, matplotlib, astropy, ephem, future, h5py, and pandas).

If you want to use fast fourier transforms, you will also need to separately install NFFT and its pynfft wrapper. The simplest way is to use conda to install both:

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

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

If you use ehtim in your publication, please cite Chael+ 2018

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
  • 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. 2019a
  • First M87 Event Horizon Telescope Results IV: Imaging the Central Supermassive Black Hole, The Event Horizon Telescope Collaboration 2019
  • VLBI Imaging of black holes via second moment regularization, Issaoun et al. 2019b
  • Using evolutionary algorithms to model relativistic jets: Application to NGC 1052, Fromm et al. 2019
  • EHT-HOPS Pipeline for Millimeter VLBI Data Reduction, Blackburn et al. 2019
  • Multi-wavelength torus-jet model for Sagittarius A*, Vincent et al. 2019
  • Discriminating Accretion States via Rotational Symmetry in Simulated Polarimetric Images of M87, Palumbo et al. 2020
  • SYMBA: An end-to-end VLBI synthetic data generation pipeline. Simulating Event Horizon Telescope observations of M87, Roelofs et al. 2020
  • Monitoring the Morphology of M87* in 2009-2017 with the Event Horizon Telescope, Wielgus et al. 2020
  • EHT imaging of the archetypal blazar 3C 279 at extreme 20 microarcsecond resolution, Kim et al. 2020

Acknowledgements

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

License

ehtim is licensed under GPLv3. See LICENSE.txt for more details.

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