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About the developer

achael
5.2K Stars 473 Forks GNU General Public License v3.0 1.9K Commits 7 Opened issues

Description

Imaging, analysis, and simulation software for radio interferometry

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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 [email protected] 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

.. code-block:: bash

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:

.. code-block:: bash

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:

.. code-block:: bash

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 oifitsnew code used for reading/writing .oifits files is a slightly modified version of Paul Boley's package at

. 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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