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, Array, and Obsdata, which provide tools for manipulating images, simulating interferometric data from images, and plotting and analyzing these data. Movie and Vex provide tools for producing time-variable simulated data and observing with real VLBI tracks from .vex files. imager is a generic imager class that can produce images from data sets in various polarizations using various data terms and regularizers.
Note
This is a pre-release of ehtim. If you have a problem please submit a pull request on the git repository and/or email achael@outlook.com
Installation
The latest stable version (1.2.11) 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).
NFFT Installation
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 NFFT and pyNFFT:
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.
Note that, unfortunately, pyNFFT is only supported for python versions 3.11 or lower. eht-imaging version 2.0 using the finufft library is in active development.
For M1/M2/M3/M4/M5 Macs (MacOS >= v12.0), install the updated Mac version of pynfft and follow the instructions on the readme to manually install fftw, nfft and then pynfft.
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 a few image analysis functions). However, the vast majority of the code will work without these dependencies.
Documentation and Tutorials
Documentation is here.
A tutorial jupyter notebook for imaging can be found in the repo at tutorials/ehtim_tutorial.ipynb
Slides for the included tutorial walk through the basic steps of reconstructing EHT images with the code
Scripts in the examples directory contain several older simple example workflows but have not been recently validated.
Citation
If you use ehtim in your publication, please cite Chael+ 2018.
The latest version is also available as a static doi on Zenodo.
Documentation
Contents:
License
ehtim is licensed under GPLv3. See LICENSE.txt for more details.