Skip to content
Snippets Groups Projects
Select Git revision
  • main default protected
  • tutorial_nifty.re_fast-resolve
2 results
Compare
Name Last commit Last update
data
.gitattributes
.gitlab-ci.yml
README.md
demo_CorrelatedFields.ipynb
demo_joint_cal_image.ipynb
demo_linearization.py
demo_poisson.ipynb
demo_radio.ipynb
demo_radio_jax.ipynb
demo_wiener_filter.ipynb
gaussian_processes.py
nifty_interfaces.py
nifty_intro.py
resolve_workshop_intro_blank.ipynb
utils.py
variational_inference_visualized.py
README.md

Nifty tutorial for radio interferometric imaging

This repository provides a hands on tutorial to perform radio imaging using information field theory.

The demo scripts nifty_interfaces.py and nifty_intro.py give and introduction into the world of nifty and how to do inference with it.

In addition, three Jupyter notebooks are available:

  • demo_CorrelatedFields.ipynb: An introduction to the correlated field model, its hyperparameters, and their effect in the statistical properties of the gaussian process.

  • demo_radio.ipynb: A mock inference task given a simplified VLBI imaging setup using the uv-coverage of the 2017 imaging campaign of the Event Horizon Telescope (eht) and an artificially generated sky brightness distribution.

  • demo_joint_cal_imag.ipynb: A resolve demo script for joint calibration and imaging. The demo uses VLA data of SN1006.

Requirements

Installation via pip

To install the packages, simply run for example

pip install matplotlib
pip install nifty8
git clone --recursive https://gitlab.mpcdf.mpg.de/ift/resolve.git
pip install resolve

or build them from their sources.

Further information

To get started with the packages, take a look at the projects homepages:

Additionally, the package nifty provides a variety of explanatory examples for imaging and general Bayesian inference tasks. See demos for more information.