Welcome to STARRED’s documentation!

About

STARRED is a two-channel deconvolution method powered by Starlet regularization and JAX automatic differentiation. It uses a Point Spread Function (PSF) narrower than the original one as kernel. It comes in the form of a Python package, and heavily depends on jax, matplotlib, numpy and scipy for its core functionality. The repository is on GitLab.

You can follow the example notebooks to learn how to use the core functionality of STARRED.

If you have already read our paper, you might want to proceed with the Installation or the Tutorial.

Contributing

Feel free to read the instructions. You can also contact the code authors Kevin Michalewicz, Martin Millon or Fred Dux.

Contents

• Installation
  • Requirements
  • Through PyPI
  • Through Anaconda
  • Manually handling the dependencies
• Contributing
  • Reporting issues or problems
  • Implementing new features
• Tutorial
• Citing
• Related published work
• FAQ
  • Which units should I use for my data?
  • Should I tune the lambda_scales and lambda_hf parameters ?
  • Why using multi-step optimisation?
  • How can I check that I am really using my GPU?
  • What is different in STARRED compared to MCS?
• Autogenerated Full API
  • Subpackages
  • Module contents

Last build of this documentation: Nov 29, 2023.