Python Fast Holographic Deconvolution
FHD is an open-source imaging algorithm for radio interferometers, specifically tested on MWA Phase I, MWA Phase II, PAPER, and HERA. There are three main use-cases for FHD: efficient image deconvolution for general radio astronomy, fast-mode Epoch of Reionization analysis, and simulation.
PyFHD is the translated library of FHD from IDL to Python, it aims to get close to the same results as the original FHD project. Do expect some minor differences compared to the original FHD project due to the many differences between IDL and Python. These differences are often due to the difference in precision between IDL and Python with IDL being single-precision (accurate upto 1e-8) and Python being double-precision (1e-16). Some of the IDL functions are double-precision but most default to single-precision.
pip install pyfhd
For full installation notes, including dependencies on PyFHD, check out the ReadTheDocs installation page.
To check if PyFHD is available on your path, run the following command:
pyfhd -v
You should see output that resembles something like this:
________________________________________________________________________
| ooooooooo. oooooooooooo ooooo ooooo oooooooooo. |
| 8888 `Y88. 8888 8 8888 888 888 Y8b |
| 888 .d88' oooo ooo 888 888 888 888 888 |
| 888ooo88P' `88. .8' 888oooo8 888ooooo888 888 888 |
| 888 `88..8' 888 888 888 888 888 |
| 888 `888' 888 888 888 888 d88' |
| o888o .8' o888o o888o o888o o888bood8P' |
| .o..P' |
| `Y8P' |
|_______________________________________________________________________|
Python Fast Holographic Deconvolution
Translated from IDL to Python as a collaboration between Astronomy Data and Computing Services (ADACS) and the Epoch of Reionisation (EoR) Team.
Repository: https://github.com/EoRImaging/PyFHD
Documentation: https://pyfhd.readthedocs.io/en/latest/
Version: 1.0.1
Git Commit Hash: aa3cddb69cb617d88cb95d8b3d177d934f1c5d01 (tutorial_adjustments)
To run the examples built into the repository and beyond, please find them here: PyFHD Examples
- PyFHD documentation
- MWA ASVO - service to obtain MWA data
- FHD repository - the original IDL code
- FHD examples - examples on how to use the original IDL code
- FHD pipeline scripts - pipeline scripts using the original IDL code
We are an open-source community that interacts and discusses issues via GitHub. We encourage collaborative development. New users are encouraged to submit issues and pull requests and to create branches for new development and exploration. Comments and suggestions are welcome.
If you wish to contribute to PyFHD, first of all thank you, second please read the contribution guide which can be found here, Contribution Guide. The contribution will cover all you need to know for developing in PyFHD from adding features, formatting adding tests and some advice in translating IDL to Python.
If you use PyFHD for a paper, the way to cite PyFHD is using the DOI link:
https://doi.org/10.5281/zenodo.15720184
From the Zenodo site, you can either copy or export the citation type you need (e.g. BibTeX).
TODO: A JOSS Paper is being done and will be submitted soon, put pre-print or JOSS paper itself here to also cite
FHD was built by Ian Sullivan and the University of Washington radio astronomy team. Maintainance is a group effort split across University of Washington and Brown University, with contributions from University of Melbourne and Arizona State University.
PyFHD is currently being created by Nichole Barry and Astronomy Data and Computing Services (ADACS) member Joel Dunstan. ADACS is a collaboration between the University of Swinburne and Curtin Institute for Data Science (CIDS) located in Curtin University.
Thank you to the previous maintainers: Jack Line - Astronomy Data and Computing Services (ADACS)
Acknowledgements to Bryna Hazelton and Paul Hancock for their advice and knowledge.