.. image:: https://github.com/thomasvrussell/sfft/blob/master/docs/sfft_logo_gwbkg.png
SFFT: Saccadic Fast Fourier Transform for image subtraction
.. image:: https://img.shields.io/pypi/v/sfft.svg :target: https://pypi.python.org/pypi/sfft :alt: Latest Version
.. image:: https://static.pepy.tech/personalized-badge/sfft?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads :target: https://pepy.tech/project/sfft
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.. .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.6463000.svg .. :target: https://doi.org/10.5281/zenodo.6463000 .. :alt: DOI
.. image:: https://img.shields.io/badge/License-MIT-yellow.svg :target: https://opensource.org/licenses/MIT | Saccadic Fast Fourier Transform (SFFT) is an algorithm for fast & accurate image subtraction in Fourier space. SFFT brings about a remarkable improvement of computational performance of around an order of magnitude compared to other published image subtraction codes.
SFFT method is the transient detection engine for several ongoing time-domain programs, including the DESIRT <https://ui.adsabs.harvard.edu/abs/2022TNSAN.107....1P/abstract>_ survey based on DECam & DESI, the DECam GW-MMADS Survey for GW Follow-ups and the JWST Cycle 3 Archival program AR 5965 <https://www.stsci.edu/jwst/science-execution/program-information?id=5965>. SFFT is also the core engine for the differential photometry pipeline of the Roman Supernova PIT <https://github.com/Roman-Supernova-PIT>.
Get started
- Documentation: https://thomasvrussell.github.io/sfft-doc/ [recommended]
- Installation: https://thomasvrussell.github.io/sfft-doc/installation/
- Tutorials: https://thomasvrussell.github.io/sfft-doc/tutorials/
- Source code: https://github.com/thomasvrussell/sfft
- Contact the author: astroleihu@gmail.com or leihu@sas.upenn.edu
Installation
To install the latest release from PyPI, use pip: ::
pip install sfft
For more detailed instructions, see the install guide <https://thomasvrussell.github.io/sfft-doc/installation/>_ in the docs.
Citing
Image Subtraction in Fourier Space, Lei Hu et al. 2022, The Astrophysical Journal, 936, 157
See ADS Link: https://ui.adsabs.harvard.edu/abs/2022ApJ...936..157H/abstract
Publications using SFFT method
See ADS Library: https://ui.adsabs.harvard.edu/public-libraries/lc4tiTR_T--92f9k0YrRQg
1 maintainer
Metadata
1 maintainer
Release files for sfft 1.7.3
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| sfft-1.7.3.tar.gz | 373.5 kB | Without provenance | Details |
Built distribution (wheel)
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|---|---|---|---|---|
| sfft-1.7.3-cp310-cp310-macosx_10_13_x86_64.whl | CPython 3.10 | CPython 3.10 | macOS 10.13+ x86-64 | Details |
Total release size: 854.7 kB
Release files / sfft-1.7.3.tar.gz
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Release files / sfft-1.7.3-cp310-cp310-macosx_10_13_x86_64.whl
| Download URL | sfft-1.7.3-cp310-cp310-macosx_10_13_x86_64.whl |
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