Skip to main content

Image Subtraction in Fourier Space

Project description

.. 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

.. image:: https://img.shields.io/badge/python-3.12-green.svg :target: https://www.python.org/downloads/release/python-312/

.. 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

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sfft-1.7.2.tar.gz (370.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

sfft-1.7.2-cp310-cp310-macosx_10_13_x86_64.whl (478.2 kB view details)

Uploaded CPython 3.10macOS 10.13+ x86-64

File details

Details for the file sfft-1.7.2.tar.gz.

File metadata

  • Download URL: sfft-1.7.2.tar.gz
  • Upload date:
  • Size: 370.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.14

File hashes

Hashes for sfft-1.7.2.tar.gz
Algorithm Hash digest
SHA256 27b6a9303c7afc16368612d6d2165b9ca85b75c96ffc87322fb51cbf6edefdd3
MD5 31e29a6bd32586b396a17f743dc70f4d
BLAKE2b-256 05eecb5947c8012e2b8b32ceb1fcf6542c457a8c5b95edd20acefa8e18dc1a85

See more details on using hashes here.

File details

Details for the file sfft-1.7.2-cp310-cp310-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for sfft-1.7.2-cp310-cp310-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 1f0529fc6c99a60d266ece6e89a3c9e010f55fe208d72e5443e113e17e7bc9db
MD5 126509f0ed44045b31da31be1c0dd400
BLAKE2b-256 77de9abd9823f6f73bf02ad242a87589f6daaffea8a793871af8971096e25a11

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page