Thanks to visit codestin.com
Credit goes to github.com

Skip to content

mperrin/stpsf

 
 

Repository files navigation

STPSF: Simulated Point Spread Functions for the James Webb and Nancy Grace Roman Space Telescopes

https://github.com/spacetelescope/stpsf/blob/stable/docs/readme_fig.png?raw=true

Badge showing current released PyPI version Github Actions CI Status https://img.shields.io/badge/ascl-1504.007-blue.svg?colorB=262255

ADVISORY: STPSF IS CURRENTLY BEING MIGRATED FROM WEBBPSF - THIS REPOSITORY IS NOT READY FOR USE

STPSF produces simulated PSFs for the James Webb Space Telescope, NASA's flagship infrared space telescope. STPSF can simulate images for any of the four science instruments plus the fine guidance sensor, including both direct imaging, coronagraphic, and spectroscopic modes.

STPSF also supports simulating PSFs for the upcoming Nancy Grace Roman Space Telescope (formerly WFIRST), including its Wide Field Instrument and a preliminary version of the Coronagraph Instrument.

Note

The current Roman WFI optical model was provided by Goddard Space Flight Center circa 2021 (the Cycle 9 reference data); a new optical model is currently being implemented in STPSF.

Developed by Marshall Perrin, Joseph Long, Shannon Osborne, Robel Geda, Bradley Sappington, Marcio Meléndez, Charles-Philippe Lajoie, Jarron Leisenring, Neil Zimmerman, Keira Brooks, Justin Otor, Trey Kulp, Lauren Chambers, Alden Jurling, and collaborators, 2010-2024.

Documentation can be found online at https://stpsf.readthedocs.io

STPSF requires input data for its simulations, including optical path difference (OPD) maps, filter transmission curves, and coronagraph Lyot mask shapes. These data files are not included in this source distribution. Please see the documentation to download the required data files.

About

Space Telescope PSF Simulation Tool

Resources

License

Code of conduct

Contributing

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 95.2%
  • Python 4.8%