PypeIt 1.1.0 stars watch

pypi DOI_latest arxiv astropy

PypeIt is a Python package for semi-automated reduction of astronomical, spectroscopic data. Its algorithms build on decades-long development of previous data reduction pipelines by the developers. The reduction procedure - including a complete list of the input parameters and available functionality - is provided by this online documentation.

PypeIt is a set of commands designed to perform the reduction without any additional coding.

This v1.1 release of PypeIt is designed to be used by both advanced spectroscopists with prior data reduction expertise and astronomers with no prior experience of data reduction. It is highly configurable and designed to be applied to any standard slit-imaging spectrograph, and can accommodate long-slit, multi-slit, as well as cross-dispersed echelle spectra.

What this version provides

What this version is missing (i.e. what we are working on)

  • Slitmask metadata slurping (e.g. Keck/DEIMOS)

  • Full 2D coadd support

  • Keck/HIRES, Keck/ESI support

  • Additional QA outputs

  • A dashboard to monitor/control PypeIt


If you are mainly here to use PypeIt to reduce your observational data then this section is for you. Ideally, you will need to go not much further than the few links in this section take you.

If you have problems, we have a very active “PypeIt Users” Slack workspace. We periodically update the invitation here. If you find a bug or have a feature request, please submit an issue.

Standard outputs

Further processing


PypeIt is an open-source, community developed package. Astronomers are encouraged to join the project and should review our Code of Conduct and PypeIt Development Procedures and Guidelines. We would also appreciate if you contact the lead developers (JXP, JFH) before beginning development activities.

The following persons have contributed substantially to the development of PypeIt.

  • J Xavier Prochaska

  • Joseph F. Hennawi

  • Kyle B. Westfall

  • Ryan J. Cooke

  • Feige Wang

  • Tiffany Hsyu

  • Frederick B. Davies

  • Emanuele Paolo Farina