pypeit 2.0.0

PypeIt Spectroscopic Reduction

PypeIt

PypeIt (pronounced “pipe it”) 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.

For a complete description of PypeIt, please see our online documentation.

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Installation and Usage

Detailed installation instructions can be found here. Briefly, after creating a fresh python environment, simply type:

pip install pypeit

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, including long-slit, multi-slit, as well as cross-dispersed echelle spectra.

The spectrographs that PypeIt supports are listed here. Specifically, look here for useful information about reducing data with certain instruments.

Example data sets: In addition to our primary code base, we maintain an extensive development suite primarily used to perform multiple layers of code testing, from basic unit tests to full end-to-end tests of all our command-line scripts. If you are new to PypeIt, you are encouraged to learn how to use the code by finding and experimenting with example data similar to your own in the RAW_DATA directory (organized by instrument and configuration) of this shared Google Drive folder.

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Community and Communication

Code of Conduct: As a project, PypeIt is committed to fostering a welcoming, diverse, and inclusive community. As a member of this community you are expected to read and follow our Code of Conduct.

Documentation: We maintain extensive online documentation that provides usage tutorials and describes the PypeIt code, reduction and processing procedures, and output data models.

Real-time Communication: We strongly encourage users to join our PypeIt Users Slack. Developers are available to answer questions and help troubleshoot issues. All are welcome to join using this invitation link; please let us know (by submitting a GitHub issue) if the invitation link has expired. Please make sure to read the guidelines before posting your question; the guidelines are also linked in the description of the #guidelines channel.

Bug Reports and Feature Requests: If you encounter a bug, please first check if this could be caused by user error; be sure to consult the online documentation and post about your issue in the Users Slack (see above). If the problem persists (particularly if it is also experienced by other users), please submit a GitHub issue. When submitting the issue, please provide as much information as possible. If you haven’t already, we may ask you to join the Users Slack for more efficient communication about the issue. Finally, you are also encouraged to submit a GitHub issue if you have a specific feature request.

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Contributing to PypeIt

We are excited to welcome your contributions to PypeIt! We acknowledge contributions take many forms, including but not limited to participating in discussions in our Users Slack Workspace; reporting issues to our GitHub repository; submitting pull requests with small bug fixes, documentation improvements, or large feature improvements; and participating in project maintenance and governance. All contributors are expected to follow our Code of Conduct.

For direct contributions to the code, please see our Development Guidelines. As mentioned therein, communication between developers is key to ensuring efforts are coordinated. Before beginning any development activities, we would appreciate communicating your intentions to the core development team, e.g., via the PypeIt Users Slack Workspace.

For information regarding our governance structure and policies, please see the PypeIt Governance documentation.

For a list of current contributors and project roles, please see our PypeIt Team listing.

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Citation

If you use PypeIt in your research, please cite the following two publications:

• Prochaska et al. (2020, JOSS): arXiv, JOSS

• Prochaska et al. (2020, Zenodo): Zenodo

We provide the relevant BibTeX entries for your convenience. Note that the Zenodo BibTex entry is for version 1.0.0 of the code; however, the Zenodo DOI is updated with each release of PypeIt. When you cite the Zenodo entry, you are encouraged to cite the specific PypeIt version you used! You are also encouraged to explicitly note the specific PypeIt version used (e.g., 1.17.3) in the article text.

If there is no place to include the relevant citations in the text of the publication, please include the following acknowledgement (provided in latex and using the provided BibTeX entries):

This research made use of \ttfamily{PypeIt} version
1.17.3,\footnote{\url{https://pypeit.readthedocs.io/en/stable/}} a Python
package for semi-automated reduction of astronomical slit-based spectroscopy
\citep{pypeit:joss_pub, pypeit:zenodo}.

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Funding

PypeIt gratefully acknowledges funding from:

• NASA ADAP (A20-0412, 20-1018)

• NSF (TI-2346210, OAC-2410837)

• JWST (JWST-AR-05464.001-A)

• W. M. Keck Observatory

• University of California Observatories

We also critically rely on important in-kind, open-source contributions from the broader astronomical community.