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Preface

The purpose of this text is to walk through image reduction and photometry using Python, especially Astropy and its affiliated packages. It assumes some basic familiarity with astronomical images and with Python. The inspiration for this work is a pair of guides written for IRAF, "A User's Guide to CCD Reductions with IRAF" (Massey 1997) and "A User's Guide to Stellar CCD Photometry with IRAF" (Massey and Davis 1992).

The focus is on optical/IR images, not spectra.

Credits

Authors

This guide was written by Matt Craig and Lauren Chambers. Editing was done by Lauren Glattly.

New contributors will be moved from the acknowledgments to the author list when they have either written roughly the equivalent of one section or provided detailed review of several sections. This is intended as a rough guideline, and when in doubt we will lean towards including people as authors rather than excluding them.

Funding

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Made possible by the Astropy Project and ScienceBetter Consulting through financial support from the Community Software Initiative at the Space Telescope Science Institute.

Acknowledgments

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The following people contributed to this work by making suggestions, testing code, or providing feedback on drafts. We are greatful for their assistance!

  • Lia Corrales
  • Kelle Cruz
  • Richard Hendricks
  • Stuart Littlefair
  • Isobel Snellenberger
  • Kris Stern
  • Thomas Stibor

If you have provided feedback and are not listed above, we apologize -- please open an issue here so we can fix it.

Resources

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This astronomical content work was inspired by, and guided by, the excellent resources below:

Software setup

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The recommended way to get set up to use this guide is to use the Anaconda Python distribution (or the much smaller miniconda installer). Once you have that, you can install everything you need with:

conda install -c astropy ccdproc photutils ipywidgets matplotlib

Data files

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The list of the data files, and their approximate sizes, is below. You can either download them one by one, or use the download helper included with these notebooks.

Use this in a terminal to download the data

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$ python download_data.py

Use this in a notebook cell to download the data

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%run download_data.py

List of data files

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