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.

Edition numbers#

The guide now has version numbers, roughly equivalent to editions in a printed book. Each number has three pieces – for example the first official stable version is 2.0.0. Think of that as roughly equivalent to “edition”, “revision” and “printing” in the physical book world.

The edition number is listed in the sidebar.

This is what changes in each of those numbers means:

  • Change in the first number, e.g. changing 2.0.0 → 3.0.0, indicates a major revision that changes the numbering of sections or adds significant new sections.

  • Changes in the middle number, e.g. changing 2.0.0 → 2.1.0, indicates suibstantive updates have been made to the content and/or important errors have been fixed. The section numbers will not change, though.

  • Changes in the last number, e.g. changing 2.0.0 → 2.0.1, indicates minor changes have been made like fixing typographic errors, broken links, and similar corrections. The section numbers will not change in these revisions. In addition, there is a “development” version available that reflects the latest changes made to the guide between releases.

Those familiar with semantic versioning from software development will recognize this as roughly the equivalent for text.

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#

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

Acknowledgments#

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

  • Nicolás Cardiel

  • Simon Conseil

  • Lia Corrales

  • Kelle Cruz

  • Adam Ginsburg

  • Johannes Goller

  • Yash Gondhalekar

  • Lia G

  • Richard Hendricks

  • Stuart Littlefair

  • Matt Phillips

  • Benjamin Shafransky

  • Isobel Snellenberger

  • Kris Stern

  • Thomas Stibor

  • Sarah Tuttle

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

Resources#

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

Software setup/getting started on your computer#

Setting up Python#

To use any of this software you will need an installation of Python. We recommend the mambaforge installer or miniconda installer (or the much larger Anaconda Python distribution if you must). Once you have that, you can install everything you need with:

conda install -c astropy ccdproc photutils ipywidgets matplotlib jupyterlab

(alternatively, mamba should work in place of conda; if you get an error message saying you need to initialize mamba then follow the instructions in that message.

Getting a copy of these notebooks#

Get a copy of these files from https://github.com/astropy/ccd-reduction-and-photometry-guide – click the green “Code” button and either clone (if you are using git) or download the zip file and extract it. If you are looking for a specific edition, e.g. 2.0.0 then go to https://github.com/astropy/ccd-reduction-and-photometry-guide/releases.

If you want to run this code in a notebook from the command line#

Open a terminal, change directory into the notebooks folder in this repository, type jupyter lab and a browser tab should open with the notebooks.

You will still need to download the data by following the instructions below in Data files

If you want to run this code in some other way (IPython, Spyder, Visual Studio Code, …)#

Spyder and Visual Studio Code can run notebooks; follow the instructions above to get the notebooks. Follow the instructions for opening notebooks in the tool you chose.

If you are copy/pasting code from the online book into one of these tools, you need two files from the repository. Follow the instructions for getting a copy of the repository, then copy the two files download_data.py and convenience_functions.py from the notebooks folder to whatever folder you plan to run the code in.

You will still need to download the data by following the instructions below in Data files

Data files#

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#

$ python download_data.py

Use this in a notebook cell to download the data#

%run download_data.py

List of data files#