We built this book on a mountain of incredibly useful open-source tools. The source code for this book converts a collection of Jupyter notebooks (Kluyver et al., 2016) and markdown files into a static website using Sphinx.
Two other textbooks, both developed using open-source software, inspired this book. One is UC Berkeley’s Data 8 course textbook, Computational and Inferential Thinking, by Ani Adhikari and John DeNero. The other is Statistics, Data Mining, and Machine Learning in Astronomy by Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas and Alexander Gray, which is a companion textbook to the AstroML python package by VanderPlas et al., 2012.
This book was initially built using a workflow developed by Sam Lau and Chris Holdgraf with support from the Data Science Education Program and the Berkeley Institute for Data Science at UC Berkeley. Jupyter Book is now part of the Executable Book Project and funded by the Alfred P. Sloan Foundation.
We’d like to thank Ani Adhikari, Chris Holdgraf, and Jacob VanderPlas, as well as Scott Bailey and Javier de la Rosa from the Stanford University Center for Interdisciplinary Digital Research, Erik Tollerud from the Space Telescope Science Institute and Astropy Project, and Eric Jonas from the Department of Computer Science at the University of Chicago for their valuable conversation, guidance, and encouragement.