Astroml.org Website Review

January 2014: the textbook accompanying astroML is now available! View it on Amazon distributed under the 3-clause BSD license. It contains a growing library of statistical and machine learning routines for an*lyzing astronomical data in Python, loaders for several open astronomical datasets, and a large suite of examples of an*lyzing and visualizing The goal of astroML is to provide a community repository for fast Python implementations of common tools and routines used for statistical data an*lysis in astronomy and astrophysics, to provide a uniform and easy-to-use interface to freely available astronomical datasets. We hope this package will be useful to researchers and students of astronomy. If you have an example you’d like to share, The astroML project was started in 2012 to accompany the book STATISTICS, DATA MINING, AND MACHINE LEARNING IN ASTRONOMY by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray, published by Princeton University Press which you can view on GitHub. If you find a mistake which is not yet noted on that page, please let us know via email or GitHub pull If you make use of any of these datasets, tools, or examples in a scientific publication, please consider citing astroML. You may @INPROCEEDINGS{astroML,  author={{Vanderplas}, J.T. and {Connolly}, A.J.  title={Introduction to astroML: Machine learning for astrophysics},  month={oct.},          and {Vanderplas}, J.T. and {Gray}, A.},  publisher={Princeton University Press}, This docu*entation is relative to astroML version 0.2 © 2012, Jake Vanderplas. Created using Sphinx