12. Other Resources¶
- Scikit-learn astronomy tutorial: http://astroML.github.com/sklearn_tutorial
This is an online tutorial which introduces the scikit-learn interface and the fundamental ideas of machine learning, and applies them within the context of astronomical data analysis. It includes several videos of the tutorial being presented at conferences.
- Scikit-learn documentation: http://scikit-learn.org
The scikit-learn documentation is extensive, and features detailed information on many of the routines used in the astroML examples.
- Matplotlib documentation: http://matplotlib.org
Matplotlib enables all the plots on this website, and its website includes extensive documentation and examples. See especially the example gallery.
- IPython documentation: http://ipython.org
IPython is is an enhanced interactive python interpreter, and also provides a framework for parallel computing and cross-platform sharing of code and results. It is an essential tool for any scientific python user.
- Python scientific lecture notes: http://scipy-lectures.github.com/
These notes cover some of the basics of scientific python, including numpy, scipy, and matplotlib. From there they move into an in-depth exploration of more advanced and specialized topics in scientific computing.