Python resources

Useful resources to know about when using Python for scientific computing:

Main sites for tools we'll be using today:

• IPython: interactive shell.
• NumPy: core package for any numerical work in Python. Needed for just about anything you'll see this week.
• SciPy: umbrella project for numerics.
• Matplotlib: 2-d plotting.

We won't have time to cover these two, but they fall under the same umbrella:

• Chaco: 2-d plotting.
• MayaVi: VTK-based 3-d visualization.

Our examples:

• Serial examples using numpy, scipy and matplotlib: in the Py4Science repository, a collection of notes and examples developed in collaboration with John Hunter, and with contributions from many in the community.
• Distributed computing: these are in the IPython1 documentation directory.

Documentation, books, tutorials:

• IPython docs (the ShowMeDo videos are particularly nice).
• IPython tutorial on the parallel features.
• API documentation for IPython1.
• The Official NumPy book: authored by Travis Oliphant.
• SciPy docs
• List of software for scientific work: covers many fields, includes links to other documents and tutorials, etc.
• Numpy examples: a comprehensive list of small examples for all the functions in NumPy.
• The SciPy Cookbook: community-contributed examples with code and discussion. Add your own!
• The STScI Tutorial: a must read document, geared towards data analysis. Includes example datasets for you to experiment.

And don't forget the mailing lists!