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:

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!*