Open writing projects/Scientific Programming with Python and Subversion/Outline

Outline

sections marked with '(modular)' can be re-written using a different technology (i.e. git instead of svn)

Why this book?
- motivation - A classic problem in the sciences is there;s lots of training in the science you can do with computers, but little training in how to do it
- Assumes no prior knowledge of Python; introduces computing tools as they are needed in the context of a typical scientific investigation. This makes it useful to both beginners and more experienced users
- goal - to make managing projects easier, but more importantly to promote good scientific practice using computing methods
Introduce scientific themes throughout the book
- Covers themes from biology, informatics, and physics? - for informatics, maybe use examples from one of the NCBI coffee breaks

What is python?
- computer language that offers easy access to high-level functions, and has a large and growing community of scientific users
Why build scientific applications in python?
- python code looks clean - easy to understand yours or your collaborators code a week later
- everything from data generation to analysis to plots can be done in python, making every aspect of your project consistent. These together promote good scientific practices (data integrity, data reproduceability)

What is source control?
- Similar to Word 'track changes' or wiki 'history' but for all the files in a project.
- A way to keep a history of every step in a process.
- Not only for computer code, but for data, plots, paper manuscripts, etc.
A modular introduction to Subversion
- What is a repository?
- How to create a repository
- How to make basic commits
- Seeing differences between versions
- Retrieving past versions
- Collaboration using subversion
Advanced Topics
- Branching and Merging

A modular introduction to matplotlib
- basic functionality - simple line, bar, histogram plots
- more sophisticated graphics - insets, labeling with text, drawing arrows
- interactive graphics - adjusting parameters for real-time fitting
An example project use of matplotlib
- bioinformatics
- physics

Python community modules (modular)
- using numpy for matrix manipulations
- using the scipy project tools
- interacting with the Gnu Scientific Library
An example project
- bioinformatics
- physics
- others?

What is unit testing?
- A way to generate automated tests of small units of code
Why do unit testing?
- example: switching a sorting algorithm - how do you know the code works the same way
  - typically done by 'eye' by running the code manually and looking at output
  - with unit tests can see if the code failed, and if it did, where exactly
Using python and nose to write unit tests? (modular)
- example of test code, and how to run the tests
  - bioinformatics
  - physics
How do I know which tests to write?
- (This one is hard)

this section could be omitted initially
What if python is not fast enough for my project?
- Several options:
  - Use psyco to 'compile' the python code
  - Identify the slow parts and write them in C/C++ and bind them to python using SWIG
Using psyco
Using C with SWIG

Case studies: This section could be omitted initially - ideally we could have an svn repo set up for people to pull from to look at the code examples at each step of the way
go through from start to finish
- initially create a repository
- the first code
- the first tests
- moving on