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

Outline

0 Introduction

• Why this book?
  • Motivation - There's lots of information about what you can do with computers in biology, chemistry, and physics, 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

Part I: Intro to scientific programming using python

1 Why use python for scientific programming?

• 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)

2 Source Control Management with Subversion

• 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.
• An 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

3 A brief introduction to python

• What the scientist needs to know to get started (References to Programming Python for more programming detail?)
  • variable assignment
  • basic control structures
  • functions
  • package structure and import
  • objects (just like packages)

4 Making scientific plots with python

• An 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

5 Crunching numbers with python

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

6 Unit testing for scientists

• 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?
  • example of test code, and how to run the tests
    • bioinformatics
    • physics
• How do I know which tests to write?
  • (This one is hard)

7 Advanced topics - using SWIG and psyco to speed up python code

• (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

Part II: Examples

• 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
• A complete case study of [blah] from start to finish
  • Creating a code repository
  • Writing your first code [Be more specific: Is this just the code for the case study or will you also talk about how to approach the scientific problem before writing the code?]
  • Writing your first tests
  • Moving on [To what?]