Open writing projects/Scientific Programming with Python and Subversion/Outline: Difference between revisions
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Revision as of 13:31, 24 March 2008
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
- variable assignment
- basic control structures
- functions
- package structure and import
- objects (just like packages)
- References to Programming Python for more detail, and A Byte of Python and Dive Into Python for more intro material
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
- example: switching a sorting algorithm - how do you know the code works the same way
- Using python and nose to write unit tests?
- example of test code, and how to run the tests
- bioinformatics
- physics
- example of test code, and how to run the tests
- 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
- Several options:
- 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
- Approaching the scientific problem with code
- deconstruct the problem into manageable parts
- bioinformatics - write the downloading and saving data files code
- physics - write the basic parts of the simulation code
- deconstruct the problem into manageable parts
- Writing your first tests
- write unit tests for these basic codes
- Getting more sophisticated
- separating your code into modules
- using objects to encapsulate the code cleanly
- Rinse, Lather, Repeat
- a general methodology for approaching the scientific problems
- start with the simplest possible task and write a script for it
- move this code into a module and write unit tests for it
- objectify the code when appropriate
- identify speed bottle-necks if needed, and speed up those parts
- a general methodology for approaching the scientific problems