Open writing projects/Scientific Programming with Python and Subversion/Outline: Difference between revisions
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* sections marked with '(modular)' can be re-written using a different technology (i.e. git instead of svn) | * sections marked with '(modular)' can be re-written using a different technology (i.e. git instead of svn) | ||
=== 0 | === 0 Introduction === | ||
* Why this book | * Why this book? | ||
** motivation - lots of training in | ** 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 | |||
** goal - to make managing projects easier, but more importantly to ''promote good scientific practice'' | |||
* Introduce scientific themes throughout the book | * Introduce scientific themes throughout the book | ||
** | ** Covers themes from biology, informatics, and physics? - for informatics, maybe use examples from one of the [http://www.ncbi.nlm.nih.gov/Coffeebreak/ NCBI coffee breaks] | ||
=== 1 Source Control Management with Subversion === | === 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 your code a week later, or collaborators code | |||
** everything can be done in python from data generation to analysis to plots making every aspect of the project consintent | |||
** these together promote ''good scientific practices'' (data integrity, data reproduceability) | |||
* An introduction to python (modular) | |||
** variable assignment | |||
** basic control structures | |||
** functions | |||
** package structure and import | |||
** objects (just like packages) | |||
=== 2 Source Control Management with Subversion === | |||
* What is source control? | * What is source control? | ||
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** Branching and Merging | ** Branching and Merging | ||
=== 3 Making scientific plots with python === | === 3 Making scientific plots with python === |
Revision as of 08:03, 24 March 2008
Outline
- sections marked with '(modular)' can be re-written using a different technology (i.e. git instead of svn)
0 Introduction
- 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
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 your code a week later, or collaborators code
- everything can be done in python from data generation to analysis to plots making every aspect of the project consintent
- these together promote good scientific practices (data integrity, data reproduceability)
- An introduction to python (modular)
- variable assignment
- basic control structures
- functions
- package structure and import
- objects (just like packages)
2 Source Control Management with Subversion
- What is source control?
- like 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.
- Introduction to subversion (modular)
- What is a repository
- How to create a repository
- How to make bosic commits
- Seeing differences between versions
- Retrieving past versions
- Collaboration using subversion
- Advanced Topics
- Branching and Merging
3 Making scientific plots with python
- An introduction to matplotlib (modular)
- 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
4 Crunching numbers with python
- 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
5 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? (modular)
- 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)
6 A Complete case study
- 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
7 Advanced topic - 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