Open writing projects/Scientific Programming with Python and Subversion/Outline: Difference between revisions
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crunching numbers example |
unit testing flesh out |
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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) | ||
=== Introductory remarks === | === 0 Introductory remarks === | ||
* Why this book | * Why this book | ||
** motivation - lots of training in what science to do with computers, but little training in how to do it | ** motivation - lots of training in what science to do with computers, but little training in how to do it | ||
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** Some physics theme? | ** Some physics theme? | ||
=== Source Control Management with Subversion === | === 1 Source Control Management with Subversion === | ||
* What is source control? | * What is source control? | ||
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** Branching and Merging | ** Branching and Merging | ||
=== An Introduction to Python === | === 2 An Introduction to Python === | ||
* What is python | * What is python | ||
| Line 44: | Line 44: | ||
** objects (just like packages) | ** objects (just like packages) | ||
=== Making scientific plots with python === | === 3 Making scientific plots with python === | ||
* An introduction to matplotlib (modular) | * An introduction to matplotlib (modular) | ||
| Line 54: | Line 54: | ||
** physics | ** physics | ||
=== Crunching numbers with python === | === 4 Crunching numbers with python === | ||
* Python community modules (modular) | * Python community modules (modular) | ||
| Line 64: | Line 64: | ||
** physics | ** physics | ||
5 | === 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 | |||
* 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) | |||
6. Complete case study - wrapping it all together | 6. Complete case study - wrapping it all together | ||
7. Advanced topic - using SWIG and psyco to speed up python code | 7. Advanced topic - using SWIG and psyco to speed up python code | ||
Revision as of 00:59, 22 March 2008
Outline
- sections marked with '(modular)' can be re-written using a different technology (i.e. git instead of svn)
0 Introductory remarks
- Why this book
- motivation - lots of training in what science to do with computers, but little training in how to do it
- for beginners - assumes no prior knowledge, introduces tools as they are needed in a typical scientific investigation using computers
- for experienced scientists - introducing new tools to do some of these tasks
- goal - to make managing projects easier, but more importantly to promote good scientific practice through these methods
- Introduce scientific themes throughout the book
- Some bioinformatics theme - maybe use an example from one of the NCBI coffee breaks
- Some physics theme?
1 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
2 An Introduction to Python
- What is python
- computer language that offers easy access to high-level functions, and has a large and growing community of scientific users
- Why 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)
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?
- 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. Complete case study - wrapping it all together
7. Advanced topic - using SWIG and psyco to speed up python code
