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Software Carpentry Overview by Software Carpentry is licensed under a Creative Commons Attribution 3.0 Unported License.

History

• Founded by Greg Wilson in 1998, teaching scientists how to use supercomputers at LANL.
• Open sourced materials 2004-present
• Currently funded by the Sloan Foundation and the Mozilla Foundation

What We Teach

• Unix Command Line Interface (Shell)
• Shell automation
• Version Control
• Python or R (unique to this bootcamp)
• Computing best practices (testing, automation)

What We Actually Teach

• A program is just another piece of lab equipment
• Programming is a human activity
• Little pieces loosely joined
• Let the computer repeat it
• Paranoia makes us productive
• Better algorithms beat better hardware

How to THINK like a programmer

Who We Teach

Who We Are

• Molly Gibson (WashU)
• Adina Howe (Argonne)
• Ryan Williams (ISU)
• Fan Yang (ISU)
• Will Trimble (Argonne)
• Jonah Duckles (OSU)

Our Goals for You

We will take you on a tour of:

• Managing and sharing Software, Data, and Manuscripts with Git
• Automating things with the shell
• Practical Programming and Analysis with R
• Good computing practices with automation and testing

Be fluent in multiple languages

You speak multiple languages when interacting with a computer. Choosing to use a new tool, library, or language can be similar to learning a new language:

• There is a high initial startup cost as you learn vocabulary, grammar, and idioms sum(x*y for x,y in itertools.izip(x_vector, y_vector))
• But once you have gained some fluency, you will find yourself capable of new things!
• You will learn faster by observing and working with others who are more skilled than you
• Aim for languages and tools that allow you to express your models and manage your data simply.

Make it work right first, make it fast later.

• "Premature optimization is the root of all evil." -- Donald Knuth
• Directing your attention to making it use less disk / less memory / less time from the start is wrongly directed attention.

Automate common actions by saving simple blocks of code into scripts

• A script is a set of commands organized into a single file
• The script is the basest unit of scientific programming, you should be comfortable writing these whenever you want to save or otherwise document or repeat your actions
• Use scripts to explore new ideas, they are easy to write and throw away

Basic strategies

• Use languages and libraries that reduce the complexity of your work
• It is worth installing a complicated or expensive software tool if your computations or model are naturally expressed with it
• Always look for opportunities to write less code
  • you will have to do less initial work (sometimes)
  • you will introduce less bugs
  • your code will be easier to understand and maintain
• Keep individual functions short, single-purpose, possible to be confident in festooning.

Back up your data!

Use version control for checkpointing and collaboration

• use local version control software to checkpoint personal code development
• checkpointing your work encourages wild ideas and late-night coding sessions
• you can easily restore back in the morning if it was a bad idea
• use distributed version control to collaborate with others
• We advocate Git, but you may be stuck with whatever your group uses

Principles of verification and validation

• verification - is your code correctly written?
• Be paranoid.
• test small things!
  
• test that what you assume is TRUE is in fact so.
• test frameworks can help you verify your code
• validation - do your computations accurately model the phenomena in question?
• not a good candidate for automation. (Not sad at all)

Document your computational work

• Save every bit of code you use for generating publishable results
• Document and comment your code for yourself as if you will need to understand it in 6 months
• use README files liberally
• as well as file-level, function-level, and inline documentation
• If any piece of code is too complex to easily describe, consider refactoring it

Schedule

• Today
• 9:00-12:00 Shell
  
• 1:00-4:30 Git
  
• Tomorrow
• 9:00-12:00 R
• 1:00-4:30 Testing, data manipulation, and automation

You sometimes need geeks.

Programming Languages for Scientific Computing

Matthew G. Knepley

Preprint: http://arxiv.org/pdf/1209.1711.pdf

Gives an overview of modern programming languages and techniques such as code generation, templates, and mixed-language designs. This is a preprint, so expect some rough spots.

Two Solitudes

Greg Wilson

Slides: http://www.slideshare.net/gvwilson/two-solitudes

Describes Greg's journey as a scientist and leader for the Software Carpentry project, provides some insight into the differences between industry and academics.

Best Practices for Scientific Computing

D. A. Aruliah, C. Titus Brown, Neil P. Chue Hong, Matt Davis, Richard T. Guy, Steven H. D. Haddock, Katy Huff, Ian Mitchell, Mark Plumbley, Ben Waugh, Ethan P. White, Greg Wilson, Paul Wilson

Preprint: http://arxiv.org/abs/1210.0530