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The University of Edinburgh

Dec 3-4, 2014

9:00 am - 5:00 pm

Instructors: Mike Jackson, Arno Proeme, Mario Antonioletti, Alistair Grant

Helpers: Emmanouil Farsarakis, Leighton Pritchard, Peter Cock

General Information

ARCHER, the UK's national supercomputing service, offers training in software development and high-performance computing to scientists and researchers across the UK. As part of our training service we are running a 2 day Software Carpentry workshop.

Software Carpentry workshops help researchers become more productive by teaching software development skills that enable more to be done, in less time, and with less pain. We will cover skills including version control, task automation, good programming practice and automated testing. These are skills that, in an ideal world, researchers would master before tackling anything with "cloud" or "peta" or "HPC" in their name, skills that enable researchers to optimise their time and provide them with a secure basis to optimise and parallelise their code.

This workshop is being run by EPCC, as part of ARCHER. The workshop is in collaboration with EPCC's PRACE Advanced Training Centre (PATC), and Software Carpentry, a Mozilla Science Lab initiative.

Who: The workshop is aimed at graduate students, post-docs and other researchers. You must have some experience of writing code or scripts and be familiar with programming concepts including conditionals, loops, arrays and functions. You should also be comfortable with using the bash shell. For an introduction to the shell, please see, for example, Software Carpentry's lessons on The Unix Shell.

Where: Room 3217, James Clerk Maxwell Building, Peter Guthrie Tait Road, Edinburgh, EH9 3FD. Get directions with OpenStreetMap or Google Maps. Directions for Getting to the James Clerk Maxwell Building. Kings Buildings Campus accessibility map (PDF), James Clerk Maxwell Building accessibility guide PDF.

Requirements: Participants must bring a laptop with a few specific software packages installed (listed below). They are also required to abide by Software Carpentry's Code of Conduct.

Contact: Please mail support@archer.ac.uk for more information.

Registration

To register, or to get more information, please, visit the ARCHER training page.

Schedule

Day 1

09:00 Introduction and software set-up
10:00 Version control with Git
10:30 Coffee break
11:00 Version control with Git
13:00 Lunch break
14:00 Building programs with Python
15:00 Coffee break
15:30 Building programs with Python
17:00 Close

Day 2

09:00 Automating tasks with Make
10:30 Coffee break
11:00 Automating tasks with Make
12:00 Lunch break
13:00 How (and how much) to test programs
14:30 Coffee break
15:00 How (and how much) to test programs
16:00 Best practices for scientific computing
17:00 Close

Syllabus

Version Control with Git

  • Creating a repository
  • Recording changes to files: add, commit, ...
  • Viewing changes: status, diff, ...
  • Ignoring files
  • Working on the web: clone, pull, push, ...
  • Resolving conflicts
  • Open licenses
  • Where to host work, and why
  • Reference...

Building programs with Python

  • Using libraries
  • Working with arrays
  • Reading and plotting data
  • Creating and using functions
  • Loops and conditionals: for, if, else, ...
  • Defensive programming
  • Using Python from the command line
  • Reference...

Automating tasks with Make

  • Make is not just for compiling code
  • Basic Tasks
  • Automatic Variables and Wildcards
  • Patterns
  • Variables
  • Reference...

How (and how much) to test programs

  • Can I do testing?
  • Big Tests and Little Tests
  • End to End
  • Unit Testing
  • Putting Tests Together
  • Reference...

Setup

To participate in a Software Carpentry workshop, you will need working copies of the software described below. Please make sure to install everything (or at least to download the installers) before the start of your workshop.

Overview

Editor

Make sure you install, or have available, a text editor that you are comfortable with using. When you're writing code, it's nice to have a text editor that is optimized for writing code, with features like automatic color-coding of key words.

The Bash Shell

Bash is a commonly-used shell. Using a shell gives you more power to do more tasks more quickly with your computer.

Git

Git is a state-of-the-art version control system. It lets you track who made changes to what when and has options for easily updating a shared or public version of your code on github.com.

Python

Python is becoming very popular in scientific computing, and it's a great language for teaching general programming concepts due to its easy-to-read syntax. We teach with Python version 2.7, since it is still the most widely used. Installing all the scientific packages for Python individually can be a bit difficult, so we recommend an all-in-one installer.

Make

Originally invented to manage compilation of programs written in languages like C, Make can be used to automatically update any set of files that depend on another set of files. This makes it a good solution for many data analysis and data management problems. While there are many build tools now in existence (e.g. ANT and CMake) they share the same fundamental concepts as Make.

Windows

Editor

nano is the editor installed by the Software Carpentry Installer, it is a basic editor integrated into the lesson material.

Notepad++ is a popular free code editor for Windows. Be aware that you must add its installation directory to your system path in order to launch it from the command line (or have other tools like Git launch it for you). Please ask your instructor to help you do this.

Git Bash

Install Git for Windows by download and running the installer. This will provide you with both Git and Bash in the Git Bash program.

Software Carpentry Installer

This installer requires an active internet connection

After installing Python and Git Bash:

  • Download the installer.
  • If the file opens directly in the browser select File→Save Page As to download it to your computer.
  • Double click on the file to run it.

Python

  • Download and install Anaconda.
  • Use all of the defaults for installation except make sure to check Make Anaconda the default Python.

Make

Once you have installed Git Bash you can install Make by:

  • Download make.exe from here
  • Place it in the bin directory where you installed Git Bash e.g. C:\Program Files (x86)\Git\bin.
  • To test: open a Git Bash window, type make, and press Enter.
  • You should see the following message 
    make: *** No targets specified and no makefile found. Stop.
    This means that Make was successfully installed. Otherwise, you'll see this error message: 
    bash: make: command not found

Mac OS X

Editor

We recommend Text Wrangler or Sublime Text. In a pinch, you can use nano, which should be pre-installed.

Bash

The default shell in all versions of Mac OS X is bash, so no need to install anything. You access bash from the Terminal (found in /Applications/Utilities). You may want to keep Terminal in your dock for this workshop.

Git

Install Git for Mac by downloading and running the installer. For older versions of OS X (10.5-10.7) use the most recent available installer available here. Use the Leopard installer for 10.5 and the Snow Leopard installer for 10.6-10.7.

Python

  • Download and install Anaconda.
  • Use all of the defaults for installation except make sure to check Make Anaconda the default Python.

Make

For OS X, version 10.9 (Mavericks) or above, download the Command Line Tools by doing: 

xcode-select  --install

For more information, see the OSX Daily blog.

If you have an older OS X version and you do not already have access to make from within your shell, you will need to install XCode (which is free, but over a gigabyte to download).

  • Go to the Apple app store
  • Search for XCode
  • Click Free
  • Click Install App

Once XCode has installed:

  • Click Applications
  • Click XCode
  • Select XCode→Preferences...
  • Click Downloads
  • Select Command Line Tools
  • Click Install

You will now be able to run make within your shell.

Linux

Editor

Kate is one option for Linux users. In a pinch, you can use nano, which should be pre-installed.

Bash

The default shell is usually bash, but if your machine is set up differently you can run it by opening a terminal and typing bash. There is no need to install anything.

Git

If Git is not already available on your machine you can try to install it via your distro's package manager (e.g. apt-get or yum).

Python

We recommend the all-in-one scientific Python installer Anaconda. (Installation requires using the shell and if you aren't comfortable doing the installation yourself just download the installer and we'll help you at the workshop.)

  1. Download the installer that matches your operating system and save it in your home folder.
  2. Open a terminal window.
  3. Type 
    bash Anaconda-
    and then press tab. The name of the file you just downloaded should appear.
  4. Press enter. You will follow the text-only prompts. When there is a colon at the bottom of the screen press the down arrow to move down through the text. Type yes and press enter to approve the license. Press enter to approve the default location for the files. Type yes and press enter to prepend Anaconda to your PATH (this makes the Anaconda distribution the default Python).

Make

Make is a standard tool on Linux systems and should already be available.

Virtual Machine

As an alternative to the above, you can use a virtual machine (VM) rather than install all the software above. To use a VM:

  1. Install VirtualBox.
  2. Download our VM image. Warning: this file is 1.7 GByte, so please download it before coming to your workshop.
  3. Load the VM into VirtualBox by selecting "Import Appliance" and loading the .ova file.

Check your setup

To check you have the correct version of Python:

To check you have the necessary software and tools:

Recognising prompts and how to exit

If you find yourself in a shell that you don't recognise, or in an editor that you can't get out of then see Recognising prompts and how to exit.

Useful links

Course materials:

Software Carpentry online lessions:

Git:

Python:

Training:

Papers:

Wilson G, Aruliah DA, Brown CT, Chue Hong NP, Davis M, et al. (2014) Best Practices for Scientific Computing. PLoS Biol 12(1): e1001745. doi:10.1371/journal.pbio.1001745.

Sandve GK, Nekrutenko A, Taylor J, Hovig E (2013) Ten Simple Rules for Reproducible Computational Research. PLoS Comput Biol 9(10): e1003285. doi:10.1371/journal.pcbi.1003285.

Noble WS (2009) A Quick Guide to Organizing Computational Biology Projects. PLoS Comput Biol 5(7): e1000424. doi:10.1371/journal.pcbi.1000424.

Ram K (2013) "git can facilitate greater reproducibility and increased transparency in science", Source Code for Biology and Medicine 2013, 8:7 doi:10.1186/1751-0473-8-7.

Glass, R. (2002) Facts and Fallacies of Software Engineering, Addison-Wesley, 2002. (PDF).

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