Despite what the title above might suggest, the purpose of this Software Carpentry bootcamp is not to teach you how to program in Python. While we do love Python for scientific computing, the goals of these modules is actually to teach you the basic, core concepts of programming that transcend languages, how they fit together, and how you can use them to become a better scientist.
By the end of these scientific Python lessons, you will be able to:
We (the instructors) recognize that you all unavoidably have come with very different levels of background in Python programming. We expect that some of you might be experienced in basic Python and a few of you of you have experience with additional modules such as numpy and scipy - for those in that category, this section of the workshop may not be as novel as the other sections. However, we hope that the method of presentation will help to solidify your existing knowledge. We also encourage you to take the opportunity to ask the instructors and volunteers about more advanced techniques that you might have heard of but do not know how to use well.
For those who have no (or almost no) background in programming in any language, you may find that these lessons proceed quickly. We encourage you to make liberal use of the helpful volunteers as we proceed through these lessons. You may also wish to consider working together with a partner to complete the exercises as a team.
Regardless of your background, you will probably feel like trying to take in all of this material is like trying to drink from a firehose. That's OK - the idea is to at least introduce you to a wide variety of topics, with the hope that you (a) will get to reinforce the most important concepts during exercises, and (b) will be able to come back to these materials later to continue mastering the concepts.
As an analogy for our overall strategy for teaching Python, picture becoming a scientific programmer as constructing a house. First you pour the foundation, then you frame the house with wood beams, then you put on a roof, seal it up, and furnish it (or something like that). We are hoping that many of you have arrived today with the foundation already poured - that is, you understand basically how to get some things done using small scripts or programs in some language. In these lessons, we will mostly focus on constructing the frame of the house together by putting in place the major structures and concepts that support all scientific programs. Once the workshop is over, you'll need to go out on your own and fill in this structure with drywall, insulation, and a couch. (We'll try to point out some useful fixtures that you might want to add to your house as we go along.)
As noted by Greg Wilson (the founder of Software Carpentry), every programming language shares seven core elements:
len
function)The lines between these are often blurry in practice - for example, a string in Python actually mixes some characteristics of a thing, a group, and a "chunk". The distinctions between these categories is not particularly relevant to the computer - they are purely a conceptual framework that helps programmers write code that does what they want it to do.
We expect that you'll find the basics of 1 and 2 fairly straightforward. We'll go quickly through those and will spend the most time on items 3-6. We won't really talk about 7, as it is not as common in scientific Python programming as it is in, say, shell scripting (pipes and redirection).
Don't worry if you don't already know what all of the above examples mean - you'll know by the end of this lesson.
Our Python tutorial during this bootcamp will rely on a number of additional files. The files that you'll need for each lesson are linked below - download these and save them in an easily accessible folder on your hard drive. Make sure that when you save each file in your browser, the appropriate file extention (i.e., .ipynb) is retained.
Scientific Programming
Read Write Tutorial
Matplotlib
To learn about these core concepts and the Python language, we'll start off by working within the IPython notebook.
To start up a notebook, open Terminal and navigate to the folder containing the
ipynb notebook files that you wish to open (or to any directory in which you'd
like to save a new notebook, if you're creating a new notebook from scratch).
Once in the directory, run the command ipython notebook
, which will launch a
local webserver and open your default browser. From there you can open an
existing notebook, create a new notebook, and start working.
Note that if you are on a Windows machine, this command may not run under
mysysgit or Cygwin. If it doesn't, open a Command Prompt (click on the Start
menu and type cmd
in the search box for Windows 7, or click on Run then type
cmd for Windows XP), navigate to the appropriate directory, and run the command
ipython notebook
.
As we go through this lesson, you can ask questions in two ways: