Day 1 / Afternoon

Doing data analysis

(Karen Cranston)

See the IPython Notebook, numpy-and-graphing.ipynb.

A bit more IPython Notebook and matplotlib

The whole IPython Notebook gallery

See: for an example of a paper where all the figures were generated with ipynb.

Matplotlib gallery:

Note that you can use


on the source code in the gallery links to load the code into ipynb and execute it.


(Titus Brown)

See the IPython Notebook, testing-with-nose.ipynb.

When we say “testing” we really mean automated testing. The central problems addressed by testing are correctness and reproducibility. (While these are linked, they are not the same!)

I don’t want to depart from the efficiency argument, though – being able to make sure your old code stays working is very important to moving forward with

There are two basic kinds of tests that I’d like to briefly discuss. One kind of test is a unit test. The other kind of test is a regression test. (There are also many more.)

Unit tests address small units of code, like functions. They are used to isolate and nail down and prove the functionality of potentially complicated little functions.

Regression tests address the overall function of code, and they are used to make sure that your code is doing the same thing today as it was yesterday.

I’ll show you examples of both.

Writing tests

We’re going to be using the nose testing framework, which is just a framework that makes it easy to find and execute tests.

Basically, ‘nose’ creates a command ‘nosetests’ that finds and runs tests. The idea is that you won’t need to register new tests.

A test function looks like this:

def test_something():
   # run some code
   # fail loudly or succeed silently

Installing Python packages; useful Python packages

Using pip, which is an interface to PyPi

See Installing Python packages; useful Python packages

Also see the IPython Notebook, using-screed.ipynb.


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