Developing Scientific Software in Python

A session at PyCon AU 2011

  • Duncan Gray

Saturday 20th August, 2011

3:10pm to 3:40pm (EST)

This presentation will outline key lessons learnt in developing scientific software in Python. Methods of maintaining and assuring code quality will be discussed, in particular:
- designing effective unit tests;
- visualising output data to discover defects; and
- designing characterisation tests to test the actual system behaviour and to identify unintended system changes.

The challenges in optimising and parallelising Python code will also be presented, including:
- profiling;
- using NumPy to optimise numerical computations;
- using C code for intensive computational tasks; and
- parallelising software to run on high performance environments such as clusters.

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Duncan Gray

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PyCon AU 2011

Australia Australia, Sydney

20th21st August 2011

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Time 3:10pm3:40pm EST

Date Sat 20th August 2011

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