Author

Mike Müller

Intended Audience

Python programmers with intermediate knowledge. Some knowledge of C is of advantage but not required.

Requirements

All participants should bring laptops with Linux, Windows, or Mac OS. Python 2.5 or Python 2.4 need to be installed as well as an editor or IDE.

The following third-party packages are needed:

• psyco (version 1.5.2 or higher)
• numpy (version 1.0.1 or higher)
• Pyrex (version 0.9.4.1 or higher)
• C compiler such as GCC or MinGW (version 3.4.2 or higher)
• ctypes (version 1.0.1 or higher) only if Python 2.4 is used (included in Python 2.5)

Summary

Although Python programs may be slow for certain types of tasks, there are many different ways to improve performance. This tutorial will demonstrate some of them. One computationally intensive example will be used with these techniques. The objectives of this course are to give the participants an overview over available techniques, to enable them to apply these techniques, and to give them the ability to decide what might be the optimal solution for a certain performance problem. Participants are strongly recommended to bring laptops because all techniques are introduced with examples and exercises.

Presenter Bio

Mike Müller lives in Leipzig, Germany and works as a consultant, programmer and trainer. He programs scientific software in Python and other languages. He has taught Python since 2004 at the Python Academy since 2006. His courses cover introductions to Python as well as special topics such as extensions or thread programming. Being an engineer who also works in research projects, he uses numerous scientific Python packages on a daily basis and, therefore, also offers a course Python for Scientist and Engineers. He is an author of PyModelData, a Python package design for reading, writing and converting data for scientific modeling. When he does not program or teach, he spends time with his wife and his two kids or works out in the gym and runs.