Proceedings of the 8th Python in Science Conference

Abstract

SciPy 2009 marks our eighth annual Python in Science conference and the second edition of the conference proceedings.The conference and these proceedings highlight the ongoing focus of the community on providing practical software tools, created to address real scientific problems.As in previous years, topics at the conference ranged from the presentation of tools and techniques for scientific work with the Python language, to reports on scientific achievement using Python.Interestingly, several people noticed that something important happened in the Scientific Python world during the last year: we are no longer constantly comparing our software with commercial packages, nor justifying the need for Python in Science.Python has now reached the level of adoption where this sort of justification is no longer necessary.The exact moment when this shift in focus occurred is difficult to identify, but that it happened was apparent during the conference. Recurring scientific themesThis year the conference spanned two days, and each day commenced with a keynote address.The first keynote was delivered by Peter Norvig, the Director of Research at Google; the second by Jonathan Guyer, a materials scientist in the Thermodynamics and Kinetics Group at the National Institute of Standards and Technology (NIST).Peter Norvig's talk was titled "What to demand from a Scientific Computing Language-even if you don't care about computing or languages", where he discussed a number of desired characteristics in a scientific computing environment.Such a platform should have the ability to share code and data with other researchers easily, provide extremely fast computations and stateof-the-art algorithms to researchers in the field, and be as easy as possible to use in a time-efficient manner.He also stressed the importance of having code that read like the mathematical ideas it expressed.Jonathan Guyer's keynote centred around "Modeling of Materials with Python".He expanded on several of the above-mentioned characteristics as he discussed the development of FiPy, a framework for solving partial differential equations based on a finite volume approach.Jonathan explained how FiPy was created to provide the most advanced numerical techniques to scientists, so that they could focus on the scientific questions at hand, while having a standard platform for sharing codes with colleagues.Importantly, FiPy has become a critical tool in Jonathan's research group and has been adopted by many of their colleagues for both research as well as teaching. Proceedings of the 8 th Python in Science Conference (SciPy 2009)Cython tutorial