English

Every autumn semester

To give doctoral students with experience of other programming languages training in implementing advanced numeric algorithms in Python/Scipy, in order to use this in other courses, in research or in industry.

Introduction to Python for students already familar with another programming language. The use of object oriented programming in scientific computing, Scipy/Numpy datastructures.

Examples of complex numerical algorithms from varying subjects in numerical analysis,

Linking to advanced numerical libraries in C and Fortran (Netlib).

Automatic tests in scientific computing. Graphical representation of mathematical results (animation). The use of Python to control system processes.

have developed an understanding for the basic principles of computational algorithms.

have improved her/his knowledge about a number of important computational problems, and ways to attack them.

have developed a programming ability (for computational purposes) at a high level.

have learnt how to code, test and evaluate the results of complex numerical algorithms, using established programme libraries.

be able to carry out a group programming project, including identifying subproblems, distribution of tasks within the group and responsibility for the completion of his/her task.

be able to account for a computational project, both in a written report and orally.

Lectures

Exercises

Written assignments

Miscellaneous

A larger programming project to be carried out in group, with a written report to be presented at a seminar. Opposition on the report of another group.

Failed, pass

Basic course in numerical analysis. Programming experience in some of the languages Java, C, C++, Fortran, Python and Matlab.

Führer, C., Solem, J. & Verdier, O.: Computing with Python: An Introduction to Python for Science and Engineering. Pearson, 2013. ISBN 9780273786436.

FMNN25F

 -01-14

FN1/Anders Gustafsson