A worked example on scientific computing with Python¶
| Authors: | Hans Petter Langtangen (hpl at simula.no) |
|---|---|
| Date: | Jul 27, 2016 |
Contents
This worked example
- fetches a data file from a web site,
- applies that file as input data for a differential equation modeling a vibrating system,
- solves the equation by a finite difference method,
- visualizes various properties of the solution and the input data.
The following programming topics are illustrated:
- basic Python constructs: variables, loops, if-tests, arrays, functions
- flexible storage of objects in lists,
- storage of objects in files (persistence),
- downloading files from the web,
- user input via the command line,
- signal processing and FFT,
- curve plotting of data,
- unit testing,
- symbolic mathematics,
- modules.
All files can be forked at https://github.com/hplgit/bumpy.
Optimal background for reading this note¶
- some interest in exploring physics through numerical simulation
- some very basic knowledge of
- differential equations
- finite difference approximations
- Python or Matlab
- significant interest in exploring Python for scientific computations to solve a real-world physical problem (with low mathematical complexity)
Notice
You can read in two ways: either as a detailed example on using Python for solving differential equations (some very basic Python knowledge is preferred) or just to get an impression of how Python can be used in a Matlab-like fashion.
If you need motivation for using Python as programming language, see Appendix: Quick motivation for programming with Python. Lists of many useful tutorials and introductions to Python, with emphasis on scientific computing, are found in Appendix: Scientific Python resources.
