In recent years, Python has grown in importance for the areas of numeric simulation and scientific computing. Python is used in a variety of projects (e.g. Numpy, Scipy, Matplotlib), as a scripting language in finite element codes, as a system language in Linux and in web development (e.g. Django).
In the course ‘Python in Numerical and Scientific Computing’ at TU Graz you will learn what you need to begin working with scientific libraries in Python.
- You will learn the basics of working with scientific libraries in Python (Python 2 and Python 3):
- Introduction to Numpy: arrays, arithmetic, indexing
- Linear algebra with Numpy/Scipy (including linear equation systems, matrix operations, sparse matrices)
- Nonlinear problems with Scipy: finding maxima and minima, data fitting
- Optimisation with Scipy and CVXOPT
- Creating graphics with Matplotlib
- Tuning numeric codes: vectorising, Cython and F2PY
- Brief presentation of advanced applications: computer algebra with SageMath, FEM with Salome and Code_Aster
- You will develop realistic and meaningful examples.
- You will learn how to tackle complex problems.
- You will learn to use the possibilities of various features of Python.
You will be able to use what you learn in this course anywhere and without additional costs, because all the software used is free.
- Experts from research and development who want to use Python in their work and are looking for an alternative or supplement to MATLAB.
To participate in the course you will need to understand the basics of numerical mathematics and programming in Python.