The Python programming language is increasingly popular. It is a versatile language for general purpose programming and accessible for novice programmers. However, it is also increasingly used for applications in the domain of scientific computing. This training introduces modules that are useful in that context.

Learning outcomes

When you complete this training you will

• be able to use numpy to represent and compute with multidimensional arrays;
• have an overview of the scope of scipy;
• be able to create data visualizations with matplotlib and bokeh;
• be able to use sympy to do symbolic computations;
• be able to represent and manipulate data in HDF5 format;
• know how to start on an image or video manipulation project.

Schedule

Total duration: 4 hours.

Subject	Duration
introduction and motivation	5 min.
numpy	80 min.
scipy	25 min.
coffee break	10 min.
matplotlib & bokeh	30 min.
sympy	25 min.
HDF5	40 min.
scikit-image & OpenCV	20 min.
wrap up	10 min.

Training materials

Slides are available in the GitHub repository, as well as example code and hands-on material.

Target audience

This training is for you if you need to use Python for scientific computing.

Prerequisites

You will need experience programming in Python. This is not a training that starts from scratch.

If you plan to do Python programming in a Linux or HPC environment you should be familiar with these as well.

For following along hands-on, you need

• laptop or desktop with internet access.
• a system set up so you can connect to an HPC system, an account on an HPC system (e.g., VSC, CECI, …), compute credits if that is required to run jobs on the HPC system if you want to use an HPC system;
• a Python environment that can run Jupyter Lab if you want to use your own system;
• access to Google Colaboratory if you prefer not to install software.

Level

• Introductory: 30 %
• Intermediate: 40 %
• Advanced: 30 %

Trainer(s)

• Geert Jan Bex (geertjan.bex@uhasselt.be)