Content

The course will be offered as a hand-on computer training workshop and will be composed of the following blocks:

1) Introduction to computer programming and Python computer language

2) Basic elements of programming: object orientation and Python data structures

3) Environmental data processing using Python (e.g. reformatting of rainfall inputs)

4) Numerical computations using Python and the Numpy module

5) Generate graphs in Python (matplotlib module)

6) Work with time series in Python (pandas module)

7) Programming in GIS environment using PCRaster-Python module

8) Dynamic modelling in Python using the PCRaster dynamic framework

Programming skills: knowledge of programming is not expected from the students and the course introduces variables, control structures and the use of functions. This introductory part relies on scientific examples to illustrate the programming concepts.

Use of libraries: many programming tasks are common enough that they come pre-packaged in the form of libraries of which use is an integral part of (scientific) programming. Typical examples include the creation of plots, the reading and writing of various file formats and numerical integration. The course presents the common aspects of using libraries and practical examples of their use.

Scientific programming: reflecting the supporting role of the course within a master’s programme, the use of programming to support scientific modeling is put forward. This part of the course includes a presentation of object-oriented programming (directly aimed at using an object to represent a scientific model) as well as rigorous programming practice.

Practical aspects: the course is based on the Python programming language. This programming language is used by several hydrology research groups involved in environmental engineering at the VUB and KU Leuven. Most classes will take place in a computer room with one computer for each student. The course content will be available online.

Course material

Presentations, exercises and data