Scientific computing in Python

Welcome!

Prof. Dr. Alexander Löw, LMU, SS2017

This lecture is different!

... experiment
... no examn
... no credits

... lot of fun (hopefully)
... new experiences

Why are you here?

Motivation? What do you expect?

What is needed?

Curiosity!

"Where I am, I want to be!"

Objectives

This course will cover

Introduction in (python) programming
Scientific Computing in Python
Data visualisation for scientific applications
Computing for environmental modelling
Computing for image analysis

Learning outcomes

At the end of the module, students should:

have an understanding of algorithm development and be able to use widely used scientific computing software to manipulate datasets and accomplish analytical tasks
have an understanding of the technical issues specific to image-based analysis, model implementation and scientific visualisation

Learning by doing!

We will try to solve as far as possible pratical scientifc problems. Only if you have a real problem you work towards, you will learn most.

Examples:

Build a simple land surface model
Build some algorithm for remote sensing image analysis
Build some agent based economic model
Build a game
Program a literature database
...

Task01

collect ideas what kind of projects you would like to work on throughout the course. We might then select 2-3 projects and work on these. ($\rightarrow$ Moodle)

Organizational matters

Assignments will be done in MOODLE digital classroom.

Task02: register there until next week.

Working with the course material

easiest way is to view the course material online on github.
as all course material is publically available, you can also download the most recent version to work on your own computer but cloning the resources



In [4]:

    
%%bash
# just uncomment the line below
# git clone git@github.com:geography-munich/sciprog.git

Credits

This course was inspired and is using material from a variety of different sources. The very valuable information from these sources is highly acknowledged. The following ressources were used in particular to compile the content of this course:



In [ ]: