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Scientific visualisation with ParaView - An introduction, 15 December 2021

This course is an introduction to Scientific Visualisation using the ParaView application.  ParaView is an open source data analysis and visualisation application.  Paraview provides state of the art algorithms and visualisation techniques.  It can analyse extremely large data sets and supports a multitude of commonly used data file formats.  Paraview is based on the Visualisation Toolkit (VTK), a powerful visualization library for C++ and Python. 

ParaView is a very generic visualization tool. This course will give an introduction to the workflow of the application and how to perform basic visualisations.  The supported data types and associated attributes will be introduced.  Demonstrating how Python can be utilised to generate data files which can be used with ParaView is another focus point of this course.

The course consists out of lectures and hands-on sessions.  During the hands-on sessions you will have the opportunity to use ParaView with actual data sets.  For the hands on part participants can use LUNARC's Aurora system, if they wish to do so.

The course will be held in English.  The course will be taught by Jonas Lindemann from LUNARC.


The course is open to SNIC users, prospective SNIC users and academics throughout the Nordic region.


  • Basic familiarity with a scripting language such as Python or Matlab
  • Either an Aurora account (can be applied for in the registration form) or access to ParaView installation on another system

Time and location

The event will be held on the 15th December from 9:00 until 12:30.  The event will be held as an online event via zoom.  Registered participants will receive a zoom link closer to the event.


Registration deadline: 5th December

Registration is now closed.


Questions and queries regarding the course should be directed to Lunarc, using the support form.

Workshop video and course materials

The workshop can be viewed here:

SNIC ParaView Workshop - YouTube

Course material including lecture:

PyVTK slides:

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