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Preparation

In this course we are going to use ParaView. ParaView is an open-source, multi-platform scientific visualization and data analysis application. It is available for Windows, macOS and Linux, see below.

We will use a number of datasets provided for the course in the exercises.

Both data files as well as ParaView binaries for Windows and Linux can be downloaded and installed using the instructions below. This course assumes that you bring your own PC/laptop, or if not, that you can work together with someone else that does have a PC/laptop.

Course materials share

All course data files and slides can be found at here. There we also provide ParaView binaries for Windows and Linux (see below).

Install ParaView

We provide ParaView binaries for Windows and Linux for this course in the course materials share (see above). For macOS there are many variations available on https://www.paraview.org/download/ and you probably know best yourself which one you need.

This guide has been written for ParaView version 5.13. Between ParaView versions small differences in GUI (and functionality) exist, but most of what is written in these notes should be easy to apply to other versions of ParaView.

OpenGL compatibility

An important difference between ParaView version 5.x and earlier versions is that new ParaView versions require a GPU that supports OpenGL 3.2 or higher. Older PCs/laptops and systems with Intel Integrated graphics may not support this. There are two options to work around this limitation:

  1. Start ParaView (5.x) from the command line with the --mesa flag, e.g. paraview --mesa. This enables CPU-based software rendering, i.e, not using a GPU at all. This will be slower than rendering on the GPU, but should work on any system.

  2. Use an older version of ParaView, like 4.4 (available from http://www.paraview.org/download). In this case the GUI and menu options will be slightly different in naming, placement and look compared that what is described in these notes.

Download and extract data

The ZIP files with data used in this workshop can be found in the course materials share linked above. Some of the data sets are provided by the University of Amsterdam.

▶ Unzip the data file to a directory of your choice.

This directory will now have 4 datasets:

  • headsq_masked.vtk: This is a CT scan of a boy's head. The data consists of a regular grid of 255x255x93 voxels, containing scalar values for the density of the scanned tissue. (The original dataset consisted of 93 separate files containing binary data, unsigned shorts with connectivity bits. For this course, we pre-processed the file for easier use).
  • wervel.csv: This is a Comma Separated Value file, containing a list of 3D points and their velocity vectors, representing a snapshot of a simulation of a tornado.
  • ALT_PRPB001A.vtk: This file contains the results of a simulation of a coral growth. The scalars in this file represent the time of growth of a coral (this data is provided courtesy of Jaap Kaandorp , Section Computational Science, UvA).
  • SMRX.vtk: This is a flow simulation of a viscous fluid through a stationary mixer (this data is provided courtesy of Drona Kandhai, Section Computational Science, UvA).

Under the Exercises section there are four exercises based on these datasets.