Paraview for Biological Data

Paraview is a powerful tool for scientific visualization, widely used in various fields, partially due to its growing support for even niche applications.

This course is provided as part of my work in my main affiliation, and you can contact me there.

Course: Paraview

Duration: 2 half days | Location: Online

We will announce the course dates soon!

Event Page

Module 1: Paraview Basics
Get to know the user interface, basic operations, what pipelines are and look how the visualization process works.
Topics: Paraview, UI, Lookup Tables, Visualization Basics.

Module 2: Data Import
Learn how to import your data into Paraview, including common file formats and troubleshooting tips.
Topics: Data Formats, Importing Data, Troubleshooting.

Module 3: Data Processing
Explore various data processing techniques available in Paraview, including filtering, thresholding, and data transformation.
Topics: Data Processing, Filtering, Thresholding, Transformation.

Module 4: Plant Data
Advanced session: how to visualize plant data, a multi-modal, partially geometric and partially volumetric data set. You will learn what considerations go into producing a visualization that conveys the right message.
Topics: Multimodal Data, Geometric Data, Volumetric Data, Visualization Principles.

Module 5: Geospatial Data
Advanced session: how to visualize geospatial data, including DEMs, satellite data and vector data. You will learn how to handle large data sets and how to create meaningful visualizations.
Topics: Geospatial Data, DEMs, Satellite Data, Vector Data, Large Data Sets.

Module 6: Scripting and Automation
Learn how to use PwPython to automate Paraview, particularly for more difficult data sets. This will be shown on the example of fluid data.
Topics: Python Scripting, Automation, Custom Visualizations.

Bonus
Module 7: Advanced Data Extraction
Introduction to data processing techniques for volumetric and geometric data. Basic principles of feature extraction and processing of either noisy data or data that is not quite where the visualization needs it to be.
Topics: Streamlines, Surface Extraction, Vorticity, Feature Extraction, Matching