Winter visualization series
With the exception of the Matplotlib session on January 20 (see details below), all sessions begin at 10am Pacific / 11am Mountain / 1pm Eastern / 2pm Atlantic. Course materials will be added to this page after each session.
Click on each session for more details and the calendar link.
Jan-20, 10am PT – Intro session: which tool is right for me?
Speaker: Alex Razoumov (SFU)
Abstract: This overview session introduces all workshops in the Series and provides high-level answers to questions such as:
- What are the differences among Python plotting libraries – Matplotlib, Seaborn, Plotly, Vega-Altair, and Plotnine – and when should you choose one over another?
- Why use ParaView for 3D scientific visualization, and what are its core capabilities?
- In what ways can you use Python scripting in ParaView? (There is more than one!)
- What are the three primary approaches to creating animations in ParaView?
- What is in-situ visualization?
- Any domain-specific visualization tools covered in this Series?
- Which scientific web visualization tools should you consider using in 2026?
We will also briefly cover tools not included in this Series, such as VisIt, Python mapping packages, Julia plotting libraries, and others, and explain why they were left out.
Jan-20, 11am PT – Plotting in Python with Matplotlib
Jan-27, 10am PT – Plotting in Python with Seaborn
Speaker: Jarno van der Kolk (Univ. of Ottawa)
Abstract: Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics and is particularly well suited for displaying multi-dimensional data to get quick insight in a dataset.
Prerequisites: Some knowledge of Python.
Jan-30, 10am PT – Plotting in Python with Plotly
Speaker: Alex Razoumov (SFU)
Abstract: Plotly is an open-source visualization library for creating interactive, publication-quality graphs in Python (and other languages), with support for a wide range of plot types. In this workshop, you will learn two ways to use Plotly: the high-level Plotly Express interface for rapid data exploration, and the more flexible Plotly Graph Objects interface for detailed customization. We will explore common plot types – line, scatter, bar, pie, heatmap – as well as more advanced and 3D visualizations. You will learn how to display figures using different rendering backends, save them as images, HTML, or JSON files, and view them inside Jupyter Notebook or JupyterHub. We will also learn how to include additional interactive elements such as sliders, buttons, and dropdown menus into your Plotly visualizations.
Feb-03, 10am PT – Plotting in Python with Vega-Altair
Speaker: Pier-Luc St-Onge (McGill Univ.)
Abstract: How can you uncover the hidden trends in your thousands of data rows? This workshop teaches you how to create interactive charts from your tabular data. You’ll master histograms, time series plots, distributions, and multi-faceted graphs to transform your tables into impactful visualizations.
Outline:
- Creating interactive charts with Altair
- Building a figure step-by-step using a grammar of graphics
- Visualizing time series and data distributions
- Creating multiple facets in a single figure
Feb-06, 10am PT – Basic ParaView
Speakers: Pier-Luc St-Onge (McGill Univ.) and Tyson Whitehead (Western Univ.)
Abstract: ParaView is an open source, multi-platform data analysis and visualization tool designed to run on a variety of hardware from individual laptops to large supercomputers. With ParaView, users can interactively visualize 2D and 3D datasets defined on structured, adaptive and unstructured meshes or particles, animate these datasets over time, and manipulate them with a variety of filters. ParaView supports both interactive visualization through a graphical user interface (GUI) and scripted visualization, including offscreen rendering, and is an easy and enjoyable tool to learn.
In this beginner’s workshop, we will explore ParaView’s interface, examine the types of data it can import, and learn how to use filters to build increasingly sophisticated visualization pipelines.
Feb-10, 10am PT – The grammar of graphics in R and Python
Feb-13, 10am PT – Introduction à ParaView
Speaker: Pier-Luc St-Onge (McGill Univ.)
Abstract: ParaView est un outil d’analyse et de visualisation de données à code source ouvert et multiplateforme, conçu pour fonctionner sur une variété de systèmes informatiques, des ordinateurs portatifs aux superordinateurs. Avec ParaView, on peut visualiser de manière interactive des ensembles de données 2D et 3D définis sur des particules ou des maillages structurés, adaptatifs ou non structurés. On peut ensuite animer ces données dans le temps et les manipuler à l’aide de divers filtres. ParaView prend en charge la visualisation interactive via une interface graphique, ainsi que la visualisation par script, y compris le rendu hors écran. C’est un outil facile et agréable à découvrir.
Dans cet atelier d’introduction, nous explorerons l’interface de ParaView, nous examinerons les types de données qu’il peut importer et nous apprendrons à utiliser des filtres pour construire des pipelines de visualisation de plus en plus sophistiqués.
Feb-24, 10am PT – ParaView scripting
Speaker: Alex Razoumov (SFU)
Abstract: ParaView’s Python library allows you to automate your visualization workflow by scripting your tasks and running them from the command line, either on your local machine or as a batch Slurm job on a remote HPC cluster, all without opening any windows. In this workshop, we will introduce ParaView’s Python tools, starting with using the Trace feature to convert GUI actions into scripts and then showing how to simplify and modify those scripts. You will learn how to run them using pvpython and pvbatch, and how to explore VTK data from the ParaView Python shell.
Prerequisites: basic ParaView knowledge (importing and examining data, working with filters)
Feb-27, 10am PT – Scripter des visualisations via ParaView
Speaker: Pier-Luc St-Onge (McGill Univ.)
Abstract: La bibliothèque Python de ParaView vous permet d’automatiser votre processus de visualisation en programmant des séquences d’actions sous la forme de scripts et en les exécutant en ligne de commande, soit localement, soit via une tâche de calcul sur un superordinateur, le tout sans ouvrir aucune fenêtre de ParaView.
Cet atelier vous présentera les outils Python de ParaView, en commençant par l’utilisation de la fonction Trace pour convertir les actions de l’interface graphique en scripts, puis en vous montrant comment simplifier et modifier ces scripts. Vous apprendrez à les exécuter avec pvpython et pvbatch, et à explorer les données VTK depuis l’interpréteur Python de ParaView.
Prérequis: connaissances de base de ParaView (importer et examiner des données, utiliser des filtres) et en programmation Python
Mar-03, 10am PT – ParaView animation
Speaker: Alex Razoumov (SFU)
Abstract: ParaView supports several approaches to animating data. In this session, we will focus on keyframe animation, where you can animate any property of any pipeline object, e.g. moving a slice through a volume by changing its origin, or animating a 2D contour through a 3D dataset by varying the contour value. We will also look at how to combine multiple timelines within a single animation. After that, we’ll work through several hands-on keyframe animation exercises and discuss how to create animations using Python scripting.
Prerequisites: basic ParaView knowledge (importing and examining data, working with filters), working knowledge of ParaView’s Python scripting
Mar-06, 10am PT – Visualisations animées avec ParaView
Speaker: Pier-Luc St-Onge (McGill Univ.)
Abstract: ParaView permet d’animer de différentes façons la visualisation de données. Cet atelier sera consacré à l’animation par images clés, soit une méthode permettant d’animer selon n’importe quelle propriété d’un objet du pipeline. Nous verrons, entre autres, comment animer le plan de coupe d’un volume et aussi comment animer un contour 2D, selon sa valeur, dans un jeu de données 3D. Nous verrons également comment combiner plusieurs chronologies au sein d’une même animation. Enfin, nous réaliserons plusieurs exercices pratiques d’animation par images clés et aborderons la création d’animations à l’aide de scripts Python.
Prérequis: connaissances de base de ParaView (importer et examiner des données, utiliser des filtres) et la compréhension de scripts Python utilisant le module ParaView.
Mar-10, 10am PT – Remote and large-scale visualization in ParaView
Mar-13, 10am PT – Introductory VMD
Mar-17, 10am PT – Programmable Filter / Source in ParaView
Speaker: Alex Razoumov (SFU)
Abstract: ParaView includes two highly flexible scripting tools – the Programmable Filter and Programmable Source – that allow you to design your own data-processing operations. With them, you can construct entirely new spatial discretizations, generate tailored 3D structures, or even import data stored in custom file formats. This hands-on session will demonstrate how these features open up new possibilities for scientific visualization workflows.
Prerequisites: To follow the hands-on exercises, please install ParaView on your computer. You can download it from the official website https://www.paraview.org/download.
Mar-20, 10am PT – Network visualization with Gephi
Speakers: Jarno van der Kolk (Univ. of Ottawa) and Alex Razoumov (SFU)
Abstract: Gephi is an open-source platform for visualizing and manipulating large graphs. It lets you visualize how different entities connect with each other. These could be social media networks, researchers publishing papers, internet traffic and so on. It also has robust statistical analysis built-in.
Prerequisites: Have Gephi installed. Downloads are available at https://gephi.org.