2026-07-23 –, Room 1.19 (Ground Floor, Shannon)
If you work with scientific data, chances are that visualization is one of your strongest tools and biggest time sinks. Whether you're dealing with images from microscopes or telescopes, complex surface reconstructions, 3D point clouds, or n-dimensional feature embeddings from neural networks, some requirements are always the same: performance, interactivity, and extensibility.
napari is a Python library for the visualization and annotation of scientific data that focuses on addressing these needs, staying cross-field and un-specialized at the core, while providing an easy way to develop powerful specialized plugins.
In this tutorial, we will learn the basics of interacting with napari and its features and how to use napari to effectively navigate n-dimensional data. Armed with this knowledge, we will simulate a typical exploratory approach to developing a new image processing workflow in Python and converting it to an easily shearable napari plugin.
Data exploration and workflow building are two often overlooked aspects of scientific data processing and visualization. Commonly discussed tools often focus on un-interactive publication figures or pre-packaged, highly specialized software suites.
If exploratory visualization constitutes a large part of your interaction with scientific data - if you often need to develop or adapt processing workflows but struggle to customize existing visualization and annotation software - if you're looking for a smoother experience that allows you to both write processing code and actually see what it does - then this tutorial is for you.
To get the most out of this tutorial, some experience with general scientific Python tooling and libraries is advised (mostly NumPy and its syntax). Ideally, you should have some idea of how data and images are represented as arrays of numbers, and the types of analyses that might be performed on these arrays, e.g. filtering and segmentation. You don’t necessarily need to be familiar with how these tools and methods work - it’s enough to know that they are out there!
Introduction to napari (~20-30 min)
We will begin with a short introduction to napari, the main components of its interface, and some important features. We will also make sure everyone can get a virtual environment set up with napari. You will have some time to play around with some sample data, get comfortable with the interface and ask some questions.
Exporatory analysis (~45-60 min)
In this section, we will simulate a typical session of data exploration, developing a small image processing workflow along the way. You will start with a small pure-python example, gradually making it more powerful and interactive by converting it to a napari widget and by integrating it with existing napari functionality such as the grid view, command palette, overlays, etc.
Making a plugin (~10-15 min)
To finish up, you will learn how to take the previously written code and convert it to a napari plugin, publishing it as a python package that everyone can easily install and reuse.
I joined the napari community during my PhD in structural biology, where I used and contributed to napari regularly, until I was invited to join the core team. I now work full time on napari as an independent contractor, improving and developing many of the features that I used or introduced during my PhD.