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SUMMARY:napari: explorative visualization and workflow building for scient
 ific data analysis - Lorenzo Gaifas\, Grzegorz Bokota
DTSTART;TZID=Poland:20260723T090000
DTEND;TZID=Poland:20260723T103000
DTSTAMP:20260907T093637Z
UID:pretalx-euroscipy-2026-TRFRMH@pretalx.com
DESCRIPTION:If you work with scientific data\, chances are that visualizat
 ion is one of your strongest tools and biggest time sinks. Whether you're 
 dealing with images from microscopes or telescopes\, complex surface recon
 structions\, 3D point clouds\, or n-dimensional feature embeddings from ne
 ural networks\, some requirements are always the same: _performance_\, _in
 teractivity_\, and _extensibility_.\nnapari is a Python library for the vi
 sualization and annotation of scientific data that focuses on addressing t
 hese needs\, staying cross-field and un-specialized at the core\, while pr
 oviding an easy way to develop powerful specialized plugins.\nIn this tuto
 rial\, we will learn the basics of interacting with napari and its feature
 s and how to use napari to effectively navigate n-dimensional data. Armed 
 with this knowledge\, we will simulate a typical exploratory approach to d
 eveloping a new image processing workflow in Python and converting it to a
 n easily shearable napari plugin.
LOCATION:Room 1.19 (Ground Floor\, Shannon)
URL:https://pretalx.com/euroscipy-2026/talk/TRFRMH/
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