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UID:pretalx-euroscipy-2026-TRFRMH@pretalx.com
DTSTART;TZID=CET:20260723T090000
DTEND;TZID=CET:20260723T103000
DESCRIPTION:If you work with scientific data\, chances are that visualizati
 on is one of your strongest tools and biggest time sinks. Whether you're d
 ealing with images from microscopes or telescopes\, complex surface recons
 tructions\, 3D point clouds\, or n-dimensional feature embeddings from neu
 ral networks\, some requirements are always the same: _performance_\, _int
 eractivity_\, and _extensibility_.\nnapari is a Python library for the vis
 ualization and annotation of scientific data that focuses on addressing th
 ese needs\, staying cross-field and un-specialized at the core\, while pro
 viding an easy way to develop powerful specialized plugins.\nIn this tutor
 ial\, we will learn the basics of interacting with napari and its features
  and how to use napari to effectively navigate n-dimensional data. Armed w
 ith this knowledge\, we will simulate a typical exploratory approach to de
 veloping a new image processing workflow in Python and converting it to an
  easily shearable napari plugin.
DTSTAMP:20260603T200039Z
LOCATION:Room 1.19 (Ground Floor\, Shannon)
SUMMARY:napari: explorative visualization and workflow building for scienti
 fic data analysis - Lorenzo Gaifas
URL:https://pretalx.com/euroscipy-2026/talk/TRFRMH/
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