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SUMMARY:From Code to app\, how to ship your tool to your non programming c
 ollaborators - Grzegorz Bokota
DTSTART;TZID=Poland:20260720T160000
DTEND;TZID=Poland:20260720T163000
DTSTAMP:20260907T002801Z
UID:pretalx-euroscipy-2026-8T33ZK@pretalx.com
DESCRIPTION:When writing Python code for scientific applications\, you may
  reach a point where you want to share your work with collaborators that d
 on't code. But what if you do not want to require them to set up Python en
 vironments or act as their tech support?\n\nThere are tools to avoid that 
 by converting your Python code into executable that can be run without Pyt
 hon setup\, and with all dependencies included.\nIn this talk I will share
  my experience with using some of those tools like PyInstaller and Conda c
 onstructor and how you could do the same. \nShowed solutions might be used
  for both CLI script and application with graphical Interface.\nImportantl
 y\, I will also mention some of the callenges that you might encounter and
  how to solve them.
LOCATION:Room 1.38 (Ground Floor\, Turing)
URL:https://pretalx.com/euroscipy-2026/talk/8T33ZK/
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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:20260907T002801Z
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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