napari: a multi-dimensional image viewer for Python
Notice bibliographique
Résumé
napari 0.6.2 ⚠️ Note: these release notes are still in draft while 0.6.2 is in release candidate testing. ⚠️ Mon, Jun 23, 2025 We're happy to announce the release of napari 0.6.2! napari is a fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images. It's built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy). For more information, examples, and documentation, please visit our website, https://napari.org. Highlights Qt controls for thick slicing (#6146) Add grid overlay (#7827) Grid mode using vispy ViewBox and linked cameras (#7870) Features table widget as builtin (#7877) Move napari into src layout (#7952) Add public API to get access to docked widgets (#7965) New Features Qt controls for thick slicing (#6146) Add automatic area and perimeter measurement for shapes + action (#7262) Add canvas color to public API (#7778) Add grid overlay (#7827) Tiling canvas overlays (#7836) Features table widget as builtin (#7877) Improvements Allow use functions from PartSegCore-compiled-backend as numba alternative for data to texture mapping (#6617) Reduce warmup of numba if non numba backend is selected (#7917) Optional rotation handle for selection box overlay + simplify inheritance for Vispy overlays (#7958) Add public API to get access to docked widgets (#7965) Implement pasting spatial information into higher dimensions (#7973) Allow to use ViewerModel as annotation of plugin constructor argument (#8002) speedup edge width set by use batched_updates context manager (#8006) Performance Allow use functions from PartSegCore-compiled-backend as numba alternative for data to texture mapping (#6617) [Shapes] Use the plural methods to update colors of all selected shapes at once (#7995) Bug Fixes Fix scalebar theme connection (#7902) Don't add widgets to non-contributable menus (#7926) Fix handle mouse events (#7936) Fix moving of first/last vertex of polygons added in ring mode (#7942) Update shapes highlight on zoom (#7953) Fix invalidate of extent cache in Layers (#7972) [Points] Fix events.data_indices for ActionType.ADDED event when adding single point (#7983) Fix interaction box initialization (#8011) Fix angles not showing correctly in UI (#8013) API Changes Expose force_sync context manager (#7908) Documentation Add example linking the cameras of two viewers (#6881) Update README to use imshow and add example to generate image (#7989) Update version switcher for 0.6.1 (docs#713) Update contributing docs page (docs#715) Update code of conduct committee members (docs#716) Add initial documentation about widget communication (docs#721) Update installation.md to link to conda getting started not miniconda (docs#726) Update governance docs (docs#729) Initial release notes for alpha of 0.6.2 (docs#734) Other Pull Requests Layer controls widgets refactor (#7355) Add codespell support (config, workflow to detect/not fix) and make it fix few typos (#7619) Add docs constraints for python 3.12 (#7714) Include Qt PyPI server for pre-releases (#7803) Refactor layer overlays visuals from VispyLayer to VispyCanvas (#7835) Use information about units when calculate scale of layers when render (#7889) Add cron check to update reader extensions (#7907) Update dask, hypothesis, numpy, tensorstore, vispy (#7948) Move export ROI implementation into qt_viewer (#7950) [pre-commit.ci] pre-commit autoupdate (#7951) Add cron check to update reader extensions v2 (#7957) Restore image in Readme (#7959) Add cron check to update reader extensions v3 (#7966) Update coverage, dask, fsspec, hypothesis, pydantic, tifffile, vispy (#7967) fix vendored script and trigger workflow on pull_request (#7968) [pre-commit.ci] pre-commit autoupdate (#7970) Remove layers_change event that is marked to be removed in 0.5.0 (#7971) [maintenance] Use Wandalen/wretry.action to auto-retry fail in --pre tests (#7986) Update hypothesis, ipython, jsonschema, tifffile (#7987) [pre-commit.ci] pre-commit autoupdate (#7988) Stop status thread on Keyboard Interruption (Ctrl+C) (#7994) Update hypothesis, magicgui, pandas, pyqt6, pytest, pytest-pretty (#8000) Update pyproject.toml to fix coverage paths (alt) (#8001) [Maintenance] Remove redundant initialization in Points layer and restructure for clarity (#8005) Update[shortcuts]: add Ctrl/Cmd-A as secondary keybinding for select_all_shapes (#8015) Fix comment and manual dispatch triggered build jobs (docs#723) 10 authors added to this release (alphabetical) (+) denotes first-time contributors 🥳 Draga Doncila Pop (docs) - @DragaDoncila Grzegorz Bokota (docs) - @Czaki Jacopo Abramo - @jacopoabramo + Juan Nunez-Iglesias - @jni Lorenzo Gaifas - @brisvag Maximilian Mayrhauser - @Llewi + Melissa Weber Mendonça - @melissawm Peter Sobolewski (docs) - @psobolewskiPhD Rahul Kumar - @rahul713rk + Tim Monko (docs) - @TimMonko 13 reviewers added to this release (alphabetical) (+) denotes first-time contributors 🥳 Ashley Anderson - @aganders3 Carol Willing - @willingc Daniel Althviz Moré - @dalthviz Draga Doncila Pop (docs) - @DragaDoncila Grzegorz Bokota (docs) - @Czaki Jacopo Abramo - @jacopoabramo + Juan Nunez-Iglesias - @jni Lorenzo Gaifas - @brisvag Melissa Weber Mendonça - @melissawm Peter Sobolewski (docs) - @psobolewskiPhD Tim Monko (docs) - @TimMonko Wouter-Michiel Vierdag - @melonora Yaroslav Halchenko - @yarikoptic
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,317 | 0,269 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».