napari: a multi-dimensional image viewer for Python
Bibliographic record
Abstract
napari 0.6.6 Wed, Oct 15, 2025 We're happy to announce the release of napari 0.6.6! 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. napari follows EffVer (Intended Effort Versioning); this is a Meso release containing awesome new features, but some effort may be needed when updating previous projects to use this version. Highlights This a small bugfix release, following up the changes in 0.6.5. Zooming in the dark? In the previous release we accidentally made the zoom tool added in v0.6.3 invisible. Whoops! No worries, it's back 🔍. "Open with napari" When using the napari bundle, it will now detect when a file can be opened with napari based on the extension. This allows you to use the open with > menu from your operative system to open files with napari! PS: Since we did quite a few changes behind the scenes on this new version of the bundle, you might experience some issues. Don't hesitate to open an issue or contact us on zulip if you do! Improvements Update menuinst configuration with file type associations (#8359) Bug Fixes Fix shape selection in a single plane when shapes are on multiple planes (#8335) Fix invisible zoom box (#8344) Bump console version to fix ipykernel bug (#8360) Build Tools Bump console version to fix ipykernel bug (#8360) Documentation Update release notes v0.6.6 (docs#868) Add info about manual trigger of conda update to release guide (docs#859) Fix version switcher for 0.6.5 (docs#861) Remove trailing comma in version switcher json (docs#862) Add release notes for v0.6.6 (docs#866) Update release notes v0.6.6, the revenge (docs#869) Other Pull Requests Add a new attr_to_settr utility function and simplify layer control widgets layer to widget setup (#8274) [pre-commit.ci] pre-commit autoupdate (#8275) ci(dependabot): bump the actions group with 9 updates (#8324) Delay settings import to avoid circular import (#8327) Add info about conda forge manual trigger to release checklist (#8328) Add sponsor badge and reorganize badges into groups (#8343) Fix vispy error traceback (#8346) Migrate license settings to modern standards (#8350) 7 authors added to this release (alphabetical) (+) denotes first-time contributors 🥳 Daniel Althviz Moré - @dalthviz Grzegorz Bokota - @Czaki Jaime Rodríguez-Guerra - @jaimergp Juan Nunez-Iglesias - @jni Lorenzo Gaifas (docs) - @brisvag Peter Sobolewski - @psobolewskiPhD Tim Monko (docs) - @TimMonko 8 reviewers added to this release (alphabetical) (+) denotes first-time contributors 🥳 Carol Willing - @willingc Daniel Althviz Moré - @dalthviz Grzegorz Bokota - @Czaki Juan Nunez-Iglesias - @jni Lorenzo Gaifas (docs) - @brisvag Melissa Weber Mendonça - @melissawm Peter Sobolewski - @psobolewskiPhD Tim Monko (docs) - @TimMonko
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.284 | 0.272 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".