napari: a multi-dimensional image viewer for Python
Bibliographic record
Abstract
napari 0.5.2 Tuesday, Aug 13, 2024 We're happy to announce the release of napari 0.5.2! napari is a fast, interactive, multi-dimensional image viewer for Python. It's designed for exploring, annotating, and analyzing multi-dimensional images. It's built on Qt (for the GUI), VisPy (for performant GPU-based rendering), and the scientific Python stack (NumPy, SciPy, and friends). For more information, examples, and documentation, please visit our website: https://napari.org/ Highlights This is primarily a bug-fix release, but we snuck a couple of new features in there, including smoother, prettier, better rendering of Labels volumes in 3D (#7100) and the ability to display scale bar at a fixed length in world coordinates, rather than having it resize dynamically to take up a small part of the screen (#7167). See below for the full list of changes! New Features Add option for smoother labels rendering in 3D (#7100) Add optional fixed length parameter to scale bar (#7167) Improvements Update shortcuts.py to have Enter be primary Shapes completion (#7063) Change shortcuts _mark_conflicts logic to always compare between strings representations of shortcuts (#7124) Allow easy call single benchmark (#7145) Bug Fixes [bugfix] update Point size slider on selection (current_size event) (#7137) Disconnect all dims events when closing viewer (#7140) Update event connection order (#7150) Run slider animation without using timer (#7158) Emit highlight event only if selection changed (#7162) [Bugfix] Only import darkdetect when needed (#7163) FIX QtViewer._open_files_dialog handing of stack (#7172) Documentation Update example annotate_segmentation_with_text.py to add link to the tutorial (#7134) Document command ID naming conventions (docs#405) Turn on warnings as error option for sphinx build (docs#409) Update version switcher for v0.5.1 (docs#468) Add documentation for run benchmark under debugger (docs#470) Update installation notes about macOS arm processors and Qt5 backends (docs#471) Fix docs CI (docs#472) Add October and July workshops from 2023 (docs#473) Add 0.5.2 release notes (docs#476) Remove warning and update date on 0.5.2 release notes (docs#478) Other Pull Requests [pre-commit.ci] pre-commit autoupdate (#7000) Add CI action to check that set milestone is the next release (#7083) Use app-model KeyBinding.to_text and KeyCode.os_symbol over Shortcut logic (#7113) Add some basic codeowners (#7118) Clean action manager to avoid side effects during tests (#7120) [maint] Drop singularity action to fix failed container action (#7121) Remove post identifier in fetching release notes (#7125) Update coverage, hypothesis, magicgui, matplotlib, napari-plugin-manager, npe2, pytest, tensorstore, tifffile, tqdm, xarray (#7138) ci(dependabot): bump the actions group across 1 directory with 2 updates (#7147) [Maint] Update version_denylist.txt to block mpl 3.9.1 on windows (#7154) [Maint] Increase timeout in test_async_out_of_bounds_layer_loaded to 500 ms (#7157) Use delete instead of getmethod for delete ready to merge label (#7160) [pre-commit.ci] pre-commit autoupdate (#7161) fix: typo in shape model name (#7166) Update dask, hypothesis, magicgui, matplotlib, pyyaml (#7169) Update docs constraints to pin sphinx<8 (#7170) Move parallel setting for coverage calculation to tox.ini (#7173) Clarify workflow names (#7174) correct typos in comments (#7175) MNT Parametrize test_open_files_dialog to check for stack True and False (#7176) Update babel, hypothesis, lxml, numpydoc, tifffile (#7179) Remove post identifier when determine deploy directory (docs#467) 12 authors added to this release (alphabetical) (+) denotes first-time contributors 🥳 andrew sweet - @andy-sweet Antoine J.-F. Salomon - @AJFSalomon + Ashley Anderson - @aganders3 Daniel Althviz Moré - @dalthviz Grzegorz Bokota (docs) - @Czaki Johannes Soltwedel - @jo-mueller Juan Nunez-Iglesias - @jni kyle i. s. harrington - @kephale Lorenzo Gaifas - @brisvag Lucy Liu (docs) - @lucyleeow Melissa Weber Mendonça (docs) - @melissawm Peter Sobolewski - @psobolewskiPhD 13 reviewers added to this release (alphabetical) (+) denotes first-time contributors 🥳 andrew sweet - @andy-sweet Antoine J.-F. Salomon - @AJFSalomon + Ashley Anderson - @aganders3 Draga Doncila Pop - @DragaDoncila Grzegorz Bokota (docs) - @Czaki Johannes Soltwedel - @jo-mueller Jordão Bragantini - @JoOkuma Juan Nunez-Iglesias - @jni Lorenzo Gaifas - @brisvag Lucy Liu (docs) - @lucyleeow Melissa Weber Mendonça (docs) - @melissawm Peter Sobolewski - @psobolewskiPhD Wouter-Michiel Vierdag - @melonora
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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) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.294 | 0.269 |
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".