napari: a multi-dimensional image viewer for Python
Bibliographic record
Abstract
napari 0.5.1 ⚠️ Note: these release notes are still in draft while 0.5.1 is in alpha/release candidate testing. ⚠️ Wednesday, Jul 24, 2024 We're happy to announce the release of napari 0.5.1! 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 napari 0.5.1 is a bugfix release hot on the heels of napari 0.5.0. It fixes a critical bug with creating viewers multiple times within a single IPython/Jupyter session (#7106), as well as regressions with viewing multiscale 3D time series (#7103) and with converting image layers to labels layers (#7095). It also fixes a bug with NumPy 2 support (#7104 and our storing of layer axis info when using the channel_axis keyword argument for images (#7089). Read on for the full list of changes since the last version from just two weeks ago! Improvements [enh] add an add_plane convenience method to ClippingPlaneList (#6921) Cleanup _image_key_bindings (#7116) Add napari-plugin-manager to optional info list (#7117) Bug Fixes Move the _is_created assignment to the top (#5078) Fix handling of units and axis_labels in add_image (#7089) Fix label conversion with proj mode (#7095) Account for displayed dimensions in multiscale translate adjustment (#7103) fix call of np.clip in _update_thumbnail (#7104) Always add Empty context key, even if action is already registered (#7106) Documentation Add link to partners in README.md (#7069) Restore README image (#7098) Update docs constraints files for new napari-sphinx-theme release (#7111) Update Makefile to be consistent (docs#448) Use plausible configuration by the PyData Sphinx Theme (docs#453) More fixes to contributing documentation guide (docs#454) Add location field to community calendar (docs#455) Fix footer items (docs#456) Add docs about the new napari-base structure (docs#457) Add draft 0.5.1 release notes (docs#464 Other Pull Requests Remove ready to merge on update of constraints PR (#6984) Add actionlint on CI (#7049) fix: set target_commitish for commit sha to fix benchmarks (#7091) Use viewer.layers instead of _layers.model().sourceModel()._root for dummy context creation (#7109) Limit setuptools vesion for minimum requirements test (#7110) [Maint] Update dockerfile for xpra source change (#7115) Update version switcher to include 0.5.0 (docs#452) deploy docs on manual trigger (docs#462) Add actionlint to prevent GHA workflow errors (docs#463) 9 authors added to this release (alphabetical) (+) denotes first-time contributors 🥳 danieldegroot2 - @danieldegroot2 + Draga Doncila Pop - @DragaDoncila Grzegorz Bokota - @Czaki jaime rodriguez-guerra - @jaimergp Juan Nunez-Iglesias (docs) - @jni Lorenzo Gaifas - @brisvag Markus Stabrin - @mstabrin Melissa Weber Mendonça (docs) - @melissawm Peter Sobolewski - @psobolewskiPhD 11 reviewers added to this release (alphabetical) (+) denotes first-time contributors 🥳 andrew sweet - @andy-sweet Draga Doncila Pop - @DragaDoncila Genevieve Buckley - @GenevieveBuckley Grzegorz Bokota - @Czaki jaime rodriguez-guerra - @jaimergp Juan Nunez-Iglesias (docs) - @jni Lorenzo Gaifas - @brisvag Lucy Liu - @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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.288 | 0.248 |
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".