holoviz/holoviews: Version 1.13.0
Bibliographic record
Abstract
This release is packed full of features and includes a general refactoring of how HoloViews renders widgets now built on top of the Panel library. Many thanks to the many contributors to this release either directly by submitting PRs or by reporting issues and making suggestions. Specifically we would like to thank @poplarShift, @jonmease, @flothesof, @julioasotodv, @ltalirz, @DancingQuanta, @ahuang, @kcpevey, @Jacob-Barkhak, @nluetts, @harmbuisman, @ceball, @mgsnuno, @srp3003, @jsignell as well as the maintainers @jbednar, @jlstevens and @philippjfr for contributing to this release. This version includes the addition of a large number of features, enhancements and bug fixes: Major features: Add link_selection to make custom linked brushing simple (#3951) link_selection builds on new support for much more powerful data-transform pipelines: new Dataset.transform method (#237, #3932), dim expressions in Dataset.select (#3920), arbitrary method calls on dim expressions (#4080), and Dataset.pipeline and Dataset.dataset properties to track provenance of data Add Annotators to allow easily drawing, editing, and annotating visual elements (#1185) Completely replaced custom Javascript widgets with Panel-based widgets allowing for customizable layout (#84, #805) Add HSpan, VSpan, Slope, Segments and Rectangles elements (#3510, #3532, #4000) Add support for cuDF GPU dataframes, cuPy backed xarrays, and GPU datashading (#3982) Other features Add spatialpandas support and redesigned geometry interfaces for consistent roundtripping (#4120) Support GIF rendering with Bokeh and Plotly backends (#2956, #4017) Support for Plotly Bars, Bounds, Box, Ellipse, HLine, Histogram, RGB, VLine and VSpan plots Add UniformNdMapping.collapse to collapse nested datastructures (#4250) Add CurveEdit and SelectionXY streams (#4119, #4167) Add apply_when helper to conditionally apply operations (#4289) Display Javascript callback errors in the notebook (#4119) Add support for linked streams in Plotly backend to enable rich interactivity (#3880, #3912) Enhancements: Support for packed values dimensions, e.g. 3D RGB/HSV arrays (#550, #3983) Allow selecting/slicing datetimes with strings (#886) Support for datashading Area, Spikes, Segments and Polygons (#4120) HeatMap now supports mixed categorical/numeric axes (#2128) Use __signature__ to generate .opts tab completions (#4193) Allow passing element-specific keywords through datashade and rasterize (#4077) (#3967) Add per_element flag to .apply accessor (#4119) Add selected plot option to control selected glyphs in bokeh (#4281) Improve default Sankey node_padding heuristic (#4253) Add hooks plot option for Plotly backend (#4157) Support for split Violin plots in bokeh (#4112) Bug fixes: Fixed radial HeatMap sizing issues (#4162) Switched to Panel for rendering machinery fixing various export issues (#3683) Handle updating of user supplied HoverTool in bokeh (#4266) Fix issues with single value datashaded plots (#3673) Fix legend layout issues (#3786) Fix linked axes issues with mixed date, categorical and numeric axes in bokeh (#3845) Fixed handling of repeated dimensions in PandasInterface (#4139) Fixed various issues related to widgets (#3868, #2885, #1677, #3212, #1059, #3027, #3777) Library compatibility: Better support for Pandas 1.0 (#4254) Compatibility with Bokeh 2.0 (#4226) Migration notes: Geometry .iloc now indexes by geometry instead of by datapoint. Convert to dataframe or dictionary before using .iloc to access individual datapoints (#4104) Padding around plot elements is now enabled by default, to revert set hv.config.node_padding = 0 (#1090) Removed Bars group_index and stack_index options, which are now controlled using the stacked option (#3985) .table is deprecated; use .collapse method instead and cast to Table (#3985) HoloMap.split_overlays is deprecated and is now a private method (#3985) Histogram.edges and Histogram.values properties are deprecated; use dimension_values (#3985) Element.collapse_data is deprecated; use the container's .collapse method instead (#3985) hv.output filename argument is deprecated; use hv.save instead (#3985)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.206 | 0.330 |
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".