Bibliographic record
Abstract
Changes and Fixes fixes to extrude() method thanks to @JeffreyWardman add utils.madcad2vedo conversion as per #976 by @JeffreyWardman add utils.camera_to_dict() add Axes(title_backface_color=...) keyword fix labels() and labels2d() add shapes.plot_scalar() plot a scalar along a line. add support for tetgenpy add transformations.compute_main_axes() add transformations.__call__() to apply a transformation fix small bug in pointcloud.distance_to() add applications.MorphPlotter() to morph a polygonal mesh to a target mesh add smooth_data() to smooth/diffuse data attributes add shapes.Tubes() add utils.Minimizer() class add CellCenters(Points) class add core.apply_transform_from_actor() add add volume.slab() add mesh.generate_random_points() to generate random points onto a surface add tetmesh.generate_random_points() to generate random points in a tet mesh rename integrate_arrays_over_domain() to integrate_data extend volume.operation() to support logic operations as per #1002 add pointcloud.relax_point_positions() method add pointcloud.auto_distance() method calculates the distance to the closest point in the same cloud of points. fixed mesh.collapse_edges() after #992 add mesh.cut_closed_surface() fix image.clone() in #1011 add transformations.TransformInterpolator class add Line.find_index_at_position() finds the index of the line vertex that is closest to a point add visual.LightKit class which provides "natural" lighting from 4 sources. add fast-simplification example by @Louis-Pujol in #992 add metadata "shape" to volume.slice_plane() in #1018 fix core.mark_boundaries() method add callbacks for cutters in #1020 and examples/volumetric/slice_plane3.py add utils.andrews_curves() function. add transformations.LinearTransform.transpose() method. add pointcloud.generate_segments() to generate a continous line from un-ordered points in 3d fix assembly.__add__() by @j042 in #1036 small fix to Ruler3D class. add plotter.render_hidden_lines() method add slot for triangle strips in constructor Mesh([verts, faces, lines, strips]) in #1019 internally use "import vedo.vtkclasses as vtki" instead of "vtk" to avoid confusion add join_with_strips() in #1043 improvements to shapes.Ellipsoid() and bug fixes in #978 by @daniel-a-diaz improvements to pointcloud.pca_ellipsoid() and bug fixes improvements to pointcloud.pca_ellipse() and bug fixes fix Plotter a toggle keypress fix viz on jupyter notebook as per #994 fix mesh.imprint() small fix to applications.Slicer2DPlotter automatically apply the actor transform to an object actor that was moved manually (via eg "InteractorStyleTrackballActor") in #1045 and #1046 by @sergei9838 add support to RectilinearGrid and StructuredGrid data (experimental). improvements to RayCastPlotter add visual.scalar_range() to control mesh coloring. fix shapes.Text3D.text() by @gioda add volume.isosurface_discrete() method Soft Breaking Changes Changes that can break existing code whose fixing is trivial: change clone2d(scale=...) to clone2d(size=...) remove shapes.StreamLines(object) becomes object.compute_streamlines() split mesh.decimate() into mesh.decimate(), mesh.decimate_pro() and mesh.decimate_binned() as per #992 modified core.clean() after #992 remove import_window() for obj files and create load_obj() by @zhouzq-thu in #891 add smooth_mls_12d(..., n=0) to fix the number of neighbors in the smoothing modified API for mesh.binarize() plotter.add_hover_legend() now returns the id of the callback. removed settings.render_lines_as_tubes and settings.hidden_line_removal, add plotter.render_hidden_lines() method fix close(), close_window() is now obsolete and removed. Hard Breaking Changes Changes that will break existing code and need active thinking and work to adapt None New/Revised Examples examples/basic/sliders_hsv.py examples/basic/buttons1.py examples/basic/buttons2.py examples/basic/input_box.py examples/advanced/warp4b.py examples/advanced/diffuse_data.py examples/advanced/moving_least_squares1D.py examples/volumetric/slab_vol.py examples/volumetric/streamlines1.py examples/volumetric/streamlines2.py examples/volumetric/streamlines3.py examples/volumetric/streamlines4.py examples/volumetric/office.py examples/volumetric/slice_plane1.py examples/volumetric/slice_plane3.py examples/volumetric/mesh2volume.py examples/volumetric/read_volume3.py examples/volumetric/rectl_grid1.py examples/volumetric/struc_grid1.py examples/volumetric/app_raycaster.py examples/volumetric/isosurfaces1.py examples/volumetric/isosurfaces2.py examples/simulations/mag_field1.py examples/pyplot/andrews_cluster.py examples/other/madcad1.py examples/other/tetgen1.py examples/other/nelder-mead.py examples/other/fast_simpl.py tests/issues/issue_968.py tests/issues/issue_1025.py tests/issues/test_force_anim.py tests/snippets/test_discourse_1956.py tests/snippets/test_ellipsoid_main_axes.py tests/snippets/test_compare_fit1.py New Contributors @daizhirui made their first contribution in https://github.com/marcomusy/vedo/pull/983 @JeffreyWardman made their first contribution in https://github.com/marcomusy/vedo/pull/988 @PeterMitrano made their first contribution in https://github.com/marcomusy/vedo/pull/1000 @Louis-Pujol made their first contribution in https://github.com/marcomusy/vedo/pull/1016 Full Changelog: https://github.com/marcomusy/vedo/compare/v2023.5.0...v2024.5.1
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.509 | 0.539 |
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".