Bibliographic record
Abstract
Main changes Major internal refactoring. Breaking changes rename internal list plt.actors which now become plt.objects rename .points() to property .vertices. Hence: mesh.points() -> mesh.vertices mesh.points(newpoints) -> mesh.vertices = newpoints rename .cell_centers() to property .cell_centers rename .faces() to property .cells rename .lines() to property .lines rename .edges() to property .edges rename .normals() and split it into property .vertex_normals and property .cell_normals rename picture.Picture2D(...) which becomes Image(...).clone2d() (see examples/pyplot/embed_matplotlib.py). rename Volume.probe_points() which becomes points.probe(volume) rename Volume.probe_line() which becomes line.probe(volume) rename Volume.probe_plane() which becomes plane.probe(volume) rename file_io.load_transform(). LinearTransform("file.mat") substitutes it. rename transform_with_landmarks() to align_with_landmarks() rename find_cells_in() to find_cells_in_bounds() rename mesh.is_inside(pt) moved to mesh.contains(pt) rename Slicer2DPlotter moved to application module.Slicer2DPlotter rename and moved method voronoi() to points.generate_voronoi() rename class Ruler to Ruler3D Other changes improvements in how vtk classes are imported (allow lazy import) improvements to method mesh.clone2d() improvements in Slicer3DPlotter thanks to @daniel-a-diaz in #925 improvements in applications.Browser add new vedo.transformations.py module. add plotter.pick_area() thanks to @ZiguoAtGitHub and @RubendeBruin feedback. add texture to npz files thanks to @zhouzq-thu in #918 add background radial gradients add utils.line_line_distance() function add utils.segment_segment_distance() function add plotter.initialize_interactor() method add object hinting by hovering mouse (see flag_labels1.py) add colors.lut_color_at(value) the color of the lookup table at value. add .show(..., screenshot="myfile.png") keyword add object.coordinates same as object.vertices add move() to move single points or objects add copy() as alias to clone() add "Roll" to camera dictionary (thanks @baba-yaga ) add applications.Slicer3DTwinPlotter thanks to @daniel-a-diaz add radii feature to smooth_mls_2d() by @jo-mueller (now store results in arrays mesh.pointdata['MLSVariance'] and mesh.pointdata['MLSValidPoint']) passing a vtkCamera to show(camera=...) triggers a copy of the input which is therefore not muted by any subsequent interaction (thanks @baba-yaga ) Bug Fixes bug fix in closest_point() thanks to @goncalo-pt bug fix in tformat thanks to @JohnsWor in #913 bug fix in windows OS in timers callbacks thanks to @jonaslindemann bug fix to non linear tranforms mode. Now it can be instantiated with a dictionary bug fix in meshlab interface thanks to @JeffreyWardman in #924 bug fix changed mp = matplotlib.colormaps[name] in colors.py New/Revised Examples examples/basic/buttons.py examples/basic/input_box.py examples/basic/sliders2.py examples/basic/spline_tool.py examples/basic/interaction_modes2.py examples/advanced/timer_callback1.py examples/advanced/timer_callback2.py examples/advanced/warp4a.py examples/advanced/warp4b.py examples/pyplot/embed_matplotlib.py examples/pyplot/plot_fxy2.py examples/simulations/springs_fem.py examples/simulations/lorenz.py examples/volumetric/numpy2volume0.py examples/volumetric/slicer1.py examples/volumetric/tet_astyle.py examples/volumetric/tet_cut1.py examples/volumetric/tet_cut2.py examples/other/flag_labels1.py
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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; both teacher heads agree on what is shown here.
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".