Phase 5: native KDEEB hammer, drop Python dependency - #23
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Reimplement edge_bundle_hammer() in C++ (src/kdeeb.cpp) as kernel density estimation edge bundling (Hurter, Ersoy & Telea 2012): sample edges into points, estimate a density field on a grid, advect points up the density gradient, smooth and resample, shrinking the bandwidth each iteration. This removes the reticulate/datashader dependency entirely: reticulate is dropped from Imports, and the .onLoad Python config, shader_env, and install_bundle_py() are removed. Bundling output differs from the old datashader-backed version. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Sixth phase (see
_docs/REFACTORING-PLAN.md). The headline dependency reduction.What
src/kdeeb.cpp(kdeeb_iter): native kernel density estimation edge bundling (Hurter, Ersoy & Telea 2012) — the algorithm behind datashader's "hammer". Density grid → separable Gaussian blur → gradient advection → smoothing → arc-length resample, with the bandwidth decaying each iteration.edge_bundle_hammer()rewritten to call it. Samebw/decayparams plus tuning knobs (npoints,iterations,grid,step,smooth).reticulateremoved fromImports; the.onLoadPython config,shader_env, andinstall_bundle_py()are deleted.edgebundlenow has no Python dependency.Tests
Contract (
x,y,index,group, 50 pts/edge), finiteness, endpoints fixed, and locality: nearby parallel edges bundle (spread 3 → ~0.01) while a distant edge stays separate.FAIL 0 | PASS 71.Notes
mark_edge_bundle(type="hammer")— worth coordinating on the N13 side.🤖 Generated with Claude Code