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one pass over the neighbour pairs (was four _pair_cross calls); the barycentre median answers degree 1 and 2 without sorting; and the search body is built from windowed model reads instead of one locked row lookup per line.
Result: {"status":"keep","total_ms":17944.4,"lg_boot_ms":703.7,"lg_decomp_ms":2453.7,"lg_graph_ms":1000,"lg_hex_ms":478,"lg_index_ms":96,"lg_listing_cold_ms":453,"lg_listing_warm_ms":411.2,"lg_nav_ms":6555.9,"lg_palette_ms":4.9,"lg_render_ms":218.4,"lg_search_ms":1308.8,"pure_graph_ms":212.3,"sm_boot_ms":432.4,"sm_decomp_ms":1267.8,"sm_graph_ms":742.7,"sm_hex_ms":440,"sm_index_ms":2.5,"sm_listing_cold_ms":264.3,"sm_listing_warm_ms":289.5,"sm_nav_ms":305.4,"sm_palette_ms":0.3,"sm_render_ms":258.6,"sm_search_ms":45,"fails":0}
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Textbook Sugiyama, the same shape IDA's own graph uses: break cycles,
longest-path layering, dummy nodes, median/transposition ordering,
priority x-coords, then port-and-channel edge routing. Pure python -- no
IDA, no Textual, no I/O -- so it is tested offline in milliseconds with no
worker, which is the whole reason the hard part is kept out of the UI.
Dummy nodes are what make routing tractable: a long edge occupies real
horizontal space, so no edge ever has to cross a box. The tests assert
exactly that over a 128-function corpus, and it holds at 0.
Two things cost real time to find. A self-loop never drains its own
in-degree, so it deadlocks the ranking and collapses the graph into three
layers, 280 columns wide -- they are dropped from the layout and drawn as
a marker. And crossing minimisation is the entire runtime: recounting
globally per candidate swap is O(n^3) and took 20.4s on a 424-block
function, against 152ms for Fenwick inversion counting plus a local
O(deg*deg) swap delta.
The result is not a painted canvas -- that function is ~13M cells. It is
an index: per-row runs, bucketed vertical intervals, and point marks,
queried one row at a time.
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