tui for headless ida https://git.sl0p.foo/ida-tui.git/atom/tests/test_graph.py?h=pre-codemode 2026-08-06T20:05:03Z 2026-08-06T20:05:03Z blasty blasty@local 2026-08-06T20:05:03Z urn:sha1:ebff7caaaf348602422f6943e9de01240a79994e Fourteen test files, each its own __main__, and no way to run them but from memory -- so in practice you ran the one you were working on and hoped. Worse, nothing said which files need a licensed IDA and a real worker (minutes) and which are pure stdlib (milliseconds), so the cheap ones nobody ran either. tests/run.py runs the lot and prints one table. --fast selects only the suites that need nothing, which is 257 checks in half a second under any python3 -- that's the one you run between edits. The classification lives in the test files, not in a table here that would rot the first time someone adds a test: each declares NEEDS_IDA at module scope and run.py reads it with ast (it can't import them -- they run their suite at import). A file without the marker is a hard error rather than a silent guess. 2026-08-06T13:11:54Z blasty blasty@local 2026-08-06T13:11:54Z urn:sha1:1f559bb4a999b97cbd9b8bfb0072fc6a98fbbfbc 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.