64 parametrised cases (8 routes/scrolls x 4 seeds each) compare the
rewritten ORT/NumPy pipeline against captures from the pre-migration
PyTorch implementation.
The pre-migration captures used torch.manual_seed + torch.randn for the
flow-ODE noise; the rewrite uses np.random.default_rng. These RNGs
produce different random numbers for the same seed, so the per-point
trajectories cannot match bit-for-bit. The suite therefore guards
*structural* equivalence:
* mouse: identical shape, start/end snapping, xy diff within
max(30 px, 20% of move distance), timestamp diff within 700 ms
* scroll: identical shape (skip with reason on quantum boundary
drift), identical deltaMode, identical total signed scroll
distance, per-event delta within 2 wheel quanta, timestamp diff
within 700 ms
Observed worst-case in this run: ~170 px xy diff on a 1681 px move
(~10% of distance, well under the 20% envelope) and ~600 ms timestamp
drift. All 64 cases pass; 0 skipped.
Goldens stored as compressed .npz under tests/unit/data/ and tracked
via Git LFS-free vanilla blobs (each file is ~kB).
Provide bundled_path() and resolve() helpers that locate ONNX
weights and JSON metadata via importlib.resources, falling back to
a user-supplied directory. Missing assets raise ModelLoadError.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Introduce AiMouseError base class plus ModelLoadError and
GenerationError subclasses so downstream consumers can catch the
umbrella or specific failure modes.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Copy of coord.py (which is already pure numpy) into the private
underscored module to be consumed by upcoming mouse.py rewrite.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
