test: capture scroll generate() golden output (pre-migration)

scripts/build_golden_scroll.py

@@ -0,0 +1,48 @@
+"""Capture golden scroll event sequences from current torch implementation."""
+from __future__ import annotations
+
+import random
+import sys
+from pathlib import Path
+
+# Allow running as `uv run python scripts/build_golden_scroll.py` from project root.
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+import numpy as np
+import torch
+
+from ai_mouse import generate_scroll
+
+CASES: list[tuple[int, int, str]] = [
+    (0,    1500, "target"),
+    (0,    500,  "precise"),
+    (0,    5000, "fast"),
+    (2000, 0,    "target"),   # upward
+    (0,    800,  "precise"),
+    (0,    3500, "fast"),
+    (1000, 1200, "precise"),  # tiny scroll
+    (0,    10000, "fast"),     # very long
+]
+SEEDS = (0, 1, 2, 3)
+
+
+def main() -> None:
+    out: dict[str, np.ndarray] = {}
+    for case_idx, (start_y, end_y, mode) in enumerate(CASES):
+        for seed in SEEDS:
+            random.seed(seed)
+            np.random.seed(seed)
+            torch.manual_seed(seed)
+            events = generate_scroll(start_y, end_y, mode=mode)
+            arr = np.array(
+                [[e["deltaY"], e["deltaMode"], e["t"]] for e in events],
+                dtype=np.int64,
+            )
+            out[f"case{case_idx}_seed{seed}"] = arr
+    out_path = Path("tests/unit/data/golden_scroll.npz")
+    np.savez_compressed(out_path, **out)
+    print(f"Wrote {len(out)} scroll golden traces to {out_path}")
+
+
+if __name__ == "__main__":
+    main()