⚡ Bolt: Vectorize novelty curve computation with np.diagonal

.jules/bolt.md

@@ -41,3 +41,7 @@
 ## 2025-02-15 - Replace Array.from(map.values()).map with a for...of loop
 **Learning:** Using `Array.from(map.values()).map(...)` creates an unnecessary intermediate array which wastes memory allocation and garbage collection time, particularly for frequently re-rendered components handling large collections.
 **Action:** Use a `for...of` loop over `map.values()` to iterate and push mapped elements directly into the final array for O(1) memory and avoiding intermediate array allocations.
+
+## 2026-07-08 - Vectorize SSM novelty extraction
+**Learning:** Extracting checkerboard kernel responses one diagonal window at a time repeats Python slicing and summation overhead for every SSM frame.
+**Action:** Sum each checkerboard offset across the full valid diagonal with `np.diagonal(...)`, and keep a loop-reference parity test so boundary scoring stays numerically stable.

services/analysis-engine/src/bandscope_analysis/sections/segmenter.py

@@ -95,7 +95,7 @@ def _checkerboard_novelty(
     The checkerboard kernel highlights transitions where the local structure
     changes (i.e., moving from one repeated section to a new one).
 
-    Iterates over valid diagonal patches while keeping the SSM frame count bounded.
+    Vectorizes diagonal patch extraction while keeping the SSM frame count bounded.
     """
     n = ssm.shape[0]
     half = kernel_size // 2
@@ -109,11 +109,16 @@ def _checkerboard_novelty(
     kernel[:half, :half] = -1.0
     kernel[half:, half:] = -1.0
 
-    # Extract all valid diagonal patches and compute dot products.
-    valid_range = range(half, n - half)
-    for i in valid_range:
-        patch = ssm[i - half : i + half, i - half : i + half]
-        novelty[i] = np.sum(patch * kernel)
+    # Sum each checkerboard offset across all valid diagonal windows at once.
+    valid = novelty[half : n - half]
+    for di in range(-half, half):
+        for dj in range(-half, half):
+            value = kernel[di + half, dj + half]
+            diagonal = np.diagonal(ssm[half + di : n - half + di, half + dj : n - half + dj])
+            if value > 0:
+                valid += diagonal
+            else:
+                valid -= diagonal
 
     # Normalize by peak absolute magnitude, preserving sign.
     max_val = np.max(np.abs(novelty))

services/analysis-engine/tests/test_segmenter.py

@@ -103,6 +103,33 @@ def test_checkerboard_novelty_short_matrix_returns_zeros() -> None:
     assert np.array_equal(novelty, np.zeros(2, dtype=np.float64))
 
 
+def test_checkerboard_novelty_matches_loop_reference() -> None:
+    """Ensure diagonal vectorization preserves checkerboard novelty values."""
+    rng = np.random.default_rng(42)
+    ssm = rng.random((48, 48), dtype=np.float64)
+    ssm = (ssm + ssm.T) / 2.0
+    kernel_size = 8
+    half = kernel_size // 2
+    expected = np.zeros(ssm.shape[0], dtype=np.float64)
+
+    kernel = np.ones((kernel_size, kernel_size), dtype=np.float64)
+    kernel[:half, :half] = -1.0
+    kernel[half:, half:] = -1.0
+    for i in range(half, ssm.shape[0] - half):
+        patch = ssm[i - half : i + half, i - half : i + half]
+        expected[i] = np.sum(patch * kernel)
+
+    max_value = np.max(np.abs(expected))
+    expected = expected / max_value
+
+    np.testing.assert_allclose(
+        _checkerboard_novelty(ssm, kernel_size=kernel_size),
+        expected,
+        rtol=1e-12,
+        atol=1e-12,
+    )
+
+
 def test_detect_boundaries_short_novelty_returns_start_only() -> None:
     """Ensure too-short novelty curves fail closed to a single start boundary."""
     boundaries = detect_boundaries(