style: black format normalizer.py and test_normalizer.py

circuitforge_core/input/gestures/normalizer.py

@@ -4,6 +4,7 @@ Landmark normalization for MediaPipe hand landmarks.
 Converts raw (21, 3) landmark array into a 63-element translation- and
 scale-invariant feature vector suitable for gesture classifiers.
 """
+
 import numpy as np
 
 
@@ -25,8 +26,8 @@ def normalize_hand(points: np.ndarray) -> np.ndarray:
         (63,) float32 feature vector.
     """
     pts = points.astype(np.float32).copy()
-    pts -= pts[0]                          # translate: wrist → origin
-    scale = float(np.linalg.norm(pts[9])) # wrist-to-middle-MCP distance
+    pts -= pts[0]  # translate: wrist → origin
+    scale = float(np.linalg.norm(pts[9]))  # wrist-to-middle-MCP distance
     if scale > 1e-6:
         pts /= scale
     return pts.flatten()

tests/test_input/test_gestures/test_normalizer.py

@@ -10,7 +10,7 @@ def _synthetic_hand(scale: float = 1.0, offset: float = 0.0) -> np.ndarray:
     for i in range(21):
         pts[i] = [offset, 0.0, 0.0]
     # Then define a few key landmarks relative to wrist
-    pts[0] = [offset, 0.0, 0.0]   # wrist
+    pts[0] = [offset, 0.0, 0.0]  # wrist
     pts[9] = [offset + scale, 0.0, 0.0]  # middle MCP at distance scale from wrist
     pts[1] = [offset + 0.1 * scale, 0.05 * scale, 0.0]  # thumb
     pts[5] = [offset + 0.4 * scale, 0.2 * scale, 0.0]  # index
@@ -32,7 +32,9 @@ def test_translation_invariance():
 def test_scale_invariance():
     pts_small = _synthetic_hand(scale=0.5)
     pts_large = _synthetic_hand(scale=2.0)
-    np.testing.assert_allclose(normalize_hand(pts_small), normalize_hand(pts_large), atol=1e-5)
+    np.testing.assert_allclose(
+        normalize_hand(pts_small), normalize_hand(pts_large), atol=1e-5
+    )
 
 
 def test_zero_scale_does_not_crash():