Implement weighted contrary motion factor

- Half of moving voices should go one direction, half opposite
- Weighted by closeness to ideal half split
- factor = 1.0 - (distance_from_half / half)
- Works with odd (near-half) and even (exact half) voices
- Analysis shows contrary_motion_steps, percent, and avg_score

src/analyze.py

@@ -45,6 +45,7 @@ def analyze_chords(
 
     # ========== Contrary Motion ==========
     contrary_motion_steps = 0
+    contrary_motion_score = 0.0
 
     # ========== DCA (Voice Stay Counts) ==========
     # Track how long each voice stays before changing
@@ -85,10 +86,20 @@ def analyze_chords(
         unique_nodes.add(node_hash)
         node_hashes.append(node_hash)
 
-        # Contrary motion: sorted_diffs[0] < 0 and sorted_diffs[-1] > 0
-        if len(cent_diffs) >= 2:
-            sorted_diffs = sorted(cent_diffs)
-            if sorted_diffs[0] < 0 and sorted_diffs[-1] > 0:
+        # Contrary motion: weighted by closeness to half split
+        # Count moving voices (cent_diff != 0)
+        num_up = sum(1 for d in cent_diffs if d > 0)
+        num_down = sum(1 for d in cent_diffs if d < 0)
+        num_moving = num_up + num_down
+
+        if num_moving >= 2:
+            ideal_up = num_moving / 2
+            distance = abs(num_up - ideal_up)
+            # Factor = 1.0 - (distance / ideal_up)
+            # Returns 0.0 if all move same direction, 1.0 if exact half split
+            contrary_factor = max(0.0, 1.0 - (distance / ideal_up))
+            contrary_motion_score += contrary_factor
+            if contrary_factor > 0:
                 contrary_motion_steps += 1
 
         # DCA: Track stay counts per voice
@@ -147,6 +158,9 @@ def analyze_chords(
         "contrary_motion_percent": (
             (contrary_motion_steps / num_steps * 100) if num_steps > 0 else 0
         ),
+        "contrary_motion_score": contrary_motion_score / num_steps
+        if num_steps > 0
+        else 0,
         # DCA
         "dca_avg_voice_stay": avg_voice_stay,
         "dca_max_voice_stay": max_voice_stay,
@@ -179,6 +193,7 @@ def format_analysis(metrics: dict) -> str:
         "--- Contrary Motion ---",
         f"Steps with contrary: {metrics['contrary_motion_steps']}",
         f"Percentage: {metrics['contrary_motion_percent']:.1f}%",
+        f"Avg score: {metrics['contrary_motion_score']:.2f}",
         "",
         "--- DCA (Voice Stay) ---",
         f"Avg stay count: {metrics['dca_avg_voice_stay']:.2f} steps",

src/graph.py

@@ -257,20 +257,27 @@ class PathFinder:
         return 1.0
 
     def _factor_contrary_motion(self, edge_data: dict, config: dict) -> float:
-        """Returns 1.0 if voices move in contrary motion, 0.0 otherwise."""
-        # Check weight - if 0, return 1.0 (neutral)
+        """Returns factor based on contrary motion.
+
+        Contrary motion: half of moving voices go one direction, half go opposite.
+        Weighted by closeness to ideal half split.
+        factor = 1.0 - (distance_from_half / half)
+        """
         if config.get("weight_contrary_motion", 0) == 0:
             return 1.0
 
-        if not config.get("contrary_motion", False):
-            return 1.0  # neutral if not configured
-
         cent_diffs = edge_data.get("cent_diffs", [])
-        if len(cent_diffs) >= 3:
-            sorted_diffs = sorted(cent_diffs)
-            if sorted_diffs[0] < 0 and sorted_diffs[-1] > 0:
-                return 1.0
-        return 0.0
+
+        num_up = sum(1 for d in cent_diffs if d > 0)
+        num_down = sum(1 for d in cent_diffs if d < 0)
+        num_moving = num_up + num_down
+
+        if num_moving < 2:
+            return 0.0  # Need at least 2 moving voices for contrary motion
+
+        ideal_up = num_moving / 2
+        distance = abs(num_up - ideal_up)
+        return max(0.0, 1.0 - (distance / ideal_up))
 
     def _factor_hamiltonian(
         self, edge: tuple, graph_path: list | None, config: dict