Refine melodic threshold to continuous scoring

- Use continuous scoring instead of binary pass/fail
- Below min: score = (cents / min)^2
- Above max: score = ((1200 - cents) / (1200 - max))^2
- Use product across voices to penalize worst offenders

src/graph.py

@@ -274,23 +274,36 @@ class PathFinder:
         }
 
     def _factor_melodic_threshold(self, edge_data: dict, config: dict) -> float:
-        """Returns 1.0 if all voice movements are within melodic threshold, 0.0 otherwise."""
-        # Check weight - if 0, return 1.0 (neutral)
-        if config.get("weight_melodic", 1) == 0:
-            return 1.0
+        """Returns continuous score based on melodic threshold.
 
+        - cents == 0: score = 1.0 (no movement is always ideal)
+        - Below min (0 < cents < min): score = (cents / min)^2
+        - Within range (min <= cents <= max): score = 1.0
+        - Above max (cents > max): score = ((1200 - cents) / (1200 - max))^2
+
+        Returns product of all voice scores.
+        """
         melodic_min = config.get("melodic_threshold_min", 0)
         melodic_max = config.get("melodic_threshold_max", float("inf"))
 
         cent_diffs = edge_data.get("cent_diffs", [])
 
-        if melodic_min is not None or melodic_max is not None:
-            for cents in cent_diffs:
-                if melodic_min is not None and cents < melodic_min:
-                    return 0.0
-                if melodic_max is not None and cents > melodic_max:
-                    return 0.0
-        return 1.0
+        if not cent_diffs:
+            return 1.0
+
+        product = 1.0
+        for cents in cent_diffs:
+            if cents == 0:
+                score = 1.0
+            elif cents < melodic_min:
+                score = (cents / melodic_min) ** 2
+            elif cents > melodic_max:
+                score = ((1200 - cents) / (1200 - melodic_max)) ** 2
+            else:
+                score = 1.0
+            product *= score
+
+        return product
 
     def _factor_direct_tuning(self, edge_data: dict, config: dict) -> float:
         """Returns 1.0 if directly tunable (or disabled), 0.0 otherwise."""