Upload gating.py

app/ml/gating.py

@@ -37,8 +37,8 @@ def gate_signal(
     signal: List[float],
     window_size: int = 128,
     step: int = 64,
-    threshold: float = 0.55,
-    temperature: float = 0.15,
+    threshold: float = 0.6,
+    temperature: float = 0.1,
     return_windows: bool = False,
     max_windows: int = 200,
 ) -> Tuple[List[float], Dict[str, Any]]:
@@ -73,6 +73,7 @@ def gate_signal(
     total_windows = 0
     selected_windows = 0
     windows_meta: List[Dict[str, Any]] = []
+    scored_windows: List[Tuple[float, List[float]]] = []
 
     for start in range(0, len(signal) - window_size + 1, step):
         window = signal[start : start + window_size]
@@ -83,6 +84,7 @@ def gate_signal(
         prob = gate_probability_with_hysteresis(sig, threshold=threshold, temperature=temperature, last_activation=last_activation)
 
         chosen = prob >= 0.5
+        scored_windows.append((sig, window))
         if chosen:
             selected.extend(window)
             selected_windows += 1
@@ -101,8 +103,24 @@ def gate_signal(
                 }
             )
 
-    if not selected:
-        selected = signal  # fall back to full signal
+    if not selected and scored_windows:
+        # If nothing selected, pick the highest-significance window to avoid empty output
+        scored_windows.sort(key=lambda x: x[0], reverse=True)
+        best_sig, best_window = scored_windows[0]
+        selected = best_window
+        selected_windows = 1
+        if return_windows and len(windows_meta) < max_windows:
+            windows_meta.insert(
+                0,
+                {
+                    "start": 0,
+                    "end": window_size,
+                    "significance": best_sig,
+                    "probability": 1.0,
+                    "selected": True,
+                    "forced": True,
+                },
+            )
 
     meta: Dict[str, Any] = {
         "total_windows": total_windows,