Update services/anomaly_service.py

services/anomaly_service.py

@@ -1,18 +1,20 @@
-import numpy as np
+import numpy as np  # Import NumPy for statistical calculations
 
+# Function to track fault counts and calculate statistics
 def track_faults(fault_counts):
     if not fault_counts:
-        return {"mean": 0, "std": 0}
-    mean = np.mean(fault_counts)
-    std = np.std(fault_counts)
+        return {"mean": 0, "std": 0}  # Return default values if no data
+    mean = np.mean(fault_counts)  # Calculate mean of fault counts
+    std = np.std(fault_counts)  # Calculate standard deviation
     return {"mean": mean, "std": std}
 
+# Function to predict potential fault escalation
 def predict_fault(fault_counts, threshold=2.5):
-    if len(fault_counts) < 5:
+    if len(fault_counts) < 5:  # Require at least 5 frames for prediction
         return False
-    stats = track_faults(fault_counts)
-    recent_counts = fault_counts[-5:]
+    stats = track_faults(fault_counts)  # Get statistics
+    recent_counts = fault_counts[-5:]  # Look at last 5 frames
     for count in recent_counts:
-        if stats["std"] > 0 and (count - stats["mean"]) / stats["std"] > threshold:
+        if stats["std"] > 0 and (count - stats["mean"]) / stats["std"] > threshold:  # Check for spike
             return True
-    return False
+    return False  # No escalation detected