Adjust computing of noise to avg highest diff

detector/compute_noise.py

@@ -37,8 +37,38 @@ def avg_diff(machine, suite, mode, first):
        return None
     return median(diffs)
 
+def equal_sign(a, b):
+    if a >= 0 and b >= 0:
+       return True
+    if a <= 0 and b <= 0:
+       return True
+    return False
+
+def avg_consequent_diff(machine, suite, mode, first):
+    prev = first
+    current = first.next()
+    diffs = []
+    consequent = 0
+    while current:
+       diff = prev.get('score') - current.get('score')
+       if diff != 0:
+           if equal_sign(diff, consequent):
+              consequent += diff
+           else:
+              diffs.append(abs(consequent))
+              consequent = diff
+
+       prev = current
+       current = current.next()
+    if consequent != 0:
+       diffs.append(abs(consequent))
+    if len(diffs) == 0:
+       return None
+    return median(diffs)
+
 for machine in tables.Machine.all():
   if machine.id != 28 and machine.id != 29 and machine.id != 26:
+  #if machine.id != 28:
     continue
   
   for mode in tables.Mode.allWith(machine):
@@ -50,7 +80,7 @@ for machine in tables.Machine.all():
         first = tables.Score.firstOfRecent(machine, suite, mode)
         if not first:
             continue
-        diff = avg_diff(machine, suite, mode, first)
+        diff = avg_consequent_diff(machine, suite, mode, first)
         if not diff:
             continue
         tables.RegressionScoreNoise.insertOrUpdate(machine, suite, mode, diff)
@@ -64,7 +94,7 @@ for machine in tables.Machine.all():
         first = tables.Breakdown.firstOfRecent(machine, suite, mode)
         if not first:
             continue
-        diff = avg_diff(machine, suite, mode, first)
+        diff = avg_consequent_diff(machine, suite, mode, first)
         if not diff:
             continue
         tables.RegressionBreakdownNoise.insertOrUpdate(machine, suite, mode, diff)