Offset the histogram data if needed

measure-analysis/measure-analysis.py

@@ -316,10 +316,11 @@ class MeasureSet:
 
         return metadata
 
-    def add_metadata(self, measure_type, units, metadata):
+    def add_metadata(self, measure_type, units, middle, metadata):
 
         self.measure_type = measure_type
         self.units = units
+        self.middle = middle
         self.metadata.update(metadata)
 
     def add_chronological(self, props_names, props):
@@ -348,12 +349,12 @@ class MeasureSet:
 
             enumerate_prop = list(enumerate(prop))
 
-            self.max.append(max(list(map(lambda x: x[0] if x[1] else 0, enumerate_prop))))
-            self.min.append(min(map(lambda x: x[0] if x[1] else 0, enumerate_prop)))
+            self.max.append(max(list(map(lambda x: (x[0] - self.middle) if x[1] else 0, enumerate_prop))))
+            self.min.append(min(map(lambda x: (x[0] - self.middle) if x[1] else 0, enumerate_prop)))
 
             sum_prop = sum(prop)
 
-            avg = sum(map(lambda x: x[0]*x[1], enumerate_prop)) / sum_prop
+            avg = sum(map(lambda x: abs(x[0] - self.middle)*x[1], enumerate_prop)) / sum_prop
             var = 0
             for x, p in enumerate_prop:
                 var += p * x**2
@@ -407,8 +408,12 @@ class MeasureSet:
         measure_type = data['measure_type']
         units = data['units']
         metadata = data['metadata']
+        if 'middle' in data:
+            middle = data['middle']
+        else:
+            middle = 0
 
-        self.add_metadata(measure_type, units, metadata)
+        self.add_metadata(measure_type, units, middle, metadata)
 
         props_names = data['props_names']
 
@@ -434,8 +439,8 @@ class MeasureSet:
 
     def generate_histogram(self, i, color, name):
 
-        bins = [i for i in range(self.max[i] + 1)]
-        vals = self.props[i][:self.max[i] + 1]
+        bins = [i for i in range(self.min[i], self.max[i] + 1)]
+        vals = self.props[i][self.min[i] + self.middle : self.max[i] + self.middle + 1]
 
         min_val = max(vals) / 140