Minor fixes

analytics/models/drop_model.py

@@ -60,7 +60,6 @@ class DropModel(Model):
                 segment_median = ax_list[antipeaks_kde[0], 0]
                 segment_min_line = ax_list[min_peak_index, 0]
                 segment_max_line = ax_list[max_peak_index, 0]
-                #print(segment_min_line, segment_max_line)
                 drop_height = 0.95 * (segment_max_line - segment_min_line)
                 drop_height_list.append(drop_height)
                 drop_length = utils.find_drop_length(segment_data, segment_min_line, segment_max_line)
@@ -132,7 +131,7 @@ class DropModel(Model):
                 delete_list.append(segment)
         for item in delete_list:
             segments.remove(item)
-        #print(segments)
+
         for idrop in self.idrops:
             segments.append(idrop)
 

analytics/models/peak_model.py

@@ -79,7 +79,7 @@ class PeakModel(Model):
         segments = []
         for i in all_maxs:
             if all_max_flatten_data[i] > extrema_list[i]:
-                segments.append(i+12)
+                segments.append(i)
 
         filtered = self.__filter_prediction(segments, data)
         return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
@@ -97,8 +97,7 @@ class PeakModel(Model):
 
         delete_list = []
         if len(segments) == 0 or len(self.ipeaks) == 0:
-            segments = []
-            return segments
+            return []
 
         pattern_data = all_max_flatten_data[self.ipeaks[0] - WINDOW_SIZE: self.ipeaks[0] + WINDOW_SIZE]
         for segment in segments:

analytics/models/trough_model.py

@@ -78,7 +78,7 @@ class TroughModel(Model):
         segments = []
         for i in all_mins:
             if all_max_flatten_data[i] < extrema_list[i]:
-                segments.append(i + 12)
+                segments.append(i)
         
         filtered = self.__filter_prediction(segments, data)
         return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]