Anti-segments in troughs model #142 (#171)

analytics/models/trough_model.py

@@ -23,6 +23,8 @@ class TroughModel(Model):
             'convolve_max': 570000,
             'convolve_min': 530000,
             'WINDOW_SIZE': 240,
+            'conv_del_min': 54000,
+            'conv_del_max': 55000,
         }
 
     def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None:
@@ -55,6 +57,20 @@ class TroughModel(Model):
             convolve_trough = scipy.signal.fftconvolve(labeled_trough, self.model_trough)
             convolve_list.append(max(auto_convolve))
             convolve_list.append(max(convolve_trough))
+            
+        del_conv_list = []
+        for segment in segments:
+            if segment['deleted']:
+                segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms'))
+                segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms'))
+                segment_data = data[segment_from_index: segment_to_index + 1]
+                if len(segment_data) == 0:
+                    continue
+                del_min_index = segment_data.idxmin()
+                deleted_trough = data[del_min_index - self.state['WINDOW_SIZE']: del_min_index + self.state['WINDOW_SIZE'] + 1]
+                deleted_trough = deleted_trough - min(deleted_trough)
+                del_conv_trough = scipy.signal.fftconvolve(deleted_trough, self.model_trough)
+                del_conv_list.append(max(del_conv_trough))  
 
         if len(confidences) > 0:
             self.state['confidence'] = float(min(confidences))
@@ -70,6 +86,16 @@ class TroughModel(Model):
             self.state['convolve_min'] = float(min(convolve_list))
         else:
             self.state['convolve_min'] = self.state['WINDOW_SIZE']
+        
+        if len(del_conv_list) > 0:
+            self.state['conv_del_min'] = float(min(del_conv_list))
+        else:
+            self.state['conv_del_min'] = self.state['WINDOW_SIZE']
+            
+        if len(del_conv_list) > 0:
+            self.state['conv_del_max'] = float(max(del_conv_list))
+        else:
+            self.state['conv_del_max'] = self.state['WINDOW_SIZE']
 
     def do_predict(self, dataframe: pd.DataFrame):
         data = dataframe['value']
@@ -102,7 +128,7 @@ class TroughModel(Model):
         if len(segments) == 0 or len(self.itroughs) == 0 :
             segments = []
             return segments  
-        pattern_data = self.model_peak
+        pattern_data = self.model_trough
         for segment in segments:
             if segment > self.state['WINDOW_SIZE']:
                 convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1]
@@ -110,6 +136,8 @@ class TroughModel(Model):
                 conv = scipy.signal.fftconvolve(convol_data, pattern_data)
                 if max(conv) > self.state['convolve_max'] * 1.1 or max(conv) < self.state['convolve_min'] * 0.9:
                     delete_list.append(segment)
+                if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
+                    delete_list.append(segment)
             else:
                 delete_list.append(segment)
         # TODO: implement filtering