172-make-peaks-detector-work

analytics/pattern_detection_model.py

@@ -56,10 +56,13 @@ class PatternDetectionModel:
         start_index, stop_index = 0, len(dataframe)
         if len(segments) > 0:
             min_time, max_time = segments_box(segments)
-            start_index = dataframe[dataframe['timestamp'] >= min_time].index[0]
-            stop_index = dataframe[dataframe['timestamp'] > max_time].index[0]
-            start_index = max(start_index - window_size, 0)
-            stop_index = min(stop_index + window_size, len(dataframe))
+            try:
+                start_index = dataframe[dataframe['timestamp'] >= min_time].index[0]
+                stop_index = dataframe[dataframe['timestamp'] > max_time].index[0]
+                start_index = max(start_index - window_size, 0)
+                stop_index = min(stop_index + window_size, len(dataframe))
+            except IndexError:
+                pass
 
         dataframe = dataframe[start_index:stop_index]
 

analytics/peaks_detector.py

@@ -34,8 +34,6 @@ class PeaksDetector:
         array = array[window_size:-window_size]
         filtered = np.subtract(array, filtered)
 
-        import matplotlib.pyplot as plt
-
         # filtered = np.convolve(array, step, mode='valid')
         # print(len(array))
         # print(len(filtered))
@@ -53,12 +51,8 @@ class PeaksDetector:
         data = filtered
         data /= data.max()
 
-        #plt.plot(array[:1000])
-        plt.plot(data[:1000])
-        plt.show()
-
         result = step_detect.find_steps(data, 0.1)
-        return [dataframe.index[x + window_size] for x in result]
+        return [(dataframe.index[x], dataframe.index[x + window_size]) for x in result]
 
     def save(self, model_filename):
         pass
@@ -68,4 +62,4 @@ class PeaksDetector:
     def load(self, model_filename):
         pass
         # with open(model_filename, 'rb') as file:
-        #     self.clf, self.scaler = pickle.load(file)
+        #     self.clf, self.scaler = pickle.load(file)