trim trailing whitespaces

analytics/analytics/analytic_unit_worker.py

@@ -29,11 +29,11 @@ class AnalyticUnitWorker:
             return new_cache
         except CancelledError as e:
             return cache
-        
+
 
     async def do_predict(self, data: pd.DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
         return self._detector.predict(data, cache)
-    
+
     def cancel(self):
         if self._training_feature is not None:
             self._training_feature.cancel()

analytics/analytics/config.py

@@ -23,7 +23,7 @@ def get_config_field(field, default_val = None):
 
     if default_val is not None:
         return default_val
-    
+
     raise Exception('Please configure {}'.format(field))
 
 ZMQ_DEV_PORT = get_config_field('ZMQ_DEV_PORT', '8002')

analytics/analytics/models/custom_model.py

@@ -3,9 +3,9 @@ import utils
 import pandas as pd
 
 
-class CustomModel(Model):    
+class CustomModel(Model):
     def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None:
         pass
-    
+
     def do_predict(self, dataframe: pd.DataFrame) -> list:
         return []

analytics/analytics/models/general_model.py

@@ -11,7 +11,6 @@ from scipy.stats import gaussian_kde
 from scipy.stats import norm
 
 
-
 class GeneralModel(Model):
 
     def __init__(self):
@@ -43,10 +42,10 @@ class GeneralModel(Model):
                 segment_data = utils.get_interval(data, center_ind, self.state['WINDOW_SIZE'])
                 segment_data = utils.subtract_min_without_nan(segment_data)
                 patterns_list.append(segment_data)
-        
+
         self.model_gen = utils.get_av_model(patterns_list)
         convolve_list = utils.get_convolve(self.ipats, self.model_gen, data, self.state['WINDOW_SIZE'])
-				
+
         del_conv_list = []
         for segment in segments:
             if segment['deleted']:
@@ -63,17 +62,17 @@ class GeneralModel(Model):
             self.state['convolve_max'] = float(max(convolve_list))
         else:
             self.state['convolve_max'] = self.state['WINDOW_SIZE'] / 3
-        
+
         if len(convolve_list) > 0:
             self.state['convolve_min'] = float(min(convolve_list))
         else:
             self.state['convolve_min'] = self.state['WINDOW_SIZE'] / 3
-		
+
         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:

analytics/analytics/models/jump_model.py

@@ -41,7 +41,7 @@ class JumpModel(Model):
                 segment_from_index, segment_to_index, segment_data = parse_segment(segment, dataframe)
                 percent_of_nans = segment_data.isnull().sum() / len(segment_data)
                 if percent_of_nans > 0 or len(segment_data) == 0:
-                    continue    
+                    continue
                 confidence = utils.find_confidence(segment_data)
                 confidences.append(confidence)
                 segment_cent_index, jump_height, jump_length = utils.find_jump_parameters(segment_data, segment_from_index)
@@ -51,10 +51,10 @@ class JumpModel(Model):
                 labeled_jump = utils.get_interval(data, segment_cent_index, self.state['WINDOW_SIZE'])
                 labeled_jump = utils.subtract_min_without_nan(labeled_jump)
                 patterns_list.append(labeled_jump)
-                
+
         self.model_jump = utils.get_av_model(patterns_list)
         convolve_list = utils.get_convolve(self.ijumps, self.model_jump, data, self.state['WINDOW_SIZE'])
-            
+
         del_conv_list = []
         for segment in segments:
             if segment['deleted']:
@@ -65,7 +65,7 @@ class JumpModel(Model):
                 deleted_jump = utils.get_interval(data, segment_cent_index, self.state['WINDOW_SIZE'])
                 deleted_jump = utils.subtract_min_without_nan(labeled_jump)
                 del_conv_jump = scipy.signal.fftconvolve(deleted_jump, self.model_jump)
-                del_conv_list.append(max(del_conv_jump)) 
+                del_conv_list.append(max(del_conv_jump))
 
         if len(confidences) > 0:
             self.state['confidence'] = float(min(confidences))
@@ -76,7 +76,7 @@ class JumpModel(Model):
             self.state['convolve_max'] = float(max(convolve_list))
         else:
             self.state['convolve_max'] = self.state['WINDOW_SIZE']
-            
+
         if len(convolve_list) > 0:
             self.state['convolve_min'] = float(min(convolve_list))
         else:
@@ -91,12 +91,12 @@ class JumpModel(Model):
             self.state['JUMP_LENGTH'] = int(max(jump_length_list))
         else:
             self.state['JUMP_LENGTH'] = 1
-            
+
         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:
@@ -113,7 +113,7 @@ class JumpModel(Model):
         variance_error = self.state['WINDOW_SIZE']
         close_patterns = utils.close_filtering(segments, variance_error)
         segments = utils.best_pat(close_patterns, data, 'max')
-            
+
         if len(segments) == 0 or len(self.ijumps) == 0 :
             segments = []
             return segments

analytics/analytics/models/peak_model.py

@@ -48,7 +48,7 @@ class PeakModel(Model):
 
         self.model_peak = utils.get_av_model(patterns_list)
         convolve_list = utils.get_convolve(self.ipeaks, self.model_peak, data, self.state['WINDOW_SIZE'])
-        
+
         del_conv_list = []
         for segment in segments:
             if segment['deleted']:
@@ -59,7 +59,7 @@ class PeakModel(Model):
                 deleted_peak = utils.get_interval(data, del_max_index, self.state['WINDOW_SIZE'])
                 deleted_peak = utils.subtract_min_without_nan(deleted_peak)
                 del_conv_peak = scipy.signal.fftconvolve(deleted_peak, self.model_peak)
-                del_conv_list.append(max(del_conv_peak))                
+                del_conv_list.append(max(del_conv_peak))
 
         if len(confidences) > 0:
             self.state['confidence'] = float(min(confidences))
@@ -75,12 +75,12 @@ class PeakModel(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:
@@ -107,7 +107,7 @@ class PeakModel(Model):
         variance_error = self.state['WINDOW_SIZE']
         close_patterns = utils.close_filtering(segments, variance_error)
         segments = utils.best_pat(close_patterns, data, 'max')
-        
+
         if len(segments) == 0 or len(self.ipeaks) == 0:
             return []
         pattern_data = self.model_peak

analytics/analytics/models/trough_model.py

@@ -40,15 +40,15 @@ class TroughModel(Model):
                     continue
                 confidence = utils.find_confidence(segment_data)
                 confidences.append(confidence)
-                segment_min_index = segment_data.idxmin() 
+                segment_min_index = segment_data.idxmin()
                 self.itroughs.append(segment_min_index)
                 labeled_trough = utils.get_interval(data, segment_min_index, self.state['WINDOW_SIZE'])
                 labeled_trough = utils.subtract_min_without_nan(labeled_trough)
                 patterns_list.append(labeled_trough)
-                
+
         self.model_trough = utils.get_av_model(patterns_list)
         convolve_list = utils.get_convolve(self.itroughs, self.model_trough, data, self.state['WINDOW_SIZE'])
-            
+
         del_conv_list = []
         for segment in segments:
             if segment['deleted']:
@@ -60,7 +60,7 @@ class TroughModel(Model):
                 deleted_trough = utils.get_interval(data, del_min_index, self.state['WINDOW_SIZE'])
                 deleted_trough = utils.subtract_min_without_nan(deleted_trough)
                 del_conv_trough = scipy.signal.fftconvolve(deleted_trough, self.model_trough)
-                del_conv_list.append(max(del_conv_trough))  
+                del_conv_list.append(max(del_conv_trough))
 
         if len(confidences) > 0:
             self.state['confidence'] = float(min(confidences))
@@ -71,17 +71,17 @@ class TroughModel(Model):
             self.state['convolve_max'] = float(max(convolve_list))
         else:
             self.state['convolve_max'] = self.state['WINDOW_SIZE']
-        
+
         if len(convolve_list) > 0:
             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:
@@ -91,7 +91,7 @@ class TroughModel(Model):
         data = dataframe['value']
         window_size = int(len(data)/SMOOTHING_COEFF) #test ws on flat data
         all_mins = argrelextrema(np.array(data), np.less)[0]
-        
+
         extrema_list = []
         for i in utils.exponential_smoothing(data - self.state['confidence'], EXP_SMOOTHING_FACTOR):
             extrema_list.append(i)
@@ -110,7 +110,7 @@ class TroughModel(Model):
         segments = utils.best_pat(close_patterns, data, 'min')
         if len(segments) == 0 or len(self.itroughs) == 0 :
             segments = []
-            return segments  
+            return segments
         pattern_data = self.model_trough
         for segment in segments:
             if segment > self.state['WINDOW_SIZE']:

analytics/analytics/services/server_service.py

@@ -16,17 +16,17 @@ class ServerMessage:
         self.method = method
         self.payload = payload
         self.request_id = request_id
-    
+
     def toJSON(self):
         result = {
-            'method': self.method   
+            'method': self.method
         }
         if self.payload is not None:
             result['payload'] = self.payload
         if self.request_id is not None:
             result['requestId'] = self.request_id
         return result
-    
+
     def fromJSON(json: dict):
         method = json['method']
         payload = None
@@ -35,7 +35,7 @@ class ServerMessage:
             payload = json['payload']
         if 'requestId' in json:
             request_id = json['requestId']
-        
+
         return ServerMessage(method, payload, request_id)
 
 class ServerService:
@@ -62,7 +62,7 @@ class ServerService:
 
     async def send_message(self, message: ServerMessage):
         await self.socket.send_string(json.dumps(message.toJSON()))
-    
+
     async def send_request(self, message: ServerMessage) -> object:
         if message.request_id is not None:
             raise ValueError('Message can`t have request_id before it is scheduled')
@@ -82,7 +82,7 @@ class ServerService:
         try:
             message_object = json.loads(text)
             message = ServerMessage.fromJSON(message_object)
-            
+
             if message.request_id is not None:
                 self.responses[message_object['requestId']] = message.payload
                 return

analytics/analytics/utils/common.py

@@ -73,7 +73,7 @@ def intersection_segment(data, median):
     for i in range(1, len(cen_ind)):
         if cen_ind[i] == cen_ind[i - 1] + 1:
             del_ind.append(i - 1)
- 
+
     return [x for (idx, x) in enumerate(cen_ind) if idx not in del_ind]
 
 def logistic_sigmoid_distribution(self, x1, x2, alpha, height):
@@ -123,7 +123,7 @@ def find_ind_median(median, segment_data):
     for i in range(len(segment_data)):
         f.append(median)
     f = np.array(f)
-    g = []    
+    g = []
     for i in segment_data:
         g.append(i)
     g = np.array(g)
@@ -139,7 +139,7 @@ def find_jump_length(segment_data, min_line, max_line):
         l.append(max_line)
     f = np.array(f)
     l = np.array(l)
-    g = []    
+    g = []
     for i in segment_data:
         g.append(i)
     g = np.array(g)
@@ -150,7 +150,7 @@ def find_jump_length(segment_data, min_line, max_line):
     else:
         print("retard alert!")
         return 0
-    
+
 def find_jump(data, height, lenght):
     j_list = []
     for i in range(len(data)-lenght-1):
@@ -168,7 +168,7 @@ def find_drop_length(segment_data, min_line, max_line):
         l.append(max_line)
     f = np.array(f)
     l = np.array(l)
-    g = []    
+    g = []
     for i in segment_data:
         g.append(i)
     g = np.array(g)
@@ -186,11 +186,11 @@ def drop_intersection(segment_data, median_line):
     for i in range(len(segment_data)):
         f.append(median_line)
     f = np.array(f)
-    g = []    
+    g = []
     for i in segment_data:
         g.append(i)
     g = np.array(g)
-    idx = np.argwhere(np.diff(np.sign(f - g)) != 0).reshape(-1) + 0 
+    idx = np.argwhere(np.diff(np.sign(f - g)) != 0).reshape(-1) + 0
     return idx
 
 def find_drop(data, height, length):
@@ -283,7 +283,7 @@ def nan_to_zero(segment, nan_list):
 def find_confidence(segment: pd.Series) -> float:
     segment_min = min(segment)
     segment_max = max(segment)
-    return 0.2 * (segment_max - segment_min)    
+    return 0.2 * (segment_max - segment_min)
 
 def get_interval(data: pd.Series, center: int, window_size: int) -> pd.Series:
     left_bound = center - window_size