Fix #231: .count(np.NaN) -> .isnull().sum()

analytics/analytics/models/drop_model.py

@@ -39,7 +39,7 @@ class DropModel(Model):
                 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]
-                percent_of_nans = segment_data.count(np.NaN) / len(segment_data)
+                percent_of_nans = segment_data.isnull().sum() / len(segment_data)
                 if percent_of_nans > 0 or len(segment_data) == 0:
                     continue
                 segment_min = min(segment_data)
@@ -164,7 +164,7 @@ class DropModel(Model):
         for segment in segments:
             if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']):
                 convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1]
-                percent_of_nans = convol_data.count(np.NaN) / len(convol_data)
+                percent_of_nans = convol_data.isnull().sum() / len(convol_data)
                 if percent_of_nans > 0.5:
                     delete_list.append(segment)
                     continue

analytics/analytics/models/general_model.py

@@ -37,7 +37,7 @@ class GeneralModel(Model):
                 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]
-                percent_of_nans = segment_data.count(np.NaN) / len(segment_data)
+                percent_of_nans = segment_data.isnull().sum() / len(segment_data)
                 if percent_of_nans > 0 or len(segment_data) == 0:
                     continue
                 x = segment_from_index + math.ceil((segment_to_index - segment_from_index) / 2)

analytics/analytics/models/jump_model.py

@@ -39,7 +39,7 @@ class JumpModel(Model):
         for segment in segments:
             if segment['labeled']:
                 segment_from_index, segment_to_index, segment_data = parse_segment(segment, dataframe)
-                percent_of_nans = segment_data.count(np.NaN) / len(segment_data)
+                percent_of_nans = segment_data.isnull().sum() / len(segment_data)
                 if percent_of_nans > 0 or len(segment_data) == 0:
                     continue    
                 segment_min = min(segment_data)
@@ -170,7 +170,7 @@ class JumpModel(Model):
         for segment in segments:
             if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']):
                 convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1]
-                percent_of_nans = convol_data.count(np.NaN) / len(convol_data)
+                percent_of_nans = convol_data.isnull().sum() / len(convol_data)
                 if percent_of_nans > 0.5:
                     delete_list.append(segment)
                     continue

analytics/analytics/models/peak_model.py

@@ -37,7 +37,7 @@ class PeakModel(Model):
                 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]
-                percent_of_nans = segment_data.count(np.NaN) / len(segment_data)
+                percent_of_nans = segment_data.isnull().sum() / len(segment_data)
                 if percent_of_nans > 0 or len(segment_data) == 0:
                     continue
                 segment_min = min(segment_data)
@@ -48,7 +48,7 @@ class PeakModel(Model):
                 labeled_peak = data[segment_max_index - self.state['WINDOW_SIZE']: segment_max_index + self.state['WINDOW_SIZE'] + 1]
                 labeled_peak = labeled_peak - min(labeled_peak)
                 patterns_list.append(labeled_peak)
-        
+
         self.model_peak = utils.get_av_model(patterns_list)
         for n in range(len(segments)): #labeled segments
             labeled_peak = data[self.ipeaks[n] - self.state['WINDOW_SIZE']: self.ipeaks[n] + self.state['WINDOW_SIZE'] + 1]
@@ -126,7 +126,7 @@ class PeakModel(Model):
             if segment > self.state['WINDOW_SIZE']:
                 convol_data = data[segment - self.state['WINDOW_SIZE']: segment + self.state['WINDOW_SIZE'] + 1]
                 convol_data = convol_data - min(convol_data)
-                percent_of_nans = convol_data.count(np.NaN) / len(convol_data)
+                percent_of_nans = convol_data.isnull().sum() / len(convol_data)
                 if percent_of_nans > 0.5:
                     delete_list.append(segment)
                     continue

analytics/analytics/models/trough_model.py

@@ -37,7 +37,7 @@ class TroughModel(Model):
                 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]
-                percent_of_nans = segment_data.count(np.NaN) / len(segment_data)
+                percent_of_nans = segment_data.isnull().sum() / len(segment_data)
                 if percent_of_nans > 0 or len(segment_data) == 0:
                     continue
                 segment_min = min(segment_data)
@@ -64,7 +64,7 @@ class TroughModel(Model):
                 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]
-                percent_of_nans = segment_data.count(np.NaN) / len(segment_data)
+                percent_of_nans = segment_data.isnull().sum() / len(segment_data)
                 if percent_of_nans > 0 or len(segment_data) == 0:
                     continue
                 del_min_index = segment_data.idxmin()
@@ -127,7 +127,7 @@ class TroughModel(Model):
             if segment > self.state['WINDOW_SIZE']:
                 convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1]
                 convol_data = convol_data - min(convol_data)
-                percent_of_nans = convol_data.count(np.NaN) / len(convol_data)
+                percent_of_nans = convol_data.isnull().sum() / len(convol_data)
                 if percent_of_nans > 0.5:
                     delete_list.append(segment)
                     continue