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

pull/1/head
rozetko 6 years ago
parent
8614499c20
commit
9986642659
5 changed files with 11 additions and 11 deletions
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  1. 4
      analytics/analytics/models/drop_model.py
  2. 2
      analytics/analytics/models/general_model.py
  3. 4
      analytics/analytics/models/jump_model.py
  4. 4
      analytics/analytics/models/peak_model.py
  5. 6
      analytics/analytics/models/trough_model.py

4
analytics/analytics/models/drop_model.py
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@ -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

2
analytics/analytics/models/general_model.py
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@ -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)

4
analytics/analytics/models/jump_model.py
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@ -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

4
analytics/analytics/models/peak_model.py
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@ -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)
@ -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

6
analytics/analytics/models/trough_model.py
Unescape View File

@ -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

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