From 9986642659ba29d7b3602657837916e79213c230 Mon Sep 17 00:00:00 2001 From: rozetko Date: Tue, 13 Nov 2018 15:26:12 +0300 Subject: [PATCH] Fix #231: .count(np.NaN) -> .isnull().sum() --- analytics/analytics/models/drop_model.py | 4 ++-- analytics/analytics/models/general_model.py | 2 +- analytics/analytics/models/jump_model.py | 4 ++-- analytics/analytics/models/peak_model.py | 6 +++--- analytics/analytics/models/trough_model.py | 6 +++--- 5 files changed, 11 insertions(+), 11 deletions(-) diff --git a/analytics/analytics/models/drop_model.py b/analytics/analytics/models/drop_model.py index 7888ac1..34decbb 100644 --- a/analytics/analytics/models/drop_model.py +++ b/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 diff --git a/analytics/analytics/models/general_model.py b/analytics/analytics/models/general_model.py index 077be97..9b3a06d 100644 --- a/analytics/analytics/models/general_model.py +++ b/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) diff --git a/analytics/analytics/models/jump_model.py b/analytics/analytics/models/jump_model.py index 369bfb3..6a8d693 100644 --- a/analytics/analytics/models/jump_model.py +++ b/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 diff --git a/analytics/analytics/models/peak_model.py b/analytics/analytics/models/peak_model.py index 8356b73..d6d8313 100644 --- a/analytics/analytics/models/peak_model.py +++ b/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 diff --git a/analytics/analytics/models/trough_model.py b/analytics/analytics/models/trough_model.py index 4cebc5c..8f1185e 100644 --- a/analytics/analytics/models/trough_model.py +++ b/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