diff --git a/analytics/models/drop_model.py b/analytics/models/drop_model.py index f5eae71..0c09858 100644 --- a/analytics/models/drop_model.py +++ b/analytics/models/drop_model.py @@ -172,7 +172,7 @@ class DropModel(Model): conv = scipy.signal.fftconvolve(convol_data, pattern_data) if conv[self.state['WINDOW_SIZE']*2] > self.state['convolve_max'] * 1.2 or conv[self.state['WINDOW_SIZE']*2] < self.state['convolve_min'] * 0.8: delete_list.append(segment) - if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: + elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: delete_list.append(segment) else: delete_list.append(segment) diff --git a/analytics/models/general_model.py b/analytics/models/general_model.py index 0a1e9f4..dc74e3e 100644 --- a/analytics/models/general_model.py +++ b/analytics/models/general_model.py @@ -23,6 +23,8 @@ class GeneralModel(Model): 'convolve_max': 240, 'convolve_min': 200, 'WINDOW_SIZE': 240, + 'conv_del_min': 100, + 'conv_del_max': 120, } self.all_conv = [] @@ -38,7 +40,7 @@ class GeneralModel(Model): segment_data = data[segment_from_index: segment_to_index + 1] if len(segment_data) == 0: continue - x = segment_from_index + int((segment_to_index - segment_from_index) / 2) + x = segment_from_index + math.ceil((segment_to_index - segment_from_index) / 2) self.ipats.append(x) segment_data = data[x - self.state['WINDOW_SIZE'] : x + self.state['WINDOW_SIZE']] segment_min = min(segment_data) @@ -53,6 +55,20 @@ class GeneralModel(Model): convolve_data = scipy.signal.fftconvolve(labeled_data, self.model_gen) convolve_list.append(max(auto_convolve)) convolve_list.append(max(convolve_data)) + + del_conv_list = [] + for segment in segments: + if segment['deleted']: + 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] + if len(segment_data) == 0: + continue + del_mid_index = segment_from_index + math.ceil((segment_to_index - segment_from_index) / 2) + deleted_pat = data[del_mid_index - self.state['WINDOW_SIZE']: del_mid_index + self.state['WINDOW_SIZE'] + 1] + deleted_pat = deleted_pat - min(deleted_pat) + del_conv_pat = scipy.signal.fftconvolve(deleted_pat, self.model_gen) + del_conv_list.append(max(del_conv_pat)) if len(convolve_list) > 0: self.state['convolve_max'] = float(max(convolve_list)) @@ -63,6 +79,16 @@ class GeneralModel(Model): 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: + self.state['conv_del_max'] = self.state['WINDOW_SIZE'] def do_predict(self, dataframe: pd.DataFrame) -> list: data = dataframe['value'] @@ -88,6 +114,8 @@ class GeneralModel(Model): for val in segments: if self.all_conv[val] < self.state['convolve_min'] * 0.8: delete_list.append(val) + elif (self.all_conv[val] < self.state['conv_del_max'] * 1.02 and self.all_conv[val] > self.state['conv_del_min'] * 0.98): + delete_list.append(val) for item in delete_list: segments.remove(item) diff --git a/analytics/models/jump_model.py b/analytics/models/jump_model.py index 8392d47..7f43b19 100644 --- a/analytics/models/jump_model.py +++ b/analytics/models/jump_model.py @@ -173,7 +173,7 @@ class JumpModel(Model): conv = scipy.signal.fftconvolve(convol_data, pattern_data) if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_min'] * 0.8: delete_list.append(segment) - if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: + elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: delete_list.append(segment) else: delete_list.append(segment) diff --git a/analytics/models/peak_model.py b/analytics/models/peak_model.py index 9f93d8e..a924ee6 100644 --- a/analytics/models/peak_model.py +++ b/analytics/models/peak_model.py @@ -134,7 +134,7 @@ class PeakModel(Model): conv = scipy.signal.fftconvolve(convol_data, pattern_data) if max(conv) > self.state['convolve_max'] * 1.05 or max(conv) < self.state['convolve_min'] * 0.95: delete_list.append(segment) - if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: + elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: delete_list.append(segment) else: delete_list.append(segment) diff --git a/analytics/models/trough_model.py b/analytics/models/trough_model.py index 03ee6a9..8baec97 100644 --- a/analytics/models/trough_model.py +++ b/analytics/models/trough_model.py @@ -136,7 +136,7 @@ class TroughModel(Model): conv = scipy.signal.fftconvolve(convol_data, pattern_data) if max(conv) > self.state['convolve_max'] * 1.1 or max(conv) < self.state['convolve_min'] * 0.9: delete_list.append(segment) - if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: + elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: delete_list.append(segment) else: delete_list.append(segment)