def do_detect(self, dataframe: pd.DataFrame) -> list:
data = utils.cut_dataframe(dataframe)
data = data['value']
possible_drops = utils.find_drop(data, self.state['DROP_HEIGHT'],
self.state['DROP_LENGTH'] + 1)
return self.__filter_detection(possible_drops, data)
def do_fit(
self,
dataframe: pd.DataFrame,
labeled_segments: List[AnalyticSegment],
deleted_segments: List[AnalyticSegment],
learning_info: LearningInfo
) -> None:
data = utils.cut_dataframe(dataframe)
data = data['value']
self.state.pattern_center = list(set(self.state.pattern_center + learning_info.segment_center_list))
self.state.pattern_model = utils.get_av_model(learning_info.patterns_list)
convolve_list = utils.get_convolve(self.state.pattern_center, self.state.pattern_model, data, self.state.window_size)
correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, self.state.window_size)
height_list = learning_info.patterns_value
del_conv_list = []
delete_pattern_width = []
delete_pattern_height = []
delete_pattern_timestamp = []
for segment in deleted_segments:
delete_pattern_timestamp.append(segment.pattern_timestamp)
deleted = utils.get_interval(data, segment.center_index, self.state.window_size)
deleted = utils.subtract_min_without_nan(deleted)
del_conv = scipy.signal.fftconvolve(deleted, self.state.pattern_model)
if len(del_conv):
del_conv_list.append(max(del_conv))
delete_pattern_height.append(utils.find_confidence(deleted)[1])
self._update_fiting_result(self.state, learning_info.confidence, convolve_list, del_conv_list, height_list)
def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list,
deleted_segments: list, learning_info: dict) -> None:
data = utils.cut_dataframe(dataframe)
data = data['value']
window_size = self.state['WINDOW_SIZE']
last_pattern_center = self.state.get('pattern_center', [])
self.state['pattern_center'] = list(
set(last_pattern_center + learning_info['segment_center_list']))
self.state['pattern_model'] = utils.get_av_model(
learning_info['patterns_list'])
convolve_list = utils.get_convolve(self.state['pattern_center'],
self.state['pattern_model'], data,
window_size)
correlation_list = utils.get_correlation(self.state['pattern_center'],
self.state['pattern_model'],
data, window_size)
height_list = learning_info['patterns_value']
del_conv_list = []
delete_pattern_width = []
delete_pattern_height = []
delete_pattern_timestamp = []
for segment in deleted_segments:
del_min_index = segment.center_index
delete_pattern_timestamp.append(segment.pattern_timestamp)
deleted = utils.get_interval(data, del_min_index, window_size)
deleted = utils.subtract_min_without_nan(deleted)
del_conv = scipy.signal.fftconvolve(deleted,
self.state['pattern_model'])
if len(del_conv): del_conv_list.append(max(del_conv))
delete_pattern_height.append(utils.find_confidence(deleted)[1])
delete_pattern_width.append(utils.find_width(deleted, False))
self._update_fiting_result(self.state, learning_info['confidence'],
convolve_list, del_conv_list, height_list)
def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
data = utils.cut_dataframe(dataframe)
data = data['value']
possible_stairs = self.get_stair_indexes(data, self.state.stair_height,
self.state.stair_length + 1)
result = self.__filter_detection(possible_stairs, data)
return [(val - 1, val + 1) for val in result]
def do_fit(self, dataframe: pd.DataFrame,
labeled_segments: List[AnalyticSegment],
deleted_segments: List[AnalyticSegment],
learning_info: LearningInfo) -> None:
data = utils.cut_dataframe(dataframe)
data = data['value']
last_pattern_center = self.state.pattern_center
self.state.pattern_center = utils.remove_duplicates_and_sort(
last_pattern_center + learning_info.segment_center_list)
self.state.pattern_model = utils.get_av_model(
learning_info.patterns_list)
convolve_list = utils.get_convolve(self.state.pattern_center,
self.state.pattern_model, data,
self.state.window_size)
correlation_list = utils.get_correlation(self.state.pattern_center,
self.state.pattern_model,
data, self.state.window_size)
del_conv_list = []
delete_pattern_timestamp = []
for segment in deleted_segments:
del_mid_index = segment.center_index
delete_pattern_timestamp.append(segment.pattern_timestamp)
deleted_pat = utils.get_interval(data, del_mid_index,
self.state.window_size)
deleted_pat = utils.subtract_min_without_nan(deleted_pat)
del_conv_pat = scipy.signal.fftconvolve(deleted_pat,
self.state.pattern_model)
if len(del_conv_pat): del_conv_list.append(max(del_conv_pat))
self.state.convolve_min, self.state.convolve_max = utils.get_min_max(
convolve_list, self.state.window_size / 3)
self.state.conv_del_min, self.state.conv_del_max = utils.get_min_max(
del_conv_list, self.state.window_size)
def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
data = utils.cut_dataframe(dataframe)
data = data['value']
possible_jumps = utils.find_jump(data, self.state.jump_height,
self.state.jump_length + 1)
result = self.__filter_detection(possible_jumps, data)
return [(val - 1, val + 1) for val in result]
def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
data = utils.cut_dataframe(dataframe)
data = data['value']
all_extremum_indexes = self.get_extremum_indexes(data)
smoothed_data = self.get_smoothed_data(data, self.state.confidence, EXP_SMOOTHING_FACTOR)
segments = self.get_possible_segments(data, smoothed_data, all_extremum_indexes)
result = self.__filter_detection(segments, data)
result = utils.get_borders_of_peaks(result, data, self.state.window_size, self.state.confidence)
return result
def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
data = utils.cut_dataframe(dataframe)
data = data['value']
pat_data = self.state.pattern_model
if pat_data.count(0) == len(pat_data):
raise ValueError('Labeled patterns must not be empty')
window_size = self.state.window_size
all_corr = utils.get_correlation_gen(data, window_size, pat_data)
all_corr_peaks = utils.find_peaks(all_corr, window_size * 2)
filtered = self.__filter_detection(all_corr_peaks, data)
filtered = list(filtered)
return [(item, item + window_size * 2) for item in filtered]
def do_detect(self, dataframe: pd.DataFrame):
data = utils.cut_dataframe(dataframe)
data = data['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)
segments = []
for i in all_mins:
if data[i] < extrema_list[i]:
segments.append(i)
return self.__filter_detection(segments, data)
def do_detect(self, dataframe: pd.DataFrame) -> list:
data = utils.cut_dataframe(dataframe)
data = data['value']
pat_data = self.state['pattern_model']
if pat_data.count(0) == len(pat_data):
raise ValueError('Labeled patterns must not be empty')
self.all_conv = []
for i in range(self.state['WINDOW_SIZE'] * 2, len(data)):
watch_data = data[i - self.state['WINDOW_SIZE'] * 2: i]
watch_data = utils.subtract_min_without_nan(watch_data)
conv = scipy.signal.fftconvolve(watch_data, pat_data)
self.all_conv.append(max(conv))
all_conv_peaks = utils.peak_finder(self.all_conv, self.state['WINDOW_SIZE'] * 2)
filtered = self.__filter_detection(all_conv_peaks, data)
return set(item + self.state['WINDOW_SIZE'] for item in filtered)
def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list, learning_info: dict) -> None:
data = utils.cut_dataframe(dataframe)
data = data['value']
last_pattern_center = self.state.get('pattern_center', [])
self.state['pattern_center'] = list(set(last_pattern_center + learning_info['segment_center_list']))
self.state['pattern_model'] = utils.get_av_model(learning_info['patterns_list'])
convolve_list = utils.get_convolve(self.state['pattern_center'], self.state['pattern_model'], data, self.state['WINDOW_SIZE'])
correlation_list = utils.get_correlation(self.state['pattern_center'], self.state['pattern_model'], data, self.state['WINDOW_SIZE'])
del_conv_list = []
delete_pattern_timestamp = []
for segment in deleted_segments:
del_mid_index = segment.center_index
delete_pattern_timestamp.append(segment.pattern_timestamp)
deleted_pat = utils.get_interval(data, del_mid_index, self.state['WINDOW_SIZE'])
deleted_pat = utils.subtract_min_without_nan(deleted_pat)
del_conv_pat = scipy.signal.fftconvolve(deleted_pat, self.state['pattern_model'])
if len(del_conv_pat): del_conv_list.append(max(del_conv_pat))
self.state['convolve_min'], self.state['convolve_max'] = utils.get_min_max(convolve_list, self.state['WINDOW_SIZE'] / 3)
self.state['conv_del_min'], self.state['conv_del_max'] = utils.get_min_max(del_conv_list, self.state['WINDOW_SIZE'])
def do_fit(self, dataframe: pd.DataFrame,
labeled_segments: List[AnalyticSegment],
deleted_segments: List[AnalyticSegment],
learning_info: LearningInfo) -> None:
data = utils.cut_dataframe(dataframe)
data = data['value']
window_size = self.state.window_size
last_pattern_center = self.state.pattern_center
self.state.pattern_center = utils.remove_duplicates_and_sort(
last_pattern_center + learning_info.segment_center_list)
self.state.pattern_model = utils.get_av_model(
learning_info.patterns_list)
convolve_list = utils.get_convolve(self.state.pattern_center,
self.state.pattern_model, data,
window_size)
correlation_list = utils.get_correlation(self.state.pattern_center,
self.state.pattern_model,
data, window_size)
height_list = learning_info.patterns_value
del_conv_list = []
delete_pattern_timestamp = []
for segment in deleted_segments:
segment_cent_index = segment.center_index
delete_pattern_timestamp.append(segment.pattern_timestamp)
deleted_stair = utils.get_interval(data, segment_cent_index,
window_size)
deleted_stair = utils.subtract_min_without_nan(deleted_stair)
del_conv_stair = scipy.signal.fftconvolve(deleted_stair,
self.state.pattern_model)
if len(del_conv_stair) > 0:
del_conv_list.append(max(del_conv_stair))
self._update_fitting_result(self.state, learning_info.confidence,
convolve_list, del_conv_list)
self.state.stair_height = int(
min(learning_info.pattern_height, default=1))
self.state.stair_length = int(
max(learning_info.pattern_width, default=1))