def find_confidence(segment: pd.Series) -> (float, float):
segment = utils.check_nan_values(segment)
segment_min = min(segment)
segment_max = max(segment)
height = segment_max - segment_min
if height:
return (CONFIDENCE_FACTOR * height, height)
else:
return (0, 0)
def get_correlation(segments: list, av_model: list, data: pd.Series, window_size: int) -> list:
labeled_segment = []
correlation_list = []
p_value_list = []
for segment in segments:
labeled_segment = utils.get_interval(data, segment, window_size)
labeled_segment = utils.subtract_min_without_nan(labeled_segment)
labeled_segment = utils.check_nan_values(labeled_segment)
correlation = pearsonr(labeled_segment, av_model)
correlation_list.append(correlation[0])
p_value_list.append(correlation[1])
return correlation_list
def get_convolve(segments: list, av_model: list, data: pd.Series, window_size: int) -> list:
labeled_segment = []
convolve_list = []
for segment in segments:
labeled_segment = utils.get_interval(data, segment, window_size)
labeled_segment = utils.subtract_min_without_nan(labeled_segment)
labeled_segment = utils.check_nan_values(labeled_segment)
auto_convolve = scipy.signal.fftconvolve(labeled_segment, labeled_segment)
convolve_segment = scipy.signal.fftconvolve(labeled_segment, av_model)
if len(auto_convolve) > 0:
convolve_list.append(max(auto_convolve))
if len(convolve_segment) > 0:
convolve_list.append(max(convolve_segment))
return convolve_list
