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| from itertools import islice | |
| import numpy as np | |
| # Sliding window function | |
| def window(seq, n=3): | |
| """https://stackoverflow.com/questions/6822725/rolling-or-sliding-window-iterator | |
| Returns a sliding window of width n over data from the iterable seq""" | |
| it = iter(seq) | |
| result = tuple(islice(it, n)) | |
| if len(result) == n: | |
| yield result | |
| for elem in it: | |
| result = result[1:] + (elem,) | |
| yield result | |
| # Compute depth scores | |
| def get_depths(scores): | |
| """Given a sequence of coherence scores of length n, compute a sequence of depth scores of similar length""" | |
| def climb(seq, i, mode='left'): | |
| """Given a sequence seq of values and index i, advance the index either to the right or left while the | |
| value keeps increasing, then return the value at new index | |
| """ | |
| if mode == 'left': | |
| while True: | |
| curr = seq[i] | |
| if i == 0: | |
| return curr | |
| i = i-1 | |
| if not seq[i] > curr: | |
| return curr | |
| if mode == 'right': | |
| while True: | |
| curr = seq[i] | |
| if i == (len(seq)-1): | |
| return curr | |
| i = i+1 | |
| if not seq[i] > curr: | |
| return curr | |
| depths = [] | |
| for i in range(len(scores)): | |
| score = scores[i] | |
| l_peak = climb(scores, i, mode='left') | |
| r_peak = climb(scores, i, mode='right') | |
| depth = 0.5 * (l_peak + r_peak - (2*score)) | |
| depths.append(depth) | |
| return np.array(depths) | |
| from scipy.signal import argrelmax | |
| # Filter out local maxima | |
| def get_local_maxima(depth_scores, order=1): | |
| """Given a sequence of depth scores, return a filtered sequence where only local maxima | |
| selected based on the given order""" | |
| maxima_ids = argrelmax(depth_scores, order=order)[0] | |
| filtered_scores = np.zeros(len(depth_scores)) | |
| filtered_scores[maxima_ids] = depth_scores[maxima_ids] | |
| return filtered_scores | |
| # Automatic threshold computation | |
| def compute_threshold(scores): | |
| """From Texttiling: https://aclanthology.org/J97-1003.pdf | |
| Automatically compute an appropriate threshold given a sequence of depth scores | |
| """ | |
| s = scores[np.nonzero(scores)] | |
| threshold = np.mean(s) - (np.std(s) / 2) | |
| # threshold = np.mean(s) - (np.std(s)) | |
| return threshold | |
| def get_threshold_segments(scores, threshold=0.1): | |
| """Given a sequence of depth scores, return indexes where the value is greater than the threshold""" | |
| segment_ids = np.where(scores >= threshold)[0] | |
| return segment_ids |