Upload utils.py

utils.py

@@ -0,0 +1,80 @@
+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