--- a/python/utils.py	Fri Jul 12 11:28:47 2013 -0400
+++ b/python/utils.py	Mon Jul 15 12:12:47 2013 -0400
@@ -138,12 +138,15 @@
 
 
 #########################
-# maths section
+# sequence section
 #########################
 
-def LCSS(l1, l2, threshold, distance, delta = float('inf')):
+def LCSS(l1, l2, similarityFunc, delta = float('inf')):
     '''returns the longest common subsequence similarity
     based on the threshold on distance between two elements of lists l1, l2
+    similarityFunc returns True or False whether the two points are considered similar
+
+    eg distance(p1, p2) < epsilon
     '''
     from numpy import zeros, int as npint
     n1 = len(l1)
@@ -151,23 +154,23 @@
     similarity = zeros((n1+1,n2+1), dtype = npint)
     for i in xrange(1,n1+1):
         for j in xrange(max(1,i-delta),min(n2+1,i+delta+1)):
-            if distance(l1[i-1], l2[j-1])<=threshold:
+            if similarityFunc(l1[i-1], l2[j-1]):
                 similarity[i][j] = similarity[i-1][j-1]+1
             else:
                 similarity[i][j] = max(similarity[i-1][j], similarity[i][j-1])
     return max(max(similarity[:,-1]), max(similarity[-1,:]))
 
-def normalizedLCSS(l1, l2, threshold, distance, delta = float('inf'), lengthMethod = min):
+def normalizedLCSS(l1, l2, similarityFunc, delta = float('inf'), lengthMethod = min):
     ''' compute the normalized LCSS
     ie, the LCSS divided by the min or mean of the indicator lengths (using lengthMethod)
     lengthMethod = lambda x,y:float(x,y)/2'''
-    return float(LCSS(l1, l2, threshold, distance, delta))/lengthMethod(len(l1), len(l2))
+    return float(LCSS(l1, l2, similarityFunc, delta))/lengthMethod(len(l1), len(l2))
 
-def DLCSS(l1, l2, threshold, distance, delta = float('inf'), lengthMethod = min):
+def DLCSS(l1, l2, similarityFunc, delta = float('inf'), lengthMethod = min):
     ''' compute the LCSS distance'''
-    return 1-normalizedLCSS(l1, l2, threshold, distance, delta, lengthMethod)
+    return 1-normalizedLCSS(l1, l2, similarityFunc, delta, lengthMethod)
 
-def alignedLCSS(_l1, _l2, threshold, distance, delta):
+def alignedLCSS(_l1, _l2, similarityFunc, delta):
     '''returns the best matching if using a finite delta by shiftinig the series alignments'''
     if len(_l2) < len(_l1): # l1 is the shortest
         l1 = _l2
@@ -180,15 +183,19 @@
     # for i in xrange(min(delta,n1), max(n1+n2-delta, n2+1)): # i is the alignment of the end of l1 in l2
     #     print l1[min(-i-1,n1):] # min(n1+n2-i,n1)
     #     print l2[max(0,i-n1):]
-    #     print LCSS(l1[min(-i-1,n1):], l2[max(0,i-n1):], threshold, distance, delta)
-    lcss = [LCSS(l1[min(-i-1,n1):], l2[max(0,i-n1):], threshold, distance, delta) for i in xrange(min(delta,n1), max(n1+n2-delta, n2+1))]
+    #     print LCSS(l1[min(-i-1,n1):], l2[max(0,i-n1):], similarityFunc, delta)
+    lcss = [LCSS(l1[min(-i-1,n1):], l2[max(0,i-n1):], similarityFunc, delta) for i in xrange(min(delta,n1), max(n1+n2-delta, n2+1))]
     return max(lcss)
 
-def normalizedAlignedLCSS(l1, l2, threshold, distance, delta, lengthMethod = min):
-    return float(alignedLCSS(l1, l2, threshold, distance, delta))/lengthMethod(len(l1), len(l2))
+def normalizedAlignedLCSS(l1, l2, similarityFunc, delta, lengthMethod = min):
+    return float(alignedLCSS(l1, l2, similarityFunc, delta))/lengthMethod(len(l1), len(l2))
 
-def alignedDLCSS(l1, l2, threshold, distance, delta, lengthMethod = min):
-    return 1-normalizedAlignedLCSS(l1, l2, threshold, distance, delta, lengthMethod)
+def alignedDLCSS(l1, l2, similarityFunc, delta, lengthMethod = min):
+    return 1-normalizedAlignedLCSS(l1, l2, similarityFunc, delta, lengthMethod)
+
+#########################
+# maths section
+#########################
 
 def framesToTime(nFrames, frameRate, initialTime = (0.,0.,0.)):
     'returns hour, minutes and seconds'