comparison Syncopation models/basic_functions.py @ 11:7d94ba423c04

update basic_functions to merge

 		subdivision_seq = subdivision_seq[0:L_max+1]
 	
 	return subdivision_seq
 
 
-def get_rhythm_category(sequence, subdivision_seq):
-	rhythm_category = 'mono'
-	for f in find_prime_factors(len(get_min_timeSpan(sequence))):
-		if not (f in subdivision_seq): 
-			rhythm_category = 'poly'
+def get_rhythm_category(velocitySequence, subdivisionSequence):
+	'''
+	The get_rhythm_category function is used to detect rhythm category: monorhythm or polyrhythm.
+	For monorhythms, all prime factors of the length of minimum time-span representation of this sequence are
+	elements of its subdivision_seq, otherwise it is polyrhythm; 
+	e.g. prime_factors of polyrhythm 100100101010 in 4/4 is [2,3] but subdivision_seq = [1,2,2] for 4/4 
+	'''
+	rhythmCategory = 'mono'
+	for f in find_prime_factors(len(get_min_timeSpan(velocitySequence))):
+		if not (f in subdivisionSequence): 
+			rhythmCategory = 'poly'
 			break
-	return rhythm_category
+	return rhythmCategory
 
 def string_to_sequence(inputString):
-	return map(int,inputString.split(','))
+	return map(int, inputString.split(','))
+
 
  # The split_by_bar function seperates the score representation of rhythm by bar lines, 
  # resulting in a list representingbar-by-bar rhythm sequence,
  # e.g. rhythm = ['|',[ts1,td1,v1], [ts2,td2,v2], '|',[ts3,td3,v3],'|'...]
  # rhythm_bybar = [ [ [ts1,td1,v1], [ts2,td2,v2] ], [ [ts3,td3,v3] ], [...]]