csong@0: '''
csong@0: Author: Chunyang Song
csong@0: Institution: Centre for Digital Music, Queen Mary University of London
csong@0: 
csong@0: '''
csong@0: 
csong@20: from basic_functions import get_H, get_min_timeSpan, get_rhythm_category
csong@20: from TMC import find_L
csong@0: 
csong@20: #def get_syncopation(seq, subdivision_seq, weight_seq, L_max, rhythm_category):
csong@20: def get_syncopation(bar, parameters = None):
csong@1: 	syncopation = None
csong@20: 	velocitySequence = bar.get_velocity_sequence()
csong@20: 	subdivisionSequence = bar.get_subdivision_sequence()
csong@20: 
csong@20: 	if get_rhythm_category(velocitySequence, subdivisionSequence) == 'poly':
csong@20: 		print 'Warning: SG model detects polyrhythms so returning None.'
csong@0: 	else:
csong@20: 		velocitySequence = get_min_timeSpan(velocitySequence)	# converting to the minimum time-span format
csong@20: 
csong@20: 		# If the parameters are not given, use the default settings
csong@20: 		if parameters == None:
csong@20: 			Lmax  = 5
csong@20: 			weightSequence = range(Lmax+1) # i.e. [0,1,2,3,4,5]
csong@20: 		else:
csong@20: 			if are_parameters_valid(parameters):
csong@20: 				Lmax = parameters['Lmax']
csong@20: 				weightSequence = parameters['W']
csong@1: 			else:
csong@20: 				pass
csong@20: 				#raise InvalidParameterError
csong@0: 
csong@20: 		L = find_L(velocitySequence, Lmax, weightSequence, subdivisionSequence) 
csong@20: 		print 'L', L
csong@20: 		if L != None:
csong@1: 			syncopation = 0
csong@1: 			# generate the metrical weights of the lowest level
csong@20: 			H = get_H(weightSequence,subdivisionSequence, L)
csong@20: 			print 'H', H
csong@0: 
csong@20: 			# The ave_dif_neighbours function calculates the (weighted) average of the difference between the note at a certain index and its neighbours in a certain metrical level
csong@20: 			def ave_dif_neighbours(index, level):
csong@20: 
csong@0: 				averages = []
csong@20: 				parameterGarma = 0.8
csong@0: 				
csong@1: 				# The findPre function is to calculate the index of the previous neighbour at a certain metrical level.
csong@20: 				def find_pre(index, level):
csong@20: 					preIndex = (index - 1)%len(H)	# using % is to restrict the index varies within range(0, len(H))
csong@20: 					while(H[preIndex] > level):
csong@20: 						preIndex = (preIndex - 1)%len(H)
csong@20: 					print 'preIndex', preIndex
csong@20: 					return preIndex
csong@0: 
csong@1: 				# The findPost function is to calculate the index of the next neighbour at a certain metrical level.
csong@20: 				def find_post(index, level):
csong@20: 					postIndex = (index + 1)%len(H)
csong@20: 					while(H[postIndex] > level):
csong@20: 						postIndex = (postIndex + 1)%len(H)
csong@20: 					print 'postIndex', postIndex
csong@20: 					return postIndex
csong@0: 				
csong@1: 				# The dif function is to calculate a difference level factor between two notes (at note position index1 and index 2) in velocity sequence
csong@0: 				def dif(index1,index2):
csong@20: 					parameterBeta = 0.5
csong@20: 					dif_v = velocitySequence[index1]-velocitySequence[index2]
csong@1: 					dif_h = abs(H[index1]-H[index2])
csong@20: 					dif = dif_v*(parameterBeta*dif_h/4+1-parameterBeta)
csong@20: 					print 'dif', dif
csong@0: 					return dif
csong@0: 
csong@1: 				# From the highest to the lowest metrical levels where the current note resides, calculate the difference between the note and its neighbours at that level
csong@1: 				for l in range(level, max(H)+1):
csong@20: 					ave = (parameterGarma*dif(index,find_pre(index,l))+dif(index,find_post(index,l)) )/(1+parameterGarma)
csong@0: 					averages.append(ave)
csong@20: 				print 'averages', averages
csong@0: 				return averages
csong@0: 
csong@1: 			# Calculate the syncopation value for each note
csong@20: 			for index in range(len(velocitySequence)):
csong@20: 				if velocitySequence[index] != 0: # Onset detected
csong@20: 					h = H[index] 
csong@20: 					# Syncopation potential according to its metrical level, which is equal to the metrical weight
csong@20: 					potential = 1 - pow(0.5,h)
csong@20: 					level = h 		# Metrical weight is equal to its metrical level
csong@20: 					syncopation += min(ave_dif_neighbours(index, level))*potential
csong@1: 			
csong@1: 	return syncopation