christopher@45: '''
christopher@45: Author: Chunyang Song
christopher@45: Institution: Centre for Digital Music, Queen Mary University of London
christopher@45: 
christopher@45: '''
christopher@45: 
christopher@71: from basic_functions import get_H, velocity_sequence_to_min_timespan, get_rhythm_category, upsample_velocity_sequence,  find_rhythm_Lmax
christopher@45: from parameter_setter import are_parameters_valid
christopher@45: 
christopher@45: def get_syncopation(bar, parameters = None):
christopher@45: 	syncopation = None
christopher@45: 	velocitySequence = bar.get_velocity_sequence()
christopher@45: 	subdivisionSequence = bar.get_subdivision_sequence()
christopher@45: 
christopher@45: 	if get_rhythm_category(velocitySequence, subdivisionSequence) == 'poly':
christopher@45: 		print 'Warning: SG model detects polyrhythms so returning None.'
christopher@71: 	elif bar.is_empty():
christopher@71: 		print 'Warning: SG model detects empty bar so returning None.'
christopher@45: 	else:
christopher@71: 		velocitySequence = velocity_sequence_to_min_timespan(velocitySequence)	# converting to the minimum time-span format
christopher@45: 
christopher@45: 		# set the defaults
christopher@71: 		Lmax  = 10
christopher@45: 		weightSequence = range(Lmax+1) # i.e. [0,1,2,3,4,5]
christopher@45: 		if parameters!= None:
christopher@45: 			if 'Lmax' in parameters:
christopher@45: 				Lmax = parameters['Lmax']				
christopher@45: 			if 'W' in parameters:
christopher@45: 				weightSequence = parameters['W']
christopher@45: 
christopher@45: 		if not are_parameters_valid(Lmax, weightSequence, subdivisionSequence):
christopher@45: 			print 'Error: the given parameters are not valid.'
christopher@45: 		else:
christopher@71: 			Lmax = find_rhythm_Lmax(velocitySequence, Lmax, weightSequence, subdivisionSequence) 
christopher@71: 			if Lmax != None:
christopher@71: 				# generate the metrical weights of level Lmax, and upsample(stretch) the velocity sequence to match the length of H
christopher@71: 				H = get_H(weightSequence,subdivisionSequence, Lmax)
christopher@71: 				#print len(velocitySequence)
christopher@71: 				#velocitySequence = upsample_velocity_sequence(velocitySequence, len(H))
christopher@71: 				#print len(velocitySequence)
christopher@71: 				
christopher@71: 				# 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
christopher@71: 				def ave_dif_neighbours(index, level):
christopher@45: 
christopher@71: 					averages = []
christopher@71: 					parameterGarma = 0.8
christopher@71: 					
christopher@71: 					# The findPre function is to calculate the index of the previous neighbour at a certain metrical level.
christopher@71: 					def find_pre(index, level):
christopher@71: 						preIndex = (index - 1)%len(H)	# using % is to restrict the index varies within range(0, len(H))
christopher@71: 						while(H[preIndex] > level):
christopher@71: 							preIndex = (preIndex - 1)%len(H)
christopher@71: 						#print 'preIndex', preIndex
christopher@71: 						return preIndex
christopher@45: 
christopher@71: 					# The findPost function is to calculate the index of the next neighbour at a certain metrical level.
christopher@71: 					def find_post(index, level):
christopher@71: 						postIndex = (index + 1)%len(H)
christopher@71: 						while(H[postIndex] > level):
christopher@71: 							postIndex = (postIndex + 1)%len(H)
christopher@71: 						#print 'postIndex', postIndex
christopher@71: 						return postIndex
christopher@71: 					
christopher@71: 					# The dif function is to calculate a difference level factor between two notes (at note position index1 and index 2) in velocity sequence
christopher@71: 					def dif(index1,index2):
christopher@71: 						parameterBeta = 0.5
christopher@71: 						dif_v = velocitySequence[index1]-velocitySequence[index2]
christopher@71: 						dif_h = abs(H[index1]-H[index2])
christopher@71: 						diffactor = (parameterBeta*dif_h/4+1-parameterBeta)
christopher@71: 						if diffactor>1:
christopher@71: 							return dif_v
christopher@71: 						else:
christopher@71: 							return dif_v*diffactor
christopher@45: 
christopher@45: 
christopher@71: 					# 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
christopher@71: 					for l in range(level, max(H)+1):
christopher@71: 						ave = (parameterGarma*dif(index,find_pre(index,l))+dif(index,find_post(index,l)) )/(1+parameterGarma)
christopher@71: 						averages.append(ave)
christopher@71: 					return averages
christopher@45: 
christopher@71: 				# if the upsampling was successfully done
christopher@71: 				if velocitySequence != None:
christopher@71: 					syncopation = 0			
christopher@71: 					# Calculate the syncopation value for each note
christopher@71: 					for index in range(len(velocitySequence)):
christopher@71: 						if velocitySequence[index] != 0: # Onset detected
christopher@71: 							h = H[index] 
christopher@71: 							# Syncopation potential according to its metrical level, which is equal to the metrical weight
christopher@71: 							potential = 1 - pow(0.5,h)
christopher@71: 							level = h 		# Metrical weight is equal to its metrical level
christopher@71: 							syncopation += min(ave_dif_neighbours(index, level))*potential
christopher@71: 				else:
christopher@71: 					print 'Try giving a bigger Lmax so that the rhythm sequence can be measured by the matching metrical weights sequence (H).'
christopher@45: 	return syncopation