annotate Syncopation models/SG.py @ 42:121d0e1f1748

Added the R plots

'''
Author: Chunyang Song
Institution: Centre for Digital Music, Queen Mary University of London

'''

from basic_functions import get_H, velocity_sequence_to_min_timespan, get_rhythm_category, upsample_velocity_sequence
from parameter_setter import are_parameters_valid

def get_syncopation(bar, parameters = None):
	syncopation = None
	velocitySequence = bar.get_velocity_sequence()
	subdivisionSequence = bar.get_subdivision_sequence()

	if get_rhythm_category(velocitySequence, subdivisionSequence) == 'poly':
		print 'Warning: SG model detects polyrhythms so returning None.'
	else:
		#velocitySequence = velocity_sequence_to_min_timespan(velocitySequence)	# converting to the minimum time-span format

		# set the defaults
		Lmax  = 5
		weightSequence = range(Lmax+1) # i.e. [0,1,2,3,4,5]
		if parameters!= None:
			if 'Lmax' in parameters:
				Lmax = parameters['Lmax']				
			if 'W' in parameters:
				weightSequence = parameters['W']

		if not are_parameters_valid(Lmax, weightSequence, subdivisionSequence):
			print 'Error: the given parameters are not valid.'
		else:
			# generate the metrical weights of level Lmax, and upsample(stretch) the velocity sequence to match the length of H
			H = get_H(weightSequence,subdivisionSequence, Lmax)
			
			velocitySequence = upsample_velocity_sequence(velocitySequence, len(H))

			# 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
			def ave_dif_neighbours(index, level):

				averages = []
				parameterGarma = 0.8
				
				# The findPre function is to calculate the index of the previous neighbour at a certain metrical level.
				def find_pre(index, level):
					preIndex = (index - 1)%len(H)	# using % is to restrict the index varies within range(0, len(H))
					while(H[preIndex] > level):
						preIndex = (preIndex - 1)%len(H)
					#print 'preIndex', preIndex
					return preIndex

				# The findPost function is to calculate the index of the next neighbour at a certain metrical level.
				def find_post(index, level):
					postIndex = (index + 1)%len(H)
					while(H[postIndex] > level):
						postIndex = (postIndex + 1)%len(H)
					#print 'postIndex', postIndex
					return postIndex
				
				# The dif function is to calculate a difference level factor between two notes (at note position index1 and index 2) in velocity sequence
				def dif(index1,index2):
					parameterBeta = 0.5
					dif_v = velocitySequence[index1]-velocitySequence[index2]
					dif_h = abs(H[index1]-H[index2])
					dif = dif_v*(parameterBeta*dif_h/4+1-parameterBeta)
					#print 'dif', dif
					return dif

				# 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
				for l in range(level, max(H)+1):
					ave = (parameterGarma*dif(index,find_pre(index,l))+dif(index,find_post(index,l)) )/(1+parameterGarma)
					averages.append(ave)
				#print 'averages', averages
				return averages

			# if the upsampling was successfully done
			if velocitySequence != None:
				syncopation = 0			
				# Calculate the syncopation value for each note
				for index in range(len(velocitySequence)):
					if velocitySequence[index] != 0: # Onset detected
						h = H[index] 
						# Syncopation potential according to its metrical level, which is equal to the metrical weight
						potential = 1 - pow(0.5,h)
						level = h 		# Metrical weight is equal to its metrical level
						syncopation += min(ave_dif_neighbours(index, level))*potential
			else:
				print 'Try giving a bigger Lmax so that the rhythm sequence can be measured by the matching metrical weights sequence (H).'
	return syncopation