diff Syncopation models/synpy/SG.py @ 71:9a60ca4ae0fb

updating models and latex files.  added
results.csv

--- a/Syncopation models/synpy/SG.py	Mon Apr 27 20:32:10 2015 +0100
+++ b/Syncopation models/synpy/SG.py	Mon May 11 23:36:25 2015 +0100
@@ -4,7 +4,7 @@
 
 '''
 
-from basic_functions import get_H, velocity_sequence_to_min_timespan, get_rhythm_category, upsample_velocity_sequence
+from basic_functions import get_H, velocity_sequence_to_min_timespan, get_rhythm_category, upsample_velocity_sequence,  find_rhythm_Lmax
 from parameter_setter import are_parameters_valid
 
 def get_syncopation(bar, parameters = None):
@@ -14,11 +14,13 @@
 
 	if get_rhythm_category(velocitySequence, subdivisionSequence) == 'poly':
 		print 'Warning: SG model detects polyrhythms so returning None.'
+	elif bar.is_empty():
+		print 'Warning: SG model detects empty bar so returning None.'
 	else:
-		#velocitySequence = velocity_sequence_to_min_timespan(velocitySequence)	# converting to the minimum time-span format
+		velocitySequence = velocity_sequence_to_min_timespan(velocitySequence)	# converting to the minimum time-span format
 
 		# set the defaults
-		Lmax  = 5
+		Lmax  = 10
 		weightSequence = range(Lmax+1) # i.e. [0,1,2,3,4,5]
 		if parameters!= None:
 			if 'Lmax' in parameters:
@@ -29,60 +31,65 @@
 		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))
+			Lmax = find_rhythm_Lmax(velocitySequence, Lmax, weightSequence, subdivisionSequence) 
+			if Lmax != None:
+				# 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)
+				#print len(velocitySequence)
+				#velocitySequence = upsample_velocity_sequence(velocitySequence, len(H))
+				#print len(velocitySequence)
+				
+				# 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):
 
-			# 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
 
-				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])
+						diffactor = (parameterBeta*dif_h/4+1-parameterBeta)
+						if diffactor>1:
+							return dif_v
+						else:
+							return dif_v*diffactor
 
-				# 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
+					# 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)
+					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).'
+				# 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