Index: python/bench-onset
===================================================================
--- python/bench-onset	(revision dbc03518695210674796a4a1c69f67ee3a288968)
+++ 	(revision )
@@ -1,160 +1,0 @@
-#! /usr/bin/python
-
-from aubio.bench.node import *
-from aubio.tasks import *
-
-class benchonset(bench):
-	
-	def dir_eval(self):
-		self.P = 100*float(self.expc-self.missed-self.merged)/(self.expc-self.missed-self.merged + self.bad+self.doubled)
-		self.R = 100*float(self.expc-self.missed-self.merged)/(self.expc-self.missed-self.merged + self.missed+self.merged)
-		if self.R < 0: self.R = 0
-		self.F = 2* self.P*self.R / (self.P+self.R)
-
-		self.values = [self.params.onsetmode, 
-		"%2.3f" % self.params.threshold,
-		self.orig,
-		self.expc,
-		self.missed,
-		self.merged,
-		self.bad,
-		self.doubled,
-		(self.orig-self.missed-self.merged),
-		"%2.3f" % (100*float(self.orig-self.missed-self.merged)/(self.orig)),
-		"%2.3f" % (100*float(self.bad+self.doubled)/(self.orig)), 
-		"%2.3f" % (100*float(self.orig-self.missed)/(self.orig)), 
-		"%2.3f" % (100*float(self.bad)/(self.orig)),
-		"%2.3f" % self.P,
-		"%2.3f" % self.R,
-		"%2.3f" % self.F  ]
-
-	def file_exec(self,input,output):
-		filetask = self.task(input,params=self.params)
-		computed_data = filetask.compute_all()
-		results = filetask.eval(computed_data)
-		self.orig    += filetask.orig
-		self.missed  += filetask.missed
-		self.merged  += filetask.merged
-		self.expc    += filetask.expc
-		self.bad     += filetask.bad
-		self.doubled += filetask.doubled
-
-
-	def run_bench(self,modes=['dual'],thresholds=[0.5]):
-		self.modes = modes
-		self.thresholds = thresholds
-
-		self.pretty_print(self.titles)
-		for mode in self.modes:
-			self.params.onsetmode = mode
-			for threshold in self.thresholds:
-				self.params.threshold = threshold
-				self.dir_exec()
-				self.dir_eval()
-				self.pretty_print(self.values)
-
-	def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]):
-		""" simple dichotomia like algorithm to optimise threshold """
-		self.modes = modes
-		self.pretty_print(self.titles)
-		for mode in self.modes:
-			steps = 10 
-			lesst = thresholds[0] 
-			topt = thresholds[1]
-			self.params.onsetmode = mode
-
-			self.params.threshold = topt 
-			self.dir_exec()
-			self.dir_eval()
-			self.pretty_print(self.values)
-			topF = self.F 
-
-			self.params.threshold = lesst 
-			self.dir_exec()
-			self.dir_eval()
-			self.pretty_print(self.values)
-			lessF = self.F 
-
-			for i in range(steps):
-				self.params.threshold = ( lesst + topt ) * .5 
-				self.dir_exec()
-				self.dir_eval()
-				self.pretty_print(self.values)
-				if self.F == 100.0 or self.F == topF: 
-					print "assuming we converged, stopping" 
-					break
-				#elif abs(self.F - topF) < 0.01 :
-				#	print "done converging"
-				#	break
-				if topF < self.F:
-					#lessF = topF
-					#lesst = topt 
-					topF = self.F
-					topt = self.params.threshold
-				elif lessF < self.F:
-					lessF = self.F
-					lesst = self.params.threshold
-				if topt == lesst:
-					lesst /= 2.
-
-	def auto_learn2(self,modes=['dual'],thresholds=[0.1,1.0]):
-		""" simple dichotomia like algorithm to optimise threshold """
-		self.modes = modes
-		self.pretty_print(self.titles)
-		for mode in self.modes:
-			steps = 10 
-			step = thresholds[1]
-			curt = thresholds[0] 
-			self.params.onsetmode = mode
-
-			self.params.threshold = curt 
-			self.dir_exec()
-			self.dir_eval()
-			self.pretty_print(self.values)
-			curexp = self.expc
-
-			for i in range(steps):
-				if curexp < self.orig:
-					#print "we found at most less onsets than annotated"
-					self.params.threshold -= step 
-					step /= 2
-				elif curexp > self.orig:
-					#print "we found more onsets than annotated"
-					self.params.threshold += step 
-					step /= 2
-				self.dir_exec()
-				self.dir_eval()
-				curexp = self.expc
-				self.pretty_print(self.values)
-				if self.orig == 100.0 or self.orig == self.expc: 
-					print "assuming we converged, stopping" 
-					break
-
-if __name__ == "__main__":
-	import sys
-	if len(sys.argv) > 1: datapath = sys.argv[1]
-	else: print "ERR: a path is required"; sys.exit(1)
-	modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual']
-	#modes = [ 'complex' ]
-	thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
-	#thresholds = [1.5]
-
-	#datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
-	respath = '/var/tmp/DB-testings'
-
-	benchonset = benchonset(datapath,respath,checkres=True,checkanno=True)
-	benchonset.params = taskparams()
-	benchonset.task = taskonset
-
-	benchonset.titles = [ 'mode', 'thres', 'orig', 'expc', 'missd', 'mergd',
-	'bad', 'doubl', 'corrt', 'GD', 'FP', 'GD-merged', 'FP-pruned',
-	'prec', 'recl', 'dist' ]
-	benchonset.formats = ["%12s" , "| %6s", "| %6s", "| %6s", "| %6s", "| %6s", 
-	"| %6s", "| %6s", "| %6s", "| %8s", "| %8s", "| %8s", "| %8s",
-	"| %6s", "| %6s", "| %6s"] 
-
-	try:
-		benchonset.auto_learn2(modes=modes)
-		#benchonset.run_bench(modes=modes)
-	except KeyboardInterrupt:
-		sys.exit(1)
Index: python/test/bench/onset/bench-onset
===================================================================
--- python/test/bench/onset/bench-onset	(revision 677b267365aa78b0ce8fa63f0939dbc41ef97ca7)
+++ python/test/bench/onset/bench-onset	(revision 677b267365aa78b0ce8fa63f0939dbc41ef97ca7)
@@ -0,0 +1,160 @@
+#! /usr/bin/python
+
+from aubio.bench.node import *
+from aubio.tasks import *
+
+class benchonset(bench):
+	
+	def dir_eval(self):
+		self.P = 100*float(self.expc-self.missed-self.merged)/(self.expc-self.missed-self.merged + self.bad+self.doubled)
+		self.R = 100*float(self.expc-self.missed-self.merged)/(self.expc-self.missed-self.merged + self.missed+self.merged)
+		if self.R < 0: self.R = 0
+		self.F = 2* self.P*self.R / (self.P+self.R)
+
+		self.values = [self.params.onsetmode, 
+		"%2.3f" % self.params.threshold,
+		self.orig,
+		self.expc,
+		self.missed,
+		self.merged,
+		self.bad,
+		self.doubled,
+		(self.orig-self.missed-self.merged),
+		"%2.3f" % (100*float(self.orig-self.missed-self.merged)/(self.orig)),
+		"%2.3f" % (100*float(self.bad+self.doubled)/(self.orig)), 
+		"%2.3f" % (100*float(self.orig-self.missed)/(self.orig)), 
+		"%2.3f" % (100*float(self.bad)/(self.orig)),
+		"%2.3f" % self.P,
+		"%2.3f" % self.R,
+		"%2.3f" % self.F  ]
+
+	def file_exec(self,input,output):
+		filetask = self.task(input,params=self.params)
+		computed_data = filetask.compute_all()
+		results = filetask.eval(computed_data)
+		self.orig    += filetask.orig
+		self.missed  += filetask.missed
+		self.merged  += filetask.merged
+		self.expc    += filetask.expc
+		self.bad     += filetask.bad
+		self.doubled += filetask.doubled
+
+
+	def run_bench(self,modes=['dual'],thresholds=[0.5]):
+		self.modes = modes
+		self.thresholds = thresholds
+
+		self.pretty_print(self.titles)
+		for mode in self.modes:
+			self.params.onsetmode = mode
+			for threshold in self.thresholds:
+				self.params.threshold = threshold
+				self.dir_exec()
+				self.dir_eval()
+				self.pretty_print(self.values)
+
+	def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]):
+		""" simple dichotomia like algorithm to optimise threshold """
+		self.modes = modes
+		self.pretty_print(self.titles)
+		for mode in self.modes:
+			steps = 10 
+			lesst = thresholds[0] 
+			topt = thresholds[1]
+			self.params.onsetmode = mode
+
+			self.params.threshold = topt 
+			self.dir_exec()
+			self.dir_eval()
+			self.pretty_print(self.values)
+			topF = self.F 
+
+			self.params.threshold = lesst 
+			self.dir_exec()
+			self.dir_eval()
+			self.pretty_print(self.values)
+			lessF = self.F 
+
+			for i in range(steps):
+				self.params.threshold = ( lesst + topt ) * .5 
+				self.dir_exec()
+				self.dir_eval()
+				self.pretty_print(self.values)
+				if self.F == 100.0 or self.F == topF: 
+					print "assuming we converged, stopping" 
+					break
+				#elif abs(self.F - topF) < 0.01 :
+				#	print "done converging"
+				#	break
+				if topF < self.F:
+					#lessF = topF
+					#lesst = topt 
+					topF = self.F
+					topt = self.params.threshold
+				elif lessF < self.F:
+					lessF = self.F
+					lesst = self.params.threshold
+				if topt == lesst:
+					lesst /= 2.
+
+	def auto_learn2(self,modes=['dual'],thresholds=[0.1,1.0]):
+		""" simple dichotomia like algorithm to optimise threshold """
+		self.modes = modes
+		self.pretty_print(self.titles)
+		for mode in self.modes:
+			steps = 10 
+			step = thresholds[1]
+			curt = thresholds[0] 
+			self.params.onsetmode = mode
+
+			self.params.threshold = curt 
+			self.dir_exec()
+			self.dir_eval()
+			self.pretty_print(self.values)
+			curexp = self.expc
+
+			for i in range(steps):
+				if curexp < self.orig:
+					#print "we found at most less onsets than annotated"
+					self.params.threshold -= step 
+					step /= 2
+				elif curexp > self.orig:
+					#print "we found more onsets than annotated"
+					self.params.threshold += step 
+					step /= 2
+				self.dir_exec()
+				self.dir_eval()
+				curexp = self.expc
+				self.pretty_print(self.values)
+				if self.orig == 100.0 or self.orig == self.expc: 
+					print "assuming we converged, stopping" 
+					break
+
+if __name__ == "__main__":
+	import sys
+	if len(sys.argv) > 1: datapath = sys.argv[1]
+	else: print "ERR: a path is required"; sys.exit(1)
+	modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual']
+	#modes = [ 'complex' ]
+	thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
+	#thresholds = [1.5]
+
+	#datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
+	respath = '/var/tmp/DB-testings'
+
+	benchonset = benchonset(datapath,respath,checkres=True,checkanno=True)
+	benchonset.params = taskparams()
+	benchonset.task = taskonset
+
+	benchonset.titles = [ 'mode', 'thres', 'orig', 'expc', 'missd', 'mergd',
+	'bad', 'doubl', 'corrt', 'GD', 'FP', 'GD-merged', 'FP-pruned',
+	'prec', 'recl', 'dist' ]
+	benchonset.formats = ["%12s" , "| %6s", "| %6s", "| %6s", "| %6s", "| %6s", 
+	"| %6s", "| %6s", "| %6s", "| %8s", "| %8s", "| %8s", "| %8s",
+	"| %6s", "| %6s", "| %6s"] 
+
+	try:
+		benchonset.auto_learn2(modes=modes)
+		#benchonset.run_bench(modes=modes)
+	except KeyboardInterrupt:
+		sys.exit(1)
