added bench-window, bench-delay, benchonset added bench-window, bench-delay, benchonset

Paul Brossier committed 20 years ago

Commit 83c67341 parent 79c04d85 files · +425 −79Browse files

Showing the changes to python (5 of 5 files). Show all

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5 files

python/test/bench/onset/Makefile.am +27 −7
python/test/bench/onset/bench-delay +72 −0
python/test/bench/onset/bench-onset +28 −72
python/test/bench/onset/bench-window +63 −0
python/test/bench/onset/benchonset.py +235 −0

python/test/bench/onset/Makefile.am+27−7

@@ -2,22 +2,42 @@ export BASEDIR=../../../.. · 1 unmodified line
2	export PYTHONPATH=$(BASEDIR)/python	2	export PYTHONPATH=$(BASEDIR)/python
3	export LD_LIBRARY_PATH=$(BASEDIR)/src/.libs:$(BASEDIR)/ext/.libs	3	export LD_LIBRARY_PATH=$(BASEDIR)/src/.libs:$(BASEDIR)/ext/.libs
4		4
5	SOURCE = \	5	DETAILSOURCE = \
6	/archives/samples/DB/PercussivePhrases/CM18/Samba_Audio \
7	/var/tmp/Onset-Mirex2005/poly_pitched \	6	/var/tmp/Onset-Mirex2005/poly_pitched \
8	/var/tmp/Onset-Mirex2005/solo_bars_and_bells \	7	/var/tmp/Onset-Mirex2005/solo_bars_and_bells \
9	/var/tmp/Onset-Mirex2005/solo_brass \
10	/var/tmp/Onset-Mirex2005/solo_drums \	8	/var/tmp/Onset-Mirex2005/solo_drums \
11	/var/tmp/Onset-Mirex2005/solo_plucked_strings \	9	/var/tmp/Onset-Mirex2005/solo_plucked_strings \
12	/var/tmp/Onset-Mirex2005/solo_singing_voice \	10	/var/tmp/Onset-Mirex2005/solo_singing_voice \
13	/var/tmp/Onset-Mirex2005/solo_sustained_strings \	11	/var/tmp/Onset-Mirex2005/solo_sustained_strings \
14	/var/tmp/Onset-Mirex2005/solo_winds \	12	/var/tmp/Onset-Mirex2005/solo_winds \
15	/var/tmp/Onset-Mirex2005/complex \	13	/var/tmp/Onset-Mirex2005/complex
16	/var/tmp/Onset-Mirex2005
17		14
18	test-aubiocut: $(patsubst %, %.aubiocut, $(SOURCE))	15	SOURCE = /var/tmp/Onset-Mirex2005
		16
		17	TESTSOURCE = \
		18	/var/tmp/Onset-Mirex2005/solo_bars_and_bells \
		19	/var/tmp/Onset-Mirex2005/solo_winds \
		20	/archives/samples/DB/PercussivePhrases/CM18/Samba_Audio
		21
		22	test-aubiocut: $(patsubst %, %.aubiocut, $(TESTSOURCE))
		23	test-aubiodelay: $(patsubst %, %.aubiodelay, $(TESTSOURCE))
		24	test-aubiowindow: $(patsubst %, %.aubiowindow, $(TESTSOURCE))
		25
		26	final-aubiocut: $(patsubst %, %.aubiocut, $(DETAILSOURCE) $(SOURCE))
		27	final-aubiodelay: $(patsubst %, %.aubiodelay, $(SOURCE))
		28	final-aubiowindow: $(patsubst %, %.aubiowindow, $(SOURCE))
19		29
20	%.aubiocut: %	30	%.aubiocut: %
21	rm -f `basename $<`.aubiocut	31	rm -f `basename $<`.aubiocut
22	./bench-onset $< \| tee `basename $<`.aubiocut	32	./bench-onset $< \| tee `basename $<`.aubiocut
23	diff `basename $<`.aubiocut.ref `basename $<`.aubiocut	33	-diff `basename $<`.aubiocut.ref `basename $<`.aubiocut
		34
		35	%.aubiodelay: %
		36	rm -f `basename $@`
		37	./bench-delay $< \| tee `basename $@`
		38	-diff `basename $@`.ref `basename $@`
		39
		40	%.aubiowindow: %
		41	rm -f `basename $@`
		42	./bench-window $< \| tee `basename $@`
		43	-diff `basename $@`.ref `basename $@`

python/test/bench/onset/bench-delay+72−0

@@ -0,0 +1,72 @@
	1	#! /usr/bin/python
	2
	3	from aubio.bench.node import *
	4	from aubio.tasks import *
	5
	6	from benchonset import mmean, stdev, benchonset
	7
	8	class mybenchonset(benchonset):
	9
	10	def run_bench(self,modes=['dual'],thresholds=[0.5]):
	11	from os.path import dirname,basename
	12	self.modes = modes
	13	self.thresholds = thresholds
	14	self.pretty_titles()
	15	for mode in self.modes:
	16	d = []
	17	outplot = "_-_".join(("delay",mode,
	18	basename(self.datadir) ))
	19
	20	self.params.onsetmode = mode
	21	self.params.threshold = thresholds[0]
	22
	23	self.params.localmin = False
	24	self.params.delay = 0.
	25
	26	self.dir_exec()
	27	self.dir_eval()
	28	self.pretty_print()
	29	self.plotdiffs(d,plottitle="Causal")
	30
	31	self.params.localmin = True
	32	self.params.delay = 0.
	33	self.dir_exec()
	34	self.dir_eval()
	35	self.pretty_print()
	36	self.plotdiffs(d,plottitle="Local min")
	37
	38	self.params.localmin = False
	39	self.params.delay = 6.
	40	self.dir_exec()
	41	self.dir_eval()
	42	self.pretty_print()
	43	self.plotdiffs(d,plottitle="Fixed delay")
	44
	45	self.plotplotdiffs(d)
	46	self.plotplotdiffs(d,outplot=outplot,extension="png")
	47	self.plotplotdiffs(d,outplot=outplot,extension="ps")
	48	self.plotplotdiffs(d,outplot=outplot,extension="svg")
	49
	50
	51	if __name__ == "__main__":
	52	import sys
	53	if len(sys.argv) > 1: datapath = sys.argv[1]
	54	else: print "ERR: a path is required"; sys.exit(1)
	55	modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual']
	56	#modes = [ 'complex' ]
	57	thresholds = [ 0.5]
	58	#thresholds = [1.5]
	59
	60	#datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
	61	respath = '/var/tmp/DB-testings'
	62
	63	benchonset = mybenchonset(datapath,respath,checkres=True,checkanno=True)
	64	benchonset.params = taskparams()
	65	benchonset.task = taskonset
	66	benchonset.valuesdict = {}
	67
	68	try:
	69	#benchonset.auto_learn2(modes=modes)
	70	benchonset.run_bench(modes=modes,thresholds=thresholds)
	71	except KeyboardInterrupt:
	72	sys.exit(1)

python/test/bench/onset/bench-onset+28−72

@@ -1,80 +1,18 @@
1	#! /usr/bin/python	1	#! /usr/bin/python
2		2
3	from aubio.bench.node import *
4	from aubio.tasks import *	3	from aubio.tasks import *
5		4
		5	from benchonset import mmean, stdev, benchonset
6		6
7		7	class mybenchonset(benchonset):
8
9	def mmean(l):
10	return sum(l)/float(len(l))
11
12	def stdev(l):
13	smean = 0
14	lmean = mmean(l)
15	for i in l:
16	smean += (i-lmean)**2
17	smean *= 1. / len(l)
18	return smean**.5
19
20	class benchonset(bench):
21
22	""" list of values to store per file """
23	valuenames = ['orig','missed','Tm','expc','bad','Td']
24	""" list of lists to store per file """
25	valuelists = ['l','labs']
26	""" list of values to print per dir """
27	printnames = [ 'mode', 'thres', 'dist', 'prec', 'recl',
28	'Ttrue', 'Tfp', 'Tfn', 'Tm', 'Td',
29	'aTtrue', 'aTfp', 'aTfn', 'aTm', 'aTd',
30	'mean', 'smean', 'amean', 'samean']
31
32	""" per dir """
33	formats = {'mode': "%12s" , 'thres': "%5.4s",
34	'dist': "%5.4s", 'prec': "%5.4s", 'recl': "%5.4s",
35	'Ttrue': "%5.4s", 'Tfp': "%5.4s", 'Tfn': "%5.4s",
36	'Tm': "%5.4s", 'Td': "%5.4s",
37	'aTtrue':"%5.4s", 'aTfp': "%5.4s", 'aTfn': "%5.4s",
38	'aTm': "%5.4s", 'aTd': "%5.4s",
39	'mean': "%5.40s", 'smean': "%5.40s",
40	'amean': "%5.40s", 'samean': "%5.40s"}
41
42	def dir_eval(self):
43	""" evaluate statistical data over the directory """
44	totaltrue = sum(self.v['expc'])-sum(self.v['bad'])-sum(self.v['Td'])
45	totalfp = sum(self.v['bad'])+sum(self.v['Td'])
46	totalfn = sum(self.v['missed'])+sum(self.v['Tm'])
47	self.P = 100*float(totaltrue)/max(totaltrue + totalfp,1)
48	self.R = 100*float(totaltrue)/max(totaltrue + totalfn,1)
49	if self.R < 0: self.R = 0
50	self.F = 2.* self.P*self.R / max(float(self.P+self.R),1)
51	N = float(len(self.reslist))
52	self.v['mode'] = self.params.onsetmode
53	self.v['thres'] = self.params.threshold
54	self.v['thres'] = "%2.3f" % self.params.threshold
55	self.v['dist'] = "%2.3f" % self.F
56	self.v['prec'] = "%2.3f" % self.P
57	self.v['recl'] = "%2.3f" % self.R
58	self.v['Ttrue'] = totaltrue
59	self.v['Tfp'] = totalfp
60	self.v['Tfn'] = totalfn
61	self.v['aTtrue'] = totaltrue/N
62	self.v['aTfp'] = totalfp/N
63	self.v['aTfn'] = totalfn/N
64	self.v['aTm'] = sum(self.v['Tm'])/N
65	self.v['aTd'] = sum(self.v['Td'])/N
66	self.v['Tm'] = sum(self.v['Tm'])
67	self.v['Td'] = sum(self.v['Td'])
68	self.v['mean'] = mmean(self.v['l'])
69	self.v['smean'] = stdev(self.v['l'])
70	self.v['amean'] = mmean(self.v['labs'])
71	self.v['samean'] = stdev(self.v['labs'])
72		8
73	def run_bench(self,modes=['dual'],thresholds=[0.5]):	9	def run_bench(self,modes=['dual'],thresholds=[0.5]):
74	self.modes = modes	10	from os.path import dirname,basename
75	self.thresholds = thresholds	11	self.thresholds = thresholds
76	self.pretty_titles()	12	self.pretty_titles()
77	for mode in self.modes:	13	d,e,f = [],[],[]
		14	for mode in modes:
		15	self.vlist = []
78	self.params.onsetmode = mode	16	self.params.onsetmode = mode
79	for threshold in self.thresholds:	17	for threshold in self.thresholds:
80	self.params.threshold = threshold	18	self.params.threshold = threshold
@@ -82,6 +20,20 @@ class benchonset(bench): · 1 unmodified line
82	self.dir_eval()	20	self.dir_eval()
83	self.pretty_print()	21	self.pretty_print()
84	#print self.v	22	#print self.v
		23	self.vlist.append(self.v)
		24	self.plotroc(d)
		25	self.plotfmeas(e)
		26	self.plotpr(f)
		27	#print vlist
		28	#self.plotplotroc(d)
		29	#self.plotplotfmeas(e)
		30	#self.plotplotpr(f)
		31	outplot = basename(self.datadir)
		32	for ext in ("png","svg","ps"):
		33	self.plotplotroc(d,outplot=outplot,extension=ext)
		34	self.plotplotfmeas(e,outplot=outplot,extension=ext)
		35	self.plotplotpr(f,outplot=outplot,extension=ext)
		36
85		37
86	def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]):	38	def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]):
87	""" simple dichotomia like algorithm to optimise threshold """	39	""" simple dichotomia like algorithm to optimise threshold """
@@ -106,6 +58,10 @@ class benchonset(bench): · 18 unmodified lines
106	lessF = self.F	58	lessF = self.F
107		59
108	for i in range(steps):	60	for i in range(steps):
		61	self.params.localmin = True
		62	self.params.delay = 1.
		63	self.dir_exec()
		64	self.dir_eval()
109	self.params.threshold = ( lesst + topt ) * .5	65	self.params.threshold = ( lesst + topt ) * .5
110	self.dir_exec()	66	self.dir_exec()
111	self.dir_eval()	67	self.dir_eval()
@@ -169,15 +125,15 @@ if __name__ == "__main__": · 57 unmodified lines
169	import sys	125	import sys
170	if len(sys.argv) > 1: datapath = sys.argv[1]	126	if len(sys.argv) > 1: datapath = sys.argv[1]
171	else: print "ERR: a path is required"; sys.exit(1)	127	else: print "ERR: a path is required"; sys.exit(1)
172	modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual']	128	modes = ['complex', 'energy', 'phase', 'hfc', 'specdiff', 'kl', 'mkl', 'dual']
173	#modes = [ 'mkl' ]
174	thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]	129	thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]
175	#thresholds = [1.5]	130	#modes = [ 'hfc' ]
		131	#thresholds = [0.1, 1.5]
176		132
177	#datapath = "%s%s" % (DATADIR,'/onset/DB/*/')	133	#datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
178	respath = '/var/tmp/DB-testings'	134	respath = '/var/tmp/DB-testings'
179		135
180	benchonset = benchonset(datapath,respath,checkres=True,checkanno=True)	136	benchonset = mybenchonset(datapath,respath,checkres=True,checkanno=True)
181	benchonset.params = taskparams()	137	benchonset.params = taskparams()
182	benchonset.task = taskonset	138	benchonset.task = taskonset
183	benchonset.valuesdict = {}	139	benchonset.valuesdict = {}

python/test/bench/onset/bench-window+63−0

@@ -0,0 +1,63 @@
	1	#! /usr/bin/python
	2
	3	from aubio.tasks import *
	4
	5	from benchonset import mmean, stdev, plotdiffs, plotplotdiffs, benchonset
	6
	7	class mybenchonset(benchonset):
	8
	9	def run_bench(self,modes=['dual'],thresholds=[0.5]):
	10	from os.path import dirname,basename
	11	self.thresholds = thresholds
	12	self.pretty_titles()
	13	for mode in modes:
	14
	15	self.params.onsetmode = mode
	16	self.params.threshold = thresholds[0]
	17	self.params.localmin = False
	18
	19	for delay in (0., 4.):
	20	d = []
	21	outplot = "_-_".join(("window",mode,"delay-%s" % delay,
	22	basename(self.datadir) ))
	23	self.params.delay = delay
	24
	25	for buf in (2048, 1024, 512):
	26	for hop in (buf/2, buf/4):
	27	self.params.bufsize = buf
	28	self.params.hopsize = hop
	29	self.params.step = float(self.params.hopsize)/float(self.params.samplerate)
	30
	31	self.dir_exec()
	32	self.dir_eval()
	33	self.pretty_print()
	34	plotdiffs(self.v,d,plottitle="%s %s" % (buf,hop))
	35
	36	plotplotdiffs(d)
	37	plotplotdiffs(d,outplot=outplot,extension="png")
	38	plotplotdiffs(d,outplot=outplot,extension="ps")
	39	plotplotdiffs(d,outplot=outplot,extension="svg")
	40
	41
	42	if __name__ == "__main__":
	43	import sys
	44	if len(sys.argv) > 1: datapath = sys.argv[1]
	45	else: print "ERR: a path is required"; sys.exit(1)
	46	modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual']
	47	#modes = [ 'complex' ]
	48	thresholds = [ 0.5]
	49	#thresholds = [1.5]
	50
	51	#datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
	52	respath = '/var/tmp/DB-testings'
	53
	54	benchonset = mybenchonset(datapath,respath,checkres=True,checkanno=True)
	55	benchonset.params = taskparams()
	56	benchonset.task = taskonset
	57	benchonset.valuesdict = {}
	58
	59	try:
	60	#benchonset.auto_learn2(modes=modes)
	61	benchonset.run_bench(modes=modes,thresholds=thresholds)
	62	except KeyboardInterrupt:
	63	sys.exit(1)

python/test/bench/onset/benchonset.py+235−0

@@ -0,0 +1,235 @@
	1	#! /usr/bin/python
	2
	3	from aubio.bench.node import *
	4	from os.path import dirname,basename
	5
	6	def mmean(l):
	7	return sum(l)/max(float(len(l)),1)
	8
	9	def stdev(l):
	10	smean = 0
	11	if not len(l): return smean
	12	lmean = mmean(l)
	13	for i in l:
	14	smean += (i-lmean)**2
	15	smean *= 1. / len(l)
	16	return smean**.5
	17
	18	class benchonset(bench):
	19
	20	""" list of values to store per file """
	21	valuenames = ['orig','missed','Tm','expc','bad','Td']
	22	""" list of lists to store per file """
	23	valuelists = ['l','labs']
	24	""" list of values to print per dir """
	25	printnames = [ 'mode', 'thres', 'dist', 'prec', 'recl',
	26	'GD', 'FP',
	27	'Torig', 'Ttrue', 'Tfp', 'Tfn', 'TTm', 'TTd',
	28	'aTtrue', 'aTfp', 'aTfn', 'aTm', 'aTd',
	29	'mean', 'smean', 'amean', 'samean']
	30
	31	""" per dir """
	32	formats = {'mode': "%12s" , 'thres': "%5.4s",
	33	'dist': "%5.4s", 'prec': "%5.4s", 'recl': "%5.4s",
	34	'Torig': "%5.4s", 'Ttrue': "%5.4s", 'Tfp': "%5.4s", 'Tfn': "%5.4s",
	35	'TTm': "%5.4s", 'TTd': "%5.4s",
	36	'aTtrue':"%5.4s", 'aTfp': "%5.4s", 'aTfn': "%5.4s",
	37	'aTm': "%5.4s", 'aTd': "%5.4s",
	38	'mean': "%5.40s", 'smean': "%5.40s",
	39	'amean': "%5.40s", 'samean': "%5.40s",
	40	"GD": "%5.4s", "FP": "%5.4s",
	41	"GDm": "%5.4s", "FPd": "%5.4s"}
	42
	43	def dir_eval(self):
	44	""" evaluate statistical data over the directory """
	45	v = self.v
	46
	47	v['mode'] = self.params.onsetmode
	48	v['thres'] = self.params.threshold
	49
	50	v['Torig'] = sum(v['orig'])
	51	v['TTm'] = sum(v['Tm'])
	52	v['TTd'] = sum(v['Td'])
	53	v['Texpc'] = sum(v['expc'])
	54	v['Tbad'] = sum(v['bad'])
	55	v['Tmissed'] = sum(v['missed'])
	56	v['aTm'] = mmean(v['Tm'])
	57	v['aTd'] = mmean(v['Td'])
	58
	59	v['mean'] = mmean(v['l'])
	60	v['smean'] = stdev(v['l'])
	61
	62	v['amean'] = mmean(v['labs'])
	63	v['samean'] = stdev(v['labs'])
	64
	65	# old type calculations
	66	# good detection rate
	67	v['GD'] = 100.*(v['Torig']-v['Tmissed']-v['TTm'])/v['Torig']
	68	# false positive rate
	69	v['FP'] = 100.*(v['Tbad']+v['TTd'])/v['Torig']
	70	# good detection counting merged detections as good
	71	v['GDm'] = 100.*(v['Torig']-v['Tmissed'])/v['Torig']
	72	# false positives counting doubled as good
	73	v['FPd'] = 100.*v['Tbad']/v['Torig']
	74
	75	# mirex type annotations
	76	totaltrue = v['Texpc']-v['Tbad']-v['TTd']
	77	totalfp = v['Tbad']+v['TTd']
	78	totalfn = v['Tmissed']+v['TTm']
	79	self.v['Ttrue'] = totaltrue
	80	self.v['Tfp'] = totalfp
	81	self.v['Tfn'] = totalfn
	82	# average over the number of annotation files
	83	N = float(len(self.reslist))
	84	self.v['aTtrue'] = totaltrue/N
	85	self.v['aTfp'] = totalfp/N
	86	self.v['aTfn'] = totalfn/N
	87
	88	# F-measure
	89	self.P = 100.*float(totaltrue)/max(totaltrue + totalfp,1)
	90	self.R = 100.*float(totaltrue)/max(totaltrue + totalfn,1)
	91	#if self.R < 0: self.R = 0
	92	self.F = 2.* self.P*self.R / max(float(self.P+self.R),1)
	93	self.v['dist'] = self.F
	94	self.v['prec'] = self.P
	95	self.v['recl'] = self.R
	96
	97	def plotroc(self,d,plottitle=""):
	98	import Gnuplot, Gnuplot.funcutils
	99	gd = []
	100	fp = []
	101	for i in self.vlist:
	102	gd.append(i['GD'])
	103	fp.append(i['FP'])
	104	d.append(Gnuplot.Data(fp, gd, with='linespoints',
	105	title="%s %s" % (plottitle,i['mode']) ))
	106
	107	def plotplotroc(self,d,outplot=0,extension='ps'):
	108	import Gnuplot, Gnuplot.funcutils
	109	from sys import exit
	110	g = Gnuplot.Gnuplot(debug=0, persist=1)
	111	if outplot:
	112	if extension == 'ps': ext, extension = '.ps' , 'postscript'
	113	elif extension == 'png': ext, extension = '.png', 'png'
	114	elif extension == 'svg': ext, extension = '.svg', 'svg'
	115	else: exit("ERR: unknown plot extension")
	116	g('set terminal %s' % extension)
	117	g('set output \'roc-%s%s\'' % (outplot,ext))
	118	xmax = 30 #max(fp)
	119	ymin = 50
	120	g('set xrange [0:%f]' % xmax)
	121	g('set yrange [%f:100]' % ymin)
	122	# grid set
	123	g('set grid')
	124	g('set xtics 0,5,%f' % xmax)
	125	g('set ytics %f,5,100' % ymin)
	126	g('set key 27,65')
	127	#g('set format \"%g\"')
	128	g.title(basename(self.datadir))
	129	g.xlabel('false positives (%)')
	130	g.ylabel('correct detections (%)')
	131	g.plot(*d)
	132
	133	def plotpr(self,d,plottitle=""):
	134	import Gnuplot, Gnuplot.funcutils
	135	x = []
	136	y = []
	137	for i in self.vlist:
	138	x.append(i['prec'])
	139	y.append(i['recl'])
	140	d.append(Gnuplot.Data(x, y, with='linespoints',
	141	title="%s %s" % (plottitle,i['mode']) ))
	142
	143	def plotplotpr(self,d,outplot=0,extension='ps'):
	144	import Gnuplot, Gnuplot.funcutils
	145	from sys import exit
	146	g = Gnuplot.Gnuplot(debug=0, persist=1)
	147	if outplot:
	148	if extension == 'ps': ext, extension = '.ps' , 'postscript'
	149	elif extension == 'png': ext, extension = '.png', 'png'
	150	elif extension == 'svg': ext, extension = '.svg', 'svg'
	151	else: exit("ERR: unknown plot extension")
	152	g('set terminal %s' % extension)
	153	g('set output \'pr-%s%s\'' % (outplot,ext))
	154	g.title(basename(self.datadir))
	155	g.xlabel('Recall (%)')
	156	g.ylabel('Precision (%)')
	157	g.plot(*d)
	158
	159	def plotfmeas(self,d,plottitle=""):
	160	import Gnuplot, Gnuplot.funcutils
	161	x,y = [],[]
	162	for i in self.vlist:
	163	x.append(i['thres'])
	164	y.append(i['dist'])
	165	d.append(Gnuplot.Data(x, y, with='linespoints',
	166	title="%s %s" % (plottitle,i['mode']) ))
	167
	168	def plotplotfmeas(self,d,outplot="",extension='ps', title="F-measure"):
	169	import Gnuplot, Gnuplot.funcutils
	170	from sys import exit
	171	g = Gnuplot.Gnuplot(debug=0, persist=1)
	172	if outplot:
	173	if extension == 'ps': terminal = 'postscript'
	174	elif extension == 'png': terminal = 'png'
	175	elif extension == 'svg': terminal = 'svg'
	176	else: exit("ERR: unknown plot extension")
	177	g('set terminal %s' % terminal)
	178	g('set output \'fmeas-%s.%s\'' % (outplot,extension))
	179	g.xlabel('threshold \\delta')
	180	g.ylabel('F-measure (%)')
	181	g('set xrange [0:1.2]')
	182	g('set yrange [0:100]')
	183	g.title(basename(self.datadir))
	184	# grid set
	185	#g('set grid')
	186	#g('set xtics 0,5,%f' % xmax)
	187	#g('set ytics %f,5,100' % ymin)
	188	#g('set key 27,65')
	189	#g('set format \"%g\"')
	190	g.plot(*d)
	191
	192	def plotdiffs(self,d,plottitle=""):
	193	import Gnuplot, Gnuplot.funcutils
	194	v = self.v
	195	l = v['l']
	196	mean = v['mean']
	197	smean = v['smean']
	198	amean = v['amean']
	199	samean = v['samean']
	200	val = []
	201	per = [0] * 100
	202	for i in range(0,100):
	203	val.append(i*.001-.05)
	204	for j in l:
	205	if abs(j-val[i]) <= 0.001:
	206	per[i] += 1
	207	total = v['Torig']
	208	for i in range(len(per)): per[i] /= total/100.
	209
	210	d.append(Gnuplot.Data(val, per, with='fsteps',
	211	title="%s %s" % (plottitle,v['mode']) ))
	212	#d.append('mean=%f,sigma=%f,eps(x) title \"\"'% (mean,smean))
	213	#d.append('mean=%f,sigma=%f,eps(x) title \"\"'% (amean,samean))
	214
	215
	216	def plotplotdiffs(self,d,outplot=0,extension='ps'):
	217	import Gnuplot, Gnuplot.funcutils
	218	from sys import exit
	219	g = Gnuplot.Gnuplot(debug=0, persist=1)
	220	if outplot:
	221	if extension == 'ps': ext, extension = '.ps' , 'postscript'
	222	elif extension == 'png': ext, extension = '.png', 'png'
	223	elif extension == 'svg': ext, extension = '.svg', 'svg'
	224	else: exit("ERR: unknown plot extension")
	225	g('set terminal %s' % extension)
	226	g('set output \'diffhist-%s%s\'' % (outplot,ext))
	227	g('eps(x) = 1./(sigma(2.3.14159)*.5) exp ( - ( x - mean ) ** 2. / ( 2. * sigma ** 2. ))')
	228	g.title(basename(self.datadir))
	229	g.xlabel('delay to hand-labelled onset (s)')
	230	g.ylabel('% number of correct detections / ms ')
	231	g('set xrange [-0.05:0.05]')
	232	g('set yrange [0:50]')
	233	g.plot(*d)
	234
	235

added bench-window, bench-delay, benchonset added bench-window, bench-delay, benchonset

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