move bench-onset in bench dir

move bench-onset in bench dir

diff --git a/python/bench-onset b/python/bench-onset
deleted file mode 100755 (executable)
index d8ede4e..0000000
--- a/python/bench-onset
+++ /dev/null
@@ -1,160 +0,0 @@
-#! /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)

diff --git a/python/test/bench/onset/bench-onset b/python/test/bench/onset/bench-onset
new file mode 100755 (executable)
index 0000000..d8ede4e
--- /dev/null
+++ b/python/test/bench/onset/bench-onset
@@ -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)