#! /usr/bin/python

from aubio.bench.node import *
from aubio.tasks import *




def mmean(l):
	return sum(l)/float(len(l))

def stdev(l):
	smean = 0
	lmean = mmean(l)
	for i in l:
		smean += (i-lmean)**2
	smean *= 1. / len(l)
	return smean**.5

class benchonset(bench):

	""" list of values to store per file """
	valuenames = ['orig','missed','Tm','expc','bad','Td']
	""" list of lists to store per file """
	valuelists = ['l','labs']
	""" list of values to print per dir """
	printnames = [ 'mode', 'thres', 'dist', 'prec', 'recl', 
		'Ttrue', 'Tfp',  'Tfn',  'Tm',   'Td',
		'aTtrue', 'aTfp', 'aTfn', 'aTm',  'aTd',  
		'mean', 'smean',  'amean', 'samean']

	""" per dir """
	formats = {'mode': "%12s" , 'thres': "%5.4s", 
		'dist':  "%5.4s", 'prec': "%5.4s", 'recl':  "%5.4s",
		'Ttrue': "%5.4s", 'Tfp':   "%5.4s", 'Tfn':   "%5.4s", 
		'Tm':    "%5.4s", 'Td':    "%5.4s",
		'aTtrue':"%5.4s", 'aTfp':  "%5.4s", 'aTfn':  "%5.4s", 
		'aTm':   "%5.4s", 'aTd':   "%5.4s",
		'mean':  "%5.40s", 'smean': "%5.40s", 
		'amean':  "%5.40s", 'samean': "%5.40s"}

	def dir_eval(self):
		""" evaluate statistical data over the directory """
		totaltrue = sum(self.v['expc'])-sum(self.v['bad'])-sum(self.v['Td'])
		totalfp = sum(self.v['bad'])+sum(self.v['Td'])
                totalfn = sum(self.v['missed'])+sum(self.v['Tm'])
		self.P = 100*float(totaltrue)/max(totaltrue + totalfp,1)
		self.R = 100*float(totaltrue)/max(totaltrue + totalfn,1)
		if self.R < 0: self.R = 0
		self.F = 2.* self.P*self.R / max(float(self.P+self.R),1)
		N = float(len(self.reslist))
		self.v['mode']      = self.params.onsetmode
		self.v['thres']     = self.params.threshold 
		self.v['thres']     = "%2.3f" % self.params.threshold
		self.v['dist']      = "%2.3f" % self.F
		self.v['prec']      = "%2.3f" % self.P
		self.v['recl']      = "%2.3f" % self.R
		self.v['Ttrue']     = totaltrue
		self.v['Tfp']       = totalfp
		self.v['Tfn']       = totalfn
		self.v['aTtrue']    = totaltrue/N
		self.v['aTfp']      = totalfp/N
		self.v['aTfn']      = totalfn/N
		self.v['aTm']       = sum(self.v['Tm'])/N
		self.v['aTd']       = sum(self.v['Td'])/N
		self.v['Tm']       = sum(self.v['Tm'])
		self.v['Td']       = sum(self.v['Td'])
		self.v['mean']      = mmean(self.v['l'])
		self.v['smean']     = stdev(self.v['l'])
		self.v['amean']     = mmean(self.v['labs'])
		self.v['samean']    = stdev(self.v['labs'])

	def run_bench(self,modes=['dual'],thresholds=[0.5]):
		self.modes = modes
		self.thresholds = thresholds
		self.pretty_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()
				#print self.v

	def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]):
		""" simple dichotomia like algorithm to optimise threshold """
		self.modes = modes
		self.pretty_titles()
		for mode in self.modes:
			steps = 11 
			lesst = thresholds[0] 
			topt = thresholds[1]
			self.params.onsetmode = mode

			self.params.threshold = topt 
			self.dir_exec()
			self.dir_eval()
			self.pretty_print()
			topF = self.F 

			self.params.threshold = lesst 
			self.dir_exec()
			self.dir_eval()
			self.pretty_print()
			lessF = self.F 

			for i in range(steps):
				self.params.threshold = ( lesst + topt ) * .5 
				self.dir_exec()
				self.dir_eval()
				self.pretty_print()
				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=[0.00001,1.0]):
		""" simple dichotomia like algorithm to optimise threshold """
		self.modes = modes
		self.pretty_titles([])
		for mode in self.modes:
			steps = 10 
			step = 0.4
			self.params.onsetmode = mode
			self.params.threshold = thresholds[0] 
			cur = 0

			for i in range(steps):
				self.dir_exec()
				self.dir_eval()
				self.pretty_print()
				new = self.P
				if self.R == 0.0:
					#print "Found maximum, highering"
					step /= 2.
					self.params.threshold -= step 
				elif new == 100.0:
					#print "Found maximum, highering"
					step *= .99
					self.params.threshold += step 
				elif cur > new:
					#print "lower"
					step /= 2.
					self.params.threshold -= step 
				elif cur < new:
					#print "higher"
					step *= .99
					self.params.threshold += step 
				else:
					print "Assuming we converged"
					break
				cur = new


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 = [ 'mkl' ]
	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]
	#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.valuesdict = {}

	try:
		#benchonset.auto_learn2(modes=modes)
		benchonset.run_bench(modes=modes,thresholds=thresholds)
	except KeyboardInterrupt:
		sys.exit(1)