#
input2 = np.concatenate((np.concatenate((np.zeros(scale / 2), Atom_test.waveform)), np.zeros(scale / 2)))
input1 = 0.01 * np.random.randn(2 * scale) + np.concatenate(
(np.concatenate((np.zeros(scale / 2 - ts), Atom_test.waveform)), np.zeros(scale / 2 + ts))
)
input3 = np.array(input2)
input4 = np.array(input1)
score = np.array([0.0])
import time
nbIt = 10000
t = time.clock()
for j in range(nbIt):
timeShift = parallelProjections.project_atom(input1, input2, score, scale)
print "C code Took", time.clock() - t
t = time.clock()
for j in range(nbIt):
Xcor = np.correlate(input4, input3, "full")
# maxI = abs(Xcor).argmax();
# max = abs(Xcor).max();
print "Numpy took ", time.clock() - t
# print "See if numpy correlate is efficient"
# print score , abs(Xcor).max();
# print timeShift , abs(Xcor).argmax() - 255;
# scoreOld = np.array([0.0]);
def getMaxAtom(self , debug = 0):
self.maxFrameIdx = floor(self.maxIdx / (0.5*self.scale))
self.maxBinIdx = self.maxIdx - self.maxFrameIdx * (0.5*self.scale)
# hack here : let us project the atom waveform on the neighbouring signal in the FFt domain,
# so that we can find the maximum correlation and best adapt the time-shift
Atom = atom.Atom(self.scale , 1 , max((self.maxFrameIdx * self.scale/2) - self.scale/4 , 0) , self.maxBinIdx , self.residualSignal.samplingFrequency)
Atom.frame = self.maxFrameIdx
# re-compute the atom amplitude for IMDCT
# if self.maxValue.real < 0:
# self.maxValue = -abs(self.maxValue)
# else:
# self.maxValue = abs(self.maxValue)
Atom.mdct_value = self.maxValue
# new version : compute also its waveform through inverse MDCT
Atom.waveform = self.synthesizeAtom(value=1)
Atom.timeShift = 0
Atom.projectionScore = 0.0
input1 = self.enframedDataMatrix[(self.maxFrameIdx-1.5) * self.scale/2 : (self.maxFrameIdx+2.5) * self.scale/2]
input2 = concatenate( (concatenate((zeros(self.scale/2) , Atom.waveform) ) , zeros(self.scale/2) ) )
# debug cases: sometimes when lot of energy on the borders , pre-echo artifacts tends
# to appears on the border and can lead to seg fault if not monitored
# therefore we need to prevent any time shift leading to positions outside of original signals
# ie in the first and last frames.
# if debug>0:
# print self.maxFrameIdx , self.maxIdx , self.frameNumber
# if self.maxFrameIdx ==1:
# plt.figure()
# plt.plot(Atom.waveform)
# plt.plot(self.enframedDataMatrix[0 : Atom.timePosition + Atom.length],'r')
# plt.show()
#
# print (self.maxFrameIdx-1.5) * self.scale/2 , (self.maxFrameIdx+2.5) * self.scale/2
# print len(input1) , len(input2)
if len(input1) != len(input2):
print self.maxFrameIdx , self.maxIdx , self.frameNumber
print len(input1) , len(input2)
#if debug>0:
print "atom in the borders , no timeShift calculated"
return Atom
# retrieve additional timeShift
if self.useC:
scoreVec = array([0.0])
Atom.timeShift = parallelProjections.project_atom(input1,input2 , scoreVec , self.scale)
if abs(Atom.timeShift) > Atom.length/2:
print "out of limits: found time shift of" , Atom.timeShift
Atom.timeShift = 0
return Atom
self.maxTimeShift = Atom.timeShift
Atom.timePosition += Atom.timeShift
# retrieve newly projected waveform
Atom.projectionScore = scoreVec[0]
Atom.waveform *= Atom.projectionScore
# Atom.waveform = input2[self.scale/2:-self.scale/2]
else:
sigFft = fft(self.enframedDataMatrix[(self.maxFrameIdx-1.5) * self.scale/2 : (self.maxFrameIdx+2.5) * self.scale/2] , 2*self.scale)
atomFft = fft(concatenate( (concatenate((zeros(self.scale/2) , Atom.waveform) ) , zeros(self.scale/2) ) ) , 2*self.scale)
Atom.timeShift , score = Xcorr.GetMaxXCorr(atomFft , sigFft , maxlag =self.scale/2)
self.maxTimeShift = Atom.timeShift
Atom.projectionScore = score
# print "found correlation max of " , float(score)/sqrt(2/float(self.scale))
# CAses That might happen: time shift result in choosing another atom instead
if abs(Atom.timeShift) > Atom.length/2:
print "out of limits: found time shift of" , Atom.timeShift
Atom.timeShift = 0
return Atom
Atom.timePosition += Atom.timeShift
# now let us re-project the atom on the signal to adjust it's energy: Only if no pathological case
# TODO optimization : pre-compute energy (better: find closed form)
if score <0:
Atom.amplitude = -sqrt(-score)
Atom.waveform = (-sqrt(-score/sum(Atom.waveform**2)) )*Atom.waveform
else:
Atom.amplitude = sqrt(score)
Atom.waveform = (sqrt(score/sum(Atom.waveform**2)) )*Atom.waveform
# projOrtho = sum(Atom.waveform * self.residualSignal.dataVec[Atom.timePosition : Atom.timePosition + Atom.length])
# if score <0:
# Atom.amplitude = -1
# Atom.waveform = -Atom.waveform
return Atom
