def createWaveform(self, scale , binIndex):
''' By default, will use the C library to create the waveform'''
if self.useC:
return parallelProjections.get_atom(int(scale) , int(binIndex))
else:
L = scale
# Check whether coefficients are initialized
if not scale in self.Windows:
self.initialize(scale)
K = L/2
temp = zeros(2*L)
temp[K + binIndex] = 1
waveform = zeros(2*L)
y = zeros(L, complex)
x = zeros(L, complex)
# we're note in Matlab anymore, loops are more straight than indexing
for i in range(1,3):
y[0:K] = temp[i*K : (i+1)*K]
#
# do the pre-twiddle
y = y * self.PreTwidCoeffs[L];
# self.fftinputs[L] = y;
# compute ifft
# self.fftplans[L].execute()
# x = self.fftoutputs[L];
x = ifft(y)
#
# # do the post-twiddle
x = x * self.PostTwidCoeff[L]
#
x = 2*math.sqrt(1/float(L))*L*x.real*self.Windows[L];
# self.fftinputs[L][0:K] += temp[i*K : (i+1)*K]*self.PreTwidCoeffs[L]
#compute fft
# # post-twiddle and store for max search
# self.projectionMatrix[i*K : (i+1)*K] = normaCoeffs*(self.inputa[0:K]* self.post_twidVec).real
# overlapp - add
# waveform[i*K : i*K +L] = waveform[i*K : i*K +L] + \
# 2*math.sqrt(1/float(L))*L*(self.fftoutputs[L]*self.PostTwidCoeff[L]).real*self.Windows[L] ;
waveform[i*K : i*K +L] = waveform[i*K : i*K +L] + x ;
# scrap zeroes on the borders
return waveform[L/2:-L/2]
def create_waveform(self, scale, binIndex):
''' By default, will use the C library to create the waveform'''
if self.use_c_optim:
return parallelProjections.get_atom(int(scale), int(binIndex))
