def computeTransform(self, startingFrame=1 , endFrame = -1):
if self.wLong is None:
self.initialize()
if endFrame <0:
endFrame = self.frameNumber -1
if startingFrame<1:
startingFrame = 1
startingFrame=1
endFrame = self.frameNumber -1
L = self.scale
K = L/2
# T = K/2
# normaCoeffs = sqrt(2/float(K))
# print startingFrame , endFrame
for l in range(-K/2,K/2,1):
parallelProjections.project(self.enframedDataMatrix,
self.bestScoreTree[l+K/2],
self.projectionMatrix[l+K/2] ,
self.locCoeff ,
self.post_twidVec ,
startingFrame,
endFrame,
self.scale ,l)
def computeTransform(self, startingFrame=1 , endFrame = -1):
""" inner product computation through MDCT """
if self.wLong is None:
self.initialize()
if endFrame <0:
endFrame = self.frameNumber -1
# debug -> changed from 1: be sure signal is properly zero -padded
if startingFrame<1:
startingFrame = 1
# Wrapping C code call for fast implementation
if self.useC:
try:
parallelProjections.project(self.enframedDataMatrix, self.bestScoreTree,
self.projectionMatrix ,
self.locCoeff ,
self.post_twidVec ,
startingFrame,
endFrame,
self.scale ,0)
except SystemError:
print sys.exc_info()[0]
print sys.exc_info()[1]
raise
except:
print "Unexpected error:", sys.exc_info()[0]
raise
else:
try:
import fftw3
except ImportError:
print '''Impossible to load fftw3, you need to use the C extension library or have
a local installation of the python fftw3 wrapper '''
return
# create a forward fft plan, since fftw is not faster for computing DCT's no need to use it for that
L = self.scale
K = L/2
T = K/2
normaCoeffs = sqrt(2/float(K))
if self.fft is None:
self.inputa = self.enframedDataMatrix[K - T: K + L - T].astype(complex)
self.outputa = None
self.fft = fftw3.Plan(self.inputa,self.outputa, direction='forward', flags=['estimate'])
self.project(startingFrame,endFrame, L , K , T , normaCoeffs)
if self.HF:
for i in range(startingFrame , endFrame):
self.bestScoreHFTree[i] = abs(self.projectionMatrix[(i+self.HFlimit)*self.scale/2 : (i+1)*self.scale/2]).max()
def computeTransform(self, startingFrame=1 , endFrame = -1 ):
if self.wLong is None:
self.initialize()
if endFrame <0:
endFrame = self.frameNumber -2
if startingFrame<2:
startingFrame = 2
# L = self.scale
# K = L/2;
############" changes ##############
# T = K/2 + self.currentTS
# new version with C calculus
parallelProjections.project(self.enframedDataMatrix, self.bestScoreTree,
self.projectionMatrix ,
self.locCoeff ,
self.post_twidVec ,
startingFrame,
endFrame,
self.scale ,
int(self.currentTS))
projectionMatrix_comp = np.zeros((N, 1), complex) scoreTree = np.zeros((P, 1)) pre_twidVec = np.array([exp(n * (-1j) * pi / L) for n in range(L)]).reshape(L, 1) post_twidVec = np.array([exp((float(n) + 0.5) * -1j * pi * (L / 2 + 1) / L) for n in range(L / 2)]).reshape(L / 2, 1) print scoreTree.shape, pre_twidVec.shape, post_twidVec.shape i = 1 j = 10 takeReal = 1 # print "Testing Bad call:" # computeMCLT.project(input_data ) print " ---Testing good call" parallelProjections.project(input_data, scoreTree, projectionMatrix_real, pre_twidVec, post_twidVec, i, j, L, 0) # if parallelFFT.clean_plans() != 1: # print "Cleaning Stage Failed" ### ##print projectionMatrix_real print scoreTree print "--- OK" # # # if computeMCLT.initialize_plans(np.array([L])) != 1: # print "Initiliazing Stage Failed" print "---Testing good call: complex" res = parallelProjections.project_mclt(input_data, scoreTree, projectionMatrix_comp, pre_twidVec, post_twidVec, i, j, L) print scoreTree
