def realTest(self):
pySig = signals.InitFromFile('../../../data/glocs.wav',forceMono=True,doNormalize=True);
pySig2 = pySig.copy()
# pySig3 = pySig.copy()
decalage = 500;
pySig.crop(0, 4.5*pySig.samplingFrequency);
pySig2.crop(decalage, decalage + 4.5*pySig.samplingFrequency);
# pySig3.crop(decalage, decalage + 4.5*pySig.samplingFrequency);
pySig.pad(16384)
pySig2.pad(16384)
# pySig3.pad(16384)
dico = [128,1024,8192];
tol = [2]*len(dico);
parallelProjections.initialize_plans(np.array(dico),np.array(tol))
classicDIco = mdct_dico.LODico(dico);
jointDico = joint_dico.SetDico(dico ,
selectNature='sum')
nbAtoms = 50;
print "%%%%%%%%%%%%%%% Testing mp with one signal %%%%%%%%%%%%%%%"
print ""
approxClassic, decayClassic = mp.mp(pySig, classicDIco, 20, nbAtoms, padSignal=False, debug=0);
print ""
# print "%%%%%%%%%%%%%%% Testing Joint mp with one signal %%%%%%%%%%%%%%%"
# print ""
# approxCommon, approxSpecList, decayList,residualSignalList = mp.mp_joint((pySig,), jointDico, 20, nbAtoms, debug=0);
# print ""
# plt.plot()
print "%%%%%%%%%%%%%%% Testing Joint mp with two signal %%%%%%%%%%%%%%%"
approxCommon, approxSpecList, decayList, residualSignalList = mp.mp_joint((pySig,pySig2),
jointDico, 20,
nbAtoms, debug=2);
print [abs(atom.getAmplitude()) for atom in approxSpecList[0].atoms]
def toleranceTests(self):
print "%%%%%%%%%%%%%%% Testing Joint mp with increased Tolerance %%%%%%%%%%%%%%%"
pySig = signals.InitFromFile('../../../data/glocs.wav',forceMono=True,doNormalize=True);
pySig2 = pySig.copy()
# pySig3 = pySig.copy()
decalage = 750;
pySig.crop(0, 4*16384);
pySig2.crop(decalage, decalage + 4*16384);
# pySig3.crop(decalage, decalage + 4.5*pySig.samplingFrequency);
pySig.pad(16384)
pySig2.pad(16384)
# pySig3.pad(16384)
dico = [128,1024];
tol = [16,2];
jointDico = joint_dico.SetDico(dico ,
selectNature='sum');
jointDicoTol = joint_dico.SetDico(dico ,
selectNature='sum',
tol = tol);
print jointDico.tolerances
nbatoms = 50;
meanApprox, currentApproxList, resEnergyList , residualSignalList = mp.mp_joint((pySig,pySig2,pySig2),
jointDico, 10, nbatoms ,debug=0)
print jointDicoTol.tolerances
meanApproxTol, currentApproxListTol, resEnergyListTol , residualSignalListTol = mp.mp_joint((pySig,pySig2,pySig2),
jointDicoTol, 10, nbatoms ,debug=0)
for i in range(len(residualSignalList)):
print np.sum(residualSignalList[i].dataVec **2), np.sum(residualSignalListTol[i].dataVec **2)
def nonLinearTest(self):
""" Youpi"""
pySig = signals.InitFromFile('../../../data/glocs.wav',forceMono=True,doNormalize=True);
pySig2 = signals.InitFromFile('../../../data/voicemale.wav',forceMono=True,doNormalize=True);
pySig3 = signals.InitFromFile('../../../data/voicefemale.wav',forceMono=True,doNormalize=True);
pySig4 = signals.InitFromFile('../../../data/orchestra.wav',forceMono=True,doNormalize=True);
decalage = 0;
Start = 1.0;
Stop = 1.5
pySig.crop(Start*pySig.samplingFrequency, Stop*pySig.samplingFrequency);
pySig2.crop(Start*pySig.samplingFrequency, Stop*pySig.samplingFrequency);
pySig3.crop(Start*pySig.samplingFrequency, Stop*pySig.samplingFrequency);
pySig4.crop(Start*pySig.samplingFrequency, Stop*pySig.samplingFrequency);
pySig2.dataVec += pySig.dataVec
pySig3.dataVec += pySig.dataVec
pySig4.dataVec += pySig.dataVec
pySig2.pad(8192)
pySig3.pad(8192)
pySig4.pad(8192)
# pySig3.pad(16384)
dico = [128,1024,8192];
nbAtoms = 200;
jointDico = joint_dico.SetDico(dico ,
selectNature='sum',
tol=[2,2,2]);
jointDicoNL = joint_dico.SetDico(dico , nonLinear = True,
selectNature='weighted',
tol=[2,2,2] , params=1);
# let us compare the two decomposition
approxCommon, approxSpecList, decayList, residualSignalList = mp.mp_joint((pySig2,pySig3,pySig4),
jointDico, 20,
nbAtoms, debug=0,
padSignal=False);
approxCommon2, approxSpecList2, decayList2, residualSignalList2 = mp.mp_joint((pySig3,pySig2,pySig4),
jointDico, 20,
nbAtoms, debug=0,
padSignal=False);
approxCommonNL, approxSpecListNL, decayListNL, residualSignalListNL = mp.mp_joint((pySig2,pySig3,pySig4),
jointDicoNL, 20,
nbAtoms, debug=0,
padSignal=False);
# shifting the order of the signals just to see if it's the same
approxCommonNL2, approxSpecListNL2, decayListNL2, residualSignalListNL2 = mp.mp_joint((pySig3,pySig2,pySig4),
jointDicoNL, 20,
nbAtoms, debug=0,
padSignal=False);
# cProfile.runctx('mp.mp_joint((pySig2,pySig3), jointDico, 10, nbAtoms ,debug=0,doClean=False,padSignal=False)' , globals() , locals())
# cProfile.runctx('mp.mp_joint((pySig2,pySig3), jointDicoNL, 10, nbAtoms ,debug=0,doClean=False,padSignal=False)' , globals() , locals())
# print [block.sigNumber for block in jointDico.blocks]
# print [block.sigNumber for block in jointDicoNL.blocks]
print decayList[0][-1],decayList2[0][-1], decayListNL[0][-1], decayListNL2[1][-1]
print decayList[1][-1],decayList2[1][-1], decayListNL[1][-1], decayListNL2[0][-1]
print decayList[2][-1],decayList2[2][-1], decayListNL[2][-1], decayListNL2[2][-1]
A = np.concatenate(jointDicoNL.blocks[2].intermediateProjection, axis = 1);
# plt.figure()
# plt.imshow(np.sqrt(np.abs(A[12000:14000,:].T)), interpolation='nearest',aspect='auto',cmap=cm.get_cmap('bone'))
# plt.colorbar()
# plt.figure()
# plt.plot(jointDico.blocks[2].bestScoreTree,'b')
# plt.plot(jointDicoNL.blocks[2].bestScoreTree,'r')
# plt.figure()
# plt.plot(jointDico.blocks[2].enframedDataMatrixList[0],'b')
# plt.plot(jointDicoNL.blocks[2].enframedDataMatrixList[0],'r:')
# plt.figure()
# plt.plot(jointDico.blocks[2].projectionMatrix,'b')
# plt.plot(jointDicoNL.blocks[2].projectionMatrix,'r')
#
print [atom.getAmplitude() for atom in approxCommonNL.atoms]
