def thprocess(self, coeffs, ths=None, thf='soft', thp=None):
ncoeffs = [coeffs[0]]
for i in range(1, len(coeffs)):
if not ths:
th = ThSelect(coeffs[i]).DonohoThEx2(len(coeffs) - i)
elif len(ths) == len(coeffs) - 1:
th = ths[i - 1]
else:
raise ValueError(
'number of th in ths[] must match levels of coeffs')
if thf == 'soft':
ncf = ThFuction(coeffs[i]).thsoft(th)
elif thf == 'hard':
ncf = ThFuction(coeffs[i]).thhard(th)
elif thf == 'thsemisf':
ncf = ThFuction(coeffs[i]).thsemisf(th, thp)
elif thf == 'thf1':
ncf = ThFuction(coeffs[i]).thf1(th, thp)
elif thf == 'thf2':
ncf = ThFuction(coeffs[i]).thf2(th, thp)
elif thf == 'thf3':
ncf = ThFuction(coeffs[i]).thf1(th, thp)
else:
raise ValueError(
'thf must be "soft","hard","thsemisf","thf1","thf2","thf3"'
)
ncoeffs.append(np.asarray(ncf))
return ncoeffs
def thprocess(coeffs, thf='soft'):
ncoeffs = [coeffs[0]]
for i in range(1, len(coeffs)):
th = ThSelect(coeffs[i]).DonohoThEx()
if thf == 'soft':
ncf = ThFuction(coeffs[i]).thsoft(th)
elif thf == 'hard':
ncf = ThFuction(coeffs[i]).thhard(th)
ncoeffs.append(np.asarray(ncf))
return ncoeffs
def spcoThf2(self):
Ecors = self.nlEcors()
ncoeffs = []
for i in range(1, len(self.coeffs) - 1):
W = self.coeffs[-i]
nW = []
ec = Ecors[i - 1]
th = ThSelect(W).DonohoThEx()
for k in range(len(W)):
thn = abs(W[k]) / abs(ec[k]) * th
if abs(W[k]) < thn:
w = 0
else:
w = W[k]
nW.append(w)
ncoeffs.insert(0, np.asarray(nW))
ncoeffs.insert(0, self.coeffs[1])
ncoeffs.insert(0, self.coeffs[0])
return ncoeffs
nce2s = spta.spcoThf2()
sf1pd = swt.swtrec(nce1s, wtname='sym6')
sf2pd = swt.swtrec(nce2s, wtname='sym6')
sf1pds.append(sf1pd)
sf2pds.append(sf2pd)
for od in ods:
swt = SWTP(od)
coeffs = swt.swtdec(wtname='sym6', delevel=4)
spta2 = SpCoThALG(coeffs)
sfp1ths = []
sfp2ths = []
P = spta2.spcoCors()
for i in range(1, len(coeffs)-1):
spta2.BTHassitIndex(i, pk=0.1, pa=4, mode='srmse')
gpth = ThSelect(P[i-1]).DonohoThEx()
sfp1th = ThSearchAlgorithm().FibonacciSearch(spta2.BTHspcothfwp1, [0, 1.5*gpth], 0.001)
sfp2th = ThSearchAlgorithm().FibonacciSearch(spta2.BTHspcothfwp2, [0, 1.5*gpth], 0.001)
sfp1ths.append(sfp1th)
sfp2ths.append(sfp2th)
npce1s = spta2.spcoThfwp1(sfp1ths, 0.05)
npce2s = spta2.spcoThfwp2(sfp2ths, 4)
sfp1pd = swt.swtrec(npce1s, wtname='sym6')
sfp2pd = swt.swtrec(npce2s, wtname='sym6')
sfp1pds.append(sfp1pd)
sfp2pds.append(sfp2pd)
sf1snrs = []
sf2snrs = []
sfwp1snrs = []
sfwp2snrs = []
ods = [od1, od2, od3]
dnames = ['Sine', 'Pulse', 'Spike']
gthss = []
bsthss = []
gthpds = []
bsthpds = []
dl = 4
for od in ods:
dwd = DWTP(od)
coeffs = dwd.dwtdec(wtname='db4', delevel=dl)
gths = []
bsths = []
for i in range(1, len(coeffs)):
bthe = BestThEstimate(coeffs[i], thfunction='thf3', thp=2 + 2 * i)
thsa = ThSearchAlgorithm()
gth = ThSelect(coeffs[i]).DonohoThEx()
bsth = thsa.FibonacciSearch(bthe.msesurefuction, [0, 1.5 * gth],
0.001)
gths.append(gth)
bsths.append(bsth)
gthss.append(gths)
bsthss.append(bsths)
for i in range(len(ods)):
dwd = DWTP(ods[i])
coeffs = dwd.dwtdec(wtname='db4', delevel=dl)
gths = gthss[i]
bsths = gthss[i]
ncoeffs1 = dwd.thprocess2(coeffs,
ths=gths,
thf='thf3',
for k in range(ln):
od1 = od[k * 1024:k * 1024 + 1024]
swt = SWTP(od1)
dwt = DWTP(od1)
coeffs = swt.swtdec(wtname='db2', delevel=dl)
dcoeffs = dwt.dwtdec(wtname='db2', delevel=dl)
n1coeffs = swt.thprocess(coeffs, thf='soft')
n2coeffs = swt.thprocess(coeffs, thf='hard')
spd1 = swt.swtrec(n1coeffs, wtname='db2')
hpd1 = swt.swtrec(n2coeffs, wtname='db2')
shes = NoiseDetection(od1, 'db2').Entropymethod1(4)
gth2s = []
gth3s = []
for i in range(1, len(coeffs)):
gth = ThSelect(coeffs[i]).DonohoThEx()
gth3 = ThSelect(dcoeffs[i]).EntropyTh2()
if shes[i - 1] > 1.5:
shes[i - 1] = 1.5
elif shes[i - 1] < 0.8:
shes[i - 1] = 0.8
else:
pass
gth2 = gth * shes[i - 1]
gth2s.append(gth2)
gth3s.append(gth3)
ncoeffs3 = swt.thprocess2(coeffs,
ths=gth2s,
thf='thf3',
thps=[2 + 2 * k for k in range(dl)])
npd1 = swt.swtrec(ncoeffs3, wtname='db2')