ssnrs = []
hsnrs = []
thsemissnrs = []
thf1snrs = []
thf2snrs = []
thf3snrs = []
smses = []
hmses = []
thsemismses = []
thf1mses = []
thf2mses = []
thf3mses = []
tds = [s.createSin(50, 60, 0), s.createPulse(50, 60, 0), s.createSpikes(0)]
for i in range(len(ods)):
sdr = DenoiseRsult(tds[i], spds[i])
hdr = DenoiseRsult(tds[i], hpds[i])
thsemisdr = DenoiseRsult(tds[i], thsemispds[i])
thf1dr = DenoiseRsult(tds[i], thf1pds[i])
thf2dr = DenoiseRsult(tds[i], thf2pds[i])
thf3dr = DenoiseRsult(tds[i], thf3pds[i])
ssnrs.append(sdr.snr())
hsnrs.append(hdr.snr())
thsemissnrs.append(thsemisdr.snr())
thf1snrs.append(thf1dr.snr())
thf2snrs.append(thf2dr.snr())
thf3snrs.append(thf3dr.snr())
smses.append(sdr.mse())
hmses.append(hdr.mse())
thsemismses.append(thsemisdr.mse())
sfp2pd = swt.swtrec(npce2s, wtname='sym6')
sfp1pds.append(sfp1pd)
sfp2pds.append(sfp2pd)
sf1snrs = []
sf2snrs = []
sfwp1snrs = []
sfwp2snrs = []
sf1mses = []
sf2mses = []
sfwp1mses = []
sfwp2mses = []
tds = [s.createSin(50, 60, 0), s.createPulse(50, 60, 0), s.createSpikes(0)]
for i in range(len(tds)):
sf1dr = DenoiseRsult(tds[i], sf1pds[i])
sf2dr = DenoiseRsult(tds[i], sf2pds[i])
sfwp1dr = DenoiseRsult(tds[i], sfp1pds[i])
sfwp2dr = DenoiseRsult(tds[i], sfp2pds[i])
sf1snrs.append(sf1dr.snr())
sf2snrs.append(sf2dr.snr())
sfwp1snrs.append(sfwp1dr.snr())
sfwp2snrs.append(sfwp2dr.snr())
sf1mses.append(sf1dr.mse())
sf2mses.append(sf2dr.mse())
sfwp1mses.append(sfwp1dr.mse())
sfwp2mses.append(sfwp2dr.mse())
snrss = [sf1snrs, sf2snrs, sfwp1snrs, sfwp2snrs]
snrsn = ['sf1snrs', 'sf2snrs', 'sfwp1snrs', 'sfwp2snrs']
ncoeffs2 = dwd.thprocess2(coeffs,
ths=bsths,
thf='thf3',
thps=[2 + 2 * k for k in range(dl)])
gthpd = dwd.dwtrec(ncoeffs1, wtname='db4')
bsthpd = dwd.dwtrec(ncoeffs2, wtname='db4')
gthpds.append(gthpd)
bsthpds.append(bsthpd)
gthsnrs = []
bsthsnrs = []
gthmses = []
bsthmses = []
tds = [s.createSin(50, 60, 0), s.createPulse(50, 60, 0), s.createSpikes(0)]
for i in range(len(tds)):
gthdr = DenoiseRsult(tds[i], gthpds[i])
bsthdr = DenoiseRsult(tds[i], bsthpds[i])
gthsnrs.append(gthdr.snr())
bsthsnrs.append(bsthdr.snr())
gthmses.append(gthdr.mse())
bsthmses.append(bsthdr.mse())
print('gthsnrs = {0}'.format(gthsnrs))
print('bsthsnrs = {0}'.format(bsthsnrs))
print('gthmses = {0}'.format(gthmses))
print('bsthmses = {0}'.format(bsthmses))
x = [x for x in range(1000)]
plt.figure()
for i in range(len(ods)):
plt.subplot(3, 2, 2 * i + 1)
plt.title('{0} signal processed by gth'.format(dnames[i]))
ssms = []
hsms = []
thsemissms = []
thf1sms = []
thf2sms = []
thf3sms = []
slrepvs = []
hlrepvs = []
thsemislrepvs = []
thf1lrepvs = []
thf2lrepvs = []
thf3lrepvs = []
tds = [s.createSin(50, 60, 0), s.createPulse(50, 60, 0), s.createSpikes(0)]
for i in range(len(ods)):
sdr = DenoiseRsult(tds[i], spds[i])
hdr = DenoiseRsult(tds[i], hpds[i])
thsemisdr = DenoiseRsult(tds[i], thsemispds[i])
thf1dr = DenoiseRsult(tds[i], thf1pds[i])
thf2dr = DenoiseRsult(tds[i], thf2pds[i])
thf3dr = DenoiseRsult(tds[i], thf3pds[i])
ssnrs.append(sdr.snr())
hsnrs.append(hdr.snr())
thsemissnrs.append(thsemisdr.snr())
thf1snrs.append(thf1dr.snr())
thf2snrs.append(thf2dr.snr())
thf3snrs.append(thf3dr.snr())
smses.append(sdr.mse())
hmses.append(hdr.mse())
thsemismses.append(thsemisdr.mse())
pd = dwd.dwtrec(ncoeffs, wtname='db4')
pdsmatrix[i].append(pd)
ssnrs = []
cvsnrs = []
gcvsnrs = []
srmsnrs = []
smses = []
cvmses = []
gcvmses = []
srmmses = []
tds = [s.createSin(50, 60, 0), s.createPulse(50, 60, 0), s.createSpikes(0)]
for i in range(len(tds)):
sdr = DenoiseRsult(tds[i], gpds[i])
cvdr = DenoiseRsult(tds[i], cvpds[i])
gcvdr = DenoiseRsult(tds[i], gcvpds[i])
srmdr = DenoiseRsult(tds[i], srmpds[i])
ssnrs.append(sdr.snr())
cvsnrs.append(cvdr.snr())
gcvsnrs.append(gcvdr.snr())
srmsnrs.append(srmdr.snr())
smses.append(sdr.mse())
cvmses.append(cvdr.mse())
gcvmses.append(gcvdr.mse())
srmmses.append(srmdr.mse())
snrss = [ssnrs, cvsnrs, gcvsnrs, srmsnrs]
if __name__ == '__main__':
ad = GetActualSignal()
ad.getwhvalue()
od1 = ad.selectvalue(start=11000, end=12024)
dl = 4
swt = SWTP(od1)
coeffs = swt.swtdec(wtname='db2', delevel=dl)
n1coeffs = swt.thprocess(coeffs, thf='soft')
n2coeffs = swt.thprocess(coeffs, thf='hard')
n3coeffs = swt.thprocess(coeffs, thf='thsemisf')
spd = swt.swtrec(n1coeffs, wtname='db2')
hpd = swt.swtrec(n2coeffs, wtname='db2')
thf1pd = swt.swtrec(n3coeffs, wtname='db2')
ad.outputdata(startidex=0, ogdata=od1, pddata=hpd)
ssm = DenoiseRsult([], spd).smooth(od1)
hsm = DenoiseRsult([], hpd).smooth(od1)
thf1sm = DenoiseRsult([], thf1pd).smooth(od1)
slrepv = DenoiseRsult([], spd).lrepv(od1, 128)
hlrepv = DenoiseRsult([], hpd).lrepv(od1, 128)
thf1lrepv = DenoiseRsult([], thf1pd).lrepv(od1, 128)
print('sf1sm = {0}'.format(ssm))
print('sfp1sm = {0}'.format(hsm))
print('sfp2sm = {0}'.format(thf1sm))
print('slrepv = {0}'.format(slrepv))
print('hlrepv = {0}'.format(hlrepv))
print('sfp2lrepv = {0}'.format(thf1lrepv))
hsnrs = []
thf3snrs = []
thf3wpsnrs = []
smses = []
hmses = []
thf3mses = []
thf3wpmses = []
tds = [
s.createSin(200, 60, 0),
s.createPulse(200, 60, 0),
s.createSpikes(0)
]
for i in range(len(tds)):
sdr = DenoiseRsult(tds[i], spds[i])
hdr = DenoiseRsult(tds[i], hpds[i])
thf3dr = DenoiseRsult(tds[i], thf3pds[i])
thf3wpdr = DenoiseRsult(tds[i], thf3wppds[i])
ssnrs.append(sdr.snr())
hsnrs.append(hdr.snr())
thf3snrs.append(thf3dr.snr())
thf3wpsnrs.append(thf3wpdr.snr())
smses.append(sdr.mse())
hmses.append(hdr.mse())
thf3mses.append(thf3dr.mse())
thf3wpmses.append(thf3wpdr.mse())
snrss = [ssnrs, hsnrs, thf3snrs, thf3wpsnrs]
ad = GetActualSignal()
ad.getwhvalue()
od1 = ad.selectvalue(start=6000, end=7024)
dl = 4
dwd = DWTP(od1)
coeffs = dwd.dwtdec(wtname='db2', 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], 0.01)
gths.append(gth)
bsths.append(bsth)
ncoeffs1 = dwd.thprocess2(coeffs,
ths=gths,
thf='thf3',
thps=[2 + 2 * k for k in range(dl)])
ncoeffs2 = dwd.thprocess2(coeffs,
ths=bsths,
thf='thf3',
thps=[2 + 2 * k for k in range(dl)])
gthpd = dwd.dwtrec(ncoeffs1, wtname='db2')
bsthpd = dwd.dwtrec(ncoeffs2, wtname='db2')
ad.outputdata(startidex=5000, ogdata=od1, pddata=bsthpd)
bsm = DenoiseRsult([], bsthpd).smooth(od1)
blrepv = DenoiseRsult([], bsthpd).lrepv(od1, 128)
print('bsm = {0}'.format(bsm))
print('blrepv = {0}'.format(blrepv))
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], 0.01)
sfp2th = ThSearchAlgorithm().FibonacciSearch(spta2.BTHspcothfwp2,
[0, 1.5 * gpth], 0.01)
gpths.append(gpth)
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')
ad.outputdata(startidex=10000, ogdata=od1, pddata=sfp2pd)
sf1sm = DenoiseRsult([], sf1pd).smooth(od1)
sfp1sm = DenoiseRsult([], sfp1pd).smooth(od1)
sfp2sm = DenoiseRsult([], sfp2pd).smooth(od1)
sf1lrepv = DenoiseRsult([], sf1pd).lrepv(od1, 128)
sfp1lrepv = DenoiseRsult([], sfp1pd).lrepv(od1, 128)
sfp2lrepv = DenoiseRsult([], sfp2pd).lrepv(od1, 128)
print('sf1sm = {0}'.format(sf1sm))
print('sfp1sm = {0}'.format(sfp1sm))
print('sfp2sm = {0}'.format(sfp2sm))
print('sf1lrepv = {0}'.format(sf1lrepv))
print('sfp1lrepv = {0}'.format(sfp1lrepv))
print('sfp2lrepv = {0}'.format(sfp2lrepv))
hpd = pywt.waverec(nhcoeffs, 'db4')
spds.append(spd)
hpds.append(hpd)
ssnrs = []
hsnrs = []
smses = []
hmses = []
ssms = []
hsms = []
slrepvs = []
hlrepvs = []
tds = [s.createSin(50, 60, 0), s.createPulse(50, 60, 0), s.createSpikes(0)]
for i in range(len(tds)):
sdr = DenoiseRsult(tds[i], spds[i])
hdr = DenoiseRsult(tds[i], hpds[i])
ssnrs.append(sdr.snr())
hsnrs.append(hdr.snr())
smses.append(sdr.mse())
hmses.append(hdr.mse())
ssms.append(sdr.smooth(ods[i]))
hsms.append(hdr.smooth(ods[i]))
slrepvs.append(sdr.lrepv(ods[i], 32))
hlrepvs.append(hdr.lrepv(ods[i], 32))
print('ssnrs = {0}'.format(ssnrs))
print('hsnrs = {0}'.format(hsnrs))
print('smses = {0}'.format(smses))
print('hmses = {0}'.format(hmses))
print()
x = [x for x in range(1000)]
plt.figure()
plt.subplot(3, 2, 1)
plt.plot(x, td1)
plt.title("True sine signal")
plt.subplot(3, 2, 2)
plt.plot(x, od1)
plt.title("Noisy sine signal")
plt.subplot(3, 2, 3)
plt.plot(x, td2)
plt.title("True pulse signal")
plt.subplot(3, 2, 4)
plt.plot(x, od2)
plt.title("Noisy pulse signal")
plt.subplot(3, 2, 5)
plt.plot(x, td3)
plt.title("True spike signal")
plt.subplot(3, 2, 6)
plt.plot(x, od3)
plt.title("Noisy spike signal")
dn1 = DenoiseRsult(td1, od1)
dn2 = DenoiseRsult(td2, od2)
dn3 = DenoiseRsult(td3, od3)
print(dn1.snr(), dn2.snr(), dn3.snr())
plt.tight_layout()
plt.show()
b = len(bceths)
nbces = DWTP(od).thprocess(bces, ths=bceths, thf='soft')
nbcoeff = BlockThMethod(coeffs[j]).recoverdata(
nbces[1:]) # 除去添加的[0]系数
nbcoeffs.append(np.asarray(nbcoeff))
blpd = DWTP(od).dwtrec(nbcoeffs, 'db4')
blpds.append(blpd)
ssnrs = []
blsnrs = []
smses = []
blmses = []
tds = [s.createSin(50, 60, 0), s.createPulse(50, 60, 0), s.createSpikes(0)]
for i in range(len(tds)):
sdr = DenoiseRsult(tds[i], spds[i])
bldr = DenoiseRsult(tds[i], blpds[i])
ssnrs.append(sdr.snr())
blsnrs.append(bldr.snr())
smses.append(sdr.mse())
blmses.append(bldr.mse())
print('ssnrs = {0}'.format(ssnrs))
print('blsnrs = {0}'.format(blsnrs))
print('smses = {0}'.format(smses))
print('blmses = {0}'.format(blmses))
x = [x for x in range(1000)]
plt.figure()
plt.subplot(3, 2, 1)
plt.title('Sine signal processed by soft threshold')
plt.plot(x, spds[0])
od3 = s.createSpikes(4)
ods = [od1, od2, od3]
td1 = s.createSin(50, 60, 0)
td2 = s.createPulse(50, 60, 0)
td3 = s.createSpikes(0)
tds = [td1, td2, td3]
for i in range(len(ods)):
dwd = DWTP(ods[i])
dwtd = DWTP(tds[i])
coeffs = dwd.dwtdec(wtname='db4', delevel=4)
tcoeffs = dwtd.dwtdec(wtname='db4', delevel=4)
gcvths = []
srmths = []
mses = []
bthe = BestThEstimate(coeffs[2], thfunction='thf3', thp=4)
for k in range(1, 100):
gcvth = bthe.gcvfunction(k / 10)
srmth = bthe.msesurefuction(k / 10)
thc = ThFuction(coeffs[2]).thf3(k / 10, 4)
mse = DenoiseRsult(tcoeffs[2], thc).mse()
gcvths.append(gcvth)
srmths.append(srmth)
mses.append(mse)
x = [x / 10 for x in range(1, 100)]
plt.figure()
l1, = plt.plot(
x, [y + 1 for y in srmths / np.max([abs(x) for x in srmths])],
linestyle='-')
l2, = plt.plot(x, mses / np.max(mses), linestyle='solid')
plt.legend(handles=[l1, l2], labels=['Sure estimation', 'MSE'])
plt.show()