od3 = s.createSpikes(4) ods = [od1, od2, od3] spds = [] hpds = [] thsemispds = [] thf1pds = [] thf2pds = [] thf3pds = [] dnames = ['Sine', 'Pulse', 'Spike'] thfname = ['soft', 'hard', 'thsemisf', 'thf1', 'thf2', 'thf3'] pdsmatrix = [spds, hpds, thsemispds, thf1pds, thf2pds, thf3pds] thps = [None, None, None, 0.5, 2, 2] for od in ods: dwd = DWTP(od) coeffs = dwd.dwtdec(wtname='db4', delevel=2) for i in range(len(pdsmatrix)): ncoeffs = dwd.thprocess(coeffs, thf=thfname[i], thp=thps[i]) pd = dwd.dwtrec(ncoeffs, wtname='db4') pdsmatrix[i].append(pd) ssnrs = [] hsnrs = [] thsemissnrs = [] thf1snrs = [] thf2snrs = [] thf3snrs = [] smses = [] hmses = [] thsemismses = []
from GetActualSignal import GetActualSignal from LayerSelect import LayerSelect from WtProcess import DWTP from NoiseDetection import NoiseDetection if __name__ == '__main__': ad = GetActualSignal() ad.getwhvalue() od1 = ad.selectvalue(start=4000, end=5024) dwt = DWTP(od1) coeffs = dwt.dwtdec(wtname='db2') ps1 = NoiseDetection(od1, 'db2').WNrecgmethod() ps2 = NoiseDetection(od1, 'db2').Entropymethod1(4) print(ps1) print(ps2)
from GetActualSignal import GetActualSignal from ThresholdSelect import ThSelect from ThresholdFunction import ThFuction import numpy as np import pywt import matplotlib.pyplot as plt from DenoiseResult import DenoiseRsult from WtProcess import DWTP from AcSNR import AcSNR if __name__ == '__main__': ad = GetActualSignal() ad.getwhvalue() od1 = ad.selectvalue(start=0, end=23552) dwt = DWTP(od1) coeffs = dwt.dwtdec(wtname='db4', delevel=4) nscoeffs = dwt.thprocess(coeffs, thf='soft') nhcoeffs = dwt.thprocess(coeffs, thf='hard') spd = dwt.dwtrec(nscoeffs, wtname='db4') hpd = dwt.dwtrec(nhcoeffs, wtname='db4') ad.outputdata(startidex=0, ogdata=od1, pddata=spd) ssm = DenoiseRsult([], spd).smooth(od1) hsm = DenoiseRsult([], hpd).smooth(od1) slrepv = DenoiseRsult([], spd).lrepv(od1, 128) hlrepv = DenoiseRsult([], hpd).lrepv(od1, 128) print('ssm = {0}'.format(ssm)) print('hsm = {0}'.format(hsm)) print('slrepv = {0}'.format(slrepv)) print('hlrepv = {0}'.format(hlrepv))
import math as mt from BlockThMethod import BlockThMethod from ThresholdSelect import ThSelect from BestThEstimation import BestThEstimate from ThSearchAlgorithm import ThSearchAlgorithm from WtProcess import DWTP from DenoiseResult import DenoiseRsult from AcSNR import AcSNR if __name__ == '__main__': 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,
gthss = [] srmthss = [] srmthss2 = [] thsmatrix = [gthss, gthss, srmthss, srmthss2] thmname = ['sth', 'hth', 'srm', 'srm'] thfname = ['soft', 'hard', 'thf3', 'thf3wp'] spds = [] hpds = [] thf3pds = [] thf3wppds = [] pdsmatrix = [spds, hpds, thf3pds, thf3wppds] dl = 5 for od in ods: dwd = DWTP(od) coeffs = dwd.dwtdec(wtname='sym6', delevel=dl) gths = [] gcvths = [] srmths = [] srmths2 = [] for i in range(1, len(coeffs)): bthe = BestThEstimate(coeffs[i], thfunction='thf3', thp=4) bthe2 = BestThEstimate(coeffs[i], thfunction='thf3', thp=2 + 2 * i) thsa = ThSearchAlgorithm() gth = ThSelect(coeffs[i]).DonohoThEx() srmth = thsa.FibonacciSearch(bthe.msesurefuction, [0, 1 * gth], 0.01) srmth2 = thsa.FibonacciSearch(bthe.msesurefuction, [0, 1 * gth], 0.01) gths.append(gth) srmths.append(srmth)