def test_max_iteration_flag(self):
S = np.random.random(200)
emd = EMD()
emd.MAX_ITERATION = 10
emd.FIXE = 20
imfs = emd.emd(S)
# There's not much to test, except that it doesn't fail.
# With low MAX_ITERATION value for random signal it's
# guaranteed to have at least 2 imfs.
self.assertTrue(imfs.shape[0]>1)
plt.savefig("ExecTime_%s.pdf" % dBase)
#%% Figs 6,7: EEG segment decomposition
# Decompostion figure parameters
Nmodes = [3, 3, 3, 4, 3] #(EMD,EEMD, CEEMDAN, EWT,VMD)
#Nmodes = [2,4,2,5,6] #(EMD,EEMD, CEEMDAN, EWT,VMD)
#VMD parameters
alpha = 2000 # % moderate bandwidth constraint
tau = 0 # % noise-tolerance (no strict fidelity enforcement)
init = 1 # % initialize omegas uniformly
tol = 1e-7 #
for kk, ind in zip(f, range(len(f))):
#fig EMD
emd = EMD()
emd.MAX_ITERATION = 2000
IMFs = emd.emd(f[kk], max_imf=Nmodes[0])
plotModes(np.flip(IMFs.T, axis=1),
Nmodes[0] + 1,
tlimits=paramsData[dBase]["tlimits"])
#plt.suptitle('EMD, %s signal'%list(f.keys())[ind])
plt.savefig("DecEMD%d_%s_%s.pdf" % (Nmodes[0], dBase, kk))
#EEMD
if __name__ == "__main__":
eemd = EEMD()
eemd.MAX_ITERATION = 2000
eIMFs = eemd(f[kk], max_imf=Nmodes[1])
plotModes(np.flip(eIMFs.T, axis=1),
Nmodes[1] + 1,
tlimits=paramsData[dBase]["tlimits"])
def fun_loadExFeats(dSet,
ri,
idx,
item,
Fs,
LPcutoff,
Nmodes,
FFTregLen=25,
gaussSigma=5,
FFTreg='gaussian'):
#load eeg
featNames = ["Group", "decTime"]
featsTuple = {
"EMD": 0,
"EEMD": 0,
"CEEMDAN": 0,
"EWT": 0,
"VMD": 0,
"Orig": 0
}
if dSet == "NSC_ND":
fLoad = loadmat("%s/data/%s/%s%d" % (dSet, item, item, ri + 1))
f = fLoad[item][:, 0]
ltemp = int(np.ceil(f.size / 2)) #to behave the same as matlab's round
fMirr = np.append(np.flip(f[0:ltemp - 1], axis=0), f)
fMirr = np.append(fMirr, np.flip(f[-ltemp - 1:-1], axis=0))
f = np.copy(fMirr)
if dSet == "BonnDataset":
f = np.loadtxt("%s/data/%s/%s%.3d.txt" % (dSet, item, item, ri + 1))
#preprocessing - LP filter and remove DC
f = f - np.mean(f)
b, a = signal.butter(4, LPcutoff / (0.5 * Fs), btype='low', analog=False)
fp = signal.filtfilt(b, a, f)
#% EMD features
tic = time.time()
emd = EMD()
emd.MAX_ITERATION = 2000
IMFs = emd.emd(fp, max_imf=Nmodes)
toc = time.time()
featsTuple["EMD"] = toc - tic #execution time (decomposition)
if Nmodes != IMFs.shape[0] - 1:
print("\nCheck number of EMD modes: %s%.3d" % (item, ri + 1))
for mi in range(IMFs.shape[0]):
featOut, labelTemp = featExtract(IMFs[mi, :], Fs, welchWin=1024)
featsTuple["EMD"] = np.append(featsTuple["EMD"], featOut)
#write feature name header
if ri == 0 and idx == 0:
for ii in labelTemp:
featNames = np.append(featNames, "%s%d" % (ii, mi))
if IMFs.shape[0] < Nmodes + 1:
featsTuple["EMD"] = np.append(
featsTuple["EMD"], np.zeros(Nfeats * (Nmodes + 1 - IMFs.shape[0])))
#% EEMD - Ensemble Empirical Mode Decomposition
tic = time.time()
if __name__ == "__main__":
eemd = EEMD(trials=200)
eemd.MAX_ITERATION = 2000
eIMFs = eemd(fp, max_imf=Nmodes)
toc = time.time()
featsTuple["EEMD"] = toc - tic #execution time (decomposition )
if Nmodes != eIMFs.shape[0] - 1:
print("\nCheck number of EEMD modes: %s%.3d" % (item, ri + 1))
#for each mode, extract features
for mi in range(eIMFs.shape[0]):
featOut, labelTemp = featExtract(eIMFs[mi, :], Fs, welchWin=1024)
featsTuple["EEMD"] = np.append(featsTuple["EEMD"], featOut)
if eIMFs.shape[0] < Nmodes + 1:
featsTuple["EEMD"] = np.append(
featsTuple["EEMD"],
np.zeros(Nfeats * (Nmodes + 1 - eIMFs.shape[0])))
#% CEEMDAN - Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
tic = time.time()
if __name__ == "__main__":
ceemdan = CEEMDAN()
ceIMFs = ceemdan(fp, max_imf=Nmodes)
toc = time.time()
featsTuple["CEEMDAN"] = toc - tic #execution time (decomposition )
if Nmodes != ceIMFs.shape[0] - 1:
print("\nCheck number of CEEMDAN modes: %s%.3d" % (item, ri + 1))
#for each mode, extract features
for mi in range(ceIMFs.shape[0]):
featOut, labelTemp = featExtract(ceIMFs[mi, :], Fs, welchWin=1024)
featsTuple["CEEMDAN"] = np.append(featsTuple["CEEMDAN"], featOut)
if ceIMFs.shape[0] < Nmodes + 1:
featsTuple["CEEMDAN"] = np.append(
featsTuple["CEEMDAN"],
np.zeros(Nfeats * (Nmodes + 1 - ceIMFs.shape[0])))
#%EWT features
tic = time.time()
ewt, _, _ = ewtpy.EWT1D(fp,
N=Nmodes,
log=0,
detect="locmax",
completion=0,
reg=FFTreg,
lengthFilter=FFTregLen,
sigmaFilter=gaussSigma)
toc = time.time()
featsTuple["EWT"] = toc - tic #execution time (decomposition )
if Nmodes != ewt.shape[1]:
print("\nCheck number of EWT modes: %s%.3d" % (item, ri + 1))
#for each mode, extract features
for mi in range(Nmodes):
featOut, labelTemp = featExtract(ewt[:, mi], Fs, welchWin=1024)
featsTuple["EWT"] = np.append(featsTuple["EWT"], featOut)
#% VMD features
DC = np.mean(fp) # no DC part imposed
tic = time.time()
vmd, _, _ = VMD(fp, alpha, tau, Nmodes, DC, init, tol)
toc = time.time()
featsTuple["VMD"] = toc - tic #execution time (decomposition )
if Nmodes != vmd.shape[0]:
print("\nCheck number of VMD modes: %s%.3d" % (item, ri + 1))
#for each mode, extract features
for mi in range(Nmodes):
featOut, labelTemp = featExtract(vmd[mi, :], Fs, welchWin=1024)
featsTuple["VMD"] = np.append(featsTuple["VMD"], featOut)
#% Original non-decomposed signal features
tic = time.time()
featOut, labelTemp = featExtract(fp, Fs, welchWin=1024)
toc = time.time()
featsTuple["Orig"] = np.append(toc - tic, featOut)
return item, featsTuple
