def _clean_use_thresh(Y, thld, wavelet):
LL = np.int32(np.floor(np.log2(len(Y))))
wres = swt(Y, wavelet, level=LL)
wres = [list(wres[i]) for i in range(len(wres))]
# xh = HardTh(xh, thld);
for i in xrange(len(wres)):
wres[i][1] = (np.abs(wres[i][1]) > thld) * wres[i][1]
# xd = mirdwt(xl,xh,h,LL);
xd = iswt(wres, wavelet)
# xn = Y - xd;
xn = Y - xd
# return the cleaned data
return xn
def _clean_use_thresh(Y,thld,wavelet):
LL = np.int32(np.floor(np.log2(len(Y))))
wres = swt(Y,wavelet,level=LL)
wres = [list(wres[i]) for i in range(len(wres))]
# xh = HardTh(xh, thld);
for i in xrange(len(wres)):
wres[i][1] = (np.abs(wres[i][1]) > thld) * wres[i][1]
# xd = mirdwt(xl,xh,h,LL);
xd = iswt(wres, wavelet)
# xn = Y - xd;
xn = Y - xd
# return the cleaned data
return xn
def _clean_find_thresh(Y, Kthr, wavelet, L):
# init
xn = None
thld = 0.0
N = len(Y)
# find the outliers
# need to replace this with blink-finding code
if False:
# Sig = median(abs(Y)/0.6745);
#Sig = np.median(np.abs(Y)/0.6745)
#Sig = np.median(np.abs(icaEEG[Comp[c],pure_range[0]:pure_range[1]])/0.6745)
Sig = np.median(np.abs(Y) / 0.6745)
# Thr = 4*Sig;
Thr = 3 * Sig
# idx = find(abs(Y) > Thr);
idx = np.nonzero(np.abs(Y) > Thr)[0]
# idx_ext = zeros(1,length(idx)*(2*L+1));
idx_ext = np.zeros(len(idx) * (2 * L + 1), dtype=np.int32)
# for k=1:length(idx),
# idx_ext((2*L+1)*(k-1)+1:(2*L+1)*k) = [idx(k)-L:idx(k)+L];
# end
for k in xrange(len(idx)):
idx_ext[(2 * L + 1) * (k):(2 * L + 1) * (k + 1) - 1] = np.arange(
idx[k] - L, idx[k] + L)
# id_noise=setdiff((1:N), idx_ext);
id_noise = np.setdiff1d(range(N), idx_ext)
# id_artef=setdiff((1:N), id_noise);
id_artef = np.setdiff1d(range(N), id_noise)
else:
id_artef, id_noise = find_blinks(Y, L)
# make sure it's not all noise or artifact
print len(id_artef), len(id_noise)
# if isempty(id_artef),
# disp(['The component #' num2str(Comp(c)) ' has passed unchanged']);
# continue;
# end
if len(id_artef) == 0:
#sys.stdout.write("passed unchanged\n")
#sys.stdout.flush()
return xn, thld
# KK = 100;
KK = 100.
# LL = floor(log2(length(Y)));
LL = np.int32(np.floor(np.log2(len(Y))))
# [xl, xh] = mrdwt(Y, h, LL);
wres = swt(Y, wavelet, level=LL)
# make it editable
wres = [list(wres[i]) for i in range(len(wres))]
# start with a low high-pass threshold and zero out wavelet
# components below that value, test to see if the artifacts look
# like the noise, if not, step up the threshold. At some point it
# should stop, but perhaps not after removing a good bit of
# signal.
# while KK > Kthr,
# thld = thld + 0.5;
# xh = HardTh(xh, thld); % x = (abs(y) > thld).*y;
# xd = mirdwt(xl,xh,h,LL);
# xn = Y - xd;
# cn=corrcoef(Y(id_noise),xn(id_noise));
# ca=corrcoef(Y(id_artef),xd(id_artef));
# KK = ca(1,2)/cn(1,2);
# end
# thld = 3.6;
# not sure where this 3.6 number came from, so I'm dropping it down to get
# more low-freq cleaning
thld = 1.1 #3.6
while KK > Kthr:
# update what's going on
#sys.stdout.write('.')
#sys.stdout.flush()
# bump up the thresh
thld += 0.5
# zero out everything below threshold in each wavelet coef
for i in xrange(len(wres)):
wres[i][1] = (np.abs(wres[i][1]) > thld) * wres[i][1]
# invert the wavelet back
xd = iswt(wres, wavelet)
# check if clean based on the ratio of correlations for noise
# and artifact data
xn = Y - xd
# cn measures the correlation between the cleaned and original
# data in the non-artifactual regions
cn = np.corrcoef(Y[id_noise], xn[id_noise])[0, 1]
# ca measures the corr b/t the signal removed from the
# artifacts and the original artifacts
ca = np.corrcoef(Y[id_artef], xd[id_artef])[0, 1]
# must not go negative, it should just be a small positive
# number if that happens
if cn <= 0.0:
cn = .000001
if ca <= 0.0:
ca = .000001
# we want the ratio of the bad things getting cleaned to the
# good things sticking around to be small, ideally both very
# close to 1.0
KK = ca / cn
sys.stdout.write('(%.2f,%.2f,%.2f) ' % (ca, cn, KK))
sys.stdout.flush()
# return the cleaned data and the thresh
return xn, thld
def _clean_find_thresh(Y,Kthr,wavelet,L):
# init
xn = None
thld = 0.0
N = len(Y)
# find the outliers
# need to replace this with blink-finding code
if False:
# Sig = median(abs(Y)/0.6745);
#Sig = np.median(np.abs(Y)/0.6745)
#Sig = np.median(np.abs(icaEEG[Comp[c],pure_range[0]:pure_range[1]])/0.6745)
Sig = np.median(np.abs(Y)/0.6745)
# Thr = 4*Sig;
Thr = 3*Sig
# idx = find(abs(Y) > Thr);
idx = np.nonzero(np.abs(Y) > Thr)[0]
# idx_ext = zeros(1,length(idx)*(2*L+1));
idx_ext = np.zeros(len(idx)*(2*L+1), dtype=np.int32)
# for k=1:length(idx),
# idx_ext((2*L+1)*(k-1)+1:(2*L+1)*k) = [idx(k)-L:idx(k)+L];
# end
for k in xrange(len(idx)):
idx_ext[(2*L+1)*(k):(2*L+1)*(k+1)-1] = np.arange(idx[k]-L,idx[k]+L)
# id_noise=setdiff((1:N), idx_ext);
id_noise = np.setdiff1d(range(N), idx_ext)
# id_artef=setdiff((1:N), id_noise);
id_artef = np.setdiff1d(range(N), id_noise)
else:
id_artef,id_noise = find_blinks(Y,L)
# make sure it's not all noise or artifact
print len(id_artef),len(id_noise)
# if isempty(id_artef),
# disp(['The component #' num2str(Comp(c)) ' has passed unchanged']);
# continue;
# end
if len(id_artef) == 0:
#sys.stdout.write("passed unchanged\n")
#sys.stdout.flush()
return xn, thld
# KK = 100;
KK = 100.
# LL = floor(log2(length(Y)));
LL = np.int32(np.floor(np.log2(len(Y))))
# [xl, xh] = mrdwt(Y, h, LL);
wres = swt(Y,wavelet,level=LL)
# make it editable
wres = [list(wres[i]) for i in range(len(wres))]
# start with a low high-pass threshold and zero out wavelet
# components below that value, test to see if the artifacts look
# like the noise, if not, step up the threshold. At some point it
# should stop, but perhaps not after removing a good bit of
# signal.
# while KK > Kthr,
# thld = thld + 0.5;
# xh = HardTh(xh, thld); % x = (abs(y) > thld).*y;
# xd = mirdwt(xl,xh,h,LL);
# xn = Y - xd;
# cn=corrcoef(Y(id_noise),xn(id_noise));
# ca=corrcoef(Y(id_artef),xd(id_artef));
# KK = ca(1,2)/cn(1,2);
# end
# thld = 3.6;
# not sure where this 3.6 number came from, so I'm dropping it down to get
# more low-freq cleaning
thld = 1.1 #3.6
while KK > Kthr:
# update what's going on
#sys.stdout.write('.')
#sys.stdout.flush()
# bump up the thresh
thld += 0.5
# zero out everything below threshold in each wavelet coef
for i in xrange(len(wres)):
wres[i][1] = (np.abs(wres[i][1]) > thld) * wres[i][1]
# invert the wavelet back
xd = iswt(wres, wavelet)
# check if clean based on the ratio of correlations for noise
# and artifact data
xn = Y-xd
# cn measures the correlation between the cleaned and original
# data in the non-artifactual regions
cn = np.corrcoef(Y[id_noise],xn[id_noise])[0,1]
# ca measures the corr b/t the signal removed from the
# artifacts and the original artifacts
ca = np.corrcoef(Y[id_artef],xd[id_artef])[0,1]
# must not go negative, it should just be a small positive
# number if that happens
if cn <= 0.0:
cn = .000001
if ca <= 0.0:
ca = .000001
# we want the ratio of the bad things getting cleaned to the
# good things sticking around to be small, ideally both very
# close to 1.0
KK = ca/cn
sys.stdout.write('(%.2f,%.2f,%.2f) '%(ca,cn,KK))
sys.stdout.flush()
# return the cleaned data and the thresh
return xn, thld
def _clean_find_thresh(Y,Kthr,wavelet,L):
# init
xn = None
thld = 0.0
N = len(Y)
# Sig = median(abs(Y)/0.6745);
#Sig = np.median(np.abs(Y)/0.6745)
#Sig = np.median(np.abs(icaEEG[Comp[c],pure_range[0]:pure_range[1]])/0.6745)
Sig = np.median(np.abs(Y)/0.6745)
# Thr = 4*Sig;
Thr = 4*Sig
# idx = find(abs(Y) > Thr);
idx = np.nonzero(np.abs(Y) > Thr)[0]
# idx_ext = zeros(1,length(idx)*(2*L+1));
idx_ext = np.zeros(len(idx)*(2*L+1), dtype=np.int32)
# for k=1:length(idx),
# idx_ext((2*L+1)*(k-1)+1:(2*L+1)*k) = [idx(k)-L:idx(k)+L];
# end
for k in xrange(len(idx)):
idx_ext[(2*L+1)*(k):(2*L+1)*(k+1)-1] = np.arange(idx[k]-L,idx[k]+L)
# id_noise=setdiff((1:N), idx_ext);
id_noise = np.setdiff1d(range(N), idx_ext)
# id_artef=setdiff((1:N), id_noise);
id_artef = np.setdiff1d(range(N), id_noise)
# if isempty(id_artef),
# disp(['The component #' num2str(Comp(c)) ' has passed unchanged']);
# continue;
# end
if len(id_artef) == 0:
#sys.stdout.write("passed unchanged\n")
#sys.stdout.flush()
return xn, thld
# thld = 3.6;
# not sure where this 3.6 number came from, so I'm dropping it down
thld = .1 #3.6
# KK = 100;
KK = 100.
# LL = floor(log2(length(Y)));
LL = np.int32(np.floor(np.log2(len(Y))))
# [xl, xh] = mrdwt(Y, h, LL);
wres = swt(Y,wavelet,level=LL)
# make it editable
wres = [list(wres[i]) for i in range(len(wres))]
# while KK > Kthr,
# thld = thld + 0.5;
# xh = HardTh(xh, thld); % x = (abs(y) > thld).*y;
# xd = mirdwt(xl,xh,h,LL);
# xn = Y - xd;
# cn=corrcoef(Y(id_noise),xn(id_noise));
# ca=corrcoef(Y(id_artef),xd(id_artef));
# KK = ca(1,2)/cn(1,2);
# end
while KK > Kthr:
sys.stdout.write('.')
sys.stdout.flush()
for i in xrange(len(wres)):
wres[i][1] = (np.abs(wres[i][1]) > thld) * wres[i][1]
xd = iswt(wres, wavelet)
xn = Y-xd
cn = np.corrcoef(Y[id_noise],xn[id_noise])
ca = np.corrcoef(Y[id_artef],xd[id_artef])
KK = ca[0,1]/cn[0,1]
thld += 0.5
# return the cleaned data and the thresh
return xn, thld
def _clean_find_thresh(Y, Kthr, wavelet, L):
# init
xn = None
thld = 0.0
N = len(Y)
# Sig = median(abs(Y)/0.6745);
#Sig = np.median(np.abs(Y)/0.6745)
#Sig = np.median(np.abs(icaEEG[Comp[c],pure_range[0]:pure_range[1]])/0.6745)
Sig = np.median(np.abs(Y) / 0.6745)
# Thr = 4*Sig;
Thr = 4 * Sig
# idx = find(abs(Y) > Thr);
idx = np.nonzero(np.abs(Y) > Thr)[0]
# idx_ext = zeros(1,length(idx)*(2*L+1));
idx_ext = np.zeros(len(idx) * (2 * L + 1), dtype=np.int32)
# for k=1:length(idx),
# idx_ext((2*L+1)*(k-1)+1:(2*L+1)*k) = [idx(k)-L:idx(k)+L];
# end
for k in xrange(len(idx)):
idx_ext[(2 * L + 1) * (k):(2 * L + 1) * (k + 1) - 1] = np.arange(
idx[k] - L, idx[k] + L)
# id_noise=setdiff((1:N), idx_ext);
id_noise = np.setdiff1d(range(N), idx_ext)
# id_artef=setdiff((1:N), id_noise);
id_artef = np.setdiff1d(range(N), id_noise)
# if isempty(id_artef),
# disp(['The component #' num2str(Comp(c)) ' has passed unchanged']);
# continue;
# end
if len(id_artef) == 0:
#sys.stdout.write("passed unchanged\n")
#sys.stdout.flush()
return xn, thld
# thld = 3.6;
# not sure where this 3.6 number came from, so I'm dropping it down
thld = .1 #3.6
# KK = 100;
KK = 100.
# LL = floor(log2(length(Y)));
LL = np.int32(np.floor(np.log2(len(Y))))
# [xl, xh] = mrdwt(Y, h, LL);
wres = swt(Y, wavelet, level=LL)
# make it editable
wres = [list(wres[i]) for i in range(len(wres))]
# while KK > Kthr,
# thld = thld + 0.5;
# xh = HardTh(xh, thld); % x = (abs(y) > thld).*y;
# xd = mirdwt(xl,xh,h,LL);
# xn = Y - xd;
# cn=corrcoef(Y(id_noise),xn(id_noise));
# ca=corrcoef(Y(id_artef),xd(id_artef));
# KK = ca(1,2)/cn(1,2);
# end
while KK > Kthr:
sys.stdout.write('.')
sys.stdout.flush()
for i in xrange(len(wres)):
wres[i][1] = (np.abs(wres[i][1]) > thld) * wres[i][1]
xd = iswt(wres, wavelet)
xn = Y - xd
cn = np.corrcoef(Y[id_noise], xn[id_noise])
ca = np.corrcoef(Y[id_artef], xd[id_artef])
KK = ca[0, 1] / cn[0, 1]
thld += 0.5
# return the cleaned data and the thresh
return xn, thld
