def freqfilt_eeg(fn_in,fn_out,btype="lp",fl=None,fh=None,border=3,windowed=False):
eeg = eegpy.open_eeg(fn_in)
out = eegpy.F32(fn_out,"w+",shape=eeg.shape,cNames=eeg.channel_names,Fs=eeg.Fs)
if btype == "lp":
if not windowed:
out[:,:] = filtfilt_low(fl,eeg[:,:],Fs=eeg.Fs,border=border)
else:
for i in range(0,out.num_datapoints,100000):
out[i:min(i+100000,out.num_datapoints),:] = filtfilt_low(fl,eeg[i:min(i+100000,out.num_datapoints),:],Fs=eeg.Fs,border=border)
elif btype == "hp":
#for i_c in range(eeg.num_channels):
if not windowed:
out[:,:] = filtfilt_high(fh,eeg[:,:],Fs=eeg.Fs,border=border)
else:
for i in range(0,out.num_datapoints,100000):
out[i:min(i+100000,out.num_datapoints),:] = filtfilt_high(fh,eeg[i:min(i+100000,out.num_datapoints),:],Fs=eeg.Fs,border=border)
elif btype == "bp":
if not windowed:
out[:,:] = filtfilt_band(fl,fh,eeg[:,:],Fs=eeg.Fs,border=border)
else:
for i in range(0,out.num_datapoints,100000):
out[i:min(i+100000,out.num_datapoints),:] = filtfilt_band(fl,fh,eeg[i:min(i+100000,out.num_datapoints),:],Fs=eeg.Fs,border=border)
elif btype == "bs":
if not windowed:
out[:,:] = filtfilt_bandstop(fl,fh,eeg[:,:],Fs=eeg.Fs,border=border)
else:
for i in range(0,out.num_datapoints,100000):
out[i:min(i+100000,out.num_datapoints),:] = filtfilt_bandstop(fl,fh,eeg[i:min(i+100000,out.num_datapoints),:],Fs=eeg.Fs,border=border)
def integrate(self,ts):
#First, calculate EEG-timecourse (independently from BOLD)
self._ts = ts
self._Fs = 1./(ts[1]-ts[0])
self._signal_rstc = self._RSTC.integrate(ts)[:,::3]
self._spls = []
for in_bd in self._input_bands:
tmp = filtfilt_band(in_bd[0],in_bd[1],self._signal_rstc[:,0],Fs=self._Fs,border=2)
power = abs(hilbert(tmp))**2
smooth_power = smooth(power,int(round(self._smooth_width*self._Fs)))
self._spls.append(splrep(ts,smooth_power))
#print "Anzahl spls:", len(self._spls)
rv = odeint(self.ode,self.y,self._ts)
return rv
def integrate(self,ts):
#First, calculate EEG-timecourse (independently from BOLD)
self._ts = ts
self._Fs = 1./(ts[1]-ts[0])
self._signal_rstc = self._RSTC.integrate(ts)[:,:]
print self._signal_rstc.shape
self._spls = np.zeros((len(self._input_bands),self._n_nodes),"O")
for i_b, in_bd in enumerate(self._input_bands):
for i_n in range(self._n_nodes):
tmp = filtfilt_band(in_bd[0],in_bd[1],self._signal_rstc[:,i_n],Fs=self._Fs,border=2)
power = abs(hilbert(tmp))**2
smooth_power = smooth(power,int(round(self._smooth_width*self._Fs)))
self._spls[i_b,i_n] = splrep(ts,smooth_power)
#print "Anzahl spls:", len(self._spls)
rv = odeint(self.ode,self.y,self._ts)
return rv
def find_all_slice_artifacts(self):
def update_pbar(num):
"""Callback for find_all_maxs"""
if show_progressbar:
pbar.update(num/2)
eeg = self.eeg
#y = abs(smooth_windowed_eeg(eeg,[self.cfsa],self._slice_width))[:,0]
#y = smooth_windowed_eeg_power(eeg,[self.cfsa],self._slice_width)[:,0]
y = filtfilt_band(1,eeg.Fs/self._slice_width,eeg[:,self.cfsa])
#pylab.plot(y[::10])
#pylab.plot(eeg[:,14])
print y.shape, self._slice_width
#import pylab
#pylab.ion()
#pylab.plot(y[0:20000:1])
#pylab.show()
#raw_input()
#pylab.plot(y[13000:20000:1])
slcs_raw = find_all_maxs(y[:1000],ratio=0.6) # First segment
slcs_raw.sort()
#print "t=", t
#slcs_raw.append(t)
offset=0
t=int(0.5*self._slice_width)
while (t>0.4*self._slice_width or (y.shape[0]-offset)>10000) and (y.shape[0]-offset)>self._slice_width*2:
#print (y.shape[0]-offset)
#print t, offset, "-",
offset = slcs_raw[-1]+self._slice_width/2
#print t, offset, "-",
#pylab.plot(y[offset:offset+self._slice_width])
#pylab.show()
#raw_input()
t=y[offset:offset+self._slice_width].argmax()
slcs_raw.append(offset+t)
#print slcs_raw[-1], slcs_raw[-1]-slcs_raw[-2], " - ",
#time.sleep(0.1)
#print t, offset
print ""
#pylab.plot(y[::10])
if show_progressbar:
pbar = ProgressBar(maxval=eeg.shape[0]/self._slice_width).start()
#slcs_raw = find_all_maxs(y[:,0],0.3,self._slice_width,20,callback=update_pbar)
print "Raw slice-positions found", len(slcs_raw), np.mean(np.diff(slcs_raw)), np.min(slcs_raw), np.max(slcs_raw)
slcs_raw_diff = np.diff(slcs_raw)
print "slcs_raw_diff: ", scoreatpercentile(slcs_raw_diff,5), scoreatpercentile(slcs_raw_diff,50), scoreatpercentile(slcs_raw_diff,95)
#raise Exception("Abbruch")
y , fn = upsample_to_memmap(eeg[:,self.cfsa],10)
slcs_raw_ups = [x*10 for x in slcs_raw]
t = slcs_raw_ups[len(slcs_raw)/2]
template = y[t-self._slice_width*10/2:t+self._slice_width*10/2]
for i in range(5):
t = slcs_raw_ups[len(slcs_raw)/2+i]
template += y[t-self._slice_width*10/2:t+self._slice_width*10/2]
template /= 6
offsets = []
for i,t in enumerate(slcs_raw_ups):
#offset = find_max_overlap(template, eeg[t-self._slice_width/2:t+self._slice_width/2,self.cfsa], 100)
offset = find_max_overlap(template, y[t-self._slice_width*10/2:t+self._slice_width*10/2], 100)
offsets.append(offset)
self.slcs_ups = [slcs_raw_ups[i]+offsets[i]+self.slice_shift for i in range(len(slcs_raw_ups))]
if show_progressbar:
pbar.finish()
print "Refined slice-positions found. Finished.", len(offsets), np.mean(offsets), np.median(offsets), np.min(offsets), np.max(offsets)
print "Percentile 0.5,5,95,99.5 of offsets: ", scoreatpercentile(offsets,0.5), scoreatpercentile(offsets,5), scoreatpercentile(offsets,95), scoreatpercentile(offsets,99.5)
#Adjusting _slice_width...
print "Old slice_width:", self._slice_width
self._new_slice_width = int(n.ceil(n.mean(n.diff(self.slcs_ups))))/10
self._new_slice_width += 3 # Make slice wider to have no zombie-timepoints
self._new_slice_width = self._new_slice_width+self._new_slice_width%2
#self._new_slice_width = (self._new_slice_width/2)*2 # make sw%2==0 (divisible by 2)
print "New slice_width:", self._new_slice_width
#raise Exception("Abbruch")
return [x/10 for x in self.slcs_ups]
def freqfilt_eeg(fn_in,
fn_out,
btype="lp",
fl=None,
fh=None,
border=3,
windowed=False):
eeg = eegpy.open_eeg(fn_in)
out = eegpy.F32(fn_out,
"w+",
shape=eeg.shape,
cNames=eeg.channel_names,
Fs=eeg.Fs)
if btype == "lp":
if not windowed:
out[:, :] = filtfilt_low(fl, eeg[:, :], Fs=eeg.Fs, border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i + 100000, out.num_datapoints), :] = filtfilt_low(
fl,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
elif btype == "hp":
#for i_c in range(eeg.num_channels):
if not windowed:
out[:, :] = filtfilt_high(fh, eeg[:, :], Fs=eeg.Fs, border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i + 100000, out.num_datapoints), :] = filtfilt_high(
fh,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
elif btype == "bp":
if not windowed:
out[:, :] = filtfilt_band(fl,
fh,
eeg[:, :],
Fs=eeg.Fs,
border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i + 100000, out.num_datapoints), :] = filtfilt_band(
fl,
fh,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
elif btype == "bs":
if not windowed:
out[:, :] = filtfilt_bandstop(fl,
fh,
eeg[:, :],
Fs=eeg.Fs,
border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i +
100000, out.num_datapoints), :] = filtfilt_bandstop(
fl,
fh,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
def find_all_slice_artifacts(self):
def update_pbar(num):
"""Callback for find_all_maxs"""
if show_progressbar:
pbar.update(num / 2)
eeg = self.eeg
#y = abs(smooth_windowed_eeg(eeg,[self.cfsa],self._slice_width))[:,0]
#y = smooth_windowed_eeg_power(eeg,[self.cfsa],self._slice_width)[:,0]
y = filtfilt_band(1, eeg.Fs / self._slice_width, eeg[:, self.cfsa])
#pylab.plot(y[::10])
#pylab.plot(eeg[:,14])
print y.shape, self._slice_width
#import pylab
#pylab.ion()
#pylab.plot(y[0:20000:1])
#pylab.show()
#raw_input()
#pylab.plot(y[13000:20000:1])
slcs_raw = find_all_maxs(y[:1000], ratio=0.6) # First segment
slcs_raw.sort()
#print "t=", t
#slcs_raw.append(t)
offset = 0
t = int(0.5 * self._slice_width)
while (t > 0.4 * self._slice_width or (y.shape[0] - offset) > 10000
) and (y.shape[0] - offset) > self._slice_width * 2:
#print (y.shape[0]-offset)
#print t, offset, "-",
offset = slcs_raw[-1] + self._slice_width / 2
#print t, offset, "-",
#pylab.plot(y[offset:offset+self._slice_width])
#pylab.show()
#raw_input()
t = y[offset:offset + self._slice_width].argmax()
slcs_raw.append(offset + t)
#print slcs_raw[-1], slcs_raw[-1]-slcs_raw[-2], " - ",
#time.sleep(0.1)
#print t, offset
print ""
#pylab.plot(y[::10])
if show_progressbar:
pbar = ProgressBar(maxval=eeg.shape[0] / self._slice_width).start()
#slcs_raw = find_all_maxs(y[:,0],0.3,self._slice_width,20,callback=update_pbar)
print "Raw slice-positions found", len(slcs_raw), np.mean(
np.diff(slcs_raw)), np.min(slcs_raw), np.max(slcs_raw)
slcs_raw_diff = np.diff(slcs_raw)
print "slcs_raw_diff: ", scoreatpercentile(
slcs_raw_diff,
5), scoreatpercentile(slcs_raw_diff,
50), scoreatpercentile(slcs_raw_diff, 95)
#raise Exception("Abbruch")
y, fn = upsample_to_memmap(eeg[:, self.cfsa], 10)
slcs_raw_ups = [x * 10 for x in slcs_raw]
t = slcs_raw_ups[len(slcs_raw) / 2]
template = y[t - self._slice_width * 10 / 2:t +
self._slice_width * 10 / 2]
for i in range(5):
t = slcs_raw_ups[len(slcs_raw) / 2 + i]
template += y[t - self._slice_width * 10 / 2:t +
self._slice_width * 10 / 2]
template /= 6
offsets = []
for i, t in enumerate(slcs_raw_ups):
#offset = find_max_overlap(template, eeg[t-self._slice_width/2:t+self._slice_width/2,self.cfsa], 100)
offset = find_max_overlap(
template, y[t - self._slice_width * 10 / 2:t +
self._slice_width * 10 / 2], 100)
offsets.append(offset)
self.slcs_ups = [
slcs_raw_ups[i] + offsets[i] + self.slice_shift
for i in range(len(slcs_raw_ups))
]
if show_progressbar:
pbar.finish()
print "Refined slice-positions found. Finished.", len(
offsets), np.mean(offsets), np.median(offsets), np.min(
offsets), np.max(offsets)
print "Percentile 0.5,5,95,99.5 of offsets: ", scoreatpercentile(
offsets, 0.5), scoreatpercentile(offsets, 5), scoreatpercentile(
offsets, 95), scoreatpercentile(offsets, 99.5)
#Adjusting _slice_width...
print "Old slice_width:", self._slice_width
self._new_slice_width = int(n.ceil(n.mean(n.diff(self.slcs_ups)))) / 10
self._new_slice_width += 3 # Make slice wider to have no zombie-timepoints
self._new_slice_width = self._new_slice_width + self._new_slice_width % 2
#self._new_slice_width = (self._new_slice_width/2)*2 # make sw%2==0 (divisible by 2)
print "New slice_width:", self._new_slice_width
#raise Exception("Abbruch")
return [x / 10 for x in self.slcs_ups]