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]