def find_slices_in_mean(self,num_slices = None,ch_num=None, width=None):
"""In the calculated average data, find all positions of slice-acquisition artifacts relative to trigger.
Two rounds: first, use a smoothed version (lower risk of failure),
second, improve values on unsmoothed signal."""
if num_slices==None:
num_slices=self._num_slices
if width==None:
width=self._slice_width
if ch_num==None:
ch_num=self._ch_for_search
#First round
smooth_data = smooth(self._mean_data[:,ch_num]**2,width)
#global test
#test=smooth_data
#smooth_data = (smooth_data**2).mean(axis=1)
#if debug:
#print smooth_data.shape, smooth_data
#pylab.clf()
#pylab.ioff()
#pylab.plot(smooth_data[:])
#pylab.show()
#raw_input()
maxs = find_maxs(smooth_data,num_maxs = num_slices, width=width)
if debug:
print "Coarse Maxs: ", maxs
template = self._mean_data[maxs[3]-width/2:maxs[3]+width/2]
for i in range(len(maxs)):
try:
maxs[i] += find_max_overlap(template,self._mean_data[maxs[i]-width/2:maxs[i]+width/2])
except Exception, e:
print "Error while refining max-position no. ", i, ", ", e
def set_timepoints_auto(self,
start,
end=10e50,
step=5500,
ch_num=0,
width=100):
"""If triggers are not good, one can try to automatically find the timepoints"""
#print "Setting timepoints automatically"
assert ch_num < self._data.shape[
1] and ch_num >= 0, "ch_num is not valid"
ts = []
t = int(start)
offset = 0
while t < self._data.shape[0] - step and t < end:
if t == int(start):
#template = self._data[t-width/2:t+width/2,ch_num]
template = self._data[t:t + step, ch_num]
ts.append(t)
else:
#offset = find_max_overlap(template, self._data[t-width/2:t+width/2,ch_num], width/2)
offset = find_max_overlap(template, self._data[t:t + step,
ch_num],
width / 2)
#print offset
ts.append(t + offset)
if debug:
print ts[-1],
t += step + offset
self.set_timepoints(ts)
return ts
def set_timepoints_auto(self,start,end=10e50,step=5500,ch_num=0,width=100):
"""If triggers are not good, one can try to automatically find the timepoints.
For fMRI, search for first volume artifact."""
#print "Setting timepoints automatically"
assert ch_num<self._data.shape[1] and ch_num>=0, "ch_num is not valid"
ts = []
t = int(start)
offset=0
while t<self._data.shape[0]-step and t<end:
if t==int(start):
#template = self._data[t-width/2:t+width/2,ch_num]
searchdata = abs(hilbert(self._data[t:t+500,ch_num]))
searchdata = smooth(searchdata,self._slice_width/2)
bp = np.where(searchdata>200)[0][0]
t = (bp+searchdata[bp:bp+self._slice_width/2].argmax())-self._slice_width/2
template = self._data[t:t+step,ch_num]
ts.append(t)
else:
#offset = find_max_overlap(template, self._data[t-width/2:t+width/2,ch_num], width/2)
offset = find_max_overlap(template, self._data[t:t+step,ch_num], width/2)
#print offset
ts.append(t+offset)
if debug:
print ts[-1],
t+=step+offset
self.set_timepoints(ts)
return ts
def find_slices_in_mean(self, num_slices=None, ch_num=None, width=None):
"""In the calculated average data, find all positions of slice-acquisition artifacts relative to trigger.
Two rounds: first, use a smoothed version (lower risk of failure),
second, improve values on unsmoothed signal."""
if num_slices == None:
num_slices = self._num_slices
if width == None:
width = self._slice_width
if ch_num == None:
ch_num = self._ch_for_search
#First round
smooth_data = smooth(self._mean_data[:, ch_num]**2, width)
#global test
#test=smooth_data
#smooth_data = (smooth_data**2).mean(axis=1)
#if debug:
#print smooth_data.shape, smooth_data
#pylab.clf()
#pylab.ioff()
#pylab.plot(smooth_data[:])
#pylab.show()
#raw_input()
maxs = find_maxs(smooth_data, num_maxs=num_slices, width=width)
if debug:
print "Coarse Maxs: ", maxs
template = self._mean_data[maxs[3] - width / 2:maxs[3] + width / 2]
for i in range(len(maxs)):
try:
maxs[i] += find_max_overlap(
template,
self._mean_data[maxs[i] - width / 2:maxs[i] + width / 2])
except Exception, e:
print "Error while refining max-position no. ", i, ", ", e
def realign_triggers(self):
if self.Ya == None:
raise ValueError("OBSFilter: No upsampled data are available - self.Ya not set yet")
self._ups_ts = [self._ts[0]*self.upsf]
template = self.Ya[self._ts[0]*self.upsf:(self._ts[0]+self._len)*self.upsf]
for i,t in enumerate(self._ts[1:]):
#pylab.clf()
artifact = self.Ya[t*self.upsf:(t+self._len)*self.upsf]
shift = find_max_overlap(template, artifact, self.upsf*10)
#pylab.plot(template[1000:3000])
#pylab.plot(artifact[1000:3000])
#pylab.text(40000,500,str(shift))
#raw_input("Press enter")
self._ups_ts.append(t*self.upsf+shift)
print "_ts", len(self._ts), self._ts
print "_ups_ts", len(self._ups_ts), self._ups_ts
def realign_triggers(self):
if self.Ya == None:
raise ValueError(
"OBSFilter: No upsampled data are available - self.Ya not set yet"
)
self._ups_ts = [self._ts[0] * self.upsf]
template = self.Ya[self._ts[0] * self.upsf:(self._ts[0] + self._len) *
self.upsf]
for i, t in enumerate(self._ts[1:]):
#pylab.clf()
artifact = self.Ya[t * self.upsf:(t + self._len) * self.upsf]
shift = find_max_overlap(template, artifact, self.upsf * 10)
#pylab.plot(template[1000:3000])
#pylab.plot(artifact[1000:3000])
#pylab.text(40000,500,str(shift))
#raw_input("Press enter")
self._ups_ts.append(t * self.upsf + shift)
print "_ts", len(self._ts), self._ts
print "_ups_ts", len(self._ups_ts), self._ups_ts
def set_timepoints_auto(self,start,end=10e50,step=5500,ch_num=0,width=100):
"""If triggers are not good, one can try to automatically find the timepoints"""
#print "Setting timepoints automatically"
assert ch_num<self._data.shape[1] and ch_num>=0, "ch_num is not valid"
ts = []
t = int(start)
offset=0
while t<self._data.shape[0]-step and t<end:
if t==int(start):
#template = self._data[t-width/2:t+width/2,ch_num]
template = self._data[t:t+step,ch_num]
ts.append(t)
else:
#offset = find_max_overlap(template, self._data[t-width/2:t+width/2,ch_num], width/2)
offset = find_max_overlap(template, self._data[t:t+step,ch_num], width/2)
#print offset
ts.append(t+offset)
if debug:
print ts[-1],
t+=step+offset
self.set_timepoints(ts)
return ts
def set_timepoints_auto(self,
start,
end=10e50,
step=5500,
ch_num=0,
width=100):
"""If triggers are not good, one can try to automatically find the timepoints.
For fMRI, search for first volume artifact."""
#print "Setting timepoints automatically"
assert ch_num < self._data.shape[
1] and ch_num >= 0, "ch_num is not valid"
ts = []
t = int(start)
offset = 0
while t < self._data.shape[0] - step and t < end:
if t == int(start):
#template = self._data[t-width/2:t+width/2,ch_num]
searchdata = abs(hilbert(self._data[t:t + 500, ch_num]))
searchdata = smooth(searchdata, self._slice_width / 2)
bp = np.where(searchdata > 200)[0][0]
t = (bp + searchdata[bp:bp + self._slice_width / 2].argmax()
) - self._slice_width / 2
template = self._data[t:t + step, ch_num]
ts.append(t)
else:
#offset = find_max_overlap(template, self._data[t-width/2:t+width/2,ch_num], width/2)
offset = find_max_overlap(template, self._data[t:t + step,
ch_num],
width / 2)
#print offset
ts.append(t + offset)
if debug:
print ts[-1],
t += step + offset
self.set_timepoints(ts)
return ts
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 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]
