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 calculate(self, cond_name=None):
"""After doing the setup, this method actually calculates the ERPs.
If no argument is supplied, then the data for all conditions are calculated."""
if cond_name == None:
cnames = self._times.keys()
else:
cnames = [cond_name]
#self.check_for_artifacts()
for c in cnames:
if debug:
print "Condition %s" % c
#assert self._datadict.has_key(c) and self._is_calculated.has_key(c), "Key in dictionary missing!"
#TODO: open file
f = eegpy.open_eeg(self._dfile_name)
#TODO: check timepoints
ndp = f.numDatapoints
tps_ok = True
if debug:
print ndp, str(f)
for t in self._times[c]:
#if debug:
# print "t = ", t
if t + self._start_end[0] < 0 or t + self._start_end[1] > ndp:
tps_ok = False
break
if tps_ok:
#getData
data = f.get_data_for_events(self._times[c], self._start_end)
if self._lp_freq != None:
data = filtfilt_low(self._lp_freq, data, Fs=f.Fs)
#Baseline-Korrektur
data -= data[-self._start_end[0] - 200:-self._start_end[0],
...].mean(axis=0)
#calculate average
self._datadict[c] = data[:, :,
~self._is_artifact[c]].mean(axis=2)
#print "Shape datadict:", self._datadict[c].shape
self._is_calculated[c] = True
else:
#TODO: some kind of error message
print "Some problem with the timepoints occured. ndp:", ndp, "max_ts:", max(
self._times[c])
pass
def calculate(self, cond_name=None):
"""After doing the setup, this method actually calculates the ERPs.
If no argument is supplied, then the data for all conditions are calculated."""
if cond_name == None:
cnames = self._times.keys()
else:
cnames = [cond_name]
#self.check_for_artifacts()
for c in cnames:
logger.debug("Condition %s" % c)
#assert self._datadict.has_key(c) and self._is_calculated.has_key(c), "Key in dictionary missing!"
f = self.eeg
#TODO: check timepoints
ndp = f.numDatapoints
tps_ok = True
logger.debug("datapoints: %s, input: %s" % (ndp, str(f)))
for t in self._times[c]:
#if debug:
# print "t = ", t
if t+self._start_end[0]<0 or t+self._start_end[1]>ndp:
tps_ok=False
break
if tps_ok:
#getData
data = f.get_data_for_events(self._times[c],self._start_end)
if self._lp_freq!=None:
data = filtfilt_low(self._lp_freq,data,Fs=f.Fs)
#Baseline-Korrektur
data -= self._get_baseline_data(data).mean(axis=0)
#calculate average
self._datadict[c] = data[:,:,~self._is_artifact[c]].mean(axis=2)
#print "Shape datadict:", self._datadict[c].shape
self._is_calculated[c] = True
else:
raise ValueError("Some problem with the timepoints occured." +
"Number of datapoints: %i, Max. timepoint: %i" %(ndp, max(self._times[c])))
def final_lowpass(self, f=90):
"""Performs simple lowpass, removing sharp edges"""
eeg = self.eeg
eeg[:] = filtfilt_low(f,eeg[:],Fs=eeg.Fs)
def final_lowpass(self, f=None):
"""Performs simple lowpass, removing sharp edges"""
if f == None:
f = self._f_flp
eeg = self._data
eeg[:] = filtfilt_low(f,eeg[:],Fs=self._Fs)
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 final_lowpass(self, f=90):
"""Performs simple lowpass, removing sharp edges"""
eeg = self.eeg
eeg[:] = filtfilt_low(f, eeg[:], Fs=eeg.Fs)
class FmriFilterContinuous(object):
"""New approach to filtering, for continuous data only.
Takes the eeg, finds slices automatically and does a kind of OBS-Filtering."""
def __init__(self, eeg=None, slice_width=78, k=20, obs_size=5, f_lp=70):
self.eeg = eeg
self._slice_width = slice_width
self.ch_for_slice_alignment = 14
#self.cfsa = self.ch_for_slice_alignment
self.num_neighbors = k
self.obs_size = obs_size
self.f_lp = f_lp
self.slice_shift = 0 #Introduced for analysis, shifting of slice-timepoints
self._pa_zeros = 5 #Number of zeros to pre-/append to slices
def get_cfsa(self):
return self.ch_for_slice_alignment
def set_cfsa(self, cfsa):
self.ch_for_slice_alignment = cfsa
cfsa = property(get_cfsa, set_cfsa)
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 remove_artifacts(self):
"""Tries to remove the artifacts.
Uses mixture of mean-subtraction and OBS-PCA-Subtraction
Needs self.slcs to be set.
Saves example data in self.examples to make evaluation of filter quality possible"""
def obs_fit_error(p, y, x):
err = n.zeros((x.shape[0]), "d")
err += p[0]
for i in range(self.obs_size):
err += y[:, i] * p[i + 1]
err -= x
return abs(err**2).sum()
def obs_fit_error_lsq(p, y, x):
err = n.zeros((x.shape[0]), "d")
err += p[0]
for i in range(self.obs_size):
err += y[:, i] * p[i + 1]
err -= x
return err
def las_fit_error_lsq(p, y, x):
"""Local artifact subtraction"""
err = p[0] * y
err -= x
return err
#Shortnames
eeg = self.eeg
slcs = self.slcs_ups
sw = self._new_slice_width * 10
k = self.num_neighbors
self.examples = []
num_examples = 10
#Loop over channels
if show_progressbar:
pbar = ProgressBar().start()
for i_ch in range(eeg.num_channels):
#Make Highpass-Version of channel
#ch = eeg[:,i_ch].copy()
y, fn = upsample_to_memmap(eeg[:, i_ch], 10)
y_hp, fn_hp = tmp_memmap(dtype=y.dtype, shape=y.shape, mode="w+")
y_hp[:] = filtfilt_high(1, y, Fs=10000.0)
y_out, fn_out = tmp_memmap(dtype=y.dtype, shape=y.shape, mode="w+")
y_out[:] = y[:]
#ch_hp = filtfilt_high(1.0,ch,Fs=eeg.Fs)
neighbors = n.zeros((sw + 2 * self._pa_zeros, k))
#Prefill
for i in range(k):
#print neighbors[:,i].shape, eeg[slcs[i]-sw/2:slcs[i]+sw/2,i_ch].shape
neighbors[:, i] = prepend_append_zeros(
y_hp[slcs[i] - sw / 2:slcs[i] + sw / 2], self._pa_zeros)
#Loop over slices and filter
next_subst = 0
for i, t in enumerate(slcs):
try:
if i > k / 2 and i < len(slcs) - k / 2:
neighbors[:, next_subst] = prepend_append_zeros(
y_hp[slcs[i + k / 2] - sw / 2:slcs[i + k / 2] +
sw / 2], self._pa_zeros)
next_subst += 1
next_subst = next_subst % k
tmp = prepend_append_zeros(y[t - sw / 2:t + sw / 2],
self._pa_zeros)
#Subtraction
#Shift/scale template
template = neighbors.mean(axis=1)
p = [1.0, 0]
p_las = leastsq(las_fit_error_lsq, p,
args=(template, tmp))[0]
#print p_las[0],
tmp -= p_las[0] * template
#Beispieldaten speichern: Teil 1
if i_ch == self.ch_for_slice_alignment:
if i % (len(slcs) / num_examples) == (
len(slcs) / num_examples) / 2:
print "examples, Teil 1"
example = {}
example["raw"] = prepend_append_zeros(
y[t - sw / 2:t + sw / 2].copy(),
self._pa_zeros)
example["mean"] = p_las[0] * template
#OBS-Fit
components = pcafilt.unmix(
neighbors) #OBS will be first 5 components
components -= components.mean(axis=0).reshape(
1, -1).repeat(components.shape[0],
0) #demeaning column-wise
obs = components[:, :self.obs_size].copy()
#Fit OBS to artifact
p = [0] + [0] * self.obs_size
#p_lsq = fmin(obs_fit_error,p,args=(obs,eeg[t-sw/2:t+sw/2,i_ch]),maxiter=1e5,maxfun=1e5)#[0]
p_lsq = leastsq(obs_fit_error_lsq, p, args=(obs, tmp))[0]
#print i,t,"p_lsq", p_lsq
fit = n.zeros((obs.shape[0]), "d")
fit += p_lsq[0]
for j in range(self.obs_size):
fit += obs[:, j] * p_lsq[j + 1]
tmp -= fit
try:
#eeg[t/10-sw/10/2:t/10+sw/10/2,i_ch] = tmp[self._pa_zeros:-self._pa_zeros][::10]
y_out[t - sw / 2:t +
sw / 2] = tmp[self._pa_zeros:-self._pa_zeros][:]
except ValueError, ve:
print i_ch, i, t, eeg[
t / 10 - sw / 10 / 2:t / 10 + sw / 10 / 2,
i_ch].shape, tmp[self._pa_zeros:-self.
_pa_zeros][::10].shape
#Beispieldaten speichern: Teil 2
if i_ch == self.ch_for_slice_alignment:
if i % (len(slcs) / num_examples) == (
len(slcs) / num_examples) / 2:
print "examples, Teil 2"
#example["fit"] = n.zeros(example["raw"].shape) #fit.copy()
example["fit"] = fit.copy()
example["obs"] = obs.copy()
example["filt1"] = (tmp + fit).copy()
example["filt2"] = tmp.copy()
self.examples.append(example)
if show_progressbar:
pbar.update(
(i_ch + i / len(slcs)) * 100 / eeg.num_channels)
except Exception, e:
print "Error occurred at slice at t=", t, ", ignored"
print e
y_out[:] = filtfilt_low(self.f_lp, y_out[:], Fs=eeg.Fs * 10)
eeg[:, i_ch] = y_out[::10]
def final_lowpass(self, f=None):
"""Performs simple lowpass, removing sharp edges"""
if f == None:
f = self._f_flp
eeg = self._data
eeg[:] = filtfilt_low(f, eeg[:], Fs=self._Fs)