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 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 test_DeltaN10W100():
#arange
data = _make_data_with_delta(100)
#act
data_smooth = smooth(data, 10)
#assert
assert data.shape == data_smooth.shape
assert _check_data_decay_from_center(data_smooth)
def test_DeltaN10W100():
# arange
data = _make_data_with_delta(100)
# act
data_smooth = smooth(data, 10)
# assert
assert data.shape == data_smooth.shape
assert _check_data_decay_from_center(data_smooth)
def test_DeltaN100W10FlatWindow():
n = 100
w = 10
# arange
data = _make_data_with_delta(n)
# act
data_smooth = smooth(data, w, "flat")
# assert
assert data.shape == data_smooth.shape
assert_array_almost_equal(data_smooth[n / 2 - w / 2 + 1 : n / 2 - w / 2 + 1 + w], np.ones((w)) / w)
def test_DeltaN100W10FlatWindow():
n = 100
w = 10
#arange
data = _make_data_with_delta(n)
#act
data_smooth = smooth(data, w, "flat")
#assert
assert data.shape == data_smooth.shape
assert_array_almost_equal(
data_smooth[n / 2 - w / 2 + 1:n / 2 - w / 2 + 1 + w],
np.ones((w)) / w)
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 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
#p.show()
# time.sleep(0.2)
inputs = []
states = []
refs = []
for i in range(5):
#inputs.append(np.diag(np.ones((10))*np.random.random()))
inputs.append(np.random.random((10, 10)))
#states.append(np.zeros((10,10)))
states.append(inputs[-1])
refs.append((-1) * np.ones((10, 10)))
for i in range(5):
inputs.append(np.random.random((10, 10)))
#inputs.append(np.diag(np.ones((10))*np.random.random()))
inputs[-1][:] = smooth(inputs[-1], 9) #+0.1
#states.append(np.zeros((10,10)))
states.append(inputs[-1])
refs.append(np.ones((10, 10)) * (1))
ct = CNNTrainer(t_sim=40, errfunc="mean_abs_diff", opt_method="simplex")
#ct = CNNTrainer(t_sim=40,errfunc="sum_sqr_diff", opt_method="powell")
#ct = CNNTrainer(t_sim=40,errfunc="mean_abs_diff", opt_method="anneal")
a, b, z = ct(inputs, states, refs)
#p.ioff()
#print "Test"
p.hot()
p.figure(1)
for i in range(10):
print i,
cnn = CNN2d(inputs[i], states[i], a, b, z)
try:
#p.show()
# time.sleep(0.2)
inputs = []
states = []
refs = []
for i in range(5):
#inputs.append(np.diag(np.ones((10))*np.random.random()))
inputs.append(np.random.random((10,10)))
#states.append(np.zeros((10,10)))
states.append(inputs[-1])
refs.append((-1)*np.ones((10,10)))
for i in range(5):
inputs.append(np.random.random((10,10)))
#inputs.append(np.diag(np.ones((10))*np.random.random()))
inputs[-1][:] = smooth(inputs[-1],9)#+0.1
#states.append(np.zeros((10,10)))
states.append(inputs[-1])
refs.append(np.ones((10,10))*(1))
ct = CNNTrainer(t_sim=40,errfunc="mean_abs_diff", opt_method="simplex")
#ct = CNNTrainer(t_sim=40,errfunc="sum_sqr_diff", opt_method="powell")
#ct = CNNTrainer(t_sim=40,errfunc="mean_abs_diff", opt_method="anneal")
a,b,z = ct(inputs,states,refs)
#p.ioff()
#print "Test"
p.hot()
p.figure(1)
for i in range(10):
print i,
cnn = CNN2d(inputs[i],states[i],a,b,z)
try:
