def FIR_hrf(self):
"""Calculate the FIR event-related estimated of the HRFs for different
kinds of events
Returns
-------
A time-series object, shape[:-2] are dimensions corresponding to the to
shape[:-2] of the EventRelatedAnalyzer data, shape[-2] corresponds to
the different kinds of events used (ordered according to the sorted
order of the unique components in the events time-series). shape[-1]
corresponds to time, and has length = len_hrf
"""
#Make a list to put the outputs in:
h = [0] * self._len_h
for i in xrange(self._len_h):
#Get the design matrix:
design = tsu.fir_design_matrix(self.events[i],self.len_hrf)
#Compute the fir estimate, in linear form:
this_h = tsa.fir(self.data[i],design)
#Reshape the linear fir estimate into a event_types*hrf_len array
u = np.unique(self.events[i])
event_types = u[np.unique(self.events[i])!=0]
h[i] =np.reshape(this_h,(event_types.shape[0],self.len_hrf))
h = np.array(h).squeeze()
return UniformTimeSeries(data=h, sampling_rate=self.sampling_rate)
def FIR(self):
"""Calculate the FIR event-related estimated of the HRFs for different
kinds of events
Returns
-------
A time-series object, shape[:-2] are dimensions corresponding to the to
shape[:-2] of the EventRelatedAnalyzer data, shape[-2] corresponds to
the different kinds of events used (ordered according to the sorted
order of the unique components in the events time-series). shape[-1]
corresponds to time, and has length = len_et
XXX code needs to be changed to use flattening (see 'eta' below)
"""
#Make a list to put the outputs in:
h = [0] * self._len_h
for i in xrange(self._len_h):
#XXX Check that the offset makes sense (there can't be an event
#happening within one offset duration of the beginning of the
#time-series:
#Get the design matrix (roll by the offset, in order to get the
#right thing):
roll_events = np.roll(self.events[i],self._offset)
design = tsu.fir_design_matrix(roll_events,self.len_et+
abs(self._offset))
#Compute the fir estimate, in linear form:
this_h = tsa.fir(self.data[i],design)
#Reshape the linear fir estimate into a event_types*hrf_len array
u = np.unique(self.events[i])
event_types = u[np.unique(self.events[i])!=0]
h[i] =np.reshape(this_h,(event_types.shape[0],self.len_et+
abs(self._offset)))
h = np.array(h).squeeze()
return ts.UniformTimeSeries(data=h,sampling_rate=self.sampling_rate,
t0=-1*self.len_et*self.sampling_interval,
time_unit=self.time_unit)
def FIR(self):
"""Calculate the FIR event-related estimated of the HRFs for different
kinds of events
Returns
-------
A time-series object, shape[:-2] are dimensions corresponding to the to
shape[:-2] of the EventRelatedAnalyzer data, shape[-2] corresponds to
the different kinds of events used (ordered according to the sorted
order of the unique components in the events time-series). shape[-1]
corresponds to time, and has length = len_et
XXX code needs to be changed to use flattening (see 'eta' below)
"""
#Make a list to put the outputs in:
h = [0] * self._len_h
for i in xrange(self._len_h):
#XXX Check that the offset makes sense (there can't be an event
#happening within one offset duration of the beginning of the
#time-series:
#Get the design matrix (roll by the offset, in order to get the
#right thing):
roll_events = np.roll(self.events[i], self.offset)
design = tsu.fir_design_matrix(roll_events, self.len_et)
#Compute the fir estimate, in linear form:
this_h = tsa.fir(self.data[i], design)
#Reshape the linear fir estimate into a event_types*hrf_len array
u = np.unique(self.events[i])
event_types = u[np.unique(self.events[i]) != 0]
h[i] = np.reshape(this_h, (event_types.shape[0], self.len_et))
h = np.array(h).squeeze()
return ts.TimeSeries(data=h,
sampling_rate=self.sampling_rate,
t0=self.offset * self.sampling_interval,
time_unit=self.time_unit)
