Ejemplo n.º 1
0
    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)
Ejemplo n.º 2
0
    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)
Ejemplo n.º 3
0
    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)