Пример #1
0
 def __setattr__(self, name, value):
     " Method called when setting an attribute."
     Population.__setattr__(self, name, value)
     if name in ['rates', 'corr', 'tau'] and hasattr(self, 'initialized'):
         # Correction of mu and sigma everytime r, c or tau is changed
         self.mu, self.sigma = self._rectify(self.rates, self.corr,
                                             self.tau)
Пример #2
0
 def __setattr__(self, name, value):
     if name == 'spike_times':
         if self.initialized:
             self.cyInstance.set_spike_times(value)
         else:
             object.__setattr__(self, name, value)
     else:
         Population.__setattr__(self, name, value)
Пример #3
0
    def __setattr__(self, name, value):
        if name == 'spike_times':
            if not isinstance(value[0], list): # several neurons
                value = [ value ]
            if not len(value) == self.size:
                Global._error('SpikeSourceArray: the size of the spike_times attribute must match the number of neurons in the population.')

            self.init['spike_times'] = value # when reset is called
            if self.initialized:
                self.cyInstance.set_spike_times(self._sort_spikes(value))
        else:
            Population.__setattr__(self, name, value)
Пример #4
0
    def __setattr__(self, name, value):
        if name == 'spike_times':
            if not isinstance(value[0], list):  # several neurons
                value = [value]
            if not len(value) == self.size:
                Global._error(
                    'SpikeSourceArray: the size of the spike_times attribute must match the number of neurons in the population.'
                )

            self.init['spike_times'] = value  # when reset is called
            if self.initialized:
                self.cyInstance.set_spike_times(self._sort_spikes(value))
        else:
            Population.__setattr__(self, name, value)
Пример #5
0
 def __setattr__(self, name, value):
     if name == 'schedule':
         if self.initialized:
             self.cyInstance.set_schedule( np.array(value) / Global.config['dt'] )
         else:
             self.init['schedule'] = value
     elif name == 'rates':
         if self.initialized:
             if len(value.shape) > 2:
                 # we need to flatten the provided data
                 flat_values = value.reshape( (value.shape[0], self.size) )
                 self.cyInstance.set_rates( flat_values )
             else:
                 self.cyInstance.set_rates( value )
         else:
             self.init['rates'] = value
     elif name == "period":
         if self.initialized:
             self.cyInstance.set_period(int(value /Global.config['dt']))
         else:
             self.init['period'] = value
     else:
         Population.__setattr__(self, name, value)
Пример #6
0
 def __setattr__(self, name, value):
     if name == 'schedule':
         if self.initialized:
             self.cyInstance.set_schedule(
                 np.array(value) / Global.config['dt'])
         else:
             self.init['schedule'] = value
     elif name == 'rates':
         if self.initialized:
             if len(value.shape) > 2:
                 # we need to flatten the provided data
                 flat_values = value.reshape((value.shape[0], self.size))
                 self.cyInstance.set_rates(flat_values)
             else:
                 self.cyInstance.set_rates(value)
         else:
             self.init['rates'] = value
     elif name == "period":
         if self.initialized:
             self.cyInstance.set_period(int(value / Global.config['dt']))
         else:
             self.init['period'] = value
     else:
         Population.__setattr__(self, name, value)
Пример #7
0
 def __setattr__(self, name, value):
     " Method called when setting an attribute."
     Population.__setattr__(self, name, value) 
     if name in ['rates', 'corr', 'tau'] and hasattr(self, 'initialized'):
         # Correction of mu and sigma everytime r, c or tau is changed
         self.mu, self.sigma = self._rectify(self.rates, self.corr, self.tau)