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)
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)
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)
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)
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)
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)
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)
