examples/example_24_large_scale_brian2_simulation/clusternet.py
def analyse(self, traj, network, current_subrun, subrun_list, network_dict):
        """Calculates average Fano Factor of a network.

        :param traj:

            Trajectory container

            Expects:

            `results.monitors.spikes_e`: Data from SpikeMonitor for excitatory neurons

            Adds:

            `results.statistics.mean_fano_factor`: Average Fano Factor

        :param network:

            The BRIAN network

        :param current_subrun:

            BrianParameter

        :param subrun_list:

            Upcoming subruns, analysis is only performed if subruns is empty,
            aka the final subrun has finished.

        :param network_dict:

            Dictionary of items shared among componetns

        """
        #Check if we finished all subruns
        if len(subrun_list)==0:
            spikes_e = traj.results.monitors.spikes_e

            time_window = traj.parameters.analysis.statistics.time_window
            start_time = traj.parameters.simulation.durations.initial_run
            end_time = start_time+traj.parameters.simulation.durations.measurement_run
            neuron_ids = traj.parameters.analysis.statistics.neuron_ids

            mean_ff = self._compute_mean_fano_factor(
                neuron_ids, spikes_e, time_window, start_time, end_time)

            traj.f_add_result('statistics.mean_fano_factor', mean_ff, comment='Average Fano '
                                                                      'Factor over all '
                                                                      'exc neurons')

            print('R_ee: %f, Mean FF: %f' % (traj.R_ee, mean_ff))
def analyse(self, traj, network, current_subrun, subrun_list, network_dict):
        #Check if we finished all subruns
        if len(subrun_list)==0:
            spikes_e = traj.results.monitors.spikes_e

            time_window = traj.parameters.analysis.statistics.time_window
            start_time = traj.parameters.simulation.durations.initial_run
            end_time = start_time+traj.parameters.simulation.durations.measurement_run
            neuron_ids = traj.parameters.analysis.statistics.neuron_ids

            mean_ff = self._compute_mean_fano_factor(
                neuron_ids, spikes_e, time_window, start_time, end_time)

            traj.f_add_result('statistics.mean_fano_factor', mean_ff, comment='Average Fano '
                                                                      'Factor over all '
                                                                      'exc neurons')

            print('R_ee: %f, Mean FF: %f' % (traj.R_ee, mean_ff))
def analyse ( self , traj , network , current_subrun , subrun_list , network_dict ) : #Check if we finished all subruns if len ( subrun_list ) == 0 : spikes_e = traj . results . monitors . spikes_e time_window = traj . parameters . analysis . statistics . time_window start_time = traj . parameters . simulation . durations . initial_run end_time = start_time + traj . parameters . simulation . durations . measurement_run neuron_ids = traj . parameters . analysis . statistics . neuron_ids mean_ff = self . _compute_mean_fano_factor ( neuron_ids , spikes_e , time_window , start_time , end_time ) traj . f_add_result ( 'statistics.mean_fano_factor' , mean_ff , comment = 'Average Fano ' 'Factor over all ' 'exc neurons' ) print ( 'R_ee: %f, Mean FF: %f' % ( traj . R_ee , mean_ff ) )
Calculates average Fano Factor of a network.

        :param traj:

            Trajectory container

            Expects:

            `results.monitors.spikes_e`: Data from SpikeMonitor for excitatory neurons

            Adds:

            `results.statistics.mean_fano_factor`: Average Fano Factor

        :param network:

            The BRIAN network

        :param current_subrun:

            BrianParameter

        :param subrun_list:

            Upcoming subruns, analysis is only performed if subruns is empty,
            aka the final subrun has finished.

        :param network_dict:

            Dictionary of items shared among componetns
Calculates average Fano Factor of a network .