def run_experiments(model,experiment_list,parameters,load_from=None):
"""
This is function called by :func:.run_workflow that executes the experiments in the `experiment_list` over the model.
Alternatively, if load_from is specified it will load an existing simulation from the path specified in load_from.
Parameters
----------
model : Model
The model to execute experiments on.
experiment_list : list
The list of experiments to execute.
parameters : ParameterSet
The parameters given to the simulation run.
load_from : str
If not None it will load the simulation from the specified directory.
Returns
-------
data_store : DataStore
The data store containing the recordings.
"""
# first lets run all the measurements required by the experiments
logger.info('Starting Experiemnts')
if load_from == None:
data_store = PickledDataStore(load=False,
parameters=MozaikExtendedParameterSet({'root_directory': Global.root_directory,'store_stimuli' : parameters.store_stimuli}))
else:
data_store = PickledDataStore(load=True,
parameters=MozaikExtendedParameterSet({'root_directory': load_from,'store_stimuli' : parameters.store_stimuli}))
data_store.set_neuron_ids(model.neuron_ids())
data_store.set_neuron_positions(model.neuron_positions())
data_store.set_neuron_annotations(model.neuron_annotations())
data_store.set_model_parameters(str(parameters))
data_store.set_sheet_parameters(str(model.sheet_parameters()))
t0 = time.time()
simulation_run_time=0
for i,experiment in enumerate(experiment_list):
logger.info('Starting experiment: ' + experiment.__class__.__name__)
stimuli = experiment.return_stimuli()
unpresented_stimuli = data_store.identify_unpresented_stimuli(stimuli)
logger.info('Running model')
simulation_run_time += experiment.run(data_store,unpresented_stimuli)
logger.info('Experiment %d/%d finished' % (i+1,len(experiment_list)))
total_run_time = time.time() - t0
mozaik_run_time = total_run_time - simulation_run_time
logger.info('Total simulation run time: %.0fs' % total_run_time)
logger.info('Simulator run time: %.0fs (%d%%)' % (simulation_run_time, int(simulation_run_time /total_run_time * 100)))
logger.info('Mozaik run time: %.0fs (%d%%)' % (mozaik_run_time, int(mozaik_run_time /total_run_time * 100)))
return data_store
def run_experiments(model, experiment_list, parameters, load_from=None):
"""
This is function called by :func:.run_workflow that executes the experiments in the `experiment_list` over the model.
Alternatively, if load_from is specified it will load an existing simulation from the path specified in load_from.
Parameters
----------
model : Model
The model to execute experiments on.
experiment_list : list
The list of experiments to execute.
parameters : ParameterSet
The parameters given to the simulation run.
load_from : str
If not None it will load the simulation from the specified directory.
Returns
-------
data_store : DataStore
The data store containing the recordings.
"""
# first lets run all the measurements required by the experiments
logger.info('Starting Experiemnts')
if load_from == None:
data_store = PickledDataStore(load=False,
parameters=MozaikExtendedParameterSet({
'root_directory':
Global.root_directory,
'store_stimuli':
parameters.store_stimuli
}))
else:
data_store = PickledDataStore(load=True,
parameters=MozaikExtendedParameterSet({
'root_directory':
load_from,
'store_stimuli':
parameters.store_stimuli
}))
data_store.set_neuron_ids(model.neuron_ids())
data_store.set_neuron_positions(model.neuron_positions())
data_store.set_neuron_annotations(model.neuron_annotations())
data_store.set_model_parameters(str(parameters))
data_store.set_sheet_parameters(str(model.sheet_parameters()))
data_store.set_experiment_parametrization_list([
(str(exp.__class__), str(exp.parameters)) for exp in experiment_list
])
t0 = time.time()
simulation_run_time = 0
for i, experiment in enumerate(experiment_list):
logger.info('Starting experiment: ' + experiment.__class__.__name__)
stimuli = experiment.return_stimuli()
unpresented_stimuli_indexes = data_store.identify_unpresented_stimuli(
stimuli)
logger.info('Running model')
simulation_run_time += experiment.run(data_store,
unpresented_stimuli_indexes)
logger.info('Experiment %d/%d finished' %
(i + 1, len(experiment_list)))
total_run_time = time.time() - t0
mozaik_run_time = total_run_time - simulation_run_time
logger.info('Total simulation run time: %.0fs' % total_run_time)
logger.info(
'Simulator run time: %.0fs (%d%%)' %
(simulation_run_time, int(simulation_run_time / total_run_time * 100)))
logger.info('Mozaik run time: %.0fs (%d%%)' %
(mozaik_run_time, int(mozaik_run_time / total_run_time * 100)))
return data_store
