def test_parallel_experiment(self):
"""
Test running an experiment in parallel
Returns
-------
"""
relative_path_to_file = '../models/Teacup.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
model = PysdModel(mdl_file=mdl_file)
model.uncertainties = [RealParameter('Room Temperature', 33, 120)]
model.outcomes = [TimeSeriesOutcome('Teacup Temperature')]
with MultiprocessingEvaluator(model, 2) as evaluator:
perform_experiments(model, 5, evaluator=evaluator)
model = NetLogoModel('predprey',
wd="./models/predatorPreyNetlogo",
model_file="Wolf Sheep Predation.nlogo")
model.run_length = 100
model.replications = 1
model.uncertainties = [
RealParameter("grass-regrowth-time", 1, 99),
RealParameter("initial-number-sheep", 1, 200),
RealParameter("initial-number-wolves", 1, 200),
RealParameter("sheep-reproduce", 1, 20),
RealParameter("wolf-reproduce", 1, 20),
]
model.outcomes = [
TimeSeriesOutcome('sheep'),
TimeSeriesOutcome('wolves'),
TimeSeriesOutcome('grass'),
TimeSeriesOutcome('TIME')
]
#perform experiments
n = 10
with MultiprocessingEvaluator(model) as evaluator:
results = perform_experiments(model, n, evaluator=evaluator)
fn = r'./data/{} runs.tar.gz'.format(n)
save_results(results, fn)
print "finish"
model.uncertainties = uncertainties
model.outcomes = [ArrayOutcome('stedin_capacity'),
ArrayOutcome('stedin_load'),
ArrayOutcome('tennet_capacity'),
ArrayOutcome('tennet_load'),
ArrayOutcome('gasunie_capacity'),
ArrayOutcome('investments'),
ArrayOutcome('stedin capex'),
ArrayOutcome('tennet capex'),
ArrayOutcome('gasunie capex'),
ArrayOutcome('collaborative capex'),
ArrayOutcome('h2_import'),
ArrayOutcome('stedin_lost'),
ArrayOutcome('first_failure'),
ArrayOutcome('missed_over_time')
]
_logger.info(f"{len(uncertainties)} uncertainties defined")
_logger.info(f"{len(model.replications)} replications defined")
n_scenarios = 2500
print(datetime.datetime.now())
# with SequentialEvaluator(model) as evaluator:
with MultiprocessingEvaluator(model, n_processes=56) as evaluator:
results = evaluator.perform_experiments(n_scenarios, uncertainty_sampling=MC)
save_results(results, f'./results/{n_scenarios}_scenarios_{len(model.replications)} replications_{datetime.datetime.now()}_MC.tar.gz')
print(datetime.datetime.now())