wd=r'./models/flu',
model_file=r'/FLUvensimV1basecase.vpm')
#outcomes
model.outcomes = [
TimeSeriesOutcome('deceased population region 1'),
TimeSeriesOutcome('infected fraction R1'),
ScalarOutcome('max infection fraction',
variable_name='infected fraction R1',
function=np.max),
ScalarOutcome('time of max',
variable_name=['infected fraction R1', 'TIME'],
function=time_of_max)
]
#create uncertainties based on csv
model.uncertainties = create_parameters(
'./models/flu/flu_uncertainties.csv')
#add policies
policies = [
Policy('no policy', model_file=r'/FLUvensimV1basecase.vpm'),
Policy('static policy', model_file=r'/FLUvensimV1static.vpm'),
Policy('adaptive policy', model_file=r'/FLUvensimV1dynamic.vpm')
]
results = perform_experiments(model,
1000,
policies=policies,
parallel=True)
model = VensimModel("fluCase", wd=r'./models/flu',
model_file=r'/FLUvensimV1basecase.vpm')
# outcomes
model.outcomes = [TimeSeriesOutcome('deceased population region 1'),
TimeSeriesOutcome('infected fraction R1'),
ScalarOutcome('max infection fraction',
variable_name='infected fraction R1',
function=np.max),
ScalarOutcome('time of max',
variable_name=[
'infected fraction R1', 'TIME'],
function=time_of_max)]
# create uncertainties based on csv
model.uncertainties = create_parameters(
'./models/flu/flu_uncertainties.csv')
# add policies
policies = [Policy('no policy',
model_file=r'/FLUvensimV1basecase.vpm'),
Policy('static policy',
model_file=r'/FLUvensimV1static.vpm'),
Policy('adaptive policy',
model_file=r'/FLUvensimV1dynamic.vpm')
]
with MultiprocessingEvaluator(model, 2) as evaluator:
results = perform_experiments(model, 1000, policies=policies,
evaluator == evaluator)