def test_perform_experiments():
# # let's make some interfaces
# model_a = DummyInterface(None, "A")
# model_b = DummyInterface(None, "B")
#
# # let's add some uncertainties to this
# shared_ab_1 = ParameterUncertainty((0,1), "shared ab 1")
# shared_ab_2 = ParameterUncertainty((0,10), "shared ab 1")
# model_a.uncertainties = [shared_ab_1, shared_ab_2]
# model_b.uncertainties = [shared_ab_1, shared_ab_2]
#
# ensemble = ModelEnsemble()
# ensemble.add_model_structures([model_a, model_b])
# what are all the test cases?
# test for error in case uncertainty by same name but different
# in other respects
# everything shared
model_a = DummyInterface(None, "A")
model_b = DummyInterface(None, "B")
model_c = DummyInterface(None, "C")
# let's add some uncertainties to this
shared_abc_1 = ParameterUncertainty((0,1), "shared abc 1")
shared_abc_2 = ParameterUncertainty((0,1), "shared abc 2")
shared_ab_1 = ParameterUncertainty((0,1), "shared ab 1")
shared_bc_1 = ParameterUncertainty((0,1), "shared bc 1")
a_1 = ParameterUncertainty((0,1), "a 1")
b_1 = ParameterUncertainty((0,1), "b 1")
model_a.uncertainties = [shared_abc_1, shared_abc_2, shared_ab_1, a_1]
model_b.uncertainties = [shared_abc_1, shared_abc_2, shared_ab_1, shared_bc_1, b_1]
model_c.uncertainties = [shared_abc_1, shared_abc_2, shared_bc_1]
#let's add an outcome to this
outcome_shared = Outcome("test", time=True)
model_a.outcomes = [outcome_shared]
model_b.outcomes = [outcome_shared]
model_c.outcomes = [outcome_shared]
ensemble = ModelEnsemble()
ensemble.parallel=True
ensemble.add_model_structures([model_a, model_b, model_c])
ema_logging.info('------------- union of uncertainties -------------')
results = ensemble.perform_experiments(10, which_uncertainties=UNION, reporting_interval=1 )
ema_logging.info('------------- intersection of uncertainties -------------')
ensemble.perform_experiments(10, which_uncertainties=INTERSECTION, reporting_interval=1)
# model.step = 1 #reduce data to be stored
wd = r'./netlogo_models'
msi = DiffusionModelInterfaceHeterogeneity(wd, 'dmodel')
ensemble = ModelEnsemble()
ensemble.set_model_structure(msi)
policies = [{'name': 'neoclassical_all_same',
'file': r'\Model_adoption_of_ISG_appliances.nlogo'},
# {'name': 'normality each run',
# 'file': r'\Model_adoption_of_ISG_appliances_-_1_run_normality_household_properties.nlogo'},
]
ensemble.add_policies(policies)
ensemble.parallel = True
n = 50
results = ensemble.perform_experiments(n)
# fig, axesdict = envelopes(results,
# outcomes_to_show=['percentage_of_households_owning_ISG_app'],
# group_by='policy',
# grouping_specifiers=['no normality',
# 'normality each run'
# # 'normality each step'
# ],
# legend=True,
# density='kde', fill=True,titles=None)
# # set the size of the figure to look reasonable nice
# fig.set_size_inches(8,5)
# # save figure
# plt.show()
# plt.savefig("./pictures/KDE comparison savings.png", dpi=75)
