def test_ode(self, model):
# Setting
eg_tau = 1440
area = {"country": "Full", "province": model.NAME}
# Population
population_data = PopulationData(filename=None)
population_data.update(model.EXAMPLE["population"], **area)
# Simulation
example_data = ExampleData(tau=eg_tau, start_date="01Jan2020")
example_data.add(model, **area)
# Model-specialized records
spe_df = example_data.specialized(**area)
assert set(spe_df.columns) == set(
[*Term.STR_COLUMNS, *model.VARIABLES])
# Non-dimensional records
nondim_df = example_data.non_dim(**area)
assert set(nondim_df.columns) == set(
[Term.TS, *list(model.VAR_DICT.keys())])
# JHU-type records
jhu_df = example_data.subset(**area)
assert set(jhu_df.columns) == set(Term.NLOC_COLUMNS)
# Calculate Rt/day parameters when parameters are None
param_dict = {p: 0 for p in model.PARAMETERS}
model_instance = model(population_data.value(**area), **param_dict)
model_instance.calc_r0()
model_instance.calc_days_dict(eg_tau)
def test_ode(self, model):
# Setting
eg_tau = 1440
# Simulation
example_data = ExampleData(tau=eg_tau)
example_data.add(model)
nondim_df = example_data.non_dim(model)
assert isinstance(nondim_df, pd.DataFrame)
nondim_cols = [Term.TS, *list(model.VAR_DICT.keys())]
assert set(nondim_df.columns) == set(nondim_cols)
clean_df = example_data.cleaned()
assert isinstance(clean_df, pd.DataFrame)
assert set(clean_df.columns) == set(Term.COLUMNS)
dim_df = example_data.subset(model)
assert isinstance(dim_df, pd.DataFrame)
assert set(dim_df.columns) == set(Term.NLOC_COLUMNS)
# Estimation
population = model.EXAMPLE["population"]
estimator = Estimator(example_data,
model=model,
population=population,
country=model.NAME,
province=Term.UNKNOWN,
tau=eg_tau)
estimator.run()
estimated_df = estimator.summary(name=model.NAME)
assert isinstance(estimated_df, pd.DataFrame)
estimator.history(show_figure=False)
estimator.accuracy(show_figure=False)
def test_error(self, model):
# Setting
eg_tau = 1440
# Simulation
example_data = ExampleData(tau=eg_tau, start_date="01Jan2020")
with pytest.raises(KeyError):
assert not example_data.specialized(model=model).empty
with pytest.raises(KeyError):
assert not example_data.non_dim(model=model).empty
example_data.add(model)
# Model-specialized records
with pytest.raises(ValueError):
assert not example_data.specialized().empty
def test_one_phase(self, model):
example_data = ExampleData()
example_data.add(model)
with pytest.raises(ValueError):
example_data.subset()
# Subset
subset_df = example_data.subset(model=model)
assert subset_df.columns.tolist() == Term.SUB_COLUMNS
example_data.subset(country=model.NAME)
example_data.subset_complement(model=model)
example_data.records(model=model)
# Specialized
specialized_df = example_data.specialized(model=model)
assert specialized_df.columns.tolist() == [Term.DATE, *model.VARIABLES]
# Non-dimensional
nondim_df = example_data.non_dim(model=model)
assert nondim_df.columns.tolist() == model.VARIABLES
assert round(nondim_df.sum().sum()) == len(nondim_df)
def test_ode_two_phases(self, population_data):
# Setting
eg_tau = 1440
# Simulation
example_data = ExampleData(tau=eg_tau)
example_data.add(SIRF, step_n=30)
example_data.add(SIRD, step_n=30)
nondim_df = example_data.non_dim(SIRF)
assert isinstance(nondim_df, pd.DataFrame)
nondim_cols = [Term.TS, *list(SIRF.VAR_DICT.keys())]
assert set(nondim_df.columns) == set(nondim_cols)
clean_df = example_data.cleaned()
assert isinstance(clean_df, pd.DataFrame)
assert set(clean_df.columns) == set(Term.COLUMNS)
dim_df = example_data.subset(SIRF)
assert isinstance(dim_df, pd.DataFrame)
assert set(dim_df.columns) == set(Term.NLOC_COLUMNS)
# Scenario analysis
population = SIRF.EXAMPLE["population"]
population_data.update(population, country=SIRF.NAME)
scenario = Scenario(example_data, population_data, country=SIRF.NAME)
scenario.trend()
