def test_interface(
self,
gdp_model_cls,
personal_bankruptcy_model_cls,
corporate_bankruptcy_model_cls,
utilisation,
):
reader = Reader(DATA_PATH)
state = state_from_utilisation(UTILISATION_NO_COVID_NO_LOCKDOWN)
econ_model = Economics(
gdp_model_cls(),
corporate_bankruptcy_model_cls(),
personal_bankruptcy_model_cls(),
)
econ_model.load(reader)
econ_model.simulate(state)
new_state = advance_state(state, utilisation)
econ_model.simulate(new_state)
# Factor of 1.1 is because of the GDP backbone model
assert (0 <= sum(new_state.gdp_state.gdp.values()) <=
new_state.gdp_state.max_gdp * 1.1)
for (
_business_size,
mapping,
) in new_state.corporate_state.proportion_solvent.items():
for sector, solvent in mapping.items():
assert 0 <= solvent <= 1
for region in Region:
assert 0 <= new_state.personal_state.personal_bankruptcy[
region] <= 1
def test_interface(
self,
gdp_model_cls,
personal_bankruptcy_model_cls,
corporate_bankruptcy_model_cls,
lockdown,
utilisations,
):
reader = Reader(DATA_PATH)
econ_model = Economics(
gdp_model_cls(),
corporate_bankruptcy_model_cls(),
personal_bankruptcy_model_cls(),
)
econ_model.load(reader)
try:
# Simulation should fail unless we start outside of lockdown
econ_model.simulate(time=0,
lockdown=lockdown,
utilisations=utilisations)
except ValueError:
if not lockdown:
raise
else:
if lockdown:
raise ValueError()
assert 0 <= sum(
econ_model.results.fraction_gdp_by_sector(0).values()) <= 1
def simulate(
self,
scenario: Scenario,
show_plots: bool = True,
figsize: Tuple = (20, 60),
scenario_name: str = "",
) -> Tuple[Economics, List[SimulateState]]:
"""
Run simulation for a given scenario
:param scenario: Scenario to run the simulation
:type scenario: Scenario
:param show_plots: Show the plots using matplotlib if set to be True
:type show_plots: bool
:param figsize: Size of the figure to be plotted. This parameter is only used when show_plots is set to be True
:type figsize: Tuple
:param scenario_name: Name of the scenario. Used in plotting
:type scenario_name: str
:return: Tuple[Economics, List[SimulateState]]
"""
# Load data for the scenario object if not done yet
if not scenario.is_loaded:
scenario.load(self.reader)
# Initialize various models given the model parameters specified in the Scenario
model_params = scenario.model_params
gdp_model = PiecewiseLinearCobbDouglasGdpModel(
**model_params.gdp_params)
cb_model = CorporateBankruptcyModel(**model_params.corporate_params)
pb_model = PersonalBankruptcyModel(**model_params.personal_params)
econ = Economics(gdp_model, cb_model, pb_model,
**model_params.economics_params)
# Load data for Economy
econ.load(self.reader)
states = []
for i in tqdm(range(scenario.simulation_end_time)):
# Load ill and dead ratio from scenario
ill = scenario.get_ill_ratio_dict(i)
dead = scenario.get_dead_ratio_dict(i)
# Load quarantine information
quarantine = scenario.get_quarantine_ratio_dict(i)
# Initialize a SimulateState object
simulate_state = scenario.generate(
time=i,
dead=dead,
ill=ill,
quarantine=quarantine,
lockdown=scenario.lockdown_start_time <= i <
scenario.lockdown_end_time,
furlough=scenario.furlough_start_time <= i <
scenario.furlough_end_time,
reader=self.reader,
)
# Pass SimulateState to Economy
econ.simulate(simulate_state)
# Keep a record of the current SimulateState
states.append(simulate_state)
# Plots
if show_plots:
fig, axes = plt.subplots(7,
1,
sharex="col",
sharey="row",
figsize=figsize)
plot_one_scenario(states,
scenario.simulation_end_time,
axes,
title_prefix=scenario_name)
return econ, states
def main(
parameters,
household_demographics,
outdir,
total_individuals,
lockdown_start,
lockdown_end,
end_time,
econ_data_dir,
gdp_model,
) -> None:
"""
Run simulations by region
"""
# set up output directory and paths
output_dir = click.format_filename(outdir)
os.makedirs(output_dir, exist_ok=True)
parameters_path = click.format_filename(parameters)
household_demographics_path = click.format_filename(household_demographics)
econ_data_dir = click.format_filename(econ_data_dir)
with open(os.path.join(econ_data_dir, "parameters.json"), "r") as f:
econ_parameters = json.load(f)
# Read regional population distributions in age
populations_df = pd.read_csv(os.path.join(econ_data_dir, "populations.csv"))
populations_by_region = {
Region[k]: {Age10Y[kk]: vv for kk, vv in v.items()}
for k, v in populations_df.set_index("region").T.to_dict().items()
}
queues = {r: multiprocessing.Queue() for r in Region}
# Setup economics model
reader = Reader(econ_data_dir)
gdp_model = ECON_MODELS[gdp_model](**econ_parameters["gdp"])
cb_model = CorporateBankruptcyModel(**econ_parameters["corporate_bankruptcy"])
pb_model = PersonalBankruptcyModel(**econ_parameters["personal_insolvency"])
econ_model = Economics(gdp_model, cb_model, pb_model)
econ_model.load(reader)
worker = functools.partial(
_worker,
output_dir=output_dir,
parameters_path=parameters_path,
household_demographics_path=household_demographics_path,
total_individuals=total_individuals,
lockdown_start=lockdown_start,
lockdown_end=lockdown_end,
end_time=end_time,
)
econ_worker = functools.partial(
_econ_worker,
model=econ_model,
total_individuals=total_individuals,
end_time=end_time,
output_dir=output_dir,
)
spread_processes = [
multiprocessing.Process(
target=worker, args=((r, populations_by_region[r], queues[r]),)
)
for r in Region
]
for p in spread_processes:
p.start()
econ_model = econ_worker(queues)
for p in spread_processes:
p.join()
plot_econ_data(econ_model, total_individuals, end_time, econ_data_dir, output_dir)
# Read regional population distributions in age
populations_df = pd.read_csv(os.path.join(econ_data_dir, "populations.csv"))
populations_by_region = {
Region[k]: {Age10Y[kk]: vv for kk, vv in v.items()}
for k, v in populations_df.set_index("region").T.to_dict().items()
}
manager = multiprocessing.Manager()
queues = {r: manager.Queue() for r in Region}
# Setup economics model
reader = Reader(econ_data_dir)
gdp_model = ECON_MODELS[gdp_model](**example_parameters["gdp"])
cb_model = CorporateBankruptcyModel(**example_parameters["corporate_bankruptcy"])
pb_model = PersonalBankruptcyModel(**example_parameters["personal_insolvency"])
econ_model = Economics(gdp_model, cb_model, pb_model)
econ_model.load(reader)
spread_worker = functools.partial(
_spread_worker,
output_dir=output_dir,
parameters_path=parameters_path,
household_demographics_path=household_demographics_path,
total_individuals=total_individuals,
lockdown_start=lockdown_start,
lockdown_end=lockdown_end,
end_time=end_time,
)
econ_worker = functools.partial(
_econ_worker,
def lockdown_then_unlock_no_corona(data_path: str = "data"):
"""
Lockdown at t=5 days, then release lockdown at t=50 days.
:param data_path:
:return:
"""
reader = Reader(data_path)
econ = Economics(
SupplyDemandGdpModel(), CorporateBankruptcyModel(), PersonalBankruptcyModel()
)
econ.load(reader)
max_utilisations = {key: 1.0 for key in itertools.product(Region, Sector, Age)}
min_utilisations = {key: 0.0 for key in itertools.product(Region, Sector, Age)}
length = 100
for i in range(length):
if 5 <= i < 50:
econ.simulate(i, True, min_utilisations)
else:
econ.simulate(i, False, max_utilisations)
df = (
pd.DataFrame(
[econ.results.fraction_gdp_by_sector(i) for i in range(1, length)],
index=range(1, length),
)
.T.sort_index()
.T.cumsum(axis=1)
)
# Plot 1
fig, ax = plt.subplots(figsize=(20, 10))
ax.fill_between(df.index, df.iloc[:, 0] * 0, df.iloc[:, 0], label=df.columns[0])
for i in range(1, df.shape[1]):
ax.fill_between(df.index, df.iloc[:, i - 1], df.iloc[:, i], label=df.columns[i])
ax.legend(ncol=2)
# Plot 2
df = pd.DataFrame(
[
econ.results.corporate_solvencies[i]
for i in econ.results.corporate_solvencies
]
)
df.plot(figsize=(20, 10))
# Plot 3
pd.DataFrame(
[
{
r: econ.results.personal_bankruptcy[i][r].personal_bankruptcy
for r in Region
}
for i in econ.results.personal_bankruptcy
]
).plot(figsize=(20, 10))
# Plot 4
pd.DataFrame(
[
{
r: econ.results.personal_bankruptcy[i][r].corporate_bankruptcy
for r in Region
}
for i in econ.results.personal_bankruptcy
]
).plot(figsize=(20, 10))