def main():
print(cs.__version__)
pprint(cs.SIRF.EXAMPLE, compact=True)
# Reproduction number
eg_dict = cs.SIRF.EXAMPLE.copy()
model_ins = cs.SIRF(
population=eg_dict["population"],
**eg_dict["param_dict"]
)
print(model_ins.calc_r0())
# Set tau value and start date of records
example_data = cs.ExampleData(tau=1440, start_date="01Jan2020")
# Add records with SIR model
model = cs.SIRF
area = {"country": "Full", "province": model.NAME}
example_data.add(model, **area)
# Change parameter values if needed
# example_data.add(model, param_dict={"kappa": 0.001, "kappa": 0.002, "rho": 0.4, "sigma": 0.0150}, **area)
# Records with model variables
df = example_data.specialized(model, **area)
# Plotting
cs.line_plot(
df.set_index("Date"),
title=f"Example data of {model.NAME} model",
y_integer=True,
filename="sirf.png"
)
def main():
warnings.simplefilter("error")
# Create output directory in example directory
code_path = Path(__file__)
output_dir = code_path.with_name("output").joinpath(code_path.stem)
output_dir.mkdir(exist_ok=True, parents=True)
# Setting
eg_tau = 1440
eg_population = 1_000_000
model = cs.SIRFV
eg_population = model.EXAMPLE["population"]
set_param_dict = model.EXAMPLE["param_dict"]
# Simulation
example_data = cs.ExampleData(tau=eg_tau)
example_data.add(model)
# Non-dimensional
nondim_df = example_data.non_dim(model)
nondim_df.to_csv(output_dir.joinpath(
f"{model.NAME}_non_dim.csv"), index=False)
cs.line_plot(
nondim_df.set_index("t"),
title=f"{model.NAME}: Example data (non-dimensional)",
ylabel=str(),
h=1.0,
filename=output_dir.joinpath(f"{model.NAME}_non_dim.png")
)
# Dimensional
dim_df = example_data.cleaned()
dim_df.to_csv(output_dir.joinpath(f"{model.NAME}_dim.csv"), index=False)
cs.line_plot(
dim_df.set_index("Date").drop("Confirmed", axis=1),
title=f"{model.NAME}: Example data (dimensional)",
h=eg_population,
y_integer=True,
filename=output_dir.joinpath(f"{model.NAME}_dim.png")
)
# Hyperparameter estimation of example data
estimator = cs.Estimator(
example_data, model=model, population=eg_population,
country=model.NAME, province=None, tau=eg_tau, omega=0.001
)
estimator.run()
estimated_df = estimator.summary(name=model.NAME)
estimated_df = estimated_df.append(
pd.Series({**set_param_dict, "tau": eg_tau}, name="set")
)
estimated_df["tau"] = estimated_df["tau"].astype(np.int64)
estimated_df.to_csv(
output_dir.joinpath(f"{model.NAME}_estimate_parameter.csv"), index=True
)
# Show the history of optimization
estimator.history(filename=output_dir.joinpath(
f"{model.NAME}_estimate_history.png")
)
# Show the accuracy as a figure
estimator.accuracy(filename=output_dir.joinpath(
f"{model.NAME}_estimate_accuracy.png")
)