def _processor(self, model, setting_df):
"""
Generate multi-processor for parameter estimation,
registering theoretical data and phase units.
Args:
model (covsirphy.ModelBase): ODE model
setting_df (pandas.DataFrame):
Index reset index
Columns
- (float): parameter values from 0 to 1.0
- Rt (float): reproduction number
- step_n (int): step number of simulation
Returns:
covsirphy.MPEstimator: multi-processor for parameter estimation
"""
units = []
# Instance to save theoretical data
example_data = ExampleData(tau=self.tau, start_date="01Jan2020")
# Population values
population = model.EXAMPLE[self.N.lower()]
population_data = PopulationData(filename=None)
# Register data for each setting
for (i,
setting_dict) in enumerate(setting_df.to_dict(orient="records")):
name = f"{model.NAME}_{i}"
step_n = setting_dict[self.STEP_N]
param_dict = {
k: v
for (k, v) in setting_dict.items() if k in model.PARAMETERS
}
# Add theoretical data
example_data.add(model,
step_n=step_n,
country=name,
param_dict=param_dict)
# Population
population_data.update(population, country=name)
# Phase unit
snl = Scenario(example_data,
population_data,
country=name,
auto_complement=False)
snl.add()
unit = snl[self.MAIN].unit("last").del_id().set_id(country=name)
units.append(unit)
# Multi-processor for parameter estimation
processor = MPEstimator(model,
jhu_data=example_data,
population_data=population_data,
tau=self.tau)
return processor.add(units)
def _init_scenario(self):
"""
Initialize the scenario classes of registered countries.
"""
self.scenario_dict = {
country: Scenario(
self.jhu_data, self.population_data, country=country, tau=self.tau)
for country in self._countries
}
def _processor(self, model, setting_df, timeout, allowance):
"""
Generate multi-processor for parameter estimation,
registering theoretical data and phase units.
Args:
model (covsirphy.ModelBase): ODE model
setting_df (pandas.DataFrame):
Index reset index
Columns
- (float): parameter values from 0 to 1.0
- Rt (float): reproduction number
- step_n (int): step number of simulation
timeout (int): time-out of run
allowance (tuple(float, float)): the allowance of the predicted value
Returns:
list[covsirphy.Scenario]: list of Scenario instances
"""
scenarios = []
# Instance to save theoretical data
example_data = ExampleData(tau=self._tau, start_date="01Jan2020")
# Population values
population = model.EXAMPLE[self.N.lower()]
population_data = PopulationData(filename=None)
# Register data for each setting
for (i, setting_dict) in enumerate(setting_df.to_dict(orient="records")):
name = f"{model.NAME}_{i}"
step_n = setting_dict[self.STEP_N]
param_dict = {k: v for (k, v) in setting_dict.items() if k in model.PARAMETERS}
# Add theoretical data
example_data.add(model, step_n=step_n, country=name, param_dict=param_dict)
# Population
population_data.update(population, country=name)
# Phase unit
snl = Scenario(country=name, auto_complement=False)
snl.register(example_data, population_data)
snl.add()
snl.estimate(model, timeout=timeout, allowance=allowance)
scenarios.append(snl)
return scenarios