def simulate(self, step_n, param_dict):
"""
Simulate the values with the parameters.
Args:
step_n (int): number of iteration
param_dict (dict): estimated parameter values
- key (str): parameter name
- value (int or float): parameter value
Returns:
(pandas.DataFrame):
Index:
reset index
Columns:
- t (int): Elapsed time divided by tau value [-]
- columns with dimensionalized variables
"""
simulator = ODESimulator()
simulator.add(model=self.model,
step_n=step_n,
population=self.population,
param_dict=param_dict,
y0_dict=self.y0_dict)
return simulator.taufree()
def simulate(self, y0_dict=None):
"""
Perform simulation with the set/estimated parameter values.
Args:
y0_dict (dict or None): dictionary of initial values or None
- key (str): variable name
- value (float): initial value
Returns:
pandas.DataFrame
Index
reset index
Columns
- Date (pd.Timestamp): Observation date
- Confirmed (int): the number of confirmed cases
- Infected (int): the number of currently infected cases
- Fatal (int): the number of fatal cases
- Recovered (int): the number of recovered cases
- Variables of the model (int): Confirmed etc.
Note:
Simulation starts at the start date of the phase.
Simulation end at the next date of the end date of the phase.
"""
self._model_is_registered()
# Initial values
y0_dict = y0_dict or {}
y0_dict.update(self.y0_dict)
diff_set = set(self._model.VARIABLES) - y0_dict.keys()
y0_dict.update({var: 0 for var in diff_set})
# Conditions
param_dict = self._ode_dict.copy()
if None in param_dict.values():
raise UnExecutedError("PhaseUnit.set_ode()")
tau = param_dict.pop(self.TAU)
last_date = self.tomorrow(self._end_date)
# Simulation
simulator = ODESimulator()
simulator.add(
model=self._model,
step_n=self.steps(self._start_date, last_date, tau),
population=self._population,
param_dict=param_dict,
y0_dict=y0_dict
)
# Dimensionalized values
df = simulator.dim(tau=tau, start_date=self._start_date)
df = self._model.restore(df)
# Return day-level data
df = df.set_index(self.DATE).resample("D").first()
df = df.loc[df.index <= self._ensure_date(last_date), :]
return df.reset_index().loc[:, self.NLOC_COLUMNS]
def add(self, model, country=None, province=None, **kwargs):
"""
Add example data.
If the country has been registered,
the start date will be the next data of the registered records.
Args:
model (subclass of cs.ModelBase): the first ODE model
country (str): country name
province (str): province name
kwargs: the other keyword arguments of ODESimulator.add()
Notes:
If country is None, the name of the model will be used.
If province is None, '-' will be used.
"""
# Arguments
model = self.validate_subclass(model, ModelBase, name="model")
arg_dict = model.EXAMPLE.copy()
arg_dict.update(kwargs)
country = country or model.NAME
province = province or self.UNKNOWN
try:
population = arg_dict["population"]
except KeyError:
raise KeyError("@population must be specified.")
# Start date and y0 values
df = self._cleaned_df.copy()
df = df.loc[
(df[self.COUNTRY] == country) & (df[self.PROVINCE] == province)
]
if df.empty:
start_date = self.start_date
else:
start_date = df.loc[df.index[-1], self.DATE]
df = model.tau_free(df, population, tau=None)
arg_dict["y0_dict"] = {
k: df.loc[df.index[0], k] for k in model.VARIABLES
}
# Simulation
simulator = ODESimulator(country=country, province=province)
simulator.add(model=model, **arg_dict)
simulator.run()
# Add the simulated records to self
dim_df = simulator.dim(tau=self.tau, start_date=start_date)
restored_df = model.restore(dim_df)
restored_df[self.COUNTRY] = country
restored_df[self.PROVINCE] = province
selected_df = restored_df.loc[:, self.COLUMNS]
self._cleaned_df = pd.concat(
[self._cleaned_df, selected_df], axis=0, ignore_index=True
)
# Set non-dimensional data
if country not in self.nondim_dict.keys():
self.nondim_dict[country] = dict()
nondim_df = simulator.non_dim()
if province in self.nondim_dict[country].keys():
nondim_df_old = self.nondim_dict[country][province].copy()
nondim_df = pd.concat([nondim_df_old, nondim_df], axis=0)
self.nondim_dict[country][province] = nondim_df.copy()
def _simulate(self, step_n, param_dict):
"""
Simulate the values with the parameters.
Args:
step_n (int): number of iteration
dict[str, int or float]: dictionary of parameter values
Returns:
pandas.DataFrame:
Index
reset index
Columns
- t (int): Elapsed time divided by tau value [-]
- columns with dimensionalized variables
"""
simulator = ODESimulator()
simulator.add(model=self.model,
step_n=step_n,
population=self.population,
param_dict=param_dict,
y0_dict=self.y0_dict)
return simulator.taufree()
def _simulate(self, name, y0_dict):
"""
Simulate ODE models with set parameter values.
Args:
name (str): phase series name
y0_dict (dict):
- key (str): variable name
- value (float): initial value
- dictionary of initial values or None
- if model will be changed in the later phase, must be specified
Returns:
(tuple)
(pandas.DataFrame): output of ODESimulator.dim()
Index: reset index
Columns:
- Date (pd.TimeStamp): Observation date
- Country (str): country/region name
- Province (str): province/prefecture/state name
- variables of the models (int): Confirmed (int) etc.
(list[pd.TimeStamp]): list of start dates
"""
phase_series = copy.deepcopy(self.series_dict[name])
# Dates and the number of steps
last_object = phase_series.last_object()
start_objects = phase_series.start_objects()
step_n_list = phase_series.number_of_steps(start_objects, last_object,
self.tau)
first_date = start_objects[0].strftime(self.DATE_FORMAT)
# Information of models
models = [self.model_dict[name] for name in phase_series.model_names()]
# Population values
population_values = phase_series.population_values()
# Create simulator
simulator = ODESimulator(self.country, province=self.province)
# Add phases to the simulator
df = phase_series.summary()
zip_iter = zip(df.index, models, step_n_list, population_values)
for (phase, model, step_n, population) in zip_iter:
param_dict = df[model.PARAMETERS].to_dict(orient="index")[phase]
if phase == self.num2str(1):
# Calculate initial values
subset_df = self.jhu_data.subset(country=self.country,
province=self.province,
start_date=first_date)
subset_df = model.tau_free(subset_df, population, tau=None)
y0_dict_phase = {
k: subset_df.loc[subset_df.index[0], k]
for k in model.VARIABLES
}
else:
if y0_dict is None:
y0_dict_phase = None
else:
y0_dict_phase = y0_dict.copy()
simulator.add(model,
step_n,
population,
param_dict=param_dict,
y0_dict=y0_dict_phase)
# Run simulation
simulator.run()
dim_df = simulator.dim(self.tau, first_date)
return dim_df, start_objects
def add(self, model, country=None, province=None, **kwargs):
"""
Add example data.
If the country has been registered,
the start date will be the next data of the registered records.
Args:
model (cs.ModelBase): the first ODE model
country (str or None): country name
province (str or None): province name
kwargs: the other keyword arguments of ODESimulator.add()
Note:
If country is None, the name of the model will be used.
If province is None, '-' will be used.
"""
# Arguments
model = self._ensure_subclass(model, ModelBase, name="model")
arg_dict = model.EXAMPLE.copy()
arg_dict.update(kwargs)
population = arg_dict["population"]
# Area
country, province = self._model_to_area(
model=model, country=country, province=province)
# Start date and y0 values
df = self._cleaned_df.copy()
df = df.loc[
(df[self.COUNTRY] == country) & (df[self.PROVINCE] == province), :
]
if df.empty:
start_date = self.start_date
else:
start_date = df.loc[
df.index[-1], self.DATE].strftime(self.DATE_FORMAT)
df = model.tau_free(df, population, tau=None)
arg_dict[self.Y0_DICT] = {
k: df.loc[df.index[0], k] for k in model.VARIABLES
}
# Simulation
simulator = ODESimulator(country=country, province=province)
simulator.add(model=model, **arg_dict)
# Specialized records
dim_df = simulator.dim(tau=self.tau, start_date=start_date)
if country not in self._specialized_dict:
self._specialized_dict[country] = {}
self._specialized_dict[country][province] = dim_df.copy()
# JHU-type records
restored_df = model.restore(dim_df)
restored_df[self.COUNTRY] = country
restored_df[self.PROVINCE] = province
selected_df = restored_df.loc[:, self.COLUMNS]
self._cleaned_df = pd.concat(
[self._cleaned_df, selected_df], axis=0, ignore_index=True
)
# Set non-dimensional data
if country not in self.nondim_dict:
self.nondim_dict[country] = {}
nondim_df = simulator.non_dim()
if province in self.nondim_dict[country]:
nondim_df_old = self.nondim_dict[country][province].copy()
nondim_df = pd.concat([nondim_df_old, nondim_df], axis=0)
self.nondim_dict[country][province] = nondim_df.copy()
