def test_r_list_to_frame():
example_r_list = robjects.r("simulated = list(n_cases = 1:10)")
as_frame = r_list_to_frame(example_r_list, ["n_cases"])
expected_frame = pd.DataFrame({
"n_cases": list(range(1, 11)),
"timestep": list(range(1, 11))
})
pd.testing.assert_frame_equal(as_frame, expected_frame)
def simulate(
self,
length: int,
state_weight: Optional[float] = None,
state: Optional[Sequence[int]] = None,
) -> pd.DataFrame:
r"""
Simulate outbreaks.
Parameters
----------
length
Number of weeks to model. ``length`` is ignored if ``state`` is given. In this case the length of ``state``
is used.
state
Use a state chain to define the status at this time point (outbreak or not). If not given, a Markov chain is
generated automatically.
state_weight
Additional weight for an outbreak which influences the distribution parameter :math:`\mu`.
Returns
-------
A ``DataFrame`` of an endemic time series where each row contains the case counts of this week.
It also contains the mean case count value based on the underlying sinus model.
"""
if self.seed:
base = robjects.packages.importr("base")
base.set_seed(self.seed)
simulated = surveillance.sim_seasonalNoise(
A=self.amplitude,
alpha=self.alpha,
beta=self.trend,
phi=self.seasonal_move,
length=length,
frequency=self.frequency,
state=robjects.NULL
if state is None else robjects.IntVector(state),
K=robjects.NULL if state_weight is None else state_weight,
)
simulated = r_list_to_frame(simulated, ["mu", "seasonalBackground"])
simulated = simulated.pipe(add_date_time_index_to_frame).rename(
columns={
"mu": "mean",
"seasonalBackground": "n_cases"
})
return simulated
def simulate(
self,
length: int,
state_weight: float = 0,
state: Optional[Sequence[int]] = None,
) -> pd.DataFrame:
"""Simulate outbreaks.
Parameters
----------
length
Number of weeks to model. ``length`` is ignored if ``state`` is given. In this case, the length of
``state`` is used.
state
Use a state chain to define the status at this time point (outbreak or not). If not given, a Markov chain is
generated automatically.
state_weight
Additional weight for an outbreak which influences the distribution parameter mu.
Returns
-------
A ``DataFrame`` of simulated case counts per week, separated into baseline and outbreak cases.
"""
if self.seed:
base.set_seed(self.seed)
simulated = surveillance.sim_pointSource(
p=self.p,
r=self.r,
length=length,
A=self.amplitude,
alpha=self.alpha,
beta=self.trend,
phi=self.seasonal_move,
frequency=self.frequency,
state=robjects.NULL
if state is None else robjects.IntVector(state),
K=state_weight,
)
simulated_as_frame = r_list_to_frame(simulated, ["observed", "state"])
return simulated_as_frame.pipe(add_date_time_index_to_frame).rename(
columns={
"observed": "n_cases",
"state": "is_outbreak"
})