def sim_chimes(scenarios: str, p: Parameters = None):
"""
Run many chime simulations.
If `params` is not None, then it is used to initialize all parameters. Any
parameter values set to something other than None in the following
optional args will update that parameter value. If `params` is None, then
all additional args must be other than None. These settings are just
for the base parameter values. We can run stuff over ranges as we see it.
:param scenarios:
:param params:
:return:
"""
base_input_params_dict = vars(p)
# Create a range of social distances
soc_dists = np.arange(0.05, 0.60, 0.05)
# array([0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ])
num_scenarios = len(soc_dists)
# We can store outputs any way we want. For this demo, just going to
# use a master list. # This will be a list of dicts of the
# result dataframes (+ 1 dict containing the scenario inputs)
results_list = []
for sd_pct in soc_dists:
sim_scenario = '{}{:.0f}'.format(scenarios, 100 * sd_pct)
# Update the parameters for this scenario
p.relative_contact_rate = sd_pct
input_params_dict = vars(p)
# Run the model
m = Model.SimSirModel(p)
# Gather results
results = gather_sim_results(m, sim_scenario, input_params_dict)
# Append results to results list
results_list.append(results.copy())
return results_list
def sim_chimes(
scenarios: str,
params: Parameters = None,
current_hospitalized: int = None,
doubling_time: float = None,
known_infected: int = None,
relative_contact_rate: float = None,
susceptible: int = None,
hospitalized: RateLos = None,
icu: RateLos = None,
ventilated: RateLos = None,
market_share: float = None,
n_days: int = None,
recovery_days: float = None,
):
"""
Run many chime simulations.
If `params` is not None, then it is used to initialize all parameters. Any
parameter values set to something other than None in the following
optional args will update that parameter value. If `params` is None, then
all additional args must be other than None. These settings are just
for the base parameter values. We can run stuff over ranges as we see it.
:param scenarios:
:param params:
:param current_hospitalized:
:param doubling_time:
:param known_infected:
:param relative_contact_rate:
:param susceptible:
:param hospitalized:
:param icu:
:param ventilated:
:param market_share:
:param n_days:
:param recovery_days:
:return:
"""
if params is not None:
params_dict = vars(params)
else:
params_dict = {
"current_hospitalized": None,
"doubling_time": None,
"known_infected": None,
"relative_contact_rate": None,
"susceptible": None,
"hospitalized": None,
"icu": None,
"ventilated": None,
"market_share": None,
"n_days": None,
"recovery_days": None,
}
# Check for parameter updates passed
vals_passed = {
key: value
for (key, value) in vars().items()
if key not in ['scenario', 'params']
}
for key, value in vals_passed.items():
if value is not None:
params_dict[key] = value
# Create Parameters object
p = Parameters(
current_hospitalized=params_dict['current_hospitalized'],
doubling_time=params_dict['doubling_time'],
known_infected=params_dict['known_infected'],
market_share=params_dict['market_share'],
n_days=params_dict['n_days'],
relative_contact_rate=params_dict['relative_contact_rate'],
susceptible=params_dict['susceptible'],
hospitalized=params_dict['hospitalized'],
icu=params_dict['icu'],
ventilated=params_dict['ventilated'],
)
base_input_params_dict = vars(p)
# Create a range of social distances
soc_dists = np.arange(0.05, 0.60, 0.05)
# array([0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55,
# 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85])
num_scenarios = len(soc_dists)
# We can store outputs any way we want. For this demo, just going to
# use a master list. # This will be a list of dicts of the
# result dataframes (+ 1 dict containing the scenario inputs)
results_list = []
for sdpct in soc_dists:
sim_scenario = '{}{:.0f}'.format(scenarios, 100 * sdpct)
# Update the parameters for this scenario
p.relative_contact_rate = sdpct
input_params_dict = vars(p)
# Run the model
m = SimSirModel(p)
# Gather results
results = gather_sim_results(m, sim_scenario, input_params_dict)
# Append results to results list
results_list.append(results.copy())
return results_list