def from_file(
cls,
config_path: str = default_config_path) -> "TransmissionConstant":
with open(config_path) as f:
config = yaml.safe_load(f)
probability = CompletionTime.from_dict(config["probability"])()
return TransmissionConstant(probability=probability)
def from_file(
cls,
time_first_infectious: float,
n: float,
alpha: float,
max_symptoms: "SymptomTag" = None,
config_path: str = default_config_path,
) -> "TransmissionXNExp":
"""
Generates transmission class from config file
Parameters
----------
time_first_infectious:
time at which the person becomes infectious
n:
exponent of x in the x^n exp(-x/alpha) function
alpha:
denominator in exponential
max_symptoms:
maximum symptoms that the person will ever have, used to lower the infectiousness of
asymptomatic and mild cases
Returns
-------
class instance
"""
with open(config_path) as f:
config = yaml.safe_load(f)
max_probability = CompletionTime.from_dict(config["max_probability"])()
norm_time = CompletionTime.from_dict(config["norm_time"])()
asymptomatic_infectious_factor = CompletionTime.from_dict(
config["asymptomatic_infectious_factor"])()
mild_infectious_factor = CompletionTime.from_dict(
config["mild_infectious_factor"])()
return TransmissionXNExp(
max_probability=max_probability,
time_first_infectious=time_first_infectious,
norm_time=norm_time,
n=n,
alpha=alpha,
max_symptoms=max_symptoms,
asymptomatic_infectious_factor=asymptomatic_infectious_factor,
mild_infectious_factor=mild_infectious_factor,
)
def test_parse_completion_time(self, constant_completion_dict):
constant = CompletionTime.from_dict(constant_completion_dict)
assert isinstance(
constant,
ConstantCompletionTime
)
exponential = CompletionTime.from_dict(
{
"type": "exponential",
"loc": 1.0,
"scale": 2.0
}
)
assert isinstance(
exponential,
ExponentialCompletionTime
)
assert exponential.loc == 1.0
assert exponential.scale == 2.0
def _load_transmission_constant(self, transmission_config: dict):
"""
Given transmission config dictionary, load parameter generators from which
transmission constant parameters will be sampled
Parameters
----------
transmission_config:
dictionary of transmission config parameters
"""
self.probability = CompletionTime.from_dict(
transmission_config["probability"])
def from_file_linked_symptoms(
cls,
time_to_symptoms_onset: float,
max_symptoms: str = None,
config_path: str = default_gamma_config_path,
) -> "TransmissionGamma":
"""
Generate transmission class reading parameters from config file, linked to
the time of symptoms onset
Parameters
----------
time_to_symptoms_onset:
time (from infection) at which the person becomes symptomatic
max_symptoms:
maximum symptoms the individual will develop, used to reduce the infectiousness
of asymptomatic and mild individuals if wanted
config_path:
path to config parameters
Returns
-------
TransmissionGamma instance
"""
with open(config_path) as f:
config = yaml.safe_load(f)
max_infectiousness = CompletionTime.from_dict(
config["max_infectiousness"])()
shape = CompletionTime.from_dict(config["shape"])()
rate = CompletionTime.from_dict(config["rate"])()
shift = CompletionTime.from_dict(
config["shift"])() + time_to_symptoms_onset
asymptomatic_infectious_factor = CompletionTime.from_dict(
config["asymptomatic_infectious_factor"])()
mild_infectious_factor = CompletionTime.from_dict(
config["mild_infectious_factor"])()
return cls(
max_infectiousness=max_infectiousness,
shape=shape,
rate=rate,
shift=shift,
max_symptoms=max_symptoms,
asymptomatic_infectious_factor=asymptomatic_infectious_factor,
mild_infectious_factor=mild_infectious_factor,
)
def _load_transmission_gamma(self, transmission_config: dict):
"""
Given transmission config dictionary, load parameter generators from which
transmission gamma parameters will be sampled
Parameters
----------
transmission_config:
dictionary of transmission config parameters
"""
self.max_infectiousness = CompletionTime.from_dict(
transmission_config["max_infectiousness"])
self.shape = CompletionTime.from_dict(transmission_config["shape"])
self.rate = CompletionTime.from_dict(transmission_config["rate"])
self.shift = CompletionTime.from_dict(transmission_config["shift"])
self.asymptomatic_infectious_factor = CompletionTime.from_dict(
transmission_config["asymptomatic_infectious_factor"])
self.mild_infectious_factor = CompletionTime.from_dict(
transmission_config["mild_infectious_factor"])
def _load_transmission_xnexp(self, transmission_config: dict):
"""
Given transmission config dictionary, load parameter generators from which
transmission xnexp parameters will be sampled
Parameters
----------
transmission_config:
dictionary of transmission config parameters
"""
self.smearing_time_first_infectious = CompletionTime.from_dict(
transmission_config["smearing_time_first_infectious"])
self.smearing_peak_position = CompletionTime.from_dict(
transmission_config["smearing_peak_position"])
self.alpha = CompletionTime.from_dict(transmission_config["alpha"])
self.max_probability = CompletionTime.from_dict(
transmission_config["max_probability"])
self.norm_time = CompletionTime.from_dict(
transmission_config["norm_time"])
self.asymptomatic_infectious_factor = CompletionTime.from_dict(
transmission_config["asymptomatic_infectious_factor"])
self.mild_infectious_factor = CompletionTime.from_dict(
transmission_config["mild_infectious_factor"])
def from_file(
cls,
max_symptoms: "SymptomTag" = None,
config_path: str = default_gamma_config_path,
) -> "TransmissionGamma":
"""
Generate transmission class reading parameters from config file
Parameters
----------
max_symptoms:
maximum symptoms the individual will develop, used to reduce the infectiousness
of asymptomatic and mild individuals if wanted
config_path:
path to config parameters
Returns
-------
TransmissionGamma instance
"""
with open(config_path) as f:
config = yaml.safe_load(f)
max_infectiousness = CompletionTime.from_dict(
config["max_infectiousness"])()
shape = CompletionTime.from_dict(config["shape"])()
rate = CompletionTime.from_dict(config["rate"])()
shift = CompletionTime.from_dict(config["shift"])()
asymptomatic_infectious_factor = CompletionTime.from_dict(
config["asymptomatic_infectious_factor"])()
mild_infectious_factor = CompletionTime.from_dict(
config["mild_infectious_factor"])()
return cls(
max_infectiousness=max_infectiousness,
shape=shape,
rate=rate,
shift=shift,
max_symptoms=max_symptoms,
asymptomatic_infectious_factor=asymptomatic_infectious_factor,
mild_infectious_factor=mild_infectious_factor,
)