""" file that has various utility functions that are general to EMOD"""
from emod_api.schema_to_class import ReadOnlyDict
[docs]class Distributions:
"""
Class with methods that return the configured distribution dictionaries. It is up to user to pipe the parameters
to correct prefix variables. For example, if your variable names are 'Sample_Size_Distribution' etc. then you would
add the prefix 'Sample_Size with the trailing underscore' to the keys of the dictionary to add to campaign/config
and pass to EMOD.
"""
[docs] @staticmethod
def constant(constant: float) -> dict:
"""
This function configures and returns dictionary of the parameters for CONSTANT_DISTRIBUTION.
Args:
constant: Each instance will receive this constant/fixed value.
Returns:
Dictionary of the distribution parameters that needs specific parameter prefix to pass to EMOD
"""
return {"Distribution": "CONSTANT_DISTRIBUTION",
"Constant": constant}
[docs] @staticmethod
def gaussian(gaussian_mean: float, gaussian_std_dev: float) -> dict:
"""
This function configures and returns dictionary of the parameters for GAUSSIAN_DISTRIBUTION.
Args:
gaussian_mean: The mean for the Gaussian distribution.
gaussian_std_dev: The standard deviation for the Gaussian distribution.
Returns:
Dictionary of the distribution parameters that needs specific parameter prefix to pass to EMOD
"""
return {"Distribution": "GAUSSIAN_DISTRIBUTION",
"Gaussian_Mean": gaussian_mean,
"Gaussian_Std_Dev": gaussian_std_dev}
[docs] @staticmethod
def exponential(exponential_mean: float) -> dict:
"""
This function configures and returns dictionary of the parameters for EXPONENTIAL_DISTRIBUTION.
Args:
exponential_mean: The mean for the exponential distribution.
Returns:
Dictionary of the distribution parameters that needs specific parameter prefix to pass to EMOD
"""
return {"Distribution": "EXPONENTIAL_DISTRIBUTION",
"Exponential": exponential_mean}
[docs] @staticmethod
def log_normal(mu: float, sigma: float) -> dict:
"""
This function configures and returns dictionary of the parameters for LOG_NORMAL_DISTRIBUTION.
Args:
mu: The mean for the log-normal distribution.
sigma: The width for the log-normal distribution.
Returns:
Dictionary of the distribution parameters that needs specific parameter prefix to pass to EMOD
"""
return {"Distribution": "LOG_NORMAL_DISTRIBUTION",
"Log_Normal_Mu": mu,
"Log_Normal_Sigma": sigma}
[docs] @staticmethod
def poisson(poisson_mean: float) -> dict:
"""
This function configures and returns dictionary of the parameters for POISSON_DISTRIBUTION.
Args:
poisson_mean: The mean for the Poisson distribution.
Returns:
Dictionary of the distribution parameters that needs specific parameter prefix to pass to EMOD
"""
return {"Distribution": "POISSON_DISTRIBUTION",
"Poisson_Mean": poisson_mean}
[docs] @staticmethod
def dual_constant(proportion_0: float, peak_2_value: float) -> dict:
"""
This function configures and returns dictionary of the parameters for DUAL_CONSTANT_DISTRIBUTION.
Args:
proportion_0: The proportion of outcomes to assign a value of zero.
peak_2_value: The value to assign to the remaining outcomes.
Returns:
Dictionary of the distribution parameters that needs specific parameter prefix to pass to EMOD
"""
return {"Distribution": "DUAL_CONSTANT_DISTRIBUTION",
"Proportion_0": proportion_0,
"Peak_2_Value": peak_2_value}
[docs] @staticmethod
def weibull(weibull_lambda: float, weibull_kappa: float) -> dict:
"""
This function configures and returns dictionary of the parameters for WEIBULL_DISTRIBUTION.
Args:
weibull_lambda: The scale value in the Weibull distribution
weibull_kappa: The shape value in a Weibull distribution.
Returns:
Dictionary of the distribution parameters that needs specific parameter prefix to pass to EMOD
"""
return {"Distribution": "WEIBULL_DISTRIBUTION",
"Lambda": weibull_lambda,
"Kappa": weibull_kappa}
[docs] @staticmethod
def dual_exponential(mean_1: float, proportion_1: float, mean_2: float) -> dict:
"""
This function configures and returns dictionary of the parameters for DUAL_EXPONENTIAL_DISTRIBUTION.
Args:
mean_1: The mean of the first exponential distribution.
proportion_1: The proportion of outcomes to assign to the first exponential distribution.
mean_2: The mean of the second exponential distribution.
Returns:
Dictionary of the distribution parameters that needs specific parameter prefix to pass to EMOD
"""
return {"Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
"Mean_1": mean_1,
"Proportion_1": proportion_1,
"Mean_2": mean_2}
[docs] @staticmethod
def set_distribution_parameters(distribution_containing_obj: ReadOnlyDict, distribution: dict, prefix: str) -> None:
"""
This function sets the distribution parameters in the schema_to_class-created dictionary.
Args:
distribution_containing_obj: ReadOnlyDict: Schema-based smart dictionary representing the structure
(intervention or demographics or config) that we're setting the distribution parameters for.
distribution: The distribution dictionary, generated by one of the distribution functions or user defined.
prefix: The prefix to be used for the distribution parameters in this intervention.
Returns:
Nothing. The intervention dictionary is updated in place.
"""
if prefix in list(distribution.keys())[0]:
# assume was generated by user and is correctly formatted
for key, value in distribution.items():
setattr(distribution_containing_obj, key, value)
else:
# assume was generated by emod_api.utils.Distributions
for key, value in distribution.items():
setattr(distribution_containing_obj, prefix + key, value)