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changes on custom distributions definitions

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This commit is contained in:
Luis Aleixo 2024-03-07 17:11:04 +01:00
parent c02737cf92
commit 3914dabebb
2 changed files with 39 additions and 42 deletions
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50
caimira/monte_carlo/data.py
View file

@ -41,50 +41,50 @@ sqrt2pi = np.sqrt(2.*np.pi)
sqrt2 = np.sqrt(2.)
def custom_distribution_lookup(dict: dict, key_part: str) -> typing.Any:
def custom_value_type_lookup(dict: dict, key_part: str) -> typing.Any:
"""
Look up a custom distribution based on a partial key.
Look up a custom value type based on a partial key.
Args:
dict (dict): The root to search.
key_part (str): The distribution key to match.
key_part (str): The value type key to match.
Returns:
str: The associated distribution.
str: The associated value.
"""
try:
for key, value in dict.items():
if (key_part in key):
return value['associated_distribution']
return value['associated_value']
except KeyError:
return f"Key '{key_part}' not found."
def evaluate_custom_distribution(dist: str, params: typing.Dict) -> typing.Any:
def evaluate_custom_value_type(dist: str, params: typing.Dict) -> typing.Any:
"""
Evaluate a custom distribution.
Evaluate a custom value type.
Args:
dist (str): The type of distribution.
params (Dict): The parameters for the distribution.
dist (str): The type of value.
params (Dict): The parameters for the value type.
Returns:
Any: The generated distribution.
Any: The generated value.
Raises:
ValueError: If the distribution type is not recognized.
ValueError: If the value type is not recognized.
"""
if dist == 'Linear Space':
return np.linspace(params['start'], params['stop'], params['num'])
elif dist == 'Normal':
if dist == 'Constant':
return params
elif dist == 'Normal distribution':
return Normal(params['normal_mean_gaussian'], params['normal_standard_deviation_gaussian'])
elif dist == 'Log-normal':
elif dist == 'Log-normal distribution':
return LogNormal(params['lognormal_mean_gaussian'], params['lognormal_standard_deviation_gaussian'])
elif dist == 'Uniform':
elif dist == 'Uniform distribution':
return Uniform(params['low'], params['high'])
else:
raise ValueError('Bad request - distribution not found.')
raise ValueError('Bad request - value type not found.')
def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) -> typing.Any:
@ -106,15 +106,15 @@ def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) ->
if isinstance(value, str):
if value == 'Custom':
custom_distribution: typing.Dict = custom_distribution_lookup(
custom_value_type: typing.Dict = custom_value_type_lookup(
root, 'custom distribution')
for d, p in custom_distribution.items():
return evaluate_custom_distribution(d, p)
for d, p in custom_value_type.items():
return evaluate_custom_value_type(d, p)
elif isinstance(value, dict):
dist: str = root[param]['associated_distribution']
dist: str = root[param]['associated_value']
params: typing.Dict = root[param]['parameters']
return evaluate_custom_distribution(dist, params)
return evaluate_custom_value_type(dist, params)
elif isinstance(value, float) or isinstance(value, int):
return value
@ -347,21 +347,21 @@ def mask_distributions(data_registry):
data_registry.mask_distributions['Type I'], 'η_inhale'),
η_exhale=param_evaluation(
data_registry.mask_distributions['Type I'], 'η_exhale')
if data_registry.mask_distributions['Type I']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
if data_registry.mask_distributions['Type I'].get('η_exhale') is not None else None
),
'FFP2': mc.Mask(
η_inhale=param_evaluation(
data_registry.mask_distributions['FFP2'], 'η_inhale'),
η_exhale=param_evaluation(
data_registry.mask_distributions['FFP2'], 'η_exhale')
if data_registry.mask_distributions['FFP2']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
if data_registry.mask_distributions['FFP2'].get('η_exhale') is not None else None
),
'Cloth': mc.Mask(
η_inhale=param_evaluation(
data_registry.mask_distributions['Cloth'], 'η_inhale'),
η_exhale=param_evaluation(
data_registry.mask_distributions['Cloth'], 'η_exhale')
if data_registry.mask_distributions['Cloth']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
if data_registry.mask_distributions['Cloth'].get('η_exhale') is not None else None
),
}

31
caimira/store/data_registry.py
View file

@ -34,14 +34,14 @@ class DataRegistry:
activity_distributions = {
"Seated": {
"inhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": -0.6872121723362303,
"lognormal_standard_deviation_gaussian": 0.10498338229297108,
},
},
"exhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": -0.6872121723362303,
"lognormal_standard_deviation_gaussian": 0.10498338229297108,
@ -50,14 +50,14 @@ class DataRegistry:
},
"Standing": {
"inhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": -0.5742377578494785,
"lognormal_standard_deviation_gaussian": 0.09373162411398223,
},
},
"exhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": -0.5742377578494785,
"lognormal_standard_deviation_gaussian": 0.09373162411398223,
@ -66,14 +66,14 @@ class DataRegistry:
},
"Light activity": {
"inhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 0.21380242785625422,
"lognormal_standard_deviation_gaussian": 0.09435378091059601,
},
},
"exhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 0.21380242785625422,
"lognormal_standard_deviation_gaussian": 0.09435378091059601,
@ -82,14 +82,14 @@ class DataRegistry:
},
"Moderate activity": {
"inhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 0.551771330362601,
"lognormal_standard_deviation_gaussian": 0.1894616357138137,
},
},
"exhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 0.551771330362601,
"lognormal_standard_deviation_gaussian": 0.1894616357138137,
@ -98,14 +98,14 @@ class DataRegistry:
},
"Heavy exercise": {
"inhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 1.1644665696723049,
"lognormal_standard_deviation_gaussian": 0.21744554768657565,
},
},
"exhalation_rate": {
"associated_distribution": "Log-normal",
"associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 1.1644665696723049,
"lognormal_standard_deviation_gaussian": 0.21744554768657565,
@ -274,37 +274,34 @@ class DataRegistry:
mask_distributions = {
"Type I": {
"η_inhale": {
"associated_distribution": "Uniform",
"associated_value": "Uniform distribution",
"parameters": {
"low": 0.25,
"high": 0.80,
},
},
"Known filtration efficiency of masks when exhaling?": "No",
"factor_exhale": 1,
},
"FFP2": {
"η_inhale": {
"associated_distribution": "Uniform",
"associated_value": "Uniform distribution",
"parameters": {
"low": 0.83,
"high": 0.91,
},
},
"Known filtration efficiency of masks when exhaling?": "No",
"factor_exhale": 1,
},
"Cloth": {
"η_inhale": {
"associated_distribution": "Uniform",
"associated_value": "Uniform distribution",
"parameters": {
"low": 0.05,
"high": 0.40,
},
},
"Known filtration efficiency of masks when exhaling?": "Yes",
"η_exhale": {
"associated_distribution": "Uniform",
"associated_value": "Uniform distribution",
"parameters": {
"low": 0.20,
"high": 0.50,