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defined global store with configuration

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Luis Aleixo 2023-10-26 16:20:44 +02:00
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commit b1aa3af332
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caimira/store/configuration.py Normal file
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import asyncio
import os
from caimira.store.global_store import GlobalStore
class Configuration:
'''
Configuration to handle data. Contains the default values used in the model.
Might suffer update from the Data Service.
'''
def __init__(self):
self.data_fetched = False
self.BLOmodel = {
'cn': {'B': 0.06,
'L': 0.2,
'O': 0.0010008,
},
'mu': {
'B': 0.989541,
'L': 1.38629,
'O': 4.97673,
},
'sigma': {
'B': 0.262364,
'L': 0.506818,
'O': 0.585005,
},
}
self.activity_distributions = {
'Seated': {
'inhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': -0.6872121723362303, 'standard_deviation_gaussian': 0.10498338229297108},
},
'exhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': -0.6872121723362303, 'standard_deviation_gaussian': 0.10498338229297108},
},
},
'Standing': {
'inhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': -0.5742377578494785, 'standard_deviation_gaussian': 0.09373162411398223},
},
'exhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': -0.5742377578494785, 'standard_deviation_gaussian': 0.09373162411398223},
},
},
'Light activity': {
'inhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': 0.21380242785625422, 'standard_deviation_gaussian': 0.09435378091059601},
},
'exhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': 0.21380242785625422, 'standard_deviation_gaussian': 0.09435378091059601},
},
},
'Moderate activity': {
'inhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': 0.551771330362601, 'standard_deviation_gaussian': 0.1894616357138137},
},
'exhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': 0.551771330362601, 'standard_deviation_gaussian': 0.1894616357138137},
},
},
'Heavy exercise': {
'inhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': 1.1644665696723049, 'standard_deviation_gaussian': 0.21744554768657565},
},
'exhalation_rate': {
'associated_distribution': 'Numpy Log-normal Distribution (random.lognormal)',
'parameters': {'mean_gaussian': 1.1644665696723049, 'standard_deviation_gaussian': 0.21744554768657565},
},
},
}
self.symptomatic_vl_frequencies = {
'log_variable': [2.46032, 2.67431, 2.85434, 3.06155, 3.25856, 3.47256, 3.66957, 3.85979, 4.09927, 4.27081,
4.47631, 4.66653, 4.87204, 5.10302, 5.27456, 5.46478, 5.6533, 5.88428, 6.07281, 6.30549,
6.48552, 6.64856, 6.85407, 7.10373, 7.30075, 7.47229, 7.66081, 7.85782, 8.05653, 8.27053,
8.48453, 8.65607, 8.90573, 9.06878, 9.27429, 9.473, 9.66152, 9.87552],
'frequencies': [0.001206885, 0.007851618, 0.008078144, 0.01502491, 0.013258014, 0.018528495, 0.020053765,
0.021896167, 0.022047184, 0.018604005, 0.01547796, 0.018075445, 0.021503523, 0.022349217,
0.025097721, 0.032875078, 0.030594727, 0.032573045, 0.034717482, 0.034792991,
0.033267721, 0.042887485, 0.036846816, 0.03876473, 0.045016819, 0.040063473, 0.04883754,
0.043944602, 0.048142864, 0.041588741, 0.048762031, 0.027921732, 0.033871788,
0.022122693, 0.016927718, 0.008833228, 0.00478598, 0.002807662],
'kernel_bandwidth': 0.1,
}
self.covid_overal_vl_data = {
'shape_factor': 3.47,
'scale_factor': 7.01,
'start': 0.01,
'stop': 0.99,
'num': 30.0,
'min_bound': 2,
'max_bound': 10,
'interpolation_fp_left': 0,
'interpolation_fp_right': 0,
'max_function': 0.2,
}
self.viable_to_RNA_ratio_distribution = {
'low': 0.01,
'high': 0.6,
}
self.infectious_dose_distribution = {
'low': 10,
'high': 100,
}
self.virus_distributions = {
'SARS_CoV_2': {
'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
'transmissibility_factor': 1,
'infectiousness_days': 14,
},
'SARS_CoV_2_ALPHA': {
'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
'transmissibility_factor': 0.78,
'infectiousness_days': 14,
},
'SARS_CoV_2_BETA': {
'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
'transmissibility_factor': 0.8,
'infectiousness_days': 14
},
'SARS_CoV_2_GAMMA': {
'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
'transmissibility_factor': 0.72,
'infectiousness_days': 14
},
'SARS_CoV_2_DELTA': {
'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
'transmissibility_factor': 0.51,
'infectiousness_days': 14
},
'SARS_CoV_2_OMICRON': {
'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
'transmissibility_factor': 0.2,
'infectiousness_days': 14
},
'SARS_CoV_2_Other': {
'viral_load_in_sputum': 'Ref: Viral load - covid_overal_vl_data',
'infectious_dose': 'Ref: Infectious dose - infectious_dose_distribution',
'viable_to_RNA_ratio': 'Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution',
'transmissibility_factor': 0.1,
'infectiousness_days': 14,
},
}
self.mask_distributions = {
'Type I': {
'η_inhale': {
'associated_distribution': 'Numpy Uniform Distribution (random.uniform)',
'parameters': {
'low': 0.25,
'high': 0.80,
},
},
'Known filtration efficiency of masks when exhaling?': 'No',
'factor_exhale': 1,
},
'FFP2': {
'η_inhale': {
'associated_distribution': 'Numpy Uniform Distribution (random.uniform)',
'parameters': {
'low': 0.83,
'high': 0.91,
},
},
'Known filtration efficiency of masks when exhaling?': 'No',
'factor_exhale': 1,
},
'Cloth': {
'η_inhale': {
'associated_distribution': 'Numpy Uniform Distribution (random.uniform)',
'parameters': {
'low': 0.05,
'high': 0.40,
},
},
'Known filtration efficiency of masks when exhaling?': 'Yes',
'η_exhale': {
'associated_distribution': 'Numpy Uniform Distribution (random.uniform)',
'parameters': {
'low': 0.20,
'high': 0.50,
},
},
'factor_exhale': 1,
},
}
self.expiration_BLO_factors = {
'Breathing': {'B': 1., 'L': 0., 'O': 0., },
'Speaking': {'B': 1., 'L': 1., 'O': 1., },
'Singing': {'B': 1., 'L': 5., 'O': 5., },
'Shouting': {'B': 1., 'L': 5., 'O': 5., },
}
self.long_range_expiration_distributions = {
'minimum_diameter': 0.1,
'maximum_diameter': 30,
}
self.short_range_expiration_distributions = {
'minimum_diameter': 0.1,
'maximum_diameter': 100,
}
self.short_range_distances = {
'minimum_distance': 0.5,
'maximum_distance': 2.,
}
####################################
self.room = {
'defaults': {
'inside_temp': 293,
'humidity_with_heating': 0.3,
'humidity_without_heating': 0.5,
},
}
self.ventilation = {
'natural': {
'discharge_factor': {
'sliding': 0.6,
},
},
'infiltration_ventilation': 0.25,
}
self.particle = {
'evaporation_factor': 0.3,
}
self.population_with_virus = {
'fraction_of_infectious_virus': 1,
}
self.concentration_model = {
'min_background_concentration': 0.,
'CO2_concentration_model': {
'CO2_atmosphere_concentration': 440.44,
'CO2_fraction_exhaled': 0.042,
},
}
self.short_range_model = {
'dilution_factor': {
'mouth_diameter': 0.02,
'exhalation_coefficient': 2,
'tstar': 2,
'penetration_coefficients': {
'𝛽r1': 0.18, '𝛽r2': .2, '𝛽x1': 2.4,
},
},
}
self.exposure_model = {
'repeats': 1,
}
self.conditional_prob_inf_given_viral_load = {
'lower_percentile': 0.05,
'upper_percentile': 0.95,
'min_vl': 2,
'max_vl': 10,
}
self.monte_carlo_sample_size = 250000
self.population_scenario_activity = {
'office': {'activity': 'Seated', 'expiration': {'Speaking': 1, 'Breathing': 2}},
'smallmeeting': {'activity': 'Seated', 'expiration': {'Speaking': 1, 'Breathing': None}},
'largemeeting': {'activity': 'Standing', 'expiration': {'Speaking': 1, 'Breathing': 2}},
'callcenter': {'activity': 'Seated', 'expiration': {'Speaking': 1}},
'controlroom-day': {'activity': 'Seated', 'expiration': {'Speaking': 1, 'Breathing': 1}},
'controlroom-night': {'activity': 'Seated', 'expiration': {'Speaking': 1, 'Breathing': 9}},
'library': {'activity': 'Seated', 'expiration': {'Breathing': 1}},
'lab': {'activity': 'Light activity', 'expiration': {'Speaking': 1, 'Breathing': 1}},
'workshop': {'activity': 'Moderate activity', 'expiration': {'Speaking': 1, 'Breathing': 1}},
'training': {'activity': 'Standing', 'expiration': {'Speaking': 1}},
'training_attendee': {'activity': 'Seated', 'expiration': {'Breathing': 1}},
'gym': {'activity': 'Heavy exercise', 'expiration': {'Breathing': 1}},
'household-day': {'activity': 'Light activity', 'expiration': {'Breathing': 5, 'Speaking': 5}},
'household-night': {'activity': 'Seated', 'expiration': {'Breathing': 7, 'Speaking': 3}},
'primary-school': {'activity': 'Light activity', 'expiration': {'Breathing': 5, 'Speaking': 5}},
'secondary-school': {'activity': 'Light activity', 'expiration': {'Breathing': 7, 'Speaking': 3}},
'university': {'activity': 'Seated', 'expiration': {'Breathing': 9, 'Speaking': 1}},
'restaurant': {'activity': 'Seated', 'expiration': {'Breathing': 1, 'Speaking': 9}},
'precise': {'activity': None, 'expiration': None},
}
async def populate_data(self):
"""
Fetches data from the API and populates the configuration object.
"""
if not self.data_fetched and os.environ.get('DATA_SERVICE_ENABLED', 'False').lower() == 'true':
# Fetch and populate data from API
await GlobalStore.populate_from_api()
data = GlobalStore.get_data()['data']
# Dynamically set attributes based on the data fetched from the API
for attr_name, value in data.items():
setattr(self, attr_name, value)
self.data_fetched = True
return
config = Configuration()
asyncio.run(config.populate_data())

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caimira/store/global_store.py Normal file
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import os
from caimira.store.data_service import DataService
class GlobalStore:
'''
Singleton pattern - ensure that there's only one instance of
GlobalStore throughout the application
'''
_instance = None
def __new__(self):
if self._instance is None:
self._instance = super().__new__(self)
self._instance = {}
return self._instance
@classmethod
async def populate_from_api(self):
data_service_credentials = {
'data_service_client_email': os.environ.get('DATA_SERVICE_CLIENT_EMAIL', None),
'data_service_client_password': os.environ.get('DATA_SERVICE_CLIENT_PASSWORD', None),
}
data_service = None
data_service_enabled = os.environ.get(
'DATA_SERVICE_ENABLED', 'False').lower() == 'true'
if data_service_enabled:
data_service = DataService(data_service_credentials)
self._instance = await data_service.fetch()
else:
print('Data service not enabled.')
@classmethod
def get_data(self):
return self._instance