injected parameters in models, model and report generator
This commit is contained in:
Luis Aleixo
parent
3cc90593c2
commit
3f75fc77cd
3 changed files with 32 additions and 47 deletions
|
|
@ -19,6 +19,7 @@ from caimira.monte_carlo.data import expiration_distribution, expiration_BLO_fac
|
|||
from .defaults import (NO_DEFAULT, DEFAULT_MC_SAMPLE_SIZE, DEFAULTS, ACTIVITIES, ACTIVITY_TYPES, COFFEE_OPTIONS_INT, CONFIDENCE_LEVEL_OPTIONS,
|
||||
MECHANICAL_VENTILATION_TYPES, MASK_TYPES, MASK_WEARING_OPTIONS, MONTH_NAMES, VACCINE_BOOSTER_TYPE, VACCINE_TYPE,
|
||||
VENTILATION_TYPES, VIRUS_TYPES, VOLUME_TYPES, WINDOWS_OPENING_REGIMES, WINDOWS_TYPES)
|
||||
from caimira.store.configuration import config
|
||||
|
||||
LOG = logging.getLogger(__name__)
|
||||
|
||||
|
|
@ -314,10 +315,10 @@ class FormData:
|
|||
|
||||
if self.arve_sensors_option == False:
|
||||
if self.room_heating_option:
|
||||
humidity = 0.3
|
||||
humidity = config.room['defaults']['humidity_with_heating']
|
||||
else:
|
||||
humidity = 0.5
|
||||
inside_temp = 293.
|
||||
humidity = config.room['defaults']['humidity_without_heating']
|
||||
inside_temp = config.room['defaults']['inside_temp']
|
||||
else:
|
||||
humidity = float(self.humidity)
|
||||
inside_temp = self.inside_temp
|
||||
|
|
@ -373,7 +374,7 @@ class FormData:
|
|||
if (self.activity_type == 'precise'):
|
||||
activity_defn, _ = self.generate_precise_activity_expiration()
|
||||
else:
|
||||
activity_defn = ACTIVITIES[ACTIVITY_TYPES.index(self.activity_type)]['activity']
|
||||
activity_defn = activity_defn = ACTIVITIES[self.activity_type]['activity']
|
||||
|
||||
population = mc.SimplePopulation(
|
||||
number=models.IntPiecewiseConstant(transition_times=tuple(transition_times), values=tuple(total_people)),
|
||||
|
|
@ -476,7 +477,8 @@ class FormData:
|
|||
# This is a minimal, always present source of ventilation, due
|
||||
# to the air infiltration from the outside.
|
||||
# See CERN-OPEN-2021-004, p. 12.
|
||||
infiltration_ventilation = models.AirChange(active=always_on, air_exch=0.25)
|
||||
residual_vent: float = config.ventilation['infiltration_ventilation'] # type: ignore
|
||||
infiltration_ventilation = models.AirChange(active=always_on, air_exch=residual_vent)
|
||||
if self.hepa_option:
|
||||
hepa = models.HEPAFilter(active=always_on, q_air_mech=self.hepa_amount)
|
||||
return models.MultipleVentilation((ventilation, hepa, infiltration_ventilation))
|
||||
|
|
@ -511,9 +513,8 @@ class FormData:
|
|||
# Initializes the virus
|
||||
virus = virus_distributions[self.virus_type]
|
||||
|
||||
activity_index = ACTIVITY_TYPES.index(self.activity_type)
|
||||
activity_defn = ACTIVITIES[activity_index]['activity']
|
||||
expiration_defn = ACTIVITIES[activity_index]['expiration']
|
||||
activity_defn = ACTIVITIES[self.activity_type]['activity']
|
||||
expiration_defn = ACTIVITIES[self.activity_type]['expiration']
|
||||
|
||||
if (self.activity_type == 'smallmeeting'):
|
||||
# Conversation of N people is approximately 1/N% of the time speaking.
|
||||
|
|
@ -538,29 +539,9 @@ class FormData:
|
|||
return infected
|
||||
|
||||
def exposed_population(self) -> mc.Population:
|
||||
scenario_activity = {
|
||||
'office': 'Seated',
|
||||
'controlroom-day': 'Seated',
|
||||
'controlroom-night': 'Seated',
|
||||
'smallmeeting': 'Seated',
|
||||
'largemeeting': 'Standing',
|
||||
'callcentre': 'Seated',
|
||||
'library': 'Seated',
|
||||
'training': 'Standing',
|
||||
'training_attendee': 'Seated',
|
||||
'lab':'Light activity',
|
||||
'workshop': 'Moderate activity',
|
||||
'gym':'Heavy exercise',
|
||||
'household-day': 'Light activity',
|
||||
'household-night': 'Seated',
|
||||
'primary-school': 'Light activity',
|
||||
'secondary-school': 'Light activity',
|
||||
'university': 'Seated',
|
||||
'restaurant': 'Seated',
|
||||
'precise': self.precise_activity['physical_activity'] if self.activity_type == 'precise' else None,
|
||||
}
|
||||
|
||||
activity_defn = scenario_activity[self.activity_type]
|
||||
activity_defn = (self.precise_activity['physical_activity']
|
||||
if self.activity_type == 'precise'
|
||||
else str(config.population_scenario_activity[self.activity_type]['activity']))
|
||||
activity = activity_distributions[activity_defn]
|
||||
|
||||
infected_occupants = self.infected_people
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ from caimira.apps.calculator import markdown_tools
|
|||
from ... import monte_carlo as mc
|
||||
from .model_generator import FormData, DEFAULT_MC_SAMPLE_SIZE
|
||||
from ... import dataclass_utils
|
||||
from caimira.store.configuration import config
|
||||
|
||||
|
||||
def model_start_end(model: models.ExposureModel):
|
||||
|
|
@ -201,8 +202,8 @@ def conditional_prob_inf_given_vl_dist(infection_probability: models._Vectorised
|
|||
for vl_log in viral_loads:
|
||||
specific_prob = infection_probability[np.where((vl_log-step/2-specific_vl)*(vl_log+step/2-specific_vl)<0)[0]] #type: ignore
|
||||
pi_means.append(specific_prob.mean())
|
||||
lower_percentiles.append(np.quantile(specific_prob, 0.05))
|
||||
upper_percentiles.append(np.quantile(specific_prob, 0.95))
|
||||
lower_percentiles.append(np.quantile(specific_prob, config.conditional_prob_inf_given_viral_load['lower_percentile']))
|
||||
upper_percentiles.append(np.quantile(specific_prob, config.conditional_prob_inf_given_viral_load['upper_percentile']))
|
||||
|
||||
return pi_means, lower_percentiles, upper_percentiles
|
||||
|
||||
|
|
@ -210,7 +211,9 @@ def conditional_prob_inf_given_vl_dist(infection_probability: models._Vectorised
|
|||
def manufacture_conditional_probability_data(exposure_model: models.ExposureModel,
|
||||
infection_probability: models._VectorisedFloat):
|
||||
|
||||
min_vl, max_vl, step = 2, 10, 8/100
|
||||
min_vl = config.conditional_prob_inf_given_viral_load['min_vl']
|
||||
max_vl = config.conditional_prob_inf_given_viral_load['max_vl']
|
||||
step = (max_vl - min_vl)/100
|
||||
viral_loads = np.arange(min_vl, max_vl, step)
|
||||
specific_vl = np.log10(exposure_model.concentration_model.virus.viral_load_in_sputum)
|
||||
pi_means, lower_percentiles, upper_percentiles = conditional_prob_inf_given_vl_dist(infection_probability, viral_loads,
|
||||
|
|
|
|||
|
|
@ -49,6 +49,7 @@ else:
|
|||
from .utils import method_cache
|
||||
|
||||
from .dataclass_utils import nested_replace
|
||||
from caimira.store.configuration import config
|
||||
|
||||
oneoverln2 = 1 / np.log(2)
|
||||
# Define types for items supporting vectorisation. In the future this may be replaced
|
||||
|
|
@ -347,7 +348,7 @@ class SlidingWindow(WindowOpening):
|
|||
Average measured value of discharge coefficient for sliding or
|
||||
side-hung windows.
|
||||
"""
|
||||
return 0.6
|
||||
return config.ventilation['natural']['discharge_factor']['sliding']
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
|
|
@ -860,7 +861,7 @@ class _PopulationWithVirus(Population):
|
|||
The fraction of infectious virus.
|
||||
|
||||
"""
|
||||
return 1.
|
||||
return config.population_with_virus['fraction_of_infectious_virus']
|
||||
|
||||
def aerosols(self):
|
||||
"""
|
||||
|
|
@ -1032,7 +1033,7 @@ class _ConcentrationModelBase:
|
|||
(in the same unit as the concentration). Its the value towards which
|
||||
the concentration will decay to.
|
||||
"""
|
||||
return 0.
|
||||
return config.concentration_model['min_background_concentration']
|
||||
|
||||
def normalization_factor(self) -> _VectorisedFloat:
|
||||
"""
|
||||
|
|
@ -1220,7 +1221,7 @@ class ConcentrationModel(_ConcentrationModelBase):
|
|||
#: evaporation factor: the particles' diameter is multiplied by this
|
||||
# factor as soon as they are in the air (but AFTER going out of the,
|
||||
# mask, if any).
|
||||
evaporation_factor: float = 0.3
|
||||
evaporation_factor: float = config.particle['evaporation_factor']
|
||||
|
||||
@property
|
||||
def population(self) -> InfectedPopulation:
|
||||
|
|
@ -1260,10 +1261,10 @@ class CO2ConcentrationModel(_ConcentrationModelBase):
|
|||
CO2_emitters: SimplePopulation
|
||||
|
||||
#: CO2 concentration in the atmosphere (in ppm)
|
||||
CO2_atmosphere_concentration: float = 440.44
|
||||
CO2_atmosphere_concentration: float = config.concentration_model['CO2_concentration_model']['CO2_atmosphere_concentration']
|
||||
|
||||
#: CO2 fraction in the exhaled air
|
||||
CO2_fraction_exhaled: float = 0.042
|
||||
CO2_fraction_exhaled: float = config.concentration_model['CO2_concentration_model']['CO2_fraction_exhaled']
|
||||
|
||||
@property
|
||||
def population(self) -> SimplePopulation:
|
||||
|
|
@ -1309,14 +1310,14 @@ class ShortRangeModel:
|
|||
The dilution factor for the respective expiratory activity type.
|
||||
'''
|
||||
# Average mouth opening diameter (m)
|
||||
mouth_diameter = 0.02
|
||||
mouth_diameter: float = config.short_range_model['dilution_factor']['mouth_diameter']
|
||||
|
||||
# Breathing rate, from m3/h to m3/s
|
||||
BR = np.array(self.activity.exhalation_rate/3600.)
|
||||
|
||||
# Exhalation coefficient. Ratio between the duration of a breathing cycle and the duration of
|
||||
# the exhalation.
|
||||
φ = 2
|
||||
φ: float = config.short_range_model['dilution_factor']['exhalation_coefficient']
|
||||
|
||||
# Exhalation airflow, as per Jia et al. (2022)
|
||||
Q_exh: _VectorisedFloat = φ * BR
|
||||
|
|
@ -1328,12 +1329,12 @@ class ShortRangeModel:
|
|||
u0 = np.array(Q_exh/Am)
|
||||
|
||||
# Duration of the expiration period(s), assuming a 4s breath-cycle
|
||||
tstar = 2.0
|
||||
tstar: float = config.short_range_model['dilution_factor']['tstar']
|
||||
|
||||
# Streamwise and radial penetration coefficients
|
||||
𝛽r1 = 0.18
|
||||
𝛽r2 = 0.2
|
||||
𝛽x1 = 2.4
|
||||
𝛽r1: float = config.short_range_model['dilution_factor']['penetration_coefficients']['𝛽r1']
|
||||
𝛽r2: float = config.short_range_model['dilution_factor']['penetration_coefficients']['𝛽r2']
|
||||
𝛽x1: float = config.short_range_model['dilution_factor']['penetration_coefficients']['𝛽x1']
|
||||
|
||||
# Parameters in the jet-like stage
|
||||
# Position of virtual origin
|
||||
|
|
@ -1489,7 +1490,7 @@ class ExposureModel:
|
|||
geographical_data: Cases
|
||||
|
||||
#: The number of times the exposure event is repeated (default 1).
|
||||
repeats: int = 1
|
||||
repeats: int = config.exposure_model['repeats']
|
||||
|
||||
def __post_init__(self):
|
||||
"""
|
||||
|
|
|
|||
Loading…
Reference in a new issue