injected parameters in models, model and report generator

caimira/apps/calculator/model_generator.py

@@ -19,6 +19,7 @@
 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 @@ def initialize_room(self) -> models.Room:
 
         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 @@ def build_CO2_model(self, sample_size=DEFAULT_MC_SAMPLE_SIZE) -> models.CO2Conce
         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 @@ def ventilation(self) -> models._VentilationBase:
         # 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 @@ def infected_population(self) -> mc.InfectedPopulation:
         # 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 @@ def infected_population(self) -> mc.InfectedPopulation:
         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

caimira/apps/calculator/report_generator.py

@@ -17,6 +17,7 @@
 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):
@@ -201,16 +202,18 @@ 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
 
 
 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, 

caimira/models.py

@@ -49,6 +49,7 @@
 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 @@ def discharge_coefficient(self) -> _VectorisedFloat:
         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 @@ def fraction_of_infectious_virus(self) -> _VectorisedFloat:
         The fraction of infectious virus.
 
         """
-        return 1.
+        return config.population_with_virus['fraction_of_infectious_virus']
 
     def aerosols(self):
         """
@@ -1032,7 +1033,7 @@ def min_background_concentration(self) -> _VectorisedFloat:
         (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 @@ def dilution_factor(self) -> _VectorisedFloat:
         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 @@ def dilution_factor(self) -> _VectorisedFloat:
         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):
         """