added exposure_model, conditional_prob_inf_given_viral_load, monte_ca…

…rlo_sample_size, infected_population_scenario_activity

caimira/apps/calculator/defaults.py

@@ -1,11 +1,13 @@
 import typing
 
+import caimira.apps.calculator.global_store.constants as constants
+
 # ------------------ Default form values ----------------------
 
 # Used to declare when an attribute of a class must have a value provided, and
 # there should be no default value used.
 NO_DEFAULT = object()
-DEFAULT_MC_SAMPLE_SIZE = 250_000
+DEFAULT_MC_SAMPLE_SIZE = constants.monte_carlo_sample_size
 
 #: The default values for undefined fields. Note that the defaults here
 #: and the defaults in the html form must not be contradictory.
@@ -79,50 +81,14 @@
 
 # ------------------ Activities ----------------------
 
-ACTIVITIES: typing.List[typing.Dict[str, typing.Any]] = [
-    # Mostly silent in the office, but 1/3rd of time speaking.
-    {'name': 'office', 'activity': 'Seated',
-        'expiration': {'Speaking': 1, 'Breathing': 2}},
-    {'name': 'smallmeeting', 'activity': 'Seated',
-        'expiration': {'Speaking': 1, 'Breathing': None}},
-    # Each infected person spends 1/3 of time speaking.
-    {'name': 'largemeeting', 'activity': 'Standing',
-        'expiration': {'Speaking': 1, 'Breathing': 2}},
-    {'name': 'callcentre', 'activity': 'Seated', 'expiration': 'Speaking'},
-    # Daytime control room shift, 50% speaking.
-    {'name': 'controlroom-day', 'activity': 'Seated',
-        'expiration': {'Speaking': 1, 'Breathing': 1}},
-    # Nightshift control room, 10% speaking.
-    {'name': 'controlroom-night', 'activity': 'Seated',
-        'expiration': {'Speaking': 1, 'Breathing': 9}},
-    {'name': 'library', 'activity': 'Seated', 'expiration': 'Breathing'},
-    # Model 1/2 of time spent speaking in a lab.
-    {'name': 'lab', 'activity': 'Light activity',
-        'expiration': {'Speaking': 1, 'Breathing': 1}},
-    # Model 1/2 of time spent speaking in a workshop.
-    {'name': 'workshop', 'activity': 'Moderate activity',
-        'expiration': {'Speaking': 1, 'Breathing': 1}},
-    {'name': 'training', 'activity': 'Standing', 'expiration': 'Speaking'},
-    {'name': 'training_attendee', 'activity': 'Seated', 'expiration': 'Breathing'},
-    {'name': 'gym', 'activity': 'Heavy exercise', 'expiration': 'Breathing'},
-    {'name': 'household-day', 'activity': 'Light activity',
-        'expiration': {'Breathing': 5, 'Speaking': 5}},
-    {'name': 'household-night', 'activity': 'Seated',
-        'expiration': {'Breathing': 7, 'Speaking': 3}},
-    {'name': 'primary-school', 'activity': 'Light activity',
-        'expiration': {'Breathing': 5, 'Speaking': 5}},
-    {'name': 'secondary-school', 'activity': 'Light activity',
-        'expiration': {'Breathing': 7, 'Speaking': 3}},
-    {'name': 'university', 'activity': 'Seated',
-        'expiration': {'Breathing': 9, 'Speaking': 1}},
-    {'name': 'restaurant', 'activity': 'Seated',
-        'expiration': {'Breathing': 1, 'Speaking': 9}},
-    {'name': 'precise', 'activity': None, 'expiration': None},
-]
+ACTIVITIES: typing.Dict[str, typing.Dict] = constants.infected_population_scenario_activity
+# ACTIVITIES: typing.List[typing.Dict[str, typing.Any]] = [
+#     {'name': 'precise', 'activity': None, 'expiration': None},
+# ]
 
 # ------------------ Validation ----------------------
 
-ACTIVITY_TYPES = [activity['name'] for activity in ACTIVITIES]
+ACTIVITY_TYPES = list(ACTIVITIES.keys())
 COFFEE_OPTIONS_INT = {'coffee_break_0': 0, 'coffee_break_1': 1,
                       'coffee_break_2': 2, 'coffee_break_4': 4}
 CONFIDENCE_LEVEL_OPTIONS = {'confidence_low': 10,

caimira/apps/calculator/global_store/constants.py

@@ -178,6 +178,39 @@
     'maximum_distance': 2.,
 }
 
+infected_population_scenario_activity = {
+    'office': {'activity': 'Seated', 'expiration': {'Speaking': 1, 'Breathing': 2}},
+    'smallmeeting': {'activity': 'Seated', 'expiration': {'Speaking': 1}},
+    '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}},
+}
+
+monte_carlo_sample_size = 250000
+
+conditional_prob_inf_given_viral_load = {
+    'lower_percentile': 0.05,
+    'upper_percentile': 0.95,
+    'min_vl': 2,
+    'max_vl': 10,
+}
+
+exposure_model = {
+    'repeats': 1
+}
 
 def update_local_reference(local_data, api_data):
     '''
@@ -195,7 +228,9 @@ def update_local_reference(local_data, api_data):
 
 
 async def populate_data():
-    global data_fetched, covid_overal_vl_data, infectious_dose_distribution, viable_to_RNA_ratio_distribution, virus_distributions, activity_distributions, mask_distributions, expiration_BLO_factors, long_range_expiration_distributions, short_range_expiration_distributions, short_range_distances
+    global data_fetched, covid_overal_vl_data, infectious_dose_distribution, viable_to_RNA_ratio_distribution, virus_distributions, activity_distributions, mask_distributions
+    global expiration_BLO_factors, long_range_expiration_distributions, short_range_expiration_distributions, short_range_distances, infected_population_scenario_activity
+    global monte_carlo_sample_size, conditional_prob_inf_given_viral_load, exposure_model
 
     if not data_fetched and os.environ.get('DATA_SERVICE_ENABLED', 'False').lower() == 'true':
         # Fetch data if it hasn't been fetched yet
@@ -224,7 +259,15 @@ async def populate_data():
             short_range_expiration_distributions, data['short_range_expiration_distributions'])
         short_range_distances = update_local_reference(
             short_range_distances, data['short_range_distances'])
-
+        infected_population_scenario_activity = update_local_reference(
+            infected_population_scenario_activity, data['infected_population_scenario_activity'])
+        monte_carlo_sample_size = update_local_reference(
+            monte_carlo_sample_size, data['monte_carlo_sample_size'])
+        conditional_prob_inf_given_viral_load = update_local_reference(
+            conditional_prob_inf_given_viral_load, data['conditional_prob_inf_given_viral_load'])
+        exposure_model = update_local_reference(
+            exposure_model, data['exposure_model'])
+
         data_fetched = True
 
 # Executed when this module is imported

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)
+import caimira.apps.calculator.global_store.constants as constants
 
 LOG = logging.getLogger(__name__)
 
@@ -373,7 +374,7 @@ def build_CO2_model(self, sample_size=DEFAULT_MC_SAMPLE_SIZE) -> models.CO2Conce
         population = mc.SimplePopulation(
             number=models.IntPiecewiseConstant(transition_times=tuple(transition_times), values=tuple(total_people)),
             presence=None,
-            activity=activity_distributions[ACTIVITIES[ACTIVITY_TYPES.index(self.activity_type)]['activity']],
+            activity=activity_distributions[ACTIVITIES[self.activity_type]['activity']],
         )
 
         # Builds a CO2 concentration model based on model inputs
@@ -506,9 +507,9 @@ 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_index = ACTIVITY_TYPES.index(self.activity_type)
+        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.
@@ -533,29 +534,11 @@ 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,
-        }
+        # scenario_activity = {
+        #     'precise': self.precise_activity['physical_activity'] if self.activity_type == 'precise' else None,
+        # }
 
-        activity_defn = scenario_activity[self.activity_type]
+        activity_defn = str(constants.infected_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
+import caimira.apps.calculator.global_store.constants as constants
 
 
 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, constants.conditional_prob_inf_given_viral_load['lower_percentile']))
+        upper_percentiles.append(np.quantile(specific_prob, constants.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 = constants.conditional_prob_inf_given_viral_load['min_vl']
+    max_vl = constants.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/tests/apps/calculator/test_model_generator.py

@@ -11,6 +11,7 @@
 from caimira.apps.calculator.model_generator import minutes_since_midnight
 from caimira import models
 from caimira.monte_carlo.data import expiration_distributions
+import caimira.apps.calculator.global_store.constants as constants
 
 
 def test_model_from_dict(baseline_form_data):
@@ -34,7 +35,7 @@ def test_model_from_dict_invalid(baseline_form_data):
     ]
 )
 def test_blend_expiration(mask_type):
-    SAMPLE_SIZE = 250000
+    SAMPLE_SIZE = constants.monte_carlo_sample_size
     TOLERANCE = 0.02
     blend = {'Breathing': 2, 'Speaking': 1}
     r = model_generator.build_expiration(blend).build_model(SAMPLE_SIZE)

caimira/tests/models/test_short_range_model.py

@@ -8,8 +8,9 @@
 from caimira.apps.calculator.model_generator import build_expiration
 from caimira.monte_carlo.data import short_range_expiration_distributions,\
         expiration_distributions, short_range_distances, activity_distributions
+import caimira.apps.calculator.global_store.constants as constants
 
-SAMPLE_SIZE = 250_000
+SAMPLE_SIZE = constants.monte_carlo_sample_size
 
 
 @pytest.fixture