changed global store directory and imported BLOmodel parameters

caimira/apps/calculator/defaults.py

@@ -1,6 +1,6 @@
 import typing
 
-import caimira.apps.calculator.global_store.constants as constants
+import caimira.global_store.constants as constants
 
 # ------------------ Default form values ----------------------
 

caimira/apps/calculator/model_generator.py

@@ -19,7 +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
+import caimira.global_store.constants as constants
 
 LOG = logging.getLogger(__name__)
 

caimira/apps/calculator/report_generator.py

@@ -17,7 +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
+import caimira.global_store.constants as constants
 
 
 def model_start_end(model: models.ExposureModel):

caimira/apps/calculator/global_store/constants.py → caimira/global_store/constants.py

@@ -1,4 +1,5 @@
 import asyncio
+import typing
 import os
 
 # Global variable to keep track of whether data has been fetched
@@ -212,33 +213,44 @@
     'repeats': 1
 }
 
-def update_local_reference(local_data, api_data):
+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},
+}
+
+def update_local_reference(local_data, api_data: typing.Union[dict, typing.Any]):
     '''
     Recursive function that iterates through the keys and values of local and remote dictionaries.
     '''
-    for key, value in api_data.items():
-        if isinstance(value, dict):
-            # If the value is a dictionary, recursively update it
-            local_data[key] = update_local_reference(
-                local_data.get(key, {}), value)
-        else:
-            # Update the value at the current key
-            local_data[key] = value
+    if isinstance(api_data, dict):
+        for key, value in api_data.items():
+            if isinstance(value, dict):
+                # If the value is a dictionary, recursively update it
+                local_data[key] = update_local_reference(
+                    local_data.get(key, {}), value)
+            else:
+                # Update the value at the current key
+                local_data[key] = value
+    else:
+        local_data = api_data
     return local_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
-    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
-
+    global data_fetched, BLOmodel, covid_overal_vl_data, infectious_dose_distribution, viable_to_RNA_ratio_distribution, virus_distributions, activity_distributions
+    global mask_distributions, expiration_BLO_factors, long_range_expiration_distributions, short_range_expiration_distributions, short_range_distances
+    global infected_population_scenario_activity, 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
-        from caimira.apps.calculator.global_store.data_store import GlobalStore
+        from caimira.global_store.data_store import GlobalStore
 
         await GlobalStore.populate_from_api()
         data = GlobalStore.get_data()['data']
 
+        BLOmodel = update_local_reference(
+            BLOmodel, data['BLOmodel'])
         infectious_dose_distribution = update_local_reference(
             infectious_dose_distribution, data['infectious_dose_distribution'])
         covid_overal_vl_data = update_local_reference(
@@ -267,7 +279,7 @@ async def populate_data():
             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/monte_carlo/data.py

@@ -7,7 +7,7 @@
 
 import caimira.monte_carlo as mc
 from caimira.monte_carlo.sampleable import LogCustom, LogNormal, LogCustomKernel, CustomKernel, Uniform, Custom, Normal
-import caimira.apps.calculator.global_store.constants as constants
+import caimira.global_store.constants as constants
 
 sqrt2pi = np.sqrt(2.*np.pi)
 sqrt2 = np.sqrt(2.)
@@ -137,15 +137,27 @@ class BLOmodel:
 
     #: cn (cm^-3) for resp. the B, L and O modes. Corresponds to the
     # total concentration of aerosols for each mode.
-    cn: typing.Tuple[float, float, float] = (0.06, 0.2, 0.0010008)
+    cn: typing.Tuple[float, float, float] = (
+        param_evaluation(constants.BLOmodel['cn'], 'B'),
+        param_evaluation(constants.BLOmodel['cn'], 'L'),
+        param_evaluation(constants.BLOmodel['cn'], 'O')
+    )
 
     # Mean of the underlying normal distributions (represents the log of a
     # diameter in microns), for resp. the B, L and O modes.
-    mu: typing.Tuple[float, float, float] = (0.989541, 1.38629, 4.97673)
+    mu: typing.Tuple[float, float, float] = (
+        param_evaluation(constants.BLOmodel['mu'], 'B'),
+        param_evaluation(constants.BLOmodel['mu'], 'L'),
+        param_evaluation(constants.BLOmodel['mu'], 'O')
+    )
 
     # Std deviation of the underlying normal distribution, for resp.
     # the B, L and O modes.
-    sigma: typing.Tuple[float, float, float] = (0.262364, 0.506818, 0.585005)
+    sigma: typing.Tuple[float, float, float] = (
+        param_evaluation(constants.BLOmodel['sigma'], 'B'),
+        param_evaluation(constants.BLOmodel['sigma'], 'L'),
+        param_evaluation(constants.BLOmodel['sigma'], 'O')
+    )
 
     def distribution(self, d):
         """

caimira/tests/apps/calculator/test_model_generator.py

@@ -11,7 +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
+import caimira.global_store.constants as constants
 
 
 def test_model_from_dict(baseline_form_data):

caimira/tests/models/test_short_range_model.py

@@ -8,7 +8,7 @@
 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
+import caimira.global_store.constants as constants
 
 SAMPLE_SIZE = constants.monte_carlo_sample_size