service data is not a form parameter but passed as an argument to the…

… model constructor

caimira/apps/calculator/__init__.py

@@ -119,9 +119,8 @@ async def post(self) -> None:
                 self.send_error(500, reason=error_message)
         try:
             # As an example
-            fetched_service_data = {'data': {'foo': 'bar', 'viral_load': {'num': 40, 'scale_factor': 7.01, 'shape_factor': 3.47, 'start': 0.01, 'stop': 0.99}}, 'version': 1}
-            requested_model_config['fetched_service_data'] = json.dumps(fetched_service_data['data'])
-            form = model_generator.FormData.from_dict(requested_model_config)
+            # fetched_service_data = {'evaporation_factor': 0.9, 'foo': 'bar', 'viral_load': {'num': 40, 'scale_factor': 7.01, 'shape_factor': 3.47, 'start': 0.01, 'stop': 0.99}}
+            form = model_generator.FormData.from_dict(requested_model_config, fetched_service_data)
 
         except Exception as err:
             if self.settings.get("debug", False):

caimira/apps/calculator/data_service.py

@@ -70,5 +70,5 @@ async def fetch(self):
         ),
         raise_error=True)
 
-        return json.loads(response.body)
+        return json.loads(response.body)['data']
 

caimira/apps/calculator/defaults.py

@@ -75,7 +75,6 @@
     'sensor_in_use': '',
     'short_range_option': 'short_range_no',
     'short_range_interactions': '[]',
-    'fetched_service_data': '{}'
 }
 
 # ------------------ Activities ----------------------

caimira/apps/calculator/model_generator.py

@@ -27,10 +27,19 @@
 
 @dataclasses.dataclass
 class ServiceData:
-    evaporation_factor: float
-    virus_distributions: dict
-
-
+    _evaporation_factor: float
+    _virus_distributions: dict
+
+    def get_evaporation_factor(self) -> float:
+        return self._evaporation_factor
+
+    def get_virus_distribution(self, virus_type: str) -> float:
+        return self._virus_distributions[virus_type]
+
+    def get_virus_distributions(self) -> dict:
+        return self._virus_distributions
+
+
 @dataclasses.dataclass
 class FormData:
     activity_type: str
@@ -98,13 +107,12 @@ class FormData:
     sensor_in_use: str
     short_range_option: str
     short_range_interactions: list
-    fetched_service_data: dict
 
+    _SERVICE_DATA: ServiceData
     _DEFAULTS: typing.ClassVar[typing.Dict[str, typing.Any]] = DEFAULTS
-    _SERVICE_DATA: ServiceData = DataGenerator().generate_data_from_parameters()
 
     @classmethod
-    def from_dict(cls, form_data: typing.Dict) -> "FormData":
+    def from_dict(cls, form_data: typing.Dict, service_data: typing.Optional[dict] = None) -> "FormData":
         # Take a copy of the form data so that we can mutate it.
         form_data = form_data.copy()
         form_data.pop('_xsrf', None)
@@ -127,10 +135,10 @@ def from_dict(cls, form_data: typing.Dict) -> "FormData":
             if key not in cls._DEFAULTS:
                 raise ValueError(f'Invalid argument "{html.escape(key)}" given')
 
-        # Populate Service Data with data that comes from the form data
-        cls._SERVICE_DATA = DataGenerator(form_data['fetched_service_data']).generate_data_from_parameters()
-        
-        instance = cls(**form_data)
+        # Populate Service Data with data that comes from the database
+        _sd = DataGenerator(service_data).generate_data_from_parameters()
+
+        instance = cls(**form_data, _SERVICE_DATA=_sd)
         instance.validate()
         return instance
 
@@ -142,7 +150,7 @@ def to_dict(cls, form: "FormData", strip_defaults: bool = False) -> dict:
         }
 
         for attr, value in form_dict.items():
-            if attr in _CAST_RULES_NATIVE_TO_FORM_ARG and attr != '_SERVICE_DATA':
+            if attr in _CAST_RULES_NATIVE_TO_FORM_ARG:
                 form_dict[attr] = _CAST_RULES_NATIVE_TO_FORM_ARG[attr](value)
 
         if strip_defaults:
@@ -356,7 +364,7 @@ def build_mc_model(self) -> mc.ExposureModel:
                 room=room,
                 ventilation=self.ventilation(),
                 infected=infected_population,
-                evaporation_factor=self._SERVICE_DATA.evaporation_factor,
+                evaporation_factor=self._SERVICE_DATA.get_evaporation_factor(),
             ),
             short_range = tuple(short_range),
             exposed=self.exposed_population(),
@@ -515,7 +523,7 @@ def generate_precise_activity_expiration(self) -> typing.Tuple[typing.Any, ...]:
 
     def infected_population(self) -> mc.InfectedPopulation:
         # Initializes the virus
-        virus = self._SERVICE_DATA.virus_distributions[self.virus_type]
+        virus = self._SERVICE_DATA.get_virus_distribution(self.virus_type)
 
         activity_index = ACTIVITY_TYPES.index(self.activity_type)
         activity_defn = ACTIVITIES[activity_index]['activity']
@@ -869,7 +877,6 @@ def baseline_raw_form_data() -> typing.Dict[str, typing.Union[str, float]]:
         'window_opening_regime': 'windows_open_permanently',
         'short_range_option': 'short_range_no',
         'short_range_interactions': '[]',
-        'fetched_service_data': '{}',
     }
 
 

caimira/monte_carlo/data.py

@@ -206,39 +206,40 @@ def generate_viral_load_distribution(self):
 
     def generate_data_from_parameters(self):        
         # From https://doi.org/10.1101/2021.10.14.21264988 and refererences therein
+        _viral_load = self.generate_viral_load_distribution()
         virus_distributions = {
             'SARS_CoV_2': mc.SARSCoV2(
-                        viral_load_in_sputum=self.generate_viral_load_distribution(),
+                        viral_load_in_sputum=_viral_load,
                         infectious_dose=infectious_dose_distribution,
                         viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
                         transmissibility_factor=1.,
                         ),
             'SARS_CoV_2_ALPHA': mc.SARSCoV2(
-                        viral_load_in_sputum=self.generate_viral_load_distribution(),
+                        viral_load_in_sputum=_viral_load,
                         infectious_dose=infectious_dose_distribution,
                         viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
                         transmissibility_factor=0.78,
                         ),
             'SARS_CoV_2_BETA': mc.SARSCoV2(
-                        viral_load_in_sputum=self.generate_viral_load_distribution(),
+                        viral_load_in_sputum=_viral_load,
                         infectious_dose=infectious_dose_distribution,
                         viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
                         transmissibility_factor=0.8,
                         ),
             'SARS_CoV_2_GAMMA': mc.SARSCoV2(
-                        viral_load_in_sputum=self.generate_viral_load_distribution(),
+                        viral_load_in_sputum=_viral_load,
                         infectious_dose=infectious_dose_distribution,
                         viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
                         transmissibility_factor=0.72,
                         ),
             'SARS_CoV_2_DELTA': mc.SARSCoV2(
-                        viral_load_in_sputum=self.generate_viral_load_distribution(),
+                        viral_load_in_sputum=_viral_load,
                         infectious_dose=infectious_dose_distribution,
                         viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
                         transmissibility_factor=0.51,
                         ),
             'SARS_CoV_2_OMICRON': mc.SARSCoV2(
-                        viral_load_in_sputum=self.generate_viral_load_distribution(),
+                        viral_load_in_sputum=_viral_load,
                         infectious_dose=infectious_dose_distribution,
                         viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
                         transmissibility_factor=0.2,

caimira/tests/apps/calculator/test_model_generator.py

@@ -10,7 +10,7 @@
 from caimira.apps.calculator.model_generator import _hours2timestring
 from caimira.apps.calculator.model_generator import minutes_since_midnight
 from caimira import models
-from caimira.monte_carlo.data import expiration_distributions, DataGenerator
+from caimira.monte_carlo.data import expiration_distributions
 
 
 def test_model_from_dict(baseline_form_data):

caimira/tests/test_full_algorithm.py

@@ -12,18 +12,19 @@
 from caimira import models,data
 from caimira.utils import method_cache
 from caimira.models import _VectorisedFloat,Interval,SpecificInterval
-from caimira.monte_carlo.sampleable import LogNormal
 from caimira.monte_carlo.data import (expiration_distributions,
         expiration_BLO_factors,short_range_expiration_distributions,
-        short_range_distances,activity_distributions, DataGenerator)
+        short_range_distances,activity_distributions)
+from caimira.monte_carlo.data import DataGenerator
 
 SAMPLE_SIZE = 1_000_000
 TOLERANCE = 0.04
 
 sqrt2pi = np.sqrt(2.*np.pi)
 sqrt2 = np.sqrt(2.)
 ln2 = np.log(2)
-
+_sd = DataGenerator().generate_data_from_parameters()
+virus_distributions: dict = _sd.get_virus_distributions()
 
 @dataclass(frozen=True)
 class SimpleConcentrationModel:
@@ -484,7 +485,6 @@ def c_model() -> mc.ConcentrationModel:
 
 @pytest.fixture
 def c_model_distr() -> mc.ConcentrationModel:
-    virus_distributions = DataGenerator().generate_data_from_parameters().virus_distributions
     return mc.ConcentrationModel(
         room=models.Room(volume=50, humidity=0.3),
         ventilation=models.AirChange(active=models.PeriodicInterval(
@@ -614,7 +614,6 @@ def expo_sr_model_distr(c_model_distr) -> mc.ExposureModel:
 
 @pytest.fixture
 def simple_expo_sr_model_distr() -> SimpleExposureModel:
-    virus_distributions = DataGenerator().generate_data_from_parameters().virus_distributions
     return SimpleExposureModel(
         infected_presence = presence,
         viral_load        = virus_distributions['SARS_CoV_2_DELTA'
@@ -717,7 +716,6 @@ def test_longrange_exposure(c_model):
     "time", [11., 12.5, 17.]
 )
 def test_longrange_concentration_with_distributions(c_model_distr,time):
-    virus_distributions = DataGenerator().generate_data_from_parameters().virus_distributions
     simple_expo_model = SimpleConcentrationModel(
         infected_presence = presence,
         viral_load        = virus_distributions['SARS_CoV_2_DELTA'
@@ -735,7 +733,6 @@ def test_longrange_concentration_with_distributions(c_model_distr,time):
 
 
 def test_longrange_exposure_with_distributions(c_model_distr):
-    virus_distributions = DataGenerator().generate_data_from_parameters().virus_distributions
     simple_expo_model = SimpleExposureModel(
         infected_presence = presence,
         viral_load        = virus_distributions['SARS_CoV_2_DELTA'

caimira/tests/test_monte_carlo_full_models.py

@@ -5,7 +5,8 @@
 
 import caimira.monte_carlo as mc
 from caimira import models,data
-from caimira.monte_carlo.data import activity_distributions, expiration_distributions, infectious_dose_distribution, viable_to_RNA_ratio_distribution, DataGenerator
+from caimira.monte_carlo.data import activity_distributions, expiration_distributions, infectious_dose_distribution, viable_to_RNA_ratio_distribution
+from caimira.monte_carlo.data import DataGenerator
 from caimira.apps.calculator.model_generator import build_expiration
 
 SAMPLE_SIZE = 500_000
@@ -33,8 +34,8 @@
     for month, temperatures in toronto_hourly_temperatures_celsius_per_hour.items()
 }
 
-virus_distributions = DataGenerator().generate_data_from_parameters().virus_distributions
-
+_sd = DataGenerator().generate_data_from_parameters()
+virus_distributions = _sd.get_virus_distributions()
 
 # References values for infection_probability and expected new cases
 # in the following tests, were obtained from the feature/mc branch

caimira/tests/test_predefined_distributions.py

@@ -2,7 +2,8 @@
 import numpy.testing as npt
 import pytest
 
-from caimira.monte_carlo.data import activity_distributions, DataGenerator
+from caimira.monte_carlo.data import activity_distributions
+from caimira.monte_carlo.data import DataGenerator
 
 
 # Mean & std deviations from https://doi.org/10.1101/2021.10.14.21264988 (Table 3)
@@ -39,6 +40,7 @@ def test_activity_distributions(distribution, mean, std):
     ]
 )
 def test_viral_load_logdistribution(distribution, mean, std):
-    virus = DataGenerator().generate_data_from_parameters().virus_distributions[distribution].build_model(size=1000000)
+    _sd = DataGenerator().generate_data_from_parameters()
+    virus = _sd.get_virus_distribution(distribution).build_model(size=1000000)
     npt.assert_allclose(np.log10(virus.viral_load_in_sputum).mean(), mean, atol=0.01)
     npt.assert_allclose(np.log10(virus.viral_load_in_sputum).std(), std, atol=0.01)