Merge https://gitlab.cern.ch/caimira/caimira into feature/no_normed_lr_concentration_in_ShortRangeModel

Author: mheie

caimira/src/caimira/calculator/models/models.py

@@ -1615,9 +1615,19 @@ def __post_init__(self):
             # the short range interaction is with does not have to be defined (?)
             raise NotImplementedError("Short range interactions with multiple infected populations not yet implemented.")
 
-        _ = self.virus
-        _ = self.room
-        _ = self.ventilation
+        viruses = [c_model.virus for c_model in self.concentration_model_list]
+        virus = viruses[0]
+        if any(v != virus for v in viruses):
+            raise ValueError("All infected must be infected with the same virus.")
+        rooms = [c_model.room for c_model in self.concentration_model_list]
+        room = rooms[0]
+        if any(r != room for r in rooms):
+            raise ValueError("All concentration models must describe the same room.")
+        
+        # TODO: test that all concentration models have overlapping ventilations
+        #       NOTE: Different concentration models can have different occupancy times, and therefore different
+        #       start and end times for which the ventilation is defined. Therefore, the ventilation objects 
+        #       must overlapp, but not neccecarily be identical.
 
         for c_model in self.concentration_model_list:
             # Check if the diameter is vectorised.
@@ -1644,27 +1654,11 @@ def concentration_model_list(self):
 
     @property
     def virus(self):
-        viruses = [c_model.virus for c_model in self.concentration_model_list]
-        virus = viruses[0]
-        if any(v != virus for v in viruses[1:]):
-            raise ValueError("All infected must be infected with the same virus.")
-        return virus
+        return self.concentration_model_list[0].virus
 
     @property
     def room(self):
-        rooms = [c_model.room for c_model in self.concentration_model_list]
-        room = rooms[0]
-        if any(r != room for r in rooms[1:]):
-            raise ValueError("All concentration models must describe the same room.")
-        return room
-    
-    @property
-    def ventilation(self):
-        ventilations = [c_model.ventilation for c_model in self.concentration_model_list]
-        ventilation = ventilations[0]
-        if any(v != ventilation for v in ventilations[1:]):
-            raise ValueError("All concentration models must have the same ventilation.")
-        return ventilation
+        return self.concentration_model_list[0].room
 
     @method_cache
     def population_state_change_times(self) -> typing.List[float]:
@@ -1709,17 +1703,19 @@ def _long_range_normed_exposure_between_bounds(self, c_model, time1: float, time
 
     def long_range_concentration(self, time: float) -> float:
         """
-        Integrated virus exposure concentration, as a function of time.
+        Total virus concentration in the room, as a function of time.
 
         It only considers the long-range concentration without the
         contribution of the short-range concentration.
 
-        Since the different ConcentrationModel objects may have different diameter bases drawn 
-        from different distributions, the concentrations from different ConcentrationModel 
-        objects must be averaged over the diameter before summed together.
+        Since the different ConcentrationModel objects may have different infected with different expirations,
+        they can have different particle diameters bases drawn from different probability distributions.
+        To Monte Carlo integrate correctly over the particle diameter, we must therefore average the concentration 
+        of each ConcentrationModel over the particle diameter before adding together all the contributions from all 
+        the ConcentrationModels.
         """
-        return sum([np.array(c_model.concentration(time)).mean() for c_model in self.concentration_model_list])
-
+        return sum([np.array(c_model.concentration(time)).mean() for c_model in self.concentration_model_list]) # Average over particle diameter
+    
     def concentration(self, time: float) -> float:
         """
         Integrated virus exposure concentration, as a function of time.
@@ -1770,7 +1766,7 @@ def long_range_deposited_exposure_between_bounds(self, time1: float, time2: floa
 
         return deposited_exposure
 
-    def deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:###
+    def deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
         """
         The number of virus per m^3 deposited on the respiratory tract
         between any two times.
@@ -1910,7 +1906,7 @@ def reproduction_number(self) -> _VectorisedFloat:
         cases directly generated by one infected case in a population.
         """
         if isinstance(self.concentration_model, list):
-            raise NotImplementedError("yet to implement dynamic infected for SR interactions")
+            raise NotImplementedError("yet to implement dynamic infected for the reproduction number")
         infected_population: InfectedPopulation = self.concentration_model.infected
         if isinstance(infected_population.number, int) and infected_population.number == 1:
             return self.expected_new_cases()

caimira/src/caimira/calculator/report/virus_report_data.py

@@ -2,6 +2,7 @@
 import base64
 import dataclasses
 import io
+import copy
 import typing
 import numpy as np
 import matplotlib.pyplot as plt
@@ -447,7 +448,15 @@ def calculate_vl_scenarios_percentiles(model: mc.ExposureModel) -> typing.Dict[s
     scenarios = {}
     for percentil in (0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99):
         vl = np.quantile(viral_load, percentil)
-        specific_vl_scenario = dataclass_utils.nested_replace(model, {'model.virus.viral_load_in_sputum': vl})
+
+        new_conc_model = copy.deepcopy(model.concentration_model)
+        if isinstance(model.concentration_model, list):
+            new_conc_model = [dataclass_utils.nested_replace(cm, {'infected.virus.viral_load_in_sputum': vl}) for cm in model.concentration_model]
+        else:
+            cm = model.concentration_model
+            new_conc_model = dataclass_utils.nested_replace(cm, {'infected.virus.viral_load_in_sputum': vl})
+
+        specific_vl_scenario = dataclass_utils.nested_replace(model, {'concentration_model': new_conc_model})
         scenarios[str(vl)] = np.mean(
             specific_vl_scenario.infection_probability())
     return {

caimira/tests/test_dynamic_infected.py

@@ -139,19 +139,6 @@ def invalid_rooms_conc_model_list(all_infected_populations):
         evaporation_factor=0.3,
     ) for vol in [75,100]]
 
-@pytest.fixture
-def invalid_vent_conc_model_list(all_infected_populations):
-    """
-    Invalid list of concentration models because the ventilations are different.
-    """
-    return [models.ConcentrationModel(
-        data_registry=data_registry,
-        room = models.Room(75, models.PiecewiseConstant((0., 24.), (293,))),
-        ventilation = models.AirChange(interesting_times, air_exch),
-        infected = all_infected_populations[0],
-        evaporation_factor=0.3,
-    ) for air_exch in [10,100]]
-
 @pytest.fixture
 def valid_conc_model_list(all_infected_populations):
     return [models.ConcentrationModel(
@@ -177,22 +164,19 @@ def get_exposure_model(concentration_model_list) -> mc.ExposureModel:
         geographical_data=models.Cases(),
     )
 
-def test_common_params(valid_conc_model_list, invalid_viruses_conc_model_list, invalid_rooms_conc_model_list, invalid_vent_conc_model_list):
+def test_common_params(valid_conc_model_list, invalid_viruses_conc_model_list, invalid_rooms_conc_model_list):
     """
-    Check that one cannot initialize an ExposureModel with ConcentrationModels 
-    with different viruses, rooms, and ventilations.
+    Check that an error is raised when initializing an ExposureModel with ConcentrationModels 
+    with different viruses and rooms.
     """
     with pytest.raises(ValueError):
         get_exposure_model(invalid_viruses_conc_model_list).build_model(1)
     with pytest.raises(ValueError):
         get_exposure_model(invalid_rooms_conc_model_list).build_model(1)
-    with pytest.raises(ValueError):
-        get_exposure_model(invalid_vent_conc_model_list).build_model(1)
 
     valid_model = get_exposure_model(valid_conc_model_list).build_model(1)
     assert isinstance(valid_model.virus, models.Virus)
     assert isinstance(valid_model.room, models.Room)
-    assert isinstance(valid_model.ventilation, models._VentilationBase)
 
 def test_population_state_change_times(valid_conc_model_list):
     expected_state_changes = [0.5, 1., 1.1, 2., 3., 5., 8.5, 12, 13., 17.5]