Merge branch 'feature/generic_population_class' into 'master'

Created a simple population class for the CO2 concentration model

Closes #296

See merge request caimira/caimira!459

caimira/apps/calculator/model_generator.py

@@ -370,12 +370,10 @@ def build_CO2_model(self, sample_size=DEFAULT_MC_SAMPLE_SIZE) -> models.CO2Conce
         total_people = [infected_population.people_present(stop) + exposed_population.people_present(stop) 
                         for _, stop in zip(transition_times[:-1], transition_times[1:])]
 
-        population = mc.Population(
+        population = mc.SimplePopulation(
             number=models.IntPiecewiseConstant(transition_times=tuple(transition_times), values=tuple(total_people)),
             presence=None,
-            mask=models.Mask.types[self.mask_type],
             activity=activity_distributions[ACTIVITIES[ACTIVITY_TYPES.index(self.activity_type)]['activity']],
-            host_immunity=0.,
         )
 
         # Builds a CO2 concentration model based on model inputs

caimira/apps/expert_co2.py

@@ -14,12 +14,10 @@
 baseline_model = models.CO2ConcentrationModel(
     room=models.Room(volume=120, humidity=0.5, inside_temp=models.PiecewiseConstant((0., 24.), (293.15,))),
     ventilation=models.HVACMechanical(active=models.PeriodicInterval(period=120, duration=120), q_air_mech=500),
-    CO2_emitters=models.Population(
+    CO2_emitters=models.SimplePopulation(
         number=10,
         presence=models.SpecificInterval(((8., 12.), (13., 17.))),
-        mask=models.Mask.types['No mask'],
         activity=models.Activity.types['Seated'],
-        host_immunity=0.,
     ),
     CO2_atmosphere_concentration=440.44,
     CO2_fraction_exhaled=0.042,

caimira/models.py

@@ -789,7 +789,7 @@ class Activity:
 
 
 @dataclass(frozen=True)
-class Population:
+class SimplePopulation:
     """
     Represents a group of people all with exactly the same behaviour and
     situation.
@@ -801,17 +801,9 @@ class Population:
     #: The times in which the people are in the room.
     presence: typing.Union[None, Interval]
 
-    #: The kind of mask being worn by the people.
-    mask: Mask
-
     #: The physical activity being carried out by the people.
     activity: Activity
 
-    #: The ratio of virions that are inactivated by the person's immunity.
-    # This parameter considers the potential antibodies in the person, 
-    # which might render inactive some RNA copies (virions).
-    host_immunity: float
-
     def __post_init__(self):
         if isinstance(self.number, int):
             if not isinstance(self.presence, Interval):
@@ -841,6 +833,22 @@ def people_present(self, time: float):
             return int(self.number.value(time))
 
 
+@dataclass(frozen=True)
+class Population(SimplePopulation):
+    """
+    Represents a group of people all with exactly the same behaviour and
+    situation, considering the usage of mask and a certain host immunity.
+
+    """
+    #: The kind of mask being worn by the people.
+    mask: Mask
+
+    #: The ratio of virions that are inactivated by the person's immunity.
+    # This parameter considers the potential antibodies in the person, 
+    # which might render inactive some RNA copies (virions).
+    host_immunity: float
+
+
 @dataclass(frozen=True)
 class _PopulationWithVirus(Population):
     #: The virus with which the population is infected.
@@ -1005,7 +1013,7 @@ class _ConcentrationModelBase:
     ventilation: _VentilationBase
 
     @property
-    def population(self) -> Population:
+    def population(self) -> SimplePopulation:
         """
         Population in the room (the emitters of what we compute the
         concentration of)
@@ -1239,7 +1247,7 @@ class CO2ConcentrationModel(_ConcentrationModelBase):
     Class used for the computation of the CO2 concentration.
     """
     #: Population in the room emitting CO2
-    CO2_emitters: Population
+    CO2_emitters: SimplePopulation
 
     #: CO2 concentration in the atmosphere (in ppm)
     CO2_atmosphere_concentration: float = 440.44
@@ -1248,7 +1256,7 @@ class CO2ConcentrationModel(_ConcentrationModelBase):
     CO2_fraction_exhaled: float = 0.042
 
     @property
-    def population(self) -> Population:
+    def population(self) -> SimplePopulation:
         return self.CO2_emitters
 
     def removal_rate(self, time: float) -> _VectorisedFloat:

caimira/tests/models/test_co2_concentration_model.py

@@ -9,12 +9,10 @@ def simple_co2_conc_model():
     return models.CO2ConcentrationModel(
         room=models.Room(200, models.PiecewiseConstant((0., 24.), (293,))),
         ventilation=models.AirChange(models.PeriodicInterval(period=120, duration=120), 0.25),
-        CO2_emitters=models.Population(
+        CO2_emitters=models.SimplePopulation(
             number=5,
             presence=models.SpecificInterval((([0., 4.], ))),
-            mask=models.Mask.types['No mask'],
             activity=models.Activity.types['Seated'],
-            host_immunity=0.,
         ),
     )
 

caimira/tests/models/test_exposure_model.py

@@ -40,18 +40,18 @@ def _normed_concentration(self, time: float) -> models._VectorisedFloat:  # noqa
 populations = [
     # A simple scalar population.
     models.Population(
-        10, halftime, models.Mask.types['Type I'],
-        models.Activity.types['Standing'], host_immunity=0.,
+        10, halftime, activity=models.Activity.types['Standing'],
+        mask=models.Mask.types['Type I'], host_immunity=0.,
     ),
     # A population with some array component for η_inhale.
     models.Population(
-        10, halftime, models.Mask(np.array([0.3, 0.35])),
-        models.Activity.types['Standing'], host_immunity=0.
+        10, halftime, activity=models.Activity.types['Standing'],
+        mask=models.Mask(np.array([0.3, 0.35])), host_immunity=0.
     ),
     # A population with some array component for inhalation_rate.
     models.Population(
-        10, halftime, models.Mask.types['Type I'],
-        models.Activity(np.array([0.51, 0.57]), 0.57), host_immunity=0.
+        10, halftime, activity=models.Activity(np.array([0.51, 0.57]), 0.57),
+        mask=models.Mask.types['Type I'], host_immunity=0.
     ),
 ]
 
@@ -212,8 +212,8 @@ def test_exposure_model_integral_accuracy(exposed_time_interval,
                                           expected_deposited_exposure, conc_model, sr_model, cases_model):
     presence_interval = models.SpecificInterval((exposed_time_interval,))
     population = models.Population(
-        10, presence_interval, models.Mask.types['Type I'],
-        models.Activity.types['Standing'], 0.,
+        10, presence_interval, models.Activity.types['Standing'],
+        models.Mask.types['Type I'], 0.,
     )
     model = ExposureModel(conc_model, sr_model, population, cases_model)
     np.testing.assert_allclose(model.deposited_exposure(), expected_deposited_exposure)
@@ -239,8 +239,8 @@ def test_infectious_dose_vectorisation(sr_model, cases_model):
 
     presence_interval = models.SpecificInterval(((0., 1.),))
     population = models.Population(
-        10, presence_interval, models.Mask.types['Type I'],
-        models.Activity.types['Standing'], 0.,
+        10, presence_interval, models.Activity.types['Standing'],
+        models.Mask.types['Type I'], 0.,
     )
     model = ExposureModel(cm, sr_model, population, cases_model)
     inf_probability = model.infection_probability()
@@ -302,8 +302,8 @@ def test_probabilistic_exposure_probability(sr_model, exposed_population, cm,
         pop, AB, cases, probabilistic_exposure_probability):
 
     population = models.Population(
-        exposed_population, models.PeriodicInterval(120, 60), models.Mask.types['Type I'],
-        models.Activity.types['Standing'], host_immunity=0.,)
+        exposed_population, models.PeriodicInterval(120, 60), models.Activity.types['Standing'], 
+        models.Mask.types['Type I'], host_immunity=0.,)
     model = ExposureModel(cm, sr_model, population, models.Cases(geographic_population=pop,
         geographic_cases=cases, ascertainment_bias=AB),)
     np.testing.assert_allclose(
@@ -402,8 +402,8 @@ def test_diameter_vectorisation_room(diameter_dependent_model, sr_model, cases_m
 )
 def test_host_immunity_vectorisation(sr_model, cases_model, cm, host_immunity, expected_probability):
     population = models.Population(
-        10, halftime, models.Mask(np.array([0.3, 0.35])),
-        models.Activity.types['Standing'], host_immunity=host_immunity
+        10, halftime, models.Activity.types['Standing'], 
+        models.Mask(np.array([0.3, 0.35])), host_immunity=host_immunity
     )
     model = ExposureModel(cm, sr_model, population, cases_model)
     inf_probability = model.infection_probability()