Added dynamic groups in backend and propagated required changes to re…

…quests

- Correction of typos

- Changes do model to accommodate group of exposure models

- Handled virus validator to accommodate group of exposure models

- Modifications to accomodate generation of results for exposure model group

- Added data registry attribute to exposuremodelgroups class

- Fixed test for dynamic models

- Defined ExposureModelGroup with required methods

- Adapted virus report data to generate results for groups of exposed population

- Build same distributions for different models

- Fixed bug with defaults

- Handled definition of a single ExposureModel root obj when only one group is defined

- Added short-range expirations per group

- Added full validation on short_range interactions with dynamic exposure model

- Added full set of tests

- Updated docstrings

- Added support for number of people in exposed population, which should be identical within each group

- Added type checks

- Added UI adjustments for expected new cases

- Adapted exposed population data format to accommodate constant total_people

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

@@ -799,7 +799,7 @@ class Activity:
 @dataclass(frozen=True)
 class SimplePopulation:
     """
-    Represents a group of people all with exactly the same behaviour and
+    Represents a group of people all with exactly the same behavior and
     situation.
 
     """
@@ -844,7 +844,7 @@ def people_present(self, time: float):
 @dataclass(frozen=True)
 class Population(SimplePopulation):
     """
-    Represents a group of people all with exactly the same behaviour and
+    Represents a group of people all with exactly the same behavior and
     situation, considering the usage of mask and a certain host immunity.
 
     """
@@ -1324,6 +1324,9 @@ class ShortRangeModel:
     #: Interpersonal distances
     distance: _VectorisedFloat
 
+    #: Expiration definition
+    expiration_def: typing.Optional[str] = None
+
     def dilution_factor(self) -> _VectorisedFloat:
         '''
         The dilution factor for the respective expiratory activity type.
@@ -1653,6 +1656,9 @@ def __post_init__(self):
         In other words, the air exchange rate from the
         ventilation, and the virus decay constant, must
         not be given as arrays.
+
+        It also checks that the number of exposed is
+        static during the simulation time.
         """
         c_model = self.concentration_model
         # Check if the diameter is vectorised.
@@ -1663,6 +1669,11 @@ def __post_init__(self):
                 c_model.ventilation.air_exchange(c_model.room, time)) for time in c_model.state_change_times()))):
             raise ValueError("If the diameter is an array, none of the ventilation parameters "
                              "or virus decay constant can be arrays at the same time.")
+
+        # Check if exposed population is static
+        if not isinstance(self.exposed.number, int) or not isinstance(self.exposed.presence, Interval):
+            raise TypeError("The exposed number must be an int and presence an Interval. "
+                            f"Got {type(self.exposed.number)} and {type(self.exposed.presence)}.")
 
     @method_cache
     def population_state_change_times(self) -> typing.List[float]:
@@ -1809,11 +1820,9 @@ def _deposited_exposure_list(self):
         The number of virus per m^3 deposited on the respiratory tract.
         """
         population_change_times = self.population_state_change_times()
-
         deposited_exposure = []
         for start, stop in zip(population_change_times[:-1], population_change_times[1:]):
             deposited_exposure.append(self.deposited_exposure_between_bounds(start, stop))
-
         return deposited_exposure
 
     def deposited_exposure(self) -> _VectorisedFloat:
@@ -1838,18 +1847,17 @@ def infection_probability(self) -> _VectorisedFloat:
         return (1 - np.prod([1 - prob for prob in self._infection_probability_list()], axis = 0)) * 100
 
     def total_probability_rule(self) -> _VectorisedFloat:
-        if (isinstance(self.concentration_model.infected.number, IntPiecewiseConstant) or
-                isinstance(self.exposed.number, IntPiecewiseConstant)):
+        if (isinstance(self.concentration_model.infected.number, IntPiecewiseConstant)):
                 raise NotImplementedError("Cannot compute total probability "
                         "(including incidence rate) with dynamic occupancy")
 
         if (self.geographical_data.geographic_population != 0 and self.geographical_data.geographic_cases != 0):
             sum_probability = 0.0
 
             # Create an equivalent exposure model but changing the number of infected cases.
-            total_people = self.concentration_model.infected.number + self.exposed.number
+            total_people = self.concentration_model.infected.number + self.exposed.number # type: ignore
             max_num_infected = (total_people if total_people < 10 else 10)
-            # The influence of a higher number of simultainious infected people (> 4 - 5) yields an almost negligible contirbution to the total probability.
+            # The influence of a higher number of simultaneous infected people (> 4 - 5) yields an almost negligible contribution to the total probability.
             # To be on the safe side, a hard coded limit with a safety margin of 2x was set.
             # Therefore we decided a hard limit of 10 infected people.
             for num_infected in range(1, max_num_infected + 1):
@@ -1872,43 +1880,88 @@ def expected_new_cases(self) -> _VectorisedFloat:
             1) Long-range exposure: take the infection_probability and multiply by the occupants exposed to long-range. 
             2) Short- and long-range exposure: take the infection_probability of long-range multiplied by the occupants exposed to long-range only, 
                plus the infection_probability of short- and long-range multiplied by the occupants exposed to short-range only.
-
-        Currently disabled when dynamic occupancy is defined for the exposed population.
         """
-
-        if (isinstance(self.concentration_model.infected.number, IntPiecewiseConstant) or
-            isinstance(self.exposed.number, IntPiecewiseConstant)):
-            raise NotImplementedError("Cannot compute expected new cases "
-                    "with dynamic occupancy")
-
+        number = self.exposed.number
         if self.short_range != ():
-            new_cases_long_range = nested_replace(self, {'short_range': [],}).infection_probability() * (self.exposed.number - self.exposed_to_short_range)
+            new_cases_long_range = nested_replace(self, {'short_range': [],}).infection_probability() * (number - self.exposed_to_short_range) # type: ignore
             return (new_cases_long_range + (self.infection_probability() * self.exposed_to_short_range)) / 100
 
-        return self.infection_probability() * self.exposed.number / 100
+        return self.infection_probability() * number / 100
 
     def reproduction_number(self) -> _VectorisedFloat:
         """
         The reproduction number can be thought of as the expected number of
         cases directly generated by one infected case in a population.
-
-        Currently disabled when dynamic occupancy is defined for both the infected and exposed population.
         """
-
-        if (isinstance(self.concentration_model.infected.number, IntPiecewiseConstant) or
-            isinstance(self.exposed.number, IntPiecewiseConstant)):
-            raise NotImplementedError("Cannot compute reproduction number "
-                    "with dynamic occupancy")
-
-        if self.concentration_model.infected.number == 1:
+        infected_population: InfectedPopulation = self.concentration_model.infected
+        if isinstance(infected_population.number, int) and infected_population.number == 1:
             return self.expected_new_cases()
 
         # Create an equivalent exposure model but with precisely
-        # one infected case.
+        # one infected case, respecting the presence interval.
         single_exposure_model = nested_replace(
             self, {
-                'concentration_model.infected.number': 1}
+                'concentration_model.infected.number': 1,
+                'concentration_model.infected.presence': infected_population.presence_interval(),
+            }
         )
-
         return single_exposure_model.expected_new_cases()
+
+
+@dataclass(frozen=True)
+class ExposureModelGroup:
+    """
+    Represents a group of exposure models. This is to handle the case
+    when different groups of people come and go in the room at different
+    times. These groups are then handled fully independently, with
+    exposure dose and probability of infection defined for each of them.
+    """
+    data_registry: DataRegistry
+
+    #: The set of exposure models for each exposed population
+    exposure_models: typing.Tuple[ExposureModel, ...]
+
+    @method_cache
+    def _deposited_exposure_list(self) -> typing.List[_VectorisedFloat]:
+        """
+        List of doses absorbed by each member of the groups.
+        """
+        return [model.deposited_exposure() for model in self.exposure_models]
+
+    @method_cache
+    def _infection_probability_list(self):
+        """
+        List of the probability of infection for each group.
+        """
+        return [model.infection_probability() for model in self.exposure_models] # type: ignore
+
+    def expected_new_cases(self) -> _VectorisedFloat:
+        """
+        Final expected number of new cases considering the
+        contribution of each individual probability of infection.
+        """
+        return np.sum([model.expected_new_cases() for model in self.exposure_models], axis=0) # type: ignore
+
+    def reproduction_number(self) -> _VectorisedFloat:
+        """
+        Reproduction number considering the contribution
+        of each individual probability of infection and
+        a single infected occupant.
+        """
+        single_exposure_models = []
+        for model in self.exposure_models:
+            if model.concentration_model.infected.number != 1:
+                model = nested_replace(
+                    self, {
+                        'model.concentration_model.infected.number': 1
+                    }
+                )
+            single_exposure_models.append(model)
+
+        single_exposure_model_group = nested_replace(
+            self, {
+                'exposure_models': single_exposure_models,
+            }
+        )
+        return single_exposure_model_group.expected_new_cases()