Bugs fixed in monte_carlo/data.py; updating ExposureModel for diameter dependent exposure

cara/models.py

@@ -923,7 +923,7 @@ class ExposureModel:
 
     def exposure(self) -> _VectorisedFloat:
         """The number of virus per meter^3."""
-        return (self._normed_exposure() *
+        return (np.array(self._normed_exposure()).mean() *
                 self.concentration_model.infected.emission_rate_when_present())
 
     def infection_probability(self) -> _VectorisedFloat:

cara/monte_carlo/data.py

@@ -1,4 +1,8 @@
+from dataclasses import dataclass
+import typing
+
 import numpy as np
+from scipy import special as sp
 
 import cara.monte_carlo as mc
 from cara.monte_carlo.sampleable import Normal,LogNormal,LogCustomKernel,CustomKernel,Uniform
@@ -138,10 +142,14 @@ mask_distributions = {
 def expiration_distribution(BLO_factors: typing.Tuple[float, float, float]):
     """
     Returns an Expiration with an aerosol diameter distribution, defined
-    by the BLO factors
+    by the BLO factors.
+    Note: integration boundaries for normalization are chosen as 0.1 and
+    30 microns respectively - this is an historical choice based on
+    previous implementations of the model (it limits the influence of
+    the O-mode).
     """
     return mc.Expiration(CustomKernel(dscan,
-                BLOmodel(BLO_factors).normalized_distribution(dscan),
+                BLOmodel(BLO_factors).normalized_distribution(dscan, 0.1, 30.),
                 kernel_bandwidth=0.1))