From f0bdda7c7d991e35ad705c424b1ae68cca7728c1 Mon Sep 17 00:00:00 2001
From: Nicolas Mounet <nicolas.mounet@cern.ch>
Date: Tue, 26 Apr 2022 11:19:33 +0200
Subject: [PATCH] Adding mask inhale efficiency factor to short-range exposure;
 adding corresponding test; some cosmetics in test_short_range_model.py

---
 cara/models.py                              |  2 +-
 cara/tests/models/test_short_range_model.py | 58 ++++++++++++++++++---
 2 files changed, 51 insertions(+), 9 deletions(-)

diff --git a/cara/models.py b/cara/models.py
index 9d7898fd..c6df3464 100644
--- a/cara/models.py
+++ b/cara/models.py
@@ -1377,7 +1377,7 @@ class ExposureModel:
         f_inf = self.concentration_model.infected.fraction_of_infectious_virus()
         deposited_exposure *= (f_inf
                 * self.concentration_model.virus.viral_load_in_sputum
-                )
+                * (1 - self.exposed.mask.inhale_efficiency()))
         # long-range concentration
         deposited_exposure += self.long_range_deposited_exposure_between_bounds(time1, time2)
 
diff --git a/cara/tests/models/test_short_range_model.py b/cara/tests/models/test_short_range_model.py
index b1dee050..ceef0bb2 100644
--- a/cara/tests/models/test_short_range_model.py
+++ b/cara/tests/models/test_short_range_model.py
@@ -6,10 +6,13 @@ import pytest
 from cara import models
 import cara.monte_carlo as mc_models
 from cara.apps.calculator.model_generator import build_expiration
-from cara.monte_carlo.data import short_range_expiration_distributions, short_range_distances, activity_distributions
+from cara.monte_carlo.data import short_range_expiration_distributions,\
+        expiration_distributions, short_range_distances, activity_distributions
 
 # TODO: seed better the random number generators
 np.random.seed(2000)
+SAMPLE_SIZE = 250_000
+
 
 @pytest.fixture
 def concentration_model() -> mc_models.ConcentrationModel:
@@ -41,8 +44,8 @@ def short_range_model():
 
 
 def test_short_range_model_ndarray(concentration_model, short_range_model):
-    concentration_model = concentration_model.build_model(250_000)
-    model = short_range_model.build_model(250_000)
+    concentration_model = concentration_model.build_model(SAMPLE_SIZE)
+    model = short_range_model.build_model(SAMPLE_SIZE)
     assert isinstance(model._normed_concentration(concentration_model, 10.75), np.ndarray)
     assert isinstance(model.short_range_concentration(concentration_model, 10.75), np.ndarray)
     assert isinstance(model._normed_jet_exposure_between_bounds(concentration_model, 10.75, 10.85), np.ndarray)
@@ -60,10 +63,10 @@ def test_short_range_model_ndarray(concentration_model, short_range_model):
     ]
 )
 def test_dilution_factor(activity, expected_dilution):
-    model = models.ShortRangeModel(expiration="Breathing",
+    model = mc_models.ShortRangeModel(expiration=short_range_expiration_distributions['Breathing'],
                                     activity=models.Activity.types[activity],
                                     presence=models.SpecificInterval(present_times=((10.5, 11.0),)),
-                                    distance=0.854)
+                                    distance=0.854).build_model(SAMPLE_SIZE)
     assert isinstance(model.dilution_factor(), np.ndarray)
     np.testing.assert_almost_equal(
         model.dilution_factor(), expected_dilution, decimal=10
@@ -105,11 +108,50 @@ def test_extract_between_bounds(short_range_model, time1, time2,
         [12.0, 0.],
     ]
 )
-def test_short_range_concentration(time, expected_short_range_concentration, concentration_model, short_range_model):
-    concentration_model = concentration_model.build_model(250_000)
-    model = short_range_model.build_model(250_000)
+def test_short_range_concentration(time, expected_short_range_concentration,
+                                   concentration_model, short_range_model):
+    concentration_model = concentration_model.build_model(SAMPLE_SIZE)
+    model = short_range_model.build_model(SAMPLE_SIZE)
     np.testing.assert_allclose(
         np.array(model.short_range_concentration(concentration_model, time)).mean(),
         expected_short_range_concentration, rtol=0.02
     )
 
+
+def test_short_range_exposure_with_ndarray_mask():
+    c_model = mc_models.ConcentrationModel(
+        room=models.Room(volume=50, humidity=0.3),
+        ventilation=models.AirChange(active=models.PeriodicInterval(period=120, duration=120),
+                                     air_exch=10_000_000,),
+        infected=mc_models.InfectedPopulation(
+            number=1,
+            presence=models.SpecificInterval(present_times=((8.5, 12.5), (13.5, 17.5))),
+            virus=models.Virus.types['SARS_CoV_2_DELTA'],
+            mask=models.Mask.types['No mask'],
+            activity=models.Activity.types['Seated'],
+            expiration=expiration_distributions['Breathing'],
+            host_immunity=0.,
+        ),
+        evaporation_factor=0.3,
+    )
+    sr_model = mc_models.ShortRangeModel(expiration=short_range_expiration_distributions['Shouting'],
+                                         activity=models.Activity.types['Heavy exercise'],
+                                         presence=models.SpecificInterval(present_times=((10.5, 11.0),)),
+                                         distance=0.854)
+    e_model = mc_models.ExposureModel(
+        concentration_model = c_model,
+        short_range = (sr_model,),
+        exposed = mc_models.Population(
+            number=1,
+            presence=models.SpecificInterval(present_times=((8.5, 12.5), (13.5, 17.5))),
+            mask=models.Mask(η_inhale=np.array([0., 0.3, 0.5])),
+            activity=models.Activity.types['Light activity'],
+            host_immunity=0.,
+        ),
+    ).build_model(SAMPLE_SIZE)
+    assert isinstance(e_model.deposited_exposure(), np.ndarray)
+    assert len(e_model.deposited_exposure()) == 3
+    np.testing.assert_allclose(e_model.deposited_exposure(),
+            e_model.deposited_exposure()[0]*np.array([1., 0.7, 0.5]),
+            rtol=1e-8)
+