Adding more quantities to test in integration tests (qR, dose); improving test structure
This commit is contained in:
Nicolas Mounet
parent
82667698d5
commit
2f61cf1a96
1 changed files with 82 additions and 62 deletions
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@ -8,15 +8,18 @@ from cara.monte_carlo.data import activity_distributions, virus_distributions
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# TODO: seed better the random number generators
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np.random.seed(2000)
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SAMPLE_SIZE = 20000
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SAMPLE_SIZE = 50000
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TOLERANCE = 0.05
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# references values for infection_probability and expected new cases
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# in the following tests, were obtained from the feature/mc branch
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def test_shared_office():
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# corresponds to the 1st line of Table 5 in CERN-OPEN-2021-04, but
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# speaking 30% of the time (instead of 1/3)
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@pytest.fixture
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def shared_office_mc():
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"""
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Corresponds to the 1st line of Table 5 in CERN-OPEN-2021-04, but
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speaking 30% of the time (instead of 1/3)
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"""
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=50, humidity=0.3),
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ventilation=models.MultipleVentilation(
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@ -45,7 +48,7 @@ def test_shared_office():
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weights=(0.3, 0.7)),
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),
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)
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exposure_mc = mc.ExposureModel(
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return mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=3,
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@ -54,15 +57,13 @@ def test_shared_office():
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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10.7, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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0.32, rtol=TOLERANCE)
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def test_classroom():
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# corresponds to the 2nd line of Table 5 in CERN-OPEN-2021-04
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@pytest.fixture
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def classroom_mc():
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"""
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Corresponds to the 2nd line of Table 5 in CERN-OPEN-2021-04
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"""
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=160, humidity=0.3),
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ventilation=models.MultipleVentilation(
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@ -88,7 +89,7 @@ def test_classroom():
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expiration=models.Expiration.types['Talking'],
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),
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)
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exposure_mc = mc.ExposureModel(
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return mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=19,
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@ -97,15 +98,13 @@ def test_classroom():
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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36.1, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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6.87, rtol=TOLERANCE)
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def test_skicabin():
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# corresponds to the 3rd line of Table 5 in CERN-OPEN-2021-04
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@pytest.fixture
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def ski_cabin_mc():
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"""
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Corresponds to the 3rd line of Table 5 in CERN-OPEN-2021-04
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"""
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=10, humidity=0.5),
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ventilation=models.AirChange(
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@ -121,7 +120,7 @@ def test_skicabin():
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expiration=models.Expiration.types['Talking'],
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),
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)
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exposure_mc = mc.ExposureModel(
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return mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=3,
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@ -130,17 +129,15 @@ def test_skicabin():
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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16.2, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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0.49, rtol=TOLERANCE)
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def test_gym():
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# corresponds to the 4th line of Table 5 in CERN-OPEN-2021-04,
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# but there the expected number of cases is wrongly reported as 0.56
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# while it should be 0.63.
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@pytest.fixture
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def gym_mc():
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"""
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Corresponds to the 4th line of Table 5 in CERN-OPEN-2021-04,
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but there the expected number of cases is wrongly reported as 0.56
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while it should be 0.63.
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"""
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=300, humidity=0.5),
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ventilation=models.AirChange(
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@ -156,7 +153,7 @@ def test_gym():
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expiration=models.Expiration.types['Breathing'],
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),
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)
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exposure_mc = mc.ExposureModel(
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return mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=28,
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@ -165,16 +162,14 @@ def test_gym():
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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2.2, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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0.63, rtol=TOLERANCE)
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def test_waiting_room():
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# corresponds to the 5th line of Table 5 in CERN-OPEN-2021-04, but
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# speaking 30% of the time (instead of 20%)
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@pytest.fixture
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def waiting_room_mc():
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"""
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Corresponds to the 5th line of Table 5 in CERN-OPEN-2021-04, but
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speaking 30% of the time (instead of 20%)
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"""
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=100, humidity=0.5),
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ventilation=models.AirChange(
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@ -193,7 +188,7 @@ def test_waiting_room():
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weights=(0.3, 0.7)),
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),
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)
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exposure_mc = mc.ExposureModel(
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return mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=14,
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@ -202,17 +197,15 @@ def test_waiting_room():
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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9.7, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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1.36, rtol=TOLERANCE)
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def test_skagit_chorale():
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# corresponds to the 6th line of Table 5 in CERN-OPEN-2021-04, but
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# infection probability should be 29.8% instead of 32%, and number of
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# new cases 17.9 instead of 21.
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@pytest.fixture
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def skagit_chorale_mc():
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"""
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Corresponds to the 6th line of Table 5 in CERN-OPEN-2021-04, but
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infection probability should be 29.8% instead of 32%, and number of
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new cases 17.9 instead of 21.
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"""
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=810, humidity=0.5),
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ventilation=models.AirChange(
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@ -228,7 +221,7 @@ def test_skagit_chorale():
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expiration=models.Expiration((5., 5., 5.)),
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),
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)
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exposure_mc = mc.ExposureModel(
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return mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=60,
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@ -237,23 +230,45 @@ def test_skagit_chorale():
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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29.8, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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17.9, rtol=TOLERANCE)
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@pytest.mark.parametrize(
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"mask_type, month, expected_probability",
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"mc_model, expected_pi, expected_new_cases, expected_dose, expected_qR",
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[
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["No mask", "Jul", 30],
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["Type I", "Jul", 10.2],
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["FFP2", "Jul", 4],
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["Type I", "Feb", 4.3],
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["shared_office_mc", 10.7, 0.32, 0.954, 10.9],
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["classroom_mc", 36.1, 6.85, 13.0, 474.4],
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["ski_cabin_mc", 16.3, 0.49, 0.599, 123.4],
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["gym_mc", 2.25, 0.63, 0.01307, 16.4],
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["waiting_room_mc", 9.72, 1.36, 0.571, 58.9],
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["skagit_chorale_mc",29.9, 17.9, 1.90, 1414],
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]
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)
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def test_report_models(mc_model, expected_pi, expected_new_cases,
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expected_dose, expected_qR, request):
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mc_model = request.getfixturevalue(mc_model)
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exposure_model = mc_model.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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expected_pi, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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expected_new_cases, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.quanta_exposure().mean(),
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expected_dose, rtol=TOLERANCE)
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npt.assert_allclose(
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exposure_model.concentration_model.infected.emission_rate_when_present().mean(),
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expected_qR, rtol=TOLERANCE)
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@pytest.mark.parametrize(
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"mask_type, month, expected_pi, expected_dose, expected_qR",
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[
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["No mask", "Jul", 30.0, 6.764, 64.9],
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["Type I", "Jul", 10.2, 1.223, 11.7],
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["FFP2", "Jul", 4.0, 1.223, 11.7],
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["Type I", "Feb", 4.25, 0.357, 11.7],
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],
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)
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def test_small_shared_office_Geneva(mask_type,month,expected_probability):
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def test_small_shared_office_Geneva(mask_type, month, expected_pi,
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expected_dose, expected_qR):
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=33, humidity=0.5),
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ventilation=models.MultipleVentilation(
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@ -293,4 +308,9 @@ def test_small_shared_office_Geneva(mask_type,month,expected_probability):
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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expected_probability, rtol=TOLERANCE)
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expected_pi, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.quanta_exposure().mean(),
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expected_dose, rtol=TOLERANCE)
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npt.assert_allclose(
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exposure_model.concentration_model.infected.emission_rate_when_present().mean(),
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expected_qR, rtol=TOLERANCE)
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