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Adding mask inhale efficiency factor to short-range exposure; adding corresponding test; some cosmetics in test_short_range_model.py

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This commit is contained in:
Nicolas Mounet 2022-04-26 11:19:33 +02:00
parent faaf1ed086
commit f0bdda7c7d
2 changed files with 51 additions and 9 deletions
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2
cara/models.py
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@ -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)

58
cara/tests/models/test_short_range_model.py
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@ -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)