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Multiplying all known concentrations in test_exposure_model by f_inf=0.5, as concentration is now with f_inf included

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Nicolas Mounet 2023-12-01 07:32:03 +01:00
parent b39131c14c
commit 5b4345e40a
1 changed files with 15 additions and 15 deletions
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30
caimira/tests/models/test_exposure_model.py
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@ -75,19 +75,19 @@ def known_concentrations(func):
@pytest.mark.parametrize(
"population, cm, expected_exposure, expected_probability", [
[populations[1], known_concentrations(lambda t: 36.),
[populations[1], known_concentrations(lambda t: 18.),
np.array([64.02320633, 59.45012016]), np.array([67.9503762594, 65.2366759251])],
[populations[2], known_concentrations(lambda t: 36.),
[populations[2], known_concentrations(lambda t: 18.),
np.array([40.91708675, 45.73086166]), np.array([51.6749232285, 55.6374622042])],
[populations[0], known_concentrations(lambda t: np.array([36., 72.])),
[populations[0], known_concentrations(lambda t: np.array([18., 36.])),
np.array([45.73086166, 91.46172332]), np.array([55.6374622042, 80.3196524031])],
[populations[1], known_concentrations(lambda t: np.array([36., 72.])),
[populations[1], known_concentrations(lambda t: np.array([18., 36.])),
np.array([64.02320633, 118.90024032]), np.array([67.9503762594, 87.9151129926])],
[populations[2], known_concentrations(lambda t: np.array([36., 72.])),
[populations[2], known_concentrations(lambda t: np.array([18., 36.])),
np.array([40.91708675, 91.46172332]), np.array([51.6749232285, 80.3196524031])],
])
def test_exposure_model_ndarray(population, cm,
@ -113,7 +113,7 @@ def test_exposure_model_ndarray(population, cm,
])
def test_exposure_model_ndarray_and_float_mix(population, expected_deposited_exposure, sr_model, cases_model):
cm = known_concentrations(
lambda t: 0. if np.floor(t) % 2 else np.array([1.2, 1.2]))
lambda t: 0. if np.floor(t) % 2 else np.array([0.6, 0.6]))
model = ExposureModel(cm, sr_model, population, cases_model)
np.testing.assert_almost_equal(
@ -130,7 +130,7 @@ def test_exposure_model_ndarray_and_float_mix(population, expected_deposited_exp
[populations[2], np.array([1.36390289, 1.52436206])],
])
def test_exposure_model_vector(population, expected_deposited_exposure, sr_model, cases_model):
cm_array = known_concentrations(lambda t: np.array([1.2, 1.2]))
cm_array = known_concentrations(lambda t: np.array([0.6, 0.6]))
model_array = ExposureModel(cm_array, sr_model, population, cases_model)
np.testing.assert_almost_equal(
model_array.deposited_exposure(), np.array(expected_deposited_exposure)
@ -138,7 +138,7 @@ def test_exposure_model_vector(population, expected_deposited_exposure, sr_model
def test_exposure_model_scalar(sr_model, cases_model):
cm_scalar = known_concentrations(lambda t: 1.2)
cm_scalar = known_concentrations(lambda t: 0.6)
model_scalar = ExposureModel(cm_scalar, sr_model, populations[0], cases_model)
expected_deposited_exposure = 1.52436206
np.testing.assert_almost_equal(
@ -234,7 +234,7 @@ def test_infectious_dose_vectorisation(sr_model, cases_model):
expiration=models.Expiration.types['Speaking'],
host_immunity=0.,
)
cm = known_concentrations(lambda t: 1.2)
cm = known_concentrations(lambda t: 0.6)
cm = replace(cm, infected=infected_population)
presence_interval = models.SpecificInterval(((0., 1.),))
@ -289,13 +289,13 @@ def test_prob_meet_infected_person(pop, cases, AB, exposed, infected, prob_meet_
@pytest.mark.parametrize(
"exposed_population, cm, pop, cases, AB, probabilistic_exposure_probability",[
[10, known_concentrations(lambda t: 36.),
[10, known_concentrations(lambda t: 18.),
100000, 68, 5, 41.50971131],
[10, known_concentrations(lambda t: 0.2),
[10, known_concentrations(lambda t: 0.1),
100000, 68, 5, 2.185785075],
[20, known_concentrations(lambda t: 72.),
[20, known_concentrations(lambda t: 36.),
100000, 68, 5, 64.09068488],
[30, known_concentrations(lambda t: 1.2),
[30, known_concentrations(lambda t: 0.6),
100000, 68, 5, 55.93154502],
])
def test_probabilistic_exposure_probability(sr_model, exposed_population, cm,
@ -396,8 +396,8 @@ def test_diameter_vectorisation_room(diameter_dependent_model, sr_model, cases_m
@pytest.mark.parametrize(
["cm", "host_immunity", "expected_probability"],
[
[known_concentrations(lambda t: 36.), np.array([0.25, 0.5]), np.array([57.40415859, 41.03956914])],
[known_concentrations(lambda t: 36.), np.array([0., 1.]), np.array([67.95037626, 0.])],
[known_concentrations(lambda t: 18.), np.array([0.25, 0.5]), np.array([57.40415859, 41.03956914])],
[known_concentrations(lambda t: 18.), np.array([0., 1.]), np.array([67.95037626, 0.])],
]
)
def test_host_immunity_vectorisation(sr_model, cases_model, cm, host_immunity, expected_probability):