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test for host immunity vectorisation

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This commit is contained in:
Luis Aleixo 2022-10-07 10:52:44 +02:00
parent 98b08a05e0
commit e82f6e58d2
1 changed files with 24 additions and 3 deletions
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27
caimira/tests/models/test_exposure_model.py
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@ -297,13 +297,13 @@ def test_prob_meet_infected_person(pop, cases, AB, exposed, infected, prob_meet_
[30, known_concentrations(lambda t: 1.2),
100000, 68, 5, 55.93154502],
])
def test_probabilistic_exposure_probability(exposed_population, cm,
def test_probabilistic_exposure_probability(sr_model, exposed_population, cm,
pop, AB, cases, probabilistic_exposure_probability):
population = models.Population(
exposed_population, models.PeriodicInterval(120, 60), models.Mask.types['Type I'],
models.Activity.types['Standing'], host_immunity=0.,)
model = ExposureModel(cm, (), population, models.Cases(geographic_population=pop,
model = ExposureModel(cm, sr_model, population, models.Cases(geographic_population=pop,
geographic_cases=cases, ascertainment_bias=AB),)
np.testing.assert_allclose(
model.total_probability_rule(), probabilistic_exposure_probability, rtol=0.05
@ -390,4 +390,25 @@ def test_diameter_vectorisation_room(diameter_dependent_model, sr_model, cases_m
ventilation = models.HVACMechanical(active=models.SpecificInterval(((0., 24.), )), q_air_mech=100.))
with pytest.raises(ValueError, match=error_message):
models.ExposureModel(concentration, sr_model, populations[0], cases_model)
@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.])],
]
)
def test_host_immunity_vectorisation(sr_model, cases_model, cm, host_immunity, expected_probability):
population = models.Population(
10, halftime, models.Mask(np.array([0.3, 0.35])),
models.Activity.types['Standing'], host_immunity=host_immunity
)
model = ExposureModel(cm, sr_model, population, cases_model)
inf_probability = model.infection_probability()
np.testing.assert_almost_equal(
inf_probability, expected_probability, decimal=1
)
assert isinstance(inf_probability, np.ndarray)
assert inf_probability.shape == (2, )