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changed and added tests on test_exposure_model

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This commit is contained in:
Luis Aleixo 2022-02-03 15:25:39 +01:00
parent 8855dfc75a
commit fb15b6f451
1 changed files with 37 additions and 27 deletions
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64
cara/tests/models/test_exposure_model.py
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@ -75,25 +75,25 @@ def known_concentrations(func):
@pytest.mark.parametrize(
"population, cm, expected_exposure, expected_probability", [
[populations[1], known_concentrations(lambda t: 36.),
np.array([432, 432]), np.array([67.9503762594, 65.2366759251])],
np.array([64.02320633, 59.45012016]), np.array([67.9503762594, 65.2366759251])],
[populations[2], known_concentrations(lambda t: 36.),
np.array([432, 432]), np.array([51.6749232285, 55.6374622042])],
np.array([40.91708675, 45.73086166]), np.array([51.6749232285, 55.6374622042])],
[populations[0], known_concentrations(lambda t: np.array([36., 72.])),
np.array([432, 864]), np.array([55.6374622042, 80.3196524031])],
np.array([45.73086166, 91.46172332]), np.array([55.6374622042, 80.3196524031])],
[populations[1], known_concentrations(lambda t: np.array([36., 72.])),
np.array([432, 864]), np.array([67.9503762594, 87.9151129926])],
np.array([64.02320633, 118.90024032]), np.array([67.9503762594, 87.9151129926])],
[populations[2], known_concentrations(lambda t: np.array([36., 72.])),
np.array([432, 864]), np.array([51.6749232285, 80.3196524031])],
np.array([40.91708675, 91.46172332]), np.array([51.6749232285, 80.3196524031])],
])
def test_exposure_model_ndarray(population, cm,
expected_exposure, expected_probability):
model = ExposureModel(cm, population)
np.testing.assert_almost_equal(
model.exposure(), expected_exposure
model.deposited_exposure(), expected_exposure
)
np.testing.assert_almost_equal(
model.infection_probability(), expected_probability, decimal=10
@ -105,33 +105,43 @@ def test_exposure_model_ndarray(population, cm,
assert model.expected_new_cases().shape == (2,)
@pytest.mark.parametrize("population", populations)
def test_exposure_model_ndarray_and_float_mix(population):
@pytest.mark.parametrize("population, expected_deposited_exposure", [
[populations[0], np.array([1.52436206, 1.52436206])],
[populations[1], np.array([2.13410688, 1.98167067])],
[populations[2], np.array([1.36390289, 1.52436206])],
])
def test_exposure_model_ndarray_and_float_mix(population, expected_deposited_exposure):
cm = known_concentrations(
lambda t: 0. if np.floor(t) % 2 else np.array([1.2, 1.2]))
model = ExposureModel(cm, population)
expected_exposure = np.array([14.4, 14.4])
np.testing.assert_almost_equal(
model.exposure(), expected_exposure
model.deposited_exposure(), expected_deposited_exposure
)
assert isinstance(model.infection_probability(), np.ndarray)
assert isinstance(model.expected_new_cases(), np.ndarray)
@pytest.mark.parametrize("population", populations)
def test_exposure_model_compare_scalar_vector(population):
cm_scalar = known_concentrations(lambda t: 1.2)
@pytest.mark.parametrize("population, expected_deposited_exposure", [
[populations[0], np.array([1.52436206, 1.52436206])],
[populations[1], np.array([2.13410688, 1.98167067])],
[populations[2], np.array([1.36390289, 1.52436206])],
])
def test_exposure_model_vector(population, expected_deposited_exposure):
cm_array = known_concentrations(lambda t: np.array([1.2, 1.2]))
model_scalar = ExposureModel(cm_scalar, population)
model_array = ExposureModel(cm_array, population)
expected_exposure = 14.4
np.testing.assert_almost_equal(
model_scalar.exposure(), expected_exposure
model_array.deposited_exposure(), np.array(expected_deposited_exposure)
)
def test_exposure_model_scalar():
cm_scalar = known_concentrations(lambda t: 1.2)
model_scalar = ExposureModel(cm_scalar, populations[0])
expected_deposited_exposure = 1.52436206
np.testing.assert_almost_equal(
model_array.exposure(), np.array([expected_exposure]*2)
model_scalar.deposited_exposure(), expected_deposited_exposure
)
@ -159,28 +169,28 @@ def conc_model():
)
# Expected exposure were computed with a trapezoidal integration, using
# Expected deposited exposure were computed with a trapezoidal integration, using
# a mesh of 10'000 pts per exposed presence interval.
@pytest.mark.parametrize(
["exposed_time_interval", "expected_exposure"],
["exposed_time_interval", "expected_deposited_exposure"],
[
[(0., 1.), 266.67176],
[(1., 1.01), 3.0879539],
[(1.01, 1.02), 3.00082435],
[(12., 12.01), 0.095063235],
[(12., 24.), 3775.65025],
[(0., 24.), 4097.8494],
[(0., 1.), 45.6008710],
[(1., 1.01), 0.5280401],
[(1.01, 1.02), 0.51314096385],
[(12., 12.01), 0.016255813185],
[(12., 24.), 645.63619275],
[(0., 24.), 700.7322474],
]
)
def test_exposure_model_integral_accuracy(exposed_time_interval,
expected_exposure, conc_model):
expected_deposited_exposure, conc_model):
presence_interval = models.SpecificInterval((exposed_time_interval,))
population = models.Population(
10, presence_interval, models.Mask.types['Type I'],
models.Activity.types['Standing'], 0.,
)
model = ExposureModel(conc_model, population)
np.testing.assert_allclose(model.exposure(), expected_exposure)
np.testing.assert_allclose(model.deposited_exposure(), expected_deposited_exposure)
def test_infectious_dose_vectorisation():