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Added tests for ExposureModel __post_init__ conditions

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Luis Aleixo 2022-08-01 11:56:22 +02:00
parent 3314d0146c
commit 5a6f4d54a1
1 changed files with 104 additions and 19 deletions
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123
caimira/tests/models/test_exposure_model.py
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@ -8,7 +8,7 @@ from dataclasses import dataclass
from caimira import models
from caimira.models import ExposureModel
from caimira.dataclass_utils import replace
from caimira.monte_carlo.data import expiration_distributions
@dataclass(frozen=True)
class KnownNormedconcentration(models.ConcentrationModel):
@ -90,9 +90,8 @@ def known_concentrations(func):
np.array([40.91708675, 91.46172332]), np.array([51.6749232285, 80.3196524031])],
])
def test_exposure_model_ndarray(population, cm,
expected_exposure, expected_probability, sr_model):
geographical_data = models.Cases()
model = ExposureModel(cm, sr_model, population, geographical_data)
expected_exposure, expected_probability, sr_model, cases_model):
model = ExposureModel(cm, sr_model, population, cases_model)
np.testing.assert_almost_equal(
model.deposited_exposure(), expected_exposure
)
@ -111,11 +110,10 @@ def test_exposure_model_ndarray(population, cm,
[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, sr_model):
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]))
geographical_data = models.Cases()
model = ExposureModel(cm, sr_model, population, geographical_data)
model = ExposureModel(cm, sr_model, population, cases_model)
np.testing.assert_almost_equal(
model.deposited_exposure(), expected_deposited_exposure
@ -130,19 +128,17 @@ def test_exposure_model_ndarray_and_float_mix(population, expected_deposited_exp
[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, sr_model):
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]))
geographical_data = models.Cases()
model_array = ExposureModel(cm_array, sr_model, population, geographical_data)
model_array = ExposureModel(cm_array, sr_model, population, cases_model)
np.testing.assert_almost_equal(
model_array.deposited_exposure(), np.array(expected_deposited_exposure)
)
def test_exposure_model_scalar(sr_model):
def test_exposure_model_scalar(sr_model, cases_model):
cm_scalar = known_concentrations(lambda t: 1.2)
geographical_data = models.Cases()
model_scalar = ExposureModel(cm_scalar, sr_model, populations[0], geographical_data)
model_scalar = ExposureModel(cm_scalar, sr_model, populations[0], cases_model)
expected_deposited_exposure = 1.52436206
np.testing.assert_almost_equal(
model_scalar.deposited_exposure(), expected_deposited_exposure
@ -173,9 +169,29 @@ def conc_model():
)
@pytest.fixture
def diameter_dependent_model(conc_model) -> models.InfectedPopulation:
# Generate a diameter dependent model
return replace(conc_model,
infected = models.InfectedPopulation(
number=1,
presence=halftime,
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.,
))
@pytest.fixture
def sr_model():
return ()
@pytest.fixture
def cases_model():
return ()
# Expected deposited exposure were computed with a trapezoidal integration, using
@ -192,18 +208,17 @@ def sr_model():
]
)
def test_exposure_model_integral_accuracy(exposed_time_interval,
expected_deposited_exposure, conc_model, sr_model):
expected_deposited_exposure, conc_model, sr_model, cases_model):
presence_interval = models.SpecificInterval((exposed_time_interval,))
population = models.Population(
10, presence_interval, models.Mask.types['Type I'],
models.Activity.types['Standing'], 0.,
)
geographical_data = models.Cases()
model = ExposureModel(conc_model, sr_model, population, geographical_data)
model = ExposureModel(conc_model, sr_model, population, cases_model)
np.testing.assert_allclose(model.deposited_exposure(), expected_deposited_exposure)
def test_infectious_dose_vectorisation(sr_model):
def test_infectious_dose_vectorisation(sr_model, cases_model):
infected_population = models.InfectedPopulation(
number=1,
presence=halftime,
@ -226,8 +241,7 @@ def test_infectious_dose_vectorisation(sr_model):
10, presence_interval, models.Mask.types['Type I'],
models.Activity.types['Standing'], 0.,
)
geographical_data = models.Cases()
model = ExposureModel(cm, sr_model, population, geographical_data)
model = ExposureModel(cm, sr_model, population, cases_model)
inf_probability = model.infection_probability()
assert isinstance(inf_probability, np.ndarray)
assert inf_probability.shape == (3, )
@ -294,3 +308,74 @@ def test_probabilistic_exposure_probability(exposed_population, cm,
np.testing.assert_allclose(
model.total_probability_rule(), probabilistic_exposure_probability, rtol=0.05
)
@pytest.mark.parametrize(
"active, outside_temp, window_height, opening_length", [
[models.PeriodicInterval(period=120, duration=120), models.PiecewiseConstant((0., 24.),
(np.array([293., 300.]),)), 1., 1.,], # Verify (ventilation) outside_temp vectorisation.
[models.PeriodicInterval(period=120, duration=120), models.PiecewiseConstant((0., 24.), (293.,)),
np.array([1., 0.5]), 1.], # Verify (ventilation) window_height vectorisation.
[models.PeriodicInterval(period=120, duration=120), models.PiecewiseConstant((0., 24.), (293.,)),
1., np.array([1., 0.5])], # Verify (ventilation) opening_length vectorisation.
]
)
def test_diameter_vectorisation_window_opening(diameter_dependent_model, sr_model, active, outside_temp,
window_height, opening_length, cases_model):
concentration = replace(diameter_dependent_model,
ventilation=models.WindowOpening(active=active,
outside_temp=outside_temp,
window_height=window_height,
opening_length=opening_length)
)
with pytest.raises(ValueError, match="Ventilation parameters and diameter cannot be arrays at the same time."):
models.ExposureModel(concentration, sr_model, populations[0], cases_model)
def test_diameter_vectorisation_hinged_window(diameter_dependent_model, sr_model, cases_model):
# Verify (ventilation) window_width vectorisation.
concentration = replace(diameter_dependent_model,
ventilation = models.HingedWindow(active=models.PeriodicInterval(period=120, duration=120),
outside_temp=models.PiecewiseConstant((0., 24.), (293.,)),
window_height=1.,
opening_length=1.,
window_width=np.array([1., 0.5]))
)
with pytest.raises(ValueError, match="Ventilation parameters and diameter cannot be arrays at the same time."):
models.ExposureModel(concentration, sr_model, populations[0], cases_model)
def test_diameter_vectorisation_HEPA_filter(diameter_dependent_model, sr_model, cases_model):
# Verify (ventilation) q_air_mech vectorisation.
concentration = replace(diameter_dependent_model,
ventilation = models.HEPAFilter(active=models.PeriodicInterval(period=120, duration=120), q_air_mech=np.array([0.5, 1.]))
)
with pytest.raises(ValueError, match="Ventilation rate and diameter cannot be arrays at the same time."):
models.ExposureModel(concentration, sr_model, populations[1], cases_model)
def test_diameter_vectorisation_air_change(diameter_dependent_model, sr_model, cases_model):
# Verify (ventilation) air_exch vectorisation.
concentration = replace(diameter_dependent_model,
ventilation = models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=np.array([0.5, 1.]))
)
with pytest.raises(ValueError, match="Ventilation rate and diameter cannot be arrays at the same time."):
models.ExposureModel(concentration, sr_model, populations[2], cases_model)
@pytest.mark.parametrize(
"volume, inside_temp, humidity, error", [
[np.array([50, 100]), models.PiecewiseConstant((0., 24.), (293,)), 0.3,
"Room volume and diameter cannot be arrays at the same time."], # Verify room volume vectorisation
[50, models.PiecewiseConstant((0., 24.), (np.array([293., 300.]),)), 0.3,
"Virus decay constant and diameter cannot be arrays at the same time."], # Verify room inside_temp vectorisation
[50, models.PiecewiseConstant((0., 24.), (293.,)), np.array([0.3, 0.5]),
"Virus decay constant and diameter cannot be arrays at the same time."], # Verify room humidity vectorisation
]
)
def test_diameter_vectorisation_room(diameter_dependent_model, sr_model, cases_model, volume, inside_temp, humidity, error):
concentration = replace(diameter_dependent_model,
room = models.Room(volume=volume, inside_temp=inside_temp, humidity=humidity))
with pytest.raises(ValueError, match=error):
models.ExposureModel(concentration, sr_model, populations[0], cases_model)