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modified dynamic tests for InfectedPopulation

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This commit is contained in:
Luis Aleixo 2023-04-06 12:27:39 +02:00
parent fe19cb5976
commit bcb1d1012a
2 changed files with 51 additions and 21 deletions
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10
caimira/models.py
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@ -818,7 +818,7 @@ class Population:
raise TypeError(f'The presence argument must be an "Interval". Got {type(self.presence)}')
else:
if self.presence is not None:
raise TypeError(f'The presence argument must be None for a IntPiecewiseConstant number.')
raise TypeError(f'The presence argument must be None for a IntPiecewiseConstant number')
def person_present(self, time: float):
# Allow back-compatibility
@ -1656,11 +1656,13 @@ class ExposureModel:
self.concentration_model.virus.transmissibility_factor)))) * 100
def total_probability_rule(self) -> _VectorisedFloat:
if (self.geographical_data.geographic_population != 0 and self.geographical_data.geographic_cases != 0):
sum_probability = 0.0
if not isinstance(self.concentration_model.infected.number, int):
if (isinstance(self.concentration_model.infected.number, IntPiecewiseContant) or
isinstance(self.exposed.number, IntPiecewiseContant)):
raise NotImplementedError("Cannot compute total probability "
"(including incidence rate) with dynamic occupancy")
if (self.geographical_data.geographic_population != 0 and self.geographical_data.geographic_cases != 0):
sum_probability = 0.0
# Create an equivalent exposure model but changing the number of infected cases.
total_people = self.concentration_model.infected.number + self.exposed.number

62
caimira/tests/models/test_dynamic_population.py
View file

@ -1,3 +1,5 @@
import re
import numpy as np
import numpy.testing as npt
import pytest
@ -48,17 +50,6 @@ def baseline_infected_population_number():
)
@pytest.fixture
def baseline_exposed_population_number():
return models.Population(
number=models.IntPiecewiseContant(
(8, 12, 13, 17), (1, 0, 1)),
presence=None,
mask=models.Mask.types['No mask'],
activity=models.Activity.types['Seated'],
host_immunity=0.,
)
@pytest.mark.parametrize(
"time",
[4., 8., 10., 12., 13., 14., 16., 20., 24.],
@ -74,6 +65,22 @@ def test_population_number(full_exposure_model: models.ExposureModel,
assert isinstance(piecewise_population_number.number,
models.IntPiecewiseContant)
assert piecewise_population_number.presence is None
with pytest.raises(
TypeError,
match=f'The presence argument must be an "Interval". Got {type(None)}'
):
dc_utils.nested_replace(
int_population_number, {'presence': None}
)
with pytest.raises(
TypeError,
match="The presence argument must be None for a IntPiecewiseConstant number"
):
dc_utils.nested_replace(
piecewise_population_number, {'presence': models.SpecificInterval(((8, 12), ))}
)
assert int_population_number.person_present(time) == piecewise_population_number.person_present(time)
assert int_population_number.people_present(time) == piecewise_population_number.people_present(time)
@ -85,14 +92,12 @@ def test_population_number(full_exposure_model: models.ExposureModel,
)
def test_concentration_model_dynamic_population(full_exposure_model: models.ExposureModel,
baseline_infected_population_number: models.InfectedPopulation,
baseline_exposed_population_number: models.Population,
time: float):
dynamic_model: models.ExposureModel = dc_utils.nested_replace(
full_exposure_model,
{
'concentration_model.infected': baseline_infected_population_number,
'exposed': baseline_exposed_population_number,
}
)
assert full_exposure_model.concentration(time) == dynamic_model.concentration(time)
@ -107,7 +112,7 @@ def test_concentration_model_dynamic_population(full_exposure_model: models.Expo
[13., 4],
[15., 5],
])
def test_sum_of_models(full_exposure_model: models.ExposureModel,
def test_linearity_with_number_of_infected(full_exposure_model: models.ExposureModel,
baseline_infected_population_number: models.InfectedPopulation,
time: float,
number_of_infected: int):
@ -128,9 +133,13 @@ def test_sum_of_models(full_exposure_model: models.ExposureModel,
)
npt.assert_almost_equal(static_multiple_exposure_model.concentration(time), dynamic_single_exposure_model.concentration(time) * number_of_infected)
npt.assert_almost_equal(static_multiple_exposure_model.deposited_exposure(), dynamic_single_exposure_model.deposited_exposure() * number_of_infected)
def test_dynamic_dose(full_exposure_model):
@pytest.mark.parametrize(
"time", (8., 9., 10., 11., 12., 13., 14.),
)
def test_dynamic_dose(full_exposure_model, time):
dynamic_infected: models.ExposureModel = dc_utils.nested_replace(
full_exposure_model,
@ -172,7 +181,26 @@ def test_dynamic_dose(full_exposure_model):
}
)
dynamic_concentration = dynamic_infected.concentration(time)
dynamic_exposure = dynamic_infected.deposited_exposure()
static_exposure = np.sum([model.deposited_exposure() for model in (single_infected, two_infected, three_infected)])
npt.assert_almost_equal(dynamic_exposure, static_exposure)
static_concentration, static_exposure = [], []
for model in (single_infected, two_infected, three_infected):
static_concentration.append(model.concentration(time))
static_exposure.append(model.deposited_exposure())
npt.assert_almost_equal(dynamic_concentration, np.sum(np.array(static_concentration)))
npt.assert_almost_equal(dynamic_exposure, np.sum(np.array(static_exposure)))
def test_dynamic_total_probability_rule(full_exposure_model: models.ExposureModel,
baseline_infected_population_number: models.InfectedPopulation):
model = dc_utils.nested_replace(full_exposure_model,
{'concentration_model.infected': baseline_infected_population_number })
with pytest.raises(
NotImplementedError,
match=re.escape("Cannot compute total probability "
"(including incidence rate) with dynamic occupancy")
):
model.total_probability_rule()