added tests on vectorisation for an integrated concentration
This commit is contained in:
Luis Aleixo
parent
1e0d0a8de2
commit
2b71d586ef
1 changed files with 77 additions and 29 deletions
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@ -10,26 +10,30 @@ from caimira import models
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@dataclass(frozen=True)
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class KnownConcentrationModelBase(models._ConcentrationModelBase):
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"""
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A ConcentrationModel where the atmosphere_concentration method is
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redefined with a value taken from a new parameter. Useful for testing.
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A _ConcentrationModelBase where all the class methods are
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redefined with a value taken from new parameters. Useful for testing.
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"""
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known_population: models.Population
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known_atmosphere_concentration: float = 0.0
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known_removal_rate: float
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known_atmosphere_concentration: float
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known_normalization_factor: float
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@property
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def population(self) -> models.Population:
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return self.known_population
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def removal_rate(self, time: float):
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return 10.
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def removal_rate(self, time: float) -> float:
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return self.known_removal_rate
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def atmosphere_concentration(self):
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def atmosphere_concentration(self) -> float:
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return self.known_atmosphere_concentration
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def normalization_factor(self):
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return 1e2
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def normalization_factor(self) -> float:
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return self.known_normalization_factor
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@pytest.mark.parametrize(
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@ -99,6 +103,17 @@ def simple_conc_model():
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)
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@pytest.fixture
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def dummy_population(simple_conc_model) -> models.Population:
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return models.Population(
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number=10,
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presence=simple_conc_model.infected.presence,
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mask=models.Mask.types['Type I'],
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activity=models.Activity.types['Seated'],
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host_immunity=0.,
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)
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@pytest.mark.parametrize(
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"time, expected_last_state_change", [
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[-15., 0.], # Out of range goes to the first state.
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@ -164,31 +179,64 @@ def test_integrated_concentration(simple_conc_model):
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npt.assert_almost_equal(c1, c2 + c3, decimal=15)
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@pytest.mark.parametrize(
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"known_atmosphere_concentration, expected_normed_integrated_concentration", [
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[0.0, 0.0017333437605308818],
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[240.0, 4.801733343835203],
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[440.0, 8.801733343835203],
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[600., 12.001733343835202],
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[1000., 20.00173334429238],
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@pytest.mark.parametrize([
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"known_atmosphere_concentration",
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"expected_normed_integrated_concentration"],
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[
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[0.0, 0.00018533333708996207],
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[240.0, 48.000185340695275],
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[440.0, 88.00018534069527],
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[600., 120.00018534069527],
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[1000., 200.0001853407918],
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]
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)
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def test_normed_integrated_concentration(
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simple_conc_model: models.ConcentrationModel,
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def test_normed_integrated_concentration_with_atmosphere_concentration(
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simple_conc_model: models.ConcentrationModel,
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dummy_population: models.Population,
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known_atmosphere_concentration: float,
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expected_normed_integrated_concentration: float):
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dummy_population = models.Population(
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number=10,
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presence=simple_conc_model.infected.presence,
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mask=models.Mask.types['Type I'],
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activity=models.Activity.types['Seated'],
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host_immunity=0.,
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)
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known_conc_model = KnownConcentrationModelBase(
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room = simple_conc_model.room,
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ventilation = simple_conc_model.ventilation,
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known_population = dummy_population,
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known_removal_rate = 100.,
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known_atmosphere_concentration = known_atmosphere_concentration,
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known_normalization_factor = 10.)
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npt.assert_almost_equal(known_conc_model.normed_integrated_concentration(0, 2), expected_normed_integrated_concentration)
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@pytest.mark.parametrize([
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"known_removal_rate",
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"known_atmosphere_concentration",
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"known_normalization_factor",
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"expected_normed_integrated_concentration"],
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[
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[np.array([0.25, 10]), 0.0, 10., np.array([0.012161005755130391, 0.0017333437605308818])],
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[100, np.array([0, 240.0]), 10., np.array([0.00018533333708996207, 48.000185340695275])],
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[100, 440.0, np.array([10., 20.]), np.array([88.00018534069527, 44.000185340695275])],
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[np.array([10, 100]), np.array([600., 800.]), 10., np.array([120.00173334473946, 160.0001853406953])],
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[np.array([50, 100,]), np.array([1000.,1100.]), np.array([10., 20.]), np.array([200.00036800764332, 110.00018534069527])],
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]
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)
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def test_normed_integrated_concentration_vectorisation(
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simple_conc_model: models.ConcentrationModel,
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dummy_population: models.Population,
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known_removal_rate: float,
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known_atmosphere_concentration: float,
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known_normalization_factor: float,
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expected_normed_integrated_concentration: float):
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known_conc_model = KnownConcentrationModelBase(
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simple_conc_model.room,
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simple_conc_model.ventilation,
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dummy_population,
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known_atmosphere_concentration)
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npt.assert_almost_equal(known_conc_model.normed_integrated_concentration(0, 2), expected_normed_integrated_concentration)
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room = simple_conc_model.room,
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ventilation = simple_conc_model.ventilation,
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known_population = dummy_population,
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known_removal_rate = known_removal_rate,
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known_atmosphere_concentration = known_atmosphere_concentration,
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known_normalization_factor = known_normalization_factor)
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integrated_concentration = known_conc_model.normed_integrated_concentration(0, 2)
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assert isinstance(integrated_concentration, np.ndarray)
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assert integrated_concentration.shape == (2, )
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npt.assert_almost_equal(integrated_concentration, expected_normed_integrated_concentration)
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