Simplifying test_exposure_model (towards unit tests rather than integration tests)
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Nicolas Mounet
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commit
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1 changed files with 69 additions and 92 deletions
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@ -1,103 +1,80 @@
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import typing
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import numpy as np
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import numpy.testing
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import pytest
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import cara.models
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from cara import models
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from cara.models import ExposureModel
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def exposure_model_from_params(params):
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always = cara.models.PeriodicInterval(240, 240) # TODO: This should be a thing on an interval.
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office_hours = cara.models.SpecificInterval(present_times=[(8,17)])
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c_model = cara.models.ConcentrationModel(
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cara.models.Room(params['volume']),
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cara.models.AirChange(always, params['air_change']),
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cara.models.InfectedPopulation(
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number=1,
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presence=office_hours,
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mask=cara.models.Mask(
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η_exhale=params['η_exhale'],
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η_leaks=params['η_leaks'],
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η_inhale=params['η_inhale'],
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),
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activity=cara.models.Activity(
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0.51,
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0.75,
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),
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virus=cara.models.Virus(
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halflife=params['virus_halflife'],
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viral_load_in_sputum=params['viral_load_in_sputum'],
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coefficient_of_infectivity=params['coefficient_of_infectivity'],
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),
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expiration=cara.models.Expiration(
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ejection_factor=(0.084, 0.009, 0.003, 0.002),
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),
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)
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)
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return cara.models.ExposureModel(
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concentration_model=c_model,
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exposed=cara.models.Population(
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number=10,
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presence=office_hours,
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activity=c_model.infected.activity,
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mask=c_model.infected.mask,
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)
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)
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@pytest.mark.parametrize(
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"override_params", [
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{'volume': np.array([100, 120])},
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{'air_change': np.array([100, 120])},
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{'virus_halflife': np.array([1.1, 1.5])},
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{'viral_load_in_sputum': np.array([5e8, 1e9])},
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{'coefficient_of_infectivity': np.array([0.02, 0.05])},
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{'η_exhale': np.array([0.92, 0.95])},
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{'η_leaks': np.array([0.15, 0.20])},
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{'η_inhale': np.array([0.3, 0.35])},
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]
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)
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def test_exposure_model_vectorisation(override_params):
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defaults = {
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'volume': 75,
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'air_change': 100,
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'virus_halflife': 1.1,
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'viral_load_in_sputum': 1e9,
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'coefficient_of_infectivity': 0.02,
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'η_exhale': 0.95,
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'η_leaks': 0.15,
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'η_inhale': 0.3,
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}
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defaults.update(override_params)
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class KnownConcentrations(models.ConcentrationModel):
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"""
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A ConcentrationModel which is based on pre-known quanta concentrations and
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which therefore doesn't need other components. Useful for testing.
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e_model = exposure_model_from_params(defaults)
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expected_new_cases = e_model.expected_new_cases()
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assert isinstance(expected_new_cases, np.ndarray)
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assert expected_new_cases.shape == (2, )
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"""
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def __init__(self, concentration_function: typing.Callable) -> None:
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self._func = concentration_function
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def concentration(self, time: float) -> models._VectorisedFloat: # noqa
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return self._func(time)
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halftime = models.PeriodicInterval(120, 60)
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populations = [
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# A simple scalar population.
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models.Population(
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10, halftime, models.Mask.types['Type I'],
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models.Activity.types['Standing'],
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),
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# A population with some array component for η_inhale.
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models.Population(
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10, halftime, models.Mask(0.95, 0.15, np.array([0.3, 0.35])),
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models.Activity.types['Standing'],
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),
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]
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@pytest.mark.parametrize(
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"vector_param", [
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'volume', 'air_change', 'virus_halflife',
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'viral_load_in_sputum', 'coefficient_of_infectivity', 'η_exhale',
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'η_leaks', 'η_inhale',
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]
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)
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def test_exposure_model_compare_scalar_vector(vector_param):
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defaults = {
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'volume': 75,
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'air_change': 100,
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'virus_halflife': 1.1,
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'viral_load_in_sputum': 1e9,
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'coefficient_of_infectivity': 0.02,
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'η_exhale': 0.95,
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'η_leaks': 0.15,
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'η_inhale': 0.3,
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}
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e_model_scalar = exposure_model_from_params(defaults)
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expected_new_cases_scalar = e_model_scalar.expected_new_cases()
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assert isinstance(expected_new_cases_scalar, float)
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"population, cm, expected_exposure",[
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[populations[1], KnownConcentrations(lambda t: 1.2), np.array([14.4, 14.4])],
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[populations[0], KnownConcentrations(lambda t: np.array([1.2, 2.4])), np.array([14.4, 28.8])],
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[populations[1], KnownConcentrations(lambda t: np.array([1.2, 2.4])), np.array([14.4, 28.8])],
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])
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def test_exposure_model_ndarray(population, cm, expected_exposure):
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model = ExposureModel(cm, population)
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np.testing.assert_almost_equal(
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model.quanta_exposure(), expected_exposure
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)
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defaults[vector_param] = np.ones(3)*defaults[vector_param]
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e_model_vector = exposure_model_from_params(defaults)
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expected_new_cases_vector = e_model_vector.expected_new_cases()
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assert isinstance(expected_new_cases_vector, np.ndarray)
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assert expected_new_cases_vector.shape == (3, )
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assert np.all(expected_new_cases_vector==expected_new_cases_scalar)
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assert isinstance(model.infection_probability(), np.ndarray)
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assert model.infection_probability().shape == (2,)
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@pytest.mark.parametrize("population", populations)
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def test_exposure_model_ndarray_and_float_mix(population):
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cm = KnownConcentrations(lambda t: 1.2 if np.floor(t) % 2 else np.array([1.2, 1.2]))
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model = ExposureModel(cm, population)
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expected_exposure = np.array([14.4, 14.4])
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np.testing.assert_almost_equal(
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model.quanta_exposure(), expected_exposure
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)
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assert isinstance(model.infection_probability(), np.ndarray)
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@pytest.mark.parametrize("population", populations)
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def test_exposure_model_compare_scalar_vector(population):
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cm_scalar = KnownConcentrations(lambda t: 1.2)
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cm_array = KnownConcentrations(lambda t: np.array([1.2, 1.2]))
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model_scalar = ExposureModel(cm_scalar, population)
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model_array = ExposureModel(cm_array, population)
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expected_exposure = 14.4
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np.testing.assert_almost_equal(
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model_scalar.quanta_exposure(), expected_exposure
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)
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np.testing.assert_almost_equal(
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model_array.quanta_exposure(), np.array([expected_exposure]*2)
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)
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