test_exposure_model values adjustments
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1 changed files with 10 additions and 10 deletions
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@ -70,20 +70,20 @@ def known_concentrations(func):
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@pytest.mark.parametrize(
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"population, cm, f_dep, expected_exposure, expected_probability",[
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[populations[1], known_concentrations(lambda t: 1.2), 1.,
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np.array([14.4, 14.4]), np.array([17.4296889121, 16.292365501])], #(1 - e**(-(0.57*(1-0.35)*14.4)/30))*100
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[populations[1], known_concentrations(lambda t: 36), 1.,
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np.array([432, 432]), np.array([99.6803184113, 99.5181053773])],
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[populations[2], known_concentrations(lambda t: 1.2), 1.,
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np.array([14.4, 14.4]), np.array([11.5205620042, 12.7855362382])],
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[populations[2], known_concentrations(lambda t: 36), 1.,
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np.array([432, 432]), np.array([97.4574432074, 98.3493482895])],
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[populations[0], known_concentrations(lambda t: np.array([1.2, 2.4])), 1.,
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np.array([14.4, 28.8]), np.array([12.7855362382, 23.9363731074])],
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[populations[0], known_concentrations(lambda t: np.array([36, 72])), 1.,
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np.array([432, 864]), np.array([98.3493482895, 99.9727534893])],
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[populations[1], known_concentrations(lambda t: np.array([1.2, 2.4])), 1.,
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np.array([14.4, 28.8]), np.array([17.4296889121, 29.9303192658])],
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[populations[1], known_concentrations(lambda t: np.array([36, 72])), 1.,
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np.array([432, 864]), np.array([99.6803184113, 99.9976777757])],
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[populations[0], known_concentrations(lambda t: 2.4), np.array([0.5, 1.]),
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28.8, np.array([12.7855362382, 23.9363731074])],
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[populations[0], known_concentrations(lambda t: 72), np.array([0.5, 1.]),
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864, np.array([98.3493482895, 99.9727534893])],
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])
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def test_exposure_model_ndarray(population, cm, f_dep,
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expected_exposure, expected_probability):
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