Fixing the remaining tests, with the new masks
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1 changed files with 10 additions and 10 deletions
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@ -60,10 +60,10 @@ populations = [
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np.array([14.4, 14.4]), np.array([99.6803184113, 99.5181053773])],
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[populations[2], KnownConcentrations(lambda t: 1.2),
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np.array([14.4, 14.4]), np.array([99.4146994564, 99.6803184113])],
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np.array([14.4, 14.4]), np.array([97.4574432074, 98.3493482895])],
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[populations[0], KnownConcentrations(lambda t: np.array([1.2, 2.4])),
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np.array([14.4, 28.8]), np.array([99.6803184113, 99.9989780368])],
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np.array([14.4, 28.8]), np.array([98.3493482895, 99.9727534893])],
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[populations[1], KnownConcentrations(lambda t: np.array([1.2, 2.4])),
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np.array([14.4, 28.8]), np.array([99.6803184113, 99.9976777757])],
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@ -123,22 +123,22 @@ def conc_model():
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models.InfectedPopulation(
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number=1,
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presence=interesting_times,
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mask=models.Mask.types['Type I'],
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Seated'],
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virus=models.Virus.types['SARS_CoV_2'],
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expiration=models.Expiration.types['Breathing'],
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expiration=models.Expiration.types['Superspreading event'],
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)
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)
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# expected quanta were computed with a trapezoidal integration, using
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# a mesh of 10'000 pts per exposed presence interval.
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@pytest.mark.parametrize("exposed_time_interval, expected_quanta", [
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[(0, 1), 0.0055680845],
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[(1, 1.01), 6.4960491e-05],
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[(1.01, 1.02), 6.3187723e-05],
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[(12, 12.01), 1.9307359e-06],
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[(12, 24), 0.079347465],
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[(0, 24), 0.086122050],
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[(0, 1), 5.4869151],
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[(1, 1.01), 0.064013521],
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[(1.01, 1.02), 0.062266596],
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[(12, 12.01), 0.0019025904],
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[(12, 24), 78.190763],
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[(0, 24), 84.866592],
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]
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)
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def test_exposure_model_integral_accuracy(exposed_time_interval,
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