20b0467f89
- extract, isolate and package it in a completely independent Python module, versioned and in a way that allows releases on PyPI.org - fixed error in placeholder for secondary school (data registry defaults) - added restriction in pytest version to install - expected number of new cases fix - data registry update (schema v2.1.1) - github update - deprecate ExpertApplication and CO2Application - changes to reflect schema update 2.0.2 - version update - Fixed error with f_inf (short-range) - new folder layout - Conditional probability data update - General fixes - Fitting results in L/S/person - CO2 fitting algorithm refinement
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import re
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import numpy as np
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import numpy.testing as npt
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import pytest
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from retry import retry
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from caimira.calculator.models import models
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from caimira.calculator.models.monte_carlo.data import expiration_distribution
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def test_multiple_wrong_weight_size():
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weights = (1., 2., 3.)
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e_base = models.Expiration(2.5)
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with pytest.raises(
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ValueError,
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match=re.escape("expirations and weigths must contain the"
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"same number of elements")
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):
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e = models.MultipleExpiration([e_base, e_base], weights)
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def test_multiple_wrong_diameters():
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weights = (1., 2., 3.)
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e1 = models.Expiration(np.array([1., 1.]))
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e2 = models.Expiration(1.)
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e3 = models.Expiration(2.)
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with pytest.raises(
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ValueError,
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match=re.escape("diameters must all be scalars")
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):
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e = models.MultipleExpiration([e1, e2, e3], weights)
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def test_multiple():
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weights = (1., 1.)
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mask = models.Mask.types['Type I']
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e1 = models.Expiration.types['Breathing']
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e2 = models.Expiration.types['Singing']
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aerosol_expected = (e1.aerosols(mask) + e2.aerosols(mask))/2.
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e = models.MultipleExpiration([e1, e2], weights)
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npt.assert_almost_equal(aerosol_expected, e.aerosols(mask))
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@retry(tries=10)
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# Expected values obtained from analytical formulas
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@pytest.mark.parametrize(
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"BLO_weights, expected_aerosols",
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[
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[(1.,0.,0.), 8.33551e-13],
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[(1.,1.,1.), 2.20071e-11],
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[(1.,5.,5.), 1.06701e-10],
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],
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)
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def test_expiration_aerosols(data_registry, BLO_weights, expected_aerosols):
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mask = models.Mask.types['No mask']
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e = expiration_distribution(data_registry, BLO_weights)
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npt.assert_allclose(e.build_model(100000).aerosols(mask).mean(),
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expected_aerosols, rtol=1e-2)
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