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Fixing tests with the change of Expiration, in particular: removing useless tests on aerosols in test_known_quantities; removing reference to Unmodulated vocalization expiration type; avoiding the use of factor_exhale as array; changing some integration test values on r0; replacing some integration tests on r0 by integration tests on quanta_exposure with a simpler model; changing values in some concentration tests

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This commit is contained in:
Nicolas Mounet 2021-05-31 23:09:22 +02:00
parent 62a4880a5b
commit 4a211aa7fc
5 changed files with 39 additions and 62 deletions
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12
cara/tests/conftest.py
View file

@ -7,11 +7,9 @@ import pytest
def baseline_model():
model = models.ConcentrationModel(
room=models.Room(volume=75),
ventilation=models.SlidingWindow(
active=models.PeriodicInterval(period=120, duration=120),
inside_temp=models.PiecewiseConstant((0,24),(293,)),
outside_temp=models.PiecewiseConstant((0,24),(283,)),
window_height=1.6, opening_length=0.6,
ventilation=models.AirChange(
active=models.SpecificInterval(((0,24),)),
air_exch=30.,
),
infected=models.InfectedPopulation(
number=1,
@ -19,7 +17,7 @@ def baseline_model():
presence=models.SpecificInterval(((0, 4), (5, 8))),
mask=models.Mask.types['No mask'],
activity=models.Activity.types['Light activity'],
expiration=models.Expiration.types['Unmodulated Vocalization'],
expiration=models.Expiration.types['Superspreading event'],
),
)
return model
@ -30,7 +28,7 @@ def baseline_exposure_model(baseline_model):
return models.ExposureModel(
baseline_model,
exposed=models.Population(
number=10,
number=1000,
presence=baseline_model.infected.presence,
activity=baseline_model.infected.activity,
mask=baseline_model.infected.mask,

8
cara/tests/models/test_concentration_model.py
View file

@ -14,7 +14,6 @@ from cara import models
{'air_change': np.array([100, 120])},
{'viral_load_in_sputum': np.array([5e8, 1e9])},
{'quantum_infectious_dose': np.array([50, 20])},
{'factor_exhale': np.array([0.92, 0.95])},
]
)
def test_concentration_model_vectorisation(override_params):
@ -24,7 +23,6 @@ def test_concentration_model_vectorisation(override_params):
'air_change': 100,
'viral_load_in_sputum': 1e9,
'quantum_infectious_dose': 50,
'factor_exhale': 0.95,
}
defaults.update(override_params)
@ -36,7 +34,7 @@ def test_concentration_model_vectorisation(override_params):
number=1,
presence=always,
mask=models.Mask(
factor_exhale=defaults['factor_exhale'],
factor_exhale=0.95,
η_inhale=0.3,
),
activity=models.Activity(
@ -47,9 +45,7 @@ def test_concentration_model_vectorisation(override_params):
viral_load_in_sputum=defaults['viral_load_in_sputum'],
quantum_infectious_dose=defaults['quantum_infectious_dose'],
),
expiration=models.Expiration(
ejection_factor=(0.084, 0.009, 0.003, 0.002),
),
expiration=models.Expiration((1., 0., 0.)),
)
)
concentrations = c_model.concentration(10)

2
cara/tests/test_expiration.py
View file

@ -28,7 +28,7 @@ def test_multiple():
npt.assert_almost_equal(e_expected.aerosols(mask), e.aerosols(mask))
# expected values obtained from separate model
# expected values obtained from another code
@pytest.mark.parametrize(
"BLO_weights, expected_aerosols",
[

77
cara/tests/test_known_quantities.py
View file

@ -6,21 +6,12 @@ import cara.models as models
import cara.data as data
def test_no_mask_aerosols(baseline_model):
exp = models.Expiration.types['Unmodulated Vocalization']
npt.assert_allclose(
exp.aerosols(models.Mask.types['No mask']),
6.077541e-12,
rtol=1e-5,
)
def test_no_mask_emission_rate(baseline_model):
rate = 151.938514
def test_no_mask_superspeading_emission_rate(baseline_model):
expected_rate = 970.
npt.assert_allclose(
[baseline_model.infected.emission_rate(t) for t in [0, 1, 4, 4.5, 5, 8, 9]],
[0, rate, rate, 0, 0, rate, 0],
rtol=1e-5
[0, expected_rate, expected_rate, 0, 0, expected_rate, 0],
rtol=1e-12
)
@ -48,23 +39,24 @@ def baseline_periodic_hepa():
def test_concentrations(baseline_model):
# expected concentrations were computed analytically
ts = [0, 4, 5, 7, 10]
concentrations = [baseline_model.concentration(t) for t in ts]
npt.assert_allclose(
concentrations,
[0.000000e+00, 2.619538e-01, 1.146999e-04, 2.619537e-01, 5.022289e-08],
rtol=1e-5
[0.000000e+00, 0.4189594, 1.6422648e-14, 0.4189594, 6.4374587e-28],
rtol=1e-6
)
def test_smooth_concentrations(baseline_model):
# We don't care about the actual concentrations in this test, but rather
# that the curve itself is smooth.
dx = 0.1
dy_limit = dx * 2 # Anything more than this is a bit steep.
dx = 0.002
dy_limit = 0.2 # Anything more than this (in relative) is a bit steep.
ts = np.arange(0, 10, dx)
concentrations = [baseline_model.concentration(t) for t in ts]
assert np.abs(np.diff(concentrations)).max() < dy_limit
assert np.abs(np.diff(concentrations)).max()/np.mean(concentrations) < dy_limit
def build_model(interval_duration):
@ -80,7 +72,7 @@ def build_model(interval_duration):
presence=models.SpecificInterval(((0, 4), (5, 8))),
mask=models.Mask.types['No mask'],
activity=models.Activity.types['Light activity'],
expiration=models.Expiration.types['Unmodulated Vocalization'],
expiration=models.Expiration.types['Superspreading event'],
),
)
return model
@ -98,8 +90,8 @@ def test_concentrations_startup(baseline_model):
def test_r0(baseline_exposure_model):
# expected r0 was computed with a trapezoidal integration, using
# a mesh of 100'000 pts per exposed presence interval.
p = baseline_exposure_model.infection_probability()
npt.assert_allclose(p, 89.001748)
r0 = baseline_exposure_model.reproduction_number()
npt.assert_allclose(r0, 973.535888)
def test_periodic_window(baseline_periodic_window, baseline_room):
@ -183,15 +175,6 @@ def test_multiple_ventilation_HEPA_HVAC_AirChange(volume, expected_value):
expected_value,rtol=1e-5)
def test_expiration_aerosols():
mask = models.Mask.types['Type I']
exp1 = models.Expiration((0.751, 0.139, 0.0139, 0.059),
particle_sizes = (0.8e-4, 1.8e-4, 3.5e-4, 5.5e-4))
exp2 = models.Expiration((0.059, 0.0139, 0.751, 0.139),
particle_sizes = (5.5e-4, 3.5e-4, 0.8e-4, 1.8e-4))
npt.assert_allclose(exp1.aerosols(mask), exp2.aerosols(mask), rtol=1e-5)
@pytest.mark.parametrize(
"time, expected_value",
[
@ -239,7 +222,7 @@ def build_hourly_dependent_model(month, intervals_open=((7.5, 8.5),),
presence=models.SpecificInterval(intervals_presence_infected),
mask=models.Mask.types['No mask'],
activity=models.Activity.types['Light activity'],
expiration=models.Expiration.types['Unmodulated Vocalization'],
expiration=models.Expiration.types['Superspreading event'],
),
)
return model
@ -260,7 +243,7 @@ def build_constant_temp_model(outside_temp, intervals_open=((7.5, 8.5),)):
presence=models.SpecificInterval(((0, 4), (5, 7.5))),
mask=models.Mask.types['No mask'],
activity=models.Activity.types['Light activity'],
expiration=models.Expiration.types['Unmodulated Vocalization'],
expiration=models.Expiration.types['Superspreading event'],
),
)
return model
@ -287,7 +270,7 @@ def build_hourly_dependent_model_multipleventilation(month, intervals_open=((7.5
presence=models.SpecificInterval(((0, 4), (5, 7.5))),
mask=models.Mask.types['No mask'],
activity=models.Activity.types['Light activity'],
expiration=models.Expiration.types['Unmodulated Vocalization'],
expiration=models.Expiration.types['Superspreading event'],
),
)
return model
@ -370,16 +353,16 @@ def build_exposure_model(concentration_model):
)
# expected r0 were computed with a trapezoidal integration, using
# expected quanta were computed with a trapezoidal integration, using
# a mesh of 100'000 pts per exposed presence interval.
@pytest.mark.parametrize(
"month, expected_r0",
"month, expected_quanta",
[
['Jan', 86.258533],
['Jun', 99.972103],
['Jan', 10.136783],
['Jun', 41.800377],
],
)
def test_r0_hourly_dep(month,expected_r0):
def test_quanta_hourly_dep(month,expected_quanta):
m = build_exposure_model(
build_hourly_dependent_model(
month,
@ -387,19 +370,19 @@ def test_r0_hourly_dep(month,expected_r0):
intervals_presence_infected=((8, 12), (13, 17))
)
)
p = m.infection_probability()
npt.assert_allclose(p, expected_r0)
quanta = m.quanta_exposure()
npt.assert_allclose(quanta, expected_quanta)
# expected r0 were computed with a trapezoidal integration, using
# expected quanta were computed with a trapezoidal integration, using
# a mesh of 100'000 pts per exposed presence interval.
@pytest.mark.parametrize(
"month, expected_r0",
"month, expected_quanta",
[
['Jan', 86.422736],
['Jun', 99.982323],
['Jan', 10.19818],
['Jun', 44.130683],
],
)
def test_r0_hourly_dep_refined(month,expected_r0):
def test_quanta_hourly_dep_refined(month,expected_quanta):
m = build_exposure_model(
build_hourly_dependent_model(
month,
@ -408,5 +391,5 @@ def test_r0_hourly_dep_refined(month,expected_r0):
temperatures=data.GenevaTemperatures,
)
)
p = m.infection_probability()
npt.assert_allclose(p, expected_r0)
quanta = m.quanta_exposure()
npt.assert_allclose(quanta, expected_quanta)

2
cara/tests/test_monte_carlo.py
View file

@ -53,7 +53,7 @@ def baseline_mc_model() -> cara.monte_carlo.ConcentrationModel:
presence=cara.models.SpecificInterval(((0, 4), (5, 8))),
mask=cara.models.Mask.types['No mask'],
activity=cara.models.Activity.types['Light activity'],
expiration=cara.models.Expiration.types['Unmodulated Vocalization'],
expiration=cara.models.Expiration.types['Breathing'],
),
)
return mc_model