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test update with new values

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This commit is contained in:
Luis Aleixo 2021-08-03 15:27:28 +02:00
parent f6340d7986
commit 4b63281f02
3 changed files with 52 additions and 40 deletions
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70
cara/tests/models/test_exposure_model.py
View file

@ -3,26 +3,27 @@ import typing
import numpy as np
import numpy.testing
import pytest
from dataclasses import dataclass
from cara import models
from cara.models import ExposureModel
@dataclass(frozen=True)
class KnownConcentrations(models.ConcentrationModel):
"""
A ConcentrationModel which is based on pre-known quanta concentrations and
A ConcentrationModel which is based on pre-known exposure concentrations and
which therefore doesn't need other components. Useful for testing.
"""
def __init__(self, concentration_function: typing.Callable) -> None:
self._func = concentration_function
concentration_function: typing.Callable
def infectious_virus_removal_rate(self, time: float) -> models._VectorisedFloat:
# very large decay constant -> same as constant concentration
return 1.e50
def _concentration_limit(self, time: float) -> models._VectorisedFloat:
return self._func(time)
return self.concentration_function(time)
def state_change_times(self):
return [0, 24]
@ -31,7 +32,7 @@ class KnownConcentrations(models.ConcentrationModel):
return 24
def concentration(self, time: float) -> models._VectorisedFloat: # noqa
return self._func(time)
return self.concentration_function(time)
halftime = models.PeriodicInterval(120, 60)
@ -52,24 +53,37 @@ populations = [
models.Activity(np.array([0.51,0.57]), 0.57),
),
]
dummyRoom = models.Room(50, 0.5)
dummyVentilation = models._VentilationBase()
dummyInfPopulation = models.InfectedPopulation(
number=1,
presence=halftime,
mask=models.Mask.types['Type I'],
activity=models.Activity.types['Standing'],
virus=models.Virus.types['SARS_CoV_2_B117'],
expiration=models.Expiration.types['Talking']
)
def known_concentrations(func):
return KnownConcentrations(dummyRoom, dummyVentilation, dummyInfPopulation, func)
@pytest.mark.parametrize(
"population, cm, f_dep, expected_exposure, expected_probability",[
[populations[1], KnownConcentrations(lambda t: 1.2), 1.,
np.array([14.4, 14.4]), np.array([99.6803184113, 99.5181053773])],
[populations[1], known_concentrations(lambda t: 1.2), 1.,
np.array([14.4, 14.4]), np.array([17.4296889121, 16.292365501])], #(1 - e**(-(0.57*(1-0.35)*14.4)/30))*100
[populations[2], KnownConcentrations(lambda t: 1.2), 1.,
np.array([14.4, 14.4]), np.array([97.4574432074, 98.3493482895])],
[populations[2], known_concentrations(lambda t: 1.2), 1.,
np.array([14.4, 14.4]), np.array([11.5205620042, 12.7855362382])],
[populations[0], KnownConcentrations(lambda t: np.array([1.2, 2.4])), 1.,
np.array([14.4, 28.8]), np.array([98.3493482895, 99.9727534893])],
[populations[0], known_concentrations(lambda t: np.array([1.2, 2.4])), 1.,
np.array([14.4, 28.8]), np.array([12.7855362382, 23.9363731074])],
[populations[1], KnownConcentrations(lambda t: np.array([1.2, 2.4])), 1.,
np.array([14.4, 28.8]), np.array([99.6803184113, 99.9976777757])],
[populations[1], known_concentrations(lambda t: np.array([1.2, 2.4])), 1.,
np.array([14.4, 28.8]), np.array([17.4296889121, 29.9303192658])],
[populations[0], KnownConcentrations(lambda t: 2.4), np.array([0.5, 1.]),
28.8, np.array([98.3493482895, 99.9727534893])],
[populations[0], known_concentrations(lambda t: 2.4), np.array([0.5, 1.]),
28.8, np.array([12.7855362382, 23.9363731074])],
])
def test_exposure_model_ndarray(population, cm, f_dep,
expected_exposure, expected_probability):
@ -89,7 +103,7 @@ def test_exposure_model_ndarray(population, cm, f_dep,
@pytest.mark.parametrize("population", populations)
def test_exposure_model_ndarray_and_float_mix(population):
cm = KnownConcentrations(lambda t: 0 if np.floor(t) % 2 else np.array([1.2, 1.2]))
cm = known_concentrations(lambda t: 0 if np.floor(t) % 2 else np.array([1.2, 1.2]))
model = ExposureModel(cm, population)
expected_exposure = np.array([14.4, 14.4])
@ -103,8 +117,8 @@ def test_exposure_model_ndarray_and_float_mix(population):
@pytest.mark.parametrize("population", populations)
def test_exposure_model_compare_scalar_vector(population):
cm_scalar = KnownConcentrations(lambda t: 1.2)
cm_array = KnownConcentrations(lambda t: np.array([1.2, 1.2]))
cm_scalar = known_concentrations(lambda t: 1.2)
cm_array = known_concentrations(lambda t: np.array([1.2, 1.2]))
model_scalar = ExposureModel(cm_scalar, population)
model_array = ExposureModel(cm_array, population)
expected_exposure = 14.4
@ -133,23 +147,23 @@ def conc_model():
)
)
# expected quanta were computed with a trapezoidal integration, using
# expected exposure were computed with a trapezoidal integration, using
# a mesh of 10'000 pts per exposed presence interval.
@pytest.mark.parametrize("exposed_time_interval, expected_quanta", [
[(0, 1), 5.3334352],
[(1, 1.01), 0.061759078],
[(1.01, 1.02), 0.060016487],
[(12, 12.01), 0.0019012647],
[(12, 24), 75.513005],
[(0, 24), 81.956988],
@pytest.mark.parametrize("exposed_time_interval, expected_exposure", [
[(0, 1), 266.67176],
[(1, 1.01), 3.0879539],
[(1.01, 1.02), 3.00082435],
[(12, 12.01), 0.095063235],
[(12, 24), 3775.65025],
[(0, 24), 4097.8494],
]
)
def test_exposure_model_integral_accuracy(exposed_time_interval,
expected_quanta, conc_model):
expected_exposure, conc_model):
presence_interval = models.SpecificInterval((exposed_time_interval,))
population = models.Population(
10, presence_interval, models.Mask.types['Type I'],
models.Activity.types['Standing'],
)
model = ExposureModel(conc_model, population, fraction_deposited=1.)
np.testing.assert_allclose(model.exposure(), expected_quanta)
np.testing.assert_allclose(model.exposure(), expected_exposure)

2
cara/tests/test_infected_population.py
View file

@ -7,14 +7,12 @@ import cara.models
@pytest.mark.parametrize(
"override_params", [
{'viral_load_in_sputum': np.array([5e8, 1e9])},
{'infectious_dose': np.array([50, 20])},
{'exhalation_rate': np.array([0.75, 0.81])},
]
)
def test_infected_population_vectorisation(override_params):
defaults = {
'viral_load_in_sputum': 1e9,
'quantum_infectious_dose': 50,
'exhalation_rate': 0.75,
}
defaults.update(override_params)

20
cara/tests/test_monte_carlo_full_models.py
View file

@ -235,12 +235,12 @@ def skagit_chorale_mc():
@pytest.mark.parametrize(
"mc_model, expected_pi, expected_new_cases, expected_dose, expected_ER",
[
["shared_office_mc", 10.7, 0.32, 0.954, 10.9],
["classroom_mc", 36.1, 6.85, 13.0, 474.4],
["ski_cabin_mc", 16.3, 0.49, 0.599, 123.4],
["gym_mc", 2.25, 0.63, 0.01307, 16.4],
["waiting_room_mc", 9.72, 1.36, 0.571, 58.9],
["skagit_chorale_mc",29.9, 17.9, 1.90, 1414],
["shared_office_mc", 10.7, 0.32, 57.24, 654],
["classroom_mc", 36.1, 6.85, 780.0, 28464],
["ski_cabin_mc", 16.3, 0.49, 35.94, 7404],
["gym_mc", 2.25, 0.63, 0.7842, 984],
["waiting_room_mc", 9.72, 1.36, 34.26, 3534],
["skagit_chorale_mc",29.9, 17.9, 190.0, 141400],
]
)
def test_report_models(mc_model, expected_pi, expected_new_cases,
@ -261,10 +261,10 @@ def test_report_models(mc_model, expected_pi, expected_new_cases,
@pytest.mark.parametrize(
"mask_type, month, expected_pi, expected_dose, expected_ER",
[
["No mask", "Jul", 30.0, 6.764, 64.9],
["Type I", "Jul", 10.2, 1.223, 11.7],
["FFP2", "Jul", 4.0, 1.223, 11.7],
["Type I", "Feb", 4.25, 0.357, 11.7],
["No mask", "Jul", 30.0, 405.84, 3894],
["Type I", "Jul", 10.2, 73.38, 702],
["FFP2", "Jul", 4.0, 73.38, 702],
["Type I", "Feb", 4.25, 21.42, 702],
],
)
def test_small_shared_office_Geneva(mask_type, month, expected_pi,