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Merge branch 'seeds_removed' into 'master'

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Removed seeds from tests

Closes #245

See merge request cara/cara!362
This commit is contained in:
Andre Henriques 2022-06-07 10:04:00 +02:00
commit 4a5311de4f
8 changed files with 30 additions and 36 deletions
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4
cara/tests/apps/calculator/test_model_generator.py
View file

@ -4,6 +4,7 @@ import typing
import numpy as np
import numpy.testing as npt
import pytest
from retry import retry
from cara.apps.calculator import model_generator
from cara.apps.calculator.model_generator import _hours2timestring
@ -11,8 +12,6 @@ from cara.apps.calculator.model_generator import minutes_since_midnight
from cara import models
from cara.monte_carlo.data import expiration_distributions
# TODO: seed better the random number generators
np.random.seed(2000)
def test_model_from_dict(baseline_form_data):
form = model_generator.FormData.from_dict(baseline_form_data)
@ -25,6 +24,7 @@ def test_model_from_dict_invalid(baseline_form_data):
model_generator.FormData.from_dict(baseline_form_data)
@retry(tries=10)
@pytest.mark.parametrize(
["mask_type"],
[

6
cara/tests/apps/calculator/test_webapp.py
View file

@ -43,7 +43,7 @@ async def test_404(http_server_client):
assert resp.code == 404
@retry()
@retry(tries=10)
class TestBasicApp(tornado.testing.AsyncHTTPTestCase):
def get_app(self):
return cara.apps.calculator.make_app()
@ -72,7 +72,7 @@ class TestBasicApp(tornado.testing.AsyncHTTPTestCase):
assert 'expected number of new cases is' in response.body.decode()
@retry()
@retry(tries=10)
class TestCernApp(tornado.testing.AsyncHTTPTestCase):
def get_app(self):
cern_theme = Path(cara.apps.calculator.__file__).parent.parent / 'themes' / 'cern'
@ -85,7 +85,7 @@ class TestCernApp(tornado.testing.AsyncHTTPTestCase):
assert 'expected number of new cases is' in response.body.decode()
retry()
retry(tries=10)
class TestOpenApp(tornado.testing.AsyncHTTPTestCase):
def get_app(self):
return cara.apps.calculator.make_app(calculator_prefix="/mycalc")

2
cara/tests/models/test_short_range_model.py
View file

@ -9,8 +9,6 @@ from cara.apps.calculator.model_generator import build_expiration
from cara.monte_carlo.data import short_range_expiration_distributions,\
expiration_distributions, short_range_distances, activity_distributions
# TODO: seed better the random number generators
np.random.seed(2000)
SAMPLE_SIZE = 250_000

2
cara/tests/test_expiration.py
View file

@ -3,6 +3,7 @@ import re
import numpy as np
import numpy.testing as npt
import pytest
from retry import retry
from cara import models
from cara.monte_carlo.data import expiration_distribution
@ -41,6 +42,7 @@ def test_multiple():
npt.assert_almost_equal(aerosol_expected, e.aerosols(mask))
@retry(tries=10)
# expected values obtained from analytical formulas
@pytest.mark.parametrize(
"BLO_weights, expected_aerosols",

44
cara/tests/test_full_algorithm.py
View file

@ -6,6 +6,7 @@ from scipy.integrate import quad
from scipy.special import erf
import numpy.testing as npt
import pytest
from retry import retry
import cara.monte_carlo as mc
from cara import models,data
@ -16,8 +17,6 @@ from cara.monte_carlo.data import (expiration_distributions,
expiration_BLO_factors,short_range_expiration_distributions,
short_range_distances,virus_distributions,activity_distributions)
# TODO: seed better the random number generators
np.random.seed(2000)
SAMPLE_SIZE = 1_000_000
TOLERANCE = 0.04
@ -478,7 +477,7 @@ def c_model() -> mc.ConcentrationModel:
host_immunity=0.,
),
evaporation_factor=0.3,
).build_model(SAMPLE_SIZE)
)
@pytest.fixture
@ -497,7 +496,7 @@ def c_model_distr() -> mc.ConcentrationModel:
host_immunity=0.,
),
evaporation_factor=0.3,
).build_model(SAMPLE_SIZE)
)
@pytest.fixture
@ -508,13 +507,13 @@ def sr_models() -> typing.Tuple[mc.ShortRangeModel, ...]:
activity = models.Activity.types['Seated'],
presence = interaction_intervals[0],
distance = 0.854,
).build_model(SAMPLE_SIZE),
),
mc.ShortRangeModel(
expiration = short_range_expiration_distributions['Breathing'],
activity = models.Activity.types['Heavy exercise'],
presence = interaction_intervals[1],
distance = 0.854,
).build_model(SAMPLE_SIZE),
),
)
@ -560,7 +559,7 @@ def expo_sr_model(c_model,sr_models) -> mc.ExposureModel:
activity=models.Activity.types['Seated'],
host_immunity=0.,
),
).build_model(SAMPLE_SIZE)
)
@pytest.fixture
@ -590,13 +589,13 @@ def expo_sr_model_distr(c_model_distr) -> mc.ExposureModel:
activity = activity_distributions['Seated'],
presence = interaction_intervals[0],
distance = short_range_distances,
).build_model(SAMPLE_SIZE),
),
mc.ShortRangeModel(
expiration = short_range_expiration_distributions['Speaking'],
activity = activity_distributions['Seated'],
presence = interaction_intervals[1],
distance = short_range_distances,
).build_model(SAMPLE_SIZE),
),
),
exposed=mc.Population(
number=1,
@ -605,11 +604,11 @@ def expo_sr_model_distr(c_model_distr) -> mc.ExposureModel:
activity=models.Activity.types['Seated'],
host_immunity=0.,
),
).build_model(SAMPLE_SIZE)
)
@pytest.fixture
def simple_expo_sr_model_distr(c_model_distr) -> SimpleExposureModel:
def simple_expo_sr_model_distr() -> SimpleExposureModel:
return SimpleExposureModel(
infected_presence = presence,
viral_load = virus_distributions['SARS_CoV_2_DELTA'
@ -650,18 +649,19 @@ def simple_expo_sr_model_distr(c_model_distr) -> SimpleExposureModel:
)
def test_longrange_concentration(time,c_model,simple_c_model):
npt.assert_allclose(
c_model.concentration(time).mean(),
c_model.build_model(SAMPLE_SIZE).concentration(time).mean(),
simple_c_model.concentration(time), rtol=TOLERANCE
)
@retry(tries=10)
@pytest.mark.parametrize(
"time", [10, 10.7, 11., 12.5, 14.75, 14.9, 17]
)
def test_shortrange_concentration(time,c_model,simple_c_model,
sr_models,simple_sr_models):
result_sr_model = np.sum([np.array(
sr_mod.short_range_concentration(c_model,time)).mean()
sr_mod.build_model(SAMPLE_SIZE).short_range_concentration(c_model.build_model(SAMPLE_SIZE),time)).mean()
for sr_mod in sr_models])
result_simple_sr_model = np.sum([np.array(
sr_mod.concentration(simple_c_model,time)).mean()
@ -686,7 +686,7 @@ def test_longrange_exposure(c_model):
sr_models = (),
)
expo_model = mc.ExposureModel(
concentration_model=c_model,
concentration_model=c_model.build_model(SAMPLE_SIZE),
short_range=(),
exposed=mc.Population(
number=1,
@ -721,7 +721,7 @@ def test_longrange_concentration_with_distributions(c_model_distr,time):
BLO_factors = expiration_BLO_factors['Breathing'],
)
npt.assert_allclose(
c_model_distr.concentration(time).mean(),
c_model_distr.build_model(SAMPLE_SIZE).concentration(time).mean(),
simple_expo_model.concentration(time).mean(), rtol=TOLERANCE
)
@ -746,7 +746,7 @@ def test_longrange_exposure_with_distributions(c_model_distr):
sr_models = (),
)
expo_model = mc.ExposureModel(
concentration_model=c_model_distr,
concentration_model=c_model_distr.build_model(SAMPLE_SIZE),
short_range=(),
exposed=mc.Population(
number=1,
@ -775,18 +775,18 @@ def test_longrange_exposure_with_distributions(c_model_distr):
)
def test_concentration_with_shortrange(expo_sr_model,simple_expo_sr_model,time):
npt.assert_allclose(
expo_sr_model.concentration(time).mean(),
expo_sr_model.build_model(SAMPLE_SIZE).concentration(time).mean(),
simple_expo_sr_model.total_concentration(time).mean(), rtol=TOLERANCE
)
def test_exposure_with_shortrange(expo_sr_model,simple_expo_sr_model):
npt.assert_allclose(
expo_sr_model.deposited_exposure().mean(),
expo_sr_model.build_model(SAMPLE_SIZE).deposited_exposure().mean(),
simple_expo_sr_model.dose().mean(), rtol=TOLERANCE
)
npt.assert_allclose(
expo_sr_model.infection_probability().mean(),
expo_sr_model.build_model(SAMPLE_SIZE).infection_probability().mean(),
simple_expo_sr_model.probability_infection().mean(), rtol=TOLERANCE
)
@ -798,7 +798,7 @@ def test_exposure_with_shortrange(expo_sr_model,simple_expo_sr_model):
def test_concentration_with_shortrange_and_distributions(
expo_sr_model_distr,simple_expo_sr_model_distr,time):
npt.assert_allclose(
expo_sr_model_distr.concentration(time).mean(),
expo_sr_model_distr.build_model(SAMPLE_SIZE).concentration(time).mean(),
simple_expo_sr_model_distr.total_concentration(time).mean(),
rtol=TOLERANCE
)
@ -807,11 +807,11 @@ def test_concentration_with_shortrange_and_distributions(
def test_exposure_with_shortrange_and_distributions(expo_sr_model_distr,
simple_expo_sr_model_distr):
npt.assert_allclose(
expo_sr_model_distr.deposited_exposure().mean(),
expo_sr_model_distr.build_model(SAMPLE_SIZE).deposited_exposure().mean(),
simple_expo_sr_model_distr.dose().mean(), rtol=0.05
)
npt.assert_allclose(
expo_sr_model_distr.infection_probability().mean(),
expo_sr_model_distr.build_model(SAMPLE_SIZE).infection_probability().mean(),
simple_expo_sr_model_distr.probability_infection().mean(),
rtol=0.03
)

3
cara/tests/test_monte_carlo_full_models.py
View file

@ -8,8 +8,6 @@ from cara import models,data
from cara.monte_carlo.data import activity_distributions, virus_distributions, expiration_distributions, infectious_dose_distribution, viable_to_RNA_ratio_distribution
from cara.apps.calculator.model_generator import build_expiration
# TODO: seed better the random number generators
np.random.seed(2000)
SAMPLE_SIZE = 500_000
TOLERANCE = 0.05
@ -336,6 +334,7 @@ def test_report_models(mc_model, expected_pi, expected_new_cases,
expected_ER, rtol=TOLERANCE)
@retry(tries=10)
@pytest.mark.parametrize(
"mask_type, month, expected_pi, expected_dose, expected_ER",
[

2
cara/tests/test_predefined_distributions.py
View file

@ -4,8 +4,6 @@ import pytest
from cara.monte_carlo.data import activity_distributions, virus_distributions
# TODO: seed better the random number generators
np.random.seed(2000)
# mean & std deviations from https://doi.org/10.1101/2021.10.14.21264988 (Table 3)

3
cara/tests/test_sampleable_distribution.py
View file

@ -4,9 +4,6 @@ import pytest
from cara.monte_carlo import sampleable
# TODO: seed better the random number generators
np.random.seed(2000)
@pytest.mark.parametrize(
"mean, std",[