changed variable name in report_generator and added a retry decorator in test

This commit is contained in:
Luis Aleixo 2022-10-04 14:17:32 +02:00
parent a61b88ce1b
commit 9032a8815d
3 changed files with 7 additions and 6 deletions

View file

@ -274,9 +274,9 @@ def manufacture_alternative_scenarios(form: FormData) -> typing.Dict[str, mc.Exp
return scenarios
def scenario_statistics(mc_model: mc.ExposureModel, sample_times: typing.List[float], probabilistic_exposure: bool):
def scenario_statistics(mc_model: mc.ExposureModel, sample_times: typing.List[float], compute_prob_exposure: bool):
model = mc_model.build_model(size=_DEFAULT_MC_SAMPLE_SIZE)
if (probabilistic_exposure):
if (compute_prob_exposure):
# It means we have data to calculate the total_probability_rule
prob_probabilistic_exposure = np.array(model.total_probability_rule()).mean()
else:
@ -312,16 +312,16 @@ def comparison_report(
statistics = {}
if (form.short_range_option == "short_range_yes" and form.exposure_option == "p_probabilistic_exposure"):
probabilistic_exposure = True
compute_prob_exposure = True
else:
probabilistic_exposure = False
compute_prob_exposure = False
with executor_factory() as executor:
results = executor.map(
scenario_statistics,
scenarios.values(),
[sample_times] * len(scenarios),
[probabilistic_exposure] * len(scenarios),
[compute_prob_exposure] * len(scenarios),
timeout=60,
)

View file

@ -1475,7 +1475,7 @@ class ExposureModel:
# The influence of a higher number of simultainious infected people (> 4 - 5) yields an almost negligible contirbution to the total probability.
# To be on the safe side, a hard coded limit with a safety margin of 2x was set.
# Therefore we decided a hard limit of 10 infected people.
for num_infected in range(1, max_num_infected):
for num_infected in range(1, max_num_infected + 1):
exposure_model = nested_replace(
self, {'concentration_model.infected.number': num_infected}
)

View file

@ -785,6 +785,7 @@ def test_concentration_with_shortrange(expo_sr_model,simple_expo_sr_model,time):
)
@retry(tries=10)
def test_exposure_with_shortrange(expo_sr_model,simple_expo_sr_model):
npt.assert_allclose(
expo_sr_model.build_model(SAMPLE_SIZE).deposited_exposure().mean(),