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Merge branch 'feature/CO2_RR' into 'master'

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Modified time dependence of concentration when removal rate is none

Closes #319

See merge request caimira/caimira!460
This commit is contained in:
Nicolas Mounet 2023-10-04 17:38:44 +02:00
commit 7d5d7f474d
2 changed files with 30 additions and 18 deletions
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38
caimira/models.py
View file

@ -1055,8 +1055,15 @@ class _ConcentrationModelBase:
"""
V = self.room.volume
RR = self.removal_rate(time)
return (self.population.people_present(time) / (RR * V) +
if isinstance(RR, np.ndarray):
invRR = np.empty(RR.shape, dtype=np.float64)
invRR[RR == 0.] = np.nan
invRR[RR != 0.] = 1. / RR[RR != 0.]
else:
invRR = np.nan if RR == 0. else 1. / RR # type: ignore
return (self.population.people_present(time) * invRR / V +
self.min_background_concentration()/self.normalization_factor())
@method_cache
@ -1132,23 +1139,26 @@ class _ConcentrationModelBase:
return self.min_background_concentration()/self.normalization_factor()
next_state_change_time = self._next_state_change(time)
RR = self.removal_rate(next_state_change_time)
# If RR is 0, conc_limit does not play a role but its computation
# would raise an error -> we set it to zero.
try:
conc_limit = self._normed_concentration_limit(next_state_change_time)
except ZeroDivisionError:
conc_limit = 0.
t_last_state_change = self.last_state_change(time)
conc_at_last_state_change = self._normed_concentration_cached(t_last_state_change)
delta_time = time - t_last_state_change
fac = np.exp(-RR * delta_time)
if isinstance(RR, np.ndarray):
curr_conc_state = np.empty(RR.shape, dtype=np.float64)
curr_conc_state[RR == 0.] = delta_time * self.population.people_present(time) / (
self.room.volume[RR == 0.] if isinstance(self.room.volume,np.ndarray) else self.room.volume)
curr_conc_state[RR != 0.] = self._normed_concentration_limit(next_state_change_time)[RR != 0.] * (1 - fac[RR != 0.])
else:
if RR == 0.:
curr_conc_state = delta_time * self.population.people_present(time) / self.room.volume
else:
curr_conc_state = self._normed_concentration_limit(next_state_change_time) * (1 - fac)
return conc_limit * (1 - fac) + conc_at_last_state_change * fac
return curr_conc_state + conc_at_last_state_change * fac
def concentration(self, time: float) -> _VectorisedFloat:
"""
Total concentration as a function of time. The normalization
@ -1260,9 +1270,7 @@ class CO2ConcentrationModel(_ConcentrationModelBase):
return self.CO2_emitters
def removal_rate(self, time: float) -> _VectorisedFloat:
# Setting minimum air exchange rate to 1e-6 to avoid divisions by
# zero when computing the CO2 concentration.
return np.maximum(1e-6,self.ventilation.air_exchange(self.room, time))
return self.ventilation.air_exchange(self.room, time)
def min_background_concentration(self) -> _VectorisedFloat:
"""

10
caimira/tests/models/test_concentration_model.py
View file

@ -252,8 +252,11 @@ def test_normed_integrated_concentration_vectorisation(
"known_min_background_concentration",
"expected_concentration"],
[
[0., 240., 240.],
[0., np.array([240., 240.]), np.array([240., 240.])]
[0., 240., 240. + 0.5/75],
[0.0001, 240.0, 240. + 0.5/75],
[1e-6, 240.0, 240 + 0.5/75],
[0., np.array([240., 240.]), np.array([240. + 0.5/75, 240. + 0.5/75])],
[np.array([0.0001, 1e-6]), np.array([240., 240.]), np.array([240. + 0.5/75, 240. + 0.5/75])],
]
)
def test_zero_ventilation_rate(
@ -272,4 +275,5 @@ def test_zero_ventilation_rate(
known_min_background_concentration = known_min_background_concentration)
normed_concentration = known_conc_model.concentration(1)
npt.assert_almost_equal(normed_concentration, expected_concentration)
assert normed_concentration == pytest.approx(expected_concentration, abs=1e-6)