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ConcentrationModel: _next_state_change used only in concentration function, now; improving docstrings and error handling

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This commit is contained in:
Nicolas Mounet 2021-05-05 12:45:02 +02:00
parent ac866491df
commit 817ca8f3bf
1 changed files with 21 additions and 9 deletions
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30
cara/models.py
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@ -644,20 +644,19 @@ class ConcentrationModel:
k = (vg * 3600) / h
return k + self.virus.decay_constant + self.ventilation.air_exchange(
self.room, self._next_state_change(time))
self.room, time)
@cached()
def _concentration_limit(self, time: float) -> _VectorisedFloat:
"""
Provides a constant that represents the theoretical asymptotic
value reached by the concentration when time goes to infinity.
It's a piecewise constant function - at any time it takes the
value it would have at the end of the current interval.
value reached by the concentration when time goes to infinity,
if all parameters were to stay time-independent.
"""
V = self.room.volume
IVRR = self.infectious_virus_removal_rate(time)
return (self.infected.emission_rate(self._next_state_change(time))) / (IVRR * V)
return (self.infected.emission_rate(time)) / (IVRR * V)
@cached()
def state_change_times(self):
@ -694,19 +693,32 @@ class ConcentrationModel:
@cached()
def concentration(self, time: float) -> _VectorisedFloat:
# Note that time is not vectorised. You can only pass a single float
# to this method.
"""
Virus quanta concentration, as a function of time.
The formulas used here assume that all parameters (ventilation,
emission rate) are constant between two state changes - only
the value of these parameters at the next state change, are used.
Note that time is not vectorised. You can only pass a single float
to this method.
"""
if time == 0:
return 0.0
IVRR = self.infectious_virus_removal_rate(time)
try:
IVRR = self.infectious_virus_removal_rate(self._next_state_change(time))
concentration_limit = self._concentration_limit(self._next_state_change(time))
except ValueError:
raise ValueError("Concentration cannot be computed at a time"
" larger than last state change (parameters"
" are not defined)")
t_last_state_change = self.last_state_change(time)
concentration_at_last_state_change = self.concentration(t_last_state_change)
delta_time = time - t_last_state_change
fac = np.exp(-IVRR * delta_time)
return self._concentration_limit(time) * (1 - fac) + concentration_at_last_state_change * fac
return concentration_limit * (1 - fac) + concentration_at_last_state_change * fac
@dataclass(frozen=True)