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Introducing cn as parameter for the Expiration, and proper normalization of the exposure

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This commit is contained in:
Nicolas Mounet 2021-09-14 12:16:53 +02:00
parent acdd22cb07
commit 30eb90099b
2 changed files with 19 additions and 18 deletions
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15
cara/models.py
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@ -922,9 +922,18 @@ class ExposureModel:
return normed_exposure * self.repeats
def exposure(self) -> _VectorisedFloat:
"""The number of virus per meter^3."""
return (np.array(self._normed_exposure()).mean() *
self.concentration_model.infected.emission_rate_when_present())
"""
The number of virus per meter^3. With sampled diameters, the
aerosol volume has to be put back in the exposure before taking
the mean, to obtain the proper result for the exposure (which
corresponds to an integration on diameters).
"""
mask = self.concentration_model.infected.mask
aerosols = self.concentration_model.infected.expiration.aerosols(mask)
emission_rate = self.concentration_model.infected.emission_rate_when_present()
return (np.array(self._normed_exposure()*aerosols).mean() *
emission_rate/aerosols)
def infection_probability(self) -> _VectorisedFloat:
exposure = self.exposure()

22
cara/monte_carlo/data.py
View file

@ -52,14 +52,6 @@ class BLOmodel:
for A,cn,mu,sigma in zip(self.BLO_factors, self.cn,
self.mu, self.sigma) )
def normalized_distribution(self, d, dmin, dmax):
"""
Return the probability distribution, normalized by its integral
between dmin and dmax (microns).
"""
norm = self.integrate(dmin, dmax)
return self.distribution(d) / norm
def integrate(self, dmin, dmax):
"""
Returns the integral between dmin and dmax (in microns) of the
@ -143,17 +135,17 @@ def expiration_distribution(BLO_factors: typing.Tuple[float, float, float]):
"""
Returns an Expiration with an aerosol diameter distribution, defined
by the BLO factors.
Note: integration boundaries for normalization are chosen as 0.1 and
30 microns respectively - this is an historical choice based on
previous implementations of the model (it limits the influence of
the O-mode).
The total concentration of aerosols is computed by integrating
the distribution between 0.1 and 30 microns - these boundaries are
an historical choice based on previous implementations of the model
(it limits the influence of the O-mode).
"""
dscan = np.linspace(0.1, 30. ,3000)
return mc.Expiration(CustomKernel(dscan,
BLOmodel(BLO_factors).normalized_distribution(dscan, 0.1, 30.),
kernel_bandwidth=0.1))
BLOmodel(BLO_factors).distribution(dscan),kernel_bandwidth=0.1),
BLOmodel(BLO_factors).integrate(0.1, 30.))
dscan = np.linspace(0.1, 30. ,3000)
expiration_distributions = {
'Breathing': expiration_distribution((1., 0., 0.)),
'Talking': expiration_distribution((1., 1., 1.)),