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use dedicated functions for sampling

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markus 2021-02-02 14:51:12 +01:00
parent a07b29dbc3
commit ccc07cbb26
1 changed files with 15 additions and 10 deletions
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25
cara/montecarlo.py
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@ -94,12 +94,21 @@ class MCInfectedPopulation(models.Population):
viral_load: typing.Optional[float] = None
@functools.lru_cache()
def _generate_viral_loads(self) -> np.ndarray:
kde_model = KernelDensity(kernel='gaussian', bandwidth=0.1)
kde_model.fit(np.asarray(log_viral_load_frequencies)[0, :][:, np.newaxis],
sample_weight=np.asarray(log_viral_load_frequencies)[1, :])
if self.viral_load is None:
kde_model = KernelDensity(kernel='gaussian', bandwidth=0.1)
kde_model.fit(np.asarray(log_viral_load_frequencies)[0, :][:, np.newaxis],
sample_weight=np.asarray(log_viral_load_frequencies)[1, :])
return kde_model.sample(n_samples=self.samples)[:, 0]
return kde_model.sample(n_samples=self.samples)[:, 0]
else:
return np.full(self.samples, self.viral_load)
@functools.lru_cache()
def _generate_breathing_rates(self) -> np.ndarray:
br_params = lognormal_parameters[self.breathing_category - 1] + (self.samples,)
return lognormal(*br_params)
def emission_rate_when_present(self) -> np.ndarray:
"""
@ -110,18 +119,14 @@ class MCInfectedPopulation(models.Population):
# Extracting only the needed information from the pre-existing Mask class
masked = self.mask.exhale_efficiency != 0
if self.viral_load is None:
viral_loads = self._generate_viral_loads()
else:
viral_loads = np.full(self.samples, self.viral_load)
viral_loads = self._generate_viral_loads()
emission_concentration = emission_concentrations[self.expiratory_activity - 1]
mask_efficiency = [0.75, 0.81, 0.81][self.expiratory_activity - 1] if masked else 0
qr_func = np.vectorize(self._calculate_qr)
br_params = lognormal_parameters[self.breathing_category - 1] + (self.samples,)
breathing_rates = lognormal(*br_params)
breathing_rates = self._generate_breathing_rates()
return qr_func(viral_loads, emission_concentration, mask_efficiency, self.qid, breathing_rates)