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make integration sampling more coarse

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markus 2021-02-18 12:30:13 +01:00
parent 8526489e96
commit 90437cf69c
1 changed files with 5 additions and 6 deletions
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11
cara/montecarlo.py
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@ -344,7 +344,7 @@ class MCExposureModel:
exposure = np.zeros(self.concentration_model.infected.samples)
for start, stop in self.exposed.presence.boundaries():
times = np.arange(start, stop, 0.001)
times = np.arange(start, stop, 0.01)
concentrations = np.asarray([self.concentration_model.concentration(t) for t in times])
integrals = np.trapz(concentrations, times, axis=0)
exposure += integrals
@ -878,11 +878,10 @@ exposure_models = [MCExposureModel(
generate_cdf_curves_vs_qr(masked=False)
# rs = [model.expected_new_cases() for model in large_population_baselines]
#
# print(f"R0 - original variant:\t{np.mean(rs[0])}")
# print(f"R0 - english variant:\t{np.mean(rs[1])}")
# print(f"Ratio between R0's:\t\t{np.mean(rs[1]) / np.mean(rs[0])}")
rs = [model.expected_new_cases() for model in large_population_baselines]
print(f"R0 - original variant:\t{np.mean(rs[0])}")
print(f"R0 - english variant:\t{np.mean(rs[1])}")
print(f"Ratio between R0's:\t\t{np.mean(rs[1]) / np.mean(rs[0])}")
#
# compare_infection_probabilities_vs_viral_loads(*exposure_models)
#