20b0467f89
- extract, isolate and package it in a completely independent Python module, versioned and in a way that allows releases on PyPI.org - fixed error in placeholder for secondary school (data registry defaults) - added restriction in pytest version to install - expected number of new cases fix - data registry update (schema v2.1.1) - github update - deprecate ExpertApplication and CO2Application - changes to reflect schema update 2.0.2 - version update - Fixed error with f_inf (short-range) - new folder layout - Conditional probability data update - General fixes - Fitting results in L/S/person - CO2 fitting algorithm refinement
80 lines
3.2 KiB
Python
80 lines
3.2 KiB
Python
import numpy as np
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import pytest
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from retry import retry
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import caimira.calculator.models.monte_carlo as mc
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from caimira.calculator.models import models
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from caimira.calculator.models.dataclass_utils import nested_replace
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from caimira.calculator.report import report_generator
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from caimira.calculator.models.monte_carlo.data import activity_distributions, virus_distributions, expiration_distributions
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@pytest.fixture
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def baseline_exposure_model(data_registry):
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concentration_mc = mc.ConcentrationModel(
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data_registry=data_registry,
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room=models.Room(volume=50, inside_temp=models.PiecewiseConstant((0., 24.), (298,)), humidity=0.5),
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ventilation=models.MultipleVentilation(
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ventilations=(
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models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.25),
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)
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),
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infected=mc.InfectedPopulation(
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data_registry=data_registry,
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number=1,
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presence=mc.SpecificInterval(present_times=((0, 3.5), (4.5, 9))),
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virus=virus_distributions(data_registry)['SARS_CoV_2_DELTA'],
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mask=models.Mask.types['No mask'],
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activity=activity_distributions(data_registry)['Seated'],
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expiration=expiration_distributions(data_registry)['Breathing'],
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host_immunity=0.,
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),
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evaporation_factor=0.3,
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)
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return mc.ExposureModel(
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data_registry=data_registry,
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concentration_model=concentration_mc,
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short_range=(),
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exposed=mc.Population(
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number=3,
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presence=mc.SpecificInterval(present_times=((0, 3.5), (4.5, 9))),
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activity=activity_distributions(data_registry)['Seated'],
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mask=models.Mask.types['No mask'],
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host_immunity=0.,
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),
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geographical_data=models.Cases(),
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)
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@retry(tries=3)
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def test_conditional_prob_inf_given_vl_dist(data_registry, baseline_exposure_model):
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viral_loads = np.array([3., 5., 7., 9.,])
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mc_model: models.ExposureModel = baseline_exposure_model.build_model(2_000_000)
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expected_pi_means = []
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expected_lower_percentiles = []
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expected_upper_percentiles = []
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for vl in viral_loads:
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model_vl: models.ExposureModel = nested_replace(
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mc_model, {
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'concentration_model.infected.virus.viral_load_in_sputum' : 10**vl,
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}
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)
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pi = model_vl.infection_probability()/100
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expected_pi_means.append(np.mean(pi))
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expected_lower_percentiles.append(np.quantile(pi, 0.05))
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expected_upper_percentiles.append(np.quantile(pi, 0.95))
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infection_probability = mc_model.infection_probability() / 100
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specific_vl = np.log10(mc_model.concentration_model.infected.virus.viral_load_in_sputum)
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step = 8/100
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actual_pi_means, actual_lower_percentiles, actual_upper_percentiles = (
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report_generator.conditional_prob_inf_given_vl_dist(infection_probability, viral_loads, specific_vl, step)
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)
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assert np.allclose(actual_pi_means, expected_pi_means, atol=0.002)
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assert np.allclose(actual_lower_percentiles, expected_lower_percentiles, atol=0.002)
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assert np.allclose(actual_upper_percentiles, expected_upper_percentiles, atol=0.002)
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