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
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
import numpy as np
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import numpy.testing as npt
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import pytest
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from caimira.calculator.models.monte_carlo.data import activity_distributions, virus_distributions
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# Mean & std deviations from https://doi.org/10.1101/2021.10.14.21264988 (Table 3)
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# NOTE: a mistake was corrected for the std deviation of the
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# "Moderate exercise" case (0.37 in the report, but should be 0.34)
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@pytest.mark.parametrize(
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"distribution, mean, std",[
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['Seated', 0.51, 0.053],
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['Standing', 0.57, 0.053],
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['Light activity', 1.24, 0.12],
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['Moderate activity', 1.77, 0.34],
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['Heavy exercise', 3.28, 0.72,],
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]
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)
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def test_activity_distributions(data_registry, distribution, mean, std):
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activity = activity_distributions(data_registry)[distribution].build_model(size=1000000)
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npt.assert_allclose(activity.inhalation_rate.mean(), mean, atol=0.01)
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npt.assert_allclose(activity.inhalation_rate.std(), std, atol=0.01)
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# Mean & std deviations from https://doi.org/10.1101/2021.10.14.21264988 (Table 3)
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# - with a refined precision on the values
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@pytest.mark.parametrize(
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"distribution, mean, std",[
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['SARS_CoV_2', 6.22, 1.80],
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['SARS_CoV_2_ALPHA', 6.22, 1.80],
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['SARS_CoV_2_GAMMA', 6.22, 1.80],
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]
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)
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def test_viral_load_logdistribution(data_registry, distribution, mean, std):
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virus = virus_distributions(data_registry)[distribution].build_model(size=1000000)
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npt.assert_allclose(np.log10(virus.viral_load_in_sputum).mean(), mean, atol=0.01)
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npt.assert_allclose(np.log10(virus.viral_load_in_sputum).std(), std, atol=0.01)
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