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lrdossan 2024-06-13 12:34:21 +02:00
parent e3472edd7e
commit f5e1ea728a
1 changed files with 12 additions and 12 deletions
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24
caimira/apps/calculator/report_generator.py
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@ -241,7 +241,7 @@ def conditional_prob_inf_given_vl_dist(
lower_percentiles.append(np.quantile(specific_prob, 0.05))
upper_percentiles.append(np.quantile(specific_prob, 0.95))
return np.array(pi_means), np.array(lower_percentiles), np.array(upper_percentiles)
return pi_means, lower_percentiles, upper_percentiles
def manufacture_conditional_probability_data(
@ -255,25 +255,25 @@ def manufacture_conditional_probability_data(
specific_vl = np.log10(exposure_model.concentration_model.virus.viral_load_in_sputum)
pi_means, lower_percentiles, upper_percentiles = conditional_prob_inf_given_vl_dist(infection_probability, viral_loads,
specific_vl, step)
log10_vl_in_sputum : models._VectorisedFloat = np.log10(exposure_model.concentration_model.infected.virus.viral_load_in_sputum)
log10_vl_in_sputum = np.log10(exposure_model.concentration_model.infected.virus.viral_load_in_sputum)
return {
'viral_loads': viral_loads,
'pi_means': pi_means,
'lower_percentiles': lower_percentiles,
'upper_percentiles': upper_percentiles,
'log10_vl_in_sputum': log10_vl_in_sputum,
'viral_loads': list(viral_loads),
'pi_means': list(pi_means),
'lower_percentiles': list(lower_percentiles),
'upper_percentiles': list(upper_percentiles),
'log10_vl_in_sputum': list(log10_vl_in_sputum),
}
def uncertainties_plot(infection_probability: models._VectorisedFloat,
conditional_probability_data: dict):
viral_loads: models._VectorisedFloat = conditional_probability_data['viral_loads']
pi_means: models._VectorisedFloat = conditional_probability_data['pi_means']
lower_percentiles: models._VectorisedFloat = conditional_probability_data['lower_percentiles']
upper_percentiles: models._VectorisedFloat = conditional_probability_data['upper_percentiles']
log10_vl_in_sputum: models._VectorisedFloat = conditional_probability_data['log10_vl_in_sputum']
viral_loads: list = conditional_probability_data['viral_loads']
pi_means: list = conditional_probability_data['pi_means']
lower_percentiles: list = conditional_probability_data['lower_percentiles']
upper_percentiles: list = conditional_probability_data['upper_percentiles']
log10_vl_in_sputum: list = conditional_probability_data['log10_vl_in_sputum']
fig, axes = plt.subplots(2, 3,
gridspec_kw={'width_ratios': [5, 0.5] + [1],