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added a method to manufacture conditional probability and send it in the report

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Luis Aleixo 2023-08-03 15:01:31 +02:00
parent 96e9aead44
commit 1abfe3cfe5
1 changed files with 17 additions and 6 deletions
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23
caimira/apps/calculator/report_generator.py
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@ -145,6 +145,9 @@ def calculate_report_data(form: FormData, model: models.ExposureModel) -> typing
expected_new_cases = np.array(model.expected_new_cases()).mean() expected_new_cases = np.array(model.expected_new_cases()).mean()
uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(model, prob))) if form.conditional_probability_plot else None uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(model, prob))) if form.conditional_probability_plot else None
exposed_presence_intervals = [list(interval) for interval in model.exposed.presence_interval().boundaries()] exposed_presence_intervals = [list(interval) for interval in model.exposed.presence_interval().boundaries()]
conditional_probability_data = {key: value for key, value in
zip(('viral_loads', 'pi_means', 'lower_percentiles', 'upper_percentiles'),
manufacture_conditional_probability_data(model, prob))}
return { return {
"model_repr": repr(model), "model_repr": repr(model),
@ -166,6 +169,7 @@ def calculate_report_data(form: FormData, model: models.ExposureModel) -> typing
"uncertainties_plot_src": uncertainties_plot_src, "uncertainties_plot_src": uncertainties_plot_src,
"CO2_concentrations": CO2_concentrations, "CO2_concentrations": CO2_concentrations,
"vl_dist": list(np.log10(model.concentration_model.virus.viral_load_in_sputum)), "vl_dist": list(np.log10(model.concentration_model.virus.viral_load_in_sputum)),
"conditional_probability_data": conditional_probability_data,
} }
@ -203,16 +207,23 @@ def conditional_prob_inf_given_vl_dist(infection_probability: models._Vectorised
return pi_means, lower_percentiles, upper_percentiles return pi_means, lower_percentiles, upper_percentiles
def manufacture_conditional_probability_data(exposure_model: models.ExposureModel,
infection_probability: models._VectorisedFloat):
min_vl, max_vl, step = 2, 10, 8/100
viral_loads = np.arange(min_vl, max_vl, step)
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)
return list(viral_loads), list(pi_means), list(lower_percentiles), list(upper_percentiles)
def uncertainties_plot(exposure_model: models.ExposureModel, prob: models._VectorisedFloat): def uncertainties_plot(exposure_model: models.ExposureModel, prob: models._VectorisedFloat):
fig = plt.figure(figsize=(4, 7), dpi=110) fig = plt.figure(figsize=(4, 7), dpi=110)
infection_probability = prob / 100 infection_probability = prob / 100
specific_vl = np.log10(exposure_model.concentration_model.infected.virus.viral_load_in_sputum) viral_loads, pi_means, lower_percentiles, upper_percentiles = manufacture_conditional_probability_data(exposure_model, infection_probability)
min_vl, max_vl, step = 2, 10, 8/100.
viral_loads = np.arange(min_vl, max_vl, step)
pi_means, lower_percentiles, upper_percentiles = conditional_prob_inf_given_vl_dist(infection_probability,
viral_loads, specific_vl, step)
fig, axs = plt.subplots(2, 3, fig, axs = plt.subplots(2, 3,
gridspec_kw={'width_ratios': [5, 0.5] + [1], gridspec_kw={'width_ratios': [5, 0.5] + [1],