From e3472edd7e46a90e33d19119b6524f902e2cf444 Mon Sep 17 00:00:00 2001 From: lrdossan Date: Thu, 13 Jun 2024 09:55:54 +0200 Subject: [PATCH] fixed variable name error --- caimira/apps/calculator/report_generator.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/caimira/apps/calculator/report_generator.py b/caimira/apps/calculator/report_generator.py index 75a5c85c..d527364d 100644 --- a/caimira/apps/calculator/report_generator.py +++ b/caimira/apps/calculator/report_generator.py @@ -176,10 +176,10 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel, exec if (form.conditional_probability_viral_loads and model.data_registry.virological_data['virus_distributions'][form.virus_type]['viral_load_in_sputum'] == ViralLoads.COVID_OVERALL.value): # type: ignore # Generate all the required data for the conditional probability plot - conditional_prob_data = manufacture_conditional_probability_data(model, prob) + conditional_probability_data = manufacture_conditional_probability_data(model, prob) # Generate the matplotlib image based on the received data - uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(prob, conditional_prob_data))) - + uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(prob, conditional_probability_data))) + return { "model_repr": repr(model), "times": list(times), @@ -267,13 +267,13 @@ def manufacture_conditional_probability_data( def uncertainties_plot(infection_probability: models._VectorisedFloat, - conditional_prob_data: dict): + conditional_probability_data: dict): - viral_loads: models._VectorisedFloat = conditional_prob_data['viral_loads'] - pi_means: models._VectorisedFloat = conditional_prob_data['pi_means'] - lower_percentiles: models._VectorisedFloat = conditional_prob_data['lower_percentiles'] - upper_percentiles: models._VectorisedFloat = conditional_prob_data['upper_percentiles'] - log10_vl_in_sputum: models._VectorisedFloat = conditional_prob_data['log10_vl_in_sputum'] + 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'] fig, axes = plt.subplots(2, 3, gridspec_kw={'width_ratios': [5, 0.5] + [1],