diff --git a/caimira/apps/calculator/__init__.py b/caimira/apps/calculator/__init__.py index 98f75762..98df93a5 100644 --- a/caimira/apps/calculator/__init__.py +++ b/caimira/apps/calculator/__init__.py @@ -193,7 +193,7 @@ class ConcentrationModel(BaseRequestHandler): ) # Re-generate the report with the conditional probability of infection plot if self.get_cookie('conditional_plot'): - form.conditional_probability_plot = True if self.get_cookie('conditional_plot') == '1' else False + form.conditional_probability_viral_loads = True if self.get_cookie('conditional_plot') == '1' else False self.clear_cookie('conditional_plot') # Clears cookie after changing the form value. report_task = executor.submit( diff --git a/caimira/apps/calculator/defaults.py b/caimira/apps/calculator/defaults.py index e12664eb..30cb928f 100644 --- a/caimira/apps/calculator/defaults.py +++ b/caimira/apps/calculator/defaults.py @@ -15,7 +15,6 @@ DEFAULTS = { 'precise_activity': '{}', 'calculator_version': NO_DEFAULT, 'ceiling_height': 0., - 'conditional_probability_plot': False, 'conditional_probability_viral_loads': False, 'CO2_fitting_result': '{}', 'exposed_coffee_break_option': 'coffee_break_0', diff --git a/caimira/apps/calculator/model_generator.py b/caimira/apps/calculator/model_generator.py index aa35ba1e..d1895848 100644 --- a/caimira/apps/calculator/model_generator.py +++ b/caimira/apps/calculator/model_generator.py @@ -31,7 +31,6 @@ class VirusFormData(FormData): arve_sensors_option: bool precise_activity: dict ceiling_height: float - conditional_probability_plot: bool conditional_probability_viral_loads: bool CO2_fitting_result: dict floor_area: float @@ -497,7 +496,6 @@ def baseline_raw_form_data() -> typing.Dict[str, typing.Union[str, float]]: 'air_changes': '', 'air_supply': '', 'ceiling_height': '', - 'conditional_probability_plot': '0', 'conditional_probability_viral_loads': '0', 'exposed_coffee_break_option': 'coffee_break_4', 'exposed_coffee_duration': '10', diff --git a/caimira/apps/calculator/report_generator.py b/caimira/apps/calculator/report_generator.py index 587c3b64..dc83ccef 100644 --- a/caimira/apps/calculator/report_generator.py +++ b/caimira/apps/calculator/report_generator.py @@ -171,25 +171,14 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel, exec expected_new_cases = np.array(model.expected_new_cases()).mean() exposed_presence_intervals = [list(interval) for interval in model.exposed.presence_interval().boundaries()] - if (model.data_registry.virological_data['virus_distributions'][form.virus_type]['viral_load_in_sputum'] == ViralLoads.COVID_OVERALL.value # type: ignore - and form.conditional_probability_plot): # Only generate this data if covid_overall_vl_data is selected. - - viral_load_in_sputum: models._VectorisedFloat = model.concentration_model.infected.virus.viral_load_in_sputum - viral_loads, pi_means, lower_percentiles, upper_percentiles = manufacture_conditional_probability_data(model, prob) - - uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(prob, viral_load_in_sputum, viral_loads, - pi_means, lower_percentiles, upper_percentiles))) - conditional_probability_data = {key: value for key, value in - zip(('viral_loads', 'pi_means', 'lower_percentiles', 'upper_percentiles'), - (viral_loads, pi_means, lower_percentiles, upper_percentiles))} - vl_dist = list(np.log10(viral_load_in_sputum)) - - else: - uncertainties_plot_src = None - conditional_probability_data = None - vl = model.concentration_model.virus.viral_load_in_sputum - if isinstance(vl, np.ndarray): vl_dist = list(np.log10(model.concentration_model.virus.viral_load_in_sputum)) - else: vl_dist = np.log10(model.concentration_model.virus.viral_load_in_sputum) + conditional_probability_data = None + uncertainties_plot_src = None + 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_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_probability_data))) return { "model_repr": repr(model), @@ -208,10 +197,9 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel, exec "prob_hist_bins": list(prob_dist_bins), "prob_probabilistic_exposure": prob_probabilistic_exposure, "expected_new_cases": expected_new_cases, - "uncertainties_plot_src": uncertainties_plot_src, "CO2_concentrations": CO2_concentrations, - "vl_dist": vl_dist, "conditional_probability_data": conditional_probability_data, + "uncertainties_plot_src": uncertainties_plot_src, } @@ -235,7 +223,6 @@ def generate_permalink(base_url, get_root_url, get_root_calculator_url, form: V def conditional_prob_inf_given_vl_dist( - data_registry: DataRegistry, infection_probability: models._VectorisedFloat, viral_loads: np.ndarray, specific_vl: float, @@ -247,7 +234,9 @@ def conditional_prob_inf_given_vl_dist( upper_percentiles = [] for vl_log in viral_loads: + # Probability of infection corresponding to a certain viral load value in the distribution specific_prob = infection_probability[np.where((vl_log-step/2-specific_vl)*(vl_log+step/2-specific_vl)<0)[0]] #type: ignore + pi_means.append(specific_prob.mean()) lower_percentiles.append(np.quantile(specific_prob, 0.05)) upper_percentiles.append(np.quantile(specific_prob, 0.95)) @@ -259,69 +248,74 @@ def manufacture_conditional_probability_data( exposure_model: models.ExposureModel, infection_probability: models._VectorisedFloat ): - data_registry: DataRegistry = exposure_model.data_registry - min_vl = 2 max_vl = 10 step = (max_vl - min_vl)/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(data_registry, infection_probability, viral_loads, + 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) + log10_vl_in_sputum = np.log10(exposure_model.concentration_model.infected.virus.viral_load_in_sputum) + + return { + '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, - viral_load_in_sputum: models._VectorisedFloat, - viral_loads: models._VectorisedFloat, - pi_means: models._VectorisedFloat, - lower_percentiles: models._VectorisedFloat, - upper_percentiles: models._VectorisedFloat): + conditional_probability_data: dict): - fig, axes = plt.subplots(2, 3, + 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, ((axs00, axs01, axs02), (axs10, axs11, axs12)) = plt.subplots(nrows=2, ncols=3, # type: ignore gridspec_kw={'width_ratios': [5, 0.5] + [1], 'height_ratios': [3, 1], 'wspace': 0}, sharey='row', sharex='col') - # Type hint for axs - axs: np.ndarray = np.array(axes) + axs01.axis('off') + axs11.axis('off') + axs12.axis('off') - for y, x in [(0, 1)] + [(1, i + 1) for i in range(2)]: - axs[y, x].axis('off') + axs01.set_visible(False) - axs[0, 1].set_visible(False) + axs00.plot(viral_loads, np.array(pi_means), label='Predictive total probability') + axs00.fill_between(viral_loads, np.array(lower_percentiles), np.array(upper_percentiles), alpha=0.1, label='5ᵗʰ and 95ᵗʰ percentile') - axs[0, 0].plot(viral_loads, np.array(pi_means)/100, label='Predictive total probability') - axs[0, 0].fill_between(viral_loads, np.array(lower_percentiles)/100, np.array(upper_percentiles)/100, alpha=0.1, label='5ᵗʰ and 95ᵗʰ percentile') + axs02.hist(infection_probability, bins=30, orientation='horizontal') + axs02.set_xticks([]) + axs02.set_xticklabels([]) + axs02.set_facecolor("lightgrey") - axs[0, 2].hist(infection_probability, bins=30, orientation='horizontal') - axs[0, 2].set_xticks([]) - axs[0, 2].set_xticklabels([]) - axs[0, 2].set_facecolor("lightgrey") + highest_bar = axs02.get_xlim()[1] + axs02.set_xlim(0, highest_bar) - highest_bar = axs[0, 2].get_xlim()[1] - axs[0, 2].set_xlim(0, highest_bar) - - axs[0, 2].text(highest_bar * 0.5, 0.5, - rf"$\bf{np.round(np.mean(infection_probability), 1)}$%", ha='center', va='center') - axs[1, 0].hist(np.log10(viral_load_in_sputum), + axs02.text(highest_bar * 0.5, 50, + "$P(I)=$\n" + rf"$\bf{np.round(np.mean(infection_probability), 1)}$%", ha='center', va='center') + axs10.hist(log10_vl_in_sputum, bins=150, range=(2, 10), color='grey') - axs[1, 0].set_facecolor("lightgrey") - axs[1, 0].set_yticks([]) - axs[1, 0].set_yticklabels([]) - axs[1, 0].set_xticks([i for i in range(2, 13, 2)]) - axs[1, 0].set_xticklabels(['$10^{' + str(i) + '}$' for i in range(2, 13, 2)]) - axs[1, 0].set_xlim(2, 10) - axs[1, 0].set_xlabel('Viral load\n(RNA copies)', fontsize=12) - axs[0, 0].set_ylabel('Conditional Probability\nof Infection', fontsize=12) + axs10.set_facecolor("lightgrey") + axs10.set_yticks([]) + axs10.set_yticklabels([]) + axs10.set_xticks([i for i in range(2, 13, 2)]) + axs10.set_xticklabels(['$10^{' + str(i) + '}$' for i in range(2, 13, 2)]) + axs10.set_xlim(2, 10) + axs10.set_xlabel('Viral load\n(RNA copies)', fontsize=12) + axs00.set_ylabel('Conditional Probability\nof Infection', fontsize=12) - axs[0, 0].text(9.5, -0.01, '$(i)$') - axs[1, 0].text(9.5, axs[1, 0].get_ylim()[1] * 0.8, '$(ii)$') - axs[0, 2].set_title('$(iii)$', fontsize=10) + axs00.text(9.5, -0.01, '$(i)$') + axs10.text(9.5, axs10.get_ylim()[1] * 0.8, '$(ii)$') + axs02.set_title('$(iii)$', fontsize=10) - axs[0, 0].legend() + axs00.legend() return fig diff --git a/caimira/apps/calculator/static/js/report.js b/caimira/apps/calculator/static/js/report.js index b1c8b3fe..7c804dd9 100644 --- a/caimira/apps/calculator/static/js/report.js +++ b/caimira/apps/calculator/static/js/report.js @@ -1,6 +1,6 @@ -function on_report_load(conditional_probability_plot) { +function on_report_load(conditional_probability_viral_loads) { // Check/uncheck uncertainties image generation - document.getElementById('conditional_probability_plot').checked = conditional_probability_plot + document.getElementById('conditional_probability_viral_loads').checked = conditional_probability_viral_loads } /* Generate the concentration plot using d3 library. */ @@ -1164,14 +1164,14 @@ function display_rename_column(bool, id) { else document.getElementById(id).style.display = 'none'; } -function conditional_probability_plot(value, is_generated) { +function conditional_probability_viral_loads(value, is_generated) { // If the image was previously generated, there is no need to reload the page. if (value && is_generated == 1) { document.getElementById('conditional_probability_div').style.display = 'block' } else if (value && is_generated == 0) { - document.getElementById('label_conditional_probability_plot').innerHTML = `Loading...`; - document.getElementById('conditional_probability_plot').setAttribute('disabled', true); + document.getElementById('label_conditional_probability_viral_loads').innerHTML = `Loading...`; + document.getElementById('conditional_probability_viral_loads').setAttribute('disabled', true); document.cookie = `conditional_plot= 1; path=/`; window.location.reload(); } diff --git a/caimira/apps/templates/base/calculator.form.html.j2 b/caimira/apps/templates/base/calculator.form.html.j2 index 745612e6..aa6271bf 100644 --- a/caimira/apps/templates/base/calculator.form.html.j2 +++ b/caimira/apps/templates/base/calculator.form.html.j2 @@ -495,7 +495,7 @@
- +
diff --git a/caimira/apps/templates/base/calculator.report.html.j2 b/caimira/apps/templates/base/calculator.report.html.j2 index 041cf3ef..0f1c7d4b 100644 --- a/caimira/apps/templates/base/calculator.report.html.j2 +++ b/caimira/apps/templates/base/calculator.report.html.j2 @@ -15,7 +15,7 @@ - +