[contrasting] Final modifications for the plot for the v4 of the article

projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py

@@ -10,7 +10,7 @@ from extreme_data.meteo_france_data.scm_models_data.safran.safran_max_snowf impo
     SafranSnowfall2020
 from projects.altitude_spatial_model.altitudes_fit.plots.plot_histogram_altitude_studies import \
     plot_shoe_plot_changes_against_altitude, plot_histogram_all_trends_against_altitudes, \
-    plot_shoe_plot_ratio_interval_size_against_altitude
+    plot_shoe_plot_ratio_interval_size_against_altitude, plot_histogram_all_models_against_altitudes
 
 from extreme_fit.model.result_from_model_fit.result_from_extremes.abstract_extract_eurocode_return_level import \
     AbstractExtractEurocodeReturnLevel
@@ -44,10 +44,10 @@ def main():
     model_must_pass_the_test = False
     AbstractExtractEurocodeReturnLevel.ALPHA_CONFIDENCE_INTERVAL_UNCERTAINTY = 0.2
 
-    fast = True
+    fast = False
     if fast is None:
-        massif_names = ['Vanoise']
-        altitudes_list = altitudes_for_groups[:]
+        massif_names = None
+        altitudes_list = altitudes_for_groups[2:3]
     elif fast:
         AbstractExtractEurocodeReturnLevel.NB_BOOTSTRAP = 10
         massif_names = ['Vanoise', 'Haute-Maurienne', 'Vercors'][:1]
@@ -80,6 +80,7 @@ def main_loop(altitudes_list, massif_names, seasons, study_classes, model_must_p
 
 
 def plot_visualizers(massif_names, visualizer_list):
+    plot_histogram_all_models_against_altitudes(massif_names, visualizer_list)
     # plot_histogram_all_trends_against_altitudes(massif_names, visualizer_list)
     # plot_shoe_plot_ratio_interval_size_against_altitude(massif_names, visualizer_list)
     # for relative in [True, False]:
@@ -95,7 +96,7 @@ def plot_visualizer(massif_names, visualizer):
     # visualizer.studies.plot_maxima_time_series(['Vanoise'])
 
     # Plot the results for the model that minimizes the individual aic
-    plot_individual_aic(visualizer)
+    # plot_individual_aic(visualizer)
 
     # Plot the results for the model that minimizes the total aic
     # plot_total_aic(model_classes, visualizer)

projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py

@@ -517,13 +517,14 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
                 coordinate_for_filter = (altitude, None)
                 unconstrained_empirical_quantiles = one_fold_fit.best_estimator.sorted_empirical_standard_gumbel_quantiles(coordinate_for_filter=coordinate_for_filter)
                 n = len(unconstrained_empirical_quantiles)
-                assert n == 61
-                standard_gumbel_quantiles = one_fold_fit.standard_gumbel_quantiles(n=n)
-                ax.plot(standard_gumbel_quantiles, unconstrained_empirical_quantiles, linestyle='None',
-                        label='{} m'.format(altitude), marker='o')
-
-                all_quantiles.extend(standard_gumbel_quantiles)
-                all_quantiles.extend(unconstrained_empirical_quantiles)
+                if n > 0:
+                    assert n == 61
+                    standard_gumbel_quantiles = one_fold_fit.standard_gumbel_quantiles(n=n)
+                    ax.plot(standard_gumbel_quantiles, unconstrained_empirical_quantiles, linestyle='None',
+                            label='{} m'.format(altitude), marker='o')
+
+                    all_quantiles.extend(standard_gumbel_quantiles)
+                    all_quantiles.extend(unconstrained_empirical_quantiles)
 
             size_label = 20
             ax.set_xlabel("Theoretical quantile", fontsize=size_label)

projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py

@@ -14,7 +14,7 @@ from projects.exceeding_snow_loads.utils import dpi_paper1_figure
 
 
 def plots(visualizer: AltitudesStudiesVisualizerForNonStationaryModels):
-    # visualizer.plot_shape_map()
+    visualizer.plot_shape_map()
     # visualizer.plot_moments()
     visualizer.plot_qqplots()
     # for std in [True, False]:

projects/altitude_spatial_model/altitudes_fit/plots/plot_histogram_altitude_studies.py

@@ -40,44 +40,44 @@ def plot_histogram_all_models_against_altitudes(massif_names, visualizer_list: L
         print(model_name_to_mean_percentage[model_name], model_name_to_mean_percentage_significant[model_name],
               model_name)
 
-    # Plot part
-    ax = plt.gca()
-    width = 5
-    size = 8
-    legend_fontsize = 10
-    labelsize = 10
-    linewidth = 1
-    tick_list = np.array([((len(visualizer_list) + 2) * i + (1 + len(visualizer_list) / 2)) * width
-                          for i in range(len(sorted_model_names))])
-    for tick_middle, model_name in zip(tick_list, sorted_model_names):
-        x_shifted = [tick_middle + width * shift / 2 for shift in range(-3, 5, 2)]
-        percentages = model_name_to_percentages[model_name]
-        percentages_significant = model_name_to_percentages_significant[model_name]
-        colors = ['white', 'yellow', 'orange', 'red']
-        labels = ['{} m - {} m (\% out of {} massifs)'.format(1000 * i, 1000 * (i + 1),
-                                                              len(v.get_valid_names(massif_names))) for i, v in
-                  enumerate(visualizer_list)]
-        for x, color, percentage, label, percentage_significant in zip(x_shifted, colors, percentages, labels,
-                                                                       percentages_significant):
-            ax.bar([x], [percentage], width=width, label=label,
-                   linewidth=2 * linewidth, edgecolor='black', color=color)
-            heights = list(range(0, math.ceil(percentage_significant), 1))[::-1]
-            for height in heights:
-                ax.bar([x], [height], width=width, linewidth=linewidth, edgecolor='black', color=color)
-
-    handles, labels = ax.get_legend_handles_labels()
-    ax.legend(handles[:len(visualizer_list)], labels[:len(visualizer_list)], prop={'size': size})
-    ax.set_xticklabels(sorted_model_names)
-    ax.set_xticks(tick_list)
-    ax.set_ylabel('Percentage of massifs (\%) ', fontsize=legend_fontsize)
-    ax.set_xlabel('Models', fontsize=legend_fontsize)
-    ax.set_ylim(bottom=0)
-    ax.yaxis.grid()
-    ax.tick_params(axis='both', which='major', labelsize=labelsize)
-
-    visualizer.plot_name = 'All models'
-    visualizer.show_or_save_to_file(add_classic_title=False, no_title=True)
-    plt.close()
+    # # Plot part
+    # ax = plt.gca()
+    # width = 5
+    # size = 8
+    # legend_fontsize = 10
+    # labelsize = 10
+    # linewidth = 1
+    # tick_list = np.array([((len(visualizer_list) + 2) * i + (1 + len(visualizer_list) / 2)) * width
+    #                       for i in range(len(sorted_model_names))])
+    # for tick_middle, model_name in zip(tick_list, sorted_model_names):
+    #     x_shifted = [tick_middle + width * shift / 2 for shift in range(-3, 5, 2)]
+    #     percentages = model_name_to_percentages[model_name]
+    #     percentages_significant = model_name_to_percentages_significant[model_name]
+    #     colors = ['white', 'yellow', 'orange', 'red']
+    #     labels = ['{} m - {} m (\% out of {} massifs)'.format(1000 * i, 1000 * (i + 1),
+    #                                                           len(v.get_valid_names(massif_names))) for i, v in
+    #               enumerate(visualizer_list)]
+    #     for x, color, percentage, label, percentage_significant in zip(x_shifted, colors, percentages, labels,
+    #                                                                    percentages_significant):
+    #         ax.bar([x], [percentage], width=width, label=label,
+    #                linewidth=2 * linewidth, edgecolor='black', color=color)
+    #         heights = list(range(0, math.ceil(percentage_significant), 1))[::-1]
+    #         for height in heights:
+    #             ax.bar([x], [height], width=width, linewidth=linewidth, edgecolor='black', color=color)
+    #
+    # handles, labels = ax.get_legend_handles_labels()
+    # ax.legend(handles[:len(visualizer_list)], labels[:len(visualizer_list)], prop={'size': size})
+    # ax.set_xticklabels(sorted_model_names)
+    # ax.set_xticks(tick_list)
+    # ax.set_ylabel('Percentage of massifs (\%) ', fontsize=legend_fontsize)
+    # ax.set_xlabel('Models', fontsize=legend_fontsize)
+    # ax.set_ylim(bottom=0)
+    # ax.yaxis.grid()
+    # ax.tick_params(axis='both', which='major', labelsize=labelsize)
+    #
+    # visualizer.plot_name = 'All models'
+    # visualizer.show_or_save_to_file(add_classic_title=False, no_title=True)
+    # plt.close()
 
 
 def plot_histogram_all_trends_against_altitudes(massif_names, visualizer_list: List[