[exceeding] final little modifications of code for the first review - v1 of the NHESS paper

extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py

@@ -172,8 +172,6 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
 
         return massif_name_to_trend_test_that_minimized_aic
 
-
-
     def get_trend_trend_test(self, massif_name, trend_test_classes):
         x, y = self.massif_name_to_years_and_maxima_for_model_fitting[massif_name]
         quantile_level = self.massif_name_to_eurocode_quantile_level_in_practice[massif_name]
@@ -226,7 +224,7 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
         ax.get_xaxis().set_visible(True)
         ax.set_xticks([])
         ax.set_xlabel('Altitude = {}m'.format(self.study.altitude), fontsize=15)
-        middle_word = 'o' if (not add_colorbar and self.study.altitude == 2700) else ''
+        middle_word = 'o' if (not add_colorbar and self.study.altitude in [1800, 2700]) else ''
         self.plot_name = 'tdlr_trends_w' + middle_word + '_colorbar'
         self.show_or_save_to_file(add_classic_title=False, tight_layout=True, no_title=True,
                                   dpi=500)

projects/exceeding_snow_loads/section_data/main_example_swe_total_plot.py

@@ -19,7 +19,7 @@ def tuples_for_examples_paper1(examples_for_the_paper=True):
     else:
         marker_altitude_massif_name_for_paper1 = [
             ('magenta', 600, 'Parpaillon'),
-            ('darkmagenta', 300, 'Devoluy'),
+            # ('darkmagenta', 300, 'Devoluy'),
             ('mediumpurple', 300, 'Aravis'),
         ]
     return marker_altitude_massif_name_for_paper1
@@ -34,7 +34,7 @@ def max_graph_annual_maxima_poster():
     save_to_file = True
     study_class = CrocusSnowLoadTotal
 
-    examples_for_the_paper = True
+    examples_for_the_paper = False
 
     ax = plt.gca()
     if examples_for_the_paper:

projects/exceeding_snow_loads/section_results/main_result_trends_and_return_levels.py

@@ -38,11 +38,11 @@ def compute_minimized_aic(visualizer):
     _ = visualizer.massif_name_to_trend_test_that_minimized_aic
     return True
 
-
 def intermediate_result(altitudes, massif_names=None,
                         model_subsets_for_uncertainty=None, uncertainty_methods=None,
                         study_class=CrocusSnowLoadTotal,
-                        multiprocessing=False):
+                        multiprocessing=False,
+                        only_histogram=False):
     """
     Plot all the trends for all altitudes
     And enable to plot uncertainty plot for some specific massif_names, uncertainty methods to be fast
@@ -56,29 +56,32 @@ def intermediate_result(altitudes, massif_names=None,
     # Load altitude to visualizer
     altitude_to_visualizer = load_altitude_to_visualizer(altitudes, massif_names, model_subsets_for_uncertainty,
                                                          study_class, uncertainty_methods)
-    # Load variable object efficiently
-    for v in altitude_to_visualizer.values():
-        _ = v.study.year_to_variable_object
-    # Compute minimized value efficiently
-    # visualizers = list()
-    if multiprocessing:
-        with Pool(NB_CORES) as p:
-            _ = p.imap(compute_minimized_aic, altitude_to_visualizer.values())
+    if only_histogram:
+        plot_uncertainty_histogram(altitude_to_visualizer)
     else:
-        for visualizer in altitude_to_visualizer.values():
-            _ = compute_minimized_aic(visualizer)
+        # Load variable object efficiently
+        for v in altitude_to_visualizer.values():
+            _ = v.study.year_to_variable_object
+        # Compute minimized value efficiently
+        # visualizers = list()
+        if multiprocessing:
+            with Pool(NB_CORES) as p:
+                _ = p.imap(compute_minimized_aic, altitude_to_visualizer.values())
+        else:
+            for visualizer in altitude_to_visualizer.values():
+                _ = compute_minimized_aic(visualizer)
 
-    # Plots
-    plot_trend_map(altitude_to_visualizer)
-    plot_trend_curves(altitude_to_visualizer={a: v for a, v in altitude_to_visualizer.items() if a >= 900})
-    plot_uncertainty_massifs(altitude_to_visualizer)
-    plot_uncertainty_histogram(altitude_to_visualizer)
-    plot_selection_curves(altitude_to_visualizer)
-    plot_intensity_against_gumbel_quantile_for_3_examples(altitude_to_visualizer)
+        # Plots
+        plot_trend_map(altitude_to_visualizer)
+        plot_trend_curves(altitude_to_visualizer={a: v for a, v in altitude_to_visualizer.items() if a >= 900})
+        plot_uncertainty_massifs(altitude_to_visualizer)
+        plot_uncertainty_histogram(altitude_to_visualizer)
+        plot_selection_curves(altitude_to_visualizer)
+        plot_intensity_against_gumbel_quantile_for_3_examples(altitude_to_visualizer)
 
-    # Additional plots
-    # uncertainty_interval_size(altitude_to_visualizer)
-    # plot_full_diagnostic(altitude_to_visualizer)
+        # Additional plots
+        # uncertainty_interval_size(altitude_to_visualizer)
+        # plot_full_diagnostic(altitude_to_visualizer)
 
 
 def major_result():
@@ -86,20 +89,24 @@ def major_result():
                            ConfidenceIntervalMethodFromExtremes.ci_mle][1:]
     # massif_names = ['Beaufortain', 'Vercors']
     massif_names = None
-    study_classes = paper_study_classes[:1]
+    study_classes = paper_study_classes[:]
     # study_classes = [CrocusSnowLoad3Days, CrocusSnowLoad5Days, CrocusSnowLoad7Days][::-1]
     altitudes = [300, 600, 900, 1800, 2700][:2]
     altitudes = [300, 600, 900, 1200, 1500, 1800]
     altitudes = paper_altitudes
     # altitudes = [900, 1800, 270{{0][:1]
     for study_class in study_classes:
-        if study_class == CrocusSnowLoadEurocode:
+        print('new stuy class', study_class)
+        if study_class is CrocusSnowLoadEurocode:
             model_subsets_for_uncertainty = [ModelSubsetForUncertainty.stationary_gumbel]
+            only_histogram = True
         else:
             model_subsets_for_uncertainty = None
+            only_histogram = False
         intermediate_result(altitudes, massif_names, model_subsets_for_uncertainty,
                             uncertainty_methods, study_class,
-                            multiprocessing=True)
+                            multiprocessing=True,
+                            only_histogram=only_histogram)
 
 
 if __name__ == '__main__':

projects/exceeding_snow_loads/section_results/plot_trend_curves.py

@@ -22,6 +22,8 @@ def plot_trend_map(altitude_to_visualizer):
             # Plot 2700 also with a colorbar
             if altitude == 2700:
                 visualizer.plot_trends(max_abs_tdrl_above_900, add_colorbar=True)
+            if altitude == 1800:
+                visualizer.plot_trends(max_abs_tdrl_above_900, add_colorbar=False)
         else:
             max_abs_tdrl_below_900 = max(altitude_to_visualizer[300].max_abs_change,
                                          altitude_to_visualizer[600].max_abs_change)

projects/exceeding_snow_loads/section_results/plot_uncertainty_curves.py

@@ -30,11 +30,11 @@ def plot_uncertainty_massifs(altitude_to_visualizer: Dict[int, StudyVisualizerFo
     visualizer = list(altitude_to_visualizer.values())[-1]
     # Subdivide massif names in group of 3
     m = 1
-    uncertainty_massif_names = visualizer.uncertainty_massif_names
-    n = (len(uncertainty_massif_names) // m)
+    massif_names_fitted = visualizer.intersection_of_massif_names_fitted
+    n = (len(massif_names_fitted) // m)
     print('total nb of massif', n)
     for i in list(range(n))[:]:
-        massif_names = uncertainty_massif_names[m * i: m * (i + 1)]
+        massif_names = massif_names_fitted[m * i: m * (i + 1)]
         print(massif_names)
         plot_subgroup_uncertainty_massifs(altitude_to_visualizer, massif_names)