[paper 1] final modifications for plots for the paper

papers/exceeding_snow_loads/check_mle_convergence_for_trends/uncertainty_interval_size.py

+from typing import Dict
+
+import pandas as pd
+
+from experiment.eurocode_data.utils import EUROCODE_ALTITUDES
+from papers.exceeding_snow_loads.paper_utils import ModelSubsetForUncertainty
+from papers.exceeding_snow_loads.study_visualizer_for_non_stationary_trends import StudyVisualizerForNonStationaryTrends
+
+
+def uncertainty_interval_size(altitude_to_visualizer: Dict[int, StudyVisualizerForNonStationaryTrends]):
+    """ Plot one graph for each non-stationary context
+    :return:
+    """
+    altitude_to_visualizer = {a: v for a, v in altitude_to_visualizer.items() if a in EUROCODE_ALTITUDES}
+    visualizer = list(altitude_to_visualizer.values())[0]
+    for a, v in altitude_to_visualizer.items():
+        print(a)
+        interval_size(v)
+
+
+def interval_size(v: StudyVisualizerForNonStationaryTrends):
+    d = v.all_massif_name_to_eurocode_uncertainty_for_minimized_aic_model_class(
+        model_subset_for_uncertainty=ModelSubsetForUncertainty.stationary_gev)
+    # what we want is the confidence interval for the shape parameter
+    d = {m: [e.confidence_interval[0], e.confidence_interval[1], e.confidence_interval[1] - e.confidence_interval[0]]
+         for m, e in d.items()}
+    df = pd.DataFrame(d).transpose()
+    print((df.head()))
+    print(df.describe())

papers/exceeding_snow_loads/paper_utils.py

@@ -32,3 +32,4 @@ class ModelSubsetForUncertainty(Enum):
     stationary_gumbel_and_gev = 1
     non_stationary_gumbel = 2
     non_stationary_gumbel_and_gev = 3
+    stationary_gev = 4

papers/exceeding_snow_loads/result_trends_and_return_levels/main_result_trends_and_return_levels.py

@@ -2,7 +2,10 @@ from multiprocessing.pool import Pool
 
 import matplotlib as mpl
 
-from experiment.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoadTotal, CrocusSnowLoad3Days
+from experiment.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoadTotal, CrocusSnowLoad3Days, \
+    CrocusSnowLoad5Days, CrocusSnowLoad7Days
+from papers.exceeding_snow_loads.check_mle_convergence_for_trends.uncertainty_interval_size import \
+    uncertainty_interval_size
 from papers.exceeding_snow_loads.paper_main_utils import load_altitude_to_visualizer
 from papers.exceeding_snow_loads.paper_utils import paper_study_classes, paper_altitudes
 from papers.exceeding_snow_loads.result_trends_and_return_levels.plot_diagnosis_risk import plot_diagnosis_risk
@@ -71,7 +74,7 @@ def intermediate_result(altitudes, massif_names=None,
     # plot_uncertainty_massifs(altitude_to_visualizer)
     plot_uncertainty_histogram(altitude_to_visualizer)
     # plot_selection_curves(altitude_to_visualizer)
-
+    # uncertainty_interval_size(altitude_to_visualizer)
 
 
 def major_result():
@@ -92,7 +95,7 @@ def major_result():
 
 if __name__ == '__main__':
     major_result()
-    # intermediate_result(altitudes=[1800], massif_names=None,
+    # intermediate_result(altitudes=[300], massif_names=None,
     #                     uncertainty_methods=[ConfidenceIntervalMethodFromExtremes.my_bayes,
     #                                          ConfidenceIntervalMethodFromExtremes.ci_mle][1:],
     #                     multiprocessing=True)

papers/exceeding_snow_loads/result_trends_and_return_levels/plot_uncertainty_histogram.py

@@ -64,7 +64,6 @@ def plot_histogram(altitude_to_visualizer, model_subset_for_uncertainty):
     ax_twiny.set_xlim(ax.get_xlim())
     ax_twiny.set_xticks(altitudes)
     nb_massif_names = [len(v.massif_names_fitted) for v in altitude_to_visualizer.values()]
-    print(nb_massif_names)
     ax_twiny.set_xticklabels(nb_massif_names)
     ax_twiny.set_xlabel('Total number of massifs at each altitude (for the percentage)', fontsize=fontsize_label)
 

papers/exceeding_snow_loads/study_visualizer_for_non_stationary_trends.py

@@ -30,7 +30,8 @@ from experiment.trend_analysis.univariate_test.extreme_trend_test.trend_test_two
     GumbelLocationAndScaleTrendTest
 from extreme_fit.model.margin_model.linear_margin_model.abstract_temporal_linear_margin_model import \
     TemporalMarginFitMethod
-from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import GumbelTemporalModel
+from extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import GumbelTemporalModel, \
+    StationaryTemporalModel
 from extreme_fit.model.result_from_model_fit.result_from_extremes.confidence_interval_method import \
     ConfidenceIntervalMethodFromExtremes
 from extreme_fit.model.result_from_model_fit.result_from_extremes.eurocode_return_level_uncertainties import \
@@ -322,6 +323,8 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
     def massif_name_and_model_subset_to_model_class(self, massif_name, model_subset_for_uncertainty):
         if model_subset_for_uncertainty is ModelSubsetForUncertainty.stationary_gumbel:
             return GumbelTemporalModel
+        if model_subset_for_uncertainty is ModelSubsetForUncertainty.stationary_gev:
+            return StationaryTemporalModel
         elif model_subset_for_uncertainty is ModelSubsetForUncertainty.stationary_gumbel_and_gev:
             return self.massif_name_to_stationary_trend_test_that_minimized_aic[massif_name].unconstrained_model_class
         elif model_subset_for_uncertainty is ModelSubsetForUncertainty.non_stationary_gumbel:
@@ -395,7 +398,7 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
         triplet = [(massif_name_to_eurocode_region[massif_name],
                     self.massif_name_to_eurocode_values[massif_name],
                     self.triplet_to_eurocode_uncertainty[(ci_method, model_subset_for_uncertainty, massif_name)])
-                   for massif_name in self.uncertainty_massif_names]
+                   for massif_name in self.massif_names_fitted]
         # First array for histogram
         a = 100 * np.array([(uncertainty.confidence_interval[0] > eurocode,
                              uncertainty.mean_estimate > eurocode,