[contrasting] use NON_STATIONARY_TREND_TEST_PAPER_2 in study visualizer for mean values

extreme_data/meteo_france_data/scm_models_data/abstract_study.py

@@ -492,6 +492,7 @@ class AbstractStudy(object):
                         massif_name_to_marker_style=None,
                         marker_style_to_label_name=None,
                         ticks_values_and_labels=None,
+                        massif_name_to_text=None,
                         fontsize_label=15,
                         ):
         if ax is None:
@@ -563,8 +564,11 @@ class AbstractStudy(object):
             for _, row in masssif_coordinate_for_display.df_all_coordinates.iterrows():
                 x, y = list(row)
                 massif_name = row.name
-                value = massif_name_to_value[massif_name]
-                str_value = str(value)
+                if massif_name_to_text is None:
+                    value = massif_name_to_value[massif_name]
+                    str_value = str(value)
+                else:
+                    str_value = massif_name_to_text[massif_name]
                 ax.text(x, y, str_value, horizontalalignment='center', verticalalignment='center', fontsize=fontsize)
 
         if scaled:

extreme_trend/trend_test_three_parameters/gev_trend_test_three_parameters.py

@@ -21,8 +21,8 @@ class GevLocationAndScaleAndShapeTrendTest(GevTrendTestThreeParameters):
     def __init__(self, years, maxima, starting_year, quantile_level=EUROCODE_QUANTILE,
                  fit_method=MarginFitMethod.extremes_fevd_mle):
         super().__init__(years, maxima, starting_year,
-                         unconstrained_model_class=StationaryTemporalModel,
-                         constrained_model_class=NonStationaryLocationAndScaleAndShapeTemporalModel,
+                         unconstrained_model_class=NonStationaryLocationAndScaleAndShapeTemporalModel,
+                         constrained_model_class=StationaryTemporalModel,
                          quantile_level=quantile_level,
                          fit_method=fit_method)
 

projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/main_snowfall_article.py

@@ -72,7 +72,7 @@ def intermediate_result(altitudes, massif_names=None,
             _ = compute_minimized_aic(visualizer)
 
     # Plots
-    # validation_plot(altitude_to_visualizer, order_derivative=0)
+    validation_plot(altitude_to_visualizer, order_derivative=0)
     # validation_plot(altitude_to_visualizer, order_derivative=1)
     plot_snowfall_mean(altitude_to_visualizer)
     # plot_snowfall_time_derivative_mean(altitude_to_visualizer)
@@ -86,7 +86,9 @@ def major_result():
     model_subsets_for_uncertainty = None
     altitudes = paper_altitudes
     altitudes = [300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600, 3900]
-    altitudes = [900, 1200]
+    # altitudes = [900, 1200]
+    draft_altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000]
+    altitudes = draft_altitudes
 
     for study_class in study_classes:
         intermediate_result(altitudes, massif_names, model_subsets_for_uncertainty,

projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/study_visualizer_for_mean_values.py

+from typing import Dict
+
 import matplotlib.pyplot as plt
 import numpy as np
 from cached_property import cached_property
@@ -5,7 +7,9 @@ from cached_property import cached_property
 from extreme_data.eurocode_data.utils import YEAR_OF_INTEREST_FOR_RETURN_LEVEL
 from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
 from extreme_fit.model.margin_model.utils import MarginFitMethod
+from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
 from extreme_trend.visualizers.study_visualizer_for_non_stationary_trends import StudyVisualizerForNonStationaryTrends
+from projects.exceeding_snow_loads.utils import NON_STATIONARY_TREND_TEST_PAPER_2
 
 
 class StudyVisualizerForMeanValues(StudyVisualizerForNonStationaryTrends):
@@ -19,6 +23,10 @@ class StudyVisualizerForMeanValues(StudyVisualizerForNonStationaryTrends):
                  non_stationary_trend_test_to_marker=None, fit_method=MarginFitMethod.extremes_fevd_mle,
                  select_only_acceptable_shape_parameter=True, fit_gev_only_on_non_null_maxima=False,
                  fit_only_time_series_with_ninety_percent_of_non_null_values=True):
+
+        # Change the type of models used in the study
+        non_stationary_trend_test_to_marker = {c: '' for c in NON_STATIONARY_TREND_TEST_PAPER_2}
+
         super().__init__(study, show, save_to_file, only_one_graph, only_first_row, vertical_kde_plot,
                          year_for_kde_plot, plot_block_maxima_quantiles, temporal_non_stationarity,
                          transformation_class, verbose, multiprocessing, complete_non_stationary_trend_analysis,
@@ -26,11 +34,19 @@ class StudyVisualizerForMeanValues(StudyVisualizerForNonStationaryTrends):
                          uncertainty_massif_names, effective_temporal_covariate, relative_change_trend_plot,
                          non_stationary_trend_test_to_marker, fit_method, select_only_acceptable_shape_parameter,
                          fit_gev_only_on_non_null_maxima, fit_only_time_series_with_ninety_percent_of_non_null_values)
-
+        assert len(self.non_stationary_trend_test) == len(NON_STATIONARY_TREND_TEST_PAPER_2)
 
     def plot_abstract_fast(self, massif_name_to_value, label, graduation=10.0, cmap=plt.cm.coolwarm, add_x_label=True,
                            negative_and_positive_values=True):
-        super().plot_abstract(massif_name_to_value, label, label, self.fit_method, graduation, cmap, add_x_label, negative_and_positive_values)
+        super().plot_abstract(massif_name_to_value, label, label, self.fit_method, graduation, cmap, add_x_label,
+                              negative_and_positive_values)
+
+    # Override the main dict massif_name_to_trend_test_that_minimized_aic
+
+    @property
+    def massif_name_to_trend_test_that_minimized_aic(self) -> Dict[str, AbstractGevTrendTest]:
+        return {m: t for m, t in super().massif_name_to_trend_test_that_minimized_aic.items()
+                if t.goodness_of_fit_anderson_test}
 
     # Study of the mean
 
@@ -55,7 +71,7 @@ class StudyVisualizerForMeanValues(StudyVisualizerForNonStationaryTrends):
         for massif_name in self.massif_name_to_trend_test_that_minimized_aic.keys():
             e = self.massif_name_to_empirical_mean[massif_name]
             m = self.massif_name_to_model_mean[massif_name]
-            relative_diference = 100 * (m-e) / e
+            relative_diference = 100 * (m - e) / e
             massif_name_to_relative_difference[massif_name] = relative_diference
         return massif_name_to_relative_difference
 
@@ -66,7 +82,7 @@ class StudyVisualizerForMeanValues(StudyVisualizerForNonStationaryTrends):
         massif_name_to_empirical_value = {}
         for massif_name, maxima in self.study.massif_name_to_annual_maxima.items():
             index = self.study.ordered_years.index(1989)
-            maxima_before, maxima_after = maxima[:index+1], maxima[index+1:]
+            maxima_before, maxima_after = maxima[:index + 1], maxima[index + 1:]
             massif_name_to_empirical_value[massif_name] = np.mean(maxima_after) / np.mean(maxima_before)
         return massif_name_to_empirical_value
 
@@ -85,6 +101,6 @@ class StudyVisualizerForMeanValues(StudyVisualizerForNonStationaryTrends):
         for massif_name in self.massif_name_to_trend_test_that_minimized_aic.keys():
             e = self.massif_name_to_change_ratio_in_empirical_mean[massif_name]
             m = self.massif_name_to_change_ratio_in_model_mean[massif_name]
-            relative_diference = 100 * (m-e) / e
+            relative_diference = 100 * (m - e) / e
             massif_name_to_relative_difference[massif_name] = relative_diference
-        return massif_name_to_relative_difference
\ No newline at end of file
+        return massif_name_to_relative_difference