[paper 2] update relative change in mean maxima

experiment/paper 2 - contrasting/main_spatial_relative_change_in_maxima_at_fixed_altitude.py

@@ -16,50 +16,54 @@ from experiment.paper_past_snow_loads.discussion_data_comparison_with_eurocode.c
 from experiment.paper_past_snow_loads.paper_utils import dpi_paper1_figure
 
 
-def density_wrt_altitude():
-    """
-    We choose these massif because each represents a different eurocode region
-    we also choose them because they belong to a different climatic area
-    :return:
-    """
-    save_to_file = False
-    study_class = [SafranTotalPrecip, SafranRainfall, SafranSnowfall, CrocusSnowLoad3Days, CrocusSnowLoadTotal][0]
-    # altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000][::-1]
-    altitudes = ALL_ALTITUDES_WITHOUT_NAN[1:]
-    altitudes = [900]
+def test():
+    study = CrocusSnowLoad3Days(altitude=1200)
+    study_visualizer = StudyVisualizer(study)
+    study_visualizer.visualize_max_graphs_poster('Queyras', altitude='noope', snow_abbreviation="ok", color='red')
+    plt.show()
 
 
+def density_wrt_altitude():
+    save_to_file = True
+    study_class = [SafranTotalPrecip, SafranRainfall, SafranSnowfall, CrocusSnowLoad3Days, CrocusSnowLoadTotal][-2]
+    altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000][::-1]
 
     for altitude in altitudes:
 
         ax = plt.gca()
-        study = study_class(altitude=altitude, nb_consecutive_days=3)
+        study = study_class(altitude=altitude)
+        # study = study_class(altitude=altitude, nb_consecutive_days=3)
         massif_name_to_value = {}
         for massif_name in study.study_massif_names:
-            s = study.observations_summer_annual_maxima.df_maxima_gev.loc[massif_name]
+            s = study.observations_annual_maxima.df_maxima_gev.loc[massif_name]
             year_limit = 1987
-            print(s)
-            df_before, df_after = s.loc[:year_limit], s.loc[year_limit+1:]
-            print(df_before, df_after)
-
+            df_before, df_after = s.loc[:year_limit], s.loc[year_limit + 1:]
+            # df_before, df_after = df_before.mean(), df_after.mean()
             df_before, df_after = df_before.median(), df_after.median()
             relative_change_value = 100 * (df_after - df_before) / df_before
             massif_name_to_value[massif_name] = relative_change_value
         print(massif_name_to_value)
-
         # Plot
         # massif_name_to_value = {m: i for i, m in enumerate(study.study_massif_names)}
         max_values = max([abs(e) for e in massif_name_to_value.values()]) + 5
         print(max_values)
+        variable_name = study.variable_name
         study.visualize_study(ax=ax, massif_name_to_value=massif_name_to_value,
                               vmin=-max_values, vmax=max_values,
                               add_colorbar=True,
                               replace_blue_by_white=False,
-                              label='Relative changes for \n{}\n at {}m (%)'.format(study.variable_name, study.altitude)
+                              show=False,
+                              label='Relative changes in mean annual maxima\n'
+                                    'of {}\n between 1958-1987 and 1988-2017\n  at {}m (%)\n'
+                                    ''.format(variable_name, study.altitude)
                               )
-        plt.show()
+        study_visualizer = StudyVisualizer(study, save_to_file=save_to_file)
+        study_visualizer.plot_name = 'relative_changes_in_maxima'
+        study_visualizer.show_or_save_to_file()
         ax.clear()
+        plt.close()
 
 
 if __name__ == '__main__':
     density_wrt_altitude()
+    # test()