[contrasting v2] fix one condition. keep all one fold fit, with at least one...

[contrasting v2] fix one condition. keep all one fold fit, with at least one valid (and not one non stationary model). change xlabel for in altitude_group.py

projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py

@@ -24,16 +24,12 @@ from extreme_data.meteo_france_data.scm_models_data.utils import Season
 
 def main():
     study_classes = [SafranSnowfall1Day, SafranSnowfall3Days, SafranSnowfall5Days, SafranSnowfall7Days][:1]
-    # study_classes = [SafranDateFirstSnowfall]
-    # study_classes = [CrocusSnowLoadTotal]
-    study_classes = [SafranPrecipitation1Day, CrocusSnowLoadTotal, SafranDateFirstSnowfall][2:]
-    study_classes = [CrocusSnowLoad3Days, SafranSnowfall3Days, SafranSnowfall5Days]
     seasons = [Season.annual, Season.winter, Season.spring, Season.automn][:1]
 
     fast = False
     if fast is None:
         massif_names = None
-        altitudes_list = altitudes_for_groups[2:3]
+        altitudes_list = altitudes_for_groups[1:2]
     elif fast:
         massif_names = ['Vanoise', 'Haute-Maurienne', 'Vercors'][:]
         altitudes_list = altitudes_for_groups[2:3]
@@ -63,18 +59,21 @@ def plot_visualizers(massif_names, visualizer_list):
     for relative in [True, False]:
         plot_shoe_plot_changes_against_altitude(massif_names, visualizer_list, relative=relative)
     # plot_coherence_curves(massif_names, visualizer_list)
-    pass
+    plot_coherence_curves(['Vanoise'], visualizer_list)
 
 
 def plot_visualizer(massif_names, visualizer):
     # Plot time series
-    visualizer.studies.plot_maxima_time_series(massif_names=massif_names)
+    # visualizer.studies.plot_maxima_time_series(massif_names)
+    visualizer.studies.plot_maxima_time_series(['Vanoise'])
     # Plot moments against altitude
     # for std in [True, False][:]:
     #     for change in [True, False, None]:
     #         studies.plot_mean_maxima_against_altitude(massif_names=massif_names, std=std, change=change)
     # Plot the results for the model that minimizes the individual aic
     plot_individual_aic(visualizer)
+
+
     # Plot the results for the model that minimizes the total aic
     # plot_total_aic(model_classes, visualizer)
     pass

projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitude_group.py

 from enum import Enum
 
 # The order is important
+MIDDLE_WORD = 'and'
+START_WORD = 'between'
 altitudes_for_groups = [
     [600, 900],
     [1200, 1500, 1800],
@@ -25,8 +27,9 @@ class AbstractAltitudeGroup(object):
 
     @property
     def xlabel(self):
-        return 'Elevation = {} m. Models are estimated with\n' \
-               'maxima from range {}, i.e. {}'.format(self.reference_altitude, self.group_id, self.formula)
+        return 'Elevation = {} m. Models are fitted with\n' \
+               'maxima from elevation range {}, i.e.\n' \
+               'maxima {}'.format(self.reference_altitude, self.group_id, self.formula)
 
     @property
     def formula(self):
@@ -40,14 +43,14 @@ class AbstractAltitudeGroup(object):
             formula = 'above 3000 m'
         else:
             min_altitude, max_altitude = 1000 * i, 1000 * (i + 1)
-            formula = 'from {} m to {} m'.format(min_altitude, max_altitude)
+            formula = '{} {} m {} {} m'.format(START_WORD, min_altitude, MIDDLE_WORD, max_altitude)
         return formula
 
     @property
     def formula_upper(self):
         f = self.formula
-        f = f.replace('from ', '')
-        f = f.replace(' to ', '-')
+        f = f.replace('{} '.format(START_WORD), '')
+        f = f.replace(' {} '.format(MIDDLE_WORD), '-')
         return f[0].upper() + f[1:]
 
 

projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py

@@ -65,6 +65,11 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
                                           type(self.altitude_group),
                                           self.display_only_model_that_pass_anderson_test)
                 self._massif_name_to_one_fold_fit[massif_name] = old_fold_fit
+        # Print number of massif without any validated fit
+        massifs_without_any_validated_fit = [massif_name
+                                             for massif_name, old_fold_fit in self._massif_name_to_one_fold_fit.items()
+                                             if not old_fold_fit.has_at_least_one_valid_model]
+        print('Not validated:', len(massifs_without_any_validated_fit), massifs_without_any_validated_fit)
         # Cache
         self._method_name_and_order_to_massif_name_to_value = {}
         self._method_name_and_order_to_max_abs = {}
@@ -97,7 +102,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
     def massif_name_to_one_fold_fit(self) -> Dict[str, OneFoldFit]:
         return {massif_name: old_fold_fit for massif_name, old_fold_fit in self._massif_name_to_one_fold_fit.items()
                 if not self.display_only_model_that_pass_anderson_test
-                or old_fold_fit.has_at_least_one_valid_non_stationary_model}
+                or old_fold_fit.has_at_least_one_valid_model}
 
     def plot_moments(self):
         for method_name in self.moment_names:
@@ -270,7 +275,8 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
                       label=label,
                       plot_name=label,
                       fontsize_label=15,
-                      add_x_label=True, graduation=0.1, massif_name_to_text=self.massif_name_to_best_name,
+                      add_x_label=True, graduation=0.1,
+                      massif_name_to_text=self.massif_name_to_best_name,
                       cmap=matplotlib.cm.get_cmap('BrBG_r'),
                       altitude=self.altitude_group.reference_altitude,
                       add_colorbar=self.add_colorbar,

projects/altitude_spatial_model/preliminary_analysis.py

@@ -43,7 +43,7 @@ class PointwiseGevStudyVisualizer(AltitudesStudies):
             print(param_name, np.mean([c for c in massif_name_to_linear_coef.values()]))
 
             # Display x label
-            xticks = [1000 * i for i in range(1, 5)]
+            xticks = [1000 * i for i in range(1, 4)]
             ax.set_xticks(xticks)
             fontsize_label = 15
             ax.tick_params(labelsize=fontsize_label)