[TREND ANALYSIS] add tdlr map with markers on it

experiment/meteo_france_data/scm_models_data/abstract_study.py

@@ -304,6 +304,7 @@ class AbstractStudy(object):
                         axis_off=False,
                         massif_name_to_hatch_boolean_list=None,
                         norm=None,
+                        massif_name_to_marker_style=None,
                         ):
         if ax is None:
             ax = plt.gca()
@@ -323,6 +324,7 @@ class AbstractStudy(object):
         massif_name_to_fill_kwargs = {massif_name: {'color': color} for massif_name, color in
                                       massif_name_to_color.items()}
         massif_names = list(massif_name_to_fill_kwargs.keys())
+        masssif_coordinate_for_display = cls.massifs_coordinates_for_display(massif_names)
 
         for coordinate_id, coords_list in cls.idx_to_coords_list.items():
             # Retrieve the list of coords (x,y) that define the contour of the massif of id coordinate_id
@@ -337,6 +339,13 @@ class AbstractStudy(object):
             # Potentially, fill the inside of the polygon with some color
             if fill and coordinate_id in cls.coordinate_id_to_massif_name:
                 massif_name = cls.coordinate_id_to_massif_name[coordinate_id]
+                if massif_name in massif_name_to_marker_style:
+                    massif_coordinate = masssif_coordinate_for_display.df_all_coordinates.loc[massif_name, :].values
+                    if massif_name in ['Maurienne', 'Mercantour']:
+                        massif_coordinate[1] -= 5000
+                    ax.plot(massif_coordinate[0],
+                            massif_coordinate[1], **massif_name_to_marker_style[massif_name])
+
                 if massif_name_to_fill_kwargs is not None and massif_name in massif_name_to_fill_kwargs:
                     fill_kwargs = massif_name_to_fill_kwargs[massif_name]
                     ax.fill(*coords_list, **fill_kwargs)
@@ -354,11 +363,7 @@ class AbstractStudy(object):
                 #             if is_hatch:
                 #                 ax.add_patch(Polygon(xy=a, fill=False, hatch=hatch))
 
-        # Display the center of the massif
-        masssif_coordinate_for_display = cls.massifs_coordinates_for_display(massif_names)
 
-        ax.scatter(masssif_coordinate_for_display.x_coordinates,
-                   masssif_coordinate_for_display.y_coordinates, s=1)
         if show_label:
             # Improve some explanation on the X axis and on the Y axis
             ax.set_xlabel('Longitude (km)')

experiment/paper_past_snow_loads/method/study_visualizer_for_non_stationary_trends.py

@@ -25,6 +25,7 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
                          transformation_class, verbose, multiprocessing, complete_non_stationary_trend_analysis,
                          normalization_under_one_observations, score_class)
         self.non_stationary_trend_test = [GevLocationTrendTest, GevScaleTrendTest, GevLocationAndScaleTrendTest]
+        self.non_stationary_trend_test_to_marker = dict(zip(self.non_stationary_trend_test, ["s", "^", "D"]))
 
     # Utils
 
@@ -64,27 +65,33 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
 
     def plot_trends(self):
         v = max(abs(min(self.massif_name_to_tdrl_value.values())), max(self.massif_name_to_tdrl_value.values()))
-        vmin, vmax = -v,  v
+        vmin, vmax = -v, v
         self.study.visualize_study(massif_name_to_value=self.massif_name_to_tdrl_value, vmin=vmin, vmax=vmax,
                                    replace_blue_by_white=False, axis_off=True, show_label=False,
-                                   add_colorbar=True)
+                                   add_colorbar=True,
+                                   massif_name_to_marker_style=self.massif_name_to_marker_style)
 
     @cached_property
     def massif_name_to_tdrl_value(self):
-        return {m: t.time_derivative_of_return_level for m, t in self.massif_name_to_minimized_aic_non_stationary_trend_test.items()}
+        return {m: t.time_derivative_of_return_level for m, t in
+                self.massif_name_to_minimized_aic_non_stationary_trend_test.items()}
 
     @property
-    def massif_name_to_minimized_aic_model_class(self):
-        return {m: t.unconstrained_model_class for m, t in self.massif_name_to_minimized_aic_non_stationary_trend_test.items()}
-
-    @property
-    def massif_name_to_model_significance_symbol(self):
-        return {}
-
+    def massif_name_to_marker_style(self):
+        d = {}
+        for m, t in self.massif_name_to_minimized_aic_non_stationary_trend_test.items():
+            d[m] = {'marker': self.non_stationary_trend_test_to_marker[type(t)],
+                    'color': 'k',
+                    'markersize': 5,
+                    'fillstyle': 'full' if t.is_significant else 'none'}
+        return d
 
     # Part 1 - Uncertainty return level plot
 
-
+    @property
+    def massif_name_to_minimized_aic_model_class(self):
+        return {m: t.unconstrained_model_class for m, t in
+                self.massif_name_to_minimized_aic_non_stationary_trend_test.items()}
 
     def massif_name_to_uncertainty(self):
         pass