[SCM][NON STATIONARY TRENDS] use latitude & longitude from SCM data for fitting

experiment/meteo_france_SCM_study/abstract_extended_study.py

@@ -44,7 +44,7 @@ class AbstractExtendedStudy(AbstractStudy):
 
     """ Properties """
 
-    def massifs_coordinates(self) -> AbstractSpatialCoordinates:
+    def massifs_coordinates_for_display(self) -> AbstractSpatialCoordinates:
         raise ValueError('Coordinates are meaningless for an extended study')
 
     @property

experiment/meteo_france_SCM_study/abstract_study.py

@@ -159,18 +159,27 @@ class AbstractStudy(object):
         return {massif_id: massif_name for massif_id, massif_name in enumerate(self.all_massif_names)}
 
     @cached_property
-    def massifs_coordinates(self) -> AbstractSpatialCoordinates:
-        # Build coordinate object from df_centroid
-        return AbstractSpatialCoordinates.from_df(self.df_spatial())
-
-    def df_spatial(self):
+    def massifs_coordinates_for_display(self) -> AbstractSpatialCoordinates:
         # Coordinate object that represents the massif coordinates in Lambert extended
-        df_centroid = self.load_df_centroid()
+        # extracted for a csv file, and used only for display purposes
+        df = self.load_df_centroid()
         for coord_column in [AbstractCoordinates.COORDINATE_X, AbstractCoordinates.COORDINATE_Y]:
-            df_centroid.loc[:, coord_column] = df_centroid[coord_column].str.replace(',', '.').astype(float)
+            df.loc[:, coord_column] = df[coord_column].str.replace(',', '.').astype(float)
         # Filter, keep massifs present at the altitude of interest
-        df_centroid = df_centroid.loc[self.study_massif_names]
-        return df_centroid
+        df = df.loc[self.study_massif_names]
+        # Build coordinate object from df_centroid
+        return AbstractSpatialCoordinates.from_df(df)
+
+    @property
+    def df_massifs_longitude_and_latitude(self) -> pd.DataFrame:
+        # DataFrame object that represents the massif coordinates in degrees extracted from the SCM data
+        any_ordered_dict = list(self.year_to_dataset_ordered_dict.values())[0]
+        longitude = np.array(any_ordered_dict.variables['longitude'])[self.altitude_mask]
+        latitude = np.array(any_ordered_dict.variables['latitude'])[self.altitude_mask]
+        index = self.altitude_to_massif_names[self.altitude]
+        columns = [AbstractSpatialCoordinates.COORDINATE_X, AbstractSpatialCoordinates.COORDINATE_Y]
+        data = dict(zip(columns, [longitude, latitude]))
+        return pd.DataFrame(data=data, index=index, columns=columns)
 
     def load_df_centroid(self) -> pd.DataFrame:
         # Load df_centroid containing all the massif names
@@ -223,7 +232,7 @@ class AbstractStudy(object):
                 # ax.scatter(x, y)
                 # ax.text(x, y, massif_name)
         # Display the center of the massif
-        ax.scatter(self.massifs_coordinates.x_coordinates, self.massifs_coordinates.y_coordinates, s=1)
+        ax.scatter(self.massifs_coordinates_for_display.x_coordinates, self.massifs_coordinates_for_display.y_coordinates, s=1)
         # Improve some explanation on the X axis and on the Y axis
         ax.set_xlabel('Longitude (km)')
         ax.xaxis.set_major_formatter(get_km_formatter())
@@ -231,7 +240,7 @@ class AbstractStudy(object):
         ax.yaxis.set_major_formatter(get_km_formatter())
         # Display the name or value of the massif
         if add_text:
-            for _, row in self.massifs_coordinates.df_all_coordinates.iterrows():
+            for _, row in self.massifs_coordinates_for_display.df_all_coordinates.iterrows():
                 x, y = list(row)
                 massif_name = row.name
                 value = massif_name_to_value[massif_name]

experiment/meteo_france_SCM_study/crocus/crocus.py

@@ -15,11 +15,6 @@ class Crocus(AbstractStudy):
         super().__init__(variable_class, *args, **kwargs)
         self.model_name = 'Crocus'
 
-    @property
-    def variable_name(self):
-        suffix = '' if self.altitude == 2400 else ' sampled every 24 hours'
-        return super().variable_name + suffix
-
     def annual_aggregation_function(self, *args, **kwargs):
         return np.mean(*args, **kwargs)
 
@@ -67,11 +62,10 @@ class CrocusDaysWithSnowOnGround(Crocus):
 
 
 if __name__ == '__main__':
-    for variable_class in [CrocusSweVariable, CrocusDepthVariable]:
+    for variable_class in [CrocusSweVariable, CrocusDepthVariable][:1]:
         study = Crocus(variable_class=variable_class, altitude=2400)
         d = study.year_to_dataset_ordered_dict[1960]
+        print(study.df_massifs_longitude_and_latitude)
         time_arr = np.array(d.variables['time'])
-        print(time_arr)
-        # print(d)
         a = study.year_to_daily_time_serie_array[1960]
         print(a.shape)

experiment/meteo_france_SCM_study/crocus/crocus_variables.py

@@ -4,8 +4,6 @@ from experiment.meteo_france_SCM_study.abstract_variable import AbstractVariable
 
 
 class CrocusVariable(AbstractVariable):
-    """Crocus data is every 6 hours. To obtain daily data, we select one data out of 4
-    (in order to have data that will still be comparable to an instantaneous variable"""
 
     def __init__(self, dataset, altitude, variable_name):
         super().__init__(dataset, altitude)

experiment/meteo_france_SCM_study/visualization/study_visualization/non_stationary_trends.py

@@ -49,9 +49,9 @@ class AbstractNonStationaryTrendTest(object):
                 if self.verbose:
                     estimator_name = get_display_name_from_object_type(estimator)
                     margin_model_name = get_display_name_from_object_type(margin_model)
-                    text = 'Fittig {} with margin: {} for starting_point={}'.format(estimator_name,
-                                                                                             margin_model_name,
-                                                                                             starting_point)
+                    text = 'Fittig {} with margin: {} for starting_point={}\n'.format(estimator_name,
+                                                                                      margin_model_name,
+                                                                                      starting_point)
                     text += 'Fit took {}s and was {}'.format(round(duration, 1), estimator.result_from_fit.convergence)
                     print(text)
             self._margin_model_class_and_starting_point_to_estimator[(margin_model_class, starting_point)] = estimator

experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py

@@ -97,7 +97,7 @@ class StudyVisualizer(object):
 
     @property
     def spatial_coordinates(self):
-        return AbstractSpatialCoordinates.from_df(df=self.study.df_spatial(),
+        return AbstractSpatialCoordinates.from_df(df=self.study.df_massifs_longitude_and_latitude,
                                                   transformation_class=self.transformation_class)
 
     @property