Index: trunk/SDToolBox/extract_data_utils.py
===================================================================
diff -u -r207 -r208
--- trunk/SDToolBox/extract_data_utils.py	(.../extract_data_utils.py)	(revision 207)
+++ trunk/SDToolBox/extract_data_utils.py	(.../extract_data_utils.py)	(revision 208)
@@ -54,7 +54,7 @@
 
 def get_list_nearest_neighbor(
     input_values: List[float], reference_list: List[float],
-) -> Tuple[List[int], List[float]]:
+) -> Tuple[np.array, List[float]]:
     """Sets the nearest neighbor for all the elements
     given in the points_list.
 
@@ -72,7 +72,7 @@
         idx, value = get_single_nearest_neighbor(point, reference_list)
         corrected_values.append(value)
         output_idx.append(idx)
-    return output_idx, corrected_values
+    return np.array(output_idx), corrected_values
 
 
 def get_matrix_nearest_neighbor(
Index: trunk/SDToolBox/extract_data.py
===================================================================
diff -u -r207 -r208
--- trunk/SDToolBox/extract_data.py	(.../extract_data.py)	(revision 207)
+++ trunk/SDToolBox/extract_data.py	(.../extract_data.py)	(revision 208)
@@ -180,51 +180,53 @@
                 Index of nearest neighbors and a Tuple of corrected Latitude and Longitude
         """
         mask_filepath = Path(mask_dir) / "ERA5_landsea_mask.nc"
-        extracted_lat_pos = 1
-        extracted_lon_pos = 0
+        extracted_lat_pos = 0
+        extracted_lon_pos = 1
         sea_mask, LON, LAT = ExtractData.get_seamask_positions(mask_filepath)
         possea = np.where(sea_mask.ravel() == 1)[0]
 
         if not input_data.is_gridded:
             index, vals = get_matrix_nearest_neighbor(
-                np.vstack((input_data._input_lon, input_data._input_lat)).T,
-                np.vstack((LON.ravel()[possea], LAT.ravel()[possea])).T,
+                np.vstack((input_data._input_lat, input_data._input_lon)).T,
+                np.vstack((LAT.ravel()[possea], LON.ravel()[possea])).T,
             )
             index_abs = possea[index].squeeze()
 
             if index_abs.size <= 0:
                 return None, None
-            indexes_lat_lon = np.unravel_index(index_abs, LON.shape)
-            unique_values, unique_idx = np.unique(vals, axis=0, return_index=True)
+            indexes_lat_lon, _ = get_unique_indices(
+                np.unravel_index(index_abs, LON.shape)
+            )
 
         else:
             index, vals = get_matrix_nearest_neighbor(
-                np.vstack((input_data._input_lon, input_data._input_lat)).T,
-                np.vstack((LON.ravel(), LAT.ravel())).T,
+                np.vstack((input_data._input_lat, input_data._input_lon)).T,
+                np.vstack((LAT.ravel(), LON.ravel())).T,
             )
-            indexes_lat_lon = np.unravel_index(index.squeeze(), LON.shape)
-            unique_values, unique_idx = np.unique(vals, axis=0, return_index=True)
+            indexes_lat_lon, _ = get_unique_indices(
+                np.unravel_index(index.squeeze(), LON.shape)
+            )
 
-            if index.shape[extracted_lon_pos] > 1:
+            if index.shape[0] > 1:
                 input_data.sea_mask = sea_mask[
-                    np.ix_(np.sort(indexes_lat_lon[0]), np.sort(indexes_lat_lon[1]),)
+                    np.ix_(indexes_lat_lon[0], indexes_lat_lon[1],)
                 ]
             else:
                 input_data.sea_mask = np.array(
                     sea_mask[indexes_lat_lon[0], indexes_lat_lon[1],]
                 )
             logging.info(f"Added sea mask to input_data: {input_data.sea_mask}")
-        if len(unique_values.shape) == 1:
+        if len(indexes_lat_lon) == 1:
             return (
                 indexes_lat_lon,
                 (
-                    np.array([unique_values[extracted_lat_pos]]),
-                    np.array([unique_values[extracted_lon_pos]]),
+                    np.array([vals[extracted_lat_pos]]),
+                    np.array([vals[extracted_lon_pos]]),
                 ),
             )
         return (
             indexes_lat_lon,
-            (unique_values[:, extracted_lat_pos], unique_values[:, extracted_lon_pos],),
+            (vals[:, extracted_lat_pos], vals[:, extracted_lon_pos],),
         )
 
     class BaseExtractor(ABC):
@@ -523,7 +525,7 @@
             ]
             output_data.data_dict[output_data.var_station_idx_key] = idx_map
             logging.info(
-                f"""Corrected nn indices: 
+                f"""Corrected nn indices:
                 Lat: {corrected_nn_lat_lon_idx[0].astype(str)}
                 Lon: {corrected_nn_lat_lon_idx[1].astype(str)}"""
             )
Index: trunk/SDToolBox/input_data.py
===================================================================
diff -u -r195 -r208
--- trunk/SDToolBox/input_data.py	(.../input_data.py)	(revision 195)
+++ trunk/SDToolBox/input_data.py	(.../input_data.py)	(revision 208)
@@ -164,7 +164,9 @@
             self.__get_corrected_longitude(lon) for lon in self._input_lon
         ]
         if self.is_gridded:
-            self._input_lat = sorted(set(self._input_lat))
+            # Latitudes go from MAX to MIN
+            self._input_lat = sorted(set(self._input_lat))[::-1]
+            # Longitudes go from MIN to MAX
             self._input_lon = list(sorted(set(self._input_lon)))
             assert len(self._input_lat) == len(
                 self._input_lon