Index: trunk/SDToolBox/extract_data.py
===================================================================
diff -u -r91 -r94
--- trunk/SDToolBox/extract_data.py	(.../extract_data.py)	(revision 91)
+++ trunk/SDToolBox/extract_data.py	(.../extract_data.py)	(revision 94)
@@ -162,15 +162,6 @@
         def get_new_file_iter(self, file_entry, file_path) -> Dict[str, str]:
             raise Exception(om.error_needs_to_be_in_subclass)
 
-        @abstractmethod
-        def get_masked_nn_lat_lon(
-                self,
-                input_data: InputData,
-                ref_dataset: Dataset,
-                cases_dict: dict,
-                mask_dir: str) -> Tuple[List[int], List[int]]:
-            raise Exception(om.error_needs_to_be_in_subclass)
-
         def set_file_iterator(
                 self,
                 input_data: InputData,
@@ -350,7 +341,8 @@
                         input_data=input_data,
                         ref_dataset=ref_dataset,
                         cases_dict=cases_dict,
-                        mask_dir=os.path.dirname(os.path.dirname(ref_file_path)))
+                        mask_dir=os.path.dirname(
+                            os.path.dirname(ref_file_path)))
 
         def _get_filtered_dict(
                 self,
@@ -454,10 +446,53 @@
             """
             kd = BallTree(data_array, leaf_size=10)
             # k=2 nearest neighbors where k1 = identity
-            _, index_found = kd.query(values)
+            _, index_found = kd.query(values, k=2)
             value_found = data_array[index_found.squeeze(), :]
             return index_found, value_found
 
+        def get_masked_nn_lat_lon(
+                self,
+                input_data: InputData,
+                ref_dataset: Dataset,
+                cases_dict: dict,
+                mask_dir: str) -> Tuple[List[int], List[int]]:
+            mask_filepath = os.path.join(mask_dir, 'ERA5_landsea_mask.nc')
+            with Dataset(mask_filepath, 'r', self.netcdf_format) \
+                    as dsmask:
+                mask = dsmask['lsm'][0, 0::2, 0::2]
+                lon = dsmask['longitude'][0::2]
+                lon[lon > 180] += -360
+                lat = dsmask['latitude'][0::2]
+                loni = np.where(lon == 180)[0][0]
+                LON, LAT = np.meshgrid(lon, lat)
+                LON = np.concatenate(
+                    (LON[:, loni+1:], LON[:, 0:loni+1]),
+                    axis=1)
+                mask = np.concatenate(
+                    (mask[:, loni+1:], mask[:, 0:loni+1]),
+                    axis=1)
+                # positions where we are in the sea
+                possea = np.where(mask.ravel() == 0.)[0]
+                index, vals = self.get_nearest_neighbor_extended(
+                    np.vstack(
+                        (
+                            input_data._input_lon,
+                            input_data._input_lat
+                        )).T,
+                    np.vstack(
+                        (
+                            LON.ravel()[possea],
+                            LAT.ravel()[possea])).T)
+                index_abs = possea[index].squeeze()
+
+                if(index_abs.size <= 0):
+                    return None
+                indexes = np.array(
+                    [
+                        np.unravel_index(ii, LON.shape)
+                        for ii in index_abs]).T.tolist()
+                return indexes[1], indexes[0]
+
         @staticmethod
         def __get_case_subset(
                 dataset: Dataset,
@@ -505,7 +540,7 @@
         }
 
         __era5_wind_mslp_dict = {
-            'msl': 'msl',
+            'msl_p': 'msl',
             'u10': 'wind_u',
             'v10': 'wind_v'
         }
@@ -596,46 +631,6 @@
                 self.file_fpath_key: file_path
             }
 
-        def get_masked_nn_lat_lon(
-                self,
-                input_data: InputData,
-                ref_dataset: Dataset,
-                cases_dict: dict,
-                mask_dir: str) -> Tuple[List[int], List[int]]:
-            mask_filepath = os.path.join(mask_dir, 'ERA5_landsea_mask.nc')
-            with Dataset(mask_filepath, 'r', self.netcdf_format) \
-                    as dsmask:
-                mask = dsmask['lsm'][0, 0::2, 0::2]
-                lon = dsmask['longitude'][0::2]
-                lon[lon > 180] += -360
-                lat = dsmask['latitude'][0::2]
-                loni = np.where(lon == 180)[0][0]
-                LON, LAT = np.meshgrid(lon, lat)
-                LON = np.concatenate(
-                    (LON[:, loni+1:], LON[:, 0:loni+1]),
-                    axis=1)
-                mask = np.concatenate(
-                    (mask[:, loni+1:], mask[:, 0:loni+1]),
-                    axis=1)
-                # positions where we are in the sea
-                possea = np.where(mask.ravel() == 0.)[0]
-                index, vals = self.get_nearest_neighbor_extended(
-                    np.vstack(
-                        (
-                            input_data._input_lon,
-                            input_data._input_lat
-                        )).T,
-                    np.vstack(
-                        (
-                            LON.ravel()[possea],
-                            LAT.ravel()[possea])).T)
-                index_abs = possea[index].squeeze()
-                indexes = np.array(
-                    [
-                        np.unravel_index(ii, LON.shape)
-                        for ii in index_abs])
-                return indexes
-
     class __EarthExtractor(BaseExtractor):
         __earth_lon_key = 'lon'
         __earth_lat_key = 'lat'
@@ -741,11 +736,3 @@
                     self.file_scenario_key: file_entry[3],
                     self.file_fpath_key: file_path
                 }
-
-        def get_masked_nn_lat_lon(
-                self,
-                input_data: InputData,
-                ref_dataset: Dataset,
-                cases_dict: dict,
-                mask_dir: str) -> Tuple[List[int], List[int]]:
-            raise Exception(om.error_function_not_implemented)