diff --git a/ocf_datapipes/select/select_spatial_slice.py b/ocf_datapipes/select/select_spatial_slice.py
index cfa7e0bfb..272cb9203 100644
--- a/ocf_datapipes/select/select_spatial_slice.py
+++ b/ocf_datapipes/select/select_spatial_slice.py
@@ -1,12 +1,13 @@
 """Select spatial slices"""
 import logging
-from typing import Union, Optional
+from typing import Optional, Union
 
 import numpy as np
 import xarray as xr
+from scipy.spatial import KDTree
 from torchdata.datapipes import functional_datapipe
 from torchdata.datapipes.iter import IterDataPipe
-from scipy.spatial import KDTree
+
 from ocf_datapipes.utils.consts import Location
 from ocf_datapipes.utils.geospatial import (
     lat_lon_to_osgb,
@@ -95,9 +96,7 @@ def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
             # Sanity check!
             assert left_idx >= 0, f"{left_idx=} must be >= 0!"
             data_width_pixels = len(xr_data[self.x_dim_name])
-            assert (
-                right_idx <= data_width_pixels
-            ), f"{right_idx=} must be <= {data_width_pixels=}"
+            assert right_idx <= data_width_pixels, f"{right_idx=} must be <= {data_width_pixels=}"
             assert top_idx >= 0, f"{top_idx=} must be >= 0!"
             data_height_pixels = len(xr_data[self.y_dim_name])
             assert (
@@ -175,15 +174,11 @@ def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
                         location, xr_data, left, bottom, right, top
                     )
 
-                    x_mask = (left <= xr_data.x_geostationary) & (
-                        xr_data.x_geostationary <= right
-                    )
+                    x_mask = (left <= xr_data.x_geostationary) & (xr_data.x_geostationary <= right)
                     y_mask = (xr_data.y_geostationary <= top) & (  # Y is flipped
                         bottom <= xr_data.y_geostationary
                     )
-                    selected = xr_data.isel(
-                        x_geostationary=x_mask, y_geostationary=y_mask
-                    )
+                    selected = xr_data.isel(x_geostationary=x_mask, y_geostationary=y_mask)
                 elif "longitude" == self.x_dim_name:
                     if location.coordinate_system == "osgb":
                         # Convert to geostationary edges
@@ -219,17 +214,11 @@ def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
                 # Select data in the region of interest and ID:
                 # This also works for unstructured grids
                 # Need to check coordinate systems match
-                if (
-                    location.coordinate_system == "osgb"
-                    and "longitude" in self.x_dim_name
-                ):
+                if location.coordinate_system == "osgb" and "longitude" in self.x_dim_name:
                     # Convert to lat_lon edges
                     left, bottom = osgb_to_lat_lon(x=left, y=bottom)
                     right, top = osgb_to_lat_lon(x=right, y=top)
-                elif (
-                    location.coordinate_system == "lat_lon"
-                    and "osgb" in self.x_dim_name
-                ):
+                elif location.coordinate_system == "lat_lon" and "osgb" in self.x_dim_name:
                     left, bottom = lat_lon_to_osgb(longitude=left, latitude=bottom)
                     right, top = lat_lon_to_osgb(longitude=right, latitude=top)
                 id_mask = (
@@ -246,16 +235,12 @@ def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
 def _convert_to_geostationary(location, xr_data, left, bottom, right, top):
     if location.coordinate_system == "osgb":
         # Convert to geostationary edges
-        _osgb_to_geostationary = load_geostationary_area_definition_and_transform_osgb(
-            xr_data
-        )
+        _osgb_to_geostationary = load_geostationary_area_definition_and_transform_osgb(xr_data)
         left, bottom = _osgb_to_geostationary(xx=left, yy=bottom)
         right, top = _osgb_to_geostationary(xx=right, yy=top)
     elif location.coordinate_system == "lat_lon":
         # Convert to geostationary edges
-        _lat_lon_to_geostationary = (
-            load_geostationary_area_definition_and_transform_latlon(xr_data)
-        )
+        _lat_lon_to_geostationary = load_geostationary_area_definition_and_transform_latlon(xr_data)
         left, bottom = _lat_lon_to_geostationary(xx=left, yy=bottom)
         right, top = _lat_lon_to_geostationary(xx=right, yy=top)
     return left, bottom, right, top
@@ -333,12 +318,8 @@ def _get_idx_of_pixel_closest_to_poi_geostationary(
     Returns:
         Location for the center pixel in geostationary coordinates
     """
-    _osgb_to_geostationary = load_geostationary_area_definition_and_transform_osgb(
-        xr_data
-    )
-    center_geostationary_tuple = _osgb_to_geostationary(
-        xx=center_osgb.x, yy=center_osgb.y
-    )
+    _osgb_to_geostationary = load_geostationary_area_definition_and_transform_osgb(xr_data)
+    center_geostationary_tuple = _osgb_to_geostationary(xx=center_osgb.x, yy=center_osgb.y)
     center_geostationary = Location(
         x=center_geostationary_tuple[0],
         y=center_geostationary_tuple[1],
@@ -346,9 +327,7 @@ def _get_idx_of_pixel_closest_to_poi_geostationary(
     )
 
     # Get the index into x and y nearest to x_center_geostationary and y_center_geostationary:
-    x_index_at_center = (
-        np.searchsorted(xr_data[x_dim_name].values, center_geostationary.x) - 1
-    )
+    x_index_at_center = np.searchsorted(xr_data[x_dim_name].values, center_geostationary.x) - 1
     # y_geostationary is in descending order:
     y_index_at_center = len(xr_data[y_dim_name]) - (
         np.searchsorted(xr_data[y_dim_name].values[::-1], center_geostationary.y) - 1