lint

deepsensor/model/model.py

@@ -810,9 +810,10 @@ def overlap_index(
                 ),
             )
 
-        def get_coordinate_extent(ds: Union[xr.DataArray, xr.Dataset], x1_ascend: bool, x2_ascend: bool) -> tuple:
-            """
-            Get coordinate extent of dataset. This method is applied to either X_t or patchwise predictions. 
+        def get_coordinate_extent(
+            ds: Union[xr.DataArray, xr.Dataset], x1_ascend: bool, x2_ascend: bool
+        ) -> tuple:
+            """Get coordinate extent of dataset. This method is applied to either X_t or patchwise predictions.
 
             Parameters
             ----------
@@ -825,12 +826,11 @@ def get_coordinate_extent(ds: Union[xr.DataArray, xr.Dataset], x1_ascend: bool,
             x2_ascend : bool
                 Whether the x2 coordinates ascend (increase) from left to right.
 
-            Returns
+            Returns:
             -------
             tuple of tuples:
                 Extents of x1 and x2 coordinates as ((min_x1, max_x1), (min_x2, max_x2)).
             """
-
             if x1_ascend:
                 ds_x1_coords = (
                     ds.coords[orig_x1_name].min().values,
@@ -853,7 +853,6 @@ def get_coordinate_extent(ds: Union[xr.DataArray, xr.Dataset], x1_ascend: bool,
                 )
             return ds_x1_coords, ds_x2_coords
 
-
         def get_index(*args, x1=True) -> Union[int, Tuple[List[int], List[int]]]:
             """Convert coordinates into pixel row/column (index).
 
@@ -930,22 +929,27 @@ def stitch_clipped_predictions(
             combined: dict
                 Dictionary object containing the stitched model predictions.
             """
-
-            # Get row/col index values of X_t.            
-            data_x1_coords, data_x2_coords= get_coordinate_extent(X_t, x1_ascend, x2_ascend)
+            # Get row/col index values of X_t.
+            data_x1_coords, data_x2_coords = get_coordinate_extent(
+                X_t, x1_ascend, x2_ascend
+            )
             data_x1_index, data_x2_index = get_index(data_x1_coords, data_x2_coords)
 
             # Iterate through patchwise predictions and slice edges prior to stitchin.
             patches_clipped = []
             for i, patch_pred in enumerate(patch_preds):
                 # get one variable name to use for coordinates and extent
-                first_key = list(patch_pred.keys())[0]   
+                first_key = list(patch_pred.keys())[0]
                 # Get row/col index values of each patch.
-                patch_x1_coords, patch_x2_coords= get_coordinate_extent(patch_pred[first_key], x1_ascend, x2_ascend)
-                patch_x1_index, patch_x2_index = get_index(patch_x1_coords, patch_x2_coords)
+                patch_x1_coords, patch_x2_coords = get_coordinate_extent(
+                    patch_pred[first_key], x1_ascend, x2_ascend
+                )
+                patch_x1_index, patch_x2_index = get_index(
+                    patch_x1_coords, patch_x2_coords
+                )
 
                 # Calculate size of border to slice of each edge of patchwise predictions.
-                # Initially set the size of all borders to the size of the overlap. 
+                # Initially set the size of all borders to the size of the overlap.
                 b_x1_min, b_x1_max = patch_overlap[0], patch_overlap[0]
                 b_x2_min, b_x2_max = patch_overlap[1], patch_overlap[1]
 
@@ -983,10 +987,10 @@ def stitch_clipped_predictions(
                 else:
                     b_x2_max = b_x2_max
 
-                # Repeat process as above for x1 coordinates. 
+                # Repeat process as above for x1 coordinates.
                 if patch_x1_index[0] == data_x1_index[0]:
                     b_x1_min = 0
-                
+
                 elif abs(patch_x1_index[1] - data_x1_index[1]) < 2:
                     b_x1_max = 0
                     b_x1_max = b_x1_max
@@ -1033,12 +1037,12 @@ def stitch_clipped_predictions(
                 }
 
                 patches_clipped.append(patch_clip)
-            
+
             # Create blank prediction object to stitch prediction values onto.
             stitched_prediction = copy.deepcopy(patch_preds[0])
             # Set prediction object extent to the same as X_t.
             for var_name, data_array in stitched_prediction.items():
-                blank_ds= xr.Dataset(
+                blank_ds = xr.Dataset(
                     coords={
                         orig_x1_name: X_t[orig_x1_name],
                         orig_x2_name: X_t[orig_x2_name],
@@ -1051,9 +1055,12 @@ def stitch_clipped_predictions(
                     blank_ds[data_var] = data_array[data_var]
                     blank_ds[data_var][:] = np.nan
                 stitched_prediction[var_name] = blank_ds
-            
+
             # Restructure prediction objects for merging
-            restructured_patches = {key: [item[key] for item in patches_clipped] for key in patches_clipped[0].keys()}
+            restructured_patches = {
+                key: [item[key] for item in patches_clipped]
+                for key in patches_clipped[0].keys()
+            }
 
             # Merge patchwise predictions to create final stiched prediction.
             # Iterate over each variable (key) in the prediction dictionary