resolved some ruff issues

Author: rijuld

tests/datasets/test_substation.py

@@ -21,11 +21,11 @@ def dataset(
         self, tmp_path: Path, monkeypatch: pytest.MonkeyPatch
     ) -> Generator[Substation, None, None]:
         """Fixture for the Substation."""
-        root = os.path.join(os.getcwd(), 'tests', 'data', 'substation') 
+        root = os.path.join(os.getcwd(), 'tests', 'data', 'substation')
 
         yield Substation(
             root=root,
-            bands=[1,2,3],
+            bands=[1, 2, 3],
             use_timepoints=True,
             mask_2d=True,
             timepoint_aggregation='median',
@@ -35,45 +35,45 @@ def dataset(
     @pytest.mark.parametrize(
         'config',
         [
-            {'bands': [1,2,3], 'use_timepoints': False, 'mask_2d': True},
+            {'bands': [1, 2, 3], 'use_timepoints': False, 'mask_2d': True},
             {
-                'bands': [1,2,3],
+                'bands': [1, 2, 3],
                 'use_timepoints': True,
                 'timepoint_aggregation': 'concat',
                 'num_of_timepoints': 4,
                 'mask_2d': False,
             },
             {
-                'bands': [1,2,3],
+                'bands': [1, 2, 3],
                 'use_timepoints': True,
                 'timepoint_aggregation': 'median',
                 'num_of_timepoints': 4,
                 'mask_2d': True,
             },
             {
-                'bands': [1,2,3],
+                'bands': [1, 2, 3],
                 'use_timepoints': True,
                 'timepoint_aggregation': 'first',
                 'num_of_timepoints': 4,
                 'mask_2d': False,
             },
             {
-                'bands': [1,2,3],
+                'bands': [1, 2, 3],
                 'use_timepoints': True,
                 'timepoint_aggregation': 'random',
                 'num_of_timepoints': 4,
                 'mask_2d': True,
             },
-            {'bands': [1,2,3], 'use_timepoints': False, 'mask_2d': False},
+            {'bands': [1, 2, 3], 'use_timepoints': False, 'mask_2d': False},
             {
-                'bands': [1,2,3],
+                'bands': [1, 2, 3],
                 'use_timepoints': False,
                 'timepoint_aggregation': 'first',
                 'num_of_timepoints': 4,
                 'mask_2d': False,
             },
             {
-                'bands': [1,2,3],
+                'bands': [1, 2, 3],
                 'use_timepoints': False,
                 'timepoint_aggregation': 'random',
                 'num_of_timepoints': 4,

torchgeo/datamodules/substation.py

@@ -1,3 +1,8 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""Substation datamodule."""
+
 from typing import Any
 
 import torch
@@ -52,6 +57,7 @@ def __init__(
             color_transforms: Color transformations to apply to the image.
             image_resize: Resizing function for the image.
             mask_resize: Resizing function for the mask.
+            num_of_timepoints: Number of timepoints to use in the dataset.
             **kwargs: Additional arguments passed to Substation.
         """
         super().__init__(Substation, batch_size, num_workers, **kwargs)
@@ -86,7 +92,7 @@ def setup(self, stage: str) -> None:
             download=True,
             checksum=False,
         )
-        
+
         generator = torch.Generator().manual_seed(0)
         total_len = len(dataset)
         val_len = int(total_len * self.val_split_pct)

torchgeo/datasets/substation.py

@@ -62,6 +62,7 @@ def __init__(
             use_timepoints: Whether to use multiple timepoints for each image.
             mask_2d: Whether to use a 2D mask.
             timepoint_aggregation: How to aggregate multiple timepoints.
+            num_of_timepoints: Number of timepoints to use for each image.
             download: Whether to download the dataset if it is not found.
             checksum: Whether to verify the dataset after downloading.
         """
@@ -126,7 +127,6 @@ def __getitem__(self, index: int) -> dict[str, Tensor]:
         mask = torch.from_numpy(mask).long()
         mask = mask.unsqueeze(dim=0)
 
-
         if self.mask_2d:
             mask_0 = 1.0 - mask
             mask = torch.concat([mask_0, mask], dim=0)