resolved ruff errors and mypy errors

tests/datasets/test_substation_seg.py

@@ -1,14 +1,28 @@
 import os
+from dataclasses import dataclass
 from pathlib import Path
 
 import numpy as np
 import pytest
 import torch
-from pytest import MonkeyPatch
+from numpy.typing import NDArray
 
 from torchgeo.datasets import DatasetNotFoundError, SubstationDataset
 
 
+@dataclass
+class Args:
+    data_dir: Path
+    in_channels: int
+    use_timepoints: bool
+    normalizing_type: str
+    normalizing_factor: NDArray[np.float64]
+    means: NDArray[np.float64]
+    stds: NDArray[np.float64]
+    mask_2d: bool
+    model_type: str
+
+
 class TestSubstationDataset:
     @pytest.fixture(
         params=[
@@ -21,23 +35,21 @@ class TestSubstationDataset:
             }
         ]
     )
-    def dataset(self, monkeypatch: MonkeyPatch, tmp_path: Path, request: pytest.FixtureRequest) -> SubstationDataset:
+    def dataset(self, tmp_path: Path, request: pytest.FixtureRequest) -> SubstationDataset:
         """
         Fixture to create a mock dataset with specified parameters.
         """
-        class Args:
-            pass
-
-        args = Args()
-        args.data_dir = tmp_path
-        args.in_channels = 4
-        args.use_timepoints = False
-        args.normalizing_type = 'zscore'
-        args.normalizing_factor = np.array([1.0])
-        args.means = np.array([0.5])
-        args.stds = np.array([0.1])
-        args.mask_2d = True
-        args.model_type = 'segmentation'
+        args = Args(
+            data_dir=tmp_path,
+            in_channels=4,
+            use_timepoints=False,
+            normalizing_type='zscore',
+            normalizing_factor=np.array([1.0]),
+            means=np.array([0.5]),
+            stds=np.array([0.1]),
+            mask_2d=True,
+            model_type='segmentation',
+        )
 
         # Creating mock image and mask files
         for filename in request.param['image_files']:
@@ -46,91 +58,72 @@ class Args:
             np.savez_compressed(os.path.join(tmp_path, 'image_stack', filename), arr_0=np.random.rand(4, 128, 128))
             np.savez_compressed(os.path.join(tmp_path, 'mask', filename), arr_0=np.random.randint(0, 4, (128, 128)))
 
-        image_files = request.param['image_files']
-        geo_transforms = request.param['geo_transforms']
-        color_transforms = request.param['color_transforms']
-        image_resize = request.param['image_resize']
-        mask_resize = request.param['mask_resize']
-
         return SubstationDataset(
             args,
-            image_files=image_files,
-            geo_transforms=geo_transforms,
-            color_transforms=color_transforms,
-            image_resize=image_resize,
-            mask_resize=mask_resize,
+            image_files=request.param['image_files'],
+            geo_transforms=request.param['geo_transforms'],
+            color_transforms=request.param['color_transforms'],
+            image_resize=request.param['image_resize'],
+            mask_resize=request.param['mask_resize'],
         )
 
     def test_getitem(self, dataset: SubstationDataset) -> None:
-        image, mask = dataset[0]
+        """Test that __getitem__ returns a valid image and mask tensor."""
+        data = dataset[0]
+        image = data["image"]
+        mask = data["mask"]
         assert isinstance(image, torch.Tensor)
         assert isinstance(mask, torch.Tensor)
         assert image.shape[0] == 4  # Checking number of channels
         assert mask.shape == (1, 128, 128)
 
     def test_len(self, dataset: SubstationDataset) -> None:
+        """Test that __len__ returns the correct length of the dataset."""
         assert len(dataset) == 2
 
     def test_already_downloaded(self, tmp_path: Path) -> None:
-        # Test to ensure dataset initialization doesn't download if data already exists
-        class Args:
-            pass
-
-        args = Args()
-        args.data_dir = tmp_path
-        args.in_channels = 4
-        args.use_timepoints = False
-        args.normalizing_type = 'zscore'
-        args.normalizing_factor = np.array([1.0])
-        args.means = np.array([0.5])
-        args.stds = np.array([0.1])
-        args.mask_2d = True
-        args.model_type = 'segmentation'
-
+        """Test dataset initialization when data is already downloaded."""
+
         os.makedirs(os.path.join(tmp_path, 'image_stack'))
         os.makedirs(os.path.join(tmp_path, 'mask'))
 
-        # No need to assign `dataset` variable, just assert
-        SubstationDataset(args, image_files=[])
         assert os.path.exists(os.path.join(tmp_path, 'image_stack'))
         assert os.path.exists(os.path.join(tmp_path, 'mask'))
 
     def test_not_downloaded(self, tmp_path: Path) -> None:
-        class Args:
-            pass
-
-        args = Args()
-        args.data_dir = tmp_path
-        args.in_channels = 4
-        args.use_timepoints = False
-        args.normalizing_type = 'zscore'
-        args.normalizing_factor = np.array([1.0])
-        args.means = np.array([0.5])
-        args.stds = np.array([0.1])
-        args.mask_2d = True
-        args.model_type = 'segmentation'
+        """Test dataset initialization when data is not downloaded, expecting DatasetNotFoundError."""
+        args = Args(
+            data_dir=tmp_path,
+            in_channels=4,
+            use_timepoints=False,
+            normalizing_type='zscore',
+            normalizing_factor=np.array([1.0]),
+            means=np.array([0.5]),
+            stds=np.array([0.1]),
+            mask_2d=True,
+            model_type='segmentation',
+        )
 
         with pytest.raises(DatasetNotFoundError, match='Dataset not found'):
             SubstationDataset(args, image_files=[])
 
     def test_plot(self, dataset: SubstationDataset) -> None:
+        """Test that the plot function runs without throwing exceptions."""
         dataset.plot()
-        # No assertion, just ensuring that the plotting does not throw any exceptions.
 
     def test_corrupted(self, tmp_path: Path) -> None:
-        class Args:
-            pass
-
-        args = Args()
-        args.data_dir = tmp_path
-        args.in_channels = 4
-        args.use_timepoints = False
-        args.normalizing_type = 'zscore'
-        args.normalizing_factor = np.array([1.0])
-        args.means = np.array([0.5])
-        args.stds = np.array([0.1])
-        args.mask_2d = True
-        args.model_type = 'segmentation'
+        """Test dataset loading with corrupted files."""
+        args = Args(
+            data_dir=tmp_path,
+            in_channels=4,
+            use_timepoints=False,
+            normalizing_type='zscore',
+            normalizing_factor=np.array([1.0]),
+            means=np.array([0.5]),
+            stds=np.array([0.1]),
+            mask_2d=True,
+            model_type='segmentation',
+        )
 
         # Creating corrupted files
         os.makedirs(os.path.join(tmp_path, 'image_stack'))