Bump black[jupyter] from 23.12.1 to 24.1.0 in /requirements (#1829)

* Bump black[jupyter] from 23.12.1 to 24.1.0 in /requirements

Bumps [black[jupyter]](https://github.com/psf/black) from 23.12.1 to 24.1.0.
- [Release notes](https://github.com/psf/black/releases)
- [Changelog](https://github.com/psf/black/blob/main/CHANGES.md)
- [Commits](https://github.com/psf/black/compare/23.12.1...24.1.0)

---
updated-dependencies:
- dependency-name: black[jupyter]
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

* Update TorchGeo

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

.pre-commit-config.yaml

@@ -12,7 +12,7 @@ repos:
              additional_dependencies: ['.[colors]']
 
     -   repo: https://github.com/psf/black
-        rev: 23.12.1
+        rev: 24.1.0
         hooks:
         -   id: black
             args: [--skip-magic-trailing-comma]

experiments/ssl4eo/landsat/chip_landsat_benchmark.py

@@ -65,9 +65,10 @@ if __name__ == "__main__":
         layer_name = "cdl"
 
     for img_path in tqdm(paths):
-        with rasterio.open(img_path) as img_src, rasterio.open(
-            args.mask_path
-        ) as mask_src:
+        with (
+            rasterio.open(img_path) as img_src,
+            rasterio.open(args.mask_path) as mask_src,
+        ):
             if mask_src.crs != img_src.crs:
                 mask_src = WarpedVRT(mask_src, crs=img_src.crs)
 

requirements/style.txt

@@ -1,5 +1,5 @@
 # style
-black[jupyter]==23.12.1
+black[jupyter]==24.1.0
 flake8==7.0.0
 isort[colors]==5.13.2
 pydocstyle[toml]==6.3.0

torchgeo/datamodules/l7irish.py

@@ -68,11 +68,9 @@ class L7IrishDataModule(GeoDataModule):
         """
         dataset = L7Irish(**self.kwargs)
         generator = torch.Generator().manual_seed(0)
-        (
-            self.train_dataset,
-            self.val_dataset,
-            self.test_dataset,
-        ) = random_bbox_assignment(dataset, [0.6, 0.2, 0.2], generator)
+        (self.train_dataset, self.val_dataset, self.test_dataset) = (
+            random_bbox_assignment(dataset, [0.6, 0.2, 0.2], generator)
+        )
 
         if stage in ["fit"]:
             self.train_batch_sampler = RandomBatchGeoSampler(

torchgeo/datamodules/l8biome.py

@@ -68,11 +68,9 @@ class L8BiomeDataModule(GeoDataModule):
         """
         dataset = L8Biome(**self.kwargs)
         generator = torch.Generator().manual_seed(0)
-        (
-            self.train_dataset,
-            self.val_dataset,
-            self.test_dataset,
-        ) = random_bbox_assignment(dataset, [0.6, 0.2, 0.2], generator)
+        (self.train_dataset, self.val_dataset, self.test_dataset) = (
+            random_bbox_assignment(dataset, [0.6, 0.2, 0.2], generator)
+        )
 
         if stage in ["fit"]:
             self.train_batch_sampler = RandomBatchGeoSampler(

torchgeo/datasets/idtrees.py

@@ -407,14 +407,12 @@ class IDTReeS(NonGeoDataset):
     @overload
     def _filter_boxes(
         self, image_size: tuple[int, int], min_size: int, boxes: Tensor, labels: Tensor
-    ) -> tuple[Tensor, Tensor]:
-        ...
+    ) -> tuple[Tensor, Tensor]: ...
 
     @overload
     def _filter_boxes(
         self, image_size: tuple[int, int], min_size: int, boxes: Tensor, labels: None
-    ) -> tuple[Tensor, None]:
-        ...
+    ) -> tuple[Tensor, None]: ...
 
     def _filter_boxes(
         self,

torchgeo/datasets/ssl4eo_benchmark.py

@@ -237,9 +237,11 @@ class SSL4EOLBenchmark(NonGeoDataset):
         download_url(
             self.url.format(self.mask_dir_name),
             self.root,
-            md5=self.mask_md5s[self.sensor.split("_")[0]][self.product]
-            if self.checksum
-            else None,
+            md5=(
+                self.mask_md5s[self.sensor.split("_")[0]][self.product]
+                if self.checksum
+                else None
+            ),
         )
 
     def _extract(self) -> None:

torchgeo/models/farseg.py

@@ -219,9 +219,11 @@ class _LightWeightDecoder(Module):
                             ),
                             BatchNorm2d(out_channels),
                             ReLU(inplace=True),
-                            UpsamplingBilinear2d(scale_factor=2)
-                            if num_upsample != 0
-                            else Identity(),
+                            (
+                                UpsamplingBilinear2d(scale_factor=2)
+                                if num_upsample != 0
+                                else Identity()
+                            ),
                         )
                         for idx in range(num_layers)
                     ]

torchgeo/samplers/utils.py

@@ -12,13 +12,11 @@ from ..datasets import BoundingBox
 
 
 @overload
-def _to_tuple(value: Union[tuple[int, int], int]) -> tuple[int, int]:
-    ...
+def _to_tuple(value: Union[tuple[int, int], int]) -> tuple[int, int]: ...
 
 
 @overload
-def _to_tuple(value: Union[tuple[float, float], float]) -> tuple[float, float]:
-    ...
+def _to_tuple(value: Union[tuple[float, float], float]) -> tuple[float, float]: ...
 
 
 def _to_tuple(value: Union[tuple[float, float], float]) -> tuple[float, float]: