Bump ruff from 0.8.5 to 0.9.1 in /requirements

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.0
+    rev: v0.9.1
     hooks:
       - id: ruff
         types_or:

docs/tutorials/transforms.ipynb

@@ -707,7 +707,7 @@
     "sample = dataset[idx]\n",
     "rgb = sample['image'][0, 1:4]\n",
     "image = T.ToPILImage()(rgb)\n",
-    "print(f\"Class Label: {dataset.classes[sample['label']]}\")\n",
+    "print(f'Class Label: {dataset.classes[sample[\"label\"]]}')\n",
     "image.resize((256, 256), resample=Image.BILINEAR)"
    ]
   },

experiments/torchgeo/run_resisc45_experiments.py

@@ -38,7 +38,7 @@ def do_work(work: 'Queue[str]', gpu_idx: int) -> bool:
     for model, lr, loss, weights in itertools.product(
         model_options, lr_options, loss_options, weight_options
     ):
-        experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_', '-')}"
+        experiment_name = f'{model}_{lr}_{loss}_{weights.replace("_", "-")}'
 
         output_dir = os.path.join('output', 'resisc45_experiments')
         log_dir = os.path.join(output_dir, 'logs')

experiments/torchgeo/run_so2sat_byol_experiments.py

@@ -39,7 +39,7 @@ def do_work(work: 'Queue[str]', gpu_idx: int) -> bool:
     for model, lr, loss, weights, bands in itertools.product(
         model_options, lr_options, loss_options, weight_options, bands_options
     ):
-        experiment_name = f"{model}_{lr}_{loss}_byol_{bands}-{weights.split('/')[-2]}"
+        experiment_name = f'{model}_{lr}_{loss}_byol_{bands}-{weights.split("/")[-2]}'
 
         output_dir = os.path.join('output', 'so2sat_experiments')
         log_dir = os.path.join(output_dir, 'logs')

experiments/torchgeo/run_so2sat_experiments.py

@@ -38,7 +38,7 @@ def do_work(work: 'Queue[str]', gpu_idx: int) -> bool:
     for model, lr, loss, weights in itertools.product(
         model_options, lr_options, loss_options, weight_options
     ):
-        experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_', '-')}"
+        experiment_name = f'{model}_{lr}_{loss}_{weights.replace("_", "-")}'
 
         output_dir = os.path.join('output', 'so2sat_experiments')
         log_dir = os.path.join(output_dir, 'logs')

experiments/torchgeo/run_so2sat_seed_experiments.py

@@ -39,7 +39,7 @@ def do_work(work: 'Queue[str]', gpu_idx: int) -> bool:
     for model, lr, loss, weights, seed in itertools.product(
         model_options, lr_options, loss_options, weight_options, seeds
     ):
-        experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_', '-')}_{seed}"
+        experiment_name = f'{model}_{lr}_{loss}_{weights.replace("_", "-")}_{seed}'
 
         output_dir = os.path.join('output', 'so2sat_seed_experiments')
         log_dir = os.path.join(output_dir, 'logs')

pyproject.toml

@@ -115,8 +115,8 @@ docs = [
 style = [
     # mypy 0.900+ required for pyproject.toml support
     "mypy>=0.900",
-    # ruff 0.8+ required for removal of ANN101, ANN102
-    "ruff>=0.8",
+    # ruff 0.9+ required for 2025 style guide
+    "ruff>=0.9",
 ]
 tests = [
     # nbmake 1.3.3+ required for variable mocking

requirements/style.txt

@@ -1,3 +1,3 @@
 # style
 mypy==1.14.1
-ruff==0.8.5
+ruff==0.9.1

tests/data/inria/data.py

@@ -68,9 +68,9 @@ def generate_test_data(root: str, n_samples: int = 2) -> str:
         lbl = np.random.randint(2, size=size, dtype=dtype)
         timg = np.random.randint(dtype_max, size=size, dtype=dtype)
 
-        img_path = os.path.join(img_dir, f'austin{i+1}.tif')
-        lbl_path = os.path.join(lbl_dir, f'austin{i+1}.tif')
-        timg_path = os.path.join(timg_dir, f'austin{i+10}.tif')
+        img_path = os.path.join(img_dir, f'austin{i + 1}.tif')
+        lbl_path = os.path.join(lbl_dir, f'austin{i + 1}.tif')
+        timg_path = os.path.join(timg_dir, f'austin{i + 10}.tif')
 
         write_data(img_path, img, driver, crs, transform)
         write_data(lbl_path, lbl, driver, crs, transform)

tests/data/seasonet/data.py

@@ -63,7 +63,7 @@
         os.remove(archive)
 
     for grid, comp in zip(grids, name_comps):
-        file_name = f"{comp[0]}_{''.join(comp[1:8])}_{'_'.join(comp[8:])}"
+        file_name = f'{comp[0]}_{"".join(comp[1:8])}_{"_".join(comp[8:])}'
         dir = os.path.join(season, f'grid{grid}', file_name)
         os.makedirs(dir)
 

tests/data/ssl4eo_benchmark_landsat/data.py

@@ -193,7 +193,7 @@ def create_tarballs(directories: str) -> None:
     # mask directory cdl
     mask_keep = ['tm_toa', 'etm_sr', 'oli_sr']
     mask_filenames = {
-        f"ssl4eo_l_{key.split('_')[0]}_cdl": val
+        f'ssl4eo_l_{key.split("_")[0]}_cdl': val
         for key, val in filenames.items()
         if key in mask_keep
     }
@@ -203,7 +203,7 @@ def create_tarballs(directories: str) -> None:
 
     # mask directory nlcd
     mask_filenames = {
-        f"ssl4eo_l_{key.split('_')[0]}_nlcd": val
+        f'ssl4eo_l_{key.split("_")[0]}_nlcd': val
         for key, val in filenames.items()
         if key in mask_keep
     }

tests/datamodules/test_digital_typhoon.py

@@ -57,14 +57,14 @@ def find_max_time_per_id(
             # Assert that each max value in train_max_values is lower
             # than in val_max_values for each key id
             for id, max_value in train_max_values.items():
-                assert (
-                    id not in val_max_values or max_value < val_max_values[id]
-                ), f'Max value for id {id} in train is not lower than in validation.'
+                assert id not in val_max_values or max_value < val_max_values[id], (
+                    f'Max value for id {id} in train is not lower than in validation.'
+                )
         else:
             train_ids = {seq['id'] for seq in train_sequences}
             val_ids = {seq['id'] for seq in val_sequences}
 
             # Assert that the intersection between train_ids and val_ids is empty
-            assert (
-                len(train_ids & val_ids) == 0
-            ), 'Train and validation datasets have overlapping ids.'
+            assert len(train_ids & val_ids) == 0, (
+                'Train and validation datasets have overlapping ids.'
+            )

torchgeo/datamodules/digital_typhoon.py

@@ -43,9 +43,9 @@ def __init__(
         """
         super().__init__(DigitalTyphoon, batch_size, num_workers, **kwargs)
 
-        assert (
-            split_by in self.valid_split_types
-        ), f'Please choose from {self.valid_split_types}'
+        assert split_by in self.valid_split_types, (
+            f'Please choose from {self.valid_split_types}'
+        )
         self.split_by = split_by
 
     def _split_dataset(

torchgeo/datamodules/ftw.py

@@ -44,9 +44,9 @@ def __init__(
         Raises:
             AssertionError: If 'countries' are specified in kwargs
         """
-        assert (
-            'countries' not in kwargs
-        ), "Please specify 'train_countries', 'val_countries', and 'test_countries' instead of 'countries' inside kwargs"
+        assert 'countries' not in kwargs, (
+            "Please specify 'train_countries', 'val_countries', and 'test_countries' instead of 'countries' inside kwargs"
+        )
 
         super().__init__(FieldsOfTheWorld, batch_size, num_workers, **kwargs)
 

torchgeo/datasets/agrifieldnet.py

@@ -149,9 +149,9 @@ def __init__(
         Raises:
             DatasetNotFoundError: If dataset is not found and *download* is False.
         """
-        assert (
-            set(classes) <= self.cmap.keys()
-        ), f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        assert set(classes) <= self.cmap.keys(), (
+            f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        )
         assert 0 in classes, 'Classes must include the background class: 0'
 
         self.paths = paths

torchgeo/datasets/bigearthnet.py

@@ -565,9 +565,9 @@ def plot(
         ax.imshow(image)
         ax.axis('off')
         if show_titles:
-            title = f"Labels: {', '.join(labels)}"
+            title = f'Labels: {", ".join(labels)}'
             if showing_predictions:
-                title += f"\nPredictions: {', '.join(predictions)}"
+                title += f'\nPredictions: {", ".join(predictions)}'
             ax.set_title(title)
 
         if suptitle is not None:

torchgeo/datasets/biomassters.py

@@ -81,14 +81,14 @@ def __init__(
         """
         self.root = root
 
-        assert (
-            split in self.valid_splits
-        ), f'Please choose one of the valid splits: {self.valid_splits}.'
+        assert split in self.valid_splits, (
+            f'Please choose one of the valid splits: {self.valid_splits}.'
+        )
         self.split = split
 
-        assert set(sensors).issubset(
-            set(self.valid_sensors)
-        ), f'Please choose a subset of valid sensors: {self.valid_sensors}.'
+        assert set(sensors).issubset(set(self.valid_sensors)), (
+            f'Please choose a subset of valid sensors: {self.valid_sensors}.'
+        )
         self.sensors = sensors
         self.as_time_series = as_time_series
 

torchgeo/datasets/cdl.py

@@ -248,9 +248,9 @@ def __init__(
             'CDL data product only exists for the following years: '
             f'{list(self.md5s.keys())}.'
         )
-        assert (
-            set(classes) <= self.cmap.keys()
-        ), f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        assert set(classes) <= self.cmap.keys(), (
+            f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        )
         assert 0 in classes, 'Classes must include the background class: 0'
 
         self.paths = paths

torchgeo/datasets/cms_mangrove_canopy.py

@@ -204,15 +204,15 @@ def __init__(
         self.checksum = checksum
 
         assert isinstance(country, str), 'Country argument must be a str.'
-        assert (
-            country in self.all_countries
-        ), f'You have selected an invalid country, please choose one of {self.all_countries}'
+        assert country in self.all_countries, (
+            f'You have selected an invalid country, please choose one of {self.all_countries}'
+        )
         self.country = country
 
         assert isinstance(measurement, str), 'Measurement must be a string.'
-        assert (
-            measurement in self.measurements
-        ), f'You have entered an invalid measurement, please choose one of {self.measurements}.'
+        assert measurement in self.measurements, (
+            f'You have entered an invalid measurement, please choose one of {self.measurements}.'
+        )
         self.measurement = measurement
 
         self.filename_glob = f'**/Mangrove_{self.measurement}_{self.country}*'

torchgeo/datasets/digital_typhoon.py

@@ -139,9 +139,9 @@ def __init__(
         self.min_feature_value = min_feature_value
         self.max_feature_value = max_feature_value
 
-        assert (
-            task in self.valid_tasks
-        ), f'Please choose one of {self.valid_tasks}, you provided {task}.'
+        assert task in self.valid_tasks, (
+            f'Please choose one of {self.valid_tasks}, you provided {task}.'
+        )
         self.task = task
 
         assert set(features).issubset(set(self.valid_features))

torchgeo/datasets/loveda.py

@@ -115,9 +115,9 @@ def __init__(
             DatasetNotFoundError: If dataset is not found and *download* is False.
         """
         assert split in self.splits
-        assert set(scene).intersection(
-            set(self.scenes)
-        ), "The possible scenes are 'rural' and/or 'urban'"
+        assert set(scene).intersection(set(self.scenes)), (
+            "The possible scenes are 'rural' and/or 'urban'"
+        )
         assert len(scene) <= 2, "There are no other scenes than 'rural' or 'urban'"
 
         self.root = root

torchgeo/datasets/mdas.py

@@ -162,13 +162,13 @@ def __init__(
         """
         self.root = root
         self.download = download
-        assert all(
-            sub in self.valid_subareas for sub in subareas
-        ), f'Subareas must be one of {self.valid_subareas}'
+        assert all(sub in self.valid_subareas for sub in subareas), (
+            f'Subareas must be one of {self.valid_subareas}'
+        )
         self.subareas = subareas
-        assert all(
-            mod in self.valid_modalities for mod in modalities
-        ), f'Modalities must be one of {self.valid_modalities}'
+        assert all(mod in self.valid_modalities for mod in modalities), (
+            f'Modalities must be one of {self.valid_modalities}'
+        )
         self.modalities = modalities
         self.transforms = transforms
         self.checksum = checksum

torchgeo/datasets/mmearth.py

@@ -206,12 +206,12 @@ def __init__(
         """
         lazy_import('h5py')
 
-        assert (
-            normalization_mode in self.norm_modes
-        ), f'Invalid normalization mode: {normalization_mode}, please choose from {self.norm_modes}'
-        assert (
-            subset in self.subsets
-        ), f'Invalid dataset version: {subset}, please choose from {self.subsets}'
+        assert normalization_mode in self.norm_modes, (
+            f'Invalid normalization mode: {normalization_mode}, please choose from {self.norm_modes}'
+        )
+        assert subset in self.subsets, (
+            f'Invalid dataset version: {subset}, please choose from {self.subsets}'
+        )
 
         self._validate_modalities(modalities)
         self.modalities = modalities

torchgeo/datasets/nlcd.py

@@ -167,9 +167,9 @@ def __init__(
             'NLCD data product only exists for the following years: '
             f'{list(self.md5s.keys())}.'
         )
-        assert (
-            set(classes) <= self.cmap.keys()
-        ), f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        assert set(classes) <= self.cmap.keys(), (
+            f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        )
         assert 0 in classes, 'Classes must include the background class: 0'
 
         self.paths = paths

torchgeo/datasets/seasonet.py

@@ -450,7 +450,7 @@ def plot(
             axs[ax].imshow(image)
             axs[ax].axis('off')
             if show_titles:
-                axs[ax].set_title(f'Image {ax+1}')
+                axs[ax].set_title(f'Image {ax + 1}')
 
         axs[ax + 1].imshow(mask, vmin=0, vmax=32, cmap=plt_cmap, interpolation='none')
         axs[ax + 1].axis('off')

torchgeo/datasets/skippd.py

@@ -104,14 +104,14 @@ def __init__(
         """
         lazy_import('h5py')
 
-        assert (
-            split in self.valid_splits
-        ), f'Please choose one of these valid data splits {self.valid_splits}.'
+        assert split in self.valid_splits, (
+            f'Please choose one of these valid data splits {self.valid_splits}.'
+        )
         self.split = split
 
-        assert (
-            task in self.valid_tasks
-        ), f'Please choose one of these valid tasks {self.valid_tasks}.'
+        assert task in self.valid_tasks, (
+            f'Please choose one of these valid tasks {self.valid_tasks}.'
+        )
         self.task = task
 
         self.root = root

torchgeo/datasets/south_africa_crop_type.py

@@ -131,9 +131,9 @@ def __init__(
         Raises:
             DatasetNotFoundError: If dataset is not found and *download* is False.
         """
-        assert (
-            set(classes) <= self.cmap.keys()
-        ), f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        assert set(classes) <= self.cmap.keys(), (
+            f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        )
         assert 0 in classes, 'Classes must include the background class: 0'
 
         self.paths = paths

torchgeo/datasets/ssl4eo_benchmark.py

@@ -138,26 +138,26 @@ def __init__(
             AssertionError: if any arguments are invalid
             DatasetNotFoundError: If dataset is not found and *download* is False.
         """
-        assert (
-            sensor in self.valid_sensors
-        ), f'Only supports one of {self.valid_sensors}, but found {sensor}.'
+        assert sensor in self.valid_sensors, (
+            f'Only supports one of {self.valid_sensors}, but found {sensor}.'
+        )
         self.sensor = sensor
-        assert (
-            product in self.valid_products
-        ), f'Only supports one of {self.valid_products}, but found {product}.'
+        assert product in self.valid_products, (
+            f'Only supports one of {self.valid_products}, but found {product}.'
+        )
         self.product = product
-        assert (
-            split in self.valid_splits
-        ), f'Only supports one of {self.valid_splits}, but found {split}.'
+        assert split in self.valid_splits, (
+            f'Only supports one of {self.valid_splits}, but found {split}.'
+        )
         self.split = split
 
         self.cmap = self.cmaps[product]
         if classes is None:
             classes = list(self.cmap.keys())
 
-        assert (
-            set(classes) <= self.cmap.keys()
-        ), f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        assert set(classes) <= self.cmap.keys(), (
+            f'Only the following classes are valid: {list(self.cmap.keys())}.'
+        )
         assert 0 in classes, 'Classes must include the background class: 0'
 
         self.root = root