fix: Fix ruff

pyproject.toml

@@ -190,6 +190,7 @@ checks = [
     "GL02", # Closing quotes should be placed in the line after the last text 
     # in the docstring
     "GL03", # Double line break found
+    "RT04", # Return value description should start with a capital letter
 ]
 exclude = [ # don't report on objects that match any of these regex
     "test_*",

src/careamics/cli/conf.py

@@ -3,12 +3,11 @@
 import sys
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional
+from typing import Annotated, Optional
 
 import click
 import typer
 import yaml
-from typing_extensions import Annotated
 
 from ..config import (
     Configuration,

src/careamics/cli/main.py

@@ -7,11 +7,10 @@
 """
 
 from pathlib import Path
-from typing import Optional
+from typing import Annotated, Optional
 
 import click
 import typer
-from typing_extensions import Annotated
 
 from ..careamist import CAREamist
 from . import conf

src/careamics/cli/utils.py

@@ -1,11 +1,11 @@
 """Utility functions for the CAREamics CLI."""
 
-from typing import Optional, Tuple
+from typing import Optional
 
 
 def handle_2D_3D_callback(
-    value: Optional[Tuple[int, int, int]]
-) -> Optional[Tuple[int, ...]]:
+    value: Optional[tuple[int, int, int]]
+) -> Optional[tuple[int, ...]]:
     """
     Callback for options that require 2D or 3D inputs.
 

src/careamics/config/architectures/architecture_model.py

@@ -1,6 +1,6 @@
 """Base model for the various CAREamics architectures."""
 
-from typing import Any, Dict
+from typing import Any
 
 from pydantic import BaseModel
 
@@ -15,7 +15,7 @@ class ArchitectureModel(BaseModel):
     architecture: str
     """Name of the architecture."""
 
-    def model_dump(self, **kwargs: Any) -> Dict[str, Any]:
+    def model_dump(self, **kwargs: Any) -> dict[str, Any]:
         """
         Dump the model as a dictionary, ignoring the architecture keyword.
 
@@ -26,7 +26,7 @@ def model_dump(self, **kwargs: Any) -> Dict[str, Any]:
 
         Returns
         -------
-        dict[str, Any]
+        {str: Any}
             Model as a dictionary.
         """
         model_dict = super().model_dump(**kwargs)

src/careamics/config/data_model.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from pprint import pformat
-from typing import Any, Literal, Optional, Union
+from typing import Annotated, Any, Literal, Optional, Union
 
 import numpy as np
 from numpy.typing import NDArray
@@ -15,7 +15,7 @@
     field_validator,
     model_validator,
 )
-from typing_extensions import Annotated, Self
+from typing_extensions import Self
 
 from .support import SupportedTransform
 from .transformations import TRANSFORMS_UNION, N2VManipulateModel

src/careamics/config/likelihood_model.py

@@ -1,11 +1,10 @@
 """Likelihood model."""
 
-from typing import Literal, Optional, Union
+from typing import Annotated, Literal, Optional, Union
 
 import numpy as np
 import torch
 from pydantic import BaseModel, ConfigDict, PlainSerializer, PlainValidator
-from typing_extensions import Annotated
 
 from careamics.models.lvae.noise_models import (
     GaussianMixtureNoiseModel,

src/careamics/config/nm_model.py

@@ -1,7 +1,7 @@
 """Noise models config."""
 
 from pathlib import Path
-from typing import Literal, Optional, Union
+from typing import Annotated, Literal, Optional, Union
 
 import numpy as np
 import torch
@@ -12,7 +12,6 @@
     PlainSerializer,
     PlainValidator,
 )
-from typing_extensions import Annotated
 
 from careamics.utils.serializers import _array_to_json, _to_numpy
 

src/careamics/config/transformations/transform_model.py

@@ -1,6 +1,6 @@
 """Parent model for the transforms."""
 
-from typing import Any, Dict
+from typing import Any
 
 from pydantic import BaseModel, ConfigDict
 
@@ -23,7 +23,7 @@ class TransformModel(BaseModel):
 
     name: str
 
-    def model_dump(self, **kwargs) -> Dict[str, Any]:
+    def model_dump(self, **kwargs) -> dict[str, Any]:
         """
         Return the model as a dictionary.
 
@@ -34,7 +34,7 @@ def model_dump(self, **kwargs) -> Dict[str, Any]:
 
         Returns
         -------
-        Dict[str, Any]
+        {str: Any}
             Dictionary representation of the model.
         """
         model_dict = super().model_dump(**kwargs)

src/careamics/config/transformations/transform_union.py

@@ -1,9 +1,8 @@
 """Type used to represent all transformations users can create."""
 
-from typing import Union
+from typing import Annotated, Union
 
 from pydantic import Discriminator
-from typing_extensions import Annotated
 
 from .n2v_manipulate_model import N2VManipulateModel
 from .xy_flip_model import XYFlipModel

src/careamics/config/validators/validator_utils.py

@@ -4,7 +4,7 @@
 These functions are used to validate dimensions and axes of inputs.
 """
 
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 _AXES = "STCZYX"
 
@@ -79,14 +79,14 @@ def value_ge_than_8_power_of_2(
 
 
 def patch_size_ge_than_8_power_of_2(
-    patch_list: Optional[Union[List[int], Union[Tuple[int, ...]]]],
+    patch_list: Optional[Union[list[int], Union[tuple[int, ...]]]],
 ) -> None:
     """
     Validate that each entry is greater or equal than 8 and a power of 2.
 
     Parameters
     ----------
-    patch_list : Optional[Union[List[int]]]
+    patch_list : list or typle of int, or None
         Patch size.
 
     Raises

src/careamics/dataset/dataset_utils/dataset_utils.py

@@ -1,7 +1,5 @@
 """Dataset utilities."""
 
-from typing import List, Tuple
-
 import numpy as np
 
 from careamics.utils.logging import get_logger
@@ -10,14 +8,14 @@
 
 
 def _get_shape_order(
-    shape_in: Tuple[int, ...], axes_in: str, ref_axes: str = "STCZYX"
-) -> Tuple[Tuple[int, ...], str, List[int]]:
+    shape_in: tuple[int, ...], axes_in: str, ref_axes: str = "STCZYX"
+) -> tuple[tuple[int, ...], str, list[int]]:
     """
     Compute a new shape for the array based on the reference axes.
 
     Parameters
     ----------
-    shape_in : Tuple[int, ...]
+    shape_in : tuple[int, ...]
         Input shape.
     axes_in : str
         Input axes.
@@ -26,7 +24,7 @@ def _get_shape_order(
 
     Returns
     -------
-    Tuple[Tuple[int, ...], str, List[int]]
+    tuple[tuple[int, ...], str, list[int]]
         New shape, new axes, indices of axes in the new axes order.
     """
     indices = [axes_in.find(k) for k in ref_axes]

src/careamics/dataset/dataset_utils/file_utils.py

@@ -2,7 +2,7 @@
 
 from fnmatch import fnmatch
 from pathlib import Path
-from typing import List, Union
+from typing import Union
 
 import numpy as np
 
@@ -12,12 +12,12 @@
 logger = get_logger(__name__)
 
 
-def get_files_size(files: List[Path]) -> float:
+def get_files_size(files: list[Path]) -> float:
     """Get files size in MB.
 
     Parameters
     ----------
-    files : List[Path]
+    files : list of pathlib.Path
         List of files.
 
     Returns
@@ -32,7 +32,7 @@ def list_files(
     data_path: Union[str, Path],
     data_type: Union[str, SupportedData],
     extension_filter: str = "",
-) -> List[Path]:
+) -> list[Path]:
     """List recursively files in `data_path` and return a sorted list.
 
     If `data_path` is a file, its name is validated against the `data_type` using
@@ -55,8 +55,8 @@ def list_files(
 
     Returns
     -------
-    List[Path]
-        List of pathlib.Path objects.
+    list[Path]
+        list of pathlib.Path objects.
 
     Raises
     ------
@@ -105,17 +105,17 @@ def list_files(
     return files
 
 
-def validate_source_target_files(src_files: List[Path], tar_files: List[Path]) -> None:
+def validate_source_target_files(src_files: list[Path], tar_files: list[Path]) -> None:
     """
     Validate source and target path lists.
 
     The two lists should have the same number of files, and the filenames should match.
 
     Parameters
     ----------
-    src_files : List[Path]
+    src_files : list of pathlib.Path
         List of source files.
-    tar_files : List[Path]
+    tar_files : list of pathlib.Path
         List of target files.
 
     Raises

src/careamics/dataset/dataset_utils/iterate_over_files.py

@@ -2,8 +2,9 @@
 
 from __future__ import annotations
 
+from collections.abc import Generator
 from pathlib import Path
-from typing import Callable, Generator, Optional, Union
+from typing import Callable, Optional, Union
 
 from numpy.typing import NDArray
 from torch.utils.data import get_worker_info

src/careamics/dataset/iterable_pred_dataset.py

@@ -2,8 +2,9 @@
 
 from __future__ import annotations
 
+from collections.abc import Generator
 from pathlib import Path
-from typing import Any, Callable, Generator
+from typing import Any, Callable
 
 from numpy.typing import NDArray
 from torch.utils.data import IterableDataset

src/careamics/dataset/iterable_tiled_pred_dataset.py

@@ -2,8 +2,9 @@
 
 from __future__ import annotations
 
+from collections.abc import Generator
 from pathlib import Path
-from typing import Any, Callable, Generator
+from typing import Any, Callable
 
 from numpy.typing import NDArray
 from torch.utils.data import IterableDataset

src/careamics/dataset/patching/random_patching.py

@@ -1,6 +1,7 @@
 """Random patching utilities."""
 
-from typing import Generator, List, Optional, Tuple, Union
+from collections.abc import Generator
+from typing import Optional, Union
 
 import numpy as np
 import zarr
@@ -11,10 +12,10 @@
 # TOOD split in testable functions
 def extract_patches_random(
     arr: np.ndarray,
-    patch_size: Union[List[int], Tuple[int, ...]],
+    patch_size: Union[list[int], tuple[int, ...]],
     target: Optional[np.ndarray] = None,
     seed: Optional[int] = None,
-) -> Generator[Tuple[np.ndarray, Optional[np.ndarray]], None, None]:
+) -> Generator[tuple[np.ndarray, Optional[np.ndarray]], None, None]:
     """
     Generate patches from an array in a random manner.
 
@@ -31,12 +32,12 @@ def extract_patches_random(
     ----------
     arr : np.ndarray
         Input image array.
-    patch_size : Tuple[int]
+    patch_size : tuple of int
         Patch sizes in each dimension.
     target : Optional[np.ndarray], optional
         Target array, by default None.
-    seed : Optional[int], optional
-        Random seed, by default None.
+    seed : int or None, default=None
+        Random seed.
 
     Yields
     ------
@@ -112,8 +113,8 @@ def extract_patches_random(
 
 def extract_patches_random_from_chunks(
     arr: zarr.Array,
-    patch_size: Union[List[int], Tuple[int, ...]],
-    chunk_size: Union[List[int], Tuple[int, ...]],
+    patch_size: Union[list[int], tuple[int, ...]],
+    chunk_size: Union[list[int], tuple[int, ...]],
     chunk_limit: Optional[int] = None,
     seed: Optional[int] = None,
 ) -> Generator[np.ndarray, None, None]:
@@ -127,9 +128,9 @@ def extract_patches_random_from_chunks(
     ----------
     arr : np.ndarray
         Input image array.
-    patch_size : Union[List[int], Tuple[int, ...]]
+    patch_size : Union[list[int], tuple[int, ...]]
         Patch sizes in each dimension.
-    chunk_size : Union[List[int], Tuple[int, ...]]
+    chunk_size : Union[list[int], tuple[int, ...]]
         Chunk sizes to load from the.
     chunk_limit : Optional[int], optional
         Number of chunks to load, by default None.