Remove support for Python 3.8

.github/workflows/tests.yml

@@ -22,7 +22,6 @@ jobs:
           windows-latest,
         ]
         python: [
-          '3.8',
           '3.9',
           '3.10',
           '3.11',

.pre-commit-config.yaml

@@ -37,7 +37,7 @@ repos:
     rev: v3.19.0
     hooks:
       - id: pyupgrade
-        args: ['--py38-plus', '--keep-runtime-typing']
+        args: ['--py39-plus', '--keep-runtime-typing']
 
   - repo: https://github.com/PyCQA/doc8
     rev: v1.1.2

docs/source/conf.py

@@ -12,7 +12,6 @@
 import os
 import sys
 from datetime import date
-from typing import List
 
 sys.path.insert(0, os.path.abspath('../../src'))
 import torchio  # noqa: E402
@@ -83,7 +82,7 @@
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns: List[str] = []
+exclude_patterns: list[str] = []
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = 'friendly'

pyproject.toml

@@ -23,7 +23,6 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
-    "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.12",
@@ -35,7 +34,7 @@ classifiers = [
     "Typing :: Typed"
 ]
 keywords = ["medical", "image processing", "pytorch", "augmentation", "mri"]
-requires-python = ">=3.8,<3.13"
+requires-python = ">=3.9,<3.13"
 dependencies = [
     "deprecated",
     "humanize",

src/torchio/data/dataset.py

@@ -1,11 +1,10 @@
 from __future__ import annotations
 
 import copy
+from collections.abc import Iterable
+from collections.abc import Sequence
 from typing import Callable
-from typing import Iterable
-from typing import List
 from typing import Optional
-from typing import Sequence
 
 from torch.utils.data import Dataset
 
@@ -107,7 +106,7 @@ def from_batch(cls, batch: dict) -> SubjectsDataset:
             batch: Dictionary generated by a data loader, containing data that
                 can be converted to instances of :class:`~.torchio.Subject`.
         """
-        subjects: List[Subject] = get_subjects_from_batch(batch)
+        subjects: list[Subject] = get_subjects_from_batch(batch)
         return cls(subjects)
 
     def dry_iter(self):

src/torchio/data/image.py

@@ -1,13 +1,10 @@
 import warnings
 from collections import Counter
+from collections.abc import Sequence
 from pathlib import Path
 from typing import Any
 from typing import Callable
-from typing import Dict
-from typing import List
 from typing import Optional
-from typing import Sequence
-from typing import Tuple
 from typing import Union
 
 import humanize
@@ -51,8 +48,8 @@
 from .io import write_image
 
 PROTECTED_KEYS = DATA, AFFINE, TYPE, PATH, STEM
-TypeBound = Tuple[float, float]
-TypeBounds = Tuple[TypeBound, TypeBound, TypeBound]
+TypeBound = tuple[float, float]
+TypeBounds = tuple[TypeBound, TypeBound, TypeBound]
 
 deprecation_message = (
     'Setting the image data with the property setter is deprecated. Use the'
@@ -139,7 +136,7 @@ def __init__(
         affine: Optional[TypeData] = None,
         check_nans: bool = False,  # removed by ITK by default
         reader: Callable = read_image,
-        **kwargs: Dict[str, Any],
+        **kwargs: dict[str, Any],
     ):
         self.check_nans = check_nans
         self.reader = reader
@@ -336,7 +333,7 @@ def width(self) -> int:
         return self.spatial_shape[0]
 
     @property
-    def orientation(self) -> Tuple[str, str, str]:
+    def orientation(self) -> tuple[str, str, str]:
         """Orientation codes."""
         return nib.aff2axcodes(self.affine)
 
@@ -349,14 +346,14 @@ def direction(self) -> TypeDirection3D:
         return direction  # type: ignore[return-value]
 
     @property
-    def spacing(self) -> Tuple[float, float, float]:
+    def spacing(self) -> tuple[float, float, float]:
         """Voxel spacing in mm."""
         _, spacing = get_rotation_and_spacing_from_affine(self.affine)
         sx, sy, sz = spacing
         return sx, sy, sz
 
     @property
-    def origin(self) -> Tuple[float, float, float]:
+    def origin(self) -> tuple[float, float, float]:
         """Center of first voxel in array, in mm."""
         ox, oy, oz = self.affine[:3, 3]
         return ox, oy, oz
@@ -479,7 +476,7 @@ def _parse_single_path(
     def _parse_path(
         self,
         path: Optional[Union[TypePath, Sequence[TypePath]]],
-    ) -> Optional[Union[Path, List[Path]]]:
+    ) -> Optional[Union[Path, list[Path]]]:
         if path is None:
             return None
         elif isinstance(path, dict):
@@ -565,7 +562,7 @@ def load(self) -> None:
         if self._loaded:
             return
 
-        paths: List[Path]
+        paths: list[Path]
         if self._is_multipath():
             paths = self.path  # type: ignore[assignment]
         else:
@@ -807,12 +804,12 @@ def show(self, viewer_path: Optional[TypePath] = None) -> None:
 
     def _crop_from_slices(
         self,
-        slices: Union[TypeSlice, Tuple[TypeSlice, ...]],
+        slices: Union[TypeSlice, tuple[TypeSlice, ...]],
     ) -> 'Image':
         from ..transforms import Crop
 
         slices_tuple = to_tuple(slices)  # type: ignore[assignment]
-        cropping: List[int] = []
+        cropping: list[int] = []
         for dim, slice_ in enumerate(slices_tuple):
             if isinstance(slice_, slice):
                 pass
@@ -914,7 +911,7 @@ def count_nonzero(self) -> int:
         """Get the number of voxels that are not 0."""
         return int(self.data.count_nonzero().item())
 
-    def count_labels(self) -> Dict[int, int]:
+    def count_labels(self) -> dict[int, int]:
         """Get the number of voxels in each label."""
         values_list = self.data.flatten().tolist()
         counter = Counter(values_list)

src/torchio/data/inference/aggregator.py

@@ -1,6 +1,5 @@
 import warnings
 from typing import Optional
-from typing import Tuple
 
 import numpy as np
 import torch
@@ -58,7 +57,7 @@ def _crop_patch(
         patch: torch.Tensor,
         location: np.ndarray,
         overlap: np.ndarray,
-    ) -> Tuple[torch.Tensor, np.ndarray]:
+    ) -> tuple[torch.Tensor, np.ndarray]:
         half_overlap = overlap // 2  # overlap is always even in grid sampler
         index_ini, index_fin = location[:3], location[3:]
 

src/torchio/data/io.py

@@ -1,7 +1,6 @@
 import warnings
 from pathlib import Path
 from typing import Optional
-from typing import Tuple
 from typing import Union
 
 import nibabel as nib
@@ -283,7 +282,7 @@ def _write_niftyreg_matrix(matrix: TypeData, txt_path: TypePath) -> None:
 
 def get_rotation_and_spacing_from_affine(
     affine: np.ndarray,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     # From https://github.com/nipy/nibabel/blob/master/nibabel/orientations.py
     rotation_zoom = affine[:3, :3]
     spacing = np.sqrt(np.sum(rotation_zoom * rotation_zoom, axis=0))
@@ -337,7 +336,7 @@ def nib_to_sitk(
 def sitk_to_nib(
     image: sitk.Image,
     keepdim: bool = False,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     data = sitk.GetArrayFromImage(image).transpose()
     data = check_uint_to_int(data)
     num_components = image.GetNumberOfComponentsPerPixel()
@@ -391,7 +390,7 @@ def get_sitk_metadata_from_ras_affine(
     affine: np.ndarray,
     is_2d: bool = False,
     lps: bool = True,
-) -> Tuple[TypeTripletFloat, TypeTripletFloat, TypeDirection]:
+) -> tuple[TypeTripletFloat, TypeTripletFloat, TypeDirection]:
     direction_ras, spacing_array = get_rotation_and_spacing_from_affine(affine)
     origin_ras = affine[:3, 3]
     origin_lps = np.dot(FLIPXY_33, origin_ras)

src/torchio/data/loader.py

@@ -1,7 +1,5 @@
 from typing import Any
 from typing import Callable
-from typing import Dict
-from typing import List
 from typing import Optional
 from typing import TypeVar
 
@@ -19,7 +17,7 @@ class SubjectsLoader(DataLoader):
     def __init__(
         self,
         dataset: Dataset,
-        collate_fn: Optional[Callable[[List[T]], Any]] = None,
+        collate_fn: Optional[Callable[[list[T]], Any]] = None,
         **kwargs,
     ):
         if collate_fn is None:
@@ -31,7 +29,7 @@ def __init__(
         )
 
     @staticmethod
-    def _collate(subjects: List[Subject]) -> Dict[str, Any]:
+    def _collate(subjects: list[Subject]) -> dict[str, Any]:
         first_subject = subjects[0]
         batch_dict = {}
         for key in first_subject.keys():

src/torchio/data/queue.py

@@ -1,6 +1,5 @@
+from collections.abc import Iterator
 from itertools import islice
-from typing import Iterator
-from typing import List
 from typing import Optional
 
 import humanize
@@ -210,7 +209,7 @@ def __init__(
         self._num_sampled_subjects = 0
         if start_background:
             self._initialize_subjects_iterable()
-        self.patches_list: List[Subject] = []
+        self.patches_list: list[Subject] = []
 
         if self.shuffle_subjects and self.subject_sampler is not None:
             raise ValueError(

src/torchio/data/sampler/grid.py

@@ -1,4 +1,4 @@
-from typing import Generator
+from collections.abc import Generator
 from typing import Optional
 from typing import Union
 

src/torchio/data/sampler/label.py

@@ -1,4 +1,3 @@
-from typing import Dict
 from typing import Optional
 
 import numpy as np
@@ -63,7 +62,7 @@ def __init__(
         self,
         patch_size: TypeSpatialShape,
         label_name: Optional[str] = None,
-        label_probabilities: Optional[Dict[int, float]] = None,
+        label_probabilities: Optional[dict[int, float]] = None,
     ):
         super().__init__(patch_size, probability_map=label_name)
         self.label_probabilities_dict = label_probabilities
@@ -106,7 +105,7 @@ def get_probability_map(self, subject: Subject) -> torch.Tensor:
     @staticmethod
     def get_probabilities_from_label_map(
         label_map: torch.Tensor,
-        label_probabilities_dict: Dict[int, float],
+        label_probabilities_dict: dict[int, float],
         patch_size: np.ndarray,
     ) -> torch.Tensor:
         """Create probability map according to label map probabilities."""

src/torchio/data/sampler/sampler.py

@@ -1,4 +1,4 @@
-from typing import Generator
+from collections.abc import Generator
 from typing import Optional
 
 import numpy as np

src/torchio/data/sampler/uniform.py

@@ -1,4 +1,4 @@
-from typing import Generator
+from collections.abc import Generator
 from typing import Optional
 
 import torch

src/torchio/data/sampler/weighted.py

@@ -1,4 +1,4 @@
-from typing import Generator
+from collections.abc import Generator
 from typing import Optional
 
 import numpy as np