Fix linting errors (#1207)

pyproject.toml

@@ -129,9 +129,23 @@ filterwarnings = [
  "ignore:invalid escape sequence",
 ]
 
-[tool.ruff]
-format.quote-style = 'single'
-lint.select = ["F", "I"]
+[tool.ruff.format]
+quote-style = 'single'
+
+[tool.ruff.lint]
+select = [
+ "B",
+ "E",
+ "F",
+ "I",
+ "W",
+]
+ignore = [
+ "E203",
+ "E501",
+ "N813",
+]
+
 
 [tool.ruff.lint.isort]
 force-single-line = true

src/torchio/data/dataset.py

@@ -82,12 +82,12 @@ def __len__(self):
  def __getitem__(self, index: int) -> Subject:
  try:
  index = int(index)
- except (RuntimeError, TypeError):
+ except (RuntimeError, TypeError) as err:
  message = (
  f'Index "{index}" must be int or compatible dtype,'
  f' but an object of type "{type(index)}" was passed'
  )
- raise ValueError(message)
+ raise ValueError(message) from err
 
  subject = self._subjects[index]
  subject = copy.deepcopy(subject) # cheap since images not loaded yet

src/torchio/data/image.py

@@ -16,6 +16,7 @@
 import SimpleITK as sitk
 import torch
 from deprecated import deprecated
+from nibabel.affines import apply_affine
 
 from ..constants import AFFINE
 from ..constants import DATA
@@ -359,8 +360,8 @@ def bounds(self) -> np.ndarray:
  """Position of centers of voxels in smallest and largest indices."""
  ini = 0, 0, 0
  fin = np.array(self.spatial_shape) - 1
- point_ini = nib.affines.apply_affine(self.affine, ini)
- point_fin = nib.affines.apply_affine(self.affine, fin)
+ point_ini = apply_affine(self.affine, ini)
+ point_fin = apply_affine(self.affine, fin)
  return np.array((point_ini, point_fin))
 
  @property
@@ -415,7 +416,7 @@ def flip_axis(axis: str) -> str:
  labels = 'LRPAISTBDV'
  first = labels[::2]
  last = labels[1::2]
- flip_dict = {a: b for a, b in zip(first + last, last + first)}
+ flip_dict = dict(zip(first + last, last + first))
  axis = axis[0].upper()
  flipped_axis = flip_dict.get(axis)
  if flipped_axis is None:
@@ -433,8 +434,8 @@ def get_bounds(self) -> TypeBounds:
  """Get minimum and maximum world coordinates occupied by the image."""
  first_index = 3 * (-0.5,)
  last_index = np.array(self.spatial_shape) - 0.5
- first_point = nib.affines.apply_affine(self.affine, first_index)
- last_point = nib.affines.apply_affine(self.affine, last_index)
+ first_point = apply_affine(self.affine, first_index)
+ last_point = apply_affine(self.affine, last_index)
  array = np.array((first_point, last_point))
  bounds_x, bounds_y, bounds_z = array.T.tolist()
  return bounds_x, bounds_y, bounds_z
@@ -445,15 +446,15 @@ def _parse_single_path(
  ) -> Path:
  try:
  path = Path(path).expanduser()
- except TypeError:
+ except TypeError as err:
  message = (
  f'Expected type str or Path but found {path} with type'
  f' {type(path)} instead'
  )
- raise TypeError(message)
- except RuntimeError:
+ raise TypeError(message) from err
+ except RuntimeError as err:
  message = f'Conversion to path not possible for variable: {path}'
- raise RuntimeError(message)
+ raise RuntimeError(message) from err
 
  if not (path.is_file() or path.is_dir()): # might be a dir with DICOM
  raise FileNotFoundError(f'File not found: "{path}"')
@@ -735,7 +736,7 @@ def get_center(self, lps: bool = False) -> TypeTripletFloat:
  """
  size = np.array(self.spatial_shape)
  center_index = (size - 1) / 2
- r, a, s = nib.affines.apply_affine(self.affine, center_index)
+ r, a, s = apply_affine(self.affine, center_index)
  if lps:
  return (-r, -a, s)
  else:

src/torchio/transforms/augmentation/intensity/random_labels_to_image.py

@@ -210,7 +210,7 @@ def parse_gaussian_parameter(
  )
  return min_value, max_value
 
- def apply_transform(self, subject: Subject) -> Subject:
+ def _guess_label_key(self, subject: Subject) -> None:
  if self.label_key is None:
  iterable = subject.get_images_dict(intensity_only=False).items()
  for name, image in iterable:
@@ -221,6 +221,9 @@ def apply_transform(self, subject: Subject) -> Subject:
  message = f'No label maps found in subject: {subject}'
  raise RuntimeError(message)
 
+ def apply_transform(self, subject: Subject) -> Subject:
+ self._guess_label_key(subject)
+
  arguments = {
  'label_key': self.label_key,
  'mean': [],

src/torchio/transforms/transform.py

@@ -314,11 +314,12 @@ def _parse_range(
 
  try:
  min_value, max_value = nums_range # type: ignore[misc]
- except (TypeError, ValueError):
- raise ValueError(
+ except (TypeError, ValueError) as err:
+ message = (
  f'If {name} is not a single number, it must be'
- f' a sequence of len 2, not {nums_range}',
+ f' a sequence of len 2, not {nums_range}'
  )
+ raise ValueError(message) from err
 
  min_is_number = isinstance(min_value, numbers.Number)
  max_is_number = isinstance(max_value, numbers.Number)

src/torchio/utils.py

@@ -205,9 +205,9 @@ def compress(
 def check_sequence(sequence: Sequence, name: str) -> None:
  try:
  iter(sequence)
- except TypeError:
+ except TypeError as err:
  message = f'"{name}" must be a sequence, not {type(name)}'
- raise TypeError(message)
+ raise TypeError(message) from err
 
 
 def get_major_sitk_version() -> int:

tests/data/test_subject.py

@@ -41,17 +41,17 @@ def test_inconsistent_shape(self):
  a=tio.ScalarImage(tensor=torch.rand(1, 2, 3, 4)),
  b=tio.ScalarImage(tensor=torch.rand(2, 2, 3, 4)),
  )
- subject.spatial_shape
+ _ = subject.spatial_shape
  with pytest.raises(RuntimeError):
- subject.shape
+ _ = subject.shape
 
  def test_inconsistent_spatial_shape(self):
  subject = tio.Subject(
  a=tio.ScalarImage(tensor=torch.rand(1, 3, 3, 4)),
  b=tio.ScalarImage(tensor=torch.rand(2, 2, 3, 4)),
  )
  with pytest.raises(RuntimeError):
- subject.spatial_shape
+ _ = subject.spatial_shape
 
  @pytest.mark.slow
  @pytest.mark.skipif(sys.platform == 'win32', reason='Unstable on Windows')
@@ -133,7 +133,7 @@ def test_delete_image(self):
  with pytest.raises(KeyError):
  subject['t1']
  with pytest.raises(AttributeError):
- subject.t1
+ _ = subject.t1
 
  def test_2d(self):
  subject = self.make_2d(self.sample_subject)

tests/transforms/test_transforms.py

@@ -1,10 +1,10 @@
 import copy
 
-import nibabel as nib
 import numpy as np
 import pytest
 import SimpleITK as sitk
 import torch
+from nibabel.nifti1 import Nifti1Image
 
 import torchio as tio
 
@@ -360,17 +360,17 @@ def test_label_keys(self):
 
  def test_nibabel_input(self):
  image = self.sample_subject.t1
- image_nib = nib.Nifti1Image(image.data[0].numpy(), image.affine)
+ image_nib = Nifti1Image(image.data[0].numpy(), image.affine)
  transformed = tio.RandomAffine()(image_nib)
  transformed.get_fdata()
- transformed.affine
+ _ = transformed.affine
 
  image = self.subject_4d.t1
  tensor_5d = image.data[np.newaxis].permute(2, 3, 4, 0, 1)
- image_nib = nib.Nifti1Image(tensor_5d.numpy(), image.affine)
+ image_nib = Nifti1Image(tensor_5d.numpy(), image.affine)
  transformed = tio.RandomAffine()(image_nib)
  transformed.get_fdata()
- transformed.affine
+ _ = transformed.affine
 
  def test_bad_shape(self):
  tensor = torch.rand(1, 2, 3)