Address some typing errors (#1253)

src/torchio/data/image.py

@@ -450,8 +450,8 @@ def get_bounds(self) -> TypeBounds:
         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
+        bounds_x, bounds_y, bounds_z = array.T.tolist()  # type: ignore[misc]
+        return bounds_x, bounds_y, bounds_z  # type: ignore[return-value]
 
     @staticmethod
     def _parse_single_path(

src/torchio/data/io.py

@@ -5,8 +5,10 @@
 
 import nibabel as nib
 import numpy as np
+import numpy.typing as npt
 import SimpleITK as sitk
 import torch
+from nibabel.filebasedimages import ImageFileError
 from nibabel.spatialimages import SpatialImage
 
 from ..constants import REPO_URL
@@ -36,7 +38,7 @@ def read_image(path: TypePath) -> TypeDataAffine:
         warnings.warn(message, stacklevel=2)
         try:
             result = _read_nibabel(path)
-        except nib.loadsave.ImageFileError as e:
+        except ImageFileError as e:
             message = (
                 f'File "{path}" not understood.'
                 ' Check supported formats by at'
@@ -56,6 +58,7 @@ def _read_nibabel(path: TypePath) -> TypeDataAffine:
     data = check_uint_to_int(data)
     tensor = torch.as_tensor(data)
     affine = img.affine
+    assert isinstance(affine, np.ndarray)
     return tensor, affine
 
 
@@ -155,19 +158,19 @@ def _write_nibabel(
     else:
         tensor = tensor[np.newaxis].permute(2, 3, 4, 0, 1)
     suffix = Path(str(path).replace('.gz', '')).suffix
-    img: Union[nib.Nifti1Image, nib.Nifti1Pair]
+    img: Union[nib.nifti1.Nifti1Image, nib.nifti1.Nifti1Pair]
     if '.nii' in suffix:
-        img = nib.Nifti1Image(np.asarray(tensor), affine)
+        img = nib.nifti1.Nifti1Image(np.asarray(tensor), affine)
     elif '.hdr' in suffix or '.img' in suffix:
-        img = nib.Nifti1Pair(np.asarray(tensor), affine)
+        img = nib.nifti1.Nifti1Pair(np.asarray(tensor), affine)
     else:
-        raise nib.loadsave.ImageFileError
-    assert isinstance(img.header, nib.Nifti1Header)
+        raise ImageFileError
+    assert isinstance(img.header, nib.nifti1.Nifti1Header)
     if num_components > 1:
         img.header.set_intent('vector')
     img.header['qform_code'] = 1
     img.header['sform_code'] = 0
-    nib.save(img, str(path))
+    nib.loadsave.save(img, str(path))
 
 
 def _write_sitk(
@@ -269,9 +272,9 @@ def _matrix_to_itk_transform(
 
 def _read_niftyreg_matrix(trsf_path: TypePath) -> torch.Tensor:
     """Read a NiftyReg matrix and return it as a NumPy array."""
-    matrix = np.loadtxt(trsf_path)
-    matrix = np.linalg.inv(matrix)
-    return torch.as_tensor(matrix)
+    read_matrix = np.loadtxt(trsf_path).astype(np.float64)
+    inverted = np.linalg.inv(read_matrix)
+    return torch.from_numpy(inverted)
 
 
 def _write_niftyreg_matrix(matrix: TypeData, txt_path: TypePath) -> None:
@@ -361,10 +364,11 @@ def sitk_to_nib(
 def get_ras_affine_from_sitk(
     sitk_object: Union[sitk.Image, sitk.ImageFileReader],
 ) -> np.ndarray:
-    spacing = np.array(sitk_object.GetSpacing())
-    direction_lps = np.array(sitk_object.GetDirection())
-    origin_lps = np.array(sitk_object.GetOrigin())
+    spacing = np.array(sitk_object.GetSpacing(), dtype=np.float64)
+    direction_lps = np.array(sitk_object.GetDirection(), dtype=np.float64)
+    origin_lps = np.array(sitk_object.GetOrigin(), dtype=np.float64)
     direction_length = len(direction_lps)
+    rotation_lps: npt.NDArray[np.float64]
     if direction_length == 9:
         rotation_lps = direction_lps.reshape(3, 3)
     elif direction_length == 4:  # ignore last dimension if 2D (1, W, H, 1)

src/torchio/data/sampler/sampler.py

@@ -76,7 +76,7 @@ def _get_crop_transform(
         crop_ini = index_ini_array.tolist()
         crop_fin = (shape - index_fin).tolist()
         start = ()
-        cropping = sum(zip(crop_ini, crop_fin), start)
+        cropping = sum(zip(crop_ini, crop_fin), start)  # type: ignore[arg-type]
         return Crop(cropping)  # type: ignore[arg-type]
 
     def __call__(

src/torchio/data/sampler/weighted.py

@@ -156,7 +156,7 @@ def clear_probability_borders(
 
         # The call tolist() is very important. Using np.uint16 as negative
         # index will not work because e.g. -np.uint16(2) == 65534
-        crop_i, crop_j, crop_k = crop_fin.tolist()
+        crop_i, crop_j, crop_k = crop_fin.tolist()  # type: ignore[misc]
         if crop_i:
             probability_map[-crop_i:, :, :] = 0
         if crop_j:

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

@@ -287,7 +287,8 @@ def add_artifact(
         self.sort_spectra(spectra, times)
         result_spectrum = torch.empty_like(spectra[0])
         last_index = result_spectrum.shape[2]
-        indices = (last_index * times).astype(int).tolist()
+        indices_array = (last_index * times).astype(int)
+        indices: list[int] = indices_array.tolist()  # type: ignore[assignment]
         indices.append(last_index)
         ini = 0
         for spectrum, fin in zip(spectra, indices):

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

@@ -68,12 +68,12 @@ def get_params(
         for _ in range(num_iterations):
             first_ini, first_fin = get_random_indices_from_shape(
                 spatial_shape,
-                patch_size.tolist(),
+                patch_size.tolist(),  # type: ignore[arg-type]
             )
             while True:
                 second_ini, second_fin = get_random_indices_from_shape(
                     spatial_shape,
-                    patch_size.tolist(),
+                    patch_size.tolist(),  # type: ignore[arg-type]
                 )
                 larger_than_initial = np.all(second_ini >= first_ini)
                 less_than_final = np.all(second_fin <= first_fin)

src/torchio/transforms/augmentation/spatial/random_affine.py

@@ -302,7 +302,7 @@ def ras_to_lps(triplet: Sequence[float]):
         radians = np.radians(degrees).tolist()
 
         # SimpleITK uses LPS
-        radians_lps = ras_to_lps(radians)
+        radians_lps = ras_to_lps(radians)  # type: ignore[arg-type]
         translation_lps = ras_to_lps(translation)
 
         transform.SetRotation(*radians_lps)

src/torchio/transforms/augmentation/spatial/random_flip.py

@@ -53,11 +53,11 @@ def apply_transform(self, subject: Subject) -> Subject:
             if i not in potential_axes:
                 axes_to_flip_hot[i] = False
         (axes,) = np.where(axes_to_flip_hot)
-        axes = axes.tolist()
-        if not axes:
+        axes_list = axes.tolist()
+        if not axes_list:
             return subject
 
-        arguments = {'axes': axes}
+        arguments = {'axes': axes_list}
         transform = Flip(**self.add_include_exclude(arguments))
         transformed = transform(subject)
         assert isinstance(transformed, Subject)