ENH: Make type hinting compatible with Python 3.9

nireports/reportlets/utils.py

@@ -69,7 +69,7 @@ def robust_set_limits(
 
 
 def _get_limits(
-    nifti_file: str | npt.NDArray,
+    nifti_file: ty.Union[str, npt.NDArray],
     only_plot_noise: bool = False,
 ) -> tuple[float, float]:
     if isinstance(nifti_file, str):
@@ -91,7 +91,7 @@ def _get_limits(
     return vmin, vmax
 
 
-def svg_compress(image: str, compress: bool | L["auto"] = "auto") -> str:
+def svg_compress(image: str, compress: ty.Union[bool, L["auto"]] = "auto") -> str:
     """Generate a blob SVG from a matplotlib figure, may perform compression."""
     # Check availability of svgo and cwebp
     has_compress = all((which("svgo"), which("cwebp")))
@@ -239,7 +239,7 @@ def combine_svg(svg_list: list[SVGFigure], axis="vertical") -> SVGFigure:
 def extract_svg(
     display_object: DisplayObject,
     dpi: int = 300,
-    compress: bool | L["auto"] = "auto",
+    compress: ty.Union[bool, L["auto"]] = "auto",
 ) -> str:
     """Remove the preamble of the svg files generated with nilearn."""
     image_svg = svg2str(display_object, dpi)
@@ -316,7 +316,7 @@ def cuts_from_bbox(mask_nii: nb.Nifti1Image, cuts: int = 3) -> dict[str, list[fl
 
 
 def _3d_in_file(
-    in_file: nb.Nifti1Image | str | os.PathLike | list[str | os.PathLike],
+    in_file: ty.Union[nb.Nifti1Image, str, os.PathLike, list[ty.Union[str, os.PathLike]]],
 ) -> nb.Nifti1Image:
     """if self.inputs.in_file is 3d, return it.
     if 4d, pick an arbitrary volume and return that.
@@ -342,7 +342,7 @@ def compose_view(
     bg_svgs: list[SVGFigure],
     fg_svgs: list[SVGFigure],
     ref: int = 0,
-    out_file: str | os.PathLike[str] = "report.svg",
+    out_file: ty.Union[str, os.PathLike[str]] = "report.svg",
 ) -> str:
     """
     Compose svgs into one standalone svg with CSS flickering animation.

nireports/tools/ndimage.py

@@ -40,7 +40,7 @@
 Mat = npt.NDArray[np.float64]
 
 
-def rotation2canonical(img: SpatialImage) -> Mat | None:
+def rotation2canonical(img: SpatialImage) -> ty.Union[Mat, None]:
     """Calculate the rotation w.r.t. cardinal axes of input image."""
     img = nb.as_closest_canonical(img)
     # XXX: SpatialImage.affine needs to be typed
@@ -52,7 +52,7 @@ def rotation2canonical(img: SpatialImage) -> Mat | None:
     return r
 
 
-def rotate_affine(img: SpatImgT, rot: Mat | None = None) -> SpatImgT:
+def rotate_affine(img: SpatImgT, rot: ty.Union[Mat, None] = None) -> SpatImgT:
     """Rewrite the affine of a spatial image."""
     if rot is None:
         return img
@@ -63,16 +63,16 @@ def rotate_affine(img: SpatImgT, rot: Mat | None = None) -> SpatImgT:
     return img.__class__(img.dataobj, affine, img.header)
 
 
-def load_api(path: str | os.PathLike[str], api: type[ImgT]) -> ImgT:
+def load_api(path: ty.Union[str, os.PathLike[str]], api: type[ImgT]) -> ImgT:
     img = nb.load(path)
     if not isinstance(img, api):
         raise TypeError(f"File {path} does not implement {api} interface")
     return img
 
 
 def _get_values_inside_a_mask(
-    main_file: str | os.PathLike[str],
-    mask_file: str | os.PathLike[str],
+    main_file: ty.Union[str, os.PathLike[str]],
+    mask_file: ty.Union[str, os.PathLike[str]],
 ) -> npt.NDArray[np.float64]:
     main_nii = load_api(main_file, SpatialImage)
     main_data = main_nii.get_fdata()

nireports/tools/timeseries.py

@@ -27,6 +27,7 @@
 # niworkflows/utils/timeseries.py
 """Extracting signals from NIfTI and CIFTI2 files."""
 
+import typing as ty
 from collections.abc import Sequence
 
 import nibabel as nb
@@ -36,7 +37,7 @@
 from .ndimage import load_api
 
 
-def get_tr(img: nb.Nifti1Image | nb.Cifti2Image) -> float:
+def get_tr(img: ty.Union[nb.Nifti1Image, nb.Cifti2Image]) -> float:
     """
     Attempt to extract repetition time from NIfTI/CIFTI header.
 
@@ -62,7 +63,7 @@ def get_tr(img: nb.Nifti1Image | nb.Cifti2Image) -> float:
 
 
 def cifti_timeseries(
-    dataset: str | nb.Cifti2Image,
+    dataset: ty.Union[str, nb.Cifti2Image],
 ) -> tuple[npt.NDArray[np.float32], dict[str, list[int]]]:
     """Extract timeseries from CIFTI2 dataset."""
     dataset = load_api(dataset, nb.Cifti2Image) if isinstance(dataset, str) else dataset
@@ -86,12 +87,12 @@ def cifti_timeseries(
 
 
 def nifti_timeseries(
-    dataset: str | nb.Nifti1Image,
-    segmentation: str | nb.Nifti1Image | None = None,
+    dataset: ty.Union[str, nb.Nifti1Image],
+    segmentation: ty.Union[str, nb.Nifti1Image, None] = None,
     labels: Sequence[str] = ("Ctx GM", "dGM", "WM+CSF", "Cb", "Crown"),
     remap_rois: bool = False,
-    lut: npt.NDArray[np.uint8] | None = None,
-) -> tuple[npt.NDArray[np.float32], dict[str, list[int]] | None]:
+    lut: ty.Union[npt.NDArray[np.uint8], None] = None,
+) -> tuple[npt.NDArray[np.float32], ty.Union[dict[str, list[int]], None]]:
     """Extract timeseries from NIfTI1/2 datasets."""
     dataset = load_api(dataset, nb.Nifti1Image) if isinstance(dataset, str) else dataset
     data: npt.NDArray[np.float32] = dataset.get_fdata(dtype="float32").reshape(