STYLE: Use the same alias consistently to import nibabel

Use the same alias consistently to import `nibabel`: prefer using `nib`
over `nb` as it is more descriptive.

docs/notebooks/bold_realignment.ipynb

@@ -11,7 +11,7 @@
     "from shutil import copy, move\n",
     "\n",
     "import nest_asyncio\n",
-    "import nibabel as nb\n",
+    "import nibabel as nib\n",
     "import nitransforms as nt\n",
     "import numpy as np\n",
     "from nipreps.synthstrip.wrappers.nipype import SynthStrip\n",
@@ -62,7 +62,7 @@
     "    avg_path = OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_boldref.nii.gz\"\n",
     "\n",
     "    if not avg_path.exists():\n",
-    "        nii = nb.load(DATA_PATH / bold_run)\n",
+    "        nii = nib.load(DATA_PATH / bold_run)\n",
     "        average = nii.get_fdata().mean(-1)\n",
     "        avg_path.parent.mkdir(exist_ok=True, parents=True)\n",
     "        nii.__class__(average, nii.affine, nii.header).to_filename(avg_path)\n",
@@ -82,7 +82,7 @@
     "    )\n",
     "\n",
     "    if not dilmask_path.exists():\n",
-    "        niimsk = nb.load(bmask_path)\n",
+    "        niimsk = nib.load(bmask_path)\n",
     "        niimsk.__class__(\n",
     "            binary_dilation(niimsk.get_fdata() > 0.0, ball(4)).astype(\"uint8\"),\n",
     "            niimsk.affine,\n",
@@ -178,7 +178,7 @@
     "def plot_profile(image_path, axis=None, indexing=None, cmap=\"gray\", label=None, figsize=(15, 1.7)):\n",
     "    \"\"\"Plots a single image slice on a given axis or a new figure if axis is None.\"\"\"\n",
     "    # Load the image\n",
-    "    image_data = nb.load(image_path).get_fdata()\n",
+    "    image_data = nib.load(image_path).get_fdata()\n",
     "\n",
     "    # Define default indexing if not provided\n",
     "    if indexing is None:\n",
@@ -322,7 +322,7 @@
     }
    ],
    "source": [
-    "datashape = nb.load(DATA_PATH / bold_runs[15]).shape\n",
+    "datashape = nib.load(DATA_PATH / bold_runs[15]).shape\n",
     "plot_profile(\n",
     "    DATA_PATH / bold_runs[15],\n",
     "    afni_realigned[15],\n",
@@ -455,7 +455,7 @@
     "        OUTPUT_DIR / bold_run.parent / f\"{bold_run.name.rsplit('_', 1)[0]}_label-brain_mask.nii.gz\"\n",
     "    )\n",
     "\n",
-    "    nii = nb.load(data_path)\n",
+    "    nii = nib.load(data_path)\n",
     "    hdr = nii.header.copy()\n",
     "    hdr.set_sform(nii.affine, code=1)\n",
     "    hdr.set_qform(nii.affine, code=1)\n",
@@ -501,7 +501,7 @@
     "        for t in range(n_timepoints)\n",
     "    ]\n",
     "\n",
-    "    nii = nb.load(DATA_PATH / bold_run)\n",
+    "    nii = nib.load(DATA_PATH / bold_run)\n",
     "    nitransforms_fd[str(bold_run)] = np.array([displacement_framewise(nii, xfm) for xfm in xfms])\n",
     "\n",
     "    hmc_xfm = nt.linear.LinearTransformsMapping(xfms)\n",
@@ -1724,7 +1724,7 @@
     "            / f\"{bold_run.name.rsplit('_', 1)[0]}_desc-realigned_bold.nii.gz\"\n",
     "        )\n",
     "\n",
-    "        datashape = nb.load(original).shape\n",
+    "        datashape = nib.load(original).shape\n",
     "\n",
     "        fig = plot_combined_profile(\n",
     "            (afni, original, nitransforms),\n",

scripts/optimize_registration.py

@@ -28,7 +28,7 @@
 from pathlib import Path
 from shutil import rmtree
 
-import nibabel as nb
+import nibabel as nib
 import nitransforms as nt
 import numpy as np
 from ConfigSpace import Configuration, ConfigurationSpace
@@ -91,7 +91,7 @@ async def ants(cmd, cwd, stdout, stderr, semaphore):
     (rng.uniform(-0.4, 0.4, size=(60, 3)), rng.uniform(-2.0, 2.0, size=(60, 3)))
 )
 CONVERSIONS = [
-    nb.affines.from_matvec(nb.eulerangles.euler2mat(*parameters[:3]), parameters[3:])
+    nib.affines.from_matvec(nib.eulerangles.euler2mat(*parameters[:3]), parameters[3:])
     for parameters in MOTION_PARAMETERS
 ]
 
@@ -118,7 +118,7 @@ async def train_coro(
         for j in (0, 1):
             fixed_path = REFERENCES[j]
             brainmask_path = DATASET_PATH / fixed_path.name.replace("desc-avg", "desc-brain_mask")
-            refnii = nb.load(fixed_path)
+            refnii = nib.load(fixed_path)
             xfm = nt.linear.Affine(T, reference=refnii)
             ref_xfms.append(xfm)
 
@@ -157,8 +157,8 @@ async def train_coro(
         fixed_path = REFERENCES[j]
         brainmask_path = DATASET_PATH / fixed_path.name.replace("desc-avg", "desc-brain_mask")
 
-        fixednii = nb.load(fixed_path)
-        movingnii = nb.load(tmp_folder / f"test-{i:04d}.nii.gz")
+        fixednii = nib.load(fixed_path)
+        movingnii = nib.load(tmp_folder / f"test-{i:04d}.nii.gz")
         xform = nt.linear.Affine(
             nt.io.itk.ITKLinearTransform.from_filename(
                 tmp_folder / f"conversion-{i:04d}0GenericAffine.mat"
@@ -168,7 +168,7 @@ async def train_coro(
             ),
         )
 
-        masknii = nb.load(brainmask_path)
+        masknii = nib.load(brainmask_path)
         initial_error = utils.displacements_within_mask(
             masknii,
             ref_xfms[i],

src/nifreeze/data/dmri.py

@@ -29,7 +29,7 @@
 
 import attr
 import h5py
-import nibabel as nb
+import nibabel as nib
 import numpy as np
 from nitransforms.linear import Affine
 
@@ -109,7 +109,7 @@ def set_transform(self, index, affine, order=3):
             dwframe = np.asanyarray(root["dataobj"][..., index])
             bvec = np.asanyarray(root["gradients"][:3, index])
 
-        dwmoving = nb.Nifti1Image(dwframe, self.affine, None)
+        dwmoving = nib.Nifti1Image(dwframe, self.affine, None)
 
         # resample and update orientation at index
         self.dataobj[..., index] = np.asanyarray(
@@ -162,7 +162,7 @@ def to_nifti(self, filename, **kwargs):
             if not insert_b0
             else np.concatenate((self.bzero[..., np.newaxis], self.dataobj), axis=-1)
         )
-        nii = nb.Nifti1Image(data, self.affine, None)
+        nii = nib.Nifti1Image(data, self.affine, None)
         nii.header.set_xyzt_units("mm")
         nii.to_filename(filename)
 
@@ -226,7 +226,7 @@ def load(
     else:
         raise RuntimeError("A gradients file is necessary")
 
-    img = nb.load(filename)
+    img = nib.load(filename)
     fulldata = img.get_fdata(dtype="float32")
     retval = DWI(
         affine=img.affine,
@@ -236,17 +236,17 @@ def load(
     retval.dataobj = fulldata[..., gradmsk]
 
     if b0_file:
-        b0img = nb.load(b0_file)
+        b0img = nib.load(b0_file)
         retval.bzero = np.asanyarray(b0img.dataobj)
     elif not np.all(gradmsk):
         retval.bzero = np.median(fulldata[..., ~gradmsk], axis=3)
 
     if brainmask_file:
-        mask = nb.load(brainmask_file)
+        mask = nib.load(brainmask_file)
         retval.brainmask = np.asanyarray(mask.dataobj)
 
     if fmap_file:
-        fmapimg = nb.load(fmap_file)
+        fmapimg = nib.load(fmap_file)
         retval.fieldmap = fmapimg.get_fdata(dtype="float32")
 
     return retval

src/nifreeze/data/pet.py

@@ -28,7 +28,7 @@
 
 import attr
 import h5py
-import nibabel as nb
+import nibabel as nib
 import numpy as np
 from nitransforms.linear import Affine
 
@@ -84,7 +84,7 @@ def set_transform(self, index, affine, order=3):
             root = in_file["/0"]
             dframe = np.asanyarray(root["dataobj"][..., index])
 
-        dmoving = nb.Nifti1Image(dframe, self.affine, None)
+        dmoving = nib.Nifti1Image(dframe, self.affine, None)
 
         # resample and update orientation at index
         self.dataobj[..., index] = np.asanyarray(
@@ -124,7 +124,7 @@ def to_filename(self, filename, compression=None, compression_opts=None):
 
     def to_nifti(self, filename, *_):
         """Write a NIfTI 1.0 file to disk."""
-        nii = nb.Nifti1Image(self.dataobj, self.affine, None)
+        nii = nib.Nifti1Image(self.dataobj, self.affine, None)
         nii.header.set_xyzt_units("mm")
         nii.to_filename(filename)
 
@@ -148,7 +148,7 @@ def load(
     if filename.name.endswith(".h5"):
         return PET.from_filename(filename)
 
-    img = nb.load(filename)
+    img = nib.load(filename)
     retval = PET(
         dataobj=img.get_fdata(dtype="float32"),
         affine=img.affine,
@@ -172,7 +172,7 @@ def load(
     assert len(retval.frame_time) == retval.dataobj.shape[-1]
 
     if brainmask_file:
-        mask = nb.load(brainmask_file)
+        mask = nib.load(brainmask_file)
         retval.brainmask = np.asanyarray(mask.dataobj)
 
     return retval

src/nifreeze/estimator.py

@@ -25,7 +25,7 @@
 from pathlib import Path
 from tempfile import TemporaryDirectory, mkstemp
 
-import nibabel as nb
+import nibabel as nib
 from tqdm import tqdm
 
 from nifreeze import utils as eutils
@@ -215,7 +215,7 @@ def _prepare_brainmask_data(brainmask, affine):
     bmask_img = None
     if brainmask is not None:
         _, bmask_img = mkstemp(suffix="_bmask.nii.gz")
-        nb.Nifti1Image(brainmask.astype("uint8"), affine, None).to_filename(bmask_img)
+        nib.Nifti1Image(brainmask.astype("uint8"), affine, None).to_filename(bmask_img)
     return bmask_img
 
 

src/nifreeze/registration/ants.py

@@ -29,7 +29,7 @@
 from pathlib import Path
 from warnings import warn
 
-import nibabel as nb
+import nibabel as nib
 import nitransforms as nt
 import numpy as np
 from nipype.interfaces.ants.registration import Registration
@@ -81,7 +81,7 @@ def _to_nifti(
         from nifreeze.data.filtering import advanced_clip
 
         data = advanced_clip(data)
-    nii = nb.Nifti1Image(
+    nii = nib.Nifti1Image(
         data,
         affine,
         None,
@@ -404,12 +404,12 @@ def generate_command(
 def _run_registration(
     fixed: Path,
     moving: Path,
-    bmask_img: nb.spatialimages.SpatialImage,
+    bmask_img: nib.spatialimages.SpatialImage,
     em_affines: np.ndarray,
     affine: np.ndarray,
     shape: tuple[int, int, int],
     bval: int,
-    fieldmap: nb.spatialimages.SpatialImage,
+    fieldmap: nib.spatialimages.SpatialImage,
     i_iter: int,
     vol_idx: int,
     dirname: Path,

src/nifreeze/registration/utils.py

@@ -33,13 +33,13 @@
 
 from itertools import product
 
-import nibabel as nb
+import nibabel as nib
 import nitransforms as nt
 import numpy as np
 
 
 def displacements_within_mask(
-    mask_img: nb.spatialimages.SpatialImage,
+    mask_img: nib.spatialimages.SpatialImage,
     test_xfm: nt.base.BaseTransform,
     reference_xfm: nt.base.BaseTransform | None = None,
 ) -> np.ndarray:
@@ -67,7 +67,7 @@ def displacements_within_mask(
     # Mask data as boolean (True for voxels inside the mask)
     maskdata = np.asanyarray(mask_img.dataobj) > 0
     # Convert voxel coordinates to world coordinates using affine transform
-    xyz = nb.affines.apply_affine(
+    xyz = nib.affines.apply_affine(
         mask_img.affine,
         np.argwhere(maskdata),
     )
@@ -80,7 +80,7 @@ def displacements_within_mask(
 
 
 def displacement_framewise(
-    img: nb.spatialimages.SpatialImage,
+    img: nib.spatialimages.SpatialImage,
     test_xfm: nt.base.BaseTransform,
     radius: float = 50.0,
 ):
@@ -107,7 +107,7 @@ def displacement_framewise(
     # Compute the center of the image in voxel space
     center_ijk = 0.5 * (np.array(img.shape[:3]) - 1)
     # Convert to world coordinates
-    center_xyz = nb.affines.apply_affine(affine, center_ijk)
+    center_xyz = nib.affines.apply_affine(affine, center_ijk)
     # Generate coordinates of points at radius distance from center
     fd_coords = np.array(list(product(*((radius, -radius),) * 3))) + center_xyz
     # Compute the average displacement from the test transformation

test/test_dmri.py

@@ -22,7 +22,7 @@
 #
 """Unit tests exercising the dMRI data structure."""
 
-import nibabel as nb
+import nibabel as nib
 import numpy as np
 import pytest
 
@@ -69,10 +69,10 @@ def _create_dwi_random_data(
     b0_dataobj,
     fieldmap_dataobj,
 ):
-    dwi = nb.Nifti1Image(dwi_dataobj, affine)
-    brainmask = nb.Nifti1Image(brainmask_dataobj, affine)
-    b0 = nb.Nifti1Image(b0_dataobj, affine)
-    fieldmap = nb.Nifti1Image(fieldmap_dataobj, affine)
+    dwi = nib.Nifti1Image(dwi_dataobj, affine)
+    brainmask = nib.Nifti1Image(brainmask_dataobj, affine)
+    b0 = nib.Nifti1Image(b0_dataobj, affine)
+    fieldmap = nib.Nifti1Image(fieldmap_dataobj, affine)
 
     return dwi, brainmask, b0, fieldmap
 
@@ -91,11 +91,11 @@ def _serialize_dwi_data(
     gradients_fname = _tmp_path / "gradients.txt"
     fieldmap_fname = _tmp_path / "fieldmap.nii.gz"
 
-    nb.save(dwi, dwi_fname)
-    nb.save(brainmask, brainmask_fname)
-    nb.save(b0, b0_fname)
+    nib.save(dwi, dwi_fname)
+    nib.save(brainmask, brainmask_fname)
+    nib.save(b0, b0_fname)
     np.savetxt(gradients_fname, gradients.T)
-    nb.save(fieldmap, fieldmap_fname)
+    nib.save(fieldmap, fieldmap_fname)
 
     return (
         dwi_fname,

test/test_integration.py

@@ -24,7 +24,7 @@
 
 from os import cpu_count
 
-import nibabel as nb
+import nibabel as nib
 import nitransforms as nt
 import numpy as np
 
@@ -37,19 +37,19 @@ def test_proximity_estimator_trivial_model(datadir, tmp_path):
     """Check the proximity of transforms estimated by the estimator with a trivial B0 model."""
 
     dwdata = DWI.from_filename(datadir / "dwi.h5")
-    b0nii = nb.Nifti1Image(dwdata.bzero, dwdata.affine, None)
-    masknii = nb.Nifti1Image(dwdata.brainmask.astype(np.uint8), dwdata.affine, None)
+    b0nii = nib.Nifti1Image(dwdata.bzero, dwdata.affine, None)
+    masknii = nib.Nifti1Image(dwdata.brainmask.astype(np.uint8), dwdata.affine, None)
 
     # Generate a list of large-yet-plausible bulk-head motion.
     xfms = nt.linear.LinearTransformsMapping(
         [
-            nb.affines.from_matvec(nb.eulerangles.euler2mat(x=0.03, z=0.005), (0.8, 0.2, 0.2)),
-            nb.affines.from_matvec(nb.eulerangles.euler2mat(x=0.02, z=0.005), (0.8, 0.2, 0.2)),
-            nb.affines.from_matvec(nb.eulerangles.euler2mat(x=0.02, z=0.02), (0.4, 0.2, 0.2)),
-            nb.affines.from_matvec(nb.eulerangles.euler2mat(x=-0.02, z=0.02), (0.4, 0.2, 0.2)),
-            nb.affines.from_matvec(nb.eulerangles.euler2mat(x=-0.02, z=0.002), (0.0, 0.2, 0.2)),
-            nb.affines.from_matvec(nb.eulerangles.euler2mat(y=-0.02, z=0.002), (0.0, 0.2, 0.2)),
-            nb.affines.from_matvec(nb.eulerangles.euler2mat(y=-0.01, z=0.002), (0.0, 0.4, 0.2)),
+            nib.affines.from_matvec(nib.eulerangles.euler2mat(x=0.03, z=0.005), (0.8, 0.2, 0.2)),
+            nib.affines.from_matvec(nib.eulerangles.euler2mat(x=0.02, z=0.005), (0.8, 0.2, 0.2)),
+            nib.affines.from_matvec(nib.eulerangles.euler2mat(x=0.02, z=0.02), (0.4, 0.2, 0.2)),
+            nib.affines.from_matvec(nib.eulerangles.euler2mat(x=-0.02, z=0.02), (0.4, 0.2, 0.2)),
+            nib.affines.from_matvec(nib.eulerangles.euler2mat(x=-0.02, z=0.002), (0.0, 0.2, 0.2)),
+            nib.affines.from_matvec(nib.eulerangles.euler2mat(y=-0.02, z=0.002), (0.0, 0.2, 0.2)),
+            nib.affines.from_matvec(nib.eulerangles.euler2mat(y=-0.01, z=0.002), (0.0, 0.4, 0.2)),
         ],
         reference=b0nii,
     )