ref: Switch to nitransforms.resampling.apply

Author: effigies

fmriprep/interfaces/resampling.py

@@ -6,6 +6,7 @@
 
 import nibabel as nb
 import nitransforms as nt
+import nitransforms.resampling
 import numpy as np
 from nipype.interfaces.base import (
     File,
@@ -689,7 +690,7 @@ def reconstruct_fieldmap(
     )
 
     if not direct:
-        fmap_img = transforms.apply(fmap_img, reference=target)
+        fmap_img = nt.resampling.apply(transforms, fmap_img, reference=target)
 
     fmap_img.header.set_intent('estimate', name='fieldmap Hz')
     fmap_img.header.set_data_dtype('float32')

fmriprep/workflows/bold/registration.py

@@ -737,6 +737,7 @@ def _conditional_downsampling(in_file, in_mask, zoom_th=4.0):
     import nibabel as nb
     import nitransforms as nt
     import numpy as np
+    from nitransforms.resampling import apply as transform
     from scipy.ndimage.filters import gaussian_filter
 
     img = nb.load(in_file)
@@ -756,14 +757,16 @@ def _conditional_downsampling(in_file, in_mask, zoom_th=4.0):
     offset = old_center - newrot.dot((newshape - 1) * 0.5)
     newaffine = nb.affines.from_matvec(newrot, offset)
 
+    identity = nt.base.TransformBase()
+
     newref = nb.Nifti1Image(np.zeros(newshape, dtype=np.uint8), newaffine)
-    nt.Affine(reference=newref).apply(img).to_filename(out_file)
+    transform(identity, img, reference=newref).to_filename(out_file)
 
     mask = nb.load(in_mask)
     mask.set_data_dtype(float)
     mdata = gaussian_filter(mask.get_fdata(dtype=float), scaling)
     floatmask = nb.Nifti1Image(mdata, mask.affine, mask.header)
-    newmask = nt.Affine(reference=newref).apply(floatmask)
+    newmask = transform(identity, floatmask, reference=newref)
     hdr = newmask.header.copy()
     hdr.set_data_dtype(np.uint8)
     newmaskdata = (newmask.get_fdata(dtype=float) > 0.5).astype(np.uint8)