Updated: offsource apply

Author: Julien Marabotto

nitransforms/nonlinear.py

@@ -14,6 +14,7 @@
 from nitransforms import io
 from nitransforms.io.base import _ensure_image
 from nitransforms.interp.bspline import grid_bspline_weights, _cubic_bspline
+from nitransforms.resampling import apply
 from nitransforms.base import (
     TransformBase,
     TransformError,
@@ -257,7 +258,7 @@ def __init__(self, coefficients, reference=None, order=3):
         if reference is not None:
             self.reference = reference
 
-            if coefficients.shape[-1] != self.ndim:
+            if coefficients.shape[-1] != self.reference.ndim:
                 raise TransformError(
                     'Number of components of the coefficients does '
                     'not match the number of dimensions')
@@ -310,19 +311,23 @@ def apply(
         spatialimage = _ensure_image(spatialimage)
 
         # If locations to be interpolated are not on a grid, run map()
+        #import pdb; pdb.set_trace()
         if not isinstance(_ref, ImageGrid):
-            return super().apply(
+            return apply(
+                super(),
                 spatialimage,
                 reference=_ref,
+                output_dtype=output_dtype,
                 order=order,
                 mode=mode,
                 cval=cval,
                 prefilter=prefilter,
-                output_dtype=output_dtype,
+                
             )
 
         # If locations to be interpolated are on a grid, generate a displacements field
-        return self.to_field(reference=reference).apply(
+        return apply(
+            self.to_field(reference=reference),
             spatialimage,
             reference=reference,
             order=order,

nitransforms/resampling.py

@@ -98,10 +98,12 @@ def apply(
     if data.ndim < transform.ndim:
         data = data[..., np.newaxis]
 
-    import pdb; pdb.set_trace()
+    if transform.ndim == 4:
+        targets = _as_homogeneous(targets.reshape(-2, targets.shape[0])).T
+
     resampled = ndi.map_coordinates(
         data,
-        _as_homogeneous(targets.reshape(-2, targets.shape[0])).T,
+        targets,
         output=output_dtype,
         order=order,
         mode=mode,

nitransforms/tests/test_base.py

@@ -94,10 +94,14 @@ def _to_hdf5(klass, x5_root):
     img = nb.load(fname)
     imgdata = np.asanyarray(img.dataobj, dtype=img.get_data_dtype())
 
+    # Test identity transform - setting reference
     xfm = TransformBase()
     with pytest.raises(TypeError):
         _ = xfm.ndim
 
+    # Test to_filename
+    xfm.to_filename("data.x5")
+
     # Test identity transform
     xfm = nitl.Affine()
     xfm.reference = fname
@@ -106,17 +110,6 @@ def _to_hdf5(klass, x5_root):
         imgdata == np.asanyarray(moved.dataobj, dtype=moved.get_data_dtype())
     )
 
-    # Test identity transform - setting reference
-    xfm = TransformBase()
-    xfm.reference = fname
-
-    with pytest.raises(TypeError):
-        _ = xfm.ndim
-    moved = apply(xfm, str(fname), reference=fname, order=0)
-    assert np.all(
-        imgdata == np.asanyarray(moved.dataobj, dtype=moved.get_data_dtype())
-    )
-
     # Test ndim returned by affine
     assert nitl.Affine().ndim == 3
     assert nitl.LinearTransformsMapping(
@@ -136,7 +129,7 @@ def _to_hdf5(klass, x5_root):
     assert np.allclose(giimoved.reshape(xfm.reference.shape), moved.get_fdata())
 
     # Test to_filename
-    xfm.to_filename("data.x5")
+    xfm.to_filename("data.xfm", fmt='itk')
 
 
 def test_SampledSpatialData(testdata_path):

nitransforms/tests/test_nonlinear.py

@@ -8,6 +8,7 @@
 
 import numpy as np
 import nibabel as nb
+from nitransforms.resampling import apply
 from nitransforms.base import TransformError
 from nitransforms.io.base import TransformFileError
 from nitransforms.nonlinear import (
@@ -247,8 +248,8 @@ def test_bspline(tmp_path, testdata_path):
     bsplxfm = BSplineFieldTransform(bs_name, reference=img_name)
     dispxfm = DenseFieldTransform(disp_name)
 
-    out_disp = dispxfm.apply(img_name)
-    out_bspl = bsplxfm.apply(img_name)
+    out_disp = apply(dispxfm,img_name)
+    out_bspl = apply(bsplxfm,img_name)
 
     out_disp.to_filename("resampled_field.nii.gz")
     out_bspl.to_filename("resampled_bsplines.nii.gz")