fix: update many apply() calls

Author: oesteban

nitransforms/cli.py

@@ -5,6 +5,7 @@
 
 from .linear import load as linload
 from .nonlinear import load as nlinload
+from .resampling import apply
 
 
 def cli_apply(pargs):
@@ -38,7 +39,8 @@ def cli_apply(pargs):
     # ensure a reference is set
     xfm.reference = pargs.ref or pargs.moving
 
-    moved = xfm.apply(
+    moved = apply(
+        xfm,
         pargs.moving,
         order=pargs.order,
         mode=pargs.mode,

nitransforms/tests/test_base.py

@@ -6,6 +6,7 @@
 
 from ..base import SpatialReference, SampledSpatialData, ImageGrid, TransformBase
 from .. import linear as nitl
+from ..resampling import apply
 
 
 def test_SpatialReference(testdata_path):
@@ -94,19 +95,21 @@ def _to_hdf5(klass, x5_root):
     # Test identity transform
     xfm = TransformBase()
     xfm.reference = fname
+
     with pytest.raises(TypeError):
         _ = xfm.ndim
-    moved = xfm.apply(fname, order=0)
+    moved = apply(xfm, fname, order=0)
     assert np.all(
         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 = xfm.apply(str(fname), reference=fname, order=0)
+    moved = apply(xfm, str(fname), reference=fname, order=0)
     assert np.all(
         imgdata == np.asanyarray(moved.dataobj, dtype=moved.get_data_dtype())
     )
@@ -126,7 +129,7 @@ def _to_hdf5(klass, x5_root):
             )
         ]
     )
-    giimoved = xfm.apply(fname, reference=gii, order=0)
+    giimoved = apply(xfm, fname, reference=gii, order=0)
     assert np.allclose(giimoved.reshape(xfm.reference.shape), moved.get_fdata())
 
     # Test to_filename

nitransforms/tests/test_io.py

@@ -28,6 +28,8 @@
 )
 from nitransforms.io.base import LinearParameters, TransformIOError, TransformFileError
 from nitransforms.conftest import _datadir, _testdir
+from nitransforms.resampling import apply
+
 
 LPS = np.diag([-1, -1, 1, 1])
 ITK_MAT = LPS.dot(np.ones((4, 4)).dot(LPS))
@@ -497,10 +499,13 @@ def test_afni_oblique(tmpdir, parameters, swapaxes, testdata_path, dir_x, dir_y,
     assert np.allclose(card_aff, nb.load("deob_3drefit.nii.gz").affine)
 
     # Check that nitransforms can emulate 3drefit -deoblique
-    nt3drefit = Affine(
-        afni._cardinal_rotation(img.affine, False),
-        reference="deob_3drefit.nii.gz",
-    ).apply("orig.nii.gz")
+    nt3drefit = apply(
+        Affine(
+            afni._cardinal_rotation(img.affine, False),
+            reference="deob_3drefit.nii.gz",
+        ),
+        "orig.nii.gz",
+    )
 
     diff = (
         np.asanyarray(img.dataobj, dtype="uint8")
@@ -509,10 +514,13 @@ def test_afni_oblique(tmpdir, parameters, swapaxes, testdata_path, dir_x, dir_y,
     assert np.sqrt((diff[10:-10, 10:-10, 10:-10] ** 2).mean()) < 0.1
 
     # Check that nitransforms can revert 3drefit -deoblique
-    nt_undo3drefit = Affine(
-        afni._cardinal_rotation(img.affine, True),
-        reference="orig.nii.gz",
-    ).apply("deob_3drefit.nii.gz")
+    nt_undo3drefit = apply(
+        Affine(
+            afni._cardinal_rotation(img.affine, True),
+            reference="orig.nii.gz",
+        ),
+        "deob_3drefit.nii.gz",
+    )
 
     diff = (
         np.asanyarray(img.dataobj, dtype="uint8")
@@ -531,16 +539,21 @@ def test_afni_oblique(tmpdir, parameters, swapaxes, testdata_path, dir_x, dir_y,
     assert np.allclose(deobaff, deobnii.affine)
 
     # Check resampling in deobliqued grid
-    ntdeobnii = Affine(np.eye(4), reference=deobnii.__class__(
-        np.zeros(deobshape, dtype="uint8"),
-        deobaff,
-        deobnii.header
-    )).apply(img, order=0)
+    ntdeobnii = apply(
+        Affine(np.eye(4), reference=deobnii.__class__(
+            np.zeros(deobshape, dtype="uint8"),
+            deobaff,
+            deobnii.header
+        )),
+        img,
+        order=0,
+    )
 
     # Generate an internal box to exclude border effects
     box = np.zeros(img.shape, dtype="uint8")
     box[10:-10, 10:-10, 10:-10] = 1
-    ntdeobmask = Affine(np.eye(4), reference=ntdeobnii).apply(
+    ntdeobmask = apply(
+        Affine(np.eye(4), reference=ntdeobnii),
         nb.Nifti1Image(box, img.affine, img.header),
         order=0,
     )

nitransforms/tests/test_linear.py

@@ -13,6 +13,7 @@
 from nibabel.affines import from_matvec
 from nitransforms import linear as nitl
 from nitransforms import io
+from nitransforms.resampling import apply
 from .utils import assert_affines_by_filename
 
 RMSE_TOL = 0.1
@@ -285,7 +286,7 @@ def test_apply_linear_transform(tmpdir, get_testdata, get_testmask, image_orient
     assert exit_code == 0
     sw_moved_mask = nb.load("resampled_brainmask.nii.gz")
 
-    nt_moved_mask = xfm.apply(msk, order=0)
+    nt_moved_mask = apply(xfm, msk, order=0)
     nt_moved_mask.set_data_dtype(msk.get_data_dtype())
     nt_moved_mask.to_filename("ntmask.nii.gz")
     diff = np.asanyarray(sw_moved_mask.dataobj) - np.asanyarray(nt_moved_mask.dataobj)
@@ -305,7 +306,7 @@ def test_apply_linear_transform(tmpdir, get_testdata, get_testmask, image_orient
     sw_moved = nb.load("resampled.nii.gz")
     sw_moved.set_data_dtype(img.get_data_dtype())
 
-    nt_moved = xfm.apply(img, order=0)
+    nt_moved = apply(xfm, img, order=0)
     diff = (
         np.asanyarray(sw_moved.dataobj, dtype=sw_moved.get_data_dtype())
         - np.asanyarray(nt_moved.dataobj, dtype=nt_moved.get_data_dtype())
@@ -314,7 +315,7 @@ def test_apply_linear_transform(tmpdir, get_testdata, get_testmask, image_orient
     # A certain tolerance is necessary because of resampling at borders
     assert np.sqrt((diff[brainmask] ** 2).mean()) < RMSE_TOL
 
-    nt_moved = xfm.apply("img.nii.gz", order=0)
+    nt_moved = apply(xfm, "img.nii.gz", order=0)
     diff = (
         np.asanyarray(sw_moved.dataobj, dtype=sw_moved.get_data_dtype())
         - np.asanyarray(nt_moved.dataobj, dtype=nt_moved.get_data_dtype())
@@ -343,22 +344,23 @@ def test_LinearTransformsMapping_apply(tmp_path, data_path, testdata_path):
     assert isinstance(hmc, nitl.LinearTransformsMapping)
 
     # Test-case: realign functional data on to sbref
-    nii = hmc.apply(
-        testdata_path / "func.nii.gz", order=1, reference=testdata_path / "sbref.nii.gz"
+    nii = apply(
+        hmc, testdata_path / "func.nii.gz", order=1, reference=testdata_path / "sbref.nii.gz"
     )
     assert nii.dataobj.shape[-1] == len(hmc)
 
     # Test-case: write out a fieldmap moved with head
     hmcinv = nitl.LinearTransformsMapping(
         np.linalg.inv(hmc.matrix), reference=testdata_path / "func.nii.gz"
     )
-    nii = hmcinv.apply(testdata_path / "fmap.nii.gz", order=1)
+    nii = apply(hmcinv, testdata_path / "fmap.nii.gz", order=1)
     assert nii.dataobj.shape[-1] == len(hmc)
 
     # Ensure a ValueError is issued when trying to do weird stuff
     hmc = nitl.LinearTransformsMapping(hmc.matrix[:1, ...])
     with pytest.raises(ValueError):
-        hmc.apply(
+        apply(
+            hmc,
             testdata_path / "func.nii.gz",
             order=1,
             reference=testdata_path / "sbref.nii.gz",