fix: update integration test (fails with new nitransforms)

test/test_integration.py

@@ -30,13 +30,15 @@
 
 from eddymotion.data.dmri import DWI
 from eddymotion.estimator import EddyMotionEstimator
+from eddymotion.registration.utils import displacements_within_mask
 
 
-def test_proximity_estimator_trivial_model(datadir):
+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)
 
     # Generate a list of large-yet-plausible bulk-head motion.
     xfms = nt.linear.LinearTransformsMapping(
@@ -56,8 +58,8 @@ def test_proximity_estimator_trivial_model(datadir):
     moved_nii = (~xfms).apply(b0nii, reference=b0nii)
 
     # Uncomment to see the moved dataset
-    # moved_nii.to_filename(tmp_path / "test.nii.gz")
-    # xfms.apply(moved_nii).to_filename(tmp_path / "ground_truth.nii.gz")
+    moved_nii.to_filename(tmp_path / "test.nii.gz")
+    xfms.apply(moved_nii).to_filename(tmp_path / "ground_truth.nii.gz")
 
     # Wrap into dataset object
     dwi_motion = DWI(
@@ -70,7 +72,7 @@ def test_proximity_estimator_trivial_model(datadir):
 
     estimator = EddyMotionEstimator()
     em_affines = estimator.estimate(
-        dwdata=dwi_motion,
+        data=dwi_motion,
         models=("b0",),
         seed=None,
         align_kwargs={
@@ -81,14 +83,18 @@ def test_proximity_estimator_trivial_model(datadir):
     )
 
     # Uncomment to see the realigned dataset
-    # nt.linear.LinearTransformsMapping(
-    #     em_affines,
-    #     reference=b0nii,
-    # ).apply(moved_nii).to_filename(tmp_path / "realigned.nii.gz")
+    nt.linear.LinearTransformsMapping(
+        em_affines,
+        reference=b0nii,
+    ).apply(moved_nii).to_filename(tmp_path / "realigned.nii.gz")
 
     # For each moved b0 volume
-    coords = xfms.reference.ndcoords.T
     for i, est in enumerate(em_affines):
-        xfm = nt.linear.Affine(xfms.matrix[i], reference=b0nii)
-        est = nt.linear.Affine(est, reference=b0nii)
-        assert np.sqrt(((xfm.map(coords) - est.map(coords)) ** 2).sum(1)).mean() < 0.2
+        assert (
+            displacements_within_mask(
+                masknii,
+                nt.linear.Affine(est),
+                xfms[i],
+            ).max()
+            < 0.2
+        )