FIX: Offsource Apply

Apply function offsourced. Tests:  139 passed, 163 Skipped, 15 Warnings

Author: Julien Marabotto

nitransforms/nonlinear.py

@@ -30,6 +30,11 @@ class DenseFieldTransform(TransformBase):
 
     __slots__ = ("_field", "_deltas")
 
+    @property
+    def ndim(self):
+        """Access the dimensions of this Desne Field Transform."""
+        return self._field.ndim - 1
+
     def __init__(self, field=None, is_deltas=True, reference=None):
         """
         Create a dense field transform.
@@ -82,11 +87,10 @@ def __init__(self, field=None, is_deltas=True, reference=None):
                 "Reference is not a spatial image"
             )
 
-        ndim = self._field.ndim - 1
-        if self._field.shape[-1] != ndim:
+        if self._field.shape[-1] != self.ndim:
             raise TransformError(
                 "The number of components of the field (%d) does not match "
-                "the number of dimensions (%d)" % (self._field.shape[-1], ndim)
+                "the number of dimensions (%d)" % (self._field.shape[-1], self.ndim)
             )
 
         if is_deltas:
@@ -245,6 +249,12 @@ class BSplineFieldTransform(TransformBase):
 
     __slots__ = ['_coeffs', '_knots', '_weights', '_order', '_moving']
 
+    @property
+    def ndim(self):
+        """Access the dimensions of this BSpline."""
+        #return ndim = self._coeffs.shape[-1]
+        return self._coeffs.ndim - 1
+
     def __init__(self, coefficients, reference=None, order=3):
         """Create a smooth deformation field using B-Spline basis."""
         super().__init__()
@@ -277,66 +287,19 @@ def to_field(self, reference=None, dtype="float32"):
         if _ref is None:
             raise TransformError("A reference must be defined")
 
-        ndim = self._coeffs.shape[-1]
-
         if self._weights is None:
             self._weights = grid_bspline_weights(_ref, self._knots)
 
-        field = np.zeros((_ref.npoints, ndim))
+        field = np.zeros((_ref.npoints, self.ndim))
 
-        for d in range(ndim):
+        for d in range(self.ndim):
             #  1 x Nvox :                          (1 x K) @ (K x Nvox)
             field[:, d] = self._coeffs[..., d].reshape(-1) @ self._weights
 
         return DenseFieldTransform(
             field.astype(dtype).reshape(*_ref.shape, -1), reference=_ref
         )
 
-    def apply(
-        self,
-        spatialimage,
-        reference=None,
-        order=3,
-        mode="constant",
-        cval=0.0,
-        prefilter=True,
-        output_dtype=None,
-    ):
-        """Apply a B-Spline transform on input data."""
-
-        _ref = (
-            self.reference if reference is None else
-            SpatialReference.factory(_ensure_image(reference))
-        )
-        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 apply(
-                super(),
-                spatialimage,
-                reference=_ref,
-                output_dtype=output_dtype,
-                order=order,
-                mode=mode,
-                cval=cval,
-                prefilter=prefilter,
-                
-            )
-
-        # If locations to be interpolated are on a grid, generate a displacements field
-        return apply(
-            self.to_field(reference=reference),
-            spatialimage,
-            reference=reference,
-            order=order,
-            mode=mode,
-            cval=cval,
-            prefilter=prefilter,
-            output_dtype=output_dtype,
-        )
-
     def map(self, x, inverse=False):
         r"""
         Apply the transformation to a list of physical coordinate points.

nitransforms/resampling.py

@@ -87,16 +87,21 @@ def apply(
         spatialimage = _nbload(str(spatialimage))
 
     data = np.asanyarray(spatialimage.dataobj)
-    
-    targets = ImageGrid(spatialimage).index(  # data should be an image
-        _as_homogeneous(transform.map(_ref.ndcoords.T), dim=_ref.ndim)
-    )
 
     if data.ndim == 4 and data.shape[-1] != len(transform):
         raise ValueError("The fourth dimension of the data does not match the tranform's shape.")
 
     if data.ndim < transform.ndim:
         data = data[..., np.newaxis]
+    
+    if hasattr(transform, 'to_field') and callable(transform.to_field):
+        targets = ImageGrid(spatialimage).index(
+            _as_homogeneous(transform.to_field(reference=reference).map(_ref.ndcoords.T), dim=_ref.ndim)
+        )
+    else:
+        targets = ImageGrid(spatialimage).index(  # data should be an image
+            _as_homogeneous(transform.map(_ref.ndcoords.T), dim=_ref.ndim)
+        )
 
     if transform.ndim == 4:
         targets = _as_homogeneous(targets.reshape(-2, targets.shape[0])).T

nitransforms/tests/test_nonlinear.py

@@ -97,13 +97,14 @@ def test_bsplines_references(testdata_path):
         ).to_field()
 
     with pytest.raises(TransformError):
-        BSplineFieldTransform(
-            testdata_path / "someones_bspline_coefficients.nii.gz"
-        ).apply(testdata_path / "someones_anatomy.nii.gz")
+        apply(
+            BSplineFieldTransform(testdata_path / "someones_bspline_coefficients.nii.gz"),
+            testdata_path / "someones_anatomy.nii.gz",
+        )
 
-    BSplineFieldTransform(
-        testdata_path / "someones_bspline_coefficients.nii.gz"
-    ).apply(
+    apply(
+        BSplineFieldTransform(
+        testdata_path / "someones_bspline_coefficients.nii.gz"),
         testdata_path / "someones_anatomy.nii.gz",
         reference=testdata_path / "someones_anatomy.nii.gz"
     )