fix: clean up scipy.signal.cubic deprecation

Resolves: nipreps#370.

sdcflows/tests/test_transform.py

@@ -348,7 +348,16 @@ def test_grid_bspline_weights():
         nb.Nifti1Image(np.zeros(target_shape), target_aff),
         nb.Nifti1Image(np.zeros(ctrl_shape), ctrl_aff),
     ).tocsr()
-    assert weights.shape == (64, 1000)
+    assert weights.shape == (np.prod(np.array(ctrl_shape) + 2), np.prod(target_shape))
+
+    # Calculate the legacy mask to index the weights below
+    legacy_mask = np.pad(
+        np.ones(ctrl_shape, dtype=bool),
+        ((1, 1), (1, 1), (1, 1)),
+    ).reshape(-1)
+
+    # Drop scipy's padding
+    weights = weights[legacy_mask]
     # Empirically determined numbers intended to indicate that something
     # significant has changed. If it turns out we've been doing this wrong,
     # these numbers will probably change.

sdcflows/transform.py

@@ -56,8 +56,8 @@
 import numpy as np
 from warnings import warn
 from scipy import ndimage as ndi
-from scipy.signal import cubic
-from scipy.sparse import vstack as sparse_vstack, kron, lil_array
+from scipy.interpolate import BSpline
+from scipy.sparse import vstack as sparse_vstack, kron
 
 import nibabel as nb
 import nitransforms as nt
@@ -325,7 +325,12 @@ def fit(
         for level in coeffs:
             wmat = grid_bspline_weights(target_reference, level)
             weights.append(wmat)
-            coeffs_data.append(level.get_fdata(dtype="float32").reshape(-1))
+
+            # Coefficients must be zero-padded because Scipy's BSpline pads the knots.
+            coeffs_data.append(np.pad(
+                level.get_fdata(dtype="float32"),
+                ((1, 1), (1, 1), (1, 1)),
+            ).reshape(-1))
 
         # Reconstruct the fieldmap (in Hz) from coefficients
         fmap = np.zeros(projected_reference.shape[:3], dtype="float32")
@@ -732,18 +737,16 @@ def grid_bspline_weights(target_nii, ctrl_nii, dtype="float32"):
         coords[axis] = np.arange(sample_shape[axis], dtype=dtype)
 
         # Calculate the index component of samples w.r.t. B-Spline knots along current axis
+        # Size of locations is L
         locs = nb.affines.apply_affine(target_to_grid, coords.T)[:, axis]
-        knots = np.arange(knots_shape[axis], dtype=dtype)
-
-        distance = np.abs(locs[np.newaxis, ...] - knots[..., np.newaxis])
-        within_support = distance < 2.0
-        d_vals, d_idxs = np.unique(distance[within_support], return_inverse=True)
-        bs_w = cubic(d_vals)
 
-        colloc_ax = lil_array((knots_shape[axis], sample_shape[axis]), dtype=dtype)
-        colloc_ax[within_support] = bs_w[d_idxs]
+        # Size of knots is K + 6 so that all locations are fully covered by basis
+        knots = np.arange(-3, knots_shape[axis] + 3, dtype=dtype)
 
-        wd.append(colloc_ax)
+        # Our original design matrices had size (K, L)
+        # However, BSpline.design_matrix() generates a size of (L, K + 2),
+        # hence the trasposition (and zero-padding of 1 at every face when using these)
+        wd.append(BSpline.design_matrix(locs, knots, 3).T)
 
     # Calculate the tensor product of the three design matrices
     return kron(kron(wd[0], wd[1]), wd[2]).astype(dtype)