fix: postpone coordinate mapping on linear array transforms

Resolves: #173.

nitransforms/linear.py

@@ -436,6 +436,7 @@ def apply(
             The data imaged after resampling to reference space.
 
         """
+
         if reference is not None and isinstance(reference, (str, Path)):
             reference = _nbload(str(reference))
 
@@ -446,40 +447,53 @@ def apply(
         if isinstance(spatialimage, (str, Path)):
             spatialimage = _nbload(str(spatialimage))
 
-        data = np.squeeze(np.asanyarray(spatialimage.dataobj))
-        output_dtype = output_dtype or data.dtype
+        # Avoid opening the data array just yet
+        input_dtype = spatialimage.header.get_data_dtype()
+        output_dtype = output_dtype or input_dtype
 
-        ycoords = self.map(_ref.ndcoords.T)
-        targets = ImageGrid(spatialimage).index(  # data should be an image
-            _as_homogeneous(np.vstack(ycoords), dim=_ref.ndim)
-        )
+        # Prepare physical coordinates of input (grid, points)
+        xcoords = _ref.ndcoords.astype("f4")
 
-        if data.ndim == 4:
-            if len(self) != data.shape[-1]:
+        # Invert target's (moving) affine once
+        ras2vox = ~Affine(spatialimage.affine)
+
+        if spatialimage.ndim == 4:
+            if len(self) != spatialimage.shape[-1]:
                 raise ValueError(
                     "Attempting to apply %d transforms on a file with "
-                    "%d timepoints" % (len(self), data.shape[-1])
+                    "%d timepoints" % (len(self), spatialimage.shape[-1])
                 )
-            targets = targets.reshape((len(self), -1, targets.shape[-1]))
-            resampled = np.stack(
-                [
-                    ndi.map_coordinates(
-                        data[..., t],
-                        targets[t, ..., : _ref.ndim].T,
-                        output=output_dtype,
-                        order=order,
-                        mode=mode,
-                        cval=cval,
-                        prefilter=prefilter,
-                    )
-                    for t in range(data.shape[-1])
-                ],
-                axis=0,
+
+            # Order F ensures individual volumes are contiguous in memory
+            # Also matches NIfTI, making final save more efficient
+            resampled = np.zeros(
+                (xcoords.T.shape[0], ) + spatialimage.shape[-1:], dtype=output_dtype, order="F"
             )
-        elif data.ndim in (2, 3):
+
+            for t in range(spatialimage.shape[-1]):
+                # Map the input coordinates on to timepoint t of the target (moving)
+                ycoords = Affine(self.matrix[t]).map(xcoords.T)[..., : _ref.ndim]
+
+                # Calculate corresponding voxel coordinates
+                yvoxels = ras2vox.map(ycoords)[..., : _ref.ndim]
+
+                # Interpolate
+                resampled[..., t] = ndi.map_coordinates(
+                    spatialimage.dataobj[..., t].astype(input_dtype, copy=False),
+                    yvoxels.T,
+                    output=output_dtype,
+                    order=order,
+                    mode=mode,
+                    cval=cval,
+                    prefilter=prefilter,
+                )
+        elif spatialimage.ndim in (2, 3):
+            ycoords = self.map(xcoords.T)[..., : _ref.ndim]
+            yvoxels = ras2vox.map(ycoords)[..., : _ref.ndim]
+
             resampled = ndi.map_coordinates(
-                data,
-                targets[..., : _ref.ndim].T,
+                spatialimage.dataobj.astype(input_dtype, copy=False),
+                yvoxels.T,
                 output=output_dtype,
                 order=order,
                 mode=mode,