enh: prepare code for easy parallelization with a process pool executor

Resolves: #214.

Author: oesteban

nitransforms/resampling.py

@@ -8,6 +8,7 @@
 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
 """Resampling utilities."""
 
+from functools import partial
 from pathlib import Path
 import numpy as np
 from nibabel.loadsave import load as _nbload
@@ -135,33 +136,37 @@ def apply(
             else None
         )
 
-        # Order F ensures individual volumes are contiguous in memory
-        # Also matches NIfTI, making final save more efficient
-        resampled = np.zeros(
-            (len(ref_ndcoords), len(transform)), dtype=input_dtype, order="F"
+        map_coordinates = partial(
+            ndi.map_coordinates,
+            order=order,
+            mode=mode,
+            cval=cval,
+            prefilter=prefilter,
         )
 
-        for t in range(n_resamplings):
-            xfm_t = transform if n_resamplings == 1 else transform[t]
+        def _apply_volume(index, data, transform, targets=None):
+            xfm_t = transform if n_resamplings == 1 else transform[index]
 
             if targets is None:
                 targets = ImageGrid(spatialimage).index(  # data should be an image
                     _as_homogeneous(xfm_t.map(ref_ndcoords), dim=_ref.ndim)
                 )
 
-            # Interpolate
-            resampled[..., t] = ndi.map_coordinates(
-                (
-                    data
-                    if data is not None
-                    else spatialimage.dataobj[..., t].astype(input_dtype, copy=False)
-                ),
-                targets,
-                order=order,
-                mode=mode,
-                cval=cval,
-                prefilter=prefilter,
+            data_t = (
+                data
+                if data is not None
+                else spatialimage.dataobj[..., index].astype(input_dtype, copy=False)
             )
+            return map_coordinates(data_t, targets)
+
+        # Order F ensures individual volumes are contiguous in memory
+        # Also matches NIfTI, making final save more efficient
+        resampled = np.zeros(
+            (len(ref_ndcoords), len(transform)), dtype=input_dtype, order="F"
+        )
+        for t in range(n_resamplings):
+            # Interpolate
+            resampled[..., t] = _apply_volume(t, data, transform, targets=targets)
 
     else:
         data = np.asanyarray(spatialimage.dataobj, dtype=input_dtype)