Merge branch 'rf/resample_$d' into 'master'

Rf/resample $d

See merge request fsl/fslpy!86

CHANGELOG.rst

@@ -21,6 +21,8 @@ Changed
 * Minimum required version of ``nibabel`` is now 2.3.
 * The :class:`.Image` class now fully delegates to ``nibabel`` for managing
   file handles.
+* The :meth:`.Image.resample` method now supports images with more than three
+  dimensions.
 
 
 Removed
@@ -38,6 +40,7 @@ Fixed
 * Make sure that FEAT ``Cluster`` objects (created by the
   :func:`.loadClusterResults` function) contain ``p`` and ``logp`` attributes,
   even when cluster thresholding was not used.
+* Fix to the :class:`.ImageWrapper` regarding complex data types.
 
 
 1.13.0 (Thursday 22nd November 2018)

fsl/data/image.py

@@ -1173,7 +1173,7 @@ class Image(Nifti):
                  order=1,
                  smooth=True):
         """Returns a copy of the data in this ``Image``, resampled to the
-        specified ``shape``.
+        specified ``newShape``.
 
         :arg newShape: Desired shape. May containg floating point values,
                        in which case the resampled image will have shape
@@ -1182,7 +1182,7 @@ class Image(Nifti):
 
         :arg sliceobj: Slice into this ``Image``. If ``None``, the whole
                        image is resampled, and it is assumed that it has the
-                       same number of dimensions as  ``shape``. A
+                       same number of dimensions as  ``newShape``. A
                        :exc:`ValueError` is raised if this is not the case.
 
         :arg dtype:    ``numpy`` data type of the resampled data. If ``None``,
@@ -1201,12 +1201,12 @@ class Image(Nifti):
 
         :returns: A tuple containing:
 
-                   - A ``numpy`` array of shape ``shape``, containing an
-                     interpolated copy of the data in this ``Image``.
+                   - A ``numpy`` array of shape ``newShape``, containing
+                     an interpolated copy of the data in this ``Image``.
 
                    - A ``numpy`` array of shape ``(4, 4)``, containing the
-                     adjusted voxel-to-world transformation for the resampled
-                     data.
+                     adjusted voxel-to-world transformation for the spatial
+                     dimensions of the resampled data.
         """
 
         if sliceobj is None: sliceobj = slice(None)
@@ -1225,7 +1225,7 @@ class Image(Nifti):
 
             ratio    = oldShape / newShape
             newShape = np.array(np.round(newShape), dtype=np.int)
-            scale    = transform.scaleOffsetXform(ratio, 0)
+            scale    = np.diag(ratio)
 
             # If interpolating and smoothing, we apply a
             # gaussian filter along axes with a resampling
@@ -1242,7 +1242,7 @@ class Image(Nifti):
                 data = ndimage.gaussian_filter(data, sigma)
 
             data = ndimage.affine_transform(data,
-                                            scale[:3, :3],
+                                            scale,
                                             output_shape=newShape,
                                             order=order)
 
@@ -1250,6 +1250,7 @@ class Image(Nifti):
             # puts the resampled image into the
             # same world coordinate system as this
             # image.
+            scale = transform.scaleOffsetXform(ratio[:3], 0)
             xform = transform.concat(self.voxToWorldMat, scale)
         else:
             xform = self.voxToWorldMat

fsl/data/imagewrapper.py

@@ -301,7 +301,14 @@ class ImageWrapper(notifier.Notifier):
 
         # Internally, we calculate and store the
         # data range for each volume/slice/vector
-        self.__volRanges = np.zeros((nvols, 2), dtype=np.float32)
+        #
+        # We use nan as a placeholder, so the
+        # dtype must be non-integral
+        dtype = self.__image.get_data_dtype()
+        if np.issubdtype(dtype, np.integer):
+            dtype = np.float32
+        self.__volRanges = np.zeros((nvols, 2),
+                                    dtype=dtype)
 
         self.__coverage[ :] = np.nan
         self.__volRanges[:] = np.nan

tests/test_image.py

@@ -1078,6 +1078,12 @@ def test_image_resample(seed):
             make_random_image(fname, shape)
             img = fslimage.Image(fname, mmap=False)
 
+            # bad shape
+            with pytest.raises(ValueError):
+                img.resample((10, 10))
+            with pytest.raises(ValueError):
+                img.resample((10, 10, 10, 10))
+
             # resampling to the same shape should be a no-op
             samei, samex = img.resample(shape)
             assert np.all(samei == img[:])
@@ -1134,18 +1140,50 @@ def test_image_resample(seed):
 
                 assert np.all(np.isclose(resvals, origvals))
 
-        # Test a 4D image
+        del img
+        img = None
+
+
+def test_image_resample_4d(seed):
+
+    fname = 'test.nii.gz'
+
+    with tempdir():
+
         make_random_image(fname, (10, 10, 10, 10))
+
+        # resample one volume
         img = fslimage.Image(fname)
         slc = (slice(None), slice(None), slice(None), 3)
-
         resampled = img.resample(img.shape[:3], slc)[0]
         assert np.all(resampled == img[..., 3])
 
+        # resample up
         resampled = img.resample((15, 15, 15), slc)[0]
         assert tuple(resampled.shape) == (15, 15, 15)
+
+        # resample down
+        resampled = img.resample((5, 5, 5), slc)[0]
+        assert tuple(resampled.shape) == (5, 5, 5)
+
+        # resample the entire image
+        resampled = img.resample((15, 15, 15, 10), None)[0]
+        assert tuple(resampled.shape) == (15, 15, 15, 10)
+
+        resampled = img.resample((5, 5, 5, 10), None)[0]
+        assert tuple(resampled.shape) == (5, 5, 5, 10)
+
+        # resample along the fourth dim
+        resampled = img.resample((15, 15, 15, 15), None)[0]
+        assert tuple(resampled.shape) == (15, 15, 15, 15)
+
+        resampled = img.resample((5, 5, 5, 15), None)[0]
+        assert tuple(resampled.shape) == (5, 5, 5, 15)
+
         del img
+        del resampled
         img = None
+        resampled = None
 
 
 def  test_Image_init_xform_nifti1():  _test_Image_init_xform(1)