BF: Fix for 'split' (concatenated?) multiframe DICOM

Can't just use number of frame indices to determine shape of data,
as the actual frames could still be split into different files.
Also can't assume a multiframe file is more than a single slice.

Author: moloney

nibabel/nicom/dicomwrappers.py

@@ -554,23 +554,20 @@ def image_shape(self):
                 raise WrapperError('Missing information, cannot remove indices with confidence.')
             derived_dim_idx = dim_seq.index(derived_tag)
             frame_indices = np.delete(frame_indices, derived_dim_idx, axis=1)
-        # account for the 2 additional dimensions (row and column) not included
-        # in the indices
-        n_dim = frame_indices.shape[1] + 2
         # Store frame indices
         self._frame_indices = frame_indices
-        if n_dim < 4:  # 3D volume
-            return rows, cols, n_frames
-        # More than 3 dimensions
+        # Determine size of any extra-spatial dimensions
         ns_unique = [len(np.unique(row)) for row in self._frame_indices.T]
-        shape = (rows, cols) + tuple(ns_unique)
-        n_vols = np.prod(shape[3:])
-        n_frames_calc = n_vols * shape[2]
-        if n_frames != n_frames_calc:
-            raise WrapperError(
-                f'Calculated # of frames ({n_frames_calc}={n_vols}*{shape[2]}) '
-                f'of shape {shape} does not match NumberOfFrames {n_frames}.'
-            )
+        shape = (rows, cols) + tuple(x for i, x in enumerate(ns_unique) if i == 0 or x != 1)
+        n_dim = len(shape)
+        if n_dim > 3:
+            n_vols = np.prod(shape[3:])
+            n_frames_calc = n_vols * shape[2]
+            if n_frames != n_frames_calc:
+                raise WrapperError(
+                    f'Calculated # of frames ({n_frames_calc}={n_vols}*{shape[2]}) '
+                    f'of shape {shape} does not match NumberOfFrames {n_frames}.'
+                )
         return tuple(shape)
 
     @cached_property
@@ -640,10 +637,11 @@ def get_data(self):
             raise WrapperError('No valid information for image shape')
         data = self.get_pixel_array()
         # Roll frames axis to last
-        data = data.transpose((1, 2, 0))
-        # Sort frames with first index changing fastest, last slowest
-        sorted_indices = np.lexsort(self._frame_indices.T)
-        data = data[..., sorted_indices]
+        if len(data.shape) > 2:
+            data = data.transpose((1, 2, 0))
+            # Sort frames with first index changing fastest, last slowest
+            sorted_indices = np.lexsort(self._frame_indices.T)
+            data = data[..., sorted_indices]
         data = data.reshape(shape, order='F')
         return self._scale_data(data)