Merge pull request #1183 from chrisfilo/fix/ants_composite

ants composite transform is a single file not a list

Author: chrisgorgo

nipype/interfaces/ants/registration.py

@@ -189,8 +189,8 @@ def _list_outputs(self):
             self.inputs.output_transform_prefix + 'Warp.nii.gz')
         outputs['inverse_warp_transform'] = os.path.abspath(
             self.inputs.output_transform_prefix + 'InverseWarp.nii.gz')
-        #outputs['metaheader'] = os.path.abspath(self.inputs.output_transform_prefix + 'velocity.mhd')
-        #outputs['metaheader_raw'] = os.path.abspath(self.inputs.output_transform_prefix + 'velocity.raw')
+        # outputs['metaheader'] = os.path.abspath(self.inputs.output_transform_prefix + 'velocity.mhd')
+        # outputs['metaheader_raw'] = os.path.abspath(self.inputs.output_transform_prefix + 'velocity.raw')
         return outputs
 
 
@@ -351,10 +351,8 @@ class RegistrationOutputSpec(TraitedSpec):
     ), desc='List of flags corresponding to the forward transforms')
     reverse_invert_flags = traits.List(traits.Bool(
     ), desc='List of flags corresponding to the reverse transforms')
-    composite_transform = traits.List(
-        File(exists=True), desc='Composite transform file')
-    inverse_composite_transform = traits.List(
-        File(exists=True), desc='Inverse composite transform file')
+    composite_transform = File(exists=True, desc='Composite transform file')
+    inverse_composite_transform = File(exists=True, desc='Inverse composite transform file')
     warped_image = File(desc="Outputs warped image")
     inverse_warped_image = File(desc="Outputs the inverse of the warped image")
     save_state = File(desc="The saved registration state to be restored")
@@ -428,7 +426,7 @@ class Registration(ANTSCommand):
     >>> reg4.inputs.collapse_output_transforms = True
     >>> outputs = reg4._list_outputs()
     >>> print outputs #doctest: +ELLIPSIS
-    {'reverse_invert_flags': [], 'inverse_composite_transform': ['.../nipype/testing/data/output_InverseComposite.h5'], 'warped_image': '.../nipype/testing/data/output_warped_image.nii.gz', 'inverse_warped_image': <undefined>, 'forward_invert_flags': [], 'reverse_transforms': [], 'save_state': <undefined>, 'composite_transform': ['.../nipype/testing/data/output_Composite.h5'], 'forward_transforms': []}
+    {'reverse_invert_flags': [], 'inverse_composite_transform': '.../nipype/testing/data/output_InverseComposite.h5', 'warped_image': '.../nipype/testing/data/output_warped_image.nii.gz', 'inverse_warped_image': <undefined>, 'forward_invert_flags': [], 'reverse_transforms': [], 'save_state': <undefined>, 'composite_transform': '.../nipype/testing/data/output_Composite.h5', 'forward_transforms': []}
 
     >>> # Test collapse transforms flag
     >>> reg4b = copy.deepcopy(reg4)
@@ -500,7 +498,7 @@ def _formatMetric(self, index):
             items = stage_inputs.items()
             indexes = range(0, len(name_input))
             # dict-comprehension only works with python 2.7 and up
-            #specs = [{k: v[i] for k, v in items} for i in indexes]
+            # specs = [{k: v[i] for k, v in items} for i in indexes]
             specs = [dict([(k, v[i]) for k, v in items]) for i in indexes]
         else:
             specs = [stage_inputs]
@@ -550,17 +548,17 @@ def _formatRegistration(self):
                 retval.append('--metric %s' % metric)
             retval.append('--convergence %s' % self._formatConvergence(ii))
             if isdefined(self.inputs.sigma_units):
-                retval.append('--smoothing-sigmas %s%s' %
+                retval.append('--smoothing-sigmas %s%s' % 
                              (self._antsJoinList(self.inputs.smoothing_sigmas[
                                  ii]),
                               self.inputs.sigma_units[ii]))
             else:
-                retval.append('--smoothing-sigmas %s' %
+                retval.append('--smoothing-sigmas %s' % 
                               self._antsJoinList(self.inputs.smoothing_sigmas[ii]))
-            retval.append('--shrink-factors %s' %
+            retval.append('--shrink-factors %s' % 
                           self._antsJoinList(self.inputs.shrink_factors[ii]))
             if isdefined(self.inputs.use_estimate_learning_rate_once):
-                retval.append('--use-estimate-learning-rate-once %d' %
+                retval.append('--use-estimate-learning-rate-once %d' % 
                               self.inputs.use_estimate_learning_rate_once[ii])
             if isdefined(self.inputs.use_histogram_matching):
                 # use_histogram_matching is either a common flag for all transforms
@@ -663,7 +661,7 @@ def _format_arg(self, opt, spec, val):
                 return self._formatWinsorizeImageIntensities()
             return ''  # Must return something for argstr!
         # This feature was removed from recent versions of antsRegistration due to corrupt outputs.
-        #elif opt == 'collapse_linear_transforms_to_fixed_image_header':
+        # elif opt == 'collapse_linear_transforms_to_fixed_image_header':
         #    return self._formatCollapseLinearTransformsToFixedImageHeader()
         return super(Registration, self)._format_arg(opt, spec, val)
 
@@ -702,12 +700,11 @@ def _list_outputs(self):
 
         if self.inputs.write_composite_transform:
             fileName = self.inputs.output_transform_prefix + 'Composite.h5'
-            outputs['composite_transform'] = [os.path.abspath(fileName)]
+            outputs['composite_transform'] = os.path.abspath(fileName)
             fileName = self.inputs.output_transform_prefix + \
                 'InverseComposite.h5'
-            outputs['inverse_composite_transform'] = [
-                os.path.abspath(fileName)]
-        else: # If composite transforms are written, then individuals are not written (as of 2014-10-26
+            outputs['inverse_composite_transform'] = os.path.abspath(fileName)
+        else:  # If composite transforms are written, then individuals are not written (as of 2014-10-26
             if not self.inputs.collapse_output_transforms:
                 transformCount = 0
                 if isdefined(self.inputs.initial_moving_transform):