Merge pull request #691 from beOn/spm_preproc

spm preproc fixes and adds

Author: satra

nipype/interfaces/spm/preprocess.py

@@ -55,8 +55,9 @@ class SliceTimingInputSpec(SPMCommandInputSpec):
 
 
 class SliceTimingOutputSpec(TraitedSpec):
-    timecorrected_files = OutputMultiPath(File(exist=True,
-                                             desc='slice time corrected files'))
+    timecorrected_files = OutputMultiPath(traits.Either(traits.List(File(exists=True)),
+                                                        File(exists=True)),
+                                          desc='slice time corrected files')
 
 
 class SliceTiming(SPMCommand):
@@ -100,15 +101,11 @@ def _list_outputs(self):
 
         filelist = filename_to_list(self.inputs.in_files)
         for f in filelist:
-            run = []
             if isinstance(f, list):
-                for inner_f in filename_to_list(f):
-                    run.append(fname_presuffix(inner_f,
-                                               prefix=self.inputs.out_prefix))
+                run = [fname_presuffix(in_f, prefix=self.inputs.out_prefix) for in_f in f]
             else:
-                realigned_run = fname_presuffix(f,
-                                                prefix=self.inputs.out_prefix)
-            outputs['timecorrected_files'].append(realigned_run)
+                run = fname_presuffix(f, prefix=self.inputs.out_prefix)
+            outputs['timecorrected_files'].append(run)
         return outputs
 
 
@@ -152,9 +149,18 @@ class RealignInputSpec(SPMCommandInputSpec):
 
 class RealignOutputSpec(TraitedSpec):
     mean_image = File(exists=True, desc='Mean image file from the realignment')
+    modified_in_files = OutputMultiPath(traits.Either(traits.List(File(exists=True)),
+                                             File(exists=True)),
+                                        desc='Copies of all files passed to in_files.\
+                                              Headers will have been modified to align all\
+                                              images with the first, or optionally to first\
+                                              do that, extract a mean image, and re-align to\
+                                              that mean image.')
     realigned_files = OutputMultiPath(traits.Either(traits.List(File(exists=True)),
                                                     File(exists=True)),
-                                      desc='Realigned files')
+                                      desc='If jobtype is write or estwrite, these will be the\
+                                            resliced files. Otherwise, they will be copies of\
+                                            in_files that have had their headers rewritten.')
     realignment_parameters = OutputMultiPath(File(exists=True),
                     desc='Estimated translation and rotation parameters')
 
@@ -213,6 +219,10 @@ def _list_outputs(self):
                                                                      use_ext=False))
             if not isinstance(imgf, list) and func_is_3d(imgf):
                 break
+        if self.inputs.jobtype == "estimate":
+            outputs['realigned_files'] = self.inputs.in_files
+        if self.inputs.jobtype == "estimate" or self.inputs.jobtype == "estwrite":
+            outputs['modified_in_files'] = self.inputs.in_files
         if self.inputs.jobtype == "write" or self.inputs.jobtype == "estwrite":
             if isinstance(self.inputs.in_files[0], list):
                 first_image = self.inputs.in_files[0][0]
@@ -367,8 +377,11 @@ class NormalizeInputSpec(SPMCommandInputSpec):
     jobtype = traits.Enum('estwrite', 'est', 'write',
                           desc='one of: est, write, estwrite (opt, estwrite)',
                           usedefault=True)
-    apply_to_files = InputMultiPath(File(exists=True), field='subj.resample',
-                               desc='files to apply transformation to (opt)', copyfile=True)
+    apply_to_files = InputMultiPath(traits.Either(File(exists=True),
+                                                  traits.List(File(exists=True))),
+                                    field='subj.resample',
+                                    desc='files to apply transformation to (opt)',
+                                    copyfile=True)
     parameter_file = File(field='subj.matname', mandatory=True,
                           xor=['source', 'template'],
                           desc='normalization parameter file*_sn.mat', copyfile=False)
@@ -481,9 +494,13 @@ def _list_outputs(self):
         elif 'write' in self.inputs.jobtype:
             outputs['normalized_files'] = []
             if isdefined(self.inputs.apply_to_files):
-                for imgf in filename_to_list(self.inputs.apply_to_files):
-                    outputs['normalized_files'].append(fname_presuffix(imgf, prefix=self.inputs.out_prefix))
-
+                filelist = filename_to_list(self.inputs.apply_to_files)
+                for f in filelist:
+                    if isinstance(f, list):
+                        run = [fname_presuffix(in_f, prefix=self.inputs.out_prefix) for in_f in f]
+                    else:
+                        run = [fname_presuffix(f, prefix=self.inputs.out_prefix)]
+                    outputs['normalized_files'].extend(run)
             if isdefined(self.inputs.source):
                 outputs['normalized_source'] = []
                 for imgf in filename_to_list(self.inputs.source):