Merge branch 'master' into afni/resample

* master: (42 commits)
  fixed applymask output
  fixed test
  Fixed some details for the PR nipy#530
  Removed accidentally added files (nipy#530)
  Updated changelog
  Updated dMRI and fMRI pre-processing
  Added motion correction pipeline
  Modified FSL FLIRT interface to provide log support
  Polished code
  buildtemplateparallel.sh - issue with input files
  Update legacy.py - buildtemplateparallel.sh
  Minor fixes
  Added MBIS to sub-package configuration list
  Standardized import to add all utilities at once
  First implementation for MBIS
  New EPI dewarp workflow (nipy#525)
  New EPI dewarping using a workflow
  api: force users to explicitly define whether to sort files
  BF: Fix SplitNifti to generate unique filenames for outputs.
  added mask input for bias field correction
  ...

Author: bpinsard

CHANGES

@@ -1,6 +1,12 @@
 Next release
 ============
+* ENH: New interfaces: nipy.Trim
 
+* ENH: Allow control over terminal output for commandline interfaces
+* ENH: New workflows for dMRI and fMRI pre-processing: added motion artifact correction
+       with rotation of the B-matrix, and susceptibility correction for EPI imaging using
+       fieldmaps. Updated eddy_correct pipeline to support both dMRI and fMRI, and new parameters.
+* ENH: Minor improvements to FSL's FUGUE and FLIRT interfaces
 
 Release 0.7.0 (Dec 18, 2012)
 ============================

nipype/interfaces/afni/base.py

@@ -42,7 +42,8 @@ def version():
            Version number as string or None if AFNI not found
 
         """
-        clout = CommandLine(command='afni_vcheck').run()
+        clout = CommandLine(command='afni_vcheck',
+                            terminal_output='allatonce').run()
         out = clout.runtime.stdout
         return out.split('\n')[1]
 
@@ -87,7 +88,8 @@ def standard_image(img_name):
         '''Grab an image from the standard location.
 
         Could be made more fancy to allow for more relocatability'''
-        clout = CommandLine('which afni').run()
+        clout = CommandLine('which afni',
+                            terminal_output='allatonce').run()
         if clout.runtime.returncode is not 0:
             return None
 
@@ -214,9 +216,10 @@ def _gen_filename(self, name):
 
             _, base, _ = split_filename(
                 getattr(self.inputs, trait_spec.name_source))
-            return self._gen_fname(basename=base, prefix=prefix, suffix=suffix, cwd='')
+            return self._gen_fname(basename=base, prefix=prefix, suffix=suffix, cwd=os.getcwd())
         else:
-            return super(AFNICommand, self)._gen_filename(name)
+            return os.path.join(os.getcwd(),
+                                super(AFNICommand, self)._gen_filename(name))
 
     def _overload_extension(self, value):
         path, base, _ = split_filename(value)
@@ -229,7 +232,8 @@ def _list_outputs(self):
             outputs = self.output_spec().get()
             for name in out_names:
                 out = self._gen_filename(name)
-                outputs[name] = os.path.abspath(out)
+                if isdefined(out):
+                    outputs[name] = os.path.abspath(out)
             return outputs
 
 

nipype/interfaces/afni/preprocess.py

@@ -12,7 +12,7 @@
 import os
 from .base import AFNIBaseCommandInputSpec, AFNIBaseCommand
 from ..base import (Directory, CommandLineInputSpec, CommandLine, TraitedSpec,
-                    traits, isdefined, File, InputMultiPath)
+                    traits, isdefined, File, InputMultiPath, Undefined)
 from ...utils.filemanip import (load_json, save_json, split_filename)
 from nipype.utils.filemanip import fname_presuffix
 from nipype.interfaces.afni.base import AFNICommand, AFNICommandInputSpec,\
@@ -292,7 +292,7 @@ class ResampleInputSpec(AFNICommandInputSpec):
 
 
 
-class Resample(AFNIBaseCommand):
+class Resample(AFNICommand):
     """Resample or reorient an image using AFNI 3dresample command
 
     For complete details, see the `3dresample Documentation.
@@ -546,7 +546,7 @@ def _gen_filename(self, name):
 
             _, base, _ = split_filename(
                 getattr(self.inputs, trait_spec.name_source))
-            return self._gen_fname(basename=base, prefix=prefix, suffix=suffix, cwd='')
+            return self._gen_fname(basename=base, prefix=prefix, suffix=suffix, cwd=os.getcwd())
         elif name == "brain_file" and isdefined(self.inputs.apply_suffix):
             suffix = ''
             prefix = ''
@@ -555,7 +555,7 @@ def _gen_filename(self, name):
 
             _, base, _ = split_filename(
                 getattr(self.inputs, trait_spec.name_source))
-            return self._gen_fname(basename=base, prefix=prefix, suffix=suffix, cwd='')
+            return self._gen_fname(basename=base, prefix=prefix, suffix=suffix, cwd=os.getcwd())
         elif name == "apply_mask" and isdefined(self.inputs.apply_suffix):
             suffix = ''
             prefix = ''
@@ -564,9 +564,10 @@ def _gen_filename(self, name):
 
             _, base, _ = split_filename(
                 getattr(self.inputs, trait_spec.name_source))
-            return self._gen_fname(basename=base, prefix=prefix, suffix=suffix, cwd='')
-        else:
-            return super(AFNICommand, self)._gen_filename(name)
+            return self._gen_fname(basename=base, prefix=prefix, suffix=suffix, cwd=os.getcwd())
+        elif hasattr(self.inputs,name) and isdefined(getattr(self.inputs,name)):
+            return super(Automask, self)._gen_filename(name)
+        return Undefined
 
     def _list_outputs(self):
         outputs = super(Automask, self)._list_outputs()

nipype/interfaces/ants/legacy.py

@@ -205,15 +205,15 @@ class buildtemplateparallel(ANTSCommand):
     def _format_arg(self, opt, spec, val):
         if opt == 'num_cores':
             if self.inputs.parallelization == 2:
-                return '-j ' + val
+                return '-j ' + str(val)
             else:
                 return ''
         if opt == 'in_files':
             if self.inputs.use_first_as_target:
                 start = '-z '
             else:
                 start = ''
-            return start + ' '.join([os.path.split(name)[1] for name in val])
+            return start + ' '.join(name for name in val)
         return super(buildtemplateparallel, self)._format_arg(opt, spec, val)
 
     def _list_outputs(self):

nipype/interfaces/ants/registration.py

@@ -264,16 +264,16 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
 
 class RegistrationOutputSpec(TraitedSpec):
     forward_transforms = traits.List(
-        File(), desc='List of output transforms for forward registration')
+        File(exists=True), desc='List of output transforms for forward registration')
     reverse_transforms = traits.List(
-        File(), desc='List of output transforms for reverse registration')
+        File(exists=True), desc='List of output transforms for reverse registration')
     forward_invert_flags = traits.List(traits.Bool(
     ), 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(), desc='Composite transform file')
+    composite_transform = traits.List(File(exists=True), desc='Composite transform file')
     inverse_composite_transform = traits.List(
-        File(), desc='Inverse composite transform file')
+        File(exists=True), desc='Inverse composite transform file')
 
 
 class Registration(ANTSCommand):
@@ -336,7 +336,6 @@ class Registration(ANTSCommand):
     _cmd = 'antsRegistration'
     input_spec = RegistrationInputSpec
     output_spec = RegistrationOutputSpec
-    _numberOfOutputTransforms = 0
     _quantilesDone = False
 
     def _optionalMetricParameters(self, index):
@@ -419,10 +418,6 @@ def _format_arg(self, opt, spec, val):
         if opt == 'moving_image_mask':
             return '--masks [ %s, %s ]' % (self.inputs.fixed_image_mask, self.inputs.moving_image_mask)
         elif opt == 'transforms':
-            if self.inputs.collapse_output_transforms:
-                self._numberOfOutputTransforms = 2  # Affine and displacement
-            else:
-                self._numberOfOutputTransforms = len(self.inputs.transforms)
             return self._formatRegistration()
         elif opt == 'initial_moving_transform':
             if self.inputs.invert_initial_moving_transform:
@@ -449,7 +444,8 @@ def _format_arg(self, opt, spec, val):
 
     def _outputFileNames(self, prefix, count, transform, inverse=False):
         self.lowDimensionalTransformMap = {'Rigid': 'Rigid.mat',
-                                           'Affine': 'Affine.mat',
+                                           #seems counterontuitive, but his is how ANTS is calling it
+                                           'Affine': 'GenericAffine.mat',
                                            'GenericAffine': 'GenericAffine.mat',
                                            'CompositeAffine': 'Affine.mat',
                                            'Similarity': 'Similarity.mat',
@@ -473,6 +469,7 @@ def _list_outputs(self):
         outputs['reverse_transforms'] = []
         outputs['reverse_invert_flags'] = []
         if not self.inputs.collapse_output_transforms:
+            transformCount = 0
             if isdefined(self.inputs.initial_moving_transform):
                 outputs['forward_transforms'].append(
                     self.inputs.initial_moving_transform)
@@ -481,20 +478,21 @@ def _list_outputs(self):
                 outputs['reverse_transforms'].insert(
                     0, self.inputs.initial_moving_transform)
                 outputs['reverse_invert_flags'].insert(0, not self.inputs.invert_initial_moving_transform)  # Prepend
-                transformCount = 1
-                for count in range(self._numberOfOutputTransforms):
-                    forwardFileName, forwardInverseMode = self._outputFileNames(self.inputs.output_transform_prefix, transformCount,
-                                                                                self.inputs.transforms[count])
-                    reverseFileName, reverseInverseMode = self._outputFileNames(self.inputs.output_transform_prefix, transformCount,
-                                                                                self.inputs.transforms[count], True)
-                    outputs['forward_transforms'].append(
-                        os.path.abspath(forwardFileName))
-                    outputs['forward_invert_flags'].append(forwardInverseMode)
-                    outputs['reverse_transforms'].insert(
-                        0, os.path.abspath(reverseFileName))
-                    outputs[
-                        'reverse_invert_flags'].insert(0, reverseInverseMode)
-                    transformCount += 1
+                transformCount += 1
+                
+            for count in range(len(self.inputs.transforms)):
+                forwardFileName, forwardInverseMode = self._outputFileNames(self.inputs.output_transform_prefix, transformCount,
+                                                                            self.inputs.transforms[count])
+                reverseFileName, reverseInverseMode = self._outputFileNames(self.inputs.output_transform_prefix, transformCount,
+                                                                            self.inputs.transforms[count], True)
+                outputs['forward_transforms'].append(
+                    os.path.abspath(forwardFileName))
+                outputs['forward_invert_flags'].append(forwardInverseMode)
+                outputs['reverse_transforms'].insert(
+                    0, os.path.abspath(reverseFileName))
+                outputs[
+                    'reverse_invert_flags'].insert(0, reverseInverseMode)
+                transformCount += 1
         else:
             transformCount = 0
             for transform in ['GenericAffine', 'SyN']:  # Only files returned by collapse_output_transforms

nipype/interfaces/ants/segmentation.py

@@ -162,6 +162,7 @@ class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
     input_image = File(argstr='--input-image %s', mandatory=True,
                         desc=('image to apply transformation to (generally a '
                               'coregistered functional)'))
+    mask_image = File(argstr='--mask-image %s')
     output_image = traits.Str(argstr='--output %s',
                              desc=('output file name'), genfile=True,
                              hash_file=False)