Merge branch 'master' of https://github.com/miykael/nipype

Conflicts:
	nipype/interfaces/spm/__init__.py
	nipype/interfaces/spm/preprocess.py

Author: miykael

nipype/interfaces/spm/preprocess.py

@@ -671,6 +671,149 @@ def _list_outputs(self):
         return outputs
 
 
+class Normalize12InputSpec(SPMCommandInputSpec):
+    image_to_align = File(exists=True, field='subj.vol',
+                          desc='file to estimate the normalization parameters with',
+                          xor=['deformation_file'],
+                          mandatory=True, 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',
+                                    copyfile=True)
+    deformation_file = File(field='subj.def', mandatory=True,
+                            xor=['image_to_align', 'tpm'],
+                            desc='file y_*.nii containing 3 deformation fields for the deformation in x, y and z dimension',
+                            copyfile=False)
+    jobtype = traits.Enum('estwrite', 'est', 'write',
+                          desc='one of: est, write, estwrite (opt, estwrite)',
+                          usedefault=True)
+    bias_regularization = traits.Enum(0, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10,
+                                      field='eoptions.biasreg',
+                                      desc='no(0) - extremely heavy (10) (opt)')
+    bias_fwhm = traits.Enum(30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,
+                            'Inf', field='eoptions.biasfwhm',
+                            desc='FWHM of Gaussian smoothness of bias (opt)')
+    tpm = File(exists=True, field='eoptions.tpm',
+               desc='template in form of a tissue probablitiy map to normalize to (opt)',
+               mandatory=False, xor=['deformation_file'],
+               copyfile=False)
+    affine_regularization_type = traits.Enum('mni', 'size', 'none', field='eoptions.affreg',
+                                              desc='mni, size, none (opt)')
+    warping_regularization = traits.List(traits.Float(), field='eoptions.reg',
+                                         minlen=5, maxlen=5,
+                                         desc='Controls balance between parameters and data (opt)')
+    smoothness = traits.Float(field='eoptions.fwhm',
+                              desc='value (in mm) to smooth the data before normalization (opt)')
+    sampling_distance = traits.Float(field='eoptions.samp',
+                                     desc='Sampling distance on data for parameter estimation (opt)')
+    write_bounding_box = traits.List(traits.List(traits.Float(),
+                                                 minlen=3, maxlen=3),
+                                     field='woptions.bb', minlen=2, maxlen=2,
+                                     desc='3x2-element list of lists representing the bounding box (in mm) to be written (opt)')
+    write_voxel_sizes = traits.List(traits.Float(), field='woptions.vox',
+                                    minlen=3, maxlen=3,
+                                    desc='3-element list representing the voxel sizes (in mm) of the written normalised images (opt)')
+    write_interp = traits.Range(low=0, high=7, field='woptions.interp',
+                                desc='degree of b-spline used for interpolation (opt)')
+
+
+class Normalize12OutputSpec(TraitedSpec):
+    deformation_field = OutputMultiPath(File(exists=True), desc='NIfTI file containing 3 deformation fields for the deformation in x, y and z dimension')
+    normalized_image = OutputMultiPath(File(exists=True), desc='Normalized file that needed to be aligned')
+    normalized_files = OutputMultiPath(File(exists=True), desc='Normalized other files')
+
+
+class Normalize12(SPMCommand):
+    """uses SPM12's new Normalise routine for warping an image to a template.
+    Spatial normalisation is now done via the segmentation routine (which was
+    known as ``New Segment`` in SPM8). Note that the normalisation in SPM12
+    is done towards a file containing multiple tissue probability maps, which
+    was not the cass in SPM8.
+
+    http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=49
+
+    Examples
+    --------
+    >>> import nipype.interfaces.spm as spm
+    >>> norm12 = spm.Normalize12()
+    >>> norm12.inputs.image_to_align = 'structural.nii'
+    >>> norm12.inputs.apply_to_files = 'functional.nii'
+    >>> norm12.run() # doctest: +SKIP
+
+    """
+
+    input_spec = Normalize12InputSpec
+    output_spec = Normalize12OutputSpec
+    _jobtype = 'spatial'
+    _jobname = 'normalise'
+
+    def _format_arg(self, opt, spec, val):
+        """Convert input to appropriate format for spm
+        """
+        if opt == 'tpm':
+            return scans_for_fname(filename_to_list(val))
+        if opt == 'image_to_align':
+            return scans_for_fname(filename_to_list(val))
+        if opt == 'apply_to_files':
+            return scans_for_fnames(filename_to_list(val))
+        if opt == 'deformation_file':
+            return np.array([list_to_filename(val)], dtype=object)
+        if opt in ['nonlinear_regularization']:
+            if len(val) != 5:
+                raise ValueError('%s must have 5 elements' % opt)
+        return super(Normalize12, self)._format_arg(opt, spec, val)
+
+    def _parse_inputs(self):
+        """validate spm normalize options if set to None ignore
+        """
+        einputs = super(Normalize12, self)._parse_inputs(skip=('jobtype',
+                                                               'apply_to_files'))
+        if isdefined(self.inputs.apply_to_files):
+            inputfiles = deepcopy(self.inputs.apply_to_files)
+            if isdefined(self.inputs.image_to_align):
+                inputfiles.extend([self.inputs.image_to_align])
+            einputs[0]['subj']['resample'] = scans_for_fnames(inputfiles)
+        jobtype = self.inputs.jobtype
+        if jobtype in ['estwrite', 'write']:
+            if not isdefined(self.inputs.apply_to_files):
+                if isdefined(self.inputs.image_to_align):
+                    einputs[0]['subj']['resample'] = scans_for_fname(self.inputs.image_to_align)
+        return [{'%s' % (jobtype): einputs[0]}]
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+
+        jobtype = self.inputs.jobtype
+        if jobtype.startswith('est'):
+            outputs['deformation_field'] = []
+            for imgf in filename_to_list(self.inputs.image_to_align):
+                outputs['deformation_field'].append(fname_presuffix(imgf,prefix='y_'))
+            outputs['deformation_field'] = list_to_filename(outputs['deformation_field'])
+
+        if self.inputs.jobtype == "estimate":
+            if isdefined(self.inputs.apply_to_files):
+                outputs['normalized_files'] = self.inputs.apply_to_files
+            outputs['normalized_image'] = fname_presuffix(self.inputs.image_to_align,
+                                                          prefix='w')
+        elif 'write' in self.inputs.jobtype:
+            outputs['normalized_files'] = []
+            if isdefined(self.inputs.apply_to_files):
+                filelist = filename_to_list(self.inputs.apply_to_files)
+                for f in filelist:
+                    if isinstance(f, list):
+                        run = [fname_presuffix(in_f,prefix='w') for in_f in f]
+                    else:
+                        run = [fname_presuffix(f,prefix='w')]
+                    outputs['normalized_files'].extend(run)
+            if isdefined(self.inputs.image_to_align):
+                outputs['normalized_image'] = fname_presuffix(self.inputs.image_to_align,
+                                                              prefix='w')
+
+        return outputs
+
+
+
 class SegmentInputSpec(SPMCommandInputSpec):
     data = InputMultiPath(File(exists=True), field='data', desc='one scan per subject',
                           copyfile=False, mandatory=True)