Merge remote-tracking branch 'origin/pr/868'

Conflicts:
	CHANGES

Author: chrisgorgo

CHANGES

@@ -16,6 +16,7 @@ Next Release
 * ENH: New data grabbing interface that works over SSH connections, SSHDataGrabber
 * ENH: New color mode for write_graph
 * ENH: You can now force MapNodes to be run serially
+* ENH: New ANTs interface: LaplacianThickness
 * FIX: MRTrix tracking algorithms were ignoring mask parameters.
 * FIX: FNIRT registration pathway and associated OpenFMRI example script
 

nipype/interfaces/afni/preprocess.py

@@ -111,10 +111,8 @@ class TShiftInputSpec(AFNICommandInputSpec):
                          desc='different interpolation methods (see 3dTShift for details)' +
                          ' default = Fourier', argstr='-%s')
 
-    tpattern = traits.Enum(('alt+z', 'alt+z2', 'alt-z',
-                            'alt-z2', 'seq+z', 'seq-z'),
-                           desc='use specified slice time pattern rather than one in header',
-                           argstr='-tpattern %s')
+    tpattern = traits.Str(desc='use specified slice time pattern rather than one in header',
+                                    argstr='-tpattern %s')
 
     rlt = traits.Bool(desc='Before shifting, remove the mean and linear trend',
                       argstr="-rlt")
@@ -560,7 +558,6 @@ class VolregInputSpec(AFNICommandInputSpec):
                    copyfile=False)
     out_file = File(name_template="%s_volreg", desc='output image file name',
                     argstr='-prefix %s', name_source="in_file")
-
     basefile = File(desc='base file for registration',
                     argstr='-base %s',
                     position=-6,
@@ -582,12 +579,18 @@ class VolregInputSpec(AFNICommandInputSpec):
                             argstr='-tshift 0')
     copyorigin = traits.Bool(desc='copy base file origin coords to output',
                              argstr='-twodup')
+    oned_matrix_save = File(name_template='%s.aff12.1D',
+                            desc='Save the matrix transformation',
+                            argstr='-1Dmatrix_save %s',
+                            keep_extension=True,
+                            name_source="in_file")
 
 
 class VolregOutputSpec(TraitedSpec):
     out_file = File(desc='registered file', exists=True)
     md1d_file = File(desc='max displacement info file', exists=True)
     oned_file = File(desc='movement parameters info file', exists=True)
+    oned_matrix_save = File(desc='matrix transformation from base to input', exists=True)
 
 
 class Volreg(AFNICommand):
@@ -1924,7 +1927,7 @@ class AFNItoNIFTI(AFNICommand):
     input_spec = AFNItoNIFTIInputSpec
     output_spec = AFNICommandOutputSpec
 
-    def _overload_extension(self, value, name=None):
+    def _overload_extension(self, value):
         path, base, ext = split_filename(value)
         if ext.lower() not in [".1d", ".nii.gz", ".1D"]:
             ext = ext + ".nii"

nipype/interfaces/ants/__init__.py

@@ -11,7 +11,7 @@
 
 
 # Segmentation Programs
-from .segmentation import Atropos, N4BiasFieldCorrection
+from .segmentation import Atropos, LaplacianThickness, N4BiasFieldCorrection
 
 # Utility Programs
 from .utils import AverageAffineTransform, AverageImages, MultiplyImages, JacobianDeterminant

nipype/interfaces/ants/segmentation.py

@@ -160,6 +160,67 @@ def _list_outputs(self):
                 outputs['posteriors'].append(os.path.abspath(self.inputs.output_posteriors_name_template % (i + 1)))
         return outputs
 
+class LaplacianThicknessInputSpec(ANTSCommandInputSpec):
+    input_wm = File(argstr='%s', mandatory=True, copyfile=True,
+                       desc=('white matter segmentation image'),
+                       position=1)
+    input_gm = File(argstr='%s', mandatory=True, copyfile=True,
+                       desc=('gray matter segmentation image'),
+                       position=2)
+    output_image = File(desc='name of output file', argstr='%s', position=3,
+                        genfile=True, hash_files=False)
+    smooth_param = traits.Float(argstr='smoothparam=%d', desc='', position=4)
+    prior_thickness = traits.Float(argstr='priorthickval=%d', desc='',
+                                   position=5)
+    dT = traits.Float(argstr='dT=%d', desc='', position=6)
+    sulcus_prior = traits.Bool(argstr='use-sulcus-prior', desc='', position=7)
+    opt_tolerance = traits.Float(argstr='optional-laplacian-tolerance=%d', 
+                                 desc='', position=8)
+
+
+class LaplacianThicknessOutputSpec(TraitedSpec):
+    output_image = File(exists=True, desc='Cortical thickness')
+
+
+class LaplacianThickness(ANTSCommand):
+    """Calculates the cortical thickness from an anatomical image
+
+    Examples
+    --------
+
+    >>> from nipype.interfaces.ants import LaplacianThickness
+    >>> cort_thick = LaplacianThickness()
+    >>> cort_thick.inputs.input_wm = 'white_matter.nii.gz'
+    >>> cort_thick.inputs.input_gm = 'gray_matter.nii.gz'
+    >>> cort_thick.inputs.output_image = 'output_thickness.nii.gz'
+    >>> cort_thick.cmdline
+    'LaplacianThickness white_matter.nii.gz gray_matter.nii.gz output_thickness.nii.gz'
+
+    """
+
+    _cmd = 'LaplacianThickness'
+    input_spec = LaplacianThicknessInputSpec
+    output_spec = LaplacianThicknessOutputSpec
+
+    def _gen_filename(self, name):
+        if name == 'output_image':
+            output = self.inputs.output_image
+            if not isdefined(output):
+                _, name, ext = split_filename(self.inputs.input_wm)
+                output = name + '_thickness' + ext
+            return output
+        return None
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        _, name, ext = split_filename(os.path.abspath(self.inputs.input_wm))
+        outputs['output_image'] = os.path.join(os.getcwd(),
+                                               ''.join((name,
+                                                        self.inputs.output_image,
+                                                        ext)))
+        return outputs
+
+
 class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
     dimension = traits.Enum(3, 2, argstr='--image-dimension %d',
                             usedefault=True,
@@ -228,4 +289,4 @@ def _gen_filename(self, name):
     def _list_outputs(self):
         outputs = self._outputs().get()
         outputs['output_image'] = os.path.abspath(self._gen_filename('output_image'))
-        return outputs
+        return outputs