Updated N4BiasFieldCorrection

Added weight_image to input spec. Added bspline_order to input spec and
updated arg formatting for bspline_fitting_distance to allow for
bspline_order to be set. Updated n_iterations and convergence_threshold
arg formatting to conform with other arg formatting for this class.

Author: blakedewey

nipype/interfaces/ants/segmentation.py

@@ -229,17 +229,16 @@ class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
                        desc=('image to apply transformation to (generally a '
                              'coregistered functional)'))
     mask_image = File(argstr='--mask-image %s')
+    weight_image = File(argstr='--weight-image %s')
     output_image = traits.Str(argstr='--output %s',
                               desc=('output file name'), genfile=True,
                               hash_files=False)
-    bspline_fitting_distance = traits.Float(argstr="--bsline-fitting [%g]")
+    bspline_fitting_distance = traits.Float(argstr="--bspline-fitting %s")
+    bspline_order = traits.Int(requires=['bspline_fitting_distance'])
     shrink_factor = traits.Int(argstr="--shrink-factor %d")
-    n_iterations = traits.List(traits.Int(), argstr="--convergence [ %s",
-                               sep="x", requires=['convergence_threshold'],
-                               position=1)
-    convergence_threshold = traits.Float(argstr=",%g]",
-                                         requires=['n_iterations'],
-                                         position=2)
+    n_iterations = traits.List(traits.Int(), argstr="--convergence %s",
+                               requires=['convergence_threshold'])
+    convergence_threshold = traits.Float(requires=['n_iterations'])
     save_bias = traits.Bool(False, mandatory=True, usedefault=True,
                             desc=('True if the estimated bias should be saved'
                                   ' to file.'), xor=['bias_image'])
@@ -315,7 +314,20 @@ def _format_arg(self, name, trait_spec, value):
            (self.inputs.save_bias or isdefined(self.inputs.bias_image))):
             bias_image = self._gen_filename('bias_image')
             output = self._gen_filename('output_image')
-            newval = '[%s,%s]' % (output, bias_image)
+            newval = '[ %s, %s ]' % (output, bias_image)
+            return trait_spec.argstr % newval
+            
+        if name == 'bspline_fitting_distance':
+            if isdefined(self.inputs.bspline_order):
+                newval = '[ %g, %d ]' % (value, self.inputs.bspline_order)
+            else:
+                newval = '[ %g ]' % value
+            return trait_spec.argstr % newval
+            
+        if ((name == 'n_iterations') and
+           (isdefined(self.inputs.convergence_threshold))):
+            newval = '[ %s, %g ]' % ('x'.join([str(elt) for elt in value]),
+                                     self.inputs.convergence_threshold)
             return trait_spec.argstr % newval
 
         return super(N4BiasFieldCorrection,

nipype/interfaces/ants/tests/test_auto_N4BiasFieldCorrection.py

@@ -7,11 +7,11 @@ def test_N4BiasFieldCorrection_inputs():
     ),
     bias_image=dict(hash_files=False,
     ),
-    bspline_fitting_distance=dict(argstr='--bsline-fitting [%g]',
+    bspline_fitting_distance=dict(argstr='--bspline-fitting %s',
     ),
-    convergence_threshold=dict(argstr=',%g]',
-    position=2,
-    requires=['n_iterations'],
+    bspline_order=dict(requires=['bspline_fitting_distance'],
+    ),
+    convergence_threshold=dict(requires=['n_iterations'],
     ),
     dimension=dict(argstr='--image-dimension %d',
     usedefault=True,
@@ -27,10 +27,8 @@ def test_N4BiasFieldCorrection_inputs():
     ),
     mask_image=dict(argstr='--mask-image %s',
     ),
-    n_iterations=dict(argstr='--convergence [ %s',
-    position=1,
+    n_iterations=dict(argstr='--convergence %s',
     requires=['convergence_threshold'],
-    sep='x',
     ),
     num_threads=dict(nohash=True,
     usedefault=True,
@@ -48,6 +46,8 @@ def test_N4BiasFieldCorrection_inputs():
     terminal_output=dict(mandatory=True,
     nohash=True,
     ),
+    weight_image=dict(argstr='--weight-image %s',
+    ),
     )
     inputs = N4BiasFieldCorrection.input_spec()