ENH: Wrapper for DenoiseImage

CHANGES

@@ -1,6 +1,7 @@
 Next release
 ============
 
+* ENH: Provides a Nipype wrapper for ANTs DenoiseImage (https://github.com/nipy/nipype/pull/1291)
 * FIX: Minor bugfix logging hash differences (https://github.com/nipy/nipype/pull/1298)
 * FIX: Use released Prov python library (https://github.com/nipy/nipype/pull/1279)
 * ENH: Support for Python 3 (https://github.com/nipy/nipype/pull/1221)

doc/devel/cmd_interface_devel.rst

@@ -198,11 +198,13 @@ output_name (optional)
      name of the output (if this is not set same name as the input will be
      assumed)
 
-keep_extension (optional - not used)
-     if you want the extension from the input to be kept
+keep_extension (optional)
+     if you want the extension from the input or name_template to be kept. The
+     name_template extension always overrides the input extension.
 
 In addition one can add functionality to your class or base class, to allow
-changing extensions specific to package or interface
+changing extensions specific to package or interface. This overload function is
+trigerred only if keep_extension is not defined.
 
 .. testcode::
 

nipype/interfaces/ants/__init__.py

@@ -12,7 +12,7 @@
 
 # Segmentation Programs
 from .segmentation import (Atropos, LaplacianThickness, N4BiasFieldCorrection, JointFusion, CorticalThickness,
-                           BrainExtraction)
+                           BrainExtraction, DenoiseImage)
 
 # Visualization Programs
 from .visualization import ConvertScalarImageToRGB, CreateTiledMosaic

nipype/interfaces/ants/segmentation.py

@@ -795,3 +795,77 @@ def _list_outputs(self):
         outputs['output_label_image'] = os.path.abspath(
             self.inputs.output_label_image)
         return outputs
+
+
+class DenoiseImageInputSpec(ANTSCommandInputSpec):
+    dimension = traits.Enum(2, 3, 4, argstr='-d %d', usedefault=False,
+                            desc='This option forces the image to be treated '
+                                 'as a specified-dimensional image. If not '
+                                 'specified, the program tries to infer the '
+                                 'dimensionality from the input image.')
+    input_image = File(exists=True, argstr="-i %s", mandatory=True,
+                       desc='A scalar image is expected as input for noise correction.')
+    noise_model = traits.Enum('Gaussian', 'Rician', argstr='-n %s', usedefault=True,
+                              desc=('Employ a Rician or Gaussian noise model.'))
+    shrink_factor = traits.Int(default_value=1, usedefault=True, argstr='-s %s',
+                               desc=('Running noise correction on large images can '
+                                     'be time consuming. To lessen computation time, '
+                                     'the input image can be resampled. The shrink '
+                                     'factor, specified as a single integer, describes '
+                                     'this resampling. Shrink factor = 1 is the default.'))
+    output_image = File(argstr="-o %s", name_source=['input_image'], hash_files=False,
+                        keep_extension=True, name_template='%s_noise_corrected',
+                        desc='The output consists of the noise corrected '
+                             'version of the input image.')
+    save_noise = traits.Bool(False, mandatory=True, usedefault=True,
+                             desc=('True if the estimated noise should be saved '
+                                   'to file.'), xor=['noise_image'])
+    noise_image = File(name_source=['input_image'], hash_files=False,
+                       keep_extension=True, name_template='%s_noise',
+                       desc='Filename for the estimated noise.')
+    verbose = traits.Bool(False, argstr="-v", desc=('Verbose output.'))
+
+
+class DenoiseImageOutputSpec(TraitedSpec):
+    output_image = File(exists=True)
+    noise_image = File(exists=True)
+
+
+class DenoiseImage(ANTSCommand):
+    """
+    Examples
+    --------
+    >>> import copy
+    >>> from nipype.interfaces.ants import DenoiseImage
+    >>> denoise = DenoiseImage()
+    >>> denoise.inputs.dimension = 3
+    >>> denoise.inputs.input_image = 'im1.nii'
+    >>> denoise.cmdline
+    'DenoiseImage -d 3 -i im1.nii -n Gaussian -o im1_noise_corrected.nii -s 1'
+
+    >>> denoise_2 = copy.deepcopy(denoise)
+    >>> denoise_2.inputs.output_image = 'output_corrected_image.nii.gz'
+    >>> denoise_2.inputs.noise_model = 'Rician'
+    >>> denoise_2.inputs.shrink_factor = 2
+    >>> denoise_2.cmdline
+    'DenoiseImage -d 3 -i im1.nii -n Rician -o output_corrected_image.nii.gz -s 2'
+
+    >>> denoise_3 = DenoiseImage()
+    >>> denoise_3.inputs.input_image = 'im1.nii'
+    >>> denoise_3.inputs.save_noise = True
+    >>> denoise_3.cmdline
+    'DenoiseImage -i im1.nii -n Gaussian -o [ im1_noise_corrected.nii, im1_noise.nii ] -s 1'
+    """
+    input_spec = DenoiseImageInputSpec
+    output_spec = DenoiseImageOutputSpec
+    _cmd = 'DenoiseImage'
+
+    def _format_arg(self, name, trait_spec, value):
+        if ((name == 'output_image') and
+                (self.inputs.save_noise or isdefined(self.inputs.noise_image))):
+            newval = '[ %s, %s ]' % (self._filename_from_source('output_image'),
+                                     self._filename_from_source('noise_image'))
+            return trait_spec.argstr % newval
+
+        return super(DenoiseImage,
+                     self)._format_arg(name, trait_spec, value)

nipype/interfaces/ants/tests/test_auto_DenoiseImage.py

@@ -0,0 +1,65 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from ....testing import assert_equal
+from ..segmentation import DenoiseImage
+
+
+def test_DenoiseImage_inputs():
+    input_map = dict(args=dict(argstr='%s',
+    ),
+    dimension=dict(argstr='-d %d',
+    usedefault=False,
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    input_image=dict(argstr='-i %s',
+    mandatory=True,
+    ),
+    noise_image=dict(hash_files=False,
+    keep_extension=True,
+    name_source=['input_image'],
+    name_template='%s_noise',
+    ),
+    noise_model=dict(argstr='-n %s',
+    usedefault=True,
+    ),
+    num_threads=dict(nohash=True,
+    usedefault=True,
+    ),
+    output_image=dict(argstr='-o %s',
+    hash_files=False,
+    keep_extension=True,
+    name_source=['input_image'],
+    name_template='%s_noise_corrected',
+    ),
+    save_noise=dict(mandatory=True,
+    usedefault=True,
+    xor=['noise_image'],
+    ),
+    shrink_factor=dict(argstr='-s %s',
+    usedefault=True,
+    ),
+    terminal_output=dict(nohash=True,
+    ),
+    verbose=dict(argstr='-v',
+    ),
+    )
+    inputs = DenoiseImage.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+
+def test_DenoiseImage_outputs():
+    output_map = dict(noise_image=dict(),
+    output_image=dict(),
+    )
+    outputs = DenoiseImage.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value

nipype/interfaces/base.py

@@ -1560,7 +1560,7 @@ def _filename_from_source(self, name, chain=None):
 
         trait_spec = self.inputs.trait(name)
         retval = getattr(self.inputs, name)
-
+        source_ext = None
         if not isdefined(retval) or "%s" in retval:
             if not trait_spec.name_source:
                 return retval
@@ -1589,7 +1589,7 @@ def _filename_from_source(self, name, chain=None):
 
                 # special treatment for files
                 try:
-                    _, base, _ = split_filename(source)
+                    _, base, source_ext = split_filename(source)
                 except AttributeError:
                     base = source
             else:
@@ -1598,14 +1598,17 @@ def _filename_from_source(self, name, chain=None):
 
                 chain.append(name)
                 base = self._filename_from_source(ns, chain)
+                if isdefined(base):
+                    _, _, source_ext = split_filename(base)
 
             chain = None
             retval = name_template % base
             _, _, ext = split_filename(retval)
-            if trait_spec.keep_extension and ext:
-                return retval
-            return self._overload_extension(retval, name)
-
+            if trait_spec.keep_extension and (ext or source_ext):
+                if (ext is None or not ext) and source_ext:
+                    retval = retval + source_ext
+            else:
+                retval = self._overload_extension(retval, name)
         return retval
 
     def _gen_filename(self, name):