[ENH] Support for multiple initial-moving-transforms for ants.Registration and ComposeMultiTransforms

nipype/interfaces/ants/__init__.py

@@ -20,4 +20,5 @@
 
 # Utility Programs
 from .utils import (AverageAffineTransform, AverageImages, MultiplyImages,
-                    CreateJacobianDeterminantImage, AffineInitializer)
+                    CreateJacobianDeterminantImage, AffineInitializer,
+                    ComposeMultiTransform)

nipype/interfaces/ants/tests/test_auto_ComposeMultiTransform.py

@@ -0,0 +1,53 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from __future__ import unicode_literals
+from ..utils import ComposeMultiTransform
+
+
+def test_ComposeMultiTransform_inputs():
+    input_map = dict(args=dict(argstr='%s',
+    ),
+    dimension=dict(argstr='%d',
+    position=0,
+    usedefault=True,
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    num_threads=dict(nohash=True,
+    usedefault=True,
+    ),
+    output_transform=dict(argstr='%s',
+    keep_ext=True,
+    name_source=['transforms'],
+    name_template='%s_composed',
+    position=1,
+    ),
+    reference_image=dict(argstr='%s',
+    position=2,
+    ),
+    terminal_output=dict(deprecated='1.0.0',
+    nohash=True,
+    ),
+    transforms=dict(argstr='%s',
+    mandatory=True,
+    position=3,
+    ),
+    )
+    inputs = ComposeMultiTransform.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            assert getattr(inputs.traits()[key], metakey) == value
+
+
+def test_ComposeMultiTransform_outputs():
+    output_map = dict(output_transform=dict(),
+    )
+    outputs = ComposeMultiTransform.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            assert getattr(outputs.traits()[key], metakey) == value

nipype/interfaces/ants/tests/test_auto_Registration.py

@@ -36,6 +36,7 @@ def test_Registration_inputs():
     usedefault=True,
     ),
     initial_moving_transform=dict(argstr='%s',
+    exists=True,
     xor=['initial_moving_transform_com'],
     ),
     initial_moving_transform_com=dict(argstr='%s',

nipype/interfaces/ants/utils.py

@@ -11,9 +11,7 @@
 
 import os
 
-from ...utils.filemanip import split_filename
-from ..base import (TraitedSpec, File, traits, isdefined, InputMultiPath,
-                    CommandLine, CommandLineInputSpec)
+from ..base import TraitedSpec, File, traits, InputMultiPath
 from .base import ANTSCommand, ANTSCommandInputSpec
 
 
@@ -59,12 +57,16 @@ def _list_outputs(self):
 class AverageImagesInputSpec(ANTSCommandInputSpec):
     dimension = traits.Enum(3, 2, argstr='%d', mandatory=True,
                             position=0, desc='image dimension (2 or 3)')
-    output_average_image = File("average.nii", argstr='%s', position=1, desc='the name of the resulting image.',
-                                usedefault=True, hash_files=False)
-    normalize = traits.Bool(argstr="%d", mandatory=True, position=2, desc='Normalize: if true, the 2nd image' +
-                            'is divided by its mean. This will select the largest image to average into.')
-    images = InputMultiPath(File(exists=True), argstr='%s', mandatory=True, position=3,
-                            desc='image to apply transformation to (generally a coregistered functional)')
+    output_average_image = File(
+        "average.nii", argstr='%s', position=1, usedefault=True, hash_files=False,
+        desc='the name of the resulting image.')
+    normalize = traits.Bool(
+        argstr="%d", mandatory=True, position=2,
+        desc='Normalize: if true, the 2nd image is divided by its mean. '
+             'This will select the largest image to average into.')
+    images = InputMultiPath(
+        File(exists=True), argstr='%s', mandatory=True, position=3,
+        desc='image to apply transformation to (generally a coregistered functional)')
 
 
 class AverageImagesOutputSpec(TraitedSpec):
@@ -101,9 +103,11 @@ def _list_outputs(self):
 class MultiplyImagesInputSpec(ANTSCommandInputSpec):
     dimension = traits.Enum(3, 2, argstr='%d', usedefault=False, mandatory=True, position=0,
                             desc='image dimension (2 or 3)')
-    first_input = File(argstr='%s', exists=True, mandatory=True, position=1, desc='image 1')
-    second_input = traits.Either(File(exists=True), traits.Float, argstr='%s', mandatory=True, position=2,
-                                 desc='image 2 or multiplication weight')
+    first_input = File(argstr='%s', exists=True,
+                       mandatory=True, position=1, desc='image 1')
+    second_input = traits.Either(
+        File(exists=True), traits.Float, argstr='%s', mandatory=True, position=2,
+        desc='image 2 or multiplication weight')
     output_product_image = File(argstr='%s', mandatory=True, position=3,
                                 desc='Outputfname.nii.gz: the name of the resulting image.')
 
@@ -138,22 +142,25 @@ def _list_outputs(self):
             self.inputs.output_product_image)
         return outputs
 
+
 class CreateJacobianDeterminantImageInputSpec(ANTSCommandInputSpec):
     imageDimension = traits.Enum(3, 2, argstr='%d', usedefault=False, mandatory=True,
-                            position=0, desc='image dimension (2 or 3)')
+                                 position=0, desc='image dimension (2 or 3)')
     deformationField = File(argstr='%s', exists=True, mandatory=True,
-                     position=1, desc='deformation transformation file')
+                            position=1, desc='deformation transformation file')
     outputImage = File(argstr='%s', mandatory=True,
-                         position=2,
-                         desc='output filename')
+                       position=2,
+                       desc='output filename')
     doLogJacobian = traits.Enum(0, 1, argstr='%d', position=3,
-                          desc='return the log jacobian')
+                                desc='return the log jacobian')
     useGeometric = traits.Enum(0, 1, argstr='%d', position=4,
-                          desc='return the geometric jacobian')
+                               desc='return the geometric jacobian')
+
 
 class CreateJacobianDeterminantImageOutputSpec(TraitedSpec):
     jacobian_image = File(exists=True, desc='jacobian image')
 
+
 class CreateJacobianDeterminantImage(ANTSCommand):
     """
     Examples
@@ -203,6 +210,7 @@ class AffineInitializerInputSpec(ANTSCommandInputSpec):
         desc=' determines if a local optimization is run at each search point for the set '
              'number of iterations')
 
+
 class AffineInitializerOutputSpec(TraitedSpec):
     out_file = File(desc='output transform file')
 
@@ -225,3 +233,38 @@ class AffineInitializer(ANTSCommand):
 
     def _list_outputs(self):
         return {'out_file': os.path.abspath(self.inputs.out_file)}
+
+
+class ComposeMultiTransformInputSpec(ANTSCommandInputSpec):
+    dimension = traits.Enum(3, 2, argstr='%d', usedefault=True, position=0,
+                            desc='image dimension (2 or 3)')
+    output_transform = File(argstr='%s', position=1, name_source=['transforms'],
+                            name_template='%s_composed', keep_ext=True,
+                            desc='the name of the resulting transform.')
+    reference_image = File(argstr='%s', position=2,
+                           desc='Reference image (only necessary when output is warpfield)')
+    transforms = InputMultiPath(File(exists=True), argstr='%s', mandatory=True,
+                                position=3, desc='transforms to average')
+
+
+class ComposeMultiTransformOutputSpec(TraitedSpec):
+    output_transform = File(exists=True, desc='Composed transform file')
+
+
+class ComposeMultiTransform(ANTSCommand):
+    """
+    Take a set of transformations and convert them to a single transformation matrix/warpfield.
+
+    Examples
+    --------
+    >>> from nipype.interfaces.ants import ComposeMultiTransform
+    >>> compose_transform = ComposeMultiTransform()
+    >>> compose_transform.inputs.dimension = 3
+    >>> compose_transform.inputs.transforms = ['struct_to_template.mat', 'func_to_struct.mat']
+    >>> compose_transform.cmdline # doctest: +ALLOW_UNICODE
+    'ComposeMultiTransform 3 struct_to_template_composed struct_to_template.mat func_to_struct.mat'
+
+    """
+    _cmd = 'ComposeMultiTransform'
+    input_spec = ComposeMultiTransformInputSpec
+    output_spec = ComposeMultiTransformOutputSpec