Merge pull request #1196 from satra/fix/antsunified

fix: address multimodal and unimodal inputs

Author: satra

examples/fmri_ants_openfmri.py

@@ -236,7 +236,7 @@ def create_reg_workflow(name='registration'):
 
     merge = pe.Node(niu.Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
-    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+    register.connect(reg, 'composite_transform', merge, 'in1')
 
     """
     Transform the mean image. First to anatomical and then to target
@@ -429,7 +429,7 @@ def create_fs_reg_workflow(name='registration'):
     reg.inputs.use_histogram_matching = [False] * 2 + [True]
     reg.inputs.winsorize_lower_quantile = 0.005
     reg.inputs.winsorize_upper_quantile = 0.995
-    reg.inputs.args = '--float'
+    reg.inputs.float = True
     reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
     reg.inputs.num_threads = 4
     reg.plugin_args = {'qsub_args': '-pe orte 4',
@@ -445,7 +445,7 @@ def create_fs_reg_workflow(name='registration'):
 
     merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
-    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+    register.connect(reg, 'composite_transform', merge, 'in1')
 
     """
     Transform the mean image. First to anatomical and then to target

examples/rsfmri_vol_surface_preprocessing.py

@@ -517,7 +517,7 @@ def create_reg_workflow(name='registration'):
     reg.inputs.use_histogram_matching = [False] * 2 + [True]
     reg.inputs.winsorize_lower_quantile = 0.005
     reg.inputs.winsorize_upper_quantile = 0.995
-    reg.inputs.args = '--float'
+    reg.inputs.float = True
     reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
     reg.inputs.num_threads = 4
     reg.plugin_args = {'qsub_args': '-l nodes=1:ppn=4'}
@@ -528,11 +528,9 @@ def create_reg_workflow(name='registration'):
     Concatenate the affine and ants transforms into a list
     """
 
-    pickfirst = lambda x: x[0]
-
     merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
-    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+    register.connect(reg, 'composite_transform', merge, 'in1')
 
     """
     Transform the mean image. First to anatomical and then to target

examples/rsfmri_vol_surface_preprocessing_nipy.py

@@ -498,7 +498,7 @@ def create_reg_workflow(name='registration'):
     reg.inputs.use_histogram_matching = [False] * 2 + [True]
     reg.inputs.winsorize_lower_quantile = 0.005
     reg.inputs.winsorize_upper_quantile = 0.995
-    reg.inputs.args = '--float'
+    reg.inputs.float = True
     reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
     reg.inputs.num_threads = 4
     reg.plugin_args = {'sbatch_args': '-c%d' % 4}
@@ -510,11 +510,9 @@ def create_reg_workflow(name='registration'):
     Concatenate the affine and ants transforms into a list
     """
 
-    pickfirst = lambda x: x[0]
-
     merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
-    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+    register.connect(reg, 'composite_transform', merge, 'in1')
 
 
     """

nipype/interfaces/ants/registration.py

@@ -7,11 +7,10 @@
    >>> os.chdir(datadir)
 """
 
-from ..base import (TraitedSpec, File, traits)
+from ..base import (TraitedSpec, File, traits, InputMultiPath)
 from .base import ANTSCommand, ANTSCommandInputSpec
 import os
-from nipype.interfaces.base import InputMultiPath
-from nipype.interfaces.traits_extension import isdefined
+from ..traits_extension import isdefined
 import numpy as np
 
 
@@ -458,15 +457,22 @@ class Registration(ANTSCommand):
 
     >>> # Test multiple metrics per stage
     >>> reg5 = copy.deepcopy(reg)
-    >>> reg5.inputs.fixed_image = [ 'fixed1.nii', 'fixed2.nii' ]
-    >>> reg5.inputs.moving_image = [ 'moving1.nii', 'moving2.nii' ]
+    >>> reg5.inputs.fixed_image = 'fixed1.nii'
+    >>> reg5.inputs.moving_image = 'moving1.nii'
     >>> reg5.inputs.metric = ['Mattes', ['Mattes', 'CC']]
     >>> reg5.inputs.metric_weight = [1, [.5,.5]]
-    >>> reg5.inputs.radius_or_number_of_bins = [32, [32,4] ]
+    >>> reg5.inputs.radius_or_number_of_bins = [32, [32, 4] ]
     >>> reg5.inputs.sampling_strategy = ['Random', None] # use default strategy in second stage
     >>> reg5.inputs.sampling_percentage = [0.05, [0.05, 0.10]]
     >>> reg5.cmdline
-    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed2.nii, moving2.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed1.nii, moving1.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
+
+    >>> # Test multiple inputs
+    >>> reg6 = copy.deepcopy(reg5)
+    >>> reg6.inputs.fixed_image = ['fixed1.nii', 'fixed2.nii']
+    >>> reg6.inputs.moving_image = ['moving1.nii', 'moving2.nii']
+    >>> reg6.cmdline
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed2.nii, moving2.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1   --write-composite-transform 1'
     """
     DEF_SAMPLING_STRATEGY = 'None'
     """The default sampling strategy argument."""
@@ -520,12 +526,12 @@ def _formatMetric(self, index):
             specs = list()
             for i in indexes:
                 temp = dict([(k, v[i]) for k, v in items])
-                if i > len( self.inputs.fixed_image ):
+                if len(self.inputs.fixed_image) == 1:
                     temp["fixed_image"] = self.inputs.fixed_image[0]
                 else:
                     temp["fixed_image"] = self.inputs.fixed_image[i]
 
-                if i > len( self.inputs.moving_image ):
+                if len(self.inputs.moving_image) == 1:
                     temp["moving_image"] = self.inputs.moving_image[0]
                 else:
                     temp["moving_image"] = self.inputs.moving_image[i]