fix: address multimodal and unimodal inputs

Author: satra

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 metrics per stage
+    >>> 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]