Merge pull request #999 from JDWarner/patch-1

FIX: Remove nonfunctional ANTs registration flag

Author: satra

nipype/interfaces/ants/registration.py

@@ -320,8 +320,6 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
         low=0.0, high=1.0, value=1.0, argstr='%s', usedefault=True, desc="The Upper quantile to clip image ranges")
     winsorize_lower_quantile = traits.Range(
         low=0.0, high=1.0, value=0.0, argstr='%s', usedefault=True, desc="The Lower quantile to clip image ranges")
-    collapse_linear_transforms_to_fixed_image_header = traits.Bool(
-        argstr='%s', default=False, usedefault=True, desc='')
 
 
 class RegistrationOutputSpec(TraitedSpec):
@@ -376,21 +374,20 @@ class Registration(ANTSCommand):
 
     >>> reg1 = copy.deepcopy(reg)
     >>> reg1.inputs.winsorize_lower_quantile = 0.025
-    >>> reg1.inputs.collapse_linear_transforms_to_fixed_image_header = False
     >>> reg1.cmdline
-    'antsRegistration --collapse-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --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, 1, 32 ] --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 [ 0.025, 1.0 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --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, 1, 32 ] --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 [ 0.025, 1.0 ]  --write-composite-transform 1'
     >>> reg1.run()  #doctest: +SKIP
 
     >>> reg2 = copy.deepcopy(reg)
     >>> reg2.inputs.winsorize_upper_quantile = 0.975
     >>> reg2.cmdline
-    'antsRegistration --collapse-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --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, 1, 32 ] --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, 0.975 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --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, 1, 32 ] --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, 0.975 ]  --write-composite-transform 1'
 
     >>> reg3 = copy.deepcopy(reg)
     >>> reg3.inputs.winsorize_lower_quantile = 0.025
     >>> reg3.inputs.winsorize_upper_quantile = 0.975
     >>> reg3.cmdline
-    'antsRegistration --collapse-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --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, 1, 32 ] --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 [ 0.025, 0.975 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --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, 1, 32 ] --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 [ 0.025, 0.975 ]  --write-composite-transform 1'
 
     >>> # Test collapse transforms flag
     >>> reg4 = copy.deepcopy(reg)
@@ -408,7 +405,7 @@ class Registration(ANTSCommand):
     >>> 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-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric CC[ fixed1.nii, moving1.nii, 1, 4, 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 CC[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, 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 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric CC[ fixed1.nii, moving1.nii, 1, 4, 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 CC[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, 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'
     """
     DEF_SAMPLING_STRATEGY = 'None'
     """The default sampling strategy argument."""
@@ -579,9 +576,11 @@ def _formatWinsorizeImageIntensities(self):
 
     def _formatCollapseLinearTransformsToFixedImageHeader(self):
         if self.inputs.collapse_linear_transforms_to_fixed_image_header:
-            return '--collapse-linear-transforms-to-fixed-image-header 1'
+            # return '--collapse-linear-transforms-to-fixed-image-header 1'
+            return ''
         else:
-            return '--collapse-linear-transforms-to-fixed-image-header 0'
+            # return '--collapse-linear-transforms-to-fixed-image-header 0'
+            return ''
 
     def _format_arg(self, opt, spec, val):
         if opt == 'fixed_image_mask':

nipype/interfaces/ants/tests/test_auto_Registration.py

@@ -5,9 +5,6 @@
 def test_Registration_inputs():
     input_map = dict(args=dict(argstr='%s',
     ),
-    collapse_linear_transforms_to_fixed_image_header=dict(argstr='%s',
-    usedefault=True,
-    ),
     collapse_output_transforms=dict(argstr='--collapse-output-transforms %d',
     usedefault=True,
     ),