Merge pull request #3695 from hjmjohnson/fix-ants-registration

ENH: Remove unused and recently unsupported antsRegistration flag

Author: effigies

nipype/interfaces/ants/registration.py

@@ -710,9 +710,9 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --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 ] \
+--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 \
+--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
     >>> reg.run()  # doctest: +SKIP
 
@@ -726,9 +726,9 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --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 ] \
+--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 \
+--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
 --use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 1.0 ]  --write-composite-transform 1'
     >>> reg1.run()  # doctest: +SKIP
 
@@ -742,9 +742,9 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --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 ] \
+--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 \
+--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 0.975 ]  --write-composite-transform 1'
 
     Clip extremely low intensity data points using winsorize_lower_quantile. All data points
@@ -759,9 +759,9 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --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 ] \
+--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 \
+--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
 --use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 0.975 ]  --write-composite-transform 1'
 
     Use float instead of double for computations (saves memory usage)
@@ -773,10 +773,10 @@ class Registration(ANTSCommand):
 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --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 \
+--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --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, 1.0 ]  \
+--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  \
 --write-composite-transform 1'
 
     Force to use double instead of float for computations (more precision and memory usage).
@@ -788,10 +788,10 @@ class Registration(ANTSCommand):
 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --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 \
+--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --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, 1.0 ]  \
+--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  \
 --write-composite-transform 1'
 
     'collapse_output_transforms' can be used to put all transformation in a single 'composite_transform'-
@@ -823,10 +823,10 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 1 --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 \
+--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --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, 1.0 ]  \
+--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  \
 --write-composite-transform 1'
 
 
@@ -857,10 +857,10 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 1 --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 \
+--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --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, 1.0 ]  \
+--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  \
 --write-composite-transform 0'
 
     One can use multiple similarity metrics in a single registration stage.The Node below first
@@ -885,10 +885,10 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --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 ] \
+--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 \
+--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
 
     ANTS Registration can also use multiple modalities to perform the registration. Here it is assumed
@@ -906,10 +906,10 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --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 ] \
+--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 \
+--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
 
     Different methods can be used for the interpolation when applying transformations.
@@ -923,9 +923,9 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --interpolation BSpline[ 3 ] --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 ] \
+--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 \
+--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
 
     >>> # Test Interpolation Parameters (MultiLabel/Gaussian)
@@ -937,10 +937,10 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --interpolation Gaussian[ 1.0, 1.0 ] \
 --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 \
+--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --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, 1.0 ]  \
+--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  \
 --write-composite-transform 1'
 
     BSplineSyN non-linear registration with custom parameters.
@@ -954,9 +954,9 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --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 BSplineSyN[ 0.25, 26, 0, 3 ] \
+--use-histogram-matching 1 --transform BSplineSyN[ 0.25, 26, 0, 3 ] \
 --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 \
+--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
 
     Mask the fixed image in the second stage of the registration (but not the first).
@@ -969,10 +969,10 @@ class Registration(ANTSCommand):
 --initialize-transforms-per-stage 0 --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 --masks [ NULL, NULL ] \
+--use-histogram-matching 1 --masks [ NULL, NULL ] \
 --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 --masks [ fixed1.nii, NULL ] \
+--use-histogram-matching 1 --masks [ fixed1.nii, NULL ] \
 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
 
     Here we use both a warpfield and a linear transformation, before registration commences.  Note that
@@ -988,10 +988,10 @@ class Registration(ANTSCommand):
 [ func_to_struct.mat, 0 ] [ ants_Warp.nii.gz, 0 ] --initialize-transforms-per-stage 0 --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 \
+--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --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, 1.0 ]  \
+--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  \
 --write-composite-transform 1'
     """
 
@@ -1155,10 +1155,9 @@ def _format_registration(self):
                 % self._format_xarray(self.inputs.shrink_factors[ii])
             )
             if isdefined(self.inputs.use_estimate_learning_rate_once):
-                retval.append(
-                    "--use-estimate-learning-rate-once %d"
-                    % self.inputs.use_estimate_learning_rate_once[ii]
-                )
+                # this flag was removed because it was never used in the ants codebase
+                # removed from Ants in commit e1e47994b on 2022-08-09
+                pass
             if isdefined(self.inputs.use_histogram_matching):
                 # use_histogram_matching is either a common flag for all transforms
                 # or a list of transform-specific flags