FIX: Remove nonfunctional ANTs registration flag

nipype/interfaces/ants/registration.py

@@ -320,8 +320,8 @@ 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='')
+    # collapse_linear_transforms_to_fixed_image_header = traits.Bool(
+    #     argstr='%s', default=False, usedefault=True, desc='')
 
 
 class RegistrationOutputSpec(TraitedSpec):
@@ -376,21 +376,20 @@ 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 +407,7 @@ 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 +578,11 @@ 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_ConvertScalarImageToRGB.py

@@ -0,0 +1,77 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from nipype.testing import assert_equal
+from nipype.interfaces.ants.visualization import ConvertScalarImageToRGB
+
+def test_ConvertScalarImageToRGB_inputs():
+    input_map = dict(args=dict(argstr='%s',
+    ),
+    colormap=dict(argstr='%s',
+    mandatory=True,
+    position=4,
+    usedefault=True,
+    ),
+    custom_color_map_file=dict(argstr='%s',
+    position=5,
+    usedefault=True,
+    ),
+    dimension=dict(argstr='%d',
+    mandatory=True,
+    position=0,
+    usedefault=True,
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    input_image=dict(argstr='%s',
+    mandatory=True,
+    position=1,
+    ),
+    mask_image=dict(argstr='%s',
+    position=3,
+    usedefault=True,
+    ),
+    maximum_RGB_output=dict(argstr='%d',
+    position=9,
+    usedefault=True,
+    ),
+    maximum_input=dict(argstr='%d',
+    mandatory=True,
+    position=7,
+    ),
+    minimum_RGB_output=dict(argstr='%d',
+    position=8,
+    usedefault=True,
+    ),
+    minimum_input=dict(argstr='%d',
+    mandatory=True,
+    position=6,
+    ),
+    num_threads=dict(nohash=True,
+    usedefault=True,
+    ),
+    output_image=dict(argstr='%s',
+    position=2,
+    usedefault=True,
+    ),
+    terminal_output=dict(mandatory=True,
+    nohash=True,
+    ),
+    )
+    inputs = ConvertScalarImageToRGB.input_spec()
+
+    for key, metadata in input_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+def test_ConvertScalarImageToRGB_outputs():
+    output_map = dict(output_image=dict(),
+    )
+    outputs = ConvertScalarImageToRGB.output_spec()
+
+    for key, metadata in output_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value
+

nipype/interfaces/ants/tests/test_auto_CreateTiledMosaic.py

@@ -0,0 +1,60 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from nipype.testing import assert_equal
+from nipype.interfaces.ants.visualization import CreateTiledMosaic
+
+def test_CreateTiledMosaic_inputs():
+    input_map = dict(alpha_value=dict(argstr='-a %.2f',
+    ),
+    args=dict(argstr='%s',
+    ),
+    direction=dict(argstr='-d %d',
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    flip_slice=dict(argstr='-f %s',
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    input_image=dict(argstr='-i %s',
+    mandatory=True,
+    ),
+    mask_image=dict(argstr='-x %s',
+    ),
+    num_threads=dict(nohash=True,
+    usedefault=True,
+    ),
+    output_image=dict(argstr='-o %s',
+    usedefault=True,
+    ),
+    pad_or_crop=dict(argstr='-p %s',
+    ),
+    permute_axes=dict(argstr='-g',
+    ),
+    rgb_image=dict(argstr='-r %s',
+    mandatory=True,
+    ),
+    slices=dict(argstr='-s %s',
+    ),
+    terminal_output=dict(mandatory=True,
+    nohash=True,
+    ),
+    tile_geometry=dict(argstr='-t %s',
+    ),
+    )
+    inputs = CreateTiledMosaic.input_spec()
+
+    for key, metadata in input_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+def test_CreateTiledMosaic_outputs():
+    output_map = dict(output_image=dict(),
+    )
+    outputs = CreateTiledMosaic.output_spec()
+
+    for key, metadata in output_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value
+

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,
     ),

nipype/interfaces/spm/tests/test_auto_Normalize.py

@@ -13,7 +13,8 @@ def test_Normalize_inputs():
     ignore_exception=dict(nohash=True,
     usedefault=True,
     ),
-    jobtype=dict(),
+    jobtype=dict(usedefault=True,
+    ),
     matlab_cmd=dict(),
     mfile=dict(usedefault=True,
     ),

nipype/interfaces/spm/tests/test_auto_Normalize12.py

@@ -25,7 +25,8 @@ def test_Normalize12_inputs():
     mandatory=True,
     xor=['deformation_file'],
     ),
-    jobtype=dict(),
+    jobtype=dict(usedefault=True,
+    ),
     matlab_cmd=dict(),
     mfile=dict(usedefault=True,
     ),