def test_ANTS_outputs():
output_map = dict(warp_transform=dict(),
metaheader=dict(),
affine_transform=dict(),
metaheader_raw=dict(),
inverse_warp_transform=dict(),
)
outputs = ANTS.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_ANTS_outputs():
output_map = dict(
affine_transform=dict(),
inverse_warp_transform=dict(),
metaheader=dict(),
metaheader_raw=dict(),
warp_transform=dict(),
)
outputs = ANTS.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_ANTS_inputs():
input_map = dict(
affine_gradient_descent_option=dict(argstr='%s', ),
args=dict(argstr='%s', ),
delta_time=dict(requires=['number_of_time_steps'], ),
dimension=dict(
argstr='%d',
position=1,
usedefault=False,
),
environ=dict(
nohash=True,
usedefault=True,
),
fixed_image=dict(mandatory=True, ),
gradient_step_length=dict(requires=['transformation_model'], ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
metric=dict(mandatory=True, ),
metric_weight=dict(requires=['metric'], ),
mi_option=dict(
argstr='--MI-option %s',
sep='x',
),
moving_image=dict(
argstr='%s',
mandatory=True,
),
num_threads=dict(
nohash=True,
usedefault=True,
),
number_of_affine_iterations=dict(
argstr='--number-of-affine-iterations %s',
sep='x',
),
number_of_iterations=dict(
argstr='--number-of-iterations %s',
sep='x',
),
number_of_time_steps=dict(requires=['gradient_step_length'], ),
output_transform_prefix=dict(
argstr='--output-naming %s',
mandatory=True,
usedefault=True,
),
radius=dict(requires=['metric'], ),
regularization=dict(argstr='%s', ),
regularization_deformation_field_sigma=dict(
requires=['regularization'], ),
regularization_gradient_field_sigma=dict(requires=['regularization'
], ),
smoothing_sigmas=dict(
argstr='--gaussian-smoothing-sigmas %s',
sep='x',
),
subsampling_factors=dict(
argstr='--subsampling-factors %s',
sep='x',
),
symmetry_type=dict(requires=['delta_time'], ),
terminal_output=dict(
mandatory=True,
nohash=True,
),
transformation_model=dict(
argstr='%s',
mandatory=True,
),
use_histogram_matching=dict(
argstr='%s',
usedefault=True,
),
)
inputs = ANTS.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_ANTS_inputs():
input_map = dict(regularization_deformation_field_sigma=dict(requires=['regularization'],
),
fixed_image=dict(mandatory=True,
),
regularization_gradient_field_sigma=dict(requires=['regularization'],
),
output_transform_prefix=dict(mandatory=True,
argstr='--output-naming %s',
usedefault=True,
),
moving_image=dict(mandatory=True,
argstr='%s',
),
radius=dict(requires=['metric'],
),
metric_weight=dict(requires=['metric'],
),
symmetry_type=dict(requires=['delta_time'],
),
regularization=dict(argstr='%s',
),
mi_option=dict(sep='x',
argstr='--MI-option %s',
),
number_of_affine_iterations=dict(sep='x',
argstr='--number-of-affine-iterations %s',
),
metric=dict(mandatory=True,
),
affine_gradient_descent_option=dict(argstr='%s',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
transformation_model=dict(mandatory=True,
argstr='%s',
),
gradient_step_length=dict(requires=['transformation_model'],
),
args=dict(argstr='%s',
),
delta_time=dict(requires=['number_of_time_steps'],
),
terminal_output=dict(mandatory=True,
nohash=True,
),
subsampling_factors=dict(sep='x',
argstr='--subsampling-factors %s',
),
num_threads=dict(nohash=True,
usedefault=True,
),
smoothing_sigmas=dict(sep='x',
argstr='--gaussian-smoothing-sigmas %s',
),
number_of_iterations=dict(sep='x',
argstr='--number-of-iterations %s',
),
number_of_time_steps=dict(requires=['gradient_step_length'],
),
environ=dict(nohash=True,
usedefault=True,
),
use_histogram_matching=dict(usedefault=True,
argstr='%s',
),
dimension=dict(position=1,
usedefault=False,
argstr='%d',
),
)
inputs = ANTS.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
