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,
),
convergence_threshold=dict(requires=['number_of_iterations'],
usedefault=True,
),
convergence_window_size=dict(requires=['convergence_threshold'],
usedefault=True,
),
dimension=dict(argstr='--dimensionality %d',
usedefault=True,
),
environ=dict(nohash=True,
usedefault=True,
),
fixed_image=dict(mandatory=True,
),
fixed_image_mask=dict(argstr='%s',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
initial_moving_transform=dict(argstr='%s',
xor=['initial_moving_transform_com'],
),
initial_moving_transform_com=dict(argstr='%s',
xor=['initial_moving_transform'],
),
interpolation=dict(argstr='%s',
usedefault=True,
),
invert_initial_moving_transform=dict(requires=['initial_moving_transform'],
xor=['initial_moving_transform_com'],
),
metric=dict(mandatory=True,
),
metric_item_trait=dict(),
metric_stage_trait=dict(),
metric_weight=dict(mandatory=True,
requires=['metric'],
usedefault=True,
),
metric_weight_item_trait=dict(),
metric_weight_stage_trait=dict(),
moving_image=dict(mandatory=True,
),
moving_image_mask=dict(requires=['fixed_image_mask'],
),
num_threads=dict(nohash=True,
usedefault=True,
),
number_of_iterations=dict(),
output_inverse_warped_image=dict(hash_files=False,
requires=['output_warped_image'],
),
output_transform_prefix=dict(argstr='%s',
usedefault=True,
),
output_warped_image=dict(hash_files=False,
),
radius_bins_item_trait=dict(),
radius_bins_stage_trait=dict(),
radius_or_number_of_bins=dict(requires=['metric_weight'],
usedefault=True,
),
sampling_percentage=dict(requires=['sampling_strategy'],
),
sampling_percentage_item_trait=dict(),
sampling_percentage_stage_trait=dict(),
sampling_strategy=dict(requires=['metric_weight'],
),
sampling_strategy_item_trait=dict(),
sampling_strategy_stage_trait=dict(),
shrink_factors=dict(mandatory=True,
),
sigma_units=dict(requires=['smoothing_sigmas'],
),
smoothing_sigmas=dict(mandatory=True,
),
terminal_output=dict(mandatory=True,
nohash=True,
),
transform_parameters=dict(),
transforms=dict(argstr='%s',
mandatory=True,
),
use_estimate_learning_rate_once=dict(),
use_histogram_matching=dict(usedefault=True,
),
winsorize_lower_quantile=dict(argstr='%s',
usedefault=True,
),
winsorize_upper_quantile=dict(argstr='%s',
usedefault=True,
),
write_composite_transform=dict(argstr='--write-composite-transform %d',
usedefault=True,
),
)
inputs = Registration.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_Registration_inputs():
input_map = dict(
args=dict(argstr='%s', ),
collapse_output_transforms=dict(
argstr='--collapse-output-transforms %d',
usedefault=True,
),
convergence_threshold=dict(
requires=['number_of_iterations'],
usedefault=True,
),
convergence_window_size=dict(
requires=['convergence_threshold'],
usedefault=True,
),
dimension=dict(
argstr='--dimensionality %d',
usedefault=True,
),
environ=dict(
nohash=True,
usedefault=True,
),
fixed_image=dict(mandatory=True, ),
fixed_image_mask=dict(argstr='%s', ),
float=dict(argstr='--float %d', ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
initial_moving_transform=dict(
argstr='%s',
xor=['initial_moving_transform_com'],
),
initial_moving_transform_com=dict(
argstr='%s',
xor=['initial_moving_transform'],
),
initialize_transforms_per_stage=dict(
argstr='--initialize-transforms-per-stage %d',
usedefault=True,
),
interpolation=dict(
argstr='%s',
usedefault=True,
),
invert_initial_moving_transform=dict(
requires=['initial_moving_transform'],
xor=['initial_moving_transform_com'],
),
metric=dict(mandatory=True, ),
metric_item_trait=dict(),
metric_stage_trait=dict(),
metric_weight=dict(
mandatory=True,
requires=['metric'],
usedefault=True,
),
metric_weight_item_trait=dict(),
metric_weight_stage_trait=dict(),
moving_image=dict(mandatory=True, ),
moving_image_mask=dict(requires=['fixed_image_mask'], ),
num_threads=dict(
nohash=True,
usedefault=True,
),
number_of_iterations=dict(),
output_inverse_warped_image=dict(
hash_files=False,
requires=['output_warped_image'],
),
output_transform_prefix=dict(
argstr='%s',
usedefault=True,
),
output_warped_image=dict(hash_files=False, ),
radius_bins_item_trait=dict(),
radius_bins_stage_trait=dict(),
radius_or_number_of_bins=dict(
requires=['metric_weight'],
usedefault=True,
),
restore_state=dict(argstr='--restore-state %s', ),
sampling_percentage=dict(requires=['sampling_strategy'], ),
sampling_percentage_item_trait=dict(),
sampling_percentage_stage_trait=dict(),
sampling_strategy=dict(requires=['metric_weight'], ),
sampling_strategy_item_trait=dict(),
sampling_strategy_stage_trait=dict(),
save_state=dict(argstr='--save-state %s', ),
shrink_factors=dict(mandatory=True, ),
sigma_units=dict(requires=['smoothing_sigmas'], ),
smoothing_sigmas=dict(mandatory=True, ),
terminal_output=dict(nohash=True, ),
transform_parameters=dict(),
transforms=dict(
argstr='%s',
mandatory=True,
),
use_estimate_learning_rate_once=dict(),
use_histogram_matching=dict(usedefault=True, ),
winsorize_lower_quantile=dict(
argstr='%s',
usedefault=True,
),
winsorize_upper_quantile=dict(
argstr='%s',
usedefault=True,
),
write_composite_transform=dict(
argstr='--write-composite-transform %d',
usedefault=True,
),
)
inputs = Registration.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
