def test_Atropos_inputs():
input_map = dict(icm_use_synchronous_update=dict(argstr='%s',
),
prior_probability_images=dict(),
intensity_images=dict(mandatory=True,
argstr='--intensity-image %s...',
),
prior_weighting=dict(),
out_classified_image_name=dict(hash_files=False,
genfile=True,
argstr='%s',
),
mrf_smoothing_factor=dict(argstr='%s',
),
convergence_threshold=dict(requires=['n_iterations'],
),
prior_probability_threshold=dict(requires=['prior_weighting'],
),
save_posteriors=dict(),
maximum_number_of_icm_terations=dict(requires=['icm_use_synchronous_update'],
),
use_mixture_model_proportions=dict(requires=['posterior_formulation'],
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
mask_image=dict(mandatory=True,
argstr='--mask-image %s',
),
mrf_radius=dict(requires=['mrf_smoothing_factor'],
),
initialization=dict(mandatory=True,
requires=['number_of_tissue_classes'],
argstr='%s',
),
args=dict(argstr='%s',
),
likelihood_model=dict(argstr='--likelihood-model %s',
),
terminal_output=dict(mandatory=True,
nohash=True,
),
output_posteriors_name_template=dict(usedefault=True,
),
num_threads=dict(nohash=True,
usedefault=True,
),
number_of_tissue_classes=dict(mandatory=True,
),
n_iterations=dict(argstr='%s',
),
environ=dict(nohash=True,
usedefault=True,
),
posterior_formulation=dict(argstr='%s',
),
dimension=dict(usedefault=True,
argstr='--image-dimensionality %d',
),
)
inputs = Atropos.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_Atropos_inputs():
input_map = dict(args=dict(argstr='%s',
),
convergence_threshold=dict(requires=['n_iterations'],
),
dimension=dict(argstr='--image-dimensionality %d',
usedefault=True,
),
environ=dict(nohash=True,
usedefault=True,
),
icm_use_synchronous_update=dict(argstr='%s',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
initialization=dict(argstr='%s',
mandatory=True,
requires=['number_of_tissue_classes'],
),
intensity_images=dict(argstr='--intensity-image %s...',
mandatory=True,
),
likelihood_model=dict(argstr='--likelihood-model %s',
),
mask_image=dict(argstr='--mask-image %s',
mandatory=True,
),
maximum_number_of_icm_terations=dict(requires=['icm_use_synchronous_update'],
),
mrf_radius=dict(requires=['mrf_smoothing_factor'],
),
mrf_smoothing_factor=dict(argstr='%s',
),
n_iterations=dict(argstr='%s',
),
num_threads=dict(nohash=True,
usedefault=True,
),
number_of_tissue_classes=dict(mandatory=True,
),
out_classified_image_name=dict(argstr='%s',
genfile=True,
hash_files=False,
),
output_posteriors_name_template=dict(usedefault=True,
),
posterior_formulation=dict(argstr='%s',
),
prior_probability_images=dict(),
prior_probability_threshold=dict(requires=['prior_weighting'],
),
prior_weighting=dict(),
save_posteriors=dict(),
terminal_output=dict(mandatory=True,
nohash=True,
),
use_mixture_model_proportions=dict(requires=['posterior_formulation'],
),
)
inputs = Atropos.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
