def test_Segment_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
paths=dict(),
clean_masks=dict(field='output.cleanup',
),
bias_fwhm=dict(field='opts.biasfwhm',
),
mask_image=dict(field='opts.msk',
),
bias_regularization=dict(field='opts.biasreg',
),
warp_frequency_cutoff=dict(field='opts.warpco',
),
affine_regularization=dict(field='opts.regtype',
),
use_v8struct=dict(min_ver='8',
usedefault=True,
),
warping_regularization=dict(field='opts.warpreg',
),
use_mcr=dict(),
gm_output_type=dict(field='output.GM',
),
tissue_prob_maps=dict(field='opts.tpm',
),
sampling_distance=dict(field='opts.samp',
),
matlab_cmd=dict(),
gaussians_per_class=dict(field='opts.ngaus',
),
mfile=dict(usedefault=True,
),
save_bias_corrected=dict(field='output.biascor',
),
data=dict(copyfile=False,
mandatory=True,
field='data',
),
wm_output_type=dict(field='output.WM',
),
csf_output_type=dict(field='output.CSF',
),
)
inputs = Segment.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_Segment_inputs():
input_map = dict(
affine_regularization=dict(field='opts.regtype', ),
bias_fwhm=dict(field='opts.biasfwhm', ),
bias_regularization=dict(field='opts.biasreg', ),
clean_masks=dict(field='output.cleanup', ),
csf_output_type=dict(field='output.CSF', ),
data=dict(
copyfile=False,
field='data',
mandatory=True,
),
gaussians_per_class=dict(field='opts.ngaus', ),
gm_output_type=dict(field='output.GM', ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
mask_image=dict(field='opts.msk', ),
matlab_cmd=dict(),
mfile=dict(usedefault=True, ),
paths=dict(),
sampling_distance=dict(field='opts.samp', ),
save_bias_corrected=dict(field='output.biascor', ),
tissue_prob_maps=dict(field='opts.tpm', ),
use_mcr=dict(),
use_v8struct=dict(
min_ver='8',
usedefault=True,
),
warp_frequency_cutoff=dict(field='opts.warpco', ),
warping_regularization=dict(field='opts.warpreg', ),
wm_output_type=dict(field='output.WM', ),
)
inputs = Segment.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_Segment_outputs():
output_map = dict(bias_corrected_image=dict(),
native_csf_image=dict(),
normalized_wm_image=dict(),
modulated_wm_image=dict(),
modulated_input_image=dict(new_name='bias_corrected_image',
deprecated='0.10',
),
native_wm_image=dict(),
inverse_transformation_mat=dict(),
transformation_mat=dict(),
normalized_csf_image=dict(),
modulated_gm_image=dict(),
modulated_csf_image=dict(),
native_gm_image=dict(),
normalized_gm_image=dict(),
)
outputs = Segment.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_Segment_outputs():
output_map = dict(
bias_corrected_image=dict(),
inverse_transformation_mat=dict(),
modulated_csf_image=dict(),
modulated_gm_image=dict(),
modulated_input_image=dict(
deprecated='0.10',
new_name='bias_corrected_image',
),
modulated_wm_image=dict(),
native_csf_image=dict(),
native_gm_image=dict(),
native_wm_image=dict(),
normalized_csf_image=dict(),
normalized_gm_image=dict(),
normalized_wm_image=dict(),
transformation_mat=dict(),
)
outputs = Segment.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
