def test_Normalize_outputs():
output_map = dict(normalization_parameters=dict(),
normalized_files=dict(),
normalized_source=dict(),
)
outputs = Normalize.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_Normalize_inputs():
input_map = dict(
DCT_period_cutoff=dict(field='eoptions.cutoff', ),
affine_regularization_type=dict(field='eoptions.regtype', ),
apply_to_files=dict(
copyfile=True,
field='subj.resample',
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
jobtype=dict(),
matlab_cmd=dict(),
mfile=dict(usedefault=True, ),
nonlinear_iterations=dict(field='eoptions.nits', ),
nonlinear_regularization=dict(field='eoptions.reg', ),
out_prefix=dict(
field='roptions.prefix',
usedefault=True,
),
parameter_file=dict(
copyfile=False,
field='subj.matname',
mandatory=True,
xor=['source', 'template'],
),
paths=dict(),
source=dict(
copyfile=True,
field='subj.source',
mandatory=True,
xor=['parameter_file'],
),
source_image_smoothing=dict(field='eoptions.smosrc', ),
source_weight=dict(
copyfile=False,
field='subj.wtsrc',
),
template=dict(
copyfile=False,
field='eoptions.template',
mandatory=True,
xor=['parameter_file'],
),
template_image_smoothing=dict(field='eoptions.smoref', ),
template_weight=dict(
copyfile=False,
field='eoptions.weight',
),
use_mcr=dict(),
use_v8struct=dict(
min_ver='8',
usedefault=True,
),
write_bounding_box=dict(field='roptions.bb', ),
write_interp=dict(field='roptions.interp', ),
write_preserve=dict(field='roptions.preserve', ),
write_voxel_sizes=dict(field='roptions.vox', ),
write_wrap=dict(field='roptions.wrap', ),
)
inputs = Normalize.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_Normalize_inputs():
input_map = dict(DCT_period_cutoff=dict(field='eoptions.cutoff',
),
affine_regularization_type=dict(field='eoptions.regype',
),
apply_to_files=dict(copyfile=True,
field='subj.resample',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
jobtype=dict(usedefault=True,
),
matlab_cmd=dict(),
mfile=dict(usedefault=True,
),
nonlinear_iterations=dict(field='eoptions.nits',
),
nonlinear_regularization=dict(field='eoptions.reg',
),
out_prefix=dict(field='roptions.prefix',
usedefault=True,
),
parameter_file=dict(copyfile=False,
field='subj.matname',
mandatory=True,
xor=['source', 'template'],
),
paths=dict(),
source=dict(copyfile=True,
field='subj.source',
mandatory=True,
xor=['parameter_file'],
),
source_image_smoothing=dict(field='eoptions.smosrc',
),
source_weight=dict(copyfile=False,
field='subj.wtsrc',
),
template=dict(copyfile=False,
field='eoptions.template',
mandatory=True,
xor=['parameter_file'],
),
template_image_smoothing=dict(field='eoptions.smoref',
),
template_weight=dict(copyfile=False,
field='eoptions.weight',
),
use_mcr=dict(),
use_v8struct=dict(min_ver='8',
usedefault=True,
),
write_bounding_box=dict(field='roptions.bb',
),
write_interp=dict(field='roptions.interp',
),
write_preserve=dict(field='roptions.preserve',
),
write_voxel_sizes=dict(field='roptions.vox',
),
write_wrap=dict(field='roptions.wrap',
),
)
inputs = Normalize.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value