def test_DTIFit_outputs():
output_map = dict(tensor_fitted=dict(), )
outputs = DTIFit.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_DTIFit_inputs():
input_map = dict(args=dict(argstr='%s',
),
bgmask=dict(argstr='-bgmask %s',
),
environ=dict(nohash=True,
usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(argstr='%s',
mandatory=True,
position=1,
),
non_linear=dict(argstr='-nonlinear',
position=3,
),
out_file=dict(argstr='> %s',
genfile=True,
position=-1,
),
scheme_file=dict(argstr='%s',
mandatory=True,
position=2,
),
terminal_output=dict(nohash=True,
),
)
inputs = DTIFit.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_DTIFit_inputs():
input_map = dict(args=dict(argstr='%s',
),
bgmask=dict(argstr='-bgmask %s',
),
environ=dict(nohash=True,
usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(argstr='%s',
mandatory=True,
position=1,
),
non_linear=dict(argstr='-nonlinear',
position=3,
),
out_file=dict(argstr='> %s',
genfile=True,
position=-1,
),
scheme_file=dict(argstr='%s',
mandatory=True,
position=2,
),
terminal_output=dict(mandatory=True,
nohash=True,
),
)
inputs = DTIFit.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_DTIFit_outputs():
output_map = dict(tensor_fitted=dict(),
)
outputs = DTIFit.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
