def test_FitTensor_inputs():
input_map = dict(args=dict(argstr='%s',
),
bval_scale=dict(argstr='-bvalue_scaling %s',
),
environ=dict(nohash=True,
usedefault=True,
),
grad_file=dict(argstr='-grad %s',
),
grad_fsl=dict(argstr='-fslgrad %s %s',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_bval=dict(),
in_bvec=dict(argstr='-fslgrad %s %s',
),
in_file=dict(argstr='%s',
mandatory=True,
position=-2,
),
in_mask=dict(argstr='-mask %s',
),
method=dict(argstr='-method %s',
),
nthreads=dict(argstr='-nthreads %d',
nohash=True,
),
out_file=dict(argstr='%s',
mandatory=True,
position=-1,
usedefault=True,
),
reg_term=dict(argstr='-regularisation %f',
),
terminal_output=dict(nohash=True,
),
)
inputs = FitTensor.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_FitTensor_inputs():
input_map = dict(
args=dict(argstr='%s', ),
bval_scale=dict(argstr='-bvalue_scaling %s', ),
environ=dict(
nohash=True,
usedefault=True,
),
grad_file=dict(argstr='-grad %s', ),
grad_fsl=dict(argstr='-fslgrad %s %s', ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_bval=dict(),
in_bvec=dict(argstr='-fslgrad %s %s', ),
in_file=dict(
argstr='%s',
mandatory=True,
position=-2,
),
in_mask=dict(argstr='-mask %s', ),
method=dict(argstr='-method %s', ),
nthreads=dict(
argstr='-nthreads %d',
nohash=True,
),
out_file=dict(
argstr='%s',
mandatory=True,
position=-1,
usedefault=True,
),
reg_term=dict(argstr='-regularisation %f', ),
terminal_output=dict(nohash=True, ),
)
inputs = FitTensor.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
