def test_DiffusionTensorStreamlineTrack_outputs():
output_map = dict(tracked=dict(), )
outputs = DiffusionTensorStreamlineTrack.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_DiffusionTensorStreamlineTrack_outputs():
output_map = dict(tracked=dict(),
)
outputs = DiffusionTensorStreamlineTrack.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_DiffusionTensorStreamlineTrack_inputs():
input_map = dict(args=dict(argstr='%s',
),
cutoff_value=dict(argstr='-cutoff %s',
units='NA',
),
desired_number_of_tracks=dict(argstr='-number %d',
),
do_not_precompute=dict(argstr='-noprecomputed',
),
environ=dict(nohash=True,
usedefault=True,
),
exclude_file=dict(argstr='-exclude %s',
xor=['exclude_file', 'exclude_spec'],
),
exclude_spec=dict(argstr='-exclude %s',
position=2,
sep=',',
units='mm',
xor=['exclude_file', 'exclude_spec'],
),
gradient_encoding_file=dict(argstr='-grad %s',
mandatory=True,
position=-2,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(argstr='%s',
mandatory=True,
position=-2,
),
include_file=dict(argstr='-include %s',
xor=['include_file', 'include_spec'],
),
include_spec=dict(argstr='-include %s',
position=2,
sep=',',
units='mm',
xor=['include_file', 'include_spec'],
),
initial_cutoff_value=dict(argstr='-initcutoff %s',
units='NA',
),
initial_direction=dict(argstr='-initdirection %s',
units='voxels',
),
inputmodel=dict(argstr='%s',
position=-3,
usedefault=True,
),
mask_file=dict(argstr='-mask %s',
xor=['mask_file', 'mask_spec'],
),
mask_spec=dict(argstr='-mask %s',
position=2,
sep=',',
units='mm',
xor=['mask_file', 'mask_spec'],
),
maximum_number_of_tracks=dict(argstr='-maxnum %d',
),
maximum_tract_length=dict(argstr='-length %s',
units='mm',
),
minimum_radius_of_curvature=dict(argstr='-curvature %s',
units='mm',
),
minimum_tract_length=dict(argstr='-minlength %s',
units='mm',
),
no_mask_interpolation=dict(argstr='-nomaskinterp',
),
out_file=dict(argstr='%s',
name_source=['in_file'],
name_template='%s_tracked.tck',
output_name='tracked.tck',
position=-1,
),
seed_file=dict(argstr='-seed %s',
xor=['seed_file', 'seed_spec'],
),
seed_spec=dict(argstr='-seed %s',
position=2,
sep=',',
units='mm',
xor=['seed_file', 'seed_spec'],
),
step_size=dict(argstr='-step %s',
units='mm',
),
stop=dict(argstr='-stop',
),
terminal_output=dict(mandatory=True,
nohash=True,
),
unidirectional=dict(argstr='-unidirectional',
),
)
inputs = DiffusionTensorStreamlineTrack.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value