def test_ModifyAffine_outputs():
output_map = dict(transformed_volumes=dict(), )
outputs = ModifyAffine.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_ModifyAffine_outputs():
output_map = dict(transformed_volumes=dict(),
)
outputs = ModifyAffine.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_ModifyAffine_inputs():
input_map = dict(
ignore_exception=dict(
nohash=True,
usedefault=True,
),
transformation_matrix=dict(usedefault=True, ),
volumes=dict(mandatory=True, ),
)
inputs = ModifyAffine.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_ModifyAffine_inputs():
input_map = dict(transformation_matrix=dict(usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
volumes=dict(mandatory=True,
),
)
inputs = ModifyAffine.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
fsl.ExtractROI(
t_min=nDelfMRI, # first volumes to be deleted
t_size=-1),
name="extract")
# smoothing with SUSAN
susan = Node(
fsl.SUSAN(brightness_threshold=2000.0,
fwhm=6.0), # smoothing filter width (6mm, isotropic)
name='susan')
# masking the fMRI with a brain mask
applymask = Node(fsl.ApplyMask(), name='applymask')
# flip left / right node
flipLR = Node(ModifyAffine(transformation_matrix=np.array(
[[-1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])),
name='flipLR')
###########
#
# READ TASK INFO IN PREPRARATION FOR THE ANALYSIS
#
###########
# Creating the layout object for this BIDS data set
layout = BIDSLayout(dataDir)
# task information file
fileEvent = layout.get(suffix='events',
task='fingerfootlips',
extension='tsv',
