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',