def test_MCFLIRT_inputs(): input_map = dict( args=dict(argstr="%s"), bins=dict(argstr="-bins %d"), cost=dict(argstr="-cost %s"), dof=dict(argstr="-dof %d"), environ=dict(nohash=True, usedefault=True), ignore_exception=dict(nohash=True, usedefault=True), in_file=dict(argstr="-in %s", mandatory=True, position=0), init=dict(argstr="-init %s"), interpolation=dict(argstr="-%s_final"), mean_vol=dict(argstr="-meanvol"), out_file=dict(argstr="-out %s", genfile=True, hash_files=False), output_type=dict(), ref_file=dict(argstr="-reffile %s"), ref_vol=dict(argstr="-refvol %d"), rotation=dict(argstr="-rotation %d"), save_mats=dict(argstr="-mats"), save_plots=dict(argstr="-plots"), save_rms=dict(argstr="-rmsabs -rmsrel"), scaling=dict(argstr="-scaling %.2f"), smooth=dict(argstr="-smooth %.2f"), stages=dict(argstr="-stages %d"), stats_imgs=dict(argstr="-stats"), terminal_output=dict(nohash=True), use_contour=dict(argstr="-edge"), use_gradient=dict(argstr="-gdt"), ) inputs = MCFLIRT.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_MCFLIRT_outputs(): output_map = dict(mat_file=dict(), mean_img=dict(), out_file=dict(), par_file=dict(), rms_files=dict(), std_img=dict(), variance_img=dict(), ) outputs = MCFLIRT.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_MCFLIRT_outputs(): output_map = dict( mat_file=dict(), mean_img=dict(), out_file=dict(), par_file=dict(), rms_files=dict(), std_img=dict(), variance_img=dict(), ) outputs = MCFLIRT.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_MCFLIRT_inputs(): input_map = dict( args=dict(argstr='%s', ), bins=dict(argstr='-bins %d', ), cost=dict(argstr='-cost %s', ), dof=dict(argstr='-dof %d', ), environ=dict( nohash=True, usedefault=True, ), ignore_exception=dict( nohash=True, usedefault=True, ), in_file=dict( argstr='-in %s', mandatory=True, position=0, ), init=dict(argstr='-init %s', ), interpolation=dict(argstr='-%s_final', ), mean_vol=dict(argstr='-meanvol', ), out_file=dict( argstr='-out %s', genfile=True, hash_files=False, ), output_type=dict(), ref_file=dict(argstr='-reffile %s', ), ref_vol=dict(argstr='-refvol %d', ), rotation=dict(argstr='-rotation %d', ), save_mats=dict(argstr='-mats', ), save_plots=dict(argstr='-plots', ), save_rms=dict(argstr='-rmsabs -rmsrel', ), scaling=dict(argstr='-scaling %.2f', ), smooth=dict(argstr='-smooth %.2f', ), stages=dict(argstr='-stages %d', ), stats_imgs=dict(argstr='-stats', ), terminal_output=dict(nohash=True, ), use_contour=dict(argstr='-edge', ), use_gradient=dict(argstr='-gdt', ), ) inputs = MCFLIRT.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value
reslice = Node(MRIConvert(vox_size=resampled_voxel_size, out_type='nii'), name='reslice') # Segment structural scan #segment = Node(Segment(affine_regularization='none'), name='segment') segment = Node(FAST(no_bias=True, segments=True, number_classes=3), name='segment') #Slice timing correction based on interleaved acquisition using FSL slicetime_correct = Node(SliceTimer(interleaved=interleave, slice_direction=slice_dir, time_repetition=TR), name='slicetime_correct') # Motion correction motion_correct = Node(MCFLIRT(save_plots=True, mean_vol=True), name='motion_correct') # Registration- using FLIRT # The BOLD image is 'in_file', the anat is 'reference', the output is 'out_file' coreg1 = Node(FLIRT(), name='coreg1') coreg2 = Node(FLIRT(apply_xfm=True), name='coreg2') # make binary mask # structural is the 'in_file', output is 'binary_file' binarize_struct = Node(Binarize(dilate=mask_dilation, erode=mask_erosion, min=1), name='binarize_struct') # apply the binary mask to the functional data