Ejemplo n.º 1
0
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
Ejemplo n.º 2
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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
Ejemplo n.º 3
0
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
Ejemplo n.º 4
0
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
Ejemplo n.º 5
0
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