Python Node.terminal_output方法代码示例

# 需要导入模块: from nipype import Node [as 别名]
# 或者: from nipype.Node import terminal_output [as 别名]
def nipype_convert(item_dicoms, prefix, with_prov, bids, tmpdir):
    """ """
    import nipype
    if with_prov:
        from nipype import config
        config.enable_provenance()
    from nipype import Node
    from nipype.interfaces.dcm2nii import Dcm2niix

    item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths

    dicom_dir = op.dirname(item_dicoms[0]) if item_dicoms else None

    convertnode = Node(Dcm2niix(), name='convert')
    convertnode.base_dir = tmpdir
    convertnode.inputs.source_names = item_dicoms
    convertnode.inputs.out_filename = op.basename(op.dirname(prefix))

    if nipype.__version__.split('.')[0] == '0':
        # deprecated since 1.0, might be needed(?) before
        convertnode.inputs.terminal_output = 'allatonce'
    else:
        convertnode.terminal_output = 'allatonce'
    convertnode.inputs.bids_format = bids
    eg = convertnode.run()

    # prov information
    prov_file = prefix + '_prov.ttl' if with_prov else None
    if prov_file:
        safe_copyfile(op.join(convertnode.base_dir,
                              convertnode.name,
                              'provenance.ttl'),
                      prov_file)

    return eg, prov_file

# 需要导入模块: from nipype import Node [as 别名]
# 或者: from nipype.Node import terminal_output [as 别名]

#.........这里部分代码省略.........
    """

    reg = Node(ants.Registration(), name='antsRegister')
    reg.inputs.output_transform_prefix = "output_"
    reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
    reg.inputs.transform_parameters = [(0.1, ), (0.1, ), (0.2, 3.0, 0.0)]
    reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[
        100, 30, 20
    ]]
    reg.inputs.dimension = 3
    reg.inputs.write_composite_transform = True
    reg.inputs.collapse_output_transforms = True
    reg.inputs.initial_moving_transform_com = True
    reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
    reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
    reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
    reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
    reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
    reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
    reg.inputs.convergence_window_size = [20] * 2 + [5]
    reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
    reg.inputs.sigma_units = ['vox'] * 3
    reg.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
    reg.inputs.use_estimate_learning_rate_once = [True] * 3
    reg.inputs.use_histogram_matching = [False] * 2 + [True]
    reg.inputs.winsorize_lower_quantile = 0.005
    reg.inputs.winsorize_upper_quantile = 0.995
    reg.inputs.float = True
    reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
    reg.inputs.num_threads = 4
    reg.plugin_args = {
        'qsub_args': '-pe orte 4',
        'sbatch_args': '--mem=6G -c 4'
    }
    register.connect(stripper, 'out_file', reg, 'moving_image')
    register.connect(inputnode, 'target_image', reg, 'fixed_image')

    """
    Concatenate the affine and ants transforms into a list
    """

    merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
    register.connect(convert2itk, 'itk_transform', merge, 'in2')
    register.connect(reg, 'composite_transform', merge, 'in1')

    """
    Transform the mean image. First to anatomical and then to target
    """

    warpmean = Node(ants.ApplyTransforms(), name='warpmean')
    warpmean.inputs.input_image_type = 0
    warpmean.inputs.interpolation = 'Linear'
    warpmean.inputs.invert_transform_flags = [False, False]
    warpmean.terminal_output = 'file'
    warpmean.inputs.args = '--float'
    # warpmean.inputs.num_threads = 4
    # warpmean.plugin_args = {'sbatch_args': '--mem=4G -c 4'}

    """
    Transform the remaining images. First to anatomical and then to target
    """

    warpall = pe.MapNode(
        ants.ApplyTransforms(), iterfield=['input_image'], name='warpall')
    warpall.inputs.input_image_type = 0
    warpall.inputs.interpolation = 'Linear'
    warpall.inputs.invert_transform_flags = [False, False]
    warpall.terminal_output = 'file'
    warpall.inputs.args = '--float'
    warpall.inputs.num_threads = 2
    warpall.plugin_args = {'sbatch_args': '--mem=6G -c 2'}
    """
    Assign all the output files
    """

    register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
    register.connect(warpall, 'output_image', outputnode, 'transformed_files')

    register.connect(inputnode, 'target_image', warpmean, 'reference_image')
    register.connect(inputnode, 'mean_image', warpmean, 'input_image')
    register.connect(merge, 'out', warpmean, 'transforms')
    register.connect(inputnode, 'target_image', warpall, 'reference_image')
    register.connect(inputnode, 'source_files', warpall, 'input_image')
    register.connect(merge, 'out', warpall, 'transforms')
    """
    Assign all the output files
    """

    register.connect(reg, 'warped_image', outputnode, 'anat2target')
    register.connect(aparcxfm, 'transformed_file', outputnode, 'aparc')
    register.connect(bbregister, 'out_fsl_file', outputnode,
                     'func2anat_transform')
    register.connect(bbregister, 'out_reg_file', outputnode, 'out_reg_file')
    register.connect(bbregister, 'min_cost_file', outputnode, 'min_cost_file')
    register.connect(mean2anat_mask, 'mask_file', outputnode, 'mean2anat_mask')
    register.connect(reg, 'composite_transform', outputnode,
                     'anat2target_transform')
    register.connect(merge, 'out', outputnode, 'transforms')

    return register