本文整理汇总了Python中nipype.Node.run_without_submitting方法的典型用法代码示例。如果您正苦于以下问题：Python Node.run_without_submitting方法的具体用法？Python Node.run_without_submitting怎么用？Python Node.run_without_submitting使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nipype.Node的用法示例。
在下文中一共展示了Node.run_without_submitting方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: create_reg_workflow
# 需要导入模块: from nipype import Node [as 别名]
# 或者: from nipype.Node import run_without_submitting [as 别名]
def create_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
name : name of workflow (default: 'registration')
Inputs::
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.anatomical_image : anatomical image to coregister to
inputspec.target_image : registration target
Outputs::
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
"""
register = Workflow(name=name)
inputnode = Node(interface=IdentityInterface(fields=['source_files',
'mean_image',
'subject_id',
'subjects_dir',
'target_image']),
name='inputspec')
outputnode = Node(interface=IdentityInterface(fields=['func2anat_transform',
'out_reg_file',
'anat2target_transform',
'transforms',
'transformed_mean',
'segmentation_files',
'anat2target',
'aparc',
'min_cost_file'
]),
name='outputspec')
# Get the subject's freesurfer source directory
fssource = Node(FreeSurferSource(),
name='fssource')
fssource.run_without_submitting = True
register.connect(inputnode, 'subject_id', fssource, 'subject_id')
register.connect(inputnode, 'subjects_dir', fssource, 'subjects_dir')
convert = Node(freesurfer.MRIConvert(out_type='nii'),
name="convert")
register.connect(fssource, 'T1', convert, 'in_file')
# Coregister the median to the surface
bbregister = Node(freesurfer.BBRegister(),
name='bbregister')
bbregister.inputs.init = 'fsl'
bbregister.inputs.contrast_type = 't2'
bbregister.inputs.out_fsl_file = True
bbregister.inputs.epi_mask = True
register.connect(inputnode, 'subject_id', bbregister, 'subject_id')
register.connect(inputnode, 'mean_image', bbregister, 'source_file')
register.connect(inputnode, 'subjects_dir', bbregister, 'subjects_dir')
"""
Estimate the tissue classes from the anatomical image. But use aparc+aseg's brain mask
"""
binarize = Node(fs.Binarize(min=0.5, out_type="nii.gz", dilate=1), name="binarize_aparc")
register.connect(fssource, ("aparc_aseg", get_aparc_aseg), binarize, "in_file")
stripper = Node(fsl.ApplyMask(), name='stripper')
register.connect(binarize, "binary_file", stripper, "mask_file")
register.connect(convert, 'out_file', stripper, 'in_file')
fast = Node(fsl.FAST(), name='fast')
register.connect(stripper, 'out_file', fast, 'in_files')
"""
Binarize the segmentation
"""
binarize = MapNode(fsl.ImageMaths(op_string='-nan -thr 0.9 -ero -bin'),
iterfield=['in_file'],
name='binarize')
register.connect(fast, 'partial_volume_files', binarize, 'in_file')
"""
Apply inverse transform to take segmentations to functional space
"""
applyxfm = MapNode(freesurfer.ApplyVolTransform(inverse=True,
interp='nearest'),
iterfield=['target_file'],
name='inverse_transform')
register.connect(inputnode, 'subjects_dir', applyxfm, 'subjects_dir')
register.connect(bbregister, 'out_reg_file', applyxfm, 'reg_file')
register.connect(binarize, 'out_file', applyxfm, 'target_file')
register.connect(inputnode, 'mean_image', applyxfm, 'source_file')
#.........这里部分代码省略.........
示例2: create_fs_reg_workflow
# 需要导入模块: from nipype import Node [as 别名]
# 或者: from nipype.Node import run_without_submitting [as 别名]
def create_fs_reg_workflow(name="registration"):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
::
name : name of workflow (default: 'registration')
Inputs::
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.target_image : registration target
Outputs::
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
Example
-------
"""
register = Workflow(name=name)
inputnode = Node(
interface=IdentityInterface(
fields=["source_files", "mean_image", "subject_id", "subjects_dir", "target_image"]
),
name="inputspec",
)
outputnode = Node(
interface=IdentityInterface(
fields=[
"func2anat_transform",
"out_reg_file",
"anat2target_transform",
"transforms",
"transformed_mean",
"transformed_files",
"min_cost_file",
"anat2target",
"aparc",
"mean2anat_mask",
]
),
name="outputspec",
)
# Get the subject's freesurfer source directory
fssource = Node(FreeSurferSource(), name="fssource")
fssource.run_without_submitting = True
register.connect(inputnode, "subject_id", fssource, "subject_id")
register.connect(inputnode, "subjects_dir", fssource, "subjects_dir")
convert = Node(freesurfer.MRIConvert(out_type="nii"), name="convert")
register.connect(fssource, "T1", convert, "in_file")
# Coregister the median to the surface
bbregister = Node(freesurfer.BBRegister(registered_file=True), name="bbregister")
bbregister.inputs.init = "fsl"
bbregister.inputs.contrast_type = "t2"
bbregister.inputs.out_fsl_file = True
bbregister.inputs.epi_mask = True
register.connect(inputnode, "subject_id", bbregister, "subject_id")
register.connect(inputnode, "mean_image", bbregister, "source_file")
register.connect(inputnode, "subjects_dir", bbregister, "subjects_dir")
# Create a mask of the median coregistered to the anatomical image
mean2anat_mask = Node(fsl.BET(mask=True), name="mean2anat_mask")
register.connect(bbregister, "registered_file", mean2anat_mask, "in_file")
"""
use aparc+aseg's brain mask
"""
binarize = Node(fs.Binarize(min=0.5, out_type="nii.gz", dilate=1), name="binarize_aparc")
register.connect(fssource, ("aparc_aseg", get_aparc_aseg), binarize, "in_file")
stripper = Node(fsl.ApplyMask(), name="stripper")
register.connect(binarize, "binary_file", stripper, "mask_file")
register.connect(convert, "out_file", stripper, "in_file")
"""
Apply inverse transform to aparc file
"""
aparcxfm = Node(freesurfer.ApplyVolTransform(inverse=True, interp="nearest"), name="aparc_inverse_transform")
register.connect(inputnode, "subjects_dir", aparcxfm, "subjects_dir")
register.connect(bbregister, "out_reg_file", aparcxfm, "reg_file")
register.connect(fssource, ("aparc_aseg", get_aparc_aseg), aparcxfm, "target_file")
register.connect(inputnode, "mean_image", aparcxfm, "source_file")
"""
Convert the BBRegister transformation to ANTS ITK format
#.........这里部分代码省略.........