本文整理汇总了Python中nipype.pipeline.engine.Node.out_data_type方法的典型用法代码示例。如果您正苦于以下问题：Python Node.out_data_type方法的具体用法？Python Node.out_data_type怎么用？Python Node.out_data_type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.pipeline.engine.Node的用法示例。
在下文中一共展示了Node.out_data_type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: func_preprocess
# 需要导入模块: from nipype.pipeline.engine import Node [as 别名]
# 或者: from nipype.pipeline.engine.Node import out_data_type [as 别名]
def func_preprocess(name = 'func_preproc'):
'''
Method to preprocess functional data after warping to anatomical space.
Accomplished after one step Distortion Correction, Motion Correction and Boundary based linear registration to
anatomical space.
Precodure includes:
# 1- skull strip
# 2- Normalize the image intensity values.
# 3- Calculate Mean of Skull stripped image
# 4- Create brain mask from Normalized data.
'''
# Define Workflow
flow = Workflow(name=name)
inputnode = Node(util.IdentityInterface(fields=['func_in']),
name='inputnode')
outputnode = Node(util.IdentityInterface(fields=['func_preproc',
'func_preproc_mean',
'func_preproc_mask']),
name = 'outputnode')
# 2- Normalize the image intensity values.
norm = Node(interface = fsl.ImageMaths(), name = 'func_normalized')
norm.inputs.op_string = '-ing 1000'
norm.out_data_type = 'float'
norm.output_type = 'NIFTI'
# 4- Create brain mask from Normalized data.
mask = Node(interface = fsl.BET(), name = 'func_preprocessed')
mask.inputs.functional = True
mask.inputs.mask = True
mask.inputs.frac = 0.5
mask.inputs.vertical_gradient = 0
mask.inputs.threshold = True
# 3- Calculate Mean of Skull stripped image
mean = Node(interface = preprocess.TStat(), name = 'func_preprocessed_mean')
mean.inputs.options = '-mean'
mean.inputs.outputtype = 'NIFTI'
flow.connect( inputnode , 'func_in' , norm, 'in_file' )
flow.connect( norm , 'out_file' , mask, 'in_file' )
flow.connect( norm , 'out_file' , mean, 'in_file' )
flow.connect( mask , 'out_file' , outputnode, 'func_preproc')
flow.connect( mask , 'mask_file' , outputnode, 'func_preproc_mask')
flow.connect( mean , 'out_file' , outputnode, 'func_preproc_mean')
return flow示例2: create_deskull_pipeline
# 需要导入模块: from nipype.pipeline.engine import Node [as 别名]
# 或者: from nipype.pipeline.engine.Node import out_data_type [as 别名]
def create_deskull_pipeline(working_dir, ds_dir, name='deskull'):
# initiate workflow
deskull_wf = Workflow(name=name)
deskull_wf.base_dir = os.path.join(working_dir,'LeiCA_resting', 'rsfMRI_preprocessing')
# set fsl output
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
# I/O NODES
inputnode = Node(util.IdentityInterface(fields=['epi_moco',
'struct_brain_mask',
'struct_2_epi_mat']),
name='inputnode')
outputnode = Node(util.IdentityInterface(fields=['epi_deskulled',
'mean_epi',
'brain_mask_epiSpace']),
name='outputnode')
ds = Node(nio.DataSink(base_directory=ds_dir), name='ds')
ds.inputs.substitutions = [('_TR_id_', 'TR_')]
# TRANSFORM BRAIN MASK TO EPI SPACE
brain_mask_epiSpace = Node(fsl.ApplyXfm(apply_xfm=True, interp='nearestneighbour'), name='brain_mask_epiSpace')
brain_mask_epiSpace.inputs.out_file = 'brain_mask_epiSpace.nii.gz'
deskull_wf.connect([(inputnode, brain_mask_epiSpace, [('struct_brain_mask', 'in_file'),
('epi_moco', 'reference'),
('struct_2_epi_mat', 'in_matrix_file')])])
deskull_wf.connect(brain_mask_epiSpace, 'out_file', outputnode, 'brain_mask_epiSpace')
deskull_wf.connect(brain_mask_epiSpace, 'out_file', ds, 'masks.brain_mask_epiSpace')
# DESKULL EPI
epi_brain = Node(fsl.maths.BinaryMaths(operation = 'mul'), name='epi_brain')
deskull_wf.connect(inputnode, 'epi_moco', epi_brain, 'in_file')
deskull_wf.connect(brain_mask_epiSpace, 'out_file', epi_brain, 'operand_file')
# GLOBAL INTENSITY NORMALIZATION
epi_intensity_norm = Node(interface=fsl.ImageMaths(), name='epi_intensity_norm')
epi_intensity_norm.inputs.op_string = '-ing 10000'
epi_intensity_norm.out_data_type = 'float'
deskull_wf.connect(epi_brain, 'out_file', epi_intensity_norm, 'in_file')
deskull_wf.connect(epi_intensity_norm, 'out_file', outputnode, 'epi_deskulled')
# CREATE MEAN EPI (INTENSITY NORMALIZED)
mean_epi = Node(fsl.maths.MeanImage(dimension='T',
out_file='rest_realigned_mean.nii.gz'),
name='mean_epi')
deskull_wf.connect(epi_intensity_norm, 'out_file', mean_epi, 'in_file')
deskull_wf.connect(mean_epi, 'out_file', ds, 'QC.mean_epi')
deskull_wf.connect(mean_epi, 'out_file', outputnode, 'mean_epi')
deskull_wf.write_graph(dotfilename=deskull_wf.name, graph2use='flat', format='pdf')
return deskull_wf