def create_workflow(wf_base_dir, input_anat, oasis_path):
'''
Method to create the nipype workflow that is executed for
preprocessing the data
Parameters
----------
wf_base_dir : string
filepath to the base directory to run the workflow
input_anat : string
filepath to the input file to run antsCorticalThickness.sh on
oasis_path : string
filepath to the oasis
Returns
-------
wf : nipype.pipeline.engine.Workflow instance
the workflow to be ran for preprocessing
'''
# Import packages
from act_interface import antsCorticalThickness
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from nipype.interfaces.utility import Function
from nipype import logging as np_logging
from nipype import config
import os
# Init variables
oasis_trt_20 = os.path.join(oasis_path,
'OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_OASIS-30.nii')
# Setup nipype workflow
if not os.path.exists(wf_base_dir):
os.makedirs(wf_base_dir)
wf = pe.Workflow(name='thickness_workflow')
wf.base_dir = wf_base_dir
# Init log directory
log_dir = wf_base_dir
# Define antsCorticalThickness node
thickness = pe.Node(antsCorticalThickness(), name='thickness')
# Set antsCorticalThickness inputs
thickness.inputs.dimension = 3
thickness.inputs.segmentation_iterations = 1
thickness.inputs.segmentation_weight = 0.25
thickness.inputs.input_skull = input_anat #-a
thickness.inputs.template = oasis_path + 'T_template0.nii.gz' #-e
thickness.inputs.brain_prob_mask = oasis_path + \
'T_template0_BrainCerebellumProbabilityMask.nii.gz' #-m
thickness.inputs.brain_seg_priors = oasis_path + \
'Priors2/priors%d.nii.gz' #-p
thickness.inputs.intensity_template = oasis_path + \
'T_template0_BrainCerebellum.nii.gz' #-t
thickness.inputs.extraction_registration_mask = oasis_path + \
'T_template0_BrainCerebellumExtractionMask.nii.gz' #-f
thickness.inputs.out_prefix = 'OUTPUT_' #-o
thickness.inputs.keep_intermediate_files = 0 #-k
# Node to run ANTs 3dROIStats
ROIstats = pe.Node(util.Function(input_names=['mask','thickness_normd'],
output_names=['roi_stats_file'],
function=roi_func),
name='ROIstats')
wf.connect(thickness, 'cortical_thickness_normalized',
ROIstats, 'thickness_normd')
ROIstats.inputs.mask = oasis_trt_20
# Create datasink node
datasink = pe.Node(nio.DataSink(), name='sinker')
datasink.inputs.base_directory = wf_base_dir
# Connect thickness outputs to datasink
wf.connect(thickness, 'brain_extraction_mask',
datasink, 'output.@brain_extr_mask')
wf.connect(thickness, 'brain_segmentation',
datasink, 'output.@brain_seg')
wf.connect(thickness, 'brain_segmentation_N4',
datasink, 'output.@brain_seg_N4')
wf.connect(thickness, 'brain_segmentation_posteriors_1',
datasink, 'output.@brain_seg_post_1')
wf.connect(thickness, 'brain_segmentation_posteriors_2',
datasink, 'output.@brain_seg_post_2')
wf.connect(thickness, 'brain_segmentation_posteriors_3',
datasink, 'output.@brain_seg_post_3')
wf.connect(thickness, 'brain_segmentation_posteriors_4',
datasink, 'output.@brain_seg_post_4')
wf.connect(thickness, 'brain_segmentation_posteriors_5',
datasink, 'output.@brain_seg_post_5')
wf.connect(thickness, 'brain_segmentation_posteriors_6',
datasink, 'output.@brain_seg_post_6')
wf.connect(thickness, 'cortical_thickness',
datasink, 'output.@cortical_thickness')
wf.connect(thickness, 'cortical_thickness_normalized',
datasink,'output.@cortical_thickness_normalized')
# Connect ROI stats output text file to datasink
wf.connect(ROIstats, 'roi_stats_file', datasink, 'output.@ROIstats')
# Setup crashfile directory and logging
wf.config['execution'] = {'hash_method': 'timestamp',
'crashdump_dir': '/home/ubuntu/crashes'}
config.update_config({'logging': {'log_directory': log_dir,
'log_to_file': True}})
np_logging.update_logging(config)
# Return the workflow
return wf
'https://s3.amazonaws.com/ndar-data/' + s3_log_path, 'N/A', 'N/A')
# And quit
sys.exit()
# --- Image file successfully extracted, setup workflow for processing ---
# Setup nipype workflow
wf_base_dir = base_path + 'work-dirs/' + img03_id_str
if not os.path.exists(wf_base_dir):
os.makedirs(wf_base_dir)
wf = pe.Workflow(name='thickness_workflow')
wf.base_dir = wf_base_dir
# Crash directory
crash_dir = wf_base_dir + '/crashes/'
log_dir = wf_base_dir
# Define antsCorticalThickness node
thickness = pe.Node(antsCorticalThickness(), name='thickness')
# Set antsCorticalThickness inputs
thickness.inputs.dimension = 3
thickness.inputs.segmentation_iterations = 1
thickness.inputs.segmentation_weight = 0.25
thickness.inputs.input_skull = nifti_file #-a
thickness.inputs.template = oasis_path + 'T_template0.nii.gz' #-e
thickness.inputs.brain_prob_mask = oasis_path + 'T_template0_BrainCerebellumProbabilityMask.nii.gz' #-m
thickness.inputs.brain_seg_priors = oasis_path + 'Priors2/priors%d.nii.gz' #-p
thickness.inputs.intensity_template = oasis_path + 'T_template0_BrainCerebellum.nii.gz' #-t
thickness.inputs.extraction_registration_mask = oasis_path + 'T_template0_BrainCerebellumExtractionMask.nii.gz' #-f
thickness.inputs.out_prefix = 'OUTPUT_' #-o
thickness.inputs.keep_intermediate_files = 0 #-k
# Node to run ANTs 3dROIStats
ROIstats = pe.Node(util.Function(input_names=['mask','thickness_normd'],
output_names=['roi_stats_file'],
