def setUp(self):
'''
Function which inits a nipype workflow using network_centrality's
create_resting_state_graphs() function to be used for unit testing
Parameters
----------
self : CentralityWorkflowTestCase
a unittest.TestCase-inherited class
Returns
-------
None
this function does not return any values, but populates the
instance attributes for:
self.wflow : nipype.pipeline.engine.Workflow object
self.wflow.base_dir : string
'''
# Import packages
import os
from CPAC.network_centrality import resting_state_centrality
# Init variables
self.wflow = resting_state_centrality.create_resting_state_graphs(
allocated_memory=1)
self.wflow.base_dir = os.path.join(RESOURCE_DIR, 'network_centrality')
def setUp(self):
'''
Function which inits a nipype workflow using network_centrality's
create_resting_state_graphs() function to be used for unit testing
Parameters
----------
self : CentralityWorkflowTestCase
a unittest.TestCase-inherited class
Returns
-------
None
this function does not return any values, but populates the
instance attributes for:
self.wflow : nipype.pipeline.engine.Workflow object
self.wflow.base_dir : string
'''
# Import packages
import os
from CPAC.network_centrality import resting_state_centrality
# Init variables
self.wflow = resting_state_centrality.create_resting_state_graphs(allocated_memory=1)
self.wflow.base_dir = os.path.join(RESOURCE_DIR, 'network_centrality')
def calc_centrality_metrics(cfg):
import os
from nipype import config
from nipype.pipeline.engine import Node, Workflow
import nipype.interfaces.utility as util
import nipype.interfaces.io as nio
import nipype.interfaces.fsl as fsl
import nipype.interfaces.freesurfer as freesurfer
import CPAC.network_centrality.resting_state_centrality as cpac_centrality
import CPAC.network_centrality.z_score as cpac_centrality_z_score
# INPUT PARAMETERS
dicom_dir = cfg['dicom_dir']
preprocessed_data_dir = cfg['preprocessed_data_dir']
working_dir = cfg['working_dir']
freesurfer_dir = cfg['freesurfer_dir']
template_dir = cfg['template_dir']
script_dir = cfg['script_dir']
ds_dir = cfg['ds_dir']
subjects_list = cfg['subjects_list']
TR_list = cfg['TR_list']
use_n_procs = cfg['use_n_procs']
plugin_name = cfg['plugin_name']
#####################################
# GENERAL SETTINGS
#####################################
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
freesurfer.FSCommand.set_default_subjects_dir(freesurfer_dir)
wf = Workflow(name='LeiCA_metrics')
wf.base_dir = os.path.join(working_dir)
nipype_cfg = dict(logging=dict(workflow_level='DEBUG'), execution={'stop_on_first_crash': False,
'remove_unnecessary_outputs': True,
'job_finished_timeout': 120})
config.update_config(nipype_cfg)
wf.config['execution']['crashdump_dir'] = os.path.join(working_dir, 'crash')
ds = Node(nio.DataSink(), name='ds')
ds.inputs.substitutions = [('_TR_id_', 'TR_')]
ds.inputs.regexp_substitutions = [('_subject_id_[A0-9]*/', ''), (
'_z_score[0-9]*/', '')] # , #('dc/_TR_id_[0-9]*/', ''), ('evc/_TR_id_[0-9]*/','')]
#####################################
# SET ITERATORS
#####################################
# GET SCAN TR_ID ITERATOR
scan_infosource = Node(util.IdentityInterface(fields=['TR_id']), name='scan_infosource')
scan_infosource.iterables = ('TR_id', TR_list)
subjects_infosource = Node(util.IdentityInterface(fields=['subject_id']), name='subjects_infosource')
subjects_infosource.iterables = ('subject_id', subjects_list)
def add_subject_id_to_ds_dir_fct(subject_id, ds_path):
import os
out_path = os.path.join(ds_path, subject_id)
return out_path
add_subject_id_to_ds_dir = Node(util.Function(input_names=['subject_id', 'ds_path'],
output_names=['out_path'],
function=add_subject_id_to_ds_dir_fct),
name='add_subject_id_to_ds_dir')
wf.connect(subjects_infosource, 'subject_id', add_subject_id_to_ds_dir, 'subject_id')
add_subject_id_to_ds_dir.inputs.ds_path = ds_dir
wf.connect(add_subject_id_to_ds_dir, 'out_path', ds, 'base_directory')
# get atlas data
templates_atlases = {'GM_mask_MNI_2mm': 'SPM_GM/SPM_GM_mask_2mm.nii.gz',
'GM_mask_MNI_3mm': 'SPM_GM/SPM_GM_mask_3mm.nii.gz',
'FSL_MNI_3mm_template': 'MNI152_T1_3mm_brain.nii.gz',
'vmhc_symm_brain': 'cpac_image_resources/symmetric/MNI152_T1_2mm_brain_symmetric.nii.gz',
'vmhc_symm_brain_3mm': 'cpac_image_resources/symmetric/MNI152_T1_3mm_brain_symmetric.nii.gz',
'vmhc_symm_skull': 'cpac_image_resources/symmetric/MNI152_T1_2mm_symmetric.nii.gz',
'vmhc_symm_brain_mask_dil': 'cpac_image_resources/symmetric/MNI152_T1_2mm_brain_mask_symmetric_dil.nii.gz',
'vmhc_config_file_2mm': 'cpac_image_resources/symmetric/T1_2_MNI152_2mm_symmetric.cnf'
}
selectfiles_anat_templates = Node(nio.SelectFiles(templates_atlases,
base_directory=template_dir),
name="selectfiles_anat_templates")
# GET SUBJECT SPECIFIC FUNCTIONAL AND STRUCTURAL DATA
selectfiles_templates = {
'epi_2_MNI_warp': '{subject_id}/rsfMRI_preprocessing/registration/epi_2_MNI_warp/TR_{TR_id}/*.nii.gz',
'epi_mask': '{subject_id}/rsfMRI_preprocessing/masks/brain_mask_epiSpace/TR_{TR_id}/*.nii.gz',
'preproc_epi_full_spectrum': '{subject_id}/rsfMRI_preprocessing/epis/01_denoised/TR_{TR_id}/*.nii.gz',
'preproc_epi_bp': '{subject_id}/rsfMRI_preprocessing/epis/02_denoised_BP/TR_{TR_id}/*.nii.gz',
'preproc_epi_bp_tNorm': '{subject_id}/rsfMRI_preprocessing/epis/03_denoised_BP_tNorm/TR_{TR_id}/*.nii.gz',
'epi_2_struct_mat': '{subject_id}/rsfMRI_preprocessing/registration/epi_2_struct_mat/TR_{TR_id}/*.mat',
't1w': '{subject_id}/raw_niftis/sMRI/t1w_reoriented.nii.gz',
't1w_brain': '{subject_id}/rsfMRI_preprocessing/struct_prep/t1w_brain/t1w_reoriented_maths.nii.gz',
'epi_bp_tNorm_MNIspace_3mm': '{subject_id}/rsfMRI_preprocessing/epis_MNI_3mm/03_denoised_BP_tNorm/TR_645/residual_filt_norm_warp.nii.gz'
}
selectfiles = Node(nio.SelectFiles(selectfiles_templates,
base_directory=preprocessed_data_dir),
name="selectfiles")
wf.connect(scan_infosource, 'TR_id', selectfiles, 'TR_id')
wf.connect(subjects_infosource, 'subject_id', selectfiles, 'subject_id')
# selectfiles.inputs.subject_id = subject_id
# CREATE TRANSFORMATIONS
# creat MNI 2 epi warp
MNI_2_epi_warp = Node(fsl.InvWarp(), name='MNI_2_epi_warp')
MNI_2_epi_warp.inputs.reference = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz')
wf.connect(selectfiles, 'epi_mask', MNI_2_epi_warp, 'reference')
wf.connect(selectfiles, 'epi_2_MNI_warp', MNI_2_epi_warp, 'warp')
#####################
# CALCULATE METRICS
#####################
# DEGREE
# fixme
# a_mem = 5
# fixme
a_mem = 20
dc = cpac_centrality.create_resting_state_graphs(allocated_memory=a_mem,
wf_name='dc') # allocated_memory = a_mem, wf_name = 'dc')
# dc.plugin_args = {'submit_specs': 'request_memory = 6000'}
# fixme
dc.plugin_args = {'submit_specs': 'request_memory = 20000'}
dc.inputs.inputspec.method_option = 0 # 0 for degree centrality, 1 for eigenvector centrality, 2 for lFCD
dc.inputs.inputspec.threshold_option = 0 # 0 for probability p_value, 1 for sparsity threshold, any other for threshold value
dc.inputs.inputspec.threshold = 0.0001
dc.inputs.inputspec.weight_options = [True,
True] # list of two booleans for binarize and weighted options respectively
wf.connect(selectfiles, 'epi_bp_tNorm_MNIspace_3mm', dc, 'inputspec.subject')
wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', dc, 'inputspec.template')
wf.connect(dc, 'outputspec.centrality_outputs', ds, 'metrics.centrality.dc.@centrality_outputs')
wf.connect(dc, 'outputspec.correlation_matrix', ds, 'metrics.centrality.dc.@correlation_matrix')
wf.connect(dc, 'outputspec.graph_outputs', ds, 'metrics.centrality.dc.@graph_outputs')
# DC Z-SCORE
dc_Z = cpac_centrality_z_score.get_cent_zscore(wf_name='dc_Z')
wf.connect(dc, 'outputspec.centrality_outputs', dc_Z, 'inputspec.input_file')
wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', dc_Z, 'inputspec.mask_file')
wf.connect(dc_Z, 'outputspec.z_score_img', ds, 'metrics.centrality.dc_z.@output')
a_mem = 20
evc = cpac_centrality.create_resting_state_graphs(allocated_memory=a_mem, wf_name='evc')
evc.plugin_args = {'submit_specs': 'request_memory = 20000'}
evc.inputs.inputspec.method_option = 1 # 0 for degree centrality, 1 for eigenvector centrality, 2 for lFCD
evc.inputs.inputspec.threshold_option = 0 # 0 for probability p_value, 1 for sparsity threshold, any other for threshold value
evc.inputs.inputspec.threshold = 0.0001
evc.inputs.inputspec.weight_options = [True,
True] # list of two booleans for binarize and weighted options respectively
wf.connect(selectfiles, 'epi_bp_tNorm_MNIspace_3mm', evc, 'inputspec.subject')
wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', evc, 'inputspec.template')
wf.connect(evc, 'outputspec.centrality_outputs', ds, 'metrics.centrality.evc.@centrality_outputs')
wf.connect(evc, 'outputspec.correlation_matrix', ds, 'metrics.centrality.evc.@correlation_matrix')
wf.connect(evc, 'outputspec.graph_outputs', ds, 'metrics.centrality.evc.@graph_outputs')
# EVC Z-SCORE
evc_Z = cpac_centrality_z_score.get_cent_zscore(wf_name='evc_Z')
wf.connect(evc, 'outputspec.centrality_outputs', evc_Z, 'inputspec.input_file')
wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', evc_Z, 'inputspec.mask_file')
wf.connect(evc_Z, 'outputspec.z_score_img', ds, 'metrics.centrality.evc_z.@output')
wf.write_graph(dotfilename=wf.name, graph2use='colored', format='pdf') # 'hierarchical')
wf.write_graph(dotfilename=wf.name, graph2use='orig', format='pdf')
wf.write_graph(dotfilename=wf.name, graph2use='flat', format='pdf')
if plugin_name == 'CondorDAGMan':
wf.run(plugin=plugin_name)
if plugin_name == 'MultiProc':
wf.run(plugin=plugin_name, plugin_args={'n_procs': use_n_procs})
