def create_check_for_s3_node(name,
file_path,
img_type='other',
creds_path=None,
dl_dir=None,
map_node=False):
if map_node:
check_s3_node = pe.MapNode(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
iterfield=['file_path'],
name='check_for_s3_%s' % name)
else:
check_s3_node = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='check_for_s3_%s' % name)
check_s3_node.inputs.set(file_path=file_path,
creds_path=creds_path,
dl_dir=dl_dir,
img_type=img_type)
return check_s3_node
def create_grp_analysis_dataflow(wf_name='gp_dataflow'):
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from CPAC.utils import select_model_files
wf = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(
fields=['ftest', 'grp_model', 'model_name'], mandatory_inputs=True),
name='inputspec')
selectmodel = pe.Node(function.Function(
input_names=['model', 'ftest', 'model_name'],
output_names=['fts_file', 'con_file', 'grp_file', 'mat_file'],
function=select_model_files,
as_module=True),
name='selectnode')
wf.connect(inputnode, 'ftest', selectmodel, 'ftest')
wf.connect(inputnode, 'grp_model', selectmodel, 'model')
wf.connect(inputnode, 'model_name', selectmodel, 'model_name')
outputnode = pe.Node(util.IdentityInterface(
fields=['fts', 'grp', 'mat', 'con'], mandatory_inputs=True),
name='outputspec')
wf.connect(selectmodel, 'mat_file', outputnode, 'mat')
wf.connect(selectmodel, 'grp_file', outputnode, 'grp')
wf.connect(selectmodel, 'fts_file', outputnode, 'fts')
wf.connect(selectmodel, 'con_file', outputnode, 'con')
return wf
def create_anat_datasource(wf_name='anat_datasource'):
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
wf = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(
fields=['subject', 'anat', 'creds_path', 'dl_dir'],
mandatory_inputs=True),
name='inputnode')
check_s3_node = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='check_for_s3')
wf.connect(inputnode, 'anat', check_s3_node, 'file_path')
wf.connect(inputnode, 'creds_path', check_s3_node, 'creds_path')
wf.connect(inputnode, 'dl_dir', check_s3_node, 'dl_dir')
check_s3_node.inputs.img_type = 'anat'
outputnode = pe.Node(util.IdentityInterface(fields=['subject', 'anat']),
name='outputspec')
wf.connect(inputnode, 'subject', outputnode, 'subject')
wf.connect(check_s3_node, 'local_path', outputnode, 'anat')
# Return the workflow
return wf
def create_general_datasource(wf_name):
from CPAC.pipeline import nipype_pipeline_engine as pe
import nipype.interfaces.utility as util
wf = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(
fields=['unique_id', 'data', 'creds_path', 'dl_dir'],
mandatory_inputs=True),
name='inputnode')
check_s3_node = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='check_for_s3')
check_s3_node.inputs.img_type = "other"
wf.connect(inputnode, 'data', check_s3_node, 'file_path')
wf.connect(inputnode, 'creds_path', check_s3_node, 'creds_path')
wf.connect(inputnode, 'dl_dir', check_s3_node, 'dl_dir')
outputnode = pe.Node(util.IdentityInterface(fields=['unique_id', 'data']),
name='outputspec')
wf.connect(inputnode, 'unique_id', outputnode, 'unique_id')
wf.connect(check_s3_node, 'local_path', outputnode, 'data')
return wf
def calc_avg(workflow, output_name, strat, num_strat, map_node=False):
"""Calculate the average of an output using AFNI 3dmaskave."""
if map_node:
calc_average = pe.MapNode(interface=preprocess.Maskave(),
name='{0}_mean_{1}'.format(output_name,
num_strat),
iterfield=['in_file'])
mean_to_csv = pe.MapNode(function.Function(input_names=['in_file',
'output_name'],
output_names=[
'output_mean'],
function=extract_output_mean,
as_module=True),
name='{0}_mean_to_txt_{1}'.format(output_name,
num_strat),
iterfield=['in_file'])
else:
calc_average = pe.Node(interface=preprocess.Maskave(),
name='{0}_mean_{1}'.format(output_name,
num_strat))
mean_to_csv = pe.Node(function.Function(input_names=['in_file',
'output_name'],
output_names=['output_mean'],
function=extract_output_mean,
as_module=True),
name='{0}_mean_to_txt_{1}'.format(output_name,
num_strat))
mean_to_csv.inputs.output_name = output_name
node, out_file = strat[output_name]
workflow.connect(node, out_file, calc_average, 'in_file')
workflow.connect(calc_average, 'out_file', mean_to_csv, 'in_file')
strat.append_name(calc_average.name)
strat.update_resource_pool({
'output_means.@{0}_average'.format(output_name): (mean_to_csv, 'output_mean')
})
return strat
def create_log(wf_name="log", scan_id=None):
"""
Workflow to create log
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
import CPAC.utils.function as function
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(fields=['workflow',
'log_dir',
'index',
'inputs']),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(fields=['out_file']),
name='outputspec')
write_log = pe.Node(function.Function(input_names=['workflow',
'log_dir',
'index',
'inputs',
'scan_id'],
output_names=['out_file'],
function=write_to_log,
as_module=True),
name='write_log')
write_log.inputs.scan_id = scan_id
wf.connect([
(
input_node, write_log, [
('workflow', 'workflow'),
('log_dir', 'log_dir'),
('index', 'index'),
('inputs', 'inputs')
]
),
(
write_log, output_node, [
('out_file', 'out_file')
]
)
])
return wf
def blip_distcor_wf(wf_name='blip_distcor'):
"""Execute AFNI 3dQWarp to calculate the distortion "unwarp" for phase
encoding direction EPI field map distortion correction.
1. Skull-strip the opposite-direction phase encoding EPI.
2. Transform the opposite-direction phase encoding EPI to the
skull-stripped functional and pass this as the base_file to
AFNI 3dQWarp (plus-minus).
3. If there is a same-direction phase encoding EPI, also skull-strip
this, and transform it to the skull-stripped functional. Then, pass
this as the in_file to AFNI 3dQWarp (plus-minus).
4. If there isn't a same-direction, pass the functional in as the
in_file of AFNI 3dQWarp (plus-minus).
5. Convert the 3dQWarp transforms to ANTs/ITK format.
6. Use antsApplyTransforms, with the original functional as both the
input and the reference, and apply the warp from 3dQWarp. The
output of this can then proceed to func_preproc.
:param wf_name:
:return:
"""
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(
fields=['func_mean', 'opposite_pe_epi', 'same_pe_epi']),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(fields=[
'blip_warp', 'blip_warp_inverse', 'new_func_mean', 'new_func_mask'
]),
name='outputspec')
skullstrip_opposite_pe = skullstrip_functional(
skullstrip_tool='afni',
wf_name="{0}_skullstrip_opp_pe".format(wf_name))
wf.connect(input_node, 'opposite_pe_epi', skullstrip_opposite_pe,
'inputspec.func')
opp_pe_to_func = pe.Node(interface=fsl.FLIRT(), name='opp_pe_to_func')
opp_pe_to_func.inputs.cost = 'corratio'
wf.connect(skullstrip_opposite_pe, 'outputspec.func_brain', opp_pe_to_func,
'in_file')
wf.connect(input_node, 'func_mean', opp_pe_to_func, 'reference')
prep_qwarp_input_imports = ['import os', 'import subprocess']
prep_qwarp_input = \
pe.Node(function.Function(input_names=['same_pe_epi',
'func_mean'],
output_names=['qwarp_input'],
function=same_pe_direction_prep,
imports=prep_qwarp_input_imports),
name='prep_qwarp_input')
wf.connect(input_node, 'same_pe_epi', prep_qwarp_input, 'same_pe_epi')
wf.connect(input_node, 'func_mean', prep_qwarp_input, 'func_mean')
calc_blip_warp = pe.Node(afni.QwarpPlusMinus(), name='calc_blip_warp')
calc_blip_warp.inputs.plusminus = True
calc_blip_warp.inputs.outputtype = "NIFTI_GZ"
calc_blip_warp.inputs.out_file = os.path.abspath("Qwarp.nii.gz")
wf.connect(opp_pe_to_func, 'out_file', calc_blip_warp, 'base_file')
wf.connect(prep_qwarp_input, 'qwarp_input', calc_blip_warp, 'in_file')
convert_afni_warp_imports = ['import os', 'import nibabel as nb']
convert_afni_warp = \
pe.Node(function.Function(input_names=['afni_warp'],
output_names=['ants_warp'],
function=convert_afni_to_ants,
imports=convert_afni_warp_imports),
name='convert_afni_warp')
wf.connect(calc_blip_warp, 'source_warp', convert_afni_warp, 'afni_warp')
# TODO: inverse source_warp (node:source_warp_inverse)
# wf.connect(###
# output_node, 'blip_warp_inverse')
undistort_func_mean = pe.Node(interface=ants.ApplyTransforms(),
name='undistort_func_mean',
mem_gb=.1)
undistort_func_mean.inputs.out_postfix = '_antswarp'
undistort_func_mean.interface.num_threads = 1
undistort_func_mean.inputs.interpolation = "LanczosWindowedSinc"
undistort_func_mean.inputs.dimension = 3
undistort_func_mean.inputs.input_image_type = 0
wf.connect(input_node, 'func_mean', undistort_func_mean, 'input_image')
wf.connect(input_node, 'func_mean', undistort_func_mean, 'reference_image')
wf.connect(convert_afni_warp, 'ants_warp', undistort_func_mean,
'transforms')
create_new_mask = skullstrip_functional(
skullstrip_tool='afni', wf_name="{0}_new_func_mask".format(wf_name))
wf.connect(undistort_func_mean, 'output_image', create_new_mask,
'inputspec.func')
wf.connect(convert_afni_warp, 'ants_warp', output_node, 'blip_warp')
wf.connect(undistort_func_mean, 'output_image', output_node,
'new_func_mean')
wf.connect(create_new_mask, 'outputspec.func_brain_mask', output_node,
'new_func_mask')
return wf
def create_func_datasource(rest_dict, wf_name='func_datasource'):
"""Return the functional timeseries-related file paths for each
series/scan, from the dictionary of functional files described in the data
configuration (sublist) YAML file.
Scan input (from inputnode) is an iterable.
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
wf = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(
fields=['subject', 'scan', 'creds_path', 'dl_dir'],
mandatory_inputs=True),
name='inputnode')
outputnode = pe.Node(util.IdentityInterface(fields=[
'subject', 'rest', 'scan', 'scan_params', 'phase_diff', 'magnitude'
]),
name='outputspec')
# have this here for now because of the big change in the data
# configuration format
check_scan = pe.Node(function.Function(
input_names=['func_scan_dct', 'scan'],
output_names=[],
function=check_func_scan,
as_module=True),
name='check_func_scan')
check_scan.inputs.func_scan_dct = rest_dict
wf.connect(inputnode, 'scan', check_scan, 'scan')
# get the functional scan itself
selectrest = pe.Node(function.Function(
input_names=['scan', 'rest_dict', 'resource'],
output_names=['file_path'],
function=get_rest,
as_module=True),
name='selectrest')
selectrest.inputs.rest_dict = rest_dict
selectrest.inputs.resource = "scan"
wf.connect(inputnode, 'scan', selectrest, 'scan')
# check to see if it's on an Amazon AWS S3 bucket, and download it, if it
# is - otherwise, just return the local file path
check_s3_node = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='check_for_s3')
wf.connect(selectrest, 'file_path', check_s3_node, 'file_path')
wf.connect(inputnode, 'creds_path', check_s3_node, 'creds_path')
wf.connect(inputnode, 'dl_dir', check_s3_node, 'dl_dir')
check_s3_node.inputs.img_type = 'func'
wf.connect(inputnode, 'subject', outputnode, 'subject')
wf.connect(check_s3_node, 'local_path', outputnode, 'rest')
wf.connect(inputnode, 'scan', outputnode, 'scan')
# scan parameters CSV
select_scan_params = pe.Node(function.Function(
input_names=['scan', 'rest_dict', 'resource'],
output_names=['file_path'],
function=get_rest,
as_module=True),
name='select_scan_params')
select_scan_params.inputs.rest_dict = rest_dict
select_scan_params.inputs.resource = "scan_parameters"
wf.connect(inputnode, 'scan', select_scan_params, 'scan')
# if the scan parameters file is on AWS S3, download it
s3_scan_params = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='s3_scan_params')
wf.connect(select_scan_params, 'file_path', s3_scan_params, 'file_path')
wf.connect(inputnode, 'creds_path', s3_scan_params, 'creds_path')
wf.connect(inputnode, 'dl_dir', s3_scan_params, 'dl_dir')
wf.connect(s3_scan_params, 'local_path', outputnode, 'scan_params')
# field map phase file, for field map distortion correction
select_fmap_phase = pe.Node(function.Function(
input_names=['scan', 'rest_dict', 'resource'],
output_names=['file_path'],
function=get_rest,
as_module=True),
name='select_fmap_phase')
select_fmap_phase.inputs.rest_dict = rest_dict
select_fmap_phase.inputs.resource = "fmap_phase"
wf.connect(inputnode, 'scan', select_fmap_phase, 'scan')
s3_fmap_phase = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='s3_fmap_phase')
s3_fmap_phase.inputs.img_type = "other"
wf.connect(select_fmap_phase, 'file_path', s3_fmap_phase, 'file_path')
wf.connect(inputnode, 'creds_path', s3_fmap_phase, 'creds_path')
wf.connect(inputnode, 'dl_dir', s3_fmap_phase, 'dl_dir')
wf.connect(s3_fmap_phase, 'local_path', outputnode, 'phase_diff')
# field map magnitude file, for field map distortion correction
select_fmap_mag = pe.Node(function.Function(
input_names=['scan', 'rest_dict', 'resource'],
output_names=['file_path'],
function=get_rest,
as_module=True),
name='select_fmap_mag')
select_fmap_mag.inputs.rest_dict = rest_dict
select_fmap_mag.inputs.resource = "fmap_mag"
wf.connect(inputnode, 'scan', select_fmap_mag, 'scan')
s3_fmap_mag = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='s3_fmap_mag')
s3_fmap_mag.inputs.img_type = "other"
wf.connect(select_fmap_mag, 'file_path', s3_fmap_mag, 'file_path')
wf.connect(inputnode, 'creds_path', s3_fmap_mag, 'creds_path')
wf.connect(inputnode, 'dl_dir', s3_fmap_mag, 'dl_dir')
wf.connect(s3_fmap_mag, 'local_path', outputnode, 'magnitude')
return wf
def create_spatial_map_dataflow(spatial_maps, wf_name='datasource_maps'):
import os
wf = pe.Workflow(name=wf_name)
spatial_map_dict = {}
for spatial_map_file in spatial_maps:
spatial_map_file = spatial_map_file.rstrip('\r\n')
base_file = os.path.basename(spatial_map_file)
try:
valid_extensions = ['.nii', '.nii.gz']
base_name = [
base_file[:-len(ext)] for ext in valid_extensions
if base_file.endswith(ext)
][0]
if base_name in spatial_map_dict:
raise ValueError(
'Files with same name not allowed: %s %s' %
(spatial_map_file, spatial_map_dict[base_name]))
spatial_map_dict[base_name] = spatial_map_file
except IndexError as e:
raise Exception('Error in spatial_map_dataflow: '
'File extension not in .nii and .nii.gz')
except Exception as e:
raise e
inputnode = pe.Node(util.IdentityInterface(
fields=['spatial_map', 'spatial_map_file', 'creds_path', 'dl_dir'],
mandatory_inputs=True),
name='inputspec')
spatial_map_keys, spatial_map_values = \
zip(*spatial_map_dict.items())
inputnode.synchronize = True
inputnode.iterables = [
('spatial_map', spatial_map_keys),
('spatial_map_file', spatial_map_values),
]
check_s3_node = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='check_for_s3')
wf.connect(inputnode, 'spatial_map_file', check_s3_node, 'file_path')
wf.connect(inputnode, 'creds_path', check_s3_node, 'creds_path')
wf.connect(inputnode, 'dl_dir', check_s3_node, 'dl_dir')
check_s3_node.inputs.img_type = 'mask'
select_spatial_map = pe.Node(util.IdentityInterface(fields=['out_file'],
mandatory_inputs=True),
name='select_spatial_map')
wf.connect(check_s3_node, 'local_path', select_spatial_map, 'out_file')
return wf
def create_fmap_datasource(fmap_dct, wf_name='fmap_datasource'):
"""Return the field map files, from the dictionary of functional files
described in the data configuration (sublist) YAML file.
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
wf = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(
fields=['subject', 'scan', 'creds_path', 'dl_dir'],
mandatory_inputs=True),
name='inputnode')
outputnode = pe.Node(util.IdentityInterface(fields=[
'subject', 'rest', 'scan', 'scan_params', 'phase_diff', 'magnitude'
]),
name='outputspec')
# get the functional scan itself
selectrest = pe.Node(function.Function(
input_names=['scan', 'rest_dict', 'resource'],
output_names=['file_path'],
function=get_rest,
as_module=True),
name='selectrest')
selectrest.inputs.rest_dict = fmap_dct
selectrest.inputs.resource = "scan"
wf.connect(inputnode, 'scan', selectrest, 'scan')
# check to see if it's on an Amazon AWS S3 bucket, and download it, if it
# is - otherwise, just return the local file path
check_s3_node = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='check_for_s3')
wf.connect(selectrest, 'file_path', check_s3_node, 'file_path')
wf.connect(inputnode, 'creds_path', check_s3_node, 'creds_path')
wf.connect(inputnode, 'dl_dir', check_s3_node, 'dl_dir')
check_s3_node.inputs.img_type = 'other'
wf.connect(inputnode, 'subject', outputnode, 'subject')
wf.connect(check_s3_node, 'local_path', outputnode, 'rest')
wf.connect(inputnode, 'scan', outputnode, 'scan')
# scan parameters CSV
select_scan_params = pe.Node(function.Function(
input_names=['scan', 'rest_dict', 'resource'],
output_names=['file_path'],
function=get_rest,
as_module=True),
name='select_scan_params')
select_scan_params.inputs.rest_dict = fmap_dct
select_scan_params.inputs.resource = "scan_parameters"
wf.connect(inputnode, 'scan', select_scan_params, 'scan')
# if the scan parameters file is on AWS S3, download it
s3_scan_params = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='s3_scan_params')
wf.connect(select_scan_params, 'file_path', s3_scan_params, 'file_path')
wf.connect(inputnode, 'creds_path', s3_scan_params, 'creds_path')
wf.connect(inputnode, 'dl_dir', s3_scan_params, 'dl_dir')
wf.connect(s3_scan_params, 'local_path', outputnode, 'scan_params')
return wf
def create_network_centrality_workflow(workflow, c, strategies, s3_config):
# TODO ASH redo?
# Check for the existence of AFNI 3dDegreeCentrality/LFCD binaries
import subprocess
try:
ret_code = subprocess.check_call(['which', '3dDegreeCentrality'],
stdout=open(os.devnull, 'wb'))
if ret_code == 0:
afni_centrality_found = True
except subprocess.CalledProcessError as exc:
afni_centrality_found = False
try:
ret_code = subprocess.check_call(['which', '3dLFCD'],
stdout=open(os.devnull, 'wb'))
if ret_code == 0:
afni_lfcd_found = True
except subprocess.CalledProcessError as exc:
afni_lfcd_found = False
if not any((
True in c.degWeightOptions,
True in c.eigWeightOptions,
True in c.lfcdWeightOptions
)):
return strategies
for num_strat, strat in enumerate(strategies[:]):
# Resample the functional mni to the centrality mask resolution
resample_functional_to_template = pe.Node(
interface=fsl.FLIRT(),
name='resample_functional_to_template_%d' % num_strat
)
resample_functional_to_template.inputs.set(
interp='trilinear',
in_matrix_file=c.identityMatrix,
apply_xfm=True
)
# Get nipype node and out file of the func mni img
node, out_file = strat['functional_to_standard']
# Resample the input functional file to template(roi/mask)
workflow.connect(node, out_file,
resample_functional_to_template, 'in_file')
workflow.connect(c.templateSpecificationFile, 'local_path',
resample_functional_to_template, 'reference')
# Init merge node for appending method output lists to one another
merge_node = pe.Node(function.Function(input_names=['deg_list',
'eig_list',
'lfcd_list'],
output_names=['merged_list'],
function=merge_lists,
as_module=True),
name='merge_node_%d' % num_strat)
# Function to connect the CPAC centrality python workflow
# into pipeline
# Degree/eigen check
if afni_centrality_found:
if True in c.degWeightOptions:
connect_afni_centrality_workflow(
workflow, c, strat, num_strat,
resample_functional_to_template, c.templateSpecificationFile, merge_node,
'degree',
c.degCorrelationThresholdOption,
c.degCorrelationThreshold
)
if True in c.eigWeightOptions:
connect_afni_centrality_workflow(
workflow, c, strat, num_strat,
resample_functional_to_template, c.templateSpecificationFile, merge_node,
'eigenvector',
c.eigCorrelationThresholdOption,
c.eigCorrelationThreshold
)
# Otherwise run the CPAC python workflow
else:
# If we're calculating degree centrality
if True in c.degWeightOptions:
connect_centrality_workflow(
workflow, c, strat, num_strat,
resample_functional_to_template, c.templateSpecificationFile, merge_node,
'degree',
c.degCorrelationThresholdOption,
c.degCorrelationThreshold,
c.degWeightOptions,
'deg_list'
)
# If we're calculating eigenvector centrality
if True in c.eigWeightOptions:
connect_centrality_workflow(
workflow, c, strat, num_strat,
resample_functional_to_template, c.templateSpecificationFile, merge_node,
'eigenvector',
c.eigCorrelationThresholdOption,
c.eigCorrelationThreshold,
c.eigWeightOptions,
'eig_list'
)
# LFCD check
if afni_lfcd_found:
# If we're calculating lFCD
if True in c.lfcdWeightOptions:
connect_afni_centrality_workflow(
workflow, c, strat, num_strat,
resample_functional_to_template, c.templateSpecificationFile, merge_node,
'lfcd',
c.lfcdCorrelationThresholdOption,
c.lfcdCorrelationThreshold
)
# Otherwise run the CPAC python workflow
else:
# If we're calculating lFCD
if True in c.lfcdWeightOptions:
connect_centrality_workflow(
workflow, c, strat, num_strat,
resample_functional_to_template, c.templateSpecificationFile, merge_node,
'lfcd',
c.lfcdCorrelationThresholdOption,
c.lfcdCorrelationThreshold,
c.lfcdWeightOptions,
'lfcd_list'
)
# Update resource pool with centrality outputs
strat.update_resource_pool({
'centrality': (merge_node, 'merged_list')
})
if 0 in c.runNetworkCentrality:
strat = strat.fork()
strategies += [strat]
return strategies
