def main(environment, experiment, pipeline, cluster, **kwargs):
"""
This function...
:param environment:
:param experiment:
:param pipeline:
:param cluster:
:param kwargs:
:return:
"""
from utilities.configFileParser import nipype_options
print("Copying Atlas directory and determining appropriate Nipype options...")
pipeline = nipype_options(
kwargs, pipeline, cluster, experiment, environment
) # Generate Nipype options
print("Getting session(s) from database...")
write_cvsubset_file(
environment,
experiment,
pipeline,
cluster,
csv_file=kwargs["--sampleFile"],
test_size=kwargs["--testSamplesize"],
hasHeader=kwargs["--header"],
)
def _main(environment, experiment, pipeline, cluster, **kwds):
from utilities.configFileParser import nipype_options
from utilities.misc import add_dict
print "Copying Atlas directory and determining appropriate Nipype options..."
pipeline = nipype_options(kwds, pipeline, cluster, experiment, environment) # Generate Nipype options
print "Getting session(s) from database..."
createAndRun(kwds['SESSIONS'], environment, experiment, pipeline, cluster)
return 0
def _SingleSession_main(environment, experiment, pipeline, cluster, **kwds):
from utilities.configFileParser import nipype_options
print("Copying Atlas directory and determining appropriate Nipype options...")
pipeline = nipype_options(kwds, pipeline, cluster, experiment, environment) # Generate Nipype options
print("Getting session(s) from database...")
createAndRun(kwds['SESSIONS'], environment, experiment, pipeline, cluster, useSentinal=kwds['--use-sentinal'],
dryRun=kwds['--dry-run'])
return 0
def _SingleSession_main(environment, experiment, pipeline, cluster, **kwds):
from utilities.configFileParser import nipype_options
print "Copying Atlas directory and determining appropriate Nipype options..."
pipeline = nipype_options(kwds, pipeline, cluster, experiment, environment) # Generate Nipype options
print "Getting session(s) from database..."
createAndRun(kwds['SESSIONS'], environment, experiment, pipeline, cluster, useSentinal=kwds['--use-sentinal'],
dryRun=kwds['--dry-run'])
return 0
def main(environment, experiment, pipeline, cluster, **kwargs):
from utilities.configFileParser import nipype_options
print "Copying Atlas directory and determining appropriate Nipype options..."
pipeline = nipype_options(kwargs, pipeline, cluster, experiment, environment) # Generate Nipype options
print "Getting session(s) from database..."
writeCVSubsetFile( environment,
experiment,
pipeline,
cluster,
csv_file=kwargs['--sampleFile'],
test_size=kwargs['--testSamplesize'],
hasHeader=kwargs['--header'] )
def main(environment, experiment, pipeline, cluster, **kwargs):
from utilities.configFileParser import nipype_options
print("Copying Atlas directory and determining appropriate Nipype options...")
pipeline = nipype_options(kwargs, pipeline, cluster, experiment, environment) # Generate Nipype options
print("Getting session(s) from database...")
writeCVSubsetFile(environment,
experiment,
pipeline,
cluster,
csv_file=kwargs['--sampleFile'],
test_size=kwargs['--testSamplesize'],
hasHeader=kwargs['--header'])
def run(argv, environment, experiment, pipeline, cluster):
from utilities.configFileParser import nipype_options
from utilities.misc import add_dict
from utilities.distributed import create_global_sge_script
print "Getting subjects from database..."
subjects = get_subjects(argv, experiment['cachedir'], environment['prefix'], experiment['dbfile']) # Build database before parallel section
if environment['cluster']:
print "Creating SGE template string..."
node_template = create_global_sge_script(cluster, environment)
else:
node_template = None
print "Copying Atlas directory and determining appropriate Nipype options..."
pipeline = nipype_options(argv, pipeline, cluster, node_template, experiment) # Generate Nipype options
master_config = {}
for configDict in [environment, experiment, pipeline, cluster]:
master_config = add_dict(master_config, configDict)
print "Dispatching jobs to the system..."
return dispatcher(master_config, subjects)
def run(argv, environment, experiment, pipeline, cluster):
from utilities.configFileParser import nipype_options
from utilities.misc import add_dict
from utilities.distributed import create_global_sge_script
print "Getting subjects from database..."
subjects = get_subjects(
argv, experiment['cachedir'], environment['prefix'],
experiment['dbfile']) # Build database before parallel section
if environment['cluster']:
print "Creating SGE template string..."
node_template = create_global_sge_script(cluster, environment)
else:
node_template = None
print "Copying Atlas directory and determining appropriate Nipype options..."
pipeline = nipype_options(argv, pipeline, cluster, node_template,
experiment) # Generate Nipype options
master_config = {}
for configDict in [environment, experiment, pipeline, cluster]:
master_config = add_dict(master_config, configDict)
print "Dispatching jobs to the system..."
return dispatcher(master_config, subjects)
def _template_runner(argv, environment, experiment, pipeline_options, cluster):
print("Getting subjects from database...")
# subjects = argv["--subjects"].split(',')
subjects, subjects_sessions_dictionary = get_subjects_sessions_dictionary(argv['SUBJECTS'],
experiment['cachedir'],
experiment['resultdir'],
environment['prefix'],
experiment['dbfile'],
argv['--use-sentinal'], argv['--use-shuffle']
) # Build database before parallel section
useSentinal = argv['--use-sentinal']
# Quick preliminary sanity check
for thisSubject in subjects:
if len(subjects_sessions_dictionary[thisSubject]) == 0:
print("ERROR: subject {0} has no sessions found. Did you supply a valid subject id on the command line?".format(thisSubject) )
sys.exit(-1)
for thisSubject in subjects:
print("Processing atlas generation for this subject: {0}".format(thisSubject))
print("="*80)
print("Copying Atlas directory and determining appropriate Nipype options...")
subj_pipeline_options = nipype_options(argv, pipeline_options, cluster, experiment, environment) # Generate Nipype options
print("Dispatching jobs to the system...")
######
###### Now start workflow construction
######
# Set universal pipeline options
nipype_config.update_config(subj_pipeline_options)
ready_for_template_building = True
for thisSession in subjects_sessions_dictionary[thisSubject]:
path_test = os.path.join(experiment['previousresult'],'*/{0}/{1}/TissueClassify/t1_average_BRAINSABC.nii.gz'.format(thisSubject,thisSession))
t1_file_result = glob.glob(path_test)
if len(t1_file_result) != 1:
print("Incorrect number of t1 images found for data grabber {0}".format(t1_file_result))
print(" at path {0}".format(path_test))
ready_for_template_building = False
if not ready_for_template_building:
print("TEMPORARY SKIPPING: Not ready to process {0}".format(thisSubject))
continue
base_output_directory = os.path.join(subj_pipeline_options['logging']['log_directory'],thisSubject)
template = pe.Workflow(name='SubjectAtlas_Template_'+thisSubject)
template.base_dir = base_output_directory
subjectNode = pe.Node(interface=IdentityInterface(fields=['subject']), run_without_submitting=True, name='99_subjectIterator')
subjectNode.inputs.subject = thisSubject
sessionsExtractorNode = pe.Node(Function(function=getSessionsFromSubjectDictionary,
input_names=['subject_session_dictionary','subject'],
output_names=['sessions']),
run_without_submitting=True, name="99_sessionsExtractor")
sessionsExtractorNode.inputs.subject_session_dictionary = subjects_sessions_dictionary
baselineOptionalDG = pe.MapNode(nio.DataGrabber(infields=['subject','session'],
outfields=[ 't2_average', 'pd_average',
'fl_average'],
run_without_submitting=True
),
run_without_submitting=True,
iterfield=['session'], name='BaselineOptional_DG')
baselineOptionalDG.inputs.base_directory = experiment['previousresult']
baselineOptionalDG.inputs.sort_filelist = True
baselineOptionalDG.inputs.raise_on_empty = False
baselineOptionalDG.inputs.template = '*'
baselineOptionalDG.inputs.field_template = {
't2_average':'*/%s/%s/TissueClassify/t2_average_BRAINSABC.nii.gz',
'pd_average':'*/%s/%s/TissueClassify/pd_average_BRAINSABC.nii.gz',
'fl_average':'*/%s/%s/TissueClassify/fl_average_BRAINSABC.nii.gz'
}
baselineOptionalDG.inputs.template_args = {
't2_average':[['subject','session']],
'pd_average':[['subject','session']],
'fl_average':[['subject','session']]
}
baselineRequiredDG = pe.MapNode(nio.DataGrabber(infields=['subject','session'],
outfields=['t1_average', 'brainMaskLabels',
'posteriorImages','passive_intensities','passive_masks',
'BCD_ACPC_Landmarks_fcsv'],
run_without_submitting=True
),
run_without_submitting=True,
iterfield=['session'], name='Baseline_DG')
baselineRequiredDG.inputs.base_directory = experiment['previousresult']
baselineRequiredDG.inputs.sort_filelist = True
baselineRequiredDG.inputs.raise_on_empty = True
baselineRequiredDG.inputs.template = '*'
posterior_files = ['AIR', 'BASAL', 'CRBLGM', 'CRBLWM', 'CSF', 'GLOBUS', 'HIPPOCAMPUS',
'NOTCSF', 'NOTGM', 'NOTVB', 'NOTWM', 'SURFGM', 'THALAMUS', 'VB', 'WM']
passive_intensities_files = [
'rho.nii.gz',
'phi.nii.gz',
'theta.nii.gz',
'l_thalamus_ProbabilityMap.nii.gz',
'r_accumben_ProbabilityMap.nii.gz',
'l_globus_ProbabilityMap.nii.gz',
'l_accumben_ProbabilityMap.nii.gz',
'l_caudate_ProbabilityMap.nii.gz',
'l_putamen_ProbabilityMap.nii.gz',
'r_thalamus_ProbabilityMap.nii.gz',
'r_putamen_ProbabilityMap.nii.gz',
'r_caudate_ProbabilityMap.nii.gz',
'r_hippocampus_ProbabilityMap.nii.gz',
'r_globus_ProbabilityMap.nii.gz',
'l_hippocampus_ProbabilityMap.nii.gz'
]
passive_mask_files = [
'template_WMPM2_labels.nii.gz',
'hncma_atlas.nii.gz',
'template_nac_labels.nii.gz',
'template_leftHemisphere.nii.gz',
'template_rightHemisphere.nii.gz',
'template_ventricles.nii.gz',
'template_headregion.nii.gz'
]
baselineRequiredDG.inputs.field_template = {'t1_average':'*/%s/%s/TissueClassify/t1_average_BRAINSABC.nii.gz',
'brainMaskLabels':'*/%s/%s/TissueClassify/complete_brainlabels_seg.nii.gz',
'BCD_ACPC_Landmarks_fcsv':'*/%s/%s/ACPCAlign/BCD_ACPC_Landmarks.fcsv',
'posteriorImages':'*/%s/%s/TissueClassify/POSTERIOR_%s.nii.gz',
'passive_intensities':'*/%s/%s/WarpedAtlas2Subject/%s',
'passive_masks':'*/%s/%s/WarpedAtlas2Subject/%s',
}
baselineRequiredDG.inputs.template_args = {'t1_average':[['subject','session']],
'brainMaskLabels':[['subject','session']],
'BCD_ACPC_Landmarks_fcsv':[['subject','session']],
'posteriorImages':[['subject','session', posterior_files]],
'passive_intensities':[['subject','session', passive_intensities_files]],
'passive_masks':[['subject','session', passive_mask_files]]
}
MergeByExtendListElementsNode = pe.Node(Function(function=MergeByExtendListElements,
input_names=['t1s', 't2s',
'pds', 'fls',
'labels', 'posteriors',
'passive_intensities', 'passive_masks'
],
output_names=['ListOfImagesDictionaries', 'registrationImageTypes',
'interpolationMapping']),
run_without_submitting=True, name="99_MergeByExtendListElements")
template.connect([(subjectNode, baselineRequiredDG, [('subject', 'subject')]),
(subjectNode, baselineOptionalDG, [('subject', 'subject')]),
(subjectNode, sessionsExtractorNode, [('subject','subject')]),
(sessionsExtractorNode, baselineRequiredDG, [('sessions', 'session')]),
(sessionsExtractorNode, baselineOptionalDG, [('sessions', 'session')]),
(baselineRequiredDG, MergeByExtendListElementsNode,
[('t1_average', 't1s'),
('brainMaskLabels', 'labels'),
(('posteriorImages',
ConvertSessionsListOfPosteriorListToDictionaryOfSessionLists), 'posteriors')
]),
(baselineOptionalDG, MergeByExtendListElementsNode,
[
('t2_average', 't2s'),
('pd_average', 'pds'),
('fl_average', 'fls')
]),
(baselineRequiredDG, MergeByExtendListElementsNode,
[
(('passive_intensities',
ConvertSessionsListOfPosteriorListToDictionaryOfSessionLists), 'passive_intensities')
]),
(baselineRequiredDG, MergeByExtendListElementsNode,
[
(('passive_masks',
ConvertSessionsListOfPosteriorListToDictionaryOfSessionLists), 'passive_masks')
])
])
myInitAvgWF = pe.Node(interface=ants.AverageImages(), name='Atlas_antsSimpleAverage') # was 'Phase1_antsSimpleAverage'
myInitAvgWF.inputs.dimension = 3
myInitAvgWF.inputs.normalize = True
myInitAvgWF.inputs.num_threads = -1
template.connect(baselineRequiredDG, 't1_average', myInitAvgWF, "images")
####################################################################################################
# TEMPLATE_BUILD_RUN_MODE = 'MULTI_IMAGE'
# if numSessions == 1:
# TEMPLATE_BUILD_RUN_MODE = 'SINGLE_IMAGE'
####################################################################################################
CLUSTER_QUEUE=cluster['queue']
CLUSTER_QUEUE_LONG=cluster['long_q']
buildTemplateIteration1 = BAWantsRegistrationTemplateBuildSingleIterationWF('iteration01',CLUSTER_QUEUE,CLUSTER_QUEUE_LONG)
# buildTemplateIteration2 = buildTemplateIteration1.clone(name='buildTemplateIteration2')
buildTemplateIteration2 = BAWantsRegistrationTemplateBuildSingleIterationWF('Iteration02',CLUSTER_QUEUE,CLUSTER_QUEUE_LONG)
CreateAtlasXMLAndCleanedDeformedAveragesNode = pe.Node(interface=Function(function=CreateAtlasXMLAndCleanedDeformedAverages,
input_names=['t1_image', 'deformed_list', 'AtlasTemplate', 'outDefinition'],
output_names=['outAtlasFullPath', 'clean_deformed_list']),
# This is a lot of work, so submit it run_without_submitting=True,
run_without_submitting=True, # HACK: THIS NODE REALLY SHOULD RUN ON THE CLUSTER!
name='99_CreateAtlasXMLAndCleanedDeformedAverages')
if subj_pipeline_options['plugin_name'].startswith('SGE'): # for some nodes, the qsub call needs to be modified on the cluster
CreateAtlasXMLAndCleanedDeformedAveragesNode.plugin_args = {'template': subj_pipeline_options['plugin_args']['template'],
'qsub_args': modify_qsub_args(cluster['queue'], 1, 1, 1),
'overwrite': True}
for bt in [buildTemplateIteration1, buildTemplateIteration2]:
BeginANTS = bt.get_node("BeginANTS")
BeginANTS.plugin_args = {'template': subj_pipeline_options['plugin_args']['template'], 'overwrite': True,
'qsub_args': modify_qsub_args(cluster['queue'], 7, 4, 16)}
wimtdeformed = bt.get_node("wimtdeformed")
wimtdeformed.plugin_args = {'template': subj_pipeline_options['plugin_args']['template'], 'overwrite': True,
'qsub_args': modify_qsub_args(cluster['queue'], 2, 2, 2)}
#AvgAffineTransform = bt.get_node("AvgAffineTransform")
#AvgAffineTransform.plugin_args = {'template': subj_pipeline_options['plugin_args']['template'], 'overwrite': True,
# 'qsub_args': modify_qsub_args(cluster['queue'], 2, 1, 1)}
wimtPassivedeformed = bt.get_node("wimtPassivedeformed")
wimtPassivedeformed.plugin_args = {'template': subj_pipeline_options['plugin_args']['template'], 'overwrite': True,
'qsub_args': modify_qsub_args(cluster['queue'], 2, 2, 4)}
# Running off previous baseline experiment
NACCommonAtlas = MakeAtlasNode(experiment['atlascache'], 'NACCommonAtlas_{0}'.format('subject'),
['S_BRAINSABCSupport'] ) ## HACK : replace 'subject' with subject id once this is a loop rather than an iterable.
template.connect([(myInitAvgWF, buildTemplateIteration1, [('output_average_image', 'inputspec.fixed_image')]),
(MergeByExtendListElementsNode, buildTemplateIteration1, [('ListOfImagesDictionaries', 'inputspec.ListOfImagesDictionaries'),
('registrationImageTypes', 'inputspec.registrationImageTypes'),
('interpolationMapping','inputspec.interpolationMapping')]),
(buildTemplateIteration1, buildTemplateIteration2, [('outputspec.template', 'inputspec.fixed_image')]),
(MergeByExtendListElementsNode, buildTemplateIteration2, [('ListOfImagesDictionaries', 'inputspec.ListOfImagesDictionaries'),
('registrationImageTypes','inputspec.registrationImageTypes'),
('interpolationMapping', 'inputspec.interpolationMapping')]),
(subjectNode, CreateAtlasXMLAndCleanedDeformedAveragesNode, [(('subject', xml_filename), 'outDefinition')]),
(NACCommonAtlas, CreateAtlasXMLAndCleanedDeformedAveragesNode, [('ExtendedAtlasDefinition_xml_in', 'AtlasTemplate')]),
(buildTemplateIteration2, CreateAtlasXMLAndCleanedDeformedAveragesNode, [('outputspec.template', 't1_image'),
('outputspec.passive_deformed_templates', 'deformed_list')]),
])
## Genearate an average lmks file.
myAverageLmk = pe.Node(interface = GenerateAverageLmkFile(), name="myAverageLmk" )
myAverageLmk.inputs.outputLandmarkFile = "AVG_LMKS.fcsv"
template.connect(baselineRequiredDG,'BCD_ACPC_Landmarks_fcsv',myAverageLmk,'inputLandmarkFiles')
# Create DataSinks
SubjectAtlas_DataSink = pe.Node(nio.DataSink(), name="Subject_DS")
SubjectAtlas_DataSink.overwrite = subj_pipeline_options['ds_overwrite']
SubjectAtlas_DataSink.inputs.base_directory = experiment['resultdir']
template.connect([(subjectNode, SubjectAtlas_DataSink, [('subject', 'container')]),
(CreateAtlasXMLAndCleanedDeformedAveragesNode, SubjectAtlas_DataSink, [('outAtlasFullPath', 'Atlas.@definitions')]),
(CreateAtlasXMLAndCleanedDeformedAveragesNode, SubjectAtlas_DataSink, [('clean_deformed_list', 'Atlas.@passive_deformed_templates')]),
(subjectNode, SubjectAtlas_DataSink, [(('subject', outputPattern), 'regexp_substitutions')]),
(buildTemplateIteration2, SubjectAtlas_DataSink, [('outputspec.template', 'Atlas.@template')]),
(myAverageLmk,SubjectAtlas_DataSink,[('outputLandmarkFile','Atlas.@outputLandmarkFile')]),
])
dotfilename = argv['--dotfilename']
if dotfilename is not None:
print("WARNING: Printing workflow, but not running pipeline")
print_workflow(template, plugin=subj_pipeline_options['plugin_name'], dotfilename=dotfilename)
else:
run_workflow(template, plugin=subj_pipeline_options['plugin_name'], plugin_args=subj_pipeline_options['plugin_args'])
def _template_runner(argv, environment, experiment, pipeline_options, cluster):
print("Getting subjects from database...")
# subjects = argv["--subjects"].split(',')
subjects, subjects_sessions_dictionary = get_subjects_sessions_dictionary(
argv['SUBJECTS'], experiment['cachedir'], experiment['resultdir'],
environment['prefix'], experiment['dbfile'], argv['--use-sentinal'],
argv['--use-shuffle']) # Build database before parallel section
useSentinal = argv['--use-sentinal']
# Quick preliminary sanity check
for thisSubject in subjects:
if len(subjects_sessions_dictionary[thisSubject]) == 0:
print(
"ERROR: subject {0} has no sessions found. Did you supply a valid subject id on the command line?"
.format(thisSubject))
sys.exit(-1)
for thisSubject in subjects:
print("Processing atlas generation for this subject: {0}".format(
thisSubject))
print("=" * 80)
print(
"Copying Atlas directory and determining appropriate Nipype options..."
)
subj_pipeline_options = nipype_options(
argv, pipeline_options, cluster, experiment,
environment) # Generate Nipype options
print("Dispatching jobs to the system...")
######
###### Now start workflow construction
######
# Set universal pipeline options
nipype_config.update_config(subj_pipeline_options)
ready_for_template_building = True
for thisSession in subjects_sessions_dictionary[thisSubject]:
path_test = os.path.join(
experiment['previousresult'],
'*/{0}/{1}/TissueClassify/t1_average_BRAINSABC.nii.gz'.format(
thisSubject, thisSession))
t1_file_result = glob.glob(path_test)
if len(t1_file_result) != 1:
print(
"Incorrect number of t1 images found for data grabber {0}".
format(t1_file_result))
print(" at path {0}".format(path_test))
ready_for_template_building = False
if not ready_for_template_building:
print("TEMPORARY SKIPPING: Not ready to process {0}".format(
thisSubject))
continue
base_output_directory = os.path.join(
subj_pipeline_options['logging']['log_directory'], thisSubject)
template = pe.Workflow(name='SubjectAtlas_Template_' + thisSubject)
template.base_dir = base_output_directory
subjectNode = pe.Node(interface=IdentityInterface(fields=['subject']),
run_without_submitting=True,
name='99_subjectIterator')
subjectNode.inputs.subject = thisSubject
sessionsExtractorNode = pe.Node(Function(
function=getSessionsFromSubjectDictionary,
input_names=['subject_session_dictionary', 'subject'],
output_names=['sessions']),
run_without_submitting=True,
name="99_sessionsExtractor")
sessionsExtractorNode.inputs.subject_session_dictionary = subjects_sessions_dictionary
baselineOptionalDG = pe.MapNode(nio.DataGrabber(
infields=['subject', 'session'],
outfields=['t2_average', 'pd_average', 'fl_average'],
run_without_submitting=True),
run_without_submitting=True,
iterfield=['session'],
name='BaselineOptional_DG')
baselineOptionalDG.inputs.base_directory = experiment['previousresult']
baselineOptionalDG.inputs.sort_filelist = True
baselineOptionalDG.inputs.raise_on_empty = False
baselineOptionalDG.inputs.template = '*'
baselineOptionalDG.inputs.field_template = {
't2_average': '*/%s/%s/TissueClassify/t2_average_BRAINSABC.nii.gz',
'pd_average': '*/%s/%s/TissueClassify/pd_average_BRAINSABC.nii.gz',
'fl_average': '*/%s/%s/TissueClassify/fl_average_BRAINSABC.nii.gz'
}
baselineOptionalDG.inputs.template_args = {
't2_average': [['subject', 'session']],
'pd_average': [['subject', 'session']],
'fl_average': [['subject', 'session']]
}
baselineRequiredDG = pe.MapNode(nio.DataGrabber(
infields=['subject', 'session'],
outfields=[
't1_average', 'brainMaskLabels', 'posteriorImages',
'passive_intensities', 'passive_masks',
'BCD_ACPC_Landmarks_fcsv'
],
run_without_submitting=True),
run_without_submitting=True,
iterfield=['session'],
name='Baseline_DG')
baselineRequiredDG.inputs.base_directory = experiment['previousresult']
baselineRequiredDG.inputs.sort_filelist = True
baselineRequiredDG.inputs.raise_on_empty = True
baselineRequiredDG.inputs.template = '*'
posterior_files = [
'AIR', 'BASAL', 'CRBLGM', 'CRBLWM', 'CSF', 'GLOBUS', 'HIPPOCAMPUS',
'NOTCSF', 'NOTGM', 'NOTVB', 'NOTWM', 'SURFGM', 'THALAMUS', 'VB',
'WM'
]
passive_intensities_files = [
'rho.nii.gz', 'phi.nii.gz', 'theta.nii.gz',
'l_thalamus_ProbabilityMap.nii.gz',
'r_accumben_ProbabilityMap.nii.gz',
'l_globus_ProbabilityMap.nii.gz',
'l_accumben_ProbabilityMap.nii.gz',
'l_caudate_ProbabilityMap.nii.gz',
'l_putamen_ProbabilityMap.nii.gz',
'r_thalamus_ProbabilityMap.nii.gz',
'r_putamen_ProbabilityMap.nii.gz',
'r_caudate_ProbabilityMap.nii.gz',
'r_hippocampus_ProbabilityMap.nii.gz',
'r_globus_ProbabilityMap.nii.gz',
'l_hippocampus_ProbabilityMap.nii.gz'
]
passive_mask_files = [
'template_WMPM2_labels.nii.gz', 'hncma_atlas.nii.gz',
'template_nac_labels.nii.gz', 'template_leftHemisphere.nii.gz',
'template_rightHemisphere.nii.gz', 'template_ventricles.nii.gz',
'template_headregion.nii.gz'
]
baselineRequiredDG.inputs.field_template = {
't1_average': '*/%s/%s/TissueClassify/t1_average_BRAINSABC.nii.gz',
'brainMaskLabels':
'*/%s/%s/TissueClassify/complete_brainlabels_seg.nii.gz',
'BCD_ACPC_Landmarks_fcsv':
'*/%s/%s/ACPCAlign/BCD_ACPC_Landmarks.fcsv',
'posteriorImages': '*/%s/%s/TissueClassify/POSTERIOR_%s.nii.gz',
'passive_intensities': '*/%s/%s/WarpedAtlas2Subject/%s',
'passive_masks': '*/%s/%s/WarpedAtlas2Subject/%s',
}
baselineRequiredDG.inputs.template_args = {
't1_average': [['subject', 'session']],
'brainMaskLabels': [['subject', 'session']],
'BCD_ACPC_Landmarks_fcsv': [['subject', 'session']],
'posteriorImages': [['subject', 'session', posterior_files]],
'passive_intensities':
[['subject', 'session', passive_intensities_files]],
'passive_masks': [['subject', 'session', passive_mask_files]]
}
MergeByExtendListElementsNode = pe.Node(
Function(function=MergeByExtendListElements,
input_names=[
't1s', 't2s', 'pds', 'fls', 'labels', 'posteriors',
'passive_intensities', 'passive_masks'
],
output_names=[
'ListOfImagesDictionaries', 'registrationImageTypes',
'interpolationMapping'
]),
run_without_submitting=True,
name="99_MergeByExtendListElements")
template.connect([
(subjectNode, baselineRequiredDG, [('subject', 'subject')]),
(subjectNode, baselineOptionalDG, [('subject', 'subject')]),
(subjectNode, sessionsExtractorNode, [('subject', 'subject')]),
(sessionsExtractorNode, baselineRequiredDG, [('sessions',
'session')]),
(sessionsExtractorNode, baselineOptionalDG, [('sessions',
'session')]),
(baselineRequiredDG, MergeByExtendListElementsNode,
[('t1_average', 't1s'), ('brainMaskLabels', 'labels'),
(('posteriorImages',
ConvertSessionsListOfPosteriorListToDictionaryOfSessionLists),
'posteriors')]),
(baselineOptionalDG, MergeByExtendListElementsNode,
[('t2_average', 't2s'), ('pd_average', 'pds'),
('fl_average', 'fls')]),
(baselineRequiredDG, MergeByExtendListElementsNode,
[(('passive_intensities',
ConvertSessionsListOfPosteriorListToDictionaryOfSessionLists),
'passive_intensities')]),
(baselineRequiredDG, MergeByExtendListElementsNode,
[(('passive_masks',
ConvertSessionsListOfPosteriorListToDictionaryOfSessionLists),
'passive_masks')])
])
myInitAvgWF = pe.Node(
interface=ants.AverageImages(),
name='Atlas_antsSimpleAverage') # was 'Phase1_antsSimpleAverage'
myInitAvgWF.inputs.dimension = 3
myInitAvgWF.inputs.normalize = True
myInitAvgWF.inputs.num_threads = -1
template.connect(baselineRequiredDG, 't1_average', myInitAvgWF,
"images")
####################################################################################################
# TEMPLATE_BUILD_RUN_MODE = 'MULTI_IMAGE'
# if numSessions == 1:
# TEMPLATE_BUILD_RUN_MODE = 'SINGLE_IMAGE'
####################################################################################################
CLUSTER_QUEUE = cluster['queue']
CLUSTER_QUEUE_LONG = cluster['long_q']
buildTemplateIteration1 = BAWantsRegistrationTemplateBuildSingleIterationWF(
'iteration01', CLUSTER_QUEUE, CLUSTER_QUEUE_LONG)
# buildTemplateIteration2 = buildTemplateIteration1.clone(name='buildTemplateIteration2')
buildTemplateIteration2 = BAWantsRegistrationTemplateBuildSingleIterationWF(
'Iteration02', CLUSTER_QUEUE, CLUSTER_QUEUE_LONG)
CreateAtlasXMLAndCleanedDeformedAveragesNode = pe.Node(
interface=Function(
function=CreateAtlasXMLAndCleanedDeformedAverages,
input_names=[
't1_image', 'deformed_list', 'AtlasTemplate',
'outDefinition'
],
output_names=['outAtlasFullPath', 'clean_deformed_list']),
# This is a lot of work, so submit it run_without_submitting=True,
run_without_submitting=
True, # HACK: THIS NODE REALLY SHOULD RUN ON THE CLUSTER!
name='99_CreateAtlasXMLAndCleanedDeformedAverages')
if subj_pipeline_options['plugin_name'].startswith(
'SGE'
): # for some nodes, the qsub call needs to be modified on the cluster
CreateAtlasXMLAndCleanedDeformedAveragesNode.plugin_args = {
'template': subj_pipeline_options['plugin_args']['template'],
'qsub_args': modify_qsub_args(cluster['queue'], 1, 1, 1),
'overwrite': True
}
for bt in [buildTemplateIteration1, buildTemplateIteration2]:
BeginANTS = bt.get_node("BeginANTS")
BeginANTS.plugin_args = {
'template':
subj_pipeline_options['plugin_args']['template'],
'overwrite': True,
'qsub_args': modify_qsub_args(cluster['queue'], 7, 4, 16)
}
wimtdeformed = bt.get_node("wimtdeformed")
wimtdeformed.plugin_args = {
'template':
subj_pipeline_options['plugin_args']['template'],
'overwrite': True,
'qsub_args': modify_qsub_args(cluster['queue'], 2, 2, 2)
}
#AvgAffineTransform = bt.get_node("AvgAffineTransform")
#AvgAffineTransform.plugin_args = {'template': subj_pipeline_options['plugin_args']['template'], 'overwrite': True,
# 'qsub_args': modify_qsub_args(cluster['queue'], 2, 1, 1)}
wimtPassivedeformed = bt.get_node("wimtPassivedeformed")
wimtPassivedeformed.plugin_args = {
'template':
subj_pipeline_options['plugin_args']['template'],
'overwrite': True,
'qsub_args': modify_qsub_args(cluster['queue'], 2, 2, 4)
}
# Running off previous baseline experiment
NACCommonAtlas = MakeAtlasNode(
experiment['atlascache'], 'NACCommonAtlas_{0}'.format('subject'),
['S_BRAINSABCSupport']
) ## HACK : replace 'subject' with subject id once this is a loop rather than an iterable.
template.connect([
(myInitAvgWF, buildTemplateIteration1,
[('output_average_image', 'inputspec.fixed_image')]),
(MergeByExtendListElementsNode, buildTemplateIteration1,
[('ListOfImagesDictionaries',
'inputspec.ListOfImagesDictionaries'),
('registrationImageTypes', 'inputspec.registrationImageTypes'),
('interpolationMapping', 'inputspec.interpolationMapping')]),
(buildTemplateIteration1, buildTemplateIteration2,
[('outputspec.template', 'inputspec.fixed_image')]),
(MergeByExtendListElementsNode, buildTemplateIteration2,
[('ListOfImagesDictionaries',
'inputspec.ListOfImagesDictionaries'),
('registrationImageTypes', 'inputspec.registrationImageTypes'),
('interpolationMapping', 'inputspec.interpolationMapping')]),
(subjectNode, CreateAtlasXMLAndCleanedDeformedAveragesNode,
[(('subject', xml_filename), 'outDefinition')]),
(NACCommonAtlas, CreateAtlasXMLAndCleanedDeformedAveragesNode,
[('ExtendedAtlasDefinition_xml_in', 'AtlasTemplate')]),
(buildTemplateIteration2,
CreateAtlasXMLAndCleanedDeformedAveragesNode, [
('outputspec.template', 't1_image'),
('outputspec.passive_deformed_templates', 'deformed_list')
]),
])
## Genearate an average lmks file.
myAverageLmk = pe.Node(interface=GenerateAverageLmkFile(),
name="myAverageLmk")
myAverageLmk.inputs.outputLandmarkFile = "AVG_LMKS.fcsv"
template.connect(baselineRequiredDG, 'BCD_ACPC_Landmarks_fcsv',
myAverageLmk, 'inputLandmarkFiles')
# Create DataSinks
SubjectAtlas_DataSink = pe.Node(nio.DataSink(), name="Subject_DS")
SubjectAtlas_DataSink.overwrite = subj_pipeline_options['ds_overwrite']
SubjectAtlas_DataSink.inputs.base_directory = experiment['resultdir']
template.connect([
(subjectNode, SubjectAtlas_DataSink, [('subject', 'container')]),
(CreateAtlasXMLAndCleanedDeformedAveragesNode,
SubjectAtlas_DataSink, [('outAtlasFullPath', 'Atlas.@definitions')
]),
(CreateAtlasXMLAndCleanedDeformedAveragesNode,
SubjectAtlas_DataSink, [('clean_deformed_list',
'Atlas.@passive_deformed_templates')]),
(subjectNode, SubjectAtlas_DataSink, [(('subject', outputPattern),
'regexp_substitutions')]),
(buildTemplateIteration2, SubjectAtlas_DataSink,
[('outputspec.template', 'Atlas.@template')]),
(myAverageLmk, SubjectAtlas_DataSink,
[('outputLandmarkFile', 'Atlas.@outputLandmarkFile')]),
])
dotfilename = argv['--dotfilename']
if dotfilename is not None:
print("WARNING: Printing workflow, but not running pipeline")
print_workflow(template,
plugin=subj_pipeline_options['plugin_name'],
dotfilename=dotfilename)
else:
run_workflow(template,
plugin=subj_pipeline_options['plugin_name'],
plugin_args=subj_pipeline_options['plugin_args'])
