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 createAndRun(sessions, environment, experiment, pipeline, cluster, useSentinal, dryRun):
from baw_exp import OpenSubjectDatabase
from utilities.misc import add_dict
from workflows.utils import run_workflow
master_config = {}
for configDict in [environment, experiment, pipeline, cluster]:
master_config = add_dict(master_config, configDict)
database = OpenSubjectDatabase(experiment['cachedir'], ['all'], environment['prefix'], experiment['dbfile'])
database.open_connection()
try:
all_sessions = database.getAllSessions()
if not set(sessions) <= set(all_sessions) and 'all' not in sessions:
missing = set(sessions) - set(all_sessions)
assert len(missing) == 0, "Requested sessions are missing from the database: {0}\n\n{1}".format(missing,all_sessions)
elif 'all' in sessions:
sessions = set(all_sessions)
else:
sessions = set(sessions)
print("!=" * 40)
print("Doing sessions {0}".format(sessions))
print("!=" * 40)
for session in sessions:
_dict = {}
subject = database.getSubjFromSession(session)
_dict['session'] = session
_dict['project'] = database.getProjFromSession(session)
_dict['subject'] = subject
_dict['T1s'] = database.getFilenamesByScantype(session, ['T1-15', 'T1-30'])
_dict['T2s'] = database.getFilenamesByScantype(session, ['T2-15', 'T2-30'])
_dict['PDs'] = database.getFilenamesByScantype(session, ['PD-15', 'PD-30'])
_dict['FLs'] = database.getFilenamesByScantype(session, ['FL-15', 'FL-30'])
_dict['OTs'] = database.getFilenamesByScantype(session, ['OTHER-15', 'OTHER-30'])
sentinal_file_basedir = os.path.join(
master_config['resultdir'],
_dict['project'],
_dict['subject'],
_dict['session']
)
sentinal_file_list = list()
sentinal_file_list.append(os.path.join(sentinal_file_basedir))
if 'denoise' in master_config['components']:
# # NO SENTINAL FILE
pass
# # Use t1 average sentinal file if specified.
if 'landmark' in master_config['components']:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"ACPCAlign",
"landmarkInitializer_atlas_to_subject_transform.h5"
))
if 'tissue_classify' in master_config['components']:
for tc_file in ["complete_brainlabels_seg.nii.gz", "t1_average_BRAINSABC.nii.gz"]:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"TissueClassify",
tc_file
))
if 'warp_atlas_to_subject' in master_config['components']:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"WarpedAtlas2Subject",
"rho.nii.gz"
))
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"WarpedAtlas2Subject",
"left_hemisphere_wm.nii.gz"
))
if 'malf_2012_neuro' in master_config['components']:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"TissueClassify",
"MALF_HDAtlas20_2015_fs_standard_label.nii.gz"
))
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"TissueClassify",
"MALF_HDAtlas20_2015_label.nii.gz"
))
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"TissueClassify",
"MALF_HDAtlas20_2015_lobar_label.nii.gz"
))
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"TissueClassify",
"MALF_HDAtlas20_2015_CSFVBInjected_label.nii.gz"
))
if 'malf_2015_wholebrain' in master_config['components']:
pass
if master_config['workflow_phase'] == 'atlas-based-reference':
atlasDirectory = os.path.join(master_config['atlascache'], 'spatialImages', 'rho.nii.gz')
else:
atlasDirectory = os.path.join(master_config['previousresult'], subject, 'Atlas', 'AVG_rho.nii.gz')
if os.path.exists(atlasDirectory):
print("LOOKING FOR DIRECTORY {0}".format(atlasDirectory))
else:
print("MISSING REQUIRED ATLAS INPUT {0}".format(atlasDirectory))
print("SKIPPING: {0} prerequisites missing".format(session))
continue
## Use different sentinal file if segmentation specified.
from workflows.baseline import DetermineIfSegmentationShouldBeDone
do_BRAINSCut_Segmentation = DetermineIfSegmentationShouldBeDone(master_config)
if do_BRAINSCut_Segmentation:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"CleanedDenoisedRFSegmentations",
"allLabels_seg.nii.gz"
))
def allPathsExists(list_of_paths):
is_missing = False
for ff in list_of_paths:
if not os.path.exists(ff):
is_missing = True
print("MISSING: {0}".format(ff))
return not is_missing
if useSentinal and allPathsExists(sentinal_file_list):
print("SKIPPING: {0} exists".format(sentinal_file_list))
else:
print("PROCESSING INCOMPLETE: at least 1 required file does not exists")
if dryRun == False:
workflow = _create_singleSession(_dict, master_config, 'Linear',
'singleSession_{0}_{1}'.format(_dict['subject'], _dict['session']))
print("Starting session {0}".format(session))
# HACK Hard-coded to SGEGraph, but --wfrun is ignored completely
run_workflow(workflow, plugin=master_config['plugin_name'],
plugin_args=master_config['plugin_args'])
else:
print("EXITING WITHOUT WORK DUE TO dryRun flag")
except:
raise
finally:
try:
database.close_connection()
except:
pass
def createAndRun(sessions, environment, experiment, pipeline, cluster,
useSentinal, dryRun):
from baw_exp import OpenSubjectDatabase
from utilities.misc import add_dict
from workflows.utils import run_workflow
master_config = {}
for configDict in [environment, experiment, pipeline, cluster]:
master_config = add_dict(master_config, configDict)
database = OpenSubjectDatabase(experiment['cachedir'], ['all'],
environment['prefix'], experiment['dbfile'])
database.open_connection()
try:
all_sessions = database.getAllSessions()
if not set(sessions) <= set(all_sessions) and 'all' not in sessions:
missing = set(sessions) - set(all_sessions)
assert len(
missing
) == 0, "Requested sessions are missing from the database: {0}\n\n{1}".format(
missing, all_sessions)
elif 'all' in sessions:
sessions = set(all_sessions)
else:
sessions = set(sessions)
print("!=" * 40)
print("Doing sessions {0}".format(sessions))
print("!=" * 40)
for session in sessions:
_dict = {}
subject = database.getSubjFromSession(session)
_dict['session'] = session
_dict['project'] = database.getProjFromSession(session)
_dict['subject'] = subject
_dict['T1s'] = database.getFilenamesByScantype(
session, ['T1-15', 'T1-30'])
_dict['T2s'] = database.getFilenamesByScantype(
session, ['T2-15', 'T2-30'])
_dict['PDs'] = database.getFilenamesByScantype(
session, ['PD-15', 'PD-30'])
_dict['FLs'] = database.getFilenamesByScantype(
session, ['FL-15', 'FL-30'])
_dict['OTs'] = database.getFilenamesByScantype(
session, ['OTHER-15', 'OTHER-30'])
sentinal_file_basedir = os.path.join(master_config['resultdir'],
_dict['project'],
_dict['subject'],
_dict['session'])
sentinal_file_list = list()
sentinal_file_list.append(os.path.join(sentinal_file_basedir))
if 'denoise' in master_config['components']:
# # NO SENTINAL FILE
pass
# # Use t1 average sentinal file if specified.
if 'landmark' in master_config['components']:
sentinal_file_list.append(
os.path.join(
sentinal_file_basedir, "ACPCAlign",
"landmarkInitializer_atlas_to_subject_transform.h5"))
if 'tissue_classify' in master_config['components']:
for tc_file in [
"complete_brainlabels_seg.nii.gz",
"t1_average_BRAINSABC.nii.gz"
]:
sentinal_file_list.append(
os.path.join(sentinal_file_basedir, "TissueClassify",
tc_file))
if 'warp_atlas_to_subject' in master_config['components']:
sentinal_file_list.append(
os.path.join(sentinal_file_basedir, "WarpedAtlas2Subject",
"rho.nii.gz"))
sentinal_file_list.append(
os.path.join(sentinal_file_basedir, "WarpedAtlas2Subject",
"left_hemisphere_wm.nii.gz"))
if 'malf_2012_neuro' in master_config['components']:
sentinal_file_list.append(
os.path.join(
sentinal_file_basedir, "TissueClassify",
"MALF_HDAtlas20_2015_fs_standard_label.nii.gz"))
sentinal_file_list.append(
os.path.join(sentinal_file_basedir, "TissueClassify",
"MALF_HDAtlas20_2015_label.nii.gz"))
sentinal_file_list.append(
os.path.join(sentinal_file_basedir, "TissueClassify",
"MALF_HDAtlas20_2015_lobar_label.nii.gz"))
sentinal_file_list.append(
os.path.join(
sentinal_file_basedir, "TissueClassify",
"MALF_HDAtlas20_2015_CSFVBInjected_label.nii.gz"))
if 'malf_2015_wholebrain' in master_config['components']:
pass
if master_config['workflow_phase'] == 'atlas-based-reference':
atlasDirectory = os.path.join(master_config['atlascache'],
'spatialImages', 'rho.nii.gz')
else:
atlasDirectory = os.path.join(master_config['previousresult'],
subject, 'Atlas',
'AVG_rho.nii.gz')
if os.path.exists(atlasDirectory):
print("LOOKING FOR DIRECTORY {0}".format(atlasDirectory))
else:
print(
"MISSING REQUIRED ATLAS INPUT {0}".format(atlasDirectory))
print("SKIPPING: {0} prerequisites missing".format(session))
continue
## Use different sentinal file if segmentation specified.
from workflows.baseline import DetermineIfSegmentationShouldBeDone
do_BRAINSCut_Segmentation = DetermineIfSegmentationShouldBeDone(
master_config)
if do_BRAINSCut_Segmentation:
sentinal_file_list.append(
os.path.join(sentinal_file_basedir,
"CleanedDenoisedRFSegmentations",
"allLabels_seg.nii.gz"))
def allPathsExists(list_of_paths):
is_missing = False
for ff in list_of_paths:
if not os.path.exists(ff):
is_missing = True
print("MISSING: {0}".format(ff))
return not is_missing
if useSentinal and allPathsExists(sentinal_file_list):
print("SKIPPING: {0} exists".format(sentinal_file_list))
else:
print(
"PROCESSING INCOMPLETE: at least 1 required file does not exists"
)
if dryRun == False:
workflow = _create_singleSession(
_dict, master_config, 'Linear',
'singleSession_{0}_{1}'.format(_dict['subject'],
_dict['session']))
print("Starting session {0}".format(session))
# HACK Hard-coded to SGEGraph, but --wfrun is ignored completely
run_workflow(workflow,
plugin=master_config['plugin_name'],
plugin_args=master_config['plugin_args'])
else:
print("EXITING WITHOUT WORK DUE TO dryRun flag")
except:
raise
finally:
try:
database.close_connection()
except:
pass
def createAndRun(sessions, environment, experiment, pipeline, cluster,
useSentinal, dryRun):
from baw_exp import OpenSubjectDatabase
from utilities.misc import add_dict
from collections import OrderedDict
import sys
from workflows.utils import run_workflow
master_config = {}
for configDict in [environment, experiment, pipeline, cluster]:
master_config = add_dict(master_config, configDict)
database = OpenSubjectDatabase(experiment['cachedir'], ['all'],
environment['prefix'], experiment['dbfile'])
database.open_connection()
try:
all_sessions = database.getAllSessions()
if not set(sessions) <= set(all_sessions) and 'all' not in sessions:
missing = set(sessions) - set(all_sessions)
assert len(
missing
) == 0, "Requested sessions are missing from the database: {0}\n\n{1}".format(
missing, all_sessions)
elif 'all' in sessions:
sessions = set(all_sessions)
else:
sessions = set(sessions)
print("!=" * 40)
print("Doing sessions {0}".format(sessions))
print("!=" * 40)
for session in sessions:
_dict = OrderedDict()
t1_list = database.getFilenamesByScantype(session,
['T1-15', 'T1-30'])
if len(t1_list) == 0:
print(
"ERROR: Skipping session {0} for subject {1} due to missing T1's"
.format(session, subject))
print("REMOVE OR FIX BEFORE CONTINUING")
continue
subject = database.getSubjFromSession(session)
_dict['session'] = session
_dict['project'] = database.getProjFromSession(session)
_dict['subject'] = subject
_dict['T1s'] = t1_list
_dict['T2s'] = database.getFilenamesByScantype(
session, ['T2-15', 'T2-30'])
_dict['BadT2'] = False
if _dict['T2s'] == database.getFilenamesByScantype(
session, ['T2-15']):
print("This T2 is not going to be used for JointFusion")
print("This T2 is not going to be used for JointFusion")
print("This T2 is not going to be used for JointFusion")
print("This T2 is not going to be used for JointFusion")
print(_dict['T2s'])
_dict['BadT2'] = True
_dict['PDs'] = database.getFilenamesByScantype(
session, ['PD-15', 'PD-30'])
_dict['FLs'] = database.getFilenamesByScantype(
session, ['FL-15', 'FL-30'])
_dict['EMSP'] = database.getFilenamesByScantype(session, ['EMSP'])
_dict['OTHERs'] = database.getFilenamesByScantype(
session, ['OTHER-15', 'OTHER-30'])
sentinal_file_basedir = os.path.join(master_config['resultdir'],
_dict['project'],
_dict['subject'],
_dict['session'])
sentinal_file_list = list()
sentinal_file_list.append(os.path.join(sentinal_file_basedir))
if 'denoise' in master_config['components']:
# # NO SENTINAL FILE
pass
# # Use t1 average sentinal file if specified.
if 'landmark' in master_config['components']:
sentinal_file_list.append(
os.path.join(
sentinal_file_basedir, "ACPCAlign",
"landmarkInitializer_atlas_to_subject_transform.h5"))
if 'tissue_classify' in master_config['components']:
for tc_file in [
"complete_brainlabels_seg.nii.gz",
"t1_average_BRAINSABC.nii.gz"
]:
sentinal_file_list.append(
os.path.join(sentinal_file_basedir, "TissueClassify",
tc_file))
if 'warp_atlas_to_subject' in master_config['components']:
warp_atlas_file_list = [
"hncma_atlas.nii.gz", "l_accumben_ProbabilityMap.nii.gz",
"l_caudate_ProbabilityMap.nii.gz",
"l_globus_ProbabilityMap.nii.gz",
"l_hippocampus_ProbabilityMap.nii.gz",
"l_putamen_ProbabilityMap.nii.gz",
"l_thalamus_ProbabilityMap.nii.gz",
"left_hemisphere_wm.nii.gz", "phi.nii.gz",
"r_accumben_ProbabilityMap.nii.gz",
"r_caudate_ProbabilityMap.nii.gz",
"r_globus_ProbabilityMap.nii.gz",
"r_hippocampus_ProbabilityMap.nii.gz",
"r_putamen_ProbabilityMap.nii.gz",
"r_thalamus_ProbabilityMap.nii.gz", "rho.nii.gz",
"right_hemisphere_wm.nii.gz",
"template_WMPM2_labels.nii.gz",
"template_headregion.nii.gz",
"template_leftHemisphere.nii.gz",
"template_nac_labels.nii.gz",
"template_rightHemisphere.nii.gz",
"template_ventricles.nii.gz", "theta.nii.gz"
]
for ff in warp_atlas_file_list:
sentinal_file_list.append(
os.path.join(sentinal_file_basedir,
"WarpedAtlas2Subject", ff))
if 'jointfusion_2015_wholebrain' in master_config['components']:
sentinal_file_list.append(
os.path.join(
sentinal_file_basedir, "TissueClassify",
"JointFusion_HDAtlas20_2015_lobar_label.nii.gz"))
sentinal_file_list.append(
os.path.join(sentinal_file_basedir, "TissueClassify",
"lobeVolumes_JSON.json"))
if master_config['workflow_phase'] == 'atlas-based-reference':
atlasDirectory = os.path.join(master_config['atlascache'],
'spatialImages', 'rho.nii.gz')
sentinal_file_list.append(atlasDirectory)
else:
atlasDirectory = os.path.join(master_config['previousresult'],
subject, 'Atlas',
'AVG_rho.nii.gz')
sentinal_file_list.append(atlasDirectory)
sentinal_file_list.append(
os.path.join(master_config['previousresult'], subject,
'Atlas', 'AVG_template_headregion.nii.gz'))
if os.path.exists(atlasDirectory):
print("LOOKING FOR DIRECTORY {0}".format(atlasDirectory))
else:
print(
"MISSING REQUIRED ATLAS INPUT {0}".format(atlasDirectory))
print("SKIPPING: {0} prerequisites missing".format(session))
continue
## Use different sentinal file if segmentation specified.
from workflows.baseline import DetermineIfSegmentationShouldBeDone
do_BRAINSCut_Segmentation = DetermineIfSegmentationShouldBeDone(
master_config)
if do_BRAINSCut_Segmentation:
sentinal_file_list.append(
os.path.join(sentinal_file_basedir,
"CleanedDenoisedRFSegmentations",
"allLabels_seg.nii.gz"))
def allPathsExists(list_of_paths):
is_missing = False
for ff in list_of_paths:
if not os.path.exists(ff):
is_missing = True
print("MISSING: {0}".format(ff))
return not is_missing
if useSentinal and allPathsExists(sentinal_file_list):
print("SKIPPING: {0} exists".format(sentinal_file_list))
else:
print(
"PROCESSING INCOMPLETE: at least 1 required file does not exists"
)
if dryRun == False:
workflow = _create_singleSession(
_dict, master_config, 'Linear',
'singleSession_{0}_{1}'.format(_dict['subject'],
_dict['session']))
print("Starting session {0}".format(session))
# HACK Hard-coded to SGEGraph, but --wfrun is ignored completely
run_workflow(workflow,
plugin=master_config['plugin_name'],
plugin_args=master_config['plugin_args'])
else:
print("EXITING WITHOUT WORK DUE TO dryRun flag")
except:
raise
finally:
try:
database.close_connection()
except:
pass
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 createAndRun(sessions, environment, experiment, pipeline, cluster, useSentinal, dryRun):
from baw_exp import OpenSubjectDatabase
from utilities.misc import add_dict
from collections import OrderedDict
import sys
from workflows.utils import run_workflow
master_config = {}
for configDict in [environment, experiment, pipeline, cluster]:
master_config = add_dict(master_config, configDict)
database = OpenSubjectDatabase(experiment['cachedir'], ['all'], environment['prefix'], experiment['dbfile'])
database.open_connection()
try:
all_sessions = database.getAllSessions()
if not set(sessions) <= set(all_sessions) and 'all' not in sessions:
missing = set(sessions) - set(all_sessions)
assert len(missing) == 0, "Requested sessions are missing from the database: {0}\n\n{1}".format(missing,
all_sessions)
elif 'all' in sessions:
sessions = set(all_sessions)
else:
sessions = set(sessions)
print("!=" * 40)
print("Doing sessions {0}".format(sessions))
print("!=" * 40)
for session in sessions:
_dict = OrderedDict()
t1_list = database.getFilenamesByScantype(session, ['T1-15', 'T1-30'])
if len(t1_list) == 0:
print("ERROR: Skipping session {0} for subject {1} due to missing T1's".format(session, subject))
print("REMOVE OR FIX BEFORE CONTINUING")
continue
subject = database.getSubjFromSession(session)
_dict['session'] = session
_dict['project'] = database.getProjFromSession(session)
_dict['subject'] = subject
_dict['T1s'] = t1_list
_dict['T2s'] = database.getFilenamesByScantype(session, ['T2-15', 'T2-30'])
_dict['BadT2'] = False
if _dict['T2s'] == database.getFilenamesByScantype(session, ['T2-15']):
print("This T2 is not going to be used for JointFusion")
print("This T2 is not going to be used for JointFusion")
print("This T2 is not going to be used for JointFusion")
print("This T2 is not going to be used for JointFusion")
print(_dict['T2s'])
_dict['BadT2'] = True
_dict['PDs'] = database.getFilenamesByScantype(session, ['PD-15', 'PD-30'])
_dict['FLs'] = database.getFilenamesByScantype(session, ['FL-15', 'FL-30'])
_dict['EMSP'] = database.getFilenamesByScantype(session, ['EMSP'])
_dict['OTHERs'] = database.getFilenamesByScantype(session, ['OTHER-15', 'OTHER-30'])
sentinal_file_basedir = os.path.join(
master_config['resultdir'],
_dict['project'],
_dict['subject'],
_dict['session']
)
sentinal_file_list = list()
sentinal_file_list.append(os.path.join(sentinal_file_basedir))
if 'denoise' in master_config['components']:
# # NO SENTINAL FILE
pass
# # Use t1 average sentinal file if specified.
if 'landmark' in master_config['components']:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"ACPCAlign",
"landmarkInitializer_atlas_to_subject_transform.h5"
))
if 'tissue_classify' in master_config['components']:
for tc_file in ["complete_brainlabels_seg.nii.gz", "t1_average_BRAINSABC.nii.gz"]:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"TissueClassify",
tc_file
))
if 'warp_atlas_to_subject' in master_config['components']:
warp_atlas_file_list = [
"hncma_atlas.nii.gz",
"l_accumben_ProbabilityMap.nii.gz",
"l_caudate_ProbabilityMap.nii.gz",
"l_globus_ProbabilityMap.nii.gz",
"l_hippocampus_ProbabilityMap.nii.gz",
"l_putamen_ProbabilityMap.nii.gz",
"l_thalamus_ProbabilityMap.nii.gz",
"left_hemisphere_wm.nii.gz",
"phi.nii.gz",
"r_accumben_ProbabilityMap.nii.gz",
"r_caudate_ProbabilityMap.nii.gz",
"r_globus_ProbabilityMap.nii.gz",
"r_hippocampus_ProbabilityMap.nii.gz",
"r_putamen_ProbabilityMap.nii.gz",
"r_thalamus_ProbabilityMap.nii.gz",
"rho.nii.gz",
"right_hemisphere_wm.nii.gz",
"template_WMPM2_labels.nii.gz",
"template_headregion.nii.gz",
"template_leftHemisphere.nii.gz",
"template_nac_labels.nii.gz",
"template_rightHemisphere.nii.gz",
"template_ventricles.nii.gz",
"theta.nii.gz"
]
for ff in warp_atlas_file_list:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"WarpedAtlas2Subject",
ff
))
if 'jointfusion_2015_wholebrain' in master_config['components']:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"TissueClassify",
"JointFusion_HDAtlas20_2015_lobar_label.nii.gz"
))
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"TissueClassify",
"lobeVolumes_JSON.json"
))
if master_config['workflow_phase'] == 'atlas-based-reference':
atlasDirectory = os.path.join(master_config['atlascache'], 'spatialImages', 'rho.nii.gz')
sentinal_file_list.append(atlasDirectory)
else:
atlasDirectory = os.path.join(master_config['previousresult'], subject, 'Atlas', 'AVG_rho.nii.gz')
sentinal_file_list.append(atlasDirectory)
sentinal_file_list.append(
os.path.join(master_config['previousresult'], subject, 'Atlas', 'AVG_template_headregion.nii.gz'))
if os.path.exists(atlasDirectory):
print("LOOKING FOR DIRECTORY {0}".format(atlasDirectory))
else:
print("MISSING REQUIRED ATLAS INPUT {0}".format(atlasDirectory))
print("SKIPPING: {0} prerequisites missing".format(session))
continue
## Use different sentinal file if segmentation specified.
from workflows.baseline import DetermineIfSegmentationShouldBeDone
do_BRAINSCut_Segmentation = DetermineIfSegmentationShouldBeDone(master_config)
if do_BRAINSCut_Segmentation:
sentinal_file_list.append(os.path.join(
sentinal_file_basedir,
"CleanedDenoisedRFSegmentations",
"allLabels_seg.nii.gz"
))
def allPathsExists(list_of_paths):
is_missing = False
for ff in list_of_paths:
if not os.path.exists(ff):
is_missing = True
print("MISSING: {0}".format(ff))
return not is_missing
if useSentinal and allPathsExists(sentinal_file_list):
print("SKIPPING: {0} exists".format(sentinal_file_list))
else:
print("PROCESSING INCOMPLETE: at least 1 required file does not exists")
if dryRun == False:
workflow = _create_singleSession(_dict, master_config, 'Linear',
'singleSession_{0}_{1}'.format(_dict['subject'], _dict['session']))
print("Starting session {0}".format(session))
# HACK Hard-coded to SGEGraph, but --wfrun is ignored completely
run_workflow(workflow, plugin=master_config['plugin_name'],
plugin_args=master_config['plugin_args'])
else:
print("EXITING WITHOUT WORK DUE TO dryRun flag")
except:
raise
finally:
try:
database.close_connection()
except:
pass
