def tbss_pipeline(self, **name_maps): # @UnusedVariable
# inputs=[FilesetSpec('fa', nifti_gz_format)],
# outputs=[FilesetSpec('tbss_mean_fa', nifti_gz_format),
# FilesetSpec('tbss_proj_fa', nifti_gz_format,
# frequency='per_study'),
# FilesetSpec('tbss_skeleton', nifti_gz_format,
# frequency='per_study'),
# FilesetSpec('tbss_skeleton_mask', nifti_gz_format,
# frequency='per_study')],
pipeline = self.new_pipeline(
name='tbss',
references=[tbss_cite, fsl_cite],
name_maps=name_maps)
# Create TBSS workflow
tbss = create_tbss_all(name='tbss')
# Connect inputs
pipeline.connect_input('fa', tbss, 'inputnode.fa_list')
# Connect outputs
pipeline.connect_output('tbss_mean_fa', tbss,
'outputnode.meanfa_file')
pipeline.connect_output('tbss_proj_fa', tbss,
'outputnode.projectedfa_file')
pipeline.connect_output('tbss_skeleton', tbss,
'outputnode.skeleton_file')
pipeline.connect_output('tbss_skeleton_mask', tbss,
'outputnode.skeleton_mask')
# Check inputs/output are connected
return pipeline
def tbss_pipeline(self, **kwargs): # @UnusedVariable
pipeline = self.create_pipeline(
name='tbss',
inputs=[DatasetSpec('fa', nifti_gz_format)],
outputs=[
DatasetSpec('tbss_mean_fa', nifti_gz_format),
DatasetSpec('tbss_proj_fa',
nifti_gz_format,
frequency='per_project'),
DatasetSpec('tbss_skeleton',
nifti_gz_format,
frequency='per_project'),
DatasetSpec('tbss_skeleton_mask',
nifti_gz_format,
frequency='per_project')
],
version=1,
citations=[tbss_cite, fsl_cite],
**kwargs)
# Create TBSS workflow
tbss = create_tbss_all(name='tbss')
# Connect inputs
pipeline.connect_input('fa', tbss, 'inputnode.fa_list')
# Connect outputs
pipeline.connect_output('tbss_mean_fa', tbss, 'outputnode.meanfa_file')
pipeline.connect_output('tbss_proj_fa', tbss,
'outputnode.projectedfa_file')
pipeline.connect_output('tbss_skeleton', tbss,
'outputnode.skeleton_file')
pipeline.connect_output('tbss_skeleton_mask', tbss,
'outputnode.skeleton_mask')
# Check inputs/output are connected
return pipeline
def create_tbss():
"""
TBSS analysis
"""
#tbss_source = get_tbss_source()
inputnode = pe.Node(interface=util.IdentityInterface(fields=['fa_list','md_list']),
name='inputspec')
tbss_all = create_tbss_all()
tbss_all.inputs.inputnode.skeleton_thresh = skeleton_thresh
tbssproc = pe.Workflow(name="tbssproc")
tbssproc.base_dir = os.path.join(os.path.abspath(workingdir), 'l2')
tbssproc.connect(inputnode, 'fa_list', tbss_all, 'inputnode.fa_list')
tbss_MD = create_tbss_non_FA(name='tbss_MD')
tbss_MD.inputs.inputnode.skeleton_thresh = tbss_all.inputs.inputnode.skeleton_thresh
tbssproc.connect([(tbss_all, tbss_MD, [('tbss2.outputnode.field_list',
'inputnode.field_list'),
('tbss3.outputnode.groupmask',
'inputnode.groupmask'),
('tbss3.outputnode.meanfa_file',
'inputnode.meanfa_file'),
('tbss4.outputnode.distance_map',
'inputnode.distance_map')]),
(inputnode, tbss_MD, [('md_list',
'inputnode.file_list')]),
])
return tbssproc
# now the TBSS analysis
tbss_source = pe.Node(
interface=nio.DataGrabber(outfiles=['fa_list', 'md_list']),
name='tbss_source')
tbss_source.inputs.base_directory = datasink.inputs.base_directory
tbss_source.inputs.template = '%s/%s_%s.nii'
tbss_source.inputs.template_args = dict(fa_list=[['FA', subject_list, 'FA']],
md_list=[['MD', subject_list, 'MD']])
tbss_source.inputs.sort_filelist = True
"""
TBSS analysis
"""
tbss_all = create_tbss_all()
tbss_all.inputs.inputnode.skeleton_thresh = 0.2
tbssproc = pe.Workflow(name="tbssproc")
tbssproc.base_dir = tbss_dir # os.path.join(os.path.abspath(resultsDir), ,'tbss', 'l2')
tbssproc.connect(tbss_source, 'fa_list', tbss_all, 'inputnode.fa_list')
tbss_MD = create_tbss_non_FA(name='tbss_MD')
tbss_MD.inputs.inputnode.skeleton_thresh = tbss_all.inputs.inputnode.skeleton_thresh
tbssproc.connect([
(tbss_all, tbss_MD,
[('tbss2.outputnode.field_list', 'inputnode.field_list'),
('tbss3.outputnode.groupmask', 'inputnode.groupmask'),
('tbss3.outputnode.meanfa_file', 'inputnode.meanfa_file'),
('tbss4.outputnode.distance_map', 'inputnode.distance_map')]),
"""
tbss_source = pe.Node(
interface=nio.DataGrabber(outfiles=['fa_list', 'md_list']),
name='tbss_source')
tbss_source.inputs.base_directory = datasink.inputs.base_directory
tbss_source.inputs.template = '%s/%s_%s.nii'
tbss_source.inputs.template_args = dict(
fa_list=[['FA', subjects_list, 'FA']],
md_list=[['MD', subjects_list, 'MD']])
tbss_source.inputs.sort_filelist = True
"""
TBSS analysis
"""
tbss_all = create_tbss_all()
tbss_all.inputs.inputnode.skeleton_thresh = 0.2
tbssproc = pe.Workflow(name="tbssproc")
tbssproc.base_dir = os.path.join(os.path.abspath(workingdir), 'l2')
tbssproc.connect(tbss_source, 'fa_list', tbss_all, 'inputnode.fa_list')
tbss_MD = create_tbss_non_FA(name='tbss_MD')
tbss_MD.inputs.inputnode.skeleton_thresh = tbss_all.inputs.inputnode.skeleton_thresh
tbssproc.connect([
(tbss_all, tbss_MD,
[('tbss2.outputnode.field_list', 'inputnode.field_list'),
('tbss3.outputnode.groupmask', 'inputnode.groupmask'),
('tbss3.outputnode.meanfa_file',
'inputnode.meanfa_file'), ('tbss4.outputnode.distance_map',
def _tbss_test_helper(estimate_skeleton):
fsl_course_dir = os.path.abspath(os.environ['FSL_COURSE_DATA'])
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
test_dir = tempfile.mkdtemp(prefix="nipype_test_tbss_")
tbss_orig_dir = os.path.join(test_dir, "tbss_all_original")
os.mkdir(tbss_orig_dir)
old_dir = os.getcwd()
os.chdir(tbss_orig_dir)
subjects = ['1260', '1549']
FA_list = [
os.path.join(fsl_course_dir, 'tbss', subject_id + '.nii.gz')
for subject_id in subjects
]
for f in FA_list:
shutil.copy(f, os.getcwd())
call(['tbss_1_preproc'] +
[subject_id + '.nii.gz' for subject_id in subjects],
env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
tbss1_orig_dir = os.path.join(test_dir, "tbss1_original")
shutil.copytree(tbss_orig_dir, tbss1_orig_dir)
call(['tbss_2_reg', '-T'],
env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
tbss2_orig_dir = os.path.join(test_dir, "tbss2_original")
shutil.copytree(tbss_orig_dir, tbss2_orig_dir)
if estimate_skeleton:
call(['tbss_3_postreg', '-S'],
env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
else:
call(['tbss_3_postreg', '-T'],
env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
tbss3_orig_dir = os.path.join(test_dir, "tbss3_original")
shutil.copytree(tbss_orig_dir, tbss3_orig_dir)
call(['tbss_4_prestats', '0.2'],
env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
tbss4_orig_dir = os.path.join(test_dir, "tbss4_original")
shutil.copytree(tbss_orig_dir, tbss4_orig_dir)
pipeline = pe.Workflow(name="test_tbss")
pipeline.base_dir = os.path.join(test_dir, "tbss_nipype")
tbss = create_tbss_all(estimate_skeleton=estimate_skeleton)
tbss.inputs.inputnode.fa_list = FA_list
tbss.inputs.inputnode.skeleton_thresh = 0.2
tbss1_original_datasource = pe.Node(nio.DataGrabber(
outfields=['fa_list', 'mask_list'], sort_filelist=False),
name='tbss1_original_datasource')
tbss1_original_datasource.inputs.base_directory = tbss1_orig_dir
tbss1_original_datasource.inputs.template = 'FA/%s_FA%s.nii.gz'
tbss1_original_datasource.inputs.template_args = dict(
fa_list=[[subjects, '']], mask_list=[[subjects, '_mask']])
tbss1_test_fa = pe.MapNode(util.AssertEqual(),
name="tbss1_fa_test",
iterfield=['volume1', 'volume2'])
tbss1_test_mask = pe.MapNode(util.AssertEqual(),
name="tbss1_mask_test",
iterfield=['volume1', 'volume2'])
pipeline.connect(tbss, 'tbss1.outputnode.fa_list', tbss1_test_fa,
'volume1')
pipeline.connect(tbss, 'tbss1.outputnode.mask_list', tbss1_test_mask,
'volume1')
pipeline.connect(tbss1_original_datasource, 'fa_list', tbss1_test_fa,
'volume2')
pipeline.connect(tbss1_original_datasource, 'mask_list', tbss1_test_mask,
'volume2')
tbss2_original_datasource = pe.Node(nio.DataGrabber(
outfields=['field_list'], sort_filelist=False),
name='tbss2_original_datasource')
tbss2_original_datasource.inputs.base_directory = tbss2_orig_dir
tbss2_original_datasource.inputs.template = 'FA/%s_FA%s.nii.gz'
tbss2_original_datasource.inputs.template_args = dict(
field_list=[[subjects, '_to_target_warp']])
tbss2_test_field = pe.MapNode(util.AssertEqual(),
name="tbss2_test_field",
iterfield=['volume1', 'volume2'])
pipeline.connect(tbss, 'tbss2.outputnode.field_list', tbss2_test_field,
'volume1')
pipeline.connect(tbss2_original_datasource, 'field_list', tbss2_test_field,
'volume2')
tbss3_original_datasource = pe.Node(nio.DataGrabber(outfields=[
'groupmask', 'skeleton_file', 'meanfa_file', 'mergefa_file'
],
sort_filelist=False),
name='tbss3_original_datasource')
tbss3_original_datasource.inputs.base_directory = tbss3_orig_dir
tbss3_original_datasource.inputs.template = 'stats/%s.nii.gz'
tbss3_original_datasource.inputs.template_args = dict(
groupmask=[['mean_FA_mask']],
skeleton_file=[['mean_FA_skeleton']],
meanfa_file=[['mean_FA']],
mergefa_file=[['all_FA']])
tbss3_test_groupmask = pe.Node(util.AssertEqual(),
name="tbss3_test_groupmask")
tbss3_test_skeleton_file = pe.Node(util.AssertEqual(),
name="tbss3_test_skeleton_file")
tbss3_test_meanfa_file = pe.Node(util.AssertEqual(),
name="tbss3_test_meanfa_file")
tbss3_test_mergefa_file = pe.Node(util.AssertEqual(),
name="tbss3_test_mergefa_file")
pipeline.connect(tbss, 'tbss3.outputnode.groupmask', tbss3_test_groupmask,
'volume1')
pipeline.connect(tbss3_original_datasource, 'groupmask',
tbss3_test_groupmask, 'volume2')
pipeline.connect(tbss, 'tbss3.outputnode.skeleton_file',
tbss3_test_skeleton_file, 'volume1')
pipeline.connect(tbss3_original_datasource, 'skeleton_file',
tbss3_test_skeleton_file, 'volume2')
pipeline.connect(tbss, 'tbss3.outputnode.meanfa_file',
tbss3_test_meanfa_file, 'volume1')
pipeline.connect(tbss3_original_datasource, 'meanfa_file',
tbss3_test_meanfa_file, 'volume2')
pipeline.connect(tbss, 'tbss3.outputnode.mergefa_file',
tbss3_test_mergefa_file, 'volume1')
pipeline.connect(tbss3_original_datasource, 'mergefa_file',
tbss3_test_mergefa_file, 'volume2')
tbss4_original_datasource = pe.Node(nio.DataGrabber(
outfields=['all_FA_skeletonised', 'mean_FA_skeleton_mask'],
sort_filelist=False),
name='tbss4_original_datasource')
tbss4_original_datasource.inputs.base_directory = tbss4_orig_dir
tbss4_original_datasource.inputs.template = 'stats/%s.nii.gz'
tbss4_original_datasource.inputs.template_args = dict(
all_FA_skeletonised=[['all_FA_skeletonised']],
mean_FA_skeleton_mask=[['mean_FA_skeleton_mask']])
tbss4_test_all_FA_skeletonised = pe.Node(
util.AssertEqual(), name="tbss4_test_all_FA_skeletonised")
tbss4_test_mean_FA_skeleton_mask = pe.Node(
util.AssertEqual(), name="tbss4_test_mean_FA_skeleton_mask")
pipeline.connect(tbss, 'tbss4.outputnode.projectedfa_file',
tbss4_test_all_FA_skeletonised, 'volume1')
pipeline.connect(tbss4_original_datasource, 'all_FA_skeletonised',
tbss4_test_all_FA_skeletonised, 'volume2')
pipeline.connect(tbss, 'tbss4.outputnode.skeleton_mask',
tbss4_test_mean_FA_skeleton_mask, 'volume1')
pipeline.connect(tbss4_original_datasource, 'mean_FA_skeleton_mask',
tbss4_test_mean_FA_skeleton_mask, 'volume2')
pipeline.run(plugin='Linear')
os.chdir(old_dir)
shutil.rmtree(test_dir)
def _tbss_test_helper(estimate_skeleton):
fsl_course_dir = os.path.abspath(os.environ['FSL_COURSE_DATA'])
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
test_dir = tempfile.mkdtemp(prefix="nipype_test_tbss_")
tbss_orig_dir = os.path.join(test_dir, "tbss_all_original")
os.mkdir(tbss_orig_dir)
old_dir = os.getcwd()
os.chdir(tbss_orig_dir)
subjects = ['1260', '1549']
FA_list = [os.path.join(fsl_course_dir, 'tbss', subject_id + '.nii.gz') for subject_id in subjects]
for f in FA_list:
shutil.copy(f, os.getcwd())
call(['tbss_1_preproc'] + [subject_id + '.nii.gz' for subject_id in subjects], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
tbss1_orig_dir = os.path.join(test_dir, "tbss1_original")
shutil.copytree(tbss_orig_dir, tbss1_orig_dir)
call(['tbss_2_reg', '-T'], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
tbss2_orig_dir = os.path.join(test_dir, "tbss2_original")
shutil.copytree(tbss_orig_dir, tbss2_orig_dir)
if estimate_skeleton:
call(['tbss_3_postreg', '-S'], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
else:
call(['tbss_3_postreg', '-T'], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
tbss3_orig_dir = os.path.join(test_dir, "tbss3_original")
shutil.copytree(tbss_orig_dir, tbss3_orig_dir)
call(['tbss_4_prestats', '0.2'], env=os.environ.update({'FSLOUTPUTTYPE': 'NIFTI_GZ'}))
tbss4_orig_dir = os.path.join(test_dir, "tbss4_original")
shutil.copytree(tbss_orig_dir, tbss4_orig_dir)
pipeline = pe.Workflow(name="test_tbss")
pipeline.base_dir = os.path.join(test_dir, "tbss_nipype")
tbss = create_tbss_all(estimate_skeleton=estimate_skeleton)
tbss.inputs.inputnode.fa_list = FA_list
tbss.inputs.inputnode.skeleton_thresh = 0.2
tbss1_original_datasource = pe.Node(nio.DataGrabber(outfields=['fa_list', 'mask_list'], sort_filelist=False), name='tbss1_original_datasource')
tbss1_original_datasource.inputs.base_directory = tbss1_orig_dir
tbss1_original_datasource.inputs.template = 'FA/%s_FA%s.nii.gz'
tbss1_original_datasource.inputs.template_args = dict(fa_list=[[subjects, '']],
mask_list=[[subjects, '_mask']])
tbss1_test_fa = pe.MapNode(util.AssertEqual(), name="tbss1_fa_test", iterfield=['volume1', 'volume2'])
tbss1_test_mask = pe.MapNode(util.AssertEqual(), name="tbss1_mask_test", iterfield=['volume1', 'volume2'])
pipeline.connect(tbss, 'tbss1.outputnode.fa_list', tbss1_test_fa, 'volume1')
pipeline.connect(tbss, 'tbss1.outputnode.mask_list', tbss1_test_mask, 'volume1')
pipeline.connect(tbss1_original_datasource, 'fa_list', tbss1_test_fa, 'volume2')
pipeline.connect(tbss1_original_datasource, 'mask_list', tbss1_test_mask, 'volume2')
tbss2_original_datasource = pe.Node(nio.DataGrabber(outfields=['field_list'], sort_filelist=False), name='tbss2_original_datasource')
tbss2_original_datasource.inputs.base_directory = tbss2_orig_dir
tbss2_original_datasource.inputs.template = 'FA/%s_FA%s.nii.gz'
tbss2_original_datasource.inputs.template_args = dict(field_list=[[subjects, '_to_target_warp']])
tbss2_test_field = pe.MapNode(util.AssertEqual(), name="tbss2_test_field", iterfield=['volume1', 'volume2'])
pipeline.connect(tbss, 'tbss2.outputnode.field_list', tbss2_test_field, 'volume1')
pipeline.connect(tbss2_original_datasource, 'field_list', tbss2_test_field, 'volume2')
tbss3_original_datasource = pe.Node(nio.DataGrabber(outfields=['groupmask',
'skeleton_file',
'meanfa_file',
'mergefa_file'], sort_filelist=False), name='tbss3_original_datasource')
tbss3_original_datasource.inputs.base_directory = tbss3_orig_dir
tbss3_original_datasource.inputs.template = 'stats/%s.nii.gz'
tbss3_original_datasource.inputs.template_args = dict(groupmask=[['mean_FA_mask']],
skeleton_file=[['mean_FA_skeleton']],
meanfa_file=[['mean_FA']],
mergefa_file=[['all_FA']])
tbss3_test_groupmask = pe.Node(util.AssertEqual(), name="tbss3_test_groupmask")
tbss3_test_skeleton_file = pe.Node(util.AssertEqual(), name="tbss3_test_skeleton_file")
tbss3_test_meanfa_file = pe.Node(util.AssertEqual(), name="tbss3_test_meanfa_file")
tbss3_test_mergefa_file = pe.Node(util.AssertEqual(), name="tbss3_test_mergefa_file")
pipeline.connect(tbss, 'tbss3.outputnode.groupmask', tbss3_test_groupmask, 'volume1')
pipeline.connect(tbss3_original_datasource, 'groupmask', tbss3_test_groupmask, 'volume2')
pipeline.connect(tbss, 'tbss3.outputnode.skeleton_file', tbss3_test_skeleton_file, 'volume1')
pipeline.connect(tbss3_original_datasource, 'skeleton_file', tbss3_test_skeleton_file, 'volume2')
pipeline.connect(tbss, 'tbss3.outputnode.meanfa_file', tbss3_test_meanfa_file, 'volume1')
pipeline.connect(tbss3_original_datasource, 'meanfa_file', tbss3_test_meanfa_file, 'volume2')
pipeline.connect(tbss, 'tbss3.outputnode.mergefa_file', tbss3_test_mergefa_file, 'volume1')
pipeline.connect(tbss3_original_datasource, 'mergefa_file', tbss3_test_mergefa_file, 'volume2')
tbss4_original_datasource = pe.Node(nio.DataGrabber(outfields=['all_FA_skeletonised',
'mean_FA_skeleton_mask'], sort_filelist=False), name='tbss4_original_datasource')
tbss4_original_datasource.inputs.base_directory = tbss4_orig_dir
tbss4_original_datasource.inputs.template = 'stats/%s.nii.gz'
tbss4_original_datasource.inputs.template_args = dict(all_FA_skeletonised=[['all_FA_skeletonised']],
mean_FA_skeleton_mask=[['mean_FA_skeleton_mask']])
tbss4_test_all_FA_skeletonised = pe.Node(util.AssertEqual(), name="tbss4_test_all_FA_skeletonised")
tbss4_test_mean_FA_skeleton_mask = pe.Node(util.AssertEqual(), name="tbss4_test_mean_FA_skeleton_mask")
pipeline.connect(tbss, 'tbss4.outputnode.projectedfa_file', tbss4_test_all_FA_skeletonised, 'volume1')
pipeline.connect(tbss4_original_datasource, 'all_FA_skeletonised', tbss4_test_all_FA_skeletonised, 'volume2')
pipeline.connect(tbss, 'tbss4.outputnode.skeleton_mask', tbss4_test_mean_FA_skeleton_mask, 'volume1')
pipeline.connect(tbss4_original_datasource, 'mean_FA_skeleton_mask', tbss4_test_mean_FA_skeleton_mask, 'volume2')
pipeline.run(plugin='Linear')
os.chdir(old_dir)
shutil.rmtree(test_dir)
