def make_neuromet1_workflow(self):
# Infosource: Iterate through subject names
infosource = Node(interface=IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = ('subject_id', self.subject_list)
# unidensource, return for every subject uni and den
unidensource = Node(interface=IdentityInterface(fields=['uniden']),
name="unidensource")
unidensource.iterables = ('uniden', ['UNI', 'DEN'])
info = dict(t1=[['subject_id', 'subject_id', 'uniden']])
datasource = Node(interface=DataGrabber(
infields=['subject_id', 'uniden'], outfields=['t1']),
name='datasource')
datasource.inputs.base_directory = self.w_dir
datasource.inputs.template = '{prefix}%s/{prefix}%s.%s_mp2rage_orig.nii.gz'.format(
prefix=self.subject_prefix)
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = False
sink = self.make_sink()
segment = self.make_segment()
mask = self.make_mask()
neuromet = Workflow(name=self.subject_prefix, base_dir=self.temp_dir)
neuromet.connect(infosource, 'subject_id', datasource, 'subject_id')
neuromet.connect(unidensource, 'uniden', datasource, 'uniden')
neuromet.connect(datasource, 't1', segment, 'ro.in_file')
# neuromet.connect()
neuromet.connect(segment, 'spm_tissues_split.gm', mask,
'sum_tissues1.in_file')
neuromet.connect(segment, 'spm_tissues_split.wm', mask,
'sum_tissues1.operand_files')
neuromet.connect(segment, 'spm_tissues_split.csf', mask,
'sum_tissues2.operand_files')
neuromet.connect(segment, 'spm_tissues_split.gm', sink, '@gm')
neuromet.connect(segment, 'spm_tissues_split.wm', sink, '@wm')
neuromet.connect(segment, 'spm_tissues_split.csf', sink, '@csf')
neuromet.connect(segment, 'seg.bias_corrected_images', sink,
'@biascorr')
# neuromet.connect(comb_imgs, 'uni_brain_den_surr_add.out_file', sink, '@img')
neuromet.connect(mask, 'gen_mask.out_file', sink, '@mask')
neuromet.connect(segment, 'ro.out_file', sink, '@ro')
return neuromet
def test_input_wrapper():
wf, node1, node2, node3 = make_simple_workflow()
s_list = ['s1', 's2']
s_node = gu.make_subject_source(s_list)
g_node = Node(DataGrabber(in_fields=["foo"], out_fields=["bar"]),
name="g_node")
iw = gu.InputWrapper(wf, s_node, g_node, node1)
yield nt.assert_equal, iw.wf, wf
yield nt.assert_equal, iw.subj_node, s_node
yield nt.assert_equal, iw.grab_node, g_node
yield nt.assert_equal, iw.in_node, node1
iw.connect_inputs()
g = wf._graph
yield nt.assert_true, s_node in g.nodes()
yield nt.assert_true, g_node in g.nodes()
yield nt.assert_true, (s_node, g_node) in g.edges()
def group_multregress_openfmri(dataset_dir,
model_id=None,
task_id=None,
l1output_dir=None,
out_dir=None,
no_reversal=False,
plugin=None,
plugin_args=None,
flamemodel='flame1',
nonparametric=False,
use_spm=False):
meta_workflow = Workflow(name='mult_regress')
meta_workflow.base_dir = work_dir
for task in task_id:
task_name = get_taskname(dataset_dir, task)
cope_ids = l1_contrasts_num(model_id, task_name, dataset_dir)
regressors_needed, contrasts, groups, subj_list = get_sub_vars(
dataset_dir, task_name, model_id)
for idx, contrast in enumerate(contrasts):
wk = Workflow(name='model_%03d_task_%03d_contrast_%s' %
(model_id, task, contrast[0][0]))
info = Node(util.IdentityInterface(
fields=['model_id', 'task_id', 'dataset_dir', 'subj_list']),
name='infosource')
info.inputs.model_id = model_id
info.inputs.task_id = task
info.inputs.dataset_dir = dataset_dir
dg = Node(DataGrabber(infields=['model_id', 'task_id', 'cope_id'],
outfields=['copes', 'varcopes']),
name='grabber')
dg.inputs.template = os.path.join(
l1output_dir,
'model%03d/task%03d/%s/%scopes/%smni/%scope%02d.nii%s')
if use_spm:
dg.inputs.template_args['copes'] = [[
'model_id', 'task_id', subj_list, '', 'spm/', '',
'cope_id', ''
]]
dg.inputs.template_args['varcopes'] = [[
'model_id', 'task_id', subj_list, 'var', 'spm/', 'var',
'cope_id', '.gz'
]]
else:
dg.inputs.template_args['copes'] = [[
'model_id', 'task_id', subj_list, '', '', '', 'cope_id',
'.gz'
]]
dg.inputs.template_args['varcopes'] = [[
'model_id', 'task_id', subj_list, 'var', '', 'var',
'cope_id', '.gz'
]]
dg.iterables = ('cope_id', cope_ids)
dg.inputs.sort_filelist = False
wk.connect(info, 'model_id', dg, 'model_id')
wk.connect(info, 'task_id', dg, 'task_id')
model = Node(MultipleRegressDesign(), name='l2model')
model.inputs.groups = groups
model.inputs.contrasts = contrasts[idx]
model.inputs.regressors = regressors_needed[idx]
mergecopes = Node(Merge(dimension='t'), name='merge_copes')
wk.connect(dg, 'copes', mergecopes, 'in_files')
if flamemodel != 'ols':
mergevarcopes = Node(Merge(dimension='t'),
name='merge_varcopes')
wk.connect(dg, 'varcopes', mergevarcopes, 'in_files')
mask_file = fsl.Info.standard_image(
'MNI152_T1_2mm_brain_mask.nii.gz')
flame = Node(FLAMEO(), name='flameo')
flame.inputs.mask_file = mask_file
flame.inputs.run_mode = flamemodel
#flame.inputs.infer_outliers = True
wk.connect(model, 'design_mat', flame, 'design_file')
wk.connect(model, 'design_con', flame, 't_con_file')
wk.connect(mergecopes, 'merged_file', flame, 'cope_file')
if flamemodel != 'ols':
wk.connect(mergevarcopes, 'merged_file', flame,
'var_cope_file')
wk.connect(model, 'design_grp', flame, 'cov_split_file')
if nonparametric:
palm = Node(Function(input_names=[
'cope_file', 'design_file', 'contrast_file', 'group_file',
'mask_file', 'cluster_threshold'
],
output_names=['palm_outputs'],
function=run_palm),
name='palm')
palm.inputs.cluster_threshold = 3.09
palm.inputs.mask_file = mask_file
palm.plugin_args = {
'sbatch_args': '-p om_all_nodes -N1 -c2 --mem=10G',
'overwrite': True
}
wk.connect(model, 'design_mat', palm, 'design_file')
wk.connect(model, 'design_con', palm, 'contrast_file')
wk.connect(mergecopes, 'merged_file', palm, 'cope_file')
wk.connect(model, 'design_grp', palm, 'group_file')
smoothest = Node(SmoothEstimate(), name='smooth_estimate')
wk.connect(flame, 'zstats', smoothest, 'zstat_file')
smoothest.inputs.mask_file = mask_file
cluster = Node(Cluster(), name='cluster')
wk.connect(smoothest, 'dlh', cluster, 'dlh')
wk.connect(smoothest, 'volume', cluster, 'volume')
cluster.inputs.connectivity = 26
cluster.inputs.threshold = 2.3
cluster.inputs.pthreshold = 0.05
cluster.inputs.out_threshold_file = True
cluster.inputs.out_index_file = True
cluster.inputs.out_localmax_txt_file = True
wk.connect(flame, 'zstats', cluster, 'in_file')
ztopval = Node(ImageMaths(op_string='-ztop', suffix='_pval'),
name='z2pval')
wk.connect(flame, 'zstats', ztopval, 'in_file')
sinker = Node(DataSink(), name='sinker')
sinker.inputs.base_directory = os.path.join(
out_dir, 'task%03d' % task, contrast[0][0])
sinker.inputs.substitutions = [('_cope_id', 'contrast'),
('_maths_', '_reversed_')]
wk.connect(flame, 'zstats', sinker, 'stats')
wk.connect(cluster, 'threshold_file', sinker, 'stats.@thr')
wk.connect(cluster, 'index_file', sinker, 'stats.@index')
wk.connect(cluster, 'localmax_txt_file', sinker, 'stats.@localmax')
if nonparametric:
wk.connect(palm, 'palm_outputs', sinker, 'stats.palm')
if not no_reversal:
zstats_reverse = Node(BinaryMaths(), name='zstats_reverse')
zstats_reverse.inputs.operation = 'mul'
zstats_reverse.inputs.operand_value = -1
wk.connect(flame, 'zstats', zstats_reverse, 'in_file')
cluster2 = cluster.clone(name='cluster2')
wk.connect(smoothest, 'dlh', cluster2, 'dlh')
wk.connect(smoothest, 'volume', cluster2, 'volume')
wk.connect(zstats_reverse, 'out_file', cluster2, 'in_file')
ztopval2 = ztopval.clone(name='ztopval2')
wk.connect(zstats_reverse, 'out_file', ztopval2, 'in_file')
wk.connect(zstats_reverse, 'out_file', sinker, 'stats.@neg')
wk.connect(cluster2, 'threshold_file', sinker,
'stats.@neg_thr')
wk.connect(cluster2, 'index_file', sinker, 'stats.@neg_index')
wk.connect(cluster2, 'localmax_txt_file', sinker,
'stats.@neg_localmax')
meta_workflow.add_nodes([wk])
return meta_workflow
derivatives_names = temps_dict.keys()
derivatives_names.remove('mean')
if 'bias_mask' in derivatives_names:
derivatives_names.remove('bias_mask')
temp_args = args['template_arguments']
# This formats the above list into a dictionary where we can much more easily associate keywords to their lists
template_arguments = {
temp_args[::2][i]: temp_args[1::2][i]
for i in range(len(temp_args) / 2)
}
data_grabber_node = Node(DataGrabber(base_directory=args['directory'],
sort_filelist=True,
raise_on_empty=False,
infields=template_arguments.keys(),
outfields=[tmp
for tmp in temps_dict]),
name='data_grabber')
data_grabber_node.inputs.template = '*'
data_grabber_node.inputs.raise_on_empty = True
data_grabber_node.inputs.drop_blank_outputs = True
data_grabber_node.inputs.field_template = temps_dict
data_grabber_node.inputs.template_args = temp_args_dict
data_grabber_node.iterables = [
(key,
[thing.rstrip('\n') for thing in open(template_arguments[key], 'r')])
for key in template_arguments.keys()
]
template = abspath(args['template'])
"""Modified script to run antsBrainExtraction on meningioma T1-contrast data.
"""
from glob import glob
import os.path as op
from nipype import DataGrabber, DataSink, IdentityInterface, Node, Workflow
from nipype.interfaces import ants
data_dir = '/om/user/jakubk/meningioma/data'
template_dir = '/om/user/jakubk/meningioma/ants_templates/OASIS-30_Atropos_template'
subjects = [op.split(path)[-1][:10] for path in glob('data/*.nii.gz')]
datasource = Node(DataGrabber(outfields=['subject_id', 't1c']),
name='datasource')
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '*'
datasource.inputs.field_template = {'t1c': '%s*.nii.gz'}
datasource.inputs.template_args = {'t1c': [['subject_id']]}
datasource.inputs.sort_filelist = True
datasource.inputs.subject_id = subjects
seg = Node(ants.BrainExtraction(), name='seg', synchronize=True)
seg.inputs.dimension = 3
seg.inputs.keep_temporary_files = 1
seg.inputs.brain_template = op.join(template_dir, 'T_template0.nii.gz')
seg.inputs.brain_probability_mask = op.join(
template_dir, 'T_template0_BrainCerebellumProbabilityMask.nii.gz')
# Node to save output files. This does not work. Why?
sinker = Node(DataSink(), name='sinker')
# Add container.
def make_neuromet_fs_workflow(self):
# Infosource: Iterate through subject names
infosource = Node(interface=IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = ('subject_id', self.subject_list)
mask_source = Node(interface=GetMaskValue(
csv_file=
'/media/drive_s/AG/AG-Floeel-Imaging/02-User/NEUROMET2/Structural_Analysis_fs7/List_UNI_DEN_Mask.xlsx'
),
name='get_mask')
# Datasource: Build subjects' filenames from IDs
info = dict(mask=[[
'subject_id', '', 'subject_id', 'mask', '_brain_bin.nii.gz'
]],
uni_bias_corr=[[
'subject_id', 'm', 'subject_id', '',
'UNI_mp2rage_orig_reoriented.nii'
]],
den_ro=[[
'subject_id', '', 'subject_id', '',
'DEN_mp2rage_orig_reoriented.nii.gz'
]])
datasource = Node(interface=DataGrabber(
infields=['subject_id', 'mask'],
outfields=['mask', 'uni_bias_corr', 'den_ro']),
name='datasource')
datasource.inputs.base_directory = self.w_dir
datasource.inputs.template = '{pref}%s/%s{pref}%s.%s%s'.format(
pref=self.subject_prefix)
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = False
sink = self.make_sink()
comb_imgs = self.make_comb_imgs()
freesurfer = self.make_freesurfer()
neuromet_fs = Workflow(
name='{pref}_fs'.format(pref=self.subject_prefix),
base_dir=self.temp_dir)
neuromet_fs.connect(infosource, 'subject_id', datasource, 'subject_id')
neuromet_fs.connect(infosource, 'subject_id', mask_source,
'subject_id')
neuromet_fs.connect(mask_source, 'mask_value', datasource, 'mask')
neuromet_fs.connect(datasource, 'uni_bias_corr', comb_imgs,
'mask_uni_bias.in_file')
neuromet_fs.connect(datasource, 'mask', comb_imgs,
'mask_uni_bias.mask_file')
neuromet_fs.connect(datasource, 'den_ro', comb_imgs,
'uni_brain_den_surr_mas.in_file')
neuromet_fs.connect(comb_imgs, 'uni_brain_den_surr_add.out_file',
freesurfer, 'fs_recon1.T1_files')
neuromet_fs.connect(datasource, 'mask', freesurfer,
'fs_mriconv.in_file')
out_dir_source = Node(interface=IdentityInterface(
fields=['out_dir'], mandatory_inputs=True),
name='out_dir_source')
out_dir_source.inputs.out_dir = self.w_dir
copy_freesurfer_dir = Node(Function(['in_dir', 'sub_id', 'out_dir'],
['out_dir'],
self.copy_freesurfer_dir),
name='copy_freesurfer_dir')
qdec = Node(interface=QDec(), name='qdec')
adj_vol = Node(interface=AdjustVolume(
diag_csv=
'/media/drive_s/AG/AG-Floeel-Imaging/02-User/NEUROMET2/Structural_Analysis_fs7/Diagnosen.csv'
),
name='adj_vol')
neuromet_fs.connect(comb_imgs, 'uni_brain_den_surr_add.out_file', sink,
'@img')
#neuromet_fs.connect(infosource, 'subject_id', copy_freesurfer_dir, 'sub_id')
#neuromet_fs.connect(freesurfer, 'segment_hp.subjects_dir', copy_freesurfer_dir, 'in_dir')
neuromet_fs.connect(freesurfer, 'segment_hp.subjects_dir', sink,
'@recon_all')
#neuromet_fs.connect(out_dir_source, 'out_dir', copy_freesurfer_dir, 'out_dir')
neuromet_fs.connect(freesurfer, 'segment_hp.subject_id', qdec,
'devnull')
neuromet_fs.connect(datasource, 'base_directory', qdec, 'basedir')
neuromet_fs.connect(qdec, 'stats_directory', adj_vol,
'stats_directory')
neuromet_fs.connect(qdec, 'stats_directory', sink, '@stat_dir')
neuromet_fs.connect(adj_vol, 'adjusted_stats', sink, '@adj_stats')
return neuromet_fs
''' define subjects '''
#sub_list = [os.path.basename(x) for x in sorted(glob(project_dir+'/data/13*'))]
sub_list = ['131217','131722','132118'] # test on one subject
''' set up nodes '''
# set up iterables
infosource = Node(IdentityInterface(fields=['subject_id']),#, 'acq_dir', 'fwhm']),
name='infosource')
infosource.iterables = [('subject_id', sub_list)]#,
#('acq_dir', acq_dir_list),
#('fwhm', fwhm_list)]
# Create DataGrabber node
dg = Node(DataGrabber(infields=['subject_id'],#, 'acq_dir'],
outfields=['anat', 'atlas', 'func']),
name='datagrabber')
# Location of the dataset folder
dg.inputs.base_directory = os.path.join(project_dir, 'data')
# Necessary default parameters
dg.inputs.template = '*'
dg.inputs.sort_filelist = True
dg.inputs.template_args = {'anat': [['subject_id']],
'atlas' : [['subject_id']],
'func': [['subject_id']]}
dg.inputs.field_template = {'anat': '%s/MNINonLinear/T1w_restore.1.60.nii.gz',
'atlas': '%s/MNINonLinear/ROIs/Atlas_ROIs.1.60.nii.gz',
'func': '%s/MNINonLinear/Results/rfMRI_REST*_7T_*/'\
"""Script to run antsBrainExtraction on meningioma T1-contrast data.
"""
import os.path as op
from nipype import Node, Workflow, DataGrabber, DataSink, MapNode
from nipype.interfaces import ants
# Node to grab data.
grab = Node(DataGrabber(outfields=['t1c']), name='grabber')
grab.inputs.base_directory = op.abspath('data')
grab.inputs.template = '*.nii.gz'
grab.inputs.field_template = {'t1c': '*.nii.gz'}
grab.inputs.sort_filelist = True
# Node to run ants.BrainExtraction.
# Segments the anatomical image and should extract brain.
template_dir = op.abspath('ants_templates/OASIS-30_Atropos_template')
seg = MapNode(ants.BrainExtraction(),
iterfield=['anatomical_image'],
name='seg')
seg.inputs.dimension = 3
seg.inputs.keep_temporary_files = 1
seg.inputs.brain_template = op.join(template_dir, 'T_template0.nii.gz')
seg.inputs.brain_probability_mask = op.join(
template_dir, 'T_template0_BrainCerebellumProbabilityMask.nii.gz')
# Node to save output files. This does not work. Why?
sinker = Node(DataSink(), name='sinker')
sinker.inputs.base_directory = op.abspath('antsBrainExtraction_output')
# Workflow.
wf = Workflow(name='antsBrainExtraction', base_dir='/om/scratch/Wed/jakubk')
from os import path as op
from nipype import DataGrabber, DataSink, Node, MapNode, Workflow
from nipype.interfaces import fsl
# Node to grab data.
grab = Node(DataGrabber(outfields=['t1', 'brain']), name='grabber')
grab.inputs.base_directory = '/om/user/jakubk/meningioma/'
grab.inputs.template = '*.nii.gz'
# Change filenames later to specify T1.
grab.inputs.field_template = {
't1': 'data/*.nii.gz',
'brain': 'ants_seg_output/brain/*.nii.gz'
}
grab.inputs.sort_filelist = True
fast = MapNode(fsl.FAST(), iterfield=['in_files'], name='fast')
fast.inputs.img_type = 1
fast.inputs.probability_maps = True
fast.iterables = ('number_classes', [3, 4, 5])
sinker = Node(DataSink(), name='sinker')
sinker.inputs.base_directory = op.abspath('fast_output')
# How can we iterate over original NIFTI files and extracted brains together?
# Run original NIFTI files.
wf = Workflow(name='fast_brain', base_dir='/om/scratch/Wed/jakubk/')
wf.connect(grab, 'brain', fast, 'in_files')
wf.connect(fast, 'probability_maps', sinker, 'prob')
wf.connect(fast, 'restored_image', sinker, 'restored')
wf.connect(fast, 'tissue_class_files', sinker, 'tissue_files')
def group_onesample_openfmri(dataset_dir,
model_id=None,
task_id=None,
l1output_dir=None,
out_dir=None,
no_reversal=False):
wk = Workflow(name='one_sample')
wk.base_dir = os.path.abspath(work_dir)
info = Node(
util.IdentityInterface(fields=['model_id', 'task_id', 'dataset_dir']),
name='infosource')
info.inputs.model_id = model_id
info.inputs.task_id = task_id
info.inputs.dataset_dir = dataset_dir
num_copes = contrasts_num(model_id, task_id, dataset_dir)
dg = Node(DataGrabber(infields=['model_id', 'task_id', 'cope_id'],
outfields=['copes', 'varcopes']),
name='grabber')
dg.inputs.template = os.path.join(
l1output_dir, 'model%03d/task%03d/*/%scopes/mni/%scope%02d.nii.gz')
dg.inputs.template_args['copes'] = [[
'model_id', 'task_id', '', '', 'cope_id'
]]
dg.inputs.template_args['varcopes'] = [[
'model_id', 'task_id', 'var', 'var', 'cope_id'
]]
dg.iterables = ('cope_id', num_copes)
dg.inputs.sort_filelist = True
wk.connect(info, 'model_id', dg, 'model_id')
wk.connect(info, 'task_id', dg, 'task_id')
model = Node(L2Model(), name='l2model')
wk.connect(dg, ('copes', get_len), model, 'num_copes')
mergecopes = Node(Merge(dimension='t'), name='merge_copes')
wk.connect(dg, 'copes', mergecopes, 'in_files')
mergevarcopes = Node(Merge(dimension='t'), name='merge_varcopes')
wk.connect(dg, 'varcopes', mergevarcopes, 'in_files')
mask_file = fsl.Info.standard_image('MNI152_T1_2mm_brain_mask.nii.gz')
flame = Node(FLAMEO(), name='flameo')
flame.inputs.mask_file = mask_file
flame.inputs.run_mode = 'flame1'
wk.connect(model, 'design_mat', flame, 'design_file')
wk.connect(model, 'design_con', flame, 't_con_file')
wk.connect(mergecopes, 'merged_file', flame, 'cope_file')
wk.connect(mergevarcopes, 'merged_file', flame, 'var_cope_file')
wk.connect(model, 'design_grp', flame, 'cov_split_file')
smoothest = Node(SmoothEstimate(), name='smooth_estimate')
wk.connect(flame, 'zstats', smoothest, 'zstat_file')
smoothest.inputs.mask_file = mask_file
cluster = Node(Cluster(), name='cluster')
wk.connect(smoothest, 'dlh', cluster, 'dlh')
wk.connect(smoothest, 'volume', cluster, 'volume')
cluster.inputs.connectivity = 26
cluster.inputs.threshold = 2.3
cluster.inputs.pthreshold = 0.05
cluster.inputs.out_threshold_file = True
cluster.inputs.out_index_file = True
cluster.inputs.out_localmax_txt_file = True
wk.connect(flame, 'zstats', cluster, 'in_file')
ztopval = Node(ImageMaths(op_string='-ztop', suffix='_pval'),
name='z2pval')
wk.connect(flame, 'zstats', ztopval, 'in_file')
sinker = Node(DataSink(), name='sinker')
sinker.inputs.base_directory = os.path.abspath(out_dir)
sinker.inputs.substitutions = [('_cope_id', 'contrast'),
('_maths__', '_reversed_')]
wk.connect(flame, 'zstats', sinker, 'stats')
wk.connect(cluster, 'threshold_file', sinker, 'stats.@thr')
wk.connect(cluster, 'index_file', sinker, 'stats.@index')
wk.connect(cluster, 'localmax_txt_file', sinker, 'stats.@localmax')
if no_reversal == False:
zstats_reverse = Node(BinaryMaths(), name='zstats_reverse')
zstats_reverse.inputs.operation = 'mul'
zstats_reverse.inputs.operand_value = -1
wk.connect(flame, 'zstats', zstats_reverse, 'in_file')
cluster2 = cluster.clone(name='cluster2')
wk.connect(smoothest, 'dlh', cluster2, 'dlh')
wk.connect(smoothest, 'volume', cluster2, 'volume')
wk.connect(zstats_reverse, 'out_file', cluster2, 'in_file')
ztopval2 = ztopval.clone(name='ztopval2')
wk.connect(zstats_reverse, 'out_file', ztopval2, 'in_file')
wk.connect(zstats_reverse, 'out_file', sinker, 'stats.@neg')
wk.connect(cluster2, 'threshold_file', sinker, 'stats.@neg_thr')
wk.connect(cluster2, 'index_file', sinker, 'stats.@neg_index')
wk.connect(cluster2, 'localmax_txt_file', sinker,
'stats.@neg_localmax')
return wk
def make_neuromet2_workflow(self):
infosource = self.make_infosource()
# Datasource: Build subjects' filenames from IDs
info = dict(
mask=[['subject_id', '', 'subject_id', 'SPMbrain_bin.nii.gz']],
uni_bias_corr=[[
'subject_id', 'm', 'subject_id',
'UNI_mp2rage_orig_reoriented.nii'
]],
den_ro=[[
'subject_id', '', 'subject_id',
'DEN_mp2rage_orig_reoriented.nii.gz'
]])
datasource = Node(interface=DataGrabber(
infields=['subject_id'],
outfields=['mask', 'uni_bias_corr', 'den_ro']),
name='datasource')
datasource.inputs.base_directory = self.w_dir
datasource.inputs.template = 'NeuroMet%s/%sNeuroMet%s.%s'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = False
sink = self.make_sink()
comb_imgs = self.make_comb_imgs()
freesurfer = self.make_freesurfer()
neuromet2 = Workflow(name='Neuromet2', base_dir=self.temp_dir)
neuromet2.connect(infosource, 'subject_id', datasource, 'subject_id')
neuromet2.connect(datasource, 'uni_bias_corr', comb_imgs,
'mask_uni_bias.in_file')
neuromet2.connect(datasource, 'mask', comb_imgs,
'mask_uni_bias.mask_file')
neuromet2.connect(datasource, 'den_ro', comb_imgs,
'uni_brain_den_surr_mas.in_file')
neuromet2.connect(comb_imgs, 'uni_brain_den_surr_add.out_file',
freesurfer, 'fs_recon1.T1_files')
neuromet2.connect(datasource, 'mask', freesurfer, 'fs_mriconv.in_file')
out_dir_source = Node(interface=IdentityInterface(
fields=['out_dir'], mandatory_inputs=True),
name='out_dir_source')
out_dir_source.inputs.out_dir = self.w_dir
copy_freesurfer_dir = Node(Function(['in_dir', 'sub_id', 'out_dir'],
['out_dir'],
self.copy_freesurfer_dir),
name='copy_freesurfer_dir')
neuromet2.connect(comb_imgs, 'uni_brain_den_surr_add.out_file', sink,
'@img')
neuromet2.connect(infosource, 'subject_id', copy_freesurfer_dir,
'sub_id')
neuromet2.connect(freesurfer, 'fs_recon3.subjects_dir',
copy_freesurfer_dir, 'in_dir')
neuromet2.connect(out_dir_source, 'out_dir', copy_freesurfer_dir,
'out_dir')
return neuromet2
def make_neuromet1_workflow(self):
infosource = self.make_infosource()
info = dict(uni=[['subject_id', 'subject_id', 'UNI']],
den=[['subject_id', 'subject_id', 'DEN']])
datasource = Node(interface=DataGrabber(infields=['subject_id'],
outfields=['uni', 'den']),
name='datasource')
datasource.inputs.base_directory = self.w_dir
datasource.inputs.template = 'NeuroMet%s/NeuroMet%s.%s_mp2rage_orig.nii.gz'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = False
sink = self.make_sink()
segment_uni = self.make_segment_uni()
segment_den = self.make_segment_den()
mask_uni = self.make_mask_uni()
mask_den = self.make_mask_den()
neuromet = Workflow(name='NeuroMet', base_dir=self.temp_dir)
neuromet.connect(infosource, 'subject_id', datasource, 'subject_id')
neuromet.connect(datasource, 'uni', segment_uni, 'ro.in_file')
neuromet.connect(datasource, 'den', segment_den, 'ro.in_file')
# neuromet.connect()
neuromet.connect(segment_uni, 'spm_tissues_split_uni.gm', mask_uni,
'sum_tissues1.in_file')
neuromet.connect(segment_uni, 'spm_tissues_split_uni.wm', mask_uni,
'sum_tissues1.operand_files')
neuromet.connect(segment_uni, 'spm_tissues_split_uni.csf', mask_uni,
'sum_tissues2.operand_files')
neuromet.connect(segment_uni, 'spm_tissues_split_uni.gm', sink,
'@gm_uni')
neuromet.connect(segment_uni, 'spm_tissues_split_uni.wm', sink,
'@wm_uni')
neuromet.connect(segment_uni, 'spm_tissues_split_uni.csf', sink,
'@csf_uni')
neuromet.connect(segment_uni, 'seg_uni.bias_corrected_images', sink,
'@biascorr_uni')
neuromet.connect(segment_den, 'spm_tissues_split_den.gm', mask_den,
'sum_tissues1.in_file')
neuromet.connect(segment_den, 'spm_tissues_split_den.wm', mask_den,
'sum_tissues1.operand_files')
neuromet.connect(segment_den, 'spm_tissues_split_den.csf', mask_den,
'sum_tissues2.operand_files')
neuromet.connect(segment_den, 'spm_tissues_split_den.gm', sink,
'@gm_den')
neuromet.connect(segment_den, 'spm_tissues_split_den.wm', sink,
'@wm_den')
neuromet.connect(segment_den, 'spm_tissues_split_den.csf', sink,
'@csf_den')
neuromet.connect(segment_den, 'seg_den.bias_corrected_images', sink,
'@biascorr_den')
# neuromet.connect(comb_imgs, 'uni_brain_den_surr_add.out_file', sink, '@img')
neuromet.connect(mask_uni, 'gen_mask.out_file', sink, '@mask_uni')
neuromet.connect(mask_den, 'gen_mask.out_file', sink, '@mask_den')
neuromet.connect(segment_den, 'ro.out_file', sink, '@ro_den')
# neuromet.connect(segment_uni, 'ro.out_file', sink, '@ro_uni')
return neuromet
# Two subjects minimum
subject_list = ['control01', 'control02']
runs = [1,2,3,4,5]
# A IdentityInterface node helps to iterate over list of subject names and run: grabs anatomical images
info_subj = Node(IdentityInterface(fields=['subject_id']), name="info_subj")
info_subj.iterables = [('subject_id', subject_list)]
# Same as above: grabs functional images
info_run = Node(IdentityInterface(fields=['run_id']), name="info_run")
info_run.iterables = [('run_id', runs)]
# DataGrabber 1 - grab_anat
data_dir = '/Users/vgonzenb/Python/nipy/fmri-rep/data/ds000171/'
grab_anat = Node(DataGrabber(infields=['subject_id'],outfields=['anat']), name = 'grab_anat', nested = True)
grab_anat.inputs.base_directory = data_dir
grab_anat.inputs.template = '*'
grab_anat.inputs.sort_filelist = True
grab_anat.inputs.template_args = {'anat': [['subject_id']]}
grab_anat.inputs.field_template = {'anat': 'sub-%s/anat/s*T1w.nii.gz'}
# DataGrabber 2 - func
grab_func = Node(DataGrabber(infields=['subject_id', 'runs'],outfields=['func']), name = 'grab_func', nested = True)
grab_func.inputs.base_directory = data_dir
grab_func.inputs.template = '*'
grab_func.inputs.sort_filelist = True
grab_func.inputs.template_args = {'func': [['subject_id', 'runs']]}
grab_func.inputs.field_template = {'func': 'sub-%s/func/sub-*run-%d_bold.nii.gz'}
def make_neuromet1_workflow(self):
# Infosource: Iterate through subject names
infosource = Node(interface=IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = ('subject_id', self.subject_list)
#unidensource, return for every subject uni and den
unidensource = Node(interface=IdentityInterface(
fields=['uniden_prefix', 'uniden_suffix']),
name="unidensource")
unidensource.iterables = [
('uniden_prefix', ['', 'derivatives/Siemens/']),
('uniden_suffix', ['T1w', 'desc-UNIDEN_MP2RAGE'])
]
unidensource.synchronize = True
split_sub_str = Node(Function(['subject_str'],
['subject_id', 'session_id'],
self.split_subject_ses),
name='split_sub_str')
info = dict(T1w=[[
'uniden_prefix', 'subject_id', 'session_id', 'anat', 'subject_id',
'session_id', 'uniden_suffix'
]])
datasource = Node(interface=DataGrabber(infields=[
'subject_id', 'session_id', 'uniden_prefix', 'uniden_suffix'
],
outfields=['T1w']),
name='datasource')
datasource.inputs.base_directory = self.bids_root
datasource.inputs.template = '%ssub-NeuroMET%s/ses-0%s/%s/sub-NeuroMET%s_ses-0%s_%s.nii.gz'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = False
sink = self.make_sink()
segment = self.make_segment()
mask = self.make_mask()
neuromet = Workflow(name='NeuroMET', base_dir=self.temp_dir)
neuromet.connect(infosource, 'subject_id', split_sub_str,
'subject_str')
neuromet.connect(split_sub_str, 'subject_id', datasource, 'subject_id')
neuromet.connect(split_sub_str, 'session_id', datasource, 'session_id')
neuromet.connect(unidensource, 'uniden_prefix', datasource,
'uniden_prefix')
neuromet.connect(unidensource, 'uniden_suffix', datasource,
'uniden_suffix')
neuromet.connect(datasource, 'T1w', segment, 'ro.in_file')
# neuromet.connect()
neuromet.connect(segment, 'spm_tissues_split.gm', mask,
'sum_tissues1.in_file')
neuromet.connect(segment, 'spm_tissues_split.wm', mask,
'sum_tissues1.operand_files')
neuromet.connect(segment, 'spm_tissues_split.csf', mask,
'sum_tissues2.operand_files')
neuromet.connect(segment, 'spm_tissues_split.gm', sink, '@gm')
neuromet.connect(segment, 'spm_tissues_split.wm', sink, '@wm')
neuromet.connect(segment, 'spm_tissues_split.csf', sink, '@csf')
neuromet.connect(segment, 'seg.bias_corrected_images', sink,
'@biascorr')
# neuromet.connect(comb_imgs, 'uni_brain_den_surr_add.out_file', sink, '@img')
neuromet.connect(mask, 'gen_mask.out_file', sink, '@mask')
neuromet.connect(segment, 'ro.out_file', sink, '@ro')
return neuromet
work_dir = os.path.join('/om2/scratch/ksitek/hcp/', analysis)
''' define subjects '''
sub_list = [os.path.basename(x) for x in sorted(glob(project_dir+'/data/13*'))]
#sub_list = ['100610'] # test on one subject
''' set up nodes '''
# set up iterables
infosource = Node(IdentityInterface(fields=['subject_id']),
name='infosource')
infosource.iterables = [('subject_id', sub_list)]
# Create DataGrabber node
dg = Node(DataGrabber(infields=[],
outfields=['dwi_file',
'bval_file',
'bvec_file',
'atlas',
'mask']),
name='datagrabber')
# Location of the dataset folder
dg.inputs.base_directory = project_dir
# Necessary default parameters
dg.inputs.template = '*'
dg.inputs.sort_filelist = True
dg.inputs.template_args = {'dwi_file': [['subject_id']],
'bval_file' : [['subject_id']],
'bvec_file': [['subject_id']],
'atlas': [['subject_id','subject_id']],
def make_neuromet_fs_workflow(self):
# Infosource: Iterate through subject names
infosource = Node(interface=IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = ('subject_id', self.subject_list)
mask_source = Node(interface=GetMaskValue(csv_file=self.mask_file),
name='get_mask')
split_sub_str = Node(Function(['subject_str'],
['subject_id', 'session_id'],
self.split_subject_ses),
name='split_sub_str')
# Datasource: Build subjects' filenames from IDs
info = dict(mask=[[
'subject_id', 'session_id', 'anat', 'subject_id', 'session_id',
'mask', 'ro_brain_bin.nii.gz'
]],
uni_bias_corr=[[
'subject_id', 'session_id', 'anat', 'subject_id',
'session_id', 'UNI', 'ro_bfcorr.nii'
]],
den_ro=[[
'subject_id', 'session_id', 'anat', 'subject_id',
'session_id', 'UNIDEN', 'ro_bfcorr.nii'
]])
datasource = Node(interface=DataGrabber(
infields=['subject_id', 'session_id', 'mask'],
outfields=['mask', 'uni_bias_corr', 'den_ro']),
name='datasource')
datasource.inputs.base_directory = self.derivatives_dir
datasource.inputs.template = 'sub-NeuroMET%s/ses-0%s/%s/sub-NeuroMET%s_ses-0%s_desc-%s_%s'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = False
sink = self.make_sink()
comb_imgs = self.make_comb_imgs()
freesurfer = self.make_freesurfer()
neuromet_fs = Workflow(name='NeuroMET', base_dir=self.temp_dir)
neuromet_fs.connect(infosource, 'subject_id', split_sub_str,
'subject_str')
neuromet_fs.connect(split_sub_str, 'subject_id', datasource,
'subject_id')
neuromet_fs.connect(split_sub_str, 'session_id', datasource,
'session_id')
neuromet_fs.connect(infosource, 'subject_id', mask_source,
'subject_id')
neuromet_fs.connect(mask_source, 'mask_value', datasource, 'mask')
neuromet_fs.connect(datasource, 'uni_bias_corr', comb_imgs,
'mask_uni_bias.in_file')
neuromet_fs.connect(datasource, 'mask', comb_imgs,
'mask_uni_bias.mask_file')
neuromet_fs.connect(datasource, 'den_ro', comb_imgs,
'uni_brain_den_surr_mas.in_file')
neuromet_fs.connect(comb_imgs, 'uni_brain_den_surr_add.out_file',
freesurfer, 'fs_recon1.T1_files')
neuromet_fs.connect(datasource, 'mask', freesurfer,
'fs_mriconv.in_file')
out_dir_source = Node(interface=IdentityInterface(
fields=['out_dir'], mandatory_inputs=True),
name='out_dir_source')
out_dir_source.inputs.out_dir = self.bids_root
make_list_str = Node(interface=Merge(2), name='make_list_of_paths')
merge_strs = Node(interface=OsPathJoin(), name='merge_sub_id_dir')
neuromet_fs.connect(comb_imgs, 'uni_brain_den_surr_add.out_file', sink,
'@img')
#neuromet_fs.connect(infosource, 'subject_id', copy_freesurfer_dir, 'sub_id')
#neuromet_fs.connect(freesurfer, 'segment_hp.subjects_dir', copy_freesurfer_dir, 'in_dir')
neuromet_fs.connect(freesurfer, 'segment_hp.subjects_dir',
make_list_str, 'in1')
neuromet_fs.connect(freesurfer, 'segment_hp.subject_id', make_list_str,
'in2')
neuromet_fs.connect(make_list_str, 'out', merge_strs, 'str_list')
neuromet_fs.connect(merge_strs, 'out_path', sink, '@recon_all')
#neuromet_fs.connect(out_dir_source, 'out_dir', copy_freesurfer_dir, 'out_dir')
#ToDo:
# 04.12.2020 QDec + Adjust volumes. It hangs if qdec is in a workflow, works a single interface
#neuromet_fs.connect(freesurfer, 'segment_hp.subject_id', qdec, 'devnull')
#neuromet_fs.connect(datasource, 'base_directory', qdec, 'basedir')
#neuromet_fs.connect(qdec, 'stats_directory', adj_vol, 'stats_directory')
#neuromet_fs.connect(qdec, 'stats_directory', sink, '@stat_dir')
#neuromet_fs.connect(adj_vol, 'adjusted_stats', sink, '@adj_stats')
return neuromet_fs
infosource_sub = Node(IdentityInterface(fields=["subject_id", "contrasts"]),
name="Infosource_Sub")
infosource_sub.inputs.contrasts = 1
x_dummy, dirs, y_dummy = os.walk(input_info_dir).next()
subject_list = dirs
#Split into parallel tasks per ID.
infosource_sub.iterables = [('subject_id', subject_list)]
##Part 2: DataGrab:
#Get WF1 MRI and Tissue files using the ID provided by Infosource
datagrab_sub = Node(DataGrabber(infields=["subject_id"],
outfields=[
"t1", "flair", "swi", "c1", "c2", "c3",
"c4", "c5", "bct1", "sim"
]),
name="DataGrabber_Sub")
datagrab_sub.inputs.base_directory = input_grab_dir
datagrab_sub.inputs.template = "*"
datagrab_sub.inputs.sort_filelist = False
#Should vary depending on the file(s) available
datagrab_sub.inputs.template_args = {
't1': [['subject_id']],
'flair': [['subject_id']],
'swi': [['subject_id']],
'c1': [['subject_id']],