def run_freesurfer(subject_id, T1_images, subjects_dir, T2_image=None):
"""Run freesurfer, convert to nidm and extract stats
"""
from nipype import freesurfer as fs
from nipype import Node
from fs_dir_to_graph import to_graph
from query_convert_fs_stats import get_collections, process_collection
recon = Node(fs.ReconAll(), name='recon')
recon.inputs.T1_files = T1_images
recon.inputs.subject_id = subject_id
recon.inputs.subjects_dir = subjects_dir
recon.inputs.openmp = 4
if T2_image:
recon.inputs.T2_file = T2_image
recon.base_dir = os.path.abspath(os.path.join('working', subject_id))
results = recon.run()
provgraph = results.provenance
newgraph = to_graph(
os.path.join(results.outputs.subjects_dir, results.outputs.subject_id))
provgraph.add_bundle(newgraph)
provgraph.rdf().serialize('test1.ttl', format='turtle')
results = get_collections(provgraph.rdf())
collections = []
for row in results:
collections.append(str(row[0]))
if len(collections) > 1:
raise ValueError('More than one freesurfer directory collection found')
provgraph, termsrdf = process_collection(provgraph, collections.pop())
rdfgraph = provgraph.rdf() + termsrdf
return provgraph, rdfgraph
def nipype_convert(item_dicoms, prefix, with_prov, bids, tmpdir):
""" """
import nipype
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
dicom_dir = op.dirname(item_dicoms[0]) if item_dicoms else None
convertnode = Node(Dcm2niix(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_dir = dicom_dir
convertnode.inputs.out_filename = op.basename(op.dirname(prefix))
if nipype.__version__.split('.')[0] == '0':
# deprecated since 1.0, might be needed(?) before
convertnode.inputs.terminal_output = 'allatonce'
else:
convertnode.terminal_output = 'allatonce'
convertnode.inputs.bids_format = bids
eg = convertnode.run()
# prov information
prov_file = prefix + '_prov.ttl' if with_prov else None
if prov_file:
safe_copyfile(
op.join(convertnode.base_dir, convertnode.name, 'provenance.ttl'),
prov_file)
return eg, prov_file
def test_neuropythy_atlas():
n = Node(function_neuropythy_atlas, 'atlas')
n.base_dir = str(ANALYSIS_PATH)
n.inputs.subject_id = 'sub-delft'
n.inputs.subjects_dir = str(FREESURFER_PATH)
n.run()
def embed_metadata_from_dicoms(bids_options, item_dicoms, outname,
outname_bids, prov_file, scaninfo, tempdirs,
with_prov):
"""
Enhance sidecar information file with more information from DICOMs
Parameters
----------
bids_options
item_dicoms
outname
outname_bids
prov_file
scaninfo
tempdirs
with_prov
Returns
-------
"""
from nipype import Node, Function
tmpdir = tempdirs(prefix='embedmeta')
# We need to assure that paths are absolute if they are relative
item_dicoms = list(map(op.abspath, item_dicoms))
embedfunc = Node(Function(input_names=[
'dcmfiles',
'niftifile',
'infofile',
'bids_info',
],
function=embed_dicom_and_nifti_metadata),
name='embedder')
embedfunc.inputs.dcmfiles = item_dicoms
embedfunc.inputs.niftifile = op.abspath(outname)
embedfunc.inputs.infofile = op.abspath(scaninfo)
embedfunc.inputs.bids_info = load_json(
op.abspath(outname_bids)) if (bids_options is not None) else None
embedfunc.base_dir = tmpdir
cwd = os.getcwd()
lgr.debug("Embedding into %s based on dicoms[0]=%s for nifti %s", scaninfo,
item_dicoms[0], outname)
try:
if op.lexists(scaninfo):
# TODO: handle annexed file case
if not op.islink(scaninfo):
set_readonly(scaninfo, False)
res = embedfunc.run()
set_readonly(scaninfo)
if with_prov:
g = res.provenance.rdf()
g.parse(prov_file, format='turtle')
g.serialize(prov_file, format='turtle')
set_readonly(prov_file)
except Exception as exc:
lgr.error("Embedding failed: %s", str(exc))
os.chdir(cwd)
def nipype_convert(item_dicoms, prefix, with_prov, bids, tmpdir):
""" """
import nipype
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
dicom_dir = op.dirname(item_dicoms[0]) if item_dicoms else None
convertnode = Node(Dcm2niix(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item_dicoms
convertnode.inputs.out_filename = op.basename(op.dirname(prefix))
if nipype.__version__.split('.')[0] == '0':
# deprecated since 1.0, might be needed(?) before
convertnode.inputs.terminal_output = 'allatonce'
else:
convertnode.terminal_output = 'allatonce'
convertnode.inputs.bids_format = bids
eg = convertnode.run()
# prov information
prov_file = prefix + '_prov.ttl' if with_prov else None
if prov_file:
safe_copyfile(op.join(convertnode.base_dir,
convertnode.name,
'provenance.ttl'),
prov_file)
return eg, prov_file
def run_freesurfer(subject_id, T1_images, subjects_dir, T2_image=None):
"""Run freesurfer, convert to nidm and extract stats
"""
from nipype import freesurfer as fs
from nipype import Node
from fs_dir_to_graph import to_graph
from query_convert_fs_stats import get_collections, process_collection
recon = Node(fs.ReconAll(), name='recon')
recon.inputs.T1_files = T1_images
recon.inputs.subject_id = subject_id
recon.inputs.subjects_dir = subjects_dir
recon.inputs.openmp = 4
if T2_image:
recon.inputs.T2_file = T2_image
recon.base_dir = os.path.abspath(os.path.join('working', subject_id))
results = recon.run()
provgraph = results.provenance
newgraph = to_graph(os.path.join(results.outputs.subjects_dir,
results.outputs.subject_id))
provgraph.add_bundle(newgraph)
provgraph.rdf().serialize('test1.ttl', format='turtle')
results = get_collections(provgraph.rdf())
collections = []
for row in results:
collections.append(str(row[0]))
if len(collections) > 1:
raise ValueError('More than one freesurfer directory collection found')
provgraph, termsrdf = process_collection(provgraph, collections.pop())
rdfgraph = provgraph.rdf() + termsrdf
return provgraph, rdfgraph
def nipype_convert(item_dicoms,
prefix,
with_prov,
bids_options,
tmpdir,
dcmconfig=None):
"""
Converts DICOMs grouped from heuristic using Nipype's Dcm2niix interface.
Parameters
----------
item_dicoms : List
DICOM files to convert
prefix : String
Heuristic output path
with_prov : Bool
Store provenance information
bids_options : List or None
If not None then output BIDS sidecar JSONs
List may contain bids specific options
tmpdir : Directory
Conversion working directory
dcmconfig : File (optional)
JSON file used for additional Dcm2niix configuration
"""
import nipype
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
fromfile = dcmconfig if dcmconfig else None
if fromfile:
lgr.info("Using custom config file %s", fromfile)
convertnode = Node(Dcm2niix(from_file=fromfile), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item_dicoms
convertnode.inputs.out_filename = prefix
if nipype.__version__.split('.')[0] == '0':
# deprecated since 1.0, might be needed(?) before
convertnode.inputs.terminal_output = 'allatonce'
else:
convertnode.terminal_output = 'allatonce'
convertnode.inputs.bids_format = bids_options is not None
eg = convertnode.run()
# prov information
prov_file = prefix + '_prov.ttl' if with_prov else None
if prov_file:
safe_copyfile(
op.join(convertnode.base_dir, convertnode.name, 'provenance.ttl'),
prov_file)
return eg, prov_file
def embed_metadata_from_dicoms(bids, item_dicoms, outname, outname_bids,
prov_file, scaninfo, tempdirs, with_prov,
min_meta):
"""
Enhance sidecar information file with more information from DICOMs
Parameters
----------
bids
item_dicoms
outname
outname_bids
prov_file
scaninfo
tempdirs
with_prov
min_meta
Returns
-------
"""
from nipype import Node, Function
tmpdir = tempdirs(prefix='embedmeta')
embedfunc = Node(Function(input_names=[
'dcmfiles', 'niftifile', 'infofile', 'bids_info', 'force', 'min_meta'
],
output_names=['outfile', 'meta'],
function=embed_nifti),
name='embedder')
embedfunc.inputs.dcmfiles = item_dicoms
embedfunc.inputs.niftifile = op.abspath(outname)
embedfunc.inputs.infofile = op.abspath(scaninfo)
embedfunc.inputs.min_meta = min_meta
if bids:
embedfunc.inputs.bids_info = load_json(op.abspath(outname_bids))
else:
embedfunc.inputs.bids_info = None
embedfunc.inputs.force = True
embedfunc.base_dir = tmpdir
cwd = os.getcwd()
try:
if op.lexists(scaninfo):
# TODO: handle annexed file case
if not op.islink(scaninfo):
set_readonly(scaninfo, False)
res = embedfunc.run()
set_readonly(scaninfo)
if with_prov:
g = res.provenance.rdf()
g.parse(prov_file, format='turtle')
g.serialize(prov_file, format='turtle')
set_readonly(prov_file)
except Exception as exc:
lgr.error("Embedding failed: %s", str(exc))
os.chdir(cwd)
def run_bet(T1_image, workdir):
"""Run freesurfer, convert to nidm and extract stats
"""
from nipype import fsl
from nipype import Node
from fs_dir_to_graph import to_graph
from query_convert_fs_stats import get_collections, process_collection
strip = Node(fsl.BET(), name='skullstripper')
strip.inputs.in_file = T1_image
strip.base_dir = workdir
results = strip.run()
provgraph = results.provenance
return provgraph
def run_bet(T1_image, workdir):
"""Run freesurfer, convert to nidm and extract stats
"""
from nipype import fsl
from nipype import Node
from fs_dir_to_graph import to_graph
from query_convert_fs_stats import get_collections, process_collection
strip = Node(fsl.BET(), name='skullstripper')
strip.inputs.in_file = T1_image
strip.base_dir = workdir
results = strip.run()
provgraph = results.provenance
return provgraph
def nipype_convert(item_dicoms, prefix, with_prov, bids, tmpdir):
""" """
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
convertnode = Node(Dcm2niix(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item_dicoms
convertnode.inputs.out_filename = op.basename(op.dirname(prefix))
convertnode.inputs.bids_format = bids
convertnode.inputs.anon_bids = False
return convertnode.run()
def extract_region(atlas_file, label):
from nipype.interfaces.base import CommandLine
from nipype.pipeline.engine import Node
import os
from glob import glob
node = Node(CommandLine('fslmaths %s -thr %s -uthr %s region_%s.nii.gz' %
(atlas_file, label, label, label)),
name='extract_roi')
cwd = os.getcwd()
print("cwd = ", cwd)
node.base_dir = cwd
node.config = {'execution': {'keep_unnecessary_outputs': 'true'}}
node.run()
single_region = os.path.realpath(
os.path.join(cwd, 'extract_roi', 'region_%s.nii.gz' % label))
print('single region mask file: ', single_region)
assert os.path.exists(single_region)
return single_region, label, atlas_file
def convert(items, anonymizer=None, symlink=True, converter=None):
prov_files = []
tmpdir = mkdtemp()
for item in items:
if isinstance(item[1], (list, tuple)):
outtypes = item[1]
else:
outtypes = [item[1]]
prefix = item[0]
print('Converting %s' % prefix)
dirname = os.path.dirname(prefix + '.ext')
print(dirname)
if not os.path.exists(dirname):
os.makedirs(dirname)
for outtype in outtypes:
print(outtype)
if outtype == 'dicom':
dicomdir = prefix + '_dicom'
if os.path.exists(dicomdir):
shutil.rmtree(dicomdir)
os.mkdir(dicomdir)
for filename in item[2]:
outfile = os.path.join(dicomdir, os.path.split(filename)[1])
if not os.path.islink(outfile):
if symlink:
os.symlink(filename, outfile)
else:
os.link(filename, outfile)
elif outtype in ['nii', 'nii.gz']:
outname = prefix + '.' + outtype
scaninfo = prefix + '_scaninfo.json'
if not os.path.exists(outname):
from nipype import config
config.enable_provenance()
from nipype import Function, Node
from nipype.interfaces.base import isdefined
print converter
if converter == 'mri_convert':
from nipype.interfaces.freesurfer.preprocess import MRIConvert
convertnode = Node(MRIConvert(), name = 'convert')
convertnode.base_dir = tmpdir
if outtype == 'nii.gz':
convertnode.inputs.out_type = 'niigz'
convertnode.inputs.in_file = item[2][0]
convertnode.inputs.out_file = outname
#cmd = 'mri_convert %s %s' % (item[2][0], outname)
#print(cmd)
#os.system(cmd)
res=convertnode.run()
elif converter == 'dcm2nii':
from nipype.interfaces.dcm2nii import Dcm2nii
convertnode = Node(Dcm2nii(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item[2]
convertnode.inputs.gzip_output = outtype == 'nii.gz'
convertnode.inputs.terminal_output = 'allatonce'
res = convertnode.run()
if isinstance(res.outputs.converted_files, list):
print("Cannot convert dicom files - series likely has multiple orientations: ", item[2])
continue
else:
shutil.copyfile(res.outputs.converted_files, outname)
if isdefined(res.outputs.bvecs):
outname_bvecs = prefix + '.bvecs'
outname_bvals = prefix + '.bvals'
shutil.copyfile(res.outputs.bvecs, outname_bvecs)
shutil.copyfile(res.outputs.bvals, outname_bvals)
prov_file = prefix + '_prov.ttl'
shutil.copyfile(os.path.join(convertnode.base_dir,
convertnode.name,
'provenance.ttl'),
prov_file)
prov_files.append(prov_file)
embedfunc = Node(Function(input_names=['dcmfiles',
'niftifile',
'infofile',
'force'],
output_names=['outfile',
'meta'],
function=embed_nifti),
name='embedder')
embedfunc.inputs.dcmfiles = item[2]
embedfunc.inputs.niftifile = outname
embedfunc.inputs.infofile = scaninfo
embedfunc.inputs.force = True
embedfunc.base_dir = tmpdir
res = embedfunc.run()
g = res.provenance.rdf()
g.parse(prov_file,
format='turtle')
g.serialize(prov_file, format='turtle')
#out_file, meta_dict = embed_nifti(item[2], outname, force=True)
os.chmod(outname, 0440)
os.chmod(scaninfo, 0440)
os.chmod(prov_file, 0440)
shutil.rmtree(tmpdir)
def embed_metadata_from_dicoms(bids, item_dicoms, outname, outname_bids,
prov_file, scaninfo, tempdirs, with_prov,
min_meta):
"""
Enhance sidecar information file with more information from DICOMs
Parameters
----------
bids
item_dicoms
outname
outname_bids
prov_file
scaninfo
tempdirs
with_prov
min_meta
Returns
-------
"""
from nipype import Node, Function
tmpdir = tempdirs(prefix='embedmeta')
# We need to assure that paths are absolute if they are relative
item_dicoms = list(map(op.abspath, item_dicoms))
embedfunc = Node(Function(input_names=['dcmfiles', 'niftifile', 'infofile',
'bids_info', 'force', 'min_meta'],
output_names=['outfile', 'meta'],
function=embed_nifti),
name='embedder')
embedfunc.inputs.dcmfiles = item_dicoms
embedfunc.inputs.niftifile = op.abspath(outname)
embedfunc.inputs.infofile = op.abspath(scaninfo)
embedfunc.inputs.min_meta = min_meta
if bids:
embedfunc.inputs.bids_info = load_json(op.abspath(outname_bids))
else:
embedfunc.inputs.bids_info = None
embedfunc.inputs.force = True
embedfunc.base_dir = tmpdir
cwd = os.getcwd()
lgr.debug("Embedding into %s based on dicoms[0]=%s for nifti %s",
scaninfo, item_dicoms[0], outname)
try:
if op.lexists(scaninfo):
# TODO: handle annexed file case
if not op.islink(scaninfo):
set_readonly(scaninfo, False)
res = embedfunc.run()
set_readonly(scaninfo)
if with_prov:
g = res.provenance.rdf()
g.parse(prov_file,
format='turtle')
g.serialize(prov_file, format='turtle')
set_readonly(prov_file)
except Exception as exc:
lgr.error("Embedding failed: %s", str(exc))
os.chdir(cwd)
contrasts = [
GainRisk_cond, GainAmb_cond, LossRisk_cond, LossAmb_cond, Risk_vs_Amb,
Gain_vs_Loss, gain_total
]
modelspec = Node(interface=model.SpecifySPMModel(), name="modelspec")
modelspec.inputs.concatenate_runs = False
modelspec.inputs.input_units = 'scans'
modelspec.inputs.output_units = 'scans'
#modelspec.inputs.outlier_files = '/media/Data/R_A_PTSD/preproccess_data/sub-1063_ses-01_task-3_bold_outliers.txt'
modelspec.inputs.time_repetition = 1. # make sure its with a dot
modelspec.inputs.high_pass_filter_cutoff = 128.
################################################
#modelspec.inputs.subject_info = subjectinfo(subject_id) # run per subject
modelspec.base_dir = '/media/Data/work'
level1design = pe.Node(interface=spm.Level1Design(),
name="level1design") #, base_dir = '/media/Data/work')
level1design.inputs.timing_units = modelspec.inputs.output_units
level1design.inputs.interscan_interval = 1.
level1design.inputs.bases = {'hrf': {'derivs': [0, 0]}}
level1design.inputs.model_serial_correlations = 'AR(1)'
#######################################################################################################################
# Initiation of a workflow
wf = Workflow(name="l1run", base_dir="/media/Data/work")
wf.connect([
(infosource, datasource, [('subject_id', 'subject_id')]),
(datasource, gunzip, [('func', 'in_file')]),
(gunzip, modelspec, [('out_file', 'functional_runs')]),
def convert(items, anonymizer=None, symlink=True, converter=None):
prov_files = []
tmpdir = mkdtemp()
for item in items:
if isinstance(item[1], (list, tuple)):
outtypes = item[1]
else:
outtypes = [item[1]]
prefix = item[0]
print('Converting %s' % prefix)
dirname = os.path.dirname(prefix + '.ext')
print(dirname)
if not os.path.exists(dirname):
os.makedirs(dirname)
for outtype in outtypes:
print(outtype)
if outtype == 'dicom':
dicomdir = prefix + '_dicom'
if os.path.exists(dicomdir):
shutil.rmtree(dicomdir)
os.mkdir(dicomdir)
for filename in item[2]:
outfile = os.path.join(dicomdir, os.path.split(filename)[1])
if not os.path.islink(outfile):
if symlink:
os.symlink(filename, outfile)
else:
os.link(filename, outfile)
elif outtype in ['nii', 'nii.gz']:
outname = prefix + '.' + outtype
scaninfo = prefix + '_scaninfo.json'
if not os.path.exists(outname):
from nipype import config
config.enable_provenance()
from nipype import Function, Node
from nipype.interfaces.base import isdefined
print converter
if converter == 'mri_convert':
from nipype.interfaces.freesurfer.preprocess import MRIConvert
convertnode = Node(MRIConvert(), name = 'convert')
convertnode.base_dir = tmpdir
if outtype == 'nii.gz':
convertnode.inputs.out_type = 'niigz'
convertnode.inputs.in_file = item[2][0]
convertnode.inputs.out_file = outname
#cmd = 'mri_convert %s %s' % (item[2][0], outname)
#print(cmd)
#os.system(cmd)
res=convertnode.run()
elif converter == 'dcm2nii':
from nipype.interfaces.dcm2nii import Dcm2nii
convertnode = Node(Dcm2nii(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item[2]
convertnode.inputs.gzip_output = outtype == 'nii.gz'
convertnode.inputs.terminal_output = 'allatonce'
res = convertnode.run()
if isinstance(res.outputs.converted_files, list):
print("Cannot convert dicom files - series likely has multiple orientations: ", item[2])
continue
else:
shutil.copyfile(res.outputs.converted_files, outname)
if isdefined(res.outputs.bvecs):
outname_bvecs = prefix + '.bvecs'
outname_bvals = prefix + '.bvals'
shutil.copyfile(res.outputs.bvecs, outname_bvecs)
shutil.copyfile(res.outputs.bvals, outname_bvals)
prov_file = prefix + '_prov.ttl'
shutil.copyfile(os.path.join(convertnode.base_dir,
convertnode.name,
'provenance.ttl'),
prov_file)
prov_files.append(prov_file)
embedfunc = Node(Function(input_names=['dcmfiles',
'niftifile',
'infofile',
'force'],
output_names=['outfile',
'meta'],
function=embed_nifti),
name='embedder')
embedfunc.inputs.dcmfiles = item[2]
embedfunc.inputs.niftifile = outname
embedfunc.inputs.infofile = scaninfo
embedfunc.inputs.force = True
embedfunc.base_dir = tmpdir
res = embedfunc.run()
g = res.provenance.rdf()
g.parse(prov_file,
format='turtle')
g.serialize(prov_file, format='turtle')
#out_file, meta_dict = embed_nifti(item[2], outname, force=True)
os.chmod(outname, 0440)
os.chmod(scaninfo, 0440)
os.chmod(prov_file, 0440)
shutil.rmtree(tmpdir)
