def run_bet(
skip_existing: bool = True
):
full_pattern = os.path.join(DATA_DIR, PATTERN)
scans = glob.iglob(full_pattern, recursive=True)
for scan in scans:
print(f'\nCurrent series: {scan}')
if skip_existing:
print('Checking for existing skull-stripping output...', end='\t')
dest = get_default_destination(scan)
if skip_existing and os.path.isfile(dest):
print(f'\u2714')
continue
print(f'\u2718')
print('Running skull-stripping with BET...')
try:
bet = Node(BET(robust=True), name='bet_node')
bet.inputs.in_file = scan
bet.inputs.out_file = dest
bet.run()
print(f'\u2714\tDone!')
except Exception as e:
print(f'\u2718')
print(e.args)
break
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 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 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 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 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_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 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 test_grab_entities_subjects(self):
subject_outputs = [{'subject': '01'}, {'subject': '02'}, {'subject': '03'}, {'subject': '04'}, {'subject': '05'}, {'subject': '06'}, {'subject': '07'}, {'subject': '08'}, {'subject': '09'}, {'subject': '10'}, {'subject': '11'}, {'subject': '12'}, {'subject': '13'}]
bl = Node(BIDSGrab(bids_dir=TEST_DATASETS['NEW_FMRIPREP_DUMMY']), name="TestBIDSGrab")
outs = bl.run()
bl_subjects = [{'subject': element['subject']} for element in outs.outputs.entities]
self.assertEqual(bl_subjects, subject_outputs)
# if __name__ == '__main__':
# import cProfile
# bl = Node(BIDSGrab(bids_dir=TEST_DATASETS['NEW_FMRIPREP_DUMMY']), name="TestBIDSGrab")
# bl.run()
# cProfile.run('bl.run()')
def testNodes():
#Node that calls a simple function
print("\n ** debug PIPELINE : simple function Node.\n")
simpleNode = Node(Function(input_names=["inputParam"], output_names=["simpleResult"], function=simpleFunction), name='simpleNode')
simpleNode.inputs.inputParam = 5
tempResult = simpleNode.run()
#print(tempResult.outputs) -- this is equvalent to below
print(simpleNode.result.outputs)
#Node that calls a shell function
TEST_DIR = abspath('/home/chidi/')
print("\n ** debug PIPELINE : shell function Node.\n")
shellPopNode = Node(Function(input_names=["shellCommandList"], output_names=["shellResult"], function=shellFunction), name='shellPopNode')
shellPopNode.inputs.shellCommandList = ['ls', TEST_DIR, '-a']
tempResult = shellPopNode.run()
#print(tempResult.outputs) -- this is equivalent to below
print(shellPopNode.result.outputs)
#Node that calls a python module that prints to std
print("\n ** debug PIPELINE : python external module Node.\n")
PYTHON='python'
SCRIPT=abspath('/home/chidi/uabiomed/develop/nipype/nkutils.py')
pythonExtNode = Node(Function(input_names=["shellCommandList"], output_names=["shellResult"], function=shellFunction), name='pythonExtNode')
pythonExtNode.inputs.shellCommandList = [PYTHON, SCRIPT]
tempResult = pythonExtNode.run()
#print(tempResult.outputs) -- this is equivalent to below
print(pythonExtNode.result.outputs)
#Node that calls an imported python module
print("\n ** debug PIPELINE : python imported module Node.\n")
pythonIntNode = Node(Function(input_names=["r","x","y"], output_names=["shellResult"], function=getArray), name='pythonIntNode')
pythonIntNode.inputs.r = 15
pythonIntNode.inputs.x = 3
pythonIntNode.inputs.y = 5
tempResult = pythonIntNode.run()
#print(tempResult.outputs) -- this is equivalent to below
print(pythonIntNode.inputs)
print(pythonIntNode.outputs)
print(pythonIntNode.result.outputs)
class CurrentInterface(object):
def __init__(self, interface, name):
self.nn = Node(interface=interface, name=name)
self.output = {}
def run(self, inputs, base_dir, dir_nm_el):
self.nn.base_dir = os.path.join(base_dir, dir_nm_el)
for key, val in inputs.items():
key = key.split(".")[-1]
setattr(self.nn.inputs, key, val)
#have to set again self._output_dir in case of mapper
self.nn._output_dir = os.path.join(self.nn.base_dir, self.nn.name)
res = self.nn.run()
return res
def rawdataChecker(input_file):
# If the input is a single DCM-file instead of a multi-dim-NifTI, we have to fetch all the other files in the series
if input_file.endswith('.dcm'):
from nipype.interfaces.io import DataFinder
from os import path
from nipype import Node
# Setup a datafinder to find the paths to the specific DICOM files
t1FinderNode = Node(DataFinder(), name='t1Finder')
t1FinderNode.inputs.match_regex = '.*\.dcm'
t1FinderNode.inputs.root_paths = path.split(input_file)[0]
return t1FinderNode.run().outputs.out_paths
else:
return input_file # If other datatype just return the same path
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 rawdataChecker(input_file):
# If the input is a single DCM-file instead of a multi-dim-NifTI, we have to fetch all the other files in the series
if input_file.endswith('.dcm'):
from nipype.interfaces.io import DataFinder
from os import path
from nipype import Node
# Setup a datafinder to find the paths to the specific DICOM files
t1FinderNode = Node(DataFinder(), name = 't1Finder')
t1FinderNode.inputs.match_regex = '.*\.dcm'
t1FinderNode.inputs.root_paths = path.split(input_file)[0]
return t1FinderNode.run().outputs.out_paths
else:
return input_file # If other datatype just return the same path
def selectFile(rootPath=r'D:\其他\老舅财务\allData'):
templates = {'path': '*\\*\\*.txt'}
# Create SelectFiles node
sf = Node(SelectFiles(templates), name='selectfiles')
# Location of the dataset folder
sf.inputs.base_directory = rootPath
# Feed {}-based placeholder strings with values
# sf.inputs.subject_id1 = '00[1,2]'
# sf.inputs.subject_id2 = '01'
# sf.inputs.ses_name = "retest"
# sf.inputs.task_name = 'covertverb'
path = sf.run().outputs.__dict__['path']
return path
def selectFile(
rootPath=r'I:\Data_Code\insomnia\workstation_MVPA_2018_05\FunImgARW1'):
templates = {'path': '*\\sw*.nii'}
# Create SelectFiles node
sf = Node(SelectFiles(templates), name='selectfiles')
# Location of the dataset folder
sf.inputs.base_directory = rootPath
# Feed {}-based placeholder strings with values
# sf.inputs.subject_id1 = '00[1,2]'
# sf.inputs.subject_id2 = '01'
# sf.inputs.ses_name = "retest"
# sf.inputs.task_name = 'covertverb'
path = sf.run().outputs.__dict__['path']
return path
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 selectFile(rootPath=r'I:\Data_Code\Doctor\RealignParameter'):
templates = {'path': '{folder}\\{id}'}
# Create SelectFiles node
sf = Node(SelectFiles(templates), name='selectfiles')
# Location of the dataset folder
sf.inputs.base_directory = rootPath
# Feed {}-based placeholder strings with values
sf.inputs.folder = '*_*'
sf.inputs.id = 'FD_Jenkinson_*'
# sf.inputs.subject_id2 = '01'
# sf.inputs.ses_name = "retest"
# sf.inputs.task_name = 'covertverb'
path = sf.run().outputs.__dict__['path']
return path
def init_fmridenoise_wf(bids_dir,
derivatives: t.List[str],
task: t.List[str],
session: t.List[str],
subject: t.List[str],
runs: t.List[int],
pipelines_paths: t.Set[str],
high_pass=0.008,
low_pass=0.08,
base_dir='/tmp/fmridenoise',
name='fmridenoise_wf'):
pipelines_paths = list(pipelines_paths)
bids_validate = Node(BIDSValidate(bids_dir=bids_dir,
derivatives=derivatives,
tasks=task,
sessions=session,
subjects=subject,
runs=runs,
pipelines=pipelines_paths),
name='BidsValidate')
result = bids_validate.run()
builder = WorkflowBuilder(bids_dir=bids_dir,
subjects=result.outputs.subjects,
tasks=result.outputs.tasks,
conf_raw=result.outputs.conf_raw,
conf_json=result.outputs.conf_json,
tr_dic=result.outputs.tr_dict,
pipelines_paths=pipelines_paths,
high_pass=high_pass,
low_pass=low_pass)
if result.outputs.fmri_prep:
builder.use_fmri_prep(result.outputs.fmri_prep)
if result.outputs.fmri_prep_aroma:
builder.use_fmri_prep_aroma(result.outputs.fmri_prep_aroma)
if result.outputs.sessions:
builder.with_sessions(result.outputs.sessions)
if result.outputs.runs:
builder.with_runs(result.outputs.runs)
return builder.build(name, base_dir)
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)
return SC_cap_row_filename, SC_dist_row_filename
import numpy as np
debugPath = '/Users/srothmei/Desktop/charite/toronto/Adalberto/debug/'
roi = 68
subid = 'Adalberto'
tracksPath = debugPath
wmBorder_file = debugPath + 'wmborder.npy'
wmborder = np.load(wmBorder_file)
affine_matrix_file = debugPath + 'affine_matrix.npy'
affine_matrix = np.load(affine_matrix_file)
from nipype import Node
from nipype.interfaces.io import DataFinder
tckFinder = Node(DataFinder(match_regex='.*\.npy', root_paths=tracksPath),
name='tckFinder')
res = tckFinder.run()
track_files = res.outputs.out_paths
#
compute_connectivity_row(roi, subid, affine_matrix, wmborder, tracksPath,
track_files)
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)
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)
path = f"{base_dir}/derivatives/fmridenoise/sub-{sub_num}"
os.makedirs(path, exist_ok=True)
out_fname = f"{path}/{basename}_{self.inputs.suffix}{ext}" # TODO: Fix lonely dot if no extension
copyfile(in_file, out_fname, copy=True)
out_files.append(out_fname)
return {'out_file': out_files}
# --- TESTS
if __name__ == '__main__':
from nipype import Node
# selector = Node(BIDSSelect(), name="pipeline_selector")
# selector.inputs.bids_dir = '/home/finc/Dropbox/Projects/fitlins/BIDS/'
# selector.inputs.derivatives = True
# selector.inputs.entities = [{'subject': '01'}]
# results = selector.run()
# print(results.outputs)
ds = Node(BIDSDataSink(
base_directory=
'/home/siegfriedwagner/Documents/git/fmridenoise/dummy_bids',
in_file=
'/home/siegfriedwagner/Documents/git/fmridenoise/dummy_bids/derivatives/sub-1/test',
entities=[{
'subject': '1'
}],
suffix='d'),
name='ds')
ds.run()
#print(ds.result)
# Releasing Mr Loggins...
dangerZone.setLevel('NOTSET')
print('Done!')
return SC_cap_row_filename, SC_dist_row_filename
import numpy as np
debugPath = '/Users/srothmei/Desktop/charite/toronto/Adalberto/debug/'
roi = 68
subid = 'Adalberto'
tracksPath = debugPath
wmBorder_file = debugPath + 'wmborder.npy'
wmborder = np.load(wmBorder_file)
affine_matrix_file = debugPath + 'affine_matrix.npy'
affine_matrix = np.load(affine_matrix_file)
from nipype import Node
from nipype.interfaces.io import DataFinder
tckFinder = Node(DataFinder(match_regex = '.*\.npy', root_paths = tracksPath), name = 'tckFinder')
res = tckFinder.run()
track_files = res.outputs.out_paths
#
compute_connectivity_row(roi, subid, affine_matrix, wmborder, tracksPath, track_files)
if __name__ == "__main__":
calc_residuals = Node(Function(function=calc_residuals,
input_names=[
'in_file', 'motion_file',
'csf_mask_path', 'wm_mask_path',
'global_signal_flag', 'const',
'check_orthogonality'
],
output_names=['out_file_list']),
name='calc_residuals')
calc_residuals.inputs.in_file = '/mnt/project1/home1/varunk/fMRI/results/resultsABIDE1_1/preprocess/motion_correction_bet/_subject_id_0050009/applyMask/sub-0050009_task-rest_run-1_bold_roi_st_mcf.nii_brain.nii.gz'
calc_residuals.inputs.motion_file = '/mnt/project1/home1/varunk/fMRI/results/resultsABIDE1_1/preprocess/_subject_id_0050009/mcflirt/sub-0050009_task-rest_run-1_bold_roi_st_mcf.nii.par'
calc_residuals.inputs.csf_mask_path = '/mnt/project1/home1/varunk/fMRI/'+\
'results/resultsABIDE1_1/preprocess/motion_correction_bet/coreg_reg/'+\
'atlas_resize_reg_directory/wf_main_masks/wf_tissue_masks/'+\
'_subject_id_0050009/csf_mask/fast__pve_0_flirt_thresh_masked.nii.gz'
calc_residuals.inputs.wm_mask_path = '/mnt/project1/home1/varunk/fMRI/'+\
'results/resultsABIDE1_1/preprocess/motion_correction_bet/coreg_reg/'+\
'atlas_resize_reg_directory/wf_main_masks/wf_tissue_masks/'+\
'_subject_id_0050009/threshold_wm/fast__pve_2_flirt_thresh.nii.gz'
calc_residuals.inputs.global_signal_flag = True
calc_residuals.inputs.const = True
calc_residuals.inputs.check_orthogonality = True
calc_residuals.run()
motscrubdat2 = np.delete(motscrubdat,scrub_vols,axis=3)
array_img = nib.Nifti1Image(motscrubdat2, nib.load(cur_dir).affine)
nib.save(array_img, '%s/rest.nii.gz'%(output_dir))
else:
nib.save(nib.load(cur_dir), '%s/rest.nii.gz'%(output_dir))
########## 2: Remove first 5 vols of func data
os.system("fslroi %s/rest.nii.gz %s/rest_delvol.nii.gz 5 350"%(output_dir,output_dir))
########## 3: Bias Field Correction
inu_n4 = Node(N4BiasFieldCorrection(
dimension=3,
input_image = '%s/%s/session_1/anat_1/anat.nii.gz'%(data_dir,subnum),
output_image = '%s/mprage_inu.nii.gz'%(output_dir))
, name='inu_n4')
res1 = inu_n4.run()
########## 4: Skullstripping
#anat
bet_anat = Node(fsl.BET(frac=0.5,
robust=True,
output_type='NIFTI_GZ',
in_file= '%s/mprage_inu.nii.gz'%(output_dir),
out_file = '%s/mprage_inu_bet.nii.gz'%(output_dir)),
name="bet_anat")
res = bet_anat.run()
#func
bet_func = Node(fsl.BET(frac=0.5,
output_type='NIFTI_GZ',
in_file= '%s/rest_delvol.nii.gz'%(output_dir),
out_file = '%s/rest_delvol_bet.nii.gz'%(output_dir),
# Import BET from the FSL interface from nipype.interfaces.fsl import BET # Import the Node module from nipype import Node # Create Node bet = Node(BET(frac=0.3), name='bet_node') # Specify node inputs bet.inputs.in_file = '/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz' bet.inputs.out_file = '/output/node_T1w_bet.nii.gz' res = bet.run()
# Setting up the second level analysis model node
level2design = Node(fsl.MultipleRegressDesign(contrasts=contrastList,
regressors=dictReg),
name='level2design')
dofCalc = Node(interface=Function(input_names=['targetDir', 'desMtx'],
output_names=['fileOut'],
function=DOFCalc),
name='dofCalc')
# creating datasink to collect outputs
datasink = Node(
DataSink(base_directory=os.path.join(outDir, 'DOFCalculation')),
name='datasink')
# directory where we create a DOF file
DOFCalc.inputs.targetDir = datasink.inputs.base_directory
# passing on the design matrix to the DOF calculation file
DOFCalc.inputs.desMtx = dictReg
# passing
# creating the workflow
dofCalc = Workflow(name="dofCalc", base_dir=outDir)
# connecting nodes
dofCalc.connect(DOFCalc, 'fileOut', datasink, 'fileOut')
# running the workflow
dofCalc.run()
# String-based templates
templates = {'anat': 'vol001/t1/0*nii',
'func': 'vol001/tap*/00*nii'}
#templates = {'anat': 'openneuro/ds000254-me/sub-1*/anat/sub*nii',
# 'func': 'openneuro/ds000254-me/sub-1*/func/sub*nii'}
# Create SelectFiles node
sf = Node(SelectFiles(templates),
name='selectfiles')
#Specify the unchanging pieces of the path
sf.inputs.base_directory = proj_dir
filelist=sf.run().outputs
filelist.func
# Magic! Build the default dict with lambda to achieve a callable nested dict easily.
img_dict=lambda:defaultdict(img_dict)
results = img_dict()
for f in filelist.func:
print(f)
tsnr=TSNR()
tsnr.inputs.in_file=f
class PipelineSelector(SimpleInterface):
input_spec = PipelineSelectorInputSpecification
output_spec = PipelineSelectorOutPutSpecification
def _run_interface(self, runtime):
js = load_pipeline_from_json(self.inputs.pipeline_path)
if not is_valid(js):
raise ValueError("""
Json file {} is not a valid pipeline,
check schema at fmridenoise.utils.json_validator.py
""".format(os.path.basename(self.inputs.pipeline_path)))
self._results['pipeline'] = js
self._results['pipeline_name'] = js['name']
return runtime
# rudimentary test # TODO: Move to this to proper unittests
if __name__ == '__main__':
from nipype import Node
import glob
reader = Node(PipelineSelector(), name="pipeline_selector")
for path in glob.glob("../pipelines/*"):
path = os.path.abspath(path)
print(path)
reader.inputs.pipeline_path = path
pipeline = reader.run()
print(pipeline.outputs)
ds.inputs.base_directory = '/data/ds000114'
# 使用%02d来设计模板
ds.inputs.template = 'sub-%02d/ses-test/func/*fingerfootlips*.nii.gz'
ds.inputs.sort_filelist = True
# 指定subject_id的具体数值
ds.inputs.subject_id = [1, 7]
results = ds.run()
results.outputs
# another method
from nipype import SelectFiles, Node
templates = {
'func':
'sub{subject_id1}\\ses-{ses_name}\\func\\sub-{subject_id2}_ses-{ses_name}_task-{task_name}.gz'
}
# Create SelectFiles node
sf = Node(SelectFiles(templates), name='selectfiles')
# Location of the dataset folder
sf.inputs.base_directory = r'D:\myCodes\MVPA_LC\Python\study\workstation'
# Feed {}-based placeholder strings with values
sf.inputs.subject_id1 = '00[1,2]'
sf.inputs.subject_id2 = '01'
sf.inputs.ses_name = "retest"
sf.inputs.task_name = 'covertverb'
path = sf.run().outputs.__dict__
