def init_spaces(checkpoint=True):
"""Initialize the :attr:`~workflow.spaces` setting."""
from niworkflows.utils.spaces import Reference, SpatialReferences
spaces = execution.output_spaces or SpatialReferences()
if not isinstance(spaces, SpatialReferences):
spaces = SpatialReferences([
ref for s in spaces.split(" ") for ref in Reference.from_string(s)
])
if checkpoint and not spaces.is_cached():
spaces.checkpoint()
# # Add the default standard space if not already present (required by several sub-workflows)
# if "MNI152NLin2009cAsym" not in spaces.get_spaces(nonstandard=False, dim=(3,)):
# spaces.add(Reference("MNI152NLin2009cAsym", {}))
# Ensure user-defined spatial references for outputs are correctly parsed.
# Certain options require normalization to a space not explicitly defined by users.
# These spaces will not be included in the final outputs.
# if workflow.use_aroma:
# # Make sure there's a normalization to FSL for AROMA to use.
# spaces.add(Reference("MNI152NLin6Asym", {"res": "2"}))
cifti_output = workflow.cifti_output
if workflow.cifti_output:
# CIFTI grayordinates to corresponding FSL-MNI resolutions.
vol_res = "2" if cifti_output == "91k" else "1"
spaces.add(Reference("fsaverage", {"den": "164k"}))
spaces.add(Reference("MNI152NLin6Asym", {"res": vol_res}))
# Make the SpatialReferences object available
workflow.spaces = spaces
def init_spaces(checkpoint=True):
"""Initialize the :attr:`~workflow.spaces` setting."""
from niworkflows.utils.spaces import Reference, SpatialReferences
spaces = execution.output_spaces or SpatialReferences()
if not isinstance(spaces, SpatialReferences):
spaces = SpatialReferences(
[ref for s in spaces.split(' ')
for ref in Reference.from_string(s)]
)
if checkpoint and not spaces.is_cached():
spaces.checkpoint()
# Add the default standard space if not already present (required by several sub-workflows)
if "Fischer344" not in spaces.get_spaces(nonstandard=False, dim=(3,)):
spaces.add(
Reference("Fischer344", {})
)
# Make the SpatialReferences object available
workflow.spaces = spaces
def parse_args(args=None, namespace=None):
"""Parse args and run further checks on the command line."""
import logging
from niworkflows.utils.spaces import Reference, SpatialReferences
parser = _build_parser()
opts = parser.parse_args(args, namespace)
config.execution.log_level = int(
max(25 - 5 * opts.verbose_count, logging.DEBUG))
config.from_dict(vars(opts))
config.loggers.init()
# Initialize --output-spaces if not defined
if config.execution.output_spaces is None:
config.execution.output_spaces = SpatialReferences(
[Reference("MNI152NLin2009cAsym", {"res": "native"})])
# Retrieve logging level
build_log = config.loggers.cli
if config.execution.fs_license_file is None:
raise RuntimeError("""\
ERROR: a valid license file is required for FreeSurfer to run. fMRIPrep looked for an existing \
license file at several paths, in this order: 1) command line argument ``--fs-license-file``; \
2) ``$FS_LICENSE`` environment variable; and 3) the ``$FREESURFER_HOME/license.txt`` path. Get it \
(for free) by registering at https://surfer.nmr.mgh.harvard.edu/registration.html"""
)
os.environ['FS_LICENSE'] = str(config.execution.fs_license_file)
# Load base plugin_settings from file if --use-plugin
if opts.use_plugin is not None:
from yaml import load as loadyml
with open(opts.use_plugin) as f:
plugin_settings = loadyml(f)
_plugin = plugin_settings.get('plugin')
if _plugin:
config.nipype.plugin = _plugin
config.nipype.plugin_args = plugin_settings.get('plugin_args', {})
config.nipype.nprocs = config.nipype.plugin_args.get(
'nprocs', config.nipype.nprocs)
# Resource management options
# Note that we're making strong assumptions about valid plugin args
# This may need to be revisited if people try to use batch plugins
if 1 < config.nipype.nprocs < config.nipype.omp_nthreads:
build_log.warning(
'Per-process threads (--omp-nthreads=%d) exceed total '
'threads (--nthreads/--n_cpus=%d)', config.nipype.omp_nthread,
config.nipype.nprocs)
bids_dir = config.execution.bids_dir
output_dir = config.execution.output_dir
work_dir = config.execution.work_dir
version = config.environment.version
if config.execution.fs_subjects_dir is None:
config.execution.fs_subjects_dir = output_dir / 'freesurfer'
# Wipe out existing work_dir
if opts.clean_workdir and work_dir.exists():
from niworkflows.utils.misc import clean_directory
build_log.log("Clearing previous fMRIPrep working directory: %s",
work_dir)
if not clean_directory(work_dir):
build_log.warning(
"Could not clear all contents of working directory: %s",
work_dir)
# Ensure input and output folders are not the same
if output_dir == bids_dir:
parser.error(
'The selected output folder is the same as the input BIDS folder. '
'Please modify the output path (suggestion: %s).' % bids_dir /
'derivatives' / ('fmriprep-%s' % version.split('+')[0]))
if bids_dir in work_dir.parents:
parser.error(
'The selected working directory is a subdirectory of the input BIDS folder. '
'Please modify the output path.')
# Validate inputs
if not opts.skip_bids_validation:
from ..utils.bids import validate_input_dir
build_log.info(
"Making sure the input data is BIDS compliant (warnings can be ignored in most "
"cases).")
validate_input_dir(config.environment.exec_env, opts.bids_dir,
opts.participant_label)
# Setup directories
config.execution.log_dir = output_dir / 'fmriprep' / 'logs'
# Check and create output and working directories
config.execution.log_dir.mkdir(exist_ok=True, parents=True)
output_dir.mkdir(exist_ok=True, parents=True)
work_dir.mkdir(exist_ok=True, parents=True)
# Force initialization of the BIDSLayout
config.execution.init()
all_subjects = config.execution.layout.get_subjects()
if config.execution.participant_label is None:
config.execution.participant_label = all_subjects
participant_label = set(config.execution.participant_label)
missing_subjects = participant_label - set(all_subjects)
if missing_subjects:
parser.error(
"One or more participant labels were not found in the BIDS directory: "
"%s." % ", ".join(missing_subjects))
config.execution.participant_label = sorted(participant_label)
config.workflow.skull_strip_template = config.workflow.skull_strip_template[
0]
def parse_args(args=None, namespace=None):
"""Parse args and run further checks on the command line."""
import logging
from niworkflows.utils.spaces import Reference, SpatialReferences
parser = _build_parser()
opts = parser.parse_args(args, namespace)
config.execution.log_level = int(
max(25 - 5 * opts.verbose_count, logging.DEBUG))
config.from_dict(vars(opts))
# Initialize --output-spaces if not defined
if config.execution.output_spaces is None:
config.execution.output_spaces = SpatialReferences(
[Reference("MNI152NLin2009cAsym", {"res": "native"})])
# Retrieve logging level
build_log = config.loggers.cli
# Load base plugin_settings from file if --use-plugin
if opts.use_plugin is not None:
import yaml
with open(opts.use_plugin) as f:
plugin_settings = yaml.load(f, Loader=yaml.FullLoader)
_plugin = plugin_settings.get("plugin")
if _plugin:
config.nipype.plugin = _plugin
config.nipype.plugin_args = plugin_settings.get("plugin_args", {})
config.nipype.nprocs = opts.nprocs or config.nipype.plugin_args.get(
"n_procs", config.nipype.nprocs)
# Resource management options
# Note that we're making strong assumptions about valid plugin args
# This may need to be revisited if people try to use batch plugins
if 1 < config.nipype.nprocs < config.nipype.omp_nthreads:
build_log.warning(
f"Per-process threads (--omp-nthreads={config.nipype.omp_nthreads}) exceed "
f"total threads (--nthreads/--n_cpus={config.nipype.nprocs})")
# Inform the user about the risk of using brain-extracted images
if config.workflow.skull_strip_t1w == "auto":
build_log.warning("""\
Option ``--skull-strip-t1w`` was set to 'auto'. A heuristic will be \
applied to determine whether the input T1w image(s) have already been skull-stripped.
If that were the case, brain extraction and INU correction will be skipped for those T1w \
inputs. Please, BEWARE OF THE RISKS TO THE CONSISTENCY of results when using varying \
processing workflows across participants. To determine whether a participant has been run \
through the shortcut pipeline (meaning, brain extraction was skipped), please check the \
citation boilerplate. When reporting results with varying pipelines, please make sure you \
mention this particular variant of fMRIPrep listing the participants for which it was \
applied.""")
bids_dir = config.execution.bids_dir
output_dir = config.execution.output_dir
work_dir = config.execution.work_dir
version = config.environment.version
if config.execution.fs_subjects_dir is None:
config.execution.fs_subjects_dir = output_dir / "freesurfer"
# Wipe out existing work_dir
if opts.clean_workdir and work_dir.exists():
from niworkflows.utils.misc import clean_directory
build_log.info(
f"Clearing previous fMRIPrep working directory: {work_dir}")
if not clean_directory(work_dir):
build_log.warning(
f"Could not clear all contents of working directory: {work_dir}"
)
# Ensure input and output folders are not the same
if output_dir == bids_dir:
parser.error(
"The selected output folder is the same as the input BIDS folder. "
"Please modify the output path (suggestion: %s)." % bids_dir /
"derivatives" / ("fmriprep-%s" % version.split("+")[0]))
if bids_dir in work_dir.parents:
parser.error(
"The selected working directory is a subdirectory of the input BIDS folder. "
"Please modify the output path.")
# Validate inputs
if not opts.skip_bids_validation:
from ..utils.bids import validate_input_dir
build_log.info(
"Making sure the input data is BIDS compliant (warnings can be ignored in most "
"cases).")
validate_input_dir(config.environment.exec_env, opts.bids_dir,
opts.participant_label)
# Setup directories
config.execution.log_dir = output_dir / "fmriprep" / "logs"
# Check and create output and working directories
config.execution.log_dir.mkdir(exist_ok=True, parents=True)
output_dir.mkdir(exist_ok=True, parents=True)
work_dir.mkdir(exist_ok=True, parents=True)
# Force initialization of the BIDSLayout
config.execution.init()
all_subjects = config.execution.layout.get_subjects()
if config.execution.participant_label is None:
config.execution.participant_label = all_subjects
participant_label = set(config.execution.participant_label)
missing_subjects = participant_label - set(all_subjects)
if missing_subjects:
parser.error(
"One or more participant labels were not found in the BIDS directory: "
"%s." % ", ".join(missing_subjects))
config.execution.participant_label = sorted(participant_label)
config.workflow.skull_strip_template = config.workflow.skull_strip_template[
0]
Exec,
Select,
MergeColumns,
ApplyTransforms,
MakeResultdicts,
ResultdictDatasink,
Vals,
FilterRegressor,
)
from ...resource import get as getresource
from ...utils import firststr, loadints
from ..constants import constants
from ..memory import MemoryCalculator
spaces = SpatialReferences([Reference("MNI152NLin6Asym", {"res": "2"})])
if not spaces.is_cached():
spaces.checkpoint()
def _aroma_column_names(melodic_mix=None, aroma_noise_ics=None):
import numpy as np
from halfpipe.utils import ncol
ncomponents = ncol(melodic_mix)
leading_zeros = int(np.ceil(np.log10(ncomponents)))
column_names = []
for i in range(1, ncomponents + 1):
if i in aroma_noise_ics:
column_names.append(f"aroma_noise_{i:0{leading_zeros}d}")
else: