class _TemplateFlowSelectInputSpec(BaseInterfaceInputSpec):
template = traits.Enum(*tf.templates(), mandatory=True, desc='Template ID')
atlas = InputMultiObject(traits.Str, desc='Specify an atlas')
resolution = InputMultiObject(traits.Int,
desc='Specify a template resolution index')
template_spec = traits.DictStrAny({'atlas': None},
usedefault=True,
desc='Template specifications')
def validate_name(ctx, param, value):
"""Check whether this template already exists in the Archive."""
from templateflow.api import templates
value = value[4:] if value.startswith("tpl-") else value
if value in templates():
raise click.BadParameter(
f"A template with name {value} already exists in the Archive.")
return value
class Reference:
"""
Represent a (non)standard space specification.
Examples
--------
>>> Reference('MNI152NLin2009cAsym')
Reference(space='MNI152NLin2009cAsym', spec={})
>>> Reference('MNI152NLin2009cAsym', {})
Reference(space='MNI152NLin2009cAsym', spec={})
>>> Reference('MNI152NLin2009cAsym', None)
Reference(space='MNI152NLin2009cAsym', spec={})
>>> Reference('MNI152NLin2009cAsym', {'res': 1})
Reference(space='MNI152NLin2009cAsym', spec={'res': 1})
>>> Reference('MNIPediatricAsym', {'cohort': '1'})
Reference(space='MNIPediatricAsym', spec={'cohort': '1'})
>>> Reference('func')
Reference(space='func', spec={})
>>> # Checks spaces with cohorts:
>>> Reference('MNIPediatricAsym')
Traceback (most recent call last):
...
ValueError: standard space "MNIPediatricAsym" is not fully defined.
...
>>> Reference(space='MNI152Lin', spec={'cohort': 1})
Traceback (most recent call last):
...
ValueError: standard space "MNI152Lin" does not accept ...
>>> Reference('MNIPediatricAsym', {'cohort': '100'})
Traceback (most recent call last):
...
ValueError: standard space "MNIPediatricAsym" does not contain ...
...
>>> Reference('MNIPediatricAsym', 'blah')
Traceback (most recent call last):
...
TypeError: ...
>>> Reference('shouldraise')
Traceback (most recent call last):
...
ValueError: space identifier "shouldraise" is invalid.
...
>>> # Check standard property
>>> Reference('func').standard
False
>>> Reference('MNI152Lin').standard
True
>>> Reference('MNIPediatricAsym', {'cohort': 1}).standard
True
>>> # Equality/inequality checks
>>> Reference('func') == Reference('func')
True
>>> Reference('func') != Reference('MNI152Lin')
True
>>> Reference('MNI152Lin', {'res': 1}) == Reference('MNI152Lin', {'res': 1})
True
>>> Reference('MNI152Lin', {'res': 1}) == Reference('MNI152Lin', {'res': 2})
False
>>> sp1 = Reference('MNIPediatricAsym', {'cohort': 1})
>>> sp2 = Reference('MNIPediatricAsym', {'cohort': 2})
>>> sp1 == sp2
False
>>> sp1 = Reference('MNIPediatricAsym', {'res': 1, 'cohort': 1})
>>> sp2 = Reference('MNIPediatricAsym', {'cohort': 1, 'res': 1})
>>> sp1 == sp2
True
"""
_standard_spaces = tuple(_tfapi.templates())
space = attr.ib(default=None, type=str)
"""Name designating this space."""
spec = attr.ib(
factory=dict,
validator=attr.validators.optional(attr.validators.instance_of(dict)),
)
"""The dictionary of specs."""
standard = attr.ib(default=False, repr=False, type=bool)
"""Whether this space is standard or not."""
dim = attr.ib(default=3, repr=False, type=int)
"""Dimensionality of the sampling manifold."""
def __attrs_post_init__(self):
"""Extract cohort out of spec."""
if self.spec is None:
object.__setattr__(self, "spec", {})
if self.space.startswith("fsaverage"):
space = self.space
object.__setattr__(self, "space", "fsaverage")
if "den" not in self.spec or space != "fsaverage":
spec = self.spec.copy()
spec["den"] = FSAVERAGE_DENSITY[space]
object.__setattr__(self, "spec", spec)
if self.space.startswith("fs"):
object.__setattr__(self, "dim", 2)
if self.space in self._standard_spaces:
object.__setattr__(self, "standard", True)
_cohorts = ["%s" % t for t in _tfapi.TF_LAYOUT.get_cohorts(template=self.space)]
if "cohort" in self.spec:
if not _cohorts:
raise ValueError(
'standard space "%s" does not accept a cohort '
"specification." % self.space
)
if str(self.spec["cohort"]) not in _cohorts:
raise ValueError(
'standard space "%s" does not contain any cohort '
'named "%s".' % (self.space, self.spec["cohort"])
)
elif _cohorts:
_cohorts = ", ".join(['"cohort-%s"' % c for c in _cohorts])
raise ValueError(
'standard space "%s" is not fully defined.\n'
"Set a valid cohort selector from: %s." % (self.space, _cohorts)
)
@property
def fullname(self):
"""
Generate a full-name combining cohort.
Examples
--------
>>> Reference('MNI152Lin').fullname
'MNI152Lin'
>>> Reference('MNIPediatricAsym', {'cohort': 1}).fullname
'MNIPediatricAsym:cohort-1'
"""
if "cohort" not in self.spec:
return self.space
return "%s:cohort-%s" % (self.space, self.spec["cohort"])
@property
def legacyname(self):
"""
Generate a legacy name for fsaverageX spaces.
Examples
--------
>>> Reference(space='fsaverage')
Reference(space='fsaverage', spec={'den': '164k'})
>>> Reference(space='fsaverage').legacyname
'fsaverage'
>>> Reference(space='fsaverage6')
Reference(space='fsaverage', spec={'den': '41k'})
>>> Reference(space='fsaverage6').legacyname
'fsaverage6'
>>> # Overwrites density of legacy "fsaverage" specifications
>>> Reference(space='fsaverage6', spec={'den': '10k'})
Reference(space='fsaverage', spec={'den': '41k'})
>>> Reference(space='fsaverage6', spec={'den': '10k'}).legacyname
'fsaverage6'
>>> # Return None if no legacy space
>>> Reference(space='fsaverage', spec={'den': '30k'}).legacyname is None
True
"""
if self.space == "fsaverage" and self.spec["den"] in FSAVERAGE_LEGACY:
return FSAVERAGE_LEGACY[self.spec["den"]]
@space.validator
def _check_name(self, attribute, value):
if value.startswith("fsaverage"):
return
valid = list(self._standard_spaces) + NONSTANDARD_REFERENCES
if value not in valid:
raise ValueError(
'space identifier "%s" is invalid.\nValid '
"identifiers are: %s" % (value, ", ".join(valid))
)
def __str__(self):
"""
Format this reference.
Examples
--------
>>> str(Reference(space='MNIPediatricAsym', spec={'cohort': 2, 'res': 1}))
'MNIPediatricAsym:cohort-2:res-1'
"""
return ":".join(
[self.space] + ["-".join((k, str(v))) for k, v in sorted(self.spec.items())]
)
@classmethod
def from_string(cls, value):
"""
Parse a string to generate the corresponding list of References.
.. testsetup::
>>> if PY_VERSION < (3, 6):
... pytest.skip("This doctest does not work on python <3.6")
Parameters
----------
value: :obj:`str`
A string containing a space specification following *fMRIPrep*'s
language for ``--output-spaces``
(e.g., ``MNIPediatricAsym:cohort-1:cohort-2:res-1:res-2``).
Returns
-------
spaces : :obj:`list` of :obj:`Reference`
A list of corresponding spaces given the input string.
Examples
--------
>>> Reference.from_string("MNI152NLin2009cAsym")
[Reference(space='MNI152NLin2009cAsym', spec={})]
>>> # Bad space name
>>> Reference.from_string("shouldraise")
Traceback (most recent call last):
...
ValueError: space identifier "shouldraise" is invalid.
...
>>> # Missing cohort
>>> Reference.from_string("MNIPediatricAsym")
Traceback (most recent call last):
...
ValueError: standard space "MNIPediatricAsym" is not fully defined.
...
>>> Reference.from_string("MNIPediatricAsym:cohort-1")
[Reference(space='MNIPediatricAsym', spec={'cohort': '1'})]
>>> Reference.from_string("MNIPediatricAsym:cohort-1:cohort-2")
[Reference(space='MNIPediatricAsym', spec={'cohort': '1'}),
Reference(space='MNIPediatricAsym', spec={'cohort': '2'})]
>>> Reference.from_string("fsaverage:den-10k:den-164k")
[Reference(space='fsaverage', spec={'den': '10k'}),
Reference(space='fsaverage', spec={'den': '164k'})]
>>> Reference.from_string("MNIPediatricAsym:cohort-5:cohort-6:res-2")
[Reference(space='MNIPediatricAsym', spec={'cohort': '5', 'res': '2'}),
Reference(space='MNIPediatricAsym', spec={'cohort': '6', 'res': '2'})]
>>> Reference.from_string("MNIPediatricAsym:cohort-5:cohort-6:res-2:res-iso1.6mm")
[Reference(space='MNIPediatricAsym', spec={'cohort': '5', 'res': '2'}),
Reference(space='MNIPediatricAsym', spec={'cohort': '5', 'res': 'iso1.6mm'}),
Reference(space='MNIPediatricAsym', spec={'cohort': '6', 'res': '2'}),
Reference(space='MNIPediatricAsym', spec={'cohort': '6', 'res': 'iso1.6mm'})]
"""
_args = value.split(":")
spec = defaultdict(list, {})
for modifier in _args[1:]:
mitems = modifier.split("-", 1)
spec[mitems[0]].append(len(mitems) == 1 or mitems[1])
allspecs = _expand_entities(spec)
return [cls(_args[0], s) for s in allspecs]
class SpatialReferences:
"""
Manage specifications of spatial references.
Examples
--------
>>> sp = SpatialReferences([
... 'func',
... 'fsnative',
... 'MNI152NLin2009cAsym',
... 'anat',
... 'fsaverage5',
... 'fsaverage6',
... ('MNIPediatricAsym', {'cohort': '2'}),
... ('MNI152NLin2009cAsym', {'res': 2}),
... ('MNI152NLin2009cAsym', {'res': 1}),
... ])
>>> sp.get_spaces(standard=False)
['func', 'fsnative', 'anat']
>>> sp.get_spaces(standard=False, dim=(3,))
['func', 'anat']
>>> sp.get_spaces(nonstandard=False)
['MNI152NLin2009cAsym', 'fsaverage', 'MNIPediatricAsym:cohort-2']
>>> sp.get_spaces(nonstandard=False, dim=(3,))
['MNI152NLin2009cAsym', 'MNIPediatricAsym:cohort-2']
>>> sp.get_fs_spaces()
['fsnative', 'fsaverage5', 'fsaverage6']
>>> sp.get_standard(full_spec=True)
[Reference(space='fsaverage', spec={'den': '10k'}),
Reference(space='fsaverage', spec={'den': '41k'}),
Reference(space='MNI152NLin2009cAsym', spec={'res': 2}),
Reference(space='MNI152NLin2009cAsym', spec={'res': 1})]
>>> sp.is_cached()
False
>>> sp.cached
Traceback (most recent call last):
...
ValueError: References have not ...
>>> sp.checkpoint()
>>> sp.is_cached()
True
>>> sp.cached.references
[Reference(space='func', spec={}),
Reference(space='fsnative', spec={}),
Reference(space='MNI152NLin2009cAsym', spec={}),
Reference(space='anat', spec={}),
Reference(space='fsaverage', spec={'den': '10k'}),
Reference(space='fsaverage', spec={'den': '41k'}),
Reference(space='MNIPediatricAsym', spec={'cohort': '2'}),
Reference(space='MNI152NLin2009cAsym', spec={'res': 2}),
Reference(space='MNI152NLin2009cAsym', spec={'res': 1})]
>>> sp.cached.get_fs_spaces()
['fsnative', 'fsaverage5', 'fsaverage6']
>>> sp.add(('MNIPediatricAsym', {'cohort': '2'}))
>>> sp.get_spaces(nonstandard=False, dim=(3,))
['MNI152NLin2009cAsym', 'MNIPediatricAsym:cohort-2']
>>> sp += [('MNIPediatricAsym', {'cohort': '2'})]
Traceback (most recent call last):
...
ValueError: space ...
>>> sp += [('MNIPediatricAsym', {'cohort': '1'})]
>>> sp.get_spaces(nonstandard=False, dim=(3,))
['MNI152NLin2009cAsym', 'MNIPediatricAsym:cohort-2', 'MNIPediatricAsym:cohort-1']
>>> sp.insert(0, ('MNIPediatricAsym', {'cohort': '3'}))
>>> sp.get_spaces(nonstandard=False, dim=(3,))
['MNIPediatricAsym:cohort-3',
'MNI152NLin2009cAsym',
'MNIPediatricAsym:cohort-2',
'MNIPediatricAsym:cohort-1']
>>> sp.insert(0, ('MNIPediatricAsym', {'cohort': '3'}))
Traceback (most recent call last):
...
ValueError: space ...
>>> sp.checkpoint()
Traceback (most recent call last):
...
ValueError: References have already ...
>>> sp.checkpoint(force=True)
>>> sp.cached.references
[Reference(space='MNIPediatricAsym', spec={'cohort': '3'}),
Reference(space='func', spec={}),
Reference(space='fsnative', spec={}),
Reference(space='MNI152NLin2009cAsym', spec={}),
Reference(space='anat', spec={}),
Reference(space='fsaverage', spec={'den': '10k'}),
Reference(space='fsaverage', spec={'den': '41k'}),
Reference(space='MNIPediatricAsym', spec={'cohort': '2'}),
Reference(space='MNI152NLin2009cAsym', spec={'res': 2}),
Reference(space='MNI152NLin2009cAsym', spec={'res': 1}),
Reference(space='MNIPediatricAsym', spec={'cohort': '1'})]
"""
__slots__ = ("_refs", "_cached")
standard_spaces = tuple(_tfapi.templates())
"""List of supported standard reference spaces."""
@staticmethod
def check_space(space):
"""Build a :class:`Reference` object."""
try:
if isinstance(space, Reference):
return space
except IndexError:
pass
spec = {}
if not isinstance(space, str):
try:
spec = space[1] or {}
except IndexError:
pass
except TypeError:
space = (None,)
space = space[0]
return Reference(space, spec)
def __init__(self, spaces=None, checkpoint=False):
"""
Maintain the bookkeeping of spaces and templates.
Internal spaces are normalizations required for pipeline execution which
can vary based on user arguments.
Output spaces are desired user outputs.
"""
self._refs = []
self._cached = None
if spaces is not None:
if isinstance(spaces, str):
spaces = [spaces]
self.__iadd__(spaces)
if checkpoint is True:
self.checkpoint()
def __iadd__(self, b):
"""Append a list of transforms to the internal list."""
if not isinstance(b, (list, tuple)):
raise TypeError("Must be a list.")
for space in b:
self.append(space)
return self
def __contains__(self, item):
"""Implement the ``in`` builtin."""
if not self.references:
return False
item = self.check_space(item)
for s in self.references:
if s == item:
return True
return False
def __str__(self):
"""
Representation of this object.
Examples
--------
>>> print(SpatialReferences())
Spatial References: <none>.
>>> print(SpatialReferences(['MNI152NLin2009cAsym']))
Spatial References: MNI152NLin2009cAsym
>>> print(SpatialReferences(['MNI152NLin2009cAsym', 'fsaverage5']))
Spatial References: MNI152NLin2009cAsym, fsaverage:den-10k
"""
spaces = ", ".join([str(s) for s in self.references]) or "<none>."
return "Spatial References: %s" % spaces
@property
def references(self):
"""Get all specified references."""
return self._refs
@property
def cached(self):
"""Get cached spaces, raise error if not cached."""
if not self.is_cached():
raise ValueError("References have not been cached")
return self._cached
def is_cached(self):
return self._cached is not None
def checkpoint(self, force=False):
"""Cache and freeze current spaces to separate attribute."""
if self.is_cached() and not force:
raise ValueError("References have already been cached")
self._cached = self.__class__(self.references)
def add(self, value):
"""Add one more space, without erroring if it exists."""
if value not in self:
self._refs += [self.check_space(value)]
def append(self, value):
"""Concatenate one more space."""
if value not in self:
self._refs += [self.check_space(value)]
return
raise ValueError('space "%s" already in spaces.' % str(value))
def insert(self, index, value, error=True):
"""Concatenate one more space."""
if value not in self:
self._refs.insert(index, self.check_space(value))
elif error is True:
raise ValueError('space "%s" already in spaces.' % str(value))
def get_spaces(self, standard=True, nonstandard=True, dim=(2, 3)):
"""
Return space names.
Parameters
----------
standard : :obj:`bool`, optional
Return standard spaces.
nonstandard : :obj:`bool`, optional
Return nonstandard spaces.
dim : :obj:`tuple`, optional
Desired dimensions of the standard spaces (default is ``(2, 3)``)
Examples
--------
>>> spaces = SpatialReferences(['MNI152NLin6Asym', ("fsaverage", {"den": "10k"})])
>>> spaces.get_spaces()
['MNI152NLin6Asym', 'fsaverage']
>>> spaces.get_spaces(standard=False)
[]
>>> spaces.get_spaces(dim=(3,))
['MNI152NLin6Asym']
>>> spaces.add(('MNI152NLin6Asym', {'res': '2'}))
>>> spaces.get_spaces()
['MNI152NLin6Asym', 'fsaverage']
>>> spaces.add(('func', {}))
>>> spaces.get_spaces()
['MNI152NLin6Asym', 'fsaverage', 'func']
>>> spaces.get_spaces(nonstandard=False)
['MNI152NLin6Asym', 'fsaverage']
>>> spaces.get_spaces(standard=False)
['func']
"""
out = []
for s in self.references:
if (
s.fullname not in out
and (s.standard is standard or s.standard is not nonstandard)
and s.dim in dim
):
out.append(s.fullname)
return out
def get_standard(self, full_spec=False, dim=(2, 3)):
"""
Return output spaces.
Parameters
----------
full_spec : :obj:`bool`
Return only fully-specified standard references (i.e., they must either
have density or resolution set).
dim : :obj:`tuple`, optional
Desired dimensions of the standard spaces (default is ``(2, 3)``)
"""
if not full_spec:
return [s for s in self.references if s.standard and s.dim in dim]
return [
s
for s in self.references
if s.standard
and s.dim in dim
and (hasspec("res", s.spec) or hasspec("den", s.spec))
]
def get_nonstandard(self, full_spec=False, dim=(2, 3)):
"""Return nonstandard spaces."""
if not full_spec:
return [s.space for s in self.references if not s.standard and s.dim in dim]
return [
s.space
for s in self.references
if not s.standard
and s.dim in dim
and (hasspec("res", s.spec) or hasspec("den", s.spec))
]
def get_fs_spaces(self):
"""
Return FreeSurfer spaces.
Discards nonlegacy fsaverage values (i.e., with nonstandard density value).
Examples
--------
>>> SpatialReferences([
... 'fsnative',
... 'fsaverage6',
... 'fsaverage5',
... 'MNI152NLin6Asym',
... ]).get_fs_spaces()
['fsnative', 'fsaverage6', 'fsaverage5']
>>> SpatialReferences([
... 'fsnative',
... 'fsaverage6',
... Reference(space='fsaverage', spec={'den': '30k'})
... ]).get_fs_spaces()
['fsnative', 'fsaverage6']
"""
return [
s.legacyname or s.space
for s in self.references
if s.legacyname or s.space == "fsnative"
]
def get_parser():
"""Build parser object"""
from smriprep.cli.utils import ParseTemplates, output_space as _output_space
from templateflow.api import templates
from packaging.version import Version
from ..__about__ import __version__
from ..workflows.bold.resampling import NONSTANDARD_REFERENCES
from .version import check_latest, is_flagged
verstr = 'fmriprep v{}'.format(__version__)
currentv = Version(__version__)
is_release = not any((currentv.is_devrelease, currentv.is_prerelease,
currentv.is_postrelease))
parser = ArgumentParser(
description='FMRIPREP: fMRI PREProcessing workflows',
formatter_class=ArgumentDefaultsHelpFormatter)
# Arguments as specified by BIDS-Apps
# required, positional arguments
# IMPORTANT: they must go directly with the parser object
parser.add_argument(
'bids_dir',
action='store',
type=Path,
help='the root folder of a BIDS valid dataset (sub-XXXXX folders should '
'be found at the top level in this folder).')
parser.add_argument(
'output_dir',
action='store',
type=Path,
help='the output path for the outcomes of preprocessing and visual '
'reports')
parser.add_argument(
'analysis_level',
choices=['participant'],
help='processing stage to be run, only "participant" in the case of '
'FMRIPREP (see BIDS-Apps specification).')
# optional arguments
parser.add_argument('--version', action='version', version=verstr)
g_bids = parser.add_argument_group('Options for filtering BIDS queries')
g_bids.add_argument(
'--skip_bids_validation',
'--skip-bids-validation',
action='store_true',
default=False,
help=
'assume the input dataset is BIDS compliant and skip the validation')
g_bids.add_argument(
'--participant_label',
'--participant-label',
action='store',
nargs='+',
help='a space delimited list of participant identifiers or a single '
'identifier (the sub- prefix can be removed)')
# Re-enable when option is actually implemented
# g_bids.add_argument('-s', '--session-id', action='store', default='single_session',
# help='select a specific session to be processed')
# Re-enable when option is actually implemented
# g_bids.add_argument('-r', '--run-id', action='store', default='single_run',
# help='select a specific run to be processed')
g_bids.add_argument('-t',
'--task-id',
action='store',
help='select a specific task to be processed')
g_bids.add_argument(
'--echo-idx',
action='store',
type=int,
help='select a specific echo to be processed in a multiecho series')
g_perfm = parser.add_argument_group('Options to handle performance')
g_perfm.add_argument('--nthreads',
'--n_cpus',
'-n-cpus',
action='store',
type=int,
help='maximum number of threads across all processes')
g_perfm.add_argument('--omp-nthreads',
action='store',
type=int,
default=0,
help='maximum number of threads per-process')
g_perfm.add_argument(
'--mem_mb',
'--mem-mb',
action='store',
default=0,
type=int,
help='upper bound memory limit for FMRIPREP processes')
g_perfm.add_argument(
'--low-mem',
action='store_true',
help='attempt to reduce memory usage (will increase disk usage '
'in working directory)')
g_perfm.add_argument('--use-plugin',
action='store',
default=None,
help='nipype plugin configuration file')
g_perfm.add_argument('--anat-only',
action='store_true',
help='run anatomical workflows only')
g_perfm.add_argument('--boilerplate',
action='store_true',
help='generate boilerplate only')
g_perfm.add_argument(
'--ignore-aroma-denoising-errors',
action='store_true',
default=False,
help='DEPRECATED (now does nothing, see --error-on-aroma-warnings) '
'- ignores the errors ICA_AROMA returns when there are no '
'components classified as either noise or signal')
g_perfm.add_argument(
'--error-on-aroma-warnings',
action='store_true',
default=False,
help='Raise an error if ICA_AROMA does not produce sensible output '
'(e.g., if all the components are classified as signal or noise)')
g_perfm.add_argument(
"-v",
"--verbose",
dest="verbose_count",
action="count",
default=0,
help="increases log verbosity for each occurence, debug level is -vvv")
g_perfm.add_argument('--debug',
action='store_true',
default=False,
help='DEPRECATED - Does not do what you want.')
g_conf = parser.add_argument_group('Workflow configuration')
g_conf.add_argument(
'--ignore',
required=False,
action='store',
nargs="+",
default=[],
choices=['fieldmaps', 'slicetiming', 'sbref'],
help=
'ignore selected aspects of the input dataset to disable corresponding '
'parts of the workflow (a space delimited list)')
g_conf.add_argument(
'--longitudinal',
action='store_true',
help='treat dataset as longitudinal - may increase runtime')
g_conf.add_argument(
'--t2s-coreg',
action='store_true',
help=
'If provided with multi-echo BOLD dataset, create T2*-map and perform '
'T2*-driven coregistration. When multi-echo data is provided and this '
'option is not enabled, standard EPI-T1 coregistration is performed '
'using the middle echo.')
g_conf.add_argument(
'--output-spaces',
nargs='+',
action=ParseTemplates,
help="""\
Standard and non-standard spaces to resample anatomical and functional images to. \
Standard spaces may be specified by the form \
``<TEMPLATE>[:res-<resolution>][:cohort-<label>][...]``, where ``<TEMPLATE>`` is \
a keyword (valid keywords: %s) or path pointing to a user-supplied template, and \
may be followed by optional, colon-separated parameters. \
Non-standard spaces (valid keywords: %s) imply specific orientations and sampling \
grids. \
Important to note, the ``res-*`` modifier does not define the resolution used for \
the spatial normalization.
For further details, please check out \
https://fmriprep.readthedocs.io/en/%s/spaces.html""" %
(', '.join('"%s"' % s
for s in templates()), ', '.join(NONSTANDARD_REFERENCES),
currentv.base_version if is_release else 'latest'))
g_conf.add_argument(
'--output-space',
required=False,
action='store',
type=str,
nargs='+',
choices=[
'T1w', 'template', 'fsnative', 'fsaverage', 'fsaverage6',
'fsaverage5'
],
help='DEPRECATED: please use ``--output-spaces`` instead.')
g_conf.add_argument(
'--template',
required=False,
action='store',
type=str,
choices=['MNI152NLin2009cAsym'],
help='volume template space (default: MNI152NLin2009cAsym). '
'DEPRECATED: please use ``--output-spaces`` instead.')
g_conf.add_argument(
'--template-resampling-grid',
required=False,
action='store',
help='Keyword ("native", "1mm", or "2mm") or path to an existing file. '
'Allows to define a reference grid for the resampling of BOLD images in template '
'space. Keyword "native" will use the original BOLD grid as reference. '
'Keywords "1mm" and "2mm" will use the corresponding isotropic template '
'resolutions. If a path is given, the grid of that image will be used. '
'It determines the field of view and resolution of the output images, '
'but is not used in normalization. '
'DEPRECATED: please use ``--output-spaces`` instead.')
g_conf.add_argument(
'--bold2t1w-dof',
action='store',
default=6,
choices=[6, 9, 12],
type=int,
help='Degrees of freedom when registering BOLD to T1w images. '
'6 degrees (rotation and translation) are used by default.')
g_conf.add_argument(
'--force-bbr',
action='store_true',
dest='use_bbr',
default=None,
help=
'Always use boundary-based registration (no goodness-of-fit checks)')
g_conf.add_argument(
'--force-no-bbr',
action='store_false',
dest='use_bbr',
default=None,
help=
'Do not use boundary-based registration (no goodness-of-fit checks)')
g_conf.add_argument(
'--medial-surface-nan',
required=False,
action='store_true',
default=False,
help=
'Replace medial wall values with NaNs on functional GIFTI files. Only '
'performed for GIFTI files mapped to a freesurfer subject (fsaverage or fsnative).'
)
g_conf.add_argument('--dummy-scans',
required=False,
action='store',
default=None,
type=int,
help='Number of non steady state volumes.')
# ICA_AROMA options
g_aroma = parser.add_argument_group(
'Specific options for running ICA_AROMA')
g_aroma.add_argument('--use-aroma',
action='store_true',
default=False,
help='add ICA_AROMA to your preprocessing stream')
g_aroma.add_argument(
'--aroma-melodic-dimensionality',
action='store',
default=-200,
type=int,
help='Exact or maximum number of MELODIC components to estimate '
'(positive = exact, negative = maximum)')
# Confounds options
g_confounds = parser.add_argument_group(
'Specific options for estimating confounds')
g_confounds.add_argument(
'--return-all-components',
required=False,
action='store_true',
default=False,
help=
'Include all components estimated in CompCor decomposition in the confounds '
'file instead of only the components sufficient to explain 50 percent of '
'BOLD variance in each CompCor mask')
g_confounds.add_argument(
'--fd-spike-threshold',
required=False,
action='store',
default=0.5,
type=float,
help=
'Threshold for flagging a frame as an outlier on the basis of framewise '
'displacement')
g_confounds.add_argument(
'--dvars-spike-threshold',
required=False,
action='store',
default=1.5,
type=float,
help=
'Threshold for flagging a frame as an outlier on the basis of standardised '
'DVARS')
# ANTs options
g_ants = parser.add_argument_group(
'Specific options for ANTs registrations')
g_ants.add_argument(
'--skull-strip-template',
action='store',
default='OASIS30ANTs',
type=_output_space,
help='select a template for skull-stripping with antsBrainExtraction')
g_ants.add_argument(
'--skull-strip-fixed-seed',
action='store_true',
help='do not use a random seed for skull-stripping - will ensure '
'run-to-run replicability when used with --omp-nthreads 1')
# Fieldmap options
g_fmap = parser.add_argument_group(
'Specific options for handling fieldmaps')
g_fmap.add_argument(
'--fmap-bspline',
action='store_true',
default=False,
help='fit a B-Spline field using least-squares (experimental)')
g_fmap.add_argument(
'--fmap-no-demean',
action='store_false',
default=True,
help='do not remove median (within mask) from fieldmap')
# SyN-unwarp options
g_syn = parser.add_argument_group(
'Specific options for SyN distortion correction')
g_syn.add_argument(
'--use-syn-sdc',
action='store_true',
default=False,
help='EXPERIMENTAL: Use fieldmap-free distortion correction')
g_syn.add_argument(
'--force-syn',
action='store_true',
default=False,
help='EXPERIMENTAL/TEMPORARY: Use SyN correction in addition to '
'fieldmap correction, if available')
# FreeSurfer options
g_fs = parser.add_argument_group(
'Specific options for FreeSurfer preprocessing')
g_fs.add_argument(
'--fs-license-file',
metavar='PATH',
type=Path,
help=
'Path to FreeSurfer license key file. Get it (for free) by registering'
' at https://surfer.nmr.mgh.harvard.edu/registration.html')
# Surface generation xor
g_surfs = parser.add_argument_group('Surface preprocessing options')
g_surfs.add_argument('--no-submm-recon',
action='store_false',
dest='hires',
help='disable sub-millimeter (hires) reconstruction')
g_surfs_xor = g_surfs.add_mutually_exclusive_group()
g_surfs_xor.add_argument('--cifti-output',
action='store_true',
default=False,
help='output BOLD files as CIFTI dtseries')
g_surfs_xor.add_argument(
'--fs-no-reconall',
'--no-freesurfer',
action='store_false',
dest='run_reconall',
help='disable FreeSurfer surface preprocessing.'
' Note : `--no-freesurfer` is deprecated and will be removed in 1.2.'
' Use `--fs-no-reconall` instead.')
g_other = parser.add_argument_group('Other options')
g_other.add_argument(
'-w',
'--work-dir',
action='store',
type=Path,
default=Path('work'),
help='path where intermediate results should be stored')
g_other.add_argument(
'--resource-monitor',
action='store_true',
default=False,
help=
'enable Nipype\'s resource monitoring to keep track of memory and CPU usage'
)
g_other.add_argument(
'--reports-only',
action='store_true',
default=False,
help=
'only generate reports, don\'t run workflows. This will only rerun report '
'aggregation, not reportlet generation for specific nodes.')
g_other.add_argument(
'--run-uuid',
action='store',
default=None,
help='Specify UUID of previous run, to include error logs in report. '
'No effect without --reports-only.')
g_other.add_argument('--write-graph',
action='store_true',
default=False,
help='Write workflow graph.')
g_other.add_argument(
'--stop-on-first-crash',
action='store_true',
default=False,
help='Force stopping on first crash, even if a work directory'
' was specified.')
g_other.add_argument(
'--notrack',
action='store_true',
default=False,
help='Opt-out of sending tracking information of this run to '
'the FMRIPREP developers. This information helps to '
'improve FMRIPREP and provides an indicator of real '
'world usage crucial for obtaining funding.')
g_other.add_argument('--sloppy',
action='store_true',
default=False,
help='Use low-quality tools for speed - TESTING ONLY')
latest = check_latest()
if latest is not None and currentv < latest:
print("""\
You are using fMRIPrep-%s, and a newer version of fMRIPrep is available: %s.
Please check out our documentation about how and when to upgrade:
https://fmriprep.readthedocs.io/en/latest/faq.html#upgrading""" %
(__version__, latest),
file=sys.stderr)
_blist = is_flagged()
if _blist[0]:
_reason = _blist[1] or 'unknown'
print("""\
WARNING: Version %s of fMRIPrep (current) has been FLAGGED
(reason: %s).
That means some severe flaw was found in it and we strongly
discourage its usage.""" % (__version__, _reason),
file=sys.stderr)
return parser
def get_parser():
"""Build parser object"""
from smriprep.cli.utils import ParseTemplates, output_space as _output_space
from templateflow.api import templates
from packaging.version import Version
from ..__about__ import __version__
from ..config import NONSTANDARD_REFERENCES
from .version import check_latest, is_flagged
verstr = 'dmriprep v{}'.format(__version__)
currentv = Version(__version__)
is_release = not any((currentv.is_devrelease, currentv.is_prerelease,
currentv.is_postrelease))
parser = ArgumentParser(
description='dMRIPrep: dMRI PREProcessing workflows',
formatter_class=ArgumentDefaultsHelpFormatter)
# Arguments as specified by BIDS-Apps
# required, positional arguments
# IMPORTANT: they must go directly with the parser object
parser.add_argument(
'bids_dir',
action='store',
type=Path,
help='the root folder of a BIDS valid dataset (sub-XXXXX folders should '
'be found at the top level in this folder).')
parser.add_argument(
'output_dir',
action='store',
type=Path,
help='the output path for the outcomes of preprocessing and visual '
'reports')
parser.add_argument(
'analysis_level',
choices=['participant'],
help='processing stage to be run, only "participant" in the case of '
'dMRIPrep (see BIDS-Apps specification).')
# optional arguments
parser.add_argument('--version', action='version', version=verstr)
g_bids = parser.add_argument_group('Options for filtering BIDS queries')
g_bids.add_argument(
'--skip-bids-validation',
action='store_true',
default=False,
help=
'assume the input dataset is BIDS compliant and skip the validation')
g_bids.add_argument(
'--participant_label',
'--participant-label',
action='store',
nargs='+',
help='a space delimited list of participant identifiers or a single '
'identifier (the sub- prefix can be removed)')
# Re-enable when option is actually implemented
# g_bids.add_argument('-s', '--session-id', action='store', default='single_session',
# help='select a specific session to be processed')
# Re-enable when option is actually implemented
# g_bids.add_argument('-r', '--run-id', action='store', default='single_run',
# help='select a specific run to be processed')
g_perfm = parser.add_argument_group('Options to handle performance')
g_perfm.add_argument('--nprocs',
'--n_cpus',
'-n-cpus',
action='store',
type=int,
help='maximum number of threads across all processes')
g_perfm.add_argument('--omp-nthreads',
action='store',
type=int,
default=0,
help='maximum number of threads per-process')
g_perfm.add_argument(
'--mem_mb',
'--mem-mb',
action='store',
default=0,
type=int,
help='upper bound memory limit for dMRIPrep processes')
g_perfm.add_argument(
'--low-mem',
action='store_true',
help='attempt to reduce memory usage (will increase disk usage '
'in working directory)')
g_perfm.add_argument('--use-plugin',
action='store',
default=None,
help='nipype plugin configuration file')
g_perfm.add_argument('--anat-only',
action='store_true',
help='run anatomical workflows only')
g_perfm.add_argument('--boilerplate',
action='store_true',
help='generate boilerplate only')
g_perfm.add_argument(
"-v",
"--verbose",
dest="verbose_count",
action="count",
default=0,
help="increases log verbosity for each occurence, debug level is -vvv")
g_conf = parser.add_argument_group('Workflow configuration')
g_conf.add_argument(
'--ignore',
required=False,
action='store',
nargs="+",
default=[],
choices=['sdc'],
help=
'ignore selected aspects of the input dataset to disable corresponding '
'parts of the workflow (a space delimited list)')
g_conf.add_argument(
'--longitudinal',
action='store_true',
help='treat dataset as longitudinal - may increase runtime')
g_conf.add_argument(
'--output-spaces',
nargs='+',
action=ParseTemplates,
help="""\
Standard and non-standard spaces to resample anatomical and functional images to. \
Standard spaces may be specified by the form \
``<TEMPLATE>[:res-<resolution>][:cohort-<label>][...]``, where ``<TEMPLATE>`` is \
a keyword (valid keywords: %s) or path pointing to a user-supplied template, and \
may be followed by optional, colon-separated parameters. \
Non-standard spaces (valid keywords: %s) imply specific orientations and sampling \
grids. \
Important to note, the ``res-*`` modifier does not define the resolution used for \
the spatial normalization.
For further details, please check out \
https://dmriprep.readthedocs.io/en/%s/spaces.html""" %
(', '.join('"%s"' % s
for s in templates()), ', '.join(NONSTANDARD_REFERENCES),
currentv.base_version if is_release else 'latest'))
# ANTs options
g_ants = parser.add_argument_group(
'Specific options for ANTs registrations')
g_ants.add_argument(
'--skull-strip-template',
action='store',
default='OASIS30ANTs',
type=_output_space,
help='select a template for skull-stripping with antsBrainExtraction')
g_ants.add_argument(
'--skull-strip-fixed-seed',
action='store_true',
help='do not use a random seed for skull-stripping - will ensure '
'run-to-run replicability when used with --omp-nthreads 1')
# Fieldmap options
g_fmap = parser.add_argument_group(
'Specific options for handling fieldmaps')
g_fmap.add_argument(
'--fmap-bspline',
action='store_true',
default=False,
help='fit a B-Spline field using least-squares (experimental)')
g_fmap.add_argument(
'--fmap-no-demean',
action='store_false',
default=True,
help='do not remove median (within mask) from fieldmap')
# SyN-unwarp options
g_syn = parser.add_argument_group(
'Specific options for SyN distortion correction')
g_syn.add_argument(
'--use-syn-sdc',
action='store_true',
default=False,
help='EXPERIMENTAL: Use fieldmap-free distortion correction')
g_syn.add_argument(
'--force-syn',
action='store_true',
default=False,
help='EXPERIMENTAL/TEMPORARY: Use SyN correction in addition to '
'fieldmap correction, if available')
# FreeSurfer options
g_fs = parser.add_argument_group(
'Specific options for FreeSurfer preprocessing')
g_fs.add_argument(
'--fs-license-file',
metavar='PATH',
type=Path,
help=
'Path to FreeSurfer license key file. Get it (for free) by registering'
' at https://surfer.nmr.mgh.harvard.edu/registration.html')
# Surface generation xor
g_surfs = parser.add_argument_group('Surface preprocessing options')
g_surfs_xor = g_surfs.add_mutually_exclusive_group()
g_surfs_xor.add_argument(
'--no-submm-recon',
action='store_false',
dest='hires',
help='disable sub-millimeter (hires) reconstruction')
g_surfs_xor.add_argument(
'--fs-no-reconall',
'--no-freesurfer',
action='store_false',
dest='run_reconall',
help='disable FreeSurfer surface preprocessing.'
' Note : `--no-freesurfer` is deprecated and will be removed in 1.2.'
' Use `--fs-no-reconall` instead.')
g_other = parser.add_argument_group('Other options')
g_other.add_argument(
'-w',
'--work-dir',
action='store',
type=Path,
default=Path('work'),
help='path where intermediate results should be stored')
g_other.add_argument(
'--resource-monitor',
action='store_true',
default=False,
help=
'enable Nipype\'s resource monitoring to keep track of memory and CPU usage'
)
g_other.add_argument(
'--reports-only',
action='store_true',
default=False,
help=
'only generate reports, don\'t run workflows. This will only rerun report '
'aggregation, not reportlet generation for specific nodes.')
g_other.add_argument(
'--run-uuid',
action='store',
default=None,
help='Specify UUID of previous run, to include error logs in report. '
'No effect without --reports-only.')
g_other.add_argument('--write-graph',
action='store_true',
default=False,
help='Write workflow graph.')
g_other.add_argument(
'--stop-on-first-crash',
action='store_true',
default=False,
help='Force stopping on first crash, even if a work directory'
' was specified.')
g_other.add_argument(
'--notrack',
action='store_true',
default=False,
help='Opt-out of sending tracking information of this run to '
'the dMRIPrep developers. This information helps to '
'improve dMRIPrep and provides an indicator of real '
'world usage crucial for obtaining funding.')
g_other.add_argument('--sloppy',
action='store_true',
default=False,
dest='debug',
help='Use low-quality tools for speed - TESTING ONLY')
latest = check_latest()
if latest is not None and currentv < latest:
print("""\
You are using dMRIPrep-%s, and a newer version of dMRIPrep is available: %s.
Please check out our documentation about how and when to upgrade:
https://dmriprep.readthedocs.io/en/latest/faq.html#upgrading""" %
(__version__, latest),
file=sys.stderr)
_blist = is_flagged()
if _blist[0]:
_reason = _blist[1] or 'unknown'
print("""\
WARNING: Version %s of dMRIPrep (current) has been FLAGGED
(reason: %s).
That means some severe flaw was found in it and we strongly
discourage its usage.""" % (__version__, _reason),
file=sys.stderr)
return parser
def get_parser():
"""Build parser object"""
from pathlib import Path
from argparse import ArgumentParser
from argparse import RawTextHelpFormatter
from templateflow.api import templates
from .utils import ParseTemplates, output_space as _output_space
from ..__about__ import __version__
parser = ArgumentParser(description='sMRIPrep: Structural MRI PREProcessing workflows',
formatter_class=RawTextHelpFormatter)
# Arguments as specified by BIDS-Apps
# required, positional arguments
# IMPORTANT: they must go directly with the parser object
parser.add_argument('bids_dir', action='store', type=Path,
help='the root folder of a BIDS valid dataset (sub-XXXXX folders should '
'be found at the top level in this folder).')
parser.add_argument('output_dir', action='store', type=Path,
help='the output path for the outcomes of preprocessing and visual '
'reports')
parser.add_argument('analysis_level', choices=['participant'],
help='processing stage to be run, only "participant" in the case of '
'sMRIPrep (see BIDS-Apps specification).')
# optional arguments
parser.add_argument('--version', action='version', version='smriprep v{}'.format(__version__))
g_bids = parser.add_argument_group('Options for filtering BIDS queries')
g_bids.add_argument('--participant-label', '--participant_label', action='store', nargs='+',
help='a space delimited list of participant identifiers or a single '
'identifier (the sub- prefix can be removed)')
g_perfm = parser.add_argument_group('Options to handle performance')
g_perfm.add_argument('--nprocs', '--ncpus', '--nthreads', '--n_cpus', '-n-cpus',
action='store', type=int,
help='number of CPUs to be used.')
g_perfm.add_argument('--omp-nthreads', action='store', type=int, default=0,
help='maximum number of threads per-process')
g_perfm.add_argument('--mem-gb', '--mem_gb', action='store', default=0, type=float,
help='upper bound memory limit for sMRIPrep processes (in GB).')
g_perfm.add_argument('--low-mem', action='store_true',
help='attempt to reduce memory usage (will increase disk usage '
'in working directory)')
g_perfm.add_argument('--use-plugin', action='store', default=None,
help='nipype plugin configuration file')
g_perfm.add_argument('--boilerplate', action='store_true',
help='generate boilerplate only')
g_perfm.add_argument("-v", "--verbose", dest="verbose_count", action="count", default=0,
help="increases log verbosity for each occurence, debug level is -vvv")
g_conf = parser.add_argument_group('Workflow configuration')
g_conf.add_argument(
'--output-spaces', nargs='+', action=ParseTemplates,
help='paths or keywords prescribing standard spaces to which normalize the input T1w image'
' (valid keywords are: %s).' % ', '.join('"%s"' % s for s in templates()))
g_conf.add_argument(
'--longitudinal', action='store_true',
help='treat dataset as longitudinal - may increase runtime')
g_conf.add_argument(
'--template', '--spatial-normalization-target', action='store', type=str,
choices=[tpl for tpl in templates() if not tpl.startswith('fs')],
help='DEPRECATED: spatial normalization target (one TemplateFlow Identifier)')
# ANTs options
g_ants = parser.add_argument_group('Specific options for ANTs registrations')
g_ants.add_argument(
'--skull-strip-template', action='store', default='OASIS30ANTs', type=_output_space,
help='select a template for skull-stripping with antsBrainExtraction')
g_ants.add_argument('--skull-strip-fixed-seed', action='store_true',
help='do not use a random seed for skull-stripping - will ensure '
'run-to-run replicability when used with --omp-nthreads 1')
# FreeSurfer options
g_fs = parser.add_argument_group('Specific options for FreeSurfer preprocessing')
g_fs.add_argument(
'--fs-license-file', metavar='PATH', type=Path,
help='Path to FreeSurfer license key file. Get it (for free) by registering'
' at https://surfer.nmr.mgh.harvard.edu/registration.html')
g_fs.add_argument(
'--fs-subjects-dir', metavar='PATH', type=Path,
help='Path to existing FreeSurfer subjects directory to reuse. '
'(default: OUTPUT_DIR/freesurfer)')
# Surface generation xor
g_surfs = parser.add_argument_group('Surface preprocessing options')
g_surfs.add_argument('--no-submm-recon', action='store_false', dest='hires',
help='disable sub-millimeter (hires) reconstruction')
g_surfs_xor = g_surfs.add_mutually_exclusive_group()
g_surfs_xor.add_argument(
'--fs-output-spaces', required=False, action='store', nargs='+',
choices=['fsnative', 'fsaverage', 'fsaverage6', 'fsaverage5'],
help="""DEPRECATED - configure Freesurfer's output spaces:
- fsnative: individual subject surface
- fsaverage*: FreeSurfer average meshes
this argument can be single value or a space delimited list,
for example: --fs-output-spaces fsnative fsaverage fsaverage5."""
)
g_surfs_xor.add_argument('--fs-no-reconall', '--no-freesurfer',
action='store_false', dest='run_reconall',
help='disable FreeSurfer surface preprocessing.'
' Note : `--no-freesurfer` is deprecated and will be removed in 1.2.'
' Use `--fs-no-reconall` instead.')
g_other = parser.add_argument_group('Other options')
g_other.add_argument('-w', '--work-dir', action='store', type=Path, default=Path('work'),
help='path where intermediate results should be stored')
g_other.add_argument(
'--resource-monitor', action='store_true', default=False,
help='enable Nipype\'s resource monitoring to keep track of memory and CPU usage')
g_other.add_argument(
'--reports-only', action='store_true', default=False,
help='only generate reports, don\'t run workflows. This will only rerun report '
'aggregation, not reportlet generation for specific nodes.')
g_other.add_argument(
'--run-uuid', action='store', default=None,
help='Specify UUID of previous run, to include error logs in report. '
'No effect without --reports-only.')
g_other.add_argument('--write-graph', action='store_true', default=False,
help='Write workflow graph.')
g_other.add_argument('--stop-on-first-crash', action='store_true', default=False,
help='Force stopping on first crash, even if a work directory'
' was specified.')
g_other.add_argument('--notrack', action='store_true', default=False,
help='Opt-out of sending tracking information of this run to '
'the sMRIPrep developers. This information helps to '
'improve sMRIPrep and provides an indicator of real '
'world usage crucial for obtaining funding.')
g_other.add_argument('--sloppy', action='store_true', default=False,
help='Use low-quality tools for speed - TESTING ONLY')
return parser
def init_anat_norm_wf(
debug,
omp_nthreads,
reportlets_dir,
template_list,
template_specs=None,
):
"""
An individual spatial normalization workflow using ``antsRegistration``.
.. workflow ::
:graph2use: orig
:simple_form: yes
from smriprep.workflows.norm import init_anat_norm_wf
wf = init_anat_norm_wf(
debug=False,
omp_nthreads=1,
reportlets_dir='.',
template_list=['MNI152NLin2009cAsym', 'MNI152NLin6Asym'],
)
**Parameters**
debug : bool
Apply sloppy arguments to speed up processing. Use with caution,
registration processes will be very inaccurate.
omp_nthreads : int
Maximum number of threads an individual process may use.
reportlets_dir : str
Directory in which to save reportlets.
template_list : list of str
List of TemplateFlow identifiers (e.g. ``MNI152NLin6Asym``) that
specifies the target template for spatial normalization. In the
future, this parameter should accept also paths to custom/private
templates with TemplateFlow's organization.
**Inputs**
moving_image
The input image that will be normalized to standard space.
moving_mask
A precise brain mask separating skull/skin/fat from brain
structures.
moving_segmentation
A brain tissue segmentation of the ``moving_image``.
moving_tpms
tissue probability maps (TPMs) corresponding to the
``moving_segmentation``.
lesion_mask
(optional) A mask to exclude regions from the cost-function
input domain to enable standardization of lesioned brains.
orig_t1w
The original T1w image from the BIDS structure.
**Outputs**
warped
The T1w after spatial normalization, in template space.
forward_transform
The T1w-to-template transform.
reverse_transform
The template-to-T1w transform.
tpl_mask
The ``moving_mask`` in template space (matches ``warped`` output).
tpl_seg
The ``moving_segmentation`` in template space (matches ``warped``
output).
tpl_tpms
The ``moving_tpms`` in template space (matches ``warped`` output).
template
The input parameter ``template`` for further use in nodes depending
on this
workflow.
"""
if not isinstance(template_list, (list, tuple)):
template_list = [template_list]
templateflow = templates()
if any(template not in templateflow for template in template_list):
raise NotImplementedError(
'This is embarrassing - custom templates are not (yet) supported.'
'Please make sure none of the options already available via TemplateFlow '
'fit your needs.')
workflow = Workflow('anat_norm_wf')
workflow.__desc__ = """\
Volume-based spatial normalization to {targets} ({targets_id}) was performed through
nonlinear registration with `antsRegistration` (ANTs {ants_ver}),
using brain-extracted versions of both T1w reference and the T1w template.
The following template{tpls} selected for spatial normalization:
""".format(ants_ver=ANTsInfo.version() or '(version unknown)',
targets='%s standard space%s' %
(defaultdict('several'.format, {
1: 'one',
2: 'two',
3: 'three',
4: 'four'
})[len(template_list)], 's' * (len(template_list) != 1)),
targets_id=', '.join(template_list),
tpls=(' was', 's were')[len(template_list) != 1])
if not template_specs:
template_specs = [{}] * len(template_list)
if len(template_list) != len(template_specs):
raise RuntimeError(
'Number of templates (%d) doesn\'t match the number of specs '
'(%d) provided.' % (len(template_list), len(template_specs)))
# Append template citations to description
for template in template_list:
template_meta = get_metadata(template)
template_refs = ['@%s' % template.lower()]
if template_meta.get('RRID', None):
template_refs += ['RRID:%s' % template_meta['RRID']]
workflow.__desc__ += """\
*{template_name}* [{template_refs}; TemplateFlow ID: {template}]""".format(
template=template,
template_name=template_meta['Name'],
template_refs=', '.join(template_refs))
workflow.__desc__ += (', ', '.')[template == template_list[-1]]
inputnode = pe.Node(niu.IdentityInterface(fields=[
'moving_image', 'moving_mask', 'moving_segmentation', 'moving_tpms',
'lesion_mask', 'orig_t1w', 'template'
]),
name='inputnode')
inputnode.iterables = [('template', template_list)]
out_fields = [
'warped', 'forward_transform', 'reverse_transform', 'tpl_mask',
'tpl_seg', 'tpl_tpms', 'template'
]
poutputnode = pe.Node(niu.IdentityInterface(fields=out_fields),
name='poutputnode')
tpl_specs = pe.Node(niu.Function(function=_select_specs),
name='tpl_specs',
run_without_submitting=True)
tpl_specs.inputs.template_list = template_list
tpl_specs.inputs.template_specs = template_specs
tpl_select = pe.Node(niu.Function(function=_get_template),
name='tpl_select',
run_without_submitting=True)
# With the improvements from poldracklab/niworkflows#342 this truncation is now necessary
trunc_mov = pe.Node(ImageMath(operation='TruncateImageIntensity',
op2='0.01 0.999 256'),
name='trunc_mov')
registration = pe.Node(RobustMNINormalization(
float=True,
flavor=['precise', 'testing'][debug],
),
name='registration',
n_procs=omp_nthreads,
mem_gb=2)
# Resample T1w-space inputs
tpl_moving = pe.Node(ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='LanczosWindowedSinc'),
name='tpl_moving')
tpl_mask = pe.Node(ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='MultiLabel'),
name='tpl_mask')
tpl_seg = pe.Node(ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='MultiLabel'),
name='tpl_seg')
tpl_tpms = pe.MapNode(ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='Gaussian'),
iterfield=['input_image'],
name='tpl_tpms')
workflow.connect([
(inputnode, tpl_specs, [('template', 'template')]),
(inputnode, tpl_select, [('template', 'template')]),
(inputnode, registration, [('template', 'template')]),
(inputnode, trunc_mov, [('moving_image', 'op1')]),
(inputnode, registration, [('moving_mask', 'moving_mask'),
('lesion_mask', 'lesion_mask')]),
(inputnode, tpl_moving, [('moving_image', 'input_image')]),
(inputnode, tpl_mask, [('moving_mask', 'input_image')]),
(tpl_specs, tpl_select, [('out', 'template_spec')]),
(tpl_specs, registration, [(('out', _drop_res), 'template_spec')]),
(tpl_select, tpl_moving, [('out', 'reference_image')]),
(tpl_select, tpl_mask, [('out', 'reference_image')]),
(tpl_select, tpl_seg, [('out', 'reference_image')]),
(tpl_select, tpl_tpms, [('out', 'reference_image')]),
(trunc_mov, registration, [('output_image', 'moving_image')]),
(registration, tpl_moving, [('composite_transform', 'transforms')]),
(registration, tpl_mask, [('composite_transform', 'transforms')]),
(inputnode, tpl_seg, [('moving_segmentation', 'input_image')]),
(registration, tpl_seg, [('composite_transform', 'transforms')]),
(inputnode, tpl_tpms, [('moving_tpms', 'input_image')]),
(registration, tpl_tpms, [('composite_transform', 'transforms')]),
(registration, poutputnode,
[('composite_transform', 'forward_transform'),
('inverse_composite_transform', 'reverse_transform')]),
(tpl_moving, poutputnode, [('output_image', 'warped')]),
(tpl_mask, poutputnode, [('output_image', 'tpl_mask')]),
(tpl_seg, poutputnode, [('output_image', 'tpl_seg')]),
(tpl_tpms, poutputnode, [('output_image', 'tpl_tpms')]),
(inputnode, poutputnode, [('template', 'template')]),
])
# Generate and store report
msk_select = pe.Node(niu.Function(
function=_get_template,
input_names=['template', 'template_spec', 'suffix', 'desc']),
name='msk_select',
run_without_submitting=True)
msk_select.inputs.desc = 'brain'
msk_select.inputs.suffix = 'mask'
norm_msk = pe.Node(niu.Function(
function=_rpt_masks,
output_names=['before', 'after'],
input_names=['mask_file', 'before', 'after', 'after_mask']),
name='norm_msk')
norm_rpt = pe.Node(SimpleBeforeAfter(), name='norm_rpt', mem_gb=0.1)
norm_rpt.inputs.after_label = 'Participant' # after
ds_t1_2_tpl_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, keep_dtype=True),
name='ds_t1_2_tpl_report',
run_without_submitting=True)
workflow.connect([
(inputnode, msk_select, [('template', 'template')]),
(inputnode, norm_rpt, [('template', 'before_label')]),
(tpl_mask, norm_msk, [('output_image', 'after_mask')]),
(tpl_specs, msk_select, [('out', 'template_spec')]),
(msk_select, norm_msk, [('out', 'mask_file')]),
(tpl_select, norm_msk, [('out', 'before')]),
(tpl_moving, norm_msk, [('output_image', 'after')]),
(norm_msk, norm_rpt, [('before', 'before'), ('after', 'after')]),
(inputnode, ds_t1_2_tpl_report, [('template', 'space'),
('orig_t1w', 'source_file')]),
(norm_rpt, ds_t1_2_tpl_report, [('out_report', 'in_file')]),
])
# Provide synchronized output
outputnode = pe.JoinNode(niu.IdentityInterface(fields=out_fields),
name='outputnode',
joinsource='inputnode')
workflow.connect([
(poutputnode, outputnode, [(f, f) for f in out_fields]),
])
return workflow
