def _run_interface(self, runtime):
# Setup useful properties
self.conf_raw = pd.read_csv(self.inputs.conf_raw, sep='\t')
with open(self.inputs.conf_json, 'r') as json_file:
self.conf_json = json.load(json_file)
self.n_volumes = len(self.conf_raw)
self.conf_prep = pd.DataFrame()
# entities
entities = parse_file_entities_with_pipelines(self.inputs.conf_raw)
# Create preprocessed confounds step-by-step
self._filter_motion_parameters()
self._filter_tissue_signals()
self._filter_acompcors()
self._create_spike_regressors()
self._create_summary_dict(subject=entities.get('subject'),
task=entities.get('task'),
session=entities.get('session'),
run=entities.get('run'))
# Store output
entities['pipeline'] = self.inputs.pipeline['name']
conf_prep = join(self.inputs.output_dir,
build_path(entities, self.conf_prep_pattern, False))
conf_summary = join(
self.inputs.output_dir,
build_path(entities, self.conf_summary_pattern, False))
self.conf_prep.to_csv(conf_prep, sep='\t', index=False, na_rep=0)
with open(conf_summary, 'w') as f:
json.dump(self.conf_summary, f)
self._results['conf_prep'] = conf_prep
self._results['conf_summary'] = conf_summary
return runtime
def _run_interface(self, runtime):
fname = self.inputs.fmri_denoised
entities = parse_file_entities(fname)
bold_img = nb.load(fname)
parcellation_file = get_parcellation_file_path(entities['space'])
masker = NiftiLabelsMasker(labels_img=parcellation_file,
standardize=True)
time_series = masker.fit_transform(bold_img, confounds=None)
corr_measure = ConnectivityMeasure(kind='correlation')
corr_mat = corr_measure.fit_transform([time_series])[0]
entities['pipeline'] = extract_pipeline_from_path(fname)
conn_file = join(self.inputs.output_dir,
build_path(entities, self.conn_file_pattern, False))
carpet_plot_file = join(
self.inputs.output_dir,
build_path(entities, self.carpet_plot_pattern, False))
matrix_plot_file = join(
self.inputs.output_dir,
build_path(entities, self.matrix_plot_pattern, False))
make_carpetplot(time_series, carpet_plot_file)
mplot = plot_matrix(corr_mat, vmin=-1, vmax=1)
mplot.figure.savefig(matrix_plot_file)
np.save(conn_file, corr_mat)
self._results['corr_mat'] = conn_file
self._results['carpet_plot'] = carpet_plot_file
self._results['matrix_plot'] = matrix_plot_file
return runtime
def test_strict_build_path(self):
# Test with strict matching--should fail
pats = ['[{session}/]{task}/r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz']
entities = {'subject': 1, 'task': "A", 'run': 2}
assert build_path(entities, pats, True)
entities = {'subject': 1, 'task': "A", 'age': 22}
assert not build_path(entities, pats, True)
def _list_outputs(self):
from bids.layout.writing import build_path
base_dir = Path(self.inputs.base_directory)
base_dir.mkdir(exist_ok=True, parents=True) # pylint: disable=E1123
path_patterns = self.inputs.path_patterns
if not isdefined(path_patterns):
path_patterns = None
out_files = []
for entities, in_file in zip(self.inputs.entities,
self.inputs.in_file):
ents = {**self.inputs.fixed_entities}
ents.update(entities)
ents = {k: snake_to_camel(str(v)) for k, v in ents.items()}
out_fname = base_dir / build_path(ents, path_patterns)
out_fname.parent.mkdir(exist_ok=True, parents=True)
_copy_or_convert(in_file, out_fname)
out_files.append(out_fname)
return {"out_file": out_files}
def build_path(entities, path_patterns, strict=False):
"""
Extension of bids.build_path that throws exception instead of returning None
Args:
entities:
A dictionary mapping entity names to entity values.
Entities with ``None`` or empty-string value will be removed.
Otherwise, entities will be cast to string values, therefore
if any format is expected (e.g., zero-padded integers), the
value should be formatted.
path_patterns:
A dictionary mapping entity names to entity values.
Entities with ``None`` or empty-string value will be removed.
Otherwise, entities will be cast to string values, therefore
if any format is expected (e.g., zero-padded integers), the
value should be formatted.
strict:
If True, all passed entities must be matched inside a
pattern in order to be a valid match. If False, extra entities will
be ignored so long as all mandatory entities are found.
Returns: built path
"""
path = writing.build_path(entities, path_patterns, strict)
if path is not None:
return path
else:
raise ValueError(
f"Unable to build path with given entities: {entities}\n and path pattern {path_patterns}"
)
def _run_interface(self, runtime):
# noinspection PyUnreachableCode
if __debug__: # sanity check
entities = [
parse_file_entities_with_pipelines(path)
for path in self.inputs.corr_mat
]
assert_all_entities_equal(entities, "session", "task", "run",
"pipeline")
n_corr_mat = len(self.inputs.corr_mat)
n_rois = 200
group_corr_mat = np.zeros((n_corr_mat, n_rois, n_rois))
for i, file in enumerate(self.inputs.corr_mat):
group_corr_mat[i, :, :] = np.load(file)
entities = parse_file_entities_with_pipelines(self.inputs.corr_mat[0])
group_corr_file = join(
self.inputs.output_dir,
build_path(entities, self.group_corr_pattern, False))
assert not exists(
group_corr_file
), f"Group connectivity file already exists {group_corr_file}"
np.save(group_corr_file, group_corr_mat)
self._results['group_corr_mat'] = group_corr_file
return runtime
def put(self, file_path: str) -> str:
bids_path = self.bids_paths.get(file_path)
if bids_path is not None:
return bids_path # already added
tags = self.database.tags(file_path)
assert isinstance(tags, dict)
bids_tags = dict()
for k, v in tags.items():
bids_entity = k
if bids_entity in entity_longnames: # map to long names
bids_entity = entity_longnames[bids_entity]
if bids_entity == "task" and tags.get("datatype") == "fmap":
assert "acq" not in tags
bids_entity = "acquisition"
if bids_entity == "run":
if not v.isdecimal(): # enforce run to be numerical
run_identifier = str(int_digest(v))[:4]
logger.warning(
f'Converting run identifier "{v}" to number "{run_identifier}" for BIDS-compliance'
)
v = run_identifier
if k in entities:
bids_tags[bids_entity] = format_like_bids(v)
else:
if tags.get("datatype") == "fmap":
if k == "suffix":
k = "fmap"
bids_tags[k] = v
bids_path_result = build_path(bids_tags, bids_config.default_path_patterns)
if bids_path_result is None:
raise ValueError(f'Unable to build BIDS-compliant path for "{file_path}"')
bids_path = str(bids_path_result)
if bids_path in self.file_paths:
if self.file_paths[bids_path] != str(file_path):
raise ValueError("Cannot assign different files to the same BIDS path")
self.bids_paths[file_path] = str(bids_path)
self.file_paths[bids_path] = str(file_path)
self.bids_tags[bids_path] = bids_tags
self._metadata[bids_path] = get_bids_metadata(self.database, file_path)
return bids_path
def bids_transform(target_project, sub_id, ses_id):
data_path = utils.get_test_data_path(target_project)
if target_project == 'EXTEND':
new_file_name = build_path(
utils.get_bids_entities(sub_id, ses_id),
('sub-{subject}/[ses-accel{ses}/]beh/sub-{subject}'
'[_ses-accel{ses}]_{suffix}.{extension}'))
elif target_project == 'BETTER':
sub_id = 'GE' + sub_id
new_file_name = build_path(utils.get_bids_entities(
sub_id, ses_id), ('sub-{subject}/[ses-{ses}/]beh/sub-{subject}'
'[_ses-{ses}]_{suffix}.{extension}'))
elif (target_project == 'BIKE_Pre') or (target_project == 'BIKE_Post'):
sub_id = utils.bike_atrain_dict[sub_id]
new_file_name = build_path(utils.get_bids_entities(
sub_id, ses_id), ('sub-{subject}/[ses-{ses}/]beh/sub-{subject}'
'[_ses-{ses}]_{suffix}.{extension}'))
elif target_project == 'PACR':
sub_id = utils.pacr_dict[sub_id]
new_file_name = build_path(utils.get_bids_entities(
sub_id, ses_id), ('sub-{subject}/[ses-{ses}/]beh/sub-{subject}'
'[_ses-{ses}]_{suffix}.{extension}'))
else:
# This should give: sub-01/ses-5/beh/sub-01_ses-5_accel.csv
# If there is a new/unknown project, the accelBIDSTransform tool will
# return only the file name with no additional data_path for the project
# To add new project information, add a conditional above this and modify
# the utils.py file to account for a new data_path
new_file_name = build_path(utils.get_bids_entities(
sub_id, ses_id), ('sub-{subject}/[ses-{ses}/]beh/sub-{subject}'
'[_ses-{ses}]_{suffix}.{extension}'))
new_file_location = os.path.join(data_path, new_file_name)
return (new_file_location)
def put(self, filepath):
if self.bidspaths_by_filepaths.get(filepath) is not None:
return # already added
tags = self.database.tags(filepath)
bidstags = dict()
for k, v in tags.items():
bidsentity = k
if bidsentity in entity_longnames:
bidsentity = entity_longnames[bidsentity]
if k in entities:
bidstags[bidsentity] = self._format_tagval(k, v)
else:
if k == "suffix" and tags.get("datatype") == "fmap":
k = "fmap"
bidstags[k] = v
bidspath = build_path(bidstags, bidsconfig.default_path_patterns)
self.bidspaths_by_filepaths[filepath] = str(bidspath)
self.filepaths_by_bidspaths[bidspath] = str(filepath)
self.bidstags_by_bidspaths[bidspath] = bidstags
def _run_interface(self, runtime):
import json
import os.path as op
import pkg_resources
from bids.layout import parse_file_entities
from bids.layout.writing import build_path
deriv_cfg = pkg_resources.resource_string(
"nibetaseries", op.join("data", "derivatives.json"))
deriv_patterns = json.loads(
deriv_cfg.decode('utf-8'))['fmriprep_path_patterns']
subject_entities = parse_file_entities(self.inputs.source_file)
betaseries_entities = parse_file_entities(self.inputs.in_file)
# hotfix
betaseries_entities['description'] = betaseries_entities['desc']
subject_entities.update(betaseries_entities)
out_file = build_path(subject_entities, deriv_patterns)
if not out_file:
raise ValueError("the provided entities do not make a valid file")
base_directory = runtime.cwd
if isdefined(self.inputs.base_directory):
base_directory = os.path.abspath(self.inputs.base_directory)
out_path = op.join(base_directory, self.out_path_base, out_file)
os.makedirs(op.dirname(out_path), exist_ok=True)
# copy the file to the output directory
copy(self.inputs.in_file, out_path)
self._results['out_file'] = out_path
return runtime
def _run_interface(self, runtime):
group_conf_summary = pd.DataFrame()
# noinspection PyUnreachableCode
if __debug__: # sanity check
entities = [
parse_file_entities_with_pipelines(path)
for path in self.inputs.conf_summary_json_files
]
assert_all_entities_equal(entities, "session", "task", "run",
"pipeline")
for summary_json_file in self.inputs.conf_summary_json_files:
with open(summary_json_file, 'r') as f:
group_conf_summary = group_conf_summary.append(
pd.DataFrame(json.load(f), index=[0]))
entities = parse_file_entities_with_pipelines(
self.inputs.conf_summary_json_files[0])
file_path = build_path(entities, self.file_pattern)
fname = os.path.join(self.inputs.output_dir, file_path)
assert not os.path.exists(
fname), f"Group confounds file already exists at {fname}"
group_conf_summary.to_csv(fname, sep='\t', index=False)
assert os.path.exists(fname), "File not created"
self._results['group_conf_summary'] = fname
return runtime
def test_build_path(self, writable_file):
# Single simple pattern
with pytest.raises(TypeError):
build_path(writable_file.entities)
pat = join(writable_file.dirname,
'{task}/sub-{subject}/run-{run}.nii.gz')
target = join(writable_file.dirname, 'rest/sub-3/run-2.nii.gz')
assert build_path(writable_file.entities, pat) == target
# Multiple simple patterns
pats = ['{session}/{task}/r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz',
'{subject}/{task}.nii.gz']
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 't-rest/3-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with optional entity
pats = ['[{session}/]{task}/r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz']
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 'rest/r-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with conditional values
pats = ['{task<func|acq>}/r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz']
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 't-rest/3-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with valid conditional values
pats = ['{task<func|rest>}/r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz']
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 'rest/r-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with optional entity with conditional values
pats = ['[{task<func|acq>}/]r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz']
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 'r-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with default value
pats = ['ses-{session|A}/r-{run}.nii.gz']
assert build_path({'run': 3}, pats) == 'ses-A/r-3.nii.gz'
# Pattern with both valid and default values
pats = ['ses-{session<A|B|C|D>|D}/r-{run}.nii.gz']
assert build_path({'run': 3}, pats) == 'ses-D/r-3.nii.gz'
pats = ['ses-{session<A|B|C|D>|D}/r-{run}.nii.gz']
assert build_path({'session': 'B', 'run': 3}, pats) == 'ses-B/r-3.nii.gz'
# Test extensions with dot and warning is issued
pats = ['ses-{session<A|B|C>|D}/r-{run}.{extension}']
with pytest.warns(UserWarning) as record:
assert build_path({'session': 'B', 'run': 3, 'extension': '.nii'},
pats) == 'ses-B/r-3.nii'
assert "defines an invalid default value" in record[0].message.args[0]
# Test expansion of optional characters
pats = ['ses-{session<[ABCD]>|D}/r-{run}.{extension}']
assert build_path({'session': 'B', 'run': 3, 'extension': '.nii'},
pats) == 'ses-B/r-3.nii'
# Test default-only patterns are correctly overriden by setting entity
entities = {
'subject': '01',
'extension': 'bvec',
'suffix': 'T1rho',
}
pats = (
"sub-{subject}[/ses-{session}]/{datatype|dwi}/sub-{subject}[_ses-{session}]"
"[_acq-{acquisition}]_{suffix|dwi}.{extension<bval|bvec|json|nii.gz|nii>|nii.gz}"
)
assert build_path(entities, pats) == 'sub-01/dwi/sub-01_T1rho.bvec'
assert build_path(entities, pats, strict=True) == 'sub-01/dwi/sub-01_T1rho.bvec'
# Test multiple paths
pats = ['ses-{session<A|B|C>|D}/r-{run}.{extension<json|nii|nii.gz>|nii.gz}']
assert sorted(
build_path({
'session': ['A', 'B'],
'run': [1, 2],
'extension': ['.nii.gz', 'json']
}, pats)) == [
'ses-A/r-1.json',
'ses-A/r-1.nii.gz',
'ses-A/r-2.json',
'ses-A/r-2.nii.gz',
'ses-B/r-1.json',
'ses-B/r-1.nii.gz',
'ses-B/r-2.json',
'ses-B/r-2.nii.gz',
]
# building blocks for generating valid file names
subject_entities = {
"subject": "01",
"session": "pre",
}
bids_bold_entities = {
**subject_entities,
"datatype": "func",
"task": "waffles",
"suffix": "bold",
"run": 1,
"extension": "nii.gz",
}
bids_bold_fname = build_path(bids_bold_entities, bids_patterns)
bids_json_entities = {
**bids_bold_entities,
"extension": "json",
}
bids_json_fname = build_path(bids_json_entities, bids_patterns)
bids_rest_entities = {
**bids_bold_entities,
"task": "rest",
}
bids_rest_fname = build_path(bids_rest_entities, bids_patterns)
bids_rest_json_entities = {
**bids_rest_entities,
def create_fname(self, fname_params, meta_params):
"""create the bids derivatives regressor file path and path names"""
self.fname = self.base_dir / build_path(fname_params, self.PATTERN)
self.meta_fname = self.base_dir / build_path(meta_params, self.PATTERN)
def create_fname(self, fname_params, meta_params):
self.fname = self.base_dir / build_path(fname_params, self.PATTERN)
self.meta_fname = self.base_dir / build_path(meta_params, self.PATTERN)
def test_build_path(self, writable_file):
# Single simple pattern
with pytest.raises(TypeError):
build_path(writable_file.entities)
pat = join(writable_file.dirname,
'{task}/sub-{subject}/run-{run}.nii.gz')
target = join(writable_file.dirname, 'rest/sub-3/run-2.nii.gz')
assert build_path(writable_file.entities, pat) == target
# Multiple simple patterns
pats = [
'{session}/{task}/r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz', '{subject}/{task}.nii.gz'
]
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 't-rest/3-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with optional entity
pats = [
'[{session}/]{task}/r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz'
]
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 'rest/r-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with conditional values
pats = [
'{task<func|acq>}/r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz'
]
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 't-rest/3-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with valid conditional values
pats = [
'{task<func|rest>}/r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz'
]
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 'rest/r-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with optional entity with conditional values
pats = [
'[{task<func|acq>}/]r-{run}.nii.gz',
't-{task}/{subject}-{run}.nii.gz'
]
pats = [join(writable_file.dirname, p) for p in pats]
target = join(writable_file.dirname, 'r-2.nii.gz')
assert build_path(writable_file.entities, pats) == target
# Pattern with default value
pats = ['sess-{session|A}/r-{run}.nii.gz']
assert build_path({'run': 3}, pats) == 'sess-A/r-3.nii.gz'
# Pattern with both valid and default values
pats = ['sess-{session<A|B|C>|D}/r-{run}.nii.gz']
assert build_path({
'session': 1,
'run': 3
}, pats) == 'sess-D/r-3.nii.gz'
pats = ['sess-{session<A|B|C>|D}/r-{run}.nii.gz']
assert build_path({
'session': 'B',
'run': 3
}, pats) == 'sess-B/r-3.nii.gz'
def collect_derivatives(derivatives_dir,
subject_id,
std_spaces,
freesurfer,
spec=None,
patterns=None):
"""Gather existing derivatives and compose a cache."""
if spec is None or patterns is None:
_spec, _patterns = tuple(
loads(
Path(
pkgrf('aslprep',
'smriprep/data/io_spec.json')).read_text()).values())
if spec is None:
spec = _spec
if patterns is None:
patterns = _patterns
derivs_cache = defaultdict(list, {})
derivatives_dir = Path(derivatives_dir)
def _check_item(item):
if not item:
return None
if isinstance(item, str):
item = [item]
result = []
for i in item:
if not (derivatives_dir / i).exists():
i = i.rstrip('.gz')
if not (derivatives_dir / i).exists():
return None
result.append(str(derivatives_dir / i))
return result
for space in [None] + std_spaces:
for k, q in spec['baseline'].items():
q['subject'] = subject_id
if space is not None:
q['space'] = space
item = _check_item(build_path(q, patterns, strict=True))
if not item:
return None
if space:
derivs_cache["std_%s" %
k] += item if len(item) == 1 else [item]
else:
derivs_cache["t1w_%s" %
k] = item[0] if len(item) == 1 else item
for space in std_spaces:
for k, q in spec['std_xfms'].items():
q['subject'] = subject_id
q['from'] = q['from'] or space
q['to'] = q['to'] or space
item = _check_item(build_path(q, patterns))
if not item:
return None
derivs_cache[k] += item
derivs_cache = dict(derivs_cache) # Back to a standard dictionary
if freesurfer:
for k, q in spec['surfaces'].items():
q['subject'] = subject_id
item = _check_item(build_path(q, patterns))
if not item:
return None
if len(item) == 1:
item = item[0]
derivs_cache[k] = item
derivs_cache['template'] = std_spaces
return derivs_cache
def bids_sessions(tmpdir_factory):
f, _ = plt.subplots()
svg_dir = tmpdir_factory.mktemp("work") / "fmriprep"
svg_dir.ensure_dir()
pattern = (
"sub-{subject}[/ses-{session}]/{datatype<figures>}/"
"sub-{subject}[_ses-{session}][_task-{task}][_acq-{acquisition}]"
"[_ce-{ceagent}][_dir-{direction}][_rec-{reconstruction}]"
"[_mod-{modality}][_run-{run}][_echo-{echo}][_space-{space}]"
"[_desc-{desc}]_{suffix<dseg|T1w|bold>}{extension<.svg>}")
subjects = ["01"]
tasks = ["t1", "t2", "t3"]
runs = ["01", "02", None]
ces = ["none", "Gd"]
descs = ["aroma", "bbregister", "carpetplot", "rois"]
# create functional data for both sessions
ses1_combos = product(subjects, ["1"], tasks, [None], runs, descs)
ses2_combos = product(subjects, ["2"], tasks, ces, [None], descs)
# have no runs in the second session (ex: dmriprep test data)
# https://github.com/nipreps/dmriprep/pull/59
all_combos = list(ses1_combos) + list(ses2_combos)
for subject, session, task, ce, run, desc in all_combos:
entities = {
"subject": subject,
"session": session,
"task": task,
"ceagent": ce,
"run": run,
"desc": desc,
"extension": ".svg",
"suffix": "bold",
"datatype": "figures",
}
bids_path = build_path(entities, pattern)
file_path = svg_dir / bids_path
file_path.ensure()
f.savefig(str(file_path))
# create anatomical data
anat_opts = [
{
"desc": "brain"
},
{
"desc": "conform"
},
{
"desc": "reconall"
},
{
"desc": "rois"
},
{
"suffix": "dseg"
},
{
"space": "MNI152NLin6Asym"
},
{
"space": "MNI152NLin2009cAsym"
},
]
anat_combos = product(subjects, anat_opts)
for subject, anat_opt in anat_combos:
anat_entities = {
"subject": subject,
"datatype": "anat",
"suffix": "t1w"
}
anat_entities.update(**anat_opt)
bids_path = build_path(entities, pattern)
file_path = svg_dir / bids_path
file_path.ensure()
f.savefig(str(file_path))
return svg_dir.dirname
def predict_derivatives(subject_id, output_spaces, freesurfer):
"""
Generate a list of the files that should be found in the output folder.
The prediction of outputs serves two purposes:
* Anticipates the set of outputs that will be generated.
* Informs the decision of whether the workflow should be staged or some
sections can be skip.
Parameters
----------
subject_id : :obj:`str`
A subject id
output_spaces : :obj:`list`
TemplateFlow identifiers of the requested output spaces
freesurfer : :obj:`bool`
Whether the ``--fs-no-reconall`` flag was used.
Examples
--------
>>> predict_derivatives('01', ['MNI152NLin2009cAsym'], False)
['sub-01/anat/sub-01_desc-brain_mask.nii.gz',
'sub-01/anat/sub-01_desc-preproc_T1w.nii.gz',
'sub-01/anat/sub-01_dseg.nii.gz',
'sub-01/anat/sub-01_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5',
'sub-01/anat/sub-01_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5',
'sub-01/anat/sub-01_label-CSF_probseg.nii.gz',
'sub-01/anat/sub-01_label-GM_probseg.nii.gz',
'sub-01/anat/sub-01_label-WM_probseg.nii.gz',
'sub-01/anat/sub-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz',
'sub-01/anat/sub-01_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz',
'sub-01/anat/sub-01_space-MNI152NLin2009cAsym_dseg.nii.gz',
'sub-01/anat/sub-01_space-MNI152NLin2009cAsym_label-CSF_probseg.nii.gz',
'sub-01/anat/sub-01_space-MNI152NLin2009cAsym_label-GM_probseg.nii.gz',
'sub-01/anat/sub-01_space-MNI152NLin2009cAsym_label-WM_probseg.nii.gz']
"""
spec = loads(
Path(pkgrf('aslprep', 'smriprep/data/io_spec.json')).read_text())
def _normalize_q(query, space=None):
query = query.copy()
query['subject'] = subject_id
if space is not None:
query['space'] = space
if 'from' in query and not query['from']:
query['from'] = output_spaces
if 'to' in query and not query['to']:
query['to'] = output_spaces
return query
queries = [
_normalize_q(q, space=None)
for q in spec['queries']['baseline'].values()
]
queries += [
_normalize_q(q, space=s) for s in output_spaces
for q in spec['queries']['baseline'].values()
]
queries += [_normalize_q(q) for q in spec['queries']['std_xfms'].values()]
if freesurfer:
queries += [
_normalize_q(q) for q in spec['queries']['surfaces'].values()
]
output = []
for q in queries:
paths = build_path(q, spec['patterns'])
if isinstance(paths, str):
output.append(paths)
elif paths:
output += paths
return sorted(output)
def bids_sessions(tmpdir_factory):
f, _ = plt.subplots()
svg_dir = tmpdir_factory.mktemp('work') / 'fmriprep'
svg_dir.ensure_dir()
pattern = (
"sub-{subject}[/ses-{session}]/{datatype<anat|func>}/"
"sub-{subject}[_ses-{session}][_task-{task}][_acq-{acquisition}]"
"[_ce-{contrast}][_dir-{direction}][_rec-{reconstruction}]"
"[_mod-{modality}][_run-{run}][_echo-{echo}][_space-{space}]"
"[_desc-{desc}]_{suffix<dseg|T1w|bold>}.{extension<svg>}")
subjects = ['01']
tasks = ['t1', 't2', 't3']
runs = ['01', '02', None]
descs = ['aroma', 'bbregister', 'carpetplot', 'rois']
# create functional data for both sessions
ses1_combos = product(subjects, ['1'], tasks, runs, descs)
ses2_combos = product(subjects, ['2'], tasks, [None], descs)
# have no runs in the second session (ex: dmriprep test data)
# https://github.com/nipreps/dmriprep/pull/59
all_combos = list(ses1_combos) + list(ses2_combos)
for subject, session, task, run, desc in all_combos:
entities = {
'subject': subject,
'session': session,
'task': task,
'run': run,
'desc': desc,
'extension': 'svg',
'suffix': 'bold',
'datatype': 'func'
}
bids_path = build_path(entities, pattern)
file_path = svg_dir / bids_path
file_path.ensure()
f.savefig(str(file_path))
# create anatomical data
anat_opts = [
{
'desc': 'brain'
},
{
'desc': 'conform'
},
{
'desc': 'reconall'
},
{
'desc': 'rois'
},
{
'suffix': 'dseg'
},
{
'space': 'MNI152NLin6Asym'
},
{
'space': 'MNI152NLin2009cAsym'
},
]
anat_combos = product(subjects, anat_opts)
for subject, anat_opt in anat_combos:
anat_entities = {
"subject": subject,
"datatype": 'anat',
"suffix": 't1w'
}
anat_entities.update(**anat_opt)
bids_path = build_path(entities, pattern)
file_path = svg_dir / bids_path
file_path.ensure()
f.savefig(str(file_path))
return svg_dir.dirname
def _run_interface(self, runtime):
# Ready the output folder
base_directory = runtime.cwd
if isdefined(self.inputs.base_directory):
base_directory = self.inputs.base_directory
base_directory = Path(base_directory).absolute()
out_path = base_directory / self.out_path_base
out_path.mkdir(exist_ok=True, parents=True)
# Ensure we have a list
in_file = listify(self.inputs.in_file)
# Read in the dictionary of metadata
if isdefined(self.inputs.meta_dict):
meta = self.inputs.meta_dict
# inputs passed in construction take priority
meta.update(self._metadata)
self._metadata = meta
# Initialize entities with those from the source file.
in_entities = [
parse_file_entities(str(relative_to_root(source_file)))
for source_file in self.inputs.source_file
]
out_entities = {
k: v
for k, v in in_entities[0].items() if all(
ent.get(k) == v for ent in in_entities[1:])
}
for drop_entity in listify(self.inputs.dismiss_entities or []):
out_entities.pop(drop_entity, None)
# Override extension with that of the input file(s)
out_entities["extension"] = [
# _splitext does not accept .surf.gii (for instance)
"".join(Path(orig_file).suffixes).lstrip(".")
for orig_file in in_file
]
compress = listify(self.inputs.compress) or [None]
if len(compress) == 1:
compress = compress * len(in_file)
for i, ext in enumerate(out_entities["extension"]):
if compress[i] is not None:
ext = regz.sub("", ext)
out_entities["extension"][
i] = f"{ext}.gz" if compress[i] else ext
# Override entities with those set as inputs
for key in self._allowed_entities:
value = getattr(self.inputs, key)
if value is not None and isdefined(value):
out_entities[key] = value
# Clean up native resolution with space
if out_entities.get("resolution") == "native" and out_entities.get(
"space"):
out_entities.pop("resolution", None)
if len(set(out_entities["extension"])) == 1:
out_entities["extension"] = out_entities["extension"][0]
# Insert custom (non-BIDS) entities from allowed_entities.
custom_entities = set(out_entities.keys()) - set(BIDS_DERIV_ENTITIES)
patterns = BIDS_DERIV_PATTERNS
if custom_entities:
# Example: f"{key}-{{{key}}}" -> "task-{task}"
custom_pat = "_".join(f"{key}-{{{key}}}"
for key in sorted(custom_entities))
patterns = [
pat.replace("_{suffix", "_".join(("", custom_pat, "{suffix")))
for pat in patterns
]
# Prepare SimpleInterface outputs object
self._results["out_file"] = []
self._results["compression"] = []
self._results["fixed_hdr"] = [False] * len(in_file)
dest_files = build_path(out_entities, path_patterns=patterns)
if not dest_files:
raise ValueError(
f"Could not build path with entities {out_entities}.")
# Make sure the interpolated values is embedded in a list, and check
dest_files = listify(dest_files)
if len(in_file) != len(dest_files):
raise ValueError(f"Input files ({len(in_file)}) not matched "
f"by interpolated patterns ({len(dest_files)}).")
for i, (orig_file, dest_file) in enumerate(zip(in_file, dest_files)):
out_file = out_path / dest_file
out_file.parent.mkdir(exist_ok=True, parents=True)
self._results["out_file"].append(str(out_file))
self._results["compression"].append(
_copy_any(orig_file, str(out_file)))
is_nifti = out_file.name.endswith(
(".nii", ".nii.gz")) and not out_file.name.endswith(
(".dtseries.nii", ".dtseries.nii.gz"))
data_dtype = self.inputs.data_dtype or DEFAULT_DTYPES[
self.inputs.suffix]
if is_nifti and any((self.inputs.check_hdr, data_dtype)):
# Do not use mmap; if we need to access the data at all, it will be to
# rewrite, risking a BusError
nii = nb.load(out_file, mmap=False)
if self.inputs.check_hdr:
hdr = nii.header
curr_units = tuple([
None if u == "unknown" else u
for u in hdr.get_xyzt_units()
])
curr_codes = (int(hdr["qform_code"]),
int(hdr["sform_code"]))
# Default to mm, use sec if data type is bold
units = (
curr_units[0] or "mm",
"sec" if out_entities["suffix"] == "bold" else None,
)
xcodes = (1, 1) # Derivative in its original scanner space
if self.inputs.space:
xcodes = ((4, 4) if self.inputs.space
in STANDARD_SPACES else (2, 2))
if curr_codes != xcodes or curr_units != units:
self._results["fixed_hdr"][i] = True
hdr.set_qform(nii.affine, xcodes[0])
hdr.set_sform(nii.affine, xcodes[1])
hdr.set_xyzt_units(*units)
# Rewrite file with new header
overwrite_header(nii, out_file)
if data_dtype == "source": # match source dtype
try:
data_dtype = nb.load(
self.inputs.source_file[0]).get_data_dtype()
except Exception:
LOGGER.warning(
f"Could not get data type of file {self.inputs.source_file[0]}"
)
data_dtype = None
if data_dtype:
if self.inputs.check_hdr:
# load updated NIfTI
nii = nb.load(out_file, mmap=False)
data_dtype = np.dtype(data_dtype)
orig_dtype = nii.get_data_dtype()
if orig_dtype != data_dtype:
LOGGER.warning(
f"Changing {out_file} dtype from {orig_dtype} to {data_dtype}"
)
# coerce dataobj to new data dtype
if np.issubdtype(data_dtype, np.integer):
new_data = np.rint(nii.dataobj).astype(data_dtype)
else:
new_data = np.asanyarray(nii.dataobj,
dtype=data_dtype)
# and set header to match
nii.set_data_dtype(data_dtype)
nii = nii.__class__(new_data, nii.affine, nii.header)
nii.to_filename(out_file)
if len(self._results["out_file"]) == 1:
meta_fields = self.inputs.copyable_trait_names()
self._metadata.update({
k: getattr(self.inputs, k)
for k in meta_fields if k not in self._static_traits
})
if self._metadata:
out_file = Path(self._results["out_file"][0])
# 1.3.x hack
# For dtseries, we have been generating weird non-BIDS JSON files.
# We can safely keep producing them to avoid breaking derivatives, but
# only the existing keys should keep going into them.
if out_file.name.endswith(".dtseries.nii"):
legacy_metadata = {}
for key in ("grayordinates", "space", "surface",
"surface_density", "volume"):
if key in self._metadata:
legacy_metadata[key] = self._metadata.pop(key)
if legacy_metadata:
sidecar = out_file.parent / f"{_splitext(str(out_file))[0]}.json"
sidecar.write_text(
dumps(legacy_metadata, sort_keys=True, indent=2))
# The future: the extension is the first . and everything after
sidecar = out_file.parent / f"{out_file.name.split('.', 1)[0]}.json"
sidecar.write_text(
dumps(self._metadata, sort_keys=True, indent=2))
self._results["out_meta"] = str(sidecar)
return runtime
def rename_fmriprep_files(fmriprep_dir, renamed_dir, dset_desc, gen_ref=False):
# copy the dataset_description file over first
os.makedirs(renamed_dir, exist_ok=True)
copyfile(dset_desc, os.path.join(renamed_dir, os.path.basename(dset_desc)))
# collect the mapping from old file names to new file names
rename_files = {}
for root, _, files in os.walk(fmriprep_dir):
for file in files:
match = re.search(DERIV_REGEX, file)
if match is not None:
file_dict = match.groupdict()
# change brainmask to desc-brain_mask
if file_dict.get('desc') == 'brainmask':
file_dict['desc'] = 'brain'
file_dict['suffix'] = 'mask'
# variant is now desc
if file_dict.get('variant'):
file_dict['desc'] = file_dict.pop('variant')
# different formats of transformation files
if file_dict.get('space') and file_dict.get('target'):
file_dict['fspace'] = file_dict.pop('space')
file_dict['tspace'] = file_dict.pop('target')
file_dict['mode'] = 'image'
file_dict['suffix'] = 'xfm'
del file_dict['desc']
if file_dict.get('target'):
file_dict['fspace'] = file_dict.pop('suffix')
file_dict['tspace'] = file_dict.pop('target')
file_dict['mode'] = 'image'
file_dict['suffix'] = 'xfm'
del file_dict['desc']
# segmentations
if file_dict.get('class'):
file_dict['label'] = file_dict.pop('class')
file_dict['suffix'] = 'probseg'
del file_dict['desc']
if file_dict.get('desc') == 'dtissue':
file_dict['suffix'] = 'dseg'
del file_dict['desc']
# freesurfer hemisphere files
if file_dict['ext'].startswith('L') or file_dict['ext'].startswith('R'):
file_dict['hemi'] = file_dict['ext'][0]
file_dict['ext'] = file_dict['ext'][2:]
file_dict['suffix'] = file_dict.pop('desc')
# aroma files
if file_dict.get('desc') == 'MELODICmix':
file_dict['desc'] = 'MELODIC'
file_dict['suffix'] = 'mixing'
if file_dict.get('desc') == 'AROMAnoiseICs':
file_dict['suffix'] = file_dict['desc']
del file_dict['desc']
# confounds file change
if file_dict.get('desc') == 'confounds':
file_dict['suffix'] = 'regressors'
# write the file to the new directory
new_file = build_path(file_dict, PATH_PATTERN)
new_root = root.replace(fmriprep_dir, renamed_dir)
new_path = os.path.join(new_root, new_file)
old_path = os.path.join(root, file)
rename_files[old_path] = new_path
os.makedirs(new_root, exist_ok=True)
copyfile(old_path, new_path)
if file_dict['suffix'] == 'bold' and gen_ref:
ref_nii = nb.load(new_path)
# take the median of the first 10 volumes
median_image_data = np.median(
ref_nii.dataobj[:, :, :, :10], axis=3)
# do not need description
del file_dict['desc']
file_dict['suffix'] = "boldref"
boldref_file = build_path(file_dict, PATH_PATTERN)
boldref_path = os.path.join(new_root, boldref_file)
nb.Nifti1Image(median_image_data, ref_nii.affine,
ref_nii.header).to_filename(boldref_path)
rename_files[boldref_path] = boldref_path
# write out log for how files were renamed
data_transfer_log = os.path.join(renamed_dir, "logs", "data_transfer.json")
os.makedirs(os.path.dirname(data_transfer_log), exist_ok=True)
with open(data_transfer_log, 'w') as fp:
json.dump(rename_files, fp)
return data_transfer_log