def _run_interface(self, runtime):
import bids
from bids.analysis import auto_model
models = self.inputs.model
if not isinstance(models, list):
database_path = self.inputs.database_path
layout = bids.BIDSLayout.load(database_path=database_path)
if not isdefined(models):
# model is not yet standardized, so validate=False
# Ignore all subject directories and .git/ and .datalad/ directories
indexer = bids.BIDSLayoutIndexer(
ignore=[re.compile(r'sub-'), re.compile(r'\.(git|datalad)')])
small_layout = bids.BIDSLayout(
layout.root, derivatives=[d.root for d in layout.derivatives.values()],
validate=False,
indexer=indexer)
# PyBIDS can double up, so find unique models
models = list(set(small_layout.get(suffix='smdl', return_type='file')))
if not models:
raise ValueError("No models found")
elif models == 'default':
models = auto_model(layout)
models = [_ensure_model(m) for m in models]
if self.inputs.selectors:
# This is almost certainly incorrect
models = [model for model in models
if all(val in model['Input'].get(key, [val])
for key, val in self.inputs.selectors.items())]
self._results['model_spec'] = models
return runtime
def run_fitlins(argv=None):
import re
from nipype import logging as nlogging
warnings.showwarning = _warn_redirect
opts = get_parser().parse_args(argv)
force_index = [
# If entry looks like `/<pattern>/`, treat `<pattern>` as a regex
re.compile(ign[1:-1]) if (ign[0], ign[-1]) == ('/', '/') else ign
# Iterate over empty tuple if undefined
for ign in opts.force_index or ()
]
ignore = [
# If entry looks like `/<pattern>/`, treat `<pattern>` as a regex
re.compile(ign[1:-1]) if (ign[0], ign[-1]) == ('/', '/') else ign
# Iterate over empty tuple if undefined
for ign in opts.ignore or ()
]
log_level = 25 + 5 * (opts.quiet - opts.verbose)
logger.setLevel(log_level)
nlogging.getLogger('nipype.workflow').setLevel(log_level)
nlogging.getLogger('nipype.interface').setLevel(log_level)
nlogging.getLogger('nipype.utils').setLevel(log_level)
if not opts.space:
# make it an explicit None
opts.space = None
if not opts.desc_label:
# make it an explicit None
opts.desc_label = None
ncpus = opts.n_cpus
if ncpus < 1:
ncpus = cpu_count()
plugin_settings = {
'plugin': 'MultiProc',
'plugin_args': {
'n_procs': ncpus,
'raise_insufficient': False,
'maxtasksperchild': 1,
},
}
if opts.mem_gb:
plugin_settings['plugin_args']['memory_gb'] = opts.mem_gb
model = default_path(opts.model, opts.bids_dir, 'model-default_smdl.json')
if opts.model in (None, 'default') and not op.exists(model):
model = 'default'
derivatives = True if not opts.derivatives else opts.derivatives
# Need this when specifying args directly (i.e. neuroscout)
# god bless neuroscout, but let's make it work for others!
if isinstance(derivatives, list) and len(derivatives) == 1:
# WRONG AND EVIL to those who have spaces in their paths... bad bad practice
# TODO - fix neuroscout
derivatives = derivatives[0].split(" ")
if opts.estimator != 'afni':
if opts.error_ts:
raise NotImplementedError(
"Saving the error time series is only implemented for"
" the afni estimator. If this is a feature you want"
f" for {opts.estimator} please let us know on github.")
if opts.derivative_label:
logger.warning('--derivative-label no longer has any effect; '
'set output directory name directly')
os.makedirs(opts.output_dir, exist_ok=True)
fub.write_derivative_description(opts.bids_dir, opts.output_dir,
vars(opts))
work_dir = mkdtemp() if opts.work_dir is None else opts.work_dir
# Go ahead and initialize the layout database
if opts.database_path is None:
database_path = Path(work_dir) / 'dbcache'
reset_database = True
else:
database_path = opts.database_path
reset_database = False
indexer = bids.BIDSLayoutIndexer(ignore=ignore, force_index=force_index)
layout = bids.BIDSLayout(
opts.bids_dir,
derivatives=derivatives,
database_path=database_path,
reset_database=reset_database,
indexer=indexer,
)
subject_list = None
if opts.participant_label is not None:
subject_list = fub.collect_participants(
layout, participant_label=opts.participant_label)
# Build main workflow
logger.log(25, INIT_MSG(version=__version__, subject_list=subject_list))
# TODO: Fix AUTO_MODEL
# if model == 'default':
# models = auto_model(layout)
# else:
# import json
# if op.exists(model):
# model_dict = json.loads(Path(model).read_text())
# models = [model_dict]
model_dict = None
if model == 'default':
retcode = 1
raise NotImplementedError(
"The default model has not been implemented yet.")
else:
import json
if op.exists(model):
model_dict = json.loads(Path(model).read_text())
if not model_dict:
raise ValueError(
f'model_dict cannot be empty. Invalid model filepath {model}.')
graph = BIDSStatsModelsGraph(layout, model_dict)
fitlins_wf = init_fitlins_wf(
database_path,
opts.output_dir,
graph=graph,
analysis_level=opts.analysis_level,
model=model,
space=opts.space,
desc=opts.desc_label,
participants=subject_list,
base_dir=work_dir,
smoothing=opts.smoothing,
drop_missing=opts.drop_missing,
drift_model=opts.drift_model,
estimator=opts.estimator,
errorts=opts.error_ts,
)
fitlins_wf.config = deepcopy(config.get_fitlins_config()._sections)
if opts.work_dir:
# dump crashes in working directory (non /tmp)
fitlins_wf.config['execution']['crashdump_dir'] = opts.work_dir
retcode = 0
if not opts.reports_only:
try:
fitlins_wf.run(**plugin_settings)
except Exception as e:
logger.critical(f"FitLins failed: {e}")
raise
run_context = {
'version': __version__,
'command': ' '.join(sys.argv),
'timestamp': time.strftime('%Y-%m-%d %H:%M:%S %z'),
}
selectors = {'desc': opts.desc_label, 'space': opts.space}
if subject_list is not None:
selectors['subject'] = subject_list
graph.load_collections(**selectors)
report_dict = build_report_dict(opts.output_dir, work_dir, graph)
write_full_report(report_dict, run_context, opts.output_dir)
return retcode
def create_bids_dataframe(self):
"""Generate the dataframe."""
# Suppress a Future Warning from pybids about leading dot included in 'extension' from version 0.14.0
# The config_bids.json file used matches the future behavior
# TODO: when reaching version 0.14.0, remove the following line
pybids.config.set_option('extension_initial_dot', True)
for path_data in self.paths_data:
path_data = os.path.join(path_data, '')
# Initialize BIDSLayoutIndexer and BIDSLayout
# validate=True by default for both indexer and layout, BIDS-validator is not skipped
# Force index for samples tsv and json files, and for subject subfolders containing microscopy files based on extensions.
# Force index of subject subfolders containing CT-scan files under "anat" or "ct" folder based on extensions and modality suffix.
# TODO: remove force indexing of microscopy files after BEP microscopy is merged in BIDS
# TODO: remove force indexing of CT-scan files after BEP CT-scan is merged in BIDS
ext_microscopy = ('.png', '.ome.tif', '.ome.tiff', '.ome.tf2',
'.ome.tf8', '.ome.btf')
ext_ct = ('.nii.gz', '.nii')
suffix_ct = ('ct', 'CT')
force_index = []
for root, dirs, files in os.walk(path_data):
for file in files:
# Microscopy
if file == "samples.tsv" or file == "samples.json":
force_index.append(file)
if (file.endswith(ext_microscopy)
and os.path.basename(root) == "microscopy" and
(root.replace(path_data, '').startswith("sub"))):
force_index.append(
os.path.join(root.replace(path_data, '')))
# CT-scan
if (file.endswith(ext_ct)
and file.split('.')[0].endswith(suffix_ct)
and (os.path.basename(root) == "anat"
or os.path.basename(root) == "ct") and
(root.replace(path_data, '').startswith("sub"))):
force_index.append(
os.path.join(root.replace(path_data, '')))
indexer = pybids.BIDSLayoutIndexer(force_index=force_index)
if self.derivatives:
self.write_derivatives_dataset_description(path_data)
layout = pybids.BIDSLayout(path_data,
config=self.bids_config,
indexer=indexer,
derivatives=self.derivatives)
# Transform layout to dataframe with all entities and json metadata
# As per pybids, derivatives don't include parsed entities, only the "path" column
df_next = layout.to_df(metadata=True)
# Add filename column
df_next.insert(1, 'filename',
df_next['path'].apply(os.path.basename))
# Drop rows with json, tsv and LICENSE files in case no extensions are provided in config file for filtering
df_next = df_next[~df_next['filename'].str.
endswith(tuple(['.json', '.tsv', 'LICENSE']))]
# Update dataframe with subject files of chosen contrasts and extensions,
# and with derivative files of chosen target_suffix from loader parameters
df_next = df_next[(
~df_next['path'].str.contains('derivatives')
& df_next['suffix'].str.contains('|'.join(self.contrast_lst))
& df_next['extension'].str.contains('|'.join(self.extensions)))
| (df_next['path'].str.contains('derivatives')
& df_next['filename'].str.
contains('|'.join(self.target_suffix)))]
if df_next[~df_next['path'].str.contains('derivatives')].empty:
# Warning if no subject files are found in path_data
logger.warning(
"No subject files were found in '{}' dataset. Skipping dataset."
.format(path_data))
else:
# Add tsv files metadata to dataframe
df_next = self.add_tsv_metadata(df_next, path_data, layout)
# TODO: check if other files are needed for EEG and DWI
# Merge dataframes
self.df = pd.concat([self.df, df_next],
join='outer',
ignore_index=True)
if self.df.empty:
# Raise error and exit if no subject files are found in any path data
raise RuntimeError(
"No subject files found. Check selection of parameters in config.json"
" and datasets compliance with BIDS specification.")
# Drop duplicated rows based on all columns except 'path'
# Keep first occurence
columns = self.df.columns.to_list()
columns.remove('path')
self.df = self.df[~(
self.df.astype(str).duplicated(subset=columns, keep='first'))]
# If indexing of derivatives is true
if self.derivatives:
# Get list of subject files with available derivatives
has_deriv, deriv = self.get_subjects_with_derivatives()
# Filter dataframe to keep subjects files with available derivatives only
if has_deriv:
self.df = self.df[
self.df['filename'].str.contains('|'.join(has_deriv))
| self.df['filename'].str.contains('|'.join(deriv))]
else:
# Raise error and exit if no derivatives are found for any subject files
raise RuntimeError("Derivatives not found.")
# Reset index
self.df.reset_index(drop=True, inplace=True)
# Drop columns with all null values
self.df.dropna(axis=1, inplace=True, how='all')
def create_bids_dataframe(self):
"""Generate the dataframe."""
for path_data in self.paths_data:
path_data = Path(path_data, '')
# Initialize BIDSLayoutIndexer and BIDSLayout
# validate=True by default for both indexer and layout, BIDS-validator is not skipped
# Force index for samples tsv and json files, and for subject subfolders containing microscopy files based on extensions.
# Force index of subject subfolders containing CT-scan files under "anat" or "ct" folder based on extensions and modality suffix.
# TODO: remove force indexing of microscopy files after Microscopy-BIDS is integrated in pybids
# TODO: remove force indexing of CT-scan files after BEP CT-scan is merged in BIDS
ext_microscopy = ('.png', '.tif', '.ome.tif', '.ome.btf')
ext_ct = ('.nii.gz', '.nii')
suffix_ct = ('ct', 'CT')
force_index = []
for path_object in path_data.glob('**/*'):
if path_object.is_file():
# Microscopy
subject_path_index = len(path_data.parts)
subject_path = path_object.parts[subject_path_index]
if path_object.name == "samples.tsv" or path_object.name == "samples.json":
force_index.append(path_object.name)
if (path_object.name.endswith(ext_microscopy) and path_object.parent.name == "micr" and
subject_path.startswith('sub')):
force_index.append(str(Path(*path_object.parent.parts[subject_path_index:])))
# CT-scan
if (path_object.name.endswith(ext_ct) and path_object.name.split('.')[0].endswith(suffix_ct) and
(path_object.parent.name == "anat" or path_object.parent.name == "ct") and
subject_path.startswith('sub')):
force_index.append(str(Path(*path_object.parent.parts[subject_path_index:])))
indexer = pybids.BIDSLayoutIndexer(force_index=force_index, validate=self.bids_validate)
if self.derivatives:
self.write_derivatives_dataset_description(path_data)
layout = pybids.BIDSLayout(str(path_data), config=self.bids_config, indexer=indexer,
derivatives=self.derivatives)
# Transform layout to dataframe with all entities and json metadata
# As per pybids, derivatives don't include parsed entities, only the "path" column
df_next = layout.to_df(metadata=True)
# Add filename column
df_next.insert(1, 'filename', df_next['path'].apply(os.path.basename))
# Drop rows with json, tsv and LICENSE files in case no extensions are provided in config file for filtering
df_next = df_next[~df_next['filename'].str.endswith(tuple(['.json', '.tsv', 'LICENSE']))]
# The following command updates the dataframe by doing 2 things:
# 1. Keep only subject files of chosen contrasts (for files that are not in the 'derivatives' folder)
# (ex: '<dataset_path>/sub-XX/anat/sub-XX_T1w.nii.gz' with contrast_lst:["T1w"])
# 2. Keep only derivatives files of chosen target_suffix (for files that are in the 'derivatives' folder)
# (ex: '<dataset_path>/derivatives/labels/sub-XX/anat/sub-XX_T1w_seg-manual.nii.gz' with target_suffix:["_seg-manual"])
df_next = df_next[(~df_next['path'].str.contains('derivatives')
& df_next['suffix'].str.contains('|'.join(self.contrast_lst)))
| (df_next['path'].str.contains('derivatives')
& (df_next['filename'].str.split('.').apply(lambda x: x[0])).str.endswith(tuple(self.target_suffix)))]
# Update dataframe with files of chosen extensions
df_next = df_next[df_next['filename'].str.endswith(tuple(self.extensions))]
# Warning if no subject files are found in path_data
if df_next[~df_next['path'].str.contains('derivatives')].empty:
logger.warning(f"No subject files were found in '{path_data}' dataset. Skipping dataset.")
else:
# Add tsv files metadata to dataframe
df_next = self.add_tsv_metadata(df_next, path_data, layout)
# TODO: check if other files are needed for EEG and DWI
# Merge dataframes
self.df = pd.concat([self.df, df_next], join='outer', ignore_index=True)
if self.df.empty:
# Raise error and exit if no subject files are found in any path data
raise RuntimeError("No subject files found. Check selection of parameters in config.json"
" and datasets compliance with BIDS specification.")
# Drop duplicated rows based on all columns except 'path'
# Keep first occurence
columns = self.df.columns.to_list()
columns.remove('path')
self.df = self.df[~(self.df.astype(str).duplicated(subset=columns, keep='first'))]
# Remove subject files without the "split_method" metadata if specified and keep all derivatives
if self.split_method:
files_remove = (self.df[(
# Path does not contain derivative string (i.e. we only target subject raw data files)
~self.df['path'].str.contains('derivatives')
# and split method metadata is null (i.e. the subject must have the split_method metadata or will be excluded)
& self.df[self.split_method].isnull())]
# Get these filesnames and convert to list.
['filename']).tolist()
if files_remove:
logger.warning(f"The following files don't have the '{self.split_method}' metadata indicated as the "
f"split_method in the configuration JSON file. Skipping these files: {files_remove}")
# Removing from dataframe all filenames which contain any of the file from files_remove field.
self.df = self.df[~self.df['filename'].str.contains('|'.join(files_remove))]
# If indexing of derivatives is true
if self.derivatives:
# Get list of subject files with available derivatives
has_deriv, deriv = self.get_subjects_with_derivatives()
# Filter dataframe to keep subjects files with available derivatives only
if has_deriv:
self.df = self.df[self.df['filename'].str.contains('|'.join(has_deriv))
| self.df['filename'].str.contains('|'.join(deriv))]
else:
# Raise error and exit if no derivatives are found for any subject files
raise RuntimeError("Derivatives not found.")
# Reset index
self.df.reset_index(drop=True, inplace=True)
# Drop columns with all null values
self.df.dropna(axis=1, inplace=True, how='all')
def create_bids_dataframe(loader_params, derivatives):
"""Create a dataframe containing all BIDS image files in a bids_path and their metadata.
Args:
loader_params (dict): Loader parameters, see :doc:`configuration_file` for more details.
derivatives (bool): If True, derivatives are indexed.
Returns:
df (pd.DataFrame): Dataframe containing all BIDS image files indexed and their metadata.
"""
# Get bids_path, bids_config, target_suffix, extensions and contrast_lst from loader parameters
bids_path = loader_params['bids_path']
bids_config = None if 'bids_config' not in loader_params else loader_params[
'bids_config']
target_suffix = loader_params['target_suffix']
# If `target_suffix` is a list of lists convert to list
if any(isinstance(t, list) for t in target_suffix):
target_suffix = list(itertools.chain.from_iterable(target_suffix))
extensions = loader_params['extensions']
contrast_lst = loader_params["contrast_params"]["contrast_lst"]
# Suppress a Future Warning from pybids about leading dot included in 'extension' from version 0.14.0
# The config_bids.json file used matches the future behavior
# TODO: when reaching version 0.14.0, remove the following line
pybids.config.set_option('extension_initial_dot', True)
# Initialize BIDSLayoutIndexer and BIDSLayout
# validate=True by default for both indexer and layout, BIDS-validator is not skipped
# Force index for samples tsv and json files, and for subject subfolders containing microscopy files based on extensions.
# TODO: remove force indexing of microscopy files after BEP microscopy is merged in BIDS
ext_microscopy = ('.png', '.ome.tif', '.ome.tiff', '.ome.tf2', '.ome.tf8',
'.ome.btf')
force_index = ['samples.tsv', 'samples.json']
if not bids_path.endswith("/"):
bids_path = bids_path + "/"
for root, dirs, files in os.walk(bids_path):
for file in files:
if file.endswith(ext_microscopy) and (root.replace(
bids_path, '').startswith("sub")):
force_index.append(os.path.join(root.replace(bids_path, '')))
indexer = pybids.BIDSLayoutIndexer(force_index=force_index)
layout = pybids.BIDSLayout(bids_path,
config=bids_config,
indexer=indexer,
derivatives=derivatives)
# Transform layout to dataframe with all entities and json metadata
# As per pybids, derivatives don't include parsed entities, only the "path" column
df = layout.to_df(metadata=True)
# Add filename and parent_path columns
df['filename'] = df['path'].apply(os.path.basename)
df['parent_path'] = df['path'].apply(os.path.dirname)
# Drop rows with json, tsv and LICENSE files in case no extensions are provided in config file for filtering
df = df[~df['filename'].str.endswith(tuple(['.json', '.tsv', 'LICENSE']))]
# Update dataframe with subject files of chosen contrasts and extensions,
# and with derivative files of chosen target_suffix from loader parameters
df = df[(~df['path'].str.contains('derivatives')
& df['suffix'].str.contains('|'.join(contrast_lst))
& df['extension'].str.contains('|'.join(extensions))) |
(df['path'].str.contains('derivatives')
& df['filename'].str.contains('|'.join(target_suffix)))]
# Add metadata from participants.tsv file, if present
# Uses pybids function
if layout.get_collections(level='dataset'):
df_participants = layout.get_collections(level='dataset',
merge=True).to_df()
df_participants.drop(['suffix'], axis=1, inplace=True)
df = pd.merge(df,
df_participants,
on='subject',
suffixes=("_x", None),
how='left')
# Add metadata from samples.tsv file, if present
# TODO: use pybids function after BEP microscopy is merged in BIDS
fname_samples = os.path.join(bids_path, "samples.tsv")
if os.path.exists(fname_samples):
df_samples = pd.read_csv(fname_samples, sep='\t')
df['participant_id'] = "sub-" + df['subject']
df['sample_id'] = "sample-" + df['sample']
df = pd.merge(df,
df_samples,
on=['participant_id', 'sample_id'],
suffixes=("_x", None),
how='left')
df.drop(['participant_id', 'sample_id'], axis=1, inplace=True)
# Add metadata from all _sessions.tsv files, if present
# Uses pybids function
if layout.get_collections(level='subject'):
df_sessions = layout.get_collections(level='subject',
merge=True).to_df()
df_sessions.drop(['suffix'], axis=1, inplace=True)
df = pd.merge(df,
df_sessions,
on=['subject', 'session'],
suffixes=("_x", None),
how='left')
# Add metadata from all _scans.tsv files, if present
# TODO: use pybids function after BEP microscopy is merged in BIDS
# TODO: verify merge behavior with EEG and DWI scans files, tested with anat and microscopy only
df_scans = pd.DataFrame()
for root, dirs, files in os.walk(bids_path):
for file in files:
if file.endswith("scans.tsv"):
df_temp = pd.read_csv(os.path.join(root, file), sep='\t')
df_scans = pd.concat([df_scans, df_temp], ignore_index=True)
if not df_scans.empty:
df_scans['filename'] = df_scans['filename'].apply(os.path.basename)
df = pd.merge(df,
df_scans,
on=['filename'],
suffixes=("_x", None),
how='left')
# TODO: check if other files are needed for EEG and DWI
# If indexing of derivatives is true
# Get list of subject files with available derivatives
if derivatives:
subject_files = df[~df['path'].str.contains('derivatives'
)]['filename'].to_list()
prefix_fnames = []
[prefix_fnames.append(s.split('.')[0]) for s in subject_files]
deriv = df[df['path'].str.contains('derivatives')]['filename'].tolist()
has_deriv = []
for p in prefix_fnames:
available = [d for d in deriv if p in d]
if available:
has_deriv.append(p)
for t in target_suffix:
if t not in str(available):
logger.warning(
"Missing target_suffix {} for subject {}.".format(
t, p))
else:
logger.warning(
"Missing derivatives for subject {}. Skipping subject.".
format(p))
# Filter dataframe to keep subjects files with available derivatives only
if has_deriv:
df = df[df['filename'].str.contains('|'.join(has_deriv))]
else:
# Raise error and exit if no derivatives are found for any subject files
raise RuntimeError("Derivatives not found.")
# Reset index
df.reset_index(drop=True, inplace=True)
# Drop columns with all null values
df.dropna(axis=1, inplace=True, how='all')
return df
