def analysis():
layout_path = join(get_test_data_path(), 'ds005')
layout = BIDSLayout(layout_path)
json_file = join(layout_path, 'models', 'ds-005_type-test_model.json')
analysis = Analysis(layout, json_file)
analysis.setup(scan_length=480, subject=['01', '02'])
return analysis
def _run_interface(self, runtime):
from bids.analysis import Analysis
from bids.layout import BIDSLayout
import re
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 self.inputs.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 self.inputs.ignore or ()
]
# If empty, then None
derivatives = self.inputs.derivatives or None
layout = BIDSLayout(self.inputs.bids_dir,
force_index=force_index,
ignore=ignore,
derivatives=derivatives)
selectors = self.inputs.selectors
analysis = Analysis(model=self.inputs.model, layout=layout)
analysis.setup(drop_na=False, **selectors)
self._load_level1(runtime, analysis)
self._load_higher_level(runtime, analysis)
return runtime
def analysis():
layout_path = join(get_test_data_path(), "ds005")
layout = BIDSLayout(layout_path)
json_file = join(layout_path, "models", "ds-005_type-test_model.json")
analysis = Analysis(layout, json_file)
analysis.setup(scan_length=480, subject=["01", "02"])
return analysis
def analysis():
layout_path = join(get_test_data_path(), 'ds005')
layout = BIDSLayout(layout_path)
json_file = join(layout_path, 'models', 'ds-005_type-test_model.json')
analysis = Analysis(layout, json_file)
analysis.setup(scan_length=480, subject=['01', '02'])
return analysis
def test_auto_model_analysis(model):
layout_path = join(get_test_data_path(), 'ds005')
layout = BIDSLayout(layout_path)
# Test to make sure an analaysis can be setup from the generated model
analysis = Analysis(layout, model)
analysis.setup(scan_length=480)
assert model['name'] == 'ds005_mixedgamblestask'
# run level
block = model['blocks'][0]
assert block['name'] == 'run'
assert block['level'] == 'run'
assert block['transformations'][0]['name'] == 'factor'
assert block['model']['HRF_variables'][0] == 'trial_type.parametric gain'
assert block['contrasts'][0]['name'] == 'run_parametric gain'
assert block['contrasts'][0]['weights'] == [1]
# subject level
block = model['blocks'][1]
assert block['name'] == 'subject'
assert block['level'] == 'subject'
assert block['model']['variables'][0] == 'run_parametric gain'
assert block['contrasts'][0]['name'] == 'subject_run_parametric gain'
# dataset level
block = model['blocks'][2]
assert block['name'] == 'dataset'
assert block['level'] == 'dataset'
assert block['model']['variables'][0] == 'subject_run_parametric gain'
assert block['contrasts'][0][
'name'] == 'dataset_subject_run_parametric gain'
def analysis_force_auto_contrasts():
layout_path = join(get_test_data_path(), 'ds005')
layout = BIDSLayout(layout_path, exclude='derivatives/')
json_file = join(layout_path, 'models', 'ds-005_type-test_model.json')
analysis = Analysis(layout, json_file)
analysis.setup(scan_length=480, subject=['01', '02'], auto_contrasts=True)
return analysis
def build_analysis(analysis,
predictor_events,
bids_dir,
run_id=None,
build=True):
tmp_dir = Path(mkdtemp())
entities = [{}]
if run_id is not None:
# Get entities of runs, and add to kwargs
for rid in run_id:
for run in analysis['runs']:
if rid == run['id']:
entities.append(get_entities(run))
break
entities = merge_dictionaries(*entities)
entities['scan_length'] = max([r['duration'] for r in analysis['runs']])
entities['task'] = analysis['task_name']
# Write out all events
paths = writeout_events(analysis, predictor_events, tmp_dir)
if build is False:
bids_analysis = None
else:
# Load events and try applying transformations
bids_layout = BIDSLayout(bids_dir,
derivatives=str(tmp_dir),
validate=False)
bids_analysis = BIDSAnalysis(bids_layout,
deepcopy(analysis.get('model')))
bids_analysis.setup(**entities)
return tmp_dir, paths, bids_analysis
def test_auto_model_analysis(model):
layout_path = join(get_test_data_path(), 'ds005')
layout = BIDSLayout(layout_path)
# Test to make sure an analaysis can be setup from the generated model
analysis = Analysis(layout, model)
analysis.setup(scan_length=480)
assert model['Name'] == 'ds005_mixedgamblestask'
# run level
block = model['Steps'][0]
assert block['Name'] == 'Run'
assert block['Level'] == 'Run'
assert block['Transformations'][0]['Name'] == 'Factor'
assert block['Contrasts'][0]['Name'] == 'run_parametric gain'
assert block['Contrasts'][0]['Weights'] == [1]
# subject level
block = model['Steps'][1]
assert block['Name'] == 'Subject'
assert block['Level'] == 'Subject'
assert block['Model']['X'][0] == 'run_parametric gain'
assert block['Contrasts'][0]['Name'] == 'subject_run_parametric gain'
# dataset level
block = model['Steps'][2]
assert block['Name'] == 'Dataset'
assert block['Level'] == 'Dataset'
assert block['Model']['X'][0] == 'subject_run_parametric gain'
assert block['Contrasts'][0]['Name'] == 'dataset_subject_run_parametric gain'
def _run_interface(self, runtime):
from bids.analysis import Analysis
from bids.layout import BIDSLayout
layout = BIDSLayout.load(database_path=self.inputs.database_path)
selectors = self.inputs.selectors
analysis = Analysis(model=self.inputs.model, layout=layout)
analysis.setup(drop_na=False, **selectors)
self._load_level1(runtime, analysis)
self._load_higher_level(runtime, analysis)
return runtime
def test_incremental_data_loading():
layout_path = join(get_test_data_path(), "ds005")
layout = BIDSLayout(layout_path)
json_file = join(layout_path, "models", "ds-005_type-test_model.json")
analysis = Analysis(layout, json_file)
analysis.setup(scan_length=480, subject=["01"], run=[1], finalize=False)
analysis.setup(scan_length=480, subject=["02"], run=[2], finalize=False)
analysis.finalize()
assert len(analysis["run"].get_collections()) == 2
def test_incremental_data_loading():
layout_path = join(get_test_data_path(), 'ds005')
layout = BIDSLayout(layout_path)
json_file = join(layout_path, 'models', 'ds-005_type-test_model.json')
analysis = Analysis(layout, json_file)
analysis.setup(scan_length=480, subject=['01'], run=[1], finalize=False)
analysis.setup(scan_length=480, subject=['02'], run=[2], finalize=False)
analysis.finalize()
assert len(analysis['run'].get_collections()) == 2
def _run_interface(self, runtime):
from bids.analysis import Analysis
from bids.layout import BIDSLayout
include = self.inputs.include_pattern
exclude = self.inputs.exclude_pattern
derivatives = self.inputs.derivatives
if not isdefined(include):
include = None
if not isdefined(exclude):
exclude = None
if not isdefined(derivatives):
exclude = False
layout = BIDSLayout(self.inputs.bids_dir, include=include,
exclude=exclude, derivatives=derivatives)
selectors = self.inputs.selectors
analysis = Analysis(model=self.inputs.model, layout=layout)
analysis.setup(drop_na=False, desc='preproc', **selectors)
self._load_level1(runtime, analysis)
self._load_higher_level(runtime, analysis)
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(" ")
pipeline_name = 'fitlins'
if opts.derivative_label:
pipeline_name += '_' + opts.derivative_label
deriv_dir = op.join(opts.output_dir, pipeline_name)
os.makedirs(deriv_dir, exist_ok=True)
bids.write_derivative_description(opts.bids_dir, deriv_dir)
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
layout = BIDSLayout(opts.bids_dir,
derivatives=derivatives,
ignore=ignore,
force_index=force_index,
database_path=database_path,
reset_database=reset_database)
subject_list = None
if opts.participant_label is not None:
subject_list = bids.collect_participants(
layout, participant_label=opts.participant_label)
# Build main workflow
logger.log(25, INIT_MSG(version=__version__, subject_list=subject_list))
fitlins_wf = init_fitlins_wf(
database_path,
deriv_dir,
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,
)
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:
retcode = 1
models = auto_model(layout) if model == 'default' else [model]
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
for model in models:
analysis = Analysis(layout, model=model)
analysis.setup(**selectors)
report_dict = build_report_dict(deriv_dir, work_dir, analysis)
write_full_report(report_dict, run_context, deriv_dir)
return retcode
def run_fitlins(argv=None):
warnings.showwarning = _warn_redirect
opts = get_parser().parse_args(argv)
if opts.debug:
logger.setLevel(logging.DEBUG)
if not opts.space:
# make it an explicit None
opts.space = None
subject_list = None
if opts.participant_label is not None:
subject_list = bids.collect_participants(
opts.bids_dir, participant_label=opts.participant_label)
ncpus = opts.n_cpus
if ncpus < 1:
ncpus = cpu_count()
plugin_settings = {
'plugin': 'MultiProc',
'plugin_args': {
'n_procs': ncpus,
'raise_insufficient': False,
'maxtasksperchild': 1,
}
}
# Build main workflow
logger.log(25, INIT_MSG(
version=__version__,
subject_list=subject_list)
)
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(" ")
pipeline_name = 'fitlins'
if opts.derivative_label:
pipeline_name += '_' + opts.derivative_label
deriv_dir = op.join(opts.output_dir, pipeline_name)
os.makedirs(deriv_dir, exist_ok=True)
bids.write_derivative_description(opts.bids_dir, deriv_dir)
work_dir = mkdtemp() if opts.work_dir is None else opts.work_dir
fitlins_wf = init_fitlins_wf(
opts.bids_dir, derivatives, deriv_dir,
analysis_level=opts.analysis_level, model=model,
space=opts.space, desc=opts.desc_label,
participants=subject_list, base_dir=work_dir,
force_index=opts.force_index, ignore=opts.ignore,
smoothing=opts.smoothing,
)
if opts.work_dir:
# dump crashes in working directory (non /tmp)
fitlins_wf.config['execution']['crashdump_dir'] = opts.work_dir
# easy to read crashfiles
fitlins_wf.config['execution']['crashfile_format'] = 'txt'
retcode = 0
if not opts.reports_only:
try:
fitlins_wf.run(**plugin_settings)
except Exception:
retcode = 1
layout = BIDSLayout(opts.bids_dir, derivatives=derivatives)
models = auto_model(layout) if model == 'default' else [model]
run_context = {'version': __version__,
'command': ' '.join(sys.argv),
'timestamp': time.strftime('%Y-%m-%d %H:%M:%S %z'),
}
selectors = {'desc': opts.desc_label}
if opts.space is not None:
selectors['space'] = opts.space
if subject_list is not None:
selectors['subject'] = subject_list
for model in models:
analysis = Analysis(layout, model=model)
analysis.setup(**selectors)
report_dict = build_report_dict(deriv_dir, work_dir, analysis)
write_full_report(report_dict, run_context, deriv_dir)
return retcode
def run_fitlins(argv=None):
warnings.showwarning = _warn_redirect
opts = get_parser().parse_args(argv)
if opts.debug:
logger.setLevel(logging.DEBUG)
subject_list = None
if opts.participant_label is not None:
subject_list = bids.collect_participants(
opts.bids_dir, participant_label=opts.participant_label)
ncpus = opts.n_cpus
if ncpus < 1:
ncpus = cpu_count()
plugin_settings = {
'plugin': 'MultiProc',
'plugin_args': {
'n_procs': ncpus,
'raise_insufficient': False,
'maxtasksperchild': 1,
}
}
# Build main workflow
logger.log(25, INIT_MSG(
version=__version__,
subject_list=subject_list)
)
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)
if len(derivatives) == 1:
derivatives = derivatives[0].split(" ")
pipeline_name = 'fitlins'
if opts.derivative_label:
pipeline_name += '_' + opts.derivative_label
deriv_dir = op.join(opts.output_dir, pipeline_name)
os.makedirs(deriv_dir, exist_ok=True)
bids.write_derivative_description(opts.bids_dir, deriv_dir)
work_dir = mkdtemp() if opts.work_dir is None else opts.work_dir
fitlins_wf = init_fitlins_wf(
opts.bids_dir, derivatives, deriv_dir, opts.space, model=model,
participants=subject_list, base_dir=work_dir,
include_pattern=opts.include, exclude_pattern=opts.exclude
)
retcode = 0
try:
fitlins_wf.run(**plugin_settings)
except Exception:
retcode = 1
layout = BIDSLayout(opts.bids_dir)
models = auto_model(layout) if model == 'default' else [model]
run_context = {'version': __version__,
'command': ' '.join(sys.argv),
'timestamp': time.strftime('%Y-%m-%d %H:%M:%S %z'),
}
for model in models:
analysis = Analysis(layout, model=model)
report_dicts = parse_directory(deriv_dir, work_dir, analysis)
write_report('unknown', report_dicts, run_context, deriv_dir)
return retcode
