def _make_flm(data_dir): # pragma: no cover
task_label = 'stopsignal'
space_label = 'MNI152NLin2009cAsym'
derivatives_folder = 'derivatives/fmriprep'
models, models_run_imgs, models_events, models_confounds = \
first_level_from_bids(
data_dir, task_label, space_label, smoothing_fwhm=5.0,
derivatives_folder=derivatives_folder)
model, imgs, _, _ = (models[0], models_run_imgs[0], models_events[0],
models_confounds[0])
subject = 'sub-' + model.subject_label
design_matrix = _make_design_matrix_for_bids_feature(data_dir, subject)
model.fit(imgs, design_matrices=[design_matrix])
return model, subject
def test_first_level_from_bids():
with InTemporaryDirectory():
bids_path = create_fake_bids_dataset(n_sub=10,
n_ses=2,
tasks=['localizer', 'main'],
n_runs=[1, 3])
# test arguments are provided correctly
with pytest.raises(TypeError):
first_level_from_bids(2, 'main', 'MNI')
with pytest.raises(ValueError):
first_level_from_bids('lolo', 'main', 'MNI')
with pytest.raises(TypeError):
first_level_from_bids(bids_path, 2, 'MNI')
with pytest.raises(TypeError):
first_level_from_bids(bids_path, 'main', 'MNI', model_init=[])
with pytest.raises(TypeError, match="space_label must be a string"):
first_level_from_bids(bids_path, 'main', space_label=42)
with pytest.raises(TypeError, match="img_filters must be a list"):
first_level_from_bids(bids_path, 'main', img_filters="foo")
with pytest.raises(TypeError, match="filters in img"):
first_level_from_bids(bids_path, 'main', img_filters=[(1, 2)])
with pytest.raises(ValueError,
match="field foo is not a possible filter."):
first_level_from_bids(bids_path,
'main',
img_filters=[("foo", "bar")])
# test output is as expected
models, m_imgs, m_events, m_confounds = first_level_from_bids(
bids_path, 'main', 'MNI', [('desc', 'preproc')])
assert len(models) == len(m_imgs)
assert len(models) == len(m_events)
assert len(models) == len(m_confounds)
# test repeated run tag error when run tag is in filenames
# can arise when desc or space is present and not specified
with pytest.raises(ValueError):
first_level_from_bids(bids_path, 'main',
'T1w') # desc not specified
# test more than one ses file error when run tag is not in filenames
# can arise when desc or space is present and not specified
with pytest.raises(ValueError):
first_level_from_bids(bids_path, 'localizer',
'T1w') # desc not specified
# test issues with confound files. There should be only one confound
# file per img. An one per image or None. Case when one is missing
confound_files = get_bids_files(os.path.join(bids_path, 'derivatives'),
file_tag='desc-confounds_timeseries')
os.remove(confound_files[-1])
with pytest.raises(ValueError):
first_level_from_bids(bids_path, 'main', 'MNI')
# test issues with event files
events_files = get_bids_files(bids_path, file_tag='events')
os.remove(events_files[0])
# one file missing
with pytest.raises(ValueError):
first_level_from_bids(bids_path, 'main', 'MNI')
for f in events_files[1:]:
os.remove(f)
# all files missing
with pytest.raises(ValueError):
first_level_from_bids(bids_path, 'main', 'MNI')
# In case different desc and spaces exist and are not selected we
# fail and ask for more specific information
shutil.rmtree(os.path.join(bids_path, 'derivatives'))
# issue if no derivatives folder is present
with pytest.raises(ValueError):
first_level_from_bids(bids_path, 'main', 'MNI')
# check runs are not repeated when ses field is not used
shutil.rmtree(bids_path)
bids_path = create_fake_bids_dataset(n_sub=10,
n_ses=1,
tasks=['localizer', 'main'],
n_runs=[1, 3],
no_session=True)
# test repeated run tag error when run tag is in filenames and not ses
# can arise when desc or space is present and not specified
with pytest.raises(ValueError):
first_level_from_bids(bids_path, 'main',
'T1w') # desc not specified
print(data_dir)
##############################################################################
# Obtain automatically FirstLevelModel objects and fit arguments
# --------------------------------------------------------------
# From the dataset directory we automatically obtain the FirstLevelModel objects
# with their subject_id filled from the :term:`BIDS` dataset. Moreover, we obtain
# for each model a dictionary with run_imgs, events and confounder regressors
# since in this case a confounds.tsv file is available in the :term:`BIDS` dataset.
# To get the first level models we only have to specify the dataset directory
# and the task_label as specified in the file names.
from nilearn.glm.first_level import first_level_from_bids
task_label = 'languagelocalizer'
models, models_run_imgs, models_events, models_confounds = \
first_level_from_bids(
data_dir, task_label,
img_filters=[('desc', 'preproc')])
#############################################################################
# Quick sanity check on fit arguments
# -----------------------------------
# Additional checks or information extraction from pre-processed data can
# be made here.
############################################################################
# We just expect one run_img per subject.
import os
print([os.path.basename(run) for run in models_run_imgs[0]])
###############################################################################
# The only confounds stored are regressors obtained from motion correction. As
# From the dataset directory we automatically obtain FirstLevelModel objects
# with their subject_id filled from the :term:`BIDS` dataset. Moreover we obtain,
# for each model, the list of run images and their respective events and
# confound regressors. Those are inferred from the confounds.tsv files
# available in the :term:`BIDS` dataset.
# To get the first level models we have to specify the dataset directory,
# the task_label and the space_label as specified in the file names.
# We also have to provide the folder with the desired derivatives, that in this
# case were produced by the fmriprep :term:`BIDS` app.
from nilearn.glm.first_level import first_level_from_bids
task_label = 'stopsignal'
space_label = 'MNI152NLin2009cAsym'
derivatives_folder = 'derivatives/fmriprep'
models, models_run_imgs, models_events, models_confounds = \
first_level_from_bids(data_dir, task_label, space_label,
smoothing_fwhm=5.0,
derivatives_folder=derivatives_folder)
#############################################################################
# Access the model and model arguments of the subject and process events.
model, imgs, events, confounds = (models[0], models_run_imgs[0],
models_events[0], models_confounds[0])
subject = 'sub-' + model.subject_label
import os
from nilearn._utils.glm import get_design_from_fslmat
fsl_design_matrix_path = os.path.join(data_dir, 'derivatives', 'task', subject,
'stopsignal.feat', 'design.mat')
design_matrix = get_design_from_fslmat(fsl_design_matrix_path,
column_names=None)
##############################################################################
# Prepare data and analysis parameters
# ------------------------------------
# Download data in :term:`BIDS` format and event information for one subject,
# and create a standard :class:`~nilearn.glm.first_level.FirstLevelModel`.
from nilearn.datasets import fetch_language_localizer_demo_dataset
data_dir, _ = fetch_language_localizer_demo_dataset()
from nilearn.glm.first_level import first_level_from_bids
models, models_run_imgs, events_dfs, models_confounds = \
first_level_from_bids(
data_dir,
'languagelocalizer',
img_filters=[('desc', 'preproc')],
)
# Grab the first subject's model, functional file, and events DataFrame
standard_glm = models[0]
fmri_file = models_run_imgs[0][0]
events_df = events_dfs[0][0]
# We will use first_level_from_bids's parameters for the other models
glm_parameters = standard_glm.get_params()
# We need to override one parameter (signal_scaling) with the value of
# scaling_axis
glm_parameters['signal_scaling'] = standard_glm.scaling_axis
##############################################################################
