def test_param_mask_deprecation_first_level_models_from_bids():
deprecation_msg = (
'The parameter "mask" will be removed in next release of Nistats. '
'Please use the parameter "mask_img" instead.'
)
mask_filepath = '~/masks/mask_01.nii.gz'
with InTemporaryDirectory():
bids_path = _create_fake_bids_dataset(n_sub=10, n_ses=2,
tasks=['localizer', 'main'],
n_runs=[1, 3])
with warnings.catch_warnings(record=True) as raised_warnings:
first_level_models_from_bids(
bids_path, 'main', 'MNI', [('variant', 'some')],
mask=mask_filepath)
first_level_models_from_bids(
bids_path, 'main', 'MNI', [('variant', 'some')],
mask_img=mask_filepath)
raised_param_deprecation_warnings = [
raised_warning_ for raised_warning_
in raised_warnings if
str(raised_warning_.message).startswith('The parameter')
]
assert len(raised_param_deprecation_warnings) == 1
for param_warning_ in raised_param_deprecation_warnings:
assert str(param_warning_.message) == deprecation_msg
assert param_warning_.category is DeprecationWarning
def test_param_mask_deprecation_first_level_models_from_bids():
deprecation_msg = (
'The parameter "mask" will be removed in next release of Nistats. '
'Please use the parameter "mask_img" instead.')
mask_filepath = '~/masks/mask_01.nii.gz'
with InTemporaryDirectory():
bids_path = _create_fake_bids_dataset(n_sub=10,
n_ses=2,
tasks=['localizer', 'main'],
n_runs=[1, 3])
with warnings.catch_warnings(record=True) as raised_warnings:
first_level_models_from_bids(bids_path,
'main',
'MNI', [('desc', 'preproc')],
mask=mask_filepath)
first_level_models_from_bids(bids_path,
'main',
'MNI', [('desc', 'preproc')],
mask_img=mask_filepath)
raised_param_deprecation_warnings = [
raised_warning_ for raised_warning_ in raised_warnings
if str(raised_warning_.message).startswith('The parameter')
]
assert len(raised_param_deprecation_warnings) == 1
for param_warning_ in raised_param_deprecation_warnings:
assert str(param_warning_.message) == deprecation_msg
assert param_warning_.category is DeprecationWarning
def test_first_level_models_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
assert_raises(TypeError, first_level_models_from_bids, 2, 'main', 'MNI')
assert_raises(ValueError, first_level_models_from_bids, 'lolo', 'main', 'MNI')
assert_raises(TypeError, first_level_models_from_bids, bids_path, 2, 'MNI')
assert_raises(TypeError, first_level_models_from_bids,
bids_path, 'main', 'MNI', model_init=[])
# test output is as expected
models, m_imgs, m_events, m_confounds = first_level_models_from_bids(
bids_path, 'main', 'MNI', [('variant', 'some')])
assert_true(len(models) == len(m_imgs))
assert_true(len(models) == len(m_events))
assert_true(len(models) == len(m_confounds))
# test repeated run tag error when run tag is in filenames
# can arise when variant or space is present and not specified
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'main', 'T1w') # variant not specified
# test more than one ses file error when run tag is not in filenames
# can arise when variant or space is present and not specified
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'localizer', 'T1w') # variant 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='confounds')
os.remove(confound_files[-1])
assert_raises(ValueError, first_level_models_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
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'main', 'MNI')
for f in events_files[1:]:
os.remove(f)
# all files missing
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'main', 'MNI')
# In case different variant 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
assert_raises(ValueError, first_level_models_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 variant or space is present and not specified
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'main', 'T1w') # variant not specified
def test_first_level_models_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
assert_raises(TypeError, first_level_models_from_bids, 2, 'main', 'MNI')
assert_raises(ValueError, first_level_models_from_bids, 'lolo', 'main', 'MNI')
assert_raises(TypeError, first_level_models_from_bids, bids_path, 2, 'MNI')
assert_raises(TypeError, first_level_models_from_bids,
bids_path, 'main', 'MNI', model_init=[])
# test output is as expected
models, m_imgs, m_events, m_confounds = first_level_models_from_bids(
bids_path, 'main', 'MNI', [('variant', 'some')])
assert_true(len(models) == len(m_imgs))
assert_true(len(models) == len(m_events))
assert_true(len(models) == len(m_confounds))
# test repeated run tag error when run tag is in filenames
# can arise when variant or space is present and not specified
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'main', 'T1w') # variant not specified
# test more than one ses file error when run tag is not in filenames
# can arise when variant or space is present and not specified
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'localizer', 'T1w') # variant 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='confounds')
os.remove(confound_files[-1])
assert_raises(ValueError, first_level_models_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
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'main', 'MNI')
for f in events_files[1:]:
os.remove(f)
# all files missing
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'main', 'MNI')
# In case different variant 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
assert_raises(ValueError, first_level_models_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 variant or space is present and not specified
assert_raises(ValueError, first_level_models_from_bids,
bids_path, 'main', 'T1w') # variant not specified
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_models_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
##############################################################################
# Obtain automatically FirstLevelModel objects and fit arguments
# --------------------------------------------------------------
# From the dataset directory we obtain automatically FirstLevelModel objects
# with their subject_id filled from the 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 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 nistats.first_level_model import first_level_models_from_bids
task_label = 'languagelocalizer'
space_label = 'MNI152nonlin2009aAsym'
_, models_run_imgs, models_events, models_confounds = \
first_level_models_from_bids(
data_dir, task_label, space_label,
img_filters=[('variant', 'smoothResamp')])
#############################################################################
# We also need to get the TR information. For that we use a json file
# of the dataset
import os
json_file = os.path.join(data_dir, 'sub-01', 'ses-02', 'func',
'sub-01_ses-02_task-languagelocalizer_bold.json')
import json
with open(json_file, 'r') as f:
t_r = json.load(f)['RepetitionTime']
#############################################################################
# Project fMRI data to the surface: First get fsaverage5
from nilearn.datasets import fetch_surf_fsaverage
data_dir, _ = fetch_bids_langloc_dataset()
##############################################################################
# Obtain automatically FirstLevelModel objects and fit arguments
# --------------------------------------------------------------
# From the dataset directory we obtain automatically FirstLevelModel objects
# with their subject_id filled from the 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 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.
task_label = 'languagelocalizer'
space_label = 'MNI152nonlin2009aAsym'
models, models_run_imgs, models_events, models_confounds = \
first_level_models_from_bids(
data_dir, task_label, space_label,
img_filters=[('variant', 'smoothResamp')])
#############################################################################
# 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.
print([os.path.basename(run) for run in models_run_imgs[0]])
###############################################################################
# The only confounds stored are regressors obtained from motion correction. As
# we can verify from the column headers of the confounds table corresponding
##############################################################################
# Obtain automatically FirstLevelModel objects and fit arguments
# --------------------------------------------------------------
# From the dataset directory we obtain automatically FirstLevelModel objects
# with their subject_id filled from the 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 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 nistats.first_level_model import first_level_models_from_bids
task_label = 'languagelocalizer'
models, models_run_imgs, models_events, models_confounds = \
first_level_models_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]])
###############################################################################
# From the dataset directory we automatically obtain FirstLevelModel objects
# with their subject_id filled from the 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 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 BIDS app.
from nistats.first_level_model import first_level_models_from_bids
task_label = 'stopsignal'
space_label = 'MNI152NLin2009cAsym'
derivatives_folder = 'derivatives/fmriprep'
models, models_run_imgs, models_events, models_confounds = \
first_level_models_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 nistats.utils 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)
# From the dataset directory we obtain automatically FirstLevelModel objects
# with their subject_id filled from the BIDS dataset. Moreover we obtain
# for each model the list of run imgs and their respective events and
# confounder regressors. Confounders are inferred from the confounds.tsv files
# available in the 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 BIDS app.
from nistats.first_level_model import first_level_models_from_bids
task_label = 'stopsignal'
space_label = 'MNI152NLin2009cAsym'
derivatives_folder = 'derivatives/fmriprep'
models, models_run_imgs, models_events, models_confounds = \
first_level_models_from_bids(
data_dir, task_label, space_label, smoothing_fwhm=5.0,
derivatives_folder=derivatives_folder)
#############################################################################
# Take 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 nistats.utils 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)
from nilearn.image import math_img
plt.set_cmap('coolwarm')
DATASET_PATH = '../bids'
out = first_level_models_from_bids(dataset_path=DATASET_PATH,
task_label='gstroop',
space_label='MNI152NLin2009cAsym',
img_filters=[],
t_r=2.0,
slice_time_ref=0.0,
hrf_model='glover',
drift_model=None,
period_cut=128,
drift_order=1,
fir_delays=[0],
min_onset=-24,
mask=None,
target_affine=None,
target_shape=None,
smoothing_fwhm=5,
memory_level=1,
standardize=False,
signal_scaling=0,
noise_model='ar1',
verbose=100,
n_jobs=2,
minimize_memory=True,
derivatives_folder='derivatives/fmriprep')
conf_cols = [
'Cosine00', 'Cosine01', 'Cosine02', 'Cosine03', 'Cosine04', 'Cosine05',
'X', 'Y', 'Z', 'RotX', 'RotY', 'RotZ'
