def test_get_design_from_fslmat():
fsl_mat_path = os.path.join(tst.tmpdir, 'fsl_mat.txt')
matrix = np.ones((5, 5))
with open(fsl_mat_path, 'w') as fsl_mat:
fsl_mat.write('/Matrix\n')
for row in matrix:
for val in row:
fsl_mat.write(str(val) + '\t')
fsl_mat.write('\n')
design_matrix = get_design_from_fslmat(fsl_mat_path)
assert_true(design_matrix.shape == matrix.shape)
def _make_design_matrix_for_bids_feature(data_dir,
subject): # pragma: no cover
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)
design_columns = [
'cond_%02d' % i for i in range(len(design_matrix.columns))
]
design_columns[0] = 'Go'
design_columns[4] = 'StopSuccess'
design_matrix.columns = design_columns
return design_matrix
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)
#############################################################################
# We identify the columns of the Go and StopSuccess conditions of the
# design matrix inferred from the FSL file, to use them later for contrast
# definition.
design_columns = ['cond_%02d' % i for i in range(len(design_matrix.columns))]
design_columns[0] = 'Go'
design_columns[4] = 'StopSuccess'
design_matrix.columns = design_columns
############################################################################
# First level model estimation (one subject)
# -------------------------------------------
# We fit the first level model for one subject.
model.fit(imgs, design_matrices=[design_matrix])
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)
#############################################################################
# We identify the columns of the Go and StopSuccess conditions of the
# design matrix inferred from the fsl file, to use them later for contrast
# definition.
design_columns = ['cond_%02d' % i for i in range(len(design_matrix.columns))]
design_columns[0] = 'Go'
design_columns[4] = 'StopSuccess'
design_matrix.columns = design_columns
############################################################################
# First level model estimation (one subject)
# -------------------------------------------
# We fit the first level model for one subject.
model.fit(imgs, design_matrices=[design_matrix])
