def test_fiac():
dataset = datasets.fetch_fiac_first_level(data_dir=tmpdir)
assert_true(isinstance(dataset.func1, _basestring))
assert_true(isinstance(dataset.func2, _basestring))
assert_true(isinstance(dataset.design_matrix1, _basestring))
assert_true(isinstance(dataset.design_matrix2, _basestring))
assert_true(isinstance(dataset.mask, _basestring))
def test_fiac():
dataset = datasets.fetch_fiac_first_level(data_dir=tmpdir)
assert_true(isinstance(dataset.func1, _basestring))
assert_true(isinstance(dataset.func2, _basestring))
assert_true(isinstance(dataset.design_matrix1, _basestring))
assert_true(isinstance(dataset.design_matrix2, _basestring))
assert_true(isinstance(dataset.mask, _basestring))
def report_flm_fiac(): # pragma: no cover
data = nistats_datasets.fetch_fiac_first_level()
fmri_img = [data['func1'], data['func2']]
from nilearn.image import mean_img
mean_img_ = mean_img(fmri_img[0])
design_files = [data['design_matrix1'], data['design_matrix2']]
design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files]
fmri_glm = FirstLevelModel(mask_img=data['mask'], minimize_memory=True)
fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices)
n_columns = design_matrices[0].shape[1]
contrasts = {
'SStSSp_minus_DStDSp': _pad_vector([1, 0, 0, -1], n_columns),
'DStDSp_minus_SStSSp': _pad_vector([-1, 0, 0, 1], n_columns),
'DSt_minus_SSt': _pad_vector([-1, -1, 1, 1], n_columns),
'DSp_minus_SSp': _pad_vector([-1, 1, -1, 1], n_columns),
'DSt_minus_SSt_for_DSp': _pad_vector([0, -1, 0, 1], n_columns),
'DSp_minus_SSp_for_DSt': _pad_vector([0, 0, -1, 1], n_columns),
'Deactivation': _pad_vector([-1, -1, -1, -1, 4], n_columns),
'Effects_of_interest': np.eye(n_columns)[:5]
}
report = make_glm_report(
fmri_glm,
contrasts,
bg_img=mean_img_,
height_control='fdr',
)
output_filename = 'generated_report_flm_fiac.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
def test_fiac():
# Create dummy 'files'
fiac_dir = os.path.join(tst.tmpdir, 'fiac_nistats', 'nipy-data-0.2',
'data', 'fiac')
fiac0_dir = os.path.join(fiac_dir, 'fiac0')
os.makedirs(fiac0_dir)
for session in [1, 2]:
# glob func data for session session + 1
session_func = os.path.join(fiac0_dir, 'run%i.nii.gz' % session)
open(session_func, 'a').close()
sess_dmtx = os.path.join(fiac0_dir, 'run%i_design.npz' % session)
open(sess_dmtx, 'a').close()
mask = os.path.join(fiac0_dir, 'mask.nii.gz')
open(mask, 'a').close()
dataset = datasets.fetch_fiac_first_level(data_dir=tst.tmpdir)
assert_true(isinstance(dataset.func1, _basestring))
assert_true(isinstance(dataset.func2, _basestring))
assert_true(isinstance(dataset.design_matrix1, _basestring))
assert_true(isinstance(dataset.design_matrix2, _basestring))
assert_true(isinstance(dataset.mask, _basestring))
from nilearn import plotting
from nilearn.image import mean_img
import nibabel as nib
from nistats.first_level_model import FirstLevelModel
from nistats import datasets
# write directory
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
#########################################################################
# Prepare data and analysis parameters
# --------------------------------------
data = datasets.fetch_fiac_first_level()
fmri_img = [data['func1'], data['func2']]
mean_img_ = mean_img(fmri_img[0])
design_files = [data['design_matrix1'], data['design_matrix2']]
design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files]
#########################################################################
# GLM estimation
# ----------------------------------
# GLM specification
fmri_glm = FirstLevelModel(mask=data['mask'], minimize_memory=True)
#########################################################################
# GLM fitting
fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices)
from nilearn import plotting
from nilearn.image import mean_img
import nibabel as nib
from nistats.glm import FirstLevelGLM
from nistats import datasets
# write directory
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
# Data and analysis parameters
data = datasets.fetch_fiac_first_level()
fmri_img = [data['func1'], data['func2']]
mean_img_ = mean_img(fmri_img[0])
design_files = [data['design_matrix1'], data['design_matrix2']]
design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files]
# GLM specification
fmri_glm = FirstLevelGLM(data['mask'], standardize=False, noise_model='ar1')
# GLM fitting
fmri_glm.fit(fmri_img, design_matrices)
# compute fixed effects of the two runs and compute related images
n_columns = design_matrices[0].shape[1]
def pad_vector(contrast_, n_columns):
return np.hstack((contrast_, np.zeros(n_columns - len(contrast_))))
