def test_fetch_spm_auditory(request_mocker, tmp_path):
import nibabel as nib
import shutil
saf = ["fM00223/fM00223_%03i.img" % index for index in range(4, 100)]
saf_ = ["fM00223/fM00223_%03i.hdr" % index for index in range(4, 100)]
data_dir = str(tmp_path / 'spm_auditory')
os.mkdir(data_dir)
subject_dir = os.path.join(data_dir, 'sub001')
os.mkdir(subject_dir)
os.mkdir(os.path.join(subject_dir, 'fM00223'))
os.mkdir(os.path.join(subject_dir, 'sM00223'))
path_img = str(tmp_path / 'tmp.img')
path_hdr = str(tmp_path / 'tmp.hdr')
nib.save(nib.Nifti1Image(np.zeros((2, 3, 4)), np.eye(4)), path_img)
shutil.copy(path_img, os.path.join(subject_dir, "sM00223/sM00223_002.img"))
shutil.copy(path_hdr, os.path.join(subject_dir, "sM00223/sM00223_002.hdr"))
for file_ in saf:
shutil.copy(path_img, os.path.join(subject_dir, file_))
for file_ in saf_:
shutil.copy(path_hdr, os.path.join(subject_dir, file_))
dataset = func.fetch_spm_auditory(data_dir=tmp_path)
assert isinstance(dataset.anat, str)
assert isinstance(dataset.func[0], str)
assert len(dataset.func) == 96
Here we fit a First Level GLM with the `minimize_memory`-argument set to `False`. By doing so, the `FirstLevelModel`-object stores the residuals, which we can then inspect. Also, the predicted time series can be extracted, which is useful to assess the quality of the model fit. """ ######################################################################### # Import modules # -------------- from nilearn.datasets.func import fetch_spm_auditory from nilearn import image from nilearn import masking import pandas as pd # load fMRI data subject_data = fetch_spm_auditory() fmri_img = image.concat_imgs(subject_data.func) # Make an average mean_img = image.mean_img(fmri_img) mask = masking.compute_epi_mask(mean_img) # Clean and smooth data fmri_img = image.clean_img(fmri_img, standardize=False) fmri_img = image.smooth_img(fmri_img, 5.) # load events events = pd.read_table(subject_data['events']) ######################################################################### # Fit model