def test_fetch_spm_auditory():
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 = os.path.join(tst.tmpdir, '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 = os.path.join(tst.tmpdir, 'tmp.img')
path_hdr = os.path.join(tst.tmpdir, '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 = datasets.fetch_spm_auditory(data_dir=tst.tmpdir)
assert_true(isinstance(dataset.anat, _basestring))
assert_true(isinstance(dataset.func[0], _basestring))
assert_equal(len(dataset.func), 96)
"""
import os
import numpy as np
import pandas as pd
import nibabel as nib
from nilearn.plotting import plot_stat_map, show
from nilearn.image import mean_img
from nistats.design_matrix import make_design_matrix
from nistats.glm import FirstLevelGLM
from nistats.datasets import fetch_spm_auditory
# fetch spm auditory data
subject_data = fetch_spm_auditory()
fmri_img = nib.concat_images(subject_data.func)
# compute bg unto which activation will be projected
mean_img = mean_img(fmri_img)
# construct experimental paradigm
tr = 7.
n_scans = 96
epoch_duration = 6 * tr # duration in seconds
conditions = ['rest', 'active'] * 8
n_blocks = len(conditions)
duration = epoch_duration * np.ones(n_blocks)
onset = np.linspace(0, (n_blocks - 1) * epoch_duration, n_blocks)
paradigm = pd.DataFrame(
{'onset': onset, 'duration': duration, 'name': conditions})
def test_fetch_spm_auditory():
dataset = datasets.fetch_spm_auditory(data_dir=tmpdir)
assert_true(isinstance(dataset.anat, _basestring))
assert_true(isinstance(dataset.func[0], _basestring))
assert_equal(len(dataset.func), 96)
:depth: 1 """ ############################################################################### # Retrieving the data # ------------------- # # .. note:: In this tutorial, we load the data using a data downloading # function. To input your own data, you will need to provide # a list of paths to your own files in the ``subject_data`` variable. # These should abide to the Brain Imaging Data Structure (BIDS) # organization. from nistats.datasets import fetch_spm_auditory subject_data = fetch_spm_auditory() print(subject_data.func) # print the list of names of functional images ############################################################################### # We can display the first functional image and the subject's anatomy: from nilearn.plotting import plot_stat_map, plot_anat, plot_img, show plot_img(subject_data.func[0]) plot_anat(subject_data.anat) ############################################################################### # Next, we concatenate all the 3D EPI image into a single 4D image, # then we average them in order to create a background # image that will be used to display the activations: from nilearn.image import concat_imgs, mean_img fmri_img = concat_imgs(subject_data.func)
def test_fetch_spm_auditory():
dataset = datasets.fetch_spm_auditory(data_dir=tmpdir)
assert_true(isinstance(dataset.anat, _basestring))
assert_true(isinstance(dataset.func[0], _basestring))
assert_equal(len(dataset.func), 96)
