def test_fetch_bids_langloc_dataset():
data_dir = os.path.join(tst.tmpdir, 'bids_langloc_example')
os.mkdir(data_dir)
main_folder = os.path.join(data_dir, 'bids_langloc_dataset')
os.mkdir(main_folder)
datadir, dl_files = datasets.fetch_bids_langloc_dataset(tst.tmpdir)
assert_true(isinstance(datadir, _basestring))
assert_true(isinstance(dl_files, list))
def test_fetch_bids_langloc_dataset(request_mocker, tmp_path):
data_dir = str(tmp_path / 'bids_langloc_example')
os.mkdir(data_dir)
main_folder = os.path.join(data_dir, 'bids_langloc_dataset')
os.mkdir(main_folder)
datadir, dl_files = datasets.fetch_bids_langloc_dataset(str(tmp_path))
assert isinstance(datadir, _basestring)
assert isinstance(dl_files, list)
:local:
:depth: 1
"""
##############################################################################
# Fetch example BIDS dataset
# --------------------------
# We download an simplified BIDS dataset made available for illustrative
# purposes. It contains only the necessary
# information to run a statistical analysis using Nistats. The raw data
# subject folders only contain bold.json and events.tsv files, while the
# derivatives folder with preprocessed files contain preproc.nii and
# confounds.tsv files.
from nistats.datasets import fetch_bids_langloc_dataset
data_dir, _ = fetch_bids_langloc_dataset()
##############################################################################
# Here is the location of the dataset on disk
print(data_dir)
##############################################################################
# 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
from scipy.stats import norm
import matplotlib.pyplot as plt
from nistats.datasets import fetch_bids_langloc_dataset
from nistats.first_level_model import first_level_models_from_bids
from nistats.second_level_model import SecondLevelModel
##############################################################################
# Fetch example BIDS dataset
# --------------------------
# We download a partial example BIDS dataset. It contains only the necessary
# information to run a statistical analysis using Nistats. The raw data
# subject folders only contain bold.json and events.tsv files, while the
# derivatives folder with preprocessed files contain preproc.nii and
# confounds.tsv files.
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,