def create_bs_imgs(ts_data, block_size, clust_mask_corr_img):
import nibabel as nib
from nilearn.masking import unmask
from pynets.fmri.estimation import timeseries_bootstrap
boot_series = timeseries_bootstrap(
ts_data.func, block_size)[0].astype('float32')
return unmask(boot_series, clust_mask_corr_img)
def test_timeseries_bootstrap():
"""Test bootstrapping a sample of time series."""
tseries = np.random.rand(100, 10)
bseries = timeseries_bootstrap(tseries, 5)
assert np.shape(bseries[0]) == np.shape(tseries)
assert len(bseries[1]) == len(tseries)