roi_cache.py 文件源码

def extract_data(exp, subj, roi_info):
    """Extract timeseries data from each ROI."""
    ts_data = {roi: [] for roi in roi_info}
    n_runs = dict(dots=12, sticks=12, rest=8)[exp]

    anal_dir = PROJECT["analysis_dir"]
    ts_temp = op.join(anal_dir, exp, subj, "reg",  "epi", "unsmoothed",
                      "run_{}", "res4d_xfm.nii.gz")

    # Extract ROI data from each run, loading images only once
    for run in range(1, n_runs + 1):
        run_data = nib.load(ts_temp.format(run)).get_data()

        for roi, info in roi_info.iteritems():
            roi_ts = extract_from_volume(run_data, info["vox_ijk"])
            ts_data[roi].append(roi_ts)

    # Combine across runs
    ts_data = {roi: np.hstack(ts_data[roi]).T for roi in roi_info}
    for roi in roi_info:
        assert ts_data[roi].shape[1] == len(roi_info[roi]["vox_ijk"])

    return ts_data