def _prepare_rerp_data(raw, events, picks=None, decim=1):
    if picks is None:
        picks = pick_types(raw.info, meg=True, eeg=True, ref_meg=True)
    info = pick_info(raw.info, picks)
    decim = int(decim)
    info["sfreq"] /= decim
    data, times = raw[:]
    data = data[picks, ::decim]
    if len(set(events[:, 0])) < len(events[:, 0]):
        raise ValueError("`events` contains duplicate time points. Make "
                         "sure all entries in the first column of `events` "
                         "are unique.")

    events = events.copy()
    events[:, 0] -= raw.first_samp
    events[:, 0] //= decim
    if len(set(events[:, 0])) < len(events[:, 0]):
        raise ValueError("After decimating, `events` contains duplicate time "
                         "points. This means some events are too closely "
                         "spaced for the requested decimation factor. Choose "
                         "different events, drop close events, or choose a "
                         "different decimation factor.")

    return data, info, events
Returns the SLA result dict corresponding to the current state.
dataset/ETHPy150Open mne-tools/mne-python/mne/stats/regression.py/_prepare_rerp_data--crossover with--dataset/ETHPy150Open openstack/rally/rally/task/sla.py/SLA.result