@verbose
def make_ad_hoc_cov(info, verbose=None):
    info = pick_info(info, pick_types(info, meg=True, eeg=True, exclude=[]))
    info._check_consistency()

    # Standard deviations to be used
    grad_std = 5e-13
    mag_std = 20e-15
    eeg_std = 0.2e-6
    logger.info('Using standard noise values '
                '(MEG grad : %6.1f fT/cm MEG mag : %6.1f fT EEG : %6.1f uV)'
                % (1e13 * grad_std, 1e15 * mag_std, 1e6 * eeg_std))

    data = np.zeros(len(info['ch_names']))
    for meg, eeg, val in zip(('grad', 'mag', False), (False, False, True),
                             (grad_std, mag_std, eeg_std)):
        data[pick_types(info, meg=meg, eeg=eeg)] = val * val
    return Covariance(data, info['ch_names'], info['bads'], info['projs'],
                      nfree=0)
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