def _pre_whiten(self, data, info, picks):
        fast_dot = _get_fast_dot()
        has_pre_whitener = hasattr(self, '_pre_whitener')
        if not has_pre_whitener and self.noise_cov is None:
            # use standardization as whitener
            # Scale (z-score) the data by channel type
            info = pick_info(info, picks)
            pre_whitener = np.empty([len(data), 1])
            for ch_type in _DATA_CH_TYPES_SPLIT:
                if _contains_ch_type(info, ch_type):
                    if ch_type == 'seeg':
                        this_picks = pick_types(info, meg=False, seeg=True)
                    elif ch_type == 'ecog':
                        this_picks = pick_types(info, meg=False, ecog=True)
                    elif ch_type == 'eeg':
                        this_picks = pick_types(info, meg=False, eeg=True)
                    else:
                        this_picks = pick_types(info, meg=ch_type)
                    pre_whitener[this_picks] = np.std(data[this_picks])
            data /= pre_whitener
        elif not has_pre_whitener and self.noise_cov is not None:
            pre_whitener, _ = compute_whitener(self.noise_cov, info, picks)
            assert data.shape[0] == pre_whitener.shape[1]
            data = fast_dot(pre_whitener, data)
        elif has_pre_whitener and self.noise_cov is None:
            data /= self._pre_whitener
            pre_whitener = self._pre_whitener
        else:
            data = fast_dot(self._pre_whitener, data)
            pre_whitener = self._pre_whitener

        return data, pre_whitener
UNIXPasswordDatabase.requestAvatarId loops through each getpwnam
dataset/ETHPy150Open mne-tools/mne-python/mne/preprocessing/ica.py/ICA._pre_whiten--crossover with--dataset/ETHPy150Open nlloyd/SubliminalCollaborator/libs/twisted/conch/test/test_checkers.py/UNIXPasswordDatabaseTests.test_loopThroughFunctions