U f{j @sddlZdddZdS)N@c Csddlm}tjt|td}t|D]}tj||}|dkrPt ||}n2|dkrb||}n |dkrv|| }n t d|||k}t ||gd}t |s&qq&t |d} | S)aFind outliers based on iterated Z-scoring. This procedure compares the absolute z-score against the threshold. After excluding local outliers, the comparison is repeated until no local outlier is present any more. Parameters ---------- X : np.ndarray of float, shape (n_elemenets,) The scores for which to find outliers. threshold : float The value above which a feature is classified as outlier. max_iter : int The maximum number of iterations. tail : {0, 1, -1} Whether to search for outliers on both extremes of the z-scores (0), or on just the positive (1) or negative (-1) side. Returns ------- bad_idx : np.ndarray of int, shape (n_features) The outlier indices. r)zscore)Zdtypez!Tail parameter %s not recognised.)Z scipy.statsrnpZzeroslenboolrangemaZ masked_arrayabs ValueErrormaxanywhere) X thresholdZmax_itertailrZmy_mask_Zthis_zZ local_badZbad_idxrU/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/preprocessing/bads.py_find_outlierss"      r)rrr)numpyrrrrrrs