- pd_data = pd.Series(tbl_dict[tst_name]["data"])
- data_t, _ = split_outliers(pd_data, outlier_const=1.5,
- window=table["window"])
- last_key = data_t.keys()[-1]
- win_size = min(data_t.size, table["window"])
- win_first_idx = data_t.size - win_size
- key_14 = data_t.keys()[win_first_idx]
- long_win_size = min(data_t.size, table["long-trend-window"])
- median_t = data_t.rolling(window=win_size, min_periods=2).median()
- median_first_idx = median_t.size - long_win_size
+ classification_lst, avgs = classify_anomalies(data_t)
+
+ win_size = min(len(data_t), table["window"])
+ long_win_size = min(len(data_t), table["long-trend-window"])
+