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