+
+ if classification == "normal":
+ index = len(classification_lst) - 1
+ else:
+ tmp_classification = "outlier" if classification == "failure" \
+ else classification
+ for idx in range(first_idx, len(classification_lst)):
+ if classification_lst[idx] == tmp_classification:
+ index = idx
+ break
+ for idx in range(index+1, len(classification_lst)):
+ if classification_lst[idx] == tmp_classification:
+ if rel_change_lst[idx] > rel_change_lst[index]:
+ index = idx
+
+ trend = round(float(median_lst[-1]) / 1000000, 2) \
+ if not isnan(median_lst[-1]) else '-'
+ sample = round(float(sample_lst[index]) / 1000000, 2) \
+ if not isnan(sample_lst[index]) else '-'
+ rel_change = rel_change_lst[index] \
+ if rel_change_lst[index] is not None else '-'
+ if not isnan(max_median):
+ if not isnan(sample_lst[index]):
+ long_trend_threshold = max_median * \
+ (table["long-trend-threshold"] / 100)
+ if sample_lst[index] < long_trend_threshold:
+ long_trend_classification = "failure"
+ else:
+ long_trend_classification = '-'
+ else:
+ long_trend_classification = "failure"