- classification = None
-
- idx = len(classification_lst) - 1
- while idx:
- if classification_lst[idx] == classification:
- break
- idx -= 1
-
- trend = round(float(trend_lst[-2]) / 1000000, 2) \
- if not isnan(trend_lst[-2]) else ''
- sample = round(float(sample_lst[idx]) / 1000000, 2) \
- if not isnan(sample_lst[idx]) else ''
- rel_change = rel_change_lst[idx] \
- if rel_change_lst[idx] is not None else ''
- tbl_lst.append([name,
- trend,
- sample,
- rel_change,
- classification])
+ tmp_classification = "outlier" if classification == "failure" \
+ else classification
+ index = None
+ for idx in range(first_idx, len(classification_lst)):
+ if classification_lst[idx] == tmp_classification:
+ if rel_change_lst[idx]:
+ index = idx
+ break
+ if index is None:
+ continue
+ for idx in range(index+1, len(classification_lst)):
+ if classification_lst[idx] == tmp_classification:
+ if rel_change_lst[idx]:
+ if (abs(rel_change_lst[idx]) >
+ abs(rel_change_lst[index])):
+ index = idx
+
+ logging.info("{}".format(name))
+ logging.info("sample_lst: {} - {}".format(len(sample_lst), sample_lst))
+ logging.info("median_lst: {} - {}".format(len(median_lst), median_lst))
+ logging.info("rel_change: {} - {}".format(len(rel_change_lst), rel_change_lst))
+ logging.info("classn_lst: {} - {}".format(len(classification_lst), classification_lst))
+ logging.info("index: {}".format(index))
+ logging.info("classifica: {}".format(classification))
+
+ try:
+ 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"
+ else:
+ long_trend_classification = '-'
+ tbl_lst.append([name,
+ trend,
+ long_trend_classification,
+ classification,
+ '-' if classification == "normal" else sample,
+ '-' if classification == "normal" else rel_change,
+ nr_outliers])
+ except IndexError as err:
+ logging.error("{}".format(err))
+ continue