- if rel_change_lst[idx] > rel_change_lst[index]:
- index = idx
-
- # if "regression" in classification_lst[first_idx:]:
- # classification = "regression"
- # elif "outlier" in classification_lst[first_idx:]:
- # classification = "outlier"
- # elif "progression" in classification_lst[first_idx:]:
- # classification = "progression"
- # elif "normal" in classification_lst[first_idx:]:
- # classification = "normal"
- # else:
- # classification = None
- #
- # nr_outliers = 0
- # consecutive_outliers = 0
- # failure = False
- # for item in classification_lst[first_idx:]:
- # if item == "outlier":
- # nr_outliers += 1
- # consecutive_outliers += 1
- # if consecutive_outliers == 3:
- # failure = True
- # else:
- # consecutive_outliers = 0
- #
- # idx = len(classification_lst) - 1
- # while idx:
- # if classification_lst[idx] == classification:
- # break
- # idx -= 1
- #
- # if failure:
- # classification = "failure"
- # elif classification == "outlier":
- # classification = "normal"
-
- 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 ''
- tbl_lst.append([name,
- trend,
- classification,
- '-' if classification == "normal" else sample,
- '-' if classification == "normal" else rel_change,
- nr_outliers])
+ if rel_change_lst[idx]:
+ if (abs(rel_change_lst[idx]) >
+ abs(rel_change_lst[index])):
+ index = idx
+
+ logging.debug("{}".format(name))
+ logging.debug("sample_lst: {} - {}".
+ format(len(sample_lst), sample_lst))
+ logging.debug("median_lst: {} - {}".
+ format(len(median_lst), median_lst))
+ logging.debug("rel_change: {} - {}".
+ format(len(rel_change_lst), rel_change_lst))
+ logging.debug("classn_lst: {} - {}".
+ format(len(classification_lst), classification_lst))
+ logging.debug("index: {}".format(index))
+ logging.debug("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 = 'normal'
+ 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