- 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 relative_change[idx] > relative_change[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])
-
- # Sort the table according to the classification
+ rel_change_long = round(
+ ((last_median_t - max_median) / max_median) * 100, 2)
+
+ logging.info("rel_change_last : {}".format(rel_change_last))
+ logging.info("rel_change_long : {}".format(rel_change_long))
+
+ tbl_lst.append(
+ [name,
+ '-' if isnan(last_median_t) else
+ round(last_median_t / 1000000, 2),
+ '-' if isnan(rel_change_last) else rel_change_last,
+ '-' if isnan(rel_change_long) else rel_change_long,
+ classification_lst[win_first_idx:].count("regression"),
+ classification_lst[win_first_idx:].count("progression"),
+ classification_lst[win_first_idx:].count("outlier")])
+
+ tbl_lst.sort(key=lambda rel: rel[0])
+