data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
outlier_const=table["outlier-const"])
# TODO: Specify window size.
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ else:
+ item.extend([None, None])
else:
item.extend([None, None])
if tbl_dict[tst_name]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
outlier_const=table["outlier-const"])
# TODO: Specify window size.
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ else:
+ item.extend([None, None])
else:
item.extend([None, None])
if item[1] is not None and item[3] is not None:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
outlier_const=table["outlier-const"])
# TODO: Specify window size.
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ else:
+ item.extend([None, None])
else:
item.extend([None, None])
if tbl_dict[tst_name]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
outlier_const=table["outlier-const"])
# TODO: Specify window size.
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ else:
+ item.extend([None, None])
else:
item.extend([None, None])
if item[1] is not None and item[3] is not None and item[1] != 0:
else:
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]:
abs(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"
+ 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 = '-'
+ long_trend_classification = "failure"
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])
+ 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
# Sort the table according to the classification
tbl_sorted = list()