for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
if history:
- for hist_data in tbl_dict[tst_name]["history"].values():
- if hist_data:
- data_t = remove_outliers(
- hist_data, outlier_const=table["outlier-const"])
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ if tbl_dict[tst_name].get("history", None) is not None:
+ for hist_data in tbl_dict[tst_name]["history"].values():
+ if hist_data:
+ data_t = remove_outliers(
+ hist_data, outlier_const=table["outlier-const"])
+ 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])
- else:
- item.extend([None, None])
+ else:
+ item.extend([None, None])
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
outlier_const=table["outlier-const"])
item.extend([None, None])
else:
item.extend([None, None])
- if item[-5] is not None and item[-3] is not None and item[-5] != 0:
- item.append(int(relative_change(float(item[-5]), float(item[-3]))))
+ if item[-4] is not None and item[-2] is not None and item[-4] != 0:
+ item.append(int(relative_change(float(item[-4]), float(item[-2]))))
if len(item) == len(header):
tbl_lst.append(item)
or isnan(stdev_t[build_nr]) \
or isnan(value):
classification_lst.append("outlier")
- elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]):
+ elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]):
classification_lst.append("regression")
- elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]):
+ elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]):
classification_lst.append("progression")
else:
classification_lst.append("normal")