for job, builds in item["data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
+ if tbl_dict.get(tst_name, None) is None:
+ continue
if tbl_dict[tst_name].get("history", None) is None:
tbl_dict[tst_name]["history"] = OrderedDict()
if tbl_dict[tst_name]["history"].get(item["title"],
None) is None:
tbl_dict[tst_name]["history"][item["title"]] = \
list()
- tbl_dict[tst_name]["history"][item["title"]].\
- append(tst_data["throughput"]["value"])
+ try:
+ tbl_dict[tst_name]["history"][item["title"]].\
+ append(tst_data["throughput"]["value"])
+ except (TypeError, KeyError):
+ pass
tbl_lst = list()
for tst_name in tbl_dict.keys():
or isnan(stdev_t[build_nr]) \
or isnan(value):
classification_lst.append("outlier")
- elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]):
+ elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]):
classification_lst.append("regression")
- elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]):
+ elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]):
classification_lst.append("progression")
else:
classification_lst.append("normal")