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():
item = [tbl_dict[tst_name]["name"], ]
if history:
- for hist_list in tbl_dict[tst_name]["history"].values():
- for hist_data in hist_list:
- 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])
+ 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])
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
outlier_const=table["outlier-const"])
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")
for nrp in range(table["window"], -1, -1):
tbl_pro = [item for item in tbl_reg if item[5] == nrp]
for nro in range(table["window"], -1, -1):
- tbl_out = [item for item in tbl_pro if item[5] == nro]
+ tbl_out = [item for item in tbl_pro if item[6] == nro]
+ tbl_out.sort(key=lambda rel: rel[2])
tbl_sorted.extend(tbl_out)
file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])