from datetime import timedelta
from utils import mean, stdev, relative_change, classify_anomalies, \
- convert_csv_to_pretty_txt
+ convert_csv_to_pretty_txt, relative_change_stdev
REGEX_NIC = re.compile(r'\d*ge\dp\d\D*\d*')
"{0} Stdev [Mpps]".format(table["reference"]["title"]),
"{0} Throughput [Mpps]".format(table["compare"]["title"]),
"{0} Stdev [Mpps]".format(table["compare"]["title"]),
- "Delta [%]"]
+ "Delta [%]", "Stdev of delta [%]"]
header_str = ",".join(header) + "\n"
except (AttributeError, KeyError) as err:
logging.error("The model is invalid, missing parameter: {0}".
if tst_data["type"] == "SOAK":
tst_name_mod = tst_name.replace("-soak", "")
if tbl_dict.get(tst_name_mod, None) is None:
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ nic = groups.group(0) if groups else ""
+ name = "{0}-{1}".format(nic, "-".join(tst_data["name"].
+ split("-")[:-1]))
tbl_dict[tst_name_mod] = {
- "name": tst_name_mod,
+ "name": name,
"ref-data": list(),
"cmp-data": list()
}
tbl_lst = list()
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
- data_t = tbl_dict[tst_name]["ref-data"]
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ data_r = tbl_dict[tst_name]["ref-data"]
+ if data_r:
+ data_r_mean = mean(data_r)
+ item.append(round(data_r_mean / 1000000, 2))
+ data_r_stdev = stdev(data_r)
+ item.append(round(data_r_stdev / 1000000, 2))
else:
+ data_r_mean = None
+ data_r_stdev = None
item.extend([None, None])
- data_t = tbl_dict[tst_name]["cmp-data"]
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ data_c = tbl_dict[tst_name]["cmp-data"]
+ if data_c:
+ data_c_mean = mean(data_c)
+ item.append(round(data_c_mean / 1000000, 2))
+ data_c_stdev = stdev(data_c)
+ item.append(round(data_c_stdev / 1000000, 2))
else:
+ data_c_mean = None
+ data_c_stdev = None
item.extend([None, None])
- 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):
+ if data_r_mean and data_c_mean:
+ delta, d_stdev = relative_change_stdev(
+ data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
+ item.append(round(delta, 2))
+ item.append(round(d_stdev, 2))
tbl_lst.append(item)
# Sort the table according to the relative change
for job, builds in table["data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
- if tst_name.lower() in table["ignore-list"]:
+ if tst_name.lower() in table.get("ignore-list", list()):
continue
if tbl_dict.get(tst_name, None) is None:
groups = re.search(REGEX_NIC, tst_data["parent"])
file_name = "vm_vhost_ip4"
feature = "-base"
+ elif "ipsecbasetnlsw" in test_name:
+ file_name = "ipsecsw"
+ feature = "-base-scale"
+
elif "ipsec" in test_name:
file_name = "ipsec"
feature = "-base-scale"
elif "8t4c" in test_name:
anchor += "8t4c"
- return url + file_name + '-' + testbed + '-' + nic + framesize + feature + \
- anchor + feature
+ return url + file_name + '-' + testbed + '-' + nic + framesize + \
+ feature.replace("-int", "").replace("-tnl", "") + anchor + feature
def table_performance_trending_dashboard_html(table, input_data):
for build in builds:
build = str(build)
for tst_name, tst_data in data[job][build].iteritems():
- if tst_name.lower() in table["ignore-list"]:
+ if tst_name.lower() in table.get("ignore-list", list()):
continue
if tbl_dict.get(tst_name, None) is None:
groups = re.search(REGEX_NIC, tst_data["parent"])