# Evaluate result:
if anomaly_classifications:
- legend_str = (f"Legend:\n[ Last trend in Mpps/Mcps | number of runs for"
- f" last trend | ")
result = u"PASS"
for job_name, job_data in anomaly_classifications.items():
data = []
with open(file_name, u'w') as txt_file:
for test_name, classification in job_data.items():
if classification == u"regression":
- tst = test_name.split(" ")[1].split(".")[1:]
- nic = tst[0].split("-")[0]
- tst_name = f"{nic}-{tst[1]}"
+ if u"2n" in test_name:
+ test_name = test_name.split("-", 2)
+ tst = test_name[2].split(".")[-1]
+ nic = test_name[1]
+ tst_name = f"{nic}-{tst}"
+ else:
+ test_name = test_name.split("-", 1)
+ tst = test_name[1].split(".")[-1]
+ nic = test_name[0].split(".")[-1]
+ tst_name = f"{nic}-{tst}"
for line in data:
if tst_name in line:
if classification in (u"regression", u"outlier"):
result = u"FAIL"
-
- txt_file.write(f"{legend_str}regression in percentage ]")
-
file_name = \
f"{spec.cpta[u'output-file']}/progressions-{job_name}.txt"
with open(file_name, u'w') as txt_file:
for test_name, classification in job_data.items():
if classification == u"progression":
- tst = test_name.split(" ")[1].split(".")[1:]
- nic = tst[0].split("-")[0]
- tst_name = f"{nic}-{tst[1]}"
+ if u"2n" in test_name:
+ test_name = test_name.split("-", 2)
+ tst = test_name[2].split(".")[-1]
+ nic = test_name[1]
+ tst_name = f"{nic}-{tst}"
+ else:
+ test_name = test_name.split("-", 1)
+ tst = test_name[1].split(".")[-1]
+ nic = test_name[0].split(".")[-1]
+ tst_name = f"{nic}-{tst}"
for line in data:
if tst_name in line:
ltc = line.split("|")[4]
txt_file.write(f"{tst_name} [ {trend}M | "
f"#{number} | {ltc}% ]\n")
-
- txt_file.write(f"{legend_str}progression in percentage ]")
else:
result = u"FAIL"