# Evaluate result:
if anomaly_classifications:
- test_reg_lst = []
- nic_reg_lst = []
- frmsize_reg_lst = []
- trend_reg_lst = []
- number_reg_lst = []
- ltc_reg_lst = []
- test_prog_lst = []
- nic_prog_lst = []
- frmsize_prog_lst = []
- trend_prog_lst = []
- number_prog_lst = []
- ltc_prog_lst = []
result = u"PASS"
- class MaxLens():
+ class MaxLens:
"""Class to store the max lengths of strings displayed in
regressions and progressions.
"""
self.run = run
self.ltc = ltc
- max_len = MaxLens(0, 0, 0, 0, 0, 0)
-
for job_name, job_data in anomaly_classifications.items():
data = []
+ test_reg_lst = []
+ nic_reg_lst = []
+ frmsize_reg_lst = []
+ trend_reg_lst = []
+ number_reg_lst = []
+ ltc_reg_lst = []
+ test_prog_lst = []
+ nic_prog_lst = []
+ frmsize_prog_lst = []
+ trend_prog_lst = []
+ number_prog_lst = []
+ ltc_prog_lst = []
+ max_len = MaxLens(0, 0, 0, 0, 0, 0)
+
+ # tb - testbed (2n-skx, 3n-dnv, etc)
tb = u"-".join(job_name.split(u"-")[-2:])
+ # data - read all txt dashboard files for tb
for file in listdir(f"{spec.cpta[u'output-file']}"):
if tb in file and u"performance-trending-dashboard" in \
file and u"txt" in file:
file_to_read = f"{spec.cpta[u'output-file']}/{file}"
- with open(f"{file_to_read}", u"rt") as input:
- data = data + input.readlines()
+ with open(f"{file_to_read}", u"rt") as f_in:
+ data = data + f_in.readlines()
for test_name, classification in job_data.items():
if classification != u"normal":
test_name = test_name.split("-", 1)
tst = test_name[1].split(".")[-1]
nic = test_name[0].split(".")[-1]
- frmsize = tst.split("-")[0].upper()
+ frmsize = tst.split("-")[0]
tst = u"-".join(tst.split("-")[1:])
tst_name = f"{nic}-{frmsize}-{tst}"
if len(tst) > max_len.tst:
number_prog_lst.append(number)
ltc_prog_lst.append(ltc)
- if classification in (u"regression", u"outlier"):
- result = u"FAIL"
-
text = u""
for idx in range(len(test_reg_lst)):
text += (
f"{u' ' * (max_len.tst - len(test_reg_lst[idx]))} "
f"{nic_reg_lst[idx]}"
f"{u' ' * (max_len.nic - len(nic_reg_lst[idx]))} "
- f"{frmsize_reg_lst[idx]}"
+ f"{frmsize_reg_lst[idx].upper()}"
f"{u' ' * (max_len.frmsize - len(frmsize_reg_lst[idx]))} "
f"{trend_reg_lst[idx]}"
f"{u' ' * (max_len.trend - len(str(trend_reg_lst[idx])))} "
f"{u' ' * (max_len.tst - len(test_prog_lst[idx]))} "
f"{nic_prog_lst[idx]}"
f"{u' ' * (max_len.nic - len(nic_prog_lst[idx]))} "
- f"{frmsize_prog_lst[idx]}"
+ f"{frmsize_prog_lst[idx].upper()}"
f"{u' ' * (max_len.frmsize - len(frmsize_prog_lst[idx]))} "
f"{trend_prog_lst[idx]}"
f"{u' ' * (max_len.trend -len(str(trend_prog_lst[idx])))} "