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:
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])))} "