row = [tst_data[u"name"], ]
for col in cols:
row_data = tst_data.get(col["title"], None)
row = [tst_data[u"name"], ]
for col in cols:
row_data = tst_data.get(col["title"], None)
- if normalize and row_data:
+ if normalize and row_data and row_data.get("mean", None) and \
+ row_data.get("stdev", None):
groups = re.search(REGEX_TOPO_ARCH, col["title"])
topo_arch = groups.group(0) if groups else ""
norm_factor = table["norm_factor"].get(topo_arch, 1.0)
groups = re.search(REGEX_TOPO_ARCH, col["title"])
topo_arch = groups.group(0) if groups else ""
norm_factor = table["norm_factor"].get(topo_arch, 1.0)
- 59 # rls2206.rc2 NDRPDR iter env 10
- 60 # rls2206.rc2 NDRPDR iter env 10
- 61 # rls2206.rc2 NDRPDR iter env 10
- 59 # rls2206.rc2 NDRPDR iter env 10
- 60 # rls2206.rc2 NDRPDR iter env 10
- 61 # rls2206.rc2 NDRPDR iter env 10
+ - 64 # rls2206.rc2 NDRPDR iter env 10
+ - 65 # rls2206.rc2 NDRPDR iter env 10
+ - 66 # rls2206.rc2 NDRPDR iter env 10
+ - 67 # rls2206.rc2 NDRPDR iter env 10
vpp-2n-icx-curr-iter-best:
csit-vpp-perf-report-iterative-2206-2n-icx:
vpp-2n-icx-curr-iter-best:
csit-vpp-perf-report-iterative-2206-2n-icx: