for l_itm in l_df:
tmp_df.extend(l_itm)
l_df = tmp_df
+ try:
+ mean_val = mean(l_df)
+ std_val = std(l_df)
+ except (TypeError, ValueError):
+ continue
d_data["name"].append(f"{test.replace(f'{drv}-', '')}-{ttype}")
- d_data["mean"].append(int(mean(l_df) * norm_factor))
- d_data["stdev"].append(int(std(l_df) * norm_factor))
+ d_data["mean"].append(int(mean_val * norm_factor))
+ d_data["stdev"].append(int(std_val * norm_factor))
d_data["unit"].append(df[C.UNIT[ttype]].to_list()[0])
return pd.DataFrame(d_data)
lst_df = list()
for itm in selected:
- if itm["ttype"] in ("NDR", "PDR"):
+ if itm["ttype"] in ("NDR", "PDR", "Latency"):
test_type = "ndrpdr"
+ elif itm["ttype"] in ("CPS", "RPS", "BPS"):
+ test_type = "hoststack"
else:
test_type = itm["ttype"].lower()
inplace=True
)
- # Change the data type from ndrpdr to one of ("NDR", "PDR")
+ # Change the data type from ndrpdr to one of ("NDR", "PDR", "Latency")
if test_type == "ndrpdr":
tmp_df = tmp_df.assign(test_type=itm["ttype"].lower())
if not tmp_df.empty:
+ if normalize:
+ if itm["ttype"] == "Latency":
+ norm_factor = C.FREQUENCY[itm["tbed"]] / C.NORM_FREQUENCY
+ else:
+ norm_factor = C.NORM_FREQUENCY / C.FREQUENCY[itm["tbed"]]
+ else:
+ norm_factor = 1.0
tmp_df = _calculate_statistics(
tmp_df,
itm["ttype"].lower(),
itm["driver"],
- C.NORM_FREQUENCY / C.FREQUENCY[itm["tbed"]] if normalize else 1
+ norm_factor
)
lst_df.append(tmp_df)
})
return selection
- unit_factor, s_unit_factor = (1e6, "M") if format == "html" else (1, str())
-
r_sel = deepcopy(selected["reference"]["selection"])
c_params = selected["compare"]
r_selection = _create_selection(r_sel)
+ if format == "html" and "Latency" not in r_sel["ttype"]:
+ unit_factor, s_unit_factor = (1e6, "M")
+ else:
+ unit_factor, s_unit_factor = (1, str())
+
# Create Table title and titles of columns with data
params = list(r_sel)
params.remove(c_params["parameter"])