phy = itm["phy"].split("-")
if len(phy) == 4:
topo, arch, nic, drv = phy
- if drv in ("dpdk", "ixgbe"):
+ if drv == "dpdk":
drv = ""
else:
drv += "-"
df = df.dropna(subset=[_VALUE[ttype], ])
if df.empty:
return list()
-
- x_axis = [d for d in df["start_time"] if d >= start and d <= end]
- if not x_axis:
+ df = df.loc[((df["start_time"] >= start) & (df["start_time"] <= end))]
+ if df.empty:
return list()
+ x_axis = df["start_time"].tolist()
+
anomalies, trend_avg, trend_stdev = _classify_anomalies(
{k: v for k, v in zip(x_axis, df[_VALUE[ttype]])}
)
u"len": 0.8,
u"title": u"Circles Marking Data Classification",
u"titleside": u"right",
- # u"titlefont": {
- # u"size": 14
- # },
u"tickmode": u"array",
u"tickvals": [0.167, 0.500, 0.833],
u"ticktext": _TICK_TEXT_LAT \