phy = itm["phy"].split("-")
if len(phy) == 4:
topo, arch, nic, drv = phy
- if drv in ("dpdk", "ixgbe"):
+ if drv == "dpdk":
drv = ""
else:
drv += "-"
drv = drv.replace("_", "-")
else:
return None
- cadence = \
- "weekly" if (arch == "aws" or itm["testtype"] != "mrr") else "daily"
- sel_topo_arch = (
- f"csit-vpp-perf-"
- f"{itm['testtype'] if itm['testtype'] == 'mrr' else 'ndrpdr'}-"
- f"{cadence}-master-{topo}-{arch}"
- )
- df_sel = data.loc[(data["job"] == sel_topo_arch)]
- regex = (
- f"^.*{nic}.*\.{itm['framesize']}-{itm['core']}-{drv}{itm['test']}-"
- f"{'mrr' if itm['testtype'] == 'mrr' else 'ndrpdr'}$"
- )
- df = df_sel.loc[
- df_sel["test_id"].apply(
- lambda x: True if re.search(regex, x) else False
- )
- ].sort_values(by="start_time", ignore_index=True)
+
+ core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+ ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
+ dut = "none" if itm["dut"] == "trex" else itm["dut"].upper()
+
+ df = data.loc[(
+ (data["dut_type"] == dut) &
+ (data["test_type"] == ttype) &
+ (data["passed"] == True)
+ )]
+ df = df[df.job.str.endswith(f"{topo}-{arch}")]
+ df = df[df.test_id.str.contains(
+ f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
+ regex=True
+ )].sort_values(by="start_time", ignore_index=True)
return df
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 \
for idx, itm in enumerate(sel):
df = select_trending_data(data, itm)
- if df is None:
+ if df is None or df.empty:
continue
- name = (
- f"{itm['phy']}-{itm['framesize']}-{itm['core']}-"
- f"{itm['test']}-{itm['testtype']}"
- )
-
+ name = "-".join((itm["dut"], itm["phy"], itm["framesize"], itm["core"],
+ itm["test"], itm["testtype"], ))
traces = _generate_trending_traces(
itm["testtype"], name, df, start, end, _COLORS[idx % len(_COLORS)]
)