X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Ftrending%2Fgraphs.py;h=36c19495a3ed7597acb4487650d773dd9dfb1c87;hb=72766c8177fb76ac5ca4cbbfe616c19ec4a9a97a;hp=4273d9d2f810a6a075cc9d5f9fe125c07a43dbf0;hpb=f63e6b83d830734fdb94b8f0384a808f189711f1;p=csit.git
diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index 4273d9d2f8..36c19495a3 100644
--- a/resources/tools/dash/app/pal/trending/graphs.py
+++ b/resources/tools/dash/app/pal/trending/graphs.py
@@ -16,7 +16,6 @@
import plotly.graph_objects as go
import pandas as pd
-import re
import hdrh.histogram
import hdrh.codec
@@ -165,30 +164,28 @@ def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
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
@@ -199,10 +196,13 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
"""
df = df.dropna(subset=[_VALUE[ttype], ])
+ if df.empty:
+ return list()
+ df = df.loc[((df["start_time"] >= start) & (df["start_time"] <= end))]
if df.empty:
return list()
- x_axis = [d for d in df["start_time"] if d >= start and d <= end]
+ 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]])}
@@ -211,18 +211,19 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
hover = list()
customdata = list()
for _, row in df.iterrows():
+ d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
hover_itm = (
- f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
"
- f" [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]}
"
+ f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}
"
+ f" [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]:,.0f}
"
f""
- f"{row['dut_type']}-ref: {row['dut_version']}
"
+ f"{d_type}-ref: {row['dut_version']}
"
f"csit-ref: {row['job']}/{row['build']}
"
f"hosts: {', '.join(row['hosts'])}"
)
if ttype == "mrr":
stdev = (
f"stdev [{row['result_receive_rate_rate_unit']}]: "
- f"{row['result_receive_rate_rate_stdev']}
"
+ f"{row['result_receive_rate_rate_stdev']:,.0f}
"
)
else:
stdev = ""
@@ -235,11 +236,12 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
hover_trend = list()
for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
+ d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
hover_itm = (
- f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
"
- f"trend [pps]: {avg}
"
- f"stdev [pps]: {stdev}
"
- f"{row['dut_type']}-ref: {row['dut_version']}
"
+ f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}
"
+ f"trend [pps]: {avg:,.0f}
"
+ f"stdev [pps]: {stdev:,.0f}
"
+ f"{d_type}-ref: {row['dut_version']}
"
f"csit-ref: {row['job']}/{row['build']}
"
f"hosts: {', '.join(row['hosts'])}"
)
@@ -285,21 +287,31 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
anomaly_x = list()
anomaly_y = list()
anomaly_color = list()
+ hover = list()
for idx, anomaly in enumerate(anomalies):
if anomaly in (u"regression", u"progression"):
anomaly_x.append(x_axis[idx])
anomaly_y.append(trend_avg[idx])
anomaly_color.append(_ANOMALY_COLOR[anomaly])
+ hover_itm = (
+ f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}
"
+ f"trend [pps]: {trend_avg[idx]:,.0f}
"
+ f"classification: {anomaly}"
+ )
+ if ttype == "pdr-lat":
+ hover_itm = hover_itm.replace("[pps]", "[us]")
+ hover.append(hover_itm)
anomaly_color.extend([0.0, 0.5, 1.0])
traces.append(
go.Scatter(
x=anomaly_x,
y=anomaly_y,
mode=u"markers",
- hoverinfo=u"none",
+ text=hover,
+ hoverinfo=u"text+name",
showlegend=False,
legendgroup=name,
- name=f"{name}-anomalies",
+ name=name,
marker={
u"size": 15,
u"symbol": u"circle-open",
@@ -315,9 +327,6 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
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 \
@@ -347,14 +356,11 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
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)]
)