import plotly.graph_objects as go
import pandas as pd
-import re
import hdrh.histogram
import hdrh.codec
from ..jumpavg import classify
-_COLORS = (
- u"#1A1110", u"#DA2647", u"#214FC6", u"#01786F", u"#BD8260", u"#FFD12A",
- u"#A6E7FF", u"#738276", u"#C95A49", u"#FC5A8D", u"#CEC8EF", u"#391285",
- u"#6F2DA8", u"#FF878D", u"#45A27D", u"#FFD0B9", u"#FD5240", u"#DB91EF",
- u"#44D7A8", u"#4F86F7", u"#84DE02", u"#FFCFF1", u"#614051"
-)
_ANOMALY_COLOR = {
u"regression": 0.0,
u"normal": 0.5,
}
+def _get_color(idx: int) -> str:
+ """
+ """
+ _COLORS = (
+ "#1A1110", "#DA2647", "#214FC6", "#01786F", "#BD8260", "#FFD12A",
+ "#A6E7FF", "#738276", "#C95A49", "#FC5A8D", "#CEC8EF", "#391285",
+ "#6F2DA8", "#FF878D", "#45A27D", "#FFD0B9", "#FD5240", "#DB91EF",
+ "#44D7A8", "#4F86F7", "#84DE02", "#FFCFF1", "#614051"
+ )
+ return _COLORS[idx % len(_COLORS)]
+
+
def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
"""
"""
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'}$"
- )
- df = df_sel.loc[
- df_sel["test_id"].apply(
- lambda x: True if re.search(regex, x) else False
- )
- ].sort_values(by="start_time", ignore_index=True)
+
+ core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+ ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
+ dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
+ dut_v101 = itm["dut"]
+
+ df = data.loc[(
+ (
+ (
+ (data["version"] == "1.0.0") &
+ (data["dut_type"].str.lower() == dut_v100)
+ ) |
+ (
+ (data["version"] == "1.0.1") &
+ (data["dut_type"].str.lower() == dut_v101)
+ )
+ ) &
+ (data["test_type"] == ttype) &
+ (data["passed"] == True)
+ )]
+ df = df[df.job.str.endswith(f"{topo}-{arch}")]
+ df = df[df.test_id.str.contains(
+ f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
+ regex=True
+ )].sort_values(by="start_time", ignore_index=True)
return df
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')}<br>"
- f"<prop> [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]}<br>"
+ f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
+ f"<prop> [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]:,.0f}<br>"
f"<stdev>"
- f"{row['dut_type']}-ref: {row['dut_version']}<br>"
+ f"{d_type}-ref: {row['dut_version']}<br>"
f"csit-ref: {row['job']}/{row['build']}<br>"
f"hosts: {', '.join(row['hosts'])}"
)
if ttype == "mrr":
stdev = (
f"stdev [{row['result_receive_rate_rate_unit']}]: "
- f"{row['result_receive_rate_rate_stdev']}<br>"
+ f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
)
else:
stdev = ""
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')}<br>"
- f"trend [pps]: {avg}<br>"
- f"stdev [pps]: {stdev}<br>"
- f"{row['dut_type']}-ref: {row['dut_version']}<br>"
+ f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
+ f"trend [pps]: {avg:,.0f}<br>"
+ f"stdev [pps]: {stdev:,.0f}<br>"
+ f"{d_type}-ref: {row['dut_version']}<br>"
f"csit-ref: {row['job']}/{row['build']}<br>"
f"hosts: {', '.join(row['hosts'])}"
)
anomaly_y.append(trend_avg[idx])
anomaly_color.append(_ANOMALY_COLOR[anomaly])
hover_itm = (
- f"date: {x_axis[idx].strftime('%d-%m-%Y %H:%M:%S')}<br>"
- f"trend [pps]: {trend_avg[idx]}<br>"
+ f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}<br>"
+ f"trend [pps]: {trend_avg[idx]:,.0f}<br>"
f"classification: {anomaly}"
)
if ttype == "pdr-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)]
+ itm["testtype"], name, df, start, end, _get_color(idx)
)
if traces:
if not fig_tput:
if itm["testtype"] == "pdr":
traces = _generate_trending_traces(
- "pdr-lat", name, df, start, end, _COLORS[idx % len(_COLORS)]
+ "pdr-lat", name, df, start, end, _get_color(idx)
)
if traces:
if not fig_lat:
legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
showlegend=bool(idx % 2),
line=dict(
- color=_COLORS[int(idx/2)],
+ color=_get_color(int(idx/2)),
dash=u"solid",
width=1 if idx % 2 else 2
),