"""
"""
+import logging
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"
-)
+_NORM_FREQUENCY = 2.0 # [GHz]
+_FREQURENCY = { # [GHz]
+ "2n-aws": 1.000,
+ "2n-dnv": 2.000,
+ "2n-clx": 2.300,
+ "2n-icx": 2.600,
+ "2n-skx": 2.500,
+ "2n-tx2": 2.500,
+ "2n-zn2": 2.900,
+ "3n-alt": 3.000,
+ "3n-aws": 1.000,
+ "3n-dnv": 2.000,
+ "3n-icx": 2.600,
+ "3n-skx": 2.500,
+ "3n-tsh": 2.200
+}
+
_ANOMALY_COLOR = {
- u"regression": 0.0,
- u"normal": 0.5,
- u"progression": 1.0
+ "regression": 0.0,
+ "normal": 0.5,
+ "progression": 1.0
}
_COLORSCALE_TPUT = [
- [0.00, u"red"],
- [0.33, u"red"],
- [0.33, u"white"],
- [0.66, u"white"],
- [0.66, u"green"],
- [1.00, u"green"]
+ [0.00, "red"],
+ [0.33, "red"],
+ [0.33, "white"],
+ [0.66, "white"],
+ [0.66, "green"],
+ [1.00, "green"]
]
-_TICK_TEXT_TPUT = [u"Regression", u"Normal", u"Progression"]
+_TICK_TEXT_TPUT = ["Regression", "Normal", "Progression"]
_COLORSCALE_LAT = [
- [0.00, u"green"],
- [0.33, u"green"],
- [0.33, u"white"],
- [0.66, u"white"],
- [0.66, u"red"],
- [1.00, u"red"]
+ [0.00, "green"],
+ [0.33, "green"],
+ [0.33, "white"],
+ [0.66, "white"],
+ [0.66, "red"],
+ [1.00, "red"]
]
-_TICK_TEXT_LAT = [u"Progression", u"Normal", u"Regression"]
+_TICK_TEXT_LAT = ["Progression", "Normal", "Regression"]
_VALUE = {
"mrr": "result_receive_rate_rate_avg",
"ndr": "result_ndr_lower_rate_value",
PERCENTILE_MAX = 99.999501
_GRAPH_LAT_HDRH_DESC = {
- u"result_latency_forward_pdr_0_hdrh": u"No-load.",
- u"result_latency_reverse_pdr_0_hdrh": u"No-load.",
- u"result_latency_forward_pdr_10_hdrh": u"Low-load, 10% PDR.",
- u"result_latency_reverse_pdr_10_hdrh": u"Low-load, 10% PDR.",
- u"result_latency_forward_pdr_50_hdrh": u"Mid-load, 50% PDR.",
- u"result_latency_reverse_pdr_50_hdrh": u"Mid-load, 50% PDR.",
- u"result_latency_forward_pdr_90_hdrh": u"High-load, 90% PDR.",
- u"result_latency_reverse_pdr_90_hdrh": u"High-load, 90% PDR."
+ "result_latency_forward_pdr_0_hdrh": "No-load.",
+ "result_latency_reverse_pdr_0_hdrh": "No-load.",
+ "result_latency_forward_pdr_10_hdrh": "Low-load, 10% PDR.",
+ "result_latency_reverse_pdr_10_hdrh": "Low-load, 10% PDR.",
+ "result_latency_forward_pdr_50_hdrh": "Mid-load, 50% PDR.",
+ "result_latency_reverse_pdr_50_hdrh": "Mid-load, 50% PDR.",
+ "result_latency_forward_pdr_90_hdrh": "High-load, 90% PDR.",
+ "result_latency_reverse_pdr_90_hdrh": "High-load, 90% PDR."
}
+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:
"""
"""
stdv = 0.0
for sample in data.values():
if isnan(sample):
- classification.append(u"outlier")
+ classification.append("outlier")
avgs.append(sample)
stdevs.append(sample)
continue
stdevs.append(stdv)
values_left -= 1
continue
- classification.append(u"normal")
+ classification.append("normal")
avgs.append(avg)
stdevs.append(stdv)
values_left -= 1
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'}$"
- )
- df = df_sel.loc[
- df_sel["test_id"].apply(
- lambda x: True if re.search(regex, x) else False
- )
- ].sort_values(by="start_time", ignore_index=True)
+
+ core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+ ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
+ dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
+ dut_v101 = itm["dut"]
+
+ df = data.loc[(
+ (
+ (
+ (data["version"] == "1.0.0") &
+ (data["dut_type"].str.lower() == dut_v100)
+ ) |
+ (
+ (data["version"] == "1.0.1") &
+ (data["dut_type"].str.lower() == dut_v101)
+ )
+ ) &
+ (data["test_type"] == ttype) &
+ (data["passed"] == True)
+ )]
+ df = df[df.job.str.endswith(f"{topo}-{arch}")]
+ df = df[df.test_id.str.contains(
+ f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
+ regex=True
+ )].sort_values(by="start_time", ignore_index=True)
return df
def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
- start: datetime, end: datetime, color: str) -> list:
+ start: datetime, end: datetime, color: str, norm_factor: float) -> list:
"""
"""
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()
+ y_data = [itm * norm_factor for itm in df[_VALUE[ttype]].tolist()]
+
anomalies, trend_avg, trend_stdev = _classify_anomalies(
- {k: v for k, v in zip(x_axis, df[_VALUE[ttype]])}
+ {k: v for k, v in zip(x_axis, y_data)}
)
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'])}"
)
traces = [
go.Scatter( # Samples
x=x_axis,
- y=df[_VALUE[ttype]],
+ y=y_data,
name=name,
mode="markers",
marker={
- u"size": 5,
- u"color": color,
- u"symbol": u"circle",
+ "size": 5,
+ "color": color,
+ "symbol": "circle",
},
text=hover,
- hoverinfo=u"text+name",
+ hoverinfo="text+name",
showlegend=True,
legendgroup=name,
customdata=customdata
name=name,
mode="lines",
line={
- u"shape": u"linear",
- u"width": 1,
- u"color": color,
+ "shape": "linear",
+ "width": 1,
+ "color": color,
},
text=hover_trend,
- hoverinfo=u"text+name",
+ hoverinfo="text+name",
showlegend=False,
legendgroup=name,
)
anomaly_color = list()
hover = list()
for idx, anomaly in enumerate(anomalies):
- if anomaly in (u"regression", u"progression"):
+ if anomaly in ("regression", "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('%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":
go.Scatter(
x=anomaly_x,
y=anomaly_y,
- mode=u"markers",
+ mode="markers",
text=hover,
- hoverinfo=u"text+name",
+ hoverinfo="text+name",
showlegend=False,
legendgroup=name,
name=name,
marker={
- u"size": 15,
- u"symbol": u"circle-open",
- u"color": anomaly_color,
- u"colorscale": _COLORSCALE_LAT \
+ "size": 15,
+ "symbol": "circle-open",
+ "color": anomaly_color,
+ "colorscale": _COLORSCALE_LAT \
if ttype == "pdr-lat" else _COLORSCALE_TPUT,
- u"showscale": True,
- u"line": {
- u"width": 2
+ "showscale": True,
+ "line": {
+ "width": 2
},
- u"colorbar": {
- u"y": 0.5,
- 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 \
+ "colorbar": {
+ "y": 0.5,
+ "len": 0.8,
+ "title": "Circles Marking Data Classification",
+ "titleside": "right",
+ "tickmode": "array",
+ "tickvals": [0.167, 0.500, 0.833],
+ "ticktext": _TICK_TEXT_LAT \
if ttype == "pdr-lat" else _TICK_TEXT_TPUT,
- u"ticks": u"",
- u"ticklen": 0,
- u"tickangle": -90,
- u"thickness": 10
+ "ticks": "",
+ "ticklen": 0,
+ "tickangle": -90,
+ "thickness": 10
}
}
)
def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
- start: datetime, end: datetime) -> tuple:
+ start: datetime, end: datetime, normalize: bool) -> tuple:
"""
"""
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"], ))
+ if normalize:
+ phy = itm["phy"].split("-")
+ topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
+ norm_factor = (_NORM_FREQUENCY / _FREQURENCY[topo_arch]) \
+ if topo_arch else 1.0
+ else:
+ norm_factor = 1.0
traces = _generate_trending_traces(
- itm["testtype"], name, df, start, end, _COLORS[idx % len(_COLORS)]
+ itm["testtype"], name, df, start, end, _get_color(idx), norm_factor
)
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), norm_factor
)
if traces:
if not fig_lat:
yaxis.append(item.value_iterated_to)
hovertext.append(
f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
- f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
+ f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
f"Latency: {item.value_iterated_to}uSec"
)
yaxis.append(item.value_iterated_to)
hovertext.append(
f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
- f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
+ f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
f"Latency: {item.value_iterated_to}uSec"
)
x=xaxis,
y=yaxis,
name=_GRAPH_LAT_HDRH_DESC[lat_name],
- mode=u"lines",
+ mode="lines",
legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
showlegend=bool(idx % 2),
line=dict(
- color=_COLORS[int(idx/2)],
- dash=u"solid",
+ color=_get_color(int(idx/2)),
+ dash="solid",
width=1 if idx % 2 else 2
),
hovertext=hovertext,
- hoverinfo=u"text"
+ hoverinfo="text"
)
)
if traces:
Code Review / csit.git / blobdiff