"""
"""
+import logging
import plotly.graph_objects as go
import pandas as pd
from ..jumpavg import classify
+_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."
}
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
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:
"""
"""
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()
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])
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"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:
"""
"""
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, _get_color(idx)
+ 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, _get_color(idx)
+ "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=_get_color(int(idx/2)),
- dash=u"solid",
+ dash="solid",
width=1 if idx % 2 else 2
),
hovertext=hovertext,
- hoverinfo=u"text"
+ hoverinfo="text"
)
)
if traces:
Code Review / csit.git / blobdiff