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UTI: Normalize trending data
[csit.git]
/
resources
/
tools
/
dash
/
app
/
pal
/
trending
/
graphs.py
diff --git
a/resources/tools/dash/app/pal/trending/graphs.py
b/resources/tools/dash/app/pal/trending/graphs.py
index
fb87cec
..
150b705
100644
(file)
--- a/
resources/tools/dash/app/pal/trending/graphs.py
+++ b/
resources/tools/dash/app/pal/trending/graphs.py
@@
-14,6
+14,7
@@
"""
"""
"""
"""
+import logging
import plotly.graph_objects as go
import pandas as pd
import plotly.graph_objects as go
import pandas as pd
@@
-26,6
+27,23
@@
from numpy import isnan
from ..jumpavg import classify
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 = {
"regression": 0.0,
"normal": 0.5,
_ANOMALY_COLOR = {
"regression": 0.0,
"normal": 0.5,
@@
-207,7
+225,7
@@
def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
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:
"""
"""
"""
"""
@@
-219,9
+237,10
@@
def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
return list()
x_axis = df["start_time"].tolist()
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(
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()
)
hover = list()
@@
-268,7
+287,7
@@
def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
traces = [
go.Scatter( # Samples
x=x_axis,
traces = [
go.Scatter( # Samples
x=x_axis,
- y=
df[_VALUE[ttype]]
,
+ y=
y_data
,
name=name,
mode="markers",
marker={
name=name,
mode="markers",
marker={
@@
-360,7
+379,7
@@
def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
- start: datetime, end: datetime) -> tuple:
+ start: datetime, end: datetime
, normalize: bool
) -> tuple:
"""
"""
"""
"""
@@
-377,8
+396,15
@@
def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
name = "-".join((itm["dut"], itm["phy"], itm["framesize"], itm["core"],
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(
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 traces:
if not fig_tput:
@@
-387,7
+413,7
@@
def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
if itm["testtype"] == "pdr":
traces = _generate_trending_traces(
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:
)
if traces:
if not fig_lat: