X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;ds=sidebyside;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Ftrending%2Fgraphs.py;h=895055816635a9d93d76a30ea7889c1ba14976c9;hb=2f6295d7c63b7e231b0198ee055468b2fc54fa94;hp=fb87cec953655689e99c859765b63398232d2f2b;hpb=d294d31e7cadd858c1c27dae9b5a85732aa95b23;p=csit.git

diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index fb87cec953..8950558166 100644
--- 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
 
@@ -26,6 +27,23 @@ from numpy import isnan
 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,
@@ -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,
-    start: datetime, end: datetime, color: str) -> list:
+    start: datetime, end: datetime, color: str, norm_factor: float) -> list:
     """
     """
 
@@ -219,18 +237,22 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
         return list()
 
     x_axis = df["start_time"].tolist()
+    if ttype == "pdr-lat":
+        y_data = [(itm / norm_factor) for itm in df[_VALUE[ttype]].tolist()]
+    else:
+        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():
+    for idx, (_, row) in enumerate(df.iterrows()):
         d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
         hover_itm = (
             f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
-            f"<prop> [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]:,.0f}<br>"
+            f"<prop> [{row[_UNIT[ttype]]}]: {y_data[idx]:,.0f}<br>"
             f"<stdev>"
             f"{d_type}-ref: {row['dut_version']}<br>"
             f"csit-ref: {row['job']}/{row['build']}<br>"
@@ -268,7 +290,7 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
     traces = [
         go.Scatter(  # Samples
             x=x_axis,
-            y=df[_VALUE[ttype]],
+            y=y_data,
             name=name,
             mode="markers",
             marker={
@@ -360,7 +382,7 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
 
 
 def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
-    start: datetime, end: datetime) -> tuple:
+    start: datetime, end: datetime, normalize: bool) -> tuple:
     """
     """
 
@@ -377,8 +399,15 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
 
         name = "-".join((itm["dut"], itm["phy"], itm["framesize"], itm["core"],
             itm["test"], itm["testtype"], ))
+        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:
@@ -387,7 +416,7 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
 
         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: