feat(uti): Values in hover as integers (no decimals)

[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 1d9fd1c..a3357e4 100644 (file)
--- a/resources/tools/dash/app/pal/trending/graphs.py
+++ b/resources/tools/dash/app/pal/trending/graphs.py
@@ -16,7 +16,6 @@
 
 import plotly.graph_objects as go
 import pandas as pd
-import re
 
 import hdrh.histogram
 import hdrh.codec
@@ -172,23 +171,21 @@ def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
             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'}$"
-    )
-    df = df_sel.loc[
-        df_sel["test_id"].apply(
-            lambda x: True if re.search(regex, x) else False
-        )
-    ].sort_values(by="start_time", ignore_index=True)
+
+    core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+    ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
+    dut = "none" if itm["dut"] == "trex" else itm["dut"].upper()
+
+    df = data.loc[(
+        (data["dut_type"] == dut) &
+        (data["test_type"] == ttype) &
+        (data["passed"] == True)
+    )]
+    df = df[df.job.str.endswith(f"{topo}-{arch}")]
+    df = df[df.test_id.str.contains(
+        f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
+        regex=True
+    )].sort_values(by="start_time", ignore_index=True)
 
     return df
 
@@ -214,18 +211,19 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
     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"<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 = ""
@@ -238,11 +236,12 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
 
     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"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'])}"
         )
@@ -296,7 +295,7 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
                 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"trend [pps]: {trend_avg[idx]:,.0f}<br>"
                     f"classification: {anomaly}"
                 )
                 if ttype == "pdr-lat":
@@ -357,14 +356,11 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
     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)]
         )