X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Ftrending%2Fgraphs.py;h=36c19495a3ed7597acb4487650d773dd9dfb1c87;hb=72766c8177fb76ac5ca4cbbfe616c19ec4a9a97a;hp=7089e2c080983cb5eb945156d0e1f35923f87de8;hpb=25c3aa43a0022d180fc9b31d68071790721c008b;p=csit.git

diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index 7089e2c080..36c19495a3 100644
--- 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
@@ -165,30 +164,28 @@ def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
     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'}$"
-    )
-    df = df_sel.loc[
-        df_sel["test_id"].apply(
-            lambda x: True if re.search(regex, x) else False
-        )
-    ].sort_values(by="start_time", ignore_index=True)
+
+    core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+    ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
+    dut = "none" if itm["dut"] == "trex" else itm["dut"].upper()
+
+    df = data.loc[(
+        (data["dut_type"] == dut) &
+        (data["test_type"] == ttype) &
+        (data["passed"] == True)
+    )]
+    df = df[df.job.str.endswith(f"{topo}-{arch}")]
+    df = df[df.test_id.str.contains(
+        f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
+        regex=True
+    )].sort_values(by="start_time", ignore_index=True)
 
     return df
 
@@ -201,11 +198,12 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
     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()
+
     anomalies, trend_avg, trend_stdev = _classify_anomalies(
         {k: v for k, v in zip(x_axis, df[_VALUE[ttype]])}
     )
@@ -213,18 +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"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 = ""
@@ -237,11 +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"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'])}"
         )
@@ -294,8 +294,8 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
                 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":
@@ -327,9 +327,6 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
                         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 \
@@ -359,14 +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)]
         )