X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Ftrending%2Fgraphs.py;h=d3164a8e45c6e78272d29ffa64195aa66d0f5052;hp=16cb5a2cb35631f0191c609242a7f7c955b9b864;hb=a6c94c7c5898fb8570f6f9ca6fdc1909d43c5dc0;hpb=c01befc28450d5c2003d25876dda0201eb827735

diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index 16cb5a2cb3..d3164a8e45 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
@@ -27,12 +26,6 @@ from numpy import isnan
 from ..jumpavg import classify
 
 
-_COLORS = (
-    u"#1A1110", u"#DA2647", u"#214FC6", u"#01786F", u"#BD8260", u"#FFD12A",
-    u"#A6E7FF", u"#738276", u"#C95A49", u"#FC5A8D", u"#CEC8EF", u"#391285",
-    u"#6F2DA8", u"#FF878D", u"#45A27D", u"#FFD0B9", u"#FD5240", u"#DB91EF",
-    u"#44D7A8", u"#4F86F7", u"#84DE02", u"#FFCFF1", u"#614051"
-)
 _ANOMALY_COLOR = {
     u"regression": 0.0,
     u"normal": 0.5,
@@ -94,6 +87,18 @@ _GRAPH_LAT_HDRH_DESC = {
 }
 
 
+def _get_color(idx: int) -> str:
+    """
+    """
+    _COLORS = (
+        "#1A1110", "#DA2647", "#214FC6", "#01786F", "#BD8260", "#FFD12A",
+        "#A6E7FF", "#738276", "#C95A49", "#FC5A8D", "#CEC8EF", "#391285",
+        "#6F2DA8", "#FF878D", "#45A27D", "#FFD0B9", "#FD5240", "#DB91EF",
+        "#44D7A8", "#4F86F7", "#84DE02", "#FFCFF1", "#614051"
+    )
+    return _COLORS[idx % len(_COLORS)]
+
+
 def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
     """
     """
@@ -172,23 +177,31 @@ 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'}$"
-    )
-    df = df_sel.loc[
-        df_sel["test_id"].apply(
-            lambda x: True if re.search(regex, x) else False
-        )
-    ].sort_values(by="start_time", ignore_index=True)
+
+    core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+    ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
+    dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
+    dut_v101 = itm["dut"]
+
+    df = data.loc[(
+        (
+            (
+                (data["version"] == "1.0.0") &
+                (data["dut_type"].str.lower() == dut_v100)
+            ) |
+            (
+                (data["version"] == "1.0.1") &
+                (data["dut_type"].str.lower() == dut_v101)
+            )
+        ) &
+        (data["test_type"] == ttype) &
+        (data["passed"] == True)
+    )]
+    df = df[df.job.str.endswith(f"{topo}-{arch}")]
+    df = df[df.test_id.str.contains(
+        f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
+        regex=True
+    )].sort_values(by="start_time", ignore_index=True)
 
     return df
 
@@ -201,11 +214,12 @@ 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 +227,19 @@ 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 +252,12 @@ 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 +310,8 @@ 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 +343,6 @@ 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 \
@@ -359,16 +372,13 @@ 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)]
+            itm["testtype"], name, df, start, end, _get_color(idx)
         )
         if traces:
             if not fig_tput:
@@ -377,7 +387,7 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
 
         if itm["testtype"] == "pdr":
             traces = _generate_trending_traces(
-                "pdr-lat", name, df, start, end, _COLORS[idx % len(_COLORS)]
+                "pdr-lat", name, df, start, end, _get_color(idx)
             )
             if traces:
                 if not fig_lat:
@@ -444,7 +454,7 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
                 legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
                 showlegend=bool(idx % 2),
                 line=dict(
-                    color=_COLORS[int(idx/2)],
+                    color=_get_color(int(idx/2)),
                     dash=u"solid",
                     width=1 if idx % 2 else 2
                 ),