+def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
+ start: datetime, end: datetime, color: str) -> list:
+ """
+ """
+
+ df = df.dropna(subset=[_VALUE[ttype], ])
+ if df.empty:
+ return list()
+ 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]])}
+ )
+
+ hover = list()
+ customdata = list()
+ for _, row in df.iterrows():
+ 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"<stdev>"
+ f"{row['dut_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>"
+ )
+ else:
+ stdev = ""
+ hover_itm = hover_itm.replace(
+ "<prop>", "latency" if ttype == "pdr-lat" else "average"
+ ).replace("<stdev>", stdev)
+ hover.append(hover_itm)
+ if ttype == "pdr-lat":
+ customdata.append(_get_hdrh_latencies(row, name))
+
+ hover_trend = list()
+ for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
+ 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"csit-ref: {row['job']}/{row['build']}<br>"
+ f"hosts: {', '.join(row['hosts'])}"