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authorTibor Frank <tifrank@cisco.com>2022-07-18 10:12:45 +0200
committerTibor Frank <tifrank@cisco.com>2022-07-18 10:14:35 +0200
commit2f6295d7c63b7e231b0198ee055468b2fc54fa94 (patch)
tree1689a4e99e6632d9398cebd2999f58fda11123db /resources/tools/dash/app/pal/trending/graphs.py
parent16fba5d038e11d16049abd62107880ae5624d383 (diff)
UTI: Small fixes and improvements
Change-Id: Iff83f4340beb51edbfcba230b60b175ac8c2d6ad Signed-off-by: Tibor Frank <tifrank@cisco.com>
Diffstat (limited to 'resources/tools/dash/app/pal/trending/graphs.py')
-rw-r--r--resources/tools/dash/app/pal/trending/graphs.py9
1 files changed, 6 insertions, 3 deletions
diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index 150b7056ba..8950558166 100644
--- a/resources/tools/dash/app/pal/trending/graphs.py
+++ b/resources/tools/dash/app/pal/trending/graphs.py
@@ -237,7 +237,10 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
return list()
x_axis = df["start_time"].tolist()
- y_data = [itm * norm_factor for itm in df[_VALUE[ttype]].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, y_data)}
@@ -245,11 +248,11 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
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>"