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-rw-r--r--csit.infra.dash/app/cdash/data/data.py2
-rw-r--r--csit.infra.dash/app/cdash/report/graphs.py19
2 files changed, 14 insertions, 7 deletions
diff --git a/csit.infra.dash/app/cdash/data/data.py b/csit.infra.dash/app/cdash/data/data.py
index 2b6733f46b..ce98476897 100644
--- a/csit.infra.dash/app/cdash/data/data.py
+++ b/csit.infra.dash/app/cdash/data/data.py
@@ -380,7 +380,7 @@ class Data:
)
return
- # Read data:
+ # Read data:
data = Data._create_dataframe_from_parquet(
path=data_set["path"],
partition_filter=partition_filter,
diff --git a/csit.infra.dash/app/cdash/report/graphs.py b/csit.infra.dash/app/cdash/report/graphs.py
index ff1428eef1..175de0f759 100644
--- a/csit.infra.dash/app/cdash/report/graphs.py
+++ b/csit.infra.dash/app/cdash/report/graphs.py
@@ -91,9 +91,12 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
:rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
"""
- def get_y_values(data, y_data_max, param, norm_factor):
- if "receive_rate" in param:
- y_vals_raw = data[param].to_list()[0]
+ def get_y_values(data, y_data_max, param, norm_factor, release=str()):
+ if param == "result_receive_rate_rate_values":
+ if release == "rls2402":
+ y_vals_raw = data["result_receive_rate_rate_avg"].to_list()
+ else:
+ y_vals_raw = data[param].to_list()[0]
else:
y_vals_raw = data[param].to_list()
y_data = [(y * norm_factor) for y in y_vals_raw]
@@ -138,8 +141,9 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
- y_data, y_tput_max = \
- get_y_values(itm_data, y_tput_max, C.VALUE_ITER[ttype], norm_factor)
+ y_data, y_tput_max = get_y_values(
+ itm_data, y_tput_max, C.VALUE_ITER[ttype], norm_factor, itm["rls"]
+ )
nr_of_samples = len(y_data)
@@ -156,12 +160,15 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
}
if itm["testtype"] == "mrr":
+ # and itm["rls"] in ("rls2306", "rls2310"):
+ trial_run = "trial"
metadata["csit-ref"] = (
f"{itm_data['job'].to_list()[0]}/",
f"{itm_data['build'].to_list()[0]}"
)
customdata = [{"metadata": metadata}, ] * nr_of_samples
else:
+ trial_run = "run"
for _, row in itm_data.iterrows():
metadata["csit-ref"] = f"{row['job']}/{row['build']}"
customdata.append({"metadata": deepcopy(metadata)})
@@ -170,7 +177,7 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
name=(
f"{idx + 1}. "
f"({nr_of_samples:02d} "
- f"run{'s' if nr_of_samples > 1 else ''}) "
+ f"{trial_run}{'s' if nr_of_samples > 1 else ''}) "
f"{itm['id']}"
),
hoverinfo=u"y+name",