diff options
| -rw-r--r-- | csit.infra.dash/app/cdash/data/data.py | 2 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/report/graphs.py | 19 |
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", |