hover = list()
customdata = list()
customdata_samples = list()
+ name_lst = name.split("-")
for idx, (_, row) in enumerate(df.iterrows()):
- d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
hover_itm = (
+ f"dut: {name_lst[0]}<br>"
+ f"infra: {'-'.join(name_lst[1:5])}<br>"
+ f"test: {'-'.join(name_lst[5:])}<br>"
f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
f"<prop> [{row[C.UNIT[ttype]]}]: {y_data[idx]:,.0f}<br>"
f"<stdev>"
f"<additional-info>"
- f"{d_type}-ref: {row['dut_version']}<br>"
+ f"{row['dut_type']}-ref: {row['dut_version']}<br>"
f"csit-ref: {row['job']}/{row['build']}<br>"
f"hosts: {', '.join(row['hosts'])}"
)
add_info = (
f"bandwidth [{row[C.UNIT['hoststack-bps']]}]: "
f"{row[C.VALUE['hoststack-bps']]:,.0f}<br>"
- f"latency [{row[C.UNIT['hoststack-lat']]}]: "
- f"{row[C.VALUE['hoststack-lat']]:,.0f}<br>"
+ f"latency [{row[C.UNIT['hoststack-latency']]}]: "
+ f"{row[C.VALUE['hoststack-latency']]:,.0f}<br>"
+ )
+ elif ttype in ("ndr", "pdr"): # Add mrr
+ test_type = f"{ttype}-bandwidth"
+ add_info = (
+ f"bandwidth [{row[C.UNIT[test_type]]}]: "
+ f"{row[C.VALUE[test_type]]:,.0f}<br>"
)
else:
add_info = str()
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"dut: {name_lst[0]}<br>"
+ f"infra: {'-'.join(name_lst[1:5])}<br>"
+ f"test: {'-'.join(name_lst[5:])}<br>"
f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
f"trend [{row[C.UNIT[ttype]]}]: {avg:,.0f}<br>"
f"stdev [{row[C.UNIT[ttype]]}]: {stdev:,.0f}<br>"
- f"{d_type}-ref: {row['dut_version']}<br>"
+ f"{row['dut_type']}-ref: {row['dut_version']}<br>"
f"csit-ref: {row['job']}/{row['build']}<br>"
f"hosts: {', '.join(row['hosts'])}"
)
"symbol": "circle",
},
text=hover,
- hoverinfo="text+name",
+ hoverinfo="text",
showlegend=True,
legendgroup=name,
customdata=customdata_samples
"color": color,
},
text=hover_trend,
- hoverinfo="text+name",
+ hoverinfo="text",
showlegend=False,
legendgroup=name,
customdata=customdata
anomaly_y.append(trend_avg[idx])
anomaly_color.append(C.ANOMALY_COLOR[anomaly])
hover_itm = (
+ f"dut: {name_lst[0]}<br>"
+ f"infra: {'-'.join(name_lst[1:5])}<br>"
+ f"test: {'-'.join(name_lst[5:])}<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}"
y=anomaly_y,
mode="markers",
text=hover,
- hoverinfo="text+name",
+ hoverinfo="text",
showlegend=False,
legendgroup=name,
name=name,
fig_tput = None
fig_lat = None
+ fig_band = None
y_units = set()
for idx, itm in enumerate(sel):
df = select_trending_data(data, itm)
fig_tput = go.Figure()
fig_tput.add_traces(traces)
+ if ttype in ("ndr", "pdr"): # Add mrr
+ traces, _ = _generate_trending_traces(
+ f"{ttype}-bandwidth",
+ itm["id"],
+ df,
+ get_color(idx),
+ norm_factor
+ )
+ if traces:
+ if not fig_band:
+ fig_band = go.Figure()
+ fig_band.add_traces(traces)
+
if itm["testtype"] == "pdr":
traces, _ = _generate_trending_traces(
"latency",
fig_layout["yaxis"]["title"] = \
f"Throughput [{'|'.join(sorted(y_units))}]"
fig_tput.update_layout(fig_layout)
+ if fig_band:
+ fig_band.update_layout(layout.get("plot-trending-bandwidth", dict()))
if fig_lat:
fig_lat.update_layout(layout.get("plot-trending-lat", dict()))
- return fig_tput, fig_lat
+ return fig_tput, fig_band, fig_lat
def graph_tm_trending(
:rtype: list
"""
traces = list()
- nr_of_metrics = len(data.tm_metric.unique())
- for idx, metric in enumerate(data.tm_metric.unique()):
+ metrics = data.tm_metric.unique().tolist()
+ for idx, metric in enumerate(metrics):
if "-pdr" in test and "='pdr'" not in metric:
continue
if "-ndr" in test and "='ndr'" not in metric:
else:
anomalies = None
if all_in_one:
- color = get_color(color_index * nr_of_metrics + idx)
+ color = get_color(color_index * len(metrics) + idx)
metric_name = f"{test}<br>{metric}"
else:
color = get_color(idx)
anomaly_color.append(C.ANOMALY_COLOR[anomaly])
hover_itm = (
f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}"
- f"<br>trend: {trend_avg[idx]:,.0f}"
+ f"<br>trend: {trend_avg[idx]:,.2f}"
f"<br>classification: {anomaly}"
)
hover.append(hover_itm)
)
)
- return traces
+ unique_metrics = set()
+ for itm in metrics:
+ unique_metrics.add(itm.split("{", 1)[0])
+ return traces, unique_metrics
tm_trending_graphs = list()
graph_layout = layout.get("plot-trending-telemetry", dict())
if all_in_one:
all_traces = list()
+ all_metrics = set()
+ all_tests = list()
for idx, test in enumerate(data.test_name.unique()):
df = data.loc[(data["test_name"] == test)]
- traces = _generate_traces(df, test, all_in_one, idx)
+ traces, metrics = _generate_traces(df, test, all_in_one, idx)
if traces:
+ all_metrics.update(metrics)
if all_in_one:
all_traces.extend(traces)
+ all_tests.append(test)
else:
graph = go.Figure()
graph.add_traces(traces)
graph.update_layout(graph_layout)
- tm_trending_graphs.append((graph, test, ))
+ tm_trending_graphs.append((graph, [test, ], ))
if all_in_one:
graph = go.Figure()
graph.add_traces(all_traces)
graph.update_layout(graph_layout)
- tm_trending_graphs.append((graph, str(), ))
+ tm_trending_graphs.append((graph, all_tests, ))
- return tm_trending_graphs
+ return tm_trending_graphs, list(all_metrics)
Code Review / csit.git / blobdiff