1 # Copyright (c) 2023 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
14 """Implementation of graphs for trending data.
17 import plotly.graph_objects as go
20 from ..utils.constants import Constants as C
21 from ..utils.utils import get_color, get_hdrh_latencies
22 from ..utils.anomalies import classify_anomalies
25 def select_trending_data(data: pd.DataFrame, itm: dict) -> pd.DataFrame:
26 """Select the data for graphs from the provided data frame.
28 :param data: Data frame with data for graphs.
29 :param itm: Item (in this case job name) which data will be selected from
31 :type data: pandas.DataFrame
33 :returns: A data frame with selected data.
34 :rtype: pandas.DataFrame
37 phy = itm["phy"].split("-")
39 topo, arch, nic, drv = phy
44 drv = drv.replace("_", "-")
48 if itm["testtype"] in ("ndr", "pdr"):
50 elif itm["testtype"] == "mrr":
52 elif itm["area"] == "hoststack":
53 test_type = "hoststack"
55 (data["test_type"] == test_type) &
56 (data["passed"] == True)
58 df = df[df.job.str.endswith(f"{topo}-{arch}")]
59 core = str() if itm["dut"] == "trex" else f"{itm['core']}"
60 ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
61 df = df[df.test_id.str.contains(
62 f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
64 )].sort_values(by="start_time", ignore_index=True)
75 """Generate the trending graph(s) - MRR, NDR, PDR and for PDR also Latences
76 (result_latency_forward_pdr_50_avg).
78 :param data: Data frame with test results.
79 :param sel: Selected tests.
80 :param layout: Layout of plot.ly graph.
81 :param normalize: If True, the data is normalized to CPU frquency
82 Constants.NORM_FREQUENCY.
83 :type data: pandas.DataFrame
87 :returns: Trending graph(s)
88 :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
95 def _generate_trending_traces(
102 """Generate the trending traces for the trending graph.
104 :param ttype: Test type (MRR, NDR, PDR).
105 :param name: The test name to be displayed as the graph title.
106 :param df: Data frame with test data.
107 :param color: The color of the trace (samples and trend line).
108 :param norm_factor: The factor used for normalization of the results to
109 CPU frequency set to Constants.NORM_FREQUENCY.
112 :type df: pandas.DataFrame
114 :type norm_factor: float
115 :returns: Traces (samples, trending line, anomalies)
119 df = df.dropna(subset=[C.VALUE[ttype], ])
121 return list(), list()
123 x_axis = df["start_time"].tolist()
124 if ttype == "latency":
125 y_data = [(v / norm_factor) for v in df[C.VALUE[ttype]].tolist()]
127 y_data = [(v * norm_factor) for v in df[C.VALUE[ttype]].tolist()]
128 units = df[C.UNIT[ttype]].unique().tolist()
130 anomalies, trend_avg, trend_stdev = classify_anomalies(
131 {k: v for k, v in zip(x_axis, y_data)}
136 customdata_samples = list()
137 for idx, (_, row) in enumerate(df.iterrows()):
139 f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
140 f"<prop> [{row[C.UNIT[ttype]]}]: {y_data[idx]:,.0f}<br>"
143 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
144 f"csit-ref: {row['job']}/{row['build']}<br>"
145 f"hosts: {', '.join(row['hosts'])}"
149 f"stdev [{row['result_receive_rate_rate_unit']}]: "
150 f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
154 if ttype in ("hoststack-cps", "hoststack-rps"):
156 f"bandwidth [{row[C.UNIT['hoststack-bps']]}]: "
157 f"{row[C.VALUE['hoststack-bps']]:,.0f}<br>"
158 f"latency [{row[C.UNIT['hoststack-lat']]}]: "
159 f"{row[C.VALUE['hoststack-lat']]:,.0f}<br>"
163 hover_itm = hover_itm.replace(
164 "<prop>", "latency" if ttype == "latency" else "average"
165 ).replace("<stdev>", stdev).replace("<additional-info>", add_info)
166 hover.append(hover_itm)
167 if ttype == "latency":
168 customdata_samples.append(get_hdrh_latencies(row, name))
169 customdata.append({"name": name})
171 customdata_samples.append(
172 {"name": name, "show_telemetry": True}
174 customdata.append({"name": name})
177 for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
179 f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
180 f"trend [{row[C.UNIT[ttype]]}]: {avg:,.0f}<br>"
181 f"stdev [{row[C.UNIT[ttype]]}]: {stdev:,.0f}<br>"
182 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
183 f"csit-ref: {row['job']}/{row['build']}<br>"
184 f"hosts: {', '.join(row['hosts'])}"
186 if ttype == "latency":
187 hover_itm = hover_itm.replace("[pps]", "[us]")
188 hover_trend.append(hover_itm)
191 go.Scatter( # Samples
202 hoverinfo="text+name",
205 customdata=customdata_samples
207 go.Scatter( # Trend line
218 hoverinfo="text+name",
221 customdata=customdata
228 anomaly_color = list()
230 for idx, anomaly in enumerate(anomalies):
231 if anomaly in ("regression", "progression"):
232 anomaly_x.append(x_axis[idx])
233 anomaly_y.append(trend_avg[idx])
234 anomaly_color.append(C.ANOMALY_COLOR[anomaly])
236 f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}<br>"
237 f"trend [pps]: {trend_avg[idx]:,.0f}<br>"
238 f"classification: {anomaly}"
240 if ttype == "latency":
241 hover_itm = hover_itm.replace("[pps]", "[us]")
242 hover.append(hover_itm)
243 anomaly_color.extend([0.0, 0.5, 1.0])
250 hoverinfo="text+name",
254 customdata=customdata,
257 "symbol": "circle-open",
258 "color": anomaly_color,
259 "colorscale": C.COLORSCALE_LAT \
260 if ttype == "latency" else C.COLORSCALE_TPUT,
268 "title": "Circles Marking Data Classification",
269 "titleside": "right",
271 "tickvals": [0.167, 0.500, 0.833],
272 "ticktext": C.TICK_TEXT_LAT \
273 if ttype == "latency" else C.TICK_TEXT_TPUT,
289 for idx, itm in enumerate(sel):
290 df = select_trending_data(data, itm)
291 if df is None or df.empty:
295 phy = itm["phy"].split("-")
296 topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
297 norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
298 if topo_arch else 1.0
302 if itm["area"] == "hoststack":
303 ttype = f"hoststack-{itm['testtype']}"
305 ttype = itm["testtype"]
307 traces, units = _generate_trending_traces(
316 fig_tput = go.Figure()
317 fig_tput.add_traces(traces)
319 if itm["testtype"] == "pdr":
320 traces, _ = _generate_trending_traces(
329 fig_lat = go.Figure()
330 fig_lat.add_traces(traces)
332 y_units.update(units)
335 fig_layout = layout.get("plot-trending-tput", dict())
336 fig_layout["yaxis"]["title"] = \
337 f"Throughput [{'|'.join(sorted(y_units))}]"
338 fig_tput.update_layout(fig_layout)
340 fig_lat.update_layout(layout.get("plot-trending-lat", dict()))
342 return fig_tput, fig_lat
345 def graph_tm_trending(
348 all_in_one: bool=False
350 """Generates one trending graph per test, each graph includes all selected
353 :param data: Data frame with telemetry data.
354 :param layout: Layout of plot.ly graph.
355 :param all_in_one: If True, all telemetry traces are placed in one graph,
356 otherwise they are split to separate graphs grouped by test ID.
357 :type data: pandas.DataFrame
359 :type all_in_one: bool
360 :returns: List of generated graphs together with test names.
361 list(tuple(plotly.graph_objects.Figure(), str()), tuple(...), ...)
368 def _generate_traces(
374 """Generates a trending graph for given test with all metrics.
376 :param data: Data frame with telemetry data for the given test.
377 :param test: The name of the test.
378 :param all_in_one: If True, all telemetry traces are placed in one
379 graph, otherwise they are split to separate graphs grouped by
381 :param color_index: The index of the test used if all_in_one is True.
382 :type data: pandas.DataFrame
384 :type all_in_one: bool
385 :type color_index: int
386 :returns: List of traces.
390 metrics = data.tm_metric.unique().tolist()
391 for idx, metric in enumerate(metrics):
392 if "-pdr" in test and "='pdr'" not in metric:
394 if "-ndr" in test and "='ndr'" not in metric:
397 df = data.loc[(data["tm_metric"] == metric)]
398 x_axis = df["start_time"].tolist()
399 y_data = [float(itm) for itm in df["tm_value"].tolist()]
401 for i, (_, row) in enumerate(df.iterrows()):
402 if row["test_type"] == "mrr":
404 f"mrr avg [{row[C.UNIT['mrr']]}]: "
405 f"{row[C.VALUE['mrr']]:,.0f}<br>"
406 f"mrr stdev [{row[C.UNIT['mrr']]}]: "
407 f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
409 elif row["test_type"] == "ndrpdr":
412 f"pdr [{row[C.UNIT['pdr']]}]: "
413 f"{row[C.VALUE['pdr']]:,.0f}<br>"
417 f"ndr [{row[C.UNIT['ndr']]}]: "
418 f"{row[C.VALUE['ndr']]:,.0f}<br>"
426 f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
427 f"value: {y_data[i]:,.2f}<br>"
429 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
430 f"csit-ref: {row['job']}/{row['build']}<br>"
433 anomalies, trend_avg, trend_stdev = classify_anomalies(
434 {k: v for k, v in zip(x_axis, y_data)}
437 for avg, stdev, (_, row) in \
438 zip(trend_avg, trend_stdev, df.iterrows()):
441 f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
442 f"trend: {avg:,.2f}<br>"
443 f"stdev: {stdev:,.2f}<br>"
444 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
445 f"csit-ref: {row['job']}/{row['build']}"
450 color = get_color(color_index * len(metrics) + idx)
451 metric_name = f"{test}<br>{metric}"
453 color = get_color(idx)
457 go.Scatter( # Samples
468 hoverinfo="text+name",
470 legendgroup=metric_name
475 go.Scatter( # Trend line
486 hoverinfo="text+name",
488 legendgroup=metric_name
494 anomaly_color = list()
496 for idx, anomaly in enumerate(anomalies):
497 if anomaly in ("regression", "progression"):
498 anomaly_x.append(x_axis[idx])
499 anomaly_y.append(trend_avg[idx])
500 anomaly_color.append(C.ANOMALY_COLOR[anomaly])
502 f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}"
503 f"<br>trend: {trend_avg[idx]:,.2f}"
504 f"<br>classification: {anomaly}"
506 hover.append(hover_itm)
507 anomaly_color.extend([0.0, 0.5, 1.0])
514 hoverinfo="text+name",
516 legendgroup=metric_name,
520 "symbol": "circle-open",
521 "color": anomaly_color,
522 "colorscale": C.COLORSCALE_TPUT,
530 "title": "Circles Marking Data Classification",
531 "titleside": "right",
533 "tickvals": [0.167, 0.500, 0.833],
534 "ticktext": C.TICK_TEXT_TPUT,
544 unique_metrics = set()
546 unique_metrics.add(itm.split("{", 1)[0])
547 return traces, unique_metrics
549 tm_trending_graphs = list()
550 graph_layout = layout.get("plot-trending-telemetry", dict())
557 for idx, test in enumerate(data.test_name.unique()):
558 df = data.loc[(data["test_name"] == test)]
559 traces, metrics = _generate_traces(df, test, all_in_one, idx)
561 all_metrics.update(metrics)
563 all_traces.extend(traces)
564 all_tests.append(test)
567 graph.add_traces(traces)
568 graph.update_layout(graph_layout)
569 tm_trending_graphs.append((graph, [test, ], ))
573 graph.add_traces(all_traces)
574 graph.update_layout(graph_layout)
575 tm_trending_graphs.append((graph, all_tests, ))
577 return tm_trending_graphs, list(all_metrics)