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.
17 import plotly.graph_objects as go
23 from ..utils.constants import Constants as C
24 from ..utils.utils import classify_anomalies, get_color
27 def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
28 """Get the HDRH latencies from the test data.
30 :param row: A row fron the data frame with test data.
31 :param name: The test name to be displayed as the graph title.
32 :type row: pandas.Series
34 :returns: Dictionary with HDRH latencies.
38 latencies = {"name": name}
39 for key in C.LAT_HDRH:
41 latencies[key] = row[key]
48 def select_trending_data(data: pd.DataFrame, itm: dict) -> pd.DataFrame:
49 """Select the data for graphs from the provided data frame.
51 :param data: Data frame with data for graphs.
52 :param itm: Item (in this case job name) which data will be selected from
54 :type data: pandas.DataFrame
56 :returns: A data frame with selected data.
57 :rtype: pandas.DataFrame
60 phy = itm["phy"].split("-")
62 topo, arch, nic, drv = phy
67 drv = drv.replace("_", "-")
71 core = str() if itm["dut"] == "trex" else f"{itm['core']}"
72 ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
75 (data["test_type"] == ttype) &
76 (data["passed"] == True)
78 df = df[df.job.str.endswith(f"{topo}-{arch}")]
79 df = df[df.test_id.str.contains(
80 f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
82 )].sort_values(by="start_time", ignore_index=True)
93 """Generate the trending graph(s) - MRR, NDR, PDR and for PDR also Latences
94 (result_latency_forward_pdr_50_avg).
96 :param data: Data frame with test results.
97 :param sel: Selected tests.
98 :param layout: Layout of plot.ly graph.
99 :param normalize: If True, the data is normalized to CPU frquency
100 Constants.NORM_FREQUENCY.
101 :type data: pandas.DataFrame
104 :type normalize: bool
105 :returns: Trending graph(s)
106 :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
113 def _generate_trending_traces(
120 """Generate the trending traces for the trending graph.
122 :param ttype: Test type (MRR, NDR, PDR).
123 :param name: The test name to be displayed as the graph title.
124 :param df: Data frame with test data.
125 :param color: The color of the trace (samples and trend line).
126 :param norm_factor: The factor used for normalization of the results to
127 CPU frequency set to Constants.NORM_FREQUENCY.
130 :type df: pandas.DataFrame
132 :type norm_factor: float
133 :returns: Traces (samples, trending line, anomalies)
137 df = df.dropna(subset=[C.VALUE[ttype], ])
141 x_axis = df["start_time"].tolist()
142 if ttype == "pdr-lat":
143 y_data = [(v / norm_factor) for v in df[C.VALUE[ttype]].tolist()]
145 y_data = [(v * norm_factor) for v in df[C.VALUE[ttype]].tolist()]
147 anomalies, trend_avg, trend_stdev = classify_anomalies(
148 {k: v for k, v in zip(x_axis, y_data)}
153 customdata_samples = list()
154 for idx, (_, row) in enumerate(df.iterrows()):
155 d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
157 f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
158 f"<prop> [{row[C.UNIT[ttype]]}]: {y_data[idx]:,.0f}<br>"
160 f"{d_type}-ref: {row['dut_version']}<br>"
161 f"csit-ref: {row['job']}/{row['build']}<br>"
162 f"hosts: {', '.join(row['hosts'])}"
166 f"stdev [{row['result_receive_rate_rate_unit']}]: "
167 f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
171 hover_itm = hover_itm.replace(
172 "<prop>", "latency" if ttype == "pdr-lat" else "average"
173 ).replace("<stdev>", stdev)
174 hover.append(hover_itm)
175 if ttype == "pdr-lat":
176 customdata_samples.append(_get_hdrh_latencies(row, name))
177 customdata.append({"name": name})
179 customdata_samples.append(
180 {"name": name, "show_telemetry": True}
182 customdata.append({"name": name})
185 for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
186 d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
188 f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
189 f"trend [pps]: {avg:,.0f}<br>"
190 f"stdev [pps]: {stdev:,.0f}<br>"
191 f"{d_type}-ref: {row['dut_version']}<br>"
192 f"csit-ref: {row['job']}/{row['build']}<br>"
193 f"hosts: {', '.join(row['hosts'])}"
195 if ttype == "pdr-lat":
196 hover_itm = hover_itm.replace("[pps]", "[us]")
197 hover_trend.append(hover_itm)
200 go.Scatter( # Samples
211 hoverinfo="text+name",
214 customdata=customdata_samples
216 go.Scatter( # Trend line
227 hoverinfo="text+name",
230 customdata=customdata
237 anomaly_color = list()
239 for idx, anomaly in enumerate(anomalies):
240 if anomaly in ("regression", "progression"):
241 anomaly_x.append(x_axis[idx])
242 anomaly_y.append(trend_avg[idx])
243 anomaly_color.append(C.ANOMALY_COLOR[anomaly])
245 f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}<br>"
246 f"trend [pps]: {trend_avg[idx]:,.0f}<br>"
247 f"classification: {anomaly}"
249 if ttype == "pdr-lat":
250 hover_itm = hover_itm.replace("[pps]", "[us]")
251 hover.append(hover_itm)
252 anomaly_color.extend([0.0, 0.5, 1.0])
259 hoverinfo="text+name",
263 customdata=customdata,
266 "symbol": "circle-open",
267 "color": anomaly_color,
268 "colorscale": C.COLORSCALE_LAT \
269 if ttype == "pdr-lat" else C.COLORSCALE_TPUT,
277 "title": "Circles Marking Data Classification",
278 "titleside": "right",
280 "tickvals": [0.167, 0.500, 0.833],
281 "ticktext": C.TICK_TEXT_LAT \
282 if ttype == "pdr-lat" else C.TICK_TEXT_TPUT,
297 for idx, itm in enumerate(sel):
298 df = select_trending_data(data, itm)
299 if df is None or df.empty:
303 phy = itm["phy"].split("-")
304 topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
305 norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
306 if topo_arch else 1.0
309 traces = _generate_trending_traces(itm["testtype"], itm["id"], df,
310 get_color(idx), norm_factor)
313 fig_tput = go.Figure()
314 fig_tput.add_traces(traces)
316 if itm["testtype"] == "pdr":
317 traces = _generate_trending_traces("pdr-lat", itm["id"], df,
318 get_color(idx), norm_factor)
321 fig_lat = go.Figure()
322 fig_lat.add_traces(traces)
325 fig_tput.update_layout(layout.get("plot-trending-tput", dict()))
327 fig_lat.update_layout(layout.get("plot-trending-lat", dict()))
329 return fig_tput, fig_lat
332 def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
333 """Generate HDR Latency histogram graphs.
335 :param data: HDRH data.
336 :param layout: Layout of plot.ly graph.
339 :returns: HDR latency Histogram.
340 :rtype: plotly.graph_objects.Figure
346 for idx, (lat_name, lat_hdrh) in enumerate(data.items()):
348 decoded = hdrh.histogram.HdrHistogram.decode(lat_hdrh)
349 except (hdrh.codec.HdrLengthException, TypeError):
356 for item in decoded.get_recorded_iterator():
357 # The real value is "percentile".
358 # For 100%, we cut that down to "x_perc" to avoid
360 percentile = item.percentile_level_iterated_to
361 x_perc = min(percentile, C.PERCENTILE_MAX)
362 xaxis.append(previous_x)
363 yaxis.append(item.value_iterated_to)
365 f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
366 f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
367 f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
368 f"Latency: {item.value_iterated_to}uSec"
370 next_x = 100.0 / (100.0 - x_perc)
372 yaxis.append(item.value_iterated_to)
374 f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
375 f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
376 f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
377 f"Latency: {item.value_iterated_to}uSec"
380 prev_perc = percentile
386 name=C.GRAPH_LAT_HDRH_DESC[lat_name],
388 legendgroup=C.GRAPH_LAT_HDRH_DESC[lat_name],
389 showlegend=bool(idx % 2),
391 color=get_color(int(idx/2)),
393 width=1 if idx % 2 else 2
401 fig.add_traces(traces)
402 layout_hdrh = layout.get("plot-hdrh-latency", None)
404 fig.update_layout(layout_hdrh)
409 def graph_tm_trending(data: pd.DataFrame, layout: dict) -> list:
410 """Generates one trending graph per test, each graph includes all selected
413 :param data: Data frame with telemetry data.
414 :param layout: Layout of plot.ly graph.
415 :type data: pandas.DataFrame
417 :returns: List of generated graphs together with test names.
418 list(tuple(plotly.graph_objects.Figure(), str()), tuple(...), ...)
428 """Generates a trending graph for given test with all metrics.
430 :param data: Data frame with telemetry data for the given test.
431 :param test: The name of the test.
432 :param layout: Layout of plot.ly graph.
433 :type data: pandas.DataFrame
436 :returns: A trending graph.
437 :rtype: plotly.graph_objects.Figure
441 for idx, metric in enumerate(data.tm_metric.unique()):
442 if "-pdr" in test and "='pdr'" not in metric:
444 if "-ndr" in test and "='ndr'" not in metric:
447 df = data.loc[(data["tm_metric"] == metric)]
448 x_axis = df["start_time"].tolist()
449 y_data = [float(itm) for itm in df["tm_value"].tolist()]
451 for i, (_, row) in enumerate(df.iterrows()):
454 f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
455 f"value: {y_data[i]:,.0f}<br>"
456 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
457 f"csit-ref: {row['job']}/{row['build']}<br>"
460 anomalies, trend_avg, trend_stdev = classify_anomalies(
461 {k: v for k, v in zip(x_axis, y_data)}
464 for avg, stdev, (_, row) in \
465 zip(trend_avg, trend_stdev, df.iterrows()):
468 f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
469 f"trend: {avg:,.0f}<br>"
470 f"stdev: {stdev:,.0f}<br>"
471 f"{row['dut_type']}-ref: {row['dut_version']}<br>"
472 f"csit-ref: {row['job']}/{row['build']}"
476 color = get_color(idx)
478 go.Scatter( # Samples
489 hoverinfo="text+name",
496 go.Scatter( # Trend line
507 hoverinfo="text+name",
515 anomaly_color = list()
517 for idx, anomaly in enumerate(anomalies):
518 if anomaly in ("regression", "progression"):
519 anomaly_x.append(x_axis[idx])
520 anomaly_y.append(trend_avg[idx])
521 anomaly_color.append(C.ANOMALY_COLOR[anomaly])
523 f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}"
524 f"<br>trend: {trend_avg[idx]:,.0f}"
525 f"<br>classification: {anomaly}"
527 hover.append(hover_itm)
528 anomaly_color.extend([0.0, 0.5, 1.0])
535 hoverinfo="text+name",
541 "symbol": "circle-open",
542 "color": anomaly_color,
543 "colorscale": C.COLORSCALE_TPUT,
551 "title": "Circles Marking Data Classification",
552 "titleside": "right",
554 "tickvals": [0.167, 0.500, 0.833],
555 "ticktext": C.TICK_TEXT_TPUT,
567 graph.add_traces(traces)
568 graph.update_layout(layout.get("plot-trending-telemetry", dict()))
573 tm_trending_graphs = list()
576 return tm_trending_graphs
578 for test in data.test_name.unique():
579 df = data.loc[(data["test_name"] == test)]
580 graph = _generate_graph(df, test, layout)
582 tm_trending_graphs.append((graph, test, ))
584 return tm_trending_graphs