1 # Copyright (c) 2020 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 """Algorithms to generate plots.
21 from collections import OrderedDict
22 from copy import deepcopy
27 import plotly.offline as ploff
28 import plotly.graph_objs as plgo
30 from plotly.subplots import make_subplots
31 from plotly.exceptions import PlotlyError
33 from pal_utils import mean, stdev
36 COLORS = [u"SkyBlue", u"Olive", u"Purple", u"Coral", u"Indigo", u"Pink",
37 u"Chocolate", u"Brown", u"Magenta", u"Cyan", u"Orange", u"Black",
38 u"Violet", u"Blue", u"Yellow", u"BurlyWood", u"CadetBlue", u"Crimson",
39 u"DarkBlue", u"DarkCyan", u"DarkGreen", u"Green", u"GoldenRod",
40 u"LightGreen", u"LightSeaGreen", u"LightSkyBlue", u"Maroon",
41 u"MediumSeaGreen", u"SeaGreen", u"LightSlateGrey"]
43 REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)-')
46 def generate_plots(spec, data):
47 """Generate all plots specified in the specification file.
49 :param spec: Specification read from the specification file.
50 :param data: Data to process.
51 :type spec: Specification
56 u"plot_nf_reconf_box_name": plot_nf_reconf_box_name,
57 u"plot_perf_box_name": plot_perf_box_name,
58 u"plot_lat_err_bars_name": plot_lat_err_bars_name,
59 u"plot_tsa_name": plot_tsa_name,
60 u"plot_http_server_perf_box": plot_http_server_perf_box,
61 u"plot_nf_heatmap": plot_nf_heatmap,
62 u"plot_lat_hdrh_bar_name": plot_lat_hdrh_bar_name,
63 u"plot_lat_hdrh_percentile": plot_lat_hdrh_percentile,
64 u"plot_hdrh_lat_by_percentile": plot_hdrh_lat_by_percentile
67 logging.info(u"Generating the plots ...")
68 for index, plot in enumerate(spec.plots):
70 logging.info(f" Plot nr {index + 1}: {plot.get(u'title', u'')}")
71 plot[u"limits"] = spec.configuration[u"limits"]
72 generator[plot[u"algorithm"]](plot, data)
73 logging.info(u" Done.")
74 except NameError as err:
76 f"Probably algorithm {plot[u'algorithm']} is not defined: "
79 logging.info(u"Done.")
82 def plot_lat_hdrh_percentile(plot, input_data):
83 """Generate the plot(s) with algorithm: plot_lat_hdrh_percentile
84 specified in the specification file.
86 :param plot: Plot to generate.
87 :param input_data: Data to process.
88 :type plot: pandas.Series
89 :type input_data: InputData
93 plot_title = plot.get(u"title", u"")
95 f" Creating the data set for the {plot.get(u'type', u'')} "
98 data = input_data.filter_tests_by_name(
99 plot, params=[u"latency", u"parent", u"tags", u"type"])
100 if data is None or len(data[0][0]) == 0:
101 logging.error(u"No data.")
106 # Prepare the data for the plot
107 directions = [u"W-E", u"E-W"]
108 for color, test in enumerate(data[0][0]):
110 if test[u"type"] in (u"NDRPDR",):
111 if u"-pdr" in plot_title.lower():
113 elif u"-ndr" in plot_title.lower():
116 logging.warning(f"Invalid test type: {test[u'type']}")
118 name = re.sub(REGEX_NIC, u"", test[u"parent"].
119 replace(u'-ndrpdr', u'').
120 replace(u'2n1l-', u''))
121 for idx, direction in enumerate(
122 (u"direction1", u"direction2", )):
124 hdr_lat = test[u"latency"][ttype][direction][u"hdrh"]
125 # TODO: Workaround, HDRH data must be aligned to 4
126 # bytes, remove when not needed.
127 hdr_lat += u"=" * (len(hdr_lat) % 4)
131 decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat)
132 for item in decoded.get_recorded_iterator():
133 percentile = item.percentile_level_iterated_to
134 if percentile != 100.0:
135 xaxis.append(100.0 / (100.0 - percentile))
136 yaxis.append(item.value_iterated_to)
139 f"Direction: {directions[idx]}<br>"
140 f"Percentile: {percentile:.5f}%<br>"
141 f"Latency: {item.value_iterated_to}uSec"
150 showlegend=bool(idx),
158 except hdrh.codec.HdrLengthException as err:
160 f"No or invalid data for HDRHistogram for the test "
165 logging.warning(f"Invalid test type: {test[u'type']}")
167 except (ValueError, KeyError) as err:
168 logging.warning(repr(err))
170 layout = deepcopy(plot[u"layout"])
172 layout[u"title"][u"text"] = \
173 f"<b>Latency:</b> {plot.get(u'graph-title', u'')}"
174 fig[u"layout"].update(layout)
177 file_type = plot.get(u"output-file-type", u".html")
178 logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
181 ploff.plot(fig, show_link=False, auto_open=False,
182 filename=f"{plot[u'output-file']}{file_type}")
183 except PlotlyError as err:
184 logging.error(f" Finished with error: {repr(err)}")
187 def plot_hdrh_lat_by_percentile(plot, input_data):
188 """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile
189 specified in the specification file.
191 :param plot: Plot to generate.
192 :param input_data: Data to process.
193 :type plot: pandas.Series
194 :type input_data: InputData
199 f" Creating the data set for the {plot.get(u'type', u'')} "
200 f"{plot.get(u'title', u'')}."
202 if plot.get(u"include", None):
203 data = input_data.filter_tests_by_name(
205 params=[u"name", u"latency", u"parent", u"tags", u"type"]
207 elif plot.get(u"filter", None):
208 data = input_data.filter_data(
210 params=[u"name", u"latency", u"parent", u"tags", u"type"],
211 continue_on_error=True
214 job = list(plot[u"data"].keys())[0]
215 build = str(plot[u"data"][job][0])
216 data = input_data.tests(job, build)
218 if data is None or len(data) == 0:
219 logging.error(u"No data.")
223 u"LAT0": u"No-load.",
224 u"PDR10": u"Low-load, 10% PDR.",
225 u"PDR50": u"Mid-load, 50% PDR.",
226 u"PDR90": u"High-load, 90% PDR.",
227 u"PDR": u"Full-load, 100% PDR.",
228 u"NDR10": u"Low-load, 10% NDR.",
229 u"NDR50": u"Mid-load, 50% NDR.",
230 u"NDR90": u"High-load, 90% NDR.",
231 u"NDR": u"Full-load, 100% NDR."
241 file_links = plot.get(u"output-file-links", None)
242 target_links = plot.get(u"target-links", None)
246 if test[u"type"] not in (u"NDRPDR",):
247 logging.warning(f"Invalid test type: {test[u'type']}")
249 name = re.sub(REGEX_NIC, u"", test[u"parent"].
250 replace(u'-ndrpdr', u'').replace(u'2n1l-', u''))
252 nic = re.search(REGEX_NIC, test[u"parent"]).group(1)
253 except (IndexError, AttributeError, KeyError, ValueError):
255 name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'')
257 logging.info(f" Generating the graph: {name_link}")
260 layout = deepcopy(plot[u"layout"])
262 for color, graph in enumerate(graphs):
263 for idx, direction in enumerate((u"direction1", u"direction2")):
267 f"<b>{desc[graph]}</b><br>"
268 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
269 f"Percentile: 0.0%<br>"
272 decoded = hdrh.histogram.HdrHistogram.decode(
273 test[u"latency"][graph][direction][u"hdrh"]
275 for item in decoded.get_recorded_iterator():
276 percentile = item.percentile_level_iterated_to
277 if percentile > 99.9:
279 xaxis.append(percentile)
280 yaxis.append(item.value_iterated_to)
282 f"<b>{desc[graph]}</b><br>"
283 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
284 f"Percentile: {percentile:.5f}%<br>"
285 f"Latency: {item.value_iterated_to}uSec"
293 legendgroup=desc[graph],
294 showlegend=bool(idx),
297 dash=u"solid" if idx % 2 else u"dash"
304 layout[u"title"][u"text"] = f"<b>Latency:</b> {name}"
305 fig.update_layout(layout)
308 file_name = f"{plot[u'output-file']}-{name_link}.html"
309 logging.info(f" Writing file {file_name}")
313 ploff.plot(fig, show_link=False, auto_open=False,
315 # Add link to the file:
316 if file_links and target_links:
317 with open(file_links, u"a") as file_handler:
320 f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n"
322 except FileNotFoundError as err:
324 f"Not possible to write the link to the file "
325 f"{file_links}\n{err}"
327 except PlotlyError as err:
328 logging.error(f" Finished with error: {repr(err)}")
330 except hdrh.codec.HdrLengthException as err:
331 logging.warning(repr(err))
334 except (ValueError, KeyError) as err:
335 logging.warning(repr(err))
339 def plot_lat_hdrh_bar_name(plot, input_data):
340 """Generate the plot(s) with algorithm: plot_lat_hdrh_bar_name
341 specified in the specification file.
343 :param plot: Plot to generate.
344 :param input_data: Data to process.
345 :type plot: pandas.Series
346 :type input_data: InputData
350 plot_title = plot.get(u"title", u"")
352 f" Creating the data set for the {plot.get(u'type', u'')} "
355 data = input_data.filter_tests_by_name(
356 plot, params=[u"latency", u"parent", u"tags", u"type"])
357 if data is None or len(data[0][0]) == 0:
358 logging.error(u"No data.")
361 # Prepare the data for the plot
362 directions = [u"W-E", u"E-W"]
365 for idx_row, test in enumerate(data[0][0]):
367 if test[u"type"] in (u"NDRPDR",):
368 if u"-pdr" in plot_title.lower():
370 elif u"-ndr" in plot_title.lower():
373 logging.warning(f"Invalid test type: {test[u'type']}")
375 name = re.sub(REGEX_NIC, u"", test[u"parent"].
376 replace(u'-ndrpdr', u'').
377 replace(u'2n1l-', u''))
379 for idx_col, direction in enumerate(
380 (u"direction1", u"direction2", )):
382 hdr_lat = test[u"latency"][ttype][direction][u"hdrh"]
383 # TODO: Workaround, HDRH data must be aligned to 4
384 # bytes, remove when not needed.
385 hdr_lat += u"=" * (len(hdr_lat) % 4)
389 decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat)
390 total_count = decoded.get_total_count()
391 for item in decoded.get_recorded_iterator():
392 xaxis.append(item.value_iterated_to)
393 prob = float(item.count_added_in_this_iter_step) / \
398 f"Direction: {directions[idx_col]}<br>"
399 f"Latency: {item.value_iterated_to}uSec<br>"
400 f"Probability: {prob:.2f}%<br>"
402 f"{item.percentile_level_iterated_to:.2f}"
404 marker_color = [COLORS[idx_row], ] * len(yaxis)
405 marker_color[xaxis.index(
406 decoded.get_value_at_percentile(50.0))] = u"red"
407 marker_color[xaxis.index(
408 decoded.get_value_at_percentile(90.0))] = u"red"
409 marker_color[xaxis.index(
410 decoded.get_value_at_percentile(95.0))] = u"red"
417 marker={u"color": marker_color},
422 except hdrh.codec.HdrLengthException as err:
424 f"No or invalid data for HDRHistogram for the test "
428 if len(histograms) == 2:
429 traces.append(histograms)
432 logging.warning(f"Invalid test type: {test[u'type']}")
434 except (ValueError, KeyError) as err:
435 logging.warning(repr(err))
438 logging.warning(f"No data for {plot_title}.")
445 [{u"type": u"bar"}, {u"type": u"bar"}] for _ in range(len(tests))
450 gridcolor=u"rgb(220, 220, 220)",
451 linecolor=u"rgb(220, 220, 220)",
456 tickcolor=u"rgb(220, 220, 220)",
459 for idx_row, test in enumerate(tests):
460 for idx_col in range(2):
462 traces[idx_row][idx_col],
477 layout = deepcopy(plot[u"layout"])
479 layout[u"title"][u"text"] = \
480 f"<b>Latency:</b> {plot.get(u'graph-title', u'')}"
481 layout[u"height"] = 250 * len(tests) + 130
483 layout[u"annotations"][2][u"y"] = 1.06 - 0.008 * len(tests)
484 layout[u"annotations"][3][u"y"] = 1.06 - 0.008 * len(tests)
486 for idx, test in enumerate(tests):
487 layout[u"annotations"].append({
492 u"text": f"<b>{test}</b>",
495 u"xanchor": u"center",
497 u"y": 1.0 - float(idx) * 1.06 / len(tests),
498 u"yanchor": u"bottom",
502 fig[u"layout"].update(layout)
505 file_type = plot.get(u"output-file-type", u".html")
506 logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
509 ploff.plot(fig, show_link=False, auto_open=False,
510 filename=f"{plot[u'output-file']}{file_type}")
511 except PlotlyError as err:
512 logging.error(f" Finished with error: {repr(err)}")
515 def plot_nf_reconf_box_name(plot, input_data):
516 """Generate the plot(s) with algorithm: plot_nf_reconf_box_name
517 specified in the specification file.
519 :param plot: Plot to generate.
520 :param input_data: Data to process.
521 :type plot: pandas.Series
522 :type input_data: InputData
527 f" Creating the data set for the {plot.get(u'type', u'')} "
528 f"{plot.get(u'title', u'')}."
530 data = input_data.filter_tests_by_name(
531 plot, params=[u"result", u"parent", u"tags", u"type"]
534 logging.error(u"No data.")
537 # Prepare the data for the plot
538 y_vals = OrderedDict()
543 if y_vals.get(test[u"parent"], None) is None:
544 y_vals[test[u"parent"]] = list()
545 loss[test[u"parent"]] = list()
547 y_vals[test[u"parent"]].append(test[u"result"][u"time"])
548 loss[test[u"parent"]].append(test[u"result"][u"loss"])
549 except (KeyError, TypeError):
550 y_vals[test[u"parent"]].append(None)
552 # Add None to the lists with missing data
554 nr_of_samples = list()
555 for val in y_vals.values():
556 if len(val) > max_len:
558 nr_of_samples.append(len(val))
559 for val in y_vals.values():
560 if len(val) < max_len:
561 val.extend([None for _ in range(max_len - len(val))])
565 df_y = pd.DataFrame(y_vals)
567 for i, col in enumerate(df_y.columns):
568 tst_name = re.sub(REGEX_NIC, u"",
569 col.lower().replace(u'-ndrpdr', u'').
570 replace(u'2n1l-', u''))
572 traces.append(plgo.Box(
573 x=[str(i + 1) + u'.'] * len(df_y[col]),
574 y=[y if y else None for y in df_y[col]],
577 f"({nr_of_samples[i]:02d} "
578 f"run{u's' if nr_of_samples[i] > 1 else u''}, "
579 f"packets lost average: {mean(loss[col]):.1f}) "
580 f"{u'-'.join(tst_name.split(u'-')[3:-2])}"
586 layout = deepcopy(plot[u"layout"])
587 layout[u"title"] = f"<b>Time Lost:</b> {layout[u'title']}"
588 layout[u"yaxis"][u"title"] = u"<b>Implied Time Lost [s]</b>"
589 layout[u"legend"][u"font"][u"size"] = 14
590 layout[u"yaxis"].pop(u"range")
591 plpl = plgo.Figure(data=traces, layout=layout)
594 file_type = plot.get(u"output-file-type", u".html")
595 logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
600 filename=f"{plot[u'output-file']}{file_type}"
602 except PlotlyError as err:
604 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
609 def plot_perf_box_name(plot, input_data):
610 """Generate the plot(s) with algorithm: plot_perf_box_name
611 specified in the specification file.
613 :param plot: Plot to generate.
614 :param input_data: Data to process.
615 :type plot: pandas.Series
616 :type input_data: InputData
621 f" Creating data set for the {plot.get(u'type', u'')} "
622 f"{plot.get(u'title', u'')}."
624 data = input_data.filter_tests_by_name(
625 plot, params=[u"throughput", u"result", u"parent", u"tags", u"type"])
627 logging.error(u"No data.")
630 # Prepare the data for the plot
631 y_vals = OrderedDict()
636 if y_vals.get(test[u"parent"], None) is None:
637 y_vals[test[u"parent"]] = list()
639 if (test[u"type"] in (u"NDRPDR", ) and
640 u"-pdr" in plot.get(u"title", u"").lower()):
641 y_vals[test[u"parent"]].\
642 append(test[u"throughput"][u"PDR"][u"LOWER"])
643 test_type = u"NDRPDR"
644 elif (test[u"type"] in (u"NDRPDR", ) and
645 u"-ndr" in plot.get(u"title", u"").lower()):
646 y_vals[test[u"parent"]]. \
647 append(test[u"throughput"][u"NDR"][u"LOWER"])
648 test_type = u"NDRPDR"
649 elif test[u"type"] in (u"SOAK", ):
650 y_vals[test[u"parent"]].\
651 append(test[u"throughput"][u"LOWER"])
653 elif test[u"type"] in (u"HOSTSTACK", ):
654 if u"LDPRELOAD" in test[u"tags"]:
655 y_vals[test[u"parent"]].append(
656 float(test[u"result"][u"bits_per_second"]) / 1e3
658 elif u"VPPECHO" in test[u"tags"]:
659 y_vals[test[u"parent"]].append(
660 (float(test[u"result"][u"client"][u"tx_data"])
662 ((float(test[u"result"][u"client"][u"time"]) +
663 float(test[u"result"][u"server"][u"time"])) /
666 test_type = u"HOSTSTACK"
669 except (KeyError, TypeError):
670 y_vals[test[u"parent"]].append(None)
672 # Add None to the lists with missing data
674 nr_of_samples = list()
675 for val in y_vals.values():
676 if len(val) > max_len:
678 nr_of_samples.append(len(val))
679 for val in y_vals.values():
680 if len(val) < max_len:
681 val.extend([None for _ in range(max_len - len(val))])
685 df_y = pd.DataFrame(y_vals)
688 for i, col in enumerate(df_y.columns):
689 tst_name = re.sub(REGEX_NIC, u"",
690 col.lower().replace(u'-ndrpdr', u'').
691 replace(u'2n1l-', u''))
693 x=[str(i + 1) + u'.'] * len(df_y[col]),
694 y=[y / 1e6 if y else None for y in df_y[col]],
697 f"({nr_of_samples[i]:02d} "
698 f"run{u's' if nr_of_samples[i] > 1 else u''}) "
703 if test_type in (u"SOAK", ):
704 kwargs[u"boxpoints"] = u"all"
706 traces.append(plgo.Box(**kwargs))
709 val_max = max(df_y[col])
711 y_max.append(int(val_max / 1e6) + 2)
712 except (ValueError, TypeError) as err:
713 logging.error(repr(err))
718 layout = deepcopy(plot[u"layout"])
719 if layout.get(u"title", None):
720 if test_type in (u"HOSTSTACK", ):
721 layout[u"title"] = f"<b>Bandwidth:</b> {layout[u'title']}"
723 layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
725 layout[u"yaxis"][u"range"] = [0, max(y_max)]
726 plpl = plgo.Figure(data=traces, layout=layout)
729 logging.info(f" Writing file {plot[u'output-file']}.html.")
734 filename=f"{plot[u'output-file']}.html"
736 except PlotlyError as err:
738 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
743 def plot_lat_err_bars_name(plot, input_data):
744 """Generate the plot(s) with algorithm: plot_lat_err_bars_name
745 specified in the specification file.
747 :param plot: Plot to generate.
748 :param input_data: Data to process.
749 :type plot: pandas.Series
750 :type input_data: InputData
754 plot_title = plot.get(u"title", u"")
756 f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
758 data = input_data.filter_tests_by_name(
759 plot, params=[u"latency", u"parent", u"tags", u"type"])
761 logging.error(u"No data.")
764 # Prepare the data for the plot
765 y_tmp_vals = OrderedDict()
770 logging.debug(f"test[u'latency']: {test[u'latency']}\n")
771 except ValueError as err:
772 logging.warning(repr(err))
773 if y_tmp_vals.get(test[u"parent"], None) is None:
774 y_tmp_vals[test[u"parent"]] = [
775 list(), # direction1, min
776 list(), # direction1, avg
777 list(), # direction1, max
778 list(), # direction2, min
779 list(), # direction2, avg
780 list() # direction2, max
783 if test[u"type"] not in (u"NDRPDR", ):
784 logging.warning(f"Invalid test type: {test[u'type']}")
786 if u"-pdr" in plot_title.lower():
788 elif u"-ndr" in plot_title.lower():
792 f"Invalid test type: {test[u'type']}"
795 y_tmp_vals[test[u"parent"]][0].append(
796 test[u"latency"][ttype][u"direction1"][u"min"])
797 y_tmp_vals[test[u"parent"]][1].append(
798 test[u"latency"][ttype][u"direction1"][u"avg"])
799 y_tmp_vals[test[u"parent"]][2].append(
800 test[u"latency"][ttype][u"direction1"][u"max"])
801 y_tmp_vals[test[u"parent"]][3].append(
802 test[u"latency"][ttype][u"direction2"][u"min"])
803 y_tmp_vals[test[u"parent"]][4].append(
804 test[u"latency"][ttype][u"direction2"][u"avg"])
805 y_tmp_vals[test[u"parent"]][5].append(
806 test[u"latency"][ttype][u"direction2"][u"max"])
807 except (KeyError, TypeError) as err:
808 logging.warning(repr(err))
814 nr_of_samples = list()
815 for key, val in y_tmp_vals.items():
816 name = re.sub(REGEX_NIC, u"", key.replace(u'-ndrpdr', u'').
817 replace(u'2n1l-', u''))
818 x_vals.append(name) # dir 1
819 y_vals.append(mean(val[1]) if val[1] else None)
820 y_mins.append(mean(val[0]) if val[0] else None)
821 y_maxs.append(mean(val[2]) if val[2] else None)
822 nr_of_samples.append(len(val[1]) if val[1] else 0)
823 x_vals.append(name) # dir 2
824 y_vals.append(mean(val[4]) if val[4] else None)
825 y_mins.append(mean(val[3]) if val[3] else None)
826 y_maxs.append(mean(val[5]) if val[5] else None)
827 nr_of_samples.append(len(val[3]) if val[3] else 0)
832 for idx, _ in enumerate(x_vals):
833 if not bool(int(idx % 2)):
834 direction = u"West-East"
836 direction = u"East-West"
838 f"No. of Runs: {nr_of_samples[idx]}<br>"
839 f"Test: {x_vals[idx]}<br>"
840 f"Direction: {direction}<br>"
842 if isinstance(y_maxs[idx], float):
843 hovertext += f"Max: {y_maxs[idx]:.2f}uSec<br>"
844 if isinstance(y_vals[idx], float):
845 hovertext += f"Mean: {y_vals[idx]:.2f}uSec<br>"
846 if isinstance(y_mins[idx], float):
847 hovertext += f"Min: {y_mins[idx]:.2f}uSec"
849 if isinstance(y_maxs[idx], float) and isinstance(y_vals[idx], float):
850 array = [y_maxs[idx] - y_vals[idx], ]
853 if isinstance(y_mins[idx], float) and isinstance(y_vals[idx], float):
854 arrayminus = [y_vals[idx] - y_mins[idx], ]
856 arrayminus = [None, ]
857 traces.append(plgo.Scatter(
861 legendgroup=x_vals[idx],
862 showlegend=bool(int(idx % 2)),
868 arrayminus=arrayminus,
869 color=COLORS[int(idx / 2)]
873 color=COLORS[int(idx / 2)],
878 annotations.append(dict(
885 text=u"E-W" if bool(int(idx % 2)) else u"W-E",
895 file_type = plot.get(u"output-file-type", u".html")
896 logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
897 layout = deepcopy(plot[u"layout"])
898 if layout.get(u"title", None):
899 layout[u"title"] = f"<b>Latency:</b> {layout[u'title']}"
900 layout[u"annotations"] = annotations
901 plpl = plgo.Figure(data=traces, layout=layout)
906 show_link=False, auto_open=False,
907 filename=f"{plot[u'output-file']}{file_type}"
909 except PlotlyError as err:
911 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
916 def plot_tsa_name(plot, input_data):
917 """Generate the plot(s) with algorithm:
919 specified in the specification file.
921 :param plot: Plot to generate.
922 :param input_data: Data to process.
923 :type plot: pandas.Series
924 :type input_data: InputData
928 plot_title = plot.get(u"title", u"")
930 f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
932 data = input_data.filter_tests_by_name(
933 plot, params=[u"throughput", u"parent", u"tags", u"type"])
935 logging.error(u"No data.")
938 y_vals = OrderedDict()
942 if y_vals.get(test[u"parent"], None) is None:
943 y_vals[test[u"parent"]] = {
949 if test[u"type"] not in (u"NDRPDR",):
952 if u"-pdr" in plot_title.lower():
954 elif u"-ndr" in plot_title.lower():
959 if u"1C" in test[u"tags"]:
960 y_vals[test[u"parent"]][u"1"]. \
961 append(test[u"throughput"][ttype][u"LOWER"])
962 elif u"2C" in test[u"tags"]:
963 y_vals[test[u"parent"]][u"2"]. \
964 append(test[u"throughput"][ttype][u"LOWER"])
965 elif u"4C" in test[u"tags"]:
966 y_vals[test[u"parent"]][u"4"]. \
967 append(test[u"throughput"][ttype][u"LOWER"])
968 except (KeyError, TypeError):
972 logging.warning(f"No data for the plot {plot.get(u'title', u'')}")
976 for test_name, test_vals in y_vals.items():
977 for key, test_val in test_vals.items():
979 avg_val = sum(test_val) / len(test_val)
980 y_vals[test_name][key] = [avg_val, len(test_val)]
981 ideal = avg_val / (int(key) * 1e6)
982 if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
983 y_1c_max[test_name] = ideal
989 pci_limit = plot[u"limits"][u"pci"][u"pci-g3-x8"]
990 for test_name, test_vals in y_vals.items():
992 if test_vals[u"1"][1]:
996 test_name.replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')
998 vals[name] = OrderedDict()
999 y_val_1 = test_vals[u"1"][0] / 1e6
1000 y_val_2 = test_vals[u"2"][0] / 1e6 if test_vals[u"2"][0] \
1002 y_val_4 = test_vals[u"4"][0] / 1e6 if test_vals[u"4"][0] \
1005 vals[name][u"val"] = [y_val_1, y_val_2, y_val_4]
1006 vals[name][u"rel"] = [1.0, None, None]
1007 vals[name][u"ideal"] = [
1008 y_1c_max[test_name],
1009 y_1c_max[test_name] * 2,
1010 y_1c_max[test_name] * 4
1012 vals[name][u"diff"] = [
1013 (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None
1015 vals[name][u"count"] = [
1022 val_max = max(vals[name][u"val"])
1023 except ValueError as err:
1024 logging.error(repr(err))
1027 y_max.append(val_max)
1030 vals[name][u"rel"][1] = round(y_val_2 / y_val_1, 2)
1031 vals[name][u"diff"][1] = \
1032 (y_val_2 - vals[name][u"ideal"][1]) * 100 / y_val_2
1034 vals[name][u"rel"][2] = round(y_val_4 / y_val_1, 2)
1035 vals[name][u"diff"][2] = \
1036 (y_val_4 - vals[name][u"ideal"][2]) * 100 / y_val_4
1037 except IndexError as err:
1038 logging.warning(f"No data for {test_name}")
1039 logging.warning(repr(err))
1042 if u"x520" in test_name:
1043 limit = plot[u"limits"][u"nic"][u"x520"]
1044 elif u"x710" in test_name:
1045 limit = plot[u"limits"][u"nic"][u"x710"]
1046 elif u"xxv710" in test_name:
1047 limit = plot[u"limits"][u"nic"][u"xxv710"]
1048 elif u"xl710" in test_name:
1049 limit = plot[u"limits"][u"nic"][u"xl710"]
1050 elif u"x553" in test_name:
1051 limit = plot[u"limits"][u"nic"][u"x553"]
1052 elif u"cx556a" in test_name:
1053 limit = plot[u"limits"][u"nic"][u"cx556a"]
1056 if limit > nic_limit:
1059 mul = 2 if u"ge2p" in test_name else 1
1060 if u"10ge" in test_name:
1061 limit = plot[u"limits"][u"link"][u"10ge"] * mul
1062 elif u"25ge" in test_name:
1063 limit = plot[u"limits"][u"link"][u"25ge"] * mul
1064 elif u"40ge" in test_name:
1065 limit = plot[u"limits"][u"link"][u"40ge"] * mul
1066 elif u"100ge" in test_name:
1067 limit = plot[u"limits"][u"link"][u"100ge"] * mul
1070 if limit > lnk_limit:
1074 annotations = list()
1079 threshold = 1.1 * max(y_max) # 10%
1080 except ValueError as err:
1084 traces.append(plgo.Scatter(
1086 y=[nic_limit, ] * len(x_vals),
1087 name=f"NIC: {nic_limit:.2f}Mpps",
1096 annotations.append(dict(
1103 text=f"NIC: {nic_limit:.2f}Mpps",
1111 y_max.append(nic_limit)
1114 if lnk_limit < threshold:
1115 traces.append(plgo.Scatter(
1117 y=[lnk_limit, ] * len(x_vals),
1118 name=f"Link: {lnk_limit:.2f}Mpps",
1127 annotations.append(dict(
1134 text=f"Link: {lnk_limit:.2f}Mpps",
1142 y_max.append(lnk_limit)
1145 if (pci_limit < threshold and
1146 (pci_limit < lnk_limit * 0.95 or lnk_limit > lnk_limit * 1.05)):
1147 traces.append(plgo.Scatter(
1149 y=[pci_limit, ] * len(x_vals),
1150 name=f"PCIe: {pci_limit:.2f}Mpps",
1159 annotations.append(dict(
1166 text=f"PCIe: {pci_limit:.2f}Mpps",
1174 y_max.append(pci_limit)
1176 # Perfect and measured:
1178 for name, val in vals.items():
1181 for idx in range(len(val[u"val"])):
1183 if isinstance(val[u"val"][idx], float):
1185 f"No. of Runs: {val[u'count'][idx]}<br>"
1186 f"Mean: {val[u'val'][idx]:.2f}Mpps<br>"
1188 if isinstance(val[u"diff"][idx], float):
1189 htext += f"Diff: {round(val[u'diff'][idx]):.0f}%<br>"
1190 if isinstance(val[u"rel"][idx], float):
1191 htext += f"Speedup: {val[u'rel'][idx]:.2f}"
1192 hovertext.append(htext)
1199 mode=u"lines+markers",
1208 hoverinfo=u"text+name"
1215 name=f"{name} perfect",
1223 text=[f"Perfect: {y:.2f}Mpps" for y in val[u"ideal"]],
1228 except (IndexError, ValueError, KeyError) as err:
1229 logging.warning(f"No data for {name}\n{repr(err)}")
1233 file_type = plot.get(u"output-file-type", u".html")
1234 logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
1235 layout = deepcopy(plot[u"layout"])
1236 if layout.get(u"title", None):
1237 layout[u"title"] = f"<b>Speedup Multi-core:</b> {layout[u'title']}"
1238 layout[u"yaxis"][u"range"] = [0, int(max(y_max) * 1.1)]
1239 layout[u"annotations"].extend(annotations)
1240 plpl = plgo.Figure(data=traces, layout=layout)
1247 filename=f"{plot[u'output-file']}{file_type}"
1249 except PlotlyError as err:
1251 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1256 def plot_http_server_perf_box(plot, input_data):
1257 """Generate the plot(s) with algorithm: plot_http_server_perf_box
1258 specified in the specification file.
1260 :param plot: Plot to generate.
1261 :param input_data: Data to process.
1262 :type plot: pandas.Series
1263 :type input_data: InputData
1266 # Transform the data
1268 f" Creating the data set for the {plot.get(u'type', u'')} "
1269 f"{plot.get(u'title', u'')}."
1271 data = input_data.filter_data(plot)
1273 logging.error(u"No data.")
1276 # Prepare the data for the plot
1281 if y_vals.get(test[u"name"], None) is None:
1282 y_vals[test[u"name"]] = list()
1284 y_vals[test[u"name"]].append(test[u"result"])
1285 except (KeyError, TypeError):
1286 y_vals[test[u"name"]].append(None)
1288 # Add None to the lists with missing data
1290 nr_of_samples = list()
1291 for val in y_vals.values():
1292 if len(val) > max_len:
1294 nr_of_samples.append(len(val))
1295 for val in y_vals.values():
1296 if len(val) < max_len:
1297 val.extend([None for _ in range(max_len - len(val))])
1301 df_y = pd.DataFrame(y_vals)
1303 for i, col in enumerate(df_y.columns):
1306 f"({nr_of_samples[i]:02d} " \
1307 f"run{u's' if nr_of_samples[i] > 1 else u''}) " \
1308 f"{col.lower().replace(u'-ndrpdr', u'')}"
1310 name_lst = name.split(u'-')
1313 for segment in name_lst:
1314 if (len(name) + len(segment) + 1) > 50 and split_name:
1317 name += segment + u'-'
1320 traces.append(plgo.Box(x=[str(i + 1) + u'.'] * len(df_y[col]),
1326 plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
1330 f" Writing file {plot[u'output-file']}"
1331 f"{plot[u'output-file-type']}."
1337 filename=f"{plot[u'output-file']}{plot[u'output-file-type']}"
1339 except PlotlyError as err:
1341 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1346 def plot_nf_heatmap(plot, input_data):
1347 """Generate the plot(s) with algorithm: plot_nf_heatmap
1348 specified in the specification file.
1350 :param plot: Plot to generate.
1351 :param input_data: Data to process.
1352 :type plot: pandas.Series
1353 :type input_data: InputData
1356 regex_cn = re.compile(r'^(\d*)R(\d*)C$')
1357 regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
1359 r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$')
1362 # Transform the data
1364 f" Creating the data set for the {plot.get(u'type', u'')} "
1365 f"{plot.get(u'title', u'')}."
1367 data = input_data.filter_data(plot, continue_on_error=True)
1368 if data is None or data.empty:
1369 logging.error(u"No data.")
1375 for tag in test[u"tags"]:
1376 groups = re.search(regex_cn, tag)
1378 chain = str(groups.group(1))
1379 node = str(groups.group(2))
1383 groups = re.search(regex_test_name, test[u"name"])
1384 if groups and len(groups.groups()) == 3:
1386 f"{str(groups.group(1))}-"
1387 f"{str(groups.group(2))}-"
1388 f"{str(groups.group(3))}"
1392 if vals.get(chain, None) is None:
1393 vals[chain] = dict()
1394 if vals[chain].get(node, None) is None:
1395 vals[chain][node] = dict(
1403 if plot[u"include-tests"] == u"MRR":
1404 result = test[u"result"][u"receive-rate"]
1405 elif plot[u"include-tests"] == u"PDR":
1406 result = test[u"throughput"][u"PDR"][u"LOWER"]
1407 elif plot[u"include-tests"] == u"NDR":
1408 result = test[u"throughput"][u"NDR"][u"LOWER"]
1415 vals[chain][node][u"vals"].append(result)
1418 logging.error(u"No data.")
1424 txt_chains.append(key_c)
1425 for key_n in vals[key_c].keys():
1426 txt_nodes.append(key_n)
1427 if vals[key_c][key_n][u"vals"]:
1428 vals[key_c][key_n][u"nr"] = len(vals[key_c][key_n][u"vals"])
1429 vals[key_c][key_n][u"mean"] = \
1430 round(mean(vals[key_c][key_n][u"vals"]) / 1000000, 1)
1431 vals[key_c][key_n][u"stdev"] = \
1432 round(stdev(vals[key_c][key_n][u"vals"]) / 1000000, 1)
1433 txt_nodes = list(set(txt_nodes))
1435 def sort_by_int(value):
1436 """Makes possible to sort a list of strings which represent integers.
1438 :param value: Integer as a string.
1440 :returns: Integer representation of input parameter 'value'.
1445 txt_chains = sorted(txt_chains, key=sort_by_int)
1446 txt_nodes = sorted(txt_nodes, key=sort_by_int)
1448 chains = [i + 1 for i in range(len(txt_chains))]
1449 nodes = [i + 1 for i in range(len(txt_nodes))]
1451 data = [list() for _ in range(len(chains))]
1452 for chain in chains:
1455 val = vals[txt_chains[chain - 1]][txt_nodes[node - 1]][u"mean"]
1456 except (KeyError, IndexError):
1458 data[chain - 1].append(val)
1461 my_green = [[0.0, u"rgb(235, 249, 242)"],
1462 [1.0, u"rgb(45, 134, 89)"]]
1464 my_blue = [[0.0, u"rgb(236, 242, 248)"],
1465 [1.0, u"rgb(57, 115, 172)"]]
1467 my_grey = [[0.0, u"rgb(230, 230, 230)"],
1468 [1.0, u"rgb(102, 102, 102)"]]
1471 annotations = list()
1473 text = (u"Test: {name}<br>"
1478 for chain, _ in enumerate(txt_chains):
1480 for node, _ in enumerate(txt_nodes):
1481 if data[chain][node] is not None:
1490 text=str(data[chain][node]),
1498 hover_line.append(text.format(
1499 name=vals[txt_chains[chain]][txt_nodes[node]][u"name"],
1500 nr=vals[txt_chains[chain]][txt_nodes[node]][u"nr"],
1501 val=data[chain][node],
1502 stdev=vals[txt_chains[chain]][txt_nodes[node]][u"stdev"]))
1503 hovertext.append(hover_line)
1511 title=plot.get(u"z-axis", u""),
1525 colorscale=my_green,
1531 for idx, item in enumerate(txt_nodes):
1549 for idx, item in enumerate(txt_chains):
1576 text=plot.get(u"x-axis", u""),
1593 text=plot.get(u"y-axis", u""),
1602 updatemenus = list([
1613 u"colorscale": [my_green, ],
1614 u"reversescale": False
1623 u"colorscale": [my_blue, ],
1624 u"reversescale": False
1633 u"colorscale": [my_grey, ],
1634 u"reversescale": False
1645 layout = deepcopy(plot[u"layout"])
1646 except KeyError as err:
1647 logging.error(f"Finished with error: No layout defined\n{repr(err)}")
1650 layout[u"annotations"] = annotations
1651 layout[u'updatemenus'] = updatemenus
1655 plpl = plgo.Figure(data=traces, layout=layout)
1658 logging.info(f" Writing file {plot[u'output-file']}.html")
1663 filename=f"{plot[u'output-file']}.html"
1665 except PlotlyError as err:
1667 f" Finished with error: {repr(err)}".replace(u"\n", u" ")