1 # Copyright (c) 2021 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 datetime import datetime
23 from copy import deepcopy
29 import plotly.offline as ploff
30 import plotly.graph_objs as plgo
31 import plotly.exceptions as plerr
33 from plotly.exceptions import PlotlyError
35 from pal_utils import mean, stdev
64 REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)-')
66 # This value depends on latency stream rate (9001 pps) and duration (5s).
67 # Keep it slightly higher to ensure rounding errors to not remove tick mark.
68 PERCENTILE_MAX = 99.999501
71 def generate_plots(spec, data):
72 """Generate all plots specified in the specification file.
74 :param spec: Specification read from the specification file.
75 :param data: Data to process.
76 :type spec: Specification
81 u"plot_nf_reconf_box_name": plot_nf_reconf_box_name,
82 u"plot_perf_box_name": plot_perf_box_name,
83 u"plot_tsa_name": plot_tsa_name,
84 u"plot_http_server_perf_box": plot_http_server_perf_box,
85 u"plot_nf_heatmap": plot_nf_heatmap,
86 u"plot_hdrh_lat_by_percentile": plot_hdrh_lat_by_percentile,
87 u"plot_hdrh_lat_by_percentile_x_log": plot_hdrh_lat_by_percentile_x_log,
88 u"plot_mrr_box_name": plot_mrr_box_name,
89 u"plot_ndrpdr_box_name": plot_ndrpdr_box_name,
90 u"plot_statistics": plot_statistics
93 logging.info(u"Generating the plots ...")
94 for index, plot in enumerate(spec.plots):
96 logging.info(f" Plot nr {index + 1}: {plot.get(u'title', u'')}")
97 plot[u"limits"] = spec.environment[u"limits"]
98 generator[plot[u"algorithm"]](plot, data)
99 logging.info(u" Done.")
100 except NameError as err:
102 f"Probably algorithm {plot[u'algorithm']} is not defined: "
105 logging.info(u"Done.")
108 def plot_statistics(plot, input_data):
109 """Generate the plot(s) with algorithm: plot_statistics
110 specified in the specification file.
112 :param plot: Plot to generate.
113 :param input_data: Data to process.
114 :type plot: pandas.Series
115 :type input_data: InputData
121 data_y_duration = list()
125 u"passed: {passed}<br>"
126 u"failed: {failed}<br>"
127 u"duration: {duration}<br>"
128 u"{sut}-ref: {build}<br>"
129 u"csit-ref: {test}-{period}-build-{build_nr}<br>"
130 u"testbed: {testbed}"
132 for job, builds in plot[u"data"].items():
133 for build_nr in builds:
135 meta = input_data.metadata(job, str(build_nr))
136 generated = meta[u"generated"]
141 int(generated[9:11]),
144 d_y_pass = meta[u"tests_passed"]
145 d_y_fail = meta[u"tests_failed"]
146 minutes = meta[u"elapsedtime"] // 60000
147 duration = f"{(minutes // 60):02d}:{(minutes % 60):02d}"
148 version = meta.get(u"version", u"")
149 except (KeyError, IndexError, ValueError, AttributeError):
152 data_y_pass.append(d_y_pass)
153 data_y_fail.append(d_y_fail)
154 data_y_duration.append(minutes)
163 hover_text.append(hover_str.format(
170 test=u"mrr" if u"mrr" in job else u"ndrpdr",
171 period=u"daily" if u"daily" in job else u"weekly",
173 testbed=meta.get(u"testbed", u"")
200 name_file = f"{plot[u'output-file']}.html"
202 logging.info(f" Writing the file {name_file}")
203 plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
204 tickvals = [0, (max(data_y_duration) // 60) * 60]
205 step = tickvals[1] / 5
207 tickvals.append(int(tickvals[0] + step * (i + 1)))
210 title=u"Duration [hh:mm]",
217 ticktext=[f"{(val // 60):02d}:{(val % 60):02d}" for val in tickvals]
220 plpl.update_layout(barmode=u"stack")
228 except plerr.PlotlyEmptyDataError:
229 logging.warning(u"No data for the plot. Skipped.")
232 def plot_hdrh_lat_by_percentile(plot, input_data):
233 """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile
234 specified in the specification file.
236 :param plot: Plot to generate.
237 :param input_data: Data to process.
238 :type plot: pandas.Series
239 :type input_data: InputData
244 f" Creating the data set for the {plot.get(u'type', u'')} "
245 f"{plot.get(u'title', u'')}."
247 if plot.get(u"include", None):
248 data = input_data.filter_tests_by_name(
250 params=[u"name", u"latency", u"parent", u"tags", u"type"]
252 elif plot.get(u"filter", None):
253 data = input_data.filter_data(
255 params=[u"name", u"latency", u"parent", u"tags", u"type"],
256 continue_on_error=True
259 job = list(plot[u"data"].keys())[0]
260 build = str(plot[u"data"][job][0])
261 data = input_data.tests(job, build)
263 if data is None or len(data) == 0:
264 logging.error(u"No data.")
268 u"LAT0": u"No-load.",
269 u"PDR10": u"Low-load, 10% PDR.",
270 u"PDR50": u"Mid-load, 50% PDR.",
271 u"PDR90": u"High-load, 90% PDR.",
272 u"PDR": u"Full-load, 100% PDR.",
273 u"NDR10": u"Low-load, 10% NDR.",
274 u"NDR50": u"Mid-load, 50% NDR.",
275 u"NDR90": u"High-load, 90% NDR.",
276 u"NDR": u"Full-load, 100% NDR."
286 file_links = plot.get(u"output-file-links", None)
287 target_links = plot.get(u"target-links", None)
291 if test[u"type"] not in (u"NDRPDR",):
292 logging.warning(f"Invalid test type: {test[u'type']}")
294 name = re.sub(REGEX_NIC, u"", test[u"parent"].
295 replace(u'-ndrpdr', u'').replace(u'2n1l-', u''))
297 nic = re.search(REGEX_NIC, test[u"parent"]).group(1)
298 except (IndexError, AttributeError, KeyError, ValueError):
300 name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'')
302 logging.info(f" Generating the graph: {name_link}")
305 layout = deepcopy(plot[u"layout"])
307 for color, graph in enumerate(graphs):
308 for idx, direction in enumerate((u"direction1", u"direction2")):
314 decoded = hdrh.histogram.HdrHistogram.decode(
315 test[u"latency"][graph][direction][u"hdrh"]
317 except hdrh.codec.HdrLengthException:
319 f"No data for direction {(u'W-E', u'E-W')[idx % 2]}"
323 for item in decoded.get_recorded_iterator():
324 percentile = item.percentile_level_iterated_to
325 xaxis.append(previous_x)
326 yaxis.append(item.value_iterated_to)
328 f"<b>{desc[graph]}</b><br>"
329 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
331 f"{previous_x:.5f}-{percentile:.5f}%<br>"
332 f"Latency: {item.value_iterated_to}uSec"
334 xaxis.append(percentile)
335 yaxis.append(item.value_iterated_to)
337 f"<b>{desc[graph]}</b><br>"
338 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
340 f"{previous_x:.5f}-{percentile:.5f}%<br>"
341 f"Latency: {item.value_iterated_to}uSec"
343 previous_x = percentile
350 legendgroup=desc[graph],
351 showlegend=bool(idx),
355 width=1 if idx % 2 else 2
362 layout[u"title"][u"text"] = f"<b>Latency:</b> {name}"
363 fig.update_layout(layout)
366 file_name = f"{plot[u'output-file']}-{name_link}.html"
367 logging.info(f" Writing file {file_name}")
371 ploff.plot(fig, show_link=False, auto_open=False,
373 # Add link to the file:
374 if file_links and target_links:
375 with open(file_links, u"a") as file_handler:
378 f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n"
380 except FileNotFoundError as err:
382 f"Not possible to write the link to the file "
383 f"{file_links}\n{err}"
385 except PlotlyError as err:
386 logging.error(f" Finished with error: {repr(err)}")
388 except hdrh.codec.HdrLengthException as err:
389 logging.warning(repr(err))
392 except (ValueError, KeyError) as err:
393 logging.warning(repr(err))
397 def plot_hdrh_lat_by_percentile_x_log(plot, input_data):
398 """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile_x_log
399 specified in the specification file.
401 :param plot: Plot to generate.
402 :param input_data: Data to process.
403 :type plot: pandas.Series
404 :type input_data: InputData
409 f" Creating the data set for the {plot.get(u'type', u'')} "
410 f"{plot.get(u'title', u'')}."
412 if plot.get(u"include", None):
413 data = input_data.filter_tests_by_name(
415 params=[u"name", u"latency", u"parent", u"tags", u"type"]
417 elif plot.get(u"filter", None):
418 data = input_data.filter_data(
420 params=[u"name", u"latency", u"parent", u"tags", u"type"],
421 continue_on_error=True
424 job = list(plot[u"data"].keys())[0]
425 build = str(plot[u"data"][job][0])
426 data = input_data.tests(job, build)
428 if data is None or len(data) == 0:
429 logging.error(u"No data.")
433 u"LAT0": u"No-load.",
434 u"PDR10": u"Low-load, 10% PDR.",
435 u"PDR50": u"Mid-load, 50% PDR.",
436 u"PDR90": u"High-load, 90% PDR.",
437 u"PDR": u"Full-load, 100% PDR.",
438 u"NDR10": u"Low-load, 10% NDR.",
439 u"NDR50": u"Mid-load, 50% NDR.",
440 u"NDR90": u"High-load, 90% NDR.",
441 u"NDR": u"Full-load, 100% NDR."
451 file_links = plot.get(u"output-file-links", None)
452 target_links = plot.get(u"target-links", None)
456 if test[u"type"] not in (u"NDRPDR",):
457 logging.warning(f"Invalid test type: {test[u'type']}")
459 name = re.sub(REGEX_NIC, u"", test[u"parent"].
460 replace(u'-ndrpdr', u'').replace(u'2n1l-', u''))
462 nic = re.search(REGEX_NIC, test[u"parent"]).group(1)
463 except (IndexError, AttributeError, KeyError, ValueError):
465 name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'')
467 logging.info(f" Generating the graph: {name_link}")
470 layout = deepcopy(plot[u"layout"])
472 for color, graph in enumerate(graphs):
473 for idx, direction in enumerate((u"direction1", u"direction2")):
480 decoded = hdrh.histogram.HdrHistogram.decode(
481 test[u"latency"][graph][direction][u"hdrh"]
483 except (hdrh.codec.HdrLengthException, TypeError):
485 f"No data for direction {(u'W-E', u'E-W')[idx % 2]}"
489 for item in decoded.get_recorded_iterator():
490 # The real value is "percentile".
491 # For 100%, we cut that down to "x_perc" to avoid
493 percentile = item.percentile_level_iterated_to
494 x_perc = min(percentile, PERCENTILE_MAX)
495 xaxis.append(previous_x)
496 yaxis.append(item.value_iterated_to)
498 f"<b>{desc[graph]}</b><br>"
499 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
500 f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
501 f"Latency: {item.value_iterated_to}uSec"
503 next_x = 100.0 / (100.0 - x_perc)
505 yaxis.append(item.value_iterated_to)
507 f"<b>{desc[graph]}</b><br>"
508 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
509 f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
510 f"Latency: {item.value_iterated_to}uSec"
513 prev_perc = percentile
520 legendgroup=desc[graph],
521 showlegend=not(bool(idx)),
525 width=1 if idx % 2 else 2
532 layout[u"title"][u"text"] = f"<b>Latency:</b> {name}"
533 x_max = log(100.0 / (100.0 - PERCENTILE_MAX), 10)
534 layout[u"xaxis"][u"range"] = [0, x_max]
535 fig.update_layout(layout)
538 file_name = f"{plot[u'output-file']}-{name_link}.html"
539 logging.info(f" Writing file {file_name}")
543 ploff.plot(fig, show_link=False, auto_open=False,
545 # Add link to the file:
546 if file_links and target_links:
547 with open(file_links, u"a") as file_handler:
550 f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n"
552 except FileNotFoundError as err:
554 f"Not possible to write the link to the file "
555 f"{file_links}\n{err}"
557 except PlotlyError as err:
558 logging.error(f" Finished with error: {repr(err)}")
560 except hdrh.codec.HdrLengthException as err:
561 logging.warning(repr(err))
564 except (ValueError, KeyError) as err:
565 logging.warning(repr(err))
569 def plot_nf_reconf_box_name(plot, input_data):
570 """Generate the plot(s) with algorithm: plot_nf_reconf_box_name
571 specified in the specification file.
573 :param plot: Plot to generate.
574 :param input_data: Data to process.
575 :type plot: pandas.Series
576 :type input_data: InputData
581 f" Creating the data set for the {plot.get(u'type', u'')} "
582 f"{plot.get(u'title', u'')}."
584 data = input_data.filter_tests_by_name(
585 plot, params=[u"result", u"parent", u"tags", u"type"]
588 logging.error(u"No data.")
591 for core in plot.get(u"core", tuple()):
592 # Prepare the data for the plot
593 y_vals = OrderedDict()
595 for item in plot.get(u"include", tuple()):
596 reg_ex = re.compile(str(item.format(core=core)).lower())
599 for test_id, test in build.iteritems():
600 if not re.match(reg_ex, str(test_id).lower()):
602 if y_vals.get(test[u"parent"], None) is None:
603 y_vals[test[u"parent"]] = list()
604 loss[test[u"parent"]] = list()
606 y_vals[test[u"parent"]].append(
607 test[u"result"][u"time"]
609 loss[test[u"parent"]].append(
610 test[u"result"][u"loss"]
612 except (KeyError, TypeError):
613 y_vals[test[u"parent"]].append(None)
615 # Add None to the lists with missing data
617 nr_of_samples = list()
618 for val in y_vals.values():
619 if len(val) > max_len:
621 nr_of_samples.append(len(val))
622 for val in y_vals.values():
623 if len(val) < max_len:
624 val.extend([None for _ in range(max_len - len(val))])
628 df_y = pd.DataFrame(y_vals)
630 for i, col in enumerate(df_y.columns):
633 col.lower().replace(u'-reconf', u'').replace(u'2n1l-', u'').
634 replace(u'2n-', u'').replace(u'-testpmd', u'')
636 traces.append(plgo.Box(
637 x=[str(i + 1) + u'.'] * len(df_y[col]),
641 f"({nr_of_samples[i]:02d} "
642 f"run{u's' if nr_of_samples[i] > 1 else u''}, "
643 f"packets lost average: {mean(loss[col]):.1f}) "
644 f"{u'-'.join(tst_name.split(u'-')[2:])}"
650 layout = deepcopy(plot[u"layout"])
651 layout[u"title"] = f"<b>Time Lost:</b> {layout[u'title']}"
652 layout[u"yaxis"][u"title"] = u"<b>Effective Blocked Time [s]</b>"
653 layout[u"legend"][u"font"][u"size"] = 14
654 layout[u"yaxis"].pop(u"range")
655 plpl = plgo.Figure(data=traces, layout=layout)
658 file_name = f"{plot[u'output-file'].format(core=core)}.html"
659 logging.info(f" Writing file {file_name}")
666 except PlotlyError as err:
668 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
672 def plot_perf_box_name(plot, input_data):
673 """Generate the plot(s) with algorithm: plot_perf_box_name
674 specified in the specification file.
676 Use only for soak and hoststack tests.
678 :param plot: Plot to generate.
679 :param input_data: Data to process.
680 :type plot: pandas.Series
681 :type input_data: InputData
686 f" Creating data set for the {plot.get(u'type', u'')} "
687 f"{plot.get(u'title', u'')}."
689 data = input_data.filter_tests_by_name(
691 params=[u"throughput", u"gbps", u"result", u"parent", u"tags", u"type"])
693 logging.error(u"No data.")
696 # Prepare the data for the plot
697 y_vals = OrderedDict()
700 for item in plot.get(u"include", tuple()):
701 reg_ex = re.compile(str(item).lower())
704 for test_id, test in build.iteritems():
705 if not re.match(reg_ex, str(test_id).lower()):
707 if y_vals.get(test[u"parent"], None) is None:
708 y_vals[test[u"parent"]] = list()
710 if test[u"type"] in (u"SOAK",):
711 y_vals[test[u"parent"]]. \
712 append(test[u"throughput"][u"LOWER"])
715 elif test[u"type"] in (u"HOSTSTACK",):
716 if u"LDPRELOAD" in test[u"tags"]:
717 y_vals[test[u"parent"]].append(
719 test[u"result"][u"bits_per_second"]
722 elif u"VPPECHO" in test[u"tags"]:
723 y_vals[test[u"parent"]].append(
725 test[u"result"][u"client"][u"tx_data"]
728 test[u"result"][u"client"][u"time"]
731 test[u"result"][u"server"][u"time"])
734 test_type = u"HOSTSTACK"
736 elif test[u"type"] in (u"LDP_NGINX",):
737 if u"TCP_CPS" in test[u"tags"]:
738 test_type = u"VSAP_CPS"
739 y_vals[test[u"parent"]].append(
740 test[u"result"][u"cps"]
742 elif u"TCP_RPS" in test[u"tags"]:
743 test_type = u"VSAP_RPS"
744 y_vals[test[u"parent"]].append(
745 test[u"result"][u"rps"]
752 except (KeyError, TypeError):
753 y_vals[test[u"parent"]].append(None)
755 # Add None to the lists with missing data
757 nr_of_samples = list()
758 for val in y_vals.values():
759 if len(val) > max_len:
761 nr_of_samples.append(len(val))
762 for val in y_vals.values():
763 if len(val) < max_len:
764 val.extend([None for _ in range(max_len - len(val))])
768 df_y = pd.DataFrame(y_vals)
771 for i, col in enumerate(df_y.columns):
772 tst_name = re.sub(REGEX_NIC, u"",
773 col.lower().replace(u'-ndrpdr', u'').
774 replace(u'2n1l-', u''))
775 if test_type in (u"VSAP_CPS", u"VSAP_RPS"):
776 data_y = [y if y else None for y in df_y[col]]
778 data_y = [y / 1e6 if y else None for y in df_y[col]]
783 f"({nr_of_samples[i]:02d} "
784 f"run{u's' if nr_of_samples[i] > 1 else u''}) "
789 if test_type in (u"SOAK", ):
790 kwargs[u"boxpoints"] = u"all"
791 kwargs[u"jitter"] = 0.3
793 traces.append(plgo.Box(**kwargs))
796 val_max = max(df_y[col])
798 y_max.append(int(val_max / 1e6))
799 except (ValueError, TypeError) as err:
800 logging.error(repr(err))
805 layout = deepcopy(plot[u"layout"])
806 layout[u"xaxis"][u"tickvals"] = [i for i in range(len(y_vals))]
807 layout[u"xaxis"][u"ticktext"] = [str(i + 1) for i in range(len(y_vals))]
808 if layout.get(u"title", None):
809 if test_type in (u"HOSTSTACK", ):
810 layout[u"title"] = f"<b>Bandwidth:</b> {layout[u'title']}"
811 elif test_type == u"VSAP_CPS":
812 layout[u"title"] = f"<b>CPS:</b> {layout[u'title']}"
813 layout[u"yaxis"][u"title"] = u"<b>Connection Rate [cps]</b>"
814 elif test_type == u"VSAP_RPS":
815 layout[u"title"] = f"<b>RPS:</b> {layout[u'title']}"
816 layout[u"yaxis"][u"title"] = u"<b>Connection Rate [rps]</b>"
818 layout[u"title"] = f"<b>Tput:</b> {layout[u'title']}"
819 if y_max and max(y_max) > 1:
820 layout[u"yaxis"][u"range"] = [0, max(y_max) + 2]
821 plpl = plgo.Figure(data=traces, layout=layout)
824 logging.info(f" Writing file {plot[u'output-file']}.html.")
829 filename=f"{plot[u'output-file']}.html"
831 except PlotlyError as err:
833 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
838 def plot_ndrpdr_box_name(plot, input_data):
839 """Generate the plot(s) with algorithm: plot_ndrpdr_box_name
840 specified in the specification file.
842 :param plot: Plot to generate.
843 :param input_data: Data to process.
844 :type plot: pandas.Series
845 :type input_data: InputData
850 f" Creating data set for the {plot.get(u'type', u'')} "
851 f"{plot.get(u'title', u'')}."
853 data = input_data.filter_tests_by_name(
855 params=[u"throughput", u"gbps", u"parent", u"tags", u"type"]
858 logging.error(u"No data.")
861 if u"-gbps" in plot.get(u"title", u"").lower():
865 value = u"throughput"
870 for ttype in plot.get(u"test-type", (u"ndr", u"pdr")):
871 for core in plot.get(u"core", tuple()):
872 # Prepare the data for the plot
874 data_y = OrderedDict()
877 for item in plot.get(u"include", tuple()):
878 reg_ex = re.compile(str(item.format(core=core)).lower())
881 for test_id, test in build.iteritems():
882 if not re.match(reg_ex, str(test_id).lower()):
884 if data_y.get(test[u"parent"], None) is None:
885 data_y[test[u"parent"]] = list()
886 test_type = test[u"type"]
890 data_y[test[u"parent"]].append(
891 test[value][ttype.upper()][u"LOWER"] *
894 except (KeyError, TypeError):
899 for idx, (key, vals) in enumerate(data_y.items()):
901 REGEX_NIC, u'', key.lower().replace(u'-ndrpdr', u'').
902 replace(u'2n1l-', u'')
905 y=[y / 1e6 if y else None for y in vals],
910 f"{u's' if len(vals) > 1 else u''}) "
915 box_points = plot.get(u"boxpoints", None)
916 if box_points and box_points in \
917 (u"all", u"outliers", u"suspectedoutliers", False):
918 kwargs[u"boxpoints"] = box_points
919 kwargs[u"jitter"] = 0.3
920 traces.append(plgo.Box(**kwargs))
922 data_y_max.append(max(vals))
923 except ValueError as err:
924 logging.warning(f"No values to use.\n{err!r}")
927 layout = deepcopy(plot[u"layout"])
928 layout[u"xaxis"][u"tickvals"] = [i for i in range(len(data_y))]
929 layout[u"xaxis"][u"ticktext"] = \
930 [str(i + 1) for i in range(len(data_y))]
931 if layout.get(u"title", None):
933 layout[u'title'].format(core=core, test_type=ttype)
934 if test_type in (u"CPS", ):
935 layout[u"title"] = f"<b>CPS:</b> {layout[u'title']}"
938 f"<b>Tput:</b> {layout[u'title']}"
940 layout[u"yaxis"][u"range"] = [0, max(data_y_max) / 1e6 + 1]
941 plpl = plgo.Figure(data=traces, layout=layout)
945 f"{plot[u'output-file'].format(core=core, test_type=ttype)}"
948 logging.info(f" Writing file {file_name}")
955 except PlotlyError as err:
957 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
961 def plot_mrr_box_name(plot, input_data):
962 """Generate the plot(s) with algorithm: plot_mrr_box_name
963 specified in the specification file.
965 :param plot: Plot to generate.
966 :param input_data: Data to process.
967 :type plot: pandas.Series
968 :type input_data: InputData
973 f" Creating data set for the {plot.get(u'type', u'')} "
974 f"{plot.get(u'title', u'')}."
976 data = input_data.filter_tests_by_name(
978 params=[u"result", u"parent", u"tags", u"type"]
981 logging.error(u"No data.")
984 for core in plot.get(u"core", tuple()):
985 # Prepare the data for the plot
991 for item in plot.get(u"include", tuple()):
992 reg_ex = re.compile(str(item.format(core=core)).lower())
995 for test_id, test in build.iteritems():
996 if not re.match(reg_ex, str(test_id).lower()):
1001 REGEX_NIC, u'', test[u'parent'].lower().
1002 replace(u'-mrr', u'').replace(u'2n1l-', u'')
1004 data_y.append(test[u"result"][u"samples"])
1007 f"({len(data_y[-1]):02d} "
1008 f"run{u's' if len(data_y[-1]) > 1 else u''}) "
1011 data_y_max.append(max(data_y[-1]))
1013 except (KeyError, TypeError):
1018 for idx, x_item in enumerate(data_x):
1021 name=data_names[idx],
1024 box_points = plot.get(u"boxpoints", None)
1025 if box_points and box_points in \
1026 (u"all", u"outliers", u"suspectedoutliers", False):
1027 kwargs[u"boxpoints"] = box_points
1028 kwargs["jitter"] = 0.3
1029 traces.append(plgo.Box(**kwargs))
1033 layout = deepcopy(plot[u"layout"])
1034 layout[u"xaxis"][u"tickvals"] = [i for i in range(len(data_y))]
1035 layout[u"xaxis"][u"ticktext"] = \
1036 [str(i + 1) for i in range(len(data_y))]
1037 if layout.get(u"title", None):
1038 layout[u"title"] = (
1039 f"<b>Tput:</b> {layout[u'title'].format(core=core)}"
1042 layout[u"yaxis"][u"range"] = [0, max(data_y_max) + 1]
1043 plpl = plgo.Figure(data=traces, layout=layout)
1046 file_name = f"{plot[u'output-file'].format(core=core)}.html"
1047 logging.info(f" Writing file {file_name}")
1054 except PlotlyError as err:
1056 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1060 def plot_tsa_name(plot, input_data):
1061 """Generate the plot(s) with algorithm:
1063 specified in the specification file.
1065 :param plot: Plot to generate.
1066 :param input_data: Data to process.
1067 :type plot: pandas.Series
1068 :type input_data: InputData
1071 # Transform the data
1072 plot_title = plot.get(u"title", u"")
1074 f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
1076 data = input_data.filter_tests_by_name(
1078 params=[u"throughput", u"gbps", u"parent", u"tags", u"type"]
1081 logging.error(u"No data.")
1084 plot_title = plot_title.lower()
1086 if u"-gbps" in plot_title:
1091 value = u"throughput"
1095 for ttype in plot.get(u"test-type", (u"ndr", u"pdr")):
1096 y_vals = OrderedDict()
1097 for item in plot.get(u"include", tuple()):
1098 reg_ex = re.compile(str(item).lower())
1101 for test_id, test in build.iteritems():
1102 if re.match(reg_ex, str(test_id).lower()):
1103 if y_vals.get(test[u"parent"], None) is None:
1104 y_vals[test[u"parent"]] = {
1110 if test[u"type"] not in (u"NDRPDR", u"CPS"):
1113 if u"1C" in test[u"tags"]:
1114 y_vals[test[u"parent"]][u"1"].append(
1115 test[value][ttype.upper()][u"LOWER"] *
1118 elif u"2C" in test[u"tags"]:
1119 y_vals[test[u"parent"]][u"2"].append(
1120 test[value][ttype.upper()][u"LOWER"] *
1123 elif u"4C" in test[u"tags"]:
1124 y_vals[test[u"parent"]][u"4"].append(
1125 test[value][ttype.upper()][u"LOWER"] *
1128 except (KeyError, TypeError):
1132 logging.warning(f"No data for the plot {plot.get(u'title', u'')}")
1136 for test_name, test_vals in y_vals.items():
1137 for key, test_val in test_vals.items():
1139 avg_val = sum(test_val) / len(test_val)
1140 y_vals[test_name][key] = [avg_val, len(test_val)]
1141 ideal = avg_val / (int(key) * 1e6)
1142 if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
1143 y_1c_max[test_name] = ideal
1145 vals = OrderedDict()
1150 for test_name, test_vals in y_vals.items():
1152 if test_vals[u"1"][1]:
1156 test_name.replace(u'-ndrpdr', u'').
1157 replace(u'2n1l-', u'')
1159 vals[name] = OrderedDict()
1160 y_val_1 = test_vals[u"1"][0] / 1e6
1161 y_val_2 = test_vals[u"2"][0] / 1e6 if test_vals[u"2"][0] \
1163 y_val_4 = test_vals[u"4"][0] / 1e6 if test_vals[u"4"][0] \
1166 vals[name][u"val"] = [y_val_1, y_val_2, y_val_4]
1167 vals[name][u"rel"] = [1.0, None, None]
1168 vals[name][u"ideal"] = [
1169 y_1c_max[test_name],
1170 y_1c_max[test_name] * 2,
1171 y_1c_max[test_name] * 4
1173 vals[name][u"diff"] = [
1174 (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1,
1178 vals[name][u"count"] = [
1185 val_max = max(vals[name][u"val"])
1186 except ValueError as err:
1187 logging.error(repr(err))
1190 y_max.append(val_max)
1193 vals[name][u"rel"][1] = round(y_val_2 / y_val_1, 2)
1194 vals[name][u"diff"][1] = \
1195 (y_val_2 - vals[name][u"ideal"][1]) * 100 / y_val_2
1197 vals[name][u"rel"][2] = round(y_val_4 / y_val_1, 2)
1198 vals[name][u"diff"][2] = \
1199 (y_val_4 - vals[name][u"ideal"][2]) * 100 / y_val_4
1200 except IndexError as err:
1201 logging.warning(f"No data for {test_name}")
1202 logging.warning(repr(err))
1205 if u"x520" in test_name:
1206 limit = plot[u"limits"][u"nic"][u"x520"]
1207 elif u"x710" in test_name:
1208 limit = plot[u"limits"][u"nic"][u"x710"]
1209 elif u"xxv710" in test_name:
1210 limit = plot[u"limits"][u"nic"][u"xxv710"]
1211 elif u"xl710" in test_name:
1212 limit = plot[u"limits"][u"nic"][u"xl710"]
1213 elif u"x553" in test_name:
1214 limit = plot[u"limits"][u"nic"][u"x553"]
1215 elif u"cx556a" in test_name:
1216 limit = plot[u"limits"][u"nic"][u"cx556a"]
1217 elif u"e810cq" in test_name:
1218 limit = plot[u"limits"][u"nic"][u"e810cq"]
1221 if limit > nic_limit:
1224 mul = 2 if u"ge2p" in test_name else 1
1225 if u"10ge" in test_name:
1226 limit = plot[u"limits"][u"link"][u"10ge"] * mul
1227 elif u"25ge" in test_name:
1228 limit = plot[u"limits"][u"link"][u"25ge"] * mul
1229 elif u"40ge" in test_name:
1230 limit = plot[u"limits"][u"link"][u"40ge"] * mul
1231 elif u"100ge" in test_name:
1232 limit = plot[u"limits"][u"link"][u"100ge"] * mul
1235 if limit > lnk_limit:
1238 if u"cx556a" in test_name:
1239 limit = plot[u"limits"][u"pci"][u"pci-g3-x8"]
1241 limit = plot[u"limits"][u"pci"][u"pci-g3-x16"]
1242 if limit > pci_limit:
1246 annotations = list()
1250 if u"-gbps" not in plot_title and u"-cps-" not in plot_title:
1254 min_limit = min((nic_limit, lnk_limit, pci_limit))
1255 if nic_limit == min_limit:
1256 traces.append(plgo.Scatter(
1258 y=[nic_limit, ] * len(x_vals),
1259 name=f"NIC: {nic_limit:.2f}Mpps",
1268 annotations.append(dict(
1275 text=f"NIC: {nic_limit:.2f}Mpps",
1283 y_max.append(nic_limit)
1284 elif lnk_limit == min_limit:
1285 traces.append(plgo.Scatter(
1287 y=[lnk_limit, ] * len(x_vals),
1288 name=f"Link: {lnk_limit:.2f}Mpps",
1297 annotations.append(dict(
1304 text=f"Link: {lnk_limit:.2f}Mpps",
1312 y_max.append(lnk_limit)
1313 elif pci_limit == min_limit:
1314 traces.append(plgo.Scatter(
1316 y=[pci_limit, ] * len(x_vals),
1317 name=f"PCIe: {pci_limit:.2f}Mpps",
1326 annotations.append(dict(
1333 text=f"PCIe: {pci_limit:.2f}Mpps",
1341 y_max.append(pci_limit)
1343 # Perfect and measured:
1345 for name, val in vals.items():
1348 for idx in range(len(val[u"val"])):
1350 if isinstance(val[u"val"][idx], float):
1352 f"No. of Runs: {val[u'count'][idx]}<br>"
1353 f"Mean: {val[u'val'][idx]:.2f}{h_unit}<br>"
1355 if isinstance(val[u"diff"][idx], float):
1356 htext += f"Diff: {round(val[u'diff'][idx]):.0f}%<br>"
1357 if isinstance(val[u"rel"][idx], float):
1358 htext += f"Speedup: {val[u'rel'][idx]:.2f}"
1359 hovertext.append(htext)
1366 mode=u"lines+markers",
1375 hoverinfo=u"text+name"
1382 name=f"{name} perfect",
1390 text=[f"Perfect: {y:.2f}Mpps" for y in val[u"ideal"]],
1395 except (IndexError, ValueError, KeyError) as err:
1396 logging.warning(f"No data for {name}\n{repr(err)}")
1400 file_name = f"{plot[u'output-file'].format(test_type=ttype)}.html"
1401 logging.info(f" Writing file {file_name}")
1402 layout = deepcopy(plot[u"layout"])
1403 if layout.get(u"title", None):
1404 layout[u"title"] = (
1405 f"<b>Speedup Multi-core:</b> "
1406 f"{layout[u'title'].format(test_type=ttype)}"
1408 layout[u"yaxis"][u"range"] = [0, int(max(y_max) * 1.1)]
1409 layout[u"annotations"].extend(annotations)
1410 plpl = plgo.Figure(data=traces, layout=layout)
1419 except PlotlyError as err:
1421 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1425 def plot_http_server_perf_box(plot, input_data):
1426 """Generate the plot(s) with algorithm: plot_http_server_perf_box
1427 specified in the specification file.
1429 :param plot: Plot to generate.
1430 :param input_data: Data to process.
1431 :type plot: pandas.Series
1432 :type input_data: InputData
1435 # Transform the data
1437 f" Creating the data set for the {plot.get(u'type', u'')} "
1438 f"{plot.get(u'title', u'')}."
1440 data = input_data.filter_data(plot)
1442 logging.error(u"No data.")
1445 # Prepare the data for the plot
1450 if y_vals.get(test[u"name"], None) is None:
1451 y_vals[test[u"name"]] = list()
1453 y_vals[test[u"name"]].append(test[u"result"])
1454 except (KeyError, TypeError):
1455 y_vals[test[u"name"]].append(None)
1457 # Add None to the lists with missing data
1459 nr_of_samples = list()
1460 for val in y_vals.values():
1461 if len(val) > max_len:
1463 nr_of_samples.append(len(val))
1464 for val in y_vals.values():
1465 if len(val) < max_len:
1466 val.extend([None for _ in range(max_len - len(val))])
1470 df_y = pd.DataFrame(y_vals)
1472 for i, col in enumerate(df_y.columns):
1475 f"({nr_of_samples[i]:02d} " \
1476 f"run{u's' if nr_of_samples[i] > 1 else u''}) " \
1477 f"{col.lower().replace(u'-ndrpdr', u'')}"
1479 name_lst = name.split(u'-')
1482 for segment in name_lst:
1483 if (len(name) + len(segment) + 1) > 50 and split_name:
1486 name += segment + u'-'
1489 traces.append(plgo.Box(x=[str(i + 1) + u'.'] * len(df_y[col]),
1495 plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
1499 f" Writing file {plot[u'output-file']}"
1500 f"{plot[u'output-file-type']}."
1506 filename=f"{plot[u'output-file']}{plot[u'output-file-type']}"
1508 except PlotlyError as err:
1510 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1515 def plot_nf_heatmap(plot, input_data):
1516 """Generate the plot(s) with algorithm: plot_nf_heatmap
1517 specified in the specification file.
1519 :param plot: Plot to generate.
1520 :param input_data: Data to process.
1521 :type plot: pandas.Series
1522 :type input_data: InputData
1525 def sort_by_int(value):
1526 """Makes possible to sort a list of strings which represent integers.
1528 :param value: Integer as a string.
1530 :returns: Integer representation of input parameter 'value'.
1535 regex_cn = re.compile(r'^(\d*)R(\d*)C$')
1536 regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
1538 r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$')
1539 # Transform the data
1541 f" Creating the data set for the {plot.get(u'type', u'')} "
1542 f"{plot.get(u'title', u'')}."
1544 in_data = input_data.filter_tests_by_name(
1546 continue_on_error=True,
1547 params=[u"throughput", u"result", u"name", u"tags", u"type"]
1549 if in_data is None or in_data.empty:
1550 logging.error(u"No data.")
1553 for ttype in plot.get(u"test-type", (u"ndr", u"pdr")):
1554 for core in plot.get(u"core", tuple()):
1556 for item in plot.get(u"include", tuple()):
1557 reg_ex = re.compile(str(item.format(core=core)).lower())
1560 for test_id, test in build.iteritems():
1561 if not re.match(reg_ex, str(test_id).lower()):
1563 for tag in test[u"tags"]:
1564 groups = re.search(regex_cn, tag)
1566 chain = str(groups.group(1))
1567 node = str(groups.group(2))
1571 groups = re.search(regex_test_name, test[u"name"])
1572 if groups and len(groups.groups()) == 3:
1574 f"{str(groups.group(1))}-"
1575 f"{str(groups.group(2))}-"
1576 f"{str(groups.group(3))}"
1580 if vals.get(chain, None) is None:
1581 vals[chain] = dict()
1582 if vals[chain].get(node, None) is None:
1583 vals[chain][node] = dict(
1592 result = test[u"result"][u"receive-rate"]
1593 elif ttype == u"pdr":
1595 test[u"throughput"][u"PDR"][u"LOWER"]
1596 elif ttype == u"ndr":
1598 test[u"throughput"][u"NDR"][u"LOWER"]
1605 vals[chain][node][u"vals"].append(result)
1608 logging.error(u"No data.")
1614 txt_chains.append(key_c)
1615 for key_n in vals[key_c].keys():
1616 txt_nodes.append(key_n)
1617 if vals[key_c][key_n][u"vals"]:
1618 vals[key_c][key_n][u"nr"] = \
1619 len(vals[key_c][key_n][u"vals"])
1620 vals[key_c][key_n][u"mean"] = \
1621 round(mean(vals[key_c][key_n][u"vals"]) / 1e6, 1)
1622 vals[key_c][key_n][u"stdev"] = \
1623 round(stdev(vals[key_c][key_n][u"vals"]) / 1e6, 1)
1624 txt_nodes = list(set(txt_nodes))
1626 txt_chains = sorted(txt_chains, key=sort_by_int)
1627 txt_nodes = sorted(txt_nodes, key=sort_by_int)
1629 chains = [i + 1 for i in range(len(txt_chains))]
1630 nodes = [i + 1 for i in range(len(txt_nodes))]
1632 data = [list() for _ in range(len(chains))]
1633 for chain in chains:
1636 val = vals[txt_chains[chain - 1]] \
1637 [txt_nodes[node - 1]][u"mean"]
1638 except (KeyError, IndexError):
1640 data[chain - 1].append(val)
1643 my_green = [[0.0, u"rgb(235, 249, 242)"],
1644 [1.0, u"rgb(45, 134, 89)"]]
1646 my_blue = [[0.0, u"rgb(236, 242, 248)"],
1647 [1.0, u"rgb(57, 115, 172)"]]
1649 my_grey = [[0.0, u"rgb(230, 230, 230)"],
1650 [1.0, u"rgb(102, 102, 102)"]]
1653 annotations = list()
1655 text = (u"Test: {name}<br>"
1660 for chain, _ in enumerate(txt_chains):
1662 for node, _ in enumerate(txt_nodes):
1663 if data[chain][node] is not None:
1672 text=str(data[chain][node]),
1680 hover_line.append(text.format(
1681 name=vals[txt_chains[chain]][txt_nodes[node]]
1683 nr=vals[txt_chains[chain]][txt_nodes[node]][u"nr"],
1684 val=data[chain][node],
1685 stdev=vals[txt_chains[chain]][txt_nodes[node]]
1688 hovertext.append(hover_line)
1696 title=plot.get(u"z-axis", u"{test_type}").
1697 format(test_type=ttype.upper()),
1711 colorscale=my_green,
1717 for idx, item in enumerate(txt_nodes):
1735 for idx, item in enumerate(txt_chains):
1762 text=plot.get(u"x-axis", u""),
1779 text=plot.get(u"y-axis", u""),
1788 updatemenus = list([
1799 u"colorscale": [my_green, ],
1800 u"reversescale": False
1809 u"colorscale": [my_blue, ],
1810 u"reversescale": False
1819 u"colorscale": [my_grey, ],
1820 u"reversescale": False
1831 layout = deepcopy(plot[u"layout"])
1832 except KeyError as err:
1834 f"Finished with error: No layout defined\n{repr(err)}"
1838 layout[u"annotations"] = annotations
1839 layout[u'updatemenus'] = updatemenus
1840 if layout.get(u"title", None):
1841 layout[u"title"] = layout[u'title'].replace(u"test_type", ttype)
1845 plpl = plgo.Figure(data=traces, layout=layout)
1849 f"{plot[u'output-file'].format(core=core, test_type=ttype)}"
1852 logging.info(f" Writing file {file_name}")
1859 except PlotlyError as err:
1861 f" Finished with error: {repr(err)}".replace(u"\n", u" ")