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]]
780 x=[str(i + 1) + u'.'] * len(df_y[col]),
784 f"({nr_of_samples[i]:02d} "
785 f"run{u's' if nr_of_samples[i] > 1 else u''}) "
790 if test_type in (u"SOAK", ):
791 kwargs[u"boxpoints"] = u"all"
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 if layout.get(u"title", None):
807 if test_type in (u"HOSTSTACK", ):
808 layout[u"title"] = f"<b>Bandwidth:</b> {layout[u'title']}"
809 elif test_type == u"VSAP_CPS":
810 layout[u"title"] = f"<b>CPS:</b> {layout[u'title']}"
811 layout[u"yaxis"][u"title"] = u"<b>Connection Rate [cps]</b>"
812 elif test_type == u"VSAP_RPS":
813 layout[u"title"] = f"<b>RPS:</b> {layout[u'title']}"
814 layout[u"yaxis"][u"title"] = u"<b>Connection Rate [rps]</b>"
816 layout[u"title"] = f"<b>Tput:</b> {layout[u'title']}"
817 if y_max and max(y_max) > 1:
818 layout[u"yaxis"][u"range"] = [0, max(y_max) + 2]
819 plpl = plgo.Figure(data=traces, layout=layout)
822 logging.info(f" Writing file {plot[u'output-file']}.html.")
827 filename=f"{plot[u'output-file']}.html"
829 except PlotlyError as err:
831 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
836 def plot_ndrpdr_box_name(plot, input_data):
837 """Generate the plot(s) with algorithm: plot_ndrpdr_box_name
838 specified in the specification file.
840 :param plot: Plot to generate.
841 :param input_data: Data to process.
842 :type plot: pandas.Series
843 :type input_data: InputData
848 f" Creating data set for the {plot.get(u'type', u'')} "
849 f"{plot.get(u'title', u'')}."
851 data = input_data.filter_tests_by_name(
853 params=[u"throughput", u"gbps", u"parent", u"tags", u"type"]
856 logging.error(u"No data.")
859 if u"-gbps" in plot.get(u"title", u"").lower():
863 value = u"throughput"
868 for ttype in plot.get(u"test-type", (u"ndr", u"pdr")):
869 for core in plot.get(u"core", tuple()):
870 # Prepare the data for the plot
872 data_y = OrderedDict()
875 for item in plot.get(u"include", tuple()):
876 reg_ex = re.compile(str(item.format(core=core)).lower())
879 for test_id, test in build.iteritems():
880 if not re.match(reg_ex, str(test_id).lower()):
882 if data_y.get(test[u"parent"], None) is None:
883 data_y[test[u"parent"]] = list()
884 test_type = test[u"type"]
888 data_y[test[u"parent"]].append(
889 test[value][ttype.upper()][u"LOWER"] *
892 except (KeyError, TypeError):
897 for idx, (key, vals) in enumerate(data_y.items()):
899 REGEX_NIC, u'', key.lower().replace(u'-ndrpdr', u'').
900 replace(u'2n1l-', u'')
904 x=[data_x[idx], ] * len(data_x),
905 y=[y / 1e6 if y else None for y in vals],
910 f"{u's' if len(vals) > 1 else u''}) "
917 data_y_max.append(max(vals))
918 except ValueError as err:
919 logging.warning(f"No values to use.\n{err!r}")
922 layout = deepcopy(plot[u"layout"])
923 if layout.get(u"title", None):
925 layout[u'title'].format(core=core, test_type=ttype)
926 if test_type in (u"CPS", ):
927 layout[u"title"] = f"<b>CPS:</b> {layout[u'title']}"
930 f"<b>Tput:</b> {layout[u'title']}"
932 layout[u"yaxis"][u"range"] = [0, max(data_y_max) / 1e6 + 1]
933 plpl = plgo.Figure(data=traces, layout=layout)
937 f"{plot[u'output-file'].format(core=core, test_type=ttype)}"
940 logging.info(f" Writing file {file_name}")
947 except PlotlyError as err:
949 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
953 def plot_mrr_box_name(plot, input_data):
954 """Generate the plot(s) with algorithm: plot_mrr_box_name
955 specified in the specification file.
957 :param plot: Plot to generate.
958 :param input_data: Data to process.
959 :type plot: pandas.Series
960 :type input_data: InputData
965 f" Creating data set for the {plot.get(u'type', u'')} "
966 f"{plot.get(u'title', u'')}."
968 data = input_data.filter_tests_by_name(
970 params=[u"result", u"parent", u"tags", u"type"]
973 logging.error(u"No data.")
976 for core in plot.get(u"core", tuple()):
977 # Prepare the data for the plot
983 for item in plot.get(u"include", tuple()):
984 reg_ex = re.compile(str(item.format(core=core)).lower())
987 for test_id, test in build.iteritems():
988 if not re.match(reg_ex, str(test_id).lower()):
993 REGEX_NIC, u'', test[u'parent'].lower().
994 replace(u'-mrr', u'').replace(u'2n1l-', u'')
996 data_y.append(test[u"result"][u"samples"])
999 f"({len(data_y[-1]):02d} "
1000 f"run{u's' if len(data_y[-1]) > 1 else u''}) "
1003 data_y_max.append(max(data_y[-1]))
1005 except (KeyError, TypeError):
1010 for idx, x_item in enumerate(data_x):
1013 x=[x_item, ] * len(data_y[idx]),
1015 name=data_names[idx],
1022 layout = deepcopy(plot[u"layout"])
1023 if layout.get(u"title", None):
1024 layout[u"title"] = (
1025 f"<b>Tput:</b> {layout[u'title'].format(core=core)}"
1028 layout[u"yaxis"][u"range"] = [0, max(data_y_max) + 1]
1029 plpl = plgo.Figure(data=traces, layout=layout)
1032 file_name = f"{plot[u'output-file'].format(core=core)}.html"
1033 logging.info(f" Writing file {file_name}")
1040 except PlotlyError as err:
1042 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1046 def plot_tsa_name(plot, input_data):
1047 """Generate the plot(s) with algorithm:
1049 specified in the specification file.
1051 :param plot: Plot to generate.
1052 :param input_data: Data to process.
1053 :type plot: pandas.Series
1054 :type input_data: InputData
1057 # Transform the data
1058 plot_title = plot.get(u"title", u"")
1060 f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
1062 data = input_data.filter_tests_by_name(
1064 params=[u"throughput", u"gbps", u"parent", u"tags", u"type"]
1067 logging.error(u"No data.")
1070 plot_title = plot_title.lower()
1072 if u"-gbps" in plot_title:
1077 value = u"throughput"
1081 for ttype in plot.get(u"test-type", (u"ndr", u"pdr")):
1082 y_vals = OrderedDict()
1083 for item in plot.get(u"include", tuple()):
1084 reg_ex = re.compile(str(item).lower())
1087 for test_id, test in build.iteritems():
1088 if re.match(reg_ex, str(test_id).lower()):
1089 if y_vals.get(test[u"parent"], None) is None:
1090 y_vals[test[u"parent"]] = {
1096 if test[u"type"] not in (u"NDRPDR", u"CPS"):
1099 if u"1C" in test[u"tags"]:
1100 y_vals[test[u"parent"]][u"1"].append(
1101 test[value][ttype.upper()][u"LOWER"] *
1104 elif u"2C" in test[u"tags"]:
1105 y_vals[test[u"parent"]][u"2"].append(
1106 test[value][ttype.upper()][u"LOWER"] *
1109 elif u"4C" in test[u"tags"]:
1110 y_vals[test[u"parent"]][u"4"].append(
1111 test[value][ttype.upper()][u"LOWER"] *
1114 except (KeyError, TypeError):
1118 logging.warning(f"No data for the plot {plot.get(u'title', u'')}")
1122 for test_name, test_vals in y_vals.items():
1123 for key, test_val in test_vals.items():
1125 avg_val = sum(test_val) / len(test_val)
1126 y_vals[test_name][key] = [avg_val, len(test_val)]
1127 ideal = avg_val / (int(key) * 1e6)
1128 if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
1129 y_1c_max[test_name] = ideal
1131 vals = OrderedDict()
1136 for test_name, test_vals in y_vals.items():
1138 if test_vals[u"1"][1]:
1142 test_name.replace(u'-ndrpdr', u'').
1143 replace(u'2n1l-', u'')
1145 vals[name] = OrderedDict()
1146 y_val_1 = test_vals[u"1"][0] / 1e6
1147 y_val_2 = test_vals[u"2"][0] / 1e6 if test_vals[u"2"][0] \
1149 y_val_4 = test_vals[u"4"][0] / 1e6 if test_vals[u"4"][0] \
1152 vals[name][u"val"] = [y_val_1, y_val_2, y_val_4]
1153 vals[name][u"rel"] = [1.0, None, None]
1154 vals[name][u"ideal"] = [
1155 y_1c_max[test_name],
1156 y_1c_max[test_name] * 2,
1157 y_1c_max[test_name] * 4
1159 vals[name][u"diff"] = [
1160 (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1,
1164 vals[name][u"count"] = [
1171 val_max = max(vals[name][u"val"])
1172 except ValueError as err:
1173 logging.error(repr(err))
1176 y_max.append(val_max)
1179 vals[name][u"rel"][1] = round(y_val_2 / y_val_1, 2)
1180 vals[name][u"diff"][1] = \
1181 (y_val_2 - vals[name][u"ideal"][1]) * 100 / y_val_2
1183 vals[name][u"rel"][2] = round(y_val_4 / y_val_1, 2)
1184 vals[name][u"diff"][2] = \
1185 (y_val_4 - vals[name][u"ideal"][2]) * 100 / y_val_4
1186 except IndexError as err:
1187 logging.warning(f"No data for {test_name}")
1188 logging.warning(repr(err))
1191 if u"x520" in test_name:
1192 limit = plot[u"limits"][u"nic"][u"x520"]
1193 elif u"x710" in test_name:
1194 limit = plot[u"limits"][u"nic"][u"x710"]
1195 elif u"xxv710" in test_name:
1196 limit = plot[u"limits"][u"nic"][u"xxv710"]
1197 elif u"xl710" in test_name:
1198 limit = plot[u"limits"][u"nic"][u"xl710"]
1199 elif u"x553" in test_name:
1200 limit = plot[u"limits"][u"nic"][u"x553"]
1201 elif u"cx556a" in test_name:
1202 limit = plot[u"limits"][u"nic"][u"cx556a"]
1203 elif u"e810cq" in test_name:
1204 limit = plot[u"limits"][u"nic"][u"e810cq"]
1207 if limit > nic_limit:
1210 mul = 2 if u"ge2p" in test_name else 1
1211 if u"10ge" in test_name:
1212 limit = plot[u"limits"][u"link"][u"10ge"] * mul
1213 elif u"25ge" in test_name:
1214 limit = plot[u"limits"][u"link"][u"25ge"] * mul
1215 elif u"40ge" in test_name:
1216 limit = plot[u"limits"][u"link"][u"40ge"] * mul
1217 elif u"100ge" in test_name:
1218 limit = plot[u"limits"][u"link"][u"100ge"] * mul
1221 if limit > lnk_limit:
1224 if u"cx556a" in test_name:
1225 limit = plot[u"limits"][u"pci"][u"pci-g3-x8"]
1227 limit = plot[u"limits"][u"pci"][u"pci-g3-x16"]
1228 if limit > pci_limit:
1232 annotations = list()
1236 if u"-gbps" not in plot_title and u"-cps-" not in plot_title:
1240 min_limit = min((nic_limit, lnk_limit, pci_limit))
1241 if nic_limit == min_limit:
1242 traces.append(plgo.Scatter(
1244 y=[nic_limit, ] * len(x_vals),
1245 name=f"NIC: {nic_limit:.2f}Mpps",
1254 annotations.append(dict(
1261 text=f"NIC: {nic_limit:.2f}Mpps",
1269 y_max.append(nic_limit)
1270 elif lnk_limit == min_limit:
1271 traces.append(plgo.Scatter(
1273 y=[lnk_limit, ] * len(x_vals),
1274 name=f"Link: {lnk_limit:.2f}Mpps",
1283 annotations.append(dict(
1290 text=f"Link: {lnk_limit:.2f}Mpps",
1298 y_max.append(lnk_limit)
1299 elif pci_limit == min_limit:
1300 traces.append(plgo.Scatter(
1302 y=[pci_limit, ] * len(x_vals),
1303 name=f"PCIe: {pci_limit:.2f}Mpps",
1312 annotations.append(dict(
1319 text=f"PCIe: {pci_limit:.2f}Mpps",
1327 y_max.append(pci_limit)
1329 # Perfect and measured:
1331 for name, val in vals.items():
1334 for idx in range(len(val[u"val"])):
1336 if isinstance(val[u"val"][idx], float):
1338 f"No. of Runs: {val[u'count'][idx]}<br>"
1339 f"Mean: {val[u'val'][idx]:.2f}{h_unit}<br>"
1341 if isinstance(val[u"diff"][idx], float):
1342 htext += f"Diff: {round(val[u'diff'][idx]):.0f}%<br>"
1343 if isinstance(val[u"rel"][idx], float):
1344 htext += f"Speedup: {val[u'rel'][idx]:.2f}"
1345 hovertext.append(htext)
1352 mode=u"lines+markers",
1361 hoverinfo=u"text+name"
1368 name=f"{name} perfect",
1376 text=[f"Perfect: {y:.2f}Mpps" for y in val[u"ideal"]],
1381 except (IndexError, ValueError, KeyError) as err:
1382 logging.warning(f"No data for {name}\n{repr(err)}")
1386 file_name = f"{plot[u'output-file'].format(test_type=ttype)}.html"
1387 logging.info(f" Writing file {file_name}")
1388 layout = deepcopy(plot[u"layout"])
1389 if layout.get(u"title", None):
1390 layout[u"title"] = (
1391 f"<b>Speedup Multi-core:</b> "
1392 f"{layout[u'title'].format(test_type=ttype)}"
1394 layout[u"yaxis"][u"range"] = [0, int(max(y_max) * 1.1)]
1395 layout[u"annotations"].extend(annotations)
1396 plpl = plgo.Figure(data=traces, layout=layout)
1405 except PlotlyError as err:
1407 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1411 def plot_http_server_perf_box(plot, input_data):
1412 """Generate the plot(s) with algorithm: plot_http_server_perf_box
1413 specified in the specification file.
1415 :param plot: Plot to generate.
1416 :param input_data: Data to process.
1417 :type plot: pandas.Series
1418 :type input_data: InputData
1421 # Transform the data
1423 f" Creating the data set for the {plot.get(u'type', u'')} "
1424 f"{plot.get(u'title', u'')}."
1426 data = input_data.filter_data(plot)
1428 logging.error(u"No data.")
1431 # Prepare the data for the plot
1436 if y_vals.get(test[u"name"], None) is None:
1437 y_vals[test[u"name"]] = list()
1439 y_vals[test[u"name"]].append(test[u"result"])
1440 except (KeyError, TypeError):
1441 y_vals[test[u"name"]].append(None)
1443 # Add None to the lists with missing data
1445 nr_of_samples = list()
1446 for val in y_vals.values():
1447 if len(val) > max_len:
1449 nr_of_samples.append(len(val))
1450 for val in y_vals.values():
1451 if len(val) < max_len:
1452 val.extend([None for _ in range(max_len - len(val))])
1456 df_y = pd.DataFrame(y_vals)
1458 for i, col in enumerate(df_y.columns):
1461 f"({nr_of_samples[i]:02d} " \
1462 f"run{u's' if nr_of_samples[i] > 1 else u''}) " \
1463 f"{col.lower().replace(u'-ndrpdr', u'')}"
1465 name_lst = name.split(u'-')
1468 for segment in name_lst:
1469 if (len(name) + len(segment) + 1) > 50 and split_name:
1472 name += segment + u'-'
1475 traces.append(plgo.Box(x=[str(i + 1) + u'.'] * len(df_y[col]),
1481 plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
1485 f" Writing file {plot[u'output-file']}"
1486 f"{plot[u'output-file-type']}."
1492 filename=f"{plot[u'output-file']}{plot[u'output-file-type']}"
1494 except PlotlyError as err:
1496 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1501 def plot_nf_heatmap(plot, input_data):
1502 """Generate the plot(s) with algorithm: plot_nf_heatmap
1503 specified in the specification file.
1505 :param plot: Plot to generate.
1506 :param input_data: Data to process.
1507 :type plot: pandas.Series
1508 :type input_data: InputData
1511 def sort_by_int(value):
1512 """Makes possible to sort a list of strings which represent integers.
1514 :param value: Integer as a string.
1516 :returns: Integer representation of input parameter 'value'.
1521 regex_cn = re.compile(r'^(\d*)R(\d*)C$')
1522 regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
1524 r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$')
1525 # Transform the data
1527 f" Creating the data set for the {plot.get(u'type', u'')} "
1528 f"{plot.get(u'title', u'')}."
1530 in_data = input_data.filter_tests_by_name(
1532 continue_on_error=True,
1533 params=[u"throughput", u"result", u"name", u"tags", u"type"]
1535 if in_data is None or in_data.empty:
1536 logging.error(u"No data.")
1539 for ttype in plot.get(u"test-type", (u"ndr", u"pdr")):
1540 for core in plot.get(u"core", tuple()):
1542 for item in plot.get(u"include", tuple()):
1543 reg_ex = re.compile(str(item.format(core=core)).lower())
1546 for test_id, test in build.iteritems():
1547 if not re.match(reg_ex, str(test_id).lower()):
1549 for tag in test[u"tags"]:
1550 groups = re.search(regex_cn, tag)
1552 chain = str(groups.group(1))
1553 node = str(groups.group(2))
1557 groups = re.search(regex_test_name, test[u"name"])
1558 if groups and len(groups.groups()) == 3:
1560 f"{str(groups.group(1))}-"
1561 f"{str(groups.group(2))}-"
1562 f"{str(groups.group(3))}"
1566 if vals.get(chain, None) is None:
1567 vals[chain] = dict()
1568 if vals[chain].get(node, None) is None:
1569 vals[chain][node] = dict(
1578 result = test[u"result"][u"receive-rate"]
1579 elif ttype == u"pdr":
1581 test[u"throughput"][u"PDR"][u"LOWER"]
1582 elif ttype == u"ndr":
1584 test[u"throughput"][u"NDR"][u"LOWER"]
1591 vals[chain][node][u"vals"].append(result)
1594 logging.error(u"No data.")
1600 txt_chains.append(key_c)
1601 for key_n in vals[key_c].keys():
1602 txt_nodes.append(key_n)
1603 if vals[key_c][key_n][u"vals"]:
1604 vals[key_c][key_n][u"nr"] = \
1605 len(vals[key_c][key_n][u"vals"])
1606 vals[key_c][key_n][u"mean"] = \
1607 round(mean(vals[key_c][key_n][u"vals"]) / 1e6, 1)
1608 vals[key_c][key_n][u"stdev"] = \
1609 round(stdev(vals[key_c][key_n][u"vals"]) / 1e6, 1)
1610 txt_nodes = list(set(txt_nodes))
1612 txt_chains = sorted(txt_chains, key=sort_by_int)
1613 txt_nodes = sorted(txt_nodes, key=sort_by_int)
1615 chains = [i + 1 for i in range(len(txt_chains))]
1616 nodes = [i + 1 for i in range(len(txt_nodes))]
1618 data = [list() for _ in range(len(chains))]
1619 for chain in chains:
1622 val = vals[txt_chains[chain - 1]] \
1623 [txt_nodes[node - 1]][u"mean"]
1624 except (KeyError, IndexError):
1626 data[chain - 1].append(val)
1629 my_green = [[0.0, u"rgb(235, 249, 242)"],
1630 [1.0, u"rgb(45, 134, 89)"]]
1632 my_blue = [[0.0, u"rgb(236, 242, 248)"],
1633 [1.0, u"rgb(57, 115, 172)"]]
1635 my_grey = [[0.0, u"rgb(230, 230, 230)"],
1636 [1.0, u"rgb(102, 102, 102)"]]
1639 annotations = list()
1641 text = (u"Test: {name}<br>"
1646 for chain, _ in enumerate(txt_chains):
1648 for node, _ in enumerate(txt_nodes):
1649 if data[chain][node] is not None:
1658 text=str(data[chain][node]),
1666 hover_line.append(text.format(
1667 name=vals[txt_chains[chain]][txt_nodes[node]]
1669 nr=vals[txt_chains[chain]][txt_nodes[node]][u"nr"],
1670 val=data[chain][node],
1671 stdev=vals[txt_chains[chain]][txt_nodes[node]]
1674 hovertext.append(hover_line)
1682 title=plot.get(u"z-axis", u"{test_type}").
1683 format(test_type=ttype.upper()),
1697 colorscale=my_green,
1703 for idx, item in enumerate(txt_nodes):
1721 for idx, item in enumerate(txt_chains):
1748 text=plot.get(u"x-axis", u""),
1765 text=plot.get(u"y-axis", u""),
1774 updatemenus = list([
1785 u"colorscale": [my_green, ],
1786 u"reversescale": False
1795 u"colorscale": [my_blue, ],
1796 u"reversescale": False
1805 u"colorscale": [my_grey, ],
1806 u"reversescale": False
1817 layout = deepcopy(plot[u"layout"])
1818 except KeyError as err:
1820 f"Finished with error: No layout defined\n{repr(err)}"
1824 layout[u"annotations"] = annotations
1825 layout[u'updatemenus'] = updatemenus
1826 if layout.get(u"title", None):
1827 layout[u"title"] = layout[u'title'].replace(u"test_type", ttype)
1831 plpl = plgo.Figure(data=traces, layout=layout)
1835 f"{plot[u'output-file'].format(core=core, test_type=ttype)}"
1838 logging.info(f" Writing file {file_name}")
1845 except PlotlyError as err:
1847 f" Finished with error: {repr(err)}".replace(u"\n", u" ")