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 fw:
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"parent", u"tags", u"type"])
627 logging.error(u"No data.")
630 # Prepare the data for the plot
631 y_vals = OrderedDict()
635 if y_vals.get(test[u"parent"], None) is None:
636 y_vals[test[u"parent"]] = list()
638 if (test[u"type"] in (u"NDRPDR", ) and
639 u"-pdr" in plot.get(u"title", u"").lower()):
640 y_vals[test[u"parent"]].\
641 append(test[u"throughput"][u"PDR"][u"LOWER"])
642 elif (test[u"type"] in (u"NDRPDR", ) and
643 u"-ndr" in plot.get(u"title", u"").lower()):
644 y_vals[test[u"parent"]]. \
645 append(test[u"throughput"][u"NDR"][u"LOWER"])
646 elif test[u"type"] in (u"SOAK", ):
647 y_vals[test[u"parent"]].\
648 append(test[u"throughput"][u"LOWER"])
651 except (KeyError, TypeError):
652 y_vals[test[u"parent"]].append(None)
654 # Add None to the lists with missing data
656 nr_of_samples = list()
657 for val in y_vals.values():
658 if len(val) > max_len:
660 nr_of_samples.append(len(val))
661 for val in y_vals.values():
662 if len(val) < max_len:
663 val.extend([None for _ in range(max_len - len(val))])
667 df_y = pd.DataFrame(y_vals)
670 for i, col in enumerate(df_y.columns):
671 tst_name = re.sub(REGEX_NIC, u"",
672 col.lower().replace(u'-ndrpdr', u'').
673 replace(u'2n1l-', u''))
676 x=[str(i + 1) + u'.'] * len(df_y[col]),
677 y=[y / 1000000 if y else None for y in df_y[col]],
680 f"({nr_of_samples[i]:02d} "
681 f"run{u's' if nr_of_samples[i] > 1 else u''}) "
688 val_max = max(df_y[col])
690 y_max.append(int(val_max / 1000000) + 2)
691 except (ValueError, TypeError) as err:
692 logging.error(repr(err))
697 layout = deepcopy(plot[u"layout"])
698 if layout.get(u"title", None):
699 layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
701 layout[u"yaxis"][u"range"] = [0, max(y_max)]
702 plpl = plgo.Figure(data=traces, layout=layout)
705 logging.info(f" Writing file {plot[u'output-file']}.html.")
710 filename=f"{plot[u'output-file']}.html"
712 except PlotlyError as err:
714 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
719 def plot_lat_err_bars_name(plot, input_data):
720 """Generate the plot(s) with algorithm: plot_lat_err_bars_name
721 specified in the specification file.
723 :param plot: Plot to generate.
724 :param input_data: Data to process.
725 :type plot: pandas.Series
726 :type input_data: InputData
730 plot_title = plot.get(u"title", u"")
732 f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
734 data = input_data.filter_tests_by_name(
735 plot, params=[u"latency", u"parent", u"tags", u"type"])
737 logging.error(u"No data.")
740 # Prepare the data for the plot
741 y_tmp_vals = OrderedDict()
746 logging.debug(f"test[u'latency']: {test[u'latency']}\n")
747 except ValueError as err:
748 logging.warning(repr(err))
749 if y_tmp_vals.get(test[u"parent"], None) is None:
750 y_tmp_vals[test[u"parent"]] = [
751 list(), # direction1, min
752 list(), # direction1, avg
753 list(), # direction1, max
754 list(), # direction2, min
755 list(), # direction2, avg
756 list() # direction2, max
759 if test[u"type"] not in (u"NDRPDR", ):
760 logging.warning(f"Invalid test type: {test[u'type']}")
762 if u"-pdr" in plot_title.lower():
764 elif u"-ndr" in plot_title.lower():
768 f"Invalid test type: {test[u'type']}"
771 y_tmp_vals[test[u"parent"]][0].append(
772 test[u"latency"][ttype][u"direction1"][u"min"])
773 y_tmp_vals[test[u"parent"]][1].append(
774 test[u"latency"][ttype][u"direction1"][u"avg"])
775 y_tmp_vals[test[u"parent"]][2].append(
776 test[u"latency"][ttype][u"direction1"][u"max"])
777 y_tmp_vals[test[u"parent"]][3].append(
778 test[u"latency"][ttype][u"direction2"][u"min"])
779 y_tmp_vals[test[u"parent"]][4].append(
780 test[u"latency"][ttype][u"direction2"][u"avg"])
781 y_tmp_vals[test[u"parent"]][5].append(
782 test[u"latency"][ttype][u"direction2"][u"max"])
783 except (KeyError, TypeError) as err:
784 logging.warning(repr(err))
790 nr_of_samples = list()
791 for key, val in y_tmp_vals.items():
792 name = re.sub(REGEX_NIC, u"", key.replace(u'-ndrpdr', u'').
793 replace(u'2n1l-', u''))
794 x_vals.append(name) # dir 1
795 y_vals.append(mean(val[1]) if val[1] else None)
796 y_mins.append(mean(val[0]) if val[0] else None)
797 y_maxs.append(mean(val[2]) if val[2] else None)
798 nr_of_samples.append(len(val[1]) if val[1] else 0)
799 x_vals.append(name) # dir 2
800 y_vals.append(mean(val[4]) if val[4] else None)
801 y_mins.append(mean(val[3]) if val[3] else None)
802 y_maxs.append(mean(val[5]) if val[5] else None)
803 nr_of_samples.append(len(val[3]) if val[3] else 0)
808 for idx, _ in enumerate(x_vals):
809 if not bool(int(idx % 2)):
810 direction = u"West-East"
812 direction = u"East-West"
814 f"No. of Runs: {nr_of_samples[idx]}<br>"
815 f"Test: {x_vals[idx]}<br>"
816 f"Direction: {direction}<br>"
818 if isinstance(y_maxs[idx], float):
819 hovertext += f"Max: {y_maxs[idx]:.2f}uSec<br>"
820 if isinstance(y_vals[idx], float):
821 hovertext += f"Mean: {y_vals[idx]:.2f}uSec<br>"
822 if isinstance(y_mins[idx], float):
823 hovertext += f"Min: {y_mins[idx]:.2f}uSec"
825 if isinstance(y_maxs[idx], float) and isinstance(y_vals[idx], float):
826 array = [y_maxs[idx] - y_vals[idx], ]
829 if isinstance(y_mins[idx], float) and isinstance(y_vals[idx], float):
830 arrayminus = [y_vals[idx] - y_mins[idx], ]
832 arrayminus = [None, ]
833 traces.append(plgo.Scatter(
837 legendgroup=x_vals[idx],
838 showlegend=bool(int(idx % 2)),
844 arrayminus=arrayminus,
845 color=COLORS[int(idx / 2)]
849 color=COLORS[int(idx / 2)],
854 annotations.append(dict(
861 text=u"E-W" if bool(int(idx % 2)) else u"W-E",
871 file_type = plot.get(u"output-file-type", u".html")
872 logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
873 layout = deepcopy(plot[u"layout"])
874 if layout.get(u"title", None):
875 layout[u"title"] = f"<b>Latency:</b> {layout[u'title']}"
876 layout[u"annotations"] = annotations
877 plpl = plgo.Figure(data=traces, layout=layout)
882 show_link=False, auto_open=False,
883 filename=f"{plot[u'output-file']}{file_type}"
885 except PlotlyError as err:
887 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
892 def plot_tsa_name(plot, input_data):
893 """Generate the plot(s) with algorithm:
895 specified in the specification file.
897 :param plot: Plot to generate.
898 :param input_data: Data to process.
899 :type plot: pandas.Series
900 :type input_data: InputData
904 plot_title = plot.get(u"title", u"")
906 f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
908 data = input_data.filter_tests_by_name(
909 plot, params=[u"throughput", u"parent", u"tags", u"type"])
911 logging.error(u"No data.")
914 y_vals = OrderedDict()
918 if y_vals.get(test[u"parent"], None) is None:
919 y_vals[test[u"parent"]] = {
925 if test[u"type"] not in (u"NDRPDR",):
928 if u"-pdr" in plot_title.lower():
930 elif u"-ndr" in plot_title.lower():
935 if u"1C" in test[u"tags"]:
936 y_vals[test[u"parent"]][u"1"]. \
937 append(test[u"throughput"][ttype][u"LOWER"])
938 elif u"2C" in test[u"tags"]:
939 y_vals[test[u"parent"]][u"2"]. \
940 append(test[u"throughput"][ttype][u"LOWER"])
941 elif u"4C" in test[u"tags"]:
942 y_vals[test[u"parent"]][u"4"]. \
943 append(test[u"throughput"][ttype][u"LOWER"])
944 except (KeyError, TypeError):
948 logging.warning(f"No data for the plot {plot.get(u'title', u'')}")
952 for test_name, test_vals in y_vals.items():
953 for key, test_val in test_vals.items():
955 avg_val = sum(test_val) / len(test_val)
956 y_vals[test_name][key] = [avg_val, len(test_val)]
957 ideal = avg_val / (int(key) * 1000000.0)
958 if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
959 y_1c_max[test_name] = ideal
965 pci_limit = plot[u"limits"][u"pci"][u"pci-g3-x8"]
966 for test_name, test_vals in y_vals.items():
968 if test_vals[u"1"][1]:
972 test_name.replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')
974 vals[name] = OrderedDict()
975 y_val_1 = test_vals[u"1"][0] / 1000000.0
976 y_val_2 = test_vals[u"2"][0] / 1000000.0 if test_vals[u"2"][0] \
978 y_val_4 = test_vals[u"4"][0] / 1000000.0 if test_vals[u"4"][0] \
981 vals[name][u"val"] = [y_val_1, y_val_2, y_val_4]
982 vals[name][u"rel"] = [1.0, None, None]
983 vals[name][u"ideal"] = [
985 y_1c_max[test_name] * 2,
986 y_1c_max[test_name] * 4
988 vals[name][u"diff"] = [
989 (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None
991 vals[name][u"count"] = [
998 val_max = max(vals[name][u"val"])
999 except ValueError as err:
1000 logging.error(repr(err))
1003 y_max.append(val_max)
1006 vals[name][u"rel"][1] = round(y_val_2 / y_val_1, 2)
1007 vals[name][u"diff"][1] = \
1008 (y_val_2 - vals[name][u"ideal"][1]) * 100 / y_val_2
1010 vals[name][u"rel"][2] = round(y_val_4 / y_val_1, 2)
1011 vals[name][u"diff"][2] = \
1012 (y_val_4 - vals[name][u"ideal"][2]) * 100 / y_val_4
1013 except IndexError as err:
1014 logging.warning(f"No data for {test_name}")
1015 logging.warning(repr(err))
1018 if u"x520" in test_name:
1019 limit = plot[u"limits"][u"nic"][u"x520"]
1020 elif u"x710" in test_name:
1021 limit = plot[u"limits"][u"nic"][u"x710"]
1022 elif u"xxv710" in test_name:
1023 limit = plot[u"limits"][u"nic"][u"xxv710"]
1024 elif u"xl710" in test_name:
1025 limit = plot[u"limits"][u"nic"][u"xl710"]
1026 elif u"x553" in test_name:
1027 limit = plot[u"limits"][u"nic"][u"x553"]
1028 elif u"cx556a" in test_name:
1029 limit = plot[u"limits"][u"nic"][u"cx556a"]
1032 if limit > nic_limit:
1035 mul = 2 if u"ge2p" in test_name else 1
1036 if u"10ge" in test_name:
1037 limit = plot[u"limits"][u"link"][u"10ge"] * mul
1038 elif u"25ge" in test_name:
1039 limit = plot[u"limits"][u"link"][u"25ge"] * mul
1040 elif u"40ge" in test_name:
1041 limit = plot[u"limits"][u"link"][u"40ge"] * mul
1042 elif u"100ge" in test_name:
1043 limit = plot[u"limits"][u"link"][u"100ge"] * mul
1046 if limit > lnk_limit:
1050 annotations = list()
1055 threshold = 1.1 * max(y_max) # 10%
1056 except ValueError as err:
1060 traces.append(plgo.Scatter(
1062 y=[nic_limit, ] * len(x_vals),
1063 name=f"NIC: {nic_limit:.2f}Mpps",
1072 annotations.append(dict(
1079 text=f"NIC: {nic_limit:.2f}Mpps",
1087 y_max.append(nic_limit)
1090 if lnk_limit < threshold:
1091 traces.append(plgo.Scatter(
1093 y=[lnk_limit, ] * len(x_vals),
1094 name=f"Link: {lnk_limit:.2f}Mpps",
1103 annotations.append(dict(
1110 text=f"Link: {lnk_limit:.2f}Mpps",
1118 y_max.append(lnk_limit)
1121 if (pci_limit < threshold and
1122 (pci_limit < lnk_limit * 0.95 or lnk_limit > lnk_limit * 1.05)):
1123 traces.append(plgo.Scatter(
1125 y=[pci_limit, ] * len(x_vals),
1126 name=f"PCIe: {pci_limit:.2f}Mpps",
1135 annotations.append(dict(
1142 text=f"PCIe: {pci_limit:.2f}Mpps",
1150 y_max.append(pci_limit)
1152 # Perfect and measured:
1154 for name, val in vals.items():
1157 for idx in range(len(val[u"val"])):
1159 if isinstance(val[u"val"][idx], float):
1161 f"No. of Runs: {val[u'count'][idx]}<br>"
1162 f"Mean: {val[u'val'][idx]:.2f}Mpps<br>"
1164 if isinstance(val[u"diff"][idx], float):
1165 htext += f"Diff: {round(val[u'diff'][idx]):.0f}%<br>"
1166 if isinstance(val[u"rel"][idx], float):
1167 htext += f"Speedup: {val[u'rel'][idx]:.2f}"
1168 hovertext.append(htext)
1175 mode=u"lines+markers",
1184 hoverinfo=u"text+name"
1191 name=f"{name} perfect",
1199 text=[f"Perfect: {y:.2f}Mpps" for y in val[u"ideal"]],
1204 except (IndexError, ValueError, KeyError) as err:
1205 logging.warning(f"No data for {name}\n{repr(err)}")
1209 file_type = plot.get(u"output-file-type", u".html")
1210 logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
1211 layout = deepcopy(plot[u"layout"])
1212 if layout.get(u"title", None):
1213 layout[u"title"] = f"<b>Speedup Multi-core:</b> {layout[u'title']}"
1214 layout[u"yaxis"][u"range"] = [0, int(max(y_max) * 1.1)]
1215 layout[u"annotations"].extend(annotations)
1216 plpl = plgo.Figure(data=traces, layout=layout)
1223 filename=f"{plot[u'output-file']}{file_type}"
1225 except PlotlyError as err:
1227 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1232 def plot_http_server_perf_box(plot, input_data):
1233 """Generate the plot(s) with algorithm: plot_http_server_perf_box
1234 specified in the specification file.
1236 :param plot: Plot to generate.
1237 :param input_data: Data to process.
1238 :type plot: pandas.Series
1239 :type input_data: InputData
1242 # Transform the data
1244 f" Creating the data set for the {plot.get(u'type', u'')} "
1245 f"{plot.get(u'title', u'')}."
1247 data = input_data.filter_data(plot)
1249 logging.error(u"No data.")
1252 # Prepare the data for the plot
1257 if y_vals.get(test[u"name"], None) is None:
1258 y_vals[test[u"name"]] = list()
1260 y_vals[test[u"name"]].append(test[u"result"])
1261 except (KeyError, TypeError):
1262 y_vals[test[u"name"]].append(None)
1264 # Add None to the lists with missing data
1266 nr_of_samples = list()
1267 for val in y_vals.values():
1268 if len(val) > max_len:
1270 nr_of_samples.append(len(val))
1271 for val in y_vals.values():
1272 if len(val) < max_len:
1273 val.extend([None for _ in range(max_len - len(val))])
1277 df_y = pd.DataFrame(y_vals)
1279 for i, col in enumerate(df_y.columns):
1282 f"({nr_of_samples[i]:02d} " \
1283 f"run{u's' if nr_of_samples[i] > 1 else u''}) " \
1284 f"{col.lower().replace(u'-ndrpdr', u'')}"
1286 name_lst = name.split(u'-')
1289 for segment in name_lst:
1290 if (len(name) + len(segment) + 1) > 50 and split_name:
1293 name += segment + u'-'
1296 traces.append(plgo.Box(x=[str(i + 1) + u'.'] * len(df_y[col]),
1302 plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
1306 f" Writing file {plot[u'output-file']}"
1307 f"{plot[u'output-file-type']}."
1313 filename=f"{plot[u'output-file']}{plot[u'output-file-type']}"
1315 except PlotlyError as err:
1317 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
1322 def plot_nf_heatmap(plot, input_data):
1323 """Generate the plot(s) with algorithm: plot_nf_heatmap
1324 specified in the specification file.
1326 :param plot: Plot to generate.
1327 :param input_data: Data to process.
1328 :type plot: pandas.Series
1329 :type input_data: InputData
1332 regex_cn = re.compile(r'^(\d*)R(\d*)C$')
1333 regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
1335 r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$')
1338 # Transform the data
1340 f" Creating the data set for the {plot.get(u'type', u'')} "
1341 f"{plot.get(u'title', u'')}."
1343 data = input_data.filter_data(plot, continue_on_error=True)
1344 if data is None or data.empty:
1345 logging.error(u"No data.")
1351 for tag in test[u"tags"]:
1352 groups = re.search(regex_cn, tag)
1354 chain = str(groups.group(1))
1355 node = str(groups.group(2))
1359 groups = re.search(regex_test_name, test[u"name"])
1360 if groups and len(groups.groups()) == 3:
1362 f"{str(groups.group(1))}-"
1363 f"{str(groups.group(2))}-"
1364 f"{str(groups.group(3))}"
1368 if vals.get(chain, None) is None:
1369 vals[chain] = dict()
1370 if vals[chain].get(node, None) is None:
1371 vals[chain][node] = dict(
1379 if plot[u"include-tests"] == u"MRR":
1380 result = test[u"result"][u"receive-rate"]
1381 elif plot[u"include-tests"] == u"PDR":
1382 result = test[u"throughput"][u"PDR"][u"LOWER"]
1383 elif plot[u"include-tests"] == u"NDR":
1384 result = test[u"throughput"][u"NDR"][u"LOWER"]
1391 vals[chain][node][u"vals"].append(result)
1394 logging.error(u"No data.")
1400 txt_chains.append(key_c)
1401 for key_n in vals[key_c].keys():
1402 txt_nodes.append(key_n)
1403 if vals[key_c][key_n][u"vals"]:
1404 vals[key_c][key_n][u"nr"] = len(vals[key_c][key_n][u"vals"])
1405 vals[key_c][key_n][u"mean"] = \
1406 round(mean(vals[key_c][key_n][u"vals"]) / 1000000, 1)
1407 vals[key_c][key_n][u"stdev"] = \
1408 round(stdev(vals[key_c][key_n][u"vals"]) / 1000000, 1)
1409 txt_nodes = list(set(txt_nodes))
1411 def sort_by_int(value):
1412 """Makes possible to sort a list of strings which represent integers.
1414 :param value: Integer as a string.
1416 :returns: Integer representation of input parameter 'value'.
1421 txt_chains = sorted(txt_chains, key=sort_by_int)
1422 txt_nodes = sorted(txt_nodes, key=sort_by_int)
1424 chains = [i + 1 for i in range(len(txt_chains))]
1425 nodes = [i + 1 for i in range(len(txt_nodes))]
1427 data = [list() for _ in range(len(chains))]
1428 for chain in chains:
1431 val = vals[txt_chains[chain - 1]][txt_nodes[node - 1]][u"mean"]
1432 except (KeyError, IndexError):
1434 data[chain - 1].append(val)
1437 my_green = [[0.0, u"rgb(235, 249, 242)"],
1438 [1.0, u"rgb(45, 134, 89)"]]
1440 my_blue = [[0.0, u"rgb(236, 242, 248)"],
1441 [1.0, u"rgb(57, 115, 172)"]]
1443 my_grey = [[0.0, u"rgb(230, 230, 230)"],
1444 [1.0, u"rgb(102, 102, 102)"]]
1447 annotations = list()
1449 text = (u"Test: {name}<br>"
1454 for chain, _ in enumerate(txt_chains):
1456 for node, _ in enumerate(txt_nodes):
1457 if data[chain][node] is not None:
1466 text=str(data[chain][node]),
1474 hover_line.append(text.format(
1475 name=vals[txt_chains[chain]][txt_nodes[node]][u"name"],
1476 nr=vals[txt_chains[chain]][txt_nodes[node]][u"nr"],
1477 val=data[chain][node],
1478 stdev=vals[txt_chains[chain]][txt_nodes[node]][u"stdev"]))
1479 hovertext.append(hover_line)
1487 title=plot.get(u"z-axis", u""),
1501 colorscale=my_green,
1507 for idx, item in enumerate(txt_nodes):
1525 for idx, item in enumerate(txt_chains):
1552 text=plot.get(u"x-axis", u""),
1569 text=plot.get(u"y-axis", u""),
1578 updatemenus = list([
1589 u"colorscale": [my_green, ],
1590 u"reversescale": False
1599 u"colorscale": [my_blue, ],
1600 u"reversescale": False
1609 u"colorscale": [my_grey, ],
1610 u"reversescale": False
1621 layout = deepcopy(plot[u"layout"])
1622 except KeyError as err:
1623 logging.error(f"Finished with error: No layout defined\n{repr(err)}")
1626 layout[u"annotations"] = annotations
1627 layout[u'updatemenus'] = updatemenus
1631 plpl = plgo.Figure(data=traces, layout=layout)
1634 logging.info(f" Writing file {plot[u'output-file']}.html")
1639 filename=f"{plot[u'output-file']}.html"
1641 except PlotlyError as err:
1643 f" Finished with error: {repr(err)}".replace(u"\n", u" ")