1 # Copyright (c) 2018 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.
20 import plotly.offline as ploff
21 import plotly.graph_objs as plgo
23 from plotly.exceptions import PlotlyError
24 from collections import OrderedDict
25 from copy import deepcopy
27 from utils import mean
30 COLORS = ["SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink",
31 "Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black",
32 "Violet", "Blue", "Yellow", "BurlyWood", "CadetBlue", "Crimson",
33 "DarkBlue", "DarkCyan", "DarkGreen", "Green", "GoldenRod",
34 "LightGreen", "LightSeaGreen", "LightSkyBlue", "Maroon",
35 "MediumSeaGreen", "SeaGreen", "LightSlateGrey"]
38 def generate_plots(spec, data):
39 """Generate all plots specified in the specification file.
41 :param spec: Specification read from the specification file.
42 :param data: Data to process.
43 :type spec: Specification
47 logging.info("Generating the plots ...")
48 for index, plot in enumerate(spec.plots):
50 logging.info(" Plot nr {0}: {1}".format(index + 1,
51 plot.get("title", "")))
52 plot["limits"] = spec.configuration["limits"]
53 eval(plot["algorithm"])(plot, data)
54 logging.info(" Done.")
55 except NameError as err:
56 logging.error("Probably algorithm '{alg}' is not defined: {err}".
57 format(alg=plot["algorithm"], err=repr(err)))
61 def plot_performance_box(plot, input_data):
62 """Generate the plot(s) with algorithm: plot_performance_box
63 specified in the specification file.
65 :param plot: Plot to generate.
66 :param input_data: Data to process.
67 :type plot: pandas.Series
68 :type input_data: InputData
72 plot_title = plot.get("title", "")
73 logging.info(" Creating the data set for the {0} '{1}'.".
74 format(plot.get("type", ""), plot_title))
75 data = input_data.filter_data(plot)
77 logging.error("No data.")
80 # Prepare the data for the plot
86 if y_vals.get(test["parent"], None) is None:
87 y_vals[test["parent"]] = list()
88 y_tags[test["parent"]] = test.get("tags", None)
90 if test["type"] in ("NDRPDR", ):
91 if "-pdr" in plot_title.lower():
92 y_vals[test["parent"]].\
93 append(test["throughput"]["PDR"]["LOWER"])
94 elif "-ndr" in plot_title.lower():
95 y_vals[test["parent"]]. \
96 append(test["throughput"]["NDR"]["LOWER"])
101 except (KeyError, TypeError):
102 y_vals[test["parent"]].append(None)
105 order = plot.get("sort", None)
107 y_sorted = OrderedDict()
108 y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
111 for suite, tags in y_tags_l.items():
113 tag = tag.split(" ")[-1]
114 if tag.lower() in tags:
117 if tag.lower() not in tags:
120 y_sorted[suite] = y_vals.pop(suite)
123 except KeyError as err:
124 logging.error("Not found: {0}".format(repr(err)))
130 # Add None to the lists with missing data
132 for val in y_sorted.values():
133 if len(val) > max_len:
135 for key, val in y_sorted.items():
136 if len(val) < max_len:
137 val.extend([None for _ in range(max_len - len(val))])
141 df = pd.DataFrame(y_sorted)
144 for i, col in enumerate(df.columns):
145 name = "{0}. {1}".format(i + 1, col.lower().replace('-ndrpdrdisc', '').
146 replace('-ndrpdr', ''))
148 traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
149 y=[y / 1000000 if y else None for y in df[col]],
153 val_max = max(df[col])
154 except ValueError as err:
155 logging.error(repr(err))
158 y_max.append(int(val_max / 1000000) + 1)
162 layout = deepcopy(plot["layout"])
163 if layout.get("title", None):
164 layout["title"] = "<b>Packet Throughput:</b> {0}". \
165 format(layout["title"])
167 layout["yaxis"]["range"] = [0, max(y_max)]
168 plpl = plgo.Figure(data=traces, layout=layout)
171 logging.info(" Writing file '{0}{1}'.".
172 format(plot["output-file"], plot["output-file-type"]))
173 ploff.plot(plpl, show_link=False, auto_open=False,
174 filename='{0}{1}'.format(plot["output-file"],
175 plot["output-file-type"]))
176 except PlotlyError as err:
177 logging.error(" Finished with error: {}".
178 format(repr(err).replace("\n", " ")))
182 def plot_latency_error_bars(plot, input_data):
183 """Generate the plot(s) with algorithm: plot_latency_error_bars
184 specified in the specification file.
186 :param plot: Plot to generate.
187 :param input_data: Data to process.
188 :type plot: pandas.Series
189 :type input_data: InputData
193 plot_title = plot.get("title", "")
194 logging.info(" Creating the data set for the {0} '{1}'.".
195 format(plot.get("type", ""), plot_title))
196 data = input_data.filter_data(plot)
198 logging.error("No data.")
201 # Prepare the data for the plot
208 logging.debug("test['latency']: {0}\n".
209 format(test["latency"]))
210 except ValueError as err:
211 logging.warning(repr(err))
212 if y_tmp_vals.get(test["parent"], None) is None:
213 y_tmp_vals[test["parent"]] = [
214 list(), # direction1, min
215 list(), # direction1, avg
216 list(), # direction1, max
217 list(), # direction2, min
218 list(), # direction2, avg
219 list() # direction2, max
221 y_tags[test["parent"]] = test.get("tags", None)
223 if test["type"] in ("NDRPDR", ):
224 if "-pdr" in plot_title.lower():
226 elif "-ndr" in plot_title.lower():
229 logging.warning("Invalid test type: {0}".
230 format(test["type"]))
232 y_tmp_vals[test["parent"]][0].append(
233 test["latency"][ttype]["direction1"]["min"])
234 y_tmp_vals[test["parent"]][1].append(
235 test["latency"][ttype]["direction1"]["avg"])
236 y_tmp_vals[test["parent"]][2].append(
237 test["latency"][ttype]["direction1"]["max"])
238 y_tmp_vals[test["parent"]][3].append(
239 test["latency"][ttype]["direction2"]["min"])
240 y_tmp_vals[test["parent"]][4].append(
241 test["latency"][ttype]["direction2"]["avg"])
242 y_tmp_vals[test["parent"]][5].append(
243 test["latency"][ttype]["direction2"]["max"])
245 logging.warning("Invalid test type: {0}".
246 format(test["type"]))
248 except (KeyError, TypeError) as err:
249 logging.warning(repr(err))
250 logging.debug("y_tmp_vals: {0}\n".format(y_tmp_vals))
252 order = plot.get("sort", None)
254 y_sorted = OrderedDict()
255 y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
258 for suite, tags in y_tags_l.items():
260 tag = tag.split(" ")[-1]
261 if tag.lower() in tags:
264 if tag.lower() not in tags:
267 y_sorted[suite] = y_tmp_vals.pop(suite)
270 except KeyError as err:
271 logging.error("Not found: {0}".format(repr(err)))
275 y_sorted = y_tmp_vals
277 logging.debug("y_sorted: {0}\n".format(y_sorted))
282 for key, val in y_sorted.items():
283 key = "-".join(key.split("-")[1:-1])
284 x_vals.append(key) # dir 1
285 y_vals.append(mean(val[1]) if val[1] else None)
286 y_mins.append(mean(val[0]) if val[0] else None)
287 y_maxs.append(mean(val[2]) if val[2] else None)
288 x_vals.append(key) # dir 2
289 y_vals.append(mean(val[4]) if val[4] else None)
290 y_mins.append(mean(val[3]) if val[3] else None)
291 y_maxs.append(mean(val[5]) if val[5] else None)
293 logging.debug("x_vals :{0}\n".format(x_vals))
294 logging.debug("y_vals :{0}\n".format(y_vals))
295 logging.debug("y_mins :{0}\n".format(y_mins))
296 logging.debug("y_maxs :{0}\n".format(y_maxs))
300 for idx in range(len(x_vals)):
301 if not bool(int(idx % 2)):
302 direction = "West - East"
304 direction = "East - West"
305 hovertext = ("Test: {test}<br>"
306 "Direction: {dir}<br>".format(test=x_vals[idx],
308 if isinstance(y_maxs[idx], float):
309 hovertext += "Max: {max:.2f}uSec<br>".format(max=y_maxs[idx])
310 if isinstance(y_vals[idx], float):
311 hovertext += "Avg: {avg:.2f}uSec<br>".format(avg=y_vals[idx])
312 if isinstance(y_mins[idx], float):
313 hovertext += "Min: {min:.2f}uSec".format(min=y_mins[idx])
315 if isinstance(y_maxs[idx], float) and isinstance(y_vals[idx], float):
316 array = [y_maxs[idx] - y_vals[idx], ]
319 if isinstance(y_mins[idx], float) and isinstance(y_vals[idx], float):
320 arrayminus = [y_vals[idx] - y_mins[idx], ]
322 arrayminus = [None, ]
323 logging.debug("y_vals[{1}] :{0}\n".format(y_vals[idx], idx))
324 logging.debug("array :{0}\n".format(array))
325 logging.debug("arrayminus :{0}\n".format(arrayminus))
326 traces.append(plgo.Scatter(
330 legendgroup=x_vals[idx],
331 showlegend=bool(int(idx % 2)),
337 arrayminus=arrayminus,
338 color=COLORS[int(idx / 2)]
342 color=COLORS[int(idx / 2)],
347 annotations.append(dict(
354 text="E-W" if bool(int(idx % 2)) else "W-E",
364 logging.info(" Writing file '{0}{1}'.".
365 format(plot["output-file"], plot["output-file-type"]))
366 layout = deepcopy(plot["layout"])
367 if layout.get("title", None):
368 layout["title"] = "<b>Packet Latency:</b> {0}".\
369 format(layout["title"])
370 layout["annotations"] = annotations
371 plpl = plgo.Figure(data=traces, layout=layout)
375 show_link=False, auto_open=False,
376 filename='{0}{1}'.format(plot["output-file"],
377 plot["output-file-type"]))
378 except PlotlyError as err:
379 logging.error(" Finished with error: {}".
380 format(str(err).replace("\n", " ")))
384 def plot_throughput_speedup_analysis(plot, input_data):
385 """Generate the plot(s) with algorithm:
386 plot_throughput_speedup_analysis
387 specified in the specification file.
389 :param plot: Plot to generate.
390 :param input_data: Data to process.
391 :type plot: pandas.Series
392 :type input_data: InputData
396 plot_title = plot.get("title", "")
397 logging.info(" Creating the data set for the {0} '{1}'.".
398 format(plot.get("type", ""), plot_title))
399 data = input_data.filter_data(plot)
401 logging.error("No data.")
409 if y_vals.get(test["parent"], None) is None:
410 y_vals[test["parent"]] = {"1": list(),
413 y_tags[test["parent"]] = test.get("tags", None)
415 if test["type"] in ("NDRPDR",):
416 if "-pdr" in plot_title.lower():
418 elif "-ndr" in plot_title.lower():
422 if "1C" in test["tags"]:
423 y_vals[test["parent"]]["1"]. \
424 append(test["throughput"][ttype]["LOWER"])
425 elif "2C" in test["tags"]:
426 y_vals[test["parent"]]["2"]. \
427 append(test["throughput"][ttype]["LOWER"])
428 elif "4C" in test["tags"]:
429 y_vals[test["parent"]]["4"]. \
430 append(test["throughput"][ttype]["LOWER"])
431 except (KeyError, TypeError):
435 logging.warning("No data for the plot '{}'".
436 format(plot.get("title", "")))
440 for test_name, test_vals in y_vals.items():
441 for key, test_val in test_vals.items():
443 avg_val = sum(test_val) / len(test_val)
444 y_vals[test_name][key] = avg_val
445 ideal = avg_val / (int(key) * 1000000.0)
446 if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
447 y_1c_max[test_name] = ideal
453 pci_limit = plot["limits"]["pci"]["pci-g3-x8"]
454 for test_name, test_vals in y_vals.items():
456 name = "-".join(test_name.split('-')[1:-1])
459 y_val_1 = test_vals["1"] / 1000000.0
460 y_val_2 = test_vals["2"] / 1000000.0 if test_vals["2"] else None
461 y_val_4 = test_vals["4"] / 1000000.0 if test_vals["4"] else None
463 vals[name]["val"] = [y_val_1, y_val_2, y_val_4]
464 vals[name]["rel"] = [1.0, None, None]
465 vals[name]["ideal"] = [y_1c_max[test_name],
466 y_1c_max[test_name] * 2,
467 y_1c_max[test_name] * 4]
468 vals[name]["diff"] = \
469 [(y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None]
472 val_max = max(max(vals[name]["val"], vals[name]["ideal"]))
473 except ValueError as err:
477 y_max.append(int((val_max / 10) + 1) * 10)
480 vals[name]["rel"][1] = round(y_val_2 / y_val_1, 2)
481 vals[name]["diff"][1] = \
482 (y_val_2 - vals[name]["ideal"][1]) * 100 / y_val_2
484 vals[name]["rel"][2] = round(y_val_4 / y_val_1, 2)
485 vals[name]["diff"][2] = \
486 (y_val_4 - vals[name]["ideal"][2]) * 100 / y_val_4
489 if "x520" in test_name:
490 limit = plot["limits"]["nic"]["x520"]
491 elif "x710" in test_name:
492 limit = plot["limits"]["nic"]["x710"]
493 elif "xxv710" in test_name:
494 limit = plot["limits"]["nic"]["xxv710"]
495 elif "xl710" in test_name:
496 limit = plot["limits"]["nic"]["xl710"]
499 if limit > nic_limit:
502 mul = 2 if "ge2p" in test_name else 1
503 if "10ge" in test_name:
504 limit = plot["limits"]["link"]["10ge"] * mul
505 elif "25ge" in test_name:
506 limit = plot["limits"]["link"]["25ge"] * mul
507 elif "40ge" in test_name:
508 limit = plot["limits"]["link"]["40ge"] * mul
509 elif "100ge" in test_name:
510 limit = plot["limits"]["link"]["100ge"] * mul
513 if limit > lnk_limit:
517 order = plot.get("sort", None)
519 y_sorted = OrderedDict()
520 y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
522 for test, tags in y_tags_l.items():
523 if tag.lower() in tags:
524 name = "-".join(test.split('-')[1:-1])
526 y_sorted[name] = vals.pop(name)
528 except KeyError as err:
529 logging.error("Not found: {0}".format(err))
541 threshold = 1.1 * max(y_max) # 10%
542 except ValueError as err:
545 nic_limit /= 1000000.0
546 if nic_limit < threshold:
547 traces.append(plgo.Scatter(
549 y=[nic_limit, ] * len(x_vals),
550 name="NIC: {0:.2f}Mpps".format(nic_limit),
559 annotations.append(dict(
566 text="NIC: {0:.2f}Mpps".format(nic_limit),
574 y_max.append(int((nic_limit / 10) + 1) * 10)
576 lnk_limit /= 1000000.0
577 if lnk_limit < threshold:
578 traces.append(plgo.Scatter(
580 y=[lnk_limit, ] * len(x_vals),
581 name="Link: {0:.2f}Mpps".format(lnk_limit),
590 annotations.append(dict(
597 text="Link: {0:.2f}Mpps".format(lnk_limit),
605 y_max.append(int((lnk_limit / 10) + 1) * 10)
607 pci_limit /= 1000000.0
608 if pci_limit < threshold:
609 traces.append(plgo.Scatter(
611 y=[pci_limit, ] * len(x_vals),
612 name="PCIe: {0:.2f}Mpps".format(pci_limit),
621 annotations.append(dict(
628 text="PCIe: {0:.2f}Mpps".format(pci_limit),
636 y_max.append(int((pci_limit / 10) + 1) * 10)
638 # Perfect and measured:
640 for name, val in y_sorted.iteritems():
642 for idx in range(len(val["val"])):
644 if isinstance(val["val"][idx], float):
645 htext += "value: {0:.2f}Mpps<br>".format(val["val"][idx])
646 if isinstance(val["diff"][idx], float):
647 htext += "diff: {0:.0f}%<br>".format(round(val["diff"][idx]))
648 if isinstance(val["rel"][idx], float):
649 htext += "speedup: {0:.2f}".format(val["rel"][idx])
650 hovertext.append(htext)
651 traces.append(plgo.Scatter(x=x_vals,
655 mode="lines+markers",
664 hoverinfo="text+name"
666 traces.append(plgo.Scatter(x=x_vals,
668 name="{0} perfect".format(name),
676 text=["perfect: {0:.2f}Mpps".format(y)
677 for y in val["ideal"]],
684 logging.info(" Writing file '{0}{1}'.".
685 format(plot["output-file"], plot["output-file-type"]))
686 layout = deepcopy(plot["layout"])
687 if layout.get("title", None):
688 layout["title"] = "<b>Speedup Multi-core:</b> {0}". \
689 format(layout["title"])
690 layout["annotations"].extend(annotations)
691 plpl = plgo.Figure(data=traces, layout=layout)
695 show_link=False, auto_open=False,
696 filename='{0}{1}'.format(plot["output-file"],
697 plot["output-file-type"]))
698 except PlotlyError as err:
699 logging.error(" Finished with error: {}".
700 format(str(err).replace("\n", " ")))
704 def plot_http_server_performance_box(plot, input_data):
705 """Generate the plot(s) with algorithm: plot_http_server_performance_box
706 specified in the specification file.
708 :param plot: Plot to generate.
709 :param input_data: Data to process.
710 :type plot: pandas.Series
711 :type input_data: InputData
715 logging.info(" Creating the data set for the {0} '{1}'.".
716 format(plot.get("type", ""), plot.get("title", "")))
717 data = input_data.filter_data(plot)
719 logging.error("No data.")
722 # Prepare the data for the plot
727 if y_vals.get(test["name"], None) is None:
728 y_vals[test["name"]] = list()
730 y_vals[test["name"]].append(test["result"])
731 except (KeyError, TypeError):
732 y_vals[test["name"]].append(None)
734 # Add None to the lists with missing data
736 for val in y_vals.values():
737 if len(val) > max_len:
739 for key, val in y_vals.items():
740 if len(val) < max_len:
741 val.extend([None for _ in range(max_len - len(val))])
745 df = pd.DataFrame(y_vals)
747 for i, col in enumerate(df.columns):
748 name = "{0}. {1}".format(i + 1, col.lower().replace('-cps', '').
750 traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
756 plpl = plgo.Figure(data=traces, layout=plot["layout"])
759 logging.info(" Writing file '{0}{1}'.".
760 format(plot["output-file"], plot["output-file-type"]))
761 ploff.plot(plpl, show_link=False, auto_open=False,
762 filename='{0}{1}'.format(plot["output-file"],
763 plot["output-file-type"]))
764 except PlotlyError as err:
765 logging.error(" Finished with error: {}".
766 format(str(err).replace("\n", " ")))