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.exceptions import PlotlyError
32 from pal_utils import mean, stdev
61 REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)-')
64 def generate_plots(spec, data):
65 """Generate all plots specified in the specification file.
67 :param spec: Specification read from the specification file.
68 :param data: Data to process.
69 :type spec: Specification
74 u"plot_nf_reconf_box_name": plot_nf_reconf_box_name,
75 u"plot_perf_box_name": plot_perf_box_name,
76 u"plot_tsa_name": plot_tsa_name,
77 u"plot_http_server_perf_box": plot_http_server_perf_box,
78 u"plot_nf_heatmap": plot_nf_heatmap,
79 u"plot_hdrh_lat_by_percentile": plot_hdrh_lat_by_percentile
82 logging.info(u"Generating the plots ...")
83 for index, plot in enumerate(spec.plots):
85 logging.info(f" Plot nr {index + 1}: {plot.get(u'title', u'')}")
86 plot[u"limits"] = spec.configuration[u"limits"]
87 generator[plot[u"algorithm"]](plot, data)
88 logging.info(u" Done.")
89 except NameError as err:
91 f"Probably algorithm {plot[u'algorithm']} is not defined: "
94 logging.info(u"Done.")
97 def plot_hdrh_lat_by_percentile(plot, input_data):
98 """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile
99 specified in the specification file.
101 :param plot: Plot to generate.
102 :param input_data: Data to process.
103 :type plot: pandas.Series
104 :type input_data: InputData
109 f" Creating the data set for the {plot.get(u'type', u'')} "
110 f"{plot.get(u'title', u'')}."
112 if plot.get(u"include", None):
113 data = input_data.filter_tests_by_name(
115 params=[u"name", u"latency", u"parent", u"tags", u"type"]
117 elif plot.get(u"filter", None):
118 data = input_data.filter_data(
120 params=[u"name", u"latency", u"parent", u"tags", u"type"],
121 continue_on_error=True
124 job = list(plot[u"data"].keys())[0]
125 build = str(plot[u"data"][job][0])
126 data = input_data.tests(job, build)
128 if data is None or len(data) == 0:
129 logging.error(u"No data.")
133 u"LAT0": u"No-load.",
134 u"PDR10": u"Low-load, 10% PDR.",
135 u"PDR50": u"Mid-load, 50% PDR.",
136 u"PDR90": u"High-load, 90% PDR.",
137 u"PDR": u"Full-load, 100% PDR.",
138 u"NDR10": u"Low-load, 10% NDR.",
139 u"NDR50": u"Mid-load, 50% NDR.",
140 u"NDR90": u"High-load, 90% NDR.",
141 u"NDR": u"Full-load, 100% NDR."
151 file_links = plot.get(u"output-file-links", None)
152 target_links = plot.get(u"target-links", None)
156 if test[u"type"] not in (u"NDRPDR",):
157 logging.warning(f"Invalid test type: {test[u'type']}")
159 name = re.sub(REGEX_NIC, u"", test[u"parent"].
160 replace(u'-ndrpdr', u'').replace(u'2n1l-', u''))
162 nic = re.search(REGEX_NIC, test[u"parent"]).group(1)
163 except (IndexError, AttributeError, KeyError, ValueError):
165 name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'')
167 logging.info(f" Generating the graph: {name_link}")
170 layout = deepcopy(plot[u"layout"])
172 for color, graph in enumerate(graphs):
173 for idx, direction in enumerate((u"direction1", u"direction2")):
177 f"<b>{desc[graph]}</b><br>"
178 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
179 f"Percentile: 0.0%<br>"
183 decoded = hdrh.histogram.HdrHistogram.decode(
184 test[u"latency"][graph][direction][u"hdrh"]
186 except hdrh.codec.HdrLengthException:
188 f"No data for direction {(u'W-E', u'E-W')[idx % 2]}"
192 for item in decoded.get_recorded_iterator():
193 percentile = item.percentile_level_iterated_to
194 if percentile > 99.9:
196 xaxis.append(percentile)
197 yaxis.append(item.value_iterated_to)
199 f"<b>{desc[graph]}</b><br>"
200 f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
201 f"Percentile: {percentile:.5f}%<br>"
202 f"Latency: {item.value_iterated_to}uSec"
210 legendgroup=desc[graph],
211 showlegend=bool(idx),
214 dash=u"dash" if idx % 2 else u"solid"
221 layout[u"title"][u"text"] = f"<b>Latency:</b> {name}"
222 fig.update_layout(layout)
225 file_name = f"{plot[u'output-file']}-{name_link}.html"
226 logging.info(f" Writing file {file_name}")
230 ploff.plot(fig, show_link=False, auto_open=False,
232 # Add link to the file:
233 if file_links and target_links:
234 with open(file_links, u"a") as file_handler:
237 f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n"
239 except FileNotFoundError as err:
241 f"Not possible to write the link to the file "
242 f"{file_links}\n{err}"
244 except PlotlyError as err:
245 logging.error(f" Finished with error: {repr(err)}")
247 except hdrh.codec.HdrLengthException as err:
248 logging.warning(repr(err))
251 except (ValueError, KeyError) as err:
252 logging.warning(repr(err))
256 def plot_nf_reconf_box_name(plot, input_data):
257 """Generate the plot(s) with algorithm: plot_nf_reconf_box_name
258 specified in the specification file.
260 :param plot: Plot to generate.
261 :param input_data: Data to process.
262 :type plot: pandas.Series
263 :type input_data: InputData
268 f" Creating the data set for the {plot.get(u'type', u'')} "
269 f"{plot.get(u'title', u'')}."
271 data = input_data.filter_tests_by_name(
272 plot, params=[u"result", u"parent", u"tags", u"type"]
275 logging.error(u"No data.")
278 # Prepare the data for the plot
279 y_vals = OrderedDict()
284 if y_vals.get(test[u"parent"], None) is None:
285 y_vals[test[u"parent"]] = list()
286 loss[test[u"parent"]] = list()
288 y_vals[test[u"parent"]].append(test[u"result"][u"time"])
289 loss[test[u"parent"]].append(test[u"result"][u"loss"])
290 except (KeyError, TypeError):
291 y_vals[test[u"parent"]].append(None)
293 # Add None to the lists with missing data
295 nr_of_samples = list()
296 for val in y_vals.values():
297 if len(val) > max_len:
299 nr_of_samples.append(len(val))
300 for val in y_vals.values():
301 if len(val) < max_len:
302 val.extend([None for _ in range(max_len - len(val))])
306 df_y = pd.DataFrame(y_vals)
308 for i, col in enumerate(df_y.columns):
309 tst_name = re.sub(REGEX_NIC, u"",
310 col.lower().replace(u'-ndrpdr', u'').
311 replace(u'2n1l-', u''))
313 traces.append(plgo.Box(
314 x=[str(i + 1) + u'.'] * len(df_y[col]),
315 y=[y if y else None for y in df_y[col]],
318 f"({nr_of_samples[i]:02d} "
319 f"run{u's' if nr_of_samples[i] > 1 else u''}, "
320 f"packets lost average: {mean(loss[col]):.1f}) "
321 f"{u'-'.join(tst_name.split(u'-')[3:-2])}"
327 layout = deepcopy(plot[u"layout"])
328 layout[u"title"] = f"<b>Time Lost:</b> {layout[u'title']}"
329 layout[u"yaxis"][u"title"] = u"<b>Effective Blocked Time [s]</b>"
330 layout[u"legend"][u"font"][u"size"] = 14
331 layout[u"yaxis"].pop(u"range")
332 plpl = plgo.Figure(data=traces, layout=layout)
335 file_type = plot.get(u"output-file-type", u".html")
336 logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
341 filename=f"{plot[u'output-file']}{file_type}"
343 except PlotlyError as err:
345 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
350 def plot_perf_box_name(plot, input_data):
351 """Generate the plot(s) with algorithm: plot_perf_box_name
352 specified in the specification file.
354 :param plot: Plot to generate.
355 :param input_data: Data to process.
356 :type plot: pandas.Series
357 :type input_data: InputData
362 f" Creating data set for the {plot.get(u'type', u'')} "
363 f"{plot.get(u'title', u'')}."
365 data = input_data.filter_tests_by_name(
367 params=[u"throughput", u"gbps", u"result", u"parent", u"tags", u"type"])
369 logging.error(u"No data.")
372 # Prepare the data for the plot
373 y_vals = OrderedDict()
378 if y_vals.get(test[u"parent"], None) is None:
379 y_vals[test[u"parent"]] = list()
381 if test[u"type"] in (u"NDRPDR", ):
382 test_type = u"NDRPDR"
383 plot_title = plot.get(u"title", u"").lower()
385 if u"-pdr" in plot_title:
387 elif u"-ndr" in plot_title:
392 if u"-gbps" in plot_title:
396 value = u"throughput"
399 y_vals[test[u"parent"]].append(
400 test[value][ttype][u"LOWER"] * multiplier
403 elif test[u"type"] in (u"SOAK", ):
404 y_vals[test[u"parent"]].\
405 append(test[u"throughput"][u"LOWER"])
408 elif test[u"type"] in (u"HOSTSTACK", ):
409 if u"LDPRELOAD" in test[u"tags"]:
410 y_vals[test[u"parent"]].append(
411 float(test[u"result"][u"bits_per_second"]) / 1e3
413 elif u"VPPECHO" in test[u"tags"]:
414 y_vals[test[u"parent"]].append(
415 (float(test[u"result"][u"client"][u"tx_data"])
417 ((float(test[u"result"][u"client"][u"time"]) +
418 float(test[u"result"][u"server"][u"time"])) /
421 test_type = u"HOSTSTACK"
426 except (KeyError, TypeError):
427 y_vals[test[u"parent"]].append(None)
429 # Add None to the lists with missing data
431 nr_of_samples = list()
432 for val in y_vals.values():
433 if len(val) > max_len:
435 nr_of_samples.append(len(val))
436 for val in y_vals.values():
437 if len(val) < max_len:
438 val.extend([None for _ in range(max_len - len(val))])
442 df_y = pd.DataFrame(y_vals)
445 for i, col in enumerate(df_y.columns):
446 tst_name = re.sub(REGEX_NIC, u"",
447 col.lower().replace(u'-ndrpdr', u'').
448 replace(u'2n1l-', u''))
450 x=[str(i + 1) + u'.'] * len(df_y[col]),
451 y=[y / 1e6 if y else None for y in df_y[col]],
454 f"({nr_of_samples[i]:02d} "
455 f"run{u's' if nr_of_samples[i] > 1 else u''}) "
460 if test_type in (u"SOAK", ):
461 kwargs[u"boxpoints"] = u"all"
463 traces.append(plgo.Box(**kwargs))
466 val_max = max(df_y[col])
468 y_max.append(int(val_max / 1e6) + 2)
469 except (ValueError, TypeError) as err:
470 logging.error(repr(err))
475 layout = deepcopy(plot[u"layout"])
476 if layout.get(u"title", None):
477 if test_type in (u"HOSTSTACK", ):
478 layout[u"title"] = f"<b>Bandwidth:</b> {layout[u'title']}"
480 layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
482 layout[u"yaxis"][u"range"] = [0, max(y_max)]
483 plpl = plgo.Figure(data=traces, layout=layout)
486 logging.info(f" Writing file {plot[u'output-file']}.html.")
491 filename=f"{plot[u'output-file']}.html"
493 except PlotlyError as err:
495 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
500 def plot_tsa_name(plot, input_data):
501 """Generate the plot(s) with algorithm:
503 specified in the specification file.
505 :param plot: Plot to generate.
506 :param input_data: Data to process.
507 :type plot: pandas.Series
508 :type input_data: InputData
512 plot_title = plot.get(u"title", u"")
514 f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
516 data = input_data.filter_tests_by_name(
518 params=[u"throughput", u"gbps", u"parent", u"tags", u"type"]
521 logging.error(u"No data.")
524 plot_title = plot_title.lower()
526 y_vals = OrderedDict()
530 if y_vals.get(test[u"parent"], None) is None:
531 y_vals[test[u"parent"]] = {
537 if test[u"type"] not in (u"NDRPDR",):
540 if u"-pdr" in plot_title:
542 elif u"-ndr" in plot_title:
547 if u"-gbps" in plot_title:
551 value = u"throughput"
554 if u"1C" in test[u"tags"]:
555 y_vals[test[u"parent"]][u"1"]. \
556 append(test[value][ttype][u"LOWER"] * multiplier)
557 elif u"2C" in test[u"tags"]:
558 y_vals[test[u"parent"]][u"2"]. \
559 append(test[value][ttype][u"LOWER"] * multiplier)
560 elif u"4C" in test[u"tags"]:
561 y_vals[test[u"parent"]][u"4"]. \
562 append(test[value][ttype][u"LOWER"] * multiplier)
563 except (KeyError, TypeError):
567 logging.warning(f"No data for the plot {plot.get(u'title', u'')}")
571 for test_name, test_vals in y_vals.items():
572 for key, test_val in test_vals.items():
574 avg_val = sum(test_val) / len(test_val)
575 y_vals[test_name][key] = [avg_val, len(test_val)]
576 ideal = avg_val / (int(key) * 1e6)
577 if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
578 y_1c_max[test_name] = ideal
584 pci_limit = plot[u"limits"][u"pci"][u"pci-g3-x8"]
585 for test_name, test_vals in y_vals.items():
587 if test_vals[u"1"][1]:
591 test_name.replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')
593 vals[name] = OrderedDict()
594 y_val_1 = test_vals[u"1"][0] / 1e6
595 y_val_2 = test_vals[u"2"][0] / 1e6 if test_vals[u"2"][0] \
597 y_val_4 = test_vals[u"4"][0] / 1e6 if test_vals[u"4"][0] \
600 vals[name][u"val"] = [y_val_1, y_val_2, y_val_4]
601 vals[name][u"rel"] = [1.0, None, None]
602 vals[name][u"ideal"] = [
604 y_1c_max[test_name] * 2,
605 y_1c_max[test_name] * 4
607 vals[name][u"diff"] = [
608 (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None
610 vals[name][u"count"] = [
617 val_max = max(vals[name][u"val"])
618 except ValueError as err:
619 logging.error(repr(err))
622 y_max.append(val_max)
625 vals[name][u"rel"][1] = round(y_val_2 / y_val_1, 2)
626 vals[name][u"diff"][1] = \
627 (y_val_2 - vals[name][u"ideal"][1]) * 100 / y_val_2
629 vals[name][u"rel"][2] = round(y_val_4 / y_val_1, 2)
630 vals[name][u"diff"][2] = \
631 (y_val_4 - vals[name][u"ideal"][2]) * 100 / y_val_4
632 except IndexError as err:
633 logging.warning(f"No data for {test_name}")
634 logging.warning(repr(err))
637 if u"x520" in test_name:
638 limit = plot[u"limits"][u"nic"][u"x520"]
639 elif u"x710" in test_name:
640 limit = plot[u"limits"][u"nic"][u"x710"]
641 elif u"xxv710" in test_name:
642 limit = plot[u"limits"][u"nic"][u"xxv710"]
643 elif u"xl710" in test_name:
644 limit = plot[u"limits"][u"nic"][u"xl710"]
645 elif u"x553" in test_name:
646 limit = plot[u"limits"][u"nic"][u"x553"]
647 elif u"cx556a" in test_name:
648 limit = plot[u"limits"][u"nic"][u"cx556a"]
651 if limit > nic_limit:
654 mul = 2 if u"ge2p" in test_name else 1
655 if u"10ge" in test_name:
656 limit = plot[u"limits"][u"link"][u"10ge"] * mul
657 elif u"25ge" in test_name:
658 limit = plot[u"limits"][u"link"][u"25ge"] * mul
659 elif u"40ge" in test_name:
660 limit = plot[u"limits"][u"link"][u"40ge"] * mul
661 elif u"100ge" in test_name:
662 limit = plot[u"limits"][u"link"][u"100ge"] * mul
665 if limit > lnk_limit:
673 if u"-gbps" not in plot_title:
675 threshold = 1.1 * max(y_max) # 10%
676 except ValueError as err:
680 traces.append(plgo.Scatter(
682 y=[nic_limit, ] * len(x_vals),
683 name=f"NIC: {nic_limit:.2f}Mpps",
692 annotations.append(dict(
699 text=f"NIC: {nic_limit:.2f}Mpps",
707 y_max.append(nic_limit)
710 if lnk_limit < threshold:
711 traces.append(plgo.Scatter(
713 y=[lnk_limit, ] * len(x_vals),
714 name=f"Link: {lnk_limit:.2f}Mpps",
723 annotations.append(dict(
730 text=f"Link: {lnk_limit:.2f}Mpps",
738 y_max.append(lnk_limit)
741 if (pci_limit < threshold and
742 (pci_limit < lnk_limit * 0.95 or lnk_limit > lnk_limit * 1.05)):
743 traces.append(plgo.Scatter(
745 y=[pci_limit, ] * len(x_vals),
746 name=f"PCIe: {pci_limit:.2f}Mpps",
755 annotations.append(dict(
762 text=f"PCIe: {pci_limit:.2f}Mpps",
770 y_max.append(pci_limit)
772 # Perfect and measured:
774 for name, val in vals.items():
777 for idx in range(len(val[u"val"])):
779 if isinstance(val[u"val"][idx], float):
781 f"No. of Runs: {val[u'count'][idx]}<br>"
782 f"Mean: {val[u'val'][idx]:.2f}Mpps<br>"
784 if isinstance(val[u"diff"][idx], float):
785 htext += f"Diff: {round(val[u'diff'][idx]):.0f}%<br>"
786 if isinstance(val[u"rel"][idx], float):
787 htext += f"Speedup: {val[u'rel'][idx]:.2f}"
788 hovertext.append(htext)
795 mode=u"lines+markers",
804 hoverinfo=u"text+name"
811 name=f"{name} perfect",
819 text=[f"Perfect: {y:.2f}Mpps" for y in val[u"ideal"]],
824 except (IndexError, ValueError, KeyError) as err:
825 logging.warning(f"No data for {name}\n{repr(err)}")
829 file_type = plot.get(u"output-file-type", u".html")
830 logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
831 layout = deepcopy(plot[u"layout"])
832 if layout.get(u"title", None):
833 layout[u"title"] = f"<b>Speedup Multi-core:</b> {layout[u'title']}"
834 layout[u"yaxis"][u"range"] = [0, int(max(y_max) * 1.1)]
835 layout[u"annotations"].extend(annotations)
836 plpl = plgo.Figure(data=traces, layout=layout)
843 filename=f"{plot[u'output-file']}{file_type}"
845 except PlotlyError as err:
847 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
852 def plot_http_server_perf_box(plot, input_data):
853 """Generate the plot(s) with algorithm: plot_http_server_perf_box
854 specified in the specification file.
856 :param plot: Plot to generate.
857 :param input_data: Data to process.
858 :type plot: pandas.Series
859 :type input_data: InputData
864 f" Creating the data set for the {plot.get(u'type', u'')} "
865 f"{plot.get(u'title', u'')}."
867 data = input_data.filter_data(plot)
869 logging.error(u"No data.")
872 # Prepare the data for the plot
877 if y_vals.get(test[u"name"], None) is None:
878 y_vals[test[u"name"]] = list()
880 y_vals[test[u"name"]].append(test[u"result"])
881 except (KeyError, TypeError):
882 y_vals[test[u"name"]].append(None)
884 # Add None to the lists with missing data
886 nr_of_samples = list()
887 for val in y_vals.values():
888 if len(val) > max_len:
890 nr_of_samples.append(len(val))
891 for val in y_vals.values():
892 if len(val) < max_len:
893 val.extend([None for _ in range(max_len - len(val))])
897 df_y = pd.DataFrame(y_vals)
899 for i, col in enumerate(df_y.columns):
902 f"({nr_of_samples[i]:02d} " \
903 f"run{u's' if nr_of_samples[i] > 1 else u''}) " \
904 f"{col.lower().replace(u'-ndrpdr', u'')}"
906 name_lst = name.split(u'-')
909 for segment in name_lst:
910 if (len(name) + len(segment) + 1) > 50 and split_name:
913 name += segment + u'-'
916 traces.append(plgo.Box(x=[str(i + 1) + u'.'] * len(df_y[col]),
922 plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
926 f" Writing file {plot[u'output-file']}"
927 f"{plot[u'output-file-type']}."
933 filename=f"{plot[u'output-file']}{plot[u'output-file-type']}"
935 except PlotlyError as err:
937 f" Finished with error: {repr(err)}".replace(u"\n", u" ")
942 def plot_nf_heatmap(plot, input_data):
943 """Generate the plot(s) with algorithm: plot_nf_heatmap
944 specified in the specification file.
946 :param plot: Plot to generate.
947 :param input_data: Data to process.
948 :type plot: pandas.Series
949 :type input_data: InputData
952 regex_cn = re.compile(r'^(\d*)R(\d*)C$')
953 regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
955 r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$')
960 f" Creating the data set for the {plot.get(u'type', u'')} "
961 f"{plot.get(u'title', u'')}."
963 data = input_data.filter_data(plot, continue_on_error=True)
964 if data is None or data.empty:
965 logging.error(u"No data.")
971 for tag in test[u"tags"]:
972 groups = re.search(regex_cn, tag)
974 chain = str(groups.group(1))
975 node = str(groups.group(2))
979 groups = re.search(regex_test_name, test[u"name"])
980 if groups and len(groups.groups()) == 3:
982 f"{str(groups.group(1))}-"
983 f"{str(groups.group(2))}-"
984 f"{str(groups.group(3))}"
988 if vals.get(chain, None) is None:
990 if vals[chain].get(node, None) is None:
991 vals[chain][node] = dict(
999 if plot[u"include-tests"] == u"MRR":
1000 result = test[u"result"][u"receive-rate"]
1001 elif plot[u"include-tests"] == u"PDR":
1002 result = test[u"throughput"][u"PDR"][u"LOWER"]
1003 elif plot[u"include-tests"] == u"NDR":
1004 result = test[u"throughput"][u"NDR"][u"LOWER"]
1011 vals[chain][node][u"vals"].append(result)
1014 logging.error(u"No data.")
1020 txt_chains.append(key_c)
1021 for key_n in vals[key_c].keys():
1022 txt_nodes.append(key_n)
1023 if vals[key_c][key_n][u"vals"]:
1024 vals[key_c][key_n][u"nr"] = len(vals[key_c][key_n][u"vals"])
1025 vals[key_c][key_n][u"mean"] = \
1026 round(mean(vals[key_c][key_n][u"vals"]) / 1000000, 1)
1027 vals[key_c][key_n][u"stdev"] = \
1028 round(stdev(vals[key_c][key_n][u"vals"]) / 1000000, 1)
1029 txt_nodes = list(set(txt_nodes))
1031 def sort_by_int(value):
1032 """Makes possible to sort a list of strings which represent integers.
1034 :param value: Integer as a string.
1036 :returns: Integer representation of input parameter 'value'.
1041 txt_chains = sorted(txt_chains, key=sort_by_int)
1042 txt_nodes = sorted(txt_nodes, key=sort_by_int)
1044 chains = [i + 1 for i in range(len(txt_chains))]
1045 nodes = [i + 1 for i in range(len(txt_nodes))]
1047 data = [list() for _ in range(len(chains))]
1048 for chain in chains:
1051 val = vals[txt_chains[chain - 1]][txt_nodes[node - 1]][u"mean"]
1052 except (KeyError, IndexError):
1054 data[chain - 1].append(val)
1057 my_green = [[0.0, u"rgb(235, 249, 242)"],
1058 [1.0, u"rgb(45, 134, 89)"]]
1060 my_blue = [[0.0, u"rgb(236, 242, 248)"],
1061 [1.0, u"rgb(57, 115, 172)"]]
1063 my_grey = [[0.0, u"rgb(230, 230, 230)"],
1064 [1.0, u"rgb(102, 102, 102)"]]
1067 annotations = list()
1069 text = (u"Test: {name}<br>"
1074 for chain, _ in enumerate(txt_chains):
1076 for node, _ in enumerate(txt_nodes):
1077 if data[chain][node] is not None:
1086 text=str(data[chain][node]),
1094 hover_line.append(text.format(
1095 name=vals[txt_chains[chain]][txt_nodes[node]][u"name"],
1096 nr=vals[txt_chains[chain]][txt_nodes[node]][u"nr"],
1097 val=data[chain][node],
1098 stdev=vals[txt_chains[chain]][txt_nodes[node]][u"stdev"]))
1099 hovertext.append(hover_line)
1107 title=plot.get(u"z-axis", u""),
1121 colorscale=my_green,
1127 for idx, item in enumerate(txt_nodes):
1145 for idx, item in enumerate(txt_chains):
1172 text=plot.get(u"x-axis", u""),
1189 text=plot.get(u"y-axis", u""),
1198 updatemenus = list([
1209 u"colorscale": [my_green, ],
1210 u"reversescale": False
1219 u"colorscale": [my_blue, ],
1220 u"reversescale": False
1229 u"colorscale": [my_grey, ],
1230 u"reversescale": False
1241 layout = deepcopy(plot[u"layout"])
1242 except KeyError as err:
1243 logging.error(f"Finished with error: No layout defined\n{repr(err)}")
1246 layout[u"annotations"] = annotations
1247 layout[u'updatemenus'] = updatemenus
1251 plpl = plgo.Figure(data=traces, layout=layout)
1254 logging.info(f" Writing file {plot[u'output-file']}.html")
1259 filename=f"{plot[u'output-file']}.html"
1261 except PlotlyError as err:
1263 f" Finished with error: {repr(err)}".replace(u"\n", u" ")