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 tables.
23 from collections import OrderedDict
24 from xml.etree import ElementTree as ET
25 from datetime import datetime as dt
26 from datetime import timedelta
27 from copy import deepcopy
29 import plotly.graph_objects as go
30 import plotly.offline as ploff
34 from numpy import nan, isnan
35 from yaml import load, FullLoader, YAMLError
37 from pal_utils import mean, stdev, classify_anomalies, \
38 convert_csv_to_pretty_txt, relative_change_stdev, relative_change
41 REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)')
44 def generate_tables(spec, data):
45 """Generate all tables specified in the specification file.
47 :param spec: Specification read from the specification file.
48 :param data: Data to process.
49 :type spec: Specification
54 u"table_merged_details": table_merged_details,
55 u"table_soak_vs_ndr": table_soak_vs_ndr,
56 u"table_perf_trending_dash": table_perf_trending_dash,
57 u"table_perf_trending_dash_html": table_perf_trending_dash_html,
58 u"table_last_failed_tests": table_last_failed_tests,
59 u"table_failed_tests": table_failed_tests,
60 u"table_failed_tests_html": table_failed_tests_html,
61 u"table_oper_data_html": table_oper_data_html,
62 u"table_comparison": table_comparison,
63 u"table_weekly_comparison": table_weekly_comparison,
64 u"table_job_spec_duration": table_job_spec_duration
67 logging.info(u"Generating the tables ...")
68 for table in spec.tables:
70 if table[u"algorithm"] == u"table_weekly_comparison":
71 table[u"testbeds"] = spec.environment.get(u"testbeds", None)
72 generator[table[u"algorithm"]](table, data)
73 except NameError as err:
75 f"Probably algorithm {table[u'algorithm']} is not defined: "
78 logging.info(u"Done.")
81 def table_job_spec_duration(table, input_data):
82 """Generate the table(s) with algorithm: table_job_spec_duration
83 specified in the specification file.
85 :param table: Table to generate.
86 :param input_data: Data to process.
87 :type table: pandas.Series
88 :type input_data: InputData
93 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
95 jb_type = table.get(u"jb-type", None)
98 if jb_type == u"iterative":
99 for line in table.get(u"lines", tuple()):
101 u"name": line.get(u"job-spec", u""),
104 for job, builds in line.get(u"data-set", dict()).items():
105 for build_nr in builds:
107 minutes = input_data.metadata(
109 )[u"elapsedtime"] // 60000
110 except (KeyError, IndexError, ValueError, AttributeError):
112 tbl_itm[u"data"].append(minutes)
113 tbl_itm[u"mean"] = mean(tbl_itm[u"data"])
114 tbl_itm[u"stdev"] = stdev(tbl_itm[u"data"])
115 tbl_lst.append(tbl_itm)
116 elif jb_type == u"coverage":
117 job = table.get(u"data", None)
120 for line in table.get(u"lines", tuple()):
123 u"name": line.get(u"job-spec", u""),
124 u"mean": input_data.metadata(
125 list(job.keys())[0], str(line[u"build"])
126 )[u"elapsedtime"] // 60000,
127 u"stdev": float(u"nan")
129 tbl_itm[u"data"] = [tbl_itm[u"mean"], ]
130 except (KeyError, IndexError, ValueError, AttributeError):
132 tbl_lst.append(tbl_itm)
134 logging.warning(f"Wrong type of job-spec: {jb_type}. Skipping.")
139 f"{int(line[u'mean'] // 60):02d}:{int(line[u'mean'] % 60):02d}"
140 if math.isnan(line[u"stdev"]):
144 f"{int(line[u'stdev'] //60):02d}:{int(line[u'stdev'] % 60):02d}"
153 f"{len(itm[u'data'])}",
154 f"{itm[u'mean']} +- {itm[u'stdev']}"
155 if itm[u"stdev"] != u"" else f"{itm[u'mean']}"
158 txt_table = prettytable.PrettyTable(
159 [u"Job Specification", u"Nr of Runs", u"Duration [HH:MM]"]
162 txt_table.add_row(row)
163 txt_table.align = u"r"
164 txt_table.align[u"Job Specification"] = u"l"
166 file_name = f"{table.get(u'output-file', u'')}.txt"
167 with open(file_name, u"wt", encoding='utf-8') as txt_file:
168 txt_file.write(str(txt_table))
171 def table_oper_data_html(table, input_data):
172 """Generate the table(s) with algorithm: html_table_oper_data
173 specified in the specification file.
175 :param table: Table to generate.
176 :param input_data: Data to process.
177 :type table: pandas.Series
178 :type input_data: InputData
181 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
184 f" Creating the data set for the {table.get(u'type', u'')} "
185 f"{table.get(u'title', u'')}."
187 data = input_data.filter_data(
189 params=[u"name", u"parent", u"telemetry-show-run", u"type"],
190 continue_on_error=True
194 data = input_data.merge_data(data)
196 sort_tests = table.get(u"sort", None)
200 ascending=(sort_tests == u"ascending")
202 data.sort_index(**args)
204 suites = input_data.filter_data(
206 continue_on_error=True,
211 suites = input_data.merge_data(suites)
213 def _generate_html_table(tst_data):
214 """Generate an HTML table with operational data for the given test.
216 :param tst_data: Test data to be used to generate the table.
217 :type tst_data: pandas.Series
218 :returns: HTML table with operational data.
223 u"header": u"#7eade7",
224 u"empty": u"#ffffff",
225 u"body": (u"#e9f1fb", u"#d4e4f7")
228 tbl = ET.Element(u"table", attrib=dict(width=u"100%", border=u"0"))
230 trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"header"]))
231 thead = ET.SubElement(
232 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
234 thead.text = tst_data[u"name"]
236 trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
237 thead = ET.SubElement(
238 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
242 if tst_data.get(u"telemetry-show-run", None) is None or \
243 isinstance(tst_data[u"telemetry-show-run"], str):
244 trow = ET.SubElement(
245 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
247 tcol = ET.SubElement(
248 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
250 tcol.text = u"No Data"
252 trow = ET.SubElement(
253 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
255 thead = ET.SubElement(
256 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
258 font = ET.SubElement(
259 thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
262 return str(ET.tostring(tbl, encoding=u"unicode"))
269 u"Cycles per Packet",
270 u"Average Vector Size"
273 for dut_data in tst_data[u"telemetry-show-run"].values():
274 trow = ET.SubElement(
275 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
277 tcol = ET.SubElement(
278 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
280 if dut_data.get(u"runtime", None) is None:
281 tcol.text = u"No Data"
285 for item in dut_data[u"runtime"].get(u"data", tuple()):
286 tid = int(item[u"labels"][u"thread_id"])
287 if runtime.get(tid, None) is None:
288 runtime[tid] = dict()
289 gnode = item[u"labels"][u"graph_node"]
290 if runtime[tid].get(gnode, None) is None:
291 runtime[tid][gnode] = dict()
293 runtime[tid][gnode][item[u"name"]] = float(item[u"value"])
295 runtime[tid][gnode][item[u"name"]] = item[u"value"]
297 threads = dict({idx: list() for idx in range(len(runtime))})
298 for idx, run_data in runtime.items():
299 for gnode, gdata in run_data.items():
300 if gdata[u"vectors"] > 0:
301 clocks = gdata[u"clocks"] / gdata[u"vectors"]
302 elif gdata[u"calls"] > 0:
303 clocks = gdata[u"clocks"] / gdata[u"calls"]
304 elif gdata[u"suspends"] > 0:
305 clocks = gdata[u"clocks"] / gdata[u"suspends"]
308 if gdata[u"calls"] > 0:
309 vectors_call = gdata[u"vectors"] / gdata[u"calls"]
312 if int(gdata[u"calls"]) + int(gdata[u"vectors"]) + \
313 int(gdata[u"suspends"]):
314 threads[idx].append([
316 int(gdata[u"calls"]),
317 int(gdata[u"vectors"]),
318 int(gdata[u"suspends"]),
323 bold = ET.SubElement(tcol, u"b")
325 f"Host IP: {dut_data.get(u'host', '')}, "
326 f"Socket: {dut_data.get(u'socket', '')}"
328 trow = ET.SubElement(
329 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
331 thead = ET.SubElement(
332 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
336 for thread_nr, thread in threads.items():
337 trow = ET.SubElement(
338 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
340 tcol = ET.SubElement(
341 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
343 bold = ET.SubElement(tcol, u"b")
344 bold.text = u"main" if thread_nr == 0 else f"worker_{thread_nr}"
345 trow = ET.SubElement(
346 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
348 for idx, col in enumerate(tbl_hdr):
349 tcol = ET.SubElement(
351 attrib=dict(align=u"right" if idx else u"left")
353 font = ET.SubElement(
354 tcol, u"font", attrib=dict(size=u"2")
356 bold = ET.SubElement(font, u"b")
358 for row_nr, row in enumerate(thread):
359 trow = ET.SubElement(
361 attrib=dict(bgcolor=colors[u"body"][row_nr % 2])
363 for idx, col in enumerate(row):
364 tcol = ET.SubElement(
366 attrib=dict(align=u"right" if idx else u"left")
368 font = ET.SubElement(
369 tcol, u"font", attrib=dict(size=u"2")
371 if isinstance(col, float):
372 font.text = f"{col:.2f}"
375 trow = ET.SubElement(
376 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
378 thead = ET.SubElement(
379 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
383 trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
384 thead = ET.SubElement(
385 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
387 font = ET.SubElement(
388 thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
392 return str(ET.tostring(tbl, encoding=u"unicode"))
394 for suite in suites.values:
396 for test_data in data.values:
397 if test_data[u"parent"] not in suite[u"name"]:
399 html_table += _generate_html_table(test_data)
403 file_name = f"{table[u'output-file']}{suite[u'name']}.rst"
404 with open(f"{file_name}", u'w') as html_file:
405 logging.info(f" Writing file: {file_name}")
406 html_file.write(u".. raw:: html\n\n\t")
407 html_file.write(html_table)
408 html_file.write(u"\n\t<p><br><br></p>\n")
410 logging.warning(u"The output file is not defined.")
412 logging.info(u" Done.")
415 def table_merged_details(table, input_data):
416 """Generate the table(s) with algorithm: table_merged_details
417 specified in the specification file.
419 :param table: Table to generate.
420 :param input_data: Data to process.
421 :type table: pandas.Series
422 :type input_data: InputData
425 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
429 f" Creating the data set for the {table.get(u'type', u'')} "
430 f"{table.get(u'title', u'')}."
432 data = input_data.filter_data(table, continue_on_error=True)
433 data = input_data.merge_data(data)
435 sort_tests = table.get(u"sort", None)
439 ascending=(sort_tests == u"ascending")
441 data.sort_index(**args)
443 suites = input_data.filter_data(
444 table, continue_on_error=True, data_set=u"suites")
445 suites = input_data.merge_data(suites)
447 # Prepare the header of the tables
449 for column in table[u"columns"]:
451 u'"{0}"'.format(str(column[u"title"]).replace(u'"', u'""'))
454 for suite in suites.values:
456 suite_name = suite[u"name"]
458 for test in data.keys():
459 if data[test][u"status"] != u"PASS" or \
460 data[test][u"parent"] not in suite_name:
463 for column in table[u"columns"]:
465 col_data = str(data[test][column[
466 u"data"].split(u" ")[1]]).replace(u'"', u'""')
467 # Do not include tests with "Test Failed" in test message
468 if u"Test Failed" in col_data:
470 col_data = col_data.replace(
471 u"No Data", u"Not Captured "
473 if column[u"data"].split(u" ")[1] in (u"name", ):
474 if len(col_data) > 30:
475 col_data_lst = col_data.split(u"-")
476 half = int(len(col_data_lst) / 2)
477 col_data = f"{u'-'.join(col_data_lst[:half])}" \
479 f"{u'-'.join(col_data_lst[half:])}"
480 col_data = f" |prein| {col_data} |preout| "
481 elif column[u"data"].split(u" ")[1] in (u"msg", ):
482 # Temporary solution: remove NDR results from message:
483 if bool(table.get(u'remove-ndr', False)):
485 col_data = col_data.split(u"\n", 1)[1]
488 col_data = col_data.replace(u'\n', u' |br| ').\
489 replace(u'\r', u'').replace(u'"', u"'")
490 col_data = f" |prein| {col_data} |preout| "
491 elif column[u"data"].split(u" ")[1] in (u"conf-history", ):
492 col_data = col_data.replace(u'\n', u' |br| ')
493 col_data = f" |prein| {col_data[:-5]} |preout| "
494 row_lst.append(f'"{col_data}"')
496 row_lst.append(u'"Not captured"')
497 if len(row_lst) == len(table[u"columns"]):
498 table_lst.append(row_lst)
500 # Write the data to file
502 separator = u"" if table[u'output-file'].endswith(u"/") else u"_"
503 file_name = f"{table[u'output-file']}{separator}{suite_name}.csv"
504 logging.info(f" Writing file: {file_name}")
505 with open(file_name, u"wt") as file_handler:
506 file_handler.write(u",".join(header) + u"\n")
507 for item in table_lst:
508 file_handler.write(u",".join(item) + u"\n")
510 logging.info(u" Done.")
513 def _tpc_modify_test_name(test_name, ignore_nic=False):
514 """Modify a test name by replacing its parts.
516 :param test_name: Test name to be modified.
517 :param ignore_nic: If True, NIC is removed from TC name.
519 :type ignore_nic: bool
520 :returns: Modified test name.
523 test_name_mod = test_name.\
524 replace(u"-ndrpdr", u"").\
525 replace(u"1t1c", u"1c").\
526 replace(u"2t1c", u"1c"). \
527 replace(u"2t2c", u"2c").\
528 replace(u"4t2c", u"2c"). \
529 replace(u"4t4c", u"4c").\
530 replace(u"8t4c", u"4c")
533 return re.sub(REGEX_NIC, u"", test_name_mod)
537 def _tpc_modify_displayed_test_name(test_name):
538 """Modify a test name which is displayed in a table by replacing its parts.
540 :param test_name: Test name to be modified.
542 :returns: Modified test name.
546 replace(u"1t1c", u"1c").\
547 replace(u"2t1c", u"1c"). \
548 replace(u"2t2c", u"2c").\
549 replace(u"4t2c", u"2c"). \
550 replace(u"4t4c", u"4c").\
551 replace(u"8t4c", u"4c")
554 def _tpc_insert_data(target, src, include_tests):
555 """Insert src data to the target structure.
557 :param target: Target structure where the data is placed.
558 :param src: Source data to be placed into the target structure.
559 :param include_tests: Which results will be included (MRR, NDR, PDR).
562 :type include_tests: str
565 if include_tests == u"MRR":
566 target[u"mean"] = src[u"result"][u"receive-rate"]
567 target[u"stdev"] = src[u"result"][u"receive-stdev"]
568 elif include_tests == u"PDR":
569 target[u"data"].append(src[u"throughput"][u"PDR"][u"LOWER"])
570 elif include_tests == u"NDR":
571 target[u"data"].append(src[u"throughput"][u"NDR"][u"LOWER"])
572 elif u"latency" in include_tests:
573 keys = include_tests.split(u"-")
575 lat = src[keys[0]][keys[1]][keys[2]][keys[3]]
576 target[u"data"].append(
577 float(u"nan") if lat == -1 else lat * 1e6
579 except (KeyError, TypeError):
583 def _tpc_generate_html_table(header, data, out_file_name, legend=u"",
584 footnote=u"", sort_data=True, title=u"",
586 """Generate html table from input data with simple sorting possibility.
588 :param header: Table header.
589 :param data: Input data to be included in the table. It is a list of lists.
590 Inner lists are rows in the table. All inner lists must be of the same
591 length. The length of these lists must be the same as the length of the
593 :param out_file_name: The name (relative or full path) where the
594 generated html table is written.
595 :param legend: The legend to display below the table.
596 :param footnote: The footnote to display below the table (and legend).
597 :param sort_data: If True the data sorting is enabled.
598 :param title: The table (and file) title.
599 :param generate_rst: If True, wrapping rst file is generated.
601 :type data: list of lists
602 :type out_file_name: str
605 :type sort_data: bool
607 :type generate_rst: bool
611 idx = header.index(u"Test Case")
617 [u"left", u"left", u"right"],
618 [u"left", u"left", u"left", u"right"]
622 [u"left", u"left", u"right"],
623 [u"left", u"left", u"left", u"right"]
625 u"width": ([15, 9], [4, 24, 10], [4, 4, 32, 10])
628 df_data = pd.DataFrame(data, columns=header)
631 df_sorted = [df_data.sort_values(
632 by=[key, header[idx]], ascending=[True, True]
633 if key != header[idx] else [False, True]) for key in header]
634 df_sorted_rev = [df_data.sort_values(
635 by=[key, header[idx]], ascending=[False, True]
636 if key != header[idx] else [True, True]) for key in header]
637 df_sorted.extend(df_sorted_rev)
641 fill_color = [[u"#d4e4f7" if idx % 2 else u"#e9f1fb"
642 for idx in range(len(df_data))]]
644 values=[f"<b>{item.replace(u',', u',<br>')}</b>" for item in header],
645 fill_color=u"#7eade7",
646 align=params[u"align-hdr"][idx],
648 family=u"Courier New",
656 for table in df_sorted:
657 columns = [table.get(col) for col in header]
660 columnwidth=params[u"width"][idx],
664 fill_color=fill_color,
665 align=params[u"align-itm"][idx],
667 family=u"Courier New",
675 menu_items = [f"<b>{itm}</b> (ascending)" for itm in header]
676 menu_items.extend([f"<b>{itm}</b> (descending)" for itm in header])
677 for idx, hdr in enumerate(menu_items):
678 visible = [False, ] * len(menu_items)
682 label=hdr.replace(u" [Mpps]", u""),
684 args=[{u"visible": visible}],
690 go.layout.Updatemenu(
697 active=len(menu_items) - 1,
698 buttons=list(buttons)
705 columnwidth=params[u"width"][idx],
708 values=[df_sorted.get(col) for col in header],
709 fill_color=fill_color,
710 align=params[u"align-itm"][idx],
712 family=u"Courier New",
723 filename=f"{out_file_name}_in.html"
729 file_name = out_file_name.split(u"/")[-1]
730 if u"vpp" in out_file_name:
731 path = u"_tmp/src/vpp_performance_tests/comparisons/"
733 path = u"_tmp/src/dpdk_performance_tests/comparisons/"
734 logging.info(f" Writing the HTML file to {path}{file_name}.rst")
735 with open(f"{path}{file_name}.rst", u"wt") as rst_file:
738 u".. |br| raw:: html\n\n <br />\n\n\n"
739 u".. |prein| raw:: html\n\n <pre>\n\n\n"
740 u".. |preout| raw:: html\n\n </pre>\n\n"
743 rst_file.write(f"{title}\n")
744 rst_file.write(f"{u'`' * len(title)}\n\n")
747 f' <iframe frameborder="0" scrolling="no" '
748 f'width="1600" height="1200" '
749 f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
755 itm_lst = legend[1:-2].split(u"\n")
757 f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
759 except IndexError as err:
760 logging.error(f"Legend cannot be written to html file\n{err}")
763 itm_lst = footnote[1:].split(u"\n")
765 f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
767 except IndexError as err:
768 logging.error(f"Footnote cannot be written to html file\n{err}")
771 def table_soak_vs_ndr(table, input_data):
772 """Generate the table(s) with algorithm: table_soak_vs_ndr
773 specified in the specification file.
775 :param table: Table to generate.
776 :param input_data: Data to process.
777 :type table: pandas.Series
778 :type input_data: InputData
781 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
785 f" Creating the data set for the {table.get(u'type', u'')} "
786 f"{table.get(u'title', u'')}."
788 data = input_data.filter_data(table, continue_on_error=True)
790 # Prepare the header of the table
794 f"Avg({table[u'reference'][u'title']})",
795 f"Stdev({table[u'reference'][u'title']})",
796 f"Avg({table[u'compare'][u'title']})",
797 f"Stdev{table[u'compare'][u'title']})",
801 header_str = u";".join(header) + u"\n"
804 f"Avg({table[u'reference'][u'title']}): "
805 f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
806 f"from a series of runs of the listed tests.\n"
807 f"Stdev({table[u'reference'][u'title']}): "
808 f"Standard deviation value of {table[u'reference'][u'title']} "
809 f"[Mpps] computed from a series of runs of the listed tests.\n"
810 f"Avg({table[u'compare'][u'title']}): "
811 f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
812 f"a series of runs of the listed tests.\n"
813 f"Stdev({table[u'compare'][u'title']}): "
814 f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
815 f"computed from a series of runs of the listed tests.\n"
816 f"Diff({table[u'reference'][u'title']},"
817 f"{table[u'compare'][u'title']}): "
818 f"Percentage change calculated for mean values.\n"
820 u"Standard deviation of percentage change calculated for mean "
823 except (AttributeError, KeyError) as err:
824 logging.error(f"The model is invalid, missing parameter: {repr(err)}")
827 # Create a list of available SOAK test results:
829 for job, builds in table[u"compare"][u"data"].items():
831 for tst_name, tst_data in data[job][str(build)].items():
832 if tst_data[u"type"] == u"SOAK":
833 tst_name_mod = tst_name.replace(u"-soak", u"")
834 if tbl_dict.get(tst_name_mod, None) is None:
835 groups = re.search(REGEX_NIC, tst_data[u"parent"])
836 nic = groups.group(0) if groups else u""
839 f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
841 tbl_dict[tst_name_mod] = {
847 tbl_dict[tst_name_mod][u"cmp-data"].append(
848 tst_data[u"throughput"][u"LOWER"])
849 except (KeyError, TypeError):
851 tests_lst = tbl_dict.keys()
853 # Add corresponding NDR test results:
854 for job, builds in table[u"reference"][u"data"].items():
856 for tst_name, tst_data in data[job][str(build)].items():
857 tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
858 replace(u"-mrr", u"")
859 if tst_name_mod not in tests_lst:
862 if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
864 if table[u"include-tests"] == u"MRR":
865 result = (tst_data[u"result"][u"receive-rate"],
866 tst_data[u"result"][u"receive-stdev"])
867 elif table[u"include-tests"] == u"PDR":
869 tst_data[u"throughput"][u"PDR"][u"LOWER"]
870 elif table[u"include-tests"] == u"NDR":
872 tst_data[u"throughput"][u"NDR"][u"LOWER"]
875 if result is not None:
876 tbl_dict[tst_name_mod][u"ref-data"].append(
878 except (KeyError, TypeError):
882 for tst_name in tbl_dict:
883 item = [tbl_dict[tst_name][u"name"], ]
884 data_r = tbl_dict[tst_name][u"ref-data"]
886 if table[u"include-tests"] == u"MRR":
887 data_r_mean = data_r[0][0]
888 data_r_stdev = data_r[0][1]
890 data_r_mean = mean(data_r)
891 data_r_stdev = stdev(data_r)
892 item.append(round(data_r_mean / 1e6, 1))
893 item.append(round(data_r_stdev / 1e6, 1))
897 item.extend([None, None])
898 data_c = tbl_dict[tst_name][u"cmp-data"]
900 if table[u"include-tests"] == u"MRR":
901 data_c_mean = data_c[0][0]
902 data_c_stdev = data_c[0][1]
904 data_c_mean = mean(data_c)
905 data_c_stdev = stdev(data_c)
906 item.append(round(data_c_mean / 1e6, 1))
907 item.append(round(data_c_stdev / 1e6, 1))
911 item.extend([None, None])
912 if data_r_mean is not None and data_c_mean is not None:
913 delta, d_stdev = relative_change_stdev(
914 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
916 item.append(round(delta))
920 item.append(round(d_stdev))
925 # Sort the table according to the relative change
926 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
928 # Generate csv tables:
929 csv_file_name = f"{table[u'output-file']}.csv"
930 with open(csv_file_name, u"wt") as file_handler:
931 file_handler.write(header_str)
933 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
935 convert_csv_to_pretty_txt(
936 csv_file_name, f"{table[u'output-file']}.txt", delimiter=u";"
938 with open(f"{table[u'output-file']}.txt", u'a') as file_handler:
939 file_handler.write(legend)
941 # Generate html table:
942 _tpc_generate_html_table(
945 table[u'output-file'],
947 title=table.get(u"title", u"")
951 def table_perf_trending_dash(table, input_data):
952 """Generate the table(s) with algorithm:
953 table_perf_trending_dash
954 specified in the specification file.
956 :param table: Table to generate.
957 :param input_data: Data to process.
958 :type table: pandas.Series
959 :type input_data: InputData
962 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
966 f" Creating the data set for the {table.get(u'type', u'')} "
967 f"{table.get(u'title', u'')}."
969 data = input_data.filter_data(table, continue_on_error=True)
971 # Prepare the header of the tables
975 u"Short-Term Change [%]",
976 u"Long-Term Change [%]",
980 header_str = u",".join(header) + u"\n"
982 incl_tests = table.get(u"include-tests", u"MRR")
984 # Prepare data to the table:
986 for job, builds in table[u"data"].items():
988 for tst_name, tst_data in data[job][str(build)].items():
989 if tst_name.lower() in table.get(u"ignore-list", list()):
991 if tbl_dict.get(tst_name, None) is None:
992 groups = re.search(REGEX_NIC, tst_data[u"parent"])
995 nic = groups.group(0)
996 tbl_dict[tst_name] = {
997 u"name": f"{nic}-{tst_data[u'name']}",
998 u"data": OrderedDict()
1001 if incl_tests == u"MRR":
1002 tbl_dict[tst_name][u"data"][str(build)] = \
1003 tst_data[u"result"][u"receive-rate"]
1004 elif incl_tests == u"NDR":
1005 tbl_dict[tst_name][u"data"][str(build)] = \
1006 tst_data[u"throughput"][u"NDR"][u"LOWER"]
1007 elif incl_tests == u"PDR":
1008 tbl_dict[tst_name][u"data"][str(build)] = \
1009 tst_data[u"throughput"][u"PDR"][u"LOWER"]
1010 except (TypeError, KeyError):
1011 pass # No data in output.xml for this test
1014 for tst_name in tbl_dict:
1015 data_t = tbl_dict[tst_name][u"data"]
1020 classification_lst, avgs, _ = classify_anomalies(data_t)
1021 except ValueError as err:
1022 logging.info(f"{err} Skipping")
1025 win_size = min(len(data_t), table[u"window"])
1026 long_win_size = min(len(data_t), table[u"long-trend-window"])
1030 [x for x in avgs[-long_win_size:-win_size]
1035 avg_week_ago = avgs[max(-win_size, -len(avgs))]
1037 if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
1038 rel_change_last = nan
1040 rel_change_last = round(
1041 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
1043 if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
1044 rel_change_long = nan
1046 rel_change_long = round(
1047 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
1049 if classification_lst:
1050 if isnan(rel_change_last) and isnan(rel_change_long):
1052 if isnan(last_avg) or isnan(rel_change_last) or \
1053 isnan(rel_change_long):
1056 [tbl_dict[tst_name][u"name"],
1057 round(last_avg / 1e6, 2),
1060 classification_lst[-win_size+1:].count(u"regression"),
1061 classification_lst[-win_size+1:].count(u"progression")])
1063 tbl_lst.sort(key=lambda rel: rel[0])
1064 tbl_lst.sort(key=lambda rel: rel[3])
1065 tbl_lst.sort(key=lambda rel: rel[2])
1068 for nrr in range(table[u"window"], -1, -1):
1069 tbl_reg = [item for item in tbl_lst if item[4] == nrr]
1070 for nrp in range(table[u"window"], -1, -1):
1071 tbl_out = [item for item in tbl_reg if item[5] == nrp]
1072 tbl_sorted.extend(tbl_out)
1074 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1076 logging.info(f" Writing file: {file_name}")
1077 with open(file_name, u"wt") as file_handler:
1078 file_handler.write(header_str)
1079 for test in tbl_sorted:
1080 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1082 logging.info(f" Writing file: {table[u'output-file']}.txt")
1083 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1086 def _generate_url(testbed, test_name):
1087 """Generate URL to a trending plot from the name of the test case.
1089 :param testbed: The testbed used for testing.
1090 :param test_name: The name of the test case.
1092 :type test_name: str
1093 :returns: The URL to the plot with the trending data for the given test
1098 if u"x520" in test_name:
1100 elif u"x710" in test_name:
1102 elif u"xl710" in test_name:
1104 elif u"xxv710" in test_name:
1106 elif u"vic1227" in test_name:
1108 elif u"vic1385" in test_name:
1110 elif u"x553" in test_name:
1112 elif u"cx556" in test_name or u"cx556a" in test_name:
1117 if u"64b" in test_name:
1119 elif u"78b" in test_name:
1121 elif u"imix" in test_name:
1122 frame_size = u"imix"
1123 elif u"9000b" in test_name:
1124 frame_size = u"9000b"
1125 elif u"1518b" in test_name:
1126 frame_size = u"1518b"
1127 elif u"114b" in test_name:
1128 frame_size = u"114b"
1132 if u"1t1c" in test_name or \
1133 (u"-1c-" in test_name and
1134 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv", u"2n-tx2")):
1136 elif u"2t2c" in test_name or \
1137 (u"-2c-" in test_name and
1138 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv", u"2n-tx2")):
1140 elif u"4t4c" in test_name or \
1141 (u"-4c-" in test_name and
1142 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv", u"2n-tx2")):
1144 elif u"2t1c" in test_name or \
1145 (u"-1c-" in test_name and
1146 testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1148 elif u"4t2c" in test_name or \
1149 (u"-2c-" in test_name and
1150 testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1152 elif u"8t4c" in test_name or \
1153 (u"-4c-" in test_name and
1154 testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1159 if u"testpmd" in test_name:
1161 elif u"l3fwd" in test_name:
1163 elif u"avf" in test_name:
1165 elif u"rdma" in test_name:
1167 elif u"dnv" in testbed or u"tsh" in testbed:
1172 if u"macip-iacl1s" in test_name:
1173 bsf = u"features-macip-iacl1"
1174 elif u"macip-iacl10s" in test_name:
1175 bsf = u"features-macip-iacl10"
1176 elif u"macip-iacl50s" in test_name:
1177 bsf = u"features-macip-iacl50"
1178 elif u"iacl1s" in test_name:
1179 bsf = u"features-iacl1"
1180 elif u"iacl10s" in test_name:
1181 bsf = u"features-iacl10"
1182 elif u"iacl50s" in test_name:
1183 bsf = u"features-iacl50"
1184 elif u"oacl1s" in test_name:
1185 bsf = u"features-oacl1"
1186 elif u"oacl10s" in test_name:
1187 bsf = u"features-oacl10"
1188 elif u"oacl50s" in test_name:
1189 bsf = u"features-oacl50"
1190 elif u"nat44det" in test_name:
1191 bsf = u"nat44det-bidir"
1192 elif u"nat44ed" in test_name and u"udir" in test_name:
1193 bsf = u"nat44ed-udir"
1194 elif u"-cps" in test_name and u"ethip4udp" in test_name:
1196 elif u"-cps" in test_name and u"ethip4tcp" in test_name:
1198 elif u"-pps" in test_name and u"ethip4udp" in test_name:
1200 elif u"-pps" in test_name and u"ethip4tcp" in test_name:
1202 elif u"-tput" in test_name and u"ethip4udp" in test_name:
1204 elif u"-tput" in test_name and u"ethip4tcp" in test_name:
1206 elif u"udpsrcscale" in test_name:
1207 bsf = u"features-udp"
1208 elif u"iacl" in test_name:
1210 elif u"policer" in test_name:
1212 elif u"adl" in test_name:
1214 elif u"cop" in test_name:
1216 elif u"nat" in test_name:
1218 elif u"macip" in test_name:
1220 elif u"scale" in test_name:
1222 elif u"base" in test_name:
1227 if u"114b" in test_name and u"vhost" in test_name:
1229 elif u"nat44" in test_name or u"-pps" in test_name or u"-cps" in test_name:
1231 if u"nat44det" in test_name:
1232 domain += u"-det-bidir"
1235 if u"udir" in test_name:
1236 domain += u"-unidir"
1237 elif u"-ethip4udp-" in test_name:
1239 elif u"-ethip4tcp-" in test_name:
1241 if u"-cps" in test_name:
1243 elif u"-pps" in test_name:
1245 elif u"-tput" in test_name:
1247 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1249 elif u"memif" in test_name:
1250 domain = u"container_memif"
1251 elif u"srv6" in test_name:
1253 elif u"vhost" in test_name:
1255 if u"vppl2xc" in test_name:
1258 driver += u"-testpmd"
1259 if u"lbvpplacp" in test_name:
1260 bsf += u"-link-bonding"
1261 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1262 domain = u"nf_service_density_vnfc"
1263 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1264 domain = u"nf_service_density_cnfc"
1265 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1266 domain = u"nf_service_density_cnfp"
1267 elif u"ipsec" in test_name:
1269 if u"sw" in test_name:
1271 elif u"hw" in test_name:
1273 elif u"ethip4vxlan" in test_name:
1274 domain = u"ip4_tunnels"
1275 elif u"ethip4udpgeneve" in test_name:
1276 domain = u"ip4_tunnels"
1277 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1279 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1281 elif u"l2xcbase" in test_name or \
1282 u"l2xcscale" in test_name or \
1283 u"l2bdbasemaclrn" in test_name or \
1284 u"l2bdscale" in test_name or \
1285 u"l2patch" in test_name:
1290 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1291 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1293 return file_name + anchor_name
1296 def table_perf_trending_dash_html(table, input_data):
1297 """Generate the table(s) with algorithm:
1298 table_perf_trending_dash_html specified in the specification
1301 :param table: Table to generate.
1302 :param input_data: Data to process.
1304 :type input_data: InputData
1309 if not table.get(u"testbed", None):
1311 f"The testbed is not defined for the table "
1312 f"{table.get(u'title', u'')}. Skipping."
1316 test_type = table.get(u"test-type", u"MRR")
1317 if test_type not in (u"MRR", u"NDR", u"PDR"):
1319 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1324 if test_type in (u"NDR", u"PDR"):
1325 lnk_dir = u"../ndrpdr_trending/"
1326 lnk_sufix = f"-{test_type.lower()}"
1328 lnk_dir = u"../trending/"
1331 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1334 with open(table[u"input-file"], u'rt') as csv_file:
1335 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1336 except FileNotFoundError as err:
1337 logging.warning(f"{err}")
1340 logging.warning(u"The input file is not defined.")
1342 except csv.Error as err:
1344 f"Not possible to process the file {table[u'input-file']}.\n"
1350 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1353 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1354 for idx, item in enumerate(csv_lst[0]):
1355 alignment = u"left" if idx == 0 else u"center"
1356 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1374 for r_idx, row in enumerate(csv_lst[1:]):
1376 color = u"regression"
1378 color = u"progression"
1381 trow = ET.SubElement(
1382 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1386 for c_idx, item in enumerate(row):
1387 tdata = ET.SubElement(
1390 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1393 if c_idx == 0 and table.get(u"add-links", True):
1394 ref = ET.SubElement(
1399 f"{_generate_url(table.get(u'testbed', ''), item)}"
1407 with open(table[u"output-file"], u'w') as html_file:
1408 logging.info(f" Writing file: {table[u'output-file']}")
1409 html_file.write(u".. raw:: html\n\n\t")
1410 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1411 html_file.write(u"\n\t<p><br><br></p>\n")
1413 logging.warning(u"The output file is not defined.")
1417 def table_last_failed_tests(table, input_data):
1418 """Generate the table(s) with algorithm: table_last_failed_tests
1419 specified in the specification file.
1421 :param table: Table to generate.
1422 :param input_data: Data to process.
1423 :type table: pandas.Series
1424 :type input_data: InputData
1427 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1429 # Transform the data
1431 f" Creating the data set for the {table.get(u'type', u'')} "
1432 f"{table.get(u'title', u'')}."
1435 data = input_data.filter_data(table, continue_on_error=True)
1437 if data is None or data.empty:
1439 f" No data for the {table.get(u'type', u'')} "
1440 f"{table.get(u'title', u'')}."
1445 for job, builds in table[u"data"].items():
1446 for build in builds:
1449 version = input_data.metadata(job, build).get(u"version", u"")
1451 input_data.metadata(job, build).get(u"elapsedtime", u"")
1453 logging.error(f"Data for {job}: {build} is not present.")
1455 tbl_list.append(build)
1456 tbl_list.append(version)
1457 failed_tests = list()
1460 for tst_data in data[job][build].values:
1461 if tst_data[u"status"] != u"FAIL":
1465 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1468 nic = groups.group(0)
1469 msg = tst_data[u'msg'].replace(u"\n", u"")
1470 msg = re.sub(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})',
1471 'xxx.xxx.xxx.xxx', msg)
1472 msg = msg.split(u'Also teardown failed')[0]
1473 failed_tests.append(f"{nic}-{tst_data[u'name']}###{msg}")
1474 tbl_list.append(passed)
1475 tbl_list.append(failed)
1476 tbl_list.append(duration)
1477 tbl_list.extend(failed_tests)
1479 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1480 logging.info(f" Writing file: {file_name}")
1481 with open(file_name, u"wt") as file_handler:
1482 for test in tbl_list:
1483 file_handler.write(f"{test}\n")
1486 def table_failed_tests(table, input_data):
1487 """Generate the table(s) with algorithm: table_failed_tests
1488 specified in the specification file.
1490 :param table: Table to generate.
1491 :param input_data: Data to process.
1492 :type table: pandas.Series
1493 :type input_data: InputData
1496 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1498 # Transform the data
1500 f" Creating the data set for the {table.get(u'type', u'')} "
1501 f"{table.get(u'title', u'')}."
1503 data = input_data.filter_data(table, continue_on_error=True)
1506 if u"NDRPDR" in table.get(u"filter", list()):
1507 test_type = u"NDRPDR"
1509 # Prepare the header of the tables
1513 u"Last Failure [Time]",
1514 u"Last Failure [VPP-Build-Id]",
1515 u"Last Failure [CSIT-Job-Build-Id]"
1518 # Generate the data for the table according to the model in the table
1522 timeperiod = timedelta(int(table.get(u"window", 7)))
1525 for job, builds in table[u"data"].items():
1526 for build in builds:
1528 for tst_name, tst_data in data[job][build].items():
1529 if tst_name.lower() in table.get(u"ignore-list", list()):
1531 if tbl_dict.get(tst_name, None) is None:
1532 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1535 nic = groups.group(0)
1536 tbl_dict[tst_name] = {
1537 u"name": f"{nic}-{tst_data[u'name']}",
1538 u"data": OrderedDict()
1541 generated = input_data.metadata(job, build).\
1542 get(u"generated", u"")
1545 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1546 if (now - then) <= timeperiod:
1547 tbl_dict[tst_name][u"data"][build] = (
1548 tst_data[u"status"],
1550 input_data.metadata(job, build).get(u"version",
1554 except (TypeError, KeyError) as err:
1555 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1559 for tst_data in tbl_dict.values():
1561 fails_last_date = u""
1562 fails_last_vpp = u""
1563 fails_last_csit = u""
1564 for val in tst_data[u"data"].values():
1565 if val[0] == u"FAIL":
1567 fails_last_date = val[1]
1568 fails_last_vpp = val[2]
1569 fails_last_csit = val[3]
1571 max_fails = fails_nr if fails_nr > max_fails else max_fails
1577 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1578 f"-build-{fails_last_csit}"
1581 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1583 for nrf in range(max_fails, -1, -1):
1584 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1585 tbl_sorted.extend(tbl_fails)
1587 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1588 logging.info(f" Writing file: {file_name}")
1589 with open(file_name, u"wt") as file_handler:
1590 file_handler.write(u",".join(header) + u"\n")
1591 for test in tbl_sorted:
1592 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1594 logging.info(f" Writing file: {table[u'output-file']}.txt")
1595 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1598 def table_failed_tests_html(table, input_data):
1599 """Generate the table(s) with algorithm: table_failed_tests_html
1600 specified in the specification file.
1602 :param table: Table to generate.
1603 :param input_data: Data to process.
1604 :type table: pandas.Series
1605 :type input_data: InputData
1610 if not table.get(u"testbed", None):
1612 f"The testbed is not defined for the table "
1613 f"{table.get(u'title', u'')}. Skipping."
1617 test_type = table.get(u"test-type", u"MRR")
1618 if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1620 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1625 if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1626 lnk_dir = u"../ndrpdr_trending/"
1629 lnk_dir = u"../trending/"
1632 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1635 with open(table[u"input-file"], u'rt') as csv_file:
1636 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1638 logging.warning(u"The input file is not defined.")
1640 except csv.Error as err:
1642 f"Not possible to process the file {table[u'input-file']}.\n"
1648 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1651 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1652 for idx, item in enumerate(csv_lst[0]):
1653 alignment = u"left" if idx == 0 else u"center"
1654 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1658 colors = (u"#e9f1fb", u"#d4e4f7")
1659 for r_idx, row in enumerate(csv_lst[1:]):
1660 background = colors[r_idx % 2]
1661 trow = ET.SubElement(
1662 failed_tests, u"tr", attrib=dict(bgcolor=background)
1666 for c_idx, item in enumerate(row):
1667 tdata = ET.SubElement(
1670 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1673 if c_idx == 0 and table.get(u"add-links", True):
1674 ref = ET.SubElement(
1679 f"{_generate_url(table.get(u'testbed', ''), item)}"
1687 with open(table[u"output-file"], u'w') as html_file:
1688 logging.info(f" Writing file: {table[u'output-file']}")
1689 html_file.write(u".. raw:: html\n\n\t")
1690 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1691 html_file.write(u"\n\t<p><br><br></p>\n")
1693 logging.warning(u"The output file is not defined.")
1697 def table_comparison(table, input_data):
1698 """Generate the table(s) with algorithm: table_comparison
1699 specified in the specification file.
1701 :param table: Table to generate.
1702 :param input_data: Data to process.
1703 :type table: pandas.Series
1704 :type input_data: InputData
1706 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1708 # Transform the data
1710 f" Creating the data set for the {table.get(u'type', u'')} "
1711 f"{table.get(u'title', u'')}."
1714 columns = table.get(u"columns", None)
1717 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1722 for idx, col in enumerate(columns):
1723 if col.get(u"data-set", None) is None:
1724 logging.warning(f"No data for column {col.get(u'title', u'')}")
1726 tag = col.get(u"tag", None)
1727 data = input_data.filter_data(
1737 data=col[u"data-set"],
1738 continue_on_error=True
1741 u"title": col.get(u"title", f"Column{idx}"),
1744 for builds in data.values:
1745 for build in builds:
1746 for tst_name, tst_data in build.items():
1747 if tag and tag not in tst_data[u"tags"]:
1750 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1751 replace(u"2n1l-", u"")
1752 if col_data[u"data"].get(tst_name_mod, None) is None:
1753 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1754 if u"across testbeds" in table[u"title"].lower() or \
1755 u"across topologies" in table[u"title"].lower():
1756 name = _tpc_modify_displayed_test_name(name)
1757 col_data[u"data"][tst_name_mod] = {
1765 target=col_data[u"data"][tst_name_mod],
1767 include_tests=table[u"include-tests"]
1770 replacement = col.get(u"data-replacement", None)
1772 rpl_data = input_data.filter_data(
1783 continue_on_error=True
1785 for builds in rpl_data.values:
1786 for build in builds:
1787 for tst_name, tst_data in build.items():
1788 if tag and tag not in tst_data[u"tags"]:
1791 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1792 replace(u"2n1l-", u"")
1793 if col_data[u"data"].get(tst_name_mod, None) is None:
1794 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1795 if u"across testbeds" in table[u"title"].lower() \
1796 or u"across topologies" in \
1797 table[u"title"].lower():
1798 name = _tpc_modify_displayed_test_name(name)
1799 col_data[u"data"][tst_name_mod] = {
1806 if col_data[u"data"][tst_name_mod][u"replace"]:
1807 col_data[u"data"][tst_name_mod][u"replace"] = False
1808 col_data[u"data"][tst_name_mod][u"data"] = list()
1810 target=col_data[u"data"][tst_name_mod],
1812 include_tests=table[u"include-tests"]
1815 if table[u"include-tests"] in (u"NDR", u"PDR") or \
1816 u"latency" in table[u"include-tests"]:
1817 for tst_name, tst_data in col_data[u"data"].items():
1818 if tst_data[u"data"]:
1819 tst_data[u"mean"] = mean(tst_data[u"data"])
1820 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1822 cols.append(col_data)
1826 for tst_name, tst_data in col[u"data"].items():
1827 if tbl_dict.get(tst_name, None) is None:
1828 tbl_dict[tst_name] = {
1829 "name": tst_data[u"name"]
1831 tbl_dict[tst_name][col[u"title"]] = {
1832 u"mean": tst_data[u"mean"],
1833 u"stdev": tst_data[u"stdev"]
1837 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1841 for tst_data in tbl_dict.values():
1842 row = [tst_data[u"name"], ]
1844 row.append(tst_data.get(col[u"title"], None))
1847 comparisons = table.get(u"comparisons", None)
1849 if comparisons and isinstance(comparisons, list):
1850 for idx, comp in enumerate(comparisons):
1852 col_ref = int(comp[u"reference"])
1853 col_cmp = int(comp[u"compare"])
1855 logging.warning(u"Comparison: No references defined! Skipping.")
1856 comparisons.pop(idx)
1858 if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1859 col_ref == col_cmp):
1860 logging.warning(f"Wrong values of reference={col_ref} "
1861 f"and/or compare={col_cmp}. Skipping.")
1862 comparisons.pop(idx)
1864 rca_file_name = comp.get(u"rca-file", None)
1867 with open(rca_file_name, u"r") as file_handler:
1870 u"title": f"RCA{idx + 1}",
1871 u"data": load(file_handler, Loader=FullLoader)
1874 except (YAMLError, IOError) as err:
1876 f"The RCA file {rca_file_name} does not exist or "
1879 logging.debug(repr(err))
1886 tbl_cmp_lst = list()
1889 new_row = deepcopy(row)
1890 for comp in comparisons:
1891 ref_itm = row[int(comp[u"reference"])]
1892 if ref_itm is None and \
1893 comp.get(u"reference-alt", None) is not None:
1894 ref_itm = row[int(comp[u"reference-alt"])]
1895 cmp_itm = row[int(comp[u"compare"])]
1896 if ref_itm is not None and cmp_itm is not None and \
1897 ref_itm[u"mean"] is not None and \
1898 cmp_itm[u"mean"] is not None and \
1899 ref_itm[u"stdev"] is not None and \
1900 cmp_itm[u"stdev"] is not None:
1902 delta, d_stdev = relative_change_stdev(
1903 ref_itm[u"mean"], cmp_itm[u"mean"],
1904 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1906 except ZeroDivisionError:
1908 if delta is None or math.isnan(delta):
1911 u"mean": delta * 1e6,
1912 u"stdev": d_stdev * 1e6
1917 tbl_cmp_lst.append(new_row)
1920 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1921 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1922 except TypeError as err:
1923 logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1925 tbl_for_csv = list()
1926 for line in tbl_cmp_lst:
1928 for idx, itm in enumerate(line[1:]):
1929 if itm is None or not isinstance(itm, dict) or\
1930 itm.get(u'mean', None) is None or \
1931 itm.get(u'stdev', None) is None:
1935 row.append(round(float(itm[u'mean']) / 1e6, 3))
1936 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1940 rca_nr = rca[u"data"].get(row[0], u"-")
1941 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1942 tbl_for_csv.append(row)
1944 header_csv = [u"Test Case", ]
1946 header_csv.append(f"Avg({col[u'title']})")
1947 header_csv.append(f"Stdev({col[u'title']})")
1948 for comp in comparisons:
1950 f"Avg({comp.get(u'title', u'')})"
1953 f"Stdev({comp.get(u'title', u'')})"
1957 header_csv.append(rca[u"title"])
1959 legend_lst = table.get(u"legend", None)
1960 if legend_lst is None:
1963 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1966 if rcas and any(rcas):
1967 footnote += u"\nRoot Cause Analysis:\n"
1970 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1972 csv_file_name = f"{table[u'output-file']}-csv.csv"
1973 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1975 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1977 for test in tbl_for_csv:
1979 u",".join([f'"{item}"' for item in test]) + u"\n"
1982 for item in legend_lst:
1983 file_handler.write(f'"{item}"\n')
1985 for itm in footnote.split(u"\n"):
1986 file_handler.write(f'"{itm}"\n')
1989 max_lens = [0, ] * len(tbl_cmp_lst[0])
1990 for line in tbl_cmp_lst:
1992 for idx, itm in enumerate(line[1:]):
1993 if itm is None or not isinstance(itm, dict) or \
1994 itm.get(u'mean', None) is None or \
1995 itm.get(u'stdev', None) is None:
2000 f"{round(float(itm[u'mean']) / 1e6, 1)} "
2001 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
2002 replace(u"nan", u"NaN")
2006 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
2007 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
2008 replace(u"nan", u"NaN")
2010 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
2011 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
2016 header = [u"Test Case", ]
2017 header.extend([col[u"title"] for col in cols])
2018 header.extend([comp.get(u"title", u"") for comp in comparisons])
2021 for line in tbl_tmp:
2023 for idx, itm in enumerate(line[1:]):
2024 if itm in (u"NT", u"NaN"):
2027 itm_lst = itm.rsplit(u"\u00B1", 1)
2029 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
2030 itm_str = u"\u00B1".join(itm_lst)
2032 if idx >= len(cols):
2034 rca = rcas[idx - len(cols)]
2037 rca_nr = rca[u"data"].get(row[0], None)
2039 hdr_len = len(header[idx + 1]) - 1
2042 rca_nr = f"[{rca_nr}]"
2044 f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
2045 f"{u' ' * (hdr_len - 4 - len(itm_str))}"
2049 tbl_final.append(row)
2051 # Generate csv tables:
2052 csv_file_name = f"{table[u'output-file']}.csv"
2053 logging.info(f" Writing the file {csv_file_name}")
2054 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2055 file_handler.write(u";".join(header) + u"\n")
2056 for test in tbl_final:
2057 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
2059 # Generate txt table:
2060 txt_file_name = f"{table[u'output-file']}.txt"
2061 logging.info(f" Writing the file {txt_file_name}")
2062 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
2064 with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
2065 file_handler.write(legend)
2066 file_handler.write(footnote)
2068 # Generate html table:
2069 _tpc_generate_html_table(
2072 table[u'output-file'],
2076 title=table.get(u"title", u"")
2080 def table_weekly_comparison(table, in_data):
2081 """Generate the table(s) with algorithm: table_weekly_comparison
2082 specified in the specification file.
2084 :param table: Table to generate.
2085 :param in_data: Data to process.
2086 :type table: pandas.Series
2087 :type in_data: InputData
2089 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
2091 # Transform the data
2093 f" Creating the data set for the {table.get(u'type', u'')} "
2094 f"{table.get(u'title', u'')}."
2097 incl_tests = table.get(u"include-tests", None)
2098 if incl_tests not in (u"NDR", u"PDR"):
2099 logging.error(f"Wrong tests to include specified ({incl_tests}).")
2102 nr_cols = table.get(u"nr-of-data-columns", None)
2103 if not nr_cols or nr_cols < 2:
2105 f"No columns specified for {table.get(u'title', u'')}. Skipping."
2109 data = in_data.filter_data(
2111 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
2112 continue_on_error=True
2117 [u"Start Timestamp", ],
2123 tb_tbl = table.get(u"testbeds", None)
2124 for job_name, job_data in data.items():
2125 for build_nr, build in job_data.items():
2131 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
2132 if tb_ip and tb_tbl:
2133 testbed = tb_tbl.get(tb_ip, u"")
2136 header[2].insert(1, build_nr)
2137 header[3].insert(1, testbed)
2139 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
2142 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
2145 for tst_name, tst_data in build.items():
2147 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
2148 if not tbl_dict.get(tst_name_mod, None):
2149 tbl_dict[tst_name_mod] = dict(
2150 name=tst_data[u'name'].rsplit(u'-', 1)[0],
2153 tbl_dict[tst_name_mod][-idx - 1] = \
2154 tst_data[u"throughput"][incl_tests][u"LOWER"]
2155 except (TypeError, IndexError, KeyError, ValueError):
2160 logging.error(u"Not enough data to build the table! Skipping")
2164 for idx, cmp in enumerate(table.get(u"comparisons", list())):
2165 idx_ref = cmp.get(u"reference", None)
2166 idx_cmp = cmp.get(u"compare", None)
2167 if idx_ref is None or idx_cmp is None:
2170 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
2171 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
2173 header[1].append(u"")
2174 header[2].append(u"")
2175 header[3].append(u"")
2176 for tst_name, tst_data in tbl_dict.items():
2177 if not cmp_dict.get(tst_name, None):
2178 cmp_dict[tst_name] = list()
2179 ref_data = tst_data.get(idx_ref, None)
2180 cmp_data = tst_data.get(idx_cmp, None)
2181 if ref_data is None or cmp_data is None:
2182 cmp_dict[tst_name].append(float(u'nan'))
2184 cmp_dict[tst_name].append(
2185 relative_change(ref_data, cmp_data)
2188 tbl_lst_none = list()
2190 for tst_name, tst_data in tbl_dict.items():
2191 itm_lst = [tst_data[u"name"], ]
2192 for idx in range(nr_cols):
2193 item = tst_data.get(-idx - 1, None)
2195 itm_lst.insert(1, None)
2197 itm_lst.insert(1, round(item / 1e6, 1))
2200 None if itm is None else round(itm, 1)
2201 for itm in cmp_dict[tst_name]
2204 if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
2205 tbl_lst_none.append(itm_lst)
2207 tbl_lst.append(itm_lst)
2209 tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
2210 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
2211 tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
2212 tbl_lst.extend(tbl_lst_none)
2214 # Generate csv table:
2215 csv_file_name = f"{table[u'output-file']}.csv"
2216 logging.info(f" Writing the file {csv_file_name}")
2217 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2219 file_handler.write(u",".join(hdr) + u"\n")
2220 for test in tbl_lst:
2221 file_handler.write(u",".join(
2223 str(item).replace(u"None", u"-").replace(u"nan", u"-").
2224 replace(u"null", u"-") for item in test
2228 txt_file_name = f"{table[u'output-file']}.txt"
2229 logging.info(f" Writing the file {txt_file_name}")
2230 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2232 # Reorganize header in txt table
2234 with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2235 for line in list(file_handler):
2236 txt_table.append(line)
2238 txt_table.insert(5, txt_table.pop(2))
2239 with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2240 file_handler.writelines(txt_table)
2244 # Generate html table:
2246 u"<br>".join(row) for row in zip(*header)
2248 _tpc_generate_html_table(
2251 table[u'output-file'],
2253 title=table.get(u"title", u""),