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:
1114 elif u"ena" in test_name:
1119 if u"64b" in test_name:
1121 elif u"78b" in test_name:
1123 elif u"imix" in test_name:
1124 frame_size = u"imix"
1125 elif u"9000b" in test_name:
1126 frame_size = u"9000b"
1127 elif u"1518b" in test_name:
1128 frame_size = u"1518b"
1129 elif u"114b" in test_name:
1130 frame_size = u"114b"
1134 if u"1t1c" in test_name or \
1135 (u"-1c-" in test_name and
1136 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv", u"2n-tx2")):
1138 elif u"2t2c" in test_name or \
1139 (u"-2c-" in test_name and
1140 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv", u"2n-tx2")):
1142 elif u"4t4c" in test_name or \
1143 (u"-4c-" in test_name and
1144 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv", u"2n-tx2")):
1146 elif u"2t1c" in test_name or \
1147 (u"-1c-" in test_name and
1149 (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2", u"2n-aws", u"3n-aws")):
1151 elif u"4t2c" in test_name or \
1152 (u"-2c-" in test_name and
1154 (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2", u"2n-aws", u"3n-aws")):
1156 elif u"8t4c" in test_name or \
1157 (u"-4c-" in test_name and
1159 (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2", u"2n-aws", u"3n-aws")):
1164 if u"testpmd" in test_name:
1166 elif u"l3fwd" in test_name:
1168 elif u"avf" in test_name:
1170 elif u"rdma" in test_name:
1172 elif u"dnv" in testbed or u"tsh" in testbed:
1174 elif u"ena" in test_name:
1179 if u"macip-iacl1s" in test_name:
1180 bsf = u"features-macip-iacl1"
1181 elif u"macip-iacl10s" in test_name:
1182 bsf = u"features-macip-iacl10"
1183 elif u"macip-iacl50s" in test_name:
1184 bsf = u"features-macip-iacl50"
1185 elif u"iacl1s" in test_name:
1186 bsf = u"features-iacl1"
1187 elif u"iacl10s" in test_name:
1188 bsf = u"features-iacl10"
1189 elif u"iacl50s" in test_name:
1190 bsf = u"features-iacl50"
1191 elif u"oacl1s" in test_name:
1192 bsf = u"features-oacl1"
1193 elif u"oacl10s" in test_name:
1194 bsf = u"features-oacl10"
1195 elif u"oacl50s" in test_name:
1196 bsf = u"features-oacl50"
1197 elif u"nat44det" in test_name:
1198 bsf = u"nat44det-bidir"
1199 elif u"nat44ed" in test_name and u"udir" in test_name:
1200 bsf = u"nat44ed-udir"
1201 elif u"-cps" in test_name and u"ethip4udp" in test_name:
1203 elif u"-cps" in test_name and u"ethip4tcp" in test_name:
1205 elif u"-pps" in test_name and u"ethip4udp" in test_name:
1207 elif u"-pps" in test_name and u"ethip4tcp" in test_name:
1209 elif u"-tput" in test_name and u"ethip4udp" in test_name:
1211 elif u"-tput" in test_name and u"ethip4tcp" in test_name:
1213 elif u"udpsrcscale" in test_name:
1214 bsf = u"features-udp"
1215 elif u"iacl" in test_name:
1217 elif u"policer" in test_name:
1219 elif u"adl" in test_name:
1221 elif u"cop" in test_name:
1223 elif u"nat" in test_name:
1225 elif u"macip" in test_name:
1227 elif u"scale" in test_name:
1229 elif u"base" in test_name:
1234 if u"114b" in test_name and u"vhost" in test_name:
1236 elif u"nat44" in test_name or u"-pps" in test_name or u"-cps" in test_name:
1238 if u"nat44det" in test_name:
1239 domain += u"-det-bidir"
1242 if u"udir" in test_name:
1243 domain += u"-unidir"
1244 elif u"-ethip4udp-" in test_name:
1246 elif u"-ethip4tcp-" in test_name:
1248 if u"-cps" in test_name:
1250 elif u"-pps" in test_name:
1252 elif u"-tput" in test_name:
1254 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1256 elif u"memif" in test_name:
1257 domain = u"container_memif"
1258 elif u"srv6" in test_name:
1260 elif u"vhost" in test_name:
1262 if u"vppl2xc" in test_name:
1265 driver += u"-testpmd"
1266 if u"lbvpplacp" in test_name:
1267 bsf += u"-link-bonding"
1268 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1269 domain = u"nf_service_density_vnfc"
1270 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1271 domain = u"nf_service_density_cnfc"
1272 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1273 domain = u"nf_service_density_cnfp"
1274 elif u"ipsec" in test_name:
1276 if u"sw" in test_name:
1278 elif u"hw" in test_name:
1280 elif u"ethip4vxlan" in test_name:
1281 domain = u"ip4_tunnels"
1282 elif u"ethip4udpgeneve" in test_name:
1283 domain = u"ip4_tunnels"
1284 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1286 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1288 elif u"l2xcbase" in test_name or \
1289 u"l2xcscale" in test_name or \
1290 u"l2bdbasemaclrn" in test_name or \
1291 u"l2bdscale" in test_name or \
1292 u"l2patch" in test_name:
1297 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1298 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1300 return file_name + anchor_name
1303 def table_perf_trending_dash_html(table, input_data):
1304 """Generate the table(s) with algorithm:
1305 table_perf_trending_dash_html specified in the specification
1308 :param table: Table to generate.
1309 :param input_data: Data to process.
1311 :type input_data: InputData
1316 if not table.get(u"testbed", None):
1318 f"The testbed is not defined for the table "
1319 f"{table.get(u'title', u'')}. Skipping."
1323 test_type = table.get(u"test-type", u"MRR")
1324 if test_type not in (u"MRR", u"NDR", u"PDR"):
1326 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1331 if test_type in (u"NDR", u"PDR"):
1332 lnk_dir = u"../ndrpdr_trending/"
1333 lnk_sufix = f"-{test_type.lower()}"
1335 lnk_dir = u"../trending/"
1338 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1341 with open(table[u"input-file"], u'rt') as csv_file:
1342 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1343 except FileNotFoundError as err:
1344 logging.warning(f"{err}")
1347 logging.warning(u"The input file is not defined.")
1349 except csv.Error as err:
1351 f"Not possible to process the file {table[u'input-file']}.\n"
1357 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1360 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1361 for idx, item in enumerate(csv_lst[0]):
1362 alignment = u"left" if idx == 0 else u"center"
1363 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1381 for r_idx, row in enumerate(csv_lst[1:]):
1383 color = u"regression"
1385 color = u"progression"
1388 trow = ET.SubElement(
1389 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1393 for c_idx, item in enumerate(row):
1394 tdata = ET.SubElement(
1397 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1400 if c_idx == 0 and table.get(u"add-links", True):
1401 ref = ET.SubElement(
1406 f"{_generate_url(table.get(u'testbed', ''), item)}"
1414 with open(table[u"output-file"], u'w') as html_file:
1415 logging.info(f" Writing file: {table[u'output-file']}")
1416 html_file.write(u".. raw:: html\n\n\t")
1417 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1418 html_file.write(u"\n\t<p><br><br></p>\n")
1420 logging.warning(u"The output file is not defined.")
1424 def table_last_failed_tests(table, input_data):
1425 """Generate the table(s) with algorithm: table_last_failed_tests
1426 specified in the specification file.
1428 :param table: Table to generate.
1429 :param input_data: Data to process.
1430 :type table: pandas.Series
1431 :type input_data: InputData
1434 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1436 # Transform the data
1438 f" Creating the data set for the {table.get(u'type', u'')} "
1439 f"{table.get(u'title', u'')}."
1442 data = input_data.filter_data(table, continue_on_error=True)
1444 if data is None or data.empty:
1446 f" No data for the {table.get(u'type', u'')} "
1447 f"{table.get(u'title', u'')}."
1452 for job, builds in table[u"data"].items():
1453 for build in builds:
1456 version = input_data.metadata(job, build).get(u"version", u"")
1458 input_data.metadata(job, build).get(u"elapsedtime", u"")
1460 logging.error(f"Data for {job}: {build} is not present.")
1462 tbl_list.append(build)
1463 tbl_list.append(version)
1464 failed_tests = list()
1467 for tst_data in data[job][build].values:
1468 if tst_data[u"status"] != u"FAIL":
1472 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1475 nic = groups.group(0)
1476 msg = tst_data[u'msg'].replace(u"\n", u"")
1477 msg = re.sub(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})',
1478 'xxx.xxx.xxx.xxx', msg)
1479 msg = msg.split(u'Also teardown failed')[0]
1480 failed_tests.append(f"{nic}-{tst_data[u'name']}###{msg}")
1481 tbl_list.append(passed)
1482 tbl_list.append(failed)
1483 tbl_list.append(duration)
1484 tbl_list.extend(failed_tests)
1486 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1487 logging.info(f" Writing file: {file_name}")
1488 with open(file_name, u"wt") as file_handler:
1489 for test in tbl_list:
1490 file_handler.write(f"{test}\n")
1493 def table_failed_tests(table, input_data):
1494 """Generate the table(s) with algorithm: table_failed_tests
1495 specified in the specification file.
1497 :param table: Table to generate.
1498 :param input_data: Data to process.
1499 :type table: pandas.Series
1500 :type input_data: InputData
1503 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1505 # Transform the data
1507 f" Creating the data set for the {table.get(u'type', u'')} "
1508 f"{table.get(u'title', u'')}."
1510 data = input_data.filter_data(table, continue_on_error=True)
1513 if u"NDRPDR" in table.get(u"filter", list()):
1514 test_type = u"NDRPDR"
1516 # Prepare the header of the tables
1520 u"Last Failure [Time]",
1521 u"Last Failure [VPP-Build-Id]",
1522 u"Last Failure [CSIT-Job-Build-Id]"
1525 # Generate the data for the table according to the model in the table
1529 timeperiod = timedelta(int(table.get(u"window", 7)))
1532 for job, builds in table[u"data"].items():
1533 for build in builds:
1535 for tst_name, tst_data in data[job][build].items():
1536 if tst_name.lower() in table.get(u"ignore-list", list()):
1538 if tbl_dict.get(tst_name, None) is None:
1539 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1542 nic = groups.group(0)
1543 tbl_dict[tst_name] = {
1544 u"name": f"{nic}-{tst_data[u'name']}",
1545 u"data": OrderedDict()
1548 generated = input_data.metadata(job, build).\
1549 get(u"generated", u"")
1552 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1553 if (now - then) <= timeperiod:
1554 tbl_dict[tst_name][u"data"][build] = (
1555 tst_data[u"status"],
1557 input_data.metadata(job, build).get(u"version",
1561 except (TypeError, KeyError) as err:
1562 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1566 for tst_data in tbl_dict.values():
1568 fails_last_date = u""
1569 fails_last_vpp = u""
1570 fails_last_csit = u""
1571 for val in tst_data[u"data"].values():
1572 if val[0] == u"FAIL":
1574 fails_last_date = val[1]
1575 fails_last_vpp = val[2]
1576 fails_last_csit = val[3]
1578 max_fails = fails_nr if fails_nr > max_fails else max_fails
1584 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1585 f"-build-{fails_last_csit}"
1588 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1590 for nrf in range(max_fails, -1, -1):
1591 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1592 tbl_sorted.extend(tbl_fails)
1594 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1595 logging.info(f" Writing file: {file_name}")
1596 with open(file_name, u"wt") as file_handler:
1597 file_handler.write(u",".join(header) + u"\n")
1598 for test in tbl_sorted:
1599 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1601 logging.info(f" Writing file: {table[u'output-file']}.txt")
1602 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1605 def table_failed_tests_html(table, input_data):
1606 """Generate the table(s) with algorithm: table_failed_tests_html
1607 specified in the specification file.
1609 :param table: Table to generate.
1610 :param input_data: Data to process.
1611 :type table: pandas.Series
1612 :type input_data: InputData
1617 if not table.get(u"testbed", None):
1619 f"The testbed is not defined for the table "
1620 f"{table.get(u'title', u'')}. Skipping."
1624 test_type = table.get(u"test-type", u"MRR")
1625 if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1627 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1632 if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1633 lnk_dir = u"../ndrpdr_trending/"
1636 lnk_dir = u"../trending/"
1639 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1642 with open(table[u"input-file"], u'rt') as csv_file:
1643 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1645 logging.warning(u"The input file is not defined.")
1647 except csv.Error as err:
1649 f"Not possible to process the file {table[u'input-file']}.\n"
1655 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1658 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1659 for idx, item in enumerate(csv_lst[0]):
1660 alignment = u"left" if idx == 0 else u"center"
1661 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1665 colors = (u"#e9f1fb", u"#d4e4f7")
1666 for r_idx, row in enumerate(csv_lst[1:]):
1667 background = colors[r_idx % 2]
1668 trow = ET.SubElement(
1669 failed_tests, u"tr", attrib=dict(bgcolor=background)
1673 for c_idx, item in enumerate(row):
1674 tdata = ET.SubElement(
1677 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1680 if c_idx == 0 and table.get(u"add-links", True):
1681 ref = ET.SubElement(
1686 f"{_generate_url(table.get(u'testbed', ''), item)}"
1694 with open(table[u"output-file"], u'w') as html_file:
1695 logging.info(f" Writing file: {table[u'output-file']}")
1696 html_file.write(u".. raw:: html\n\n\t")
1697 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1698 html_file.write(u"\n\t<p><br><br></p>\n")
1700 logging.warning(u"The output file is not defined.")
1704 def table_comparison(table, input_data):
1705 """Generate the table(s) with algorithm: table_comparison
1706 specified in the specification file.
1708 :param table: Table to generate.
1709 :param input_data: Data to process.
1710 :type table: pandas.Series
1711 :type input_data: InputData
1713 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1715 # Transform the data
1717 f" Creating the data set for the {table.get(u'type', u'')} "
1718 f"{table.get(u'title', u'')}."
1721 columns = table.get(u"columns", None)
1724 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1729 for idx, col in enumerate(columns):
1730 if col.get(u"data-set", None) is None:
1731 logging.warning(f"No data for column {col.get(u'title', u'')}")
1733 tag = col.get(u"tag", None)
1734 data = input_data.filter_data(
1744 data=col[u"data-set"],
1745 continue_on_error=True
1748 u"title": col.get(u"title", f"Column{idx}"),
1751 for builds in data.values:
1752 for build in builds:
1753 for tst_name, tst_data in build.items():
1754 if tag and tag not in tst_data[u"tags"]:
1757 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1758 replace(u"2n1l-", u"")
1759 if col_data[u"data"].get(tst_name_mod, None) is None:
1760 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1761 if u"across testbeds" in table[u"title"].lower() or \
1762 u"across topologies" in table[u"title"].lower():
1763 name = _tpc_modify_displayed_test_name(name)
1764 col_data[u"data"][tst_name_mod] = {
1772 target=col_data[u"data"][tst_name_mod],
1774 include_tests=table[u"include-tests"]
1777 replacement = col.get(u"data-replacement", None)
1779 rpl_data = input_data.filter_data(
1790 continue_on_error=True
1792 for builds in rpl_data.values:
1793 for build in builds:
1794 for tst_name, tst_data in build.items():
1795 if tag and tag not in tst_data[u"tags"]:
1798 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1799 replace(u"2n1l-", u"")
1800 if col_data[u"data"].get(tst_name_mod, None) is None:
1801 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1802 if u"across testbeds" in table[u"title"].lower() \
1803 or u"across topologies" in \
1804 table[u"title"].lower():
1805 name = _tpc_modify_displayed_test_name(name)
1806 col_data[u"data"][tst_name_mod] = {
1813 if col_data[u"data"][tst_name_mod][u"replace"]:
1814 col_data[u"data"][tst_name_mod][u"replace"] = False
1815 col_data[u"data"][tst_name_mod][u"data"] = list()
1817 target=col_data[u"data"][tst_name_mod],
1819 include_tests=table[u"include-tests"]
1822 if table[u"include-tests"] in (u"NDR", u"PDR") or \
1823 u"latency" in table[u"include-tests"]:
1824 for tst_name, tst_data in col_data[u"data"].items():
1825 if tst_data[u"data"]:
1826 tst_data[u"mean"] = mean(tst_data[u"data"])
1827 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1829 cols.append(col_data)
1833 for tst_name, tst_data in col[u"data"].items():
1834 if tbl_dict.get(tst_name, None) is None:
1835 tbl_dict[tst_name] = {
1836 "name": tst_data[u"name"]
1838 tbl_dict[tst_name][col[u"title"]] = {
1839 u"mean": tst_data[u"mean"],
1840 u"stdev": tst_data[u"stdev"]
1844 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1848 for tst_data in tbl_dict.values():
1849 row = [tst_data[u"name"], ]
1851 row.append(tst_data.get(col[u"title"], None))
1854 comparisons = table.get(u"comparisons", None)
1856 if comparisons and isinstance(comparisons, list):
1857 for idx, comp in enumerate(comparisons):
1859 col_ref = int(comp[u"reference"])
1860 col_cmp = int(comp[u"compare"])
1862 logging.warning(u"Comparison: No references defined! Skipping.")
1863 comparisons.pop(idx)
1865 if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1866 col_ref == col_cmp):
1867 logging.warning(f"Wrong values of reference={col_ref} "
1868 f"and/or compare={col_cmp}. Skipping.")
1869 comparisons.pop(idx)
1871 rca_file_name = comp.get(u"rca-file", None)
1874 with open(rca_file_name, u"r") as file_handler:
1877 u"title": f"RCA{idx + 1}",
1878 u"data": load(file_handler, Loader=FullLoader)
1881 except (YAMLError, IOError) as err:
1883 f"The RCA file {rca_file_name} does not exist or "
1886 logging.debug(repr(err))
1893 tbl_cmp_lst = list()
1896 new_row = deepcopy(row)
1897 for comp in comparisons:
1898 ref_itm = row[int(comp[u"reference"])]
1899 if ref_itm is None and \
1900 comp.get(u"reference-alt", None) is not None:
1901 ref_itm = row[int(comp[u"reference-alt"])]
1902 cmp_itm = row[int(comp[u"compare"])]
1903 if ref_itm is not None and cmp_itm is not None and \
1904 ref_itm[u"mean"] is not None and \
1905 cmp_itm[u"mean"] is not None and \
1906 ref_itm[u"stdev"] is not None and \
1907 cmp_itm[u"stdev"] is not None:
1909 delta, d_stdev = relative_change_stdev(
1910 ref_itm[u"mean"], cmp_itm[u"mean"],
1911 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1913 except ZeroDivisionError:
1915 if delta is None or math.isnan(delta):
1918 u"mean": delta * 1e6,
1919 u"stdev": d_stdev * 1e6
1924 tbl_cmp_lst.append(new_row)
1927 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1928 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1929 except TypeError as err:
1930 logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1932 tbl_for_csv = list()
1933 for line in tbl_cmp_lst:
1935 for idx, itm in enumerate(line[1:]):
1936 if itm is None or not isinstance(itm, dict) or\
1937 itm.get(u'mean', None) is None or \
1938 itm.get(u'stdev', None) is None:
1942 row.append(round(float(itm[u'mean']) / 1e6, 3))
1943 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1947 rca_nr = rca[u"data"].get(row[0], u"-")
1948 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1949 tbl_for_csv.append(row)
1951 header_csv = [u"Test Case", ]
1953 header_csv.append(f"Avg({col[u'title']})")
1954 header_csv.append(f"Stdev({col[u'title']})")
1955 for comp in comparisons:
1957 f"Avg({comp.get(u'title', u'')})"
1960 f"Stdev({comp.get(u'title', u'')})"
1964 header_csv.append(rca[u"title"])
1966 legend_lst = table.get(u"legend", None)
1967 if legend_lst is None:
1970 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1973 if rcas and any(rcas):
1974 footnote += u"\nRoot Cause Analysis:\n"
1977 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1979 csv_file_name = f"{table[u'output-file']}-csv.csv"
1980 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1982 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1984 for test in tbl_for_csv:
1986 u",".join([f'"{item}"' for item in test]) + u"\n"
1989 for item in legend_lst:
1990 file_handler.write(f'"{item}"\n')
1992 for itm in footnote.split(u"\n"):
1993 file_handler.write(f'"{itm}"\n')
1996 max_lens = [0, ] * len(tbl_cmp_lst[0])
1997 for line in tbl_cmp_lst:
1999 for idx, itm in enumerate(line[1:]):
2000 if itm is None or not isinstance(itm, dict) or \
2001 itm.get(u'mean', None) is None or \
2002 itm.get(u'stdev', None) is None:
2007 f"{round(float(itm[u'mean']) / 1e6, 1)} "
2008 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
2009 replace(u"nan", u"NaN")
2013 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
2014 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
2015 replace(u"nan", u"NaN")
2017 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
2018 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
2023 header = [u"Test Case", ]
2024 header.extend([col[u"title"] for col in cols])
2025 header.extend([comp.get(u"title", u"") for comp in comparisons])
2028 for line in tbl_tmp:
2030 for idx, itm in enumerate(line[1:]):
2031 if itm in (u"NT", u"NaN"):
2034 itm_lst = itm.rsplit(u"\u00B1", 1)
2036 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
2037 itm_str = u"\u00B1".join(itm_lst)
2039 if idx >= len(cols):
2041 rca = rcas[idx - len(cols)]
2044 rca_nr = rca[u"data"].get(row[0], None)
2046 hdr_len = len(header[idx + 1]) - 1
2049 rca_nr = f"[{rca_nr}]"
2051 f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
2052 f"{u' ' * (hdr_len - 4 - len(itm_str))}"
2056 tbl_final.append(row)
2058 # Generate csv tables:
2059 csv_file_name = f"{table[u'output-file']}.csv"
2060 logging.info(f" Writing the file {csv_file_name}")
2061 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2062 file_handler.write(u";".join(header) + u"\n")
2063 for test in tbl_final:
2064 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
2066 # Generate txt table:
2067 txt_file_name = f"{table[u'output-file']}.txt"
2068 logging.info(f" Writing the file {txt_file_name}")
2069 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
2071 with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
2072 file_handler.write(legend)
2073 file_handler.write(footnote)
2075 # Generate html table:
2076 _tpc_generate_html_table(
2079 table[u'output-file'],
2083 title=table.get(u"title", u"")
2087 def table_weekly_comparison(table, in_data):
2088 """Generate the table(s) with algorithm: table_weekly_comparison
2089 specified in the specification file.
2091 :param table: Table to generate.
2092 :param in_data: Data to process.
2093 :type table: pandas.Series
2094 :type in_data: InputData
2096 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
2098 # Transform the data
2100 f" Creating the data set for the {table.get(u'type', u'')} "
2101 f"{table.get(u'title', u'')}."
2104 incl_tests = table.get(u"include-tests", None)
2105 if incl_tests not in (u"NDR", u"PDR"):
2106 logging.error(f"Wrong tests to include specified ({incl_tests}).")
2109 nr_cols = table.get(u"nr-of-data-columns", None)
2110 if not nr_cols or nr_cols < 2:
2112 f"No columns specified for {table.get(u'title', u'')}. Skipping."
2116 data = in_data.filter_data(
2118 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
2119 continue_on_error=True
2124 [u"Start Timestamp", ],
2130 tb_tbl = table.get(u"testbeds", None)
2131 for job_name, job_data in data.items():
2132 for build_nr, build in job_data.items():
2138 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
2139 if tb_ip and tb_tbl:
2140 testbed = tb_tbl.get(tb_ip, u"")
2143 header[2].insert(1, build_nr)
2144 header[3].insert(1, testbed)
2146 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
2149 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
2152 for tst_name, tst_data in build.items():
2154 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
2155 if not tbl_dict.get(tst_name_mod, None):
2156 tbl_dict[tst_name_mod] = dict(
2157 name=tst_data[u'name'].rsplit(u'-', 1)[0],
2160 tbl_dict[tst_name_mod][-idx - 1] = \
2161 tst_data[u"throughput"][incl_tests][u"LOWER"]
2162 except (TypeError, IndexError, KeyError, ValueError):
2167 logging.error(u"Not enough data to build the table! Skipping")
2171 for idx, cmp in enumerate(table.get(u"comparisons", list())):
2172 idx_ref = cmp.get(u"reference", None)
2173 idx_cmp = cmp.get(u"compare", None)
2174 if idx_ref is None or idx_cmp is None:
2177 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
2178 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
2180 header[1].append(u"")
2181 header[2].append(u"")
2182 header[3].append(u"")
2183 for tst_name, tst_data in tbl_dict.items():
2184 if not cmp_dict.get(tst_name, None):
2185 cmp_dict[tst_name] = list()
2186 ref_data = tst_data.get(idx_ref, None)
2187 cmp_data = tst_data.get(idx_cmp, None)
2188 if ref_data is None or cmp_data is None:
2189 cmp_dict[tst_name].append(float(u'nan'))
2191 cmp_dict[tst_name].append(
2192 relative_change(ref_data, cmp_data)
2195 tbl_lst_none = list()
2197 for tst_name, tst_data in tbl_dict.items():
2198 itm_lst = [tst_data[u"name"], ]
2199 for idx in range(nr_cols):
2200 item = tst_data.get(-idx - 1, None)
2202 itm_lst.insert(1, None)
2204 itm_lst.insert(1, round(item / 1e6, 1))
2207 None if itm is None else round(itm, 1)
2208 for itm in cmp_dict[tst_name]
2211 if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
2212 tbl_lst_none.append(itm_lst)
2214 tbl_lst.append(itm_lst)
2216 tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
2217 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
2218 tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
2219 tbl_lst.extend(tbl_lst_none)
2221 # Generate csv table:
2222 csv_file_name = f"{table[u'output-file']}.csv"
2223 logging.info(f" Writing the file {csv_file_name}")
2224 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2226 file_handler.write(u",".join(hdr) + u"\n")
2227 for test in tbl_lst:
2228 file_handler.write(u",".join(
2230 str(item).replace(u"None", u"-").replace(u"nan", u"-").
2231 replace(u"null", u"-") for item in test
2235 txt_file_name = f"{table[u'output-file']}.txt"
2236 logging.info(f" Writing the file {txt_file_name}")
2237 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2239 # Reorganize header in txt table
2241 with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2242 for line in list(file_handler):
2243 txt_table.append(line)
2245 txt_table.insert(5, txt_table.pop(2))
2246 with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2247 file_handler.writelines(txt_table)
2251 # Generate html table:
2253 u"<br>".join(row) for row in zip(*header)
2255 _tpc_generate_html_table(
2258 table[u'output-file'],
2260 title=table.get(u"title", u""),