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"Number of runs [#]",
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 nr_of_last_avgs = 0;
1038 for x in reversed(avgs):
1040 nr_of_last_avgs += 1
1044 if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
1045 rel_change_last = nan
1047 rel_change_last = round(
1048 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
1050 if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
1051 rel_change_long = nan
1053 rel_change_long = round(
1054 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
1056 if classification_lst:
1057 if isnan(rel_change_last) and isnan(rel_change_long):
1059 if isnan(last_avg) or isnan(rel_change_last) or \
1060 isnan(rel_change_long):
1063 [tbl_dict[tst_name][u"name"],
1064 round(last_avg / 1e6, 2),
1067 classification_lst[-win_size+1:].count(u"regression"),
1068 classification_lst[-win_size+1:].count(u"progression")])
1070 tbl_lst.sort(key=lambda rel: rel[0])
1071 tbl_lst.sort(key=lambda rel: rel[2])
1072 tbl_lst.sort(key=lambda rel: rel[3])
1073 tbl_lst.sort(key=lambda rel: rel[5], reverse=True)
1074 tbl_lst.sort(key=lambda rel: rel[4], reverse=True)
1076 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1078 logging.info(f" Writing file: {file_name}")
1079 with open(file_name, u"wt") as file_handler:
1080 file_handler.write(header_str)
1081 for test in tbl_lst:
1082 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1084 logging.info(f" Writing file: {table[u'output-file']}.txt")
1085 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1088 def _generate_url(testbed, test_name):
1089 """Generate URL to a trending plot from the name of the test case.
1091 :param testbed: The testbed used for testing.
1092 :param test_name: The name of the test case.
1094 :type test_name: str
1095 :returns: The URL to the plot with the trending data for the given test
1100 if u"x520" in test_name:
1102 elif u"x710" in test_name:
1104 elif u"xl710" in test_name:
1106 elif u"xxv710" in test_name:
1108 elif u"vic1227" in test_name:
1110 elif u"vic1385" in test_name:
1112 elif u"x553" in test_name:
1114 elif u"cx556" in test_name or u"cx556a" in test_name:
1116 elif u"ena" in test_name:
1121 if u"64b" in test_name:
1123 elif u"78b" in test_name:
1125 elif u"imix" in test_name:
1126 frame_size = u"imix"
1127 elif u"9000b" in test_name:
1128 frame_size = u"9000b"
1129 elif u"1518b" in test_name:
1130 frame_size = u"1518b"
1131 elif u"114b" in test_name:
1132 frame_size = u"114b"
1136 if u"1t1c" in test_name or \
1137 (u"-1c-" 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"2t2c" in test_name or \
1141 (u"-2c-" 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"4t4c" in test_name or \
1145 (u"-4c-" in test_name and
1146 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv", u"2n-tx2")):
1148 elif u"2t1c" in test_name or \
1149 (u"-1c-" in test_name and
1151 (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2", u"2n-aws", u"3n-aws")):
1153 elif u"4t2c" in test_name or \
1154 (u"-2c-" in test_name and
1156 (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2", u"2n-aws", u"3n-aws")):
1158 elif u"8t4c" in test_name or \
1159 (u"-4c-" in test_name and
1161 (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2", u"2n-aws", u"3n-aws")):
1166 if u"testpmd" in test_name:
1168 elif u"l3fwd" in test_name:
1170 elif u"avf" in test_name:
1172 elif u"af-xdp" in test_name or u"af_xdp" in test_name:
1174 elif u"rdma" in test_name:
1176 elif u"dnv" in testbed or u"tsh" in testbed:
1178 elif u"ena" in test_name:
1183 if u"macip-iacl1s" in test_name:
1184 bsf = u"features-macip-iacl1"
1185 elif u"macip-iacl10s" in test_name:
1186 bsf = u"features-macip-iacl10"
1187 elif u"macip-iacl50s" in test_name:
1188 bsf = u"features-macip-iacl50"
1189 elif u"iacl1s" in test_name:
1190 bsf = u"features-iacl1"
1191 elif u"iacl10s" in test_name:
1192 bsf = u"features-iacl10"
1193 elif u"iacl50s" in test_name:
1194 bsf = u"features-iacl50"
1195 elif u"oacl1s" in test_name:
1196 bsf = u"features-oacl1"
1197 elif u"oacl10s" in test_name:
1198 bsf = u"features-oacl10"
1199 elif u"oacl50s" in test_name:
1200 bsf = u"features-oacl50"
1201 elif u"nat44det" in test_name:
1202 bsf = u"nat44det-bidir"
1203 elif u"nat44ed" in test_name and u"udir" in test_name:
1204 bsf = u"nat44ed-udir"
1205 elif u"-cps" in test_name and u"ethip4udp" in test_name:
1207 elif u"-cps" in test_name and u"ethip4tcp" in test_name:
1209 elif u"-pps" in test_name and u"ethip4udp" in test_name:
1211 elif u"-pps" in test_name and u"ethip4tcp" in test_name:
1213 elif u"-tput" in test_name and u"ethip4udp" in test_name:
1215 elif u"-tput" in test_name and u"ethip4tcp" in test_name:
1217 elif u"udpsrcscale" in test_name:
1218 bsf = u"features-udp"
1219 elif u"iacl" in test_name:
1221 elif u"policer" in test_name:
1223 elif u"adl" in test_name:
1225 elif u"cop" in test_name:
1227 elif u"nat" in test_name:
1229 elif u"macip" in test_name:
1231 elif u"scale" in test_name:
1233 elif u"base" in test_name:
1238 if u"114b" in test_name and u"vhost" in test_name:
1240 elif u"nat44" in test_name or u"-pps" in test_name or u"-cps" in test_name:
1242 if u"nat44det" in test_name:
1243 domain += u"-det-bidir"
1246 if u"udir" in test_name:
1247 domain += u"-unidir"
1248 elif u"-ethip4udp-" in test_name:
1250 elif u"-ethip4tcp-" in test_name:
1252 if u"-cps" in test_name:
1254 elif u"-pps" in test_name:
1256 elif u"-tput" in test_name:
1258 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1260 elif u"memif" in test_name:
1261 domain = u"container_memif"
1262 elif u"srv6" in test_name:
1264 elif u"vhost" in test_name:
1266 if u"vppl2xc" in test_name:
1269 driver += u"-testpmd"
1270 if u"lbvpplacp" in test_name:
1271 bsf += u"-link-bonding"
1272 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1273 domain = u"nf_service_density_vnfc"
1274 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1275 domain = u"nf_service_density_cnfc"
1276 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1277 domain = u"nf_service_density_cnfp"
1278 elif u"ipsec" in test_name:
1280 if u"sw" in test_name:
1282 elif u"hw" in test_name:
1284 elif u"spe" in test_name:
1286 elif u"ethip4vxlan" in test_name:
1287 domain = u"ip4_tunnels"
1288 elif u"ethip4udpgeneve" in test_name:
1289 domain = u"ip4_tunnels"
1290 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1292 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1294 elif u"l2xcbase" in test_name or \
1295 u"l2xcscale" in test_name or \
1296 u"l2bdbasemaclrn" in test_name or \
1297 u"l2bdscale" in test_name or \
1298 u"l2patch" in test_name:
1303 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1304 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1306 return file_name + anchor_name
1309 def table_perf_trending_dash_html(table, input_data):
1310 """Generate the table(s) with algorithm:
1311 table_perf_trending_dash_html specified in the specification
1314 :param table: Table to generate.
1315 :param input_data: Data to process.
1317 :type input_data: InputData
1322 if not table.get(u"testbed", None):
1324 f"The testbed is not defined for the table "
1325 f"{table.get(u'title', u'')}. Skipping."
1329 test_type = table.get(u"test-type", u"MRR")
1330 if test_type not in (u"MRR", u"NDR", u"PDR"):
1332 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1337 if test_type in (u"NDR", u"PDR"):
1338 lnk_dir = u"../ndrpdr_trending/"
1339 lnk_sufix = f"-{test_type.lower()}"
1341 lnk_dir = u"../trending/"
1344 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1347 with open(table[u"input-file"], u'rt') as csv_file:
1348 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1349 except FileNotFoundError as err:
1350 logging.warning(f"{err}")
1353 logging.warning(u"The input file is not defined.")
1355 except csv.Error as err:
1357 f"Not possible to process the file {table[u'input-file']}.\n"
1363 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1366 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1367 for idx, item in enumerate(csv_lst[0]):
1368 alignment = u"left" if idx == 0 else u"center"
1369 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1387 for r_idx, row in enumerate(csv_lst[1:]):
1389 color = u"regression"
1391 color = u"progression"
1394 trow = ET.SubElement(
1395 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1399 for c_idx, item in enumerate(row):
1400 tdata = ET.SubElement(
1403 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1406 if c_idx == 0 and table.get(u"add-links", True):
1407 ref = ET.SubElement(
1412 f"{_generate_url(table.get(u'testbed', ''), item)}"
1420 with open(table[u"output-file"], u'w') as html_file:
1421 logging.info(f" Writing file: {table[u'output-file']}")
1422 html_file.write(u".. raw:: html\n\n\t")
1423 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1424 html_file.write(u"\n\t<p><br><br></p>\n")
1426 logging.warning(u"The output file is not defined.")
1430 def table_last_failed_tests(table, input_data):
1431 """Generate the table(s) with algorithm: table_last_failed_tests
1432 specified in the specification file.
1434 :param table: Table to generate.
1435 :param input_data: Data to process.
1436 :type table: pandas.Series
1437 :type input_data: InputData
1440 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1442 # Transform the data
1444 f" Creating the data set for the {table.get(u'type', u'')} "
1445 f"{table.get(u'title', u'')}."
1448 data = input_data.filter_data(table, continue_on_error=True)
1450 if data is None or data.empty:
1452 f" No data for the {table.get(u'type', u'')} "
1453 f"{table.get(u'title', u'')}."
1458 for job, builds in table[u"data"].items():
1459 for build in builds:
1462 version = input_data.metadata(job, build).get(u"version", u"")
1464 input_data.metadata(job, build).get(u"elapsedtime", u"")
1466 logging.error(f"Data for {job}: {build} is not present.")
1468 tbl_list.append(build)
1469 tbl_list.append(version)
1470 failed_tests = list()
1473 for tst_data in data[job][build].values:
1474 if tst_data[u"status"] != u"FAIL":
1478 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1481 nic = groups.group(0)
1482 msg = tst_data[u'msg'].replace(u"\n", u"")
1483 msg = re.sub(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})',
1484 'xxx.xxx.xxx.xxx', msg)
1485 msg = msg.split(u'Also teardown failed')[0]
1486 failed_tests.append(f"{nic}-{tst_data[u'name']}###{msg}")
1487 tbl_list.append(passed)
1488 tbl_list.append(failed)
1489 tbl_list.append(duration)
1490 tbl_list.extend(failed_tests)
1492 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1493 logging.info(f" Writing file: {file_name}")
1494 with open(file_name, u"wt") as file_handler:
1495 for test in tbl_list:
1496 file_handler.write(f"{test}\n")
1499 def table_failed_tests(table, input_data):
1500 """Generate the table(s) with algorithm: table_failed_tests
1501 specified in the specification file.
1503 :param table: Table to generate.
1504 :param input_data: Data to process.
1505 :type table: pandas.Series
1506 :type input_data: InputData
1509 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1511 # Transform the data
1513 f" Creating the data set for the {table.get(u'type', u'')} "
1514 f"{table.get(u'title', u'')}."
1516 data = input_data.filter_data(table, continue_on_error=True)
1519 if u"NDRPDR" in table.get(u"filter", list()):
1520 test_type = u"NDRPDR"
1522 # Prepare the header of the tables
1526 u"Last Failure [Time]",
1527 u"Last Failure [VPP-Build-Id]",
1528 u"Last Failure [CSIT-Job-Build-Id]"
1531 # Generate the data for the table according to the model in the table
1535 timeperiod = timedelta(int(table.get(u"window", 7)))
1538 for job, builds in table[u"data"].items():
1539 for build in builds:
1541 for tst_name, tst_data in data[job][build].items():
1542 if tst_name.lower() in table.get(u"ignore-list", list()):
1544 if tbl_dict.get(tst_name, None) is None:
1545 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1548 nic = groups.group(0)
1549 tbl_dict[tst_name] = {
1550 u"name": f"{nic}-{tst_data[u'name']}",
1551 u"data": OrderedDict()
1554 generated = input_data.metadata(job, build).\
1555 get(u"generated", u"")
1558 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1559 if (now - then) <= timeperiod:
1560 tbl_dict[tst_name][u"data"][build] = (
1561 tst_data[u"status"],
1563 input_data.metadata(job, build).get(u"version",
1567 except (TypeError, KeyError) as err:
1568 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1572 for tst_data in tbl_dict.values():
1574 fails_last_date = u""
1575 fails_last_vpp = u""
1576 fails_last_csit = u""
1577 for val in tst_data[u"data"].values():
1578 if val[0] == u"FAIL":
1580 fails_last_date = val[1]
1581 fails_last_vpp = val[2]
1582 fails_last_csit = val[3]
1584 max_fails = fails_nr if fails_nr > max_fails else max_fails
1590 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1591 f"-build-{fails_last_csit}"
1594 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1596 for nrf in range(max_fails, -1, -1):
1597 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1598 tbl_sorted.extend(tbl_fails)
1600 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1601 logging.info(f" Writing file: {file_name}")
1602 with open(file_name, u"wt") as file_handler:
1603 file_handler.write(u",".join(header) + u"\n")
1604 for test in tbl_sorted:
1605 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1607 logging.info(f" Writing file: {table[u'output-file']}.txt")
1608 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1611 def table_failed_tests_html(table, input_data):
1612 """Generate the table(s) with algorithm: table_failed_tests_html
1613 specified in the specification file.
1615 :param table: Table to generate.
1616 :param input_data: Data to process.
1617 :type table: pandas.Series
1618 :type input_data: InputData
1623 if not table.get(u"testbed", None):
1625 f"The testbed is not defined for the table "
1626 f"{table.get(u'title', u'')}. Skipping."
1630 test_type = table.get(u"test-type", u"MRR")
1631 if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1633 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1638 if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1639 lnk_dir = u"../ndrpdr_trending/"
1642 lnk_dir = u"../trending/"
1645 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1648 with open(table[u"input-file"], u'rt') as csv_file:
1649 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1651 logging.warning(u"The input file is not defined.")
1653 except csv.Error as err:
1655 f"Not possible to process the file {table[u'input-file']}.\n"
1661 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1664 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1665 for idx, item in enumerate(csv_lst[0]):
1666 alignment = u"left" if idx == 0 else u"center"
1667 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1671 colors = (u"#e9f1fb", u"#d4e4f7")
1672 for r_idx, row in enumerate(csv_lst[1:]):
1673 background = colors[r_idx % 2]
1674 trow = ET.SubElement(
1675 failed_tests, u"tr", attrib=dict(bgcolor=background)
1679 for c_idx, item in enumerate(row):
1680 tdata = ET.SubElement(
1683 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1686 if c_idx == 0 and table.get(u"add-links", True):
1687 ref = ET.SubElement(
1692 f"{_generate_url(table.get(u'testbed', ''), item)}"
1700 with open(table[u"output-file"], u'w') as html_file:
1701 logging.info(f" Writing file: {table[u'output-file']}")
1702 html_file.write(u".. raw:: html\n\n\t")
1703 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1704 html_file.write(u"\n\t<p><br><br></p>\n")
1706 logging.warning(u"The output file is not defined.")
1710 def table_comparison(table, input_data):
1711 """Generate the table(s) with algorithm: table_comparison
1712 specified in the specification file.
1714 :param table: Table to generate.
1715 :param input_data: Data to process.
1716 :type table: pandas.Series
1717 :type input_data: InputData
1719 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1721 # Transform the data
1723 f" Creating the data set for the {table.get(u'type', u'')} "
1724 f"{table.get(u'title', u'')}."
1727 columns = table.get(u"columns", None)
1730 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1735 for idx, col in enumerate(columns):
1736 if col.get(u"data-set", None) is None:
1737 logging.warning(f"No data for column {col.get(u'title', u'')}")
1739 tag = col.get(u"tag", None)
1740 data = input_data.filter_data(
1750 data=col[u"data-set"],
1751 continue_on_error=True
1754 u"title": col.get(u"title", f"Column{idx}"),
1757 for builds in data.values:
1758 for build in builds:
1759 for tst_name, tst_data in build.items():
1760 if tag and tag not in tst_data[u"tags"]:
1763 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1764 replace(u"2n1l-", u"")
1765 if col_data[u"data"].get(tst_name_mod, None) is None:
1766 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1767 if u"across testbeds" in table[u"title"].lower() or \
1768 u"across topologies" in table[u"title"].lower():
1769 name = _tpc_modify_displayed_test_name(name)
1770 col_data[u"data"][tst_name_mod] = {
1778 target=col_data[u"data"][tst_name_mod],
1780 include_tests=table[u"include-tests"]
1783 replacement = col.get(u"data-replacement", None)
1785 rpl_data = input_data.filter_data(
1796 continue_on_error=True
1798 for builds in rpl_data.values:
1799 for build in builds:
1800 for tst_name, tst_data in build.items():
1801 if tag and tag not in tst_data[u"tags"]:
1804 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1805 replace(u"2n1l-", u"")
1806 if col_data[u"data"].get(tst_name_mod, None) is None:
1807 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1808 if u"across testbeds" in table[u"title"].lower() \
1809 or u"across topologies" in \
1810 table[u"title"].lower():
1811 name = _tpc_modify_displayed_test_name(name)
1812 col_data[u"data"][tst_name_mod] = {
1819 if col_data[u"data"][tst_name_mod][u"replace"]:
1820 col_data[u"data"][tst_name_mod][u"replace"] = False
1821 col_data[u"data"][tst_name_mod][u"data"] = list()
1823 target=col_data[u"data"][tst_name_mod],
1825 include_tests=table[u"include-tests"]
1828 if table[u"include-tests"] in (u"NDR", u"PDR") or \
1829 u"latency" in table[u"include-tests"]:
1830 for tst_name, tst_data in col_data[u"data"].items():
1831 if tst_data[u"data"]:
1832 tst_data[u"mean"] = mean(tst_data[u"data"])
1833 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1835 cols.append(col_data)
1839 for tst_name, tst_data in col[u"data"].items():
1840 if tbl_dict.get(tst_name, None) is None:
1841 tbl_dict[tst_name] = {
1842 "name": tst_data[u"name"]
1844 tbl_dict[tst_name][col[u"title"]] = {
1845 u"mean": tst_data[u"mean"],
1846 u"stdev": tst_data[u"stdev"]
1850 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1854 for tst_data in tbl_dict.values():
1855 row = [tst_data[u"name"], ]
1857 row.append(tst_data.get(col[u"title"], None))
1860 comparisons = table.get(u"comparisons", None)
1862 if comparisons and isinstance(comparisons, list):
1863 for idx, comp in enumerate(comparisons):
1865 col_ref = int(comp[u"reference"])
1866 col_cmp = int(comp[u"compare"])
1868 logging.warning(u"Comparison: No references defined! Skipping.")
1869 comparisons.pop(idx)
1871 if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1872 col_ref == col_cmp):
1873 logging.warning(f"Wrong values of reference={col_ref} "
1874 f"and/or compare={col_cmp}. Skipping.")
1875 comparisons.pop(idx)
1877 rca_file_name = comp.get(u"rca-file", None)
1880 with open(rca_file_name, u"r") as file_handler:
1883 u"title": f"RCA{idx + 1}",
1884 u"data": load(file_handler, Loader=FullLoader)
1887 except (YAMLError, IOError) as err:
1889 f"The RCA file {rca_file_name} does not exist or "
1892 logging.debug(repr(err))
1899 tbl_cmp_lst = list()
1902 new_row = deepcopy(row)
1903 for comp in comparisons:
1904 ref_itm = row[int(comp[u"reference"])]
1905 if ref_itm is None and \
1906 comp.get(u"reference-alt", None) is not None:
1907 ref_itm = row[int(comp[u"reference-alt"])]
1908 cmp_itm = row[int(comp[u"compare"])]
1909 if ref_itm is not None and cmp_itm is not None and \
1910 ref_itm[u"mean"] is not None and \
1911 cmp_itm[u"mean"] is not None and \
1912 ref_itm[u"stdev"] is not None and \
1913 cmp_itm[u"stdev"] is not None:
1915 delta, d_stdev = relative_change_stdev(
1916 ref_itm[u"mean"], cmp_itm[u"mean"],
1917 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1919 except ZeroDivisionError:
1921 if delta is None or math.isnan(delta):
1924 u"mean": delta * 1e6,
1925 u"stdev": d_stdev * 1e6
1930 tbl_cmp_lst.append(new_row)
1933 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1934 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1935 except TypeError as err:
1936 logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1938 tbl_for_csv = list()
1939 for line in tbl_cmp_lst:
1941 for idx, itm in enumerate(line[1:]):
1942 if itm is None or not isinstance(itm, dict) or\
1943 itm.get(u'mean', None) is None or \
1944 itm.get(u'stdev', None) is None:
1948 row.append(round(float(itm[u'mean']) / 1e6, 3))
1949 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1953 rca_nr = rca[u"data"].get(row[0], u"-")
1954 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1955 tbl_for_csv.append(row)
1957 header_csv = [u"Test Case", ]
1959 header_csv.append(f"Avg({col[u'title']})")
1960 header_csv.append(f"Stdev({col[u'title']})")
1961 for comp in comparisons:
1963 f"Avg({comp.get(u'title', u'')})"
1966 f"Stdev({comp.get(u'title', u'')})"
1970 header_csv.append(rca[u"title"])
1972 legend_lst = table.get(u"legend", None)
1973 if legend_lst is None:
1976 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1979 if rcas and any(rcas):
1980 footnote += u"\nRoot Cause Analysis:\n"
1983 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1985 csv_file_name = f"{table[u'output-file']}-csv.csv"
1986 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1988 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1990 for test in tbl_for_csv:
1992 u",".join([f'"{item}"' for item in test]) + u"\n"
1995 for item in legend_lst:
1996 file_handler.write(f'"{item}"\n')
1998 for itm in footnote.split(u"\n"):
1999 file_handler.write(f'"{itm}"\n')
2002 max_lens = [0, ] * len(tbl_cmp_lst[0])
2003 for line in tbl_cmp_lst:
2005 for idx, itm in enumerate(line[1:]):
2006 if itm is None or not isinstance(itm, dict) or \
2007 itm.get(u'mean', None) is None or \
2008 itm.get(u'stdev', None) is None:
2013 f"{round(float(itm[u'mean']) / 1e6, 2)} "
2014 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 2)}".
2015 replace(u"nan", u"NaN")
2019 f"{round(float(itm[u'mean']) / 1e6, 2):+} "
2020 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 2)}".
2021 replace(u"nan", u"NaN")
2023 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
2024 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
2029 header = [u"Test Case", ]
2030 header.extend([col[u"title"] for col in cols])
2031 header.extend([comp.get(u"title", u"") for comp in comparisons])
2034 for line in tbl_tmp:
2036 for idx, itm in enumerate(line[1:]):
2037 if itm in (u"NT", u"NaN"):
2040 itm_lst = itm.rsplit(u"\u00B1", 1)
2042 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
2043 itm_str = u"\u00B1".join(itm_lst)
2045 if idx >= len(cols):
2047 rca = rcas[idx - len(cols)]
2050 rca_nr = rca[u"data"].get(row[0], None)
2052 hdr_len = len(header[idx + 1]) - 1
2055 rca_nr = f"[{rca_nr}]"
2057 f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
2058 f"{u' ' * (hdr_len - 4 - len(itm_str))}"
2062 tbl_final.append(row)
2064 # Generate csv tables:
2065 csv_file_name = f"{table[u'output-file']}.csv"
2066 logging.info(f" Writing the file {csv_file_name}")
2067 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2068 file_handler.write(u";".join(header) + u"\n")
2069 for test in tbl_final:
2070 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
2072 # Generate txt table:
2073 txt_file_name = f"{table[u'output-file']}.txt"
2074 logging.info(f" Writing the file {txt_file_name}")
2075 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
2077 with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
2078 file_handler.write(legend)
2079 file_handler.write(footnote)
2081 # Generate html table:
2082 _tpc_generate_html_table(
2085 table[u'output-file'],
2089 title=table.get(u"title", u"")
2093 def table_weekly_comparison(table, in_data):
2094 """Generate the table(s) with algorithm: table_weekly_comparison
2095 specified in the specification file.
2097 :param table: Table to generate.
2098 :param in_data: Data to process.
2099 :type table: pandas.Series
2100 :type in_data: InputData
2102 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
2104 # Transform the data
2106 f" Creating the data set for the {table.get(u'type', u'')} "
2107 f"{table.get(u'title', u'')}."
2110 incl_tests = table.get(u"include-tests", None)
2111 if incl_tests not in (u"NDR", u"PDR"):
2112 logging.error(f"Wrong tests to include specified ({incl_tests}).")
2115 nr_cols = table.get(u"nr-of-data-columns", None)
2116 if not nr_cols or nr_cols < 2:
2118 f"No columns specified for {table.get(u'title', u'')}. Skipping."
2122 data = in_data.filter_data(
2124 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
2125 continue_on_error=True
2130 [u"Start Timestamp", ],
2136 tb_tbl = table.get(u"testbeds", None)
2137 for job_name, job_data in data.items():
2138 for build_nr, build in job_data.items():
2144 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
2145 if tb_ip and tb_tbl:
2146 testbed = tb_tbl.get(tb_ip, u"")
2149 header[2].insert(1, build_nr)
2150 header[3].insert(1, testbed)
2152 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
2155 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
2158 for tst_name, tst_data in build.items():
2160 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
2161 if not tbl_dict.get(tst_name_mod, None):
2162 tbl_dict[tst_name_mod] = dict(
2163 name=tst_data[u'name'].rsplit(u'-', 1)[0],
2166 tbl_dict[tst_name_mod][-idx - 1] = \
2167 tst_data[u"throughput"][incl_tests][u"LOWER"]
2168 except (TypeError, IndexError, KeyError, ValueError):
2173 logging.error(u"Not enough data to build the table! Skipping")
2177 for idx, cmp in enumerate(table.get(u"comparisons", list())):
2178 idx_ref = cmp.get(u"reference", None)
2179 idx_cmp = cmp.get(u"compare", None)
2180 if idx_ref is None or idx_cmp is None:
2183 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
2184 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
2186 header[1].append(u"")
2187 header[2].append(u"")
2188 header[3].append(u"")
2189 for tst_name, tst_data in tbl_dict.items():
2190 if not cmp_dict.get(tst_name, None):
2191 cmp_dict[tst_name] = list()
2192 ref_data = tst_data.get(idx_ref, None)
2193 cmp_data = tst_data.get(idx_cmp, None)
2194 if ref_data is None or cmp_data is None:
2195 cmp_dict[tst_name].append(float(u'nan'))
2197 cmp_dict[tst_name].append(
2198 relative_change(ref_data, cmp_data)
2201 tbl_lst_none = list()
2203 for tst_name, tst_data in tbl_dict.items():
2204 itm_lst = [tst_data[u"name"], ]
2205 for idx in range(nr_cols):
2206 item = tst_data.get(-idx - 1, None)
2208 itm_lst.insert(1, None)
2210 itm_lst.insert(1, round(item / 1e6, 1))
2213 None if itm is None else round(itm, 1)
2214 for itm in cmp_dict[tst_name]
2217 if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
2218 tbl_lst_none.append(itm_lst)
2220 tbl_lst.append(itm_lst)
2222 tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
2223 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
2224 tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
2225 tbl_lst.extend(tbl_lst_none)
2227 # Generate csv table:
2228 csv_file_name = f"{table[u'output-file']}.csv"
2229 logging.info(f" Writing the file {csv_file_name}")
2230 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2232 file_handler.write(u",".join(hdr) + u"\n")
2233 for test in tbl_lst:
2234 file_handler.write(u",".join(
2236 str(item).replace(u"None", u"-").replace(u"nan", u"-").
2237 replace(u"null", u"-") for item in test
2241 txt_file_name = f"{table[u'output-file']}.txt"
2242 logging.info(f" Writing the file {txt_file_name}")
2243 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2245 # Reorganize header in txt table
2247 with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2248 for line in list(file_handler):
2249 txt_table.append(line)
2251 txt_table.insert(5, txt_table.pop(2))
2252 with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2253 file_handler.writelines(txt_table)
2257 # Generate html table:
2259 u"<br>".join(row) for row in zip(*header)
2261 _tpc_generate_html_table(
2264 table[u'output-file'],
2266 title=table.get(u"title", u""),