1 # Copyright (c) 2020 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
14 """Algorithms to generate tables.
22 from collections import OrderedDict
23 from xml.etree import ElementTree as ET
24 from datetime import datetime as dt
25 from datetime import timedelta
26 from copy import deepcopy
28 import plotly.graph_objects as go
29 import plotly.offline as ploff
32 from numpy import nan, isnan
33 from yaml import load, FullLoader, YAMLError
35 from pal_utils import mean, stdev, classify_anomalies, \
36 convert_csv_to_pretty_txt, relative_change_stdev, relative_change
39 REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)')
42 def generate_tables(spec, data):
43 """Generate all tables specified in the specification file.
45 :param spec: Specification read from the specification file.
46 :param data: Data to process.
47 :type spec: Specification
52 u"table_merged_details": table_merged_details,
53 u"table_soak_vs_ndr": table_soak_vs_ndr,
54 u"table_perf_trending_dash": table_perf_trending_dash,
55 u"table_perf_trending_dash_html": table_perf_trending_dash_html,
56 u"table_last_failed_tests": table_last_failed_tests,
57 u"table_failed_tests": table_failed_tests,
58 u"table_failed_tests_html": table_failed_tests_html,
59 u"table_oper_data_html": table_oper_data_html,
60 u"table_comparison": table_comparison,
61 u"table_weekly_comparison": table_weekly_comparison
64 logging.info(u"Generating the tables ...")
65 for table in spec.tables:
67 if table[u"algorithm"] == u"table_weekly_comparison":
68 table[u"testbeds"] = spec.environment.get(u"testbeds", None)
69 generator[table[u"algorithm"]](table, data)
70 except NameError as err:
72 f"Probably algorithm {table[u'algorithm']} is not defined: "
75 logging.info(u"Done.")
78 def table_oper_data_html(table, input_data):
79 """Generate the table(s) with algorithm: html_table_oper_data
80 specified in the specification file.
82 :param table: Table to generate.
83 :param input_data: Data to process.
84 :type table: pandas.Series
85 :type input_data: InputData
88 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
91 f" Creating the data set for the {table.get(u'type', u'')} "
92 f"{table.get(u'title', u'')}."
94 data = input_data.filter_data(
96 params=[u"name", u"parent", u"show-run", u"type"],
97 continue_on_error=True
101 data = input_data.merge_data(data)
103 sort_tests = table.get(u"sort", None)
107 ascending=(sort_tests == u"ascending")
109 data.sort_index(**args)
111 suites = input_data.filter_data(
113 continue_on_error=True,
118 suites = input_data.merge_data(suites)
120 def _generate_html_table(tst_data):
121 """Generate an HTML table with operational data for the given test.
123 :param tst_data: Test data to be used to generate the table.
124 :type tst_data: pandas.Series
125 :returns: HTML table with operational data.
130 u"header": u"#7eade7",
131 u"empty": u"#ffffff",
132 u"body": (u"#e9f1fb", u"#d4e4f7")
135 tbl = ET.Element(u"table", attrib=dict(width=u"100%", border=u"0"))
137 trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"header"]))
138 thead = ET.SubElement(
139 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
141 thead.text = tst_data[u"name"]
143 trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
144 thead = ET.SubElement(
145 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
149 if tst_data.get(u"show-run", u"No Data") == u"No Data":
150 trow = ET.SubElement(
151 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
153 tcol = ET.SubElement(
154 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
156 tcol.text = u"No Data"
158 trow = ET.SubElement(
159 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
161 thead = ET.SubElement(
162 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
164 font = ET.SubElement(
165 thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
168 return str(ET.tostring(tbl, encoding=u"unicode"))
175 u"Cycles per Packet",
176 u"Average Vector Size"
179 for dut_data in tst_data[u"show-run"].values():
180 trow = ET.SubElement(
181 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
183 tcol = ET.SubElement(
184 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
186 if dut_data.get(u"threads", None) is None:
187 tcol.text = u"No Data"
190 bold = ET.SubElement(tcol, u"b")
192 f"Host IP: {dut_data.get(u'host', '')}, "
193 f"Socket: {dut_data.get(u'socket', '')}"
195 trow = ET.SubElement(
196 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
198 thead = ET.SubElement(
199 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
203 for thread_nr, thread in dut_data[u"threads"].items():
204 trow = ET.SubElement(
205 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
207 tcol = ET.SubElement(
208 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
210 bold = ET.SubElement(tcol, u"b")
211 bold.text = u"main" if thread_nr == 0 else f"worker_{thread_nr}"
212 trow = ET.SubElement(
213 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
215 for idx, col in enumerate(tbl_hdr):
216 tcol = ET.SubElement(
218 attrib=dict(align=u"right" if idx else u"left")
220 font = ET.SubElement(
221 tcol, u"font", attrib=dict(size=u"2")
223 bold = ET.SubElement(font, u"b")
225 for row_nr, row in enumerate(thread):
226 trow = ET.SubElement(
228 attrib=dict(bgcolor=colors[u"body"][row_nr % 2])
230 for idx, col in enumerate(row):
231 tcol = ET.SubElement(
233 attrib=dict(align=u"right" if idx else u"left")
235 font = ET.SubElement(
236 tcol, u"font", attrib=dict(size=u"2")
238 if isinstance(col, float):
239 font.text = f"{col:.2f}"
242 trow = ET.SubElement(
243 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
245 thead = ET.SubElement(
246 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
250 trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
251 thead = ET.SubElement(
252 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
254 font = ET.SubElement(
255 thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
259 return str(ET.tostring(tbl, encoding=u"unicode"))
261 for suite in suites.values:
263 for test_data in data.values:
264 if test_data[u"parent"] not in suite[u"name"]:
266 html_table += _generate_html_table(test_data)
270 file_name = f"{table[u'output-file']}{suite[u'name']}.rst"
271 with open(f"{file_name}", u'w') as html_file:
272 logging.info(f" Writing file: {file_name}")
273 html_file.write(u".. raw:: html\n\n\t")
274 html_file.write(html_table)
275 html_file.write(u"\n\t<p><br><br></p>\n")
277 logging.warning(u"The output file is not defined.")
279 logging.info(u" Done.")
282 def table_merged_details(table, input_data):
283 """Generate the table(s) with algorithm: table_merged_details
284 specified in the specification file.
286 :param table: Table to generate.
287 :param input_data: Data to process.
288 :type table: pandas.Series
289 :type input_data: InputData
292 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
296 f" Creating the data set for the {table.get(u'type', u'')} "
297 f"{table.get(u'title', u'')}."
299 data = input_data.filter_data(table, continue_on_error=True)
300 data = input_data.merge_data(data)
302 sort_tests = table.get(u"sort", None)
306 ascending=(sort_tests == u"ascending")
308 data.sort_index(**args)
310 suites = input_data.filter_data(
311 table, continue_on_error=True, data_set=u"suites")
312 suites = input_data.merge_data(suites)
314 # Prepare the header of the tables
316 for column in table[u"columns"]:
318 u'"{0}"'.format(str(column[u"title"]).replace(u'"', u'""'))
321 for suite in suites.values:
323 suite_name = suite[u"name"]
325 for test in data.keys():
326 if data[test][u"parent"] not in suite_name:
329 for column in table[u"columns"]:
331 col_data = str(data[test][column[
332 u"data"].split(u" ")[1]]).replace(u'"', u'""')
333 # Do not include tests with "Test Failed" in test message
334 if u"Test Failed" in col_data:
336 col_data = col_data.replace(
337 u"No Data", u"Not Captured "
339 if column[u"data"].split(u" ")[1] in (u"name", ):
340 if len(col_data) > 30:
341 col_data_lst = col_data.split(u"-")
342 half = int(len(col_data_lst) / 2)
343 col_data = f"{u'-'.join(col_data_lst[:half])}" \
345 f"{u'-'.join(col_data_lst[half:])}"
346 col_data = f" |prein| {col_data} |preout| "
347 elif column[u"data"].split(u" ")[1] in (u"msg", ):
348 # Temporary solution: remove NDR results from message:
349 if bool(table.get(u'remove-ndr', False)):
351 col_data = col_data.split(u" |br| ", 1)[1]
354 col_data = f" |prein| {col_data} |preout| "
355 elif column[u"data"].split(u" ")[1] in \
356 (u"conf-history", u"show-run"):
357 col_data = col_data.replace(u" |br| ", u"", 1)
358 col_data = f" |prein| {col_data[:-5]} |preout| "
359 row_lst.append(f'"{col_data}"')
361 row_lst.append(u'"Not captured"')
362 if len(row_lst) == len(table[u"columns"]):
363 table_lst.append(row_lst)
365 # Write the data to file
367 separator = u"" if table[u'output-file'].endswith(u"/") else u"_"
368 file_name = f"{table[u'output-file']}{separator}{suite_name}.csv"
369 logging.info(f" Writing file: {file_name}")
370 with open(file_name, u"wt") as file_handler:
371 file_handler.write(u",".join(header) + u"\n")
372 for item in table_lst:
373 file_handler.write(u",".join(item) + u"\n")
375 logging.info(u" Done.")
378 def _tpc_modify_test_name(test_name, ignore_nic=False):
379 """Modify a test name by replacing its parts.
381 :param test_name: Test name to be modified.
382 :param ignore_nic: If True, NIC is removed from TC name.
384 :type ignore_nic: bool
385 :returns: Modified test name.
388 test_name_mod = test_name.\
389 replace(u"-ndrpdrdisc", u""). \
390 replace(u"-ndrpdr", u"").\
391 replace(u"-pdrdisc", u""). \
392 replace(u"-ndrdisc", u"").\
393 replace(u"-pdr", u""). \
394 replace(u"-ndr", u""). \
395 replace(u"1t1c", u"1c").\
396 replace(u"2t1c", u"1c"). \
397 replace(u"2t2c", u"2c").\
398 replace(u"4t2c", u"2c"). \
399 replace(u"4t4c", u"4c").\
400 replace(u"8t4c", u"4c")
403 return re.sub(REGEX_NIC, u"", test_name_mod)
407 def _tpc_modify_displayed_test_name(test_name):
408 """Modify a test name which is displayed in a table by replacing its parts.
410 :param test_name: Test name to be modified.
412 :returns: Modified test name.
416 replace(u"1t1c", u"1c").\
417 replace(u"2t1c", u"1c"). \
418 replace(u"2t2c", u"2c").\
419 replace(u"4t2c", u"2c"). \
420 replace(u"4t4c", u"4c").\
421 replace(u"8t4c", u"4c")
424 def _tpc_insert_data(target, src, include_tests):
425 """Insert src data to the target structure.
427 :param target: Target structure where the data is placed.
428 :param src: Source data to be placed into the target stucture.
429 :param include_tests: Which results will be included (MRR, NDR, PDR).
432 :type include_tests: str
435 if include_tests == u"MRR":
436 target[u"mean"] = src[u"result"][u"receive-rate"]
437 target[u"stdev"] = src[u"result"][u"receive-stdev"]
438 elif include_tests == u"PDR":
439 target[u"data"].append(src[u"throughput"][u"PDR"][u"LOWER"])
440 elif include_tests == u"NDR":
441 target[u"data"].append(src[u"throughput"][u"NDR"][u"LOWER"])
442 except (KeyError, TypeError):
446 def _tpc_generate_html_table(header, data, out_file_name, legend=u"",
447 footnote=u"", sort_data=True, title=u"",
449 """Generate html table from input data with simple sorting possibility.
451 :param header: Table header.
452 :param data: Input data to be included in the table. It is a list of lists.
453 Inner lists are rows in the table. All inner lists must be of the same
454 length. The length of these lists must be the same as the length of the
456 :param out_file_name: The name (relative or full path) where the
457 generated html table is written.
458 :param legend: The legend to display below the table.
459 :param footnote: The footnote to display below the table (and legend).
460 :param sort_data: If True the data sorting is enabled.
461 :param title: The table (and file) title.
462 :param generate_rst: If True, wrapping rst file is generated.
464 :type data: list of lists
465 :type out_file_name: str
468 :type sort_data: bool
470 :type generate_rst: bool
474 idx = header.index(u"Test Case")
480 [u"left", u"left", u"right"],
481 [u"left", u"left", u"left", u"right"]
485 [u"left", u"left", u"right"],
486 [u"left", u"left", u"left", u"right"]
488 u"width": ([15, 9], [4, 24, 10], [4, 4, 32, 10])
491 df_data = pd.DataFrame(data, columns=header)
494 df_sorted = [df_data.sort_values(
495 by=[key, header[idx]], ascending=[True, True]
496 if key != header[idx] else [False, True]) for key in header]
497 df_sorted_rev = [df_data.sort_values(
498 by=[key, header[idx]], ascending=[False, True]
499 if key != header[idx] else [True, True]) for key in header]
500 df_sorted.extend(df_sorted_rev)
504 fill_color = [[u"#d4e4f7" if idx % 2 else u"#e9f1fb"
505 for idx in range(len(df_data))]]
507 values=[f"<b>{item.replace(u',', u',<br>')}</b>" for item in header],
508 fill_color=u"#7eade7",
509 align=params[u"align-hdr"][idx],
511 family=u"Courier New",
519 for table in df_sorted:
520 columns = [table.get(col) for col in header]
523 columnwidth=params[u"width"][idx],
527 fill_color=fill_color,
528 align=params[u"align-itm"][idx],
530 family=u"Courier New",
538 menu_items = [f"<b>{itm}</b> (ascending)" for itm in header]
539 menu_items.extend([f"<b>{itm}</b> (descending)" for itm in header])
540 for idx, hdr in enumerate(menu_items):
541 visible = [False, ] * len(menu_items)
545 label=hdr.replace(u" [Mpps]", u""),
547 args=[{u"visible": visible}],
553 go.layout.Updatemenu(
560 active=len(menu_items) - 1,
561 buttons=list(buttons)
568 columnwidth=params[u"width"][idx],
571 values=[df_sorted.get(col) for col in header],
572 fill_color=fill_color,
573 align=params[u"align-itm"][idx],
575 family=u"Courier New",
586 filename=f"{out_file_name}_in.html"
592 file_name = out_file_name.split(u"/")[-1]
593 if u"vpp" in out_file_name:
594 path = u"_tmp/src/vpp_performance_tests/comparisons/"
596 path = u"_tmp/src/dpdk_performance_tests/comparisons/"
597 with open(f"{path}{file_name}.rst", u"wt") as rst_file:
600 u".. |br| raw:: html\n\n <br />\n\n\n"
601 u".. |prein| raw:: html\n\n <pre>\n\n\n"
602 u".. |preout| raw:: html\n\n </pre>\n\n"
605 rst_file.write(f"{title}\n")
606 rst_file.write(f"{u'`' * len(title)}\n\n")
609 f' <iframe frameborder="0" scrolling="no" '
610 f'width="1600" height="1200" '
611 f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
615 # TODO: Use html (rst) list for legend and footnote
617 rst_file.write(legend[1:].replace(u"\n", u" |br| "))
619 rst_file.write(footnote.replace(u"\n", u" |br| ")[1:])
622 def table_soak_vs_ndr(table, input_data):
623 """Generate the table(s) with algorithm: table_soak_vs_ndr
624 specified in the specification file.
626 :param table: Table to generate.
627 :param input_data: Data to process.
628 :type table: pandas.Series
629 :type input_data: InputData
632 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
636 f" Creating the data set for the {table.get(u'type', u'')} "
637 f"{table.get(u'title', u'')}."
639 data = input_data.filter_data(table, continue_on_error=True)
641 # Prepare the header of the table
645 f"Avg({table[u'reference'][u'title']})",
646 f"Stdev({table[u'reference'][u'title']})",
647 f"Avg({table[u'compare'][u'title']})",
648 f"Stdev{table[u'compare'][u'title']})",
652 header_str = u";".join(header) + u"\n"
655 f"Avg({table[u'reference'][u'title']}): "
656 f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
657 f"from a series of runs of the listed tests.\n"
658 f"Stdev({table[u'reference'][u'title']}): "
659 f"Standard deviation value of {table[u'reference'][u'title']} "
660 f"[Mpps] computed from a series of runs of the listed tests.\n"
661 f"Avg({table[u'compare'][u'title']}): "
662 f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
663 f"a series of runs of the listed tests.\n"
664 f"Stdev({table[u'compare'][u'title']}): "
665 f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
666 f"computed from a series of runs of the listed tests.\n"
667 f"Diff({table[u'reference'][u'title']},"
668 f"{table[u'compare'][u'title']}): "
669 f"Percentage change calculated for mean values.\n"
671 u"Standard deviation of percentage change calculated for mean "
674 except (AttributeError, KeyError) as err:
675 logging.error(f"The model is invalid, missing parameter: {repr(err)}")
678 # Create a list of available SOAK test results:
680 for job, builds in table[u"compare"][u"data"].items():
682 for tst_name, tst_data in data[job][str(build)].items():
683 if tst_data[u"type"] == u"SOAK":
684 tst_name_mod = tst_name.replace(u"-soak", u"")
685 if tbl_dict.get(tst_name_mod, None) is None:
686 groups = re.search(REGEX_NIC, tst_data[u"parent"])
687 nic = groups.group(0) if groups else u""
690 f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
692 tbl_dict[tst_name_mod] = {
698 tbl_dict[tst_name_mod][u"cmp-data"].append(
699 tst_data[u"throughput"][u"LOWER"])
700 except (KeyError, TypeError):
702 tests_lst = tbl_dict.keys()
704 # Add corresponding NDR test results:
705 for job, builds in table[u"reference"][u"data"].items():
707 for tst_name, tst_data in data[job][str(build)].items():
708 tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
709 replace(u"-mrr", u"")
710 if tst_name_mod not in tests_lst:
713 if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
715 if table[u"include-tests"] == u"MRR":
716 result = (tst_data[u"result"][u"receive-rate"],
717 tst_data[u"result"][u"receive-stdev"])
718 elif table[u"include-tests"] == u"PDR":
720 tst_data[u"throughput"][u"PDR"][u"LOWER"]
721 elif table[u"include-tests"] == u"NDR":
723 tst_data[u"throughput"][u"NDR"][u"LOWER"]
726 if result is not None:
727 tbl_dict[tst_name_mod][u"ref-data"].append(
729 except (KeyError, TypeError):
733 for tst_name in tbl_dict:
734 item = [tbl_dict[tst_name][u"name"], ]
735 data_r = tbl_dict[tst_name][u"ref-data"]
737 if table[u"include-tests"] == u"MRR":
738 data_r_mean = data_r[0][0]
739 data_r_stdev = data_r[0][1]
741 data_r_mean = mean(data_r)
742 data_r_stdev = stdev(data_r)
743 item.append(round(data_r_mean / 1e6, 1))
744 item.append(round(data_r_stdev / 1e6, 1))
748 item.extend([None, None])
749 data_c = tbl_dict[tst_name][u"cmp-data"]
751 if table[u"include-tests"] == u"MRR":
752 data_c_mean = data_c[0][0]
753 data_c_stdev = data_c[0][1]
755 data_c_mean = mean(data_c)
756 data_c_stdev = stdev(data_c)
757 item.append(round(data_c_mean / 1e6, 1))
758 item.append(round(data_c_stdev / 1e6, 1))
762 item.extend([None, None])
763 if data_r_mean is not None and data_c_mean is not None:
764 delta, d_stdev = relative_change_stdev(
765 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
767 item.append(round(delta))
771 item.append(round(d_stdev))
776 # Sort the table according to the relative change
777 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
779 # Generate csv tables:
780 csv_file = f"{table[u'output-file']}.csv"
781 with open(csv_file, u"wt") as file_handler:
782 file_handler.write(header_str)
784 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
786 convert_csv_to_pretty_txt(
787 csv_file, f"{table[u'output-file']}.txt", delimiter=u";"
789 with open(f"{table[u'output-file']}.txt", u'a') as txt_file:
790 txt_file.write(legend)
792 # Generate html table:
793 _tpc_generate_html_table(
796 table[u'output-file'],
798 title=table.get(u"title", u"")
802 def table_perf_trending_dash(table, input_data):
803 """Generate the table(s) with algorithm:
804 table_perf_trending_dash
805 specified in the specification file.
807 :param table: Table to generate.
808 :param input_data: Data to process.
809 :type table: pandas.Series
810 :type input_data: InputData
813 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
817 f" Creating the data set for the {table.get(u'type', u'')} "
818 f"{table.get(u'title', u'')}."
820 data = input_data.filter_data(table, continue_on_error=True)
822 # Prepare the header of the tables
826 u"Short-Term Change [%]",
827 u"Long-Term Change [%]",
831 header_str = u",".join(header) + u"\n"
833 incl_tests = table.get(u"include-tests", u"MRR")
835 # Prepare data to the table:
837 for job, builds in table[u"data"].items():
839 for tst_name, tst_data in data[job][str(build)].items():
840 if tst_name.lower() in table.get(u"ignore-list", list()):
842 if tbl_dict.get(tst_name, None) is None:
843 groups = re.search(REGEX_NIC, tst_data[u"parent"])
846 nic = groups.group(0)
847 tbl_dict[tst_name] = {
848 u"name": f"{nic}-{tst_data[u'name']}",
849 u"data": OrderedDict()
852 if incl_tests == u"MRR":
853 tbl_dict[tst_name][u"data"][str(build)] = \
854 tst_data[u"result"][u"receive-rate"]
855 elif incl_tests == u"NDR":
856 tbl_dict[tst_name][u"data"][str(build)] = \
857 tst_data[u"throughput"][u"NDR"][u"LOWER"]
858 elif incl_tests == u"PDR":
859 tbl_dict[tst_name][u"data"][str(build)] = \
860 tst_data[u"throughput"][u"PDR"][u"LOWER"]
861 except (TypeError, KeyError):
862 pass # No data in output.xml for this test
865 for tst_name in tbl_dict:
866 data_t = tbl_dict[tst_name][u"data"]
870 classification_lst, avgs, _ = classify_anomalies(data_t)
872 win_size = min(len(data_t), table[u"window"])
873 long_win_size = min(len(data_t), table[u"long-trend-window"])
877 [x for x in avgs[-long_win_size:-win_size]
882 avg_week_ago = avgs[max(-win_size, -len(avgs))]
884 if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
885 rel_change_last = nan
887 rel_change_last = round(
888 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
890 if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
891 rel_change_long = nan
893 rel_change_long = round(
894 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
896 if classification_lst:
897 if isnan(rel_change_last) and isnan(rel_change_long):
899 if isnan(last_avg) or isnan(rel_change_last) or \
900 isnan(rel_change_long):
903 [tbl_dict[tst_name][u"name"],
904 round(last_avg / 1e6, 2),
907 classification_lst[-win_size+1:].count(u"regression"),
908 classification_lst[-win_size+1:].count(u"progression")])
910 tbl_lst.sort(key=lambda rel: rel[0])
913 for nrr in range(table[u"window"], -1, -1):
914 tbl_reg = [item for item in tbl_lst if item[4] == nrr]
915 for nrp in range(table[u"window"], -1, -1):
916 tbl_out = [item for item in tbl_reg if item[5] == nrp]
917 tbl_out.sort(key=lambda rel: rel[2])
918 tbl_sorted.extend(tbl_out)
920 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
922 logging.info(f" Writing file: {file_name}")
923 with open(file_name, u"wt") as file_handler:
924 file_handler.write(header_str)
925 for test in tbl_sorted:
926 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
928 logging.info(f" Writing file: {table[u'output-file']}.txt")
929 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
932 def _generate_url(testbed, test_name):
933 """Generate URL to a trending plot from the name of the test case.
935 :param testbed: The testbed used for testing.
936 :param test_name: The name of the test case.
939 :returns: The URL to the plot with the trending data for the given test
944 if u"x520" in test_name:
946 elif u"x710" in test_name:
948 elif u"xl710" in test_name:
950 elif u"xxv710" in test_name:
952 elif u"vic1227" in test_name:
954 elif u"vic1385" in test_name:
956 elif u"x553" in test_name:
958 elif u"cx556" in test_name or u"cx556a" in test_name:
963 if u"64b" in test_name:
965 elif u"78b" in test_name:
967 elif u"imix" in test_name:
969 elif u"9000b" in test_name:
970 frame_size = u"9000b"
971 elif u"1518b" in test_name:
972 frame_size = u"1518b"
973 elif u"114b" in test_name:
978 if u"1t1c" in test_name or \
979 (u"-1c-" in test_name and
980 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
982 elif u"2t2c" in test_name or \
983 (u"-2c-" in test_name and
984 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
986 elif u"4t4c" in test_name or \
987 (u"-4c-" in test_name and
988 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
990 elif u"2t1c" in test_name or \
991 (u"-1c-" in test_name and
992 testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
994 elif u"4t2c" in test_name or \
995 (u"-2c-" in test_name and
996 testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
998 elif u"8t4c" in test_name or \
999 (u"-4c-" in test_name and
1000 testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
1005 if u"testpmd" in test_name:
1007 elif u"l3fwd" in test_name:
1009 elif u"avf" in test_name:
1011 elif u"rdma" in test_name:
1013 elif u"dnv" in testbed or u"tsh" in testbed:
1018 if u"acl" in test_name or \
1019 u"macip" in test_name or \
1020 u"nat" in test_name or \
1021 u"policer" in test_name or \
1022 u"cop" in test_name:
1024 elif u"scale" in test_name:
1026 elif u"base" in test_name:
1031 if u"114b" in test_name and u"vhost" in test_name:
1033 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1035 elif u"memif" in test_name:
1036 domain = u"container_memif"
1037 elif u"srv6" in test_name:
1039 elif u"vhost" in test_name:
1041 if u"vppl2xc" in test_name:
1044 driver += u"-testpmd"
1045 if u"lbvpplacp" in test_name:
1046 bsf += u"-link-bonding"
1047 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1048 domain = u"nf_service_density_vnfc"
1049 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1050 domain = u"nf_service_density_cnfc"
1051 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1052 domain = u"nf_service_density_cnfp"
1053 elif u"ipsec" in test_name:
1055 if u"sw" in test_name:
1057 elif u"hw" in test_name:
1059 elif u"ethip4vxlan" in test_name:
1060 domain = u"ip4_tunnels"
1061 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1063 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1065 elif u"l2xcbase" in test_name or \
1066 u"l2xcscale" in test_name or \
1067 u"l2bdbasemaclrn" in test_name or \
1068 u"l2bdscale" in test_name or \
1069 u"l2patch" in test_name:
1074 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1075 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1077 return file_name + anchor_name
1080 def table_perf_trending_dash_html(table, input_data):
1081 """Generate the table(s) with algorithm:
1082 table_perf_trending_dash_html specified in the specification
1085 :param table: Table to generate.
1086 :param input_data: Data to process.
1088 :type input_data: InputData
1093 if not table.get(u"testbed", None):
1095 f"The testbed is not defined for the table "
1096 f"{table.get(u'title', u'')}."
1100 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1103 with open(table[u"input-file"], u'rt') as csv_file:
1104 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1106 logging.warning(u"The input file is not defined.")
1108 except csv.Error as err:
1110 f"Not possible to process the file {table[u'input-file']}.\n"
1116 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1119 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1120 for idx, item in enumerate(csv_lst[0]):
1121 alignment = u"left" if idx == 0 else u"center"
1122 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1140 for r_idx, row in enumerate(csv_lst[1:]):
1142 color = u"regression"
1144 color = u"progression"
1147 trow = ET.SubElement(
1148 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1152 for c_idx, item in enumerate(row):
1153 tdata = ET.SubElement(
1156 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1159 if c_idx == 0 and table.get(u"add-links", True):
1160 ref = ET.SubElement(
1164 href=f"../trending/"
1165 f"{_generate_url(table.get(u'testbed', ''), item)}"
1172 with open(table[u"output-file"], u'w') as html_file:
1173 logging.info(f" Writing file: {table[u'output-file']}")
1174 html_file.write(u".. raw:: html\n\n\t")
1175 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1176 html_file.write(u"\n\t<p><br><br></p>\n")
1178 logging.warning(u"The output file is not defined.")
1182 def table_last_failed_tests(table, input_data):
1183 """Generate the table(s) with algorithm: table_last_failed_tests
1184 specified in the specification file.
1186 :param table: Table to generate.
1187 :param input_data: Data to process.
1188 :type table: pandas.Series
1189 :type input_data: InputData
1192 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1194 # Transform the data
1196 f" Creating the data set for the {table.get(u'type', u'')} "
1197 f"{table.get(u'title', u'')}."
1200 data = input_data.filter_data(table, continue_on_error=True)
1202 if data is None or data.empty:
1204 f" No data for the {table.get(u'type', u'')} "
1205 f"{table.get(u'title', u'')}."
1210 for job, builds in table[u"data"].items():
1211 for build in builds:
1214 version = input_data.metadata(job, build).get(u"version", u"")
1216 logging.error(f"Data for {job}: {build} is not present.")
1218 tbl_list.append(build)
1219 tbl_list.append(version)
1220 failed_tests = list()
1223 for tst_data in data[job][build].values:
1224 if tst_data[u"status"] != u"FAIL":
1228 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1231 nic = groups.group(0)
1232 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1233 tbl_list.append(str(passed))
1234 tbl_list.append(str(failed))
1235 tbl_list.extend(failed_tests)
1237 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1238 logging.info(f" Writing file: {file_name}")
1239 with open(file_name, u"wt") as file_handler:
1240 for test in tbl_list:
1241 file_handler.write(test + u'\n')
1244 def table_failed_tests(table, input_data):
1245 """Generate the table(s) with algorithm: table_failed_tests
1246 specified in the specification file.
1248 :param table: Table to generate.
1249 :param input_data: Data to process.
1250 :type table: pandas.Series
1251 :type input_data: InputData
1254 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1256 # Transform the data
1258 f" Creating the data set for the {table.get(u'type', u'')} "
1259 f"{table.get(u'title', u'')}."
1261 data = input_data.filter_data(table, continue_on_error=True)
1264 if u"NDRPDR" in table.get(u"filter", list()):
1265 test_type = u"NDRPDR"
1267 # Prepare the header of the tables
1271 u"Last Failure [Time]",
1272 u"Last Failure [VPP-Build-Id]",
1273 u"Last Failure [CSIT-Job-Build-Id]"
1276 # Generate the data for the table according to the model in the table
1280 timeperiod = timedelta(int(table.get(u"window", 7)))
1283 for job, builds in table[u"data"].items():
1284 for build in builds:
1286 for tst_name, tst_data in data[job][build].items():
1287 if tst_name.lower() in table.get(u"ignore-list", list()):
1289 if tbl_dict.get(tst_name, None) is None:
1290 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1293 nic = groups.group(0)
1294 tbl_dict[tst_name] = {
1295 u"name": f"{nic}-{tst_data[u'name']}",
1296 u"data": OrderedDict()
1299 generated = input_data.metadata(job, build).\
1300 get(u"generated", u"")
1303 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1304 if (now - then) <= timeperiod:
1305 tbl_dict[tst_name][u"data"][build] = (
1306 tst_data[u"status"],
1308 input_data.metadata(job, build).get(u"version",
1312 except (TypeError, KeyError) as err:
1313 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1317 for tst_data in tbl_dict.values():
1319 fails_last_date = u""
1320 fails_last_vpp = u""
1321 fails_last_csit = u""
1322 for val in tst_data[u"data"].values():
1323 if val[0] == u"FAIL":
1325 fails_last_date = val[1]
1326 fails_last_vpp = val[2]
1327 fails_last_csit = val[3]
1329 max_fails = fails_nr if fails_nr > max_fails else max_fails
1335 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1336 f"-build-{fails_last_csit}"
1339 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1341 for nrf in range(max_fails, -1, -1):
1342 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1343 tbl_sorted.extend(tbl_fails)
1345 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1346 logging.info(f" Writing file: {file_name}")
1347 with open(file_name, u"wt") as file_handler:
1348 file_handler.write(u",".join(header) + u"\n")
1349 for test in tbl_sorted:
1350 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1352 logging.info(f" Writing file: {table[u'output-file']}.txt")
1353 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1356 def table_failed_tests_html(table, input_data):
1357 """Generate the table(s) with algorithm: table_failed_tests_html
1358 specified in the specification file.
1360 :param table: Table to generate.
1361 :param input_data: Data to process.
1362 :type table: pandas.Series
1363 :type input_data: InputData
1368 if not table.get(u"testbed", None):
1370 f"The testbed is not defined for the table "
1371 f"{table.get(u'title', u'')}."
1375 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1378 with open(table[u"input-file"], u'rt') as csv_file:
1379 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1381 logging.warning(u"The input file is not defined.")
1383 except csv.Error as err:
1385 f"Not possible to process the file {table[u'input-file']}.\n"
1391 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1394 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1395 for idx, item in enumerate(csv_lst[0]):
1396 alignment = u"left" if idx == 0 else u"center"
1397 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1401 colors = (u"#e9f1fb", u"#d4e4f7")
1402 for r_idx, row in enumerate(csv_lst[1:]):
1403 background = colors[r_idx % 2]
1404 trow = ET.SubElement(
1405 failed_tests, u"tr", attrib=dict(bgcolor=background)
1409 for c_idx, item in enumerate(row):
1410 tdata = ET.SubElement(
1413 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1417 ref = ET.SubElement(
1421 href=f"../trending/"
1422 f"{_generate_url(table.get(u'testbed', ''), item)}"
1429 with open(table[u"output-file"], u'w') as html_file:
1430 logging.info(f" Writing file: {table[u'output-file']}")
1431 html_file.write(u".. raw:: html\n\n\t")
1432 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1433 html_file.write(u"\n\t<p><br><br></p>\n")
1435 logging.warning(u"The output file is not defined.")
1439 def table_comparison(table, input_data):
1440 """Generate the table(s) with algorithm: table_comparison
1441 specified in the specification file.
1443 :param table: Table to generate.
1444 :param input_data: Data to process.
1445 :type table: pandas.Series
1446 :type input_data: InputData
1448 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1450 # Transform the data
1452 f" Creating the data set for the {table.get(u'type', u'')} "
1453 f"{table.get(u'title', u'')}."
1456 columns = table.get(u"columns", None)
1459 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1464 for idx, col in enumerate(columns):
1465 if col.get(u"data-set", None) is None:
1466 logging.warning(f"No data for column {col.get(u'title', u'')}")
1468 tag = col.get(u"tag", None)
1469 data = input_data.filter_data(
1471 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1472 data=col[u"data-set"],
1473 continue_on_error=True
1476 u"title": col.get(u"title", f"Column{idx}"),
1479 for builds in data.values:
1480 for build in builds:
1481 for tst_name, tst_data in build.items():
1482 if tag and tag not in tst_data[u"tags"]:
1485 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1486 replace(u"2n1l-", u"")
1487 if col_data[u"data"].get(tst_name_mod, None) is None:
1488 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1489 if u"across testbeds" in table[u"title"].lower() or \
1490 u"across topologies" in table[u"title"].lower():
1491 name = _tpc_modify_displayed_test_name(name)
1492 col_data[u"data"][tst_name_mod] = {
1500 target=col_data[u"data"][tst_name_mod],
1502 include_tests=table[u"include-tests"]
1505 replacement = col.get(u"data-replacement", None)
1507 rpl_data = input_data.filter_data(
1509 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1511 continue_on_error=True
1513 for builds in rpl_data.values:
1514 for build in builds:
1515 for tst_name, tst_data in build.items():
1516 if tag and tag not in tst_data[u"tags"]:
1519 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1520 replace(u"2n1l-", u"")
1521 if col_data[u"data"].get(tst_name_mod, None) is None:
1522 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1523 if u"across testbeds" in table[u"title"].lower() \
1524 or u"across topologies" in \
1525 table[u"title"].lower():
1526 name = _tpc_modify_displayed_test_name(name)
1527 col_data[u"data"][tst_name_mod] = {
1534 if col_data[u"data"][tst_name_mod][u"replace"]:
1535 col_data[u"data"][tst_name_mod][u"replace"] = False
1536 col_data[u"data"][tst_name_mod][u"data"] = list()
1538 target=col_data[u"data"][tst_name_mod],
1540 include_tests=table[u"include-tests"]
1543 if table[u"include-tests"] in (u"NDR", u"PDR"):
1544 for tst_name, tst_data in col_data[u"data"].items():
1545 if tst_data[u"data"]:
1546 tst_data[u"mean"] = mean(tst_data[u"data"])
1547 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1549 cols.append(col_data)
1553 for tst_name, tst_data in col[u"data"].items():
1554 if tbl_dict.get(tst_name, None) is None:
1555 tbl_dict[tst_name] = {
1556 "name": tst_data[u"name"]
1558 tbl_dict[tst_name][col[u"title"]] = {
1559 u"mean": tst_data[u"mean"],
1560 u"stdev": tst_data[u"stdev"]
1564 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1568 for tst_data in tbl_dict.values():
1569 row = [tst_data[u"name"], ]
1571 row.append(tst_data.get(col[u"title"], None))
1574 comparisons = table.get(u"comparisons", None)
1575 if comparisons and isinstance(comparisons, list):
1576 for idx, comp in enumerate(comparisons):
1578 col_ref = int(comp[u"reference"])
1579 col_cmp = int(comp[u"compare"])
1581 logging.warning(u"Comparison: No references defined! Skipping.")
1582 comparisons.pop(idx)
1584 if not (0 < col_ref <= len(cols) and
1585 0 < col_cmp <= len(cols)) or \
1587 logging.warning(f"Wrong values of reference={col_ref} "
1588 f"and/or compare={col_cmp}. Skipping.")
1589 comparisons.pop(idx)
1592 tbl_cmp_lst = list()
1595 new_row = deepcopy(row)
1597 for comp in comparisons:
1598 ref_itm = row[int(comp[u"reference"])]
1599 if ref_itm is None and \
1600 comp.get(u"reference-alt", None) is not None:
1601 ref_itm = row[int(comp[u"reference-alt"])]
1602 cmp_itm = row[int(comp[u"compare"])]
1603 if ref_itm is not None and cmp_itm is not None and \
1604 ref_itm[u"mean"] is not None and \
1605 cmp_itm[u"mean"] is not None and \
1606 ref_itm[u"stdev"] is not None and \
1607 cmp_itm[u"stdev"] is not None:
1608 delta, d_stdev = relative_change_stdev(
1609 ref_itm[u"mean"], cmp_itm[u"mean"],
1610 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1614 u"mean": delta * 1e6,
1615 u"stdev": d_stdev * 1e6
1620 new_row.append(None)
1622 tbl_cmp_lst.append(new_row)
1624 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1625 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1628 rca_in = table.get(u"rca", None)
1629 if rca_in and isinstance(rca_in, list):
1630 for idx, itm in enumerate(rca_in):
1632 with open(itm.get(u"data", u""), u"r") as rca_file:
1635 u"title": itm.get(u"title", f"RCA{idx}"),
1636 u"data": load(rca_file, Loader=FullLoader)
1639 except (YAMLError, IOError) as err:
1641 f"The RCA file {itm.get(u'data', u'')} does not exist or "
1644 logging.debug(repr(err))
1646 tbl_for_csv = list()
1647 for line in tbl_cmp_lst:
1649 for idx, itm in enumerate(line[1:]):
1654 row.append(round(float(itm[u'mean']) / 1e6, 3))
1655 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1657 rca_nr = rca[u"data"].get(row[0], u"-")
1658 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1659 tbl_for_csv.append(row)
1661 header_csv = [u"Test Case", ]
1663 header_csv.append(f"Avg({col[u'title']})")
1664 header_csv.append(f"Stdev({col[u'title']})")
1665 for comp in comparisons:
1667 f"Avg({comp.get(u'title', u'')})"
1670 f"Stdev({comp.get(u'title', u'')})"
1672 header_csv.extend([rca[u"title"] for rca in rcas])
1674 legend_lst = table.get(u"legend", None)
1675 if legend_lst is None:
1678 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1682 footnote += f"\n{rca[u'title']}:\n"
1683 footnote += rca[u"data"].get(u"footnote", u"")
1685 csv_file = f"{table[u'output-file']}-csv.csv"
1686 with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1688 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1690 for test in tbl_for_csv:
1692 u",".join([f'"{item}"' for item in test]) + u"\n"
1695 for item in legend_lst:
1696 file_handler.write(f'"{item}"\n')
1698 for itm in footnote.split(u"\n"):
1699 file_handler.write(f'"{itm}"\n')
1702 max_lens = [0, ] * len(tbl_cmp_lst[0])
1703 for line in tbl_cmp_lst:
1705 for idx, itm in enumerate(line[1:]):
1711 f"{round(float(itm[u'mean']) / 1e6, 1)} "
1712 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1713 replace(u"nan", u"NaN")
1717 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1718 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1719 replace(u"nan", u"NaN")
1721 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1722 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1728 for line in tbl_tmp:
1730 for idx, itm in enumerate(line[1:]):
1731 if itm in (u"NT", u"NaN"):
1734 itm_lst = itm.rsplit(u"\u00B1", 1)
1736 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1737 row.append(u"\u00B1".join(itm_lst))
1739 rca_nr = rca[u"data"].get(row[0], u"-")
1740 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1742 tbl_final.append(row)
1744 header = [u"Test Case", ]
1745 header.extend([col[u"title"] for col in cols])
1746 header.extend([comp.get(u"title", u"") for comp in comparisons])
1747 header.extend([rca[u"title"] for rca in rcas])
1749 # Generate csv tables:
1750 csv_file = f"{table[u'output-file']}.csv"
1751 with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1752 file_handler.write(u";".join(header) + u"\n")
1753 for test in tbl_final:
1754 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1756 # Generate txt table:
1757 txt_file_name = f"{table[u'output-file']}.txt"
1758 convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
1760 with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
1761 txt_file.write(legend)
1762 txt_file.write(footnote)
1764 # Generate html table:
1765 _tpc_generate_html_table(
1768 table[u'output-file'],
1772 title=table.get(u"title", u"")
1776 def table_weekly_comparison(table, in_data):
1777 """Generate the table(s) with algorithm: table_weekly_comparison
1778 specified in the specification file.
1780 :param table: Table to generate.
1781 :param in_data: Data to process.
1782 :type table: pandas.Series
1783 :type in_data: InputData
1785 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1787 # Transform the data
1789 f" Creating the data set for the {table.get(u'type', u'')} "
1790 f"{table.get(u'title', u'')}."
1793 incl_tests = table.get(u"include-tests", None)
1794 if incl_tests not in (u"NDR", u"PDR"):
1795 logging.error(f"Wrong tests to include specified ({incl_tests}).")
1798 nr_cols = table.get(u"nr-of-data-columns", None)
1799 if not nr_cols or nr_cols < 2:
1801 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1805 data = in_data.filter_data(
1807 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1808 continue_on_error=True
1813 [u"Start Timestamp", ],
1819 tb_tbl = table.get(u"testbeds", None)
1820 for job_name, job_data in data.items():
1821 for build_nr, build in job_data.items():
1827 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1828 if tb_ip and tb_tbl:
1829 testbed = tb_tbl.get(tb_ip, u"")
1832 header[2].insert(1, build_nr)
1833 header[3].insert(1, testbed)
1835 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1838 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1841 for tst_name, tst_data in build.items():
1843 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1844 if not tbl_dict.get(tst_name_mod, None):
1845 tbl_dict[tst_name_mod] = dict(
1846 name=tst_data[u'name'].rsplit(u'-', 1)[0],
1849 tbl_dict[tst_name_mod][-idx - 1] = \
1850 tst_data[u"throughput"][incl_tests][u"LOWER"]
1851 except (TypeError, IndexError, KeyError, ValueError):
1856 logging.error(u"Not enough data to build the table! Skipping")
1860 for idx, cmp in enumerate(table.get(u"comparisons", list())):
1861 idx_ref = cmp.get(u"reference", None)
1862 idx_cmp = cmp.get(u"compare", None)
1863 if idx_ref is None or idx_cmp is None:
1866 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1867 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1869 header[1].append(u"")
1870 header[2].append(u"")
1871 header[3].append(u"")
1872 for tst_name, tst_data in tbl_dict.items():
1873 if not cmp_dict.get(tst_name, None):
1874 cmp_dict[tst_name] = list()
1875 ref_data = tst_data.get(idx_ref, None)
1876 cmp_data = tst_data.get(idx_cmp, None)
1877 if ref_data is None or cmp_data is None:
1878 cmp_dict[tst_name].append(float('nan'))
1880 cmp_dict[tst_name].append(
1881 relative_change(ref_data, cmp_data)
1885 for tst_name, tst_data in tbl_dict.items():
1886 itm_lst = [tst_data[u"name"], ]
1887 for idx in range(nr_cols):
1888 item = tst_data.get(-idx - 1, None)
1890 itm_lst.insert(1, None)
1892 itm_lst.insert(1, round(item / 1e6, 1))
1895 None if itm is None else round(itm, 1)
1896 for itm in cmp_dict[tst_name]
1899 tbl_lst.append(itm_lst)
1901 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1902 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
1904 # Generate csv table:
1905 csv_file = f"{table[u'output-file']}.csv"
1906 with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1908 file_handler.write(u",".join(hdr) + u"\n")
1909 for test in tbl_lst:
1910 file_handler.write(u",".join(
1912 str(item).replace(u"None", u"-").replace(u"nan", u"-").
1913 replace(u"null", u"-") for item in test
1917 txt_file = f"{table[u'output-file']}.txt"
1918 convert_csv_to_pretty_txt(csv_file, txt_file, delimiter=u",")
1920 # Reorganize header in txt table
1922 with open(txt_file, u"rt", encoding='utf-8') as file_handler:
1923 for line in file_handler:
1924 txt_table.append(line)
1926 txt_table.insert(5, txt_table.pop(2))
1927 with open(txt_file, u"wt", encoding='utf-8') as file_handler:
1928 file_handler.writelines(txt_table)
1932 # Generate html table:
1934 u"<br>".join(row) for row in zip(*header)
1936 _tpc_generate_html_table(
1939 table[u'output-file'],
1941 title=table.get(u"title", u""),