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 rst_file.write(legend[1:].replace(u"\n", u" |br| "))
617 rst_file.write(footnote.replace(u"\n", u" |br| ")[1:])
620 def table_soak_vs_ndr(table, input_data):
621 """Generate the table(s) with algorithm: table_soak_vs_ndr
622 specified in the specification file.
624 :param table: Table to generate.
625 :param input_data: Data to process.
626 :type table: pandas.Series
627 :type input_data: InputData
630 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
634 f" Creating the data set for the {table.get(u'type', u'')} "
635 f"{table.get(u'title', u'')}."
637 data = input_data.filter_data(table, continue_on_error=True)
639 # Prepare the header of the table
643 f"Avg({table[u'reference'][u'title']})",
644 f"Stdev({table[u'reference'][u'title']})",
645 f"Avg({table[u'compare'][u'title']})",
646 f"Stdev{table[u'compare'][u'title']})",
650 header_str = u";".join(header) + u"\n"
653 f"Avg({table[u'reference'][u'title']}): "
654 f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
655 f"from a series of runs of the listed tests.\n"
656 f"Stdev({table[u'reference'][u'title']}): "
657 f"Standard deviation value of {table[u'reference'][u'title']} "
658 f"[Mpps] computed from a series of runs of the listed tests.\n"
659 f"Avg({table[u'compare'][u'title']}): "
660 f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
661 f"a series of runs of the listed tests.\n"
662 f"Stdev({table[u'compare'][u'title']}): "
663 f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
664 f"computed from a series of runs of the listed tests.\n"
665 f"Diff({table[u'reference'][u'title']},"
666 f"{table[u'compare'][u'title']}): "
667 f"Percentage change calculated for mean values.\n"
669 u"Standard deviation of percentage change calculated for mean "
673 except (AttributeError, KeyError) as err:
674 logging.error(f"The model is invalid, missing parameter: {repr(err)}")
677 # Create a list of available SOAK test results:
679 for job, builds in table[u"compare"][u"data"].items():
681 for tst_name, tst_data in data[job][str(build)].items():
682 if tst_data[u"type"] == u"SOAK":
683 tst_name_mod = tst_name.replace(u"-soak", u"")
684 if tbl_dict.get(tst_name_mod, None) is None:
685 groups = re.search(REGEX_NIC, tst_data[u"parent"])
686 nic = groups.group(0) if groups else u""
689 f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
691 tbl_dict[tst_name_mod] = {
697 tbl_dict[tst_name_mod][u"cmp-data"].append(
698 tst_data[u"throughput"][u"LOWER"])
699 except (KeyError, TypeError):
701 tests_lst = tbl_dict.keys()
703 # Add corresponding NDR test results:
704 for job, builds in table[u"reference"][u"data"].items():
706 for tst_name, tst_data in data[job][str(build)].items():
707 tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
708 replace(u"-mrr", u"")
709 if tst_name_mod not in tests_lst:
712 if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
714 if table[u"include-tests"] == u"MRR":
715 result = (tst_data[u"result"][u"receive-rate"],
716 tst_data[u"result"][u"receive-stdev"])
717 elif table[u"include-tests"] == u"PDR":
719 tst_data[u"throughput"][u"PDR"][u"LOWER"]
720 elif table[u"include-tests"] == u"NDR":
722 tst_data[u"throughput"][u"NDR"][u"LOWER"]
725 if result is not None:
726 tbl_dict[tst_name_mod][u"ref-data"].append(
728 except (KeyError, TypeError):
732 for tst_name in tbl_dict:
733 item = [tbl_dict[tst_name][u"name"], ]
734 data_r = tbl_dict[tst_name][u"ref-data"]
736 if table[u"include-tests"] == u"MRR":
737 data_r_mean = data_r[0][0]
738 data_r_stdev = data_r[0][1]
740 data_r_mean = mean(data_r)
741 data_r_stdev = stdev(data_r)
742 item.append(round(data_r_mean / 1e6, 1))
743 item.append(round(data_r_stdev / 1e6, 1))
747 item.extend([None, None])
748 data_c = tbl_dict[tst_name][u"cmp-data"]
750 if table[u"include-tests"] == u"MRR":
751 data_c_mean = data_c[0][0]
752 data_c_stdev = data_c[0][1]
754 data_c_mean = mean(data_c)
755 data_c_stdev = stdev(data_c)
756 item.append(round(data_c_mean / 1e6, 1))
757 item.append(round(data_c_stdev / 1e6, 1))
761 item.extend([None, None])
762 if data_r_mean is not None and data_c_mean is not None:
763 delta, d_stdev = relative_change_stdev(
764 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
766 item.append(round(delta))
770 item.append(round(d_stdev))
775 # Sort the table according to the relative change
776 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
778 # Generate csv tables:
779 csv_file = f"{table[u'output-file']}.csv"
780 with open(csv_file, u"wt") as file_handler:
781 file_handler.write(header_str)
783 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
785 convert_csv_to_pretty_txt(
786 csv_file, f"{table[u'output-file']}.txt", delimiter=u";"
788 with open(f"{table[u'output-file']}.txt", u'a') as txt_file:
789 txt_file.write(legend)
791 # Generate html table:
792 _tpc_generate_html_table(
795 table[u'output-file'],
797 title=table.get(u"title", u"")
801 def table_perf_trending_dash(table, input_data):
802 """Generate the table(s) with algorithm:
803 table_perf_trending_dash
804 specified in the specification file.
806 :param table: Table to generate.
807 :param input_data: Data to process.
808 :type table: pandas.Series
809 :type input_data: InputData
812 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
816 f" Creating the data set for the {table.get(u'type', u'')} "
817 f"{table.get(u'title', u'')}."
819 data = input_data.filter_data(table, continue_on_error=True)
821 # Prepare the header of the tables
825 u"Short-Term Change [%]",
826 u"Long-Term Change [%]",
830 header_str = u",".join(header) + u"\n"
832 # Prepare data to the table:
834 for job, builds in table[u"data"].items():
836 for tst_name, tst_data in data[job][str(build)].items():
837 if tst_name.lower() in table.get(u"ignore-list", list()):
839 if tbl_dict.get(tst_name, None) is None:
840 groups = re.search(REGEX_NIC, tst_data[u"parent"])
843 nic = groups.group(0)
844 tbl_dict[tst_name] = {
845 u"name": f"{nic}-{tst_data[u'name']}",
846 u"data": OrderedDict()
849 tbl_dict[tst_name][u"data"][str(build)] = \
850 tst_data[u"result"][u"receive-rate"]
851 except (TypeError, KeyError):
852 pass # No data in output.xml for this test
855 for tst_name in tbl_dict:
856 data_t = tbl_dict[tst_name][u"data"]
860 classification_lst, avgs = classify_anomalies(data_t)
862 win_size = min(len(data_t), table[u"window"])
863 long_win_size = min(len(data_t), table[u"long-trend-window"])
867 [x for x in avgs[-long_win_size:-win_size]
872 avg_week_ago = avgs[max(-win_size, -len(avgs))]
874 if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
875 rel_change_last = nan
877 rel_change_last = round(
878 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
880 if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
881 rel_change_long = nan
883 rel_change_long = round(
884 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
886 if classification_lst:
887 if isnan(rel_change_last) and isnan(rel_change_long):
889 if isnan(last_avg) or isnan(rel_change_last) or \
890 isnan(rel_change_long):
893 [tbl_dict[tst_name][u"name"],
894 round(last_avg / 1e6, 2),
897 classification_lst[-win_size:].count(u"regression"),
898 classification_lst[-win_size:].count(u"progression")])
900 tbl_lst.sort(key=lambda rel: rel[0])
903 for nrr in range(table[u"window"], -1, -1):
904 tbl_reg = [item for item in tbl_lst if item[4] == nrr]
905 for nrp in range(table[u"window"], -1, -1):
906 tbl_out = [item for item in tbl_reg if item[5] == nrp]
907 tbl_out.sort(key=lambda rel: rel[2])
908 tbl_sorted.extend(tbl_out)
910 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
912 logging.info(f" Writing file: {file_name}")
913 with open(file_name, u"wt") as file_handler:
914 file_handler.write(header_str)
915 for test in tbl_sorted:
916 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
918 logging.info(f" Writing file: {table[u'output-file']}.txt")
919 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
922 def _generate_url(testbed, test_name):
923 """Generate URL to a trending plot from the name of the test case.
925 :param testbed: The testbed used for testing.
926 :param test_name: The name of the test case.
929 :returns: The URL to the plot with the trending data for the given test
934 if u"x520" in test_name:
936 elif u"x710" in test_name:
938 elif u"xl710" in test_name:
940 elif u"xxv710" in test_name:
942 elif u"vic1227" in test_name:
944 elif u"vic1385" in test_name:
946 elif u"x553" in test_name:
948 elif u"cx556" in test_name or u"cx556a" in test_name:
953 if u"64b" in test_name:
955 elif u"78b" in test_name:
957 elif u"imix" in test_name:
959 elif u"9000b" in test_name:
960 frame_size = u"9000b"
961 elif u"1518b" in test_name:
962 frame_size = u"1518b"
963 elif u"114b" in test_name:
968 if u"1t1c" in test_name or \
969 (u"-1c-" in test_name and
970 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
972 elif u"2t2c" in test_name or \
973 (u"-2c-" in test_name and
974 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
976 elif u"4t4c" in test_name or \
977 (u"-4c-" in test_name and
978 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
980 elif u"2t1c" in test_name or \
981 (u"-1c-" in test_name and
982 testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
984 elif u"4t2c" in test_name or \
985 (u"-2c-" in test_name and
986 testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
988 elif u"8t4c" in test_name or \
989 (u"-4c-" in test_name and
990 testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
995 if u"testpmd" in test_name:
997 elif u"l3fwd" in test_name:
999 elif u"avf" in test_name:
1001 elif u"rdma" in test_name:
1003 elif u"dnv" in testbed or u"tsh" in testbed:
1008 if u"acl" in test_name or \
1009 u"macip" in test_name or \
1010 u"nat" in test_name or \
1011 u"policer" in test_name or \
1012 u"cop" in test_name:
1014 elif u"scale" in test_name:
1016 elif u"base" in test_name:
1021 if u"114b" in test_name and u"vhost" in test_name:
1023 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1025 elif u"memif" in test_name:
1026 domain = u"container_memif"
1027 elif u"srv6" in test_name:
1029 elif u"vhost" in test_name:
1031 if u"vppl2xc" in test_name:
1034 driver += u"-testpmd"
1035 if u"lbvpplacp" in test_name:
1036 bsf += u"-link-bonding"
1037 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1038 domain = u"nf_service_density_vnfc"
1039 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1040 domain = u"nf_service_density_cnfc"
1041 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1042 domain = u"nf_service_density_cnfp"
1043 elif u"ipsec" in test_name:
1045 if u"sw" in test_name:
1047 elif u"hw" in test_name:
1049 elif u"ethip4vxlan" in test_name:
1050 domain = u"ip4_tunnels"
1051 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1053 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1055 elif u"l2xcbase" in test_name or \
1056 u"l2xcscale" in test_name or \
1057 u"l2bdbasemaclrn" in test_name or \
1058 u"l2bdscale" in test_name or \
1059 u"l2patch" in test_name:
1064 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1065 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1067 return file_name + anchor_name
1070 def table_perf_trending_dash_html(table, input_data):
1071 """Generate the table(s) with algorithm:
1072 table_perf_trending_dash_html specified in the specification
1075 :param table: Table to generate.
1076 :param input_data: Data to process.
1078 :type input_data: InputData
1083 if not table.get(u"testbed", None):
1085 f"The testbed is not defined for the table "
1086 f"{table.get(u'title', u'')}."
1090 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1093 with open(table[u"input-file"], u'rt') as csv_file:
1094 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1096 logging.warning(u"The input file is not defined.")
1098 except csv.Error as err:
1100 f"Not possible to process the file {table[u'input-file']}.\n"
1106 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1109 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1110 for idx, item in enumerate(csv_lst[0]):
1111 alignment = u"left" if idx == 0 else u"center"
1112 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1130 for r_idx, row in enumerate(csv_lst[1:]):
1132 color = u"regression"
1134 color = u"progression"
1137 trow = ET.SubElement(
1138 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1142 for c_idx, item in enumerate(row):
1143 tdata = ET.SubElement(
1146 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1150 ref = ET.SubElement(
1154 href=f"../trending/"
1155 f"{_generate_url(table.get(u'testbed', ''), item)}"
1162 with open(table[u"output-file"], u'w') as html_file:
1163 logging.info(f" Writing file: {table[u'output-file']}")
1164 html_file.write(u".. raw:: html\n\n\t")
1165 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1166 html_file.write(u"\n\t<p><br><br></p>\n")
1168 logging.warning(u"The output file is not defined.")
1172 def table_last_failed_tests(table, input_data):
1173 """Generate the table(s) with algorithm: table_last_failed_tests
1174 specified in the specification file.
1176 :param table: Table to generate.
1177 :param input_data: Data to process.
1178 :type table: pandas.Series
1179 :type input_data: InputData
1182 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1184 # Transform the data
1186 f" Creating the data set for the {table.get(u'type', u'')} "
1187 f"{table.get(u'title', u'')}."
1190 data = input_data.filter_data(table, continue_on_error=True)
1192 if data is None or data.empty:
1194 f" No data for the {table.get(u'type', u'')} "
1195 f"{table.get(u'title', u'')}."
1200 for job, builds in table[u"data"].items():
1201 for build in builds:
1204 version = input_data.metadata(job, build).get(u"version", u"")
1206 logging.error(f"Data for {job}: {build} is not present.")
1208 tbl_list.append(build)
1209 tbl_list.append(version)
1210 failed_tests = list()
1213 for tst_data in data[job][build].values:
1214 if tst_data[u"status"] != u"FAIL":
1218 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1221 nic = groups.group(0)
1222 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1223 tbl_list.append(str(passed))
1224 tbl_list.append(str(failed))
1225 tbl_list.extend(failed_tests)
1227 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1228 logging.info(f" Writing file: {file_name}")
1229 with open(file_name, u"wt") as file_handler:
1230 for test in tbl_list:
1231 file_handler.write(test + u'\n')
1234 def table_failed_tests(table, input_data):
1235 """Generate the table(s) with algorithm: table_failed_tests
1236 specified in the specification file.
1238 :param table: Table to generate.
1239 :param input_data: Data to process.
1240 :type table: pandas.Series
1241 :type input_data: InputData
1244 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1246 # Transform the data
1248 f" Creating the data set for the {table.get(u'type', u'')} "
1249 f"{table.get(u'title', u'')}."
1251 data = input_data.filter_data(table, continue_on_error=True)
1253 # Prepare the header of the tables
1257 u"Last Failure [Time]",
1258 u"Last Failure [VPP-Build-Id]",
1259 u"Last Failure [CSIT-Job-Build-Id]"
1262 # Generate the data for the table according to the model in the table
1266 timeperiod = timedelta(int(table.get(u"window", 7)))
1269 for job, builds in table[u"data"].items():
1270 for build in builds:
1272 for tst_name, tst_data in data[job][build].items():
1273 if tst_name.lower() in table.get(u"ignore-list", list()):
1275 if tbl_dict.get(tst_name, None) is None:
1276 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1279 nic = groups.group(0)
1280 tbl_dict[tst_name] = {
1281 u"name": f"{nic}-{tst_data[u'name']}",
1282 u"data": OrderedDict()
1285 generated = input_data.metadata(job, build).\
1286 get(u"generated", u"")
1289 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1290 if (now - then) <= timeperiod:
1291 tbl_dict[tst_name][u"data"][build] = (
1292 tst_data[u"status"],
1294 input_data.metadata(job, build).get(u"version",
1298 except (TypeError, KeyError) as err:
1299 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1303 for tst_data in tbl_dict.values():
1305 fails_last_date = u""
1306 fails_last_vpp = u""
1307 fails_last_csit = u""
1308 for val in tst_data[u"data"].values():
1309 if val[0] == u"FAIL":
1311 fails_last_date = val[1]
1312 fails_last_vpp = val[2]
1313 fails_last_csit = val[3]
1315 max_fails = fails_nr if fails_nr > max_fails else max_fails
1322 f"mrr-daily-build-{fails_last_csit}"
1326 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1328 for nrf in range(max_fails, -1, -1):
1329 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1330 tbl_sorted.extend(tbl_fails)
1332 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1333 logging.info(f" Writing file: {file_name}")
1334 with open(file_name, u"wt") as file_handler:
1335 file_handler.write(u",".join(header) + u"\n")
1336 for test in tbl_sorted:
1337 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1339 logging.info(f" Writing file: {table[u'output-file']}.txt")
1340 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1343 def table_failed_tests_html(table, input_data):
1344 """Generate the table(s) with algorithm: table_failed_tests_html
1345 specified in the specification file.
1347 :param table: Table to generate.
1348 :param input_data: Data to process.
1349 :type table: pandas.Series
1350 :type input_data: InputData
1355 if not table.get(u"testbed", None):
1357 f"The testbed is not defined for the table "
1358 f"{table.get(u'title', u'')}."
1362 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1365 with open(table[u"input-file"], u'rt') as csv_file:
1366 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1368 logging.warning(u"The input file is not defined.")
1370 except csv.Error as err:
1372 f"Not possible to process the file {table[u'input-file']}.\n"
1378 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1381 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1382 for idx, item in enumerate(csv_lst[0]):
1383 alignment = u"left" if idx == 0 else u"center"
1384 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1388 colors = (u"#e9f1fb", u"#d4e4f7")
1389 for r_idx, row in enumerate(csv_lst[1:]):
1390 background = colors[r_idx % 2]
1391 trow = ET.SubElement(
1392 failed_tests, u"tr", attrib=dict(bgcolor=background)
1396 for c_idx, item in enumerate(row):
1397 tdata = ET.SubElement(
1400 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1404 ref = ET.SubElement(
1408 href=f"../trending/"
1409 f"{_generate_url(table.get(u'testbed', ''), item)}"
1416 with open(table[u"output-file"], u'w') as html_file:
1417 logging.info(f" Writing file: {table[u'output-file']}")
1418 html_file.write(u".. raw:: html\n\n\t")
1419 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1420 html_file.write(u"\n\t<p><br><br></p>\n")
1422 logging.warning(u"The output file is not defined.")
1426 def table_comparison(table, input_data):
1427 """Generate the table(s) with algorithm: table_comparison
1428 specified in the specification file.
1430 :param table: Table to generate.
1431 :param input_data: Data to process.
1432 :type table: pandas.Series
1433 :type input_data: InputData
1435 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1437 # Transform the data
1439 f" Creating the data set for the {table.get(u'type', u'')} "
1440 f"{table.get(u'title', u'')}."
1443 columns = table.get(u"columns", None)
1446 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1451 for idx, col in enumerate(columns):
1452 if col.get(u"data-set", None) is None:
1453 logging.warning(f"No data for column {col.get(u'title', u'')}")
1455 tag = col.get(u"tag", None)
1456 data = input_data.filter_data(
1458 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1459 data=col[u"data-set"],
1460 continue_on_error=True
1463 u"title": col.get(u"title", f"Column{idx}"),
1466 for builds in data.values:
1467 for build in builds:
1468 for tst_name, tst_data in build.items():
1469 if tag and tag not in tst_data[u"tags"]:
1472 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1473 replace(u"2n1l-", u"")
1474 if col_data[u"data"].get(tst_name_mod, None) is None:
1475 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1476 if u"across testbeds" in table[u"title"].lower() or \
1477 u"across topologies" in table[u"title"].lower():
1478 name = _tpc_modify_displayed_test_name(name)
1479 col_data[u"data"][tst_name_mod] = {
1487 target=col_data[u"data"][tst_name_mod],
1489 include_tests=table[u"include-tests"]
1492 replacement = col.get(u"data-replacement", None)
1494 rpl_data = input_data.filter_data(
1496 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1498 continue_on_error=True
1500 for builds in rpl_data.values:
1501 for build in builds:
1502 for tst_name, tst_data in build.items():
1503 if tag and tag not in tst_data[u"tags"]:
1506 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1507 replace(u"2n1l-", u"")
1508 if col_data[u"data"].get(tst_name_mod, None) is None:
1509 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1510 if u"across testbeds" in table[u"title"].lower() \
1511 or u"across topologies" in \
1512 table[u"title"].lower():
1513 name = _tpc_modify_displayed_test_name(name)
1514 col_data[u"data"][tst_name_mod] = {
1521 if col_data[u"data"][tst_name_mod][u"replace"]:
1522 col_data[u"data"][tst_name_mod][u"replace"] = False
1523 col_data[u"data"][tst_name_mod][u"data"] = list()
1525 target=col_data[u"data"][tst_name_mod],
1527 include_tests=table[u"include-tests"]
1530 if table[u"include-tests"] in (u"NDR", u"PDR"):
1531 for tst_name, tst_data in col_data[u"data"].items():
1532 if tst_data[u"data"]:
1533 tst_data[u"mean"] = mean(tst_data[u"data"])
1534 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1536 cols.append(col_data)
1540 for tst_name, tst_data in col[u"data"].items():
1541 if tbl_dict.get(tst_name, None) is None:
1542 tbl_dict[tst_name] = {
1543 "name": tst_data[u"name"]
1545 tbl_dict[tst_name][col[u"title"]] = {
1546 u"mean": tst_data[u"mean"],
1547 u"stdev": tst_data[u"stdev"]
1551 for tst_data in tbl_dict.values():
1552 row = [tst_data[u"name"], ]
1554 row.append(tst_data.get(col[u"title"], None))
1557 comparisons = table.get(u"comparisons", None)
1558 if comparisons and isinstance(comparisons, list):
1559 for idx, comp in enumerate(comparisons):
1561 col_ref = int(comp[u"reference"])
1562 col_cmp = int(comp[u"compare"])
1564 logging.warning(u"Comparison: No references defined! Skipping.")
1565 comparisons.pop(idx)
1567 if not (0 < col_ref <= len(cols) and
1568 0 < col_cmp <= len(cols)) or \
1570 logging.warning(f"Wrong values of reference={col_ref} "
1571 f"and/or compare={col_cmp}. Skipping.")
1572 comparisons.pop(idx)
1575 tbl_cmp_lst = list()
1578 new_row = deepcopy(row)
1580 for comp in comparisons:
1581 ref_itm = row[int(comp[u"reference"])]
1582 if ref_itm is None and \
1583 comp.get(u"reference-alt", None) is not None:
1584 ref_itm = row[int(comp[u"reference-alt"])]
1585 cmp_itm = row[int(comp[u"compare"])]
1586 if ref_itm is not None and cmp_itm is not None and \
1587 ref_itm[u"mean"] is not None and \
1588 cmp_itm[u"mean"] is not None and \
1589 ref_itm[u"stdev"] is not None and \
1590 cmp_itm[u"stdev"] is not None:
1591 delta, d_stdev = relative_change_stdev(
1592 ref_itm[u"mean"], cmp_itm[u"mean"],
1593 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1597 u"mean": delta * 1e6,
1598 u"stdev": d_stdev * 1e6
1603 new_row.append(None)
1605 tbl_cmp_lst.append(new_row)
1607 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1608 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1611 rca_in = table.get(u"rca", None)
1612 if rca_in and isinstance(rca_in, list):
1613 for idx, itm in enumerate(rca_in):
1615 with open(itm.get(u"data", u""), u"r") as rca_file:
1618 u"title": itm.get(u"title", f"RCA{idx}"),
1619 u"data": load(rca_file, Loader=FullLoader)
1622 except (YAMLError, IOError) as err:
1624 f"The RCA file {itm.get(u'data', u'')} does not exist or "
1627 logging.debug(repr(err))
1629 tbl_for_csv = list()
1630 for line in tbl_cmp_lst:
1632 for idx, itm in enumerate(line[1:]):
1637 row.append(round(float(itm[u'mean']) / 1e6, 3))
1638 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1640 rca_nr = rca[u"data"].get(row[0], u"-")
1641 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1642 tbl_for_csv.append(row)
1644 header_csv = [u"Test Case", ]
1646 header_csv.append(f"Avg({col[u'title']})")
1647 header_csv.append(f"Stdev({col[u'title']})")
1648 for comp in comparisons:
1650 f"Avg({comp.get(u'title', u'')})"
1653 f"Stdev({comp.get(u'title', u'')})"
1655 header_csv.extend([rca[u"title"] for rca in rcas])
1657 legend_lst = table.get(u"legend", None)
1658 if legend_lst is None:
1661 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1665 footnote += f"\n{rca[u'title']}:\n"
1666 footnote += rca[u"data"].get(u"footnote", u"")
1668 csv_file = f"{table[u'output-file']}-csv.csv"
1669 with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1671 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1673 for test in tbl_for_csv:
1675 u",".join([f'"{item}"' for item in test]) + u"\n"
1678 for item in legend_lst:
1679 file_handler.write(f'"{item}"\n')
1681 for itm in footnote.split(u"\n"):
1682 file_handler.write(f'"{itm}"\n')
1685 max_lens = [0, ] * len(tbl_cmp_lst[0])
1686 for line in tbl_cmp_lst:
1688 for idx, itm in enumerate(line[1:]):
1694 f"{round(float(itm[u'mean']) / 1e6, 1)} "
1695 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1696 replace(u"nan", u"NaN")
1700 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1701 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1702 replace(u"nan", u"NaN")
1704 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1705 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1711 for line in tbl_tmp:
1713 for idx, itm in enumerate(line[1:]):
1714 if itm in (u"NT", u"NaN"):
1717 itm_lst = itm.rsplit(u"\u00B1", 1)
1719 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1720 row.append(u"\u00B1".join(itm_lst))
1722 rca_nr = rca[u"data"].get(row[0], u"-")
1723 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1725 tbl_final.append(row)
1727 header = [u"Test Case", ]
1728 header.extend([col[u"title"] for col in cols])
1729 header.extend([comp.get(u"title", u"") for comp in comparisons])
1730 header.extend([rca[u"title"] for rca in rcas])
1732 # Generate csv tables:
1733 csv_file = f"{table[u'output-file']}.csv"
1734 with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1735 file_handler.write(u";".join(header) + u"\n")
1736 for test in tbl_final:
1737 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1739 # Generate txt table:
1740 txt_file_name = f"{table[u'output-file']}.txt"
1741 convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
1743 with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
1744 txt_file.write(legend)
1745 txt_file.write(footnote)
1746 if legend or footnote:
1747 txt_file.write(u"\n:END")
1749 # Generate html table:
1750 _tpc_generate_html_table(
1753 table[u'output-file'],
1757 title=table.get(u"title", u"")
1761 def table_weekly_comparison(table, in_data):
1762 """Generate the table(s) with algorithm: table_weekly_comparison
1763 specified in the specification file.
1765 :param table: Table to generate.
1766 :param in_data: Data to process.
1767 :type table: pandas.Series
1768 :type in_data: InputData
1770 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1772 # Transform the data
1774 f" Creating the data set for the {table.get(u'type', u'')} "
1775 f"{table.get(u'title', u'')}."
1778 incl_tests = table.get(u"include-tests", None)
1779 if incl_tests not in (u"NDR", u"PDR"):
1780 logging.error(f"Wrong tests to include specified ({incl_tests}).")
1783 nr_cols = table.get(u"nr-of-data-columns", None)
1784 if not nr_cols or nr_cols < 2:
1786 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1790 data = in_data.filter_data(
1792 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1793 continue_on_error=True
1804 tb_tbl = table.get(u"testbeds", None)
1805 for job_name, job_data in data.items():
1806 for build_nr, build in job_data.items():
1812 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1813 if tb_ip and tb_tbl:
1814 testbed = tb_tbl.get(tb_ip, u"")
1817 header[2].insert(1, build_nr)
1818 header[3].insert(1, testbed)
1820 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1823 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1826 for tst_name, tst_data in build.items():
1828 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1829 if not tbl_dict.get(tst_name_mod, None):
1830 tbl_dict[tst_name_mod] = dict(
1831 name=tst_data[u'name'].rsplit(u'-', 1)[0],
1834 tbl_dict[tst_name_mod][-idx - 1] = \
1835 tst_data[u"throughput"][incl_tests][u"LOWER"]
1836 except (TypeError, IndexError, KeyError, ValueError):
1841 logging.error(u"Not enough data to build the table! Skipping")
1845 for idx, cmp in enumerate(table.get(u"comparisons", list())):
1846 idx_ref = cmp.get(u"reference", None)
1847 idx_cmp = cmp.get(u"compare", None)
1848 if idx_ref is None or idx_cmp is None:
1850 header[0].append(f"Diff{idx + 1}")
1851 header[1].append(header[0][idx_ref - idx - 1])
1852 header[2].append(u"vs")
1853 header[3].append(header[0][idx_cmp - idx - 1])
1854 for tst_name, tst_data in tbl_dict.items():
1855 if not cmp_dict.get(tst_name, None):
1856 cmp_dict[tst_name] = list()
1857 ref_data = tst_data.get(idx_ref, None)
1858 cmp_data = tst_data.get(idx_cmp, None)
1859 if ref_data is None or cmp_data is None:
1860 cmp_dict[tst_name].append(float('nan'))
1862 cmp_dict[tst_name].append(
1863 relative_change(ref_data, cmp_data)
1867 for tst_name, tst_data in tbl_dict.items():
1868 itm_lst = [tst_data[u"name"], ]
1869 for idx in range(nr_cols):
1870 item = tst_data.get(-idx - 1, None)
1872 itm_lst.insert(1, None)
1874 itm_lst.insert(1, round(item / 1e6, 1))
1877 None if itm is None else round(itm, 1)
1878 for itm in cmp_dict[tst_name]
1881 tbl_lst.append(itm_lst)
1883 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1884 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
1886 # Generate csv table:
1887 csv_file = f"{table[u'output-file']}.csv"
1888 with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1890 file_handler.write(u",".join(hdr) + u"\n")
1891 for test in tbl_lst:
1892 file_handler.write(u",".join(
1894 str(item).replace(u"None", u"-").replace(u"nan", u"-").
1895 replace(u"null", u"-") for item in test
1899 txt_file = f"{table[u'output-file']}.txt"
1900 convert_csv_to_pretty_txt(csv_file, txt_file, delimiter=u",")
1902 # Reorganize header in txt table
1904 with open(txt_file, u"rt", encoding='utf-8') as file_handler:
1905 for line in file_handler:
1906 txt_table.append(line)
1908 txt_table.insert(5, txt_table.pop(2))
1909 with open(txt_file, u"wt", encoding='utf-8') as file_handler:
1910 file_handler.writelines(txt_table)
1914 # Generate html table:
1916 u"<br>".join(row) for row in zip(*header)
1918 _tpc_generate_html_table(
1921 table[u'output-file'],
1923 title=table.get(u"title", u""),