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:
961 if u"64b" in test_name:
963 elif u"78b" in test_name:
965 elif u"imix" in test_name:
967 elif u"9000b" in test_name:
968 frame_size = u"9000b"
969 elif u"1518b" in test_name:
970 frame_size = u"1518b"
971 elif u"114b" in test_name:
976 if u"1t1c" in test_name or \
977 (u"-1c-" in test_name and
978 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
980 elif u"2t2c" in test_name or \
981 (u"-2c-" in test_name and
982 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
984 elif u"4t4c" in test_name or \
985 (u"-4c-" in test_name and
986 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
988 elif u"2t1c" in test_name or \
989 (u"-1c-" in test_name and
990 testbed in (u"2n-skx", u"3n-skx")):
992 elif u"4t2c" in test_name:
994 elif u"8t4c" in test_name:
999 if u"testpmd" in test_name:
1001 elif u"l3fwd" in test_name:
1003 elif u"avf" in test_name:
1005 elif u"dnv" in testbed or u"tsh" in testbed:
1010 if u"acl" in test_name or \
1011 u"macip" in test_name or \
1012 u"nat" in test_name or \
1013 u"policer" in test_name or \
1014 u"cop" in test_name:
1016 elif u"scale" in test_name:
1018 elif u"base" in test_name:
1023 if u"114b" in test_name and u"vhost" in test_name:
1025 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1027 elif u"memif" in test_name:
1028 domain = u"container_memif"
1029 elif u"srv6" in test_name:
1031 elif u"vhost" in test_name:
1033 if u"vppl2xc" in test_name:
1036 driver += u"-testpmd"
1037 if u"lbvpplacp" in test_name:
1038 bsf += u"-link-bonding"
1039 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1040 domain = u"nf_service_density_vnfc"
1041 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1042 domain = u"nf_service_density_cnfc"
1043 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1044 domain = u"nf_service_density_cnfp"
1045 elif u"ipsec" in test_name:
1047 if u"sw" in test_name:
1049 elif u"hw" in test_name:
1051 elif u"ethip4vxlan" in test_name:
1052 domain = u"ip4_tunnels"
1053 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1055 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1057 elif u"l2xcbase" in test_name or \
1058 u"l2xcscale" in test_name or \
1059 u"l2bdbasemaclrn" in test_name or \
1060 u"l2bdscale" in test_name or \
1061 u"l2patch" in test_name:
1066 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1067 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1069 return file_name + anchor_name
1072 def table_perf_trending_dash_html(table, input_data):
1073 """Generate the table(s) with algorithm:
1074 table_perf_trending_dash_html specified in the specification
1077 :param table: Table to generate.
1078 :param input_data: Data to process.
1080 :type input_data: InputData
1085 if not table.get(u"testbed", None):
1087 f"The testbed is not defined for the table "
1088 f"{table.get(u'title', u'')}."
1092 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1095 with open(table[u"input-file"], u'rt') as csv_file:
1096 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1098 logging.warning(u"The input file is not defined.")
1100 except csv.Error as err:
1102 f"Not possible to process the file {table[u'input-file']}.\n"
1108 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1111 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1112 for idx, item in enumerate(csv_lst[0]):
1113 alignment = u"left" if idx == 0 else u"center"
1114 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1132 for r_idx, row in enumerate(csv_lst[1:]):
1134 color = u"regression"
1136 color = u"progression"
1139 trow = ET.SubElement(
1140 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1144 for c_idx, item in enumerate(row):
1145 tdata = ET.SubElement(
1148 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1151 if c_idx == 0 and table.get(u"add-links", True):
1152 ref = ET.SubElement(
1156 href=f"../trending/"
1157 f"{_generate_url(table.get(u'testbed', ''), item)}"
1164 with open(table[u"output-file"], u'w') as html_file:
1165 logging.info(f" Writing file: {table[u'output-file']}")
1166 html_file.write(u".. raw:: html\n\n\t")
1167 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1168 html_file.write(u"\n\t<p><br><br></p>\n")
1170 logging.warning(u"The output file is not defined.")
1174 def table_last_failed_tests(table, input_data):
1175 """Generate the table(s) with algorithm: table_last_failed_tests
1176 specified in the specification file.
1178 :param table: Table to generate.
1179 :param input_data: Data to process.
1180 :type table: pandas.Series
1181 :type input_data: InputData
1184 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1186 # Transform the data
1188 f" Creating the data set for the {table.get(u'type', u'')} "
1189 f"{table.get(u'title', u'')}."
1192 data = input_data.filter_data(table, continue_on_error=True)
1194 if data is None or data.empty:
1196 f" No data for the {table.get(u'type', u'')} "
1197 f"{table.get(u'title', u'')}."
1202 for job, builds in table[u"data"].items():
1203 for build in builds:
1206 version = input_data.metadata(job, build).get(u"version", u"")
1208 logging.error(f"Data for {job}: {build} is not present.")
1210 tbl_list.append(build)
1211 tbl_list.append(version)
1212 failed_tests = list()
1215 for tst_data in data[job][build].values:
1216 if tst_data[u"status"] != u"FAIL":
1220 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1223 nic = groups.group(0)
1224 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1225 tbl_list.append(str(passed))
1226 tbl_list.append(str(failed))
1227 tbl_list.extend(failed_tests)
1229 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1230 logging.info(f" Writing file: {file_name}")
1231 with open(file_name, u"wt") as file_handler:
1232 for test in tbl_list:
1233 file_handler.write(test + u'\n')
1236 def table_failed_tests(table, input_data):
1237 """Generate the table(s) with algorithm: table_failed_tests
1238 specified in the specification file.
1240 :param table: Table to generate.
1241 :param input_data: Data to process.
1242 :type table: pandas.Series
1243 :type input_data: InputData
1246 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1248 # Transform the data
1250 f" Creating the data set for the {table.get(u'type', u'')} "
1251 f"{table.get(u'title', u'')}."
1253 data = input_data.filter_data(table, continue_on_error=True)
1255 # Prepare the header of the tables
1259 u"Last Failure [Time]",
1260 u"Last Failure [VPP-Build-Id]",
1261 u"Last Failure [CSIT-Job-Build-Id]"
1264 # Generate the data for the table according to the model in the table
1268 timeperiod = timedelta(int(table.get(u"window", 7)))
1271 for job, builds in table[u"data"].items():
1272 for build in builds:
1274 for tst_name, tst_data in data[job][build].items():
1275 if tst_name.lower() in table.get(u"ignore-list", list()):
1277 if tbl_dict.get(tst_name, None) is None:
1278 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1281 nic = groups.group(0)
1282 tbl_dict[tst_name] = {
1283 u"name": f"{nic}-{tst_data[u'name']}",
1284 u"data": OrderedDict()
1287 generated = input_data.metadata(job, build).\
1288 get(u"generated", u"")
1291 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1292 if (now - then) <= timeperiod:
1293 tbl_dict[tst_name][u"data"][build] = (
1294 tst_data[u"status"],
1296 input_data.metadata(job, build).get(u"version",
1300 except (TypeError, KeyError) as err:
1301 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1305 for tst_data in tbl_dict.values():
1307 fails_last_date = u""
1308 fails_last_vpp = u""
1309 fails_last_csit = u""
1310 for val in tst_data[u"data"].values():
1311 if val[0] == u"FAIL":
1313 fails_last_date = val[1]
1314 fails_last_vpp = val[2]
1315 fails_last_csit = val[3]
1317 max_fails = fails_nr if fails_nr > max_fails else max_fails
1324 f"mrr-daily-build-{fails_last_csit}"
1328 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1330 for nrf in range(max_fails, -1, -1):
1331 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1332 tbl_sorted.extend(tbl_fails)
1334 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1335 logging.info(f" Writing file: {file_name}")
1336 with open(file_name, u"wt") as file_handler:
1337 file_handler.write(u",".join(header) + u"\n")
1338 for test in tbl_sorted:
1339 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1341 logging.info(f" Writing file: {table[u'output-file']}.txt")
1342 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1345 def table_failed_tests_html(table, input_data):
1346 """Generate the table(s) with algorithm: table_failed_tests_html
1347 specified in the specification file.
1349 :param table: Table to generate.
1350 :param input_data: Data to process.
1351 :type table: pandas.Series
1352 :type input_data: InputData
1357 if not table.get(u"testbed", None):
1359 f"The testbed is not defined for the table "
1360 f"{table.get(u'title', u'')}."
1364 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1367 with open(table[u"input-file"], u'rt') as csv_file:
1368 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1370 logging.warning(u"The input file is not defined.")
1372 except csv.Error as err:
1374 f"Not possible to process the file {table[u'input-file']}.\n"
1380 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1383 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1384 for idx, item in enumerate(csv_lst[0]):
1385 alignment = u"left" if idx == 0 else u"center"
1386 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1390 colors = (u"#e9f1fb", u"#d4e4f7")
1391 for r_idx, row in enumerate(csv_lst[1:]):
1392 background = colors[r_idx % 2]
1393 trow = ET.SubElement(
1394 failed_tests, u"tr", attrib=dict(bgcolor=background)
1398 for c_idx, item in enumerate(row):
1399 tdata = ET.SubElement(
1402 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1406 ref = ET.SubElement(
1410 href=f"../trending/"
1411 f"{_generate_url(table.get(u'testbed', ''), item)}"
1418 with open(table[u"output-file"], u'w') as html_file:
1419 logging.info(f" Writing file: {table[u'output-file']}")
1420 html_file.write(u".. raw:: html\n\n\t")
1421 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1422 html_file.write(u"\n\t<p><br><br></p>\n")
1424 logging.warning(u"The output file is not defined.")
1428 def table_comparison(table, input_data):
1429 """Generate the table(s) with algorithm: table_comparison
1430 specified in the specification file.
1432 :param table: Table to generate.
1433 :param input_data: Data to process.
1434 :type table: pandas.Series
1435 :type input_data: InputData
1437 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1439 # Transform the data
1441 f" Creating the data set for the {table.get(u'type', u'')} "
1442 f"{table.get(u'title', u'')}."
1445 columns = table.get(u"columns", None)
1448 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1453 for idx, col in enumerate(columns):
1454 if col.get(u"data-set", None) is None:
1455 logging.warning(f"No data for column {col.get(u'title', u'')}")
1457 tag = col.get(u"tag", None)
1458 data = input_data.filter_data(
1460 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1461 data=col[u"data-set"],
1462 continue_on_error=True
1465 u"title": col.get(u"title", f"Column{idx}"),
1468 for builds in data.values:
1469 for build in builds:
1470 for tst_name, tst_data in build.items():
1471 if tag and tag not in tst_data[u"tags"]:
1474 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1475 replace(u"2n1l-", u"")
1476 if col_data[u"data"].get(tst_name_mod, None) is None:
1477 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1478 if u"across testbeds" in table[u"title"].lower() or \
1479 u"across topologies" in table[u"title"].lower():
1480 name = _tpc_modify_displayed_test_name(name)
1481 col_data[u"data"][tst_name_mod] = {
1489 target=col_data[u"data"][tst_name_mod],
1491 include_tests=table[u"include-tests"]
1494 replacement = col.get(u"data-replacement", None)
1496 rpl_data = input_data.filter_data(
1498 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1500 continue_on_error=True
1502 for builds in rpl_data.values:
1503 for build in builds:
1504 for tst_name, tst_data in build.items():
1505 if tag and tag not in tst_data[u"tags"]:
1508 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1509 replace(u"2n1l-", u"")
1510 if col_data[u"data"].get(tst_name_mod, None) is None:
1511 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1512 if u"across testbeds" in table[u"title"].lower() \
1513 or u"across topologies" in \
1514 table[u"title"].lower():
1515 name = _tpc_modify_displayed_test_name(name)
1516 col_data[u"data"][tst_name_mod] = {
1523 if col_data[u"data"][tst_name_mod][u"replace"]:
1524 col_data[u"data"][tst_name_mod][u"replace"] = False
1525 col_data[u"data"][tst_name_mod][u"data"] = list()
1527 target=col_data[u"data"][tst_name_mod],
1529 include_tests=table[u"include-tests"]
1532 if table[u"include-tests"] in (u"NDR", u"PDR"):
1533 for tst_name, tst_data in col_data[u"data"].items():
1534 if tst_data[u"data"]:
1535 tst_data[u"mean"] = mean(tst_data[u"data"])
1536 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1538 cols.append(col_data)
1542 for tst_name, tst_data in col[u"data"].items():
1543 if tbl_dict.get(tst_name, None) is None:
1544 tbl_dict[tst_name] = {
1545 "name": tst_data[u"name"]
1547 tbl_dict[tst_name][col[u"title"]] = {
1548 u"mean": tst_data[u"mean"],
1549 u"stdev": tst_data[u"stdev"]
1553 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1557 for tst_data in tbl_dict.values():
1558 row = [tst_data[u"name"], ]
1560 row.append(tst_data.get(col[u"title"], None))
1563 comparisons = table.get(u"comparisons", None)
1564 if comparisons and isinstance(comparisons, list):
1565 for idx, comp in enumerate(comparisons):
1567 col_ref = int(comp[u"reference"])
1568 col_cmp = int(comp[u"compare"])
1570 logging.warning(u"Comparison: No references defined! Skipping.")
1571 comparisons.pop(idx)
1573 if not (0 < col_ref <= len(cols) and
1574 0 < col_cmp <= len(cols)) or \
1576 logging.warning(f"Wrong values of reference={col_ref} "
1577 f"and/or compare={col_cmp}. Skipping.")
1578 comparisons.pop(idx)
1581 tbl_cmp_lst = list()
1584 new_row = deepcopy(row)
1586 for comp in comparisons:
1587 ref_itm = row[int(comp[u"reference"])]
1588 if ref_itm is None and \
1589 comp.get(u"reference-alt", None) is not None:
1590 ref_itm = row[int(comp[u"reference-alt"])]
1591 cmp_itm = row[int(comp[u"compare"])]
1592 if ref_itm is not None and cmp_itm is not None and \
1593 ref_itm[u"mean"] is not None and \
1594 cmp_itm[u"mean"] is not None and \
1595 ref_itm[u"stdev"] is not None and \
1596 cmp_itm[u"stdev"] is not None:
1597 delta, d_stdev = relative_change_stdev(
1598 ref_itm[u"mean"], cmp_itm[u"mean"],
1599 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1603 u"mean": delta * 1e6,
1604 u"stdev": d_stdev * 1e6
1609 new_row.append(None)
1611 tbl_cmp_lst.append(new_row)
1613 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1614 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1617 rca_in = table.get(u"rca", None)
1618 if rca_in and isinstance(rca_in, list):
1619 for idx, itm in enumerate(rca_in):
1621 with open(itm.get(u"data", u""), u"r") as rca_file:
1624 u"title": itm.get(u"title", f"RCA{idx}"),
1625 u"data": load(rca_file, Loader=FullLoader)
1628 except (YAMLError, IOError) as err:
1630 f"The RCA file {itm.get(u'data', u'')} does not exist or "
1633 logging.debug(repr(err))
1635 tbl_for_csv = list()
1636 for line in tbl_cmp_lst:
1638 for idx, itm in enumerate(line[1:]):
1643 row.append(round(float(itm[u'mean']) / 1e6, 3))
1644 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1646 rca_nr = rca[u"data"].get(row[0], u"-")
1647 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1648 tbl_for_csv.append(row)
1650 header_csv = [u"Test Case", ]
1652 header_csv.append(f"Avg({col[u'title']})")
1653 header_csv.append(f"Stdev({col[u'title']})")
1654 for comp in comparisons:
1656 f"Avg({comp.get(u'title', u'')})"
1659 f"Stdev({comp.get(u'title', u'')})"
1661 header_csv.extend([rca[u"title"] for rca in rcas])
1663 legend_lst = table.get(u"legend", None)
1664 if legend_lst is None:
1667 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1671 footnote += f"\n{rca[u'title']}:\n"
1672 footnote += rca[u"data"].get(u"footnote", u"")
1674 csv_file = f"{table[u'output-file']}-csv.csv"
1675 with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1677 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1679 for test in tbl_for_csv:
1681 u",".join([f'"{item}"' for item in test]) + u"\n"
1684 for item in legend_lst:
1685 file_handler.write(f'"{item}"\n')
1687 for itm in footnote.split(u"\n"):
1688 file_handler.write(f'"{itm}"\n')
1691 max_lens = [0, ] * len(tbl_cmp_lst[0])
1692 for line in tbl_cmp_lst:
1694 for idx, itm in enumerate(line[1:]):
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")
1706 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1707 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1708 replace(u"nan", u"NaN")
1710 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1711 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1717 for line in tbl_tmp:
1719 for idx, itm in enumerate(line[1:]):
1720 if itm in (u"NT", u"NaN"):
1723 itm_lst = itm.rsplit(u"\u00B1", 1)
1725 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1726 row.append(u"\u00B1".join(itm_lst))
1728 rca_nr = rca[u"data"].get(row[0], u"-")
1729 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1731 tbl_final.append(row)
1733 header = [u"Test Case", ]
1734 header.extend([col[u"title"] for col in cols])
1735 header.extend([comp.get(u"title", u"") for comp in comparisons])
1736 header.extend([rca[u"title"] for rca in rcas])
1738 # Generate csv tables:
1739 csv_file = f"{table[u'output-file']}.csv"
1740 with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1741 file_handler.write(u";".join(header) + u"\n")
1742 for test in tbl_final:
1743 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1745 # Generate txt table:
1746 txt_file_name = f"{table[u'output-file']}.txt"
1747 convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
1749 with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
1750 txt_file.write(legend)
1751 txt_file.write(footnote)
1753 # Generate html table:
1754 _tpc_generate_html_table(
1757 table[u'output-file'],
1761 title=table.get(u"title", u"")
1765 def table_weekly_comparison(table, in_data):
1766 """Generate the table(s) with algorithm: table_weekly_comparison
1767 specified in the specification file.
1769 :param table: Table to generate.
1770 :param in_data: Data to process.
1771 :type table: pandas.Series
1772 :type in_data: InputData
1774 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1776 # Transform the data
1778 f" Creating the data set for the {table.get(u'type', u'')} "
1779 f"{table.get(u'title', u'')}."
1782 incl_tests = table.get(u"include-tests", None)
1783 if incl_tests not in (u"NDR", u"PDR"):
1784 logging.error(f"Wrong tests to include specified ({incl_tests}).")
1787 nr_cols = table.get(u"nr-of-data-columns", None)
1788 if not nr_cols or nr_cols < 2:
1790 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1794 data = in_data.filter_data(
1796 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1797 continue_on_error=True
1802 [u"Start Timestamp", ],
1808 tb_tbl = table.get(u"testbeds", None)
1809 for job_name, job_data in data.items():
1810 for build_nr, build in job_data.items():
1816 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1817 if tb_ip and tb_tbl:
1818 testbed = tb_tbl.get(tb_ip, u"")
1821 header[2].insert(1, build_nr)
1822 header[3].insert(1, testbed)
1824 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1827 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1830 for tst_name, tst_data in build.items():
1832 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1833 if not tbl_dict.get(tst_name_mod, None):
1834 tbl_dict[tst_name_mod] = dict(
1835 name=tst_data[u'name'].rsplit(u'-', 1)[0],
1838 tbl_dict[tst_name_mod][-idx - 1] = \
1839 tst_data[u"throughput"][incl_tests][u"LOWER"]
1840 except (TypeError, IndexError, KeyError, ValueError):
1845 logging.error(u"Not enough data to build the table! Skipping")
1849 for idx, cmp in enumerate(table.get(u"comparisons", list())):
1850 idx_ref = cmp.get(u"reference", None)
1851 idx_cmp = cmp.get(u"compare", None)
1852 if idx_ref is None or idx_cmp is None:
1855 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1856 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1858 header[1].append(u"")
1859 header[2].append(u"")
1860 header[3].append(u"")
1861 for tst_name, tst_data in tbl_dict.items():
1862 if not cmp_dict.get(tst_name, None):
1863 cmp_dict[tst_name] = list()
1864 ref_data = tst_data.get(idx_ref, None)
1865 cmp_data = tst_data.get(idx_cmp, None)
1866 if ref_data is None or cmp_data is None:
1867 cmp_dict[tst_name].append(float('nan'))
1869 cmp_dict[tst_name].append(
1870 relative_change(ref_data, cmp_data)
1874 for tst_name, tst_data in tbl_dict.items():
1875 itm_lst = [tst_data[u"name"], ]
1876 for idx in range(nr_cols):
1877 item = tst_data.get(-idx - 1, None)
1879 itm_lst.insert(1, None)
1881 itm_lst.insert(1, round(item / 1e6, 1))
1884 None if itm is None else round(itm, 1)
1885 for itm in cmp_dict[tst_name]
1888 tbl_lst.append(itm_lst)
1890 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1891 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
1893 # Generate csv table:
1894 csv_file = f"{table[u'output-file']}.csv"
1895 with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1897 file_handler.write(u",".join(hdr) + u"\n")
1898 for test in tbl_lst:
1899 file_handler.write(u",".join(
1901 str(item).replace(u"None", u"-").replace(u"nan", u"-").
1902 replace(u"null", u"-") for item in test
1906 txt_file = f"{table[u'output-file']}.txt"
1907 convert_csv_to_pretty_txt(csv_file, txt_file, delimiter=u",")
1909 # Reorganize header in txt table
1911 with open(txt_file, u"rt", encoding='utf-8') as file_handler:
1912 for line in file_handler:
1913 txt_table.append(line)
1915 txt_table.insert(5, txt_table.pop(2))
1916 with open(txt_file, u"wt", encoding='utf-8') as file_handler:
1917 file_handler.writelines(txt_table)
1921 # Generate html table:
1923 u"<br>".join(row) for row in zip(*header)
1925 _tpc_generate_html_table(
1928 table[u'output-file'],
1930 title=table.get(u"title", u""),