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 logging.info(f" Writing the HTML file to {path}{file_name}.rst")
598 with open(f"{path}{file_name}.rst", u"wt") as rst_file:
601 u".. |br| raw:: html\n\n <br />\n\n\n"
602 u".. |prein| raw:: html\n\n <pre>\n\n\n"
603 u".. |preout| raw:: html\n\n </pre>\n\n"
606 rst_file.write(f"{title}\n")
607 rst_file.write(f"{u'`' * len(title)}\n\n")
610 f' <iframe frameborder="0" scrolling="no" '
611 f'width="1600" height="1200" '
612 f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
618 itm_lst = legend[1:-2].split(u"\n")
620 f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
622 except IndexError as err:
623 logging.error(f"Legend cannot be written to html file\n{err}")
626 itm_lst = footnote[1:].split(u"\n")
628 f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
630 except IndexError as err:
631 logging.error(f"Footnote cannot be written to html file\n{err}")
634 def table_soak_vs_ndr(table, input_data):
635 """Generate the table(s) with algorithm: table_soak_vs_ndr
636 specified in the specification file.
638 :param table: Table to generate.
639 :param input_data: Data to process.
640 :type table: pandas.Series
641 :type input_data: InputData
644 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
648 f" Creating the data set for the {table.get(u'type', u'')} "
649 f"{table.get(u'title', u'')}."
651 data = input_data.filter_data(table, continue_on_error=True)
653 # Prepare the header of the table
657 f"Avg({table[u'reference'][u'title']})",
658 f"Stdev({table[u'reference'][u'title']})",
659 f"Avg({table[u'compare'][u'title']})",
660 f"Stdev{table[u'compare'][u'title']})",
664 header_str = u";".join(header) + u"\n"
667 f"Avg({table[u'reference'][u'title']}): "
668 f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
669 f"from a series of runs of the listed tests.\n"
670 f"Stdev({table[u'reference'][u'title']}): "
671 f"Standard deviation value of {table[u'reference'][u'title']} "
672 f"[Mpps] computed from a series of runs of the listed tests.\n"
673 f"Avg({table[u'compare'][u'title']}): "
674 f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
675 f"a series of runs of the listed tests.\n"
676 f"Stdev({table[u'compare'][u'title']}): "
677 f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
678 f"computed from a series of runs of the listed tests.\n"
679 f"Diff({table[u'reference'][u'title']},"
680 f"{table[u'compare'][u'title']}): "
681 f"Percentage change calculated for mean values.\n"
683 u"Standard deviation of percentage change calculated for mean "
686 except (AttributeError, KeyError) as err:
687 logging.error(f"The model is invalid, missing parameter: {repr(err)}")
690 # Create a list of available SOAK test results:
692 for job, builds in table[u"compare"][u"data"].items():
694 for tst_name, tst_data in data[job][str(build)].items():
695 if tst_data[u"type"] == u"SOAK":
696 tst_name_mod = tst_name.replace(u"-soak", u"")
697 if tbl_dict.get(tst_name_mod, None) is None:
698 groups = re.search(REGEX_NIC, tst_data[u"parent"])
699 nic = groups.group(0) if groups else u""
702 f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
704 tbl_dict[tst_name_mod] = {
710 tbl_dict[tst_name_mod][u"cmp-data"].append(
711 tst_data[u"throughput"][u"LOWER"])
712 except (KeyError, TypeError):
714 tests_lst = tbl_dict.keys()
716 # Add corresponding NDR test results:
717 for job, builds in table[u"reference"][u"data"].items():
719 for tst_name, tst_data in data[job][str(build)].items():
720 tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
721 replace(u"-mrr", u"")
722 if tst_name_mod not in tests_lst:
725 if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
727 if table[u"include-tests"] == u"MRR":
728 result = (tst_data[u"result"][u"receive-rate"],
729 tst_data[u"result"][u"receive-stdev"])
730 elif table[u"include-tests"] == u"PDR":
732 tst_data[u"throughput"][u"PDR"][u"LOWER"]
733 elif table[u"include-tests"] == u"NDR":
735 tst_data[u"throughput"][u"NDR"][u"LOWER"]
738 if result is not None:
739 tbl_dict[tst_name_mod][u"ref-data"].append(
741 except (KeyError, TypeError):
745 for tst_name in tbl_dict:
746 item = [tbl_dict[tst_name][u"name"], ]
747 data_r = tbl_dict[tst_name][u"ref-data"]
749 if table[u"include-tests"] == u"MRR":
750 data_r_mean = data_r[0][0]
751 data_r_stdev = data_r[0][1]
753 data_r_mean = mean(data_r)
754 data_r_stdev = stdev(data_r)
755 item.append(round(data_r_mean / 1e6, 1))
756 item.append(round(data_r_stdev / 1e6, 1))
760 item.extend([None, None])
761 data_c = tbl_dict[tst_name][u"cmp-data"]
763 if table[u"include-tests"] == u"MRR":
764 data_c_mean = data_c[0][0]
765 data_c_stdev = data_c[0][1]
767 data_c_mean = mean(data_c)
768 data_c_stdev = stdev(data_c)
769 item.append(round(data_c_mean / 1e6, 1))
770 item.append(round(data_c_stdev / 1e6, 1))
774 item.extend([None, None])
775 if data_r_mean is not None and data_c_mean is not None:
776 delta, d_stdev = relative_change_stdev(
777 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
779 item.append(round(delta))
783 item.append(round(d_stdev))
788 # Sort the table according to the relative change
789 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
791 # Generate csv tables:
792 csv_file_name = f"{table[u'output-file']}.csv"
793 with open(csv_file_name, u"wt") as file_handler:
794 file_handler.write(header_str)
796 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
798 convert_csv_to_pretty_txt(
799 csv_file_name, f"{table[u'output-file']}.txt", delimiter=u";"
801 with open(f"{table[u'output-file']}.txt", u'a') as file_handler:
802 file_handler.write(legend)
804 # Generate html table:
805 _tpc_generate_html_table(
808 table[u'output-file'],
810 title=table.get(u"title", u"")
814 def table_perf_trending_dash(table, input_data):
815 """Generate the table(s) with algorithm:
816 table_perf_trending_dash
817 specified in the specification file.
819 :param table: Table to generate.
820 :param input_data: Data to process.
821 :type table: pandas.Series
822 :type input_data: InputData
825 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
829 f" Creating the data set for the {table.get(u'type', u'')} "
830 f"{table.get(u'title', u'')}."
832 data = input_data.filter_data(table, continue_on_error=True)
834 # Prepare the header of the tables
838 u"Short-Term Change [%]",
839 u"Long-Term Change [%]",
843 header_str = u",".join(header) + u"\n"
845 incl_tests = table.get(u"include-tests", u"MRR")
847 # Prepare data to the table:
849 for job, builds in table[u"data"].items():
851 for tst_name, tst_data in data[job][str(build)].items():
852 if tst_name.lower() in table.get(u"ignore-list", list()):
854 if tbl_dict.get(tst_name, None) is None:
855 groups = re.search(REGEX_NIC, tst_data[u"parent"])
858 nic = groups.group(0)
859 tbl_dict[tst_name] = {
860 u"name": f"{nic}-{tst_data[u'name']}",
861 u"data": OrderedDict()
864 if incl_tests == u"MRR":
865 tbl_dict[tst_name][u"data"][str(build)] = \
866 tst_data[u"result"][u"receive-rate"]
867 elif incl_tests == u"NDR":
868 tbl_dict[tst_name][u"data"][str(build)] = \
869 tst_data[u"throughput"][u"NDR"][u"LOWER"]
870 elif incl_tests == u"PDR":
871 tbl_dict[tst_name][u"data"][str(build)] = \
872 tst_data[u"throughput"][u"PDR"][u"LOWER"]
873 except (TypeError, KeyError):
874 pass # No data in output.xml for this test
877 for tst_name in tbl_dict:
878 data_t = tbl_dict[tst_name][u"data"]
882 classification_lst, avgs, _ = classify_anomalies(data_t)
884 win_size = min(len(data_t), table[u"window"])
885 long_win_size = min(len(data_t), table[u"long-trend-window"])
889 [x for x in avgs[-long_win_size:-win_size]
894 avg_week_ago = avgs[max(-win_size, -len(avgs))]
896 if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
897 rel_change_last = nan
899 rel_change_last = round(
900 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
902 if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
903 rel_change_long = nan
905 rel_change_long = round(
906 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
908 if classification_lst:
909 if isnan(rel_change_last) and isnan(rel_change_long):
911 if isnan(last_avg) or isnan(rel_change_last) or \
912 isnan(rel_change_long):
915 [tbl_dict[tst_name][u"name"],
916 round(last_avg / 1e6, 2),
919 classification_lst[-win_size+1:].count(u"regression"),
920 classification_lst[-win_size+1:].count(u"progression")])
922 tbl_lst.sort(key=lambda rel: rel[0])
925 for nrr in range(table[u"window"], -1, -1):
926 tbl_reg = [item for item in tbl_lst if item[4] == nrr]
927 for nrp in range(table[u"window"], -1, -1):
928 tbl_out = [item for item in tbl_reg if item[5] == nrp]
929 tbl_out.sort(key=lambda rel: rel[2])
930 tbl_sorted.extend(tbl_out)
932 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
934 logging.info(f" Writing file: {file_name}")
935 with open(file_name, u"wt") as file_handler:
936 file_handler.write(header_str)
937 for test in tbl_sorted:
938 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
940 logging.info(f" Writing file: {table[u'output-file']}.txt")
941 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
944 def _generate_url(testbed, test_name):
945 """Generate URL to a trending plot from the name of the test case.
947 :param testbed: The testbed used for testing.
948 :param test_name: The name of the test case.
951 :returns: The URL to the plot with the trending data for the given test
956 if u"x520" in test_name:
958 elif u"x710" in test_name:
960 elif u"xl710" in test_name:
962 elif u"xxv710" in test_name:
964 elif u"vic1227" in test_name:
966 elif u"vic1385" in test_name:
968 elif u"x553" in test_name:
970 elif u"cx556" in test_name or u"cx556a" in test_name:
975 if u"64b" in test_name:
977 elif u"78b" in test_name:
979 elif u"imix" in test_name:
981 elif u"9000b" in test_name:
982 frame_size = u"9000b"
983 elif u"1518b" in test_name:
984 frame_size = u"1518b"
985 elif u"114b" in test_name:
990 if u"1t1c" in test_name or \
991 (u"-1c-" in test_name and
992 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
994 elif u"2t2c" in test_name or \
995 (u"-2c-" in test_name and
996 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
998 elif u"4t4c" in test_name or \
999 (u"-4c-" in test_name and
1000 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
1002 elif u"2t1c" in test_name or \
1003 (u"-1c-" in test_name and
1004 testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
1006 elif u"4t2c" in test_name or \
1007 (u"-2c-" in test_name and
1008 testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
1010 elif u"8t4c" in test_name or \
1011 (u"-4c-" in test_name and
1012 testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
1017 if u"testpmd" in test_name:
1019 elif u"l3fwd" in test_name:
1021 elif u"avf" in test_name:
1023 elif u"rdma" in test_name:
1025 elif u"dnv" in testbed or u"tsh" in testbed:
1030 if u"acl" in test_name or \
1031 u"macip" in test_name or \
1032 u"nat" in test_name or \
1033 u"policer" in test_name or \
1034 u"cop" in test_name:
1036 elif u"scale" in test_name:
1038 elif u"base" in test_name:
1043 if u"114b" in test_name and u"vhost" in test_name:
1045 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1047 elif u"memif" in test_name:
1048 domain = u"container_memif"
1049 elif u"srv6" in test_name:
1051 elif u"vhost" in test_name:
1053 if u"vppl2xc" in test_name:
1056 driver += u"-testpmd"
1057 if u"lbvpplacp" in test_name:
1058 bsf += u"-link-bonding"
1059 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1060 domain = u"nf_service_density_vnfc"
1061 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1062 domain = u"nf_service_density_cnfc"
1063 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1064 domain = u"nf_service_density_cnfp"
1065 elif u"ipsec" in test_name:
1067 if u"sw" in test_name:
1069 elif u"hw" in test_name:
1071 elif u"ethip4vxlan" in test_name:
1072 domain = u"ip4_tunnels"
1073 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1075 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1077 elif u"l2xcbase" in test_name or \
1078 u"l2xcscale" in test_name or \
1079 u"l2bdbasemaclrn" in test_name or \
1080 u"l2bdscale" in test_name or \
1081 u"l2patch" in test_name:
1086 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1087 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1089 return file_name + anchor_name
1092 def table_perf_trending_dash_html(table, input_data):
1093 """Generate the table(s) with algorithm:
1094 table_perf_trending_dash_html specified in the specification
1097 :param table: Table to generate.
1098 :param input_data: Data to process.
1100 :type input_data: InputData
1105 if not table.get(u"testbed", None):
1107 f"The testbed is not defined for the table "
1108 f"{table.get(u'title', u'')}."
1112 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1115 with open(table[u"input-file"], u'rt') as csv_file:
1116 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1118 logging.warning(u"The input file is not defined.")
1120 except csv.Error as err:
1122 f"Not possible to process the file {table[u'input-file']}.\n"
1128 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1131 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1132 for idx, item in enumerate(csv_lst[0]):
1133 alignment = u"left" if idx == 0 else u"center"
1134 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1152 for r_idx, row in enumerate(csv_lst[1:]):
1154 color = u"regression"
1156 color = u"progression"
1159 trow = ET.SubElement(
1160 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1164 for c_idx, item in enumerate(row):
1165 tdata = ET.SubElement(
1168 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1171 if c_idx == 0 and table.get(u"add-links", True):
1172 ref = ET.SubElement(
1176 href=f"../trending/"
1177 f"{_generate_url(table.get(u'testbed', ''), item)}"
1184 with open(table[u"output-file"], u'w') as html_file:
1185 logging.info(f" Writing file: {table[u'output-file']}")
1186 html_file.write(u".. raw:: html\n\n\t")
1187 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1188 html_file.write(u"\n\t<p><br><br></p>\n")
1190 logging.warning(u"The output file is not defined.")
1194 def table_last_failed_tests(table, input_data):
1195 """Generate the table(s) with algorithm: table_last_failed_tests
1196 specified in the specification file.
1198 :param table: Table to generate.
1199 :param input_data: Data to process.
1200 :type table: pandas.Series
1201 :type input_data: InputData
1204 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1206 # Transform the data
1208 f" Creating the data set for the {table.get(u'type', u'')} "
1209 f"{table.get(u'title', u'')}."
1212 data = input_data.filter_data(table, continue_on_error=True)
1214 if data is None or data.empty:
1216 f" No data for the {table.get(u'type', u'')} "
1217 f"{table.get(u'title', u'')}."
1222 for job, builds in table[u"data"].items():
1223 for build in builds:
1226 version = input_data.metadata(job, build).get(u"version", u"")
1228 logging.error(f"Data for {job}: {build} is not present.")
1230 tbl_list.append(build)
1231 tbl_list.append(version)
1232 failed_tests = list()
1235 for tst_data in data[job][build].values:
1236 if tst_data[u"status"] != u"FAIL":
1240 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1243 nic = groups.group(0)
1244 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1245 tbl_list.append(str(passed))
1246 tbl_list.append(str(failed))
1247 tbl_list.extend(failed_tests)
1249 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1250 logging.info(f" Writing file: {file_name}")
1251 with open(file_name, u"wt") as file_handler:
1252 for test in tbl_list:
1253 file_handler.write(test + u'\n')
1256 def table_failed_tests(table, input_data):
1257 """Generate the table(s) with algorithm: table_failed_tests
1258 specified in the specification file.
1260 :param table: Table to generate.
1261 :param input_data: Data to process.
1262 :type table: pandas.Series
1263 :type input_data: InputData
1266 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1268 # Transform the data
1270 f" Creating the data set for the {table.get(u'type', u'')} "
1271 f"{table.get(u'title', u'')}."
1273 data = input_data.filter_data(table, continue_on_error=True)
1275 # Prepare the header of the tables
1279 u"Last Failure [Time]",
1280 u"Last Failure [VPP-Build-Id]",
1281 u"Last Failure [CSIT-Job-Build-Id]"
1284 # Generate the data for the table according to the model in the table
1288 timeperiod = timedelta(int(table.get(u"window", 7)))
1291 for job, builds in table[u"data"].items():
1292 for build in builds:
1294 for tst_name, tst_data in data[job][build].items():
1295 if tst_name.lower() in table.get(u"ignore-list", list()):
1297 if tbl_dict.get(tst_name, None) is None:
1298 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1301 nic = groups.group(0)
1302 tbl_dict[tst_name] = {
1303 u"name": f"{nic}-{tst_data[u'name']}",
1304 u"data": OrderedDict()
1307 generated = input_data.metadata(job, build).\
1308 get(u"generated", u"")
1311 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1312 if (now - then) <= timeperiod:
1313 tbl_dict[tst_name][u"data"][build] = (
1314 tst_data[u"status"],
1316 input_data.metadata(job, build).get(u"version",
1320 except (TypeError, KeyError) as err:
1321 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1325 for tst_data in tbl_dict.values():
1327 fails_last_date = u""
1328 fails_last_vpp = u""
1329 fails_last_csit = u""
1330 for val in tst_data[u"data"].values():
1331 if val[0] == u"FAIL":
1333 fails_last_date = val[1]
1334 fails_last_vpp = val[2]
1335 fails_last_csit = val[3]
1337 max_fails = fails_nr if fails_nr > max_fails else max_fails
1344 f"mrr-daily-build-{fails_last_csit}"
1348 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1350 for nrf in range(max_fails, -1, -1):
1351 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1352 tbl_sorted.extend(tbl_fails)
1354 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1355 logging.info(f" Writing file: {file_name}")
1356 with open(file_name, u"wt") as file_handler:
1357 file_handler.write(u",".join(header) + u"\n")
1358 for test in tbl_sorted:
1359 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1361 logging.info(f" Writing file: {table[u'output-file']}.txt")
1362 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1365 def table_failed_tests_html(table, input_data):
1366 """Generate the table(s) with algorithm: table_failed_tests_html
1367 specified in the specification file.
1369 :param table: Table to generate.
1370 :param input_data: Data to process.
1371 :type table: pandas.Series
1372 :type input_data: InputData
1377 if not table.get(u"testbed", None):
1379 f"The testbed is not defined for the table "
1380 f"{table.get(u'title', u'')}."
1384 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1387 with open(table[u"input-file"], u'rt') as csv_file:
1388 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1390 logging.warning(u"The input file is not defined.")
1392 except csv.Error as err:
1394 f"Not possible to process the file {table[u'input-file']}.\n"
1400 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1403 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1404 for idx, item in enumerate(csv_lst[0]):
1405 alignment = u"left" if idx == 0 else u"center"
1406 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1410 colors = (u"#e9f1fb", u"#d4e4f7")
1411 for r_idx, row in enumerate(csv_lst[1:]):
1412 background = colors[r_idx % 2]
1413 trow = ET.SubElement(
1414 failed_tests, u"tr", attrib=dict(bgcolor=background)
1418 for c_idx, item in enumerate(row):
1419 tdata = ET.SubElement(
1422 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1426 ref = ET.SubElement(
1430 href=f"../trending/"
1431 f"{_generate_url(table.get(u'testbed', ''), item)}"
1438 with open(table[u"output-file"], u'w') as html_file:
1439 logging.info(f" Writing file: {table[u'output-file']}")
1440 html_file.write(u".. raw:: html\n\n\t")
1441 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1442 html_file.write(u"\n\t<p><br><br></p>\n")
1444 logging.warning(u"The output file is not defined.")
1448 def table_comparison(table, input_data):
1449 """Generate the table(s) with algorithm: table_comparison
1450 specified in the specification file.
1452 :param table: Table to generate.
1453 :param input_data: Data to process.
1454 :type table: pandas.Series
1455 :type input_data: InputData
1457 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1459 # Transform the data
1461 f" Creating the data set for the {table.get(u'type', u'')} "
1462 f"{table.get(u'title', u'')}."
1465 columns = table.get(u"columns", None)
1468 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1473 for idx, col in enumerate(columns):
1474 if col.get(u"data-set", None) is None:
1475 logging.warning(f"No data for column {col.get(u'title', u'')}")
1477 tag = col.get(u"tag", None)
1478 data = input_data.filter_data(
1480 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1481 data=col[u"data-set"],
1482 continue_on_error=True
1485 u"title": col.get(u"title", f"Column{idx}"),
1488 for builds in data.values:
1489 for build in builds:
1490 for tst_name, tst_data in build.items():
1491 if tag and tag not in tst_data[u"tags"]:
1494 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1495 replace(u"2n1l-", u"")
1496 if col_data[u"data"].get(tst_name_mod, None) is None:
1497 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1498 if u"across testbeds" in table[u"title"].lower() or \
1499 u"across topologies" in table[u"title"].lower():
1500 name = _tpc_modify_displayed_test_name(name)
1501 col_data[u"data"][tst_name_mod] = {
1509 target=col_data[u"data"][tst_name_mod],
1511 include_tests=table[u"include-tests"]
1514 replacement = col.get(u"data-replacement", None)
1516 rpl_data = input_data.filter_data(
1518 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1520 continue_on_error=True
1522 for builds in rpl_data.values:
1523 for build in builds:
1524 for tst_name, tst_data in build.items():
1525 if tag and tag not in tst_data[u"tags"]:
1528 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1529 replace(u"2n1l-", u"")
1530 if col_data[u"data"].get(tst_name_mod, None) is None:
1531 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1532 if u"across testbeds" in table[u"title"].lower() \
1533 or u"across topologies" in \
1534 table[u"title"].lower():
1535 name = _tpc_modify_displayed_test_name(name)
1536 col_data[u"data"][tst_name_mod] = {
1543 if col_data[u"data"][tst_name_mod][u"replace"]:
1544 col_data[u"data"][tst_name_mod][u"replace"] = False
1545 col_data[u"data"][tst_name_mod][u"data"] = list()
1547 target=col_data[u"data"][tst_name_mod],
1549 include_tests=table[u"include-tests"]
1552 if table[u"include-tests"] in (u"NDR", u"PDR"):
1553 for tst_name, tst_data in col_data[u"data"].items():
1554 if tst_data[u"data"]:
1555 tst_data[u"mean"] = mean(tst_data[u"data"])
1556 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1558 cols.append(col_data)
1562 for tst_name, tst_data in col[u"data"].items():
1563 if tbl_dict.get(tst_name, None) is None:
1564 tbl_dict[tst_name] = {
1565 "name": tst_data[u"name"]
1567 tbl_dict[tst_name][col[u"title"]] = {
1568 u"mean": tst_data[u"mean"],
1569 u"stdev": tst_data[u"stdev"]
1573 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1577 for tst_data in tbl_dict.values():
1578 row = [tst_data[u"name"], ]
1580 row.append(tst_data.get(col[u"title"], None))
1583 comparisons = table.get(u"comparisons", None)
1585 if comparisons and isinstance(comparisons, list):
1586 for idx, comp in enumerate(comparisons):
1588 col_ref = int(comp[u"reference"])
1589 col_cmp = int(comp[u"compare"])
1591 logging.warning(u"Comparison: No references defined! Skipping.")
1592 comparisons.pop(idx)
1594 if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1595 col_ref == col_cmp):
1596 logging.warning(f"Wrong values of reference={col_ref} "
1597 f"and/or compare={col_cmp}. Skipping.")
1598 comparisons.pop(idx)
1600 rca_file_name = comp.get(u"rca-file", None)
1603 with open(rca_file_name, u"r") as file_handler:
1606 u"title": f"RCA{idx + 1}",
1607 u"data": load(file_handler, Loader=FullLoader)
1610 except (YAMLError, IOError) as err:
1612 f"The RCA file {rca_file_name} does not exist or "
1615 logging.debug(repr(err))
1622 tbl_cmp_lst = list()
1625 new_row = deepcopy(row)
1626 for comp in comparisons:
1627 ref_itm = row[int(comp[u"reference"])]
1628 if ref_itm is None and \
1629 comp.get(u"reference-alt", None) is not None:
1630 ref_itm = row[int(comp[u"reference-alt"])]
1631 cmp_itm = row[int(comp[u"compare"])]
1632 if ref_itm is not None and cmp_itm is not None and \
1633 ref_itm[u"mean"] is not None and \
1634 cmp_itm[u"mean"] is not None and \
1635 ref_itm[u"stdev"] is not None and \
1636 cmp_itm[u"stdev"] is not None:
1637 delta, d_stdev = relative_change_stdev(
1638 ref_itm[u"mean"], cmp_itm[u"mean"],
1639 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1644 u"mean": delta * 1e6,
1645 u"stdev": d_stdev * 1e6
1650 tbl_cmp_lst.append(new_row)
1653 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1654 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1655 except TypeError as err:
1656 logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1658 tbl_for_csv = list()
1659 for line in tbl_cmp_lst:
1661 for idx, itm in enumerate(line[1:]):
1662 if itm is None or not isinstance(itm, dict) or\
1663 itm.get(u'mean', None) is None or \
1664 itm.get(u'stdev', None) is None:
1668 row.append(round(float(itm[u'mean']) / 1e6, 3))
1669 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1673 rca_nr = rca[u"data"].get(row[0], u"-")
1674 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1675 tbl_for_csv.append(row)
1677 header_csv = [u"Test Case", ]
1679 header_csv.append(f"Avg({col[u'title']})")
1680 header_csv.append(f"Stdev({col[u'title']})")
1681 for comp in comparisons:
1683 f"Avg({comp.get(u'title', u'')})"
1686 f"Stdev({comp.get(u'title', u'')})"
1690 header_csv.append(rca[u"title"])
1692 legend_lst = table.get(u"legend", None)
1693 if legend_lst is None:
1696 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1699 if rcas and any(rcas):
1700 footnote += u"\nRoot Cause Analysis:\n"
1703 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1705 csv_file_name = f"{table[u'output-file']}-csv.csv"
1706 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1708 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1710 for test in tbl_for_csv:
1712 u",".join([f'"{item}"' for item in test]) + u"\n"
1715 for item in legend_lst:
1716 file_handler.write(f'"{item}"\n')
1718 for itm in footnote.split(u"\n"):
1719 file_handler.write(f'"{itm}"\n')
1722 max_lens = [0, ] * len(tbl_cmp_lst[0])
1723 for line in tbl_cmp_lst:
1725 for idx, itm in enumerate(line[1:]):
1726 if itm is None or not isinstance(itm, dict) or \
1727 itm.get(u'mean', None) is None or \
1728 itm.get(u'stdev', None) is None:
1733 f"{round(float(itm[u'mean']) / 1e6, 1)} "
1734 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1735 replace(u"nan", u"NaN")
1739 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1740 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1741 replace(u"nan", u"NaN")
1743 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1744 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1749 header = [u"Test Case", ]
1750 header.extend([col[u"title"] for col in cols])
1751 header.extend([comp.get(u"title", u"") for comp in comparisons])
1754 for line in tbl_tmp:
1756 for idx, itm in enumerate(line[1:]):
1757 if itm in (u"NT", u"NaN"):
1760 itm_lst = itm.rsplit(u"\u00B1", 1)
1762 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1763 itm_str = u"\u00B1".join(itm_lst)
1765 if idx >= len(cols):
1767 rca = rcas[idx - len(cols)]
1770 rca_nr = rca[u"data"].get(row[0], None)
1772 hdr_len = len(header[idx + 1]) - 1
1775 rca_nr = f"[{rca_nr}]"
1777 f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
1778 f"{u' ' * (hdr_len - 4 - len(itm_str))}"
1782 tbl_final.append(row)
1784 # Generate csv tables:
1785 csv_file_name = f"{table[u'output-file']}.csv"
1786 logging.info(f" Writing the file {csv_file_name}")
1787 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1788 file_handler.write(u";".join(header) + u"\n")
1789 for test in tbl_final:
1790 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1792 # Generate txt table:
1793 txt_file_name = f"{table[u'output-file']}.txt"
1794 logging.info(f" Writing the file {txt_file_name}")
1795 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
1797 with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
1798 file_handler.write(legend)
1799 file_handler.write(footnote)
1801 # Generate html table:
1802 _tpc_generate_html_table(
1805 table[u'output-file'],
1809 title=table.get(u"title", u"")
1813 def table_weekly_comparison(table, in_data):
1814 """Generate the table(s) with algorithm: table_weekly_comparison
1815 specified in the specification file.
1817 :param table: Table to generate.
1818 :param in_data: Data to process.
1819 :type table: pandas.Series
1820 :type in_data: InputData
1822 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1824 # Transform the data
1826 f" Creating the data set for the {table.get(u'type', u'')} "
1827 f"{table.get(u'title', u'')}."
1830 incl_tests = table.get(u"include-tests", None)
1831 if incl_tests not in (u"NDR", u"PDR"):
1832 logging.error(f"Wrong tests to include specified ({incl_tests}).")
1835 nr_cols = table.get(u"nr-of-data-columns", None)
1836 if not nr_cols or nr_cols < 2:
1838 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1842 data = in_data.filter_data(
1844 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1845 continue_on_error=True
1850 [u"Start Timestamp", ],
1856 tb_tbl = table.get(u"testbeds", None)
1857 for job_name, job_data in data.items():
1858 for build_nr, build in job_data.items():
1864 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1865 if tb_ip and tb_tbl:
1866 testbed = tb_tbl.get(tb_ip, u"")
1869 header[2].insert(1, build_nr)
1870 header[3].insert(1, testbed)
1872 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1875 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1878 for tst_name, tst_data in build.items():
1880 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1881 if not tbl_dict.get(tst_name_mod, None):
1882 tbl_dict[tst_name_mod] = dict(
1883 name=tst_data[u'name'].rsplit(u'-', 1)[0],
1886 tbl_dict[tst_name_mod][-idx - 1] = \
1887 tst_data[u"throughput"][incl_tests][u"LOWER"]
1888 except (TypeError, IndexError, KeyError, ValueError):
1893 logging.error(u"Not enough data to build the table! Skipping")
1897 for idx, cmp in enumerate(table.get(u"comparisons", list())):
1898 idx_ref = cmp.get(u"reference", None)
1899 idx_cmp = cmp.get(u"compare", None)
1900 if idx_ref is None or idx_cmp is None:
1903 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1904 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1906 header[1].append(u"")
1907 header[2].append(u"")
1908 header[3].append(u"")
1909 for tst_name, tst_data in tbl_dict.items():
1910 if not cmp_dict.get(tst_name, None):
1911 cmp_dict[tst_name] = list()
1912 ref_data = tst_data.get(idx_ref, None)
1913 cmp_data = tst_data.get(idx_cmp, None)
1914 if ref_data is None or cmp_data is None:
1915 cmp_dict[tst_name].append(float('nan'))
1917 cmp_dict[tst_name].append(
1918 relative_change(ref_data, cmp_data)
1922 for tst_name, tst_data in tbl_dict.items():
1923 itm_lst = [tst_data[u"name"], ]
1924 for idx in range(nr_cols):
1925 item = tst_data.get(-idx - 1, None)
1927 itm_lst.insert(1, None)
1929 itm_lst.insert(1, round(item / 1e6, 1))
1932 None if itm is None else round(itm, 1)
1933 for itm in cmp_dict[tst_name]
1936 tbl_lst.append(itm_lst)
1938 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1939 tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
1941 # Generate csv table:
1942 csv_file_name = f"{table[u'output-file']}.csv"
1943 logging.info(f" Writing the file {csv_file_name}")
1944 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1946 file_handler.write(u",".join(hdr) + u"\n")
1947 for test in tbl_lst:
1948 file_handler.write(u",".join(
1950 str(item).replace(u"None", u"-").replace(u"nan", u"-").
1951 replace(u"null", u"-") for item in test
1955 txt_file_name = f"{table[u'output-file']}.txt"
1956 logging.info(f" Writing the file {txt_file_name}")
1957 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
1959 # Reorganize header in txt table
1961 with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
1962 for line in file_handler:
1963 txt_table.append(line)
1965 txt_table.insert(5, txt_table.pop(2))
1966 with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
1967 file_handler.writelines(txt_table)
1971 # Generate html table:
1973 u"<br>".join(row) for row in zip(*header)
1975 _tpc_generate_html_table(
1978 table[u'output-file'],
1980 title=table.get(u"title", u""),