1 # Copyright (c) 2021 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
14 """Algorithms to generate tables.
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])
923 tbl_lst.sort(key=lambda rel: rel[3])
924 tbl_lst.sort(key=lambda rel: rel[2])
927 for nrr in range(table[u"window"], -1, -1):
928 tbl_reg = [item for item in tbl_lst if item[4] == nrr]
929 for nrp in range(table[u"window"], -1, -1):
930 tbl_out = [item for item in tbl_reg if item[5] == nrp]
931 tbl_sorted.extend(tbl_out)
933 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
935 logging.info(f" Writing file: {file_name}")
936 with open(file_name, u"wt") as file_handler:
937 file_handler.write(header_str)
938 for test in tbl_sorted:
939 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
941 logging.info(f" Writing file: {table[u'output-file']}.txt")
942 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
945 def _generate_url(testbed, test_name):
946 """Generate URL to a trending plot from the name of the test case.
948 :param testbed: The testbed used for testing.
949 :param test_name: The name of the test case.
952 :returns: The URL to the plot with the trending data for the given test
957 if u"x520" in test_name:
959 elif u"x710" in test_name:
961 elif u"xl710" in test_name:
963 elif u"xxv710" in test_name:
965 elif u"vic1227" in test_name:
967 elif u"vic1385" in test_name:
969 elif u"x553" in test_name:
971 elif u"cx556" in test_name or u"cx556a" in test_name:
976 if u"64b" in test_name:
978 elif u"78b" in test_name:
980 elif u"imix" in test_name:
982 elif u"9000b" in test_name:
983 frame_size = u"9000b"
984 elif u"1518b" in test_name:
985 frame_size = u"1518b"
986 elif u"114b" in test_name:
991 if u"1t1c" in test_name or \
992 (u"-1c-" in test_name and
993 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
995 elif u"2t2c" in test_name or \
996 (u"-2c-" in test_name and
997 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
999 elif u"4t4c" in test_name or \
1000 (u"-4c-" in test_name and
1001 testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
1003 elif u"2t1c" in test_name or \
1004 (u"-1c-" in test_name and
1005 testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1007 elif u"4t2c" in test_name or \
1008 (u"-2c-" in test_name and
1009 testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1011 elif u"8t4c" in test_name or \
1012 (u"-4c-" in test_name and
1013 testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1018 if u"testpmd" in test_name:
1020 elif u"l3fwd" in test_name:
1022 elif u"avf" in test_name:
1024 elif u"rdma" in test_name:
1026 elif u"dnv" in testbed or u"tsh" in testbed:
1031 if u"macip-iacl1s" in test_name:
1032 bsf = u"features-macip-iacl1"
1033 elif u"macip-iacl10s" in test_name:
1034 bsf = u"features-macip-iacl01"
1035 elif u"macip-iacl50s" in test_name:
1036 bsf = u"features-macip-iacl50"
1037 elif u"iacl1s" in test_name:
1038 bsf = u"features-iacl1"
1039 elif u"iacl10s" in test_name:
1040 bsf = u"features-iacl10"
1041 elif u"iacl50s" in test_name:
1042 bsf = u"features-iacl50"
1043 elif u"oacl1s" in test_name:
1044 bsf = u"features-oacl1"
1045 elif u"oacl10s" in test_name:
1046 bsf = u"features-oacl10"
1047 elif u"oacl50s" in test_name:
1048 bsf = u"features-oacl50"
1049 elif u"nat44det" in test_name:
1050 bsf = u"nat44det-bidir"
1051 elif u"nat44ed" in test_name and u"udir" in test_name:
1052 bsf = u"nat44ed-udir"
1053 elif u"-cps" in test_name and u"ethip4udp" in test_name:
1055 elif u"-cps" in test_name and u"ethip4tcp" in test_name:
1057 elif u"-pps" in test_name and u"ethip4udp" in test_name:
1059 elif u"-pps" in test_name and u"ethip4tcp" in test_name:
1061 elif u"udpsrcscale" in test_name:
1062 bsf = u"features-udp"
1063 elif u"iacl" in test_name:
1065 elif u"policer" in test_name:
1067 elif u"adl" in test_name:
1069 elif u"cop" in test_name:
1071 elif u"nat" in test_name:
1073 elif u"macip" in test_name:
1075 elif u"scale" in test_name:
1077 elif u"base" in test_name:
1082 if u"114b" in test_name and u"vhost" in test_name:
1084 elif u"nat44" in test_name or u"-pps" in test_name or u"-cps" in test_name:
1086 if u"nat44det" in test_name:
1087 domain += u"-det-bidir"
1090 if u"udir" in test_name:
1091 domain += u"-unidir"
1092 elif u"-ethip4udp-" in test_name:
1094 elif u"-ethip4tcp-" in test_name:
1096 if u"-cps" in test_name:
1098 elif u"-pps" in test_name:
1100 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1102 elif u"memif" in test_name:
1103 domain = u"container_memif"
1104 elif u"srv6" in test_name:
1106 elif u"vhost" in test_name:
1108 if u"vppl2xc" in test_name:
1111 driver += u"-testpmd"
1112 if u"lbvpplacp" in test_name:
1113 bsf += u"-link-bonding"
1114 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1115 domain = u"nf_service_density_vnfc"
1116 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1117 domain = u"nf_service_density_cnfc"
1118 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1119 domain = u"nf_service_density_cnfp"
1120 elif u"ipsec" in test_name:
1122 if u"sw" in test_name:
1124 elif u"hw" in test_name:
1126 elif u"ethip4vxlan" in test_name:
1127 domain = u"ip4_tunnels"
1128 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1130 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1132 elif u"l2xcbase" in test_name or \
1133 u"l2xcscale" in test_name or \
1134 u"l2bdbasemaclrn" in test_name or \
1135 u"l2bdscale" in test_name or \
1136 u"l2patch" in test_name:
1141 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1142 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1144 return file_name + anchor_name
1147 def table_perf_trending_dash_html(table, input_data):
1148 """Generate the table(s) with algorithm:
1149 table_perf_trending_dash_html specified in the specification
1152 :param table: Table to generate.
1153 :param input_data: Data to process.
1155 :type input_data: InputData
1160 if not table.get(u"testbed", None):
1162 f"The testbed is not defined for the table "
1163 f"{table.get(u'title', u'')}. Skipping."
1167 test_type = table.get(u"test-type", u"MRR")
1168 if test_type not in (u"MRR", u"NDR", u"PDR"):
1170 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1175 if test_type in (u"NDR", u"PDR"):
1176 lnk_dir = u"../ndrpdr_trending/"
1177 lnk_sufix = f"-{test_type.lower()}"
1179 lnk_dir = u"../trending/"
1182 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1185 with open(table[u"input-file"], u'rt') as csv_file:
1186 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1188 logging.warning(u"The input file is not defined.")
1190 except csv.Error as err:
1192 f"Not possible to process the file {table[u'input-file']}.\n"
1198 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1201 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1202 for idx, item in enumerate(csv_lst[0]):
1203 alignment = u"left" if idx == 0 else u"center"
1204 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1222 for r_idx, row in enumerate(csv_lst[1:]):
1224 color = u"regression"
1226 color = u"progression"
1229 trow = ET.SubElement(
1230 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1234 for c_idx, item in enumerate(row):
1235 tdata = ET.SubElement(
1238 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1241 if c_idx == 0 and table.get(u"add-links", True):
1242 ref = ET.SubElement(
1247 f"{_generate_url(table.get(u'testbed', ''), item)}"
1255 with open(table[u"output-file"], u'w') as html_file:
1256 logging.info(f" Writing file: {table[u'output-file']}")
1257 html_file.write(u".. raw:: html\n\n\t")
1258 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1259 html_file.write(u"\n\t<p><br><br></p>\n")
1261 logging.warning(u"The output file is not defined.")
1265 def table_last_failed_tests(table, input_data):
1266 """Generate the table(s) with algorithm: table_last_failed_tests
1267 specified in the specification file.
1269 :param table: Table to generate.
1270 :param input_data: Data to process.
1271 :type table: pandas.Series
1272 :type input_data: InputData
1275 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1277 # Transform the data
1279 f" Creating the data set for the {table.get(u'type', u'')} "
1280 f"{table.get(u'title', u'')}."
1283 data = input_data.filter_data(table, continue_on_error=True)
1285 if data is None or data.empty:
1287 f" No data for the {table.get(u'type', u'')} "
1288 f"{table.get(u'title', u'')}."
1293 for job, builds in table[u"data"].items():
1294 for build in builds:
1297 version = input_data.metadata(job, build).get(u"version", u"")
1299 logging.error(f"Data for {job}: {build} is not present.")
1301 tbl_list.append(build)
1302 tbl_list.append(version)
1303 failed_tests = list()
1306 for tst_data in data[job][build].values:
1307 if tst_data[u"status"] != u"FAIL":
1311 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1314 nic = groups.group(0)
1315 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1316 tbl_list.append(str(passed))
1317 tbl_list.append(str(failed))
1318 tbl_list.extend(failed_tests)
1320 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1321 logging.info(f" Writing file: {file_name}")
1322 with open(file_name, u"wt") as file_handler:
1323 for test in tbl_list:
1324 file_handler.write(test + u'\n')
1327 def table_failed_tests(table, input_data):
1328 """Generate the table(s) with algorithm: table_failed_tests
1329 specified in the specification file.
1331 :param table: Table to generate.
1332 :param input_data: Data to process.
1333 :type table: pandas.Series
1334 :type input_data: InputData
1337 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1339 # Transform the data
1341 f" Creating the data set for the {table.get(u'type', u'')} "
1342 f"{table.get(u'title', u'')}."
1344 data = input_data.filter_data(table, continue_on_error=True)
1347 if u"NDRPDR" in table.get(u"filter", list()):
1348 test_type = u"NDRPDR"
1350 # Prepare the header of the tables
1354 u"Last Failure [Time]",
1355 u"Last Failure [VPP-Build-Id]",
1356 u"Last Failure [CSIT-Job-Build-Id]"
1359 # Generate the data for the table according to the model in the table
1363 timeperiod = timedelta(int(table.get(u"window", 7)))
1366 for job, builds in table[u"data"].items():
1367 for build in builds:
1369 for tst_name, tst_data in data[job][build].items():
1370 if tst_name.lower() in table.get(u"ignore-list", list()):
1372 if tbl_dict.get(tst_name, None) is None:
1373 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1376 nic = groups.group(0)
1377 tbl_dict[tst_name] = {
1378 u"name": f"{nic}-{tst_data[u'name']}",
1379 u"data": OrderedDict()
1382 generated = input_data.metadata(job, build).\
1383 get(u"generated", u"")
1386 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1387 if (now - then) <= timeperiod:
1388 tbl_dict[tst_name][u"data"][build] = (
1389 tst_data[u"status"],
1391 input_data.metadata(job, build).get(u"version",
1395 except (TypeError, KeyError) as err:
1396 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1400 for tst_data in tbl_dict.values():
1402 fails_last_date = u""
1403 fails_last_vpp = u""
1404 fails_last_csit = u""
1405 for val in tst_data[u"data"].values():
1406 if val[0] == u"FAIL":
1408 fails_last_date = val[1]
1409 fails_last_vpp = val[2]
1410 fails_last_csit = val[3]
1412 max_fails = fails_nr if fails_nr > max_fails else max_fails
1418 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1419 f"-build-{fails_last_csit}"
1422 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1424 for nrf in range(max_fails, -1, -1):
1425 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1426 tbl_sorted.extend(tbl_fails)
1428 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1429 logging.info(f" Writing file: {file_name}")
1430 with open(file_name, u"wt") as file_handler:
1431 file_handler.write(u",".join(header) + u"\n")
1432 for test in tbl_sorted:
1433 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1435 logging.info(f" Writing file: {table[u'output-file']}.txt")
1436 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1439 def table_failed_tests_html(table, input_data):
1440 """Generate the table(s) with algorithm: table_failed_tests_html
1441 specified in the specification file.
1443 :param table: Table to generate.
1444 :param input_data: Data to process.
1445 :type table: pandas.Series
1446 :type input_data: InputData
1451 if not table.get(u"testbed", None):
1453 f"The testbed is not defined for the table "
1454 f"{table.get(u'title', u'')}. Skipping."
1458 test_type = table.get(u"test-type", u"MRR")
1459 if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1461 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1466 if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1467 lnk_dir = u"../ndrpdr_trending/"
1470 lnk_dir = u"../trending/"
1473 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1476 with open(table[u"input-file"], u'rt') as csv_file:
1477 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1479 logging.warning(u"The input file is not defined.")
1481 except csv.Error as err:
1483 f"Not possible to process the file {table[u'input-file']}.\n"
1489 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1492 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1493 for idx, item in enumerate(csv_lst[0]):
1494 alignment = u"left" if idx == 0 else u"center"
1495 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1499 colors = (u"#e9f1fb", u"#d4e4f7")
1500 for r_idx, row in enumerate(csv_lst[1:]):
1501 background = colors[r_idx % 2]
1502 trow = ET.SubElement(
1503 failed_tests, u"tr", attrib=dict(bgcolor=background)
1507 for c_idx, item in enumerate(row):
1508 tdata = ET.SubElement(
1511 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1514 if c_idx == 0 and table.get(u"add-links", True):
1515 ref = ET.SubElement(
1520 f"{_generate_url(table.get(u'testbed', ''), item)}"
1528 with open(table[u"output-file"], u'w') as html_file:
1529 logging.info(f" Writing file: {table[u'output-file']}")
1530 html_file.write(u".. raw:: html\n\n\t")
1531 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1532 html_file.write(u"\n\t<p><br><br></p>\n")
1534 logging.warning(u"The output file is not defined.")
1538 def table_comparison(table, input_data):
1539 """Generate the table(s) with algorithm: table_comparison
1540 specified in the specification file.
1542 :param table: Table to generate.
1543 :param input_data: Data to process.
1544 :type table: pandas.Series
1545 :type input_data: InputData
1547 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1549 # Transform the data
1551 f" Creating the data set for the {table.get(u'type', u'')} "
1552 f"{table.get(u'title', u'')}."
1555 columns = table.get(u"columns", None)
1558 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1563 for idx, col in enumerate(columns):
1564 if col.get(u"data-set", None) is None:
1565 logging.warning(f"No data for column {col.get(u'title', u'')}")
1567 tag = col.get(u"tag", None)
1568 data = input_data.filter_data(
1570 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1571 data=col[u"data-set"],
1572 continue_on_error=True
1575 u"title": col.get(u"title", f"Column{idx}"),
1578 for builds in data.values:
1579 for build in builds:
1580 for tst_name, tst_data in build.items():
1581 if tag and tag not in tst_data[u"tags"]:
1584 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1585 replace(u"2n1l-", u"")
1586 if col_data[u"data"].get(tst_name_mod, None) is None:
1587 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1588 if u"across testbeds" in table[u"title"].lower() or \
1589 u"across topologies" in table[u"title"].lower():
1590 name = _tpc_modify_displayed_test_name(name)
1591 col_data[u"data"][tst_name_mod] = {
1599 target=col_data[u"data"][tst_name_mod],
1601 include_tests=table[u"include-tests"]
1604 replacement = col.get(u"data-replacement", None)
1606 rpl_data = input_data.filter_data(
1608 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1610 continue_on_error=True
1612 for builds in rpl_data.values:
1613 for build in builds:
1614 for tst_name, tst_data in build.items():
1615 if tag and tag not in tst_data[u"tags"]:
1618 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1619 replace(u"2n1l-", u"")
1620 if col_data[u"data"].get(tst_name_mod, None) is None:
1621 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1622 if u"across testbeds" in table[u"title"].lower() \
1623 or u"across topologies" in \
1624 table[u"title"].lower():
1625 name = _tpc_modify_displayed_test_name(name)
1626 col_data[u"data"][tst_name_mod] = {
1633 if col_data[u"data"][tst_name_mod][u"replace"]:
1634 col_data[u"data"][tst_name_mod][u"replace"] = False
1635 col_data[u"data"][tst_name_mod][u"data"] = list()
1637 target=col_data[u"data"][tst_name_mod],
1639 include_tests=table[u"include-tests"]
1642 if table[u"include-tests"] in (u"NDR", u"PDR"):
1643 for tst_name, tst_data in col_data[u"data"].items():
1644 if tst_data[u"data"]:
1645 tst_data[u"mean"] = mean(tst_data[u"data"])
1646 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1648 cols.append(col_data)
1652 for tst_name, tst_data in col[u"data"].items():
1653 if tbl_dict.get(tst_name, None) is None:
1654 tbl_dict[tst_name] = {
1655 "name": tst_data[u"name"]
1657 tbl_dict[tst_name][col[u"title"]] = {
1658 u"mean": tst_data[u"mean"],
1659 u"stdev": tst_data[u"stdev"]
1663 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1667 for tst_data in tbl_dict.values():
1668 row = [tst_data[u"name"], ]
1670 row.append(tst_data.get(col[u"title"], None))
1673 comparisons = table.get(u"comparisons", None)
1675 if comparisons and isinstance(comparisons, list):
1676 for idx, comp in enumerate(comparisons):
1678 col_ref = int(comp[u"reference"])
1679 col_cmp = int(comp[u"compare"])
1681 logging.warning(u"Comparison: No references defined! Skipping.")
1682 comparisons.pop(idx)
1684 if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1685 col_ref == col_cmp):
1686 logging.warning(f"Wrong values of reference={col_ref} "
1687 f"and/or compare={col_cmp}. Skipping.")
1688 comparisons.pop(idx)
1690 rca_file_name = comp.get(u"rca-file", None)
1693 with open(rca_file_name, u"r") as file_handler:
1696 u"title": f"RCA{idx + 1}",
1697 u"data": load(file_handler, Loader=FullLoader)
1700 except (YAMLError, IOError) as err:
1702 f"The RCA file {rca_file_name} does not exist or "
1705 logging.debug(repr(err))
1712 tbl_cmp_lst = list()
1715 new_row = deepcopy(row)
1716 for comp in comparisons:
1717 ref_itm = row[int(comp[u"reference"])]
1718 if ref_itm is None and \
1719 comp.get(u"reference-alt", None) is not None:
1720 ref_itm = row[int(comp[u"reference-alt"])]
1721 cmp_itm = row[int(comp[u"compare"])]
1722 if ref_itm is not None and cmp_itm is not None and \
1723 ref_itm[u"mean"] is not None and \
1724 cmp_itm[u"mean"] is not None and \
1725 ref_itm[u"stdev"] is not None and \
1726 cmp_itm[u"stdev"] is not None:
1727 delta, d_stdev = relative_change_stdev(
1728 ref_itm[u"mean"], cmp_itm[u"mean"],
1729 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1734 u"mean": delta * 1e6,
1735 u"stdev": d_stdev * 1e6
1740 tbl_cmp_lst.append(new_row)
1743 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1744 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1745 except TypeError as err:
1746 logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1748 tbl_for_csv = list()
1749 for line in tbl_cmp_lst:
1751 for idx, itm in enumerate(line[1:]):
1752 if itm is None or not isinstance(itm, dict) or\
1753 itm.get(u'mean', None) is None or \
1754 itm.get(u'stdev', None) is None:
1758 row.append(round(float(itm[u'mean']) / 1e6, 3))
1759 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1763 rca_nr = rca[u"data"].get(row[0], u"-")
1764 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1765 tbl_for_csv.append(row)
1767 header_csv = [u"Test Case", ]
1769 header_csv.append(f"Avg({col[u'title']})")
1770 header_csv.append(f"Stdev({col[u'title']})")
1771 for comp in comparisons:
1773 f"Avg({comp.get(u'title', u'')})"
1776 f"Stdev({comp.get(u'title', u'')})"
1780 header_csv.append(rca[u"title"])
1782 legend_lst = table.get(u"legend", None)
1783 if legend_lst is None:
1786 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1789 if rcas and any(rcas):
1790 footnote += u"\nRoot Cause Analysis:\n"
1793 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1795 csv_file_name = f"{table[u'output-file']}-csv.csv"
1796 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1798 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1800 for test in tbl_for_csv:
1802 u",".join([f'"{item}"' for item in test]) + u"\n"
1805 for item in legend_lst:
1806 file_handler.write(f'"{item}"\n')
1808 for itm in footnote.split(u"\n"):
1809 file_handler.write(f'"{itm}"\n')
1812 max_lens = [0, ] * len(tbl_cmp_lst[0])
1813 for line in tbl_cmp_lst:
1815 for idx, itm in enumerate(line[1:]):
1816 if itm is None or not isinstance(itm, dict) or \
1817 itm.get(u'mean', None) is None or \
1818 itm.get(u'stdev', None) is None:
1823 f"{round(float(itm[u'mean']) / 1e6, 1)} "
1824 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1825 replace(u"nan", u"NaN")
1829 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1830 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1831 replace(u"nan", u"NaN")
1833 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1834 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1839 header = [u"Test Case", ]
1840 header.extend([col[u"title"] for col in cols])
1841 header.extend([comp.get(u"title", u"") for comp in comparisons])
1844 for line in tbl_tmp:
1846 for idx, itm in enumerate(line[1:]):
1847 if itm in (u"NT", u"NaN"):
1850 itm_lst = itm.rsplit(u"\u00B1", 1)
1852 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1853 itm_str = u"\u00B1".join(itm_lst)
1855 if idx >= len(cols):
1857 rca = rcas[idx - len(cols)]
1860 rca_nr = rca[u"data"].get(row[0], None)
1862 hdr_len = len(header[idx + 1]) - 1
1865 rca_nr = f"[{rca_nr}]"
1867 f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
1868 f"{u' ' * (hdr_len - 4 - len(itm_str))}"
1872 tbl_final.append(row)
1874 # Generate csv tables:
1875 csv_file_name = f"{table[u'output-file']}.csv"
1876 logging.info(f" Writing the file {csv_file_name}")
1877 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1878 file_handler.write(u";".join(header) + u"\n")
1879 for test in tbl_final:
1880 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1882 # Generate txt table:
1883 txt_file_name = f"{table[u'output-file']}.txt"
1884 logging.info(f" Writing the file {txt_file_name}")
1885 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
1887 with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
1888 file_handler.write(legend)
1889 file_handler.write(footnote)
1891 # Generate html table:
1892 _tpc_generate_html_table(
1895 table[u'output-file'],
1899 title=table.get(u"title", u"")
1903 def table_weekly_comparison(table, in_data):
1904 """Generate the table(s) with algorithm: table_weekly_comparison
1905 specified in the specification file.
1907 :param table: Table to generate.
1908 :param in_data: Data to process.
1909 :type table: pandas.Series
1910 :type in_data: InputData
1912 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1914 # Transform the data
1916 f" Creating the data set for the {table.get(u'type', u'')} "
1917 f"{table.get(u'title', u'')}."
1920 incl_tests = table.get(u"include-tests", None)
1921 if incl_tests not in (u"NDR", u"PDR"):
1922 logging.error(f"Wrong tests to include specified ({incl_tests}).")
1925 nr_cols = table.get(u"nr-of-data-columns", None)
1926 if not nr_cols or nr_cols < 2:
1928 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1932 data = in_data.filter_data(
1934 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1935 continue_on_error=True
1940 [u"Start Timestamp", ],
1946 tb_tbl = table.get(u"testbeds", None)
1947 for job_name, job_data in data.items():
1948 for build_nr, build in job_data.items():
1954 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1955 if tb_ip and tb_tbl:
1956 testbed = tb_tbl.get(tb_ip, u"")
1959 header[2].insert(1, build_nr)
1960 header[3].insert(1, testbed)
1962 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1965 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1968 for tst_name, tst_data in build.items():
1970 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1971 if not tbl_dict.get(tst_name_mod, None):
1972 tbl_dict[tst_name_mod] = dict(
1973 name=tst_data[u'name'].rsplit(u'-', 1)[0],
1976 tbl_dict[tst_name_mod][-idx - 1] = \
1977 tst_data[u"throughput"][incl_tests][u"LOWER"]
1978 except (TypeError, IndexError, KeyError, ValueError):
1983 logging.error(u"Not enough data to build the table! Skipping")
1987 for idx, cmp in enumerate(table.get(u"comparisons", list())):
1988 idx_ref = cmp.get(u"reference", None)
1989 idx_cmp = cmp.get(u"compare", None)
1990 if idx_ref is None or idx_cmp is None:
1993 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1994 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1996 header[1].append(u"")
1997 header[2].append(u"")
1998 header[3].append(u"")
1999 for tst_name, tst_data in tbl_dict.items():
2000 if not cmp_dict.get(tst_name, None):
2001 cmp_dict[tst_name] = list()
2002 ref_data = tst_data.get(idx_ref, None)
2003 cmp_data = tst_data.get(idx_cmp, None)
2004 if ref_data is None or cmp_data is None:
2005 cmp_dict[tst_name].append(float(u'nan'))
2007 cmp_dict[tst_name].append(
2008 relative_change(ref_data, cmp_data)
2011 tbl_lst_none = list()
2013 for tst_name, tst_data in tbl_dict.items():
2014 itm_lst = [tst_data[u"name"], ]
2015 for idx in range(nr_cols):
2016 item = tst_data.get(-idx - 1, None)
2018 itm_lst.insert(1, None)
2020 itm_lst.insert(1, round(item / 1e6, 1))
2023 None if itm is None else round(itm, 1)
2024 for itm in cmp_dict[tst_name]
2027 if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
2028 tbl_lst_none.append(itm_lst)
2030 tbl_lst.append(itm_lst)
2032 tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
2033 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
2034 tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
2035 tbl_lst.extend(tbl_lst_none)
2037 # Generate csv table:
2038 csv_file_name = f"{table[u'output-file']}.csv"
2039 logging.info(f" Writing the file {csv_file_name}")
2040 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2042 file_handler.write(u",".join(hdr) + u"\n")
2043 for test in tbl_lst:
2044 file_handler.write(u",".join(
2046 str(item).replace(u"None", u"-").replace(u"nan", u"-").
2047 replace(u"null", u"-") for item in test
2051 txt_file_name = f"{table[u'output-file']}.txt"
2052 logging.info(f" Writing the file {txt_file_name}")
2053 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2055 # Reorganize header in txt table
2057 with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2058 for line in file_handler:
2059 txt_table.append(line)
2061 txt_table.insert(5, txt_table.pop(2))
2062 with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2063 file_handler.writelines(txt_table)
2067 # Generate html table:
2069 u"<br>".join(row) for row in zip(*header)
2071 _tpc_generate_html_table(
2074 table[u'output-file'],
2076 title=table.get(u"title", u""),