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])
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"cop" in test_name:
1069 elif u"nat" in test_name:
1071 elif u"macip" in test_name:
1073 elif u"scale" in test_name:
1075 elif u"base" in test_name:
1080 if u"114b" in test_name and u"vhost" in test_name:
1082 elif u"nat44" in test_name or u"-pps" in test_name or u"-cps" in test_name:
1084 if u"nat44det" in test_name:
1085 domain += u"-det-bidir"
1088 if u"udir" in test_name:
1089 domain += u"-unidir"
1090 elif u"-ethip4udp-" in test_name:
1092 elif u"-ethip4tcp-" in test_name:
1094 if u"-cps" in test_name:
1096 elif u"-pps" in test_name:
1098 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1100 elif u"memif" in test_name:
1101 domain = u"container_memif"
1102 elif u"srv6" in test_name:
1104 elif u"vhost" in test_name:
1106 if u"vppl2xc" in test_name:
1109 driver += u"-testpmd"
1110 if u"lbvpplacp" in test_name:
1111 bsf += u"-link-bonding"
1112 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1113 domain = u"nf_service_density_vnfc"
1114 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1115 domain = u"nf_service_density_cnfc"
1116 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1117 domain = u"nf_service_density_cnfp"
1118 elif u"ipsec" in test_name:
1120 if u"sw" in test_name:
1122 elif u"hw" in test_name:
1124 elif u"ethip4vxlan" in test_name:
1125 domain = u"ip4_tunnels"
1126 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1128 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1130 elif u"l2xcbase" in test_name or \
1131 u"l2xcscale" in test_name or \
1132 u"l2bdbasemaclrn" in test_name or \
1133 u"l2bdscale" in test_name or \
1134 u"l2patch" in test_name:
1139 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1140 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1142 return file_name + anchor_name
1145 def table_perf_trending_dash_html(table, input_data):
1146 """Generate the table(s) with algorithm:
1147 table_perf_trending_dash_html specified in the specification
1150 :param table: Table to generate.
1151 :param input_data: Data to process.
1153 :type input_data: InputData
1158 if not table.get(u"testbed", None):
1160 f"The testbed is not defined for the table "
1161 f"{table.get(u'title', u'')}. Skipping."
1165 test_type = table.get(u"test-type", u"MRR")
1166 if test_type not in (u"MRR", u"NDR", u"PDR"):
1168 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1173 if test_type in (u"NDR", u"PDR"):
1174 lnk_dir = u"../ndrpdr_trending/"
1175 lnk_sufix = f"-{test_type.lower()}"
1177 lnk_dir = u"../trending/"
1180 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1183 with open(table[u"input-file"], u'rt') as csv_file:
1184 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1186 logging.warning(u"The input file is not defined.")
1188 except csv.Error as err:
1190 f"Not possible to process the file {table[u'input-file']}.\n"
1196 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1199 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1200 for idx, item in enumerate(csv_lst[0]):
1201 alignment = u"left" if idx == 0 else u"center"
1202 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1220 for r_idx, row in enumerate(csv_lst[1:]):
1222 color = u"regression"
1224 color = u"progression"
1227 trow = ET.SubElement(
1228 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1232 for c_idx, item in enumerate(row):
1233 tdata = ET.SubElement(
1236 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1239 if c_idx == 0 and table.get(u"add-links", True):
1240 ref = ET.SubElement(
1245 f"{_generate_url(table.get(u'testbed', ''), item)}"
1253 with open(table[u"output-file"], u'w') as html_file:
1254 logging.info(f" Writing file: {table[u'output-file']}")
1255 html_file.write(u".. raw:: html\n\n\t")
1256 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1257 html_file.write(u"\n\t<p><br><br></p>\n")
1259 logging.warning(u"The output file is not defined.")
1263 def table_last_failed_tests(table, input_data):
1264 """Generate the table(s) with algorithm: table_last_failed_tests
1265 specified in the specification file.
1267 :param table: Table to generate.
1268 :param input_data: Data to process.
1269 :type table: pandas.Series
1270 :type input_data: InputData
1273 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1275 # Transform the data
1277 f" Creating the data set for the {table.get(u'type', u'')} "
1278 f"{table.get(u'title', u'')}."
1281 data = input_data.filter_data(table, continue_on_error=True)
1283 if data is None or data.empty:
1285 f" No data for the {table.get(u'type', u'')} "
1286 f"{table.get(u'title', u'')}."
1291 for job, builds in table[u"data"].items():
1292 for build in builds:
1295 version = input_data.metadata(job, build).get(u"version", u"")
1297 logging.error(f"Data for {job}: {build} is not present.")
1299 tbl_list.append(build)
1300 tbl_list.append(version)
1301 failed_tests = list()
1304 for tst_data in data[job][build].values:
1305 if tst_data[u"status"] != u"FAIL":
1309 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1312 nic = groups.group(0)
1313 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1314 tbl_list.append(str(passed))
1315 tbl_list.append(str(failed))
1316 tbl_list.extend(failed_tests)
1318 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1319 logging.info(f" Writing file: {file_name}")
1320 with open(file_name, u"wt") as file_handler:
1321 for test in tbl_list:
1322 file_handler.write(test + u'\n')
1325 def table_failed_tests(table, input_data):
1326 """Generate the table(s) with algorithm: table_failed_tests
1327 specified in the specification file.
1329 :param table: Table to generate.
1330 :param input_data: Data to process.
1331 :type table: pandas.Series
1332 :type input_data: InputData
1335 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1337 # Transform the data
1339 f" Creating the data set for the {table.get(u'type', u'')} "
1340 f"{table.get(u'title', u'')}."
1342 data = input_data.filter_data(table, continue_on_error=True)
1345 if u"NDRPDR" in table.get(u"filter", list()):
1346 test_type = u"NDRPDR"
1348 # Prepare the header of the tables
1352 u"Last Failure [Time]",
1353 u"Last Failure [VPP-Build-Id]",
1354 u"Last Failure [CSIT-Job-Build-Id]"
1357 # Generate the data for the table according to the model in the table
1361 timeperiod = timedelta(int(table.get(u"window", 7)))
1364 for job, builds in table[u"data"].items():
1365 for build in builds:
1367 for tst_name, tst_data in data[job][build].items():
1368 if tst_name.lower() in table.get(u"ignore-list", list()):
1370 if tbl_dict.get(tst_name, None) is None:
1371 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1374 nic = groups.group(0)
1375 tbl_dict[tst_name] = {
1376 u"name": f"{nic}-{tst_data[u'name']}",
1377 u"data": OrderedDict()
1380 generated = input_data.metadata(job, build).\
1381 get(u"generated", u"")
1384 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1385 if (now - then) <= timeperiod:
1386 tbl_dict[tst_name][u"data"][build] = (
1387 tst_data[u"status"],
1389 input_data.metadata(job, build).get(u"version",
1393 except (TypeError, KeyError) as err:
1394 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1398 for tst_data in tbl_dict.values():
1400 fails_last_date = u""
1401 fails_last_vpp = u""
1402 fails_last_csit = u""
1403 for val in tst_data[u"data"].values():
1404 if val[0] == u"FAIL":
1406 fails_last_date = val[1]
1407 fails_last_vpp = val[2]
1408 fails_last_csit = val[3]
1410 max_fails = fails_nr if fails_nr > max_fails else max_fails
1416 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1417 f"-build-{fails_last_csit}"
1420 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1422 for nrf in range(max_fails, -1, -1):
1423 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1424 tbl_sorted.extend(tbl_fails)
1426 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1427 logging.info(f" Writing file: {file_name}")
1428 with open(file_name, u"wt") as file_handler:
1429 file_handler.write(u",".join(header) + u"\n")
1430 for test in tbl_sorted:
1431 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1433 logging.info(f" Writing file: {table[u'output-file']}.txt")
1434 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1437 def table_failed_tests_html(table, input_data):
1438 """Generate the table(s) with algorithm: table_failed_tests_html
1439 specified in the specification file.
1441 :param table: Table to generate.
1442 :param input_data: Data to process.
1443 :type table: pandas.Series
1444 :type input_data: InputData
1449 if not table.get(u"testbed", None):
1451 f"The testbed is not defined for the table "
1452 f"{table.get(u'title', u'')}. Skipping."
1456 test_type = table.get(u"test-type", u"MRR")
1457 if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1459 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1464 if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1465 lnk_dir = u"../ndrpdr_trending/"
1468 lnk_dir = u"../trending/"
1471 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1474 with open(table[u"input-file"], u'rt') as csv_file:
1475 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1477 logging.warning(u"The input file is not defined.")
1479 except csv.Error as err:
1481 f"Not possible to process the file {table[u'input-file']}.\n"
1487 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1490 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1491 for idx, item in enumerate(csv_lst[0]):
1492 alignment = u"left" if idx == 0 else u"center"
1493 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1497 colors = (u"#e9f1fb", u"#d4e4f7")
1498 for r_idx, row in enumerate(csv_lst[1:]):
1499 background = colors[r_idx % 2]
1500 trow = ET.SubElement(
1501 failed_tests, u"tr", attrib=dict(bgcolor=background)
1505 for c_idx, item in enumerate(row):
1506 tdata = ET.SubElement(
1509 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1512 if c_idx == 0 and table.get(u"add-links", True):
1513 ref = ET.SubElement(
1518 f"{_generate_url(table.get(u'testbed', ''), item)}"
1526 with open(table[u"output-file"], u'w') as html_file:
1527 logging.info(f" Writing file: {table[u'output-file']}")
1528 html_file.write(u".. raw:: html\n\n\t")
1529 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1530 html_file.write(u"\n\t<p><br><br></p>\n")
1532 logging.warning(u"The output file is not defined.")
1536 def table_comparison(table, input_data):
1537 """Generate the table(s) with algorithm: table_comparison
1538 specified in the specification file.
1540 :param table: Table to generate.
1541 :param input_data: Data to process.
1542 :type table: pandas.Series
1543 :type input_data: InputData
1545 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1547 # Transform the data
1549 f" Creating the data set for the {table.get(u'type', u'')} "
1550 f"{table.get(u'title', u'')}."
1553 columns = table.get(u"columns", None)
1556 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1561 for idx, col in enumerate(columns):
1562 if col.get(u"data-set", None) is None:
1563 logging.warning(f"No data for column {col.get(u'title', u'')}")
1565 tag = col.get(u"tag", None)
1566 data = input_data.filter_data(
1568 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1569 data=col[u"data-set"],
1570 continue_on_error=True
1573 u"title": col.get(u"title", f"Column{idx}"),
1576 for builds in data.values:
1577 for build in builds:
1578 for tst_name, tst_data in build.items():
1579 if tag and tag not in tst_data[u"tags"]:
1582 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1583 replace(u"2n1l-", u"")
1584 if col_data[u"data"].get(tst_name_mod, None) is None:
1585 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1586 if u"across testbeds" in table[u"title"].lower() or \
1587 u"across topologies" in table[u"title"].lower():
1588 name = _tpc_modify_displayed_test_name(name)
1589 col_data[u"data"][tst_name_mod] = {
1597 target=col_data[u"data"][tst_name_mod],
1599 include_tests=table[u"include-tests"]
1602 replacement = col.get(u"data-replacement", None)
1604 rpl_data = input_data.filter_data(
1606 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1608 continue_on_error=True
1610 for builds in rpl_data.values:
1611 for build in builds:
1612 for tst_name, tst_data in build.items():
1613 if tag and tag not in tst_data[u"tags"]:
1616 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1617 replace(u"2n1l-", u"")
1618 if col_data[u"data"].get(tst_name_mod, None) is None:
1619 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1620 if u"across testbeds" in table[u"title"].lower() \
1621 or u"across topologies" in \
1622 table[u"title"].lower():
1623 name = _tpc_modify_displayed_test_name(name)
1624 col_data[u"data"][tst_name_mod] = {
1631 if col_data[u"data"][tst_name_mod][u"replace"]:
1632 col_data[u"data"][tst_name_mod][u"replace"] = False
1633 col_data[u"data"][tst_name_mod][u"data"] = list()
1635 target=col_data[u"data"][tst_name_mod],
1637 include_tests=table[u"include-tests"]
1640 if table[u"include-tests"] in (u"NDR", u"PDR"):
1641 for tst_name, tst_data in col_data[u"data"].items():
1642 if tst_data[u"data"]:
1643 tst_data[u"mean"] = mean(tst_data[u"data"])
1644 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1646 cols.append(col_data)
1650 for tst_name, tst_data in col[u"data"].items():
1651 if tbl_dict.get(tst_name, None) is None:
1652 tbl_dict[tst_name] = {
1653 "name": tst_data[u"name"]
1655 tbl_dict[tst_name][col[u"title"]] = {
1656 u"mean": tst_data[u"mean"],
1657 u"stdev": tst_data[u"stdev"]
1661 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1665 for tst_data in tbl_dict.values():
1666 row = [tst_data[u"name"], ]
1668 row.append(tst_data.get(col[u"title"], None))
1671 comparisons = table.get(u"comparisons", None)
1673 if comparisons and isinstance(comparisons, list):
1674 for idx, comp in enumerate(comparisons):
1676 col_ref = int(comp[u"reference"])
1677 col_cmp = int(comp[u"compare"])
1679 logging.warning(u"Comparison: No references defined! Skipping.")
1680 comparisons.pop(idx)
1682 if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1683 col_ref == col_cmp):
1684 logging.warning(f"Wrong values of reference={col_ref} "
1685 f"and/or compare={col_cmp}. Skipping.")
1686 comparisons.pop(idx)
1688 rca_file_name = comp.get(u"rca-file", None)
1691 with open(rca_file_name, u"r") as file_handler:
1694 u"title": f"RCA{idx + 1}",
1695 u"data": load(file_handler, Loader=FullLoader)
1698 except (YAMLError, IOError) as err:
1700 f"The RCA file {rca_file_name} does not exist or "
1703 logging.debug(repr(err))
1710 tbl_cmp_lst = list()
1713 new_row = deepcopy(row)
1714 for comp in comparisons:
1715 ref_itm = row[int(comp[u"reference"])]
1716 if ref_itm is None and \
1717 comp.get(u"reference-alt", None) is not None:
1718 ref_itm = row[int(comp[u"reference-alt"])]
1719 cmp_itm = row[int(comp[u"compare"])]
1720 if ref_itm is not None and cmp_itm is not None and \
1721 ref_itm[u"mean"] is not None and \
1722 cmp_itm[u"mean"] is not None and \
1723 ref_itm[u"stdev"] is not None and \
1724 cmp_itm[u"stdev"] is not None:
1725 delta, d_stdev = relative_change_stdev(
1726 ref_itm[u"mean"], cmp_itm[u"mean"],
1727 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1732 u"mean": delta * 1e6,
1733 u"stdev": d_stdev * 1e6
1738 tbl_cmp_lst.append(new_row)
1741 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1742 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1743 except TypeError as err:
1744 logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1746 tbl_for_csv = list()
1747 for line in tbl_cmp_lst:
1749 for idx, itm in enumerate(line[1:]):
1750 if itm is None or not isinstance(itm, dict) or\
1751 itm.get(u'mean', None) is None or \
1752 itm.get(u'stdev', None) is None:
1756 row.append(round(float(itm[u'mean']) / 1e6, 3))
1757 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1761 rca_nr = rca[u"data"].get(row[0], u"-")
1762 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1763 tbl_for_csv.append(row)
1765 header_csv = [u"Test Case", ]
1767 header_csv.append(f"Avg({col[u'title']})")
1768 header_csv.append(f"Stdev({col[u'title']})")
1769 for comp in comparisons:
1771 f"Avg({comp.get(u'title', u'')})"
1774 f"Stdev({comp.get(u'title', u'')})"
1778 header_csv.append(rca[u"title"])
1780 legend_lst = table.get(u"legend", None)
1781 if legend_lst is None:
1784 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1787 if rcas and any(rcas):
1788 footnote += u"\nRoot Cause Analysis:\n"
1791 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1793 csv_file_name = f"{table[u'output-file']}-csv.csv"
1794 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1796 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1798 for test in tbl_for_csv:
1800 u",".join([f'"{item}"' for item in test]) + u"\n"
1803 for item in legend_lst:
1804 file_handler.write(f'"{item}"\n')
1806 for itm in footnote.split(u"\n"):
1807 file_handler.write(f'"{itm}"\n')
1810 max_lens = [0, ] * len(tbl_cmp_lst[0])
1811 for line in tbl_cmp_lst:
1813 for idx, itm in enumerate(line[1:]):
1814 if itm is None or not isinstance(itm, dict) or \
1815 itm.get(u'mean', None) is None or \
1816 itm.get(u'stdev', None) is None:
1821 f"{round(float(itm[u'mean']) / 1e6, 1)} "
1822 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1823 replace(u"nan", u"NaN")
1827 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1828 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1829 replace(u"nan", u"NaN")
1831 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1832 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1837 header = [u"Test Case", ]
1838 header.extend([col[u"title"] for col in cols])
1839 header.extend([comp.get(u"title", u"") for comp in comparisons])
1842 for line in tbl_tmp:
1844 for idx, itm in enumerate(line[1:]):
1845 if itm in (u"NT", u"NaN"):
1848 itm_lst = itm.rsplit(u"\u00B1", 1)
1850 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1851 itm_str = u"\u00B1".join(itm_lst)
1853 if idx >= len(cols):
1855 rca = rcas[idx - len(cols)]
1858 rca_nr = rca[u"data"].get(row[0], None)
1860 hdr_len = len(header[idx + 1]) - 1
1863 rca_nr = f"[{rca_nr}]"
1865 f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
1866 f"{u' ' * (hdr_len - 4 - len(itm_str))}"
1870 tbl_final.append(row)
1872 # Generate csv tables:
1873 csv_file_name = f"{table[u'output-file']}.csv"
1874 logging.info(f" Writing the file {csv_file_name}")
1875 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1876 file_handler.write(u";".join(header) + u"\n")
1877 for test in tbl_final:
1878 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1880 # Generate txt table:
1881 txt_file_name = f"{table[u'output-file']}.txt"
1882 logging.info(f" Writing the file {txt_file_name}")
1883 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
1885 with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
1886 file_handler.write(legend)
1887 file_handler.write(footnote)
1889 # Generate html table:
1890 _tpc_generate_html_table(
1893 table[u'output-file'],
1897 title=table.get(u"title", u"")
1901 def table_weekly_comparison(table, in_data):
1902 """Generate the table(s) with algorithm: table_weekly_comparison
1903 specified in the specification file.
1905 :param table: Table to generate.
1906 :param in_data: Data to process.
1907 :type table: pandas.Series
1908 :type in_data: InputData
1910 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1912 # Transform the data
1914 f" Creating the data set for the {table.get(u'type', u'')} "
1915 f"{table.get(u'title', u'')}."
1918 incl_tests = table.get(u"include-tests", None)
1919 if incl_tests not in (u"NDR", u"PDR"):
1920 logging.error(f"Wrong tests to include specified ({incl_tests}).")
1923 nr_cols = table.get(u"nr-of-data-columns", None)
1924 if not nr_cols or nr_cols < 2:
1926 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1930 data = in_data.filter_data(
1932 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1933 continue_on_error=True
1938 [u"Start Timestamp", ],
1944 tb_tbl = table.get(u"testbeds", None)
1945 for job_name, job_data in data.items():
1946 for build_nr, build in job_data.items():
1952 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1953 if tb_ip and tb_tbl:
1954 testbed = tb_tbl.get(tb_ip, u"")
1957 header[2].insert(1, build_nr)
1958 header[3].insert(1, testbed)
1960 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1963 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1966 for tst_name, tst_data in build.items():
1968 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1969 if not tbl_dict.get(tst_name_mod, None):
1970 tbl_dict[tst_name_mod] = dict(
1971 name=tst_data[u'name'].rsplit(u'-', 1)[0],
1974 tbl_dict[tst_name_mod][-idx - 1] = \
1975 tst_data[u"throughput"][incl_tests][u"LOWER"]
1976 except (TypeError, IndexError, KeyError, ValueError):
1981 logging.error(u"Not enough data to build the table! Skipping")
1985 for idx, cmp in enumerate(table.get(u"comparisons", list())):
1986 idx_ref = cmp.get(u"reference", None)
1987 idx_cmp = cmp.get(u"compare", None)
1988 if idx_ref is None or idx_cmp is None:
1991 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1992 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1994 header[1].append(u"")
1995 header[2].append(u"")
1996 header[3].append(u"")
1997 for tst_name, tst_data in tbl_dict.items():
1998 if not cmp_dict.get(tst_name, None):
1999 cmp_dict[tst_name] = list()
2000 ref_data = tst_data.get(idx_ref, None)
2001 cmp_data = tst_data.get(idx_cmp, None)
2002 if ref_data is None or cmp_data is None:
2003 cmp_dict[tst_name].append(float(u'nan'))
2005 cmp_dict[tst_name].append(
2006 relative_change(ref_data, cmp_data)
2009 tbl_lst_none = list()
2011 for tst_name, tst_data in tbl_dict.items():
2012 itm_lst = [tst_data[u"name"], ]
2013 for idx in range(nr_cols):
2014 item = tst_data.get(-idx - 1, None)
2016 itm_lst.insert(1, None)
2018 itm_lst.insert(1, round(item / 1e6, 1))
2021 None if itm is None else round(itm, 1)
2022 for itm in cmp_dict[tst_name]
2025 if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
2026 tbl_lst_none.append(itm_lst)
2028 tbl_lst.append(itm_lst)
2030 tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
2031 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
2032 tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
2033 tbl_lst.extend(tbl_lst_none)
2035 # Generate csv table:
2036 csv_file_name = f"{table[u'output-file']}.csv"
2037 logging.info(f" Writing the file {csv_file_name}")
2038 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2040 file_handler.write(u",".join(hdr) + u"\n")
2041 for test in tbl_lst:
2042 file_handler.write(u",".join(
2044 str(item).replace(u"None", u"-").replace(u"nan", u"-").
2045 replace(u"null", u"-") for item in test
2049 txt_file_name = f"{table[u'output-file']}.txt"
2050 logging.info(f" Writing the file {txt_file_name}")
2051 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2053 # Reorganize header in txt table
2055 with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2056 for line in file_handler:
2057 txt_table.append(line)
2059 txt_table.insert(5, txt_table.pop(2))
2060 with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2061 file_handler.writelines(txt_table)
2065 # Generate html table:
2067 u"<br>".join(row) for row in zip(*header)
2069 _tpc_generate_html_table(
2072 table[u'output-file'],
2074 title=table.get(u"title", u""),