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")):
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")):
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")):
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:
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"-vps" in test_name:
1084 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1086 elif u"memif" in test_name:
1087 domain = u"container_memif"
1088 elif u"srv6" in test_name:
1090 elif u"vhost" in test_name:
1092 if u"vppl2xc" in test_name:
1095 driver += u"-testpmd"
1096 if u"lbvpplacp" in test_name:
1097 bsf += u"-link-bonding"
1098 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1099 domain = u"nf_service_density_vnfc"
1100 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1101 domain = u"nf_service_density_cnfc"
1102 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1103 domain = u"nf_service_density_cnfp"
1104 elif u"ipsec" in test_name:
1106 if u"sw" in test_name:
1108 elif u"hw" in test_name:
1110 elif u"ethip4vxlan" in test_name:
1111 domain = u"ip4_tunnels"
1112 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1114 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1116 elif u"l2xcbase" in test_name or \
1117 u"l2xcscale" in test_name or \
1118 u"l2bdbasemaclrn" in test_name or \
1119 u"l2bdscale" in test_name or \
1120 u"l2patch" in test_name:
1125 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1126 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1128 return file_name + anchor_name
1131 def table_perf_trending_dash_html(table, input_data):
1132 """Generate the table(s) with algorithm:
1133 table_perf_trending_dash_html specified in the specification
1136 :param table: Table to generate.
1137 :param input_data: Data to process.
1139 :type input_data: InputData
1144 if not table.get(u"testbed", None):
1146 f"The testbed is not defined for the table "
1147 f"{table.get(u'title', u'')}. Skipping."
1151 test_type = table.get(u"test-type", u"MRR")
1152 if test_type not in (u"MRR", u"NDR", u"PDR"):
1154 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1159 if test_type in (u"NDR", u"PDR"):
1160 lnk_dir = u"../ndrpdr_trending/"
1161 lnk_sufix = f"-{test_type.lower()}"
1163 lnk_dir = u"../trending/"
1166 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1169 with open(table[u"input-file"], u'rt') as csv_file:
1170 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1172 logging.warning(u"The input file is not defined.")
1174 except csv.Error as err:
1176 f"Not possible to process the file {table[u'input-file']}.\n"
1182 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1185 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1186 for idx, item in enumerate(csv_lst[0]):
1187 alignment = u"left" if idx == 0 else u"center"
1188 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1206 for r_idx, row in enumerate(csv_lst[1:]):
1208 color = u"regression"
1210 color = u"progression"
1213 trow = ET.SubElement(
1214 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1218 for c_idx, item in enumerate(row):
1219 tdata = ET.SubElement(
1222 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1225 if c_idx == 0 and table.get(u"add-links", True):
1226 ref = ET.SubElement(
1231 f"{_generate_url(table.get(u'testbed', ''), item)}"
1239 with open(table[u"output-file"], u'w') as html_file:
1240 logging.info(f" Writing file: {table[u'output-file']}")
1241 html_file.write(u".. raw:: html\n\n\t")
1242 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1243 html_file.write(u"\n\t<p><br><br></p>\n")
1245 logging.warning(u"The output file is not defined.")
1249 def table_last_failed_tests(table, input_data):
1250 """Generate the table(s) with algorithm: table_last_failed_tests
1251 specified in the specification file.
1253 :param table: Table to generate.
1254 :param input_data: Data to process.
1255 :type table: pandas.Series
1256 :type input_data: InputData
1259 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1261 # Transform the data
1263 f" Creating the data set for the {table.get(u'type', u'')} "
1264 f"{table.get(u'title', u'')}."
1267 data = input_data.filter_data(table, continue_on_error=True)
1269 if data is None or data.empty:
1271 f" No data for the {table.get(u'type', u'')} "
1272 f"{table.get(u'title', u'')}."
1277 for job, builds in table[u"data"].items():
1278 for build in builds:
1281 version = input_data.metadata(job, build).get(u"version", u"")
1283 logging.error(f"Data for {job}: {build} is not present.")
1285 tbl_list.append(build)
1286 tbl_list.append(version)
1287 failed_tests = list()
1290 for tst_data in data[job][build].values:
1291 if tst_data[u"status"] != u"FAIL":
1295 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1298 nic = groups.group(0)
1299 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1300 tbl_list.append(str(passed))
1301 tbl_list.append(str(failed))
1302 tbl_list.extend(failed_tests)
1304 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1305 logging.info(f" Writing file: {file_name}")
1306 with open(file_name, u"wt") as file_handler:
1307 for test in tbl_list:
1308 file_handler.write(test + u'\n')
1311 def table_failed_tests(table, input_data):
1312 """Generate the table(s) with algorithm: table_failed_tests
1313 specified in the specification file.
1315 :param table: Table to generate.
1316 :param input_data: Data to process.
1317 :type table: pandas.Series
1318 :type input_data: InputData
1321 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1323 # Transform the data
1325 f" Creating the data set for the {table.get(u'type', u'')} "
1326 f"{table.get(u'title', u'')}."
1328 data = input_data.filter_data(table, continue_on_error=True)
1331 if u"NDRPDR" in table.get(u"filter", list()):
1332 test_type = u"NDRPDR"
1334 # Prepare the header of the tables
1338 u"Last Failure [Time]",
1339 u"Last Failure [VPP-Build-Id]",
1340 u"Last Failure [CSIT-Job-Build-Id]"
1343 # Generate the data for the table according to the model in the table
1347 timeperiod = timedelta(int(table.get(u"window", 7)))
1350 for job, builds in table[u"data"].items():
1351 for build in builds:
1353 for tst_name, tst_data in data[job][build].items():
1354 if tst_name.lower() in table.get(u"ignore-list", list()):
1356 if tbl_dict.get(tst_name, None) is None:
1357 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1360 nic = groups.group(0)
1361 tbl_dict[tst_name] = {
1362 u"name": f"{nic}-{tst_data[u'name']}",
1363 u"data": OrderedDict()
1366 generated = input_data.metadata(job, build).\
1367 get(u"generated", u"")
1370 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1371 if (now - then) <= timeperiod:
1372 tbl_dict[tst_name][u"data"][build] = (
1373 tst_data[u"status"],
1375 input_data.metadata(job, build).get(u"version",
1379 except (TypeError, KeyError) as err:
1380 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1384 for tst_data in tbl_dict.values():
1386 fails_last_date = u""
1387 fails_last_vpp = u""
1388 fails_last_csit = u""
1389 for val in tst_data[u"data"].values():
1390 if val[0] == u"FAIL":
1392 fails_last_date = val[1]
1393 fails_last_vpp = val[2]
1394 fails_last_csit = val[3]
1396 max_fails = fails_nr if fails_nr > max_fails else max_fails
1402 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1403 f"-build-{fails_last_csit}"
1406 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1408 for nrf in range(max_fails, -1, -1):
1409 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1410 tbl_sorted.extend(tbl_fails)
1412 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1413 logging.info(f" Writing file: {file_name}")
1414 with open(file_name, u"wt") as file_handler:
1415 file_handler.write(u",".join(header) + u"\n")
1416 for test in tbl_sorted:
1417 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1419 logging.info(f" Writing file: {table[u'output-file']}.txt")
1420 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1423 def table_failed_tests_html(table, input_data):
1424 """Generate the table(s) with algorithm: table_failed_tests_html
1425 specified in the specification file.
1427 :param table: Table to generate.
1428 :param input_data: Data to process.
1429 :type table: pandas.Series
1430 :type input_data: InputData
1435 if not table.get(u"testbed", None):
1437 f"The testbed is not defined for the table "
1438 f"{table.get(u'title', u'')}. Skipping."
1442 test_type = table.get(u"test-type", u"MRR")
1443 if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1445 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1450 if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1451 lnk_dir = u"../ndrpdr_trending/"
1454 lnk_dir = u"../trending/"
1457 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1460 with open(table[u"input-file"], u'rt') as csv_file:
1461 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1463 logging.warning(u"The input file is not defined.")
1465 except csv.Error as err:
1467 f"Not possible to process the file {table[u'input-file']}.\n"
1473 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1476 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1477 for idx, item in enumerate(csv_lst[0]):
1478 alignment = u"left" if idx == 0 else u"center"
1479 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1483 colors = (u"#e9f1fb", u"#d4e4f7")
1484 for r_idx, row in enumerate(csv_lst[1:]):
1485 background = colors[r_idx % 2]
1486 trow = ET.SubElement(
1487 failed_tests, u"tr", attrib=dict(bgcolor=background)
1491 for c_idx, item in enumerate(row):
1492 tdata = ET.SubElement(
1495 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1498 if c_idx == 0 and table.get(u"add-links", True):
1499 ref = ET.SubElement(
1504 f"{_generate_url(table.get(u'testbed', ''), item)}"
1512 with open(table[u"output-file"], u'w') as html_file:
1513 logging.info(f" Writing file: {table[u'output-file']}")
1514 html_file.write(u".. raw:: html\n\n\t")
1515 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1516 html_file.write(u"\n\t<p><br><br></p>\n")
1518 logging.warning(u"The output file is not defined.")
1522 def table_comparison(table, input_data):
1523 """Generate the table(s) with algorithm: table_comparison
1524 specified in the specification file.
1526 :param table: Table to generate.
1527 :param input_data: Data to process.
1528 :type table: pandas.Series
1529 :type input_data: InputData
1531 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1533 # Transform the data
1535 f" Creating the data set for the {table.get(u'type', u'')} "
1536 f"{table.get(u'title', u'')}."
1539 columns = table.get(u"columns", None)
1542 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1547 for idx, col in enumerate(columns):
1548 if col.get(u"data-set", None) is None:
1549 logging.warning(f"No data for column {col.get(u'title', u'')}")
1551 tag = col.get(u"tag", None)
1552 data = input_data.filter_data(
1554 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1555 data=col[u"data-set"],
1556 continue_on_error=True
1559 u"title": col.get(u"title", f"Column{idx}"),
1562 for builds in data.values:
1563 for build in builds:
1564 for tst_name, tst_data in build.items():
1565 if tag and tag not in tst_data[u"tags"]:
1568 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1569 replace(u"2n1l-", u"")
1570 if col_data[u"data"].get(tst_name_mod, None) is None:
1571 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1572 if u"across testbeds" in table[u"title"].lower() or \
1573 u"across topologies" in table[u"title"].lower():
1574 name = _tpc_modify_displayed_test_name(name)
1575 col_data[u"data"][tst_name_mod] = {
1583 target=col_data[u"data"][tst_name_mod],
1585 include_tests=table[u"include-tests"]
1588 replacement = col.get(u"data-replacement", None)
1590 rpl_data = input_data.filter_data(
1592 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1594 continue_on_error=True
1596 for builds in rpl_data.values:
1597 for build in builds:
1598 for tst_name, tst_data in build.items():
1599 if tag and tag not in tst_data[u"tags"]:
1602 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1603 replace(u"2n1l-", u"")
1604 if col_data[u"data"].get(tst_name_mod, None) is None:
1605 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1606 if u"across testbeds" in table[u"title"].lower() \
1607 or u"across topologies" in \
1608 table[u"title"].lower():
1609 name = _tpc_modify_displayed_test_name(name)
1610 col_data[u"data"][tst_name_mod] = {
1617 if col_data[u"data"][tst_name_mod][u"replace"]:
1618 col_data[u"data"][tst_name_mod][u"replace"] = False
1619 col_data[u"data"][tst_name_mod][u"data"] = list()
1621 target=col_data[u"data"][tst_name_mod],
1623 include_tests=table[u"include-tests"]
1626 if table[u"include-tests"] in (u"NDR", u"PDR"):
1627 for tst_name, tst_data in col_data[u"data"].items():
1628 if tst_data[u"data"]:
1629 tst_data[u"mean"] = mean(tst_data[u"data"])
1630 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1632 cols.append(col_data)
1636 for tst_name, tst_data in col[u"data"].items():
1637 if tbl_dict.get(tst_name, None) is None:
1638 tbl_dict[tst_name] = {
1639 "name": tst_data[u"name"]
1641 tbl_dict[tst_name][col[u"title"]] = {
1642 u"mean": tst_data[u"mean"],
1643 u"stdev": tst_data[u"stdev"]
1647 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1651 for tst_data in tbl_dict.values():
1652 row = [tst_data[u"name"], ]
1654 row.append(tst_data.get(col[u"title"], None))
1657 comparisons = table.get(u"comparisons", None)
1659 if comparisons and isinstance(comparisons, list):
1660 for idx, comp in enumerate(comparisons):
1662 col_ref = int(comp[u"reference"])
1663 col_cmp = int(comp[u"compare"])
1665 logging.warning(u"Comparison: No references defined! Skipping.")
1666 comparisons.pop(idx)
1668 if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1669 col_ref == col_cmp):
1670 logging.warning(f"Wrong values of reference={col_ref} "
1671 f"and/or compare={col_cmp}. Skipping.")
1672 comparisons.pop(idx)
1674 rca_file_name = comp.get(u"rca-file", None)
1677 with open(rca_file_name, u"r") as file_handler:
1680 u"title": f"RCA{idx + 1}",
1681 u"data": load(file_handler, Loader=FullLoader)
1684 except (YAMLError, IOError) as err:
1686 f"The RCA file {rca_file_name} does not exist or "
1689 logging.debug(repr(err))
1696 tbl_cmp_lst = list()
1699 new_row = deepcopy(row)
1700 for comp in comparisons:
1701 ref_itm = row[int(comp[u"reference"])]
1702 if ref_itm is None and \
1703 comp.get(u"reference-alt", None) is not None:
1704 ref_itm = row[int(comp[u"reference-alt"])]
1705 cmp_itm = row[int(comp[u"compare"])]
1706 if ref_itm is not None and cmp_itm is not None and \
1707 ref_itm[u"mean"] is not None and \
1708 cmp_itm[u"mean"] is not None and \
1709 ref_itm[u"stdev"] is not None and \
1710 cmp_itm[u"stdev"] is not None:
1711 delta, d_stdev = relative_change_stdev(
1712 ref_itm[u"mean"], cmp_itm[u"mean"],
1713 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1718 u"mean": delta * 1e6,
1719 u"stdev": d_stdev * 1e6
1724 tbl_cmp_lst.append(new_row)
1727 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1728 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1729 except TypeError as err:
1730 logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1732 tbl_for_csv = list()
1733 for line in tbl_cmp_lst:
1735 for idx, itm in enumerate(line[1:]):
1736 if itm is None or not isinstance(itm, dict) or\
1737 itm.get(u'mean', None) is None or \
1738 itm.get(u'stdev', None) is None:
1742 row.append(round(float(itm[u'mean']) / 1e6, 3))
1743 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1747 rca_nr = rca[u"data"].get(row[0], u"-")
1748 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1749 tbl_for_csv.append(row)
1751 header_csv = [u"Test Case", ]
1753 header_csv.append(f"Avg({col[u'title']})")
1754 header_csv.append(f"Stdev({col[u'title']})")
1755 for comp in comparisons:
1757 f"Avg({comp.get(u'title', u'')})"
1760 f"Stdev({comp.get(u'title', u'')})"
1764 header_csv.append(rca[u"title"])
1766 legend_lst = table.get(u"legend", None)
1767 if legend_lst is None:
1770 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1773 if rcas and any(rcas):
1774 footnote += u"\nRoot Cause Analysis:\n"
1777 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1779 csv_file_name = f"{table[u'output-file']}-csv.csv"
1780 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1782 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1784 for test in tbl_for_csv:
1786 u",".join([f'"{item}"' for item in test]) + u"\n"
1789 for item in legend_lst:
1790 file_handler.write(f'"{item}"\n')
1792 for itm in footnote.split(u"\n"):
1793 file_handler.write(f'"{itm}"\n')
1796 max_lens = [0, ] * len(tbl_cmp_lst[0])
1797 for line in tbl_cmp_lst:
1799 for idx, itm in enumerate(line[1:]):
1800 if itm is None or not isinstance(itm, dict) or \
1801 itm.get(u'mean', None) is None or \
1802 itm.get(u'stdev', None) is None:
1807 f"{round(float(itm[u'mean']) / 1e6, 1)} "
1808 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1809 replace(u"nan", u"NaN")
1813 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1814 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1815 replace(u"nan", u"NaN")
1817 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1818 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1823 header = [u"Test Case", ]
1824 header.extend([col[u"title"] for col in cols])
1825 header.extend([comp.get(u"title", u"") for comp in comparisons])
1828 for line in tbl_tmp:
1830 for idx, itm in enumerate(line[1:]):
1831 if itm in (u"NT", u"NaN"):
1834 itm_lst = itm.rsplit(u"\u00B1", 1)
1836 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1837 itm_str = u"\u00B1".join(itm_lst)
1839 if idx >= len(cols):
1841 rca = rcas[idx - len(cols)]
1844 rca_nr = rca[u"data"].get(row[0], None)
1846 hdr_len = len(header[idx + 1]) - 1
1849 rca_nr = f"[{rca_nr}]"
1851 f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
1852 f"{u' ' * (hdr_len - 4 - len(itm_str))}"
1856 tbl_final.append(row)
1858 # Generate csv tables:
1859 csv_file_name = f"{table[u'output-file']}.csv"
1860 logging.info(f" Writing the file {csv_file_name}")
1861 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1862 file_handler.write(u";".join(header) + u"\n")
1863 for test in tbl_final:
1864 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1866 # Generate txt table:
1867 txt_file_name = f"{table[u'output-file']}.txt"
1868 logging.info(f" Writing the file {txt_file_name}")
1869 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
1871 with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
1872 file_handler.write(legend)
1873 file_handler.write(footnote)
1875 # Generate html table:
1876 _tpc_generate_html_table(
1879 table[u'output-file'],
1883 title=table.get(u"title", u"")
1887 def table_weekly_comparison(table, in_data):
1888 """Generate the table(s) with algorithm: table_weekly_comparison
1889 specified in the specification file.
1891 :param table: Table to generate.
1892 :param in_data: Data to process.
1893 :type table: pandas.Series
1894 :type in_data: InputData
1896 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1898 # Transform the data
1900 f" Creating the data set for the {table.get(u'type', u'')} "
1901 f"{table.get(u'title', u'')}."
1904 incl_tests = table.get(u"include-tests", None)
1905 if incl_tests not in (u"NDR", u"PDR"):
1906 logging.error(f"Wrong tests to include specified ({incl_tests}).")
1909 nr_cols = table.get(u"nr-of-data-columns", None)
1910 if not nr_cols or nr_cols < 2:
1912 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1916 data = in_data.filter_data(
1918 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1919 continue_on_error=True
1924 [u"Start Timestamp", ],
1930 tb_tbl = table.get(u"testbeds", None)
1931 for job_name, job_data in data.items():
1932 for build_nr, build in job_data.items():
1938 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1939 if tb_ip and tb_tbl:
1940 testbed = tb_tbl.get(tb_ip, u"")
1943 header[2].insert(1, build_nr)
1944 header[3].insert(1, testbed)
1946 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1949 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1952 for tst_name, tst_data in build.items():
1954 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1955 if not tbl_dict.get(tst_name_mod, None):
1956 tbl_dict[tst_name_mod] = dict(
1957 name=tst_data[u'name'].rsplit(u'-', 1)[0],
1960 tbl_dict[tst_name_mod][-idx - 1] = \
1961 tst_data[u"throughput"][incl_tests][u"LOWER"]
1962 except (TypeError, IndexError, KeyError, ValueError):
1967 logging.error(u"Not enough data to build the table! Skipping")
1971 for idx, cmp in enumerate(table.get(u"comparisons", list())):
1972 idx_ref = cmp.get(u"reference", None)
1973 idx_cmp = cmp.get(u"compare", None)
1974 if idx_ref is None or idx_cmp is None:
1977 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1978 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1980 header[1].append(u"")
1981 header[2].append(u"")
1982 header[3].append(u"")
1983 for tst_name, tst_data in tbl_dict.items():
1984 if not cmp_dict.get(tst_name, None):
1985 cmp_dict[tst_name] = list()
1986 ref_data = tst_data.get(idx_ref, None)
1987 cmp_data = tst_data.get(idx_cmp, None)
1988 if ref_data is None or cmp_data is None:
1989 cmp_dict[tst_name].append(float(u'nan'))
1991 cmp_dict[tst_name].append(
1992 relative_change(ref_data, cmp_data)
1995 tbl_lst_none = list()
1997 for tst_name, tst_data in tbl_dict.items():
1998 itm_lst = [tst_data[u"name"], ]
1999 for idx in range(nr_cols):
2000 item = tst_data.get(-idx - 1, None)
2002 itm_lst.insert(1, None)
2004 itm_lst.insert(1, round(item / 1e6, 1))
2007 None if itm is None else round(itm, 1)
2008 for itm in cmp_dict[tst_name]
2011 if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
2012 tbl_lst_none.append(itm_lst)
2014 tbl_lst.append(itm_lst)
2016 tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
2017 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
2018 tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
2019 tbl_lst.extend(tbl_lst_none)
2021 # Generate csv table:
2022 csv_file_name = f"{table[u'output-file']}.csv"
2023 logging.info(f" Writing the file {csv_file_name}")
2024 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2026 file_handler.write(u",".join(hdr) + u"\n")
2027 for test in tbl_lst:
2028 file_handler.write(u",".join(
2030 str(item).replace(u"None", u"-").replace(u"nan", u"-").
2031 replace(u"null", u"-") for item in test
2035 txt_file_name = f"{table[u'output-file']}.txt"
2036 logging.info(f" Writing the file {txt_file_name}")
2037 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2039 # Reorganize header in txt table
2041 with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2042 for line in file_handler:
2043 txt_table.append(line)
2045 txt_table.insert(5, txt_table.pop(2))
2046 with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2047 file_handler.writelines(txt_table)
2051 # Generate html table:
2053 u"<br>".join(row) for row in zip(*header)
2055 _tpc_generate_html_table(
2058 table[u'output-file'],
2060 title=table.get(u"title", u""),