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"udpsrcscale" in test_name:
1050 bsf = u"features-udp"
1051 elif u"iacl" in test_name:
1053 elif u"policer" in test_name:
1055 elif u"cop" in test_name:
1057 elif u"nat" in test_name:
1059 elif u"macip" in test_name:
1061 elif u"scale" in test_name:
1063 elif u"base" in test_name:
1068 if u"114b" in test_name and u"vhost" in test_name:
1070 elif u"testpmd" in test_name or u"l3fwd" in test_name:
1072 elif u"memif" in test_name:
1073 domain = u"container_memif"
1074 elif u"srv6" in test_name:
1076 elif u"vhost" in test_name:
1078 if u"vppl2xc" in test_name:
1081 driver += u"-testpmd"
1082 if u"lbvpplacp" in test_name:
1083 bsf += u"-link-bonding"
1084 elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1085 domain = u"nf_service_density_vnfc"
1086 elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1087 domain = u"nf_service_density_cnfc"
1088 elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1089 domain = u"nf_service_density_cnfp"
1090 elif u"ipsec" in test_name:
1092 if u"sw" in test_name:
1094 elif u"hw" in test_name:
1096 elif u"ethip4vxlan" in test_name:
1097 domain = u"ip4_tunnels"
1098 elif u"ip4base" in test_name or u"ip4scale" in test_name:
1100 elif u"ip6base" in test_name or u"ip6scale" in test_name:
1102 elif u"l2xcbase" in test_name or \
1103 u"l2xcscale" in test_name or \
1104 u"l2bdbasemaclrn" in test_name or \
1105 u"l2bdscale" in test_name or \
1106 u"l2patch" in test_name:
1111 file_name = u"-".join((domain, testbed, nic)) + u".html#"
1112 anchor_name = u"-".join((frame_size, cores, bsf, driver))
1114 return file_name + anchor_name
1117 def table_perf_trending_dash_html(table, input_data):
1118 """Generate the table(s) with algorithm:
1119 table_perf_trending_dash_html specified in the specification
1122 :param table: Table to generate.
1123 :param input_data: Data to process.
1125 :type input_data: InputData
1130 if not table.get(u"testbed", None):
1132 f"The testbed is not defined for the table "
1133 f"{table.get(u'title', u'')}. Skipping."
1137 test_type = table.get(u"test-type", u"MRR")
1138 if test_type not in (u"MRR", u"NDR", u"PDR"):
1140 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1145 if test_type in (u"NDR", u"PDR"):
1146 lnk_dir = u"../ndrpdr_trending/"
1147 lnk_sufix = f"-{test_type.lower()}"
1149 lnk_dir = u"../trending/"
1152 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1155 with open(table[u"input-file"], u'rt') as csv_file:
1156 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1158 logging.warning(u"The input file is not defined.")
1160 except csv.Error as err:
1162 f"Not possible to process the file {table[u'input-file']}.\n"
1168 dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1171 trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1172 for idx, item in enumerate(csv_lst[0]):
1173 alignment = u"left" if idx == 0 else u"center"
1174 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1192 for r_idx, row in enumerate(csv_lst[1:]):
1194 color = u"regression"
1196 color = u"progression"
1199 trow = ET.SubElement(
1200 dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1204 for c_idx, item in enumerate(row):
1205 tdata = ET.SubElement(
1208 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1211 if c_idx == 0 and table.get(u"add-links", True):
1212 ref = ET.SubElement(
1217 f"{_generate_url(table.get(u'testbed', ''), item)}"
1225 with open(table[u"output-file"], u'w') as html_file:
1226 logging.info(f" Writing file: {table[u'output-file']}")
1227 html_file.write(u".. raw:: html\n\n\t")
1228 html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1229 html_file.write(u"\n\t<p><br><br></p>\n")
1231 logging.warning(u"The output file is not defined.")
1235 def table_last_failed_tests(table, input_data):
1236 """Generate the table(s) with algorithm: table_last_failed_tests
1237 specified in the specification file.
1239 :param table: Table to generate.
1240 :param input_data: Data to process.
1241 :type table: pandas.Series
1242 :type input_data: InputData
1245 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1247 # Transform the data
1249 f" Creating the data set for the {table.get(u'type', u'')} "
1250 f"{table.get(u'title', u'')}."
1253 data = input_data.filter_data(table, continue_on_error=True)
1255 if data is None or data.empty:
1257 f" No data for the {table.get(u'type', u'')} "
1258 f"{table.get(u'title', u'')}."
1263 for job, builds in table[u"data"].items():
1264 for build in builds:
1267 version = input_data.metadata(job, build).get(u"version", u"")
1269 logging.error(f"Data for {job}: {build} is not present.")
1271 tbl_list.append(build)
1272 tbl_list.append(version)
1273 failed_tests = list()
1276 for tst_data in data[job][build].values:
1277 if tst_data[u"status"] != u"FAIL":
1281 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1284 nic = groups.group(0)
1285 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1286 tbl_list.append(str(passed))
1287 tbl_list.append(str(failed))
1288 tbl_list.extend(failed_tests)
1290 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1291 logging.info(f" Writing file: {file_name}")
1292 with open(file_name, u"wt") as file_handler:
1293 for test in tbl_list:
1294 file_handler.write(test + u'\n')
1297 def table_failed_tests(table, input_data):
1298 """Generate the table(s) with algorithm: table_failed_tests
1299 specified in the specification file.
1301 :param table: Table to generate.
1302 :param input_data: Data to process.
1303 :type table: pandas.Series
1304 :type input_data: InputData
1307 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1309 # Transform the data
1311 f" Creating the data set for the {table.get(u'type', u'')} "
1312 f"{table.get(u'title', u'')}."
1314 data = input_data.filter_data(table, continue_on_error=True)
1317 if u"NDRPDR" in table.get(u"filter", list()):
1318 test_type = u"NDRPDR"
1320 # Prepare the header of the tables
1324 u"Last Failure [Time]",
1325 u"Last Failure [VPP-Build-Id]",
1326 u"Last Failure [CSIT-Job-Build-Id]"
1329 # Generate the data for the table according to the model in the table
1333 timeperiod = timedelta(int(table.get(u"window", 7)))
1336 for job, builds in table[u"data"].items():
1337 for build in builds:
1339 for tst_name, tst_data in data[job][build].items():
1340 if tst_name.lower() in table.get(u"ignore-list", list()):
1342 if tbl_dict.get(tst_name, None) is None:
1343 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1346 nic = groups.group(0)
1347 tbl_dict[tst_name] = {
1348 u"name": f"{nic}-{tst_data[u'name']}",
1349 u"data": OrderedDict()
1352 generated = input_data.metadata(job, build).\
1353 get(u"generated", u"")
1356 then = dt.strptime(generated, u"%Y%m%d %H:%M")
1357 if (now - then) <= timeperiod:
1358 tbl_dict[tst_name][u"data"][build] = (
1359 tst_data[u"status"],
1361 input_data.metadata(job, build).get(u"version",
1365 except (TypeError, KeyError) as err:
1366 logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1370 for tst_data in tbl_dict.values():
1372 fails_last_date = u""
1373 fails_last_vpp = u""
1374 fails_last_csit = u""
1375 for val in tst_data[u"data"].values():
1376 if val[0] == u"FAIL":
1378 fails_last_date = val[1]
1379 fails_last_vpp = val[2]
1380 fails_last_csit = val[3]
1382 max_fails = fails_nr if fails_nr > max_fails else max_fails
1388 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1389 f"-build-{fails_last_csit}"
1392 tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1394 for nrf in range(max_fails, -1, -1):
1395 tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1396 tbl_sorted.extend(tbl_fails)
1398 file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1399 logging.info(f" Writing file: {file_name}")
1400 with open(file_name, u"wt") as file_handler:
1401 file_handler.write(u",".join(header) + u"\n")
1402 for test in tbl_sorted:
1403 file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1405 logging.info(f" Writing file: {table[u'output-file']}.txt")
1406 convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1409 def table_failed_tests_html(table, input_data):
1410 """Generate the table(s) with algorithm: table_failed_tests_html
1411 specified in the specification file.
1413 :param table: Table to generate.
1414 :param input_data: Data to process.
1415 :type table: pandas.Series
1416 :type input_data: InputData
1421 if not table.get(u"testbed", None):
1423 f"The testbed is not defined for the table "
1424 f"{table.get(u'title', u'')}. Skipping."
1428 test_type = table.get(u"test-type", u"MRR")
1429 if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1431 f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1436 if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1437 lnk_dir = u"../ndrpdr_trending/"
1440 lnk_dir = u"../trending/"
1443 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1446 with open(table[u"input-file"], u'rt') as csv_file:
1447 csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1449 logging.warning(u"The input file is not defined.")
1451 except csv.Error as err:
1453 f"Not possible to process the file {table[u'input-file']}.\n"
1459 failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1462 trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1463 for idx, item in enumerate(csv_lst[0]):
1464 alignment = u"left" if idx == 0 else u"center"
1465 thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1469 colors = (u"#e9f1fb", u"#d4e4f7")
1470 for r_idx, row in enumerate(csv_lst[1:]):
1471 background = colors[r_idx % 2]
1472 trow = ET.SubElement(
1473 failed_tests, u"tr", attrib=dict(bgcolor=background)
1477 for c_idx, item in enumerate(row):
1478 tdata = ET.SubElement(
1481 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1484 if c_idx == 0 and table.get(u"add-links", True):
1485 ref = ET.SubElement(
1490 f"{_generate_url(table.get(u'testbed', ''), item)}"
1498 with open(table[u"output-file"], u'w') as html_file:
1499 logging.info(f" Writing file: {table[u'output-file']}")
1500 html_file.write(u".. raw:: html\n\n\t")
1501 html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1502 html_file.write(u"\n\t<p><br><br></p>\n")
1504 logging.warning(u"The output file is not defined.")
1508 def table_comparison(table, input_data):
1509 """Generate the table(s) with algorithm: table_comparison
1510 specified in the specification file.
1512 :param table: Table to generate.
1513 :param input_data: Data to process.
1514 :type table: pandas.Series
1515 :type input_data: InputData
1517 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1519 # Transform the data
1521 f" Creating the data set for the {table.get(u'type', u'')} "
1522 f"{table.get(u'title', u'')}."
1525 columns = table.get(u"columns", None)
1528 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1533 for idx, col in enumerate(columns):
1534 if col.get(u"data-set", None) is None:
1535 logging.warning(f"No data for column {col.get(u'title', u'')}")
1537 tag = col.get(u"tag", None)
1538 data = input_data.filter_data(
1540 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1541 data=col[u"data-set"],
1542 continue_on_error=True
1545 u"title": col.get(u"title", f"Column{idx}"),
1548 for builds in data.values:
1549 for build in builds:
1550 for tst_name, tst_data in build.items():
1551 if tag and tag not in tst_data[u"tags"]:
1554 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1555 replace(u"2n1l-", u"")
1556 if col_data[u"data"].get(tst_name_mod, None) is None:
1557 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1558 if u"across testbeds" in table[u"title"].lower() or \
1559 u"across topologies" in table[u"title"].lower():
1560 name = _tpc_modify_displayed_test_name(name)
1561 col_data[u"data"][tst_name_mod] = {
1569 target=col_data[u"data"][tst_name_mod],
1571 include_tests=table[u"include-tests"]
1574 replacement = col.get(u"data-replacement", None)
1576 rpl_data = input_data.filter_data(
1578 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1580 continue_on_error=True
1582 for builds in rpl_data.values:
1583 for build in builds:
1584 for tst_name, tst_data in build.items():
1585 if tag and tag not in tst_data[u"tags"]:
1588 _tpc_modify_test_name(tst_name, ignore_nic=True).\
1589 replace(u"2n1l-", u"")
1590 if col_data[u"data"].get(tst_name_mod, None) is None:
1591 name = tst_data[u'name'].rsplit(u'-', 1)[0]
1592 if u"across testbeds" in table[u"title"].lower() \
1593 or u"across topologies" in \
1594 table[u"title"].lower():
1595 name = _tpc_modify_displayed_test_name(name)
1596 col_data[u"data"][tst_name_mod] = {
1603 if col_data[u"data"][tst_name_mod][u"replace"]:
1604 col_data[u"data"][tst_name_mod][u"replace"] = False
1605 col_data[u"data"][tst_name_mod][u"data"] = list()
1607 target=col_data[u"data"][tst_name_mod],
1609 include_tests=table[u"include-tests"]
1612 if table[u"include-tests"] in (u"NDR", u"PDR"):
1613 for tst_name, tst_data in col_data[u"data"].items():
1614 if tst_data[u"data"]:
1615 tst_data[u"mean"] = mean(tst_data[u"data"])
1616 tst_data[u"stdev"] = stdev(tst_data[u"data"])
1618 cols.append(col_data)
1622 for tst_name, tst_data in col[u"data"].items():
1623 if tbl_dict.get(tst_name, None) is None:
1624 tbl_dict[tst_name] = {
1625 "name": tst_data[u"name"]
1627 tbl_dict[tst_name][col[u"title"]] = {
1628 u"mean": tst_data[u"mean"],
1629 u"stdev": tst_data[u"stdev"]
1633 logging.warning(f"No data for table {table.get(u'title', u'')}!")
1637 for tst_data in tbl_dict.values():
1638 row = [tst_data[u"name"], ]
1640 row.append(tst_data.get(col[u"title"], None))
1643 comparisons = table.get(u"comparisons", None)
1644 if comparisons and isinstance(comparisons, list):
1645 for idx, comp in enumerate(comparisons):
1647 col_ref = int(comp[u"reference"])
1648 col_cmp = int(comp[u"compare"])
1650 logging.warning(u"Comparison: No references defined! Skipping.")
1651 comparisons.pop(idx)
1653 if not (0 < col_ref <= len(cols) and
1654 0 < col_cmp <= len(cols)) or \
1656 logging.warning(f"Wrong values of reference={col_ref} "
1657 f"and/or compare={col_cmp}. Skipping.")
1658 comparisons.pop(idx)
1661 tbl_cmp_lst = list()
1664 new_row = deepcopy(row)
1666 for comp in comparisons:
1667 ref_itm = row[int(comp[u"reference"])]
1668 if ref_itm is None and \
1669 comp.get(u"reference-alt", None) is not None:
1670 ref_itm = row[int(comp[u"reference-alt"])]
1671 cmp_itm = row[int(comp[u"compare"])]
1672 if ref_itm is not None and cmp_itm is not None and \
1673 ref_itm[u"mean"] is not None and \
1674 cmp_itm[u"mean"] is not None and \
1675 ref_itm[u"stdev"] is not None and \
1676 cmp_itm[u"stdev"] is not None:
1677 delta, d_stdev = relative_change_stdev(
1678 ref_itm[u"mean"], cmp_itm[u"mean"],
1679 ref_itm[u"stdev"], cmp_itm[u"stdev"]
1683 u"mean": delta * 1e6,
1684 u"stdev": d_stdev * 1e6
1689 new_row.append(None)
1691 tbl_cmp_lst.append(new_row)
1693 tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1694 tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1697 rca_in = table.get(u"rca", None)
1698 if rca_in and isinstance(rca_in, list):
1699 for idx, itm in enumerate(rca_in):
1701 with open(itm.get(u"data", u""), u"r") as rca_file:
1704 u"title": itm.get(u"title", f"RCA{idx}"),
1705 u"data": load(rca_file, Loader=FullLoader)
1708 except (YAMLError, IOError) as err:
1710 f"The RCA file {itm.get(u'data', u'')} does not exist or "
1713 logging.debug(repr(err))
1715 tbl_for_csv = list()
1716 for line in tbl_cmp_lst:
1718 for idx, itm in enumerate(line[1:]):
1719 if itm is None or not isinstance(itm, dict) or\
1720 itm.get(u'mean', None) is None or \
1721 itm.get(u'stdev', None) is None:
1725 row.append(round(float(itm[u'mean']) / 1e6, 3))
1726 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1728 rca_nr = rca[u"data"].get(row[0], u"-")
1729 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1730 tbl_for_csv.append(row)
1732 header_csv = [u"Test Case", ]
1734 header_csv.append(f"Avg({col[u'title']})")
1735 header_csv.append(f"Stdev({col[u'title']})")
1736 for comp in comparisons:
1738 f"Avg({comp.get(u'title', u'')})"
1741 f"Stdev({comp.get(u'title', u'')})"
1743 header_csv.extend([rca[u"title"] for rca in rcas])
1745 legend_lst = table.get(u"legend", None)
1746 if legend_lst is None:
1749 legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1753 footnote += u"\nRCA:\n"
1755 footnote += rca[u"data"].get(u"footnote", u"")
1757 csv_file_name = f"{table[u'output-file']}-csv.csv"
1758 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1760 u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1762 for test in tbl_for_csv:
1764 u",".join([f'"{item}"' for item in test]) + u"\n"
1767 for item in legend_lst:
1768 file_handler.write(f'"{item}"\n')
1770 for itm in footnote.split(u"\n"):
1771 file_handler.write(f'"{itm}"\n')
1774 max_lens = [0, ] * len(tbl_cmp_lst[0])
1775 for line in tbl_cmp_lst:
1777 for idx, itm in enumerate(line[1:]):
1778 if itm is None or not isinstance(itm, dict) or \
1779 itm.get(u'mean', None) is None or \
1780 itm.get(u'stdev', None) is None:
1785 f"{round(float(itm[u'mean']) / 1e6, 1)} "
1786 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1787 replace(u"nan", u"NaN")
1791 f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1792 f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1793 replace(u"nan", u"NaN")
1795 if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1796 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1802 for line in tbl_tmp:
1804 for idx, itm in enumerate(line[1:]):
1805 if itm in (u"NT", u"NaN"):
1808 itm_lst = itm.rsplit(u"\u00B1", 1)
1810 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1811 row.append(u"\u00B1".join(itm_lst))
1813 rca_nr = rca[u"data"].get(row[0], u"-")
1814 row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1816 tbl_final.append(row)
1818 header = [u"Test Case", ]
1819 header.extend([col[u"title"] for col in cols])
1820 header.extend([comp.get(u"title", u"") for comp in comparisons])
1821 header.extend([rca[u"title"] for rca in rcas])
1823 # Generate csv tables:
1824 csv_file_name = f"{table[u'output-file']}.csv"
1825 logging.info(f" Writing the file {csv_file_name}")
1826 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1827 file_handler.write(u";".join(header) + u"\n")
1828 for test in tbl_final:
1829 file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1831 # Generate txt table:
1832 txt_file_name = f"{table[u'output-file']}.txt"
1833 logging.info(f" Writing the file {txt_file_name}")
1834 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
1836 with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
1837 file_handler.write(legend)
1838 file_handler.write(footnote)
1840 # Generate html table:
1841 _tpc_generate_html_table(
1844 table[u'output-file'],
1848 title=table.get(u"title", u"")
1852 def table_weekly_comparison(table, in_data):
1853 """Generate the table(s) with algorithm: table_weekly_comparison
1854 specified in the specification file.
1856 :param table: Table to generate.
1857 :param in_data: Data to process.
1858 :type table: pandas.Series
1859 :type in_data: InputData
1861 logging.info(f" Generating the table {table.get(u'title', u'')} ...")
1863 # Transform the data
1865 f" Creating the data set for the {table.get(u'type', u'')} "
1866 f"{table.get(u'title', u'')}."
1869 incl_tests = table.get(u"include-tests", None)
1870 if incl_tests not in (u"NDR", u"PDR"):
1871 logging.error(f"Wrong tests to include specified ({incl_tests}).")
1874 nr_cols = table.get(u"nr-of-data-columns", None)
1875 if not nr_cols or nr_cols < 2:
1877 f"No columns specified for {table.get(u'title', u'')}. Skipping."
1881 data = in_data.filter_data(
1883 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1884 continue_on_error=True
1889 [u"Start Timestamp", ],
1895 tb_tbl = table.get(u"testbeds", None)
1896 for job_name, job_data in data.items():
1897 for build_nr, build in job_data.items():
1903 tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1904 if tb_ip and tb_tbl:
1905 testbed = tb_tbl.get(tb_ip, u"")
1908 header[2].insert(1, build_nr)
1909 header[3].insert(1, testbed)
1911 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1914 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1917 for tst_name, tst_data in build.items():
1919 _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1920 if not tbl_dict.get(tst_name_mod, None):
1921 tbl_dict[tst_name_mod] = dict(
1922 name=tst_data[u'name'].rsplit(u'-', 1)[0],
1925 tbl_dict[tst_name_mod][-idx - 1] = \
1926 tst_data[u"throughput"][incl_tests][u"LOWER"]
1927 except (TypeError, IndexError, KeyError, ValueError):
1932 logging.error(u"Not enough data to build the table! Skipping")
1936 for idx, cmp in enumerate(table.get(u"comparisons", list())):
1937 idx_ref = cmp.get(u"reference", None)
1938 idx_cmp = cmp.get(u"compare", None)
1939 if idx_ref is None or idx_cmp is None:
1942 f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1943 f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1945 header[1].append(u"")
1946 header[2].append(u"")
1947 header[3].append(u"")
1948 for tst_name, tst_data in tbl_dict.items():
1949 if not cmp_dict.get(tst_name, None):
1950 cmp_dict[tst_name] = list()
1951 ref_data = tst_data.get(idx_ref, None)
1952 cmp_data = tst_data.get(idx_cmp, None)
1953 if ref_data is None or cmp_data is None:
1954 cmp_dict[tst_name].append(float(u'nan'))
1956 cmp_dict[tst_name].append(
1957 relative_change(ref_data, cmp_data)
1960 tbl_lst_none = list()
1962 for tst_name, tst_data in tbl_dict.items():
1963 itm_lst = [tst_data[u"name"], ]
1964 for idx in range(nr_cols):
1965 item = tst_data.get(-idx - 1, None)
1967 itm_lst.insert(1, None)
1969 itm_lst.insert(1, round(item / 1e6, 1))
1972 None if itm is None else round(itm, 1)
1973 for itm in cmp_dict[tst_name]
1976 if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
1977 tbl_lst_none.append(itm_lst)
1979 tbl_lst.append(itm_lst)
1981 tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
1982 tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1983 tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
1984 tbl_lst.extend(tbl_lst_none)
1986 # Generate csv table:
1987 csv_file_name = f"{table[u'output-file']}.csv"
1988 logging.info(f" Writing the file {csv_file_name}")
1989 with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1991 file_handler.write(u",".join(hdr) + u"\n")
1992 for test in tbl_lst:
1993 file_handler.write(u",".join(
1995 str(item).replace(u"None", u"-").replace(u"nan", u"-").
1996 replace(u"null", u"-") for item in test
2000 txt_file_name = f"{table[u'output-file']}.txt"
2001 logging.info(f" Writing the file {txt_file_name}")
2002 convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2004 # Reorganize header in txt table
2006 with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2007 for line in file_handler:
2008 txt_table.append(line)
2010 txt_table.insert(5, txt_table.pop(2))
2011 with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2012 file_handler.writelines(txt_table)
2016 # Generate html table:
2018 u"<br>".join(row) for row in zip(*header)
2020 _tpc_generate_html_table(
2023 table[u'output-file'],
2025 title=table.get(u"title", u""),