resources/tools/presentation/generator_tables.py

   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:
   5 #
   6 #     http://www.apache.org/licenses/LICENSE-2.0
   7 #
   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.
  13
  14 """Algorithms to generate tables.
  15 """
  16
  17
  18 import logging
  19 import csv
  20 import re
  21
  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
  27
  28 import plotly.graph_objects as go
  29 import plotly.offline as ploff
  30 import pandas as pd
  31
  32 from numpy import nan, isnan
  33 from yaml import load, FullLoader, YAMLError
  34
  35 from pal_utils import mean, stdev, classify_anomalies, \
  36     convert_csv_to_pretty_txt, relative_change_stdev, relative_change
  37
  38
  39 REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)')
  40
  41
  42 def generate_tables(spec, data):
  43     """Generate all tables specified in the specification file.
  44
  45     :param spec: Specification read from the specification file.
  46     :param data: Data to process.
  47     :type spec: Specification
  48     :type data: InputData
  49     """
  50
  51     generator = {
  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
  62     }
  63
  64     logging.info(u"Generating the tables ...")
  65     for table in spec.tables:
  66         try:
  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:
  71             logging.error(
  72                 f"Probably algorithm {table[u'algorithm']} is not defined: "
  73                 f"{repr(err)}"
  74             )
  75     logging.info(u"Done.")
  76
  77
  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.
  81
  82     :param table: Table to generate.
  83     :param input_data: Data to process.
  84     :type table: pandas.Series
  85     :type input_data: InputData
  86     """
  87
  88     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
  89     # Transform the data
  90     logging.info(
  91         f"    Creating the data set for the {table.get(u'type', u'')} "
  92         f"{table.get(u'title', u'')}."
  93     )
  94     data = input_data.filter_data(
  95         table,
  96         params=[u"name", u"parent", u"show-run", u"type"],
  97         continue_on_error=True
  98     )
  99     if data.empty:
 100         return
 101     data = input_data.merge_data(data)
 102
 103     sort_tests = table.get(u"sort", None)
 104     if sort_tests:
 105         args = dict(
 106             inplace=True,
 107             ascending=(sort_tests == u"ascending")
 108         )
 109         data.sort_index(**args)
 110
 111     suites = input_data.filter_data(
 112         table,
 113         continue_on_error=True,
 114         data_set=u"suites"
 115     )
 116     if suites.empty:
 117         return
 118     suites = input_data.merge_data(suites)
 119
 120     def _generate_html_table(tst_data):
 121         """Generate an HTML table with operational data for the given test.
 122
 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.
 126         :rtype: str
 127         """
 128
 129         colors = {
 130             u"header": u"#7eade7",
 131             u"empty": u"#ffffff",
 132             u"body": (u"#e9f1fb", u"#d4e4f7")
 133         }
 134
 135         tbl = ET.Element(u"table", attrib=dict(width=u"100%", border=u"0"))
 136
 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")
 140         )
 141         thead.text = tst_data[u"name"]
 142
 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")
 146         )
 147         thead.text = u"\t"
 148
 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"])
 152             )
 153             tcol = ET.SubElement(
 154                 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 155             )
 156             tcol.text = u"No Data"
 157
 158             trow = ET.SubElement(
 159                 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 160             )
 161             thead = ET.SubElement(
 162                 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 163             )
 164             font = ET.SubElement(
 165                 thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
 166             )
 167             font.text = u"."
 168             return str(ET.tostring(tbl, encoding=u"unicode"))
 169
 170         tbl_hdr = (
 171             u"Name",
 172             u"Nr of Vectors",
 173             u"Nr of Packets",
 174             u"Suspends",
 175             u"Cycles per Packet",
 176             u"Average Vector Size"
 177         )
 178
 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"])
 182             )
 183             tcol = ET.SubElement(
 184                 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 185             )
 186             if dut_data.get(u"threads", None) is None:
 187                 tcol.text = u"No Data"
 188                 continue
 189
 190             bold = ET.SubElement(tcol, u"b")
 191             bold.text = (
 192                 f"Host IP: {dut_data.get(u'host', '')}, "
 193                 f"Socket: {dut_data.get(u'socket', '')}"
 194             )
 195             trow = ET.SubElement(
 196                 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 197             )
 198             thead = ET.SubElement(
 199                 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 200             )
 201             thead.text = u"\t"
 202
 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"])
 206                 )
 207                 tcol = ET.SubElement(
 208                     trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 209                 )
 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"])
 214                 )
 215                 for idx, col in enumerate(tbl_hdr):
 216                     tcol = ET.SubElement(
 217                         trow, u"td",
 218                         attrib=dict(align=u"right" if idx else u"left")
 219                     )
 220                     font = ET.SubElement(
 221                         tcol, u"font", attrib=dict(size=u"2")
 222                     )
 223                     bold = ET.SubElement(font, u"b")
 224                     bold.text = col
 225                 for row_nr, row in enumerate(thread):
 226                     trow = ET.SubElement(
 227                         tbl, u"tr",
 228                         attrib=dict(bgcolor=colors[u"body"][row_nr % 2])
 229                     )
 230                     for idx, col in enumerate(row):
 231                         tcol = ET.SubElement(
 232                             trow, u"td",
 233                             attrib=dict(align=u"right" if idx else u"left")
 234                         )
 235                         font = ET.SubElement(
 236                             tcol, u"font", attrib=dict(size=u"2")
 237                         )
 238                         if isinstance(col, float):
 239                             font.text = f"{col:.2f}"
 240                         else:
 241                             font.text = str(col)
 242                 trow = ET.SubElement(
 243                     tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 244                 )
 245                 thead = ET.SubElement(
 246                     trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 247                 )
 248                 thead.text = u"\t"
 249
 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")
 253         )
 254         font = ET.SubElement(
 255             thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
 256         )
 257         font.text = u"."
 258
 259         return str(ET.tostring(tbl, encoding=u"unicode"))
 260
 261     for suite in suites.values:
 262         html_table = str()
 263         for test_data in data.values:
 264             if test_data[u"parent"] not in suite[u"name"]:
 265                 continue
 266             html_table += _generate_html_table(test_data)
 267         if not html_table:
 268             continue
 269         try:
 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")
 276         except KeyError:
 277             logging.warning(u"The output file is not defined.")
 278             return
 279     logging.info(u"  Done.")
 280
 281
 282 def table_merged_details(table, input_data):
 283     """Generate the table(s) with algorithm: table_merged_details
 284     specified in the specification file.
 285
 286     :param table: Table to generate.
 287     :param input_data: Data to process.
 288     :type table: pandas.Series
 289     :type input_data: InputData
 290     """
 291
 292     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 293
 294     # Transform the data
 295     logging.info(
 296         f"    Creating the data set for the {table.get(u'type', u'')} "
 297         f"{table.get(u'title', u'')}."
 298     )
 299     data = input_data.filter_data(table, continue_on_error=True)
 300     data = input_data.merge_data(data)
 301
 302     sort_tests = table.get(u"sort", None)
 303     if sort_tests:
 304         args = dict(
 305             inplace=True,
 306             ascending=(sort_tests == u"ascending")
 307         )
 308         data.sort_index(**args)
 309
 310     suites = input_data.filter_data(
 311         table, continue_on_error=True, data_set=u"suites")
 312     suites = input_data.merge_data(suites)
 313
 314     # Prepare the header of the tables
 315     header = list()
 316     for column in table[u"columns"]:
 317         header.append(
 318             u'"{0}"'.format(str(column[u"title"]).replace(u'"', u'""'))
 319         )
 320
 321     for suite in suites.values:
 322         # Generate data
 323         suite_name = suite[u"name"]
 324         table_lst = list()
 325         for test in data.keys():
 326             if data[test][u"parent"] not in suite_name:
 327                 continue
 328             row_lst = list()
 329             for column in table[u"columns"]:
 330                 try:
 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:
 335                         continue
 336                     col_data = col_data.replace(
 337                         u"No Data", u"Not Captured     "
 338                     )
 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])}" \
 344                                        f"- |br| " \
 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)):
 350                             try:
 351                                 col_data = col_data.split(u" |br| ", 1)[1]
 352                             except IndexError:
 353                                 pass
 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}"')
 360                 except KeyError:
 361                     row_lst.append(u'"Not captured"')
 362             if len(row_lst) == len(table[u"columns"]):
 363                 table_lst.append(row_lst)
 364
 365         # Write the data to file
 366         if table_lst:
 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")
 374
 375     logging.info(u"  Done.")
 376
 377
 378 def _tpc_modify_test_name(test_name, ignore_nic=False):
 379     """Modify a test name by replacing its parts.
 380
 381     :param test_name: Test name to be modified.
 382     :param ignore_nic: If True, NIC is removed from TC name.
 383     :type test_name: str
 384     :type ignore_nic: bool
 385     :returns: Modified test name.
 386     :rtype: str
 387     """
 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")
 401
 402     if ignore_nic:
 403         return re.sub(REGEX_NIC, u"", test_name_mod)
 404     return test_name_mod
 405
 406
 407 def _tpc_modify_displayed_test_name(test_name):
 408     """Modify a test name which is displayed in a table by replacing its parts.
 409
 410     :param test_name: Test name to be modified.
 411     :type test_name: str
 412     :returns: Modified test name.
 413     :rtype: str
 414     """
 415     return 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")
 422
 423
 424 def _tpc_insert_data(target, src, include_tests):
 425     """Insert src data to the target structure.
 426
 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).
 430     :type target: list
 431     :type src: dict
 432     :type include_tests: str
 433     """
 434     try:
 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):
 443         pass
 444
 445
 446 def _tpc_generate_html_table(header, data, out_file_name, legend=u"",
 447                              footnote=u"", sort_data=True, title=u"",
 448                              generate_rst=True):
 449     """Generate html table from input data with simple sorting possibility.
 450
 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
 455         header.
 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.
 463     :type header: list
 464     :type data: list of lists
 465     :type out_file_name: str
 466     :type legend: str
 467     :type footnote: str
 468     :type sort_data: bool
 469     :type title: str
 470     :type generate_rst: bool
 471     """
 472
 473     try:
 474         idx = header.index(u"Test Case")
 475     except ValueError:
 476         idx = 0
 477     params = {
 478         u"align-hdr": (
 479             [u"left", u"right"],
 480             [u"left", u"left", u"right"],
 481             [u"left", u"left", u"left", u"right"]
 482         ),
 483         u"align-itm": (
 484             [u"left", u"right"],
 485             [u"left", u"left", u"right"],
 486             [u"left", u"left", u"left", u"right"]
 487         ),
 488         u"width": ([15, 9], [4, 24, 10], [4, 4, 32, 10])
 489     }
 490
 491     df_data = pd.DataFrame(data, columns=header)
 492
 493     if sort_data:
 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)
 501     else:
 502         df_sorted = df_data
 503
 504     fill_color = [[u"#d4e4f7" if idx % 2 else u"#e9f1fb"
 505                    for idx in range(len(df_data))]]
 506     table_header = dict(
 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],
 510         font=dict(
 511             family=u"Courier New",
 512             size=12
 513         )
 514     )
 515
 516     fig = go.Figure()
 517
 518     if sort_data:
 519         for table in df_sorted:
 520             columns = [table.get(col) for col in header]
 521             fig.add_trace(
 522                 go.Table(
 523                     columnwidth=params[u"width"][idx],
 524                     header=table_header,
 525                     cells=dict(
 526                         values=columns,
 527                         fill_color=fill_color,
 528                         align=params[u"align-itm"][idx],
 529                         font=dict(
 530                             family=u"Courier New",
 531                             size=12
 532                         )
 533                     )
 534                 )
 535             )
 536
 537         buttons = list()
 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)
 542             visible[idx] = True
 543             buttons.append(
 544                 dict(
 545                     label=hdr.replace(u" [Mpps]", u""),
 546                     method=u"update",
 547                     args=[{u"visible": visible}],
 548                 )
 549             )
 550
 551         fig.update_layout(
 552             updatemenus=[
 553                 go.layout.Updatemenu(
 554                     type=u"dropdown",
 555                     direction=u"down",
 556                     x=0.0,
 557                     xanchor=u"left",
 558                     y=1.002,
 559                     yanchor=u"bottom",
 560                     active=len(menu_items) - 1,
 561                     buttons=list(buttons)
 562                 )
 563             ],
 564         )
 565     else:
 566         fig.add_trace(
 567             go.Table(
 568                 columnwidth=params[u"width"][idx],
 569                 header=table_header,
 570                 cells=dict(
 571                     values=[df_sorted.get(col) for col in header],
 572                     fill_color=fill_color,
 573                     align=params[u"align-itm"][idx],
 574                     font=dict(
 575                         family=u"Courier New",
 576                         size=12
 577                     )
 578                 )
 579             )
 580         )
 581
 582     ploff.plot(
 583         fig,
 584         show_link=False,
 585         auto_open=False,
 586         filename=f"{out_file_name}_in.html"
 587     )
 588
 589     if not generate_rst:
 590         return
 591
 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/"
 595     else:
 596         path = u"_tmp/src/dpdk_performance_tests/comparisons/"
 597     with open(f"{path}{file_name}.rst", u"wt") as rst_file:
 598         rst_file.write(
 599             u"\n"
 600             u".. |br| raw:: html\n\n    <br />\n\n\n"
 601             u".. |prein| raw:: html\n\n    <pre>\n\n\n"
 602             u".. |preout| raw:: html\n\n    </pre>\n\n"
 603         )
 604         if title:
 605             rst_file.write(f"{title}\n")
 606             rst_file.write(f"{u'`' * len(title)}\n\n")
 607         rst_file.write(
 608             u".. raw:: html\n\n"
 609             f'    <iframe frameborder="0" scrolling="no" '
 610             f'width="1600" height="1200" '
 611             f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
 612             f'</iframe>\n\n'
 613         )
 614
 615         # TODO: Use html (rst) list for legend and footnote
 616         if legend:
 617             rst_file.write(legend[1:].replace(u"\n", u" |br| "))
 618         if footnote:
 619             rst_file.write(footnote.replace(u"\n", u" |br| ")[1:])
 620
 621
 622 def table_soak_vs_ndr(table, input_data):
 623     """Generate the table(s) with algorithm: table_soak_vs_ndr
 624     specified in the specification file.
 625
 626     :param table: Table to generate.
 627     :param input_data: Data to process.
 628     :type table: pandas.Series
 629     :type input_data: InputData
 630     """
 631
 632     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 633
 634     # Transform the data
 635     logging.info(
 636         f"    Creating the data set for the {table.get(u'type', u'')} "
 637         f"{table.get(u'title', u'')}."
 638     )
 639     data = input_data.filter_data(table, continue_on_error=True)
 640
 641     # Prepare the header of the table
 642     try:
 643         header = [
 644             u"Test Case",
 645             f"Avg({table[u'reference'][u'title']})",
 646             f"Stdev({table[u'reference'][u'title']})",
 647             f"Avg({table[u'compare'][u'title']})",
 648             f"Stdev{table[u'compare'][u'title']})",
 649             u"Diff",
 650             u"Stdev(Diff)"
 651         ]
 652         header_str = u";".join(header) + u"\n"
 653         legend = (
 654             u"\nLegend:\n"
 655             f"Avg({table[u'reference'][u'title']}): "
 656             f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
 657             f"from a series of runs of the listed tests.\n"
 658             f"Stdev({table[u'reference'][u'title']}): "
 659             f"Standard deviation value of {table[u'reference'][u'title']} "
 660             f"[Mpps] computed from a series of runs of the listed tests.\n"
 661             f"Avg({table[u'compare'][u'title']}): "
 662             f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
 663             f"a series of runs of the listed tests.\n"
 664             f"Stdev({table[u'compare'][u'title']}): "
 665             f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
 666             f"computed from a series of runs of the listed tests.\n"
 667             f"Diff({table[u'reference'][u'title']},"
 668             f"{table[u'compare'][u'title']}): "
 669             f"Percentage change calculated for mean values.\n"
 670             u"Stdev(Diff): "
 671             u"Standard deviation of percentage change calculated for mean "
 672             u"values."
 673         )
 674     except (AttributeError, KeyError) as err:
 675         logging.error(f"The model is invalid, missing parameter: {repr(err)}")
 676         return
 677
 678     # Create a list of available SOAK test results:
 679     tbl_dict = dict()
 680     for job, builds in table[u"compare"][u"data"].items():
 681         for build in builds:
 682             for tst_name, tst_data in data[job][str(build)].items():
 683                 if tst_data[u"type"] == u"SOAK":
 684                     tst_name_mod = tst_name.replace(u"-soak", u"")
 685                     if tbl_dict.get(tst_name_mod, None) is None:
 686                         groups = re.search(REGEX_NIC, tst_data[u"parent"])
 687                         nic = groups.group(0) if groups else u""
 688                         name = (
 689                             f"{nic}-"
 690                             f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
 691                         )
 692                         tbl_dict[tst_name_mod] = {
 693                             u"name": name,
 694                             u"ref-data": list(),
 695                             u"cmp-data": list()
 696                         }
 697                     try:
 698                         tbl_dict[tst_name_mod][u"cmp-data"].append(
 699                             tst_data[u"throughput"][u"LOWER"])
 700                     except (KeyError, TypeError):
 701                         pass
 702     tests_lst = tbl_dict.keys()
 703
 704     # Add corresponding NDR test results:
 705     for job, builds in table[u"reference"][u"data"].items():
 706         for build in builds:
 707             for tst_name, tst_data in data[job][str(build)].items():
 708                 tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
 709                     replace(u"-mrr", u"")
 710                 if tst_name_mod not in tests_lst:
 711                     continue
 712                 try:
 713                     if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
 714                         continue
 715                     if table[u"include-tests"] == u"MRR":
 716                         result = (tst_data[u"result"][u"receive-rate"],
 717                                   tst_data[u"result"][u"receive-stdev"])
 718                     elif table[u"include-tests"] == u"PDR":
 719                         result = \
 720                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 721                     elif table[u"include-tests"] == u"NDR":
 722                         result = \
 723                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 724                     else:
 725                         result = None
 726                     if result is not None:
 727                         tbl_dict[tst_name_mod][u"ref-data"].append(
 728                             result)
 729                 except (KeyError, TypeError):
 730                     continue
 731
 732     tbl_lst = list()
 733     for tst_name in tbl_dict:
 734         item = [tbl_dict[tst_name][u"name"], ]
 735         data_r = tbl_dict[tst_name][u"ref-data"]
 736         if data_r:
 737             if table[u"include-tests"] == u"MRR":
 738                 data_r_mean = data_r[0][0]
 739                 data_r_stdev = data_r[0][1]
 740             else:
 741                 data_r_mean = mean(data_r)
 742                 data_r_stdev = stdev(data_r)
 743             item.append(round(data_r_mean / 1e6, 1))
 744             item.append(round(data_r_stdev / 1e6, 1))
 745         else:
 746             data_r_mean = None
 747             data_r_stdev = None
 748             item.extend([None, None])
 749         data_c = tbl_dict[tst_name][u"cmp-data"]
 750         if data_c:
 751             if table[u"include-tests"] == u"MRR":
 752                 data_c_mean = data_c[0][0]
 753                 data_c_stdev = data_c[0][1]
 754             else:
 755                 data_c_mean = mean(data_c)
 756                 data_c_stdev = stdev(data_c)
 757             item.append(round(data_c_mean / 1e6, 1))
 758             item.append(round(data_c_stdev / 1e6, 1))
 759         else:
 760             data_c_mean = None
 761             data_c_stdev = None
 762             item.extend([None, None])
 763         if data_r_mean is not None and data_c_mean is not None:
 764             delta, d_stdev = relative_change_stdev(
 765                 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
 766             try:
 767                 item.append(round(delta))
 768             except ValueError:
 769                 item.append(delta)
 770             try:
 771                 item.append(round(d_stdev))
 772             except ValueError:
 773                 item.append(d_stdev)
 774             tbl_lst.append(item)
 775
 776     # Sort the table according to the relative change
 777     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
 778
 779     # Generate csv tables:
 780     csv_file = f"{table[u'output-file']}.csv"
 781     with open(csv_file, u"wt") as file_handler:
 782         file_handler.write(header_str)
 783         for test in tbl_lst:
 784             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
 785
 786     convert_csv_to_pretty_txt(
 787         csv_file, f"{table[u'output-file']}.txt", delimiter=u";"
 788     )
 789     with open(f"{table[u'output-file']}.txt", u'a') as txt_file:
 790         txt_file.write(legend)
 791
 792     # Generate html table:
 793     _tpc_generate_html_table(
 794         header,
 795         tbl_lst,
 796         table[u'output-file'],
 797         legend=legend,
 798         title=table.get(u"title", u"")
 799     )
 800
 801
 802 def table_perf_trending_dash(table, input_data):
 803     """Generate the table(s) with algorithm:
 804     table_perf_trending_dash
 805     specified in the specification file.
 806
 807     :param table: Table to generate.
 808     :param input_data: Data to process.
 809     :type table: pandas.Series
 810     :type input_data: InputData
 811     """
 812
 813     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 814
 815     # Transform the data
 816     logging.info(
 817         f"    Creating the data set for the {table.get(u'type', u'')} "
 818         f"{table.get(u'title', u'')}."
 819     )
 820     data = input_data.filter_data(table, continue_on_error=True)
 821
 822     # Prepare the header of the tables
 823     header = [
 824         u"Test Case",
 825         u"Trend [Mpps]",
 826         u"Short-Term Change [%]",
 827         u"Long-Term Change [%]",
 828         u"Regressions [#]",
 829         u"Progressions [#]"
 830     ]
 831     header_str = u",".join(header) + u"\n"
 832
 833     incl_tests = table.get(u"include-tests", u"MRR")
 834
 835     # Prepare data to the table:
 836     tbl_dict = dict()
 837     for job, builds in table[u"data"].items():
 838         for build in builds:
 839             for tst_name, tst_data in data[job][str(build)].items():
 840                 if tst_name.lower() in table.get(u"ignore-list", list()):
 841                     continue
 842                 if tbl_dict.get(tst_name, None) is None:
 843                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
 844                     if not groups:
 845                         continue
 846                     nic = groups.group(0)
 847                     tbl_dict[tst_name] = {
 848                         u"name": f"{nic}-{tst_data[u'name']}",
 849                         u"data": OrderedDict()
 850                     }
 851                 try:
 852                     if incl_tests == u"MRR":
 853                         tbl_dict[tst_name][u"data"][str(build)] = \
 854                             tst_data[u"result"][u"receive-rate"]
 855                     elif incl_tests == u"NDR":
 856                         tbl_dict[tst_name][u"data"][str(build)] = \
 857                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 858                     elif incl_tests == u"PDR":
 859                         tbl_dict[tst_name][u"data"][str(build)] = \
 860                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 861                 except (TypeError, KeyError):
 862                     pass  # No data in output.xml for this test
 863
 864     tbl_lst = list()
 865     for tst_name in tbl_dict:
 866         data_t = tbl_dict[tst_name][u"data"]
 867         if len(data_t) < 2:
 868             continue
 869
 870         classification_lst, avgs, _ = classify_anomalies(data_t)
 871
 872         win_size = min(len(data_t), table[u"window"])
 873         long_win_size = min(len(data_t), table[u"long-trend-window"])
 874
 875         try:
 876             max_long_avg = max(
 877                 [x for x in avgs[-long_win_size:-win_size]
 878                  if not isnan(x)])
 879         except ValueError:
 880             max_long_avg = nan
 881         last_avg = avgs[-1]
 882         avg_week_ago = avgs[max(-win_size, -len(avgs))]
 883
 884         if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
 885             rel_change_last = nan
 886         else:
 887             rel_change_last = round(
 888                 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
 889
 890         if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
 891             rel_change_long = nan
 892         else:
 893             rel_change_long = round(
 894                 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
 895
 896         if classification_lst:
 897             if isnan(rel_change_last) and isnan(rel_change_long):
 898                 continue
 899             if isnan(last_avg) or isnan(rel_change_last) or \
 900                     isnan(rel_change_long):
 901                 continue
 902             tbl_lst.append(
 903                 [tbl_dict[tst_name][u"name"],
 904                  round(last_avg / 1e6, 2),
 905                  rel_change_last,
 906                  rel_change_long,
 907                  classification_lst[-win_size+1:].count(u"regression"),
 908                  classification_lst[-win_size+1:].count(u"progression")])
 909
 910     tbl_lst.sort(key=lambda rel: rel[0])
 911
 912     tbl_sorted = list()
 913     for nrr in range(table[u"window"], -1, -1):
 914         tbl_reg = [item for item in tbl_lst if item[4] == nrr]
 915         for nrp in range(table[u"window"], -1, -1):
 916             tbl_out = [item for item in tbl_reg if item[5] == nrp]
 917             tbl_out.sort(key=lambda rel: rel[2])
 918             tbl_sorted.extend(tbl_out)
 919
 920     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
 921
 922     logging.info(f"    Writing file: {file_name}")
 923     with open(file_name, u"wt") as file_handler:
 924         file_handler.write(header_str)
 925         for test in tbl_sorted:
 926             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
 927
 928     logging.info(f"    Writing file: {table[u'output-file']}.txt")
 929     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
 930
 931
 932 def _generate_url(testbed, test_name):
 933     """Generate URL to a trending plot from the name of the test case.
 934
 935     :param testbed: The testbed used for testing.
 936     :param test_name: The name of the test case.
 937     :type testbed: str
 938     :type test_name: str
 939     :returns: The URL to the plot with the trending data for the given test
 940         case.
 941     :rtype str
 942     """
 943
 944     if u"x520" in test_name:
 945         nic = u"x520"
 946     elif u"x710" in test_name:
 947         nic = u"x710"
 948     elif u"xl710" in test_name:
 949         nic = u"xl710"
 950     elif u"xxv710" in test_name:
 951         nic = u"xxv710"
 952     elif u"vic1227" in test_name:
 953         nic = u"vic1227"
 954     elif u"vic1385" in test_name:
 955         nic = u"vic1385"
 956     elif u"x553" in test_name:
 957         nic = u"x553"
 958     elif u"cx556" in test_name or u"cx556a" in test_name:
 959         nic = u"cx556a"
 960     else:
 961         nic = u""
 962
 963     if u"64b" in test_name:
 964         frame_size = u"64b"
 965     elif u"78b" in test_name:
 966         frame_size = u"78b"
 967     elif u"imix" in test_name:
 968         frame_size = u"imix"
 969     elif u"9000b" in test_name:
 970         frame_size = u"9000b"
 971     elif u"1518b" in test_name:
 972         frame_size = u"1518b"
 973     elif u"114b" in test_name:
 974         frame_size = u"114b"
 975     else:
 976         frame_size = u""
 977
 978     if u"1t1c" in test_name or \
 979         (u"-1c-" in test_name and
 980          testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 981         cores = u"1t1c"
 982     elif u"2t2c" in test_name or \
 983          (u"-2c-" in test_name and
 984           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 985         cores = u"2t2c"
 986     elif u"4t4c" in test_name or \
 987          (u"-4c-" in test_name and
 988           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 989         cores = u"4t4c"
 990     elif u"2t1c" in test_name or \
 991          (u"-1c-" in test_name and
 992           testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
 993         cores = u"2t1c"
 994     elif u"4t2c" in test_name or \
 995          (u"-2c-" in test_name and
 996           testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
 997         cores = u"4t2c"
 998     elif u"8t4c" in test_name or \
 999          (u"-4c-" in test_name and
1000           testbed in (u"2n-skx", u"3n-skx", u"2n-clx")):
1001         cores = u"8t4c"
1002     else:
1003         cores = u""
1004
1005     if u"testpmd" in test_name:
1006         driver = u"testpmd"
1007     elif u"l3fwd" in test_name:
1008         driver = u"l3fwd"
1009     elif u"avf" in test_name:
1010         driver = u"avf"
1011     elif u"rdma" in test_name:
1012         driver = u"rdma"
1013     elif u"dnv" in testbed or u"tsh" in testbed:
1014         driver = u"ixgbe"
1015     else:
1016         driver = u"dpdk"
1017
1018     if u"acl" in test_name or \
1019             u"macip" in test_name or \
1020             u"nat" in test_name or \
1021             u"policer" in test_name or \
1022             u"cop" in test_name:
1023         bsf = u"features"
1024     elif u"scale" in test_name:
1025         bsf = u"scale"
1026     elif u"base" in test_name:
1027         bsf = u"base"
1028     else:
1029         bsf = u"base"
1030
1031     if u"114b" in test_name and u"vhost" in test_name:
1032         domain = u"vts"
1033     elif u"testpmd" in test_name or u"l3fwd" in test_name:
1034         domain = u"dpdk"
1035     elif u"memif" in test_name:
1036         domain = u"container_memif"
1037     elif u"srv6" in test_name:
1038         domain = u"srv6"
1039     elif u"vhost" in test_name:
1040         domain = u"vhost"
1041         if u"vppl2xc" in test_name:
1042             driver += u"-vpp"
1043         else:
1044             driver += u"-testpmd"
1045         if u"lbvpplacp" in test_name:
1046             bsf += u"-link-bonding"
1047     elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1048         domain = u"nf_service_density_vnfc"
1049     elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1050         domain = u"nf_service_density_cnfc"
1051     elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1052         domain = u"nf_service_density_cnfp"
1053     elif u"ipsec" in test_name:
1054         domain = u"ipsec"
1055         if u"sw" in test_name:
1056             bsf += u"-sw"
1057         elif u"hw" in test_name:
1058             bsf += u"-hw"
1059     elif u"ethip4vxlan" in test_name:
1060         domain = u"ip4_tunnels"
1061     elif u"ip4base" in test_name or u"ip4scale" in test_name:
1062         domain = u"ip4"
1063     elif u"ip6base" in test_name or u"ip6scale" in test_name:
1064         domain = u"ip6"
1065     elif u"l2xcbase" in test_name or \
1066             u"l2xcscale" in test_name or \
1067             u"l2bdbasemaclrn" in test_name or \
1068             u"l2bdscale" in test_name or \
1069             u"l2patch" in test_name:
1070         domain = u"l2"
1071     else:
1072         domain = u""
1073
1074     file_name = u"-".join((domain, testbed, nic)) + u".html#"
1075     anchor_name = u"-".join((frame_size, cores, bsf, driver))
1076
1077     return file_name + anchor_name
1078
1079
1080 def table_perf_trending_dash_html(table, input_data):
1081     """Generate the table(s) with algorithm:
1082     table_perf_trending_dash_html specified in the specification
1083     file.
1084
1085     :param table: Table to generate.
1086     :param input_data: Data to process.
1087     :type table: dict
1088     :type input_data: InputData
1089     """
1090
1091     _ = input_data
1092
1093     if not table.get(u"testbed", None):
1094         logging.error(
1095             f"The testbed is not defined for the table "
1096             f"{table.get(u'title', u'')}."
1097         )
1098         return
1099
1100     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1101
1102     try:
1103         with open(table[u"input-file"], u'rt') as csv_file:
1104             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1105     except KeyError:
1106         logging.warning(u"The input file is not defined.")
1107         return
1108     except csv.Error as err:
1109         logging.warning(
1110             f"Not possible to process the file {table[u'input-file']}.\n"
1111             f"{repr(err)}"
1112         )
1113         return
1114
1115     # Table:
1116     dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1117
1118     # Table header:
1119     trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1120     for idx, item in enumerate(csv_lst[0]):
1121         alignment = u"left" if idx == 0 else u"center"
1122         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1123         thead.text = item
1124
1125     # Rows:
1126     colors = {
1127         u"regression": (
1128             u"#ffcccc",
1129             u"#ff9999"
1130         ),
1131         u"progression": (
1132             u"#c6ecc6",
1133             u"#9fdf9f"
1134         ),
1135         u"normal": (
1136             u"#e9f1fb",
1137             u"#d4e4f7"
1138         )
1139     }
1140     for r_idx, row in enumerate(csv_lst[1:]):
1141         if int(row[4]):
1142             color = u"regression"
1143         elif int(row[5]):
1144             color = u"progression"
1145         else:
1146             color = u"normal"
1147         trow = ET.SubElement(
1148             dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1149         )
1150
1151         # Columns:
1152         for c_idx, item in enumerate(row):
1153             tdata = ET.SubElement(
1154                 trow,
1155                 u"td",
1156                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1157             )
1158             # Name:
1159             if c_idx == 0 and table.get(u"add-links", True):
1160                 ref = ET.SubElement(
1161                     tdata,
1162                     u"a",
1163                     attrib=dict(
1164                         href=f"../trending/"
1165                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1166                     )
1167                 )
1168                 ref.text = item
1169             else:
1170                 tdata.text = item
1171     try:
1172         with open(table[u"output-file"], u'w') as html_file:
1173             logging.info(f"    Writing file: {table[u'output-file']}")
1174             html_file.write(u".. raw:: html\n\n\t")
1175             html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1176             html_file.write(u"\n\t<p><br><br></p>\n")
1177     except KeyError:
1178         logging.warning(u"The output file is not defined.")
1179         return
1180
1181
1182 def table_last_failed_tests(table, input_data):
1183     """Generate the table(s) with algorithm: table_last_failed_tests
1184     specified in the specification file.
1185
1186     :param table: Table to generate.
1187     :param input_data: Data to process.
1188     :type table: pandas.Series
1189     :type input_data: InputData
1190     """
1191
1192     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1193
1194     # Transform the data
1195     logging.info(
1196         f"    Creating the data set for the {table.get(u'type', u'')} "
1197         f"{table.get(u'title', u'')}."
1198     )
1199
1200     data = input_data.filter_data(table, continue_on_error=True)
1201
1202     if data is None or data.empty:
1203         logging.warning(
1204             f"    No data for the {table.get(u'type', u'')} "
1205             f"{table.get(u'title', u'')}."
1206         )
1207         return
1208
1209     tbl_list = list()
1210     for job, builds in table[u"data"].items():
1211         for build in builds:
1212             build = str(build)
1213             try:
1214                 version = input_data.metadata(job, build).get(u"version", u"")
1215             except KeyError:
1216                 logging.error(f"Data for {job}: {build} is not present.")
1217                 return
1218             tbl_list.append(build)
1219             tbl_list.append(version)
1220             failed_tests = list()
1221             passed = 0
1222             failed = 0
1223             for tst_data in data[job][build].values:
1224                 if tst_data[u"status"] != u"FAIL":
1225                     passed += 1
1226                     continue
1227                 failed += 1
1228                 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1229                 if not groups:
1230                     continue
1231                 nic = groups.group(0)
1232                 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1233             tbl_list.append(str(passed))
1234             tbl_list.append(str(failed))
1235             tbl_list.extend(failed_tests)
1236
1237     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1238     logging.info(f"    Writing file: {file_name}")
1239     with open(file_name, u"wt") as file_handler:
1240         for test in tbl_list:
1241             file_handler.write(test + u'\n')
1242
1243
1244 def table_failed_tests(table, input_data):
1245     """Generate the table(s) with algorithm: table_failed_tests
1246     specified in the specification file.
1247
1248     :param table: Table to generate.
1249     :param input_data: Data to process.
1250     :type table: pandas.Series
1251     :type input_data: InputData
1252     """
1253
1254     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1255
1256     # Transform the data
1257     logging.info(
1258         f"    Creating the data set for the {table.get(u'type', u'')} "
1259         f"{table.get(u'title', u'')}."
1260     )
1261     data = input_data.filter_data(table, continue_on_error=True)
1262
1263     # Prepare the header of the tables
1264     header = [
1265         u"Test Case",
1266         u"Failures [#]",
1267         u"Last Failure [Time]",
1268         u"Last Failure [VPP-Build-Id]",
1269         u"Last Failure [CSIT-Job-Build-Id]"
1270     ]
1271
1272     # Generate the data for the table according to the model in the table
1273     # specification
1274
1275     now = dt.utcnow()
1276     timeperiod = timedelta(int(table.get(u"window", 7)))
1277
1278     tbl_dict = dict()
1279     for job, builds in table[u"data"].items():
1280         for build in builds:
1281             build = str(build)
1282             for tst_name, tst_data in data[job][build].items():
1283                 if tst_name.lower() in table.get(u"ignore-list", list()):
1284                     continue
1285                 if tbl_dict.get(tst_name, None) is None:
1286                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
1287                     if not groups:
1288                         continue
1289                     nic = groups.group(0)
1290                     tbl_dict[tst_name] = {
1291                         u"name": f"{nic}-{tst_data[u'name']}",
1292                         u"data": OrderedDict()
1293                     }
1294                 try:
1295                     generated = input_data.metadata(job, build).\
1296                         get(u"generated", u"")
1297                     if not generated:
1298                         continue
1299                     then = dt.strptime(generated, u"%Y%m%d %H:%M")
1300                     if (now - then) <= timeperiod:
1301                         tbl_dict[tst_name][u"data"][build] = (
1302                             tst_data[u"status"],
1303                             generated,
1304                             input_data.metadata(job, build).get(u"version",
1305                                                                 u""),
1306                             build
1307                         )
1308                 except (TypeError, KeyError) as err:
1309                     logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1310
1311     max_fails = 0
1312     tbl_lst = list()
1313     for tst_data in tbl_dict.values():
1314         fails_nr = 0
1315         fails_last_date = u""
1316         fails_last_vpp = u""
1317         fails_last_csit = u""
1318         for val in tst_data[u"data"].values():
1319             if val[0] == u"FAIL":
1320                 fails_nr += 1
1321                 fails_last_date = val[1]
1322                 fails_last_vpp = val[2]
1323                 fails_last_csit = val[3]
1324         if fails_nr:
1325             max_fails = fails_nr if fails_nr > max_fails else max_fails
1326             tbl_lst.append(
1327                 [
1328                     tst_data[u"name"],
1329                     fails_nr,
1330                     fails_last_date,
1331                     fails_last_vpp,
1332                     f"mrr-daily-build-{fails_last_csit}"
1333                 ]
1334             )
1335
1336     tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1337     tbl_sorted = list()
1338     for nrf in range(max_fails, -1, -1):
1339         tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1340         tbl_sorted.extend(tbl_fails)
1341
1342     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1343     logging.info(f"    Writing file: {file_name}")
1344     with open(file_name, u"wt") as file_handler:
1345         file_handler.write(u",".join(header) + u"\n")
1346         for test in tbl_sorted:
1347             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1348
1349     logging.info(f"    Writing file: {table[u'output-file']}.txt")
1350     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1351
1352
1353 def table_failed_tests_html(table, input_data):
1354     """Generate the table(s) with algorithm: table_failed_tests_html
1355     specified in the specification file.
1356
1357     :param table: Table to generate.
1358     :param input_data: Data to process.
1359     :type table: pandas.Series
1360     :type input_data: InputData
1361     """
1362
1363     _ = input_data
1364
1365     if not table.get(u"testbed", None):
1366         logging.error(
1367             f"The testbed is not defined for the table "
1368             f"{table.get(u'title', u'')}."
1369         )
1370         return
1371
1372     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1373
1374     try:
1375         with open(table[u"input-file"], u'rt') as csv_file:
1376             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1377     except KeyError:
1378         logging.warning(u"The input file is not defined.")
1379         return
1380     except csv.Error as err:
1381         logging.warning(
1382             f"Not possible to process the file {table[u'input-file']}.\n"
1383             f"{repr(err)}"
1384         )
1385         return
1386
1387     # Table:
1388     failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1389
1390     # Table header:
1391     trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1392     for idx, item in enumerate(csv_lst[0]):
1393         alignment = u"left" if idx == 0 else u"center"
1394         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1395         thead.text = item
1396
1397     # Rows:
1398     colors = (u"#e9f1fb", u"#d4e4f7")
1399     for r_idx, row in enumerate(csv_lst[1:]):
1400         background = colors[r_idx % 2]
1401         trow = ET.SubElement(
1402             failed_tests, u"tr", attrib=dict(bgcolor=background)
1403         )
1404
1405         # Columns:
1406         for c_idx, item in enumerate(row):
1407             tdata = ET.SubElement(
1408                 trow,
1409                 u"td",
1410                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1411             )
1412             # Name:
1413             if c_idx == 0:
1414                 ref = ET.SubElement(
1415                     tdata,
1416                     u"a",
1417                     attrib=dict(
1418                         href=f"../trending/"
1419                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1420                     )
1421                 )
1422                 ref.text = item
1423             else:
1424                 tdata.text = item
1425     try:
1426         with open(table[u"output-file"], u'w') as html_file:
1427             logging.info(f"    Writing file: {table[u'output-file']}")
1428             html_file.write(u".. raw:: html\n\n\t")
1429             html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1430             html_file.write(u"\n\t<p><br><br></p>\n")
1431     except KeyError:
1432         logging.warning(u"The output file is not defined.")
1433         return
1434
1435
1436 def table_comparison(table, input_data):
1437     """Generate the table(s) with algorithm: table_comparison
1438     specified in the specification file.
1439
1440     :param table: Table to generate.
1441     :param input_data: Data to process.
1442     :type table: pandas.Series
1443     :type input_data: InputData
1444     """
1445     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1446
1447     # Transform the data
1448     logging.info(
1449         f"    Creating the data set for the {table.get(u'type', u'')} "
1450         f"{table.get(u'title', u'')}."
1451     )
1452
1453     columns = table.get(u"columns", None)
1454     if not columns:
1455         logging.error(
1456             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1457         )
1458         return
1459
1460     cols = list()
1461     for idx, col in enumerate(columns):
1462         if col.get(u"data-set", None) is None:
1463             logging.warning(f"No data for column {col.get(u'title', u'')}")
1464             continue
1465         tag = col.get(u"tag", None)
1466         data = input_data.filter_data(
1467             table,
1468             params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1469             data=col[u"data-set"],
1470             continue_on_error=True
1471         )
1472         col_data = {
1473             u"title": col.get(u"title", f"Column{idx}"),
1474             u"data": dict()
1475         }
1476         for builds in data.values:
1477             for build in builds:
1478                 for tst_name, tst_data in build.items():
1479                     if tag and tag not in tst_data[u"tags"]:
1480                         continue
1481                     tst_name_mod = \
1482                         _tpc_modify_test_name(tst_name, ignore_nic=True).\
1483                         replace(u"2n1l-", u"")
1484                     if col_data[u"data"].get(tst_name_mod, None) is None:
1485                         name = tst_data[u'name'].rsplit(u'-', 1)[0]
1486                         if u"across testbeds" in table[u"title"].lower() or \
1487                                 u"across topologies" in table[u"title"].lower():
1488                             name = _tpc_modify_displayed_test_name(name)
1489                         col_data[u"data"][tst_name_mod] = {
1490                             u"name": name,
1491                             u"replace": True,
1492                             u"data": list(),
1493                             u"mean": None,
1494                             u"stdev": None
1495                         }
1496                     _tpc_insert_data(
1497                         target=col_data[u"data"][tst_name_mod],
1498                         src=tst_data,
1499                         include_tests=table[u"include-tests"]
1500                     )
1501
1502         replacement = col.get(u"data-replacement", None)
1503         if replacement:
1504             rpl_data = input_data.filter_data(
1505                 table,
1506                 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1507                 data=replacement,
1508                 continue_on_error=True
1509             )
1510             for builds in rpl_data.values:
1511                 for build in builds:
1512                     for tst_name, tst_data in build.items():
1513                         if tag and tag not in tst_data[u"tags"]:
1514                             continue
1515                         tst_name_mod = \
1516                             _tpc_modify_test_name(tst_name, ignore_nic=True).\
1517                             replace(u"2n1l-", u"")
1518                         if col_data[u"data"].get(tst_name_mod, None) is None:
1519                             name = tst_data[u'name'].rsplit(u'-', 1)[0]
1520                             if u"across testbeds" in table[u"title"].lower() \
1521                                     or u"across topologies" in \
1522                                     table[u"title"].lower():
1523                                 name = _tpc_modify_displayed_test_name(name)
1524                             col_data[u"data"][tst_name_mod] = {
1525                                 u"name": name,
1526                                 u"replace": False,
1527                                 u"data": list(),
1528                                 u"mean": None,
1529                                 u"stdev": None
1530                             }
1531                         if col_data[u"data"][tst_name_mod][u"replace"]:
1532                             col_data[u"data"][tst_name_mod][u"replace"] = False
1533                             col_data[u"data"][tst_name_mod][u"data"] = list()
1534                         _tpc_insert_data(
1535                             target=col_data[u"data"][tst_name_mod],
1536                             src=tst_data,
1537                             include_tests=table[u"include-tests"]
1538                         )
1539
1540         if table[u"include-tests"] in (u"NDR", u"PDR"):
1541             for tst_name, tst_data in col_data[u"data"].items():
1542                 if tst_data[u"data"]:
1543                     tst_data[u"mean"] = mean(tst_data[u"data"])
1544                     tst_data[u"stdev"] = stdev(tst_data[u"data"])
1545
1546         cols.append(col_data)
1547
1548     tbl_dict = dict()
1549     for col in cols:
1550         for tst_name, tst_data in col[u"data"].items():
1551             if tbl_dict.get(tst_name, None) is None:
1552                 tbl_dict[tst_name] = {
1553                     "name": tst_data[u"name"]
1554                 }
1555             tbl_dict[tst_name][col[u"title"]] = {
1556                 u"mean": tst_data[u"mean"],
1557                 u"stdev": tst_data[u"stdev"]
1558             }
1559
1560     if not tbl_dict:
1561         logging.warning(f"No data for table {table.get(u'title', u'')}!")
1562         return
1563
1564     tbl_lst = list()
1565     for tst_data in tbl_dict.values():
1566         row = [tst_data[u"name"], ]
1567         for col in cols:
1568             row.append(tst_data.get(col[u"title"], None))
1569         tbl_lst.append(row)
1570
1571     comparisons = table.get(u"comparisons", None)
1572     if comparisons and isinstance(comparisons, list):
1573         for idx, comp in enumerate(comparisons):
1574             try:
1575                 col_ref = int(comp[u"reference"])
1576                 col_cmp = int(comp[u"compare"])
1577             except KeyError:
1578                 logging.warning(u"Comparison: No references defined! Skipping.")
1579                 comparisons.pop(idx)
1580                 continue
1581             if not (0 < col_ref <= len(cols) and
1582                     0 < col_cmp <= len(cols)) or \
1583                     col_ref == col_cmp:
1584                 logging.warning(f"Wrong values of reference={col_ref} "
1585                                 f"and/or compare={col_cmp}. Skipping.")
1586                 comparisons.pop(idx)
1587                 continue
1588
1589     tbl_cmp_lst = list()
1590     if comparisons:
1591         for row in tbl_lst:
1592             new_row = deepcopy(row)
1593             add_to_tbl = False
1594             for comp in comparisons:
1595                 ref_itm = row[int(comp[u"reference"])]
1596                 if ref_itm is None and \
1597                         comp.get(u"reference-alt", None) is not None:
1598                     ref_itm = row[int(comp[u"reference-alt"])]
1599                 cmp_itm = row[int(comp[u"compare"])]
1600                 if ref_itm is not None and cmp_itm is not None and \
1601                         ref_itm[u"mean"] is not None and \
1602                         cmp_itm[u"mean"] is not None and \
1603                         ref_itm[u"stdev"] is not None and \
1604                         cmp_itm[u"stdev"] is not None:
1605                     delta, d_stdev = relative_change_stdev(
1606                         ref_itm[u"mean"], cmp_itm[u"mean"],
1607                         ref_itm[u"stdev"], cmp_itm[u"stdev"]
1608                     )
1609                     new_row.append(
1610                         {
1611                             u"mean": delta * 1e6,
1612                             u"stdev": d_stdev * 1e6
1613                         }
1614                     )
1615                     add_to_tbl = True
1616                 else:
1617                     new_row.append(None)
1618             if add_to_tbl:
1619                 tbl_cmp_lst.append(new_row)
1620
1621     tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1622     tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1623
1624     rcas = list()
1625     rca_in = table.get(u"rca", None)
1626     if rca_in and isinstance(rca_in, list):
1627         for idx, itm in enumerate(rca_in):
1628             try:
1629                 with open(itm.get(u"data", u""), u"r") as rca_file:
1630                     rcas.append(
1631                         {
1632                             u"title": itm.get(u"title", f"RCA{idx}"),
1633                             u"data": load(rca_file, Loader=FullLoader)
1634                         }
1635                     )
1636             except (YAMLError, IOError) as err:
1637                 logging.warning(
1638                     f"The RCA file {itm.get(u'data', u'')} does not exist or "
1639                     f"it is corrupted!"
1640                 )
1641                 logging.debug(repr(err))
1642
1643     tbl_for_csv = list()
1644     for line in tbl_cmp_lst:
1645         row = [line[0], ]
1646         for idx, itm in enumerate(line[1:]):
1647             if itm is None or not isinstance(itm, dict) or\
1648                     itm.get(u'mean', None) is None or \
1649                     itm.get(u'stdev', None) is None:
1650                 row.append(u"NT")
1651                 row.append(u"NT")
1652             else:
1653                 row.append(round(float(itm[u'mean']) / 1e6, 3))
1654                 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1655         for rca in rcas:
1656             rca_nr = rca[u"data"].get(row[0], u"-")
1657             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1658         tbl_for_csv.append(row)
1659
1660     header_csv = [u"Test Case", ]
1661     for col in cols:
1662         header_csv.append(f"Avg({col[u'title']})")
1663         header_csv.append(f"Stdev({col[u'title']})")
1664     for comp in comparisons:
1665         header_csv.append(
1666             f"Avg({comp.get(u'title', u'')})"
1667         )
1668         header_csv.append(
1669             f"Stdev({comp.get(u'title', u'')})"
1670         )
1671     header_csv.extend([rca[u"title"] for rca in rcas])
1672
1673     legend_lst = table.get(u"legend", None)
1674     if legend_lst is None:
1675         legend = u""
1676     else:
1677         legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1678
1679     footnote = u""
1680     for rca in rcas:
1681         footnote += f"\n{rca[u'title']}:\n"
1682         footnote += rca[u"data"].get(u"footnote", u"")
1683
1684     csv_file = f"{table[u'output-file']}-csv.csv"
1685     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1686         file_handler.write(
1687             u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1688         )
1689         for test in tbl_for_csv:
1690             file_handler.write(
1691                 u",".join([f'"{item}"' for item in test]) + u"\n"
1692             )
1693         if legend_lst:
1694             for item in legend_lst:
1695                 file_handler.write(f'"{item}"\n')
1696         if footnote:
1697             for itm in footnote.split(u"\n"):
1698                 file_handler.write(f'"{itm}"\n')
1699
1700     tbl_tmp = list()
1701     max_lens = [0, ] * len(tbl_cmp_lst[0])
1702     for line in tbl_cmp_lst:
1703         row = [line[0], ]
1704         for idx, itm in enumerate(line[1:]):
1705             if itm is None:
1706                 new_itm = u"NT"
1707             else:
1708                 if idx < len(cols):
1709                     new_itm = (
1710                         f"{round(float(itm[u'mean']) / 1e6, 1)} "
1711                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1712                         replace(u"nan", u"NaN")
1713                     )
1714                 else:
1715                     new_itm = (
1716                         f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1717                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1718                         replace(u"nan", u"NaN")
1719                     )
1720             if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1721                 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1722             row.append(new_itm)
1723
1724         tbl_tmp.append(row)
1725
1726     tbl_final = list()
1727     for line in tbl_tmp:
1728         row = [line[0], ]
1729         for idx, itm in enumerate(line[1:]):
1730             if itm in (u"NT", u"NaN"):
1731                 row.append(itm)
1732                 continue
1733             itm_lst = itm.rsplit(u"\u00B1", 1)
1734             itm_lst[-1] = \
1735                 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1736             row.append(u"\u00B1".join(itm_lst))
1737         for rca in rcas:
1738             rca_nr = rca[u"data"].get(row[0], u"-")
1739             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1740
1741         tbl_final.append(row)
1742
1743     header = [u"Test Case", ]
1744     header.extend([col[u"title"] for col in cols])
1745     header.extend([comp.get(u"title", u"") for comp in comparisons])
1746     header.extend([rca[u"title"] for rca in rcas])
1747
1748     # Generate csv tables:
1749     csv_file = f"{table[u'output-file']}.csv"
1750     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1751         file_handler.write(u";".join(header) + u"\n")
1752         for test in tbl_final:
1753             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1754
1755     # Generate txt table:
1756     txt_file_name = f"{table[u'output-file']}.txt"
1757     convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
1758
1759     with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
1760         txt_file.write(legend)
1761         txt_file.write(footnote)
1762
1763     # Generate html table:
1764     _tpc_generate_html_table(
1765         header,
1766         tbl_final,
1767         table[u'output-file'],
1768         legend=legend,
1769         footnote=footnote,
1770         sort_data=False,
1771         title=table.get(u"title", u"")
1772     )
1773
1774
1775 def table_weekly_comparison(table, in_data):
1776     """Generate the table(s) with algorithm: table_weekly_comparison
1777     specified in the specification file.
1778
1779     :param table: Table to generate.
1780     :param in_data: Data to process.
1781     :type table: pandas.Series
1782     :type in_data: InputData
1783     """
1784     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1785
1786     # Transform the data
1787     logging.info(
1788         f"    Creating the data set for the {table.get(u'type', u'')} "
1789         f"{table.get(u'title', u'')}."
1790     )
1791
1792     incl_tests = table.get(u"include-tests", None)
1793     if incl_tests not in (u"NDR", u"PDR"):
1794         logging.error(f"Wrong tests to include specified ({incl_tests}).")
1795         return
1796
1797     nr_cols = table.get(u"nr-of-data-columns", None)
1798     if not nr_cols or nr_cols < 2:
1799         logging.error(
1800             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1801         )
1802         return
1803
1804     data = in_data.filter_data(
1805         table,
1806         params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1807         continue_on_error=True
1808     )
1809
1810     header = [
1811         [u"VPP Version", ],
1812         [u"Start Timestamp", ],
1813         [u"CSIT Build", ],
1814         [u"CSIT Testbed", ]
1815     ]
1816     tbl_dict = dict()
1817     idx = 0
1818     tb_tbl = table.get(u"testbeds", None)
1819     for job_name, job_data in data.items():
1820         for build_nr, build in job_data.items():
1821             if idx >= nr_cols:
1822                 break
1823             if build.empty:
1824                 continue
1825
1826             tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1827             if tb_ip and tb_tbl:
1828                 testbed = tb_tbl.get(tb_ip, u"")
1829             else:
1830                 testbed = u""
1831             header[2].insert(1, build_nr)
1832             header[3].insert(1, testbed)
1833             header[1].insert(
1834                 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1835             )
1836             header[0].insert(
1837                 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1838             )
1839
1840             for tst_name, tst_data in build.items():
1841                 tst_name_mod = \
1842                     _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1843                 if not tbl_dict.get(tst_name_mod, None):
1844                     tbl_dict[tst_name_mod] = dict(
1845                         name=tst_data[u'name'].rsplit(u'-', 1)[0],
1846                     )
1847                 try:
1848                     tbl_dict[tst_name_mod][-idx - 1] = \
1849                         tst_data[u"throughput"][incl_tests][u"LOWER"]
1850                 except (TypeError, IndexError, KeyError, ValueError):
1851                     pass
1852             idx += 1
1853
1854     if idx < nr_cols:
1855         logging.error(u"Not enough data to build the table! Skipping")
1856         return
1857
1858     cmp_dict = dict()
1859     for idx, cmp in enumerate(table.get(u"comparisons", list())):
1860         idx_ref = cmp.get(u"reference", None)
1861         idx_cmp = cmp.get(u"compare", None)
1862         if idx_ref is None or idx_cmp is None:
1863             continue
1864         header[0].append(
1865             f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1866             f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1867         )
1868         header[1].append(u"")
1869         header[2].append(u"")
1870         header[3].append(u"")
1871         for tst_name, tst_data in tbl_dict.items():
1872             if not cmp_dict.get(tst_name, None):
1873                 cmp_dict[tst_name] = list()
1874             ref_data = tst_data.get(idx_ref, None)
1875             cmp_data = tst_data.get(idx_cmp, None)
1876             if ref_data is None or cmp_data is None:
1877                 cmp_dict[tst_name].append(float('nan'))
1878             else:
1879                 cmp_dict[tst_name].append(
1880                     relative_change(ref_data, cmp_data)
1881                 )
1882
1883     tbl_lst = list()
1884     for tst_name, tst_data in tbl_dict.items():
1885         itm_lst = [tst_data[u"name"], ]
1886         for idx in range(nr_cols):
1887             item = tst_data.get(-idx - 1, None)
1888             if item is None:
1889                 itm_lst.insert(1, None)
1890             else:
1891                 itm_lst.insert(1, round(item / 1e6, 1))
1892         itm_lst.extend(
1893             [
1894                 None if itm is None else round(itm, 1)
1895                 for itm in cmp_dict[tst_name]
1896             ]
1897         )
1898         tbl_lst.append(itm_lst)
1899
1900     tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1901     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
1902
1903     # Generate csv table:
1904     csv_file = f"{table[u'output-file']}.csv"
1905     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1906         for hdr in header:
1907             file_handler.write(u",".join(hdr) + u"\n")
1908         for test in tbl_lst:
1909             file_handler.write(u",".join(
1910                 [
1911                     str(item).replace(u"None", u"-").replace(u"nan", u"-").
1912                     replace(u"null", u"-") for item in test
1913                 ]
1914             ) + u"\n")
1915
1916     txt_file = f"{table[u'output-file']}.txt"
1917     convert_csv_to_pretty_txt(csv_file, txt_file, delimiter=u",")
1918
1919     # Reorganize header in txt table
1920     txt_table = list()
1921     with open(txt_file, u"rt", encoding='utf-8') as file_handler:
1922         for line in file_handler:
1923             txt_table.append(line)
1924     try:
1925         txt_table.insert(5, txt_table.pop(2))
1926         with open(txt_file, u"wt", encoding='utf-8') as file_handler:
1927             file_handler.writelines(txt_table)
1928     except IndexError:
1929         pass
1930
1931     # Generate html table:
1932     hdr_html = [
1933         u"<br>".join(row) for row in zip(*header)
1934     ]
1935     _tpc_generate_html_table(
1936         hdr_html,
1937         tbl_lst,
1938         table[u'output-file'],
1939         sort_data=True,
1940         title=table.get(u"title", u""),
1941         generate_rst=False
1942     )