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     test_type = u"MRR"
1264     if u"NDRPDR" in table.get(u"filter", list()):
1265         test_type = u"NDRPDR"
1266
1267     # Prepare the header of the tables
1268     header = [
1269         u"Test Case",
1270         u"Failures [#]",
1271         u"Last Failure [Time]",
1272         u"Last Failure [VPP-Build-Id]",
1273         u"Last Failure [CSIT-Job-Build-Id]"
1274     ]
1275
1276     # Generate the data for the table according to the model in the table
1277     # specification
1278
1279     now = dt.utcnow()
1280     timeperiod = timedelta(int(table.get(u"window", 7)))
1281
1282     tbl_dict = dict()
1283     for job, builds in table[u"data"].items():
1284         for build in builds:
1285             build = str(build)
1286             for tst_name, tst_data in data[job][build].items():
1287                 if tst_name.lower() in table.get(u"ignore-list", list()):
1288                     continue
1289                 if tbl_dict.get(tst_name, None) is None:
1290                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
1291                     if not groups:
1292                         continue
1293                     nic = groups.group(0)
1294                     tbl_dict[tst_name] = {
1295                         u"name": f"{nic}-{tst_data[u'name']}",
1296                         u"data": OrderedDict()
1297                     }
1298                 try:
1299                     generated = input_data.metadata(job, build).\
1300                         get(u"generated", u"")
1301                     if not generated:
1302                         continue
1303                     then = dt.strptime(generated, u"%Y%m%d %H:%M")
1304                     if (now - then) <= timeperiod:
1305                         tbl_dict[tst_name][u"data"][build] = (
1306                             tst_data[u"status"],
1307                             generated,
1308                             input_data.metadata(job, build).get(u"version",
1309                                                                 u""),
1310                             build
1311                         )
1312                 except (TypeError, KeyError) as err:
1313                     logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1314
1315     max_fails = 0
1316     tbl_lst = list()
1317     for tst_data in tbl_dict.values():
1318         fails_nr = 0
1319         fails_last_date = u""
1320         fails_last_vpp = u""
1321         fails_last_csit = u""
1322         for val in tst_data[u"data"].values():
1323             if val[0] == u"FAIL":
1324                 fails_nr += 1
1325                 fails_last_date = val[1]
1326                 fails_last_vpp = val[2]
1327                 fails_last_csit = val[3]
1328         if fails_nr:
1329             max_fails = fails_nr if fails_nr > max_fails else max_fails
1330             tbl_lst.append([
1331                 tst_data[u"name"],
1332                 fails_nr,
1333                 fails_last_date,
1334                 fails_last_vpp,
1335                 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1336                 f"-build-{fails_last_csit}"
1337             ])
1338
1339     tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1340     tbl_sorted = list()
1341     for nrf in range(max_fails, -1, -1):
1342         tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1343         tbl_sorted.extend(tbl_fails)
1344
1345     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1346     logging.info(f"    Writing file: {file_name}")
1347     with open(file_name, u"wt") as file_handler:
1348         file_handler.write(u",".join(header) + u"\n")
1349         for test in tbl_sorted:
1350             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1351
1352     logging.info(f"    Writing file: {table[u'output-file']}.txt")
1353     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1354
1355
1356 def table_failed_tests_html(table, input_data):
1357     """Generate the table(s) with algorithm: table_failed_tests_html
1358     specified in the specification file.
1359
1360     :param table: Table to generate.
1361     :param input_data: Data to process.
1362     :type table: pandas.Series
1363     :type input_data: InputData
1364     """
1365
1366     _ = input_data
1367
1368     if not table.get(u"testbed", None):
1369         logging.error(
1370             f"The testbed is not defined for the table "
1371             f"{table.get(u'title', u'')}."
1372         )
1373         return
1374
1375     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1376
1377     try:
1378         with open(table[u"input-file"], u'rt') as csv_file:
1379             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1380     except KeyError:
1381         logging.warning(u"The input file is not defined.")
1382         return
1383     except csv.Error as err:
1384         logging.warning(
1385             f"Not possible to process the file {table[u'input-file']}.\n"
1386             f"{repr(err)}"
1387         )
1388         return
1389
1390     # Table:
1391     failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1392
1393     # Table header:
1394     trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1395     for idx, item in enumerate(csv_lst[0]):
1396         alignment = u"left" if idx == 0 else u"center"
1397         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1398         thead.text = item
1399
1400     # Rows:
1401     colors = (u"#e9f1fb", u"#d4e4f7")
1402     for r_idx, row in enumerate(csv_lst[1:]):
1403         background = colors[r_idx % 2]
1404         trow = ET.SubElement(
1405             failed_tests, u"tr", attrib=dict(bgcolor=background)
1406         )
1407
1408         # Columns:
1409         for c_idx, item in enumerate(row):
1410             tdata = ET.SubElement(
1411                 trow,
1412                 u"td",
1413                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1414             )
1415             # Name:
1416             if c_idx == 0:
1417                 ref = ET.SubElement(
1418                     tdata,
1419                     u"a",
1420                     attrib=dict(
1421                         href=f"../trending/"
1422                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1423                     )
1424                 )
1425                 ref.text = item
1426             else:
1427                 tdata.text = item
1428     try:
1429         with open(table[u"output-file"], u'w') as html_file:
1430             logging.info(f"    Writing file: {table[u'output-file']}")
1431             html_file.write(u".. raw:: html\n\n\t")
1432             html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1433             html_file.write(u"\n\t<p><br><br></p>\n")
1434     except KeyError:
1435         logging.warning(u"The output file is not defined.")
1436         return
1437
1438
1439 def table_comparison(table, input_data):
1440     """Generate the table(s) with algorithm: table_comparison
1441     specified in the specification file.
1442
1443     :param table: Table to generate.
1444     :param input_data: Data to process.
1445     :type table: pandas.Series
1446     :type input_data: InputData
1447     """
1448     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1449
1450     # Transform the data
1451     logging.info(
1452         f"    Creating the data set for the {table.get(u'type', u'')} "
1453         f"{table.get(u'title', u'')}."
1454     )
1455
1456     columns = table.get(u"columns", None)
1457     if not columns:
1458         logging.error(
1459             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1460         )
1461         return
1462
1463     cols = list()
1464     for idx, col in enumerate(columns):
1465         if col.get(u"data-set", None) is None:
1466             logging.warning(f"No data for column {col.get(u'title', u'')}")
1467             continue
1468         tag = col.get(u"tag", None)
1469         data = input_data.filter_data(
1470             table,
1471             params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1472             data=col[u"data-set"],
1473             continue_on_error=True
1474         )
1475         col_data = {
1476             u"title": col.get(u"title", f"Column{idx}"),
1477             u"data": dict()
1478         }
1479         for builds in data.values:
1480             for build in builds:
1481                 for tst_name, tst_data in build.items():
1482                     if tag and tag not in tst_data[u"tags"]:
1483                         continue
1484                     tst_name_mod = \
1485                         _tpc_modify_test_name(tst_name, ignore_nic=True).\
1486                         replace(u"2n1l-", u"")
1487                     if col_data[u"data"].get(tst_name_mod, None) is None:
1488                         name = tst_data[u'name'].rsplit(u'-', 1)[0]
1489                         if u"across testbeds" in table[u"title"].lower() or \
1490                                 u"across topologies" in table[u"title"].lower():
1491                             name = _tpc_modify_displayed_test_name(name)
1492                         col_data[u"data"][tst_name_mod] = {
1493                             u"name": name,
1494                             u"replace": True,
1495                             u"data": list(),
1496                             u"mean": None,
1497                             u"stdev": None
1498                         }
1499                     _tpc_insert_data(
1500                         target=col_data[u"data"][tst_name_mod],
1501                         src=tst_data,
1502                         include_tests=table[u"include-tests"]
1503                     )
1504
1505         replacement = col.get(u"data-replacement", None)
1506         if replacement:
1507             rpl_data = input_data.filter_data(
1508                 table,
1509                 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1510                 data=replacement,
1511                 continue_on_error=True
1512             )
1513             for builds in rpl_data.values:
1514                 for build in builds:
1515                     for tst_name, tst_data in build.items():
1516                         if tag and tag not in tst_data[u"tags"]:
1517                             continue
1518                         tst_name_mod = \
1519                             _tpc_modify_test_name(tst_name, ignore_nic=True).\
1520                             replace(u"2n1l-", u"")
1521                         if col_data[u"data"].get(tst_name_mod, None) is None:
1522                             name = tst_data[u'name'].rsplit(u'-', 1)[0]
1523                             if u"across testbeds" in table[u"title"].lower() \
1524                                     or u"across topologies" in \
1525                                     table[u"title"].lower():
1526                                 name = _tpc_modify_displayed_test_name(name)
1527                             col_data[u"data"][tst_name_mod] = {
1528                                 u"name": name,
1529                                 u"replace": False,
1530                                 u"data": list(),
1531                                 u"mean": None,
1532                                 u"stdev": None
1533                             }
1534                         if col_data[u"data"][tst_name_mod][u"replace"]:
1535                             col_data[u"data"][tst_name_mod][u"replace"] = False
1536                             col_data[u"data"][tst_name_mod][u"data"] = list()
1537                         _tpc_insert_data(
1538                             target=col_data[u"data"][tst_name_mod],
1539                             src=tst_data,
1540                             include_tests=table[u"include-tests"]
1541                         )
1542
1543         if table[u"include-tests"] in (u"NDR", u"PDR"):
1544             for tst_name, tst_data in col_data[u"data"].items():
1545                 if tst_data[u"data"]:
1546                     tst_data[u"mean"] = mean(tst_data[u"data"])
1547                     tst_data[u"stdev"] = stdev(tst_data[u"data"])
1548
1549         cols.append(col_data)
1550
1551     tbl_dict = dict()
1552     for col in cols:
1553         for tst_name, tst_data in col[u"data"].items():
1554             if tbl_dict.get(tst_name, None) is None:
1555                 tbl_dict[tst_name] = {
1556                     "name": tst_data[u"name"]
1557                 }
1558             tbl_dict[tst_name][col[u"title"]] = {
1559                 u"mean": tst_data[u"mean"],
1560                 u"stdev": tst_data[u"stdev"]
1561             }
1562
1563     if not tbl_dict:
1564         logging.warning(f"No data for table {table.get(u'title', u'')}!")
1565         return
1566
1567     tbl_lst = list()
1568     for tst_data in tbl_dict.values():
1569         row = [tst_data[u"name"], ]
1570         for col in cols:
1571             row.append(tst_data.get(col[u"title"], None))
1572         tbl_lst.append(row)
1573
1574     comparisons = table.get(u"comparisons", None)
1575     if comparisons and isinstance(comparisons, list):
1576         for idx, comp in enumerate(comparisons):
1577             try:
1578                 col_ref = int(comp[u"reference"])
1579                 col_cmp = int(comp[u"compare"])
1580             except KeyError:
1581                 logging.warning(u"Comparison: No references defined! Skipping.")
1582                 comparisons.pop(idx)
1583                 continue
1584             if not (0 < col_ref <= len(cols) and
1585                     0 < col_cmp <= len(cols)) or \
1586                     col_ref == col_cmp:
1587                 logging.warning(f"Wrong values of reference={col_ref} "
1588                                 f"and/or compare={col_cmp}. Skipping.")
1589                 comparisons.pop(idx)
1590                 continue
1591
1592     tbl_cmp_lst = list()
1593     if comparisons:
1594         for row in tbl_lst:
1595             new_row = deepcopy(row)
1596             add_to_tbl = False
1597             for comp in comparisons:
1598                 ref_itm = row[int(comp[u"reference"])]
1599                 if ref_itm is None and \
1600                         comp.get(u"reference-alt", None) is not None:
1601                     ref_itm = row[int(comp[u"reference-alt"])]
1602                 cmp_itm = row[int(comp[u"compare"])]
1603                 if ref_itm is not None and cmp_itm is not None and \
1604                         ref_itm[u"mean"] is not None and \
1605                         cmp_itm[u"mean"] is not None and \
1606                         ref_itm[u"stdev"] is not None and \
1607                         cmp_itm[u"stdev"] is not None:
1608                     delta, d_stdev = relative_change_stdev(
1609                         ref_itm[u"mean"], cmp_itm[u"mean"],
1610                         ref_itm[u"stdev"], cmp_itm[u"stdev"]
1611                     )
1612                     new_row.append(
1613                         {
1614                             u"mean": delta * 1e6,
1615                             u"stdev": d_stdev * 1e6
1616                         }
1617                     )
1618                     add_to_tbl = True
1619                 else:
1620                     new_row.append(None)
1621             if add_to_tbl:
1622                 tbl_cmp_lst.append(new_row)
1623
1624     tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1625     tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1626
1627     rcas = list()
1628     rca_in = table.get(u"rca", None)
1629     if rca_in and isinstance(rca_in, list):
1630         for idx, itm in enumerate(rca_in):
1631             try:
1632                 with open(itm.get(u"data", u""), u"r") as rca_file:
1633                     rcas.append(
1634                         {
1635                             u"title": itm.get(u"title", f"RCA{idx}"),
1636                             u"data": load(rca_file, Loader=FullLoader)
1637                         }
1638                     )
1639             except (YAMLError, IOError) as err:
1640                 logging.warning(
1641                     f"The RCA file {itm.get(u'data', u'')} does not exist or "
1642                     f"it is corrupted!"
1643                 )
1644                 logging.debug(repr(err))
1645
1646     tbl_for_csv = list()
1647     for line in tbl_cmp_lst:
1648         row = [line[0], ]
1649         for idx, itm in enumerate(line[1:]):
1650             if itm is None:
1651                 row.append(u"NT")
1652                 row.append(u"NT")
1653             else:
1654                 row.append(round(float(itm[u'mean']) / 1e6, 3))
1655                 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1656         for rca in rcas:
1657             rca_nr = rca[u"data"].get(row[0], u"-")
1658             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1659         tbl_for_csv.append(row)
1660
1661     header_csv = [u"Test Case", ]
1662     for col in cols:
1663         header_csv.append(f"Avg({col[u'title']})")
1664         header_csv.append(f"Stdev({col[u'title']})")
1665     for comp in comparisons:
1666         header_csv.append(
1667             f"Avg({comp.get(u'title', u'')})"
1668         )
1669         header_csv.append(
1670             f"Stdev({comp.get(u'title', u'')})"
1671         )
1672     header_csv.extend([rca[u"title"] for rca in rcas])
1673
1674     legend_lst = table.get(u"legend", None)
1675     if legend_lst is None:
1676         legend = u""
1677     else:
1678         legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1679
1680     footnote = u""
1681     for rca in rcas:
1682         footnote += f"\n{rca[u'title']}:\n"
1683         footnote += rca[u"data"].get(u"footnote", u"")
1684
1685     csv_file = f"{table[u'output-file']}-csv.csv"
1686     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1687         file_handler.write(
1688             u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1689         )
1690         for test in tbl_for_csv:
1691             file_handler.write(
1692                 u",".join([f'"{item}"' for item in test]) + u"\n"
1693             )
1694         if legend_lst:
1695             for item in legend_lst:
1696                 file_handler.write(f'"{item}"\n')
1697         if footnote:
1698             for itm in footnote.split(u"\n"):
1699                 file_handler.write(f'"{itm}"\n')
1700
1701     tbl_tmp = list()
1702     max_lens = [0, ] * len(tbl_cmp_lst[0])
1703     for line in tbl_cmp_lst:
1704         row = [line[0], ]
1705         for idx, itm in enumerate(line[1:]):
1706             if itm is None:
1707                 new_itm = u"NT"
1708             else:
1709                 if idx < len(cols):
1710                     new_itm = (
1711                         f"{round(float(itm[u'mean']) / 1e6, 1)} "
1712                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1713                         replace(u"nan", u"NaN")
1714                     )
1715                 else:
1716                     new_itm = (
1717                         f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1718                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1719                         replace(u"nan", u"NaN")
1720                     )
1721             if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1722                 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1723             row.append(new_itm)
1724
1725         tbl_tmp.append(row)
1726
1727     tbl_final = list()
1728     for line in tbl_tmp:
1729         row = [line[0], ]
1730         for idx, itm in enumerate(line[1:]):
1731             if itm in (u"NT", u"NaN"):
1732                 row.append(itm)
1733                 continue
1734             itm_lst = itm.rsplit(u"\u00B1", 1)
1735             itm_lst[-1] = \
1736                 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1737             row.append(u"\u00B1".join(itm_lst))
1738         for rca in rcas:
1739             rca_nr = rca[u"data"].get(row[0], u"-")
1740             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1741
1742         tbl_final.append(row)
1743
1744     header = [u"Test Case", ]
1745     header.extend([col[u"title"] for col in cols])
1746     header.extend([comp.get(u"title", u"") for comp in comparisons])
1747     header.extend([rca[u"title"] for rca in rcas])
1748
1749     # Generate csv tables:
1750     csv_file = f"{table[u'output-file']}.csv"
1751     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1752         file_handler.write(u";".join(header) + u"\n")
1753         for test in tbl_final:
1754             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1755
1756     # Generate txt table:
1757     txt_file_name = f"{table[u'output-file']}.txt"
1758     convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
1759
1760     with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
1761         txt_file.write(legend)
1762         txt_file.write(footnote)
1763
1764     # Generate html table:
1765     _tpc_generate_html_table(
1766         header,
1767         tbl_final,
1768         table[u'output-file'],
1769         legend=legend,
1770         footnote=footnote,
1771         sort_data=False,
1772         title=table.get(u"title", u"")
1773     )
1774
1775
1776 def table_weekly_comparison(table, in_data):
1777     """Generate the table(s) with algorithm: table_weekly_comparison
1778     specified in the specification file.
1779
1780     :param table: Table to generate.
1781     :param in_data: Data to process.
1782     :type table: pandas.Series
1783     :type in_data: InputData
1784     """
1785     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1786
1787     # Transform the data
1788     logging.info(
1789         f"    Creating the data set for the {table.get(u'type', u'')} "
1790         f"{table.get(u'title', u'')}."
1791     )
1792
1793     incl_tests = table.get(u"include-tests", None)
1794     if incl_tests not in (u"NDR", u"PDR"):
1795         logging.error(f"Wrong tests to include specified ({incl_tests}).")
1796         return
1797
1798     nr_cols = table.get(u"nr-of-data-columns", None)
1799     if not nr_cols or nr_cols < 2:
1800         logging.error(
1801             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1802         )
1803         return
1804
1805     data = in_data.filter_data(
1806         table,
1807         params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1808         continue_on_error=True
1809     )
1810
1811     header = [
1812         [u"VPP Version", ],
1813         [u"Start Timestamp", ],
1814         [u"CSIT Build", ],
1815         [u"CSIT Testbed", ]
1816     ]
1817     tbl_dict = dict()
1818     idx = 0
1819     tb_tbl = table.get(u"testbeds", None)
1820     for job_name, job_data in data.items():
1821         for build_nr, build in job_data.items():
1822             if idx >= nr_cols:
1823                 break
1824             if build.empty:
1825                 continue
1826
1827             tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1828             if tb_ip and tb_tbl:
1829                 testbed = tb_tbl.get(tb_ip, u"")
1830             else:
1831                 testbed = u""
1832             header[2].insert(1, build_nr)
1833             header[3].insert(1, testbed)
1834             header[1].insert(
1835                 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1836             )
1837             header[0].insert(
1838                 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1839             )
1840
1841             for tst_name, tst_data in build.items():
1842                 tst_name_mod = \
1843                     _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1844                 if not tbl_dict.get(tst_name_mod, None):
1845                     tbl_dict[tst_name_mod] = dict(
1846                         name=tst_data[u'name'].rsplit(u'-', 1)[0],
1847                     )
1848                 try:
1849                     tbl_dict[tst_name_mod][-idx - 1] = \
1850                         tst_data[u"throughput"][incl_tests][u"LOWER"]
1851                 except (TypeError, IndexError, KeyError, ValueError):
1852                     pass
1853             idx += 1
1854
1855     if idx < nr_cols:
1856         logging.error(u"Not enough data to build the table! Skipping")
1857         return
1858
1859     cmp_dict = dict()
1860     for idx, cmp in enumerate(table.get(u"comparisons", list())):
1861         idx_ref = cmp.get(u"reference", None)
1862         idx_cmp = cmp.get(u"compare", None)
1863         if idx_ref is None or idx_cmp is None:
1864             continue
1865         header[0].append(
1866             f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1867             f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1868         )
1869         header[1].append(u"")
1870         header[2].append(u"")
1871         header[3].append(u"")
1872         for tst_name, tst_data in tbl_dict.items():
1873             if not cmp_dict.get(tst_name, None):
1874                 cmp_dict[tst_name] = list()
1875             ref_data = tst_data.get(idx_ref, None)
1876             cmp_data = tst_data.get(idx_cmp, None)
1877             if ref_data is None or cmp_data is None:
1878                 cmp_dict[tst_name].append(float('nan'))
1879             else:
1880                 cmp_dict[tst_name].append(
1881                     relative_change(ref_data, cmp_data)
1882                 )
1883
1884     tbl_lst = list()
1885     for tst_name, tst_data in tbl_dict.items():
1886         itm_lst = [tst_data[u"name"], ]
1887         for idx in range(nr_cols):
1888             item = tst_data.get(-idx - 1, None)
1889             if item is None:
1890                 itm_lst.insert(1, None)
1891             else:
1892                 itm_lst.insert(1, round(item / 1e6, 1))
1893         itm_lst.extend(
1894             [
1895                 None if itm is None else round(itm, 1)
1896                 for itm in cmp_dict[tst_name]
1897             ]
1898         )
1899         tbl_lst.append(itm_lst)
1900
1901     tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1902     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
1903
1904     # Generate csv table:
1905     csv_file = f"{table[u'output-file']}.csv"
1906     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1907         for hdr in header:
1908             file_handler.write(u",".join(hdr) + u"\n")
1909         for test in tbl_lst:
1910             file_handler.write(u",".join(
1911                 [
1912                     str(item).replace(u"None", u"-").replace(u"nan", u"-").
1913                     replace(u"null", u"-") for item in test
1914                 ]
1915             ) + u"\n")
1916
1917     txt_file = f"{table[u'output-file']}.txt"
1918     convert_csv_to_pretty_txt(csv_file, txt_file, delimiter=u",")
1919
1920     # Reorganize header in txt table
1921     txt_table = list()
1922     with open(txt_file, u"rt", encoding='utf-8') as file_handler:
1923         for line in file_handler:
1924             txt_table.append(line)
1925     try:
1926         txt_table.insert(5, txt_table.pop(2))
1927         with open(txt_file, u"wt", encoding='utf-8') as file_handler:
1928             file_handler.writelines(txt_table)
1929     except IndexError:
1930         pass
1931
1932     # Generate html table:
1933     hdr_html = [
1934         u"<br>".join(row) for row in zip(*header)
1935     ]
1936     _tpc_generate_html_table(
1937         hdr_html,
1938         tbl_lst,
1939         table[u'output-file'],
1940         sort_data=True,
1941         title=table.get(u"title", u""),
1942         generate_rst=False
1943     )