resources/tools/presentation/generator_tables.py

   1 # Copyright (c) 2021 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     logging.info(f"    Writing the HTML file to {path}{file_name}.rst")
 598     with open(f"{path}{file_name}.rst", u"wt") as rst_file:
 599         rst_file.write(
 600             u"\n"
 601             u".. |br| raw:: html\n\n    <br />\n\n\n"
 602             u".. |prein| raw:: html\n\n    <pre>\n\n\n"
 603             u".. |preout| raw:: html\n\n    </pre>\n\n"
 604         )
 605         if title:
 606             rst_file.write(f"{title}\n")
 607             rst_file.write(f"{u'`' * len(title)}\n\n")
 608         rst_file.write(
 609             u".. raw:: html\n\n"
 610             f'    <iframe frameborder="0" scrolling="no" '
 611             f'width="1600" height="1200" '
 612             f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
 613             f'</iframe>\n\n'
 614         )
 615
 616         if legend:
 617             try:
 618                 itm_lst = legend[1:-2].split(u"\n")
 619                 rst_file.write(
 620                     f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
 621                 )
 622             except IndexError as err:
 623                 logging.error(f"Legend cannot be written to html file\n{err}")
 624         if footnote:
 625             try:
 626                 itm_lst = footnote[1:].split(u"\n")
 627                 rst_file.write(
 628                     f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
 629                 )
 630             except IndexError as err:
 631                 logging.error(f"Footnote cannot be written to html file\n{err}")
 632
 633
 634 def table_soak_vs_ndr(table, input_data):
 635     """Generate the table(s) with algorithm: table_soak_vs_ndr
 636     specified in the specification file.
 637
 638     :param table: Table to generate.
 639     :param input_data: Data to process.
 640     :type table: pandas.Series
 641     :type input_data: InputData
 642     """
 643
 644     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 645
 646     # Transform the data
 647     logging.info(
 648         f"    Creating the data set for the {table.get(u'type', u'')} "
 649         f"{table.get(u'title', u'')}."
 650     )
 651     data = input_data.filter_data(table, continue_on_error=True)
 652
 653     # Prepare the header of the table
 654     try:
 655         header = [
 656             u"Test Case",
 657             f"Avg({table[u'reference'][u'title']})",
 658             f"Stdev({table[u'reference'][u'title']})",
 659             f"Avg({table[u'compare'][u'title']})",
 660             f"Stdev{table[u'compare'][u'title']})",
 661             u"Diff",
 662             u"Stdev(Diff)"
 663         ]
 664         header_str = u";".join(header) + u"\n"
 665         legend = (
 666             u"\nLegend:\n"
 667             f"Avg({table[u'reference'][u'title']}): "
 668             f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
 669             f"from a series of runs of the listed tests.\n"
 670             f"Stdev({table[u'reference'][u'title']}): "
 671             f"Standard deviation value of {table[u'reference'][u'title']} "
 672             f"[Mpps] computed from a series of runs of the listed tests.\n"
 673             f"Avg({table[u'compare'][u'title']}): "
 674             f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
 675             f"a series of runs of the listed tests.\n"
 676             f"Stdev({table[u'compare'][u'title']}): "
 677             f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
 678             f"computed from a series of runs of the listed tests.\n"
 679             f"Diff({table[u'reference'][u'title']},"
 680             f"{table[u'compare'][u'title']}): "
 681             f"Percentage change calculated for mean values.\n"
 682             u"Stdev(Diff): "
 683             u"Standard deviation of percentage change calculated for mean "
 684             u"values."
 685         )
 686     except (AttributeError, KeyError) as err:
 687         logging.error(f"The model is invalid, missing parameter: {repr(err)}")
 688         return
 689
 690     # Create a list of available SOAK test results:
 691     tbl_dict = dict()
 692     for job, builds in table[u"compare"][u"data"].items():
 693         for build in builds:
 694             for tst_name, tst_data in data[job][str(build)].items():
 695                 if tst_data[u"type"] == u"SOAK":
 696                     tst_name_mod = tst_name.replace(u"-soak", u"")
 697                     if tbl_dict.get(tst_name_mod, None) is None:
 698                         groups = re.search(REGEX_NIC, tst_data[u"parent"])
 699                         nic = groups.group(0) if groups else u""
 700                         name = (
 701                             f"{nic}-"
 702                             f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
 703                         )
 704                         tbl_dict[tst_name_mod] = {
 705                             u"name": name,
 706                             u"ref-data": list(),
 707                             u"cmp-data": list()
 708                         }
 709                     try:
 710                         tbl_dict[tst_name_mod][u"cmp-data"].append(
 711                             tst_data[u"throughput"][u"LOWER"])
 712                     except (KeyError, TypeError):
 713                         pass
 714     tests_lst = tbl_dict.keys()
 715
 716     # Add corresponding NDR test results:
 717     for job, builds in table[u"reference"][u"data"].items():
 718         for build in builds:
 719             for tst_name, tst_data in data[job][str(build)].items():
 720                 tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
 721                     replace(u"-mrr", u"")
 722                 if tst_name_mod not in tests_lst:
 723                     continue
 724                 try:
 725                     if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
 726                         continue
 727                     if table[u"include-tests"] == u"MRR":
 728                         result = (tst_data[u"result"][u"receive-rate"],
 729                                   tst_data[u"result"][u"receive-stdev"])
 730                     elif table[u"include-tests"] == u"PDR":
 731                         result = \
 732                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 733                     elif table[u"include-tests"] == u"NDR":
 734                         result = \
 735                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 736                     else:
 737                         result = None
 738                     if result is not None:
 739                         tbl_dict[tst_name_mod][u"ref-data"].append(
 740                             result)
 741                 except (KeyError, TypeError):
 742                     continue
 743
 744     tbl_lst = list()
 745     for tst_name in tbl_dict:
 746         item = [tbl_dict[tst_name][u"name"], ]
 747         data_r = tbl_dict[tst_name][u"ref-data"]
 748         if data_r:
 749             if table[u"include-tests"] == u"MRR":
 750                 data_r_mean = data_r[0][0]
 751                 data_r_stdev = data_r[0][1]
 752             else:
 753                 data_r_mean = mean(data_r)
 754                 data_r_stdev = stdev(data_r)
 755             item.append(round(data_r_mean / 1e6, 1))
 756             item.append(round(data_r_stdev / 1e6, 1))
 757         else:
 758             data_r_mean = None
 759             data_r_stdev = None
 760             item.extend([None, None])
 761         data_c = tbl_dict[tst_name][u"cmp-data"]
 762         if data_c:
 763             if table[u"include-tests"] == u"MRR":
 764                 data_c_mean = data_c[0][0]
 765                 data_c_stdev = data_c[0][1]
 766             else:
 767                 data_c_mean = mean(data_c)
 768                 data_c_stdev = stdev(data_c)
 769             item.append(round(data_c_mean / 1e6, 1))
 770             item.append(round(data_c_stdev / 1e6, 1))
 771         else:
 772             data_c_mean = None
 773             data_c_stdev = None
 774             item.extend([None, None])
 775         if data_r_mean is not None and data_c_mean is not None:
 776             delta, d_stdev = relative_change_stdev(
 777                 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
 778             try:
 779                 item.append(round(delta))
 780             except ValueError:
 781                 item.append(delta)
 782             try:
 783                 item.append(round(d_stdev))
 784             except ValueError:
 785                 item.append(d_stdev)
 786             tbl_lst.append(item)
 787
 788     # Sort the table according to the relative change
 789     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
 790
 791     # Generate csv tables:
 792     csv_file_name = f"{table[u'output-file']}.csv"
 793     with open(csv_file_name, u"wt") as file_handler:
 794         file_handler.write(header_str)
 795         for test in tbl_lst:
 796             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
 797
 798     convert_csv_to_pretty_txt(
 799         csv_file_name, f"{table[u'output-file']}.txt", delimiter=u";"
 800     )
 801     with open(f"{table[u'output-file']}.txt", u'a') as file_handler:
 802         file_handler.write(legend)
 803
 804     # Generate html table:
 805     _tpc_generate_html_table(
 806         header,
 807         tbl_lst,
 808         table[u'output-file'],
 809         legend=legend,
 810         title=table.get(u"title", u"")
 811     )
 812
 813
 814 def table_perf_trending_dash(table, input_data):
 815     """Generate the table(s) with algorithm:
 816     table_perf_trending_dash
 817     specified in the specification file.
 818
 819     :param table: Table to generate.
 820     :param input_data: Data to process.
 821     :type table: pandas.Series
 822     :type input_data: InputData
 823     """
 824
 825     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 826
 827     # Transform the data
 828     logging.info(
 829         f"    Creating the data set for the {table.get(u'type', u'')} "
 830         f"{table.get(u'title', u'')}."
 831     )
 832     data = input_data.filter_data(table, continue_on_error=True)
 833
 834     # Prepare the header of the tables
 835     header = [
 836         u"Test Case",
 837         u"Trend [Mpps]",
 838         u"Short-Term Change [%]",
 839         u"Long-Term Change [%]",
 840         u"Regressions [#]",
 841         u"Progressions [#]"
 842     ]
 843     header_str = u",".join(header) + u"\n"
 844
 845     incl_tests = table.get(u"include-tests", u"MRR")
 846
 847     # Prepare data to the table:
 848     tbl_dict = dict()
 849     for job, builds in table[u"data"].items():
 850         for build in builds:
 851             for tst_name, tst_data in data[job][str(build)].items():
 852                 if tst_name.lower() in table.get(u"ignore-list", list()):
 853                     continue
 854                 if tbl_dict.get(tst_name, None) is None:
 855                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
 856                     if not groups:
 857                         continue
 858                     nic = groups.group(0)
 859                     tbl_dict[tst_name] = {
 860                         u"name": f"{nic}-{tst_data[u'name']}",
 861                         u"data": OrderedDict()
 862                     }
 863                 try:
 864                     if incl_tests == u"MRR":
 865                         tbl_dict[tst_name][u"data"][str(build)] = \
 866                             tst_data[u"result"][u"receive-rate"]
 867                     elif incl_tests == u"NDR":
 868                         tbl_dict[tst_name][u"data"][str(build)] = \
 869                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 870                     elif incl_tests == u"PDR":
 871                         tbl_dict[tst_name][u"data"][str(build)] = \
 872                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 873                 except (TypeError, KeyError):
 874                     pass  # No data in output.xml for this test
 875
 876     tbl_lst = list()
 877     for tst_name in tbl_dict:
 878         data_t = tbl_dict[tst_name][u"data"]
 879         if len(data_t) < 2:
 880             continue
 881
 882         classification_lst, avgs, _ = classify_anomalies(data_t)
 883
 884         win_size = min(len(data_t), table[u"window"])
 885         long_win_size = min(len(data_t), table[u"long-trend-window"])
 886
 887         try:
 888             max_long_avg = max(
 889                 [x for x in avgs[-long_win_size:-win_size]
 890                  if not isnan(x)])
 891         except ValueError:
 892             max_long_avg = nan
 893         last_avg = avgs[-1]
 894         avg_week_ago = avgs[max(-win_size, -len(avgs))]
 895
 896         if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
 897             rel_change_last = nan
 898         else:
 899             rel_change_last = round(
 900                 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
 901
 902         if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
 903             rel_change_long = nan
 904         else:
 905             rel_change_long = round(
 906                 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
 907
 908         if classification_lst:
 909             if isnan(rel_change_last) and isnan(rel_change_long):
 910                 continue
 911             if isnan(last_avg) or isnan(rel_change_last) or \
 912                     isnan(rel_change_long):
 913                 continue
 914             tbl_lst.append(
 915                 [tbl_dict[tst_name][u"name"],
 916                  round(last_avg / 1e6, 2),
 917                  rel_change_last,
 918                  rel_change_long,
 919                  classification_lst[-win_size+1:].count(u"regression"),
 920                  classification_lst[-win_size+1:].count(u"progression")])
 921
 922     tbl_lst.sort(key=lambda rel: rel[0])
 923     tbl_lst.sort(key=lambda rel: rel[3])
 924     tbl_lst.sort(key=lambda rel: rel[2])
 925
 926     tbl_sorted = list()
 927     for nrr in range(table[u"window"], -1, -1):
 928         tbl_reg = [item for item in tbl_lst if item[4] == nrr]
 929         for nrp in range(table[u"window"], -1, -1):
 930             tbl_out = [item for item in tbl_reg if item[5] == nrp]
 931             tbl_sorted.extend(tbl_out)
 932
 933     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
 934
 935     logging.info(f"    Writing file: {file_name}")
 936     with open(file_name, u"wt") as file_handler:
 937         file_handler.write(header_str)
 938         for test in tbl_sorted:
 939             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
 940
 941     logging.info(f"    Writing file: {table[u'output-file']}.txt")
 942     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
 943
 944
 945 def _generate_url(testbed, test_name):
 946     """Generate URL to a trending plot from the name of the test case.
 947
 948     :param testbed: The testbed used for testing.
 949     :param test_name: The name of the test case.
 950     :type testbed: str
 951     :type test_name: str
 952     :returns: The URL to the plot with the trending data for the given test
 953         case.
 954     :rtype str
 955     """
 956
 957     if u"x520" in test_name:
 958         nic = u"x520"
 959     elif u"x710" in test_name:
 960         nic = u"x710"
 961     elif u"xl710" in test_name:
 962         nic = u"xl710"
 963     elif u"xxv710" in test_name:
 964         nic = u"xxv710"
 965     elif u"vic1227" in test_name:
 966         nic = u"vic1227"
 967     elif u"vic1385" in test_name:
 968         nic = u"vic1385"
 969     elif u"x553" in test_name:
 970         nic = u"x553"
 971     elif u"cx556" in test_name or u"cx556a" in test_name:
 972         nic = u"cx556a"
 973     else:
 974         nic = u""
 975
 976     if u"64b" in test_name:
 977         frame_size = u"64b"
 978     elif u"78b" in test_name:
 979         frame_size = u"78b"
 980     elif u"imix" in test_name:
 981         frame_size = u"imix"
 982     elif u"9000b" in test_name:
 983         frame_size = u"9000b"
 984     elif u"1518b" in test_name:
 985         frame_size = u"1518b"
 986     elif u"114b" in test_name:
 987         frame_size = u"114b"
 988     else:
 989         frame_size = u""
 990
 991     if u"1t1c" in test_name or \
 992         (u"-1c-" in test_name and
 993          testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 994         cores = u"1t1c"
 995     elif u"2t2c" in test_name or \
 996          (u"-2c-" in test_name and
 997           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 998         cores = u"2t2c"
 999     elif u"4t4c" in test_name or \
1000          (u"-4c-" in test_name and
1001           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
1002         cores = u"4t4c"
1003     elif u"2t1c" in test_name or \
1004          (u"-1c-" in test_name and
1005           testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1006         cores = u"2t1c"
1007     elif u"4t2c" in test_name or \
1008          (u"-2c-" in test_name and
1009           testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1010         cores = u"4t2c"
1011     elif u"8t4c" in test_name or \
1012          (u"-4c-" in test_name and
1013           testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1014         cores = u"8t4c"
1015     else:
1016         cores = u""
1017
1018     if u"testpmd" in test_name:
1019         driver = u"testpmd"
1020     elif u"l3fwd" in test_name:
1021         driver = u"l3fwd"
1022     elif u"avf" in test_name:
1023         driver = u"avf"
1024     elif u"rdma" in test_name:
1025         driver = u"rdma"
1026     elif u"dnv" in testbed or u"tsh" in testbed:
1027         driver = u"ixgbe"
1028     else:
1029         driver = u"dpdk"
1030
1031     if u"macip-iacl1s" in test_name:
1032         bsf = u"features-macip-iacl1"
1033     elif u"macip-iacl10s" in test_name:
1034         bsf = u"features-macip-iacl01"
1035     elif u"macip-iacl50s" in test_name:
1036         bsf = u"features-macip-iacl50"
1037     elif u"iacl1s" in test_name:
1038         bsf = u"features-iacl1"
1039     elif u"iacl10s" in test_name:
1040         bsf = u"features-iacl10"
1041     elif u"iacl50s" in test_name:
1042         bsf = u"features-iacl50"
1043     elif u"oacl1s" in test_name:
1044         bsf = u"features-oacl1"
1045     elif u"oacl10s" in test_name:
1046         bsf = u"features-oacl10"
1047     elif u"oacl50s" in test_name:
1048         bsf = u"features-oacl50"
1049     elif u"nat44det" in test_name:
1050         bsf = u"nat44det-bidir"
1051     elif u"nat44ed" in test_name and u"udir" in test_name:
1052         bsf = u"nat44ed-udir"
1053     elif u"-cps" in test_name and u"ethip4udp" in test_name:
1054         bsf = u"udp-cps"
1055     elif u"-cps" in test_name and u"ethip4tcp" in test_name:
1056         bsf = u"tcp-cps"
1057     elif u"-pps" in test_name and u"ethip4udp" in test_name:
1058         bsf = u"udp-pps"
1059     elif u"-pps" in test_name and u"ethip4tcp" in test_name:
1060         bsf = u"tcp-pps"
1061     elif u"udpsrcscale" in test_name:
1062         bsf = u"features-udp"
1063     elif u"iacl" in test_name:
1064         bsf = u"features"
1065     elif u"policer" in test_name:
1066         bsf = u"features"
1067     elif u"adl" in test_name:
1068         bsf = u"features"
1069     elif u"cop" in test_name:
1070         bsf = u"features"
1071     elif u"nat" in test_name:
1072         bsf = u"features"
1073     elif u"macip" in test_name:
1074         bsf = u"features"
1075     elif u"scale" in test_name:
1076         bsf = u"scale"
1077     elif u"base" in test_name:
1078         bsf = u"base"
1079     else:
1080         bsf = u"base"
1081
1082     if u"114b" in test_name and u"vhost" in test_name:
1083         domain = u"vts"
1084     elif u"nat44" in test_name or u"-pps" in test_name or u"-cps" in test_name:
1085         domain = u"nat44"
1086         if u"nat44det" in test_name:
1087             domain += u"-det-bidir"
1088         else:
1089             domain += u"-ed"
1090         if u"udir" in test_name:
1091             domain += u"-unidir"
1092         elif u"-ethip4udp-" in test_name:
1093             domain += u"-udp"
1094         elif u"-ethip4tcp-" in test_name:
1095             domain += u"-tcp"
1096         if u"-cps" in test_name:
1097             domain += u"-cps"
1098         elif u"-pps" in test_name:
1099             domain += u"-pps"
1100     elif u"testpmd" in test_name or u"l3fwd" in test_name:
1101         domain = u"dpdk"
1102     elif u"memif" in test_name:
1103         domain = u"container_memif"
1104     elif u"srv6" in test_name:
1105         domain = u"srv6"
1106     elif u"vhost" in test_name:
1107         domain = u"vhost"
1108         if u"vppl2xc" in test_name:
1109             driver += u"-vpp"
1110         else:
1111             driver += u"-testpmd"
1112         if u"lbvpplacp" in test_name:
1113             bsf += u"-link-bonding"
1114     elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1115         domain = u"nf_service_density_vnfc"
1116     elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1117         domain = u"nf_service_density_cnfc"
1118     elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1119         domain = u"nf_service_density_cnfp"
1120     elif u"ipsec" in test_name:
1121         domain = u"ipsec"
1122         if u"sw" in test_name:
1123             bsf += u"-sw"
1124         elif u"hw" in test_name:
1125             bsf += u"-hw"
1126     elif u"ethip4vxlan" in test_name:
1127         domain = u"ip4_tunnels"
1128     elif u"ip4base" in test_name or u"ip4scale" in test_name:
1129         domain = u"ip4"
1130     elif u"ip6base" in test_name or u"ip6scale" in test_name:
1131         domain = u"ip6"
1132     elif u"l2xcbase" in test_name or \
1133             u"l2xcscale" in test_name or \
1134             u"l2bdbasemaclrn" in test_name or \
1135             u"l2bdscale" in test_name or \
1136             u"l2patch" in test_name:
1137         domain = u"l2"
1138     else:
1139         domain = u""
1140
1141     file_name = u"-".join((domain, testbed, nic)) + u".html#"
1142     anchor_name = u"-".join((frame_size, cores, bsf, driver))
1143
1144     return file_name + anchor_name
1145
1146
1147 def table_perf_trending_dash_html(table, input_data):
1148     """Generate the table(s) with algorithm:
1149     table_perf_trending_dash_html specified in the specification
1150     file.
1151
1152     :param table: Table to generate.
1153     :param input_data: Data to process.
1154     :type table: dict
1155     :type input_data: InputData
1156     """
1157
1158     _ = input_data
1159
1160     if not table.get(u"testbed", None):
1161         logging.error(
1162             f"The testbed is not defined for the table "
1163             f"{table.get(u'title', u'')}. Skipping."
1164         )
1165         return
1166
1167     test_type = table.get(u"test-type", u"MRR")
1168     if test_type not in (u"MRR", u"NDR", u"PDR"):
1169         logging.error(
1170             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1171             f"Skipping."
1172         )
1173         return
1174
1175     if test_type in (u"NDR", u"PDR"):
1176         lnk_dir = u"../ndrpdr_trending/"
1177         lnk_sufix = f"-{test_type.lower()}"
1178     else:
1179         lnk_dir = u"../trending/"
1180         lnk_sufix = u""
1181
1182     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1183
1184     try:
1185         with open(table[u"input-file"], u'rt') as csv_file:
1186             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1187     except KeyError:
1188         logging.warning(u"The input file is not defined.")
1189         return
1190     except csv.Error as err:
1191         logging.warning(
1192             f"Not possible to process the file {table[u'input-file']}.\n"
1193             f"{repr(err)}"
1194         )
1195         return
1196
1197     # Table:
1198     dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1199
1200     # Table header:
1201     trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1202     for idx, item in enumerate(csv_lst[0]):
1203         alignment = u"left" if idx == 0 else u"center"
1204         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1205         thead.text = item
1206
1207     # Rows:
1208     colors = {
1209         u"regression": (
1210             u"#ffcccc",
1211             u"#ff9999"
1212         ),
1213         u"progression": (
1214             u"#c6ecc6",
1215             u"#9fdf9f"
1216         ),
1217         u"normal": (
1218             u"#e9f1fb",
1219             u"#d4e4f7"
1220         )
1221     }
1222     for r_idx, row in enumerate(csv_lst[1:]):
1223         if int(row[4]):
1224             color = u"regression"
1225         elif int(row[5]):
1226             color = u"progression"
1227         else:
1228             color = u"normal"
1229         trow = ET.SubElement(
1230             dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1231         )
1232
1233         # Columns:
1234         for c_idx, item in enumerate(row):
1235             tdata = ET.SubElement(
1236                 trow,
1237                 u"td",
1238                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1239             )
1240             # Name:
1241             if c_idx == 0 and table.get(u"add-links", True):
1242                 ref = ET.SubElement(
1243                     tdata,
1244                     u"a",
1245                     attrib=dict(
1246                         href=f"{lnk_dir}"
1247                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1248                              f"{lnk_sufix}"
1249                     )
1250                 )
1251                 ref.text = item
1252             else:
1253                 tdata.text = item
1254     try:
1255         with open(table[u"output-file"], u'w') as html_file:
1256             logging.info(f"    Writing file: {table[u'output-file']}")
1257             html_file.write(u".. raw:: html\n\n\t")
1258             html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1259             html_file.write(u"\n\t<p><br><br></p>\n")
1260     except KeyError:
1261         logging.warning(u"The output file is not defined.")
1262         return
1263
1264
1265 def table_last_failed_tests(table, input_data):
1266     """Generate the table(s) with algorithm: table_last_failed_tests
1267     specified in the specification file.
1268
1269     :param table: Table to generate.
1270     :param input_data: Data to process.
1271     :type table: pandas.Series
1272     :type input_data: InputData
1273     """
1274
1275     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1276
1277     # Transform the data
1278     logging.info(
1279         f"    Creating the data set for the {table.get(u'type', u'')} "
1280         f"{table.get(u'title', u'')}."
1281     )
1282
1283     data = input_data.filter_data(table, continue_on_error=True)
1284
1285     if data is None or data.empty:
1286         logging.warning(
1287             f"    No data for the {table.get(u'type', u'')} "
1288             f"{table.get(u'title', u'')}."
1289         )
1290         return
1291
1292     tbl_list = list()
1293     for job, builds in table[u"data"].items():
1294         for build in builds:
1295             build = str(build)
1296             try:
1297                 version = input_data.metadata(job, build).get(u"version", u"")
1298             except KeyError:
1299                 logging.error(f"Data for {job}: {build} is not present.")
1300                 return
1301             tbl_list.append(build)
1302             tbl_list.append(version)
1303             failed_tests = list()
1304             passed = 0
1305             failed = 0
1306             for tst_data in data[job][build].values:
1307                 if tst_data[u"status"] != u"FAIL":
1308                     passed += 1
1309                     continue
1310                 failed += 1
1311                 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1312                 if not groups:
1313                     continue
1314                 nic = groups.group(0)
1315                 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1316             tbl_list.append(str(passed))
1317             tbl_list.append(str(failed))
1318             tbl_list.extend(failed_tests)
1319
1320     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1321     logging.info(f"    Writing file: {file_name}")
1322     with open(file_name, u"wt") as file_handler:
1323         for test in tbl_list:
1324             file_handler.write(test + u'\n')
1325
1326
1327 def table_failed_tests(table, input_data):
1328     """Generate the table(s) with algorithm: table_failed_tests
1329     specified in the specification file.
1330
1331     :param table: Table to generate.
1332     :param input_data: Data to process.
1333     :type table: pandas.Series
1334     :type input_data: InputData
1335     """
1336
1337     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1338
1339     # Transform the data
1340     logging.info(
1341         f"    Creating the data set for the {table.get(u'type', u'')} "
1342         f"{table.get(u'title', u'')}."
1343     )
1344     data = input_data.filter_data(table, continue_on_error=True)
1345
1346     test_type = u"MRR"
1347     if u"NDRPDR" in table.get(u"filter", list()):
1348         test_type = u"NDRPDR"
1349
1350     # Prepare the header of the tables
1351     header = [
1352         u"Test Case",
1353         u"Failures [#]",
1354         u"Last Failure [Time]",
1355         u"Last Failure [VPP-Build-Id]",
1356         u"Last Failure [CSIT-Job-Build-Id]"
1357     ]
1358
1359     # Generate the data for the table according to the model in the table
1360     # specification
1361
1362     now = dt.utcnow()
1363     timeperiod = timedelta(int(table.get(u"window", 7)))
1364
1365     tbl_dict = dict()
1366     for job, builds in table[u"data"].items():
1367         for build in builds:
1368             build = str(build)
1369             for tst_name, tst_data in data[job][build].items():
1370                 if tst_name.lower() in table.get(u"ignore-list", list()):
1371                     continue
1372                 if tbl_dict.get(tst_name, None) is None:
1373                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
1374                     if not groups:
1375                         continue
1376                     nic = groups.group(0)
1377                     tbl_dict[tst_name] = {
1378                         u"name": f"{nic}-{tst_data[u'name']}",
1379                         u"data": OrderedDict()
1380                     }
1381                 try:
1382                     generated = input_data.metadata(job, build).\
1383                         get(u"generated", u"")
1384                     if not generated:
1385                         continue
1386                     then = dt.strptime(generated, u"%Y%m%d %H:%M")
1387                     if (now - then) <= timeperiod:
1388                         tbl_dict[tst_name][u"data"][build] = (
1389                             tst_data[u"status"],
1390                             generated,
1391                             input_data.metadata(job, build).get(u"version",
1392                                                                 u""),
1393                             build
1394                         )
1395                 except (TypeError, KeyError) as err:
1396                     logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1397
1398     max_fails = 0
1399     tbl_lst = list()
1400     for tst_data in tbl_dict.values():
1401         fails_nr = 0
1402         fails_last_date = u""
1403         fails_last_vpp = u""
1404         fails_last_csit = u""
1405         for val in tst_data[u"data"].values():
1406             if val[0] == u"FAIL":
1407                 fails_nr += 1
1408                 fails_last_date = val[1]
1409                 fails_last_vpp = val[2]
1410                 fails_last_csit = val[3]
1411         if fails_nr:
1412             max_fails = fails_nr if fails_nr > max_fails else max_fails
1413             tbl_lst.append([
1414                 tst_data[u"name"],
1415                 fails_nr,
1416                 fails_last_date,
1417                 fails_last_vpp,
1418                 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1419                 f"-build-{fails_last_csit}"
1420             ])
1421
1422     tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1423     tbl_sorted = list()
1424     for nrf in range(max_fails, -1, -1):
1425         tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1426         tbl_sorted.extend(tbl_fails)
1427
1428     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1429     logging.info(f"    Writing file: {file_name}")
1430     with open(file_name, u"wt") as file_handler:
1431         file_handler.write(u",".join(header) + u"\n")
1432         for test in tbl_sorted:
1433             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1434
1435     logging.info(f"    Writing file: {table[u'output-file']}.txt")
1436     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1437
1438
1439 def table_failed_tests_html(table, input_data):
1440     """Generate the table(s) with algorithm: table_failed_tests_html
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
1449     _ = input_data
1450
1451     if not table.get(u"testbed", None):
1452         logging.error(
1453             f"The testbed is not defined for the table "
1454             f"{table.get(u'title', u'')}. Skipping."
1455         )
1456         return
1457
1458     test_type = table.get(u"test-type", u"MRR")
1459     if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1460         logging.error(
1461             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1462             f"Skipping."
1463         )
1464         return
1465
1466     if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1467         lnk_dir = u"../ndrpdr_trending/"
1468         lnk_sufix = u"-pdr"
1469     else:
1470         lnk_dir = u"../trending/"
1471         lnk_sufix = u""
1472
1473     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1474
1475     try:
1476         with open(table[u"input-file"], u'rt') as csv_file:
1477             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1478     except KeyError:
1479         logging.warning(u"The input file is not defined.")
1480         return
1481     except csv.Error as err:
1482         logging.warning(
1483             f"Not possible to process the file {table[u'input-file']}.\n"
1484             f"{repr(err)}"
1485         )
1486         return
1487
1488     # Table:
1489     failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1490
1491     # Table header:
1492     trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1493     for idx, item in enumerate(csv_lst[0]):
1494         alignment = u"left" if idx == 0 else u"center"
1495         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1496         thead.text = item
1497
1498     # Rows:
1499     colors = (u"#e9f1fb", u"#d4e4f7")
1500     for r_idx, row in enumerate(csv_lst[1:]):
1501         background = colors[r_idx % 2]
1502         trow = ET.SubElement(
1503             failed_tests, u"tr", attrib=dict(bgcolor=background)
1504         )
1505
1506         # Columns:
1507         for c_idx, item in enumerate(row):
1508             tdata = ET.SubElement(
1509                 trow,
1510                 u"td",
1511                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1512             )
1513             # Name:
1514             if c_idx == 0 and table.get(u"add-links", True):
1515                 ref = ET.SubElement(
1516                     tdata,
1517                     u"a",
1518                     attrib=dict(
1519                         href=f"{lnk_dir}"
1520                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1521                              f"{lnk_sufix}"
1522                     )
1523                 )
1524                 ref.text = item
1525             else:
1526                 tdata.text = item
1527     try:
1528         with open(table[u"output-file"], u'w') as html_file:
1529             logging.info(f"    Writing file: {table[u'output-file']}")
1530             html_file.write(u".. raw:: html\n\n\t")
1531             html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1532             html_file.write(u"\n\t<p><br><br></p>\n")
1533     except KeyError:
1534         logging.warning(u"The output file is not defined.")
1535         return
1536
1537
1538 def table_comparison(table, input_data):
1539     """Generate the table(s) with algorithm: table_comparison
1540     specified in the specification file.
1541
1542     :param table: Table to generate.
1543     :param input_data: Data to process.
1544     :type table: pandas.Series
1545     :type input_data: InputData
1546     """
1547     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1548
1549     # Transform the data
1550     logging.info(
1551         f"    Creating the data set for the {table.get(u'type', u'')} "
1552         f"{table.get(u'title', u'')}."
1553     )
1554
1555     columns = table.get(u"columns", None)
1556     if not columns:
1557         logging.error(
1558             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1559         )
1560         return
1561
1562     cols = list()
1563     for idx, col in enumerate(columns):
1564         if col.get(u"data-set", None) is None:
1565             logging.warning(f"No data for column {col.get(u'title', u'')}")
1566             continue
1567         tag = col.get(u"tag", None)
1568         data = input_data.filter_data(
1569             table,
1570             params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1571             data=col[u"data-set"],
1572             continue_on_error=True
1573         )
1574         col_data = {
1575             u"title": col.get(u"title", f"Column{idx}"),
1576             u"data": dict()
1577         }
1578         for builds in data.values:
1579             for build in builds:
1580                 for tst_name, tst_data in build.items():
1581                     if tag and tag not in tst_data[u"tags"]:
1582                         continue
1583                     tst_name_mod = \
1584                         _tpc_modify_test_name(tst_name, ignore_nic=True).\
1585                         replace(u"2n1l-", u"")
1586                     if col_data[u"data"].get(tst_name_mod, None) is None:
1587                         name = tst_data[u'name'].rsplit(u'-', 1)[0]
1588                         if u"across testbeds" in table[u"title"].lower() or \
1589                                 u"across topologies" in table[u"title"].lower():
1590                             name = _tpc_modify_displayed_test_name(name)
1591                         col_data[u"data"][tst_name_mod] = {
1592                             u"name": name,
1593                             u"replace": True,
1594                             u"data": list(),
1595                             u"mean": None,
1596                             u"stdev": None
1597                         }
1598                     _tpc_insert_data(
1599                         target=col_data[u"data"][tst_name_mod],
1600                         src=tst_data,
1601                         include_tests=table[u"include-tests"]
1602                     )
1603
1604         replacement = col.get(u"data-replacement", None)
1605         if replacement:
1606             rpl_data = input_data.filter_data(
1607                 table,
1608                 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1609                 data=replacement,
1610                 continue_on_error=True
1611             )
1612             for builds in rpl_data.values:
1613                 for build in builds:
1614                     for tst_name, tst_data in build.items():
1615                         if tag and tag not in tst_data[u"tags"]:
1616                             continue
1617                         tst_name_mod = \
1618                             _tpc_modify_test_name(tst_name, ignore_nic=True).\
1619                             replace(u"2n1l-", u"")
1620                         if col_data[u"data"].get(tst_name_mod, None) is None:
1621                             name = tst_data[u'name'].rsplit(u'-', 1)[0]
1622                             if u"across testbeds" in table[u"title"].lower() \
1623                                     or u"across topologies" in \
1624                                     table[u"title"].lower():
1625                                 name = _tpc_modify_displayed_test_name(name)
1626                             col_data[u"data"][tst_name_mod] = {
1627                                 u"name": name,
1628                                 u"replace": False,
1629                                 u"data": list(),
1630                                 u"mean": None,
1631                                 u"stdev": None
1632                             }
1633                         if col_data[u"data"][tst_name_mod][u"replace"]:
1634                             col_data[u"data"][tst_name_mod][u"replace"] = False
1635                             col_data[u"data"][tst_name_mod][u"data"] = list()
1636                         _tpc_insert_data(
1637                             target=col_data[u"data"][tst_name_mod],
1638                             src=tst_data,
1639                             include_tests=table[u"include-tests"]
1640                         )
1641
1642         if table[u"include-tests"] in (u"NDR", u"PDR"):
1643             for tst_name, tst_data in col_data[u"data"].items():
1644                 if tst_data[u"data"]:
1645                     tst_data[u"mean"] = mean(tst_data[u"data"])
1646                     tst_data[u"stdev"] = stdev(tst_data[u"data"])
1647
1648         cols.append(col_data)
1649
1650     tbl_dict = dict()
1651     for col in cols:
1652         for tst_name, tst_data in col[u"data"].items():
1653             if tbl_dict.get(tst_name, None) is None:
1654                 tbl_dict[tst_name] = {
1655                     "name": tst_data[u"name"]
1656                 }
1657             tbl_dict[tst_name][col[u"title"]] = {
1658                 u"mean": tst_data[u"mean"],
1659                 u"stdev": tst_data[u"stdev"]
1660             }
1661
1662     if not tbl_dict:
1663         logging.warning(f"No data for table {table.get(u'title', u'')}!")
1664         return
1665
1666     tbl_lst = list()
1667     for tst_data in tbl_dict.values():
1668         row = [tst_data[u"name"], ]
1669         for col in cols:
1670             row.append(tst_data.get(col[u"title"], None))
1671         tbl_lst.append(row)
1672
1673     comparisons = table.get(u"comparisons", None)
1674     rcas = list()
1675     if comparisons and isinstance(comparisons, list):
1676         for idx, comp in enumerate(comparisons):
1677             try:
1678                 col_ref = int(comp[u"reference"])
1679                 col_cmp = int(comp[u"compare"])
1680             except KeyError:
1681                 logging.warning(u"Comparison: No references defined! Skipping.")
1682                 comparisons.pop(idx)
1683                 continue
1684             if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1685                     col_ref == col_cmp):
1686                 logging.warning(f"Wrong values of reference={col_ref} "
1687                                 f"and/or compare={col_cmp}. Skipping.")
1688                 comparisons.pop(idx)
1689                 continue
1690             rca_file_name = comp.get(u"rca-file", None)
1691             if rca_file_name:
1692                 try:
1693                     with open(rca_file_name, u"r") as file_handler:
1694                         rcas.append(
1695                             {
1696                                 u"title": f"RCA{idx + 1}",
1697                                 u"data": load(file_handler, Loader=FullLoader)
1698                             }
1699                         )
1700                 except (YAMLError, IOError) as err:
1701                     logging.warning(
1702                         f"The RCA file {rca_file_name} does not exist or "
1703                         f"it is corrupted!"
1704                     )
1705                     logging.debug(repr(err))
1706                     rcas.append(None)
1707             else:
1708                 rcas.append(None)
1709     else:
1710         comparisons = None
1711
1712     tbl_cmp_lst = list()
1713     if comparisons:
1714         for row in tbl_lst:
1715             new_row = deepcopy(row)
1716             for comp in comparisons:
1717                 ref_itm = row[int(comp[u"reference"])]
1718                 if ref_itm is None and \
1719                         comp.get(u"reference-alt", None) is not None:
1720                     ref_itm = row[int(comp[u"reference-alt"])]
1721                 cmp_itm = row[int(comp[u"compare"])]
1722                 if ref_itm is not None and cmp_itm is not None and \
1723                         ref_itm[u"mean"] is not None and \
1724                         cmp_itm[u"mean"] is not None and \
1725                         ref_itm[u"stdev"] is not None and \
1726                         cmp_itm[u"stdev"] is not None:
1727                     delta, d_stdev = relative_change_stdev(
1728                         ref_itm[u"mean"], cmp_itm[u"mean"],
1729                         ref_itm[u"stdev"], cmp_itm[u"stdev"]
1730                     )
1731                     if delta is None:
1732                         break
1733                     new_row.append({
1734                         u"mean": delta * 1e6,
1735                         u"stdev": d_stdev * 1e6
1736                     })
1737                 else:
1738                     break
1739             else:
1740                 tbl_cmp_lst.append(new_row)
1741
1742     try:
1743         tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1744         tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1745     except TypeError as err:
1746         logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1747
1748     tbl_for_csv = list()
1749     for line in tbl_cmp_lst:
1750         row = [line[0], ]
1751         for idx, itm in enumerate(line[1:]):
1752             if itm is None or not isinstance(itm, dict) or\
1753                     itm.get(u'mean', None) is None or \
1754                     itm.get(u'stdev', None) is None:
1755                 row.append(u"NT")
1756                 row.append(u"NT")
1757             else:
1758                 row.append(round(float(itm[u'mean']) / 1e6, 3))
1759                 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1760         for rca in rcas:
1761             if rca is None:
1762                 continue
1763             rca_nr = rca[u"data"].get(row[0], u"-")
1764             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1765         tbl_for_csv.append(row)
1766
1767     header_csv = [u"Test Case", ]
1768     for col in cols:
1769         header_csv.append(f"Avg({col[u'title']})")
1770         header_csv.append(f"Stdev({col[u'title']})")
1771     for comp in comparisons:
1772         header_csv.append(
1773             f"Avg({comp.get(u'title', u'')})"
1774         )
1775         header_csv.append(
1776             f"Stdev({comp.get(u'title', u'')})"
1777         )
1778     for rca in rcas:
1779         if rca:
1780             header_csv.append(rca[u"title"])
1781
1782     legend_lst = table.get(u"legend", None)
1783     if legend_lst is None:
1784         legend = u""
1785     else:
1786         legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1787
1788     footnote = u""
1789     if rcas and any(rcas):
1790         footnote += u"\nRoot Cause Analysis:\n"
1791         for rca in rcas:
1792             if rca:
1793                 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1794
1795     csv_file_name = f"{table[u'output-file']}-csv.csv"
1796     with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1797         file_handler.write(
1798             u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1799         )
1800         for test in tbl_for_csv:
1801             file_handler.write(
1802                 u",".join([f'"{item}"' for item in test]) + u"\n"
1803             )
1804         if legend_lst:
1805             for item in legend_lst:
1806                 file_handler.write(f'"{item}"\n')
1807         if footnote:
1808             for itm in footnote.split(u"\n"):
1809                 file_handler.write(f'"{itm}"\n')
1810
1811     tbl_tmp = list()
1812     max_lens = [0, ] * len(tbl_cmp_lst[0])
1813     for line in tbl_cmp_lst:
1814         row = [line[0], ]
1815         for idx, itm in enumerate(line[1:]):
1816             if itm is None or not isinstance(itm, dict) or \
1817                     itm.get(u'mean', None) is None or \
1818                     itm.get(u'stdev', None) is None:
1819                 new_itm = u"NT"
1820             else:
1821                 if idx < len(cols):
1822                     new_itm = (
1823                         f"{round(float(itm[u'mean']) / 1e6, 1)} "
1824                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1825                         replace(u"nan", u"NaN")
1826                     )
1827                 else:
1828                     new_itm = (
1829                         f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1830                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1831                         replace(u"nan", u"NaN")
1832                     )
1833             if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1834                 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1835             row.append(new_itm)
1836
1837         tbl_tmp.append(row)
1838
1839     header = [u"Test Case", ]
1840     header.extend([col[u"title"] for col in cols])
1841     header.extend([comp.get(u"title", u"") for comp in comparisons])
1842
1843     tbl_final = list()
1844     for line in tbl_tmp:
1845         row = [line[0], ]
1846         for idx, itm in enumerate(line[1:]):
1847             if itm in (u"NT", u"NaN"):
1848                 row.append(itm)
1849                 continue
1850             itm_lst = itm.rsplit(u"\u00B1", 1)
1851             itm_lst[-1] = \
1852                 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1853             itm_str = u"\u00B1".join(itm_lst)
1854
1855             if idx >= len(cols):
1856                 # Diffs
1857                 rca = rcas[idx - len(cols)]
1858                 if rca:
1859                     # Add rcas to diffs
1860                     rca_nr = rca[u"data"].get(row[0], None)
1861                     if rca_nr:
1862                         hdr_len = len(header[idx + 1]) - 1
1863                         if hdr_len < 19:
1864                             hdr_len = 19
1865                         rca_nr = f"[{rca_nr}]"
1866                         itm_str = (
1867                             f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
1868                             f"{u' ' * (hdr_len - 4 - len(itm_str))}"
1869                             f"{itm_str}"
1870                         )
1871             row.append(itm_str)
1872         tbl_final.append(row)
1873
1874     # Generate csv tables:
1875     csv_file_name = f"{table[u'output-file']}.csv"
1876     logging.info(f"    Writing the file {csv_file_name}")
1877     with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1878         file_handler.write(u";".join(header) + u"\n")
1879         for test in tbl_final:
1880             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1881
1882     # Generate txt table:
1883     txt_file_name = f"{table[u'output-file']}.txt"
1884     logging.info(f"    Writing the file {txt_file_name}")
1885     convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
1886
1887     with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
1888         file_handler.write(legend)
1889         file_handler.write(footnote)
1890
1891     # Generate html table:
1892     _tpc_generate_html_table(
1893         header,
1894         tbl_final,
1895         table[u'output-file'],
1896         legend=legend,
1897         footnote=footnote,
1898         sort_data=False,
1899         title=table.get(u"title", u"")
1900     )
1901
1902
1903 def table_weekly_comparison(table, in_data):
1904     """Generate the table(s) with algorithm: table_weekly_comparison
1905     specified in the specification file.
1906
1907     :param table: Table to generate.
1908     :param in_data: Data to process.
1909     :type table: pandas.Series
1910     :type in_data: InputData
1911     """
1912     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1913
1914     # Transform the data
1915     logging.info(
1916         f"    Creating the data set for the {table.get(u'type', u'')} "
1917         f"{table.get(u'title', u'')}."
1918     )
1919
1920     incl_tests = table.get(u"include-tests", None)
1921     if incl_tests not in (u"NDR", u"PDR"):
1922         logging.error(f"Wrong tests to include specified ({incl_tests}).")
1923         return
1924
1925     nr_cols = table.get(u"nr-of-data-columns", None)
1926     if not nr_cols or nr_cols < 2:
1927         logging.error(
1928             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1929         )
1930         return
1931
1932     data = in_data.filter_data(
1933         table,
1934         params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1935         continue_on_error=True
1936     )
1937
1938     header = [
1939         [u"VPP Version", ],
1940         [u"Start Timestamp", ],
1941         [u"CSIT Build", ],
1942         [u"CSIT Testbed", ]
1943     ]
1944     tbl_dict = dict()
1945     idx = 0
1946     tb_tbl = table.get(u"testbeds", None)
1947     for job_name, job_data in data.items():
1948         for build_nr, build in job_data.items():
1949             if idx >= nr_cols:
1950                 break
1951             if build.empty:
1952                 continue
1953
1954             tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1955             if tb_ip and tb_tbl:
1956                 testbed = tb_tbl.get(tb_ip, u"")
1957             else:
1958                 testbed = u""
1959             header[2].insert(1, build_nr)
1960             header[3].insert(1, testbed)
1961             header[1].insert(
1962                 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1963             )
1964             header[0].insert(
1965                 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1966             )
1967
1968             for tst_name, tst_data in build.items():
1969                 tst_name_mod = \
1970                     _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1971                 if not tbl_dict.get(tst_name_mod, None):
1972                     tbl_dict[tst_name_mod] = dict(
1973                         name=tst_data[u'name'].rsplit(u'-', 1)[0],
1974                     )
1975                 try:
1976                     tbl_dict[tst_name_mod][-idx - 1] = \
1977                         tst_data[u"throughput"][incl_tests][u"LOWER"]
1978                 except (TypeError, IndexError, KeyError, ValueError):
1979                     pass
1980             idx += 1
1981
1982     if idx < nr_cols:
1983         logging.error(u"Not enough data to build the table! Skipping")
1984         return
1985
1986     cmp_dict = dict()
1987     for idx, cmp in enumerate(table.get(u"comparisons", list())):
1988         idx_ref = cmp.get(u"reference", None)
1989         idx_cmp = cmp.get(u"compare", None)
1990         if idx_ref is None or idx_cmp is None:
1991             continue
1992         header[0].append(
1993             f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1994             f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1995         )
1996         header[1].append(u"")
1997         header[2].append(u"")
1998         header[3].append(u"")
1999         for tst_name, tst_data in tbl_dict.items():
2000             if not cmp_dict.get(tst_name, None):
2001                 cmp_dict[tst_name] = list()
2002             ref_data = tst_data.get(idx_ref, None)
2003             cmp_data = tst_data.get(idx_cmp, None)
2004             if ref_data is None or cmp_data is None:
2005                 cmp_dict[tst_name].append(float(u'nan'))
2006             else:
2007                 cmp_dict[tst_name].append(
2008                     relative_change(ref_data, cmp_data)
2009                 )
2010
2011     tbl_lst_none = list()
2012     tbl_lst = list()
2013     for tst_name, tst_data in tbl_dict.items():
2014         itm_lst = [tst_data[u"name"], ]
2015         for idx in range(nr_cols):
2016             item = tst_data.get(-idx - 1, None)
2017             if item is None:
2018                 itm_lst.insert(1, None)
2019             else:
2020                 itm_lst.insert(1, round(item / 1e6, 1))
2021         itm_lst.extend(
2022             [
2023                 None if itm is None else round(itm, 1)
2024                 for itm in cmp_dict[tst_name]
2025             ]
2026         )
2027         if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
2028             tbl_lst_none.append(itm_lst)
2029         else:
2030             tbl_lst.append(itm_lst)
2031
2032     tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
2033     tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
2034     tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
2035     tbl_lst.extend(tbl_lst_none)
2036
2037     # Generate csv table:
2038     csv_file_name = f"{table[u'output-file']}.csv"
2039     logging.info(f"    Writing the file {csv_file_name}")
2040     with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2041         for hdr in header:
2042             file_handler.write(u",".join(hdr) + u"\n")
2043         for test in tbl_lst:
2044             file_handler.write(u",".join(
2045                 [
2046                     str(item).replace(u"None", u"-").replace(u"nan", u"-").
2047                     replace(u"null", u"-") for item in test
2048                 ]
2049             ) + u"\n")
2050
2051     txt_file_name = f"{table[u'output-file']}.txt"
2052     logging.info(f"    Writing the file {txt_file_name}")
2053     convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2054
2055     # Reorganize header in txt table
2056     txt_table = list()
2057     with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2058         for line in file_handler:
2059             txt_table.append(line)
2060     try:
2061         txt_table.insert(5, txt_table.pop(2))
2062         with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2063             file_handler.writelines(txt_table)
2064     except IndexError:
2065         pass
2066
2067     # Generate html table:
2068     hdr_html = [
2069         u"<br>".join(row) for row in zip(*header)
2070     ]
2071     _tpc_generate_html_table(
2072         hdr_html,
2073         tbl_lst,
2074         table[u'output-file'],
2075         sort_data=True,
2076         title=table.get(u"title", u""),
2077         generate_rst=False
2078     )