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     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"cop" in test_name:
1068         bsf = u"features"
1069     elif u"nat" in test_name:
1070         bsf = u"features"
1071     elif u"macip" in test_name:
1072         bsf = u"features"
1073     elif u"scale" in test_name:
1074         bsf = u"scale"
1075     elif u"base" in test_name:
1076         bsf = u"base"
1077     else:
1078         bsf = u"base"
1079
1080     if u"114b" in test_name and u"vhost" in test_name:
1081         domain = u"vts"
1082     elif u"nat44" in test_name or u"-pps" in test_name or u"-cps" in test_name:
1083         domain = u"nat44"
1084         if u"nat44det" in test_name:
1085             domain += u"-det-bidir"
1086         else:
1087             domain += u"-ed"
1088         if u"udir" in test_name:
1089             domain += u"-unidir"
1090         elif u"-ethip4udp-" in test_name:
1091             domain += u"-udp"
1092         elif u"-ethip4tcp-" in test_name:
1093             domain += u"-tcp"
1094         if u"-cps" in test_name:
1095             domain += u"-cps"
1096         elif u"-pps" in test_name:
1097             domain += u"-pps"
1098     elif u"testpmd" in test_name or u"l3fwd" in test_name:
1099         domain = u"dpdk"
1100     elif u"memif" in test_name:
1101         domain = u"container_memif"
1102     elif u"srv6" in test_name:
1103         domain = u"srv6"
1104     elif u"vhost" in test_name:
1105         domain = u"vhost"
1106         if u"vppl2xc" in test_name:
1107             driver += u"-vpp"
1108         else:
1109             driver += u"-testpmd"
1110         if u"lbvpplacp" in test_name:
1111             bsf += u"-link-bonding"
1112     elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1113         domain = u"nf_service_density_vnfc"
1114     elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1115         domain = u"nf_service_density_cnfc"
1116     elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1117         domain = u"nf_service_density_cnfp"
1118     elif u"ipsec" in test_name:
1119         domain = u"ipsec"
1120         if u"sw" in test_name:
1121             bsf += u"-sw"
1122         elif u"hw" in test_name:
1123             bsf += u"-hw"
1124     elif u"ethip4vxlan" in test_name:
1125         domain = u"ip4_tunnels"
1126     elif u"ip4base" in test_name or u"ip4scale" in test_name:
1127         domain = u"ip4"
1128     elif u"ip6base" in test_name or u"ip6scale" in test_name:
1129         domain = u"ip6"
1130     elif u"l2xcbase" in test_name or \
1131             u"l2xcscale" in test_name or \
1132             u"l2bdbasemaclrn" in test_name or \
1133             u"l2bdscale" in test_name or \
1134             u"l2patch" in test_name:
1135         domain = u"l2"
1136     else:
1137         domain = u""
1138
1139     file_name = u"-".join((domain, testbed, nic)) + u".html#"
1140     anchor_name = u"-".join((frame_size, cores, bsf, driver))
1141
1142     return file_name + anchor_name
1143
1144
1145 def table_perf_trending_dash_html(table, input_data):
1146     """Generate the table(s) with algorithm:
1147     table_perf_trending_dash_html specified in the specification
1148     file.
1149
1150     :param table: Table to generate.
1151     :param input_data: Data to process.
1152     :type table: dict
1153     :type input_data: InputData
1154     """
1155
1156     _ = input_data
1157
1158     if not table.get(u"testbed", None):
1159         logging.error(
1160             f"The testbed is not defined for the table "
1161             f"{table.get(u'title', u'')}. Skipping."
1162         )
1163         return
1164
1165     test_type = table.get(u"test-type", u"MRR")
1166     if test_type not in (u"MRR", u"NDR", u"PDR"):
1167         logging.error(
1168             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1169             f"Skipping."
1170         )
1171         return
1172
1173     if test_type in (u"NDR", u"PDR"):
1174         lnk_dir = u"../ndrpdr_trending/"
1175         lnk_sufix = f"-{test_type.lower()}"
1176     else:
1177         lnk_dir = u"../trending/"
1178         lnk_sufix = u""
1179
1180     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1181
1182     try:
1183         with open(table[u"input-file"], u'rt') as csv_file:
1184             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1185     except KeyError:
1186         logging.warning(u"The input file is not defined.")
1187         return
1188     except csv.Error as err:
1189         logging.warning(
1190             f"Not possible to process the file {table[u'input-file']}.\n"
1191             f"{repr(err)}"
1192         )
1193         return
1194
1195     # Table:
1196     dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1197
1198     # Table header:
1199     trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1200     for idx, item in enumerate(csv_lst[0]):
1201         alignment = u"left" if idx == 0 else u"center"
1202         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1203         thead.text = item
1204
1205     # Rows:
1206     colors = {
1207         u"regression": (
1208             u"#ffcccc",
1209             u"#ff9999"
1210         ),
1211         u"progression": (
1212             u"#c6ecc6",
1213             u"#9fdf9f"
1214         ),
1215         u"normal": (
1216             u"#e9f1fb",
1217             u"#d4e4f7"
1218         )
1219     }
1220     for r_idx, row in enumerate(csv_lst[1:]):
1221         if int(row[4]):
1222             color = u"regression"
1223         elif int(row[5]):
1224             color = u"progression"
1225         else:
1226             color = u"normal"
1227         trow = ET.SubElement(
1228             dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1229         )
1230
1231         # Columns:
1232         for c_idx, item in enumerate(row):
1233             tdata = ET.SubElement(
1234                 trow,
1235                 u"td",
1236                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1237             )
1238             # Name:
1239             if c_idx == 0 and table.get(u"add-links", True):
1240                 ref = ET.SubElement(
1241                     tdata,
1242                     u"a",
1243                     attrib=dict(
1244                         href=f"{lnk_dir}"
1245                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1246                              f"{lnk_sufix}"
1247                     )
1248                 )
1249                 ref.text = item
1250             else:
1251                 tdata.text = item
1252     try:
1253         with open(table[u"output-file"], u'w') as html_file:
1254             logging.info(f"    Writing file: {table[u'output-file']}")
1255             html_file.write(u".. raw:: html\n\n\t")
1256             html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1257             html_file.write(u"\n\t<p><br><br></p>\n")
1258     except KeyError:
1259         logging.warning(u"The output file is not defined.")
1260         return
1261
1262
1263 def table_last_failed_tests(table, input_data):
1264     """Generate the table(s) with algorithm: table_last_failed_tests
1265     specified in the specification file.
1266
1267     :param table: Table to generate.
1268     :param input_data: Data to process.
1269     :type table: pandas.Series
1270     :type input_data: InputData
1271     """
1272
1273     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1274
1275     # Transform the data
1276     logging.info(
1277         f"    Creating the data set for the {table.get(u'type', u'')} "
1278         f"{table.get(u'title', u'')}."
1279     )
1280
1281     data = input_data.filter_data(table, continue_on_error=True)
1282
1283     if data is None or data.empty:
1284         logging.warning(
1285             f"    No data for the {table.get(u'type', u'')} "
1286             f"{table.get(u'title', u'')}."
1287         )
1288         return
1289
1290     tbl_list = list()
1291     for job, builds in table[u"data"].items():
1292         for build in builds:
1293             build = str(build)
1294             try:
1295                 version = input_data.metadata(job, build).get(u"version", u"")
1296             except KeyError:
1297                 logging.error(f"Data for {job}: {build} is not present.")
1298                 return
1299             tbl_list.append(build)
1300             tbl_list.append(version)
1301             failed_tests = list()
1302             passed = 0
1303             failed = 0
1304             for tst_data in data[job][build].values:
1305                 if tst_data[u"status"] != u"FAIL":
1306                     passed += 1
1307                     continue
1308                 failed += 1
1309                 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1310                 if not groups:
1311                     continue
1312                 nic = groups.group(0)
1313                 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1314             tbl_list.append(str(passed))
1315             tbl_list.append(str(failed))
1316             tbl_list.extend(failed_tests)
1317
1318     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1319     logging.info(f"    Writing file: {file_name}")
1320     with open(file_name, u"wt") as file_handler:
1321         for test in tbl_list:
1322             file_handler.write(test + u'\n')
1323
1324
1325 def table_failed_tests(table, input_data):
1326     """Generate the table(s) with algorithm: table_failed_tests
1327     specified in the specification file.
1328
1329     :param table: Table to generate.
1330     :param input_data: Data to process.
1331     :type table: pandas.Series
1332     :type input_data: InputData
1333     """
1334
1335     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1336
1337     # Transform the data
1338     logging.info(
1339         f"    Creating the data set for the {table.get(u'type', u'')} "
1340         f"{table.get(u'title', u'')}."
1341     )
1342     data = input_data.filter_data(table, continue_on_error=True)
1343
1344     test_type = u"MRR"
1345     if u"NDRPDR" in table.get(u"filter", list()):
1346         test_type = u"NDRPDR"
1347
1348     # Prepare the header of the tables
1349     header = [
1350         u"Test Case",
1351         u"Failures [#]",
1352         u"Last Failure [Time]",
1353         u"Last Failure [VPP-Build-Id]",
1354         u"Last Failure [CSIT-Job-Build-Id]"
1355     ]
1356
1357     # Generate the data for the table according to the model in the table
1358     # specification
1359
1360     now = dt.utcnow()
1361     timeperiod = timedelta(int(table.get(u"window", 7)))
1362
1363     tbl_dict = dict()
1364     for job, builds in table[u"data"].items():
1365         for build in builds:
1366             build = str(build)
1367             for tst_name, tst_data in data[job][build].items():
1368                 if tst_name.lower() in table.get(u"ignore-list", list()):
1369                     continue
1370                 if tbl_dict.get(tst_name, None) is None:
1371                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
1372                     if not groups:
1373                         continue
1374                     nic = groups.group(0)
1375                     tbl_dict[tst_name] = {
1376                         u"name": f"{nic}-{tst_data[u'name']}",
1377                         u"data": OrderedDict()
1378                     }
1379                 try:
1380                     generated = input_data.metadata(job, build).\
1381                         get(u"generated", u"")
1382                     if not generated:
1383                         continue
1384                     then = dt.strptime(generated, u"%Y%m%d %H:%M")
1385                     if (now - then) <= timeperiod:
1386                         tbl_dict[tst_name][u"data"][build] = (
1387                             tst_data[u"status"],
1388                             generated,
1389                             input_data.metadata(job, build).get(u"version",
1390                                                                 u""),
1391                             build
1392                         )
1393                 except (TypeError, KeyError) as err:
1394                     logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1395
1396     max_fails = 0
1397     tbl_lst = list()
1398     for tst_data in tbl_dict.values():
1399         fails_nr = 0
1400         fails_last_date = u""
1401         fails_last_vpp = u""
1402         fails_last_csit = u""
1403         for val in tst_data[u"data"].values():
1404             if val[0] == u"FAIL":
1405                 fails_nr += 1
1406                 fails_last_date = val[1]
1407                 fails_last_vpp = val[2]
1408                 fails_last_csit = val[3]
1409         if fails_nr:
1410             max_fails = fails_nr if fails_nr > max_fails else max_fails
1411             tbl_lst.append([
1412                 tst_data[u"name"],
1413                 fails_nr,
1414                 fails_last_date,
1415                 fails_last_vpp,
1416                 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1417                 f"-build-{fails_last_csit}"
1418             ])
1419
1420     tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1421     tbl_sorted = list()
1422     for nrf in range(max_fails, -1, -1):
1423         tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1424         tbl_sorted.extend(tbl_fails)
1425
1426     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1427     logging.info(f"    Writing file: {file_name}")
1428     with open(file_name, u"wt") as file_handler:
1429         file_handler.write(u",".join(header) + u"\n")
1430         for test in tbl_sorted:
1431             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1432
1433     logging.info(f"    Writing file: {table[u'output-file']}.txt")
1434     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1435
1436
1437 def table_failed_tests_html(table, input_data):
1438     """Generate the table(s) with algorithm: table_failed_tests_html
1439     specified in the specification file.
1440
1441     :param table: Table to generate.
1442     :param input_data: Data to process.
1443     :type table: pandas.Series
1444     :type input_data: InputData
1445     """
1446
1447     _ = input_data
1448
1449     if not table.get(u"testbed", None):
1450         logging.error(
1451             f"The testbed is not defined for the table "
1452             f"{table.get(u'title', u'')}. Skipping."
1453         )
1454         return
1455
1456     test_type = table.get(u"test-type", u"MRR")
1457     if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1458         logging.error(
1459             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1460             f"Skipping."
1461         )
1462         return
1463
1464     if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1465         lnk_dir = u"../ndrpdr_trending/"
1466         lnk_sufix = u"-pdr"
1467     else:
1468         lnk_dir = u"../trending/"
1469         lnk_sufix = u""
1470
1471     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1472
1473     try:
1474         with open(table[u"input-file"], u'rt') as csv_file:
1475             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1476     except KeyError:
1477         logging.warning(u"The input file is not defined.")
1478         return
1479     except csv.Error as err:
1480         logging.warning(
1481             f"Not possible to process the file {table[u'input-file']}.\n"
1482             f"{repr(err)}"
1483         )
1484         return
1485
1486     # Table:
1487     failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1488
1489     # Table header:
1490     trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1491     for idx, item in enumerate(csv_lst[0]):
1492         alignment = u"left" if idx == 0 else u"center"
1493         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1494         thead.text = item
1495
1496     # Rows:
1497     colors = (u"#e9f1fb", u"#d4e4f7")
1498     for r_idx, row in enumerate(csv_lst[1:]):
1499         background = colors[r_idx % 2]
1500         trow = ET.SubElement(
1501             failed_tests, u"tr", attrib=dict(bgcolor=background)
1502         )
1503
1504         # Columns:
1505         for c_idx, item in enumerate(row):
1506             tdata = ET.SubElement(
1507                 trow,
1508                 u"td",
1509                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1510             )
1511             # Name:
1512             if c_idx == 0 and table.get(u"add-links", True):
1513                 ref = ET.SubElement(
1514                     tdata,
1515                     u"a",
1516                     attrib=dict(
1517                         href=f"{lnk_dir}"
1518                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1519                              f"{lnk_sufix}"
1520                     )
1521                 )
1522                 ref.text = item
1523             else:
1524                 tdata.text = item
1525     try:
1526         with open(table[u"output-file"], u'w') as html_file:
1527             logging.info(f"    Writing file: {table[u'output-file']}")
1528             html_file.write(u".. raw:: html\n\n\t")
1529             html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1530             html_file.write(u"\n\t<p><br><br></p>\n")
1531     except KeyError:
1532         logging.warning(u"The output file is not defined.")
1533         return
1534
1535
1536 def table_comparison(table, input_data):
1537     """Generate the table(s) with algorithm: table_comparison
1538     specified in the specification file.
1539
1540     :param table: Table to generate.
1541     :param input_data: Data to process.
1542     :type table: pandas.Series
1543     :type input_data: InputData
1544     """
1545     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1546
1547     # Transform the data
1548     logging.info(
1549         f"    Creating the data set for the {table.get(u'type', u'')} "
1550         f"{table.get(u'title', u'')}."
1551     )
1552
1553     columns = table.get(u"columns", None)
1554     if not columns:
1555         logging.error(
1556             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1557         )
1558         return
1559
1560     cols = list()
1561     for idx, col in enumerate(columns):
1562         if col.get(u"data-set", None) is None:
1563             logging.warning(f"No data for column {col.get(u'title', u'')}")
1564             continue
1565         tag = col.get(u"tag", None)
1566         data = input_data.filter_data(
1567             table,
1568             params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1569             data=col[u"data-set"],
1570             continue_on_error=True
1571         )
1572         col_data = {
1573             u"title": col.get(u"title", f"Column{idx}"),
1574             u"data": dict()
1575         }
1576         for builds in data.values:
1577             for build in builds:
1578                 for tst_name, tst_data in build.items():
1579                     if tag and tag not in tst_data[u"tags"]:
1580                         continue
1581                     tst_name_mod = \
1582                         _tpc_modify_test_name(tst_name, ignore_nic=True).\
1583                         replace(u"2n1l-", u"")
1584                     if col_data[u"data"].get(tst_name_mod, None) is None:
1585                         name = tst_data[u'name'].rsplit(u'-', 1)[0]
1586                         if u"across testbeds" in table[u"title"].lower() or \
1587                                 u"across topologies" in table[u"title"].lower():
1588                             name = _tpc_modify_displayed_test_name(name)
1589                         col_data[u"data"][tst_name_mod] = {
1590                             u"name": name,
1591                             u"replace": True,
1592                             u"data": list(),
1593                             u"mean": None,
1594                             u"stdev": None
1595                         }
1596                     _tpc_insert_data(
1597                         target=col_data[u"data"][tst_name_mod],
1598                         src=tst_data,
1599                         include_tests=table[u"include-tests"]
1600                     )
1601
1602         replacement = col.get(u"data-replacement", None)
1603         if replacement:
1604             rpl_data = input_data.filter_data(
1605                 table,
1606                 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1607                 data=replacement,
1608                 continue_on_error=True
1609             )
1610             for builds in rpl_data.values:
1611                 for build in builds:
1612                     for tst_name, tst_data in build.items():
1613                         if tag and tag not in tst_data[u"tags"]:
1614                             continue
1615                         tst_name_mod = \
1616                             _tpc_modify_test_name(tst_name, ignore_nic=True).\
1617                             replace(u"2n1l-", u"")
1618                         if col_data[u"data"].get(tst_name_mod, None) is None:
1619                             name = tst_data[u'name'].rsplit(u'-', 1)[0]
1620                             if u"across testbeds" in table[u"title"].lower() \
1621                                     or u"across topologies" in \
1622                                     table[u"title"].lower():
1623                                 name = _tpc_modify_displayed_test_name(name)
1624                             col_data[u"data"][tst_name_mod] = {
1625                                 u"name": name,
1626                                 u"replace": False,
1627                                 u"data": list(),
1628                                 u"mean": None,
1629                                 u"stdev": None
1630                             }
1631                         if col_data[u"data"][tst_name_mod][u"replace"]:
1632                             col_data[u"data"][tst_name_mod][u"replace"] = False
1633                             col_data[u"data"][tst_name_mod][u"data"] = list()
1634                         _tpc_insert_data(
1635                             target=col_data[u"data"][tst_name_mod],
1636                             src=tst_data,
1637                             include_tests=table[u"include-tests"]
1638                         )
1639
1640         if table[u"include-tests"] in (u"NDR", u"PDR"):
1641             for tst_name, tst_data in col_data[u"data"].items():
1642                 if tst_data[u"data"]:
1643                     tst_data[u"mean"] = mean(tst_data[u"data"])
1644                     tst_data[u"stdev"] = stdev(tst_data[u"data"])
1645
1646         cols.append(col_data)
1647
1648     tbl_dict = dict()
1649     for col in cols:
1650         for tst_name, tst_data in col[u"data"].items():
1651             if tbl_dict.get(tst_name, None) is None:
1652                 tbl_dict[tst_name] = {
1653                     "name": tst_data[u"name"]
1654                 }
1655             tbl_dict[tst_name][col[u"title"]] = {
1656                 u"mean": tst_data[u"mean"],
1657                 u"stdev": tst_data[u"stdev"]
1658             }
1659
1660     if not tbl_dict:
1661         logging.warning(f"No data for table {table.get(u'title', u'')}!")
1662         return
1663
1664     tbl_lst = list()
1665     for tst_data in tbl_dict.values():
1666         row = [tst_data[u"name"], ]
1667         for col in cols:
1668             row.append(tst_data.get(col[u"title"], None))
1669         tbl_lst.append(row)
1670
1671     comparisons = table.get(u"comparisons", None)
1672     rcas = list()
1673     if comparisons and isinstance(comparisons, list):
1674         for idx, comp in enumerate(comparisons):
1675             try:
1676                 col_ref = int(comp[u"reference"])
1677                 col_cmp = int(comp[u"compare"])
1678             except KeyError:
1679                 logging.warning(u"Comparison: No references defined! Skipping.")
1680                 comparisons.pop(idx)
1681                 continue
1682             if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1683                     col_ref == col_cmp):
1684                 logging.warning(f"Wrong values of reference={col_ref} "
1685                                 f"and/or compare={col_cmp}. Skipping.")
1686                 comparisons.pop(idx)
1687                 continue
1688             rca_file_name = comp.get(u"rca-file", None)
1689             if rca_file_name:
1690                 try:
1691                     with open(rca_file_name, u"r") as file_handler:
1692                         rcas.append(
1693                             {
1694                                 u"title": f"RCA{idx + 1}",
1695                                 u"data": load(file_handler, Loader=FullLoader)
1696                             }
1697                         )
1698                 except (YAMLError, IOError) as err:
1699                     logging.warning(
1700                         f"The RCA file {rca_file_name} does not exist or "
1701                         f"it is corrupted!"
1702                     )
1703                     logging.debug(repr(err))
1704                     rcas.append(None)
1705             else:
1706                 rcas.append(None)
1707     else:
1708         comparisons = None
1709
1710     tbl_cmp_lst = list()
1711     if comparisons:
1712         for row in tbl_lst:
1713             new_row = deepcopy(row)
1714             for comp in comparisons:
1715                 ref_itm = row[int(comp[u"reference"])]
1716                 if ref_itm is None and \
1717                         comp.get(u"reference-alt", None) is not None:
1718                     ref_itm = row[int(comp[u"reference-alt"])]
1719                 cmp_itm = row[int(comp[u"compare"])]
1720                 if ref_itm is not None and cmp_itm is not None and \
1721                         ref_itm[u"mean"] is not None and \
1722                         cmp_itm[u"mean"] is not None and \
1723                         ref_itm[u"stdev"] is not None and \
1724                         cmp_itm[u"stdev"] is not None:
1725                     delta, d_stdev = relative_change_stdev(
1726                         ref_itm[u"mean"], cmp_itm[u"mean"],
1727                         ref_itm[u"stdev"], cmp_itm[u"stdev"]
1728                     )
1729                     if delta is None:
1730                         break
1731                     new_row.append({
1732                         u"mean": delta * 1e6,
1733                         u"stdev": d_stdev * 1e6
1734                     })
1735                 else:
1736                     break
1737             else:
1738                 tbl_cmp_lst.append(new_row)
1739
1740     try:
1741         tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1742         tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1743     except TypeError as err:
1744         logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1745
1746     tbl_for_csv = list()
1747     for line in tbl_cmp_lst:
1748         row = [line[0], ]
1749         for idx, itm in enumerate(line[1:]):
1750             if itm is None or not isinstance(itm, dict) or\
1751                     itm.get(u'mean', None) is None or \
1752                     itm.get(u'stdev', None) is None:
1753                 row.append(u"NT")
1754                 row.append(u"NT")
1755             else:
1756                 row.append(round(float(itm[u'mean']) / 1e6, 3))
1757                 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1758         for rca in rcas:
1759             if rca is None:
1760                 continue
1761             rca_nr = rca[u"data"].get(row[0], u"-")
1762             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1763         tbl_for_csv.append(row)
1764
1765     header_csv = [u"Test Case", ]
1766     for col in cols:
1767         header_csv.append(f"Avg({col[u'title']})")
1768         header_csv.append(f"Stdev({col[u'title']})")
1769     for comp in comparisons:
1770         header_csv.append(
1771             f"Avg({comp.get(u'title', u'')})"
1772         )
1773         header_csv.append(
1774             f"Stdev({comp.get(u'title', u'')})"
1775         )
1776     for rca in rcas:
1777         if rca:
1778             header_csv.append(rca[u"title"])
1779
1780     legend_lst = table.get(u"legend", None)
1781     if legend_lst is None:
1782         legend = u""
1783     else:
1784         legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1785
1786     footnote = u""
1787     if rcas and any(rcas):
1788         footnote += u"\nRoot Cause Analysis:\n"
1789         for rca in rcas:
1790             if rca:
1791                 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1792
1793     csv_file_name = f"{table[u'output-file']}-csv.csv"
1794     with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1795         file_handler.write(
1796             u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1797         )
1798         for test in tbl_for_csv:
1799             file_handler.write(
1800                 u",".join([f'"{item}"' for item in test]) + u"\n"
1801             )
1802         if legend_lst:
1803             for item in legend_lst:
1804                 file_handler.write(f'"{item}"\n')
1805         if footnote:
1806             for itm in footnote.split(u"\n"):
1807                 file_handler.write(f'"{itm}"\n')
1808
1809     tbl_tmp = list()
1810     max_lens = [0, ] * len(tbl_cmp_lst[0])
1811     for line in tbl_cmp_lst:
1812         row = [line[0], ]
1813         for idx, itm in enumerate(line[1:]):
1814             if itm is None or not isinstance(itm, dict) or \
1815                     itm.get(u'mean', None) is None or \
1816                     itm.get(u'stdev', None) is None:
1817                 new_itm = u"NT"
1818             else:
1819                 if idx < len(cols):
1820                     new_itm = (
1821                         f"{round(float(itm[u'mean']) / 1e6, 1)} "
1822                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1823                         replace(u"nan", u"NaN")
1824                     )
1825                 else:
1826                     new_itm = (
1827                         f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1828                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1829                         replace(u"nan", u"NaN")
1830                     )
1831             if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1832                 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1833             row.append(new_itm)
1834
1835         tbl_tmp.append(row)
1836
1837     header = [u"Test Case", ]
1838     header.extend([col[u"title"] for col in cols])
1839     header.extend([comp.get(u"title", u"") for comp in comparisons])
1840
1841     tbl_final = list()
1842     for line in tbl_tmp:
1843         row = [line[0], ]
1844         for idx, itm in enumerate(line[1:]):
1845             if itm in (u"NT", u"NaN"):
1846                 row.append(itm)
1847                 continue
1848             itm_lst = itm.rsplit(u"\u00B1", 1)
1849             itm_lst[-1] = \
1850                 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1851             itm_str = u"\u00B1".join(itm_lst)
1852
1853             if idx >= len(cols):
1854                 # Diffs
1855                 rca = rcas[idx - len(cols)]
1856                 if rca:
1857                     # Add rcas to diffs
1858                     rca_nr = rca[u"data"].get(row[0], None)
1859                     if rca_nr:
1860                         hdr_len = len(header[idx + 1]) - 1
1861                         if hdr_len < 19:
1862                             hdr_len = 19
1863                         rca_nr = f"[{rca_nr}]"
1864                         itm_str = (
1865                             f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
1866                             f"{u' ' * (hdr_len - 4 - len(itm_str))}"
1867                             f"{itm_str}"
1868                         )
1869             row.append(itm_str)
1870         tbl_final.append(row)
1871
1872     # Generate csv tables:
1873     csv_file_name = f"{table[u'output-file']}.csv"
1874     logging.info(f"    Writing the file {csv_file_name}")
1875     with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1876         file_handler.write(u";".join(header) + u"\n")
1877         for test in tbl_final:
1878             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1879
1880     # Generate txt table:
1881     txt_file_name = f"{table[u'output-file']}.txt"
1882     logging.info(f"    Writing the file {txt_file_name}")
1883     convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
1884
1885     with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
1886         file_handler.write(legend)
1887         file_handler.write(footnote)
1888
1889     # Generate html table:
1890     _tpc_generate_html_table(
1891         header,
1892         tbl_final,
1893         table[u'output-file'],
1894         legend=legend,
1895         footnote=footnote,
1896         sort_data=False,
1897         title=table.get(u"title", u"")
1898     )
1899
1900
1901 def table_weekly_comparison(table, in_data):
1902     """Generate the table(s) with algorithm: table_weekly_comparison
1903     specified in the specification file.
1904
1905     :param table: Table to generate.
1906     :param in_data: Data to process.
1907     :type table: pandas.Series
1908     :type in_data: InputData
1909     """
1910     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1911
1912     # Transform the data
1913     logging.info(
1914         f"    Creating the data set for the {table.get(u'type', u'')} "
1915         f"{table.get(u'title', u'')}."
1916     )
1917
1918     incl_tests = table.get(u"include-tests", None)
1919     if incl_tests not in (u"NDR", u"PDR"):
1920         logging.error(f"Wrong tests to include specified ({incl_tests}).")
1921         return
1922
1923     nr_cols = table.get(u"nr-of-data-columns", None)
1924     if not nr_cols or nr_cols < 2:
1925         logging.error(
1926             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1927         )
1928         return
1929
1930     data = in_data.filter_data(
1931         table,
1932         params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1933         continue_on_error=True
1934     )
1935
1936     header = [
1937         [u"VPP Version", ],
1938         [u"Start Timestamp", ],
1939         [u"CSIT Build", ],
1940         [u"CSIT Testbed", ]
1941     ]
1942     tbl_dict = dict()
1943     idx = 0
1944     tb_tbl = table.get(u"testbeds", None)
1945     for job_name, job_data in data.items():
1946         for build_nr, build in job_data.items():
1947             if idx >= nr_cols:
1948                 break
1949             if build.empty:
1950                 continue
1951
1952             tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1953             if tb_ip and tb_tbl:
1954                 testbed = tb_tbl.get(tb_ip, u"")
1955             else:
1956                 testbed = u""
1957             header[2].insert(1, build_nr)
1958             header[3].insert(1, testbed)
1959             header[1].insert(
1960                 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1961             )
1962             header[0].insert(
1963                 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1964             )
1965
1966             for tst_name, tst_data in build.items():
1967                 tst_name_mod = \
1968                     _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1969                 if not tbl_dict.get(tst_name_mod, None):
1970                     tbl_dict[tst_name_mod] = dict(
1971                         name=tst_data[u'name'].rsplit(u'-', 1)[0],
1972                     )
1973                 try:
1974                     tbl_dict[tst_name_mod][-idx - 1] = \
1975                         tst_data[u"throughput"][incl_tests][u"LOWER"]
1976                 except (TypeError, IndexError, KeyError, ValueError):
1977                     pass
1978             idx += 1
1979
1980     if idx < nr_cols:
1981         logging.error(u"Not enough data to build the table! Skipping")
1982         return
1983
1984     cmp_dict = dict()
1985     for idx, cmp in enumerate(table.get(u"comparisons", list())):
1986         idx_ref = cmp.get(u"reference", None)
1987         idx_cmp = cmp.get(u"compare", None)
1988         if idx_ref is None or idx_cmp is None:
1989             continue
1990         header[0].append(
1991             f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1992             f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1993         )
1994         header[1].append(u"")
1995         header[2].append(u"")
1996         header[3].append(u"")
1997         for tst_name, tst_data in tbl_dict.items():
1998             if not cmp_dict.get(tst_name, None):
1999                 cmp_dict[tst_name] = list()
2000             ref_data = tst_data.get(idx_ref, None)
2001             cmp_data = tst_data.get(idx_cmp, None)
2002             if ref_data is None or cmp_data is None:
2003                 cmp_dict[tst_name].append(float(u'nan'))
2004             else:
2005                 cmp_dict[tst_name].append(
2006                     relative_change(ref_data, cmp_data)
2007                 )
2008
2009     tbl_lst_none = list()
2010     tbl_lst = list()
2011     for tst_name, tst_data in tbl_dict.items():
2012         itm_lst = [tst_data[u"name"], ]
2013         for idx in range(nr_cols):
2014             item = tst_data.get(-idx - 1, None)
2015             if item is None:
2016                 itm_lst.insert(1, None)
2017             else:
2018                 itm_lst.insert(1, round(item / 1e6, 1))
2019         itm_lst.extend(
2020             [
2021                 None if itm is None else round(itm, 1)
2022                 for itm in cmp_dict[tst_name]
2023             ]
2024         )
2025         if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
2026             tbl_lst_none.append(itm_lst)
2027         else:
2028             tbl_lst.append(itm_lst)
2029
2030     tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
2031     tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
2032     tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
2033     tbl_lst.extend(tbl_lst_none)
2034
2035     # Generate csv table:
2036     csv_file_name = f"{table[u'output-file']}.csv"
2037     logging.info(f"    Writing the file {csv_file_name}")
2038     with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2039         for hdr in header:
2040             file_handler.write(u",".join(hdr) + u"\n")
2041         for test in tbl_lst:
2042             file_handler.write(u",".join(
2043                 [
2044                     str(item).replace(u"None", u"-").replace(u"nan", u"-").
2045                     replace(u"null", u"-") for item in test
2046                 ]
2047             ) + u"\n")
2048
2049     txt_file_name = f"{table[u'output-file']}.txt"
2050     logging.info(f"    Writing the file {txt_file_name}")
2051     convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2052
2053     # Reorganize header in txt table
2054     txt_table = list()
2055     with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2056         for line in file_handler:
2057             txt_table.append(line)
2058     try:
2059         txt_table.insert(5, txt_table.pop(2))
2060         with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2061             file_handler.writelines(txt_table)
2062     except IndexError:
2063         pass
2064
2065     # Generate html table:
2066     hdr_html = [
2067         u"<br>".join(row) for row in zip(*header)
2068     ]
2069     _tpc_generate_html_table(
2070         hdr_html,
2071         tbl_lst,
2072         table[u'output-file'],
2073         sort_data=True,
2074         title=table.get(u"title", u""),
2075         generate_rst=False
2076     )