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