resources/tools/presentation/generator_tables.py

   1 # Copyright (c) 2020 Cisco and/or its affiliates.
   2 # Licensed under the Apache License, Version 2.0 (the "License");
   3 # you may not use this file except in compliance with the License.
   4 # You may obtain a copy of the License at:
   5 #
   6 #     http://www.apache.org/licenses/LICENSE-2.0
   7 #
   8 # Unless required by applicable law or agreed to in writing, software
   9 # distributed under the License is distributed on an "AS IS" BASIS,
  10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  11 # See the License for the specific language governing permissions and
  12 # limitations under the License.
  13
  14 """Algorithms to generate tables.
  15 """
  16
  17
  18 import logging
  19 import csv
  20 import re
  21
  22 from collections import OrderedDict
  23 from xml.etree import ElementTree as ET
  24 from datetime import datetime as dt
  25 from datetime import timedelta
  26 from copy import deepcopy
  27
  28 import plotly.graph_objects as go
  29 import plotly.offline as ploff
  30 import pandas as pd
  31
  32 from numpy import nan, isnan
  33 from yaml import load, FullLoader, YAMLError
  34
  35 from pal_utils import mean, stdev, classify_anomalies, \
  36     convert_csv_to_pretty_txt, relative_change_stdev, relative_change
  37
  38
  39 REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)')
  40
  41
  42 def generate_tables(spec, data):
  43     """Generate all tables specified in the specification file.
  44
  45     :param spec: Specification read from the specification file.
  46     :param data: Data to process.
  47     :type spec: Specification
  48     :type data: InputData
  49     """
  50
  51     generator = {
  52         u"table_merged_details": table_merged_details,
  53         u"table_soak_vs_ndr": table_soak_vs_ndr,
  54         u"table_perf_trending_dash": table_perf_trending_dash,
  55         u"table_perf_trending_dash_html": table_perf_trending_dash_html,
  56         u"table_last_failed_tests": table_last_failed_tests,
  57         u"table_failed_tests": table_failed_tests,
  58         u"table_failed_tests_html": table_failed_tests_html,
  59         u"table_oper_data_html": table_oper_data_html,
  60         u"table_comparison": table_comparison,
  61         u"table_weekly_comparison": table_weekly_comparison
  62     }
  63
  64     logging.info(u"Generating the tables ...")
  65     for table in spec.tables:
  66         try:
  67             if table[u"algorithm"] == u"table_weekly_comparison":
  68                 table[u"testbeds"] = spec.environment.get(u"testbeds", None)
  69             generator[table[u"algorithm"]](table, data)
  70         except NameError as err:
  71             logging.error(
  72                 f"Probably algorithm {table[u'algorithm']} is not defined: "
  73                 f"{repr(err)}"
  74             )
  75     logging.info(u"Done.")
  76
  77
  78 def table_oper_data_html(table, input_data):
  79     """Generate the table(s) with algorithm: html_table_oper_data
  80     specified in the specification file.
  81
  82     :param table: Table to generate.
  83     :param input_data: Data to process.
  84     :type table: pandas.Series
  85     :type input_data: InputData
  86     """
  87
  88     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
  89     # Transform the data
  90     logging.info(
  91         f"    Creating the data set for the {table.get(u'type', u'')} "
  92         f"{table.get(u'title', u'')}."
  93     )
  94     data = input_data.filter_data(
  95         table,
  96         params=[u"name", u"parent", u"show-run", u"type"],
  97         continue_on_error=True
  98     )
  99     if data.empty:
 100         return
 101     data = input_data.merge_data(data)
 102
 103     sort_tests = table.get(u"sort", None)
 104     if sort_tests:
 105         args = dict(
 106             inplace=True,
 107             ascending=(sort_tests == u"ascending")
 108         )
 109         data.sort_index(**args)
 110
 111     suites = input_data.filter_data(
 112         table,
 113         continue_on_error=True,
 114         data_set=u"suites"
 115     )
 116     if suites.empty:
 117         return
 118     suites = input_data.merge_data(suites)
 119
 120     def _generate_html_table(tst_data):
 121         """Generate an HTML table with operational data for the given test.
 122
 123         :param tst_data: Test data to be used to generate the table.
 124         :type tst_data: pandas.Series
 125         :returns: HTML table with operational data.
 126         :rtype: str
 127         """
 128
 129         colors = {
 130             u"header": u"#7eade7",
 131             u"empty": u"#ffffff",
 132             u"body": (u"#e9f1fb", u"#d4e4f7")
 133         }
 134
 135         tbl = ET.Element(u"table", attrib=dict(width=u"100%", border=u"0"))
 136
 137         trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"header"]))
 138         thead = ET.SubElement(
 139             trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 140         )
 141         thead.text = tst_data[u"name"]
 142
 143         trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
 144         thead = ET.SubElement(
 145             trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 146         )
 147         thead.text = u"\t"
 148
 149         if tst_data.get(u"show-run", u"No Data") == u"No Data":
 150             trow = ET.SubElement(
 151                 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 152             )
 153             tcol = ET.SubElement(
 154                 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 155             )
 156             tcol.text = u"No Data"
 157
 158             trow = ET.SubElement(
 159                 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 160             )
 161             thead = ET.SubElement(
 162                 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 163             )
 164             font = ET.SubElement(
 165                 thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
 166             )
 167             font.text = u"."
 168             return str(ET.tostring(tbl, encoding=u"unicode"))
 169
 170         tbl_hdr = (
 171             u"Name",
 172             u"Nr of Vectors",
 173             u"Nr of Packets",
 174             u"Suspends",
 175             u"Cycles per Packet",
 176             u"Average Vector Size"
 177         )
 178
 179         for dut_data in tst_data[u"show-run"].values():
 180             trow = ET.SubElement(
 181                 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 182             )
 183             tcol = ET.SubElement(
 184                 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 185             )
 186             if dut_data.get(u"threads", None) is None:
 187                 tcol.text = u"No Data"
 188                 continue
 189
 190             bold = ET.SubElement(tcol, u"b")
 191             bold.text = (
 192                 f"Host IP: {dut_data.get(u'host', '')}, "
 193                 f"Socket: {dut_data.get(u'socket', '')}"
 194             )
 195             trow = ET.SubElement(
 196                 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 197             )
 198             thead = ET.SubElement(
 199                 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 200             )
 201             thead.text = u"\t"
 202
 203             for thread_nr, thread in dut_data[u"threads"].items():
 204                 trow = ET.SubElement(
 205                     tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 206                 )
 207                 tcol = ET.SubElement(
 208                     trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 209                 )
 210                 bold = ET.SubElement(tcol, u"b")
 211                 bold.text = u"main" if thread_nr == 0 else f"worker_{thread_nr}"
 212                 trow = ET.SubElement(
 213                     tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 214                 )
 215                 for idx, col in enumerate(tbl_hdr):
 216                     tcol = ET.SubElement(
 217                         trow, u"td",
 218                         attrib=dict(align=u"right" if idx else u"left")
 219                     )
 220                     font = ET.SubElement(
 221                         tcol, u"font", attrib=dict(size=u"2")
 222                     )
 223                     bold = ET.SubElement(font, u"b")
 224                     bold.text = col
 225                 for row_nr, row in enumerate(thread):
 226                     trow = ET.SubElement(
 227                         tbl, u"tr",
 228                         attrib=dict(bgcolor=colors[u"body"][row_nr % 2])
 229                     )
 230                     for idx, col in enumerate(row):
 231                         tcol = ET.SubElement(
 232                             trow, u"td",
 233                             attrib=dict(align=u"right" if idx else u"left")
 234                         )
 235                         font = ET.SubElement(
 236                             tcol, u"font", attrib=dict(size=u"2")
 237                         )
 238                         if isinstance(col, float):
 239                             font.text = f"{col:.2f}"
 240                         else:
 241                             font.text = str(col)
 242                 trow = ET.SubElement(
 243                     tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 244                 )
 245                 thead = ET.SubElement(
 246                     trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 247                 )
 248                 thead.text = u"\t"
 249
 250         trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
 251         thead = ET.SubElement(
 252             trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 253         )
 254         font = ET.SubElement(
 255             thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
 256         )
 257         font.text = u"."
 258
 259         return str(ET.tostring(tbl, encoding=u"unicode"))
 260
 261     for suite in suites.values:
 262         html_table = str()
 263         for test_data in data.values:
 264             if test_data[u"parent"] not in suite[u"name"]:
 265                 continue
 266             html_table += _generate_html_table(test_data)
 267         if not html_table:
 268             continue
 269         try:
 270             file_name = f"{table[u'output-file']}{suite[u'name']}.rst"
 271             with open(f"{file_name}", u'w') as html_file:
 272                 logging.info(f"    Writing file: {file_name}")
 273                 html_file.write(u".. raw:: html\n\n\t")
 274                 html_file.write(html_table)
 275                 html_file.write(u"\n\t<p><br><br></p>\n")
 276         except KeyError:
 277             logging.warning(u"The output file is not defined.")
 278             return
 279     logging.info(u"  Done.")
 280
 281
 282 def table_merged_details(table, input_data):
 283     """Generate the table(s) with algorithm: table_merged_details
 284     specified in the specification file.
 285
 286     :param table: Table to generate.
 287     :param input_data: Data to process.
 288     :type table: pandas.Series
 289     :type input_data: InputData
 290     """
 291
 292     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 293
 294     # Transform the data
 295     logging.info(
 296         f"    Creating the data set for the {table.get(u'type', u'')} "
 297         f"{table.get(u'title', u'')}."
 298     )
 299     data = input_data.filter_data(table, continue_on_error=True)
 300     data = input_data.merge_data(data)
 301
 302     sort_tests = table.get(u"sort", None)
 303     if sort_tests:
 304         args = dict(
 305             inplace=True,
 306             ascending=(sort_tests == u"ascending")
 307         )
 308         data.sort_index(**args)
 309
 310     suites = input_data.filter_data(
 311         table, continue_on_error=True, data_set=u"suites")
 312     suites = input_data.merge_data(suites)
 313
 314     # Prepare the header of the tables
 315     header = list()
 316     for column in table[u"columns"]:
 317         header.append(
 318             u'"{0}"'.format(str(column[u"title"]).replace(u'"', u'""'))
 319         )
 320
 321     for suite in suites.values:
 322         # Generate data
 323         suite_name = suite[u"name"]
 324         table_lst = list()
 325         for test in data.keys():
 326             if data[test][u"parent"] not in suite_name:
 327                 continue
 328             row_lst = list()
 329             for column in table[u"columns"]:
 330                 try:
 331                     col_data = str(data[test][column[
 332                         u"data"].split(u" ")[1]]).replace(u'"', u'""')
 333                     # Do not include tests with "Test Failed" in test message
 334                     if u"Test Failed" in col_data:
 335                         continue
 336                     col_data = col_data.replace(
 337                         u"No Data", u"Not Captured     "
 338                     )
 339                     if column[u"data"].split(u" ")[1] in (u"name", ):
 340                         if len(col_data) > 30:
 341                             col_data_lst = col_data.split(u"-")
 342                             half = int(len(col_data_lst) / 2)
 343                             col_data = f"{u'-'.join(col_data_lst[:half])}" \
 344                                        f"- |br| " \
 345                                        f"{u'-'.join(col_data_lst[half:])}"
 346                         col_data = f" |prein| {col_data} |preout| "
 347                     elif column[u"data"].split(u" ")[1] in (u"msg", ):
 348                         # Temporary solution: remove NDR results from message:
 349                         if bool(table.get(u'remove-ndr', False)):
 350                             try:
 351                                 col_data = col_data.split(u" |br| ", 1)[1]
 352                             except IndexError:
 353                                 pass
 354                         col_data = f" |prein| {col_data} |preout| "
 355                     elif column[u"data"].split(u" ")[1] in \
 356                             (u"conf-history", u"show-run"):
 357                         col_data = col_data.replace(u" |br| ", u"", 1)
 358                         col_data = f" |prein| {col_data[:-5]} |preout| "
 359                     row_lst.append(f'"{col_data}"')
 360                 except KeyError:
 361                     row_lst.append(u'"Not captured"')
 362             if len(row_lst) == len(table[u"columns"]):
 363                 table_lst.append(row_lst)
 364
 365         # Write the data to file
 366         if table_lst:
 367             separator = u"" if table[u'output-file'].endswith(u"/") else u"_"
 368             file_name = f"{table[u'output-file']}{separator}{suite_name}.csv"
 369             logging.info(f"      Writing file: {file_name}")
 370             with open(file_name, u"wt") as file_handler:
 371                 file_handler.write(u",".join(header) + u"\n")
 372                 for item in table_lst:
 373                     file_handler.write(u",".join(item) + u"\n")
 374
 375     logging.info(u"  Done.")
 376
 377
 378 def _tpc_modify_test_name(test_name, ignore_nic=False):
 379     """Modify a test name by replacing its parts.
 380
 381     :param test_name: Test name to be modified.
 382     :param ignore_nic: If True, NIC is removed from TC name.
 383     :type test_name: str
 384     :type ignore_nic: bool
 385     :returns: Modified test name.
 386     :rtype: str
 387     """
 388     test_name_mod = test_name.\
 389         replace(u"-ndrpdrdisc", u""). \
 390         replace(u"-ndrpdr", u"").\
 391         replace(u"-pdrdisc", u""). \
 392         replace(u"-ndrdisc", u"").\
 393         replace(u"-pdr", u""). \
 394         replace(u"-ndr", u""). \
 395         replace(u"1t1c", u"1c").\
 396         replace(u"2t1c", u"1c"). \
 397         replace(u"2t2c", u"2c").\
 398         replace(u"4t2c", u"2c"). \
 399         replace(u"4t4c", u"4c").\
 400         replace(u"8t4c", u"4c")
 401
 402     if ignore_nic:
 403         return re.sub(REGEX_NIC, u"", test_name_mod)
 404     return test_name_mod
 405
 406
 407 def _tpc_modify_displayed_test_name(test_name):
 408     """Modify a test name which is displayed in a table by replacing its parts.
 409
 410     :param test_name: Test name to be modified.
 411     :type test_name: str
 412     :returns: Modified test name.
 413     :rtype: str
 414     """
 415     return test_name.\
 416         replace(u"1t1c", u"1c").\
 417         replace(u"2t1c", u"1c"). \
 418         replace(u"2t2c", u"2c").\
 419         replace(u"4t2c", u"2c"). \
 420         replace(u"4t4c", u"4c").\
 421         replace(u"8t4c", u"4c")
 422
 423
 424 def _tpc_insert_data(target, src, include_tests):
 425     """Insert src data to the target structure.
 426
 427     :param target: Target structure where the data is placed.
 428     :param src: Source data to be placed into the target stucture.
 429     :param include_tests: Which results will be included (MRR, NDR, PDR).
 430     :type target: list
 431     :type src: dict
 432     :type include_tests: str
 433     """
 434     try:
 435         if include_tests == u"MRR":
 436             target[u"mean"] = src[u"result"][u"receive-rate"]
 437             target[u"stdev"] = src[u"result"][u"receive-stdev"]
 438         elif include_tests == u"PDR":
 439             target[u"data"].append(src[u"throughput"][u"PDR"][u"LOWER"])
 440         elif include_tests == u"NDR":
 441             target[u"data"].append(src[u"throughput"][u"NDR"][u"LOWER"])
 442     except (KeyError, TypeError):
 443         pass
 444
 445
 446 def _tpc_generate_html_table(header, data, out_file_name, legend=u"",
 447                              footnote=u"", sort_data=True, title=u"",
 448                              generate_rst=True):
 449     """Generate html table from input data with simple sorting possibility.
 450
 451     :param header: Table header.
 452     :param data: Input data to be included in the table. It is a list of lists.
 453         Inner lists are rows in the table. All inner lists must be of the same
 454         length. The length of these lists must be the same as the length of the
 455         header.
 456     :param out_file_name: The name (relative or full path) where the
 457         generated html table is written.
 458     :param legend: The legend to display below the table.
 459     :param footnote: The footnote to display below the table (and legend).
 460     :param sort_data: If True the data sorting is enabled.
 461     :param title: The table (and file) title.
 462     :param generate_rst: If True, wrapping rst file is generated.
 463     :type header: list
 464     :type data: list of lists
 465     :type out_file_name: str
 466     :type legend: str
 467     :type footnote: str
 468     :type sort_data: bool
 469     :type title: str
 470     :type generate_rst: bool
 471     """
 472
 473     try:
 474         idx = header.index(u"Test Case")
 475     except ValueError:
 476         idx = 0
 477     params = {
 478         u"align-hdr": (
 479             [u"left", u"right"],
 480             [u"left", u"left", u"right"],
 481             [u"left", u"left", u"left", u"right"]
 482         ),
 483         u"align-itm": (
 484             [u"left", u"right"],
 485             [u"left", u"left", u"right"],
 486             [u"left", u"left", u"left", u"right"]
 487         ),
 488         u"width": ([15, 9], [4, 24, 10], [4, 4, 32, 10])
 489     }
 490
 491     df_data = pd.DataFrame(data, columns=header)
 492
 493     if sort_data:
 494         df_sorted = [df_data.sort_values(
 495             by=[key, header[idx]], ascending=[True, True]
 496             if key != header[idx] else [False, True]) for key in header]
 497         df_sorted_rev = [df_data.sort_values(
 498             by=[key, header[idx]], ascending=[False, True]
 499             if key != header[idx] else [True, True]) for key in header]
 500         df_sorted.extend(df_sorted_rev)
 501     else:
 502         df_sorted = df_data
 503
 504     fill_color = [[u"#d4e4f7" if idx % 2 else u"#e9f1fb"
 505                    for idx in range(len(df_data))]]
 506     table_header = dict(
 507         values=[f"<b>{item.replace(u',', u',<br>')}</b>" for item in header],
 508         fill_color=u"#7eade7",
 509         align=params[u"align-hdr"][idx],
 510         font=dict(
 511             family=u"Courier New",
 512             size=12
 513         )
 514     )
 515
 516     fig = go.Figure()
 517
 518     if sort_data:
 519         for table in df_sorted:
 520             columns = [table.get(col) for col in header]
 521             fig.add_trace(
 522                 go.Table(
 523                     columnwidth=params[u"width"][idx],
 524                     header=table_header,
 525                     cells=dict(
 526                         values=columns,
 527                         fill_color=fill_color,
 528                         align=params[u"align-itm"][idx],
 529                         font=dict(
 530                             family=u"Courier New",
 531                             size=12
 532                         )
 533                     )
 534                 )
 535             )
 536
 537         buttons = list()
 538         menu_items = [f"<b>{itm}</b> (ascending)" for itm in header]
 539         menu_items.extend([f"<b>{itm}</b> (descending)" for itm in header])
 540         for idx, hdr in enumerate(menu_items):
 541             visible = [False, ] * len(menu_items)
 542             visible[idx] = True
 543             buttons.append(
 544                 dict(
 545                     label=hdr.replace(u" [Mpps]", u""),
 546                     method=u"update",
 547                     args=[{u"visible": visible}],
 548                 )
 549             )
 550
 551         fig.update_layout(
 552             updatemenus=[
 553                 go.layout.Updatemenu(
 554                     type=u"dropdown",
 555                     direction=u"down",
 556                     x=0.0,
 557                     xanchor=u"left",
 558                     y=1.002,
 559                     yanchor=u"bottom",
 560                     active=len(menu_items) - 1,
 561                     buttons=list(buttons)
 562                 )
 563             ],
 564         )
 565     else:
 566         fig.add_trace(
 567             go.Table(
 568                 columnwidth=params[u"width"][idx],
 569                 header=table_header,
 570                 cells=dict(
 571                     values=[df_sorted.get(col) for col in header],
 572                     fill_color=fill_color,
 573                     align=params[u"align-itm"][idx],
 574                     font=dict(
 575                         family=u"Courier New",
 576                         size=12
 577                     )
 578                 )
 579             )
 580         )
 581
 582     ploff.plot(
 583         fig,
 584         show_link=False,
 585         auto_open=False,
 586         filename=f"{out_file_name}_in.html"
 587     )
 588
 589     if not generate_rst:
 590         return
 591
 592     file_name = out_file_name.split(u"/")[-1]
 593     if u"vpp" in out_file_name:
 594         path = u"_tmp/src/vpp_performance_tests/comparisons/"
 595     else:
 596         path = u"_tmp/src/dpdk_performance_tests/comparisons/"
 597     with open(f"{path}{file_name}.rst", u"wt") as rst_file:
 598         rst_file.write(
 599             u"\n"
 600             u".. |br| raw:: html\n\n    <br />\n\n\n"
 601             u".. |prein| raw:: html\n\n    <pre>\n\n\n"
 602             u".. |preout| raw:: html\n\n    </pre>\n\n"
 603         )
 604         if title:
 605             rst_file.write(f"{title}\n")
 606             rst_file.write(f"{u'`' * len(title)}\n\n")
 607         rst_file.write(
 608             u".. raw:: html\n\n"
 609             f'    <iframe frameborder="0" scrolling="no" '
 610             f'width="1600" height="1200" '
 611             f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
 612             f'</iframe>\n\n'
 613         )
 614
 615         # TODO: Use html (rst) list for legend and footnote
 616         if legend:
 617             rst_file.write(legend[1:].replace(u"\n", u" |br| "))
 618         if footnote:
 619             rst_file.write(footnote.replace(u"\n", u" |br| ")[1:])
 620
 621
 622 def table_soak_vs_ndr(table, input_data):
 623     """Generate the table(s) with algorithm: table_soak_vs_ndr
 624     specified in the specification file.
 625
 626     :param table: Table to generate.
 627     :param input_data: Data to process.
 628     :type table: pandas.Series
 629     :type input_data: InputData
 630     """
 631
 632     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 633
 634     # Transform the data
 635     logging.info(
 636         f"    Creating the data set for the {table.get(u'type', u'')} "
 637         f"{table.get(u'title', u'')}."
 638     )
 639     data = input_data.filter_data(table, continue_on_error=True)
 640
 641     # Prepare the header of the table
 642     try:
 643         header = [
 644             u"Test Case",
 645             f"Avg({table[u'reference'][u'title']})",
 646             f"Stdev({table[u'reference'][u'title']})",
 647             f"Avg({table[u'compare'][u'title']})",
 648             f"Stdev{table[u'compare'][u'title']})",
 649             u"Diff",
 650             u"Stdev(Diff)"
 651         ]
 652         header_str = u";".join(header) + u"\n"
 653         legend = (
 654             u"\nLegend:\n"
 655             f"Avg({table[u'reference'][u'title']}): "
 656             f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
 657             f"from a series of runs of the listed tests.\n"
 658             f"Stdev({table[u'reference'][u'title']}): "
 659             f"Standard deviation value of {table[u'reference'][u'title']} "
 660             f"[Mpps] computed from a series of runs of the listed tests.\n"
 661             f"Avg({table[u'compare'][u'title']}): "
 662             f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
 663             f"a series of runs of the listed tests.\n"
 664             f"Stdev({table[u'compare'][u'title']}): "
 665             f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
 666             f"computed from a series of runs of the listed tests.\n"
 667             f"Diff({table[u'reference'][u'title']},"
 668             f"{table[u'compare'][u'title']}): "
 669             f"Percentage change calculated for mean values.\n"
 670             u"Stdev(Diff): "
 671             u"Standard deviation of percentage change calculated for mean "
 672             u"values."
 673         )
 674     except (AttributeError, KeyError) as err:
 675         logging.error(f"The model is invalid, missing parameter: {repr(err)}")
 676         return
 677
 678     # Create a list of available SOAK test results:
 679     tbl_dict = dict()
 680     for job, builds in table[u"compare"][u"data"].items():
 681         for build in builds:
 682             for tst_name, tst_data in data[job][str(build)].items():
 683                 if tst_data[u"type"] == u"SOAK":
 684                     tst_name_mod = tst_name.replace(u"-soak", u"")
 685                     if tbl_dict.get(tst_name_mod, None) is None:
 686                         groups = re.search(REGEX_NIC, tst_data[u"parent"])
 687                         nic = groups.group(0) if groups else u""
 688                         name = (
 689                             f"{nic}-"
 690                             f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
 691                         )
 692                         tbl_dict[tst_name_mod] = {
 693                             u"name": name,
 694                             u"ref-data": list(),
 695                             u"cmp-data": list()
 696                         }
 697                     try:
 698                         tbl_dict[tst_name_mod][u"cmp-data"].append(
 699                             tst_data[u"throughput"][u"LOWER"])
 700                     except (KeyError, TypeError):
 701                         pass
 702     tests_lst = tbl_dict.keys()
 703
 704     # Add corresponding NDR test results:
 705     for job, builds in table[u"reference"][u"data"].items():
 706         for build in builds:
 707             for tst_name, tst_data in data[job][str(build)].items():
 708                 tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
 709                     replace(u"-mrr", u"")
 710                 if tst_name_mod not in tests_lst:
 711                     continue
 712                 try:
 713                     if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
 714                         continue
 715                     if table[u"include-tests"] == u"MRR":
 716                         result = (tst_data[u"result"][u"receive-rate"],
 717                                   tst_data[u"result"][u"receive-stdev"])
 718                     elif table[u"include-tests"] == u"PDR":
 719                         result = \
 720                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 721                     elif table[u"include-tests"] == u"NDR":
 722                         result = \
 723                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 724                     else:
 725                         result = None
 726                     if result is not None:
 727                         tbl_dict[tst_name_mod][u"ref-data"].append(
 728                             result)
 729                 except (KeyError, TypeError):
 730                     continue
 731
 732     tbl_lst = list()
 733     for tst_name in tbl_dict:
 734         item = [tbl_dict[tst_name][u"name"], ]
 735         data_r = tbl_dict[tst_name][u"ref-data"]
 736         if data_r:
 737             if table[u"include-tests"] == u"MRR":
 738                 data_r_mean = data_r[0][0]
 739                 data_r_stdev = data_r[0][1]
 740             else:
 741                 data_r_mean = mean(data_r)
 742                 data_r_stdev = stdev(data_r)
 743             item.append(round(data_r_mean / 1e6, 1))
 744             item.append(round(data_r_stdev / 1e6, 1))
 745         else:
 746             data_r_mean = None
 747             data_r_stdev = None
 748             item.extend([None, None])
 749         data_c = tbl_dict[tst_name][u"cmp-data"]
 750         if data_c:
 751             if table[u"include-tests"] == u"MRR":
 752                 data_c_mean = data_c[0][0]
 753                 data_c_stdev = data_c[0][1]
 754             else:
 755                 data_c_mean = mean(data_c)
 756                 data_c_stdev = stdev(data_c)
 757             item.append(round(data_c_mean / 1e6, 1))
 758             item.append(round(data_c_stdev / 1e6, 1))
 759         else:
 760             data_c_mean = None
 761             data_c_stdev = None
 762             item.extend([None, None])
 763         if data_r_mean is not None and data_c_mean is not None:
 764             delta, d_stdev = relative_change_stdev(
 765                 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
 766             try:
 767                 item.append(round(delta))
 768             except ValueError:
 769                 item.append(delta)
 770             try:
 771                 item.append(round(d_stdev))
 772             except ValueError:
 773                 item.append(d_stdev)
 774             tbl_lst.append(item)
 775
 776     # Sort the table according to the relative change
 777     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
 778
 779     # Generate csv tables:
 780     csv_file = f"{table[u'output-file']}.csv"
 781     with open(csv_file, u"wt") as file_handler:
 782         file_handler.write(header_str)
 783         for test in tbl_lst:
 784             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
 785
 786     convert_csv_to_pretty_txt(
 787         csv_file, f"{table[u'output-file']}.txt", delimiter=u";"
 788     )
 789     with open(f"{table[u'output-file']}.txt", u'a') as txt_file:
 790         txt_file.write(legend)
 791
 792     # Generate html table:
 793     _tpc_generate_html_table(
 794         header,
 795         tbl_lst,
 796         table[u'output-file'],
 797         legend=legend,
 798         title=table.get(u"title", u"")
 799     )
 800
 801
 802 def table_perf_trending_dash(table, input_data):
 803     """Generate the table(s) with algorithm:
 804     table_perf_trending_dash
 805     specified in the specification file.
 806
 807     :param table: Table to generate.
 808     :param input_data: Data to process.
 809     :type table: pandas.Series
 810     :type input_data: InputData
 811     """
 812
 813     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 814
 815     # Transform the data
 816     logging.info(
 817         f"    Creating the data set for the {table.get(u'type', u'')} "
 818         f"{table.get(u'title', u'')}."
 819     )
 820     data = input_data.filter_data(table, continue_on_error=True)
 821
 822     # Prepare the header of the tables
 823     header = [
 824         u"Test Case",
 825         u"Trend [Mpps]",
 826         u"Short-Term Change [%]",
 827         u"Long-Term Change [%]",
 828         u"Regressions [#]",
 829         u"Progressions [#]"
 830     ]
 831     header_str = u",".join(header) + u"\n"
 832
 833     incl_tests = table.get(u"include-tests", u"MRR")
 834
 835     # Prepare data to the table:
 836     tbl_dict = dict()
 837     for job, builds in table[u"data"].items():
 838         for build in builds:
 839             for tst_name, tst_data in data[job][str(build)].items():
 840                 if tst_name.lower() in table.get(u"ignore-list", list()):
 841                     continue
 842                 if tbl_dict.get(tst_name, None) is None:
 843                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
 844                     if not groups:
 845                         continue
 846                     nic = groups.group(0)
 847                     tbl_dict[tst_name] = {
 848                         u"name": f"{nic}-{tst_data[u'name']}",
 849                         u"data": OrderedDict()
 850                     }
 851                 try:
 852                     if incl_tests == u"MRR":
 853                         tbl_dict[tst_name][u"data"][str(build)] = \
 854                             tst_data[u"result"][u"receive-rate"]
 855                     elif incl_tests == u"NDR":
 856                         tbl_dict[tst_name][u"data"][str(build)] = \
 857                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 858                     elif incl_tests == u"PDR":
 859                         tbl_dict[tst_name][u"data"][str(build)] = \
 860                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 861                 except (TypeError, KeyError):
 862                     pass  # No data in output.xml for this test
 863
 864     tbl_lst = list()
 865     for tst_name in tbl_dict:
 866         data_t = tbl_dict[tst_name][u"data"]
 867         if len(data_t) < 2:
 868             continue
 869
 870         classification_lst, avgs, _ = classify_anomalies(data_t)
 871
 872         win_size = min(len(data_t), table[u"window"])
 873         long_win_size = min(len(data_t), table[u"long-trend-window"])
 874
 875         try:
 876             max_long_avg = max(
 877                 [x for x in avgs[-long_win_size:-win_size]
 878                  if not isnan(x)])
 879         except ValueError:
 880             max_long_avg = nan
 881         last_avg = avgs[-1]
 882         avg_week_ago = avgs[max(-win_size, -len(avgs))]
 883
 884         if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
 885             rel_change_last = nan
 886         else:
 887             rel_change_last = round(
 888                 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
 889
 890         if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
 891             rel_change_long = nan
 892         else:
 893             rel_change_long = round(
 894                 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
 895
 896         if classification_lst:
 897             if isnan(rel_change_last) and isnan(rel_change_long):
 898                 continue
 899             if isnan(last_avg) or isnan(rel_change_last) or \
 900                     isnan(rel_change_long):
 901                 continue
 902             tbl_lst.append(
 903                 [tbl_dict[tst_name][u"name"],
 904                  round(last_avg / 1e6, 2),
 905                  rel_change_last,
 906                  rel_change_long,
 907                  classification_lst[-win_size+1:].count(u"regression"),
 908                  classification_lst[-win_size+1:].count(u"progression")])
 909
 910     tbl_lst.sort(key=lambda rel: rel[0])
 911     tbl_lst.sort(key=lambda rel: rel[3])
 912     tbl_lst.sort(key=lambda rel: rel[2])
 913
 914     tbl_sorted = list()
 915     for nrr in range(table[u"window"], -1, -1):
 916         tbl_reg = [item for item in tbl_lst if item[4] == nrr]
 917         for nrp in range(table[u"window"], -1, -1):
 918             tbl_out = [item for item in tbl_reg if item[5] == nrp]
 919             tbl_sorted.extend(tbl_out)
 920
 921     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
 922
 923     logging.info(f"    Writing file: {file_name}")
 924     with open(file_name, u"wt") as file_handler:
 925         file_handler.write(header_str)
 926         for test in tbl_sorted:
 927             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
 928
 929     logging.info(f"    Writing file: {table[u'output-file']}.txt")
 930     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
 931
 932
 933 def _generate_url(testbed, test_name):
 934     """Generate URL to a trending plot from the name of the test case.
 935
 936     :param testbed: The testbed used for testing.
 937     :param test_name: The name of the test case.
 938     :type testbed: str
 939     :type test_name: str
 940     :returns: The URL to the plot with the trending data for the given test
 941         case.
 942     :rtype str
 943     """
 944
 945     if u"x520" in test_name:
 946         nic = u"x520"
 947     elif u"x710" in test_name:
 948         nic = u"x710"
 949     elif u"xl710" in test_name:
 950         nic = u"xl710"
 951     elif u"xxv710" in test_name:
 952         nic = u"xxv710"
 953     elif u"vic1227" in test_name:
 954         nic = u"vic1227"
 955     elif u"vic1385" in test_name:
 956         nic = u"vic1385"
 957     elif u"x553" in test_name:
 958         nic = u"x553"
 959     else:
 960         nic = u""
 961
 962     if u"64b" in test_name:
 963         frame_size = u"64b"
 964     elif u"78b" in test_name:
 965         frame_size = u"78b"
 966     elif u"imix" in test_name:
 967         frame_size = u"imix"
 968     elif u"9000b" in test_name:
 969         frame_size = u"9000b"
 970     elif u"1518b" in test_name:
 971         frame_size = u"1518b"
 972     elif u"114b" in test_name:
 973         frame_size = u"114b"
 974     else:
 975         frame_size = u""
 976
 977     if u"1t1c" in test_name or \
 978         (u"-1c-" in test_name and
 979          testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 980         cores = u"1t1c"
 981     elif u"2t2c" in test_name or \
 982          (u"-2c-" in test_name and
 983           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 984         cores = u"2t2c"
 985     elif u"4t4c" in test_name or \
 986          (u"-4c-" in test_name and
 987           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 988         cores = u"4t4c"
 989     elif u"2t1c" in test_name or \
 990          (u"-1c-" in test_name and
 991           testbed in (u"2n-skx", u"3n-skx")):
 992         cores = u"2t1c"
 993     elif u"4t2c" in test_name:
 994         cores = u"4t2c"
 995     elif u"8t4c" in test_name:
 996         cores = u"8t4c"
 997     else:
 998         cores = u""
 999
1000     if u"testpmd" in test_name:
1001         driver = u"testpmd"
1002     elif u"l3fwd" in test_name:
1003         driver = u"l3fwd"
1004     elif u"avf" in test_name:
1005         driver = u"avf"
1006     elif u"dnv" in testbed or u"tsh" in testbed:
1007         driver = u"ixgbe"
1008     else:
1009         driver = u"dpdk"
1010
1011     if u"macip-iacl1s" in test_name:
1012         bsf = u"features-macip-iacl1"
1013     elif u"macip-iacl10s" in test_name:
1014         bsf = u"features-macip-iacl01"
1015     elif u"macip-iacl50s" in test_name:
1016         bsf = u"features-macip-iacl50"
1017     elif u"iacl1s" in test_name:
1018         bsf = u"features-iacl1"
1019     elif u"iacl10s" in test_name:
1020         bsf = u"features-iacl10"
1021     elif u"iacl50s" in test_name:
1022         bsf = u"features-iacl50"
1023     elif u"oacl1s" in test_name:
1024         bsf = u"features-oacl1"
1025     elif u"oacl10s" in test_name:
1026         bsf = u"features-oacl10"
1027     elif u"oacl50s" in test_name:
1028         bsf = u"features-oacl50"
1029     elif u"udpsrcscale" in test_name:
1030         bsf = u"features-udp"
1031     elif u"iacl" in test_name:
1032         bsf = u"features"
1033     elif u"policer" in test_name:
1034         bsf = u"features"
1035     elif u"cop" in test_name:
1036         bsf = u"features"
1037     elif u"nat" in test_name:
1038         bsf = u"features"
1039     elif u"macip" in test_name:
1040         bsf = u"features"
1041     elif u"scale" in test_name:
1042         bsf = u"scale"
1043     elif u"base" in test_name:
1044         bsf = u"base"
1045     else:
1046         bsf = u"base"
1047
1048     if u"114b" in test_name and u"vhost" in test_name:
1049         domain = u"vts"
1050     elif u"testpmd" in test_name or u"l3fwd" in test_name:
1051         domain = u"dpdk"
1052     elif u"memif" in test_name:
1053         domain = u"container_memif"
1054     elif u"srv6" in test_name:
1055         domain = u"srv6"
1056     elif u"vhost" in test_name:
1057         domain = u"vhost"
1058         if u"vppl2xc" in test_name:
1059             driver += u"-vpp"
1060         else:
1061             driver += u"-testpmd"
1062         if u"lbvpplacp" in test_name:
1063             bsf += u"-link-bonding"
1064     elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1065         domain = u"nf_service_density_vnfc"
1066     elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1067         domain = u"nf_service_density_cnfc"
1068     elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1069         domain = u"nf_service_density_cnfp"
1070     elif u"ipsec" in test_name:
1071         domain = u"ipsec"
1072         if u"sw" in test_name:
1073             bsf += u"-sw"
1074         elif u"hw" in test_name:
1075             bsf += u"-hw"
1076     elif u"ethip4vxlan" in test_name:
1077         domain = u"ip4_tunnels"
1078     elif u"ip4base" in test_name or u"ip4scale" in test_name:
1079         domain = u"ip4"
1080     elif u"ip6base" in test_name or u"ip6scale" in test_name:
1081         domain = u"ip6"
1082     elif u"l2xcbase" in test_name or \
1083             u"l2xcscale" in test_name or \
1084             u"l2bdbasemaclrn" in test_name or \
1085             u"l2bdscale" in test_name or \
1086             u"l2patch" in test_name:
1087         domain = u"l2"
1088     else:
1089         domain = u""
1090
1091     file_name = u"-".join((domain, testbed, nic)) + u".html#"
1092     anchor_name = u"-".join((frame_size, cores, bsf, driver))
1093
1094     return file_name + anchor_name
1095
1096
1097 def table_perf_trending_dash_html(table, input_data):
1098     """Generate the table(s) with algorithm:
1099     table_perf_trending_dash_html specified in the specification
1100     file.
1101
1102     :param table: Table to generate.
1103     :param input_data: Data to process.
1104     :type table: dict
1105     :type input_data: InputData
1106     """
1107
1108     _ = input_data
1109
1110     if not table.get(u"testbed", None):
1111         logging.error(
1112             f"The testbed is not defined for the table "
1113             f"{table.get(u'title', u'')}. Skipping."
1114         )
1115         return
1116
1117     test_type = table.get(u"test-type", u"MRR")
1118     if test_type not in (u"MRR", u"NDR", u"PDR"):
1119         logging.error(
1120             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1121             f"Skipping."
1122         )
1123         return
1124
1125     if test_type in (u"NDR", u"PDR"):
1126         lnk_dir = u"../ndrpdr_trending/"
1127         lnk_sufix = f"-{test_type.lower()}"
1128     else:
1129         lnk_dir = u"../trending/"
1130         lnk_sufix = u""
1131
1132     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1133
1134     try:
1135         with open(table[u"input-file"], u'rt') as csv_file:
1136             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1137     except KeyError:
1138         logging.warning(u"The input file is not defined.")
1139         return
1140     except csv.Error as err:
1141         logging.warning(
1142             f"Not possible to process the file {table[u'input-file']}.\n"
1143             f"{repr(err)}"
1144         )
1145         return
1146
1147     # Table:
1148     dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1149
1150     # Table header:
1151     trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1152     for idx, item in enumerate(csv_lst[0]):
1153         alignment = u"left" if idx == 0 else u"center"
1154         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1155         thead.text = item
1156
1157     # Rows:
1158     colors = {
1159         u"regression": (
1160             u"#ffcccc",
1161             u"#ff9999"
1162         ),
1163         u"progression": (
1164             u"#c6ecc6",
1165             u"#9fdf9f"
1166         ),
1167         u"normal": (
1168             u"#e9f1fb",
1169             u"#d4e4f7"
1170         )
1171     }
1172     for r_idx, row in enumerate(csv_lst[1:]):
1173         if int(row[4]):
1174             color = u"regression"
1175         elif int(row[5]):
1176             color = u"progression"
1177         else:
1178             color = u"normal"
1179         trow = ET.SubElement(
1180             dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1181         )
1182
1183         # Columns:
1184         for c_idx, item in enumerate(row):
1185             tdata = ET.SubElement(
1186                 trow,
1187                 u"td",
1188                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1189             )
1190             # Name:
1191             if c_idx == 0 and table.get(u"add-links", True):
1192                 ref = ET.SubElement(
1193                     tdata,
1194                     u"a",
1195                     attrib=dict(
1196                         href=f"{lnk_dir}"
1197                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1198                              f"{lnk_sufix}"
1199                     )
1200                 )
1201                 ref.text = item
1202             else:
1203                 tdata.text = item
1204     try:
1205         with open(table[u"output-file"], u'w') as html_file:
1206             logging.info(f"    Writing file: {table[u'output-file']}")
1207             html_file.write(u".. raw:: html\n\n\t")
1208             html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1209             html_file.write(u"\n\t<p><br><br></p>\n")
1210     except KeyError:
1211         logging.warning(u"The output file is not defined.")
1212         return
1213
1214
1215 def table_last_failed_tests(table, input_data):
1216     """Generate the table(s) with algorithm: table_last_failed_tests
1217     specified in the specification file.
1218
1219     :param table: Table to generate.
1220     :param input_data: Data to process.
1221     :type table: pandas.Series
1222     :type input_data: InputData
1223     """
1224
1225     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1226
1227     # Transform the data
1228     logging.info(
1229         f"    Creating the data set for the {table.get(u'type', u'')} "
1230         f"{table.get(u'title', u'')}."
1231     )
1232
1233     data = input_data.filter_data(table, continue_on_error=True)
1234
1235     if data is None or data.empty:
1236         logging.warning(
1237             f"    No data for the {table.get(u'type', u'')} "
1238             f"{table.get(u'title', u'')}."
1239         )
1240         return
1241
1242     tbl_list = list()
1243     for job, builds in table[u"data"].items():
1244         for build in builds:
1245             build = str(build)
1246             try:
1247                 version = input_data.metadata(job, build).get(u"version", u"")
1248             except KeyError:
1249                 logging.error(f"Data for {job}: {build} is not present.")
1250                 return
1251             tbl_list.append(build)
1252             tbl_list.append(version)
1253             failed_tests = list()
1254             passed = 0
1255             failed = 0
1256             for tst_data in data[job][build].values:
1257                 if tst_data[u"status"] != u"FAIL":
1258                     passed += 1
1259                     continue
1260                 failed += 1
1261                 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1262                 if not groups:
1263                     continue
1264                 nic = groups.group(0)
1265                 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1266             tbl_list.append(str(passed))
1267             tbl_list.append(str(failed))
1268             tbl_list.extend(failed_tests)
1269
1270     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1271     logging.info(f"    Writing file: {file_name}")
1272     with open(file_name, u"wt") as file_handler:
1273         for test in tbl_list:
1274             file_handler.write(test + u'\n')
1275
1276
1277 def table_failed_tests(table, input_data):
1278     """Generate the table(s) with algorithm: table_failed_tests
1279     specified in the specification file.
1280
1281     :param table: Table to generate.
1282     :param input_data: Data to process.
1283     :type table: pandas.Series
1284     :type input_data: InputData
1285     """
1286
1287     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1288
1289     # Transform the data
1290     logging.info(
1291         f"    Creating the data set for the {table.get(u'type', u'')} "
1292         f"{table.get(u'title', u'')}."
1293     )
1294     data = input_data.filter_data(table, continue_on_error=True)
1295
1296     test_type = u"MRR"
1297     if u"NDRPDR" in table.get(u"filter", list()):
1298         test_type = u"NDRPDR"
1299
1300     # Prepare the header of the tables
1301     header = [
1302         u"Test Case",
1303         u"Failures [#]",
1304         u"Last Failure [Time]",
1305         u"Last Failure [VPP-Build-Id]",
1306         u"Last Failure [CSIT-Job-Build-Id]"
1307     ]
1308
1309     # Generate the data for the table according to the model in the table
1310     # specification
1311
1312     now = dt.utcnow()
1313     timeperiod = timedelta(int(table.get(u"window", 7)))
1314
1315     tbl_dict = dict()
1316     for job, builds in table[u"data"].items():
1317         for build in builds:
1318             build = str(build)
1319             for tst_name, tst_data in data[job][build].items():
1320                 if tst_name.lower() in table.get(u"ignore-list", list()):
1321                     continue
1322                 if tbl_dict.get(tst_name, None) is None:
1323                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
1324                     if not groups:
1325                         continue
1326                     nic = groups.group(0)
1327                     tbl_dict[tst_name] = {
1328                         u"name": f"{nic}-{tst_data[u'name']}",
1329                         u"data": OrderedDict()
1330                     }
1331                 try:
1332                     generated = input_data.metadata(job, build).\
1333                         get(u"generated", u"")
1334                     if not generated:
1335                         continue
1336                     then = dt.strptime(generated, u"%Y%m%d %H:%M")
1337                     if (now - then) <= timeperiod:
1338                         tbl_dict[tst_name][u"data"][build] = (
1339                             tst_data[u"status"],
1340                             generated,
1341                             input_data.metadata(job, build).get(u"version",
1342                                                                 u""),
1343                             build
1344                         )
1345                 except (TypeError, KeyError) as err:
1346                     logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1347
1348     max_fails = 0
1349     tbl_lst = list()
1350     for tst_data in tbl_dict.values():
1351         fails_nr = 0
1352         fails_last_date = u""
1353         fails_last_vpp = u""
1354         fails_last_csit = u""
1355         for val in tst_data[u"data"].values():
1356             if val[0] == u"FAIL":
1357                 fails_nr += 1
1358                 fails_last_date = val[1]
1359                 fails_last_vpp = val[2]
1360                 fails_last_csit = val[3]
1361         if fails_nr:
1362             max_fails = fails_nr if fails_nr > max_fails else max_fails
1363             tbl_lst.append([
1364                 tst_data[u"name"],
1365                 fails_nr,
1366                 fails_last_date,
1367                 fails_last_vpp,
1368                 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1369                 f"-build-{fails_last_csit}"
1370             ])
1371
1372     tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1373     tbl_sorted = list()
1374     for nrf in range(max_fails, -1, -1):
1375         tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1376         tbl_sorted.extend(tbl_fails)
1377
1378     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1379     logging.info(f"    Writing file: {file_name}")
1380     with open(file_name, u"wt") as file_handler:
1381         file_handler.write(u",".join(header) + u"\n")
1382         for test in tbl_sorted:
1383             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1384
1385     logging.info(f"    Writing file: {table[u'output-file']}.txt")
1386     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1387
1388
1389 def table_failed_tests_html(table, input_data):
1390     """Generate the table(s) with algorithm: table_failed_tests_html
1391     specified in the specification file.
1392
1393     :param table: Table to generate.
1394     :param input_data: Data to process.
1395     :type table: pandas.Series
1396     :type input_data: InputData
1397     """
1398
1399     _ = input_data
1400
1401     if not table.get(u"testbed", None):
1402         logging.error(
1403             f"The testbed is not defined for the table "
1404             f"{table.get(u'title', u'')}. Skipping."
1405         )
1406         return
1407
1408     test_type = table.get(u"test-type", u"MRR")
1409     if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1410         logging.error(
1411             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1412             f"Skipping."
1413         )
1414         return
1415
1416     if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1417         lnk_dir = u"../ndrpdr_trending/"
1418         lnk_sufix = u"-pdr"
1419     else:
1420         lnk_dir = u"../trending/"
1421         lnk_sufix = u""
1422
1423     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1424
1425     try:
1426         with open(table[u"input-file"], u'rt') as csv_file:
1427             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1428     except KeyError:
1429         logging.warning(u"The input file is not defined.")
1430         return
1431     except csv.Error as err:
1432         logging.warning(
1433             f"Not possible to process the file {table[u'input-file']}.\n"
1434             f"{repr(err)}"
1435         )
1436         return
1437
1438     # Table:
1439     failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1440
1441     # Table header:
1442     trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1443     for idx, item in enumerate(csv_lst[0]):
1444         alignment = u"left" if idx == 0 else u"center"
1445         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1446         thead.text = item
1447
1448     # Rows:
1449     colors = (u"#e9f1fb", u"#d4e4f7")
1450     for r_idx, row in enumerate(csv_lst[1:]):
1451         background = colors[r_idx % 2]
1452         trow = ET.SubElement(
1453             failed_tests, u"tr", attrib=dict(bgcolor=background)
1454         )
1455
1456         # Columns:
1457         for c_idx, item in enumerate(row):
1458             tdata = ET.SubElement(
1459                 trow,
1460                 u"td",
1461                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1462             )
1463             # Name:
1464             if c_idx == 0 and table.get(u"add-links", True):
1465                 ref = ET.SubElement(
1466                     tdata,
1467                     u"a",
1468                     attrib=dict(
1469                         href=f"{lnk_dir}"
1470                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1471                              f"{lnk_sufix}"
1472                     )
1473                 )
1474                 ref.text = item
1475             else:
1476                 tdata.text = item
1477     try:
1478         with open(table[u"output-file"], u'w') as html_file:
1479             logging.info(f"    Writing file: {table[u'output-file']}")
1480             html_file.write(u".. raw:: html\n\n\t")
1481             html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1482             html_file.write(u"\n\t<p><br><br></p>\n")
1483     except KeyError:
1484         logging.warning(u"The output file is not defined.")
1485         return
1486
1487
1488 def table_comparison(table, input_data):
1489     """Generate the table(s) with algorithm: table_comparison
1490     specified in the specification file.
1491
1492     :param table: Table to generate.
1493     :param input_data: Data to process.
1494     :type table: pandas.Series
1495     :type input_data: InputData
1496     """
1497     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1498
1499     # Transform the data
1500     logging.info(
1501         f"    Creating the data set for the {table.get(u'type', u'')} "
1502         f"{table.get(u'title', u'')}."
1503     )
1504
1505     columns = table.get(u"columns", None)
1506     if not columns:
1507         logging.error(
1508             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1509         )
1510         return
1511
1512     cols = list()
1513     for idx, col in enumerate(columns):
1514         if col.get(u"data-set", None) is None:
1515             logging.warning(f"No data for column {col.get(u'title', u'')}")
1516             continue
1517         tag = col.get(u"tag", None)
1518         data = input_data.filter_data(
1519             table,
1520             params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1521             data=col[u"data-set"],
1522             continue_on_error=True
1523         )
1524         col_data = {
1525             u"title": col.get(u"title", f"Column{idx}"),
1526             u"data": dict()
1527         }
1528         for builds in data.values:
1529             for build in builds:
1530                 for tst_name, tst_data in build.items():
1531                     if tag and tag not in tst_data[u"tags"]:
1532                         continue
1533                     tst_name_mod = \
1534                         _tpc_modify_test_name(tst_name, ignore_nic=True).\
1535                         replace(u"2n1l-", u"")
1536                     if col_data[u"data"].get(tst_name_mod, None) is None:
1537                         name = tst_data[u'name'].rsplit(u'-', 1)[0]
1538                         if u"across testbeds" in table[u"title"].lower() or \
1539                                 u"across topologies" in table[u"title"].lower():
1540                             name = _tpc_modify_displayed_test_name(name)
1541                         col_data[u"data"][tst_name_mod] = {
1542                             u"name": name,
1543                             u"replace": True,
1544                             u"data": list(),
1545                             u"mean": None,
1546                             u"stdev": None
1547                         }
1548                     _tpc_insert_data(
1549                         target=col_data[u"data"][tst_name_mod],
1550                         src=tst_data,
1551                         include_tests=table[u"include-tests"]
1552                     )
1553
1554         replacement = col.get(u"data-replacement", None)
1555         if replacement:
1556             rpl_data = input_data.filter_data(
1557                 table,
1558                 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1559                 data=replacement,
1560                 continue_on_error=True
1561             )
1562             for builds in rpl_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() \
1573                                     or u"across topologies" in \
1574                                     table[u"title"].lower():
1575                                 name = _tpc_modify_displayed_test_name(name)
1576                             col_data[u"data"][tst_name_mod] = {
1577                                 u"name": name,
1578                                 u"replace": False,
1579                                 u"data": list(),
1580                                 u"mean": None,
1581                                 u"stdev": None
1582                             }
1583                         if col_data[u"data"][tst_name_mod][u"replace"]:
1584                             col_data[u"data"][tst_name_mod][u"replace"] = False
1585                             col_data[u"data"][tst_name_mod][u"data"] = list()
1586                         _tpc_insert_data(
1587                             target=col_data[u"data"][tst_name_mod],
1588                             src=tst_data,
1589                             include_tests=table[u"include-tests"]
1590                         )
1591
1592         if table[u"include-tests"] in (u"NDR", u"PDR"):
1593             for tst_name, tst_data in col_data[u"data"].items():
1594                 if tst_data[u"data"]:
1595                     tst_data[u"mean"] = mean(tst_data[u"data"])
1596                     tst_data[u"stdev"] = stdev(tst_data[u"data"])
1597
1598         cols.append(col_data)
1599
1600     tbl_dict = dict()
1601     for col in cols:
1602         for tst_name, tst_data in col[u"data"].items():
1603             if tbl_dict.get(tst_name, None) is None:
1604                 tbl_dict[tst_name] = {
1605                     "name": tst_data[u"name"]
1606                 }
1607             tbl_dict[tst_name][col[u"title"]] = {
1608                 u"mean": tst_data[u"mean"],
1609                 u"stdev": tst_data[u"stdev"]
1610             }
1611
1612     if not tbl_dict:
1613         logging.warning(f"No data for table {table.get(u'title', u'')}!")
1614         return
1615
1616     tbl_lst = list()
1617     for tst_data in tbl_dict.values():
1618         row = [tst_data[u"name"], ]
1619         for col in cols:
1620             row.append(tst_data.get(col[u"title"], None))
1621         tbl_lst.append(row)
1622
1623     comparisons = table.get(u"comparisons", None)
1624     if comparisons and isinstance(comparisons, list):
1625         for idx, comp in enumerate(comparisons):
1626             try:
1627                 col_ref = int(comp[u"reference"])
1628                 col_cmp = int(comp[u"compare"])
1629             except KeyError:
1630                 logging.warning(u"Comparison: No references defined! Skipping.")
1631                 comparisons.pop(idx)
1632                 continue
1633             if not (0 < col_ref <= len(cols) and
1634                     0 < col_cmp <= len(cols)) or \
1635                     col_ref == col_cmp:
1636                 logging.warning(f"Wrong values of reference={col_ref} "
1637                                 f"and/or compare={col_cmp}. Skipping.")
1638                 comparisons.pop(idx)
1639                 continue
1640
1641     tbl_cmp_lst = list()
1642     if comparisons:
1643         for row in tbl_lst:
1644             new_row = deepcopy(row)
1645             add_to_tbl = False
1646             for comp in comparisons:
1647                 ref_itm = row[int(comp[u"reference"])]
1648                 if ref_itm is None and \
1649                         comp.get(u"reference-alt", None) is not None:
1650                     ref_itm = row[int(comp[u"reference-alt"])]
1651                 cmp_itm = row[int(comp[u"compare"])]
1652                 if ref_itm is not None and cmp_itm is not None and \
1653                         ref_itm[u"mean"] is not None and \
1654                         cmp_itm[u"mean"] is not None and \
1655                         ref_itm[u"stdev"] is not None and \
1656                         cmp_itm[u"stdev"] is not None:
1657                     delta, d_stdev = relative_change_stdev(
1658                         ref_itm[u"mean"], cmp_itm[u"mean"],
1659                         ref_itm[u"stdev"], cmp_itm[u"stdev"]
1660                     )
1661                     new_row.append(
1662                         {
1663                             u"mean": delta * 1e6,
1664                             u"stdev": d_stdev * 1e6
1665                         }
1666                     )
1667                     add_to_tbl = True
1668                 else:
1669                     new_row.append(None)
1670             if add_to_tbl:
1671                 tbl_cmp_lst.append(new_row)
1672
1673     tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1674     tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1675
1676     rcas = list()
1677     rca_in = table.get(u"rca", None)
1678     if rca_in and isinstance(rca_in, list):
1679         for idx, itm in enumerate(rca_in):
1680             try:
1681                 with open(itm.get(u"data", u""), u"r") as rca_file:
1682                     rcas.append(
1683                         {
1684                             u"title": itm.get(u"title", f"RCA{idx}"),
1685                             u"data": load(rca_file, Loader=FullLoader)
1686                         }
1687                     )
1688             except (YAMLError, IOError) as err:
1689                 logging.warning(
1690                     f"The RCA file {itm.get(u'data', u'')} does not exist or "
1691                     f"it is corrupted!"
1692                 )
1693                 logging.debug(repr(err))
1694
1695     tbl_for_csv = list()
1696     for line in tbl_cmp_lst:
1697         row = [line[0], ]
1698         for idx, itm in enumerate(line[1:]):
1699             if itm is None:
1700                 row.append(u"NT")
1701                 row.append(u"NT")
1702             else:
1703                 row.append(round(float(itm[u'mean']) / 1e6, 3))
1704                 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1705         for rca in rcas:
1706             rca_nr = rca[u"data"].get(row[0], u"-")
1707             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1708         tbl_for_csv.append(row)
1709
1710     header_csv = [u"Test Case", ]
1711     for col in cols:
1712         header_csv.append(f"Avg({col[u'title']})")
1713         header_csv.append(f"Stdev({col[u'title']})")
1714     for comp in comparisons:
1715         header_csv.append(
1716             f"Avg({comp.get(u'title', u'')})"
1717         )
1718         header_csv.append(
1719             f"Stdev({comp.get(u'title', u'')})"
1720         )
1721     header_csv.extend([rca[u"title"] for rca in rcas])
1722
1723     legend_lst = table.get(u"legend", None)
1724     if legend_lst is None:
1725         legend = u""
1726     else:
1727         legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1728
1729     footnote = u""
1730     for rca in rcas:
1731         footnote += f"\n{rca[u'title']}:\n"
1732         footnote += rca[u"data"].get(u"footnote", u"")
1733
1734     csv_file = f"{table[u'output-file']}-csv.csv"
1735     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1736         file_handler.write(
1737             u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1738         )
1739         for test in tbl_for_csv:
1740             file_handler.write(
1741                 u",".join([f'"{item}"' for item in test]) + u"\n"
1742             )
1743         if legend_lst:
1744             for item in legend_lst:
1745                 file_handler.write(f'"{item}"\n')
1746         if footnote:
1747             for itm in footnote.split(u"\n"):
1748                 file_handler.write(f'"{itm}"\n')
1749
1750     tbl_tmp = list()
1751     max_lens = [0, ] * len(tbl_cmp_lst[0])
1752     for line in tbl_cmp_lst:
1753         row = [line[0], ]
1754         for idx, itm in enumerate(line[1:]):
1755             if itm is None:
1756                 new_itm = u"NT"
1757             else:
1758                 if idx < len(cols):
1759                     new_itm = (
1760                         f"{round(float(itm[u'mean']) / 1e6, 1)} "
1761                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1762                         replace(u"nan", u"NaN")
1763                     )
1764                 else:
1765                     new_itm = (
1766                         f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1767                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1768                         replace(u"nan", u"NaN")
1769                     )
1770             if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1771                 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1772             row.append(new_itm)
1773
1774         tbl_tmp.append(row)
1775
1776     tbl_final = list()
1777     for line in tbl_tmp:
1778         row = [line[0], ]
1779         for idx, itm in enumerate(line[1:]):
1780             if itm in (u"NT", u"NaN"):
1781                 row.append(itm)
1782                 continue
1783             itm_lst = itm.rsplit(u"\u00B1", 1)
1784             itm_lst[-1] = \
1785                 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1786             row.append(u"\u00B1".join(itm_lst))
1787         for rca in rcas:
1788             rca_nr = rca[u"data"].get(row[0], u"-")
1789             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1790
1791         tbl_final.append(row)
1792
1793     header = [u"Test Case", ]
1794     header.extend([col[u"title"] for col in cols])
1795     header.extend([comp.get(u"title", u"") for comp in comparisons])
1796     header.extend([rca[u"title"] for rca in rcas])
1797
1798     # Generate csv tables:
1799     csv_file = f"{table[u'output-file']}.csv"
1800     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1801         file_handler.write(u";".join(header) + u"\n")
1802         for test in tbl_final:
1803             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1804
1805     # Generate txt table:
1806     txt_file_name = f"{table[u'output-file']}.txt"
1807     convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
1808
1809     with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
1810         txt_file.write(legend)
1811         txt_file.write(footnote)
1812
1813     # Generate html table:
1814     _tpc_generate_html_table(
1815         header,
1816         tbl_final,
1817         table[u'output-file'],
1818         legend=legend,
1819         footnote=footnote,
1820         sort_data=False,
1821         title=table.get(u"title", u"")
1822     )
1823
1824
1825 def table_weekly_comparison(table, in_data):
1826     """Generate the table(s) with algorithm: table_weekly_comparison
1827     specified in the specification file.
1828
1829     :param table: Table to generate.
1830     :param in_data: Data to process.
1831     :type table: pandas.Series
1832     :type in_data: InputData
1833     """
1834     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1835
1836     # Transform the data
1837     logging.info(
1838         f"    Creating the data set for the {table.get(u'type', u'')} "
1839         f"{table.get(u'title', u'')}."
1840     )
1841
1842     incl_tests = table.get(u"include-tests", None)
1843     if incl_tests not in (u"NDR", u"PDR"):
1844         logging.error(f"Wrong tests to include specified ({incl_tests}).")
1845         return
1846
1847     nr_cols = table.get(u"nr-of-data-columns", None)
1848     if not nr_cols or nr_cols < 2:
1849         logging.error(
1850             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1851         )
1852         return
1853
1854     data = in_data.filter_data(
1855         table,
1856         params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1857         continue_on_error=True
1858     )
1859
1860     header = [
1861         [u"VPP Version", ],
1862         [u"Start Timestamp", ],
1863         [u"CSIT Build", ],
1864         [u"CSIT Testbed", ]
1865     ]
1866     tbl_dict = dict()
1867     idx = 0
1868     tb_tbl = table.get(u"testbeds", None)
1869     for job_name, job_data in data.items():
1870         for build_nr, build in job_data.items():
1871             if idx >= nr_cols:
1872                 break
1873             if build.empty:
1874                 continue
1875
1876             tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1877             if tb_ip and tb_tbl:
1878                 testbed = tb_tbl.get(tb_ip, u"")
1879             else:
1880                 testbed = u""
1881             header[2].insert(1, build_nr)
1882             header[3].insert(1, testbed)
1883             header[1].insert(
1884                 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1885             )
1886             header[0].insert(
1887                 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1888             )
1889
1890             for tst_name, tst_data in build.items():
1891                 tst_name_mod = \
1892                     _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1893                 if not tbl_dict.get(tst_name_mod, None):
1894                     tbl_dict[tst_name_mod] = dict(
1895                         name=tst_data[u'name'].rsplit(u'-', 1)[0],
1896                     )
1897                 try:
1898                     tbl_dict[tst_name_mod][-idx - 1] = \
1899                         tst_data[u"throughput"][incl_tests][u"LOWER"]
1900                 except (TypeError, IndexError, KeyError, ValueError):
1901                     pass
1902             idx += 1
1903
1904     if idx < nr_cols:
1905         logging.error(u"Not enough data to build the table! Skipping")
1906         return
1907
1908     cmp_dict = dict()
1909     for idx, cmp in enumerate(table.get(u"comparisons", list())):
1910         idx_ref = cmp.get(u"reference", None)
1911         idx_cmp = cmp.get(u"compare", None)
1912         if idx_ref is None or idx_cmp is None:
1913             continue
1914         header[0].append(
1915             f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1916             f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1917         )
1918         header[1].append(u"")
1919         header[2].append(u"")
1920         header[3].append(u"")
1921         for tst_name, tst_data in tbl_dict.items():
1922             if not cmp_dict.get(tst_name, None):
1923                 cmp_dict[tst_name] = list()
1924             ref_data = tst_data.get(idx_ref, None)
1925             cmp_data = tst_data.get(idx_cmp, None)
1926             if ref_data is None or cmp_data is None:
1927                 cmp_dict[tst_name].append(float('nan'))
1928             else:
1929                 cmp_dict[tst_name].append(
1930                     relative_change(ref_data, cmp_data)
1931                 )
1932
1933     tbl_lst = list()
1934     for tst_name, tst_data in tbl_dict.items():
1935         itm_lst = [tst_data[u"name"], ]
1936         for idx in range(nr_cols):
1937             item = tst_data.get(-idx - 1, None)
1938             if item is None:
1939                 itm_lst.insert(1, None)
1940             else:
1941                 itm_lst.insert(1, round(item / 1e6, 1))
1942         itm_lst.extend(
1943             [
1944                 None if itm is None else round(itm, 1)
1945                 for itm in cmp_dict[tst_name]
1946             ]
1947         )
1948         tbl_lst.append(itm_lst)
1949
1950     tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1951     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
1952
1953     # Generate csv table:
1954     csv_file = f"{table[u'output-file']}.csv"
1955     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1956         for hdr in header:
1957             file_handler.write(u",".join(hdr) + u"\n")
1958         for test in tbl_lst:
1959             file_handler.write(u",".join(
1960                 [
1961                     str(item).replace(u"None", u"-").replace(u"nan", u"-").
1962                     replace(u"null", u"-") for item in test
1963                 ]
1964             ) + u"\n")
1965
1966     txt_file = f"{table[u'output-file']}.txt"
1967     convert_csv_to_pretty_txt(csv_file, txt_file, delimiter=u",")
1968
1969     # Reorganize header in txt table
1970     txt_table = list()
1971     with open(txt_file, u"rt", encoding='utf-8') as file_handler:
1972         for line in file_handler:
1973             txt_table.append(line)
1974     try:
1975         txt_table.insert(5, txt_table.pop(2))
1976         with open(txt_file, u"wt", encoding='utf-8') as file_handler:
1977             file_handler.writelines(txt_table)
1978     except IndexError:
1979         pass
1980
1981     # Generate html table:
1982     hdr_html = [
1983         u"<br>".join(row) for row in zip(*header)
1984     ]
1985     _tpc_generate_html_table(
1986         hdr_html,
1987         tbl_lst,
1988         table[u'output-file'],
1989         sort_data=True,
1990         title=table.get(u"title", u""),
1991         generate_rst=False
1992     )