resources/tools/presentation/generator_tables.py

   1 # Copyright (c) 2020 Cisco and/or its affiliates.
   2 # Licensed under the Apache License, Version 2.0 (the "License");
   3 # you may not use this file except in compliance with the License.
   4 # You may obtain a copy of the License at:
   5 #
   6 #     http://www.apache.org/licenses/LICENSE-2.0
   7 #
   8 # Unless required by applicable law or agreed to in writing, software
   9 # distributed under the License is distributed on an "AS IS" BASIS,
  10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  11 # See the License for the specific language governing permissions and
  12 # limitations under the License.
  13
  14 """Algorithms to generate tables.
  15 """
  16
  17
  18 import logging
  19 import csv
  20 import re
  21
  22 from collections import OrderedDict
  23 from xml.etree import ElementTree as ET
  24 from datetime import datetime as dt
  25 from datetime import timedelta
  26 from copy import deepcopy
  27
  28 import plotly.graph_objects as go
  29 import plotly.offline as ploff
  30 import pandas as pd
  31
  32 from numpy import nan, isnan
  33 from yaml import load, FullLoader, YAMLError
  34
  35 from pal_utils import mean, stdev, classify_anomalies, \
  36     convert_csv_to_pretty_txt, relative_change_stdev, relative_change
  37
  38
  39 REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)')
  40
  41
  42 def generate_tables(spec, data):
  43     """Generate all tables specified in the specification file.
  44
  45     :param spec: Specification read from the specification file.
  46     :param data: Data to process.
  47     :type spec: Specification
  48     :type data: InputData
  49     """
  50
  51     generator = {
  52         u"table_merged_details": table_merged_details,
  53         u"table_soak_vs_ndr": table_soak_vs_ndr,
  54         u"table_perf_trending_dash": table_perf_trending_dash,
  55         u"table_perf_trending_dash_html": table_perf_trending_dash_html,
  56         u"table_last_failed_tests": table_last_failed_tests,
  57         u"table_failed_tests": table_failed_tests,
  58         u"table_failed_tests_html": table_failed_tests_html,
  59         u"table_oper_data_html": table_oper_data_html,
  60         u"table_comparison": table_comparison,
  61         u"table_weekly_comparison": table_weekly_comparison
  62     }
  63
  64     logging.info(u"Generating the tables ...")
  65     for table in spec.tables:
  66         try:
  67             if table[u"algorithm"] == u"table_weekly_comparison":
  68                 table[u"testbeds"] = spec.environment.get(u"testbeds", None)
  69             generator[table[u"algorithm"]](table, data)
  70         except NameError as err:
  71             logging.error(
  72                 f"Probably algorithm {table[u'algorithm']} is not defined: "
  73                 f"{repr(err)}"
  74             )
  75     logging.info(u"Done.")
  76
  77
  78 def table_oper_data_html(table, input_data):
  79     """Generate the table(s) with algorithm: html_table_oper_data
  80     specified in the specification file.
  81
  82     :param table: Table to generate.
  83     :param input_data: Data to process.
  84     :type table: pandas.Series
  85     :type input_data: InputData
  86     """
  87
  88     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
  89     # Transform the data
  90     logging.info(
  91         f"    Creating the data set for the {table.get(u'type', u'')} "
  92         f"{table.get(u'title', u'')}."
  93     )
  94     data = input_data.filter_data(
  95         table,
  96         params=[u"name", u"parent", u"show-run", u"type"],
  97         continue_on_error=True
  98     )
  99     if data.empty:
 100         return
 101     data = input_data.merge_data(data)
 102
 103     sort_tests = table.get(u"sort", None)
 104     if sort_tests:
 105         args = dict(
 106             inplace=True,
 107             ascending=(sort_tests == u"ascending")
 108         )
 109         data.sort_index(**args)
 110
 111     suites = input_data.filter_data(
 112         table,
 113         continue_on_error=True,
 114         data_set=u"suites"
 115     )
 116     if suites.empty:
 117         return
 118     suites = input_data.merge_data(suites)
 119
 120     def _generate_html_table(tst_data):
 121         """Generate an HTML table with operational data for the given test.
 122
 123         :param tst_data: Test data to be used to generate the table.
 124         :type tst_data: pandas.Series
 125         :returns: HTML table with operational data.
 126         :rtype: str
 127         """
 128
 129         colors = {
 130             u"header": u"#7eade7",
 131             u"empty": u"#ffffff",
 132             u"body": (u"#e9f1fb", u"#d4e4f7")
 133         }
 134
 135         tbl = ET.Element(u"table", attrib=dict(width=u"100%", border=u"0"))
 136
 137         trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"header"]))
 138         thead = ET.SubElement(
 139             trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 140         )
 141         thead.text = tst_data[u"name"]
 142
 143         trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
 144         thead = ET.SubElement(
 145             trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 146         )
 147         thead.text = u"\t"
 148
 149         if tst_data.get(u"show-run", u"No Data") == u"No Data":
 150             trow = ET.SubElement(
 151                 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 152             )
 153             tcol = ET.SubElement(
 154                 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 155             )
 156             tcol.text = u"No Data"
 157
 158             trow = ET.SubElement(
 159                 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 160             )
 161             thead = ET.SubElement(
 162                 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 163             )
 164             font = ET.SubElement(
 165                 thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
 166             )
 167             font.text = u"."
 168             return str(ET.tostring(tbl, encoding=u"unicode"))
 169
 170         tbl_hdr = (
 171             u"Name",
 172             u"Nr of Vectors",
 173             u"Nr of Packets",
 174             u"Suspends",
 175             u"Cycles per Packet",
 176             u"Average Vector Size"
 177         )
 178
 179         for dut_data in tst_data[u"show-run"].values():
 180             trow = ET.SubElement(
 181                 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 182             )
 183             tcol = ET.SubElement(
 184                 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 185             )
 186             if dut_data.get(u"threads", None) is None:
 187                 tcol.text = u"No Data"
 188                 continue
 189
 190             bold = ET.SubElement(tcol, u"b")
 191             bold.text = (
 192                 f"Host IP: {dut_data.get(u'host', '')}, "
 193                 f"Socket: {dut_data.get(u'socket', '')}"
 194             )
 195             trow = ET.SubElement(
 196                 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 197             )
 198             thead = ET.SubElement(
 199                 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 200             )
 201             thead.text = u"\t"
 202
 203             for thread_nr, thread in dut_data[u"threads"].items():
 204                 trow = ET.SubElement(
 205                     tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 206                 )
 207                 tcol = ET.SubElement(
 208                     trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 209                 )
 210                 bold = ET.SubElement(tcol, u"b")
 211                 bold.text = u"main" if thread_nr == 0 else f"worker_{thread_nr}"
 212                 trow = ET.SubElement(
 213                     tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 214                 )
 215                 for idx, col in enumerate(tbl_hdr):
 216                     tcol = ET.SubElement(
 217                         trow, u"td",
 218                         attrib=dict(align=u"right" if idx else u"left")
 219                     )
 220                     font = ET.SubElement(
 221                         tcol, u"font", attrib=dict(size=u"2")
 222                     )
 223                     bold = ET.SubElement(font, u"b")
 224                     bold.text = col
 225                 for row_nr, row in enumerate(thread):
 226                     trow = ET.SubElement(
 227                         tbl, u"tr",
 228                         attrib=dict(bgcolor=colors[u"body"][row_nr % 2])
 229                     )
 230                     for idx, col in enumerate(row):
 231                         tcol = ET.SubElement(
 232                             trow, u"td",
 233                             attrib=dict(align=u"right" if idx else u"left")
 234                         )
 235                         font = ET.SubElement(
 236                             tcol, u"font", attrib=dict(size=u"2")
 237                         )
 238                         if isinstance(col, float):
 239                             font.text = f"{col:.2f}"
 240                         else:
 241                             font.text = str(col)
 242                 trow = ET.SubElement(
 243                     tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 244                 )
 245                 thead = ET.SubElement(
 246                     trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 247                 )
 248                 thead.text = u"\t"
 249
 250         trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
 251         thead = ET.SubElement(
 252             trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 253         )
 254         font = ET.SubElement(
 255             thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
 256         )
 257         font.text = u"."
 258
 259         return str(ET.tostring(tbl, encoding=u"unicode"))
 260
 261     for suite in suites.values:
 262         html_table = str()
 263         for test_data in data.values:
 264             if test_data[u"parent"] not in suite[u"name"]:
 265                 continue
 266             html_table += _generate_html_table(test_data)
 267         if not html_table:
 268             continue
 269         try:
 270             file_name = f"{table[u'output-file']}{suite[u'name']}.rst"
 271             with open(f"{file_name}", u'w') as html_file:
 272                 logging.info(f"    Writing file: {file_name}")
 273                 html_file.write(u".. raw:: html\n\n\t")
 274                 html_file.write(html_table)
 275                 html_file.write(u"\n\t<p><br><br></p>\n")
 276         except KeyError:
 277             logging.warning(u"The output file is not defined.")
 278             return
 279     logging.info(u"  Done.")
 280
 281
 282 def table_merged_details(table, input_data):
 283     """Generate the table(s) with algorithm: table_merged_details
 284     specified in the specification file.
 285
 286     :param table: Table to generate.
 287     :param input_data: Data to process.
 288     :type table: pandas.Series
 289     :type input_data: InputData
 290     """
 291
 292     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 293
 294     # Transform the data
 295     logging.info(
 296         f"    Creating the data set for the {table.get(u'type', u'')} "
 297         f"{table.get(u'title', u'')}."
 298     )
 299     data = input_data.filter_data(table, continue_on_error=True)
 300     data = input_data.merge_data(data)
 301
 302     sort_tests = table.get(u"sort", None)
 303     if sort_tests:
 304         args = dict(
 305             inplace=True,
 306             ascending=(sort_tests == u"ascending")
 307         )
 308         data.sort_index(**args)
 309
 310     suites = input_data.filter_data(
 311         table, continue_on_error=True, data_set=u"suites")
 312     suites = input_data.merge_data(suites)
 313
 314     # Prepare the header of the tables
 315     header = list()
 316     for column in table[u"columns"]:
 317         header.append(
 318             u'"{0}"'.format(str(column[u"title"]).replace(u'"', u'""'))
 319         )
 320
 321     for suite in suites.values:
 322         # Generate data
 323         suite_name = suite[u"name"]
 324         table_lst = list()
 325         for test in data.keys():
 326             if data[test][u"parent"] not in suite_name:
 327                 continue
 328             row_lst = list()
 329             for column in table[u"columns"]:
 330                 try:
 331                     col_data = str(data[test][column[
 332                         u"data"].split(u" ")[1]]).replace(u'"', u'""')
 333                     # Do not include tests with "Test Failed" in test message
 334                     if u"Test Failed" in col_data:
 335                         continue
 336                     col_data = col_data.replace(
 337                         u"No Data", u"Not Captured     "
 338                     )
 339                     if column[u"data"].split(u" ")[1] in (u"name", ):
 340                         if len(col_data) > 30:
 341                             col_data_lst = col_data.split(u"-")
 342                             half = int(len(col_data_lst) / 2)
 343                             col_data = f"{u'-'.join(col_data_lst[:half])}" \
 344                                        f"- |br| " \
 345                                        f"{u'-'.join(col_data_lst[half:])}"
 346                         col_data = f" |prein| {col_data} |preout| "
 347                     elif column[u"data"].split(u" ")[1] in (u"msg", ):
 348                         # Temporary solution: remove NDR results from message:
 349                         if bool(table.get(u'remove-ndr', False)):
 350                             try:
 351                                 col_data = col_data.split(u" |br| ", 1)[1]
 352                             except IndexError:
 353                                 pass
 354                         col_data = f" |prein| {col_data} |preout| "
 355                     elif column[u"data"].split(u" ")[1] in \
 356                             (u"conf-history", u"show-run"):
 357                         col_data = col_data.replace(u" |br| ", u"", 1)
 358                         col_data = f" |prein| {col_data[:-5]} |preout| "
 359                     row_lst.append(f'"{col_data}"')
 360                 except KeyError:
 361                     row_lst.append(u'"Not captured"')
 362             if len(row_lst) == len(table[u"columns"]):
 363                 table_lst.append(row_lst)
 364
 365         # Write the data to file
 366         if table_lst:
 367             separator = u"" if table[u'output-file'].endswith(u"/") else u"_"
 368             file_name = f"{table[u'output-file']}{separator}{suite_name}.csv"
 369             logging.info(f"      Writing file: {file_name}")
 370             with open(file_name, u"wt") as file_handler:
 371                 file_handler.write(u",".join(header) + u"\n")
 372                 for item in table_lst:
 373                     file_handler.write(u",".join(item) + u"\n")
 374
 375     logging.info(u"  Done.")
 376
 377
 378 def _tpc_modify_test_name(test_name, ignore_nic=False):
 379     """Modify a test name by replacing its parts.
 380
 381     :param test_name: Test name to be modified.
 382     :param ignore_nic: If True, NIC is removed from TC name.
 383     :type test_name: str
 384     :type ignore_nic: bool
 385     :returns: Modified test name.
 386     :rtype: str
 387     """
 388     test_name_mod = test_name.\
 389         replace(u"-ndrpdrdisc", u""). \
 390         replace(u"-ndrpdr", u"").\
 391         replace(u"-pdrdisc", u""). \
 392         replace(u"-ndrdisc", u"").\
 393         replace(u"-pdr", u""). \
 394         replace(u"-ndr", u""). \
 395         replace(u"1t1c", u"1c").\
 396         replace(u"2t1c", u"1c"). \
 397         replace(u"2t2c", u"2c").\
 398         replace(u"4t2c", u"2c"). \
 399         replace(u"4t4c", u"4c").\
 400         replace(u"8t4c", u"4c")
 401
 402     if ignore_nic:
 403         return re.sub(REGEX_NIC, u"", test_name_mod)
 404     return test_name_mod
 405
 406
 407 def _tpc_modify_displayed_test_name(test_name):
 408     """Modify a test name which is displayed in a table by replacing its parts.
 409
 410     :param test_name: Test name to be modified.
 411     :type test_name: str
 412     :returns: Modified test name.
 413     :rtype: str
 414     """
 415     return test_name.\
 416         replace(u"1t1c", u"1c").\
 417         replace(u"2t1c", u"1c"). \
 418         replace(u"2t2c", u"2c").\
 419         replace(u"4t2c", u"2c"). \
 420         replace(u"4t4c", u"4c").\
 421         replace(u"8t4c", u"4c")
 422
 423
 424 def _tpc_insert_data(target, src, include_tests):
 425     """Insert src data to the target structure.
 426
 427     :param target: Target structure where the data is placed.
 428     :param src: Source data to be placed into the target stucture.
 429     :param include_tests: Which results will be included (MRR, NDR, PDR).
 430     :type target: list
 431     :type src: dict
 432     :type include_tests: str
 433     """
 434     try:
 435         if include_tests == u"MRR":
 436             target[u"mean"] = src[u"result"][u"receive-rate"]
 437             target[u"stdev"] = src[u"result"][u"receive-stdev"]
 438         elif include_tests == u"PDR":
 439             target[u"data"].append(src[u"throughput"][u"PDR"][u"LOWER"])
 440         elif include_tests == u"NDR":
 441             target[u"data"].append(src[u"throughput"][u"NDR"][u"LOWER"])
 442     except (KeyError, TypeError):
 443         pass
 444
 445
 446 def _tpc_generate_html_table(header, data, out_file_name, legend=u"",
 447                              footnote=u"", sort_data=True, title=u"",
 448                              generate_rst=True):
 449     """Generate html table from input data with simple sorting possibility.
 450
 451     :param header: Table header.
 452     :param data: Input data to be included in the table. It is a list of lists.
 453         Inner lists are rows in the table. All inner lists must be of the same
 454         length. The length of these lists must be the same as the length of the
 455         header.
 456     :param out_file_name: The name (relative or full path) where the
 457         generated html table is written.
 458     :param legend: The legend to display below the table.
 459     :param footnote: The footnote to display below the table (and legend).
 460     :param sort_data: If True the data sorting is enabled.
 461     :param title: The table (and file) title.
 462     :param generate_rst: If True, wrapping rst file is generated.
 463     :type header: list
 464     :type data: list of lists
 465     :type out_file_name: str
 466     :type legend: str
 467     :type footnote: str
 468     :type sort_data: bool
 469     :type title: str
 470     :type generate_rst: bool
 471     """
 472
 473     try:
 474         idx = header.index(u"Test Case")
 475     except ValueError:
 476         idx = 0
 477     params = {
 478         u"align-hdr": (
 479             [u"left", u"right"],
 480             [u"left", u"left", u"right"],
 481             [u"left", u"left", u"left", u"right"]
 482         ),
 483         u"align-itm": (
 484             [u"left", u"right"],
 485             [u"left", u"left", u"right"],
 486             [u"left", u"left", u"left", u"right"]
 487         ),
 488         u"width": ([15, 9], [4, 24, 10], [4, 4, 32, 10])
 489     }
 490
 491     df_data = pd.DataFrame(data, columns=header)
 492
 493     if sort_data:
 494         df_sorted = [df_data.sort_values(
 495             by=[key, header[idx]], ascending=[True, True]
 496             if key != header[idx] else [False, True]) for key in header]
 497         df_sorted_rev = [df_data.sort_values(
 498             by=[key, header[idx]], ascending=[False, True]
 499             if key != header[idx] else [True, True]) for key in header]
 500         df_sorted.extend(df_sorted_rev)
 501     else:
 502         df_sorted = df_data
 503
 504     fill_color = [[u"#d4e4f7" if idx % 2 else u"#e9f1fb"
 505                    for idx in range(len(df_data))]]
 506     table_header = dict(
 507         values=[f"<b>{item.replace(u',', u',<br>')}</b>" for item in header],
 508         fill_color=u"#7eade7",
 509         align=params[u"align-hdr"][idx],
 510         font=dict(
 511             family=u"Courier New",
 512             size=12
 513         )
 514     )
 515
 516     fig = go.Figure()
 517
 518     if sort_data:
 519         for table in df_sorted:
 520             columns = [table.get(col) for col in header]
 521             fig.add_trace(
 522                 go.Table(
 523                     columnwidth=params[u"width"][idx],
 524                     header=table_header,
 525                     cells=dict(
 526                         values=columns,
 527                         fill_color=fill_color,
 528                         align=params[u"align-itm"][idx],
 529                         font=dict(
 530                             family=u"Courier New",
 531                             size=12
 532                         )
 533                     )
 534                 )
 535             )
 536
 537         buttons = list()
 538         menu_items = [f"<b>{itm}</b> (ascending)" for itm in header]
 539         menu_items.extend([f"<b>{itm}</b> (descending)" for itm in header])
 540         for idx, hdr in enumerate(menu_items):
 541             visible = [False, ] * len(menu_items)
 542             visible[idx] = True
 543             buttons.append(
 544                 dict(
 545                     label=hdr.replace(u" [Mpps]", u""),
 546                     method=u"update",
 547                     args=[{u"visible": visible}],
 548                 )
 549             )
 550
 551         fig.update_layout(
 552             updatemenus=[
 553                 go.layout.Updatemenu(
 554                     type=u"dropdown",
 555                     direction=u"down",
 556                     x=0.0,
 557                     xanchor=u"left",
 558                     y=1.002,
 559                     yanchor=u"bottom",
 560                     active=len(menu_items) - 1,
 561                     buttons=list(buttons)
 562                 )
 563             ],
 564         )
 565     else:
 566         fig.add_trace(
 567             go.Table(
 568                 columnwidth=params[u"width"][idx],
 569                 header=table_header,
 570                 cells=dict(
 571                     values=[df_sorted.get(col) for col in header],
 572                     fill_color=fill_color,
 573                     align=params[u"align-itm"][idx],
 574                     font=dict(
 575                         family=u"Courier New",
 576                         size=12
 577                     )
 578                 )
 579             )
 580         )
 581
 582     ploff.plot(
 583         fig,
 584         show_link=False,
 585         auto_open=False,
 586         filename=f"{out_file_name}_in.html"
 587     )
 588
 589     if not generate_rst:
 590         return
 591
 592     file_name = out_file_name.split(u"/")[-1]
 593     if u"vpp" in out_file_name:
 594         path = u"_tmp/src/vpp_performance_tests/comparisons/"
 595     else:
 596         path = u"_tmp/src/dpdk_performance_tests/comparisons/"
 597     with open(f"{path}{file_name}.rst", u"wt") as rst_file:
 598         rst_file.write(
 599             u"\n"
 600             u".. |br| raw:: html\n\n    <br />\n\n\n"
 601             u".. |prein| raw:: html\n\n    <pre>\n\n\n"
 602             u".. |preout| raw:: html\n\n    </pre>\n\n"
 603         )
 604         if title:
 605             rst_file.write(f"{title}\n")
 606             rst_file.write(f"{u'`' * len(title)}\n\n")
 607         rst_file.write(
 608             u".. raw:: html\n\n"
 609             f'    <iframe frameborder="0" scrolling="no" '
 610             f'width="1600" height="1200" '
 611             f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
 612             f'</iframe>\n\n'
 613         )
 614
 615         # TODO: Use html (rst) list for legend and footnote
 616         if legend:
 617             rst_file.write(legend[1:].replace(u"\n", u" |br| "))
 618         if footnote:
 619             rst_file.write(footnote.replace(u"\n", u" |br| ")[1:])
 620
 621
 622 def table_soak_vs_ndr(table, input_data):
 623     """Generate the table(s) with algorithm: table_soak_vs_ndr
 624     specified in the specification file.
 625
 626     :param table: Table to generate.
 627     :param input_data: Data to process.
 628     :type table: pandas.Series
 629     :type input_data: InputData
 630     """
 631
 632     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 633
 634     # Transform the data
 635     logging.info(
 636         f"    Creating the data set for the {table.get(u'type', u'')} "
 637         f"{table.get(u'title', u'')}."
 638     )
 639     data = input_data.filter_data(table, continue_on_error=True)
 640
 641     # Prepare the header of the table
 642     try:
 643         header = [
 644             u"Test Case",
 645             f"Avg({table[u'reference'][u'title']})",
 646             f"Stdev({table[u'reference'][u'title']})",
 647             f"Avg({table[u'compare'][u'title']})",
 648             f"Stdev{table[u'compare'][u'title']})",
 649             u"Diff",
 650             u"Stdev(Diff)"
 651         ]
 652         header_str = u";".join(header) + u"\n"
 653         legend = (
 654             u"\nLegend:\n"
 655             f"Avg({table[u'reference'][u'title']}): "
 656             f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
 657             f"from a series of runs of the listed tests.\n"
 658             f"Stdev({table[u'reference'][u'title']}): "
 659             f"Standard deviation value of {table[u'reference'][u'title']} "
 660             f"[Mpps] computed from a series of runs of the listed tests.\n"
 661             f"Avg({table[u'compare'][u'title']}): "
 662             f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
 663             f"a series of runs of the listed tests.\n"
 664             f"Stdev({table[u'compare'][u'title']}): "
 665             f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
 666             f"computed from a series of runs of the listed tests.\n"
 667             f"Diff({table[u'reference'][u'title']},"
 668             f"{table[u'compare'][u'title']}): "
 669             f"Percentage change calculated for mean values.\n"
 670             u"Stdev(Diff): "
 671             u"Standard deviation of percentage change calculated for mean "
 672             u"values."
 673         )
 674     except (AttributeError, KeyError) as err:
 675         logging.error(f"The model is invalid, missing parameter: {repr(err)}")
 676         return
 677
 678     # Create a list of available SOAK test results:
 679     tbl_dict = dict()
 680     for job, builds in table[u"compare"][u"data"].items():
 681         for build in builds:
 682             for tst_name, tst_data in data[job][str(build)].items():
 683                 if tst_data[u"type"] == u"SOAK":
 684                     tst_name_mod = tst_name.replace(u"-soak", u"")
 685                     if tbl_dict.get(tst_name_mod, None) is None:
 686                         groups = re.search(REGEX_NIC, tst_data[u"parent"])
 687                         nic = groups.group(0) if groups else u""
 688                         name = (
 689                             f"{nic}-"
 690                             f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
 691                         )
 692                         tbl_dict[tst_name_mod] = {
 693                             u"name": name,
 694                             u"ref-data": list(),
 695                             u"cmp-data": list()
 696                         }
 697                     try:
 698                         tbl_dict[tst_name_mod][u"cmp-data"].append(
 699                             tst_data[u"throughput"][u"LOWER"])
 700                     except (KeyError, TypeError):
 701                         pass
 702     tests_lst = tbl_dict.keys()
 703
 704     # Add corresponding NDR test results:
 705     for job, builds in table[u"reference"][u"data"].items():
 706         for build in builds:
 707             for tst_name, tst_data in data[job][str(build)].items():
 708                 tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
 709                     replace(u"-mrr", u"")
 710                 if tst_name_mod not in tests_lst:
 711                     continue
 712                 try:
 713                     if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
 714                         continue
 715                     if table[u"include-tests"] == u"MRR":
 716                         result = (tst_data[u"result"][u"receive-rate"],
 717                                   tst_data[u"result"][u"receive-stdev"])
 718                     elif table[u"include-tests"] == u"PDR":
 719                         result = \
 720                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 721                     elif table[u"include-tests"] == u"NDR":
 722                         result = \
 723                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 724                     else:
 725                         result = None
 726                     if result is not None:
 727                         tbl_dict[tst_name_mod][u"ref-data"].append(
 728                             result)
 729                 except (KeyError, TypeError):
 730                     continue
 731
 732     tbl_lst = list()
 733     for tst_name in tbl_dict:
 734         item = [tbl_dict[tst_name][u"name"], ]
 735         data_r = tbl_dict[tst_name][u"ref-data"]
 736         if data_r:
 737             if table[u"include-tests"] == u"MRR":
 738                 data_r_mean = data_r[0][0]
 739                 data_r_stdev = data_r[0][1]
 740             else:
 741                 data_r_mean = mean(data_r)
 742                 data_r_stdev = stdev(data_r)
 743             item.append(round(data_r_mean / 1e6, 1))
 744             item.append(round(data_r_stdev / 1e6, 1))
 745         else:
 746             data_r_mean = None
 747             data_r_stdev = None
 748             item.extend([None, None])
 749         data_c = tbl_dict[tst_name][u"cmp-data"]
 750         if data_c:
 751             if table[u"include-tests"] == u"MRR":
 752                 data_c_mean = data_c[0][0]
 753                 data_c_stdev = data_c[0][1]
 754             else:
 755                 data_c_mean = mean(data_c)
 756                 data_c_stdev = stdev(data_c)
 757             item.append(round(data_c_mean / 1e6, 1))
 758             item.append(round(data_c_stdev / 1e6, 1))
 759         else:
 760             data_c_mean = None
 761             data_c_stdev = None
 762             item.extend([None, None])
 763         if data_r_mean is not None and data_c_mean is not None:
 764             delta, d_stdev = relative_change_stdev(
 765                 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
 766             try:
 767                 item.append(round(delta))
 768             except ValueError:
 769                 item.append(delta)
 770             try:
 771                 item.append(round(d_stdev))
 772             except ValueError:
 773                 item.append(d_stdev)
 774             tbl_lst.append(item)
 775
 776     # Sort the table according to the relative change
 777     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
 778
 779     # Generate csv tables:
 780     csv_file = f"{table[u'output-file']}.csv"
 781     with open(csv_file, u"wt") as file_handler:
 782         file_handler.write(header_str)
 783         for test in tbl_lst:
 784             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
 785
 786     convert_csv_to_pretty_txt(
 787         csv_file, f"{table[u'output-file']}.txt", delimiter=u";"
 788     )
 789     with open(f"{table[u'output-file']}.txt", u'a') as txt_file:
 790         txt_file.write(legend)
 791
 792     # Generate html table:
 793     _tpc_generate_html_table(
 794         header,
 795         tbl_lst,
 796         table[u'output-file'],
 797         legend=legend,
 798         title=table.get(u"title", u"")
 799     )
 800
 801
 802 def table_perf_trending_dash(table, input_data):
 803     """Generate the table(s) with algorithm:
 804     table_perf_trending_dash
 805     specified in the specification file.
 806
 807     :param table: Table to generate.
 808     :param input_data: Data to process.
 809     :type table: pandas.Series
 810     :type input_data: InputData
 811     """
 812
 813     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 814
 815     # Transform the data
 816     logging.info(
 817         f"    Creating the data set for the {table.get(u'type', u'')} "
 818         f"{table.get(u'title', u'')}."
 819     )
 820     data = input_data.filter_data(table, continue_on_error=True)
 821
 822     # Prepare the header of the tables
 823     header = [
 824         u"Test Case",
 825         u"Trend [Mpps]",
 826         u"Short-Term Change [%]",
 827         u"Long-Term Change [%]",
 828         u"Regressions [#]",
 829         u"Progressions [#]"
 830     ]
 831     header_str = u",".join(header) + u"\n"
 832
 833     incl_tests = table.get(u"include-tests", u"MRR")
 834
 835     # Prepare data to the table:
 836     tbl_dict = dict()
 837     for job, builds in table[u"data"].items():
 838         for build in builds:
 839             for tst_name, tst_data in data[job][str(build)].items():
 840                 if tst_name.lower() in table.get(u"ignore-list", list()):
 841                     continue
 842                 if tbl_dict.get(tst_name, None) is None:
 843                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
 844                     if not groups:
 845                         continue
 846                     nic = groups.group(0)
 847                     tbl_dict[tst_name] = {
 848                         u"name": f"{nic}-{tst_data[u'name']}",
 849                         u"data": OrderedDict()
 850                     }
 851                 try:
 852                     if incl_tests == u"MRR":
 853                         tbl_dict[tst_name][u"data"][str(build)] = \
 854                             tst_data[u"result"][u"receive-rate"]
 855                     elif incl_tests == u"NDR":
 856                         tbl_dict[tst_name][u"data"][str(build)] = \
 857                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 858                     elif incl_tests == u"PDR":
 859                         tbl_dict[tst_name][u"data"][str(build)] = \
 860                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 861                 except (TypeError, KeyError):
 862                     pass  # No data in output.xml for this test
 863
 864     tbl_lst = list()
 865     for tst_name in tbl_dict:
 866         data_t = tbl_dict[tst_name][u"data"]
 867         if len(data_t) < 2:
 868             continue
 869
 870         classification_lst, avgs, _ = classify_anomalies(data_t)
 871
 872         win_size = min(len(data_t), table[u"window"])
 873         long_win_size = min(len(data_t), table[u"long-trend-window"])
 874
 875         try:
 876             max_long_avg = max(
 877                 [x for x in avgs[-long_win_size:-win_size]
 878                  if not isnan(x)])
 879         except ValueError:
 880             max_long_avg = nan
 881         last_avg = avgs[-1]
 882         avg_week_ago = avgs[max(-win_size, -len(avgs))]
 883
 884         if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
 885             rel_change_last = nan
 886         else:
 887             rel_change_last = round(
 888                 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
 889
 890         if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
 891             rel_change_long = nan
 892         else:
 893             rel_change_long = round(
 894                 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
 895
 896         if classification_lst:
 897             if isnan(rel_change_last) and isnan(rel_change_long):
 898                 continue
 899             if isnan(last_avg) or isnan(rel_change_last) or \
 900                     isnan(rel_change_long):
 901                 continue
 902             tbl_lst.append(
 903                 [tbl_dict[tst_name][u"name"],
 904                  round(last_avg / 1e6, 2),
 905                  rel_change_last,
 906                  rel_change_long,
 907                  classification_lst[-win_size+1:].count(u"regression"),
 908                  classification_lst[-win_size+1:].count(u"progression")])
 909
 910     tbl_lst.sort(key=lambda rel: rel[0])
 911
 912     tbl_sorted = list()
 913     for nrr in range(table[u"window"], -1, -1):
 914         tbl_reg = [item for item in tbl_lst if item[4] == nrr]
 915         for nrp in range(table[u"window"], -1, -1):
 916             tbl_out = [item for item in tbl_reg if item[5] == nrp]
 917             tbl_out.sort(key=lambda rel: rel[2])
 918             tbl_sorted.extend(tbl_out)
 919
 920     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
 921
 922     logging.info(f"    Writing file: {file_name}")
 923     with open(file_name, u"wt") as file_handler:
 924         file_handler.write(header_str)
 925         for test in tbl_sorted:
 926             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
 927
 928     logging.info(f"    Writing file: {table[u'output-file']}.txt")
 929     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
 930
 931
 932 def _generate_url(testbed, test_name):
 933     """Generate URL to a trending plot from the name of the test case.
 934
 935     :param testbed: The testbed used for testing.
 936     :param test_name: The name of the test case.
 937     :type testbed: str
 938     :type test_name: str
 939     :returns: The URL to the plot with the trending data for the given test
 940         case.
 941     :rtype str
 942     """
 943
 944     if u"x520" in test_name:
 945         nic = u"x520"
 946     elif u"x710" in test_name:
 947         nic = u"x710"
 948     elif u"xl710" in test_name:
 949         nic = u"xl710"
 950     elif u"xxv710" in test_name:
 951         nic = u"xxv710"
 952     elif u"vic1227" in test_name:
 953         nic = u"vic1227"
 954     elif u"vic1385" in test_name:
 955         nic = u"vic1385"
 956     elif u"x553" in test_name:
 957         nic = u"x553"
 958     else:
 959         nic = u""
 960
 961     if u"64b" in test_name:
 962         frame_size = u"64b"
 963     elif u"78b" in test_name:
 964         frame_size = u"78b"
 965     elif u"imix" in test_name:
 966         frame_size = u"imix"
 967     elif u"9000b" in test_name:
 968         frame_size = u"9000b"
 969     elif u"1518b" in test_name:
 970         frame_size = u"1518b"
 971     elif u"114b" in test_name:
 972         frame_size = u"114b"
 973     else:
 974         frame_size = u""
 975
 976     if u"1t1c" in test_name or \
 977         (u"-1c-" in test_name and
 978          testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 979         cores = u"1t1c"
 980     elif u"2t2c" in test_name or \
 981          (u"-2c-" in test_name and
 982           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 983         cores = u"2t2c"
 984     elif u"4t4c" in test_name or \
 985          (u"-4c-" in test_name and
 986           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 987         cores = u"4t4c"
 988     elif u"2t1c" in test_name or \
 989          (u"-1c-" in test_name and
 990           testbed in (u"2n-skx", u"3n-skx")):
 991         cores = u"2t1c"
 992     elif u"4t2c" in test_name:
 993         cores = u"4t2c"
 994     elif u"8t4c" in test_name:
 995         cores = u"8t4c"
 996     else:
 997         cores = u""
 998
 999     if u"testpmd" in test_name:
1000         driver = u"testpmd"
1001     elif u"l3fwd" in test_name:
1002         driver = u"l3fwd"
1003     elif u"avf" in test_name:
1004         driver = u"avf"
1005     elif u"dnv" in testbed or u"tsh" in testbed:
1006         driver = u"ixgbe"
1007     else:
1008         driver = u"dpdk"
1009
1010     if u"macip-iacl1s" in test_name:
1011         bsf = u"features-macip-iacl1"
1012     elif u"macip-iacl10s" in test_name:
1013         bsf = u"features-macip-iacl01"
1014     elif u"macip-iacl50s" in test_name:
1015         bsf = u"features-macip-iacl50"
1016     elif u"iacl1s" in test_name:
1017         bsf = u"features-iacl1"
1018     elif u"iacl10s" in test_name:
1019         bsf = u"features-iacl10"
1020     elif u"iacl50s" in test_name:
1021         bsf = u"features-iacl50"
1022     elif u"oacl1s" in test_name:
1023         bsf = u"features-oacl1"
1024     elif u"oacl10s" in test_name:
1025         bsf = u"features-oacl10"
1026     elif u"oacl50s" in test_name:
1027         bsf = u"features-oacl50"
1028     elif u"udpsrcscale" in test_name:
1029         bsf = u"features-udp"
1030     elif u"iacl" in test_name:
1031         bsf = u"features"
1032     elif u"policer" in test_name:
1033         bsf = u"features"
1034     elif u"cop" in test_name:
1035         bsf = u"features"
1036     elif u"nat" in test_name:
1037         bsf = u"features"
1038     elif u"macip" in test_name:
1039         bsf = u"features"
1040     elif u"scale" in test_name:
1041         bsf = u"scale"
1042     elif u"base" in test_name:
1043         bsf = u"base"
1044     else:
1045         bsf = u"base"
1046
1047     if u"114b" in test_name and u"vhost" in test_name:
1048         domain = u"vts"
1049     elif u"testpmd" in test_name or u"l3fwd" in test_name:
1050         domain = u"dpdk"
1051     elif u"memif" in test_name:
1052         domain = u"container_memif"
1053     elif u"srv6" in test_name:
1054         domain = u"srv6"
1055     elif u"vhost" in test_name:
1056         domain = u"vhost"
1057         if u"vppl2xc" in test_name:
1058             driver += u"-vpp"
1059         else:
1060             driver += u"-testpmd"
1061         if u"lbvpplacp" in test_name:
1062             bsf += u"-link-bonding"
1063     elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1064         domain = u"nf_service_density_vnfc"
1065     elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1066         domain = u"nf_service_density_cnfc"
1067     elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1068         domain = u"nf_service_density_cnfp"
1069     elif u"ipsec" in test_name:
1070         domain = u"ipsec"
1071         if u"sw" in test_name:
1072             bsf += u"-sw"
1073         elif u"hw" in test_name:
1074             bsf += u"-hw"
1075     elif u"ethip4vxlan" in test_name:
1076         domain = u"ip4_tunnels"
1077     elif u"ip4base" in test_name or u"ip4scale" in test_name:
1078         domain = u"ip4"
1079     elif u"ip6base" in test_name or u"ip6scale" in test_name:
1080         domain = u"ip6"
1081     elif u"l2xcbase" in test_name or \
1082             u"l2xcscale" in test_name or \
1083             u"l2bdbasemaclrn" in test_name or \
1084             u"l2bdscale" in test_name or \
1085             u"l2patch" in test_name:
1086         domain = u"l2"
1087     else:
1088         domain = u""
1089
1090     file_name = u"-".join((domain, testbed, nic)) + u".html#"
1091     anchor_name = u"-".join((frame_size, cores, bsf, driver))
1092
1093     return file_name + anchor_name
1094
1095
1096 def table_perf_trending_dash_html(table, input_data):
1097     """Generate the table(s) with algorithm:
1098     table_perf_trending_dash_html specified in the specification
1099     file.
1100
1101     :param table: Table to generate.
1102     :param input_data: Data to process.
1103     :type table: dict
1104     :type input_data: InputData
1105     """
1106
1107     _ = input_data
1108
1109     if not table.get(u"testbed", None):
1110         logging.error(
1111             f"The testbed is not defined for the table "
1112             f"{table.get(u'title', u'')}. Skipping."
1113         )
1114         return
1115
1116     test_type = table.get(u"test-type", u"MRR")
1117     if test_type not in (u"MRR", u"NDR", u"PDR"):
1118         logging.error(
1119             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1120             f"Skipping."
1121         )
1122         return
1123
1124     if test_type in (u"NDR", u"PDR"):
1125         lnk_dir = u"../ndrpdr_trending/"
1126         lnk_sufix = f"-{test_type.lower()}"
1127     else:
1128         lnk_dir = u"../trending/"
1129         lnk_sufix = u""
1130
1131     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1132
1133     try:
1134         with open(table[u"input-file"], u'rt') as csv_file:
1135             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1136     except KeyError:
1137         logging.warning(u"The input file is not defined.")
1138         return
1139     except csv.Error as err:
1140         logging.warning(
1141             f"Not possible to process the file {table[u'input-file']}.\n"
1142             f"{repr(err)}"
1143         )
1144         return
1145
1146     # Table:
1147     dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1148
1149     # Table header:
1150     trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1151     for idx, item in enumerate(csv_lst[0]):
1152         alignment = u"left" if idx == 0 else u"center"
1153         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1154         thead.text = item
1155
1156     # Rows:
1157     colors = {
1158         u"regression": (
1159             u"#ffcccc",
1160             u"#ff9999"
1161         ),
1162         u"progression": (
1163             u"#c6ecc6",
1164             u"#9fdf9f"
1165         ),
1166         u"normal": (
1167             u"#e9f1fb",
1168             u"#d4e4f7"
1169         )
1170     }
1171     for r_idx, row in enumerate(csv_lst[1:]):
1172         if int(row[4]):
1173             color = u"regression"
1174         elif int(row[5]):
1175             color = u"progression"
1176         else:
1177             color = u"normal"
1178         trow = ET.SubElement(
1179             dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1180         )
1181
1182         # Columns:
1183         for c_idx, item in enumerate(row):
1184             tdata = ET.SubElement(
1185                 trow,
1186                 u"td",
1187                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1188             )
1189             # Name:
1190             if c_idx == 0 and table.get(u"add-links", True):
1191                 ref = ET.SubElement(
1192                     tdata,
1193                     u"a",
1194                     attrib=dict(
1195                         href=f"{lnk_dir}"
1196                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1197                              f"{lnk_sufix}"
1198                     )
1199                 )
1200                 ref.text = item
1201             else:
1202                 tdata.text = item
1203     try:
1204         with open(table[u"output-file"], u'w') as html_file:
1205             logging.info(f"    Writing file: {table[u'output-file']}")
1206             html_file.write(u".. raw:: html\n\n\t")
1207             html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1208             html_file.write(u"\n\t<p><br><br></p>\n")
1209     except KeyError:
1210         logging.warning(u"The output file is not defined.")
1211         return
1212
1213
1214 def table_last_failed_tests(table, input_data):
1215     """Generate the table(s) with algorithm: table_last_failed_tests
1216     specified in the specification file.
1217
1218     :param table: Table to generate.
1219     :param input_data: Data to process.
1220     :type table: pandas.Series
1221     :type input_data: InputData
1222     """
1223
1224     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1225
1226     # Transform the data
1227     logging.info(
1228         f"    Creating the data set for the {table.get(u'type', u'')} "
1229         f"{table.get(u'title', u'')}."
1230     )
1231
1232     data = input_data.filter_data(table, continue_on_error=True)
1233
1234     if data is None or data.empty:
1235         logging.warning(
1236             f"    No data for the {table.get(u'type', u'')} "
1237             f"{table.get(u'title', u'')}."
1238         )
1239         return
1240
1241     tbl_list = list()
1242     for job, builds in table[u"data"].items():
1243         for build in builds:
1244             build = str(build)
1245             try:
1246                 version = input_data.metadata(job, build).get(u"version", u"")
1247             except KeyError:
1248                 logging.error(f"Data for {job}: {build} is not present.")
1249                 return
1250             tbl_list.append(build)
1251             tbl_list.append(version)
1252             failed_tests = list()
1253             passed = 0
1254             failed = 0
1255             for tst_data in data[job][build].values:
1256                 if tst_data[u"status"] != u"FAIL":
1257                     passed += 1
1258                     continue
1259                 failed += 1
1260                 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1261                 if not groups:
1262                     continue
1263                 nic = groups.group(0)
1264                 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1265             tbl_list.append(str(passed))
1266             tbl_list.append(str(failed))
1267             tbl_list.extend(failed_tests)
1268
1269     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1270     logging.info(f"    Writing file: {file_name}")
1271     with open(file_name, u"wt") as file_handler:
1272         for test in tbl_list:
1273             file_handler.write(test + u'\n')
1274
1275
1276 def table_failed_tests(table, input_data):
1277     """Generate the table(s) with algorithm: table_failed_tests
1278     specified in the specification file.
1279
1280     :param table: Table to generate.
1281     :param input_data: Data to process.
1282     :type table: pandas.Series
1283     :type input_data: InputData
1284     """
1285
1286     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1287
1288     # Transform the data
1289     logging.info(
1290         f"    Creating the data set for the {table.get(u'type', u'')} "
1291         f"{table.get(u'title', u'')}."
1292     )
1293     data = input_data.filter_data(table, continue_on_error=True)
1294
1295     test_type = u"MRR"
1296     if u"NDRPDR" in table.get(u"filter", list()):
1297         test_type = u"NDRPDR"
1298
1299     # Prepare the header of the tables
1300     header = [
1301         u"Test Case",
1302         u"Failures [#]",
1303         u"Last Failure [Time]",
1304         u"Last Failure [VPP-Build-Id]",
1305         u"Last Failure [CSIT-Job-Build-Id]"
1306     ]
1307
1308     # Generate the data for the table according to the model in the table
1309     # specification
1310
1311     now = dt.utcnow()
1312     timeperiod = timedelta(int(table.get(u"window", 7)))
1313
1314     tbl_dict = dict()
1315     for job, builds in table[u"data"].items():
1316         for build in builds:
1317             build = str(build)
1318             for tst_name, tst_data in data[job][build].items():
1319                 if tst_name.lower() in table.get(u"ignore-list", list()):
1320                     continue
1321                 if tbl_dict.get(tst_name, None) is None:
1322                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
1323                     if not groups:
1324                         continue
1325                     nic = groups.group(0)
1326                     tbl_dict[tst_name] = {
1327                         u"name": f"{nic}-{tst_data[u'name']}",
1328                         u"data": OrderedDict()
1329                     }
1330                 try:
1331                     generated = input_data.metadata(job, build).\
1332                         get(u"generated", u"")
1333                     if not generated:
1334                         continue
1335                     then = dt.strptime(generated, u"%Y%m%d %H:%M")
1336                     if (now - then) <= timeperiod:
1337                         tbl_dict[tst_name][u"data"][build] = (
1338                             tst_data[u"status"],
1339                             generated,
1340                             input_data.metadata(job, build).get(u"version",
1341                                                                 u""),
1342                             build
1343                         )
1344                 except (TypeError, KeyError) as err:
1345                     logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1346
1347     max_fails = 0
1348     tbl_lst = list()
1349     for tst_data in tbl_dict.values():
1350         fails_nr = 0
1351         fails_last_date = u""
1352         fails_last_vpp = u""
1353         fails_last_csit = u""
1354         for val in tst_data[u"data"].values():
1355             if val[0] == u"FAIL":
1356                 fails_nr += 1
1357                 fails_last_date = val[1]
1358                 fails_last_vpp = val[2]
1359                 fails_last_csit = val[3]
1360         if fails_nr:
1361             max_fails = fails_nr if fails_nr > max_fails else max_fails
1362             tbl_lst.append([
1363                 tst_data[u"name"],
1364                 fails_nr,
1365                 fails_last_date,
1366                 fails_last_vpp,
1367                 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1368                 f"-build-{fails_last_csit}"
1369             ])
1370
1371     tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1372     tbl_sorted = list()
1373     for nrf in range(max_fails, -1, -1):
1374         tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1375         tbl_sorted.extend(tbl_fails)
1376
1377     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1378     logging.info(f"    Writing file: {file_name}")
1379     with open(file_name, u"wt") as file_handler:
1380         file_handler.write(u",".join(header) + u"\n")
1381         for test in tbl_sorted:
1382             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1383
1384     logging.info(f"    Writing file: {table[u'output-file']}.txt")
1385     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1386
1387
1388 def table_failed_tests_html(table, input_data):
1389     """Generate the table(s) with algorithm: table_failed_tests_html
1390     specified in the specification file.
1391
1392     :param table: Table to generate.
1393     :param input_data: Data to process.
1394     :type table: pandas.Series
1395     :type input_data: InputData
1396     """
1397
1398     _ = input_data
1399
1400     if not table.get(u"testbed", None):
1401         logging.error(
1402             f"The testbed is not defined for the table "
1403             f"{table.get(u'title', u'')}. Skipping."
1404         )
1405         return
1406
1407     test_type = table.get(u"test-type", u"MRR")
1408     if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1409         logging.error(
1410             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1411             f"Skipping."
1412         )
1413         return
1414
1415     if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1416         lnk_dir = u"../ndrpdr_trending/"
1417         lnk_sufix = u"-pdr"
1418     else:
1419         lnk_dir = u"../trending/"
1420         lnk_sufix = u""
1421
1422     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1423
1424     try:
1425         with open(table[u"input-file"], u'rt') as csv_file:
1426             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1427     except KeyError:
1428         logging.warning(u"The input file is not defined.")
1429         return
1430     except csv.Error as err:
1431         logging.warning(
1432             f"Not possible to process the file {table[u'input-file']}.\n"
1433             f"{repr(err)}"
1434         )
1435         return
1436
1437     # Table:
1438     failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1439
1440     # Table header:
1441     trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1442     for idx, item in enumerate(csv_lst[0]):
1443         alignment = u"left" if idx == 0 else u"center"
1444         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1445         thead.text = item
1446
1447     # Rows:
1448     colors = (u"#e9f1fb", u"#d4e4f7")
1449     for r_idx, row in enumerate(csv_lst[1:]):
1450         background = colors[r_idx % 2]
1451         trow = ET.SubElement(
1452             failed_tests, u"tr", attrib=dict(bgcolor=background)
1453         )
1454
1455         # Columns:
1456         for c_idx, item in enumerate(row):
1457             tdata = ET.SubElement(
1458                 trow,
1459                 u"td",
1460                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1461             )
1462             # Name:
1463             if c_idx == 0 and table.get(u"add-links", True):
1464                 ref = ET.SubElement(
1465                     tdata,
1466                     u"a",
1467                     attrib=dict(
1468                         href=f"{lnk_dir}"
1469                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1470                              f"{lnk_sufix}"
1471                     )
1472                 )
1473                 ref.text = item
1474             else:
1475                 tdata.text = item
1476     try:
1477         with open(table[u"output-file"], u'w') as html_file:
1478             logging.info(f"    Writing file: {table[u'output-file']}")
1479             html_file.write(u".. raw:: html\n\n\t")
1480             html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1481             html_file.write(u"\n\t<p><br><br></p>\n")
1482     except KeyError:
1483         logging.warning(u"The output file is not defined.")
1484         return
1485
1486
1487 def table_comparison(table, input_data):
1488     """Generate the table(s) with algorithm: table_comparison
1489     specified in the specification file.
1490
1491     :param table: Table to generate.
1492     :param input_data: Data to process.
1493     :type table: pandas.Series
1494     :type input_data: InputData
1495     """
1496     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1497
1498     # Transform the data
1499     logging.info(
1500         f"    Creating the data set for the {table.get(u'type', u'')} "
1501         f"{table.get(u'title', u'')}."
1502     )
1503
1504     columns = table.get(u"columns", None)
1505     if not columns:
1506         logging.error(
1507             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1508         )
1509         return
1510
1511     cols = list()
1512     for idx, col in enumerate(columns):
1513         if col.get(u"data-set", None) is None:
1514             logging.warning(f"No data for column {col.get(u'title', u'')}")
1515             continue
1516         tag = col.get(u"tag", None)
1517         data = input_data.filter_data(
1518             table,
1519             params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1520             data=col[u"data-set"],
1521             continue_on_error=True
1522         )
1523         col_data = {
1524             u"title": col.get(u"title", f"Column{idx}"),
1525             u"data": dict()
1526         }
1527         for builds in data.values:
1528             for build in builds:
1529                 for tst_name, tst_data in build.items():
1530                     if tag and tag not in tst_data[u"tags"]:
1531                         continue
1532                     tst_name_mod = \
1533                         _tpc_modify_test_name(tst_name, ignore_nic=True).\
1534                         replace(u"2n1l-", u"")
1535                     if col_data[u"data"].get(tst_name_mod, None) is None:
1536                         name = tst_data[u'name'].rsplit(u'-', 1)[0]
1537                         if u"across testbeds" in table[u"title"].lower() or \
1538                                 u"across topologies" in table[u"title"].lower():
1539                             name = _tpc_modify_displayed_test_name(name)
1540                         col_data[u"data"][tst_name_mod] = {
1541                             u"name": name,
1542                             u"replace": True,
1543                             u"data": list(),
1544                             u"mean": None,
1545                             u"stdev": None
1546                         }
1547                     _tpc_insert_data(
1548                         target=col_data[u"data"][tst_name_mod],
1549                         src=tst_data,
1550                         include_tests=table[u"include-tests"]
1551                     )
1552
1553         replacement = col.get(u"data-replacement", None)
1554         if replacement:
1555             rpl_data = input_data.filter_data(
1556                 table,
1557                 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1558                 data=replacement,
1559                 continue_on_error=True
1560             )
1561             for builds in rpl_data.values:
1562                 for build in builds:
1563                     for tst_name, tst_data in build.items():
1564                         if tag and tag not in tst_data[u"tags"]:
1565                             continue
1566                         tst_name_mod = \
1567                             _tpc_modify_test_name(tst_name, ignore_nic=True).\
1568                             replace(u"2n1l-", u"")
1569                         if col_data[u"data"].get(tst_name_mod, None) is None:
1570                             name = tst_data[u'name'].rsplit(u'-', 1)[0]
1571                             if u"across testbeds" in table[u"title"].lower() \
1572                                     or u"across topologies" in \
1573                                     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": False,
1578                                 u"data": list(),
1579                                 u"mean": None,
1580                                 u"stdev": None
1581                             }
1582                         if col_data[u"data"][tst_name_mod][u"replace"]:
1583                             col_data[u"data"][tst_name_mod][u"replace"] = False
1584                             col_data[u"data"][tst_name_mod][u"data"] = list()
1585                         _tpc_insert_data(
1586                             target=col_data[u"data"][tst_name_mod],
1587                             src=tst_data,
1588                             include_tests=table[u"include-tests"]
1589                         )
1590
1591         if table[u"include-tests"] in (u"NDR", u"PDR"):
1592             for tst_name, tst_data in col_data[u"data"].items():
1593                 if tst_data[u"data"]:
1594                     tst_data[u"mean"] = mean(tst_data[u"data"])
1595                     tst_data[u"stdev"] = stdev(tst_data[u"data"])
1596
1597         cols.append(col_data)
1598
1599     tbl_dict = dict()
1600     for col in cols:
1601         for tst_name, tst_data in col[u"data"].items():
1602             if tbl_dict.get(tst_name, None) is None:
1603                 tbl_dict[tst_name] = {
1604                     "name": tst_data[u"name"]
1605                 }
1606             tbl_dict[tst_name][col[u"title"]] = {
1607                 u"mean": tst_data[u"mean"],
1608                 u"stdev": tst_data[u"stdev"]
1609             }
1610
1611     if not tbl_dict:
1612         logging.warning(f"No data for table {table.get(u'title', u'')}!")
1613         return
1614
1615     tbl_lst = list()
1616     for tst_data in tbl_dict.values():
1617         row = [tst_data[u"name"], ]
1618         for col in cols:
1619             row.append(tst_data.get(col[u"title"], None))
1620         tbl_lst.append(row)
1621
1622     comparisons = table.get(u"comparisons", None)
1623     if comparisons and isinstance(comparisons, list):
1624         for idx, comp in enumerate(comparisons):
1625             try:
1626                 col_ref = int(comp[u"reference"])
1627                 col_cmp = int(comp[u"compare"])
1628             except KeyError:
1629                 logging.warning(u"Comparison: No references defined! Skipping.")
1630                 comparisons.pop(idx)
1631                 continue
1632             if not (0 < col_ref <= len(cols) and
1633                     0 < col_cmp <= len(cols)) or \
1634                     col_ref == col_cmp:
1635                 logging.warning(f"Wrong values of reference={col_ref} "
1636                                 f"and/or compare={col_cmp}. Skipping.")
1637                 comparisons.pop(idx)
1638                 continue
1639
1640     tbl_cmp_lst = list()
1641     if comparisons:
1642         for row in tbl_lst:
1643             new_row = deepcopy(row)
1644             add_to_tbl = False
1645             for comp in comparisons:
1646                 ref_itm = row[int(comp[u"reference"])]
1647                 if ref_itm is None and \
1648                         comp.get(u"reference-alt", None) is not None:
1649                     ref_itm = row[int(comp[u"reference-alt"])]
1650                 cmp_itm = row[int(comp[u"compare"])]
1651                 if ref_itm is not None and cmp_itm is not None and \
1652                         ref_itm[u"mean"] is not None and \
1653                         cmp_itm[u"mean"] is not None and \
1654                         ref_itm[u"stdev"] is not None and \
1655                         cmp_itm[u"stdev"] is not None:
1656                     delta, d_stdev = relative_change_stdev(
1657                         ref_itm[u"mean"], cmp_itm[u"mean"],
1658                         ref_itm[u"stdev"], cmp_itm[u"stdev"]
1659                     )
1660                     new_row.append(
1661                         {
1662                             u"mean": delta * 1e6,
1663                             u"stdev": d_stdev * 1e6
1664                         }
1665                     )
1666                     add_to_tbl = True
1667                 else:
1668                     new_row.append(None)
1669             if add_to_tbl:
1670                 tbl_cmp_lst.append(new_row)
1671
1672     tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1673     tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1674
1675     rcas = list()
1676     rca_in = table.get(u"rca", None)
1677     if rca_in and isinstance(rca_in, list):
1678         for idx, itm in enumerate(rca_in):
1679             try:
1680                 with open(itm.get(u"data", u""), u"r") as rca_file:
1681                     rcas.append(
1682                         {
1683                             u"title": itm.get(u"title", f"RCA{idx}"),
1684                             u"data": load(rca_file, Loader=FullLoader)
1685                         }
1686                     )
1687             except (YAMLError, IOError) as err:
1688                 logging.warning(
1689                     f"The RCA file {itm.get(u'data', u'')} does not exist or "
1690                     f"it is corrupted!"
1691                 )
1692                 logging.debug(repr(err))
1693
1694     tbl_for_csv = list()
1695     for line in tbl_cmp_lst:
1696         row = [line[0], ]
1697         for idx, itm in enumerate(line[1:]):
1698             if itm is None:
1699                 row.append(u"NT")
1700                 row.append(u"NT")
1701             else:
1702                 row.append(round(float(itm[u'mean']) / 1e6, 3))
1703                 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1704         for rca in rcas:
1705             rca_nr = rca[u"data"].get(row[0], u"-")
1706             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1707         tbl_for_csv.append(row)
1708
1709     header_csv = [u"Test Case", ]
1710     for col in cols:
1711         header_csv.append(f"Avg({col[u'title']})")
1712         header_csv.append(f"Stdev({col[u'title']})")
1713     for comp in comparisons:
1714         header_csv.append(
1715             f"Avg({comp.get(u'title', u'')})"
1716         )
1717         header_csv.append(
1718             f"Stdev({comp.get(u'title', u'')})"
1719         )
1720     header_csv.extend([rca[u"title"] for rca in rcas])
1721
1722     legend_lst = table.get(u"legend", None)
1723     if legend_lst is None:
1724         legend = u""
1725     else:
1726         legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1727
1728     footnote = u""
1729     for rca in rcas:
1730         footnote += f"\n{rca[u'title']}:\n"
1731         footnote += rca[u"data"].get(u"footnote", u"")
1732
1733     csv_file = f"{table[u'output-file']}-csv.csv"
1734     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1735         file_handler.write(
1736             u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1737         )
1738         for test in tbl_for_csv:
1739             file_handler.write(
1740                 u",".join([f'"{item}"' for item in test]) + u"\n"
1741             )
1742         if legend_lst:
1743             for item in legend_lst:
1744                 file_handler.write(f'"{item}"\n')
1745         if footnote:
1746             for itm in footnote.split(u"\n"):
1747                 file_handler.write(f'"{itm}"\n')
1748
1749     tbl_tmp = list()
1750     max_lens = [0, ] * len(tbl_cmp_lst[0])
1751     for line in tbl_cmp_lst:
1752         row = [line[0], ]
1753         for idx, itm in enumerate(line[1:]):
1754             if itm is None:
1755                 new_itm = u"NT"
1756             else:
1757                 if idx < len(cols):
1758                     new_itm = (
1759                         f"{round(float(itm[u'mean']) / 1e6, 1)} "
1760                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1761                         replace(u"nan", u"NaN")
1762                     )
1763                 else:
1764                     new_itm = (
1765                         f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1766                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1767                         replace(u"nan", u"NaN")
1768                     )
1769             if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1770                 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1771             row.append(new_itm)
1772
1773         tbl_tmp.append(row)
1774
1775     tbl_final = list()
1776     for line in tbl_tmp:
1777         row = [line[0], ]
1778         for idx, itm in enumerate(line[1:]):
1779             if itm in (u"NT", u"NaN"):
1780                 row.append(itm)
1781                 continue
1782             itm_lst = itm.rsplit(u"\u00B1", 1)
1783             itm_lst[-1] = \
1784                 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1785             row.append(u"\u00B1".join(itm_lst))
1786         for rca in rcas:
1787             rca_nr = rca[u"data"].get(row[0], u"-")
1788             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1789
1790         tbl_final.append(row)
1791
1792     header = [u"Test Case", ]
1793     header.extend([col[u"title"] for col in cols])
1794     header.extend([comp.get(u"title", u"") for comp in comparisons])
1795     header.extend([rca[u"title"] for rca in rcas])
1796
1797     # Generate csv tables:
1798     csv_file = f"{table[u'output-file']}.csv"
1799     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1800         file_handler.write(u";".join(header) + u"\n")
1801         for test in tbl_final:
1802             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1803
1804     # Generate txt table:
1805     txt_file_name = f"{table[u'output-file']}.txt"
1806     convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
1807
1808     with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
1809         txt_file.write(legend)
1810         txt_file.write(footnote)
1811
1812     # Generate html table:
1813     _tpc_generate_html_table(
1814         header,
1815         tbl_final,
1816         table[u'output-file'],
1817         legend=legend,
1818         footnote=footnote,
1819         sort_data=False,
1820         title=table.get(u"title", u"")
1821     )
1822
1823
1824 def table_weekly_comparison(table, in_data):
1825     """Generate the table(s) with algorithm: table_weekly_comparison
1826     specified in the specification file.
1827
1828     :param table: Table to generate.
1829     :param in_data: Data to process.
1830     :type table: pandas.Series
1831     :type in_data: InputData
1832     """
1833     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1834
1835     # Transform the data
1836     logging.info(
1837         f"    Creating the data set for the {table.get(u'type', u'')} "
1838         f"{table.get(u'title', u'')}."
1839     )
1840
1841     incl_tests = table.get(u"include-tests", None)
1842     if incl_tests not in (u"NDR", u"PDR"):
1843         logging.error(f"Wrong tests to include specified ({incl_tests}).")
1844         return
1845
1846     nr_cols = table.get(u"nr-of-data-columns", None)
1847     if not nr_cols or nr_cols < 2:
1848         logging.error(
1849             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1850         )
1851         return
1852
1853     data = in_data.filter_data(
1854         table,
1855         params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1856         continue_on_error=True
1857     )
1858
1859     header = [
1860         [u"VPP Version", ],
1861         [u"Start Timestamp", ],
1862         [u"CSIT Build", ],
1863         [u"CSIT Testbed", ]
1864     ]
1865     tbl_dict = dict()
1866     idx = 0
1867     tb_tbl = table.get(u"testbeds", None)
1868     for job_name, job_data in data.items():
1869         for build_nr, build in job_data.items():
1870             if idx >= nr_cols:
1871                 break
1872             if build.empty:
1873                 continue
1874
1875             tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1876             if tb_ip and tb_tbl:
1877                 testbed = tb_tbl.get(tb_ip, u"")
1878             else:
1879                 testbed = u""
1880             header[2].insert(1, build_nr)
1881             header[3].insert(1, testbed)
1882             header[1].insert(
1883                 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1884             )
1885             header[0].insert(
1886                 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1887             )
1888
1889             for tst_name, tst_data in build.items():
1890                 tst_name_mod = \
1891                     _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1892                 if not tbl_dict.get(tst_name_mod, None):
1893                     tbl_dict[tst_name_mod] = dict(
1894                         name=tst_data[u'name'].rsplit(u'-', 1)[0],
1895                     )
1896                 try:
1897                     tbl_dict[tst_name_mod][-idx - 1] = \
1898                         tst_data[u"throughput"][incl_tests][u"LOWER"]
1899                 except (TypeError, IndexError, KeyError, ValueError):
1900                     pass
1901             idx += 1
1902
1903     if idx < nr_cols:
1904         logging.error(u"Not enough data to build the table! Skipping")
1905         return
1906
1907     cmp_dict = dict()
1908     for idx, cmp in enumerate(table.get(u"comparisons", list())):
1909         idx_ref = cmp.get(u"reference", None)
1910         idx_cmp = cmp.get(u"compare", None)
1911         if idx_ref is None or idx_cmp is None:
1912             continue
1913         header[0].append(
1914             f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1915             f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1916         )
1917         header[1].append(u"")
1918         header[2].append(u"")
1919         header[3].append(u"")
1920         for tst_name, tst_data in tbl_dict.items():
1921             if not cmp_dict.get(tst_name, None):
1922                 cmp_dict[tst_name] = list()
1923             ref_data = tst_data.get(idx_ref, None)
1924             cmp_data = tst_data.get(idx_cmp, None)
1925             if ref_data is None or cmp_data is None:
1926                 cmp_dict[tst_name].append(float('nan'))
1927             else:
1928                 cmp_dict[tst_name].append(
1929                     relative_change(ref_data, cmp_data)
1930                 )
1931
1932     tbl_lst = list()
1933     for tst_name, tst_data in tbl_dict.items():
1934         itm_lst = [tst_data[u"name"], ]
1935         for idx in range(nr_cols):
1936             item = tst_data.get(-idx - 1, None)
1937             if item is None:
1938                 itm_lst.insert(1, None)
1939             else:
1940                 itm_lst.insert(1, round(item / 1e6, 1))
1941         itm_lst.extend(
1942             [
1943                 None if itm is None else round(itm, 1)
1944                 for itm in cmp_dict[tst_name]
1945             ]
1946         )
1947         tbl_lst.append(itm_lst)
1948
1949     tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1950     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
1951
1952     # Generate csv table:
1953     csv_file = f"{table[u'output-file']}.csv"
1954     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1955         for hdr in header:
1956             file_handler.write(u",".join(hdr) + u"\n")
1957         for test in tbl_lst:
1958             file_handler.write(u",".join(
1959                 [
1960                     str(item).replace(u"None", u"-").replace(u"nan", u"-").
1961                     replace(u"null", u"-") for item in test
1962                 ]
1963             ) + u"\n")
1964
1965     txt_file = f"{table[u'output-file']}.txt"
1966     convert_csv_to_pretty_txt(csv_file, txt_file, delimiter=u",")
1967
1968     # Reorganize header in txt table
1969     txt_table = list()
1970     with open(txt_file, u"rt", encoding='utf-8') as file_handler:
1971         for line in file_handler:
1972             txt_table.append(line)
1973     try:
1974         txt_table.insert(5, txt_table.pop(2))
1975         with open(txt_file, u"wt", encoding='utf-8') as file_handler:
1976             file_handler.writelines(txt_table)
1977     except IndexError:
1978         pass
1979
1980     # Generate html table:
1981     hdr_html = [
1982         u"<br>".join(row) for row in zip(*header)
1983     ]
1984     _tpc_generate_html_table(
1985         hdr_html,
1986         tbl_lst,
1987         table[u'output-file'],
1988         sort_data=True,
1989         title=table.get(u"title", u""),
1990         generate_rst=False
1991     )