resources/tools/presentation/generator_tables.py

   1 # Copyright (c) 2021 Cisco and/or its affiliates.
   2 # Licensed under the Apache License, Version 2.0 (the "License");
   3 # you may not use this file except in compliance with the License.
   4 # You may obtain a copy of the License at:
   5 #
   6 #     http://www.apache.org/licenses/LICENSE-2.0
   7 #
   8 # Unless required by applicable law or agreed to in writing, software
   9 # distributed under the License is distributed on an "AS IS" BASIS,
  10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  11 # See the License for the specific language governing permissions and
  12 # limitations under the License.
  13
  14 """Algorithms to generate tables.
  15 """
  16
  17
  18 import logging
  19 import csv
  20 import re
  21
  22 from collections import OrderedDict
  23 from xml.etree import ElementTree as ET
  24 from datetime import datetime as dt
  25 from datetime import timedelta
  26 from copy import deepcopy
  27
  28 import plotly.graph_objects as go
  29 import plotly.offline as ploff
  30 import pandas as pd
  31
  32 from numpy import nan, isnan
  33 from yaml import load, FullLoader, YAMLError
  34
  35 from pal_utils import mean, stdev, classify_anomalies, \
  36     convert_csv_to_pretty_txt, relative_change_stdev, relative_change
  37
  38
  39 REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)')
  40
  41
  42 def generate_tables(spec, data):
  43     """Generate all tables specified in the specification file.
  44
  45     :param spec: Specification read from the specification file.
  46     :param data: Data to process.
  47     :type spec: Specification
  48     :type data: InputData
  49     """
  50
  51     generator = {
  52         u"table_merged_details": table_merged_details,
  53         u"table_soak_vs_ndr": table_soak_vs_ndr,
  54         u"table_perf_trending_dash": table_perf_trending_dash,
  55         u"table_perf_trending_dash_html": table_perf_trending_dash_html,
  56         u"table_last_failed_tests": table_last_failed_tests,
  57         u"table_failed_tests": table_failed_tests,
  58         u"table_failed_tests_html": table_failed_tests_html,
  59         u"table_oper_data_html": table_oper_data_html,
  60         u"table_comparison": table_comparison,
  61         u"table_weekly_comparison": table_weekly_comparison
  62     }
  63
  64     logging.info(u"Generating the tables ...")
  65     for table in spec.tables:
  66         try:
  67             if table[u"algorithm"] == u"table_weekly_comparison":
  68                 table[u"testbeds"] = spec.environment.get(u"testbeds", None)
  69             generator[table[u"algorithm"]](table, data)
  70         except NameError as err:
  71             logging.error(
  72                 f"Probably algorithm {table[u'algorithm']} is not defined: "
  73                 f"{repr(err)}"
  74             )
  75     logging.info(u"Done.")
  76
  77
  78 def table_oper_data_html(table, input_data):
  79     """Generate the table(s) with algorithm: html_table_oper_data
  80     specified in the specification file.
  81
  82     :param table: Table to generate.
  83     :param input_data: Data to process.
  84     :type table: pandas.Series
  85     :type input_data: InputData
  86     """
  87
  88     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
  89     # Transform the data
  90     logging.info(
  91         f"    Creating the data set for the {table.get(u'type', u'')} "
  92         f"{table.get(u'title', u'')}."
  93     )
  94     data = input_data.filter_data(
  95         table,
  96         params=[u"name", u"parent", u"show-run", u"type"],
  97         continue_on_error=True
  98     )
  99     if data.empty:
 100         return
 101     data = input_data.merge_data(data)
 102
 103     sort_tests = table.get(u"sort", None)
 104     if sort_tests:
 105         args = dict(
 106             inplace=True,
 107             ascending=(sort_tests == u"ascending")
 108         )
 109         data.sort_index(**args)
 110
 111     suites = input_data.filter_data(
 112         table,
 113         continue_on_error=True,
 114         data_set=u"suites"
 115     )
 116     if suites.empty:
 117         return
 118     suites = input_data.merge_data(suites)
 119
 120     def _generate_html_table(tst_data):
 121         """Generate an HTML table with operational data for the given test.
 122
 123         :param tst_data: Test data to be used to generate the table.
 124         :type tst_data: pandas.Series
 125         :returns: HTML table with operational data.
 126         :rtype: str
 127         """
 128
 129         colors = {
 130             u"header": u"#7eade7",
 131             u"empty": u"#ffffff",
 132             u"body": (u"#e9f1fb", u"#d4e4f7")
 133         }
 134
 135         tbl = ET.Element(u"table", attrib=dict(width=u"100%", border=u"0"))
 136
 137         trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"header"]))
 138         thead = ET.SubElement(
 139             trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 140         )
 141         thead.text = tst_data[u"name"]
 142
 143         trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
 144         thead = ET.SubElement(
 145             trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 146         )
 147         thead.text = u"\t"
 148
 149         if tst_data.get(u"show-run", u"No Data") == u"No Data":
 150             trow = ET.SubElement(
 151                 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 152             )
 153             tcol = ET.SubElement(
 154                 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 155             )
 156             tcol.text = u"No Data"
 157
 158             trow = ET.SubElement(
 159                 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 160             )
 161             thead = ET.SubElement(
 162                 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 163             )
 164             font = ET.SubElement(
 165                 thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
 166             )
 167             font.text = u"."
 168             return str(ET.tostring(tbl, encoding=u"unicode"))
 169
 170         tbl_hdr = (
 171             u"Name",
 172             u"Nr of Vectors",
 173             u"Nr of Packets",
 174             u"Suspends",
 175             u"Cycles per Packet",
 176             u"Average Vector Size"
 177         )
 178
 179         for dut_data in tst_data[u"show-run"].values():
 180             trow = ET.SubElement(
 181                 tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 182             )
 183             tcol = ET.SubElement(
 184                 trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 185             )
 186             if dut_data.get(u"threads", None) is None:
 187                 tcol.text = u"No Data"
 188                 continue
 189
 190             bold = ET.SubElement(tcol, u"b")
 191             bold.text = (
 192                 f"Host IP: {dut_data.get(u'host', '')}, "
 193                 f"Socket: {dut_data.get(u'socket', '')}"
 194             )
 195             trow = ET.SubElement(
 196                 tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 197             )
 198             thead = ET.SubElement(
 199                 trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 200             )
 201             thead.text = u"\t"
 202
 203             for thread_nr, thread in dut_data[u"threads"].items():
 204                 trow = ET.SubElement(
 205                     tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 206                 )
 207                 tcol = ET.SubElement(
 208                     trow, u"td", attrib=dict(align=u"left", colspan=u"6")
 209                 )
 210                 bold = ET.SubElement(tcol, u"b")
 211                 bold.text = u"main" if thread_nr == 0 else f"worker_{thread_nr}"
 212                 trow = ET.SubElement(
 213                     tbl, u"tr", attrib=dict(bgcolor=colors[u"header"])
 214                 )
 215                 for idx, col in enumerate(tbl_hdr):
 216                     tcol = ET.SubElement(
 217                         trow, u"td",
 218                         attrib=dict(align=u"right" if idx else u"left")
 219                     )
 220                     font = ET.SubElement(
 221                         tcol, u"font", attrib=dict(size=u"2")
 222                     )
 223                     bold = ET.SubElement(font, u"b")
 224                     bold.text = col
 225                 for row_nr, row in enumerate(thread):
 226                     trow = ET.SubElement(
 227                         tbl, u"tr",
 228                         attrib=dict(bgcolor=colors[u"body"][row_nr % 2])
 229                     )
 230                     for idx, col in enumerate(row):
 231                         tcol = ET.SubElement(
 232                             trow, u"td",
 233                             attrib=dict(align=u"right" if idx else u"left")
 234                         )
 235                         font = ET.SubElement(
 236                             tcol, u"font", attrib=dict(size=u"2")
 237                         )
 238                         if isinstance(col, float):
 239                             font.text = f"{col:.2f}"
 240                         else:
 241                             font.text = str(col)
 242                 trow = ET.SubElement(
 243                     tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"])
 244                 )
 245                 thead = ET.SubElement(
 246                     trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 247                 )
 248                 thead.text = u"\t"
 249
 250         trow = ET.SubElement(tbl, u"tr", attrib=dict(bgcolor=colors[u"empty"]))
 251         thead = ET.SubElement(
 252             trow, u"th", attrib=dict(align=u"left", colspan=u"6")
 253         )
 254         font = ET.SubElement(
 255             thead, u"font", attrib=dict(size=u"12px", color=u"#ffffff")
 256         )
 257         font.text = u"."
 258
 259         return str(ET.tostring(tbl, encoding=u"unicode"))
 260
 261     for suite in suites.values:
 262         html_table = str()
 263         for test_data in data.values:
 264             if test_data[u"parent"] not in suite[u"name"]:
 265                 continue
 266             html_table += _generate_html_table(test_data)
 267         if not html_table:
 268             continue
 269         try:
 270             file_name = f"{table[u'output-file']}{suite[u'name']}.rst"
 271             with open(f"{file_name}", u'w') as html_file:
 272                 logging.info(f"    Writing file: {file_name}")
 273                 html_file.write(u".. raw:: html\n\n\t")
 274                 html_file.write(html_table)
 275                 html_file.write(u"\n\t<p><br><br></p>\n")
 276         except KeyError:
 277             logging.warning(u"The output file is not defined.")
 278             return
 279     logging.info(u"  Done.")
 280
 281
 282 def table_merged_details(table, input_data):
 283     """Generate the table(s) with algorithm: table_merged_details
 284     specified in the specification file.
 285
 286     :param table: Table to generate.
 287     :param input_data: Data to process.
 288     :type table: pandas.Series
 289     :type input_data: InputData
 290     """
 291
 292     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 293
 294     # Transform the data
 295     logging.info(
 296         f"    Creating the data set for the {table.get(u'type', u'')} "
 297         f"{table.get(u'title', u'')}."
 298     )
 299     data = input_data.filter_data(table, continue_on_error=True)
 300     data = input_data.merge_data(data)
 301
 302     sort_tests = table.get(u"sort", None)
 303     if sort_tests:
 304         args = dict(
 305             inplace=True,
 306             ascending=(sort_tests == u"ascending")
 307         )
 308         data.sort_index(**args)
 309
 310     suites = input_data.filter_data(
 311         table, continue_on_error=True, data_set=u"suites")
 312     suites = input_data.merge_data(suites)
 313
 314     # Prepare the header of the tables
 315     header = list()
 316     for column in table[u"columns"]:
 317         header.append(
 318             u'"{0}"'.format(str(column[u"title"]).replace(u'"', u'""'))
 319         )
 320
 321     for suite in suites.values:
 322         # Generate data
 323         suite_name = suite[u"name"]
 324         table_lst = list()
 325         for test in data.keys():
 326             if data[test][u"parent"] not in suite_name:
 327                 continue
 328             row_lst = list()
 329             for column in table[u"columns"]:
 330                 try:
 331                     col_data = str(data[test][column[
 332                         u"data"].split(u" ")[1]]).replace(u'"', u'""')
 333                     # Do not include tests with "Test Failed" in test message
 334                     if u"Test Failed" in col_data:
 335                         continue
 336                     col_data = col_data.replace(
 337                         u"No Data", u"Not Captured     "
 338                     )
 339                     if column[u"data"].split(u" ")[1] in (u"name", ):
 340                         if len(col_data) > 30:
 341                             col_data_lst = col_data.split(u"-")
 342                             half = int(len(col_data_lst) / 2)
 343                             col_data = f"{u'-'.join(col_data_lst[:half])}" \
 344                                        f"- |br| " \
 345                                        f"{u'-'.join(col_data_lst[half:])}"
 346                         col_data = f" |prein| {col_data} |preout| "
 347                     elif column[u"data"].split(u" ")[1] in (u"msg", ):
 348                         # Temporary solution: remove NDR results from message:
 349                         if bool(table.get(u'remove-ndr', False)):
 350                             try:
 351                                 col_data = col_data.split(u" |br| ", 1)[1]
 352                             except IndexError:
 353                                 pass
 354                         col_data = f" |prein| {col_data} |preout| "
 355                     elif column[u"data"].split(u" ")[1] in \
 356                             (u"conf-history", u"show-run"):
 357                         col_data = col_data.replace(u" |br| ", u"", 1)
 358                         col_data = f" |prein| {col_data[:-5]} |preout| "
 359                     row_lst.append(f'"{col_data}"')
 360                 except KeyError:
 361                     row_lst.append(u'"Not captured"')
 362             if len(row_lst) == len(table[u"columns"]):
 363                 table_lst.append(row_lst)
 364
 365         # Write the data to file
 366         if table_lst:
 367             separator = u"" if table[u'output-file'].endswith(u"/") else u"_"
 368             file_name = f"{table[u'output-file']}{separator}{suite_name}.csv"
 369             logging.info(f"      Writing file: {file_name}")
 370             with open(file_name, u"wt") as file_handler:
 371                 file_handler.write(u",".join(header) + u"\n")
 372                 for item in table_lst:
 373                     file_handler.write(u",".join(item) + u"\n")
 374
 375     logging.info(u"  Done.")
 376
 377
 378 def _tpc_modify_test_name(test_name, ignore_nic=False):
 379     """Modify a test name by replacing its parts.
 380
 381     :param test_name: Test name to be modified.
 382     :param ignore_nic: If True, NIC is removed from TC name.
 383     :type test_name: str
 384     :type ignore_nic: bool
 385     :returns: Modified test name.
 386     :rtype: str
 387     """
 388     test_name_mod = test_name.\
 389         replace(u"-ndrpdrdisc", u""). \
 390         replace(u"-ndrpdr", u"").\
 391         replace(u"-pdrdisc", u""). \
 392         replace(u"-ndrdisc", u"").\
 393         replace(u"-pdr", u""). \
 394         replace(u"-ndr", u""). \
 395         replace(u"1t1c", u"1c").\
 396         replace(u"2t1c", u"1c"). \
 397         replace(u"2t2c", u"2c").\
 398         replace(u"4t2c", u"2c"). \
 399         replace(u"4t4c", u"4c").\
 400         replace(u"8t4c", u"4c")
 401
 402     if ignore_nic:
 403         return re.sub(REGEX_NIC, u"", test_name_mod)
 404     return test_name_mod
 405
 406
 407 def _tpc_modify_displayed_test_name(test_name):
 408     """Modify a test name which is displayed in a table by replacing its parts.
 409
 410     :param test_name: Test name to be modified.
 411     :type test_name: str
 412     :returns: Modified test name.
 413     :rtype: str
 414     """
 415     return test_name.\
 416         replace(u"1t1c", u"1c").\
 417         replace(u"2t1c", u"1c"). \
 418         replace(u"2t2c", u"2c").\
 419         replace(u"4t2c", u"2c"). \
 420         replace(u"4t4c", u"4c").\
 421         replace(u"8t4c", u"4c")
 422
 423
 424 def _tpc_insert_data(target, src, include_tests):
 425     """Insert src data to the target structure.
 426
 427     :param target: Target structure where the data is placed.
 428     :param src: Source data to be placed into the target stucture.
 429     :param include_tests: Which results will be included (MRR, NDR, PDR).
 430     :type target: list
 431     :type src: dict
 432     :type include_tests: str
 433     """
 434     try:
 435         if include_tests == u"MRR":
 436             target[u"mean"] = src[u"result"][u"receive-rate"]
 437             target[u"stdev"] = src[u"result"][u"receive-stdev"]
 438         elif include_tests == u"PDR":
 439             target[u"data"].append(src[u"throughput"][u"PDR"][u"LOWER"])
 440         elif include_tests == u"NDR":
 441             target[u"data"].append(src[u"throughput"][u"NDR"][u"LOWER"])
 442     except (KeyError, TypeError):
 443         pass
 444
 445
 446 def _tpc_generate_html_table(header, data, out_file_name, legend=u"",
 447                              footnote=u"", sort_data=True, title=u"",
 448                              generate_rst=True):
 449     """Generate html table from input data with simple sorting possibility.
 450
 451     :param header: Table header.
 452     :param data: Input data to be included in the table. It is a list of lists.
 453         Inner lists are rows in the table. All inner lists must be of the same
 454         length. The length of these lists must be the same as the length of the
 455         header.
 456     :param out_file_name: The name (relative or full path) where the
 457         generated html table is written.
 458     :param legend: The legend to display below the table.
 459     :param footnote: The footnote to display below the table (and legend).
 460     :param sort_data: If True the data sorting is enabled.
 461     :param title: The table (and file) title.
 462     :param generate_rst: If True, wrapping rst file is generated.
 463     :type header: list
 464     :type data: list of lists
 465     :type out_file_name: str
 466     :type legend: str
 467     :type footnote: str
 468     :type sort_data: bool
 469     :type title: str
 470     :type generate_rst: bool
 471     """
 472
 473     try:
 474         idx = header.index(u"Test Case")
 475     except ValueError:
 476         idx = 0
 477     params = {
 478         u"align-hdr": (
 479             [u"left", u"right"],
 480             [u"left", u"left", u"right"],
 481             [u"left", u"left", u"left", u"right"]
 482         ),
 483         u"align-itm": (
 484             [u"left", u"right"],
 485             [u"left", u"left", u"right"],
 486             [u"left", u"left", u"left", u"right"]
 487         ),
 488         u"width": ([15, 9], [4, 24, 10], [4, 4, 32, 10])
 489     }
 490
 491     df_data = pd.DataFrame(data, columns=header)
 492
 493     if sort_data:
 494         df_sorted = [df_data.sort_values(
 495             by=[key, header[idx]], ascending=[True, True]
 496             if key != header[idx] else [False, True]) for key in header]
 497         df_sorted_rev = [df_data.sort_values(
 498             by=[key, header[idx]], ascending=[False, True]
 499             if key != header[idx] else [True, True]) for key in header]
 500         df_sorted.extend(df_sorted_rev)
 501     else:
 502         df_sorted = df_data
 503
 504     fill_color = [[u"#d4e4f7" if idx % 2 else u"#e9f1fb"
 505                    for idx in range(len(df_data))]]
 506     table_header = dict(
 507         values=[f"<b>{item.replace(u',', u',<br>')}</b>" for item in header],
 508         fill_color=u"#7eade7",
 509         align=params[u"align-hdr"][idx],
 510         font=dict(
 511             family=u"Courier New",
 512             size=12
 513         )
 514     )
 515
 516     fig = go.Figure()
 517
 518     if sort_data:
 519         for table in df_sorted:
 520             columns = [table.get(col) for col in header]
 521             fig.add_trace(
 522                 go.Table(
 523                     columnwidth=params[u"width"][idx],
 524                     header=table_header,
 525                     cells=dict(
 526                         values=columns,
 527                         fill_color=fill_color,
 528                         align=params[u"align-itm"][idx],
 529                         font=dict(
 530                             family=u"Courier New",
 531                             size=12
 532                         )
 533                     )
 534                 )
 535             )
 536
 537         buttons = list()
 538         menu_items = [f"<b>{itm}</b> (ascending)" for itm in header]
 539         menu_items.extend([f"<b>{itm}</b> (descending)" for itm in header])
 540         for idx, hdr in enumerate(menu_items):
 541             visible = [False, ] * len(menu_items)
 542             visible[idx] = True
 543             buttons.append(
 544                 dict(
 545                     label=hdr.replace(u" [Mpps]", u""),
 546                     method=u"update",
 547                     args=[{u"visible": visible}],
 548                 )
 549             )
 550
 551         fig.update_layout(
 552             updatemenus=[
 553                 go.layout.Updatemenu(
 554                     type=u"dropdown",
 555                     direction=u"down",
 556                     x=0.0,
 557                     xanchor=u"left",
 558                     y=1.002,
 559                     yanchor=u"bottom",
 560                     active=len(menu_items) - 1,
 561                     buttons=list(buttons)
 562                 )
 563             ],
 564         )
 565     else:
 566         fig.add_trace(
 567             go.Table(
 568                 columnwidth=params[u"width"][idx],
 569                 header=table_header,
 570                 cells=dict(
 571                     values=[df_sorted.get(col) for col in header],
 572                     fill_color=fill_color,
 573                     align=params[u"align-itm"][idx],
 574                     font=dict(
 575                         family=u"Courier New",
 576                         size=12
 577                     )
 578                 )
 579             )
 580         )
 581
 582     ploff.plot(
 583         fig,
 584         show_link=False,
 585         auto_open=False,
 586         filename=f"{out_file_name}_in.html"
 587     )
 588
 589     if not generate_rst:
 590         return
 591
 592     file_name = out_file_name.split(u"/")[-1]
 593     if u"vpp" in out_file_name:
 594         path = u"_tmp/src/vpp_performance_tests/comparisons/"
 595     else:
 596         path = u"_tmp/src/dpdk_performance_tests/comparisons/"
 597     logging.info(f"    Writing the HTML file to {path}{file_name}.rst")
 598     with open(f"{path}{file_name}.rst", u"wt") as rst_file:
 599         rst_file.write(
 600             u"\n"
 601             u".. |br| raw:: html\n\n    <br />\n\n\n"
 602             u".. |prein| raw:: html\n\n    <pre>\n\n\n"
 603             u".. |preout| raw:: html\n\n    </pre>\n\n"
 604         )
 605         if title:
 606             rst_file.write(f"{title}\n")
 607             rst_file.write(f"{u'`' * len(title)}\n\n")
 608         rst_file.write(
 609             u".. raw:: html\n\n"
 610             f'    <iframe frameborder="0" scrolling="no" '
 611             f'width="1600" height="1200" '
 612             f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
 613             f'</iframe>\n\n'
 614         )
 615
 616         if legend:
 617             try:
 618                 itm_lst = legend[1:-2].split(u"\n")
 619                 rst_file.write(
 620                     f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
 621                 )
 622             except IndexError as err:
 623                 logging.error(f"Legend cannot be written to html file\n{err}")
 624         if footnote:
 625             try:
 626                 itm_lst = footnote[1:].split(u"\n")
 627                 rst_file.write(
 628                     f"{itm_lst[0]}\n\n- " + u'\n- '.join(itm_lst[1:]) + u"\n\n"
 629                 )
 630             except IndexError as err:
 631                 logging.error(f"Footnote cannot be written to html file\n{err}")
 632
 633
 634 def table_soak_vs_ndr(table, input_data):
 635     """Generate the table(s) with algorithm: table_soak_vs_ndr
 636     specified in the specification file.
 637
 638     :param table: Table to generate.
 639     :param input_data: Data to process.
 640     :type table: pandas.Series
 641     :type input_data: InputData
 642     """
 643
 644     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 645
 646     # Transform the data
 647     logging.info(
 648         f"    Creating the data set for the {table.get(u'type', u'')} "
 649         f"{table.get(u'title', u'')}."
 650     )
 651     data = input_data.filter_data(table, continue_on_error=True)
 652
 653     # Prepare the header of the table
 654     try:
 655         header = [
 656             u"Test Case",
 657             f"Avg({table[u'reference'][u'title']})",
 658             f"Stdev({table[u'reference'][u'title']})",
 659             f"Avg({table[u'compare'][u'title']})",
 660             f"Stdev{table[u'compare'][u'title']})",
 661             u"Diff",
 662             u"Stdev(Diff)"
 663         ]
 664         header_str = u";".join(header) + u"\n"
 665         legend = (
 666             u"\nLegend:\n"
 667             f"Avg({table[u'reference'][u'title']}): "
 668             f"Mean value of {table[u'reference'][u'title']} [Mpps] computed "
 669             f"from a series of runs of the listed tests.\n"
 670             f"Stdev({table[u'reference'][u'title']}): "
 671             f"Standard deviation value of {table[u'reference'][u'title']} "
 672             f"[Mpps] computed from a series of runs of the listed tests.\n"
 673             f"Avg({table[u'compare'][u'title']}): "
 674             f"Mean value of {table[u'compare'][u'title']} [Mpps] computed from "
 675             f"a series of runs of the listed tests.\n"
 676             f"Stdev({table[u'compare'][u'title']}): "
 677             f"Standard deviation value of {table[u'compare'][u'title']} [Mpps] "
 678             f"computed from a series of runs of the listed tests.\n"
 679             f"Diff({table[u'reference'][u'title']},"
 680             f"{table[u'compare'][u'title']}): "
 681             f"Percentage change calculated for mean values.\n"
 682             u"Stdev(Diff): "
 683             u"Standard deviation of percentage change calculated for mean "
 684             u"values."
 685         )
 686     except (AttributeError, KeyError) as err:
 687         logging.error(f"The model is invalid, missing parameter: {repr(err)}")
 688         return
 689
 690     # Create a list of available SOAK test results:
 691     tbl_dict = dict()
 692     for job, builds in table[u"compare"][u"data"].items():
 693         for build in builds:
 694             for tst_name, tst_data in data[job][str(build)].items():
 695                 if tst_data[u"type"] == u"SOAK":
 696                     tst_name_mod = tst_name.replace(u"-soak", u"")
 697                     if tbl_dict.get(tst_name_mod, None) is None:
 698                         groups = re.search(REGEX_NIC, tst_data[u"parent"])
 699                         nic = groups.group(0) if groups else u""
 700                         name = (
 701                             f"{nic}-"
 702                             f"{u'-'.join(tst_data[u'name'].split(u'-')[:-1])}"
 703                         )
 704                         tbl_dict[tst_name_mod] = {
 705                             u"name": name,
 706                             u"ref-data": list(),
 707                             u"cmp-data": list()
 708                         }
 709                     try:
 710                         tbl_dict[tst_name_mod][u"cmp-data"].append(
 711                             tst_data[u"throughput"][u"LOWER"])
 712                     except (KeyError, TypeError):
 713                         pass
 714     tests_lst = tbl_dict.keys()
 715
 716     # Add corresponding NDR test results:
 717     for job, builds in table[u"reference"][u"data"].items():
 718         for build in builds:
 719             for tst_name, tst_data in data[job][str(build)].items():
 720                 tst_name_mod = tst_name.replace(u"-ndrpdr", u"").\
 721                     replace(u"-mrr", u"")
 722                 if tst_name_mod not in tests_lst:
 723                     continue
 724                 try:
 725                     if tst_data[u"type"] not in (u"NDRPDR", u"MRR", u"BMRR"):
 726                         continue
 727                     if table[u"include-tests"] == u"MRR":
 728                         result = (tst_data[u"result"][u"receive-rate"],
 729                                   tst_data[u"result"][u"receive-stdev"])
 730                     elif table[u"include-tests"] == u"PDR":
 731                         result = \
 732                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 733                     elif table[u"include-tests"] == u"NDR":
 734                         result = \
 735                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 736                     else:
 737                         result = None
 738                     if result is not None:
 739                         tbl_dict[tst_name_mod][u"ref-data"].append(
 740                             result)
 741                 except (KeyError, TypeError):
 742                     continue
 743
 744     tbl_lst = list()
 745     for tst_name in tbl_dict:
 746         item = [tbl_dict[tst_name][u"name"], ]
 747         data_r = tbl_dict[tst_name][u"ref-data"]
 748         if data_r:
 749             if table[u"include-tests"] == u"MRR":
 750                 data_r_mean = data_r[0][0]
 751                 data_r_stdev = data_r[0][1]
 752             else:
 753                 data_r_mean = mean(data_r)
 754                 data_r_stdev = stdev(data_r)
 755             item.append(round(data_r_mean / 1e6, 1))
 756             item.append(round(data_r_stdev / 1e6, 1))
 757         else:
 758             data_r_mean = None
 759             data_r_stdev = None
 760             item.extend([None, None])
 761         data_c = tbl_dict[tst_name][u"cmp-data"]
 762         if data_c:
 763             if table[u"include-tests"] == u"MRR":
 764                 data_c_mean = data_c[0][0]
 765                 data_c_stdev = data_c[0][1]
 766             else:
 767                 data_c_mean = mean(data_c)
 768                 data_c_stdev = stdev(data_c)
 769             item.append(round(data_c_mean / 1e6, 1))
 770             item.append(round(data_c_stdev / 1e6, 1))
 771         else:
 772             data_c_mean = None
 773             data_c_stdev = None
 774             item.extend([None, None])
 775         if data_r_mean is not None and data_c_mean is not None:
 776             delta, d_stdev = relative_change_stdev(
 777                 data_r_mean, data_c_mean, data_r_stdev, data_c_stdev)
 778             try:
 779                 item.append(round(delta))
 780             except ValueError:
 781                 item.append(delta)
 782             try:
 783                 item.append(round(d_stdev))
 784             except ValueError:
 785                 item.append(d_stdev)
 786             tbl_lst.append(item)
 787
 788     # Sort the table according to the relative change
 789     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
 790
 791     # Generate csv tables:
 792     csv_file_name = f"{table[u'output-file']}.csv"
 793     with open(csv_file_name, u"wt") as file_handler:
 794         file_handler.write(header_str)
 795         for test in tbl_lst:
 796             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
 797
 798     convert_csv_to_pretty_txt(
 799         csv_file_name, f"{table[u'output-file']}.txt", delimiter=u";"
 800     )
 801     with open(f"{table[u'output-file']}.txt", u'a') as file_handler:
 802         file_handler.write(legend)
 803
 804     # Generate html table:
 805     _tpc_generate_html_table(
 806         header,
 807         tbl_lst,
 808         table[u'output-file'],
 809         legend=legend,
 810         title=table.get(u"title", u"")
 811     )
 812
 813
 814 def table_perf_trending_dash(table, input_data):
 815     """Generate the table(s) with algorithm:
 816     table_perf_trending_dash
 817     specified in the specification file.
 818
 819     :param table: Table to generate.
 820     :param input_data: Data to process.
 821     :type table: pandas.Series
 822     :type input_data: InputData
 823     """
 824
 825     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
 826
 827     # Transform the data
 828     logging.info(
 829         f"    Creating the data set for the {table.get(u'type', u'')} "
 830         f"{table.get(u'title', u'')}."
 831     )
 832     data = input_data.filter_data(table, continue_on_error=True)
 833
 834     # Prepare the header of the tables
 835     header = [
 836         u"Test Case",
 837         u"Trend [Mpps]",
 838         u"Short-Term Change [%]",
 839         u"Long-Term Change [%]",
 840         u"Regressions [#]",
 841         u"Progressions [#]"
 842     ]
 843     header_str = u",".join(header) + u"\n"
 844
 845     incl_tests = table.get(u"include-tests", u"MRR")
 846
 847     # Prepare data to the table:
 848     tbl_dict = dict()
 849     for job, builds in table[u"data"].items():
 850         for build in builds:
 851             for tst_name, tst_data in data[job][str(build)].items():
 852                 if tst_name.lower() in table.get(u"ignore-list", list()):
 853                     continue
 854                 if tbl_dict.get(tst_name, None) is None:
 855                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
 856                     if not groups:
 857                         continue
 858                     nic = groups.group(0)
 859                     tbl_dict[tst_name] = {
 860                         u"name": f"{nic}-{tst_data[u'name']}",
 861                         u"data": OrderedDict()
 862                     }
 863                 try:
 864                     if incl_tests == u"MRR":
 865                         tbl_dict[tst_name][u"data"][str(build)] = \
 866                             tst_data[u"result"][u"receive-rate"]
 867                     elif incl_tests == u"NDR":
 868                         tbl_dict[tst_name][u"data"][str(build)] = \
 869                             tst_data[u"throughput"][u"NDR"][u"LOWER"]
 870                     elif incl_tests == u"PDR":
 871                         tbl_dict[tst_name][u"data"][str(build)] = \
 872                             tst_data[u"throughput"][u"PDR"][u"LOWER"]
 873                 except (TypeError, KeyError):
 874                     pass  # No data in output.xml for this test
 875
 876     tbl_lst = list()
 877     for tst_name in tbl_dict:
 878         data_t = tbl_dict[tst_name][u"data"]
 879         if len(data_t) < 2:
 880             continue
 881
 882         classification_lst, avgs, _ = classify_anomalies(data_t)
 883
 884         win_size = min(len(data_t), table[u"window"])
 885         long_win_size = min(len(data_t), table[u"long-trend-window"])
 886
 887         try:
 888             max_long_avg = max(
 889                 [x for x in avgs[-long_win_size:-win_size]
 890                  if not isnan(x)])
 891         except ValueError:
 892             max_long_avg = nan
 893         last_avg = avgs[-1]
 894         avg_week_ago = avgs[max(-win_size, -len(avgs))]
 895
 896         if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
 897             rel_change_last = nan
 898         else:
 899             rel_change_last = round(
 900                 ((last_avg - avg_week_ago) / avg_week_ago) * 1e2, 2)
 901
 902         if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
 903             rel_change_long = nan
 904         else:
 905             rel_change_long = round(
 906                 ((last_avg - max_long_avg) / max_long_avg) * 1e2, 2)
 907
 908         if classification_lst:
 909             if isnan(rel_change_last) and isnan(rel_change_long):
 910                 continue
 911             if isnan(last_avg) or isnan(rel_change_last) or \
 912                     isnan(rel_change_long):
 913                 continue
 914             tbl_lst.append(
 915                 [tbl_dict[tst_name][u"name"],
 916                  round(last_avg / 1e6, 2),
 917                  rel_change_last,
 918                  rel_change_long,
 919                  classification_lst[-win_size+1:].count(u"regression"),
 920                  classification_lst[-win_size+1:].count(u"progression")])
 921
 922     tbl_lst.sort(key=lambda rel: rel[0])
 923     tbl_lst.sort(key=lambda rel: rel[3])
 924     tbl_lst.sort(key=lambda rel: rel[2])
 925
 926     tbl_sorted = list()
 927     for nrr in range(table[u"window"], -1, -1):
 928         tbl_reg = [item for item in tbl_lst if item[4] == nrr]
 929         for nrp in range(table[u"window"], -1, -1):
 930             tbl_out = [item for item in tbl_reg if item[5] == nrp]
 931             tbl_sorted.extend(tbl_out)
 932
 933     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
 934
 935     logging.info(f"    Writing file: {file_name}")
 936     with open(file_name, u"wt") as file_handler:
 937         file_handler.write(header_str)
 938         for test in tbl_sorted:
 939             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
 940
 941     logging.info(f"    Writing file: {table[u'output-file']}.txt")
 942     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
 943
 944
 945 def _generate_url(testbed, test_name):
 946     """Generate URL to a trending plot from the name of the test case.
 947
 948     :param testbed: The testbed used for testing.
 949     :param test_name: The name of the test case.
 950     :type testbed: str
 951     :type test_name: str
 952     :returns: The URL to the plot with the trending data for the given test
 953         case.
 954     :rtype str
 955     """
 956
 957     if u"x520" in test_name:
 958         nic = u"x520"
 959     elif u"x710" in test_name:
 960         nic = u"x710"
 961     elif u"xl710" in test_name:
 962         nic = u"xl710"
 963     elif u"xxv710" in test_name:
 964         nic = u"xxv710"
 965     elif u"vic1227" in test_name:
 966         nic = u"vic1227"
 967     elif u"vic1385" in test_name:
 968         nic = u"vic1385"
 969     elif u"x553" in test_name:
 970         nic = u"x553"
 971     elif u"cx556" in test_name or u"cx556a" in test_name:
 972         nic = u"cx556a"
 973     else:
 974         nic = u""
 975
 976     if u"64b" in test_name:
 977         frame_size = u"64b"
 978     elif u"78b" in test_name:
 979         frame_size = u"78b"
 980     elif u"imix" in test_name:
 981         frame_size = u"imix"
 982     elif u"9000b" in test_name:
 983         frame_size = u"9000b"
 984     elif u"1518b" in test_name:
 985         frame_size = u"1518b"
 986     elif u"114b" in test_name:
 987         frame_size = u"114b"
 988     else:
 989         frame_size = u""
 990
 991     if u"1t1c" in test_name or \
 992         (u"-1c-" in test_name and
 993          testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 994         cores = u"1t1c"
 995     elif u"2t2c" in test_name or \
 996          (u"-2c-" in test_name and
 997           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
 998         cores = u"2t2c"
 999     elif u"4t4c" in test_name or \
1000          (u"-4c-" in test_name and
1001           testbed in (u"3n-hsw", u"3n-tsh", u"2n-dnv", u"3n-dnv")):
1002         cores = u"4t4c"
1003     elif u"2t1c" in test_name or \
1004          (u"-1c-" in test_name and
1005           testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1006         cores = u"2t1c"
1007     elif u"4t2c" in test_name or \
1008          (u"-2c-" in test_name and
1009           testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1010         cores = u"4t2c"
1011     elif u"8t4c" in test_name or \
1012          (u"-4c-" in test_name and
1013           testbed in (u"2n-skx", u"3n-skx", u"2n-clx", u"2n-zn2")):
1014         cores = u"8t4c"
1015     else:
1016         cores = u""
1017
1018     if u"testpmd" in test_name:
1019         driver = u"testpmd"
1020     elif u"l3fwd" in test_name:
1021         driver = u"l3fwd"
1022     elif u"avf" in test_name:
1023         driver = u"avf"
1024     elif u"rdma" in test_name:
1025         driver = u"rdma"
1026     elif u"dnv" in testbed or u"tsh" in testbed:
1027         driver = u"ixgbe"
1028     else:
1029         driver = u"dpdk"
1030
1031     if u"macip-iacl1s" in test_name:
1032         bsf = u"features-macip-iacl1"
1033     elif u"macip-iacl10s" in test_name:
1034         bsf = u"features-macip-iacl10"
1035     elif u"macip-iacl50s" in test_name:
1036         bsf = u"features-macip-iacl50"
1037     elif u"iacl1s" in test_name:
1038         bsf = u"features-iacl1"
1039     elif u"iacl10s" in test_name:
1040         bsf = u"features-iacl10"
1041     elif u"iacl50s" in test_name:
1042         bsf = u"features-iacl50"
1043     elif u"oacl1s" in test_name:
1044         bsf = u"features-oacl1"
1045     elif u"oacl10s" in test_name:
1046         bsf = u"features-oacl10"
1047     elif u"oacl50s" in test_name:
1048         bsf = u"features-oacl50"
1049     elif u"nat44det" in test_name:
1050         bsf = u"nat44det-bidir"
1051     elif u"nat44ed" in test_name and u"udir" in test_name:
1052         bsf = u"nat44ed-udir"
1053     elif u"-cps" in test_name and u"ethip4udp" in test_name:
1054         bsf = u"udp-cps"
1055     elif u"-cps" in test_name and u"ethip4tcp" in test_name:
1056         bsf = u"tcp-cps"
1057     elif u"-pps" in test_name and u"ethip4udp" in test_name:
1058         bsf = u"udp-pps"
1059     elif u"-pps" in test_name and u"ethip4tcp" in test_name:
1060         bsf = u"tcp-pps"
1061     elif u"udpsrcscale" in test_name:
1062         bsf = u"features-udp"
1063     elif u"iacl" in test_name:
1064         bsf = u"features"
1065     elif u"policer" in test_name:
1066         bsf = u"features"
1067     elif u"adl" in test_name:
1068         bsf = u"features"
1069     elif u"cop" in test_name:
1070         bsf = u"features"
1071     elif u"nat" in test_name:
1072         bsf = u"features"
1073     elif u"macip" in test_name:
1074         bsf = u"features"
1075     elif u"scale" in test_name:
1076         bsf = u"scale"
1077     elif u"base" in test_name:
1078         bsf = u"base"
1079     else:
1080         bsf = u"base"
1081
1082     if u"114b" in test_name and u"vhost" in test_name:
1083         domain = u"vts"
1084     elif u"nat44" in test_name or u"-pps" in test_name or u"-cps" in test_name:
1085         domain = u"nat44"
1086         if u"nat44det" in test_name:
1087             domain += u"-det-bidir"
1088         else:
1089             domain += u"-ed"
1090         if u"udir" in test_name:
1091             domain += u"-unidir"
1092         elif u"-ethip4udp-" in test_name:
1093             domain += u"-udp"
1094         elif u"-ethip4tcp-" in test_name:
1095             domain += u"-tcp"
1096         if u"-cps" in test_name:
1097             domain += u"-cps"
1098         elif u"-pps" in test_name:
1099             domain += u"-pps"
1100     elif u"testpmd" in test_name or u"l3fwd" in test_name:
1101         domain = u"dpdk"
1102     elif u"memif" in test_name:
1103         domain = u"container_memif"
1104     elif u"srv6" in test_name:
1105         domain = u"srv6"
1106     elif u"vhost" in test_name:
1107         domain = u"vhost"
1108         if u"vppl2xc" in test_name:
1109             driver += u"-vpp"
1110         else:
1111             driver += u"-testpmd"
1112         if u"lbvpplacp" in test_name:
1113             bsf += u"-link-bonding"
1114     elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1115         domain = u"nf_service_density_vnfc"
1116     elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1117         domain = u"nf_service_density_cnfc"
1118     elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1119         domain = u"nf_service_density_cnfp"
1120     elif u"ipsec" in test_name:
1121         domain = u"ipsec"
1122         if u"sw" in test_name:
1123             bsf += u"-sw"
1124         elif u"hw" in test_name:
1125             bsf += u"-hw"
1126     elif u"ethip4vxlan" in test_name:
1127         domain = u"ip4_tunnels"
1128     elif u"ethip4udpgeneve" in test_name:
1129         domain = u"ip4_tunnels"
1130     elif u"ip4base" in test_name or u"ip4scale" in test_name:
1131         domain = u"ip4"
1132     elif u"ip6base" in test_name or u"ip6scale" in test_name:
1133         domain = u"ip6"
1134     elif u"l2xcbase" in test_name or \
1135             u"l2xcscale" in test_name or \
1136             u"l2bdbasemaclrn" in test_name or \
1137             u"l2bdscale" in test_name or \
1138             u"l2patch" in test_name:
1139         domain = u"l2"
1140     else:
1141         domain = u""
1142
1143     file_name = u"-".join((domain, testbed, nic)) + u".html#"
1144     anchor_name = u"-".join((frame_size, cores, bsf, driver))
1145
1146     return file_name + anchor_name
1147
1148
1149 def table_perf_trending_dash_html(table, input_data):
1150     """Generate the table(s) with algorithm:
1151     table_perf_trending_dash_html specified in the specification
1152     file.
1153
1154     :param table: Table to generate.
1155     :param input_data: Data to process.
1156     :type table: dict
1157     :type input_data: InputData
1158     """
1159
1160     _ = input_data
1161
1162     if not table.get(u"testbed", None):
1163         logging.error(
1164             f"The testbed is not defined for the table "
1165             f"{table.get(u'title', u'')}. Skipping."
1166         )
1167         return
1168
1169     test_type = table.get(u"test-type", u"MRR")
1170     if test_type not in (u"MRR", u"NDR", u"PDR"):
1171         logging.error(
1172             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1173             f"Skipping."
1174         )
1175         return
1176
1177     if test_type in (u"NDR", u"PDR"):
1178         lnk_dir = u"../ndrpdr_trending/"
1179         lnk_sufix = f"-{test_type.lower()}"
1180     else:
1181         lnk_dir = u"../trending/"
1182         lnk_sufix = u""
1183
1184     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1185
1186     try:
1187         with open(table[u"input-file"], u'rt') as csv_file:
1188             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1189     except KeyError:
1190         logging.warning(u"The input file is not defined.")
1191         return
1192     except csv.Error as err:
1193         logging.warning(
1194             f"Not possible to process the file {table[u'input-file']}.\n"
1195             f"{repr(err)}"
1196         )
1197         return
1198
1199     # Table:
1200     dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1201
1202     # Table header:
1203     trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1204     for idx, item in enumerate(csv_lst[0]):
1205         alignment = u"left" if idx == 0 else u"center"
1206         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1207         thead.text = item
1208
1209     # Rows:
1210     colors = {
1211         u"regression": (
1212             u"#ffcccc",
1213             u"#ff9999"
1214         ),
1215         u"progression": (
1216             u"#c6ecc6",
1217             u"#9fdf9f"
1218         ),
1219         u"normal": (
1220             u"#e9f1fb",
1221             u"#d4e4f7"
1222         )
1223     }
1224     for r_idx, row in enumerate(csv_lst[1:]):
1225         if int(row[4]):
1226             color = u"regression"
1227         elif int(row[5]):
1228             color = u"progression"
1229         else:
1230             color = u"normal"
1231         trow = ET.SubElement(
1232             dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1233         )
1234
1235         # Columns:
1236         for c_idx, item in enumerate(row):
1237             tdata = ET.SubElement(
1238                 trow,
1239                 u"td",
1240                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1241             )
1242             # Name:
1243             if c_idx == 0 and table.get(u"add-links", True):
1244                 ref = ET.SubElement(
1245                     tdata,
1246                     u"a",
1247                     attrib=dict(
1248                         href=f"{lnk_dir}"
1249                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1250                              f"{lnk_sufix}"
1251                     )
1252                 )
1253                 ref.text = item
1254             else:
1255                 tdata.text = item
1256     try:
1257         with open(table[u"output-file"], u'w') as html_file:
1258             logging.info(f"    Writing file: {table[u'output-file']}")
1259             html_file.write(u".. raw:: html\n\n\t")
1260             html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1261             html_file.write(u"\n\t<p><br><br></p>\n")
1262     except KeyError:
1263         logging.warning(u"The output file is not defined.")
1264         return
1265
1266
1267 def table_last_failed_tests(table, input_data):
1268     """Generate the table(s) with algorithm: table_last_failed_tests
1269     specified in the specification file.
1270
1271     :param table: Table to generate.
1272     :param input_data: Data to process.
1273     :type table: pandas.Series
1274     :type input_data: InputData
1275     """
1276
1277     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1278
1279     # Transform the data
1280     logging.info(
1281         f"    Creating the data set for the {table.get(u'type', u'')} "
1282         f"{table.get(u'title', u'')}."
1283     )
1284
1285     data = input_data.filter_data(table, continue_on_error=True)
1286
1287     if data is None or data.empty:
1288         logging.warning(
1289             f"    No data for the {table.get(u'type', u'')} "
1290             f"{table.get(u'title', u'')}."
1291         )
1292         return
1293
1294     tbl_list = list()
1295     for job, builds in table[u"data"].items():
1296         for build in builds:
1297             build = str(build)
1298             try:
1299                 version = input_data.metadata(job, build).get(u"version", u"")
1300             except KeyError:
1301                 logging.error(f"Data for {job}: {build} is not present.")
1302                 return
1303             tbl_list.append(build)
1304             tbl_list.append(version)
1305             failed_tests = list()
1306             passed = 0
1307             failed = 0
1308             for tst_data in data[job][build].values:
1309                 if tst_data[u"status"] != u"FAIL":
1310                     passed += 1
1311                     continue
1312                 failed += 1
1313                 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1314                 if not groups:
1315                     continue
1316                 nic = groups.group(0)
1317                 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1318             tbl_list.append(str(passed))
1319             tbl_list.append(str(failed))
1320             tbl_list.extend(failed_tests)
1321
1322     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1323     logging.info(f"    Writing file: {file_name}")
1324     with open(file_name, u"wt") as file_handler:
1325         for test in tbl_list:
1326             file_handler.write(test + u'\n')
1327
1328
1329 def table_failed_tests(table, input_data):
1330     """Generate the table(s) with algorithm: table_failed_tests
1331     specified in the specification file.
1332
1333     :param table: Table to generate.
1334     :param input_data: Data to process.
1335     :type table: pandas.Series
1336     :type input_data: InputData
1337     """
1338
1339     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1340
1341     # Transform the data
1342     logging.info(
1343         f"    Creating the data set for the {table.get(u'type', u'')} "
1344         f"{table.get(u'title', u'')}."
1345     )
1346     data = input_data.filter_data(table, continue_on_error=True)
1347
1348     test_type = u"MRR"
1349     if u"NDRPDR" in table.get(u"filter", list()):
1350         test_type = u"NDRPDR"
1351
1352     # Prepare the header of the tables
1353     header = [
1354         u"Test Case",
1355         u"Failures [#]",
1356         u"Last Failure [Time]",
1357         u"Last Failure [VPP-Build-Id]",
1358         u"Last Failure [CSIT-Job-Build-Id]"
1359     ]
1360
1361     # Generate the data for the table according to the model in the table
1362     # specification
1363
1364     now = dt.utcnow()
1365     timeperiod = timedelta(int(table.get(u"window", 7)))
1366
1367     tbl_dict = dict()
1368     for job, builds in table[u"data"].items():
1369         for build in builds:
1370             build = str(build)
1371             for tst_name, tst_data in data[job][build].items():
1372                 if tst_name.lower() in table.get(u"ignore-list", list()):
1373                     continue
1374                 if tbl_dict.get(tst_name, None) is None:
1375                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
1376                     if not groups:
1377                         continue
1378                     nic = groups.group(0)
1379                     tbl_dict[tst_name] = {
1380                         u"name": f"{nic}-{tst_data[u'name']}",
1381                         u"data": OrderedDict()
1382                     }
1383                 try:
1384                     generated = input_data.metadata(job, build).\
1385                         get(u"generated", u"")
1386                     if not generated:
1387                         continue
1388                     then = dt.strptime(generated, u"%Y%m%d %H:%M")
1389                     if (now - then) <= timeperiod:
1390                         tbl_dict[tst_name][u"data"][build] = (
1391                             tst_data[u"status"],
1392                             generated,
1393                             input_data.metadata(job, build).get(u"version",
1394                                                                 u""),
1395                             build
1396                         )
1397                 except (TypeError, KeyError) as err:
1398                     logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1399
1400     max_fails = 0
1401     tbl_lst = list()
1402     for tst_data in tbl_dict.values():
1403         fails_nr = 0
1404         fails_last_date = u""
1405         fails_last_vpp = u""
1406         fails_last_csit = u""
1407         for val in tst_data[u"data"].values():
1408             if val[0] == u"FAIL":
1409                 fails_nr += 1
1410                 fails_last_date = val[1]
1411                 fails_last_vpp = val[2]
1412                 fails_last_csit = val[3]
1413         if fails_nr:
1414             max_fails = fails_nr if fails_nr > max_fails else max_fails
1415             tbl_lst.append([
1416                 tst_data[u"name"],
1417                 fails_nr,
1418                 fails_last_date,
1419                 fails_last_vpp,
1420                 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1421                 f"-build-{fails_last_csit}"
1422             ])
1423
1424     tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1425     tbl_sorted = list()
1426     for nrf in range(max_fails, -1, -1):
1427         tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1428         tbl_sorted.extend(tbl_fails)
1429
1430     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1431     logging.info(f"    Writing file: {file_name}")
1432     with open(file_name, u"wt") as file_handler:
1433         file_handler.write(u",".join(header) + u"\n")
1434         for test in tbl_sorted:
1435             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1436
1437     logging.info(f"    Writing file: {table[u'output-file']}.txt")
1438     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1439
1440
1441 def table_failed_tests_html(table, input_data):
1442     """Generate the table(s) with algorithm: table_failed_tests_html
1443     specified in the specification file.
1444
1445     :param table: Table to generate.
1446     :param input_data: Data to process.
1447     :type table: pandas.Series
1448     :type input_data: InputData
1449     """
1450
1451     _ = input_data
1452
1453     if not table.get(u"testbed", None):
1454         logging.error(
1455             f"The testbed is not defined for the table "
1456             f"{table.get(u'title', u'')}. Skipping."
1457         )
1458         return
1459
1460     test_type = table.get(u"test-type", u"MRR")
1461     if test_type not in (u"MRR", u"NDR", u"PDR", u"NDRPDR"):
1462         logging.error(
1463             f"Test type {table.get(u'test-type', u'MRR')} is not defined. "
1464             f"Skipping."
1465         )
1466         return
1467
1468     if test_type in (u"NDRPDR", u"NDR", u"PDR"):
1469         lnk_dir = u"../ndrpdr_trending/"
1470         lnk_sufix = u"-pdr"
1471     else:
1472         lnk_dir = u"../trending/"
1473         lnk_sufix = u""
1474
1475     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1476
1477     try:
1478         with open(table[u"input-file"], u'rt') as csv_file:
1479             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1480     except KeyError:
1481         logging.warning(u"The input file is not defined.")
1482         return
1483     except csv.Error as err:
1484         logging.warning(
1485             f"Not possible to process the file {table[u'input-file']}.\n"
1486             f"{repr(err)}"
1487         )
1488         return
1489
1490     # Table:
1491     failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1492
1493     # Table header:
1494     trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1495     for idx, item in enumerate(csv_lst[0]):
1496         alignment = u"left" if idx == 0 else u"center"
1497         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1498         thead.text = item
1499
1500     # Rows:
1501     colors = (u"#e9f1fb", u"#d4e4f7")
1502     for r_idx, row in enumerate(csv_lst[1:]):
1503         background = colors[r_idx % 2]
1504         trow = ET.SubElement(
1505             failed_tests, u"tr", attrib=dict(bgcolor=background)
1506         )
1507
1508         # Columns:
1509         for c_idx, item in enumerate(row):
1510             tdata = ET.SubElement(
1511                 trow,
1512                 u"td",
1513                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1514             )
1515             # Name:
1516             if c_idx == 0 and table.get(u"add-links", True):
1517                 ref = ET.SubElement(
1518                     tdata,
1519                     u"a",
1520                     attrib=dict(
1521                         href=f"{lnk_dir}"
1522                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1523                              f"{lnk_sufix}"
1524                     )
1525                 )
1526                 ref.text = item
1527             else:
1528                 tdata.text = item
1529     try:
1530         with open(table[u"output-file"], u'w') as html_file:
1531             logging.info(f"    Writing file: {table[u'output-file']}")
1532             html_file.write(u".. raw:: html\n\n\t")
1533             html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1534             html_file.write(u"\n\t<p><br><br></p>\n")
1535     except KeyError:
1536         logging.warning(u"The output file is not defined.")
1537         return
1538
1539
1540 def table_comparison(table, input_data):
1541     """Generate the table(s) with algorithm: table_comparison
1542     specified in the specification file.
1543
1544     :param table: Table to generate.
1545     :param input_data: Data to process.
1546     :type table: pandas.Series
1547     :type input_data: InputData
1548     """
1549     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1550
1551     # Transform the data
1552     logging.info(
1553         f"    Creating the data set for the {table.get(u'type', u'')} "
1554         f"{table.get(u'title', u'')}."
1555     )
1556
1557     columns = table.get(u"columns", None)
1558     if not columns:
1559         logging.error(
1560             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1561         )
1562         return
1563
1564     cols = list()
1565     for idx, col in enumerate(columns):
1566         if col.get(u"data-set", None) is None:
1567             logging.warning(f"No data for column {col.get(u'title', u'')}")
1568             continue
1569         tag = col.get(u"tag", None)
1570         data = input_data.filter_data(
1571             table,
1572             params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1573             data=col[u"data-set"],
1574             continue_on_error=True
1575         )
1576         col_data = {
1577             u"title": col.get(u"title", f"Column{idx}"),
1578             u"data": dict()
1579         }
1580         for builds in data.values:
1581             for build in builds:
1582                 for tst_name, tst_data in build.items():
1583                     if tag and tag not in tst_data[u"tags"]:
1584                         continue
1585                     tst_name_mod = \
1586                         _tpc_modify_test_name(tst_name, ignore_nic=True).\
1587                         replace(u"2n1l-", u"")
1588                     if col_data[u"data"].get(tst_name_mod, None) is None:
1589                         name = tst_data[u'name'].rsplit(u'-', 1)[0]
1590                         if u"across testbeds" in table[u"title"].lower() or \
1591                                 u"across topologies" in table[u"title"].lower():
1592                             name = _tpc_modify_displayed_test_name(name)
1593                         col_data[u"data"][tst_name_mod] = {
1594                             u"name": name,
1595                             u"replace": True,
1596                             u"data": list(),
1597                             u"mean": None,
1598                             u"stdev": None
1599                         }
1600                     _tpc_insert_data(
1601                         target=col_data[u"data"][tst_name_mod],
1602                         src=tst_data,
1603                         include_tests=table[u"include-tests"]
1604                     )
1605
1606         replacement = col.get(u"data-replacement", None)
1607         if replacement:
1608             rpl_data = input_data.filter_data(
1609                 table,
1610                 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1611                 data=replacement,
1612                 continue_on_error=True
1613             )
1614             for builds in rpl_data.values:
1615                 for build in builds:
1616                     for tst_name, tst_data in build.items():
1617                         if tag and tag not in tst_data[u"tags"]:
1618                             continue
1619                         tst_name_mod = \
1620                             _tpc_modify_test_name(tst_name, ignore_nic=True).\
1621                             replace(u"2n1l-", u"")
1622                         if col_data[u"data"].get(tst_name_mod, None) is None:
1623                             name = tst_data[u'name'].rsplit(u'-', 1)[0]
1624                             if u"across testbeds" in table[u"title"].lower() \
1625                                     or u"across topologies" in \
1626                                     table[u"title"].lower():
1627                                 name = _tpc_modify_displayed_test_name(name)
1628                             col_data[u"data"][tst_name_mod] = {
1629                                 u"name": name,
1630                                 u"replace": False,
1631                                 u"data": list(),
1632                                 u"mean": None,
1633                                 u"stdev": None
1634                             }
1635                         if col_data[u"data"][tst_name_mod][u"replace"]:
1636                             col_data[u"data"][tst_name_mod][u"replace"] = False
1637                             col_data[u"data"][tst_name_mod][u"data"] = list()
1638                         _tpc_insert_data(
1639                             target=col_data[u"data"][tst_name_mod],
1640                             src=tst_data,
1641                             include_tests=table[u"include-tests"]
1642                         )
1643
1644         if table[u"include-tests"] in (u"NDR", u"PDR"):
1645             for tst_name, tst_data in col_data[u"data"].items():
1646                 if tst_data[u"data"]:
1647                     tst_data[u"mean"] = mean(tst_data[u"data"])
1648                     tst_data[u"stdev"] = stdev(tst_data[u"data"])
1649
1650         cols.append(col_data)
1651
1652     tbl_dict = dict()
1653     for col in cols:
1654         for tst_name, tst_data in col[u"data"].items():
1655             if tbl_dict.get(tst_name, None) is None:
1656                 tbl_dict[tst_name] = {
1657                     "name": tst_data[u"name"]
1658                 }
1659             tbl_dict[tst_name][col[u"title"]] = {
1660                 u"mean": tst_data[u"mean"],
1661                 u"stdev": tst_data[u"stdev"]
1662             }
1663
1664     if not tbl_dict:
1665         logging.warning(f"No data for table {table.get(u'title', u'')}!")
1666         return
1667
1668     tbl_lst = list()
1669     for tst_data in tbl_dict.values():
1670         row = [tst_data[u"name"], ]
1671         for col in cols:
1672             row.append(tst_data.get(col[u"title"], None))
1673         tbl_lst.append(row)
1674
1675     comparisons = table.get(u"comparisons", None)
1676     rcas = list()
1677     if comparisons and isinstance(comparisons, list):
1678         for idx, comp in enumerate(comparisons):
1679             try:
1680                 col_ref = int(comp[u"reference"])
1681                 col_cmp = int(comp[u"compare"])
1682             except KeyError:
1683                 logging.warning(u"Comparison: No references defined! Skipping.")
1684                 comparisons.pop(idx)
1685                 continue
1686             if not (0 < col_ref <= len(cols) and 0 < col_cmp <= len(cols) or
1687                     col_ref == col_cmp):
1688                 logging.warning(f"Wrong values of reference={col_ref} "
1689                                 f"and/or compare={col_cmp}. Skipping.")
1690                 comparisons.pop(idx)
1691                 continue
1692             rca_file_name = comp.get(u"rca-file", None)
1693             if rca_file_name:
1694                 try:
1695                     with open(rca_file_name, u"r") as file_handler:
1696                         rcas.append(
1697                             {
1698                                 u"title": f"RCA{idx + 1}",
1699                                 u"data": load(file_handler, Loader=FullLoader)
1700                             }
1701                         )
1702                 except (YAMLError, IOError) as err:
1703                     logging.warning(
1704                         f"The RCA file {rca_file_name} does not exist or "
1705                         f"it is corrupted!"
1706                     )
1707                     logging.debug(repr(err))
1708                     rcas.append(None)
1709             else:
1710                 rcas.append(None)
1711     else:
1712         comparisons = None
1713
1714     tbl_cmp_lst = list()
1715     if comparisons:
1716         for row in tbl_lst:
1717             new_row = deepcopy(row)
1718             for comp in comparisons:
1719                 ref_itm = row[int(comp[u"reference"])]
1720                 if ref_itm is None and \
1721                         comp.get(u"reference-alt", None) is not None:
1722                     ref_itm = row[int(comp[u"reference-alt"])]
1723                 cmp_itm = row[int(comp[u"compare"])]
1724                 if ref_itm is not None and cmp_itm is not None and \
1725                         ref_itm[u"mean"] is not None and \
1726                         cmp_itm[u"mean"] is not None and \
1727                         ref_itm[u"stdev"] is not None and \
1728                         cmp_itm[u"stdev"] is not None:
1729                     delta, d_stdev = relative_change_stdev(
1730                         ref_itm[u"mean"], cmp_itm[u"mean"],
1731                         ref_itm[u"stdev"], cmp_itm[u"stdev"]
1732                     )
1733                     if delta is None:
1734                         break
1735                     new_row.append({
1736                         u"mean": delta * 1e6,
1737                         u"stdev": d_stdev * 1e6
1738                     })
1739                 else:
1740                     break
1741             else:
1742                 tbl_cmp_lst.append(new_row)
1743
1744     try:
1745         tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1746         tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1747     except TypeError as err:
1748         logging.warning(f"Empty data element in table\n{tbl_cmp_lst}\n{err}")
1749
1750     tbl_for_csv = list()
1751     for line in tbl_cmp_lst:
1752         row = [line[0], ]
1753         for idx, itm in enumerate(line[1:]):
1754             if itm is None or not isinstance(itm, dict) or\
1755                     itm.get(u'mean', None) is None or \
1756                     itm.get(u'stdev', None) is None:
1757                 row.append(u"NT")
1758                 row.append(u"NT")
1759             else:
1760                 row.append(round(float(itm[u'mean']) / 1e6, 3))
1761                 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1762         for rca in rcas:
1763             if rca is None:
1764                 continue
1765             rca_nr = rca[u"data"].get(row[0], u"-")
1766             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1767         tbl_for_csv.append(row)
1768
1769     header_csv = [u"Test Case", ]
1770     for col in cols:
1771         header_csv.append(f"Avg({col[u'title']})")
1772         header_csv.append(f"Stdev({col[u'title']})")
1773     for comp in comparisons:
1774         header_csv.append(
1775             f"Avg({comp.get(u'title', u'')})"
1776         )
1777         header_csv.append(
1778             f"Stdev({comp.get(u'title', u'')})"
1779         )
1780     for rca in rcas:
1781         if rca:
1782             header_csv.append(rca[u"title"])
1783
1784     legend_lst = table.get(u"legend", None)
1785     if legend_lst is None:
1786         legend = u""
1787     else:
1788         legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1789
1790     footnote = u""
1791     if rcas and any(rcas):
1792         footnote += u"\nRoot Cause Analysis:\n"
1793         for rca in rcas:
1794             if rca:
1795                 footnote += f"{rca[u'data'].get(u'footnote', u'')}\n"
1796
1797     csv_file_name = f"{table[u'output-file']}-csv.csv"
1798     with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1799         file_handler.write(
1800             u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1801         )
1802         for test in tbl_for_csv:
1803             file_handler.write(
1804                 u",".join([f'"{item}"' for item in test]) + u"\n"
1805             )
1806         if legend_lst:
1807             for item in legend_lst:
1808                 file_handler.write(f'"{item}"\n')
1809         if footnote:
1810             for itm in footnote.split(u"\n"):
1811                 file_handler.write(f'"{itm}"\n')
1812
1813     tbl_tmp = list()
1814     max_lens = [0, ] * len(tbl_cmp_lst[0])
1815     for line in tbl_cmp_lst:
1816         row = [line[0], ]
1817         for idx, itm in enumerate(line[1:]):
1818             if itm is None or not isinstance(itm, dict) or \
1819                     itm.get(u'mean', None) is None or \
1820                     itm.get(u'stdev', None) is None:
1821                 new_itm = u"NT"
1822             else:
1823                 if idx < len(cols):
1824                     new_itm = (
1825                         f"{round(float(itm[u'mean']) / 1e6, 1)} "
1826                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1827                         replace(u"nan", u"NaN")
1828                     )
1829                 else:
1830                     new_itm = (
1831                         f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1832                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1833                         replace(u"nan", u"NaN")
1834                     )
1835             if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1836                 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1837             row.append(new_itm)
1838
1839         tbl_tmp.append(row)
1840
1841     header = [u"Test Case", ]
1842     header.extend([col[u"title"] for col in cols])
1843     header.extend([comp.get(u"title", u"") for comp in comparisons])
1844
1845     tbl_final = list()
1846     for line in tbl_tmp:
1847         row = [line[0], ]
1848         for idx, itm in enumerate(line[1:]):
1849             if itm in (u"NT", u"NaN"):
1850                 row.append(itm)
1851                 continue
1852             itm_lst = itm.rsplit(u"\u00B1", 1)
1853             itm_lst[-1] = \
1854                 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1855             itm_str = u"\u00B1".join(itm_lst)
1856
1857             if idx >= len(cols):
1858                 # Diffs
1859                 rca = rcas[idx - len(cols)]
1860                 if rca:
1861                     # Add rcas to diffs
1862                     rca_nr = rca[u"data"].get(row[0], None)
1863                     if rca_nr:
1864                         hdr_len = len(header[idx + 1]) - 1
1865                         if hdr_len < 19:
1866                             hdr_len = 19
1867                         rca_nr = f"[{rca_nr}]"
1868                         itm_str = (
1869                             f"{u' ' * (4 - len(rca_nr))}{rca_nr}"
1870                             f"{u' ' * (hdr_len - 4 - len(itm_str))}"
1871                             f"{itm_str}"
1872                         )
1873             row.append(itm_str)
1874         tbl_final.append(row)
1875
1876     # Generate csv tables:
1877     csv_file_name = f"{table[u'output-file']}.csv"
1878     logging.info(f"    Writing the file {csv_file_name}")
1879     with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
1880         file_handler.write(u";".join(header) + u"\n")
1881         for test in tbl_final:
1882             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1883
1884     # Generate txt table:
1885     txt_file_name = f"{table[u'output-file']}.txt"
1886     logging.info(f"    Writing the file {txt_file_name}")
1887     convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u";")
1888
1889     with open(txt_file_name, u'a', encoding='utf-8') as file_handler:
1890         file_handler.write(legend)
1891         file_handler.write(footnote)
1892
1893     # Generate html table:
1894     _tpc_generate_html_table(
1895         header,
1896         tbl_final,
1897         table[u'output-file'],
1898         legend=legend,
1899         footnote=footnote,
1900         sort_data=False,
1901         title=table.get(u"title", u"")
1902     )
1903
1904
1905 def table_weekly_comparison(table, in_data):
1906     """Generate the table(s) with algorithm: table_weekly_comparison
1907     specified in the specification file.
1908
1909     :param table: Table to generate.
1910     :param in_data: Data to process.
1911     :type table: pandas.Series
1912     :type in_data: InputData
1913     """
1914     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1915
1916     # Transform the data
1917     logging.info(
1918         f"    Creating the data set for the {table.get(u'type', u'')} "
1919         f"{table.get(u'title', u'')}."
1920     )
1921
1922     incl_tests = table.get(u"include-tests", None)
1923     if incl_tests not in (u"NDR", u"PDR"):
1924         logging.error(f"Wrong tests to include specified ({incl_tests}).")
1925         return
1926
1927     nr_cols = table.get(u"nr-of-data-columns", None)
1928     if not nr_cols or nr_cols < 2:
1929         logging.error(
1930             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1931         )
1932         return
1933
1934     data = in_data.filter_data(
1935         table,
1936         params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1937         continue_on_error=True
1938     )
1939
1940     header = [
1941         [u"VPP Version", ],
1942         [u"Start Timestamp", ],
1943         [u"CSIT Build", ],
1944         [u"CSIT Testbed", ]
1945     ]
1946     tbl_dict = dict()
1947     idx = 0
1948     tb_tbl = table.get(u"testbeds", None)
1949     for job_name, job_data in data.items():
1950         for build_nr, build in job_data.items():
1951             if idx >= nr_cols:
1952                 break
1953             if build.empty:
1954                 continue
1955
1956             tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1957             if tb_ip and tb_tbl:
1958                 testbed = tb_tbl.get(tb_ip, u"")
1959             else:
1960                 testbed = u""
1961             header[2].insert(1, build_nr)
1962             header[3].insert(1, testbed)
1963             header[1].insert(
1964                 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1965             )
1966             header[0].insert(
1967                 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1968             )
1969
1970             for tst_name, tst_data in build.items():
1971                 tst_name_mod = \
1972                     _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1973                 if not tbl_dict.get(tst_name_mod, None):
1974                     tbl_dict[tst_name_mod] = dict(
1975                         name=tst_data[u'name'].rsplit(u'-', 1)[0],
1976                     )
1977                 try:
1978                     tbl_dict[tst_name_mod][-idx - 1] = \
1979                         tst_data[u"throughput"][incl_tests][u"LOWER"]
1980                 except (TypeError, IndexError, KeyError, ValueError):
1981                     pass
1982             idx += 1
1983
1984     if idx < nr_cols:
1985         logging.error(u"Not enough data to build the table! Skipping")
1986         return
1987
1988     cmp_dict = dict()
1989     for idx, cmp in enumerate(table.get(u"comparisons", list())):
1990         idx_ref = cmp.get(u"reference", None)
1991         idx_cmp = cmp.get(u"compare", None)
1992         if idx_ref is None or idx_cmp is None:
1993             continue
1994         header[0].append(
1995             f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1996             f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1997         )
1998         header[1].append(u"")
1999         header[2].append(u"")
2000         header[3].append(u"")
2001         for tst_name, tst_data in tbl_dict.items():
2002             if not cmp_dict.get(tst_name, None):
2003                 cmp_dict[tst_name] = list()
2004             ref_data = tst_data.get(idx_ref, None)
2005             cmp_data = tst_data.get(idx_cmp, None)
2006             if ref_data is None or cmp_data is None:
2007                 cmp_dict[tst_name].append(float(u'nan'))
2008             else:
2009                 cmp_dict[tst_name].append(
2010                     relative_change(ref_data, cmp_data)
2011                 )
2012
2013     tbl_lst_none = list()
2014     tbl_lst = list()
2015     for tst_name, tst_data in tbl_dict.items():
2016         itm_lst = [tst_data[u"name"], ]
2017         for idx in range(nr_cols):
2018             item = tst_data.get(-idx - 1, None)
2019             if item is None:
2020                 itm_lst.insert(1, None)
2021             else:
2022                 itm_lst.insert(1, round(item / 1e6, 1))
2023         itm_lst.extend(
2024             [
2025                 None if itm is None else round(itm, 1)
2026                 for itm in cmp_dict[tst_name]
2027             ]
2028         )
2029         if str(itm_lst[-1]) == u"nan" or itm_lst[-1] is None:
2030             tbl_lst_none.append(itm_lst)
2031         else:
2032             tbl_lst.append(itm_lst)
2033
2034     tbl_lst_none.sort(key=lambda rel: rel[0], reverse=False)
2035     tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
2036     tbl_lst.sort(key=lambda rel: rel[-1], reverse=False)
2037     tbl_lst.extend(tbl_lst_none)
2038
2039     # Generate csv table:
2040     csv_file_name = f"{table[u'output-file']}.csv"
2041     logging.info(f"    Writing the file {csv_file_name}")
2042     with open(csv_file_name, u"wt", encoding='utf-8') as file_handler:
2043         for hdr in header:
2044             file_handler.write(u",".join(hdr) + u"\n")
2045         for test in tbl_lst:
2046             file_handler.write(u",".join(
2047                 [
2048                     str(item).replace(u"None", u"-").replace(u"nan", u"-").
2049                     replace(u"null", u"-") for item in test
2050                 ]
2051             ) + u"\n")
2052
2053     txt_file_name = f"{table[u'output-file']}.txt"
2054     logging.info(f"    Writing the file {txt_file_name}")
2055     convert_csv_to_pretty_txt(csv_file_name, txt_file_name, delimiter=u",")
2056
2057     # Reorganize header in txt table
2058     txt_table = list()
2059     with open(txt_file_name, u"rt", encoding='utf-8') as file_handler:
2060         for line in file_handler:
2061             txt_table.append(line)
2062     try:
2063         txt_table.insert(5, txt_table.pop(2))
2064         with open(txt_file_name, u"wt", encoding='utf-8') as file_handler:
2065             file_handler.writelines(txt_table)
2066     except IndexError:
2067         pass
2068
2069     # Generate html table:
2070     hdr_html = [
2071         u"<br>".join(row) for row in zip(*header)
2072     ]
2073     _tpc_generate_html_table(
2074         hdr_html,
2075         tbl_lst,
2076         table[u'output-file'],
2077         sort_data=True,
2078         title=table.get(u"title", u""),
2079         generate_rst=False
2080     )