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"acl" in test_name or \
1011             u"macip" in test_name or \
1012             u"nat" in test_name or \
1013             u"policer" in test_name or \
1014             u"cop" in test_name:
1015         bsf = u"features"
1016     elif u"scale" in test_name:
1017         bsf = u"scale"
1018     elif u"base" in test_name:
1019         bsf = u"base"
1020     else:
1021         bsf = u"base"
1022
1023     if u"114b" in test_name and u"vhost" in test_name:
1024         domain = u"vts"
1025     elif u"testpmd" in test_name or u"l3fwd" in test_name:
1026         domain = u"dpdk"
1027     elif u"memif" in test_name:
1028         domain = u"container_memif"
1029     elif u"srv6" in test_name:
1030         domain = u"srv6"
1031     elif u"vhost" in test_name:
1032         domain = u"vhost"
1033         if u"vppl2xc" in test_name:
1034             driver += u"-vpp"
1035         else:
1036             driver += u"-testpmd"
1037         if u"lbvpplacp" in test_name:
1038             bsf += u"-link-bonding"
1039     elif u"ch" in test_name and u"vh" in test_name and u"vm" in test_name:
1040         domain = u"nf_service_density_vnfc"
1041     elif u"ch" in test_name and u"mif" in test_name and u"dcr" in test_name:
1042         domain = u"nf_service_density_cnfc"
1043     elif u"pl" in test_name and u"mif" in test_name and u"dcr" in test_name:
1044         domain = u"nf_service_density_cnfp"
1045     elif u"ipsec" in test_name:
1046         domain = u"ipsec"
1047         if u"sw" in test_name:
1048             bsf += u"-sw"
1049         elif u"hw" in test_name:
1050             bsf += u"-hw"
1051     elif u"ethip4vxlan" in test_name:
1052         domain = u"ip4_tunnels"
1053     elif u"ip4base" in test_name or u"ip4scale" in test_name:
1054         domain = u"ip4"
1055     elif u"ip6base" in test_name or u"ip6scale" in test_name:
1056         domain = u"ip6"
1057     elif u"l2xcbase" in test_name or \
1058             u"l2xcscale" in test_name or \
1059             u"l2bdbasemaclrn" in test_name or \
1060             u"l2bdscale" in test_name or \
1061             u"l2patch" in test_name:
1062         domain = u"l2"
1063     else:
1064         domain = u""
1065
1066     file_name = u"-".join((domain, testbed, nic)) + u".html#"
1067     anchor_name = u"-".join((frame_size, cores, bsf, driver))
1068
1069     return file_name + anchor_name
1070
1071
1072 def table_perf_trending_dash_html(table, input_data):
1073     """Generate the table(s) with algorithm:
1074     table_perf_trending_dash_html specified in the specification
1075     file.
1076
1077     :param table: Table to generate.
1078     :param input_data: Data to process.
1079     :type table: dict
1080     :type input_data: InputData
1081     """
1082
1083     _ = input_data
1084
1085     if not table.get(u"testbed", None):
1086         logging.error(
1087             f"The testbed is not defined for the table "
1088             f"{table.get(u'title', u'')}."
1089         )
1090         return
1091
1092     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1093
1094     try:
1095         with open(table[u"input-file"], u'rt') as csv_file:
1096             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1097     except KeyError:
1098         logging.warning(u"The input file is not defined.")
1099         return
1100     except csv.Error as err:
1101         logging.warning(
1102             f"Not possible to process the file {table[u'input-file']}.\n"
1103             f"{repr(err)}"
1104         )
1105         return
1106
1107     # Table:
1108     dashboard = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1109
1110     # Table header:
1111     trow = ET.SubElement(dashboard, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1112     for idx, item in enumerate(csv_lst[0]):
1113         alignment = u"left" if idx == 0 else u"center"
1114         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1115         thead.text = item
1116
1117     # Rows:
1118     colors = {
1119         u"regression": (
1120             u"#ffcccc",
1121             u"#ff9999"
1122         ),
1123         u"progression": (
1124             u"#c6ecc6",
1125             u"#9fdf9f"
1126         ),
1127         u"normal": (
1128             u"#e9f1fb",
1129             u"#d4e4f7"
1130         )
1131     }
1132     for r_idx, row in enumerate(csv_lst[1:]):
1133         if int(row[4]):
1134             color = u"regression"
1135         elif int(row[5]):
1136             color = u"progression"
1137         else:
1138             color = u"normal"
1139         trow = ET.SubElement(
1140             dashboard, u"tr", attrib=dict(bgcolor=colors[color][r_idx % 2])
1141         )
1142
1143         # Columns:
1144         for c_idx, item in enumerate(row):
1145             tdata = ET.SubElement(
1146                 trow,
1147                 u"td",
1148                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1149             )
1150             # Name:
1151             if c_idx == 0 and table.get(u"add-links", True):
1152                 ref = ET.SubElement(
1153                     tdata,
1154                     u"a",
1155                     attrib=dict(
1156                         href=f"../trending/"
1157                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1158                     )
1159                 )
1160                 ref.text = item
1161             else:
1162                 tdata.text = item
1163     try:
1164         with open(table[u"output-file"], u'w') as html_file:
1165             logging.info(f"    Writing file: {table[u'output-file']}")
1166             html_file.write(u".. raw:: html\n\n\t")
1167             html_file.write(str(ET.tostring(dashboard, encoding=u"unicode")))
1168             html_file.write(u"\n\t<p><br><br></p>\n")
1169     except KeyError:
1170         logging.warning(u"The output file is not defined.")
1171         return
1172
1173
1174 def table_last_failed_tests(table, input_data):
1175     """Generate the table(s) with algorithm: table_last_failed_tests
1176     specified in the specification file.
1177
1178     :param table: Table to generate.
1179     :param input_data: Data to process.
1180     :type table: pandas.Series
1181     :type input_data: InputData
1182     """
1183
1184     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1185
1186     # Transform the data
1187     logging.info(
1188         f"    Creating the data set for the {table.get(u'type', u'')} "
1189         f"{table.get(u'title', u'')}."
1190     )
1191
1192     data = input_data.filter_data(table, continue_on_error=True)
1193
1194     if data is None or data.empty:
1195         logging.warning(
1196             f"    No data for the {table.get(u'type', u'')} "
1197             f"{table.get(u'title', u'')}."
1198         )
1199         return
1200
1201     tbl_list = list()
1202     for job, builds in table[u"data"].items():
1203         for build in builds:
1204             build = str(build)
1205             try:
1206                 version = input_data.metadata(job, build).get(u"version", u"")
1207             except KeyError:
1208                 logging.error(f"Data for {job}: {build} is not present.")
1209                 return
1210             tbl_list.append(build)
1211             tbl_list.append(version)
1212             failed_tests = list()
1213             passed = 0
1214             failed = 0
1215             for tst_data in data[job][build].values:
1216                 if tst_data[u"status"] != u"FAIL":
1217                     passed += 1
1218                     continue
1219                 failed += 1
1220                 groups = re.search(REGEX_NIC, tst_data[u"parent"])
1221                 if not groups:
1222                     continue
1223                 nic = groups.group(0)
1224                 failed_tests.append(f"{nic}-{tst_data[u'name']}")
1225             tbl_list.append(str(passed))
1226             tbl_list.append(str(failed))
1227             tbl_list.extend(failed_tests)
1228
1229     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1230     logging.info(f"    Writing file: {file_name}")
1231     with open(file_name, u"wt") as file_handler:
1232         for test in tbl_list:
1233             file_handler.write(test + u'\n')
1234
1235
1236 def table_failed_tests(table, input_data):
1237     """Generate the table(s) with algorithm: table_failed_tests
1238     specified in the specification file.
1239
1240     :param table: Table to generate.
1241     :param input_data: Data to process.
1242     :type table: pandas.Series
1243     :type input_data: InputData
1244     """
1245
1246     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1247
1248     # Transform the data
1249     logging.info(
1250         f"    Creating the data set for the {table.get(u'type', u'')} "
1251         f"{table.get(u'title', u'')}."
1252     )
1253     data = input_data.filter_data(table, continue_on_error=True)
1254
1255     test_type = u"MRR"
1256     if u"NDRPDR" in table.get(u"filter", list()):
1257         test_type = u"NDRPDR"
1258
1259     # Prepare the header of the tables
1260     header = [
1261         u"Test Case",
1262         u"Failures [#]",
1263         u"Last Failure [Time]",
1264         u"Last Failure [VPP-Build-Id]",
1265         u"Last Failure [CSIT-Job-Build-Id]"
1266     ]
1267
1268     # Generate the data for the table according to the model in the table
1269     # specification
1270
1271     now = dt.utcnow()
1272     timeperiod = timedelta(int(table.get(u"window", 7)))
1273
1274     tbl_dict = dict()
1275     for job, builds in table[u"data"].items():
1276         for build in builds:
1277             build = str(build)
1278             for tst_name, tst_data in data[job][build].items():
1279                 if tst_name.lower() in table.get(u"ignore-list", list()):
1280                     continue
1281                 if tbl_dict.get(tst_name, None) is None:
1282                     groups = re.search(REGEX_NIC, tst_data[u"parent"])
1283                     if not groups:
1284                         continue
1285                     nic = groups.group(0)
1286                     tbl_dict[tst_name] = {
1287                         u"name": f"{nic}-{tst_data[u'name']}",
1288                         u"data": OrderedDict()
1289                     }
1290                 try:
1291                     generated = input_data.metadata(job, build).\
1292                         get(u"generated", u"")
1293                     if not generated:
1294                         continue
1295                     then = dt.strptime(generated, u"%Y%m%d %H:%M")
1296                     if (now - then) <= timeperiod:
1297                         tbl_dict[tst_name][u"data"][build] = (
1298                             tst_data[u"status"],
1299                             generated,
1300                             input_data.metadata(job, build).get(u"version",
1301                                                                 u""),
1302                             build
1303                         )
1304                 except (TypeError, KeyError) as err:
1305                     logging.warning(f"tst_name: {tst_name} - err: {repr(err)}")
1306
1307     max_fails = 0
1308     tbl_lst = list()
1309     for tst_data in tbl_dict.values():
1310         fails_nr = 0
1311         fails_last_date = u""
1312         fails_last_vpp = u""
1313         fails_last_csit = u""
1314         for val in tst_data[u"data"].values():
1315             if val[0] == u"FAIL":
1316                 fails_nr += 1
1317                 fails_last_date = val[1]
1318                 fails_last_vpp = val[2]
1319                 fails_last_csit = val[3]
1320         if fails_nr:
1321             max_fails = fails_nr if fails_nr > max_fails else max_fails
1322             tbl_lst.append([
1323                 tst_data[u"name"],
1324                 fails_nr,
1325                 fails_last_date,
1326                 fails_last_vpp,
1327                 f"{u'mrr-daily' if test_type == u'MRR' else u'ndrpdr-weekly'}"
1328                 f"-build-{fails_last_csit}"
1329             ])
1330
1331     tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
1332     tbl_sorted = list()
1333     for nrf in range(max_fails, -1, -1):
1334         tbl_fails = [item for item in tbl_lst if item[1] == nrf]
1335         tbl_sorted.extend(tbl_fails)
1336
1337     file_name = f"{table[u'output-file']}{table[u'output-file-ext']}"
1338     logging.info(f"    Writing file: {file_name}")
1339     with open(file_name, u"wt") as file_handler:
1340         file_handler.write(u",".join(header) + u"\n")
1341         for test in tbl_sorted:
1342             file_handler.write(u",".join([str(item) for item in test]) + u'\n')
1343
1344     logging.info(f"    Writing file: {table[u'output-file']}.txt")
1345     convert_csv_to_pretty_txt(file_name, f"{table[u'output-file']}.txt")
1346
1347
1348 def table_failed_tests_html(table, input_data):
1349     """Generate the table(s) with algorithm: table_failed_tests_html
1350     specified in the specification file.
1351
1352     :param table: Table to generate.
1353     :param input_data: Data to process.
1354     :type table: pandas.Series
1355     :type input_data: InputData
1356     """
1357
1358     _ = input_data
1359
1360     if not table.get(u"testbed", None):
1361         logging.error(
1362             f"The testbed is not defined for the table "
1363             f"{table.get(u'title', u'')}."
1364         )
1365         return
1366
1367     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1368
1369     try:
1370         with open(table[u"input-file"], u'rt') as csv_file:
1371             csv_lst = list(csv.reader(csv_file, delimiter=u',', quotechar=u'"'))
1372     except KeyError:
1373         logging.warning(u"The input file is not defined.")
1374         return
1375     except csv.Error as err:
1376         logging.warning(
1377             f"Not possible to process the file {table[u'input-file']}.\n"
1378             f"{repr(err)}"
1379         )
1380         return
1381
1382     # Table:
1383     failed_tests = ET.Element(u"table", attrib=dict(width=u"100%", border=u'0'))
1384
1385     # Table header:
1386     trow = ET.SubElement(failed_tests, u"tr", attrib=dict(bgcolor=u"#7eade7"))
1387     for idx, item in enumerate(csv_lst[0]):
1388         alignment = u"left" if idx == 0 else u"center"
1389         thead = ET.SubElement(trow, u"th", attrib=dict(align=alignment))
1390         thead.text = item
1391
1392     # Rows:
1393     colors = (u"#e9f1fb", u"#d4e4f7")
1394     for r_idx, row in enumerate(csv_lst[1:]):
1395         background = colors[r_idx % 2]
1396         trow = ET.SubElement(
1397             failed_tests, u"tr", attrib=dict(bgcolor=background)
1398         )
1399
1400         # Columns:
1401         for c_idx, item in enumerate(row):
1402             tdata = ET.SubElement(
1403                 trow,
1404                 u"td",
1405                 attrib=dict(align=u"left" if c_idx == 0 else u"center")
1406             )
1407             # Name:
1408             if c_idx == 0:
1409                 ref = ET.SubElement(
1410                     tdata,
1411                     u"a",
1412                     attrib=dict(
1413                         href=f"../trending/"
1414                              f"{_generate_url(table.get(u'testbed', ''), item)}"
1415                     )
1416                 )
1417                 ref.text = item
1418             else:
1419                 tdata.text = item
1420     try:
1421         with open(table[u"output-file"], u'w') as html_file:
1422             logging.info(f"    Writing file: {table[u'output-file']}")
1423             html_file.write(u".. raw:: html\n\n\t")
1424             html_file.write(str(ET.tostring(failed_tests, encoding=u"unicode")))
1425             html_file.write(u"\n\t<p><br><br></p>\n")
1426     except KeyError:
1427         logging.warning(u"The output file is not defined.")
1428         return
1429
1430
1431 def table_comparison(table, input_data):
1432     """Generate the table(s) with algorithm: table_comparison
1433     specified in the specification file.
1434
1435     :param table: Table to generate.
1436     :param input_data: Data to process.
1437     :type table: pandas.Series
1438     :type input_data: InputData
1439     """
1440     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1441
1442     # Transform the data
1443     logging.info(
1444         f"    Creating the data set for the {table.get(u'type', u'')} "
1445         f"{table.get(u'title', u'')}."
1446     )
1447
1448     columns = table.get(u"columns", None)
1449     if not columns:
1450         logging.error(
1451             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1452         )
1453         return
1454
1455     cols = list()
1456     for idx, col in enumerate(columns):
1457         if col.get(u"data-set", None) is None:
1458             logging.warning(f"No data for column {col.get(u'title', u'')}")
1459             continue
1460         tag = col.get(u"tag", None)
1461         data = input_data.filter_data(
1462             table,
1463             params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1464             data=col[u"data-set"],
1465             continue_on_error=True
1466         )
1467         col_data = {
1468             u"title": col.get(u"title", f"Column{idx}"),
1469             u"data": dict()
1470         }
1471         for builds in data.values:
1472             for build in builds:
1473                 for tst_name, tst_data in build.items():
1474                     if tag and tag not in tst_data[u"tags"]:
1475                         continue
1476                     tst_name_mod = \
1477                         _tpc_modify_test_name(tst_name, ignore_nic=True).\
1478                         replace(u"2n1l-", u"")
1479                     if col_data[u"data"].get(tst_name_mod, None) is None:
1480                         name = tst_data[u'name'].rsplit(u'-', 1)[0]
1481                         if u"across testbeds" in table[u"title"].lower() or \
1482                                 u"across topologies" in table[u"title"].lower():
1483                             name = _tpc_modify_displayed_test_name(name)
1484                         col_data[u"data"][tst_name_mod] = {
1485                             u"name": name,
1486                             u"replace": True,
1487                             u"data": list(),
1488                             u"mean": None,
1489                             u"stdev": None
1490                         }
1491                     _tpc_insert_data(
1492                         target=col_data[u"data"][tst_name_mod],
1493                         src=tst_data,
1494                         include_tests=table[u"include-tests"]
1495                     )
1496
1497         replacement = col.get(u"data-replacement", None)
1498         if replacement:
1499             rpl_data = input_data.filter_data(
1500                 table,
1501                 params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1502                 data=replacement,
1503                 continue_on_error=True
1504             )
1505             for builds in rpl_data.values:
1506                 for build in builds:
1507                     for tst_name, tst_data in build.items():
1508                         if tag and tag not in tst_data[u"tags"]:
1509                             continue
1510                         tst_name_mod = \
1511                             _tpc_modify_test_name(tst_name, ignore_nic=True).\
1512                             replace(u"2n1l-", u"")
1513                         if col_data[u"data"].get(tst_name_mod, None) is None:
1514                             name = tst_data[u'name'].rsplit(u'-', 1)[0]
1515                             if u"across testbeds" in table[u"title"].lower() \
1516                                     or u"across topologies" in \
1517                                     table[u"title"].lower():
1518                                 name = _tpc_modify_displayed_test_name(name)
1519                             col_data[u"data"][tst_name_mod] = {
1520                                 u"name": name,
1521                                 u"replace": False,
1522                                 u"data": list(),
1523                                 u"mean": None,
1524                                 u"stdev": None
1525                             }
1526                         if col_data[u"data"][tst_name_mod][u"replace"]:
1527                             col_data[u"data"][tst_name_mod][u"replace"] = False
1528                             col_data[u"data"][tst_name_mod][u"data"] = list()
1529                         _tpc_insert_data(
1530                             target=col_data[u"data"][tst_name_mod],
1531                             src=tst_data,
1532                             include_tests=table[u"include-tests"]
1533                         )
1534
1535         if table[u"include-tests"] in (u"NDR", u"PDR"):
1536             for tst_name, tst_data in col_data[u"data"].items():
1537                 if tst_data[u"data"]:
1538                     tst_data[u"mean"] = mean(tst_data[u"data"])
1539                     tst_data[u"stdev"] = stdev(tst_data[u"data"])
1540
1541         cols.append(col_data)
1542
1543     tbl_dict = dict()
1544     for col in cols:
1545         for tst_name, tst_data in col[u"data"].items():
1546             if tbl_dict.get(tst_name, None) is None:
1547                 tbl_dict[tst_name] = {
1548                     "name": tst_data[u"name"]
1549                 }
1550             tbl_dict[tst_name][col[u"title"]] = {
1551                 u"mean": tst_data[u"mean"],
1552                 u"stdev": tst_data[u"stdev"]
1553             }
1554
1555     if not tbl_dict:
1556         logging.warning(f"No data for table {table.get(u'title', u'')}!")
1557         return
1558
1559     tbl_lst = list()
1560     for tst_data in tbl_dict.values():
1561         row = [tst_data[u"name"], ]
1562         for col in cols:
1563             row.append(tst_data.get(col[u"title"], None))
1564         tbl_lst.append(row)
1565
1566     comparisons = table.get(u"comparisons", None)
1567     if comparisons and isinstance(comparisons, list):
1568         for idx, comp in enumerate(comparisons):
1569             try:
1570                 col_ref = int(comp[u"reference"])
1571                 col_cmp = int(comp[u"compare"])
1572             except KeyError:
1573                 logging.warning(u"Comparison: No references defined! Skipping.")
1574                 comparisons.pop(idx)
1575                 continue
1576             if not (0 < col_ref <= len(cols) and
1577                     0 < col_cmp <= len(cols)) or \
1578                     col_ref == col_cmp:
1579                 logging.warning(f"Wrong values of reference={col_ref} "
1580                                 f"and/or compare={col_cmp}. Skipping.")
1581                 comparisons.pop(idx)
1582                 continue
1583
1584     tbl_cmp_lst = list()
1585     if comparisons:
1586         for row in tbl_lst:
1587             new_row = deepcopy(row)
1588             add_to_tbl = False
1589             for comp in comparisons:
1590                 ref_itm = row[int(comp[u"reference"])]
1591                 if ref_itm is None and \
1592                         comp.get(u"reference-alt", None) is not None:
1593                     ref_itm = row[int(comp[u"reference-alt"])]
1594                 cmp_itm = row[int(comp[u"compare"])]
1595                 if ref_itm is not None and cmp_itm is not None and \
1596                         ref_itm[u"mean"] is not None and \
1597                         cmp_itm[u"mean"] is not None and \
1598                         ref_itm[u"stdev"] is not None and \
1599                         cmp_itm[u"stdev"] is not None:
1600                     delta, d_stdev = relative_change_stdev(
1601                         ref_itm[u"mean"], cmp_itm[u"mean"],
1602                         ref_itm[u"stdev"], cmp_itm[u"stdev"]
1603                     )
1604                     new_row.append(
1605                         {
1606                             u"mean": delta * 1e6,
1607                             u"stdev": d_stdev * 1e6
1608                         }
1609                     )
1610                     add_to_tbl = True
1611                 else:
1612                     new_row.append(None)
1613             if add_to_tbl:
1614                 tbl_cmp_lst.append(new_row)
1615
1616     tbl_cmp_lst.sort(key=lambda rel: rel[0], reverse=False)
1617     tbl_cmp_lst.sort(key=lambda rel: rel[-1][u'mean'], reverse=True)
1618
1619     rcas = list()
1620     rca_in = table.get(u"rca", None)
1621     if rca_in and isinstance(rca_in, list):
1622         for idx, itm in enumerate(rca_in):
1623             try:
1624                 with open(itm.get(u"data", u""), u"r") as rca_file:
1625                     rcas.append(
1626                         {
1627                             u"title": itm.get(u"title", f"RCA{idx}"),
1628                             u"data": load(rca_file, Loader=FullLoader)
1629                         }
1630                     )
1631             except (YAMLError, IOError) as err:
1632                 logging.warning(
1633                     f"The RCA file {itm.get(u'data', u'')} does not exist or "
1634                     f"it is corrupted!"
1635                 )
1636                 logging.debug(repr(err))
1637
1638     tbl_for_csv = list()
1639     for line in tbl_cmp_lst:
1640         row = [line[0], ]
1641         for idx, itm in enumerate(line[1:]):
1642             if itm is None:
1643                 row.append(u"NT")
1644                 row.append(u"NT")
1645             else:
1646                 row.append(round(float(itm[u'mean']) / 1e6, 3))
1647                 row.append(round(float(itm[u'stdev']) / 1e6, 3))
1648         for rca in rcas:
1649             rca_nr = rca[u"data"].get(row[0], u"-")
1650             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1651         tbl_for_csv.append(row)
1652
1653     header_csv = [u"Test Case", ]
1654     for col in cols:
1655         header_csv.append(f"Avg({col[u'title']})")
1656         header_csv.append(f"Stdev({col[u'title']})")
1657     for comp in comparisons:
1658         header_csv.append(
1659             f"Avg({comp.get(u'title', u'')})"
1660         )
1661         header_csv.append(
1662             f"Stdev({comp.get(u'title', u'')})"
1663         )
1664     header_csv.extend([rca[u"title"] for rca in rcas])
1665
1666     legend_lst = table.get(u"legend", None)
1667     if legend_lst is None:
1668         legend = u""
1669     else:
1670         legend = u"\n" + u"\n".join(legend_lst) + u"\n"
1671
1672     footnote = u""
1673     for rca in rcas:
1674         footnote += f"\n{rca[u'title']}:\n"
1675         footnote += rca[u"data"].get(u"footnote", u"")
1676
1677     csv_file = f"{table[u'output-file']}-csv.csv"
1678     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1679         file_handler.write(
1680             u",".join([f'"{itm}"' for itm in header_csv]) + u"\n"
1681         )
1682         for test in tbl_for_csv:
1683             file_handler.write(
1684                 u",".join([f'"{item}"' for item in test]) + u"\n"
1685             )
1686         if legend_lst:
1687             for item in legend_lst:
1688                 file_handler.write(f'"{item}"\n')
1689         if footnote:
1690             for itm in footnote.split(u"\n"):
1691                 file_handler.write(f'"{itm}"\n')
1692
1693     tbl_tmp = list()
1694     max_lens = [0, ] * len(tbl_cmp_lst[0])
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                 new_itm = u"NT"
1700             else:
1701                 if idx < len(cols):
1702                     new_itm = (
1703                         f"{round(float(itm[u'mean']) / 1e6, 1)} "
1704                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1705                         replace(u"nan", u"NaN")
1706                     )
1707                 else:
1708                     new_itm = (
1709                         f"{round(float(itm[u'mean']) / 1e6, 1):+} "
1710                         f"\u00B1{round(float(itm[u'stdev']) / 1e6, 1)}".
1711                         replace(u"nan", u"NaN")
1712                     )
1713             if len(new_itm.rsplit(u" ", 1)[-1]) > max_lens[idx]:
1714                 max_lens[idx] = len(new_itm.rsplit(u" ", 1)[-1])
1715             row.append(new_itm)
1716
1717         tbl_tmp.append(row)
1718
1719     tbl_final = list()
1720     for line in tbl_tmp:
1721         row = [line[0], ]
1722         for idx, itm in enumerate(line[1:]):
1723             if itm in (u"NT", u"NaN"):
1724                 row.append(itm)
1725                 continue
1726             itm_lst = itm.rsplit(u"\u00B1", 1)
1727             itm_lst[-1] = \
1728                 f"{u' ' * (max_lens[idx] - len(itm_lst[-1]))}{itm_lst[-1]}"
1729             row.append(u"\u00B1".join(itm_lst))
1730         for rca in rcas:
1731             rca_nr = rca[u"data"].get(row[0], u"-")
1732             row.append(f"[{rca_nr}]" if rca_nr != u"-" else u"-")
1733
1734         tbl_final.append(row)
1735
1736     header = [u"Test Case", ]
1737     header.extend([col[u"title"] for col in cols])
1738     header.extend([comp.get(u"title", u"") for comp in comparisons])
1739     header.extend([rca[u"title"] for rca in rcas])
1740
1741     # Generate csv tables:
1742     csv_file = f"{table[u'output-file']}.csv"
1743     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1744         file_handler.write(u";".join(header) + u"\n")
1745         for test in tbl_final:
1746             file_handler.write(u";".join([str(item) for item in test]) + u"\n")
1747
1748     # Generate txt table:
1749     txt_file_name = f"{table[u'output-file']}.txt"
1750     convert_csv_to_pretty_txt(csv_file, txt_file_name, delimiter=u";")
1751
1752     with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
1753         txt_file.write(legend)
1754         txt_file.write(footnote)
1755
1756     # Generate html table:
1757     _tpc_generate_html_table(
1758         header,
1759         tbl_final,
1760         table[u'output-file'],
1761         legend=legend,
1762         footnote=footnote,
1763         sort_data=False,
1764         title=table.get(u"title", u"")
1765     )
1766
1767
1768 def table_weekly_comparison(table, in_data):
1769     """Generate the table(s) with algorithm: table_weekly_comparison
1770     specified in the specification file.
1771
1772     :param table: Table to generate.
1773     :param in_data: Data to process.
1774     :type table: pandas.Series
1775     :type in_data: InputData
1776     """
1777     logging.info(f"  Generating the table {table.get(u'title', u'')} ...")
1778
1779     # Transform the data
1780     logging.info(
1781         f"    Creating the data set for the {table.get(u'type', u'')} "
1782         f"{table.get(u'title', u'')}."
1783     )
1784
1785     incl_tests = table.get(u"include-tests", None)
1786     if incl_tests not in (u"NDR", u"PDR"):
1787         logging.error(f"Wrong tests to include specified ({incl_tests}).")
1788         return
1789
1790     nr_cols = table.get(u"nr-of-data-columns", None)
1791     if not nr_cols or nr_cols < 2:
1792         logging.error(
1793             f"No columns specified for {table.get(u'title', u'')}. Skipping."
1794         )
1795         return
1796
1797     data = in_data.filter_data(
1798         table,
1799         params=[u"throughput", u"result", u"name", u"parent", u"tags"],
1800         continue_on_error=True
1801     )
1802
1803     header = [
1804         [u"VPP Version", ],
1805         [u"Start Timestamp", ],
1806         [u"CSIT Build", ],
1807         [u"CSIT Testbed", ]
1808     ]
1809     tbl_dict = dict()
1810     idx = 0
1811     tb_tbl = table.get(u"testbeds", None)
1812     for job_name, job_data in data.items():
1813         for build_nr, build in job_data.items():
1814             if idx >= nr_cols:
1815                 break
1816             if build.empty:
1817                 continue
1818
1819             tb_ip = in_data.metadata(job_name, build_nr).get(u"testbed", u"")
1820             if tb_ip and tb_tbl:
1821                 testbed = tb_tbl.get(tb_ip, u"")
1822             else:
1823                 testbed = u""
1824             header[2].insert(1, build_nr)
1825             header[3].insert(1, testbed)
1826             header[1].insert(
1827                 1, in_data.metadata(job_name, build_nr).get(u"generated", u"")
1828             )
1829             header[0].insert(
1830                 1, in_data.metadata(job_name, build_nr).get(u"version", u"")
1831             )
1832
1833             for tst_name, tst_data in build.items():
1834                 tst_name_mod = \
1835                     _tpc_modify_test_name(tst_name).replace(u"2n1l-", u"")
1836                 if not tbl_dict.get(tst_name_mod, None):
1837                     tbl_dict[tst_name_mod] = dict(
1838                         name=tst_data[u'name'].rsplit(u'-', 1)[0],
1839                     )
1840                 try:
1841                     tbl_dict[tst_name_mod][-idx - 1] = \
1842                         tst_data[u"throughput"][incl_tests][u"LOWER"]
1843                 except (TypeError, IndexError, KeyError, ValueError):
1844                     pass
1845             idx += 1
1846
1847     if idx < nr_cols:
1848         logging.error(u"Not enough data to build the table! Skipping")
1849         return
1850
1851     cmp_dict = dict()
1852     for idx, cmp in enumerate(table.get(u"comparisons", list())):
1853         idx_ref = cmp.get(u"reference", None)
1854         idx_cmp = cmp.get(u"compare", None)
1855         if idx_ref is None or idx_cmp is None:
1856             continue
1857         header[0].append(
1858             f"Diff({header[0][idx_ref - idx].split(u'~')[-1]} vs "
1859             f"{header[0][idx_cmp - idx].split(u'~')[-1]})"
1860         )
1861         header[1].append(u"")
1862         header[2].append(u"")
1863         header[3].append(u"")
1864         for tst_name, tst_data in tbl_dict.items():
1865             if not cmp_dict.get(tst_name, None):
1866                 cmp_dict[tst_name] = list()
1867             ref_data = tst_data.get(idx_ref, None)
1868             cmp_data = tst_data.get(idx_cmp, None)
1869             if ref_data is None or cmp_data is None:
1870                 cmp_dict[tst_name].append(float('nan'))
1871             else:
1872                 cmp_dict[tst_name].append(
1873                     relative_change(ref_data, cmp_data)
1874                 )
1875
1876     tbl_lst = list()
1877     for tst_name, tst_data in tbl_dict.items():
1878         itm_lst = [tst_data[u"name"], ]
1879         for idx in range(nr_cols):
1880             item = tst_data.get(-idx - 1, None)
1881             if item is None:
1882                 itm_lst.insert(1, None)
1883             else:
1884                 itm_lst.insert(1, round(item / 1e6, 1))
1885         itm_lst.extend(
1886             [
1887                 None if itm is None else round(itm, 1)
1888                 for itm in cmp_dict[tst_name]
1889             ]
1890         )
1891         tbl_lst.append(itm_lst)
1892
1893     tbl_lst.sort(key=lambda rel: rel[0], reverse=False)
1894     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
1895
1896     # Generate csv table:
1897     csv_file = f"{table[u'output-file']}.csv"
1898     with open(csv_file, u"wt", encoding='utf-8') as file_handler:
1899         for hdr in header:
1900             file_handler.write(u",".join(hdr) + u"\n")
1901         for test in tbl_lst:
1902             file_handler.write(u",".join(
1903                 [
1904                     str(item).replace(u"None", u"-").replace(u"nan", u"-").
1905                     replace(u"null", u"-") for item in test
1906                 ]
1907             ) + u"\n")
1908
1909     txt_file = f"{table[u'output-file']}.txt"
1910     convert_csv_to_pretty_txt(csv_file, txt_file, delimiter=u",")
1911
1912     # Reorganize header in txt table
1913     txt_table = list()
1914     with open(txt_file, u"rt", encoding='utf-8') as file_handler:
1915         for line in file_handler:
1916             txt_table.append(line)
1917     try:
1918         txt_table.insert(5, txt_table.pop(2))
1919         with open(txt_file, u"wt", encoding='utf-8') as file_handler:
1920             file_handler.writelines(txt_table)
1921     except IndexError:
1922         pass
1923
1924     # Generate html table:
1925     hdr_html = [
1926         u"<br>".join(row) for row in zip(*header)
1927     ]
1928     _tpc_generate_html_table(
1929         hdr_html,
1930         tbl_lst,
1931         table[u'output-file'],
1932         sort_data=True,
1933         title=table.get(u"title", u""),
1934         generate_rst=False
1935     )