resources/tools/presentation/generator_tables.py

   1 # Copyright (c) 2017 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 prettytable
  21 import pandas as pd
  22
  23 from string import replace
  24 from math import isnan
  25 from xml.etree import ElementTree as ET
  26
  27 from errors import PresentationError
  28 from utils import mean, stdev, relative_change, remove_outliers, find_outliers
  29
  30
  31 def generate_tables(spec, data):
  32     """Generate all tables specified in the specification file.
  33
  34     :param spec: Specification read from the specification file.
  35     :param data: Data to process.
  36     :type spec: Specification
  37     :type data: InputData
  38     """
  39
  40     logging.info("Generating the tables ...")
  41     for table in spec.tables:
  42         try:
  43             eval(table["algorithm"])(table, data)
  44         except NameError:
  45             logging.error("The algorithm '{0}' is not defined.".
  46                           format(table["algorithm"]))
  47     logging.info("Done.")
  48
  49
  50 def table_details(table, input_data):
  51     """Generate the table(s) with algorithm: table_detailed_test_results
  52     specified in the specification file.
  53
  54     :param table: Table to generate.
  55     :param input_data: Data to process.
  56     :type table: pandas.Series
  57     :type input_data: InputData
  58     """
  59
  60     logging.info("  Generating the table {0} ...".
  61                  format(table.get("title", "")))
  62
  63     # Transform the data
  64     data = input_data.filter_data(table)
  65
  66     # Prepare the header of the tables
  67     header = list()
  68     for column in table["columns"]:
  69         header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
  70
  71     # Generate the data for the table according to the model in the table
  72     # specification
  73     job = table["data"].keys()[0]
  74     build = str(table["data"][job][0])
  75     try:
  76         suites = input_data.suites(job, build)
  77     except KeyError:
  78         logging.error("    No data available. The table will not be generated.")
  79         return
  80
  81     for suite_longname, suite in suites.iteritems():
  82         # Generate data
  83         suite_name = suite["name"]
  84         table_lst = list()
  85         for test in data[job][build].keys():
  86             if data[job][build][test]["parent"] in suite_name:
  87                 row_lst = list()
  88                 for column in table["columns"]:
  89                     try:
  90                         col_data = str(data[job][build][test][column["data"].
  91                                        split(" ")[1]]).replace('"', '""')
  92                         if column["data"].split(" ")[1] in ("vat-history",
  93                                                             "show-run"):
  94                             col_data = replace(col_data, " |br| ", "",
  95                                                maxreplace=1)
  96                             col_data = " |prein| {0} |preout| ".\
  97                                 format(col_data[:-5])
  98                         row_lst.append('"{0}"'.format(col_data))
  99                     except KeyError:
 100                         row_lst.append("No data")
 101                 table_lst.append(row_lst)
 102
 103         # Write the data to file
 104         if table_lst:
 105             file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
 106                                             table["output-file-ext"])
 107             logging.info("      Writing file: '{}'".format(file_name))
 108             with open(file_name, "w") as file_handler:
 109                 file_handler.write(",".join(header) + "\n")
 110                 for item in table_lst:
 111                     file_handler.write(",".join(item) + "\n")
 112
 113     logging.info("  Done.")
 114
 115
 116 def table_merged_details(table, input_data):
 117     """Generate the table(s) with algorithm: table_merged_details
 118     specified in the specification file.
 119
 120     :param table: Table to generate.
 121     :param input_data: Data to process.
 122     :type table: pandas.Series
 123     :type input_data: InputData
 124     """
 125
 126     logging.info("  Generating the table {0} ...".
 127                  format(table.get("title", "")))
 128
 129     # Transform the data
 130     data = input_data.filter_data(table)
 131     data = input_data.merge_data(data)
 132     data.sort_index(inplace=True)
 133
 134     suites = input_data.filter_data(table, data_set="suites")
 135     suites = input_data.merge_data(suites)
 136
 137     # Prepare the header of the tables
 138     header = list()
 139     for column in table["columns"]:
 140         header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
 141
 142     for _, suite in suites.iteritems():
 143         # Generate data
 144         suite_name = suite["name"]
 145         table_lst = list()
 146         for test in data.keys():
 147             if data[test]["parent"] in suite_name:
 148                 row_lst = list()
 149                 for column in table["columns"]:
 150                     try:
 151                         col_data = str(data[test][column["data"].
 152                                        split(" ")[1]]).replace('"', '""')
 153                         if column["data"].split(" ")[1] in ("vat-history",
 154                                                             "show-run"):
 155                             col_data = replace(col_data, " |br| ", "",
 156                                                maxreplace=1)
 157                             col_data = " |prein| {0} |preout| ".\
 158                                 format(col_data[:-5])
 159                         row_lst.append('"{0}"'.format(col_data))
 160                     except KeyError:
 161                         row_lst.append("No data")
 162                 table_lst.append(row_lst)
 163
 164         # Write the data to file
 165         if table_lst:
 166             file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
 167                                             table["output-file-ext"])
 168             logging.info("      Writing file: '{}'".format(file_name))
 169             with open(file_name, "w") as file_handler:
 170                 file_handler.write(",".join(header) + "\n")
 171                 for item in table_lst:
 172                     file_handler.write(",".join(item) + "\n")
 173
 174     logging.info("  Done.")
 175
 176
 177 def table_performance_improvements(table, input_data):
 178     """Generate the table(s) with algorithm: table_performance_improvements
 179     specified in the specification file.
 180
 181     :param table: Table to generate.
 182     :param input_data: Data to process.
 183     :type table: pandas.Series
 184     :type input_data: InputData
 185     """
 186
 187     def _write_line_to_file(file_handler, data):
 188         """Write a line to the .csv file.
 189
 190         :param file_handler: File handler for the csv file. It must be open for
 191          writing text.
 192         :param data: Item to be written to the file.
 193         :type file_handler: BinaryIO
 194         :type data: list
 195         """
 196
 197         line_lst = list()
 198         for item in data:
 199             if isinstance(item["data"], str):
 200                 # Remove -?drdisc from the end
 201                 if item["data"].endswith("drdisc"):
 202                     item["data"] = item["data"][:-8]
 203                 line_lst.append(item["data"])
 204             elif isinstance(item["data"], float):
 205                 line_lst.append("{:.1f}".format(item["data"]))
 206             elif item["data"] is None:
 207                 line_lst.append("")
 208         file_handler.write(",".join(line_lst) + "\n")
 209
 210     logging.info("  Generating the table {0} ...".
 211                  format(table.get("title", "")))
 212
 213     # Read the template
 214     file_name = table.get("template", None)
 215     if file_name:
 216         try:
 217             tmpl = _read_csv_template(file_name)
 218         except PresentationError:
 219             logging.error("  The template '{0}' does not exist. Skipping the "
 220                           "table.".format(file_name))
 221             return None
 222     else:
 223         logging.error("The template is not defined. Skipping the table.")
 224         return None
 225
 226     # Transform the data
 227     data = input_data.filter_data(table)
 228
 229     # Prepare the header of the tables
 230     header = list()
 231     for column in table["columns"]:
 232         header.append(column["title"])
 233
 234     # Generate the data for the table according to the model in the table
 235     # specification
 236     tbl_lst = list()
 237     for tmpl_item in tmpl:
 238         tbl_item = list()
 239         for column in table["columns"]:
 240             cmd = column["data"].split(" ")[0]
 241             args = column["data"].split(" ")[1:]
 242             if cmd == "template":
 243                 try:
 244                     val = float(tmpl_item[int(args[0])])
 245                 except ValueError:
 246                     val = tmpl_item[int(args[0])]
 247                 tbl_item.append({"data": val})
 248             elif cmd == "data":
 249                 jobs = args[0:-1]
 250                 operation = args[-1]
 251                 data_lst = list()
 252                 for job in jobs:
 253                     for build in data[job]:
 254                         try:
 255                             data_lst.append(float(build[tmpl_item[0]]
 256                                                   ["throughput"]["value"]))
 257                         except (KeyError, TypeError):
 258                             # No data, ignore
 259                             continue
 260                 if data_lst:
 261                     tbl_item.append({"data": (eval(operation)(data_lst)) /
 262                                              1000000})
 263                 else:
 264                     tbl_item.append({"data": None})
 265             elif cmd == "operation":
 266                 operation = args[0]
 267                 try:
 268                     nr1 = float(tbl_item[int(args[1])]["data"])
 269                     nr2 = float(tbl_item[int(args[2])]["data"])
 270                     if nr1 and nr2:
 271                         tbl_item.append({"data": eval(operation)(nr1, nr2)})
 272                     else:
 273                         tbl_item.append({"data": None})
 274                 except (IndexError, ValueError, TypeError):
 275                     logging.error("No data for {0}".format(tbl_item[0]["data"]))
 276                     tbl_item.append({"data": None})
 277                     continue
 278             else:
 279                 logging.error("Not supported command {0}. Skipping the table.".
 280                               format(cmd))
 281                 return None
 282         tbl_lst.append(tbl_item)
 283
 284     # Sort the table according to the relative change
 285     tbl_lst.sort(key=lambda rel: rel[-1]["data"], reverse=True)
 286
 287     # Create the tables and write them to the files
 288     file_names = [
 289         "{0}_ndr_top{1}".format(table["output-file"], table["output-file-ext"]),
 290         "{0}_pdr_top{1}".format(table["output-file"], table["output-file-ext"]),
 291         "{0}_ndr_low{1}".format(table["output-file"], table["output-file-ext"]),
 292         "{0}_pdr_low{1}".format(table["output-file"], table["output-file-ext"])
 293     ]
 294
 295     for file_name in file_names:
 296         logging.info("    Writing the file '{0}'".format(file_name))
 297         with open(file_name, "w") as file_handler:
 298             file_handler.write(",".join(header) + "\n")
 299             for item in tbl_lst:
 300                 if isinstance(item[-1]["data"], float):
 301                     rel_change = round(item[-1]["data"], 1)
 302                 else:
 303                     rel_change = item[-1]["data"]
 304                 if "ndr_top" in file_name \
 305                         and "ndr" in item[0]["data"] \
 306                         and rel_change >= 10.0:
 307                     _write_line_to_file(file_handler, item)
 308                 elif "pdr_top" in file_name \
 309                         and "pdr" in item[0]["data"] \
 310                         and rel_change >= 10.0:
 311                     _write_line_to_file(file_handler, item)
 312                 elif "ndr_low" in file_name \
 313                         and "ndr" in item[0]["data"] \
 314                         and rel_change < 10.0:
 315                     _write_line_to_file(file_handler, item)
 316                 elif "pdr_low" in file_name \
 317                         and "pdr" in item[0]["data"] \
 318                         and rel_change < 10.0:
 319                     _write_line_to_file(file_handler, item)
 320
 321     logging.info("  Done.")
 322
 323
 324 def _read_csv_template(file_name):
 325     """Read the template from a .csv file.
 326
 327     :param file_name: Name / full path / relative path of the file to read.
 328     :type file_name: str
 329     :returns: Data from the template as list (lines) of lists (items on line).
 330     :rtype: list
 331     :raises: PresentationError if it is not possible to read the file.
 332     """
 333
 334     try:
 335         with open(file_name, 'r') as csv_file:
 336             tmpl_data = list()
 337             for line in csv_file:
 338                 tmpl_data.append(line[:-1].split(","))
 339         return tmpl_data
 340     except IOError as err:
 341         raise PresentationError(str(err), level="ERROR")
 342
 343
 344 def table_performance_comparison(table, input_data):
 345     """Generate the table(s) with algorithm: table_performance_comparison
 346     specified in the specification file.
 347
 348     :param table: Table to generate.
 349     :param input_data: Data to process.
 350     :type table: pandas.Series
 351     :type input_data: InputData
 352     """
 353
 354     logging.info("  Generating the table {0} ...".
 355                  format(table.get("title", "")))
 356
 357     # Transform the data
 358     data = input_data.filter_data(table)
 359
 360     # Prepare the header of the tables
 361     try:
 362         header = ["Test case",
 363                   "{0} Throughput [Mpps]".format(table["reference"]["title"]),
 364                   "{0} stdev [Mpps]".format(table["reference"]["title"]),
 365                   "{0} Throughput [Mpps]".format(table["compare"]["title"]),
 366                   "{0} stdev [Mpps]".format(table["compare"]["title"]),
 367                   "Change [%]"]
 368         header_str = ",".join(header) + "\n"
 369     except (AttributeError, KeyError) as err:
 370         logging.error("The model is invalid, missing parameter: {0}".
 371                       format(err))
 372         return
 373
 374     # Prepare data to the table:
 375     tbl_dict = dict()
 376     for job, builds in table["reference"]["data"].items():
 377         for build in builds:
 378             for tst_name, tst_data in data[job][str(build)].iteritems():
 379                 if tbl_dict.get(tst_name, None) is None:
 380                     name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
 381                                             "-".join(tst_data["name"].
 382                                                      split("-")[1:]))
 383                     tbl_dict[tst_name] = {"name": name,
 384                                           "ref-data": list(),
 385                                           "cmp-data": list()}
 386                 try:
 387                     tbl_dict[tst_name]["ref-data"].\
 388                         append(tst_data["throughput"]["value"])
 389                 except TypeError:
 390                     pass  # No data in output.xml for this test
 391
 392     for job, builds in table["compare"]["data"].items():
 393         for build in builds:
 394             for tst_name, tst_data in data[job][str(build)].iteritems():
 395                 try:
 396                     tbl_dict[tst_name]["cmp-data"].\
 397                         append(tst_data["throughput"]["value"])
 398                 except KeyError:
 399                     pass
 400                 except TypeError:
 401                     tbl_dict.pop(tst_name, None)
 402
 403     tbl_lst = list()
 404     for tst_name in tbl_dict.keys():
 405         item = [tbl_dict[tst_name]["name"], ]
 406         if tbl_dict[tst_name]["ref-data"]:
 407             data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
 408                                      table["outlier-const"])
 409             item.append(round(mean(data_t) / 1000000, 2))
 410             item.append(round(stdev(data_t) / 1000000, 2))
 411         else:
 412             item.extend([None, None])
 413         if tbl_dict[tst_name]["cmp-data"]:
 414             data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
 415                                      table["outlier-const"])
 416             item.append(round(mean(data_t) / 1000000, 2))
 417             item.append(round(stdev(data_t) / 1000000, 2))
 418         else:
 419             item.extend([None, None])
 420         if item[1] is not None and item[3] is not None:
 421             item.append(int(relative_change(float(item[1]), float(item[3]))))
 422         if len(item) == 6:
 423             tbl_lst.append(item)
 424
 425     # Sort the table according to the relative change
 426     tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
 427
 428     # Generate tables:
 429     # All tests in csv:
 430     tbl_names = ["{0}-ndr-1t1c-full{1}".format(table["output-file"],
 431                                                table["output-file-ext"]),
 432                  "{0}-ndr-2t2c-full{1}".format(table["output-file"],
 433                                                table["output-file-ext"]),
 434                  "{0}-ndr-4t4c-full{1}".format(table["output-file"],
 435                                                table["output-file-ext"]),
 436                  "{0}-pdr-1t1c-full{1}".format(table["output-file"],
 437                                                table["output-file-ext"]),
 438                  "{0}-pdr-2t2c-full{1}".format(table["output-file"],
 439                                                table["output-file-ext"]),
 440                  "{0}-pdr-4t4c-full{1}".format(table["output-file"],
 441                                                table["output-file-ext"])
 442                  ]
 443     for file_name in tbl_names:
 444         logging.info("      Writing file: '{0}'".format(file_name))
 445         with open(file_name, "w") as file_handler:
 446             file_handler.write(header_str)
 447             for test in tbl_lst:
 448                 if (file_name.split("-")[-3] in test[0] and    # NDR vs PDR
 449                         file_name.split("-")[-2] in test[0]):  # cores
 450                     test[0] = "-".join(test[0].split("-")[:-1])
 451                     file_handler.write(",".join([str(item) for item in test]) +
 452                                        "\n")
 453
 454     # All tests in txt:
 455     tbl_names_txt = ["{0}-ndr-1t1c-full.txt".format(table["output-file"]),
 456                      "{0}-ndr-2t2c-full.txt".format(table["output-file"]),
 457                      "{0}-ndr-4t4c-full.txt".format(table["output-file"]),
 458                      "{0}-pdr-1t1c-full.txt".format(table["output-file"]),
 459                      "{0}-pdr-2t2c-full.txt".format(table["output-file"]),
 460                      "{0}-pdr-4t4c-full.txt".format(table["output-file"])
 461                      ]
 462
 463     for i, txt_name in enumerate(tbl_names_txt):
 464         txt_table = None
 465         logging.info("      Writing file: '{0}'".format(txt_name))
 466         with open(tbl_names[i], 'rb') as csv_file:
 467             csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
 468             for row in csv_content:
 469                 if txt_table is None:
 470                     txt_table = prettytable.PrettyTable(row)
 471                 else:
 472                     txt_table.add_row(row)
 473             txt_table.align["Test case"] = "l"
 474         with open(txt_name, "w") as txt_file:
 475             txt_file.write(str(txt_table))
 476
 477     # Selected tests in csv:
 478     input_file = "{0}-ndr-1t1c-full{1}".format(table["output-file"],
 479                                                table["output-file-ext"])
 480     with open(input_file, "r") as in_file:
 481         lines = list()
 482         for line in in_file:
 483             lines.append(line)
 484
 485     output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"],
 486                                                table["output-file-ext"])
 487     logging.info("      Writing file: '{0}'".format(output_file))
 488     with open(output_file, "w") as out_file:
 489         out_file.write(header_str)
 490         for i, line in enumerate(lines[1:]):
 491             if i == table["nr-of-tests-shown"]:
 492                 break
 493             out_file.write(line)
 494
 495     output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"],
 496                                                   table["output-file-ext"])
 497     logging.info("      Writing file: '{0}'".format(output_file))
 498     with open(output_file, "w") as out_file:
 499         out_file.write(header_str)
 500         for i, line in enumerate(lines[-1:0:-1]):
 501             if i == table["nr-of-tests-shown"]:
 502                 break
 503             out_file.write(line)
 504
 505     input_file = "{0}-pdr-1t1c-full{1}".format(table["output-file"],
 506                                                table["output-file-ext"])
 507     with open(input_file, "r") as in_file:
 508         lines = list()
 509         for line in in_file:
 510             lines.append(line)
 511
 512     output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"],
 513                                                table["output-file-ext"])
 514     logging.info("      Writing file: '{0}'".format(output_file))
 515     with open(output_file, "w") as out_file:
 516         out_file.write(header_str)
 517         for i, line in enumerate(lines[1:]):
 518             if i == table["nr-of-tests-shown"]:
 519                 break
 520             out_file.write(line)
 521
 522     output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"],
 523                                                   table["output-file-ext"])
 524     logging.info("      Writing file: '{0}'".format(output_file))
 525     with open(output_file, "w") as out_file:
 526         out_file.write(header_str)
 527         for i, line in enumerate(lines[-1:0:-1]):
 528             if i == table["nr-of-tests-shown"]:
 529                 break
 530             out_file.write(line)
 531
 532
 533 def table_performance_trending_dashboard(table, input_data):
 534     """Generate the table(s) with algorithm: table_performance_comparison
 535     specified in the specification file.
 536
 537     :param table: Table to generate.
 538     :param input_data: Data to process.
 539     :type table: pandas.Series
 540     :type input_data: InputData
 541     """
 542
 543     logging.info("  Generating the table {0} ...".
 544                  format(table.get("title", "")))
 545
 546     # Transform the data
 547     data = input_data.filter_data(table)
 548
 549     # Prepare the header of the tables
 550     header = ["Test case",
 551               "Thput trend [Mpps]",
 552               "Anomaly [Mpps]",
 553               "Change [%]",
 554               "Classification"]
 555     header_str = ",".join(header) + "\n"
 556
 557     # Prepare data to the table:
 558     tbl_dict = dict()
 559     for job, builds in table["data"].items():
 560         for build in builds:
 561             for tst_name, tst_data in data[job][str(build)].iteritems():
 562                 if tbl_dict.get(tst_name, None) is None:
 563                     name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
 564                                             "-".join(tst_data["name"].
 565                                                      split("-")[1:]))
 566                     tbl_dict[tst_name] = {"name": name,
 567                                           "data": list()}
 568                 try:
 569                     tbl_dict[tst_name]["data"]. \
 570                         append(tst_data["result"]["throughput"])
 571                 except (TypeError, KeyError):
 572                     pass  # No data in output.xml for this test
 573
 574     tbl_lst = list()
 575     for tst_name in tbl_dict.keys():
 576         if len(tbl_dict[tst_name]["data"]) > 2:
 577             sample_lst = tbl_dict[tst_name]["data"]
 578             pd_data = pd.Series(sample_lst)
 579             win_size = pd_data.size \
 580                 if pd_data.size < table["window"] else table["window"]
 581             # Test name:
 582             name = tbl_dict[tst_name]["name"]
 583
 584             # Trend list:
 585             trend_lst = list(pd_data.rolling(window=win_size, min_periods=2).
 586                              median())
 587             # Stdevs list:
 588             t_data, _ = find_outliers(pd_data)
 589             t_data_lst = list(t_data)
 590             stdev_lst = list(t_data.rolling(window=win_size, min_periods=2).
 591                              std())
 592
 593             rel_change_lst = [None, ]
 594             classification_lst = [None, ]
 595             for idx in range(1, len(trend_lst)):
 596                 # Relative changes list:
 597                 if not isnan(sample_lst[idx]) \
 598                         and not isnan(trend_lst[idx])\
 599                         and trend_lst[idx] != 0:
 600                     rel_change_lst.append(
 601                         int(relative_change(float(trend_lst[idx]),
 602                                             float(sample_lst[idx]))))
 603                 else:
 604                     rel_change_lst.append(None)
 605                 # Classification list:
 606                 if isnan(t_data_lst[idx]) or isnan(stdev_lst[idx]):
 607                     classification_lst.append("outlier")
 608                 elif sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]):
 609                     classification_lst.append("regression")
 610                 elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]):
 611                     classification_lst.append("progression")
 612                 else:
 613                     classification_lst.append("normal")
 614
 615             last_idx = len(sample_lst) - 1
 616             first_idx = last_idx - int(table["evaluated-window"])
 617             if first_idx < 0:
 618                 first_idx = 0
 619
 620             if "regression" in classification_lst[first_idx:]:
 621                 classification = "regression"
 622             elif "outlier" in classification_lst[first_idx:]:
 623                 classification = "outlier"
 624             elif "progression" in classification_lst[first_idx:]:
 625                 classification = "progression"
 626             else:
 627                 classification = "normal"
 628
 629             idx = len(classification_lst) - 1
 630             while idx:
 631                 if classification_lst[idx] == classification:
 632                     break
 633                 idx -= 1
 634
 635             trend = round(float(trend_lst[-2]) / 1000000, 2) \
 636                 if not isnan(trend_lst[-2]) else ''
 637             sample = round(float(sample_lst[idx]) / 1000000, 2) \
 638                 if not isnan(sample_lst[idx]) else ''
 639             rel_change = rel_change_lst[idx] \
 640                 if rel_change_lst[idx] is not None else ''
 641             tbl_lst.append([name,
 642                             trend,
 643                             sample,
 644                             rel_change,
 645                             classification])
 646
 647     # Sort the table according to the classification
 648     tbl_sorted = list()
 649     for classification in ("regression", "outlier", "progression", "normal"):
 650         tbl_tmp = [item for item in tbl_lst if item[4] == classification]
 651         tbl_tmp.sort(key=lambda rel: rel[0])
 652         tbl_sorted.extend(tbl_tmp)
 653
 654     file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
 655
 656     logging.info("      Writing file: '{0}'".format(file_name))
 657     with open(file_name, "w") as file_handler:
 658         file_handler.write(header_str)
 659         for test in tbl_sorted:
 660             file_handler.write(",".join([str(item) for item in test]) + '\n')
 661
 662     txt_file_name = "{0}.txt".format(table["output-file"])
 663     txt_table = None
 664     logging.info("      Writing file: '{0}'".format(txt_file_name))
 665     with open(file_name, 'rb') as csv_file:
 666         csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
 667         for row in csv_content:
 668             if txt_table is None:
 669                 txt_table = prettytable.PrettyTable(row)
 670             else:
 671                 txt_table.add_row(row)
 672         txt_table.align["Test case"] = "l"
 673     with open(txt_file_name, "w") as txt_file:
 674         txt_file.write(str(txt_table))
 675
 676
 677 def table_performance_trending_dashboard_html(table, input_data):
 678     """Generate the table(s) with algorithm:
 679     table_performance_trending_dashboard_html specified in the specification
 680     file.
 681
 682     :param table: Table to generate.
 683     :param input_data: Data to process.
 684     :type table: pandas.Series
 685     :type input_data: InputData
 686     """
 687
 688     logging.info("  Generating the table {0} ...".
 689                  format(table.get("title", "")))
 690
 691     try:
 692         with open(table["input-file"], 'rb') as csv_file:
 693             csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
 694             csv_lst = [item for item in csv_content]
 695     except KeyError:
 696         logging.warning("The input file is not defined.")
 697         return
 698     except csv.Error as err:
 699         logging.warning("Not possible to process the file '{0}'.\n{1}".
 700                         format(table["input-file"], err))
 701         return
 702
 703     # Table:
 704     dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
 705
 706     # Table header:
 707     tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#6699ff"))
 708     for idx, item in enumerate(csv_lst[0]):
 709         alignment = "left" if idx == 0 else "right"
 710         th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
 711         th.text = item
 712
 713     # Rows:
 714     for r_idx, row in enumerate(csv_lst[1:]):
 715         background = "#D4E4F7" if r_idx % 2 else "white"
 716         tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
 717
 718         # Columns:
 719         for c_idx, item in enumerate(row):
 720             alignment = "left" if c_idx == 0 else "center"
 721             td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
 722             if c_idx == 4:
 723                 if item == "regression":
 724                     td.set("bgcolor", "#eca1a6")
 725                 elif item == "outlier":
 726                     td.set("bgcolor", "#d6cbd3")
 727                 elif item == "progression":
 728                     td.set("bgcolor", "#bdcebe")
 729             td.text = item
 730
 731     try:
 732         with open(table["output-file"], 'w') as html_file:
 733             logging.info("      Writing file: '{0}'".
 734                          format(table["output-file"]))
 735             html_file.write(".. raw:: html\n\n\t")
 736             html_file.write(ET.tostring(dashboard))
 737             html_file.write("\n\t<p><br><br></p>\n")
 738     except KeyError:
 739         logging.warning("The output file is not defined.")
 740         return