CSIT-1110: Integrate anomaly detection into PAL

+ Keep the original detection,
+ add the new one as subdirectory
  (both in source and in rendered tree).
- The new detection is not rebased over "Add dpdk mrr tests to trending".

New detection features:
+ Do not remove (nor detect) outliers.
+ Trend line shows the constant average within a group.
+ Anomaly circles are placed at the changed average.
+ Small bias against too similar averages.
+ Should be ready for moving the detection library out to pip.

Change-Id: I7ab1a92b79eeeed53ba65a071b1305e927816a89
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

1 files changed, 995 insertions, 0 deletions

diff --git a/resources/tools/presentation/new/generator_tables.py b/resources/tools/presentation/new/generator_tables.py
new file mode 100644
index 0000000000..12f160145b
--- /dev/null
+++ b/resources/tools/presentation/new/generator_tables.py
@@ -0,0 +1,995 @@
+# Copyright (c) 2018 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Algorithms to generate tables.
+"""
+
+
+import logging
+import csv
+import prettytable
+import pandas as pd
+
+from string import replace
+from collections import OrderedDict
+from numpy import nan, isnan
+from xml.etree import ElementTree as ET
+
+from errors import PresentationError
+from utils import mean, stdev, relative_change, classify_anomalies
+
+
+def generate_tables(spec, data):
+    """Generate all tables specified in the specification file.
+
+    :param spec: Specification read from the specification file.
+    :param data: Data to process.
+    :type spec: Specification
+    :type data: InputData
+    """
+
+    logging.info("Generating the tables ...")
+    for table in spec.tables:
+        try:
+            eval(table["algorithm"])(table, data)
+        except NameError as err:
+            logging.error("Probably algorithm '{alg}' is not defined: {err}".
+                          format(alg=table["algorithm"], err=repr(err)))
+    logging.info("Done.")
+
+
+def table_details(table, input_data):
+    """Generate the table(s) with algorithm: table_detailed_test_results
+    specified in the specification file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    # Transform the data
+    logging.info("    Creating the data set for the {0} '{1}'.".
+                 format(table.get("type", ""), table.get("title", "")))
+    data = input_data.filter_data(table)
+
+    # Prepare the header of the tables
+    header = list()
+    for column in table["columns"]:
+        header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
+
+    # Generate the data for the table according to the model in the table
+    # specification
+    job = table["data"].keys()[0]
+    build = str(table["data"][job][0])
+    try:
+        suites = input_data.suites(job, build)
+    except KeyError:
+        logging.error("    No data available. The table will not be generated.")
+        return
+
+    for suite_longname, suite in suites.iteritems():
+        # Generate data
+        suite_name = suite["name"]
+        table_lst = list()
+        for test in data[job][build].keys():
+            if data[job][build][test]["parent"] in suite_name:
+                row_lst = list()
+                for column in table["columns"]:
+                    try:
+                        col_data = str(data[job][build][test][column["data"].
+                                       split(" ")[1]]).replace('"', '""')
+                        if column["data"].split(" ")[1] in ("vat-history",
+                                                            "show-run"):
+                            col_data = replace(col_data, " |br| ", "",
+                                               maxreplace=1)
+                            col_data = " |prein| {0} |preout| ".\
+                                format(col_data[:-5])
+                        row_lst.append('"{0}"'.format(col_data))
+                    except KeyError:
+                        row_lst.append("No data")
+                table_lst.append(row_lst)
+
+        # Write the data to file
+        if table_lst:
+            file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
+                                            table["output-file-ext"])
+            logging.info("      Writing file: '{}'".format(file_name))
+            with open(file_name, "w") as file_handler:
+                file_handler.write(",".join(header) + "\n")
+                for item in table_lst:
+                    file_handler.write(",".join(item) + "\n")
+
+    logging.info("  Done.")
+
+
+def table_merged_details(table, input_data):
+    """Generate the table(s) with algorithm: table_merged_details
+    specified in the specification file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    # Transform the data
+    logging.info("    Creating the data set for the {0} '{1}'.".
+                 format(table.get("type", ""), table.get("title", "")))
+    data = input_data.filter_data(table)
+    data = input_data.merge_data(data)
+    data.sort_index(inplace=True)
+
+    logging.info("    Creating the data set for the {0} '{1}'.".
+                 format(table.get("type", ""), table.get("title", "")))
+    suites = input_data.filter_data(table, data_set="suites")
+    suites = input_data.merge_data(suites)
+
+    # Prepare the header of the tables
+    header = list()
+    for column in table["columns"]:
+        header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
+
+    for _, suite in suites.iteritems():
+        # Generate data
+        suite_name = suite["name"]
+        table_lst = list()
+        for test in data.keys():
+            if data[test]["parent"] in suite_name:
+                row_lst = list()
+                for column in table["columns"]:
+                    try:
+                        col_data = str(data[test][column["data"].
+                                       split(" ")[1]]).replace('"', '""')
+                        if column["data"].split(" ")[1] in ("vat-history",
+                                                            "show-run"):
+                            col_data = replace(col_data, " |br| ", "",
+                                               maxreplace=1)
+                            col_data = " |prein| {0} |preout| ".\
+                                format(col_data[:-5])
+                        row_lst.append('"{0}"'.format(col_data))
+                    except KeyError:
+                        row_lst.append("No data")
+                table_lst.append(row_lst)
+
+        # Write the data to file
+        if table_lst:
+            file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
+                                            table["output-file-ext"])
+            logging.info("      Writing file: '{}'".format(file_name))
+            with open(file_name, "w") as file_handler:
+                file_handler.write(",".join(header) + "\n")
+                for item in table_lst:
+                    file_handler.write(",".join(item) + "\n")
+
+    logging.info("  Done.")
+
+
+def table_performance_improvements(table, input_data):
+    """Generate the table(s) with algorithm: table_performance_improvements
+    specified in the specification file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    def _write_line_to_file(file_handler, data):
+        """Write a line to the .csv file.
+
+        :param file_handler: File handler for the csv file. It must be open for
+         writing text.
+        :param data: Item to be written to the file.
+        :type file_handler: BinaryIO
+        :type data: list
+        """
+
+        line_lst = list()
+        for item in data:
+            if isinstance(item["data"], str):
+                # Remove -?drdisc from the end
+                if item["data"].endswith("drdisc"):
+                    item["data"] = item["data"][:-8]
+                line_lst.append(item["data"])
+            elif isinstance(item["data"], float):
+                line_lst.append("{:.1f}".format(item["data"]))
+            elif item["data"] is None:
+                line_lst.append("")
+        file_handler.write(",".join(line_lst) + "\n")
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    # Read the template
+    file_name = table.get("template", None)
+    if file_name:
+        try:
+            tmpl = _read_csv_template(file_name)
+        except PresentationError:
+            logging.error("  The template '{0}' does not exist. Skipping the "
+                          "table.".format(file_name))
+            return None
+    else:
+        logging.error("The template is not defined. Skipping the table.")
+        return None
+
+    # Transform the data
+    logging.info("    Creating the data set for the {0} '{1}'.".
+                 format(table.get("type", ""), table.get("title", "")))
+    data = input_data.filter_data(table)
+
+    # Prepare the header of the tables
+    header = list()
+    for column in table["columns"]:
+        header.append(column["title"])
+
+    # Generate the data for the table according to the model in the table
+    # specification
+    tbl_lst = list()
+    for tmpl_item in tmpl:
+        tbl_item = list()
+        for column in table["columns"]:
+            cmd = column["data"].split(" ")[0]
+            args = column["data"].split(" ")[1:]
+            if cmd == "template":
+                try:
+                    val = float(tmpl_item[int(args[0])])
+                except ValueError:
+                    val = tmpl_item[int(args[0])]
+                tbl_item.append({"data": val})
+            elif cmd == "data":
+                jobs = args[0:-1]
+                operation = args[-1]
+                data_lst = list()
+                for job in jobs:
+                    for build in data[job]:
+                        try:
+                            data_lst.append(float(build[tmpl_item[0]]
+                                                  ["throughput"]["value"]))
+                        except (KeyError, TypeError):
+                            # No data, ignore
+                            continue
+                if data_lst:
+                    tbl_item.append({"data": (eval(operation)(data_lst)) /
+                                             1000000})
+                else:
+                    tbl_item.append({"data": None})
+            elif cmd == "operation":
+                operation = args[0]
+                try:
+                    nr1 = float(tbl_item[int(args[1])]["data"])
+                    nr2 = float(tbl_item[int(args[2])]["data"])
+                    if nr1 and nr2:
+                        tbl_item.append({"data": eval(operation)(nr1, nr2)})
+                    else:
+                        tbl_item.append({"data": None})
+                except (IndexError, ValueError, TypeError):
+                    logging.error("No data for {0}".format(tbl_item[0]["data"]))
+                    tbl_item.append({"data": None})
+                    continue
+            else:
+                logging.error("Not supported command {0}. Skipping the table.".
+                              format(cmd))
+                return None
+        tbl_lst.append(tbl_item)
+
+    # Sort the table according to the relative change
+    tbl_lst.sort(key=lambda rel: rel[-1]["data"], reverse=True)
+
+    # Create the tables and write them to the files
+    file_names = [
+        "{0}_ndr_top{1}".format(table["output-file"], table["output-file-ext"]),
+        "{0}_pdr_top{1}".format(table["output-file"], table["output-file-ext"]),
+        "{0}_ndr_low{1}".format(table["output-file"], table["output-file-ext"]),
+        "{0}_pdr_low{1}".format(table["output-file"], table["output-file-ext"])
+    ]
+
+    for file_name in file_names:
+        logging.info("    Writing the file '{0}'".format(file_name))
+        with open(file_name, "w") as file_handler:
+            file_handler.write(",".join(header) + "\n")
+            for item in tbl_lst:
+                if isinstance(item[-1]["data"], float):
+                    rel_change = round(item[-1]["data"], 1)
+                else:
+                    rel_change = item[-1]["data"]
+                if "ndr_top" in file_name \
+                        and "ndr" in item[0]["data"] \
+                        and rel_change >= 10.0:
+                    _write_line_to_file(file_handler, item)
+                elif "pdr_top" in file_name \
+                        and "pdr" in item[0]["data"] \
+                        and rel_change >= 10.0:
+                    _write_line_to_file(file_handler, item)
+                elif "ndr_low" in file_name \
+                        and "ndr" in item[0]["data"] \
+                        and rel_change < 10.0:
+                    _write_line_to_file(file_handler, item)
+                elif "pdr_low" in file_name \
+                        and "pdr" in item[0]["data"] \
+                        and rel_change < 10.0:
+                    _write_line_to_file(file_handler, item)
+
+    logging.info("  Done.")
+
+
+def _read_csv_template(file_name):
+    """Read the template from a .csv file.
+
+    :param file_name: Name / full path / relative path of the file to read.
+    :type file_name: str
+    :returns: Data from the template as list (lines) of lists (items on line).
+    :rtype: list
+    :raises: PresentationError if it is not possible to read the file.
+    """
+
+    try:
+        with open(file_name, 'r') as csv_file:
+            tmpl_data = list()
+            for line in csv_file:
+                tmpl_data.append(line[:-1].split(","))
+        return tmpl_data
+    except IOError as err:
+        raise PresentationError(str(err), level="ERROR")
+
+
+def table_performance_comparison(table, input_data):
+    """Generate the table(s) with algorithm: table_performance_comparison
+    specified in the specification file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    # Transform the data
+    logging.info("    Creating the data set for the {0} '{1}'.".
+                 format(table.get("type", ""), table.get("title", "")))
+    data = input_data.filter_data(table, continue_on_error=True)
+
+    # Prepare the header of the tables
+    try:
+        header = ["Test case", ]
+
+        history = table.get("history", None)
+        if history:
+            for item in history:
+                header.extend(
+                    ["{0} Throughput [Mpps]".format(item["title"]),
+                     "{0} Stdev [Mpps]".format(item["title"])])
+        header.extend(
+            ["{0} Throughput [Mpps]".format(table["reference"]["title"]),
+             "{0} Stdev [Mpps]".format(table["reference"]["title"]),
+             "{0} Throughput [Mpps]".format(table["compare"]["title"]),
+             "{0} Stdev [Mpps]".format(table["compare"]["title"]),
+             "Change [%]"])
+        header_str = ",".join(header) + "\n"
+    except (AttributeError, KeyError) as err:
+        logging.error("The model is invalid, missing parameter: {0}".
+                      format(err))
+        return
+
+    # Prepare data to the table:
+    tbl_dict = dict()
+    for job, builds in table["reference"]["data"].items():
+        for build in builds:
+            for tst_name, tst_data in data[job][str(build)].iteritems():
+                if tbl_dict.get(tst_name, None) is None:
+                    name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
+                                            "-".join(tst_data["name"].
+                                                     split("-")[1:]))
+                    tbl_dict[tst_name] = {"name": name,
+                                          "ref-data": list(),
+                                          "cmp-data": list()}
+                try:
+                    tbl_dict[tst_name]["ref-data"].\
+                        append(tst_data["throughput"]["value"])
+                except TypeError:
+                    pass  # No data in output.xml for this test
+
+    for job, builds in table["compare"]["data"].items():
+        for build in builds:
+            for tst_name, tst_data in data[job][str(build)].iteritems():
+                try:
+                    tbl_dict[tst_name]["cmp-data"].\
+                        append(tst_data["throughput"]["value"])
+                except KeyError:
+                    pass
+                except TypeError:
+                    tbl_dict.pop(tst_name, None)
+    if history:
+        for item in history:
+            for job, builds in item["data"].items():
+                for build in builds:
+                    for tst_name, tst_data in data[job][str(build)].iteritems():
+                        if tbl_dict.get(tst_name, None) is None:
+                            continue
+                        if tbl_dict[tst_name].get("history", None) is None:
+                            tbl_dict[tst_name]["history"] = OrderedDict()
+                        if tbl_dict[tst_name]["history"].get(item["title"],
+                                                             None) is None:
+                            tbl_dict[tst_name]["history"][item["title"]] = \
+                                list()
+                        try:
+                            tbl_dict[tst_name]["history"][item["title"]].\
+                                append(tst_data["throughput"]["value"])
+                        except (TypeError, KeyError):
+                            pass
+
+    tbl_lst = list()
+    for tst_name in tbl_dict.keys():
+        item = [tbl_dict[tst_name]["name"], ]
+        if history:
+            if tbl_dict[tst_name].get("history", None) is not None:
+                for hist_data in tbl_dict[tst_name]["history"].values():
+                    if hist_data:
+                        item.append(round(mean(hist_data) / 1000000, 2))
+                        item.append(round(stdev(hist_data) / 1000000, 2))
+                    else:
+                        item.extend([None, None])
+            else:
+                item.extend([None, None])
+        if tbl_dict[tst_name]["ref-data"]:
+            data_t = tbl_dict[tst_name]["ref-data"]
+            # TODO: Specify window size.
+            if data_t:
+                item.append(round(mean(data_t) / 1000000, 2))
+                item.append(round(stdev(data_t) / 1000000, 2))
+            else:
+                item.extend([None, None])
+        else:
+            item.extend([None, None])
+        if tbl_dict[tst_name]["cmp-data"]:
+            data_t = tbl_dict[tst_name]["cmp-data"]
+            # TODO: Specify window size.
+            if data_t:
+                item.append(round(mean(data_t) / 1000000, 2))
+                item.append(round(stdev(data_t) / 1000000, 2))
+            else:
+                item.extend([None, None])
+        else:
+            item.extend([None, None])
+        if item[-4] is not None and item[-2] is not None and item[-4] != 0:
+            item.append(int(relative_change(float(item[-4]), float(item[-2]))))
+        if len(item) == len(header):
+            tbl_lst.append(item)
+
+    # Sort the table according to the relative change
+    tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+
+    # Generate tables:
+    # All tests in csv:
+    tbl_names = ["{0}-ndr-1t1c-full{1}".format(table["output-file"],
+                                               table["output-file-ext"]),
+                 "{0}-ndr-2t2c-full{1}".format(table["output-file"],
+                                               table["output-file-ext"]),
+                 "{0}-ndr-4t4c-full{1}".format(table["output-file"],
+                                               table["output-file-ext"]),
+                 "{0}-pdr-1t1c-full{1}".format(table["output-file"],
+                                               table["output-file-ext"]),
+                 "{0}-pdr-2t2c-full{1}".format(table["output-file"],
+                                               table["output-file-ext"]),
+                 "{0}-pdr-4t4c-full{1}".format(table["output-file"],
+                                               table["output-file-ext"])
+                 ]
+    for file_name in tbl_names:
+        logging.info("      Writing file: '{0}'".format(file_name))
+        with open(file_name, "w") as file_handler:
+            file_handler.write(header_str)
+            for test in tbl_lst:
+                if (file_name.split("-")[-3] in test[0] and    # NDR vs PDR
+                        file_name.split("-")[-2] in test[0]):  # cores
+                    test[0] = "-".join(test[0].split("-")[:-1])
+                    file_handler.write(",".join([str(item) for item in test]) +
+                                       "\n")
+
+    # All tests in txt:
+    tbl_names_txt = ["{0}-ndr-1t1c-full.txt".format(table["output-file"]),
+                     "{0}-ndr-2t2c-full.txt".format(table["output-file"]),
+                     "{0}-ndr-4t4c-full.txt".format(table["output-file"]),
+                     "{0}-pdr-1t1c-full.txt".format(table["output-file"]),
+                     "{0}-pdr-2t2c-full.txt".format(table["output-file"]),
+                     "{0}-pdr-4t4c-full.txt".format(table["output-file"])
+                     ]
+
+    for i, txt_name in enumerate(tbl_names_txt):
+        txt_table = None
+        logging.info("      Writing file: '{0}'".format(txt_name))
+        with open(tbl_names[i], 'rb') as csv_file:
+            csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+            for row in csv_content:
+                if txt_table is None:
+                    txt_table = prettytable.PrettyTable(row)
+                else:
+                    txt_table.add_row(row)
+            txt_table.align["Test case"] = "l"
+        with open(txt_name, "w") as txt_file:
+            txt_file.write(str(txt_table))
+
+    # Selected tests in csv:
+    input_file = "{0}-ndr-1t1c-full{1}".format(table["output-file"],
+                                               table["output-file-ext"])
+    with open(input_file, "r") as in_file:
+        lines = list()
+        for line in in_file:
+            lines.append(line)
+
+    output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"],
+                                               table["output-file-ext"])
+    logging.info("      Writing file: '{0}'".format(output_file))
+    with open(output_file, "w") as out_file:
+        out_file.write(header_str)
+        for i, line in enumerate(lines[1:]):
+            if i == table["nr-of-tests-shown"]:
+                break
+            out_file.write(line)
+
+    output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"],
+                                                  table["output-file-ext"])
+    logging.info("      Writing file: '{0}'".format(output_file))
+    with open(output_file, "w") as out_file:
+        out_file.write(header_str)
+        for i, line in enumerate(lines[-1:0:-1]):
+            if i == table["nr-of-tests-shown"]:
+                break
+            out_file.write(line)
+
+    input_file = "{0}-pdr-1t1c-full{1}".format(table["output-file"],
+                                               table["output-file-ext"])
+    with open(input_file, "r") as in_file:
+        lines = list()
+        for line in in_file:
+            lines.append(line)
+
+    output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"],
+                                               table["output-file-ext"])
+    logging.info("      Writing file: '{0}'".format(output_file))
+    with open(output_file, "w") as out_file:
+        out_file.write(header_str)
+        for i, line in enumerate(lines[1:]):
+            if i == table["nr-of-tests-shown"]:
+                break
+            out_file.write(line)
+
+    output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"],
+                                                  table["output-file-ext"])
+    logging.info("      Writing file: '{0}'".format(output_file))
+    with open(output_file, "w") as out_file:
+        out_file.write(header_str)
+        for i, line in enumerate(lines[-1:0:-1]):
+            if i == table["nr-of-tests-shown"]:
+                break
+            out_file.write(line)
+
+
+def table_performance_comparison_mrr(table, input_data):
+    """Generate the table(s) with algorithm: table_performance_comparison_mrr
+    specified in the specification file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    # Transform the data
+    logging.info("    Creating the data set for the {0} '{1}'.".
+                 format(table.get("type", ""), table.get("title", "")))
+    data = input_data.filter_data(table, continue_on_error=True)
+
+    # Prepare the header of the tables
+    try:
+        header = ["Test case",
+                  "{0} Throughput [Mpps]".format(table["reference"]["title"]),
+                  "{0} stdev [Mpps]".format(table["reference"]["title"]),
+                  "{0} Throughput [Mpps]".format(table["compare"]["title"]),
+                  "{0} stdev [Mpps]".format(table["compare"]["title"]),
+                  "Change [%]"]
+        header_str = ",".join(header) + "\n"
+    except (AttributeError, KeyError) as err:
+        logging.error("The model is invalid, missing parameter: {0}".
+                      format(err))
+        return
+
+    # Prepare data to the table:
+    tbl_dict = dict()
+    for job, builds in table["reference"]["data"].items():
+        for build in builds:
+            for tst_name, tst_data in data[job][str(build)].iteritems():
+                if tbl_dict.get(tst_name, None) is None:
+                    name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
+                                            "-".join(tst_data["name"].
+                                                     split("-")[1:]))
+                    tbl_dict[tst_name] = {"name": name,
+                                          "ref-data": list(),
+                                          "cmp-data": list()}
+                try:
+                    tbl_dict[tst_name]["ref-data"].\
+                        append(tst_data["result"]["throughput"])
+                except TypeError:
+                    pass  # No data in output.xml for this test
+
+    for job, builds in table["compare"]["data"].items():
+        for build in builds:
+            for tst_name, tst_data in data[job][str(build)].iteritems():
+                try:
+                    tbl_dict[tst_name]["cmp-data"].\
+                        append(tst_data["result"]["throughput"])
+                except KeyError:
+                    pass
+                except TypeError:
+                    tbl_dict.pop(tst_name, None)
+
+    tbl_lst = list()
+    for tst_name in tbl_dict.keys():
+        item = [tbl_dict[tst_name]["name"], ]
+        if tbl_dict[tst_name]["ref-data"]:
+            data_t = tbl_dict[tst_name]["ref-data"]
+            # TODO: Specify window size.
+            if data_t:
+                item.append(round(mean(data_t) / 1000000, 2))
+                item.append(round(stdev(data_t) / 1000000, 2))
+            else:
+                item.extend([None, None])
+        else:
+            item.extend([None, None])
+        if tbl_dict[tst_name]["cmp-data"]:
+            data_t = tbl_dict[tst_name]["cmp-data"]
+            # TODO: Specify window size.
+            if data_t:
+                item.append(round(mean(data_t) / 1000000, 2))
+                item.append(round(stdev(data_t) / 1000000, 2))
+            else:
+                item.extend([None, None])
+        else:
+            item.extend([None, None])
+        if item[1] is not None and item[3] is not None and item[1] != 0:
+            item.append(int(relative_change(float(item[1]), float(item[3]))))
+        if len(item) == 6:
+            tbl_lst.append(item)
+
+    # Sort the table according to the relative change
+    tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+
+    # Generate tables:
+    # All tests in csv:
+    tbl_names = ["{0}-1t1c-full{1}".format(table["output-file"],
+                                           table["output-file-ext"]),
+                 "{0}-2t2c-full{1}".format(table["output-file"],
+                                           table["output-file-ext"]),
+                 "{0}-4t4c-full{1}".format(table["output-file"],
+                                           table["output-file-ext"])
+                 ]
+    for file_name in tbl_names:
+        logging.info("      Writing file: '{0}'".format(file_name))
+        with open(file_name, "w") as file_handler:
+            file_handler.write(header_str)
+            for test in tbl_lst:
+                if file_name.split("-")[-2] in test[0]:  # cores
+                    test[0] = "-".join(test[0].split("-")[:-1])
+                    file_handler.write(",".join([str(item) for item in test]) +
+                                       "\n")
+
+    # All tests in txt:
+    tbl_names_txt = ["{0}-1t1c-full.txt".format(table["output-file"]),
+                     "{0}-2t2c-full.txt".format(table["output-file"]),
+                     "{0}-4t4c-full.txt".format(table["output-file"])
+                     ]
+
+    for i, txt_name in enumerate(tbl_names_txt):
+        txt_table = None
+        logging.info("      Writing file: '{0}'".format(txt_name))
+        with open(tbl_names[i], 'rb') as csv_file:
+            csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+            for row in csv_content:
+                if txt_table is None:
+                    txt_table = prettytable.PrettyTable(row)
+                else:
+                    txt_table.add_row(row)
+            txt_table.align["Test case"] = "l"
+        with open(txt_name, "w") as txt_file:
+            txt_file.write(str(txt_table))
+
+
+def table_performance_trending_dashboard(table, input_data):
+    """Generate the table(s) with algorithm: table_performance_comparison
+    specified in the specification file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    # Transform the data
+    logging.info("    Creating the data set for the {0} '{1}'.".
+                 format(table.get("type", ""), table.get("title", "")))
+    data = input_data.filter_data(table, continue_on_error=True)
+
+    # Prepare the header of the tables
+    header = ["Test Case",
+              "Trend [Mpps]",
+              "Short-Term Change [%]",
+              "Long-Term Change [%]",
+              "Regressions [#]",
+              "Progressions [#]",
+              "Outliers [#]"
+              ]
+    header_str = ",".join(header) + "\n"
+
+    # Prepare data to the table:
+    tbl_dict = dict()
+    for job, builds in table["data"].items():
+        for build in builds:
+            for tst_name, tst_data in data[job][str(build)].iteritems():
+                if tst_name.lower() in table["ignore-list"]:
+                    continue
+                if tbl_dict.get(tst_name, None) is None:
+                    name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
+                                            "-".join(tst_data["name"].
+                                                     split("-")[1:]))
+                    tbl_dict[tst_name] = {"name": name,
+                                          "data": OrderedDict()}
+                try:
+                    tbl_dict[tst_name]["data"][str(build)] =  \
+                        tst_data["result"]["throughput"]
+                except (TypeError, KeyError):
+                    pass  # No data in output.xml for this test
+
+    tbl_lst = list()
+    for tst_name in tbl_dict.keys():
+        if len(tbl_dict[tst_name]["data"]) < 3:
+            continue
+
+        pd_data = pd.Series(tbl_dict[tst_name]["data"])
+        last_key = pd_data.keys()[-1]
+        win_size = min(pd_data.size, table["window"])
+        win_first_idx = pd_data.size - win_size
+        key_14 = pd_data.keys()[win_first_idx]
+        long_win_size = min(pd_data.size, table["long-trend-window"])
+        median_t = pd_data.rolling(window=win_size, min_periods=2).median()
+        median_first_idx = median_t.size - long_win_size
+        try:
+            max_median = max(
+                [x for x in median_t.values[median_first_idx:-win_size]
+                 if not isnan(x)])
+        except ValueError:
+            max_median = nan
+        try:
+            last_median_t = median_t[last_key]
+        except KeyError:
+            last_median_t = nan
+        try:
+            median_t_14 = median_t[key_14]
+        except KeyError:
+            median_t_14 = nan
+
+        if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0:
+            rel_change_last = nan
+        else:
+            rel_change_last = round(
+                ((last_median_t - median_t_14) / median_t_14) * 100, 2)
+
+        if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
+            rel_change_long = nan
+        else:
+            rel_change_long = round(
+                ((last_median_t - max_median) / max_median) * 100, 2)
+
+        # Classification list:
+        classification_lst, _ = classify_anomalies(pd_data)
+
+        if classification_lst:
+            if isnan(rel_change_last) and isnan(rel_change_long):
+                continue
+            tbl_lst.append(
+                [tbl_dict[tst_name]["name"],
+                 '-' if isnan(last_median_t) else
+                 round(last_median_t / 1000000, 2),
+                 '-' if isnan(rel_change_last) else rel_change_last,
+                 '-' if isnan(rel_change_long) else rel_change_long,
+                 classification_lst[win_first_idx:].count("regression"),
+                 classification_lst[win_first_idx:].count("progression"),
+                 classification_lst[win_first_idx:].count("outlier")])
+
+    tbl_lst.sort(key=lambda rel: rel[0])
+
+    tbl_sorted = list()
+    for nrr in range(table["window"], -1, -1):
+        tbl_reg = [item for item in tbl_lst if item[4] == nrr]
+        for nrp in range(table["window"], -1, -1):
+            tbl_pro = [item for item in tbl_reg if item[5] == nrp]
+            for nro in range(table["window"], -1, -1):
+                tbl_out = [item for item in tbl_pro if item[6] == nro]
+                tbl_out.sort(key=lambda rel: rel[2])
+                tbl_sorted.extend(tbl_out)
+
+    file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
+
+    logging.info("      Writing file: '{0}'".format(file_name))
+    with open(file_name, "w") as file_handler:
+        file_handler.write(header_str)
+        for test in tbl_sorted:
+            file_handler.write(",".join([str(item) for item in test]) + '\n')
+
+    txt_file_name = "{0}.txt".format(table["output-file"])
+    txt_table = None
+    logging.info("      Writing file: '{0}'".format(txt_file_name))
+    with open(file_name, 'rb') as csv_file:
+        csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+        for row in csv_content:
+            if txt_table is None:
+                txt_table = prettytable.PrettyTable(row)
+            else:
+                txt_table.add_row(row)
+        txt_table.align["Test case"] = "l"
+    with open(txt_file_name, "w") as txt_file:
+        txt_file.write(str(txt_table))
+
+
+def table_performance_trending_dashboard_html(table, input_data):
+    """Generate the table(s) with algorithm:
+    table_performance_trending_dashboard_html specified in the specification
+    file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    try:
+        with open(table["input-file"], 'rb') as csv_file:
+            csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+            csv_lst = [item for item in csv_content]
+    except KeyError:
+        logging.warning("The input file is not defined.")
+        return
+    except csv.Error as err:
+        logging.warning("Not possible to process the file '{0}'.\n{1}".
+                        format(table["input-file"], err))
+        return
+
+    # Table:
+    dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
+
+    # Table header:
+    tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7"))
+    for idx, item in enumerate(csv_lst[0]):
+        alignment = "left" if idx == 0 else "center"
+        th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
+        th.text = item
+
+    # Rows:
+    colors = {"regression": ("#ffcccc", "#ff9999"),
+              "progression": ("#c6ecc6", "#9fdf9f"),
+              "outlier": ("#e6e6e6", "#cccccc"),
+              "normal": ("#e9f1fb", "#d4e4f7")}
+    for r_idx, row in enumerate(csv_lst[1:]):
+        if int(row[4]):
+            color = "regression"
+        elif int(row[5]):
+            color = "progression"
+        elif int(row[6]):
+            color = "outlier"
+        else:
+            color = "normal"
+        background = colors[color][r_idx % 2]
+        tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
+
+        # Columns:
+        for c_idx, item in enumerate(row):
+            alignment = "left" if c_idx == 0 else "center"
+            td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
+            # Name:
+            url = "../trending/"
+            file_name = ""
+            anchor = "#"
+            feature = ""
+            if c_idx == 0:
+                if "memif" in item:
+                    file_name = "container_memif.html"
+
+                elif "srv6" in item:
+                    file_name = "srv6.html"
+
+                elif "vhost" in item:
+                    if "l2xcbase" in item or "l2bdbasemaclrn" in item:
+                        file_name = "vm_vhost_l2.html"
+                    elif "ip4base" in item:
+                        file_name = "vm_vhost_ip4.html"
+
+                elif "ipsec" in item:
+                    file_name = "ipsec.html"
+
+                elif "ethip4lispip" in item or "ethip4vxlan" in item:
+                    file_name = "ip4_tunnels.html"
+
+                elif "ip4base" in item or "ip4scale" in item:
+                    file_name = "ip4.html"
+                    if "iacl" in item or "snat" in item or "cop" in item:
+                        feature = "-features"
+
+                elif "ip6base" in item or "ip6scale" in item:
+                    file_name = "ip6.html"
+
+                elif "l2xcbase" in item or "l2xcscale" in item \
+                        or "l2bdbasemaclrn" in item or "l2bdscale" in item \
+                        or "l2dbbasemaclrn" in item or "l2dbscale" in item:
+                    file_name = "l2.html"
+                    if "iacl" in item:
+                        feature = "-features"
+
+                if "x520" in item:
+                    anchor += "x520-"
+                elif "x710" in item:
+                    anchor += "x710-"
+                elif "xl710" in item:
+                    anchor += "xl710-"
+
+                if "64b" in item:
+                    anchor += "64b-"
+                elif "78b" in item:
+                    anchor += "78b-"
+                elif "imix" in item:
+                    anchor += "imix-"
+                elif "9000b" in item:
+                    anchor += "9000b-"
+                elif "1518" in item:
+                    anchor += "1518b-"
+
+                if "1t1c" in item:
+                    anchor += "1t1c"
+                elif "2t2c" in item:
+                    anchor += "2t2c"
+                elif "4t4c" in item:
+                    anchor += "4t4c"
+
+                url = url + file_name + anchor + feature
+
+                ref = ET.SubElement(td, "a", attrib=dict(href=url))
+                ref.text = item
+
+            if c_idx > 0:
+                td.text = item
+
+    try:
+        with open(table["output-file"], 'w') as html_file:
+            logging.info("      Writing file: '{0}'".
+                         format(table["output-file"]))
+            html_file.write(".. raw:: html\n\n\t")
+            html_file.write(ET.tostring(dashboard))
+            html_file.write("\n\t<p><br><br></p>\n")
+    except KeyError:
+        logging.warning("The output file is not defined.")
+        return