summaryrefslogtreecommitdiffstats
path: root/test/test_dhcp.py
AgeCommit message (Expand)AuthorFilesLines
2019-03-11VPP-1508: Use scapy.compat to manage packet level library differences.Paul Vinciguerra1-2/+3
2019-03-04test framework: Fix wrapper functions to match API message names.Ole Troan1-10/+10
2019-01-31DHCP-test: more robust sleep checkNeale Ranns1-8/+13
2019-01-30Use IP and MAC API types for neighborsNeale Ranns1-5/+5
2018-12-18PAPI: Add MACAddress object wrapper for vl_api_mac_address_tOle Troan1-11/+11
2018-12-10Test framework: StringIO fixes for Python3Ole Troan1-2/+1
2018-07-18make_test: dhcp - add lease time test and wrong XID in OFFER testJan Gelety1-7/+102
2018-06-07DHCP Client DumpNeale Ranns1-0/+23
2018-03-23Add client hw address to DHCP offer and ack in test caseNeale Ranns1-6/+11
2018-03-23Drop dhcp pkts w/ hardware address mismatchesDave Barach1-0/+1
2018-03-19Scapy upgrade to 2.4.0.rc5Neale Ranns1-1/+2
2018-01-23For DHCP client configuration control the setting of the broadcast flag in theNeale Ranns1-4/+105
2018-01-09test: consolidate the multiple versions of send_and_*Neale Ranns1-7/+0
2017-11-16Add Support of DHCP VSS Type 0 where VPN-ID is ASCIIJohn Lo1-170/+298
2017-10-25VPP-1033: Python API support arbitrary sized input parameters.Ole Troan1-1/+2
2017-09-11FIB table add/delete APINeale Ranns1-3/+21
2017-08-03DHCP Client: receive unicast ACKsNeale Ranns1-21/+71
2017-07-18DHCP client option 61 "client_id"Neale Ranns1-2/+32
2017-07-18DHCP client - remove interface address when DHCP de-configuredNeale Ranns1-21/+137
2017-04-19Learn IP6 link-local ND entries from NSs sourced from link-local addressNeale Ranns1-7/+1
2017-03-07DHCP Multiple Servers (VPP-602, VPP-605)Neale Ranns1-44/+312
2017-03-03Changing the IP table for an interface is an error if the interface already h...Neale Ranns1-0/+7
2017-02-16Consolidate DHCP v4 and V6 implementation. No functional change intendedNeale Ranns1-1/+1
2017-02-21dhcp: multiple additionsNeale Ranns1-82/+84
2017-01-27IP Multicast FIB (mfib)Neale Ranns1-16/+0
2017-01-26DHCPv[46] proxy testsNeale Ranns1-0/+739
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# Copyright (c) 2020 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 plots.
"""


import re
import logging

from collections import OrderedDict
from copy import deepcopy

import hdrh.histogram
import hdrh.codec
import pandas as pd
import plotly.offline as ploff
import plotly.graph_objs as plgo

from plotly.exceptions import PlotlyError

from pal_utils import mean, stdev


COLORS = (
    u"#1A1110",
    u"#DA2647",
    u"#214FC6",
    u"#01786F",
    u"#BD8260",
    u"#FFD12A",
    u"#A6E7FF",
    u"#738276",
    u"#C95A49",
    u"#FC5A8D",
    u"#CEC8EF",
    u"#391285",
    u"#6F2DA8",
    u"#FF878D",
    u"#45A27D",
    u"#FFD0B9",
    u"#FD5240",
    u"#DB91EF",
    u"#44D7A8",
    u"#4F86F7",
    u"#84DE02",
    u"#FFCFF1",
    u"#614051"
)

REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)-')


def generate_plots(spec, data):
    """Generate all plots specified in the specification file.

    :param spec: Specification read from the specification file.
    :param data: Data to process.
    :type spec: Specification
    :type data: InputData
    """

    generator = {
        u"plot_nf_reconf_box_name": plot_nf_reconf_box_name,
        u"plot_perf_box_name": plot_perf_box_name,
        u"plot_tsa_name": plot_tsa_name,
        u"plot_http_server_perf_box": plot_http_server_perf_box,
        u"plot_nf_heatmap": plot_nf_heatmap,
        u"plot_hdrh_lat_by_percentile": plot_hdrh_lat_by_percentile
    }

    logging.info(u"Generating the plots ...")
    for index, plot in enumerate(spec.plots):
        try:
            logging.info(f"  Plot nr {index + 1}: {plot.get(u'title', u'')}")
            plot[u"limits"] = spec.configuration[u"limits"]
            generator[plot[u"algorithm"]](plot, data)
            logging.info(u"  Done.")
        except NameError as err:
            logging.error(
                f"Probably algorithm {plot[u'algorithm']} is not defined: "
                f"{repr(err)}"
            )
    logging.info(u"Done.")


def plot_hdrh_lat_by_percentile(plot, input_data):
    """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile
    specified in the specification file.

    :param plot: Plot to generate.
    :param input_data: Data to process.
    :type plot: pandas.Series
    :type input_data: InputData
    """

    # Transform the data
    logging.info(
        f"    Creating the data set for the {plot.get(u'type', u'')} "
        f"{plot.get(u'title', u'')}."
    )
    if plot.get(u"include", None):
        data = input_data.filter_tests_by_name(
            plot,
            params=[u"name", u"latency", u"parent", u"tags", u"type"]
        )[0][0]
    elif plot.get(u"filter", None):
        data = input_data.filter_data(
            plot,
            params=[u"name", u"latency", u"parent", u"tags", u"type"],
            continue_on_error=True
        )[0][0]
    else:
        job = list(plot[u"data"].keys())[0]
        build = str(plot[u"data"][job][0])
        data = input_data.tests(job, build)

    if data is None or len(data) == 0:
        logging.error(u"No data.")
        return

    desc = {
        u"LAT0": u"No-load.",
        u"PDR10": u"Low-load, 10% PDR.",
        u"PDR50": u"Mid-load, 50% PDR.",
        u"PDR90": u"High-load, 90% PDR.",
        u"PDR": u"Full-load, 100% PDR.",
        u"NDR10": u"Low-load, 10% NDR.",
        u"NDR50": u"Mid-load, 50% NDR.",
        u"NDR90": u"High-load, 90% NDR.",
        u"NDR": u"Full-load, 100% NDR."
    }

    graphs = [
        u"LAT0",
        u"PDR10",
        u"PDR50",
        u"PDR90"
    ]

    file_links = plot.get(u"output-file-links", None)
    target_links = plot.get(u"target-links", None)

    for test in data:
        try:
            if test[u"type"] not in (u"NDRPDR",):
                logging.warning(f"Invalid test type: {test[u'type']}")
                continue
            name = re.sub(REGEX_NIC, u"", test[u"parent"].
                          replace(u'-ndrpdr', u'').replace(u'2n1l-', u''))
            try:
                nic = re.search(REGEX_NIC, test[u"parent"]).group(1)
            except (IndexError, AttributeError, KeyError, ValueError):
                nic = u""
            name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'')

            logging.info(f"    Generating the graph: {name_link}")

            fig = plgo.Figure()
            layout = deepcopy(plot[u"layout"])

            for color, graph in enumerate(graphs):
                for idx, direction in enumerate((u"direction1", u"direction2")):
                    xaxis = [0.0, ]
                    yaxis = [0.0, ]
                    hovertext = [
                        f"<b>{desc[graph]}</b><br>"
                        f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
                        f"Percentile: 0.0%<br>"
                        f"Latency: 0.0uSec"
                    ]
                    try:
                        decoded = hdrh.histogram.HdrHistogram.decode(
                            test[u"latency"][graph][direction][u"hdrh"]
                        )
                    except hdrh.codec.HdrLengthException:
                        logging.warning(
                            f"No data for direction {(u'W-E', u'E-W')[idx % 2]}"
                        )
                        continue

                    for item in decoded.get_recorded_iterator():
                        percentile = item.percentile_level_iterated_to
                        if percentile > 99.9:
                            continue
                        xaxis.append(percentile)
                        yaxis.append(item.value_iterated_to)
                        hovertext.append(
                            f"<b>{desc[graph]}</b><br>"
                            f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
                            f"Percentile: {percentile:.5f}%<br>"
                            f"Latency: {item.value_iterated_to}uSec"
                        )
                    fig.add_trace(
                        plgo.Scatter(
                            x=xaxis,
                            y=yaxis,
                            name=desc[graph],
                            mode=u"lines",
                            legendgroup=desc[graph],
                            showlegend=bool(idx),
                            line=dict(
                                color=COLORS[color],
                                dash=u"dash" if idx % 2 else u"solid"
                            ),
                            hovertext=hovertext,
                            hoverinfo=u"text"
                        )
                    )

            layout[u"title"][u"text"] = f"<b>Latency:</b> {name}"
            fig.update_layout(layout)

            # Create plot
            file_name = f"{plot[u'output-file']}-{name_link}.html"
            logging.info(f"    Writing file {file_name}")

            try:
                # Export Plot
                ploff.plot(fig, show_link=False, auto_open=False,
                           filename=file_name)
                # Add link to the file:
                if file_links and target_links:
                    with open(file_links, u"a") as file_handler:
                        file_handler.write(
                            f"- `{name_link} "
                            f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n"
                        )
            except FileNotFoundError as err:
                logging.error(
                    f"Not possible to write the link to the file "
                    f"{file_links}\n{err}"
                )
            except PlotlyError as err:
                logging.error(f"   Finished with error: {repr(err)}")

        except hdrh.codec.HdrLengthException as err:
            logging.warning(repr(err))
            continue

        except (ValueError, KeyError) as err:
            logging.warning(repr(err))
            continue


def plot_nf_reconf_box_name(plot, input_data):
    """Generate the plot(s) with algorithm: plot_nf_reconf_box_name
    specified in the specification file.

    :param plot: Plot to generate.
    :param input_data: Data to process.
    :type plot: pandas.Series
    :type input_data: InputData
    """

    # Transform the data
    logging.info(
        f"    Creating the data set for the {plot.get(u'type', u'')} "
        f"{plot.get(u'title', u'')}."
    )
    data = input_data.filter_tests_by_name(
        plot, params=[u"result", u"parent", u"tags", u"type"]
    )
    if data is None:
        logging.error(u"No data.")
        return

    # Prepare the data for the plot
    y_vals = OrderedDict()
    loss = dict()
    for job in data:
        for build in job:
            for test in build:
                if y_vals.get(test[u"parent"], None) is None:
                    y_vals[test[u"parent"]] = list()
                    loss[test[u"parent"]] = list()
                try:
                    y_vals[test[u"parent"]].append(test[u"result"][u"time"])
                    loss[test[u"parent"]].append(test[u"result"][u"loss"])
                except (KeyError, TypeError):
                    y_vals[test[u"parent"]].append(None)

    # Add None to the lists with missing data
    max_len = 0
    nr_of_samples = list()
    for val in y_vals.values():
        if len(val) > max_len:
            max_len = len(val)
        nr_of_samples.append(len(val))
    for val in y_vals.values():
        if len(val) < max_len:
            val.extend([None for _ in range(max_len - len(val))])

    # Add plot traces
    traces = list()
    df_y = pd.DataFrame(y_vals)
    df_y.head()
    for i, col in enumerate(df_y.columns):
        tst_name = re.sub(REGEX_NIC, u"",
                          col.lower().replace(u'-ndrpdr', u'').
                          replace(u'2n1l-', u''))

        traces.append(plgo.Box(
            x=[str(i + 1) + u'.'] * len(df_y[col]),
            y=[y if y else None for y in df_y[col]],
            name=(
                f"{i + 1}. "
                f"({nr_of_samples[i]:02d} "
                f"run{u's' if nr_of_samples[i] > 1 else u''}, "
                f"packets lost average: {mean(loss[col]):.1f}) "
                f"{u'-'.join(tst_name.split(u'-')[3:-2])}"
            ),
            hoverinfo=u"y+name"
        ))
    try:
        # Create plot
        layout = deepcopy(plot[u"layout"])
        layout[u"title"] = f"<b>Time Lost:</b> {layout[u'title']}"
        layout[u"yaxis"][u"title"] = u"<b>Effective Blocked Time [s]</b>"
        layout[u"legend"][u"font"][u"size"] = 14
        layout[u"yaxis"].pop(u"range")
        plpl = plgo.Figure(data=traces, layout=layout)

        # Export Plot
        file_type = plot.get(u"output-file-type", u".html")
        logging.info(f"    Writing file {plot[u'output-file']}{file_type}.")
        ploff.plot(
            plpl,
            show_link=False,
            auto_open=False,
            filename=f"{plot[u'output-file']}{file_type}"
        )
    except PlotlyError as err:
        logging.error(
            f"   Finished with error: {repr(err)}".replace(u"\n", u" ")
        )
        return


def plot_perf_box_name(plot, input_data):
    """Generate the plot(s) with algorithm: plot_perf_box_name
    specified in the specification file.

    :param plot: Plot to generate.
    :param input_data: Data to process.
    :type plot: pandas.Series
    :type input_data: InputData
    """

    # Transform the data
    logging.info(
        f"    Creating data set for the {plot.get(u'type', u'')} "
        f"{plot.get(u'title', u'')}."
    )
    data = input_data.filter_tests_by_name(
        plot,
        params=[u"throughput", u"gbps", u"result", u"parent", u"tags", u"type"])
    if data is None:
        logging.error(u"No data.")
        return

    # Prepare the data for the plot
    plot_title = plot.get(u"title", u"").lower()

    if u"-gbps" in plot_title:
        value = u"gbps"
        multiplier = 1e6
    else:
        value = u"throughput"
        multiplier = 1.0
    y_vals = OrderedDict()
    test_type = u""
    for job in data:
        for build in job:
            for test in build:
                if y_vals.get(test[u"parent"], None) is None:
                    y_vals[test[u"parent"]] = list()
                try:
                    if test[u"type"] in (u"NDRPDR", ):
                        test_type = u"NDRPDR"

                        if u"-pdr" in plot_title:
                            ttype = u"PDR"
                        elif u"-ndr" in plot_title:
                            ttype = u"NDR"
                        else:
                            raise RuntimeError(
                                u"Wrong title. No information about test type. "
                                u"Add '-ndr' or '-pdr' to the test title."
                            )

                        y_vals[test[u"parent"]].append(
                            test[value][ttype][u"LOWER"] * multiplier
                        )

                    elif test[u"type"] in (u"SOAK", ):
                        y_vals[test[u"parent"]].\
                            append(test[u"throughput"][u"LOWER"])
                        test_type = u"SOAK"

                    elif test[u"type"] in (u"HOSTSTACK", ):
                        if u"LDPRELOAD" in test[u"tags"]:
                            y_vals[test[u"parent"]].append(
                                float(test[u"result"][u"bits_per_second"]) / 1e3
                            )
                        elif u"VPPECHO" in test[u"tags"]:
                            y_vals[test[u"parent"]].append(
                                (float(test[u"result"][u"client"][u"tx_data"])
                                 * 8 / 1e3) /
                                ((float(test[u"result"][u"client"][u"time"]) +
                                  float(test[u"result"][u"server"][u"time"])) /
                                 2)
                            )
                        test_type = u"HOSTSTACK"

                    else:
                        continue

                except (KeyError, TypeError):
                    y_vals[test[u"parent"]].append(None)

    # Add None to the lists with missing data
    max_len = 0
    nr_of_samples = list()
    for val in y_vals.values():
        if len(val) > max_len:
            max_len = len(val)
        nr_of_samples.append(len(val))
    for val in y_vals.values():
        if len(val) < max_len:
            val.extend([None for _ in range(max_len - len(val))])

    # Add plot traces
    traces = list()
    df_y = pd.DataFrame(y_vals)
    df_y.head()
    y_max = list()
    for i, col in enumerate(df_y.columns):
        tst_name = re.sub(REGEX_NIC, u"",
                          col.lower().replace(u'-ndrpdr', u'').
                          replace(u'2n1l-', u''))
        kwargs = dict(
            x=[str(i + 1) + u'.'] * len(df_y[col]),
            y=[y / 1e6 if y else None for y in df_y[col]],
            name=(
                f"{i + 1}. "
                f"({nr_of_samples[i]:02d} "
                f"run{u's' if nr_of_samples[i] > 1 else u''}) "
                f"{tst_name}"
            ),
            hoverinfo=u"y+name"
        )
        if test_type in (u"SOAK", ):
            kwargs[u"boxpoints"] = u"all"

        traces.append(plgo.Box(**kwargs))

        try:
            val_max = max(df_y[col])
            if val_max:
                y_max.append(int(val_max / 1e6) + 2)
        except (ValueError, TypeError) as err:
            logging.error(repr(err))
            continue

    try:
        # Create plot
        layout = deepcopy(plot[u"layout"])
        if layout.get(u"title", None):
            if test_type in (u"HOSTSTACK", ):
                layout[u"title"] = f"<b>Bandwidth:</b> {layout[u'title']}"
            else:
                layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
        if y_max:
            layout[u"yaxis"][u"range"] = [0, max(y_max)]
        plpl = plgo.Figure(data=traces, layout=layout)

        # Export Plot
        logging.info(f"    Writing file {plot[u'output-file']}.html.")
        ploff.plot(
            plpl,
            show_link=False,
            auto_open=False,
            filename=f"{plot[u'output-file']}.html"
        )
    except PlotlyError as err:
        logging.error(
            f"   Finished with error: {repr(err)}".replace(u"\n", u" ")
        )
        return


def plot_tsa_name(plot, input_data):
    """Generate the plot(s) with algorithm:
    plot_tsa_name
    specified in the specification file.

    :param plot: Plot to generate.
    :param input_data: Data to process.
    :type plot: pandas.Series
    :type input_data: InputData
    """

    # Transform the data
    plot_title = plot.get(u"title", u"")
    logging.info(
        f"    Creating data set for the {plot.get(u'type', u'')} {plot_title}."
    )
    data = input_data.filter_tests_by_name(
        plot,
        params=[u"throughput", u"gbps", u"parent", u"tags", u"type"]
    )
    if data is None:
        logging.error(u"No data.")
        return

    plot_title = plot_title.lower()

    if u"-gbps" in plot_title:
        value = u"gbps"
        h_unit = u"Gbps"
        multiplier = 1e6
    else:
        value = u"throughput"
        h_unit = u"Mpps"
        multiplier = 1.0

    y_vals = OrderedDict()
    for job in data:
        for build in job:
            for test in build:
                if y_vals.get(test[u"parent"], None) is None:
                    y_vals[test[u"parent"]] = {
                        u"1": list(),
                        u"2": list(),
                        u"4": list()
                    }
                try:
                    if test[u"type"] not in (u"NDRPDR",):
                        continue

                    if u"-pdr" in plot_title:
                        ttype = u"PDR"
                    elif u"-ndr" in plot_title:
                        ttype = u"NDR"
                    else:
                        continue

                    if u"1C" in test[u"tags"]:
                        y_vals[test[u"parent"]][u"1"]. \
                            append(test[value][ttype][u"LOWER"] * multiplier)
                    elif u"2C" in test[u"tags"]:
                        y_vals[test[u"parent"]][u"2"]. \
                            append(test[value][ttype][u"LOWER"] * multiplier)
                    elif u"4C" in test[u"tags"]:
                        y_vals[test[u"parent"]][u"4"]. \
                            append(test[value][ttype][u"LOWER"] * multiplier)
                except (KeyError, TypeError):
                    pass

    if not y_vals:
        logging.warning(f"No data for the plot {plot.get(u'title', u'')}")
        return

    y_1c_max = dict()
    for test_name, test_vals in y_vals.items():
        for key, test_val in test_vals.items():
            if test_val:
                avg_val = sum(test_val) / len(test_val)
                y_vals[test_name][key] = [avg_val, len(test_val)]
                ideal = avg_val / (int(key) * 1e6)
                if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
                    y_1c_max[test_name] = ideal

    vals = OrderedDict()
    y_max = list()
    nic_limit = 0
    lnk_limit = 0
    pci_limit = plot[u"limits"][u"pci"][u"pci-g3-x8"]
    for test_name, test_vals in y_vals.items():
        try:
            if test_vals[u"1"][1]:
                name = re.sub(
                    REGEX_NIC,
                    u"",
                    test_name.replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')
                )
                vals[name] = OrderedDict()
                y_val_1 = test_vals[u"1"][0] / 1e6
                y_val_2 = test_vals[u"2"][0] / 1e6 if test_vals[u"2"][0] \
                    else None
                y_val_4 = test_vals[u"4"][0] / 1e6 if test_vals[u"4"][0] \
                    else None

                vals[name][u"val"] = [y_val_1, y_val_2, y_val_4]
                vals[name][u"rel"] = [1.0, None, None]
                vals[name][u"ideal"] = [
                    y_1c_max[test_name],
                    y_1c_max[test_name] * 2,
                    y_1c_max[test_name] * 4
                ]
                vals[name][u"diff"] = [
                    (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None
                ]
                vals[name][u"count"] = [
                    test_vals[u"1"][1],
                    test_vals[u"2"][1],
                    test_vals[u"4"][1]
                ]

                try:
                    val_max = max(vals[name][u"val"])
                except ValueError as err:
                    logging.error(repr(err))
                    continue
                if val_max:
                    y_max.append(val_max)

                if y_val_2:
                    vals[name][u"rel"][1] = round(y_val_2 / y_val_1, 2)
                    vals[name][u"diff"][1] = \
                        (y_val_2 - vals[name][u"ideal"][1]) * 100 / y_val_2
                if y_val_4:
                    vals[name][u"rel"][2] = round(y_val_4 / y_val_1, 2)
                    vals[name][u"diff"][2] = \
                        (y_val_4 - vals[name][u"ideal"][2]) * 100 / y_val_4
        except IndexError as err:
            logging.warning(f"No data for {test_name}")
            logging.warning(repr(err))

        # Limits:
        if u"x520" in test_name:
            limit = plot[u"limits"][u"nic"][u"x520"]
        elif u"x710" in test_name:
            limit = plot[u"limits"][u"nic"][u"x710"]
        elif u"xxv710" in test_name:
            limit = plot[u"limits"][u"nic"][u"xxv710"]
        elif u"xl710" in test_name:
            limit = plot[u"limits"][u"nic"][u"xl710"]
        elif u"x553" in test_name:
            limit = plot[u"limits"][u"nic"][u"x553"]
        elif u"cx556a" in test_name:
            limit = plot[u"limits"][u"nic"][u"cx556a"]
        else:
            limit = 0
        if limit > nic_limit:
            nic_limit = limit

        mul = 2 if u"ge2p" in test_name else 1
        if u"10ge" in test_name:
            limit = plot[u"limits"][u"link"][u"10ge"] * mul
        elif u"25ge" in test_name:
            limit = plot[u"limits"][u"link"][u"25ge"] * mul
        elif u"40ge" in test_name:
            limit = plot[u"limits"][u"link"][u"40ge"] * mul
        elif u"100ge" in test_name:
            limit = plot[u"limits"][u"link"][u"100ge"] * mul
        else:
            limit = 0
        if limit > lnk_limit:
            lnk_limit = limit

    traces = list()
    annotations = list()
    x_vals = [1, 2, 4]

    # Limits:
    if u"-gbps" not in plot_title:
        try:
            threshold = 1.1 * max(y_max)  # 10%
        except ValueError as err:
            logging.error(err)
            return
        nic_limit /= 1e6
        traces.append(plgo.Scatter(
            x=x_vals,
            y=[nic_limit, ] * len(x_vals),
            name=f"NIC: {nic_limit:.2f}Mpps",
            showlegend=False,
            mode=u"lines",
            line=dict(
                dash=u"dot",
                color=COLORS[-1],
                width=1),
            hoverinfo=u"none"
        ))
        annotations.append(dict(
            x=1,
            y=nic_limit,
            xref=u"x",
            yref=u"y",
            xanchor=u"left",
            yanchor=u"bottom",
            text=f"NIC: {nic_limit:.2f}Mpps",
            font=dict(
                size=14,
                color=COLORS[-1],
            ),
            align=u"left",
            showarrow=False
        ))
        y_max.append(nic_limit)

        lnk_limit /= 1e6
        if lnk_limit < threshold:
            traces.append(plgo.Scatter(
                x=x_vals,
                y=[lnk_limit, ] * len(x_vals),
                name=f"Link: {lnk_limit:.2f}Mpps",
                showlegend=False,
                mode=u"lines",
                line=dict(
                    dash=u"dot",
                    color=COLORS[-2],
                    width=1),
                hoverinfo=u"none"
            ))
            annotations.append(dict(
                x=1,
                y=lnk_limit,
                xref=u"x",
                yref=u"y",
                xanchor=u"left",
                yanchor=u"bottom",
                text=f"Link: {lnk_limit:.2f}Mpps",
                font=dict(
                    size=14,
                    color=COLORS[-2],
                ),
                align=u"left",
                showarrow=False
            ))
            y_max.append(lnk_limit)

        pci_limit /= 1e6
        if (pci_limit < threshold and
                (pci_limit < lnk_limit * 0.95 or lnk_limit > lnk_limit * 1.05)):
            traces.append(plgo.Scatter(
                x=x_vals,
                y=[pci_limit, ] * len(x_vals),
                name=f"PCIe: {pci_limit:.2f}Mpps",
                showlegend=False,
                mode=u"lines",
                line=dict(
                    dash=u"dot",
                    color=COLORS[-3],
                    width=1),
                hoverinfo=u"none"
            ))
            annotations.append(dict(
                x=1,
                y=pci_limit,
                xref=u"x",
                yref=u"y",
                xanchor=u"left",
                yanchor=u"bottom",
                text=f"PCIe: {pci_limit:.2f}Mpps",
                font=dict(
                    size=14,
                    color=COLORS[-3],
                ),
                align=u"left",
                showarrow=False
            ))
            y_max.append(pci_limit)

    # Perfect and measured:
    cidx = 0
    for name, val in vals.items():
        hovertext = list()
        try:
            for idx in range(len(val[u"val"])):
                htext = ""
                if isinstance(val[u"val"][idx], float):
                    htext += (
                        f"No. of Runs: {val[u'count'][idx]}<br>"
                        f"Mean: {val[u'val'][idx]:.2f}{h_unit}<br>"
                    )
                if isinstance(val[u"diff"][idx], float):
                    htext += f"Diff: {round(val[u'diff'][idx]):.0f}%<br>"
                if isinstance(val[u"rel"][idx], float):
                    htext += f"Speedup: {val[u'rel'][idx]:.2f}"
                hovertext.append(htext)
            traces.append(
                plgo.Scatter(
                    x=x_vals,
                    y=val[u"val"],
                    name=name,
                    legendgroup=name,
                    mode=u"lines+markers",
                    line=dict(
                        color=COLORS[cidx],
                        width=2),
                    marker=dict(
                        symbol=u"circle",
                        size=10
                    ),
                    text=hovertext,
                    hoverinfo=u"text+name"
                )
            )
            traces.append(
                plgo.Scatter(
                    x=x_vals,
                    y=val[u"ideal"],
                    name=f"{name} perfect",
                    legendgroup=name,
                    showlegend=False,
                    mode=u"lines",
                    line=dict(
                        color=COLORS[cidx],
                        width=2,
                        dash=u"dash"),
                    text=[f"Perfect: {y:.2f}Mpps" for y in val[u"ideal"]],
                    hoverinfo=u"text"
                )
            )
            cidx += 1
        except (IndexError, ValueError, KeyError) as err:
            logging.warning(f"No data for {name}\n{repr(err)}")

    try:
        # Create plot
        file_type = plot.get(u"output-file-type", u".html")
        logging.info(f"    Writing file {plot[u'output-file']}{file_type}.")
        layout = deepcopy(plot[u"layout"])
        if layout.get(u"title", None):
            layout[u"title"] = f"<b>Speedup Multi-core:</b> {layout[u'title']}"
        layout[u"yaxis"][u"range"] = [0, int(max(y_max) * 1.1)]
        layout[u"annotations"].extend(annotations)
        plpl = plgo.Figure(data=traces, layout=layout)

        # Export Plot
        ploff.plot(
            plpl,
            show_link=False,
            auto_open=False,
            filename=f"{plot[u'output-file']}{file_type}"
        )
    except PlotlyError as err:
        logging.error(
            f"   Finished with error: {repr(err)}".replace(u"\n", u" ")
        )
        return


def plot_http_server_perf_box(plot, input_data):
    """Generate the plot(s) with algorithm: plot_http_server_perf_box
    specified in the specification file.

    :param plot: Plot to generate.
    :param input_data: Data to process.
    :type plot: pandas.Series
    :type input_data: InputData
    """

    # Transform the data
    logging.info(
        f"    Creating the data set for the {plot.get(u'type', u'')} "
        f"{plot.get(u'title', u'')}."
    )
    data = input_data.filter_data(plot)
    if data is None:
        logging.error(u"No data.")
        return

    # Prepare the data for the plot
    y_vals = dict()
    for job in data:
        for build in job:
            for test in build:
                if y_vals.get(test[u"name"], None) is None:
                    y_vals[test[u"name"]] = list()
                try:
                    y_vals[test[u"name"]].append(test[u"result"])
                except (KeyError, TypeError):
                    y_vals[test[u"name"]].append(None)

    # Add None to the lists with missing data
    max_len = 0
    nr_of_samples = list()
    for val in y_vals.values():
        if len(val) > max_len:
            max_len = len(val)
        nr_of_samples.append(len(val))
    for val in y_vals.values():
        if len(val) < max_len:
            val.extend([None for _ in range(max_len - len(val))])

    # Add plot traces
    traces = list()
    df_y = pd.DataFrame(y_vals)
    df_y.head()
    for i, col in enumerate(df_y.columns):
        name = \
            f"{i + 1}. " \
            f"({nr_of_samples[i]:02d} " \
            f"run{u's' if nr_of_samples[i] > 1 else u''}) " \
            f"{col.lower().replace(u'-ndrpdr', u'')}"
        if len(name) > 50:
            name_lst = name.split(u'-')
            name = u""
            split_name = True
            for segment in name_lst:
                if (len(name) + len(segment) + 1) > 50 and split_name:
                    name += u"<br>    "
                    split_name = False
                name += segment + u'-'
            name = name[:-1]

        traces.append(plgo.Box(x=[str(i + 1) + u'.'] * len(df_y[col]),
                               y=df_y[col],
                               name=name,
                               **plot[u"traces"]))
    try:
        # Create plot
        plpl = plgo.Figure(data=traces, layout=plot[u"layout"])

        # Export Plot
        logging.info(
            f"    Writing file {plot[u'output-file']}"
            f"{plot[u'output-file-type']}."
        )
        ploff.plot(
            plpl,
            show_link=False,
            auto_open=False,
            filename=f"{plot[u'output-file']}{plot[u'output-file-type']}"
        )
    except PlotlyError as err:
        logging.error(
            f"   Finished with error: {repr(err)}".replace(u"\n", u" ")
        )
        return


def plot_nf_heatmap(plot, input_data):
    """Generate the plot(s) with algorithm: plot_nf_heatmap
    specified in the specification file.

    :param plot: Plot to generate.
    :param input_data: Data to process.
    :type plot: pandas.Series
    :type input_data: InputData
    """

    regex_cn = re.compile(r'^(\d*)R(\d*)C$')
    regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
                                 r'(\d+mif|\d+vh)-'
                                 r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$')
    vals = dict()

    # Transform the data
    logging.info(
        f"    Creating the data set for the {plot.get(u'type', u'')} "
        f"{plot.get(u'title', u'')}."
    )
    data = input_data.filter_data(plot, continue_on_error=True)
    if data is None or data.empty:
        logging.error(u"No data.")
        return

    for job in data:
        for build in job:
            for test in build:
                for tag in test[u"tags"]:
                    groups = re.search(regex_cn, tag)
                    if groups:
                        chain = str(groups.group(1))
                        node = str(groups.group(2))
                        break
                else:
                    continue
                groups = re.search(regex_test_name, test[u"name"])
                if groups and len(groups.groups()) == 3:
                    hover_name = (
                        f"{str(groups.group(1))}-"
                        f"{str(groups.group(2))}-"
                        f"{str(groups.group(3))}"
                    )
                else:
                    hover_name = u""
                if vals.get(chain, None) is None:
                    vals[chain] = dict()
                if vals[chain].get(node, None) is None:
                    vals[chain][node] = dict(
                        name=hover_name,
                        vals=list(),
                        nr=None,
                        mean=None,
                        stdev=None
                    )
                try:
                    if plot[u"include-tests"] == u"MRR":
                        result = test[u"result"][u"receive-rate"]
                    elif plot[u"include-tests"] == u"PDR":
                        result = test[u"throughput"][u"PDR"][u"LOWER"]
                    elif plot[u"include-tests"] == u"NDR":
                        result = test[u"throughput"][u"NDR"][u"LOWER"]
                    else:
                        result = None
                except TypeError:
                    result = None

                if result:
                    vals[chain][node][u"vals"].append(result)

    if not vals:
        logging.error(u"No data.")
        return

    txt_chains = list()
    txt_nodes = list()
    for key_c in vals:
        txt_chains.append(key_c)
        for key_n in vals[key_c].keys():
            txt_nodes.append(key_n)
            if vals[key_c][key_n][u"vals"]:
                vals[key_c][key_n][u"nr"] = len(vals[key_c][key_n][u"vals"])
                vals[key_c][key_n][u"mean"] = \
                    round(mean(vals[key_c][key_n][u"vals"]) / 1000000, 1)
                vals[key_c][key_n][u"stdev"] = \
                    round(stdev(vals[key_c][key_n][u"vals"]) / 1000000, 1)
    txt_nodes = list(set(txt_nodes))

    def sort_by_int(value):
        """Makes possible to sort a list of strings which represent integers.

        :param value: Integer as a string.
        :type value: str
        :returns: Integer representation of input parameter 'value'.
        :rtype: int
        """
        return int(value)

    txt_chains = sorted(txt_chains, key=sort_by_int)
    txt_nodes = sorted(txt_nodes, key=sort_by_int)

    chains = [i + 1 for i in range(len(txt_chains))]
    nodes = [i + 1 for i in range(len(txt_nodes))]

    data = [list() for _ in range(len(chains))]
    for chain in chains:
        for node in nodes:
            try:
                val = vals[txt_chains[chain - 1]][txt_nodes[node - 1]][u"mean"]
            except (KeyError, IndexError):
                val = None
            data[chain - 1].append(val)

    # Color scales:
    my_green = [[0.0, u"rgb(235, 249, 242)"],
                [1.0, u"rgb(45, 134, 89)"]]

    my_blue = [[0.0, u"rgb(236, 242, 248)"],
               [1.0, u"rgb(57, 115, 172)"]]

    my_grey = [[0.0, u"rgb(230, 230, 230)"],
               [1.0, u"rgb(102, 102, 102)"]]

    hovertext = list()
    annotations = list()

    text = (u"Test: {name}<br>"
            u"Runs: {nr}<br>"
            u"Thput: {val}<br>"
            u"StDev: {stdev}")

    for chain, _ in enumerate(txt_chains):
        hover_line = list()
        for node, _ in enumerate(txt_nodes):
            if data[chain][node] is not None:
                annotations.append(
                    dict(
                        x=node+1,
                        y=chain+1,
                        xref=u"x",
                        yref=u"y",
                        xanchor=u"center",
                        yanchor=u"middle",
                        text=str(data[chain][node]),
                        font=dict(
                            size=14,
                        ),
                        align=u"center",
                        showarrow=False
                    )
                )
                hover_line.append(text.format(
                    name=vals[txt_chains[chain]][txt_nodes[node]][u"name"],
                    nr=vals[txt_chains[chain]][txt_nodes[node]][u"nr"],
                    val=data[chain][node],
                    stdev=vals[txt_chains[chain]][txt_nodes[node]][u"stdev"]))
        hovertext.append(hover_line)

    traces = [
        plgo.Heatmap(
            x=nodes,
            y=chains,
            z=data,
            colorbar=dict(
                title=plot.get(u"z-axis", u""),
                titleside=u"right",
                titlefont=dict(
                    size=16
                ),
                tickfont=dict(
                    size=16,
                ),
                tickformat=u".1f",
                yanchor=u"bottom",
                y=-0.02,
                len=0.925,
            ),
            showscale=True,
            colorscale=my_green,
            text=hovertext,
            hoverinfo=u"text"
        )
    ]

    for idx, item in enumerate(txt_nodes):
        # X-axis, numbers:
        annotations.append(
            dict(
                x=idx+1,
                y=0.05,
                xref=u"x",
                yref=u"y",
                xanchor=u"center",
                yanchor=u"top",
                text=item,
                font=dict(
                    size=16,
                ),
                align=u"center",
                showarrow=False
            )
        )
    for idx, item in enumerate(txt_chains):
        # Y-axis, numbers:
        annotations.append(
            dict(
                x=0.35,
                y=idx+1,
                xref=u"x",
                yref=u"y",
                xanchor=u"right",
                yanchor=u"middle",
                text=item,
                font=dict(
                    size=16,
                ),
                align=u"center",
                showarrow=False
            )
        )
    # X-axis, title:
    annotations.append(
        dict(
            x=0.55,
            y=-0.15,
            xref=u"paper",
            yref=u"y",
            xanchor=u"center",
            yanchor=u"bottom",
            text=plot.get(u"x-axis", u""),
            font=dict(
                size=16,
            ),
            align=u"center",
            showarrow=False
        )
    )
    # Y-axis, title:
    annotations.append(
        dict(
            x=-0.1,
            y=0.5,
            xref=u"x",
            yref=u"paper",
            xanchor=u"center",
            yanchor=u"middle",
            text=plot.get(u"y-axis", u""),
            font=dict(
                size=16,
            ),
            align=u"center",
            textangle=270,
            showarrow=False
        )
    )
    updatemenus = list([
        dict(
            x=1.0,
            y=0.0,
            xanchor=u"right",
            yanchor=u"bottom",
            direction=u"up",
            buttons=list([
                dict(
                    args=[
                        {
                            u"colorscale": [my_green, ],
                            u"reversescale": False
                        }
                    ],
                    label=u"Green",
                    method=u"update"
                ),
                dict(
                    args=[
                        {
                            u"colorscale": [my_blue, ],
                            u"reversescale": False
                        }
                    ],
                    label=u"Blue",
                    method=u"update"
                ),
                dict(
                    args=[
                        {
                            u"colorscale": [my_grey, ],
                            u"reversescale": False
                        }
                    ],
                    label=u"Grey",
                    method=u"update"
                )
            ])
        )
    ])

    try:
        layout = deepcopy(plot[u"layout"])
    except KeyError as err:
        logging.error(f"Finished with error: No layout defined\n{repr(err)}")
        return

    layout[u"annotations"] = annotations
    layout[u'updatemenus'] = updatemenus

    try:
        # Create plot
        plpl = plgo.Figure(data=traces, layout=layout)

        # Export Plot
        logging.info(f"    Writing file {plot[u'output-file']}.html")
        ploff.plot(
            plpl,
            show_link=False,
            auto_open=False,
            filename=f"{plot[u'output-file']}.html"
        )
    except PlotlyError as err:
        logging.error(
            f"   Finished with error: {repr(err)}".replace(u"\n", u" ")
        )
        return