# Copyright (c) 2021 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

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

from collections import OrderedDict
from copy import deepcopy
from math import log

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]*)-')

# This value depends on latency stream rate (9001 pps) and duration (5s).
# Keep it slightly higher to ensure rounding errors to not remove tick mark.
PERCENTILE_MAX = 99.999501


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,
        u"plot_hdrh_lat_by_percentile_x_log": plot_hdrh_lat_by_percentile_x_log,
        u"plot_mrr_error_bars_name": plot_mrr_error_bars_name,
        u"plot_mrr_box_name": plot_mrr_box_name
    }

    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")):
                    previous_x = 0.0
                    xaxis = list()
                    yaxis = list()
                    hovertext = list()
                    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
                        xaxis.append(previous_x)
                        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: {previous_x:.5f}-{percentile:.5f}%<br>"
                            f"Latency: {item.value_iterated_to}uSec"
                        )
                        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: {previous_x:.5f}-{percentile:.5f}%<br>"
                            f"Latency: {item.value_iterated_to}uSec"
                        )
                        previous_x = percentile
                    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"solid",
                                width=1 if idx % 2 else 2
                            ),
                            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_hdrh_lat_by_percentile_x_log(plot, input_data):
    """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile_x_log
    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")):
                    previous_x = 0.0
                    prev_perc = 0.0
                    xaxis = list()
                    yaxis = list()
                    hovertext = list()
                    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():
                        # The real value is "percentile".
                        # For 100%, we cut that down to "x_perc" to avoid
                        # infinity.
                        percentile = item.percentile_level_iterated_to
                        x_perc = min(percentile, PERCENTILE_MAX)
                        xaxis.append(previous_x)
                        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: {prev_perc:.5f}-{percentile:.5f}%<br>"
                            f"Latency: {item.value_iterated_to}uSec"
                        )
                        next_x = 100.0 / (100.0 - x_perc)
                        xaxis.append(next_x)
                        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: {prev_perc:.5f}-{percentile:.5f}%<br>"
                            f"Latency: {item.value_iterated_to}uSec"
                        )
                        previous_x = next_x
                        prev_perc = percentile
                    fig.add_trace(
                        plgo.Scatter(
                            x=xaxis,
                            y=yaxis,
                            name=desc[graph],
                            mode=u"lines",
                            legendgroup=desc[graph],
                            showlegend=not(bool(idx)),
                            line=dict(
                                color=COLORS[color],
                                dash=u"solid",
                                width=1 if idx % 2 else 2
                            ),
                            hovertext=hovertext,
                            hoverinfo=u"text"
                        )
                    )

            layout[u"title"][u"text"] = f"<b>Latency:</b> {name}"
            x_max = log(100.0 / (100.0 - PERCENTILE_MAX), 10)
            layout[u"xaxis"][u"range"] = [0, x_max]
            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'-reconf', u'').
                          replace(u'2n1l-', u'').replace(u'2n-', u'').
                          replace(u'-testpmd', u''))

        traces.append(plgo.Box(
            x=[str(i + 1) + u'.'] * len(df_y[col]),
            y=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'-')[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 item in plot.get(u"include", tuple()):
        reg_ex = re.compile(str(item).lower())
        for job in data:
            for build in job:
                for test_id, test in build.iteritems():
                    if not re.match(reg_ex, str(test_id).lower()):
                        continue
                    if y_vals.get(test[u"parent"], None) is None:
                        y_vals[test[u"parent"]] = list()
                    try:
                        if test[u"type"] in (u"NDRPDR", u"CPS"):
                            test_type = test[u"type"]

                            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 "
                                    u"type. Add '-ndr' or '-pdr' to the test "
                                    u"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']}"
            elif test_type in (u"CPS", ):
                layout[u"title"] = f"<b>CPS:</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_mrr_box_name(plot, input_data):
    """Generate the plot(s) with algorithm: plot_mrr_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"result", u"parent", u"tags", u"type"])
    if data is None:
        logging.error(u"No data.")
        return

    # Prepare the data for the plot
    data_x = list()
    data_names = list()
    data_y = list()
    data_y_max = list()
    idx = 1
    for item in plot.get(u"include", tuple()):
        reg_ex = re.compile(str(item).lower())
        for job in data:
            for build in job:
                for test_id, test in build.iteritems():
                    if not re.match(reg_ex, str(test_id).lower()):
                        continue
                    try:
                        data_x.append(idx)
                        name = re.sub(REGEX_NIC, u'', test[u'parent'].lower().
                                      replace(u'-mrr', u'').
                                      replace(u'2n1l-', u''))
                        data_y.append(test[u"result"][u"samples"])
                        data_names.append(
                            f"{idx}."
                            f"({len(data_y[-1]):02d} "
                            f"run{u's' if len(data_y[-1]) > 1 else u''}) "
                            f"{name}"
                        )
                        data_y_max.append(max(test[u"result"][u"samples"]))
                        idx += 1
                    except (KeyError, TypeError):
                        pass

    # Add plot traces
    traces = list()
    for idx in range(len(data_x)):
        traces.append(
            plgo.Box(
                x=[data_x[idx], ] * len(data_y[idx]),
                y=data_y[idx],
                name=data_names[idx],
                hoverinfo=u"y+name"
            )
        )

    try:
        # Create plot
        layout = deepcopy(plot[u"layout"])
        if layout.get(u"title", None):
            layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
        if data_y_max:
            layout[u"yaxis"][u"range"] = [0, max(data_y_max) + 1]
        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_mrr_error_bars_name(plot, input_data):
    """Generate the plot(s) with algorithm: plot_mrr_error_bars_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"result", u"parent", u"tags", u"type"])
    if data is None:
        logging.error(u"No data.")
        return

    # Prepare the data for the plot
    data_x = list()
    data_names = list()
    data_y_avg = list()
    data_y_stdev = list()
    data_y_max = 0
    hover_info = list()
    idx = 1
    for item in plot.get(u"include", tuple()):
        reg_ex = re.compile(str(item).lower())
        for job in data:
            for build in job:
                for test_id, test in build.iteritems():
                    if not re.match(reg_ex, str(test_id).lower()):
                        continue
                    try:
                        data_x.append(idx)
                        name = re.sub(REGEX_NIC, u'', test[u'parent'].lower().
                                      replace(u'-mrr', u'').
                                      replace(u'2n1l-', u''))
                        data_names.append(f"{idx}. {name}")
                        data_y_avg.append(
                            round(test[u"result"][u"receive-rate"], 3)
                        )
                        data_y_stdev.append(
                            round(test[u"result"][u"receive-stdev"], 3)
                        )
                        hover_info.append(
                            f"{data_names[-1]}<br>"
                            f"average [Gbps]: {data_y_avg[-1]}<br>"
                            f"stdev [Gbps]: {data_y_stdev[-1]}"
                        )
                        if data_y_avg[-1] + data_y_stdev[-1] > data_y_max:
                            data_y_max = data_y_avg[-1] + data_y_stdev[-1]
                        idx += 1
                    except (KeyError, TypeError):
                        pass

    # Add plot traces
    traces = list()
    for idx in range(len(data_x)):
        traces.append(
            plgo.Scatter(
                x=[data_x[idx], ],
                y=[data_y_avg[idx], ],
                error_y=dict(
                    type=u"data",
                    array=[data_y_stdev[idx], ],
                    visible=True
                ),
                name=data_names[idx],
                mode=u"markers",
                text=hover_info[idx],
                hoverinfo=u"text"
            )
        )

    try:
        # Create plot
        layout = deepcopy(plot[u"layout"])
        if layout.get(u"title", None):
            layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
        if data_y_max:
            layout[u"yaxis"][u"range"] = [0, int(data_y_max) + 1]
        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 item in plot.get(u"include", tuple()):
        reg_ex = re.compile(str(item).lower())
        for job in data:
            for build in job:
                for test_id, test in build.iteritems():
                    if re.match(reg_ex, str(test_id).lower()):
                        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", u"CPS"):
                                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 = 0
    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

        if u"cx556a" in test_name:
            limit = plot[u"limits"][u"pci"][u"pci-g3-x8"]
        else:
            limit = plot[u"limits"][u"pci"][u"pci-g3-x16"]
        if limit > pci_limit:
            pci_limit = limit

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

    # Limits:
    if u"-gbps" not in plot_title and u"-cps-" not in plot_title:
        nic_limit /= 1e6
        lnk_limit /= 1e6
        pci_limit /= 1e6
        min_limit = min((nic_limit, lnk_limit, pci_limit))
        if nic_limit == min_limit:
            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)
        elif lnk_limit == min_limit:
            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[-1],
                    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[-1],
                ),
                align=u"left",
                showarrow=False
            ))
            y_max.append(lnk_limit)
        elif pci_limit == min_limit:
            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[-1],
                    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[-1],
                ),
                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