+++ /dev/null
-# 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.subplots import make_subplots
-from plotly.exceptions import PlotlyError
-
-from pal_utils import mean, stdev
-
-
-COLORS = [u"SkyBlue", u"Olive", u"Purple", u"Coral", u"Indigo", u"Pink",
- u"Chocolate", u"Brown", u"Magenta", u"Cyan", u"Orange", u"Black",
- u"Violet", u"Blue", u"Yellow", u"BurlyWood", u"CadetBlue", u"Crimson",
- u"DarkBlue", u"DarkCyan", u"DarkGreen", u"Green", u"GoldenRod",
- u"LightGreen", u"LightSeaGreen", u"LightSkyBlue", u"Maroon",
- u"MediumSeaGreen", u"SeaGreen", u"LightSlateGrey"]
-
-REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*)-')
-
-
-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_lat_err_bars_name": plot_lat_err_bars_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_lat_hdrh_bar_name": plot_lat_hdrh_bar_name,
- u"plot_lat_hdrh_percentile": plot_lat_hdrh_percentile,
- 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_lat_hdrh_percentile(plot, input_data):
- """Generate the plot(s) with algorithm: plot_lat_hdrh_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
- plot_title = plot.get(u"title", u"")
- logging.info(
- f" Creating the data set for the {plot.get(u'type', u'')} "
- f"{plot_title}."
- )
- data = input_data.filter_tests_by_name(
- plot, params=[u"latency", u"parent", u"tags", u"type"])
- if data is None or len(data[0][0]) == 0:
- logging.error(u"No data.")
- return
-
- fig = plgo.Figure()
-
- # Prepare the data for the plot
- directions = [u"W-E", u"E-W"]
- for color, test in enumerate(data[0][0]):
- try:
- if test[u"type"] in (u"NDRPDR",):
- if u"-pdr" in plot_title.lower():
- ttype = u"PDR"
- elif u"-ndr" in plot_title.lower():
- ttype = u"NDR"
- else:
- 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''))
- for idx, direction in enumerate(
- (u"direction1", u"direction2", )):
- try:
- hdr_lat = test[u"latency"][ttype][direction][u"hdrh"]
- # TODO: Workaround, HDRH data must be aligned to 4
- # bytes, remove when not needed.
- hdr_lat += u"=" * (len(hdr_lat) % 4)
- xaxis = list()
- yaxis = list()
- hovertext = list()
- decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat)
- for item in decoded.get_recorded_iterator():
- percentile = item.percentile_level_iterated_to
- if percentile != 100.0:
- xaxis.append(100.0 / (100.0 - percentile))
- yaxis.append(item.value_iterated_to)
- hovertext.append(
- f"Test: {name}<br>"
- f"Direction: {directions[idx]}<br>"
- f"Percentile: {percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- fig.add_trace(
- plgo.Scatter(
- x=xaxis,
- y=yaxis,
- name=name,
- mode=u"lines",
- legendgroup=name,
- showlegend=bool(idx),
- line=dict(
- color=COLORS[color]
- ),
- hovertext=hovertext,
- hoverinfo=u"text"
- )
- )
- except hdrh.codec.HdrLengthException as err:
- logging.warning(
- f"No or invalid data for HDRHistogram for the test "
- f"{name}\n{err}"
- )
- continue
- else:
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- except (ValueError, KeyError) as err:
- logging.warning(repr(err))
-
- layout = deepcopy(plot[u"layout"])
-
- layout[u"title"][u"text"] = \
- f"<b>Latency:</b> {plot.get(u'graph-title', u'')}"
- fig[u"layout"].update(layout)
-
- # Create plot
- file_type = plot.get(u"output-file-type", u".html")
- logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
- try:
- # Export Plot
- ploff.plot(fig, 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)}")
-
-
-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"latency", u"throughput", u"parent", u"tags", u"type"]
- )[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
-
- 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:
- nic = u""
- name_link = f"{nic}-{test[u'name']}"
-
- logging.info(f" Generating the graph: {name_link}")
-
- 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."
- }
-
- 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"
- ]
- decoded = hdrh.histogram.HdrHistogram.decode(
- test[u"latency"][graph][direction][u"hdrh"]
- )
- 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"solid" if idx % 2 else u"dash"
- ),
- 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 fw:
- fw.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_lat_hdrh_bar_name(plot, input_data):
- """Generate the plot(s) with algorithm: plot_lat_hdrh_bar_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 the data set for the {plot.get(u'type', u'')} "
- f"{plot_title}."
- )
- data = input_data.filter_tests_by_name(
- plot, params=[u"latency", u"parent", u"tags", u"type"])
- if data is None or len(data[0][0]) == 0:
- logging.error(u"No data.")
- return
-
- # Prepare the data for the plot
- directions = [u"W-E", u"E-W"]
- tests = list()
- traces = list()
- for idx_row, test in enumerate(data[0][0]):
- try:
- if test[u"type"] in (u"NDRPDR",):
- if u"-pdr" in plot_title.lower():
- ttype = u"PDR"
- elif u"-ndr" in plot_title.lower():
- ttype = u"NDR"
- else:
- 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''))
- histograms = list()
- for idx_col, direction in enumerate(
- (u"direction1", u"direction2", )):
- try:
- hdr_lat = test[u"latency"][ttype][direction][u"hdrh"]
- # TODO: Workaround, HDRH data must be aligned to 4
- # bytes, remove when not needed.
- hdr_lat += u"=" * (len(hdr_lat) % 4)
- xaxis = list()
- yaxis = list()
- hovertext = list()
- decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat)
- total_count = decoded.get_total_count()
- for item in decoded.get_recorded_iterator():
- xaxis.append(item.value_iterated_to)
- prob = float(item.count_added_in_this_iter_step) / \
- total_count * 100
- yaxis.append(prob)
- hovertext.append(
- f"Test: {name}<br>"
- f"Direction: {directions[idx_col]}<br>"
- f"Latency: {item.value_iterated_to}uSec<br>"
- f"Probability: {prob:.2f}%<br>"
- f"Percentile: "
- f"{item.percentile_level_iterated_to:.2f}"
- )
- marker_color = [COLORS[idx_row], ] * len(yaxis)
- marker_color[xaxis.index(
- decoded.get_value_at_percentile(50.0))] = u"red"
- marker_color[xaxis.index(
- decoded.get_value_at_percentile(90.0))] = u"red"
- marker_color[xaxis.index(
- decoded.get_value_at_percentile(95.0))] = u"red"
- histograms.append(
- plgo.Bar(
- x=xaxis,
- y=yaxis,
- showlegend=False,
- name=name,
- marker={u"color": marker_color},
- hovertext=hovertext,
- hoverinfo=u"text"
- )
- )
- except hdrh.codec.HdrLengthException as err:
- logging.warning(
- f"No or invalid data for HDRHistogram for the test "
- f"{name}\n{err}"
- )
- continue
- if len(histograms) == 2:
- traces.append(histograms)
- tests.append(name)
- else:
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- except (ValueError, KeyError) as err:
- logging.warning(repr(err))
-
- if not tests:
- logging.warning(f"No data for {plot_title}.")
- return
-
- fig = make_subplots(
- rows=len(tests),
- cols=2,
- specs=[
- [{u"type": u"bar"}, {u"type": u"bar"}] for _ in range(len(tests))
- ]
- )
-
- layout_axes = dict(
- gridcolor=u"rgb(220, 220, 220)",
- linecolor=u"rgb(220, 220, 220)",
- linewidth=1,
- showgrid=True,
- showline=True,
- showticklabels=True,
- tickcolor=u"rgb(220, 220, 220)",
- )
-
- for idx_row, test in enumerate(tests):
- for idx_col in range(2):
- fig.add_trace(
- traces[idx_row][idx_col],
- row=idx_row + 1,
- col=idx_col + 1
- )
- fig.update_xaxes(
- row=idx_row + 1,
- col=idx_col + 1,
- **layout_axes
- )
- fig.update_yaxes(
- row=idx_row + 1,
- col=idx_col + 1,
- **layout_axes
- )
-
- layout = deepcopy(plot[u"layout"])
-
- layout[u"title"][u"text"] = \
- f"<b>Latency:</b> {plot.get(u'graph-title', u'')}"
- layout[u"height"] = 250 * len(tests) + 130
-
- layout[u"annotations"][2][u"y"] = 1.06 - 0.008 * len(tests)
- layout[u"annotations"][3][u"y"] = 1.06 - 0.008 * len(tests)
-
- for idx, test in enumerate(tests):
- layout[u"annotations"].append({
- u"font": {
- u"size": 14
- },
- u"showarrow": False,
- u"text": f"<b>{test}</b>",
- u"textangle": 0,
- u"x": 0.5,
- u"xanchor": u"center",
- u"xref": u"paper",
- u"y": 1.0 - float(idx) * 1.06 / len(tests),
- u"yanchor": u"bottom",
- u"yref": u"paper"
- })
-
- fig[u"layout"].update(layout)
-
- # Create plot
- file_type = plot.get(u"output-file-type", u".html")
- logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
- try:
- # Export Plot
- ploff.plot(fig, 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)}")
-
-
-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>Implied Time Lost [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"parent", u"tags", u"type"])
- if data is None:
- logging.error(u"No data.")
- return
-
- # Prepare the data for the plot
- 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"]] = list()
- try:
- if (test[u"type"] in (u"NDRPDR", ) and
- u"-pdr" in plot.get(u"title", u"").lower()):
- y_vals[test[u"parent"]].\
- append(test[u"throughput"][u"PDR"][u"LOWER"])
- elif (test[u"type"] in (u"NDRPDR", ) and
- u"-ndr" in plot.get(u"title", u"").lower()):
- y_vals[test[u"parent"]]. \
- append(test[u"throughput"][u"NDR"][u"LOWER"])
- elif test[u"type"] in (u"SOAK", ):
- y_vals[test[u"parent"]].\
- append(test[u"throughput"][u"LOWER"])
- 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''))
- traces.append(
- plgo.Box(
- x=[str(i + 1) + u'.'] * len(df_y[col]),
- y=[y / 1000000 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"
- )
- )
- try:
- val_max = max(df_y[col])
- if val_max:
- y_max.append(int(val_max / 1000000) + 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):
- 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_lat_err_bars_name(plot, input_data):
- """Generate the plot(s) with algorithm: plot_lat_err_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
- 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"latency", u"parent", u"tags", u"type"])
- if data is None:
- logging.error(u"No data.")
- return
-
- # Prepare the data for the plot
- y_tmp_vals = OrderedDict()
- for job in data:
- for build in job:
- for test in build:
- try:
- logging.debug(f"test[u'latency']: {test[u'latency']}\n")
- except ValueError as err:
- logging.warning(repr(err))
- if y_tmp_vals.get(test[u"parent"], None) is None:
- y_tmp_vals[test[u"parent"]] = [
- list(), # direction1, min
- list(), # direction1, avg
- list(), # direction1, max
- list(), # direction2, min
- list(), # direction2, avg
- list() # direction2, max
- ]
- try:
- if test[u"type"] not in (u"NDRPDR", ):
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- if u"-pdr" in plot_title.lower():
- ttype = u"PDR"
- elif u"-ndr" in plot_title.lower():
- ttype = u"NDR"
- else:
- logging.warning(
- f"Invalid test type: {test[u'type']}"
- )
- continue
- y_tmp_vals[test[u"parent"]][0].append(
- test[u"latency"][ttype][u"direction1"][u"min"])
- y_tmp_vals[test[u"parent"]][1].append(
- test[u"latency"][ttype][u"direction1"][u"avg"])
- y_tmp_vals[test[u"parent"]][2].append(
- test[u"latency"][ttype][u"direction1"][u"max"])
- y_tmp_vals[test[u"parent"]][3].append(
- test[u"latency"][ttype][u"direction2"][u"min"])
- y_tmp_vals[test[u"parent"]][4].append(
- test[u"latency"][ttype][u"direction2"][u"avg"])
- y_tmp_vals[test[u"parent"]][5].append(
- test[u"latency"][ttype][u"direction2"][u"max"])
- except (KeyError, TypeError) as err:
- logging.warning(repr(err))
-
- x_vals = list()
- y_vals = list()
- y_mins = list()
- y_maxs = list()
- nr_of_samples = list()
- for key, val in y_tmp_vals.items():
- name = re.sub(REGEX_NIC, u"", key.replace(u'-ndrpdr', u'').
- replace(u'2n1l-', u''))
- x_vals.append(name) # dir 1
- y_vals.append(mean(val[1]) if val[1] else None)
- y_mins.append(mean(val[0]) if val[0] else None)
- y_maxs.append(mean(val[2]) if val[2] else None)
- nr_of_samples.append(len(val[1]) if val[1] else 0)
- x_vals.append(name) # dir 2
- y_vals.append(mean(val[4]) if val[4] else None)
- y_mins.append(mean(val[3]) if val[3] else None)
- y_maxs.append(mean(val[5]) if val[5] else None)
- nr_of_samples.append(len(val[3]) if val[3] else 0)
-
- traces = list()
- annotations = list()
-
- for idx, _ in enumerate(x_vals):
- if not bool(int(idx % 2)):
- direction = u"West-East"
- else:
- direction = u"East-West"
- hovertext = (
- f"No. of Runs: {nr_of_samples[idx]}<br>"
- f"Test: {x_vals[idx]}<br>"
- f"Direction: {direction}<br>"
- )
- if isinstance(y_maxs[idx], float):
- hovertext += f"Max: {y_maxs[idx]:.2f}uSec<br>"
- if isinstance(y_vals[idx], float):
- hovertext += f"Mean: {y_vals[idx]:.2f}uSec<br>"
- if isinstance(y_mins[idx], float):
- hovertext += f"Min: {y_mins[idx]:.2f}uSec"
-
- if isinstance(y_maxs[idx], float) and isinstance(y_vals[idx], float):
- array = [y_maxs[idx] - y_vals[idx], ]
- else:
- array = [None, ]
- if isinstance(y_mins[idx], float) and isinstance(y_vals[idx], float):
- arrayminus = [y_vals[idx] - y_mins[idx], ]
- else:
- arrayminus = [None, ]
- traces.append(plgo.Scatter(
- x=[idx, ],
- y=[y_vals[idx], ],
- name=x_vals[idx],
- legendgroup=x_vals[idx],
- showlegend=bool(int(idx % 2)),
- mode=u"markers",
- error_y=dict(
- type=u"data",
- symmetric=False,
- array=array,
- arrayminus=arrayminus,
- color=COLORS[int(idx / 2)]
- ),
- marker=dict(
- size=10,
- color=COLORS[int(idx / 2)],
- ),
- text=hovertext,
- hoverinfo=u"text",
- ))
- annotations.append(dict(
- x=idx,
- y=0,
- xref=u"x",
- yref=u"y",
- xanchor=u"center",
- yanchor=u"top",
- text=u"E-W" if bool(int(idx % 2)) else u"W-E",
- font=dict(
- size=16,
- ),
- align=u"center",
- showarrow=False
- ))
-
- 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>Latency:</b> {layout[u'title']}"
- layout[u"annotations"] = 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_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"parent", u"tags", u"type"])
- if data is None:
- logging.error(u"No data.")
- return
-
- 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.lower():
- ttype = u"PDR"
- elif u"-ndr" in plot_title.lower():
- ttype = u"NDR"
- else:
- continue
-
- if u"1C" in test[u"tags"]:
- y_vals[test[u"parent"]][u"1"]. \
- append(test[u"throughput"][ttype][u"LOWER"])
- elif u"2C" in test[u"tags"]:
- y_vals[test[u"parent"]][u"2"]. \
- append(test[u"throughput"][ttype][u"LOWER"])
- elif u"4C" in test[u"tags"]:
- y_vals[test[u"parent"]][u"4"]. \
- append(test[u"throughput"][ttype][u"LOWER"])
- 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) * 1000000.0)
- 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] / 1000000.0
- y_val_2 = test_vals[u"2"][0] / 1000000.0 if test_vals[u"2"][0] \
- else None
- y_val_4 = test_vals[u"4"][0] / 1000000.0 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"]
- 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:
- try:
- threshold = 1.1 * max(y_max) # 10%
- except ValueError as err:
- logging.error(err)
- return
- nic_limit /= 1000000.0
- 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 /= 1000000.0
- 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 /= 1000000.0
- 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}Mpps<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$')
- regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
- r'(\d+mif|\d+vh)-'
- r'(\d+vm\d+t|\d+dcr\d+t).*$')
- 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
Code Review / csit.git / blobdiff