-# Copyright (c) 2017 Cisco and/or its affiliates.
+# Copyright (c) 2019 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:
"""
+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 utils import mean
+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):
:type data: InputData
"""
- logging.info("Generating the plots ...")
+ 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
+ }
+
+ logging.info(u"Generating the plots ...")
for index, plot in enumerate(spec.plots):
try:
- logging.info(" Plot nr {0}:".format(index + 1))
- eval(plot["algorithm"])(plot, data)
- except NameError:
- logging.error("The algorithm '{0}' is not defined.".
- format(plot["algorithm"]))
- logging.info("Done.")
+ 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_performance_box(plot, input_data):
- """Generate the plot(s) with algorithm: table_detailed_test_results
+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.
:type input_data: InputData
"""
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
+ # 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'').
+ replace(u'avf-', 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_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
- data = input_data.filter_data(plot)
+ 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'').
+ replace(u'avf-', 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("No data.")
+ logging.error(u"No data.")
return
# Prepare the data for the plot
- y_vals = dict()
+ y_vals = OrderedDict()
+ loss = dict()
for job in data:
for build in job:
for test in build:
- if y_vals.get(test["parent"], None) is None:
- y_vals[test["parent"]] = list()
+ 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["parent"]].append(test["throughput"]["value"])
+ 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["parent"]].append(None)
+ 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)
- for key, val in y_vals.items():
+ 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 = pd.DataFrame(y_vals)
- df.head()
- for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-ndrpdrdisc', ''))
- traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
- y=df[col],
- name=name,
- **plot["traces"]))
+ 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'').replace(u'avf-', 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
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
+ 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
- logging.info(" Writing file '{0}{1}'.".
- format(plot["output-file"], plot["output-file-type"]))
- ploff.plot(plpl,
- show_link=False, auto_open=False,
- filename='{0}{1}'.format(plot["output-file"],
- plot["output-file-type"]))
+ 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(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
return
- logging.info(" Done.")
-
-def plot_latency_box(plot, input_data):
- """Generate the plot(s) with algorithm: plot_latency_box
+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.
:type input_data: InputData
"""
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
+ # 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'').replace(u'avf-', 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
- data = input_data.filter_data(plot)
+ 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("No data.")
+ logging.error(u"No data.")
return
# Prepare the data for the plot
- y_tmp_vals = dict()
+ y_tmp_vals = OrderedDict()
for job in data:
for build in job:
for test in build:
- if y_tmp_vals.get(test["parent"], None) is None:
- y_tmp_vals[test["parent"]] = [
+ 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, max
]
try:
- y_tmp_vals[test["parent"]][0].append(
- test["latency"]["direction1"]["50"]["min"])
- y_tmp_vals[test["parent"]][1].append(
- test["latency"]["direction1"]["50"]["avg"])
- y_tmp_vals[test["parent"]][2].append(
- test["latency"]["direction1"]["50"]["max"])
- y_tmp_vals[test["parent"]][3].append(
- test["latency"]["direction2"]["50"]["min"])
- y_tmp_vals[test["parent"]][4].append(
- test["latency"]["direction2"]["50"]["avg"])
- y_tmp_vals[test["parent"]][5].append(
- test["latency"]["direction2"]["50"]["max"])
+ 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'').replace(u'avf-', 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
- y_vals = dict()
- for key, values in y_tmp_vals.items():
- y_vals[key] = list()
- for val in values:
- if val:
- average = mean(val)
- else:
- average = None
- y_vals[key].append(average)
- y_vals[key].append(average) # Twice for plot.ly
+ 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'').
+ replace(u'avf-', 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
- # Add plot traces
traces = list()
+ annotations = list()
+ x_vals = [1, 2, 4]
+
+ # Limits:
try:
- df = pd.DataFrame(y_vals)
- df.head()
+ threshold = 1.1 * max(y_max) # 10%
except ValueError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
- return
-
- for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-ndrpdrdisc', ''))
- traces.append(plgo.Box(x=['TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1'],
- y=df[col],
- name=name,
- **plot["traces"]))
+ 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
- logging.info(" Writing file '{0}{1}'.".
- format(plot["output-file"], plot["output-file-type"]))
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
+ plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
# Export Plot
- ploff.plot(plpl,
- show_link=False, auto_open=False,
- filename='{0}{1}'.format(plot["output-file"],
- plot["output-file-type"]))
+ 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(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
return
- logging.info(" Done.")
+
+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']}"
+ 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