# 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:
#
# 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 pandas as pd
import plotly.offline as ploff
import plotly.graph_objs as plgo
from plotly.exceptions import PlotlyError
from collections import OrderedDict
from copy import deepcopy
from utils import mean, stdev
COLORS = ["SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink",
"Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black",
"Violet", "Blue", "Yellow", "BurlyWood", "CadetBlue", "Crimson",
"DarkBlue", "DarkCyan", "DarkGreen", "Green", "GoldenRod",
"LightGreen", "LightSeaGreen", "LightSkyBlue", "Maroon",
"MediumSeaGreen", "SeaGreen", "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
"""
logging.info("Generating the plots ...")
for index, plot in enumerate(spec.plots):
try:
logging.info(" Plot nr {0}: {1}".format(index + 1,
plot.get("title", "")))
plot["limits"] = spec.configuration["limits"]
eval(plot["algorithm"])(plot, data)
logging.info(" Done.")
except NameError as err:
logging.error("Probably algorithm '{alg}' is not defined: {err}".
format(alg=plot["algorithm"], err=repr(err)))
logging.info("Done.")
def plot_service_density_reconf_box_name(plot, input_data):
"""Generate the plot(s) with algorithm: plot_service_density_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
plot_title = plot.get("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot_title))
data = input_data.filter_tests_by_name(
plot, params=["result", "parent", "tags", "type"])
if data is None:
logging.error("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["parent"], None) is None:
y_vals[test["parent"]] = list()
loss[test["parent"]] = list()
try:
y_vals[test["parent"]].append(test["result"]["time"])
loss[test["parent"]].append(test["result"]["loss"])
except (KeyError, TypeError):
y_vals[test["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 key, val in y_vals.items():
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()
y_max = list()
for i, col in enumerate(df.columns):
tst_name = re.sub(REGEX_NIC, "",
col.lower().replace('-ndrpdr', '').
replace('2n1l-', ''))
tst_name = "-".join(tst_name.split("-")[3:-2])
name = "{nr}. ({samples:02d} run{plural}, avg pkt loss: {loss:.1f}, " \
"stdev: {stdev:.2f}) {name}".format(
nr=(i + 1),
samples=nr_of_samples[i],
plural='s' if nr_of_samples[i] > 1 else '',
name=tst_name,
loss=mean(loss[col]) / 1000000,
stdev=stdev(loss[col]) / 1000000)
traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
y=[y if y else None for y in df[col]],
name=name,
hoverinfo="x+y",
boxpoints="outliers",
whiskerwidth=0))
try:
val_max = max(df[col])
except ValueError as err:
logging.error(repr(err))
continue
if val_max:
y_max.append(int(val_max) + 1)
try:
# Create plot
layout = deepcopy(plot["layout"])
layout["title"] = "Time Lost: {0}".format(layout["title"])
layout["yaxis"]["title"] = "Implied Time Lost [s]"
layout["legend"]["font"]["size"] = 14
if y_max:
layout["yaxis"]["range"] = [0, max(y_max)]
plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
file_type = plot.get("output-file-type", ".html")
logging.info(" Writing file '{0}{1}'.".
format(plot["output-file"], file_type))
ploff.plot(plpl, show_link=False, auto_open=False,
filename='{0}{1}'.format(plot["output-file"], file_type))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(repr(err).replace("\n", " ")))
return
def plot_performance_box_name(plot, input_data):
"""Generate the plot(s) with algorithm: plot_performance_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
plot_title = plot.get("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot_title))
data = input_data.filter_tests_by_name(
plot, params=["throughput", "parent", "tags", "type"])
if data is None:
logging.error("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["parent"], None) is None:
y_vals[test["parent"]] = list()
try:
if test["type"] in ("NDRPDR", ):
if "-pdr" in plot_title.lower():
y_vals[test["parent"]].\
append(test["throughput"]["PDR"]["LOWER"])
elif "-ndr" in plot_title.lower():
y_vals[test["parent"]]. \
append(test["throughput"]["NDR"]["LOWER"])
else:
continue
elif test["type"] in ("SOAK", ):
y_vals[test["parent"]].\
append(test["throughput"]["LOWER"])
else:
continue
except (KeyError, TypeError):
y_vals[test["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 key, val in y_vals.items():
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()
y_max = list()
for i, col in enumerate(df.columns):
tst_name = re.sub(REGEX_NIC, "",
col.lower().replace('-ndrpdr', '').
replace('2n1l-', ''))
name = "{nr}. ({samples:02d} run{plural}) {name}".\
format(nr=(i + 1),
samples=nr_of_samples[i],
plural='s' if nr_of_samples[i] > 1 else '',
name=tst_name)
logging.debug(name)
traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
y=[y / 1000000 if y else None for y in df[col]],
name=name,
hoverinfo="x+y",
boxpoints="outliers",
whiskerwidth=0))
try:
val_max = max(df[col])
except ValueError as err:
logging.error(repr(err))
continue
if val_max:
y_max.append(int(val_max / 1000000) + 2)
try:
# Create plot
layout = deepcopy(plot["layout"])
if layout.get("title", None):
layout["title"] = "Throughput: {0}". \
format(layout["title"])
if y_max:
layout["yaxis"]["range"] = [0, max(y_max)]
plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
file_type = plot.get("output-file-type", ".html")
logging.info(" Writing file '{0}{1}'.".
format(plot["output-file"], file_type))
ploff.plot(plpl, show_link=False, auto_open=False,
filename='{0}{1}'.format(plot["output-file"], file_type))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(repr(err).replace("\n", " ")))
return
def plot_latency_error_bars_name(plot, input_data):
"""Generate the plot(s) with algorithm: plot_latency_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
plot_title = plot.get("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot_title))
data = input_data.filter_tests_by_name(
plot, params=["latency", "parent", "tags", "type"])
if data is None:
logging.error("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("test['latency']: {0}\n".
format(test["latency"]))
except ValueError as err:
logging.warning(repr(err))
if y_tmp_vals.get(test["parent"], None) is None:
y_tmp_vals[test["parent"]] = [
list(), # direction1, min
list(), # direction1, avg
list(), # direction1, max
list(), # direction2, min
list(), # direction2, avg
list() # direction2, max
]
try:
if test["type"] in ("NDRPDR", ):
if "-pdr" in plot_title.lower():
ttype = "PDR"
elif "-ndr" in plot_title.lower():
ttype = "NDR"
else:
logging.warning("Invalid test type: {0}".
format(test["type"]))
continue
y_tmp_vals[test["parent"]][0].append(
test["latency"][ttype]["direction1"]["min"])
y_tmp_vals[test["parent"]][1].append(
test["latency"][ttype]["direction1"]["avg"])
y_tmp_vals[test["parent"]][2].append(
test["latency"][ttype]["direction1"]["max"])
y_tmp_vals[test["parent"]][3].append(
test["latency"][ttype]["direction2"]["min"])
y_tmp_vals[test["parent"]][4].append(
test["latency"][ttype]["direction2"]["avg"])
y_tmp_vals[test["parent"]][5].append(
test["latency"][ttype]["direction2"]["max"])
else:
logging.warning("Invalid test type: {0}".
format(test["type"]))
continue
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, "", key.replace('-ndrpdr', '').
replace('2n1l-', ''))
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 range(len(x_vals)):
if not bool(int(idx % 2)):
direction = "West-East"
else:
direction = "East-West"
hovertext = ("No. of Runs: {nr}
"
"Test: {test}
"
"Direction: {dir}
".format(test=x_vals[idx],
dir=direction,
nr=nr_of_samples[idx]))
if isinstance(y_maxs[idx], float):
hovertext += "Max: {max:.2f}uSec
".format(max=y_maxs[idx])
if isinstance(y_vals[idx], float):
hovertext += "Mean: {avg:.2f}uSec
".format(avg=y_vals[idx])
if isinstance(y_mins[idx], float):
hovertext += "Min: {min:.2f}uSec".format(min=y_mins[idx])
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="markers",
error_y=dict(
type='data',
symmetric=False,
array=array,
arrayminus=arrayminus,
color=COLORS[int(idx / 2)]
),
marker=dict(
size=10,
color=COLORS[int(idx / 2)],
),
text=hovertext,
hoverinfo="text",
))
annotations.append(dict(
x=idx,
y=0,
xref="x",
yref="y",
xanchor="center",
yanchor="top",
text="E-W" if bool(int(idx % 2)) else "W-E",
font=dict(
size=16,
),
align="center",
showarrow=False
))
try:
# Create plot
file_type = plot.get("output-file-type", ".html")
logging.info(" Writing file '{0}{1}'.".
format(plot["output-file"], file_type))
layout = deepcopy(plot["layout"])
if layout.get("title", None):
layout["title"] = "Latency: {0}".\
format(layout["title"])
layout["annotations"] = annotations
plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
ploff.plot(plpl,
show_link=False, auto_open=False,
filename='{0}{1}'.format(plot["output-file"], file_type))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(str(err).replace("\n", " ")))
return
def plot_throughput_speedup_analysis_name(plot, input_data):
"""Generate the plot(s) with algorithm:
plot_throughput_speedup_analysis_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("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot_title))
data = input_data.filter_tests_by_name(
plot, params=["throughput", "parent", "tags", "type"])
if data is None:
logging.error("No data.")
return
y_vals = OrderedDict()
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"]] = {"1": list(),
"2": list(),
"4": list()}
try:
if test["type"] in ("NDRPDR",):
if "-pdr" in plot_title.lower():
ttype = "PDR"
elif "-ndr" in plot_title.lower():
ttype = "NDR"
else:
continue
if "1C" in test["tags"]:
y_vals[test["parent"]]["1"]. \
append(test["throughput"][ttype]["LOWER"])
elif "2C" in test["tags"]:
y_vals[test["parent"]]["2"]. \
append(test["throughput"][ttype]["LOWER"])
elif "4C" in test["tags"]:
y_vals[test["parent"]]["4"]. \
append(test["throughput"][ttype]["LOWER"])
except (KeyError, TypeError):
pass
if not y_vals:
logging.warning("No data for the plot '{}'".
format(plot.get("title", "")))
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["limits"]["pci"]["pci-g3-x8"]
for test_name, test_vals in y_vals.items():
try:
if test_vals["1"][1]:
name = re.sub(REGEX_NIC, "", test_name.replace('-ndrpdr', '').
replace('2n1l-', ''))
vals[name] = OrderedDict()
y_val_1 = test_vals["1"][0] / 1000000.0
y_val_2 = test_vals["2"][0] / 1000000.0 if test_vals["2"][0] \
else None
y_val_4 = test_vals["4"][0] / 1000000.0 if test_vals["4"][0] \
else None
vals[name]["val"] = [y_val_1, y_val_2, y_val_4]
vals[name]["rel"] = [1.0, None, None]
vals[name]["ideal"] = [y_1c_max[test_name],
y_1c_max[test_name] * 2,
y_1c_max[test_name] * 4]
vals[name]["diff"] = [(y_val_1 - y_1c_max[test_name]) * 100 /
y_val_1, None, None]
vals[name]["count"] = [test_vals["1"][1],
test_vals["2"][1],
test_vals["4"][1]]
try:
val_max = max(vals[name]["val"])
except ValueError as err:
logging.error(repr(err))
continue
if val_max:
y_max.append(val_max)
if y_val_2:
vals[name]["rel"][1] = round(y_val_2 / y_val_1, 2)
vals[name]["diff"][1] = \
(y_val_2 - vals[name]["ideal"][1]) * 100 / y_val_2
if y_val_4:
vals[name]["rel"][2] = round(y_val_4 / y_val_1, 2)
vals[name]["diff"][2] = \
(y_val_4 - vals[name]["ideal"][2]) * 100 / y_val_4
except IndexError as err:
logging.warning("No data for '{0}'".format(test_name))
logging.warning(repr(err))
# Limits:
if "x520" in test_name:
limit = plot["limits"]["nic"]["x520"]
elif "x710" in test_name:
limit = plot["limits"]["nic"]["x710"]
elif "xxv710" in test_name:
limit = plot["limits"]["nic"]["xxv710"]
elif "xl710" in test_name:
limit = plot["limits"]["nic"]["xl710"]
elif "x553" in test_name:
limit = plot["limits"]["nic"]["x553"]
else:
limit = 0
if limit > nic_limit:
nic_limit = limit
mul = 2 if "ge2p" in test_name else 1
if "10ge" in test_name:
limit = plot["limits"]["link"]["10ge"] * mul
elif "25ge" in test_name:
limit = plot["limits"]["link"]["25ge"] * mul
elif "40ge" in test_name:
limit = plot["limits"]["link"]["40ge"] * mul
elif "100ge" in test_name:
limit = plot["limits"]["link"]["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="NIC: {0:.2f}Mpps".format(nic_limit),
showlegend=False,
mode="lines",
line=dict(
dash="dot",
color=COLORS[-1],
width=1),
hoverinfo="none"
))
annotations.append(dict(
x=1,
y=nic_limit,
xref="x",
yref="y",
xanchor="left",
yanchor="bottom",
text="NIC: {0:.2f}Mpps".format(nic_limit),
font=dict(
size=14,
color=COLORS[-1],
),
align="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="Link: {0:.2f}Mpps".format(lnk_limit),
showlegend=False,
mode="lines",
line=dict(
dash="dot",
color=COLORS[-2],
width=1),
hoverinfo="none"
))
annotations.append(dict(
x=1,
y=lnk_limit,
xref="x",
yref="y",
xanchor="left",
yanchor="bottom",
text="Link: {0:.2f}Mpps".format(lnk_limit),
font=dict(
size=14,
color=COLORS[-2],
),
align="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="PCIe: {0:.2f}Mpps".format(pci_limit),
showlegend=False,
mode="lines",
line=dict(
dash="dot",
color=COLORS[-3],
width=1),
hoverinfo="none"
))
annotations.append(dict(
x=1,
y=pci_limit,
xref="x",
yref="y",
xanchor="left",
yanchor="bottom",
text="PCIe: {0:.2f}Mpps".format(pci_limit),
font=dict(
size=14,
color=COLORS[-3],
),
align="left",
showarrow=False
))
y_max.append(pci_limit)
# Perfect and measured:
cidx = 0
for name, val in vals.iteritems():
hovertext = list()
try:
for idx in range(len(val["val"])):
htext = ""
if isinstance(val["val"][idx], float):
htext += "No. of Runs: {1}
" \
"Mean: {0:.2f}Mpps
".format(val["val"][idx],
val["count"][idx])
if isinstance(val["diff"][idx], float):
htext += "Diff: {0:.0f}%
".format(
round(val["diff"][idx]))
if isinstance(val["rel"][idx], float):
htext += "Speedup: {0:.2f}".format(val["rel"][idx])
hovertext.append(htext)
traces.append(plgo.Scatter(x=x_vals,
y=val["val"],
name=name,
legendgroup=name,
mode="lines+markers",
line=dict(
color=COLORS[cidx],
width=2),
marker=dict(
symbol="circle",
size=10
),
text=hovertext,
hoverinfo="text+name"
))
traces.append(plgo.Scatter(x=x_vals,
y=val["ideal"],
name="{0} perfect".format(name),
legendgroup=name,
showlegend=False,
mode="lines",
line=dict(
color=COLORS[cidx],
width=2,
dash="dash"),
text=["Perfect: {0:.2f}Mpps".format(y)
for y in val["ideal"]],
hoverinfo="text"
))
cidx += 1
except (IndexError, ValueError, KeyError) as err:
logging.warning("No data for '{0}'".format(name))
logging.warning(repr(err))
try:
# Create plot
file_type = plot.get("output-file-type", ".html")
logging.info(" Writing file '{0}{1}'.".
format(plot["output-file"], file_type))
layout = deepcopy(plot["layout"])
if layout.get("title", None):
layout["title"] = "Speedup Multi-core: {0}". \
format(layout["title"])
layout["yaxis"]["range"] = [0, int(max(y_max) * 1.1)]
layout["annotations"].extend(annotations)
plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
ploff.plot(plpl,
show_link=False, auto_open=False,
filename='{0}{1}'.format(plot["output-file"], file_type))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(repr(err).replace("\n", " ")))
return
def plot_performance_box(plot, input_data):
"""Generate the plot(s) with algorithm: plot_performance_box
specified in the specification file.
TODO: Remove when not needed.
: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("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot_title))
data = input_data.filter_data(plot)
if data is None:
logging.error("No data.")
return
# Prepare the data for the plot
y_vals = dict()
y_tags = 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()
y_tags[test["parent"]] = test.get("tags", None)
try:
if test["type"] in ("NDRPDR", ):
if "-pdr" in plot_title.lower():
y_vals[test["parent"]].\
append(test["throughput"]["PDR"]["LOWER"])
elif "-ndr" in plot_title.lower():
y_vals[test["parent"]]. \
append(test["throughput"]["NDR"]["LOWER"])
else:
continue
elif test["type"] in ("SOAK", ):
y_vals[test["parent"]].\
append(test["throughput"]["LOWER"])
else:
continue
except (KeyError, TypeError):
y_vals[test["parent"]].append(None)
# Sort the tests
order = plot.get("sort", None)
if order and y_tags:
y_sorted = OrderedDict()
y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
for tag in order:
logging.debug(tag)
for suite, tags in y_tags_l.items():
if "not " in tag:
tag = tag.split(" ")[-1]
if tag.lower() in tags:
continue
else:
if tag.lower() not in tags:
continue
try:
y_sorted[suite] = y_vals.pop(suite)
y_tags_l.pop(suite)
logging.debug(suite)
except KeyError as err:
logging.error("Not found: {0}".format(repr(err)))
finally:
break
else:
y_sorted = y_vals
# Add None to the lists with missing data
max_len = 0
nr_of_samples = list()
for val in y_sorted.values():
if len(val) > max_len:
max_len = len(val)
nr_of_samples.append(len(val))
for key, val in y_sorted.items():
if len(val) < max_len:
val.extend([None for _ in range(max_len - len(val))])
# Add plot traces
traces = list()
df = pd.DataFrame(y_sorted)
df.head()
y_max = list()
for i, col in enumerate(df.columns):
tst_name = re.sub(REGEX_NIC, "",
col.lower().replace('-ndrpdr', '').
replace('2n1l-', ''))
name = "{nr}. ({samples:02d} run{plural}) {name}".\
format(nr=(i + 1),
samples=nr_of_samples[i],
plural='s' if nr_of_samples[i] > 1 else '',
name=tst_name)
logging.debug(name)
traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
y=[y / 1000000 if y else None for y in df[col]],
name=name,
**plot["traces"]))
try:
val_max = max(df[col])
except ValueError as err:
logging.error(repr(err))
continue
if val_max:
y_max.append(int(val_max / 1000000) + 2)
try:
# Create plot
layout = deepcopy(plot["layout"])
if layout.get("title", None):
layout["title"] = "Throughput: {0}". \
format(layout["title"])
if y_max:
layout["yaxis"]["range"] = [0, max(y_max)]
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"]))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(repr(err).replace("\n", " ")))
return
def plot_soak_bars(plot, input_data):
"""Generate the plot(s) with algorithm: plot_soak_bars
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("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot_title))
data = input_data.filter_data(plot)
if data is None:
logging.error("No data.")
return
# Prepare the data for the plot
y_vals = dict()
y_tags = dict()
for job in data:
for build in job:
for test in build:
if y_vals.get(test["parent"], None) is None:
y_tags[test["parent"]] = test.get("tags", None)
try:
if test["type"] in ("SOAK", ):
y_vals[test["parent"]] = test["throughput"]
else:
continue
except (KeyError, TypeError):
y_vals[test["parent"]] = dict()
# Sort the tests
order = plot.get("sort", None)
if order and y_tags:
y_sorted = OrderedDict()
y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
for tag in order:
logging.debug(tag)
for suite, tags in y_tags_l.items():
if "not " in tag:
tag = tag.split(" ")[-1]
if tag.lower() in tags:
continue
else:
if tag.lower() not in tags:
continue
try:
y_sorted[suite] = y_vals.pop(suite)
y_tags_l.pop(suite)
logging.debug(suite)
except KeyError as err:
logging.error("Not found: {0}".format(repr(err)))
finally:
break
else:
y_sorted = y_vals
idx = 0
y_max = 0
traces = list()
for test_name, test_data in y_sorted.items():
idx += 1
name = "{nr}. {name}".\
format(nr=idx, name=test_name.lower().replace('-soak', ''))
if len(name) > 50:
name_lst = name.split('-')
name = ""
split_name = True
for segment in name_lst:
if (len(name) + len(segment) + 1) > 50 and split_name:
name += "
"
split_name = False
name += segment + '-'
name = name[:-1]
y_val = test_data.get("LOWER", None)
if y_val:
y_val /= 1000000
if y_val > y_max:
y_max = y_val
time = "No Information"
result = "No Information"
hovertext = ("{name}
"
"Packet Throughput: {val:.2f}Mpps
"
"Final Duration: {time}
"
"Result: {result}".format(name=name,
val=y_val,
time=time,
result=result))
traces.append(plgo.Bar(x=[str(idx) + '.', ],
y=[y_val, ],
name=name,
text=hovertext,
hoverinfo="text"))
try:
# Create plot
layout = deepcopy(plot["layout"])
if layout.get("title", None):
layout["title"] = "Packet Throughput: {0}". \
format(layout["title"])
if y_max:
layout["yaxis"]["range"] = [0, y_max + 1]
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"]))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(repr(err).replace("\n", " ")))
return
def plot_soak_boxes(plot, input_data):
"""Generate the plot(s) with algorithm: plot_soak_boxes
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("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot_title))
data = input_data.filter_data(plot)
if data is None:
logging.error("No data.")
return
# Prepare the data for the plot
y_vals = dict()
y_tags = dict()
for job in data:
for build in job:
for test in build:
if y_vals.get(test["parent"], None) is None:
y_tags[test["parent"]] = test.get("tags", None)
try:
if test["type"] in ("SOAK", ):
y_vals[test["parent"]] = test["throughput"]
else:
continue
except (KeyError, TypeError):
y_vals[test["parent"]] = dict()
# Sort the tests
order = plot.get("sort", None)
if order and y_tags:
y_sorted = OrderedDict()
y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
for tag in order:
logging.debug(tag)
for suite, tags in y_tags_l.items():
if "not " in tag:
tag = tag.split(" ")[-1]
if tag.lower() in tags:
continue
else:
if tag.lower() not in tags:
continue
try:
y_sorted[suite] = y_vals.pop(suite)
y_tags_l.pop(suite)
logging.debug(suite)
except KeyError as err:
logging.error("Not found: {0}".format(repr(err)))
finally:
break
else:
y_sorted = y_vals
idx = 0
y_max = 0
traces = list()
for test_name, test_data in y_sorted.items():
idx += 1
name = "{nr}. {name}".\
format(nr=idx, name=test_name.lower().replace('-soak', '').
replace('2n1l-', ''))
if len(name) > 55:
name_lst = name.split('-')
name = ""
split_name = True
for segment in name_lst:
if (len(name) + len(segment) + 1) > 55 and split_name:
name += "
"
split_name = False
name += segment + '-'
name = name[:-1]
y_val = test_data.get("UPPER", None)
if y_val:
y_val /= 1000000
if y_val > y_max:
y_max = y_val
y_base = test_data.get("LOWER", None)
if y_base:
y_base /= 1000000
hovertext = ("Upper bound: {upper:.2f}
"
"Lower bound: {lower:.2f}".format(upper=y_val,
lower=y_base))
traces.append(plgo.Bar(x=[str(idx) + '.', ],
# +0.05 to see the value in case lower == upper
y=[y_val - y_base + 0.05, ],
base=y_base,
name=name,
text=hovertext,
hoverinfo="text"))
try:
# Create plot
layout = deepcopy(plot["layout"])
if layout.get("title", None):
layout["title"] = "Throughput: {0}". \
format(layout["title"])
if y_max:
layout["yaxis"]["range"] = [0, y_max + 1]
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"]))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(repr(err).replace("\n", " ")))
return
def plot_latency_error_bars(plot, input_data):
"""Generate the plot(s) with algorithm: plot_latency_error_bars
specified in the specification file.
TODO: Remove when not needed.
: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("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot_title))
data = input_data.filter_data(plot)
if data is None:
logging.error("No data.")
return
# Prepare the data for the plot
y_tmp_vals = dict()
y_tags = dict()
for job in data:
for build in job:
for test in build:
try:
logging.debug("test['latency']: {0}\n".
format(test["latency"]))
except ValueError as err:
logging.warning(repr(err))
if y_tmp_vals.get(test["parent"], None) is None:
y_tmp_vals[test["parent"]] = [
list(), # direction1, min
list(), # direction1, avg
list(), # direction1, max
list(), # direction2, min
list(), # direction2, avg
list() # direction2, max
]
y_tags[test["parent"]] = test.get("tags", None)
try:
if test["type"] in ("NDRPDR", ):
if "-pdr" in plot_title.lower():
ttype = "PDR"
elif "-ndr" in plot_title.lower():
ttype = "NDR"
else:
logging.warning("Invalid test type: {0}".
format(test["type"]))
continue
y_tmp_vals[test["parent"]][0].append(
test["latency"][ttype]["direction1"]["min"])
y_tmp_vals[test["parent"]][1].append(
test["latency"][ttype]["direction1"]["avg"])
y_tmp_vals[test["parent"]][2].append(
test["latency"][ttype]["direction1"]["max"])
y_tmp_vals[test["parent"]][3].append(
test["latency"][ttype]["direction2"]["min"])
y_tmp_vals[test["parent"]][4].append(
test["latency"][ttype]["direction2"]["avg"])
y_tmp_vals[test["parent"]][5].append(
test["latency"][ttype]["direction2"]["max"])
else:
logging.warning("Invalid test type: {0}".
format(test["type"]))
continue
except (KeyError, TypeError) as err:
logging.warning(repr(err))
logging.debug("y_tmp_vals: {0}\n".format(y_tmp_vals))
# Sort the tests
order = plot.get("sort", None)
if order and y_tags:
y_sorted = OrderedDict()
y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
for tag in order:
logging.debug(tag)
for suite, tags in y_tags_l.items():
if "not " in tag:
tag = tag.split(" ")[-1]
if tag.lower() in tags:
continue
else:
if tag.lower() not in tags:
continue
try:
y_sorted[suite] = y_tmp_vals.pop(suite)
y_tags_l.pop(suite)
logging.debug(suite)
except KeyError as err:
logging.error("Not found: {0}".format(repr(err)))
finally:
break
else:
y_sorted = y_tmp_vals
logging.debug("y_sorted: {0}\n".format(y_sorted))
x_vals = list()
y_vals = list()
y_mins = list()
y_maxs = list()
nr_of_samples = list()
for key, val in y_sorted.items():
name = re.sub(REGEX_NIC, "", key.replace('-ndrpdr', '').
replace('2n1l-', ''))
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)
logging.debug("x_vals :{0}\n".format(x_vals))
logging.debug("y_vals :{0}\n".format(y_vals))
logging.debug("y_mins :{0}\n".format(y_mins))
logging.debug("y_maxs :{0}\n".format(y_maxs))
logging.debug("nr_of_samples :{0}\n".format(nr_of_samples))
traces = list()
annotations = list()
for idx in range(len(x_vals)):
if not bool(int(idx % 2)):
direction = "West-East"
else:
direction = "East-West"
hovertext = ("No. of Runs: {nr}
"
"Test: {test}
"
"Direction: {dir}
".format(test=x_vals[idx],
dir=direction,
nr=nr_of_samples[idx]))
if isinstance(y_maxs[idx], float):
hovertext += "Max: {max:.2f}uSec
".format(max=y_maxs[idx])
if isinstance(y_vals[idx], float):
hovertext += "Mean: {avg:.2f}uSec
".format(avg=y_vals[idx])
if isinstance(y_mins[idx], float):
hovertext += "Min: {min:.2f}uSec".format(min=y_mins[idx])
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, ]
logging.debug("y_vals[{1}] :{0}\n".format(y_vals[idx], idx))
logging.debug("array :{0}\n".format(array))
logging.debug("arrayminus :{0}\n".format(arrayminus))
traces.append(plgo.Scatter(
x=[idx, ],
y=[y_vals[idx], ],
name=x_vals[idx],
legendgroup=x_vals[idx],
showlegend=bool(int(idx % 2)),
mode="markers",
error_y=dict(
type='data',
symmetric=False,
array=array,
arrayminus=arrayminus,
color=COLORS[int(idx / 2)]
),
marker=dict(
size=10,
color=COLORS[int(idx / 2)],
),
text=hovertext,
hoverinfo="text",
))
annotations.append(dict(
x=idx,
y=0,
xref="x",
yref="y",
xanchor="center",
yanchor="top",
text="E-W" if bool(int(idx % 2)) else "W-E",
font=dict(
size=16,
),
align="center",
showarrow=False
))
try:
# Create plot
logging.info(" Writing file '{0}{1}'.".
format(plot["output-file"], plot["output-file-type"]))
layout = deepcopy(plot["layout"])
if layout.get("title", None):
layout["title"] = "Latency: {0}".\
format(layout["title"])
layout["annotations"] = annotations
plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
ploff.plot(plpl,
show_link=False, auto_open=False,
filename='{0}{1}'.format(plot["output-file"],
plot["output-file-type"]))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(str(err).replace("\n", " ")))
return
def plot_throughput_speedup_analysis(plot, input_data):
"""Generate the plot(s) with algorithm:
plot_throughput_speedup_analysis
specified in the specification file.
TODO: Remove when not needed.
: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("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot_title))
data = input_data.filter_data(plot)
if data is None:
logging.error("No data.")
return
y_vals = dict()
y_tags = 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"]] = {"1": list(),
"2": list(),
"4": list()}
y_tags[test["parent"]] = test.get("tags", None)
try:
if test["type"] in ("NDRPDR",):
if "-pdr" in plot_title.lower():
ttype = "PDR"
elif "-ndr" in plot_title.lower():
ttype = "NDR"
else:
continue
if "1C" in test["tags"]:
y_vals[test["parent"]]["1"]. \
append(test["throughput"][ttype]["LOWER"])
elif "2C" in test["tags"]:
y_vals[test["parent"]]["2"]. \
append(test["throughput"][ttype]["LOWER"])
elif "4C" in test["tags"]:
y_vals[test["parent"]]["4"]. \
append(test["throughput"][ttype]["LOWER"])
except (KeyError, TypeError):
pass
if not y_vals:
logging.warning("No data for the plot '{}'".
format(plot.get("title", "")))
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 = dict()
y_max = list()
nic_limit = 0
lnk_limit = 0
pci_limit = plot["limits"]["pci"]["pci-g3-x8"]
for test_name, test_vals in y_vals.items():
try:
if test_vals["1"][1]:
name = re.sub(REGEX_NIC, "", test_name.replace('-ndrpdr', '').
replace('2n1l-', ''))
vals[name] = dict()
y_val_1 = test_vals["1"][0] / 1000000.0
y_val_2 = test_vals["2"][0] / 1000000.0 if test_vals["2"][0] \
else None
y_val_4 = test_vals["4"][0] / 1000000.0 if test_vals["4"][0] \
else None
vals[name]["val"] = [y_val_1, y_val_2, y_val_4]
vals[name]["rel"] = [1.0, None, None]
vals[name]["ideal"] = [y_1c_max[test_name],
y_1c_max[test_name] * 2,
y_1c_max[test_name] * 4]
vals[name]["diff"] = [(y_val_1 - y_1c_max[test_name]) * 100 /
y_val_1, None, None]
vals[name]["count"] = [test_vals["1"][1],
test_vals["2"][1],
test_vals["4"][1]]
try:
# val_max = max(max(vals[name]["val"], vals[name]["ideal"]))
val_max = max(vals[name]["val"])
except ValueError as err:
logging.error(err)
continue
if val_max:
# y_max.append(int((val_max / 10) + 1) * 10)
y_max.append(val_max)
if y_val_2:
vals[name]["rel"][1] = round(y_val_2 / y_val_1, 2)
vals[name]["diff"][1] = \
(y_val_2 - vals[name]["ideal"][1]) * 100 / y_val_2
if y_val_4:
vals[name]["rel"][2] = round(y_val_4 / y_val_1, 2)
vals[name]["diff"][2] = \
(y_val_4 - vals[name]["ideal"][2]) * 100 / y_val_4
except IndexError as err:
logging.warning("No data for '{0}'".format(test_name))
logging.warning(repr(err))
# Limits:
if "x520" in test_name:
limit = plot["limits"]["nic"]["x520"]
elif "x710" in test_name:
limit = plot["limits"]["nic"]["x710"]
elif "xxv710" in test_name:
limit = plot["limits"]["nic"]["xxv710"]
elif "xl710" in test_name:
limit = plot["limits"]["nic"]["xl710"]
elif "x553" in test_name:
limit = plot["limits"]["nic"]["x553"]
else:
limit = 0
if limit > nic_limit:
nic_limit = limit
mul = 2 if "ge2p" in test_name else 1
if "10ge" in test_name:
limit = plot["limits"]["link"]["10ge"] * mul
elif "25ge" in test_name:
limit = plot["limits"]["link"]["25ge"] * mul
elif "40ge" in test_name:
limit = plot["limits"]["link"]["40ge"] * mul
elif "100ge" in test_name:
limit = plot["limits"]["link"]["100ge"] * mul
else:
limit = 0
if limit > lnk_limit:
lnk_limit = limit
# Sort the tests
order = plot.get("sort", None)
if order and y_tags:
y_sorted = OrderedDict()
y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
for tag in order:
for test, tags in y_tags_l.items():
if tag.lower() in tags:
name = re.sub(REGEX_NIC, "",
test.replace('-ndrpdr', '').
replace('2n1l-', ''))
try:
y_sorted[name] = vals.pop(name)
y_tags_l.pop(test)
except KeyError as err:
logging.error("Not found: {0}".format(err))
finally:
break
else:
y_sorted = vals
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
# if nic_limit < threshold:
traces.append(plgo.Scatter(
x=x_vals,
y=[nic_limit, ] * len(x_vals),
name="NIC: {0:.2f}Mpps".format(nic_limit),
showlegend=False,
mode="lines",
line=dict(
dash="dot",
color=COLORS[-1],
width=1),
hoverinfo="none"
))
annotations.append(dict(
x=1,
y=nic_limit,
xref="x",
yref="y",
xanchor="left",
yanchor="bottom",
text="NIC: {0:.2f}Mpps".format(nic_limit),
font=dict(
size=14,
color=COLORS[-1],
),
align="left",
showarrow=False
))
# y_max.append(int((nic_limit / 10) + 1) * 10)
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="Link: {0:.2f}Mpps".format(lnk_limit),
showlegend=False,
mode="lines",
line=dict(
dash="dot",
color=COLORS[-2],
width=1),
hoverinfo="none"
))
annotations.append(dict(
x=1,
y=lnk_limit,
xref="x",
yref="y",
xanchor="left",
yanchor="bottom",
text="Link: {0:.2f}Mpps".format(lnk_limit),
font=dict(
size=14,
color=COLORS[-2],
),
align="left",
showarrow=False
))
# y_max.append(int((lnk_limit / 10) + 1) * 10)
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="PCIe: {0:.2f}Mpps".format(pci_limit),
showlegend=False,
mode="lines",
line=dict(
dash="dot",
color=COLORS[-3],
width=1),
hoverinfo="none"
))
annotations.append(dict(
x=1,
y=pci_limit,
xref="x",
yref="y",
xanchor="left",
yanchor="bottom",
text="PCIe: {0:.2f}Mpps".format(pci_limit),
font=dict(
size=14,
color=COLORS[-3],
),
align="left",
showarrow=False
))
# y_max.append(int((pci_limit / 10) + 1) * 10)
y_max.append(pci_limit)
# Perfect and measured:
cidx = 0
for name, val in y_sorted.iteritems():
hovertext = list()
try:
for idx in range(len(val["val"])):
htext = ""
if isinstance(val["val"][idx], float):
htext += "No. of Runs: {1}
" \
"Mean: {0:.2f}Mpps
".format(val["val"][idx],
val["count"][idx])
if isinstance(val["diff"][idx], float):
htext += "Diff: {0:.0f}%
".format(round(val["diff"][idx]))
if isinstance(val["rel"][idx], float):
htext += "Speedup: {0:.2f}".format(val["rel"][idx])
hovertext.append(htext)
traces.append(plgo.Scatter(x=x_vals,
y=val["val"],
name=name,
legendgroup=name,
mode="lines+markers",
line=dict(
color=COLORS[cidx],
width=2),
marker=dict(
symbol="circle",
size=10
),
text=hovertext,
hoverinfo="text+name"
))
traces.append(plgo.Scatter(x=x_vals,
y=val["ideal"],
name="{0} perfect".format(name),
legendgroup=name,
showlegend=False,
mode="lines",
line=dict(
color=COLORS[cidx],
width=2,
dash="dash"),
text=["Perfect: {0:.2f}Mpps".format(y)
for y in val["ideal"]],
hoverinfo="text"
))
cidx += 1
except (IndexError, ValueError, KeyError) as err:
logging.warning("No data for '{0}'".format(name))
logging.warning(repr(err))
try:
# Create plot
logging.info(" Writing file '{0}{1}'.".
format(plot["output-file"], plot["output-file-type"]))
layout = deepcopy(plot["layout"])
if layout.get("title", None):
layout["title"] = "Speedup Multi-core: {0}". \
format(layout["title"])
# layout["yaxis"]["range"] = [0, int((max(y_max) / 10) + 1) * 10]
layout["yaxis"]["range"] = [0, int(max(y_max) * 1.1)]
layout["annotations"].extend(annotations)
plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
ploff.plot(plpl,
show_link=False, auto_open=False,
filename='{0}{1}'.format(plot["output-file"],
plot["output-file-type"]))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(str(err).replace("\n", " ")))
return
def plot_http_server_performance_box(plot, input_data):
"""Generate the plot(s) with algorithm: plot_http_server_performance_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(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot.get("title", "")))
data = input_data.filter_data(plot)
if data is None:
logging.error("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["name"], None) is None:
y_vals[test["name"]] = list()
try:
y_vals[test["name"]].append(test["result"])
except (KeyError, TypeError):
y_vals[test["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 key, val in y_vals.items():
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 = "{nr}. ({samples:02d} run{plural}) {name}".\
format(nr=(i + 1),
samples=nr_of_samples[i],
plural='s' if nr_of_samples[i] > 1 else '',
name=col.lower().replace('-ndrpdr', ''))
if len(name) > 50:
name_lst = name.split('-')
name = ""
split_name = True
for segment in name_lst:
if (len(name) + len(segment) + 1) > 50 and split_name:
name += "
"
split_name = False
name += segment + '-'
name = name[:-1]
traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
y=df[col],
name=name,
**plot["traces"]))
try:
# Create plot
plpl = plgo.Figure(data=traces, layout=plot["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"]))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(str(err).replace("\n", " ")))
return
def plot_service_density_heatmap(plot, input_data):
"""Generate the plot(s) with algorithm: plot_service_density_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$')
REGEX_TEST_NAME = re.compile(r'^.*-(\d+vhost|\d+memif)-'
r'(\d+chain|\d+pipe)-'
r'(\d+vm|\d+dcr|\d+drc).*$')
txt_chains = list()
txt_nodes = list()
vals = dict()
# Transform the data
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot.get("title", "")))
data = input_data.filter_data(plot, continue_on_error=True)
if data is None or data.empty:
logging.error("No data.")
return
for job in data:
for build in job:
for test in build:
for tag in test['tags']:
groups = re.search(REGEX_CN, tag)
if groups:
c = str(groups.group(1))
n = str(groups.group(2))
break
else:
continue
groups = re.search(REGEX_TEST_NAME, test["name"])
if groups and len(groups.groups()) == 3:
hover_name = "{vhost}-{chain}-{vm}".format(
vhost=str(groups.group(1)),
chain=str(groups.group(2)),
vm=str(groups.group(3)))
else:
hover_name = ""
if vals.get(c, None) is None:
vals[c] = dict()
if vals[c].get(n, None) is None:
vals[c][n] = dict(name=hover_name,
vals=list(),
nr=None,
mean=None,
stdev=None)
try:
if plot["include-tests"] == "MRR":
result = test["result"]["receive-rate"].avg
elif plot["include-tests"] == "PDR":
result = test["throughput"]["PDR"]["LOWER"]
elif plot["include-tests"] == "NDR":
result = test["throughput"]["NDR"]["LOWER"]
else:
result = None
except TypeError:
result = None
if result:
vals[c][n]["vals"].append(result)
if not vals:
logging.error("No data.")
return
for key_c in vals.keys():
txt_chains.append(key_c)
for key_n in vals[key_c].keys():
txt_nodes.append(key_n)
if vals[key_c][key_n]["vals"]:
vals[key_c][key_n]["nr"] = len(vals[key_c][key_n]["vals"])
vals[key_c][key_n]["mean"] = \
round(mean(vals[key_c][key_n]["vals"]) / 1000000, 1)
vals[key_c][key_n]["stdev"] = \
round(stdev(vals[key_c][key_n]["vals"]) / 1000000, 1)
txt_nodes = list(set(txt_nodes))
txt_chains = sorted(txt_chains, key=lambda chain: int(chain))
txt_nodes = sorted(txt_nodes, key=lambda node: int(node))
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 c in chains:
for n in nodes:
try:
val = vals[txt_chains[c - 1]][txt_nodes[n - 1]]["mean"]
except (KeyError, IndexError):
val = None
data[c - 1].append(val)
# Colorscales:
my_green = [[0.0, 'rgb(235, 249, 242)'],
[1.0, 'rgb(45, 134, 89)']]
my_blue = [[0.0, 'rgb(236, 242, 248)'],
[1.0, 'rgb(57, 115, 172)']]
my_grey = [[0.0, 'rgb(230, 230, 230)'],
[1.0, 'rgb(102, 102, 102)']]
hovertext = list()
annotations = list()
text = ("Test: {name}
"
"Runs: {nr}
"
"Thput: {val}
"
"StDev: {stdev}")
for c in range(len(txt_chains)):
hover_line = list()
for n in range(len(txt_nodes)):
if data[c][n] is not None:
annotations.append(dict(
x=n+1,
y=c+1,
xref="x",
yref="y",
xanchor="center",
yanchor="middle",
text=str(data[c][n]),
font=dict(
size=14,
),
align="center",
showarrow=False
))
hover_line.append(text.format(
name=vals[txt_chains[c]][txt_nodes[n]]["name"],
nr=vals[txt_chains[c]][txt_nodes[n]]["nr"],
val=data[c][n],
stdev=vals[txt_chains[c]][txt_nodes[n]]["stdev"]))
hovertext.append(hover_line)
traces = [
plgo.Heatmap(x=nodes,
y=chains,
z=data,
colorbar=dict(
title=plot.get("z-axis", ""),
titleside="right",
titlefont=dict(
size=16
),
tickfont=dict(
size=16,
),
tickformat=".1f",
yanchor="bottom",
y=-0.02,
len=0.925,
),
showscale=True,
colorscale=my_green,
text=hovertext,
hoverinfo="text")
]
for idx, item in enumerate(txt_nodes):
# X-axis, numbers:
annotations.append(dict(
x=idx+1,
y=0.05,
xref="x",
yref="y",
xanchor="center",
yanchor="top",
text=item,
font=dict(
size=16,
),
align="center",
showarrow=False
))
for idx, item in enumerate(txt_chains):
# Y-axis, numbers:
annotations.append(dict(
x=0.35,
y=idx+1,
xref="x",
yref="y",
xanchor="right",
yanchor="middle",
text=item,
font=dict(
size=16,
),
align="center",
showarrow=False
))
# X-axis, title:
annotations.append(dict(
x=0.55,
y=-0.15,
xref="paper",
yref="y",
xanchor="center",
yanchor="bottom",
text=plot.get("x-axis", ""),
font=dict(
size=16,
),
align="center",
showarrow=False
))
# Y-axis, title:
annotations.append(dict(
x=-0.1,
y=0.5,
xref="x",
yref="paper",
xanchor="center",
yanchor="middle",
text=plot.get("y-axis", ""),
font=dict(
size=16,
),
align="center",
textangle=270,
showarrow=False
))
updatemenus = list([
dict(
x=1.0,
y=0.0,
xanchor='right',
yanchor='bottom',
direction='up',
buttons=list([
dict(
args=[{"colorscale": [my_green, ], "reversescale": False}],
label="Green",
method="update"
),
dict(
args=[{"colorscale": [my_blue, ], "reversescale": False}],
label="Blue",
method="update"
),
dict(
args=[{"colorscale": [my_grey, ], "reversescale": False}],
label="Grey",
method="update"
)
])
)
])
try:
layout = deepcopy(plot["layout"])
except KeyError as err:
logging.error("Finished with error: No layout defined")
logging.error(repr(err))
return
layout["annotations"] = annotations
layout['updatemenus'] = updatemenus
try:
# Create plot
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"]))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(str(err).replace("\n", " ")))
return
def plot_service_density_heatmap_compare(plot, input_data):
"""Generate the plot(s) with algorithm: plot_service_density_heatmap_compare
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$')
REGEX_TEST_NAME = re.compile(r'^.*-(\d+ch|\d+pl)-'
r'(\d+vh|\d+mif)-'
r'(\d+vm|\d+dcr).*$')
REGEX_THREADS = re.compile(r'^(\d+)(VM|DCR)(\d+)T$')
txt_chains = list()
txt_nodes = list()
vals = dict()
# Transform the data
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot.get("title", "")))
data = input_data.filter_data(plot, continue_on_error=True)
if data is None or data.empty:
logging.error("No data.")
return
for job in data:
for build in job:
for test in build:
for tag in test['tags']:
groups = re.search(REGEX_CN, tag)
if groups:
c = str(groups.group(1))
n = str(groups.group(2))
break
else:
continue
groups = re.search(REGEX_TEST_NAME, test["name"])
if groups and len(groups.groups()) == 3:
hover_name = "{chain}-{vhost}-{vm}".format(
chain=str(groups.group(1)),
vhost=str(groups.group(2)),
vm=str(groups.group(3)))
else:
hover_name = ""
if vals.get(c, None) is None:
vals[c] = dict()
if vals[c].get(n, None) is None:
vals[c][n] = dict(name=hover_name,
vals_r=list(),
vals_c=list(),
nr_r=None,
nr_c=None,
mean_r=None,
mean_c=None,
stdev_r=None,
stdev_c=None)
try:
if plot["include-tests"] == "MRR":
result = test["result"]["receive-rate"].avg
elif plot["include-tests"] == "PDR":
result = test["throughput"]["PDR"]["LOWER"]
elif plot["include-tests"] == "NDR":
result = test["throughput"]["NDR"]["LOWER"]
else:
result = None
except TypeError:
result = None
if result:
for tag in test['tags']:
groups = re.search(REGEX_THREADS, tag)
if groups and len(groups.groups()) == 3:
if str(groups.group(3)) == \
plot["reference"]["include"]:
vals[c][n]["vals_r"].append(result)
elif str(groups.group(3)) == \
plot["compare"]["include"]:
vals[c][n]["vals_c"].append(result)
break
if not vals:
logging.error("No data.")
return
for key_c in vals.keys():
txt_chains.append(key_c)
for key_n in vals[key_c].keys():
txt_nodes.append(key_n)
if vals[key_c][key_n]["vals_r"]:
vals[key_c][key_n]["nr_r"] = len(vals[key_c][key_n]["vals_r"])
vals[key_c][key_n]["mean_r"] = \
mean(vals[key_c][key_n]["vals_r"])
vals[key_c][key_n]["stdev_r"] = \
round(stdev(vals[key_c][key_n]["vals_r"]) / 1000000, 1)
if vals[key_c][key_n]["vals_c"]:
vals[key_c][key_n]["nr_c"] = len(vals[key_c][key_n]["vals_c"])
vals[key_c][key_n]["mean_c"] = \
mean(vals[key_c][key_n]["vals_c"])
vals[key_c][key_n]["stdev_c"] = \
round(stdev(vals[key_c][key_n]["vals_c"]) / 1000000, 1)
txt_nodes = list(set(txt_nodes))
txt_chains = sorted(txt_chains, key=lambda chain: int(chain))
txt_nodes = sorted(txt_nodes, key=lambda node: int(node))
chains = [i + 1 for i in range(len(txt_chains))]
nodes = [i + 1 for i in range(len(txt_nodes))]
data_r = [list() for _ in range(len(chains))]
data_c = [list() for _ in range(len(chains))]
diff = [list() for _ in range(len(chains))]
for c in chains:
for n in nodes:
try:
val_r = vals[txt_chains[c - 1]][txt_nodes[n - 1]]["mean_r"]
except (KeyError, IndexError):
val_r = None
try:
val_c = vals[txt_chains[c - 1]][txt_nodes[n - 1]]["mean_c"]
except (KeyError, IndexError):
val_c = None
if val_c is not None and val_r:
val_d = (val_c - val_r) * 100 / val_r
else:
val_d = None
if val_r is not None:
val_r = round(val_r / 1000000, 1)
data_r[c - 1].append(val_r)
if val_c is not None:
val_c = round(val_c / 1000000, 1)
data_c[c - 1].append(val_c)
if val_d is not None:
val_d = int(round(val_d, 0))
diff[c - 1].append(val_d)
# Colorscales:
my_green = [[0.0, 'rgb(235, 249, 242)'],
[1.0, 'rgb(45, 134, 89)']]
my_blue = [[0.0, 'rgb(236, 242, 248)'],
[1.0, 'rgb(57, 115, 172)']]
my_grey = [[0.0, 'rgb(230, 230, 230)'],
[1.0, 'rgb(102, 102, 102)']]
hovertext = list()
annotations = list()
annotations_r = list()
annotations_c = list()
annotations_diff = list()
text = ("Test: {name}"
"
{title_r}: {text_r}"
"
{title_c}: {text_c}{text_diff}")
text_r = "Thput: {val_r}; StDev: {stdev_r}; Runs: {nr_r}"
text_c = "Thput: {val_c}; StDev: {stdev_c}; Runs: {nr_c}"
text_diff = "
Relative Difference {title_c} vs. {title_r}: {diff}%"
for c in range(len(txt_chains)):
hover_line = list()
for n in range(len(txt_nodes)):
point = dict(
x=n + 1,
y=c + 1,
xref="x",
yref="y",
xanchor="center",
yanchor="middle",
text="",
font=dict(
size=14,
),
align="center",
showarrow=False
)
point_text_r = "Not present"
point_text_c = "Not present"
point_text_diff = ""
try:
point_r = data_r[c][n]
if point_r is not None:
point_text_r = text_r.format(
val_r=point_r,
stdev_r=vals[txt_chains[c]][txt_nodes[n]]["stdev_r"],
nr_r=vals[txt_chains[c]][txt_nodes[n]]["nr_r"])
except KeyError:
point_r = None
point["text"] = "" if point_r is None else point_r
annotations_r.append(deepcopy(point))
try:
point_c = data_c[c][n]
if point_c is not None:
point_text_c = text_c.format(
val_c=point_c,
stdev_c=vals[txt_chains[c]][txt_nodes[n]]["stdev_c"],
nr_c=vals[txt_chains[c]][txt_nodes[n]]["nr_c"])
except KeyError:
point_c = None
point["text"] = "" if point_c is None else point_c
annotations_c.append(deepcopy(point))
try:
point_d = diff[c][n]
if point_d is not None:
point_text_diff = text_diff.format(
title_r=plot["reference"]["name"],
title_c=plot["compare"]["name"],
diff=point_d)
except KeyError:
point_d = None
point["text"] = "" if point_d is None else point_d
annotations_diff.append(deepcopy(point))
try:
name = vals[txt_chains[c]][txt_nodes[n]]["name"]
except KeyError:
continue
hover_line.append(text.format(
name=name,
title_r=plot["reference"]["name"],
text_r=point_text_r,
title_c=plot["compare"]["name"],
text_c=point_text_c,
text_diff=point_text_diff
))
hovertext.append(hover_line)
traces = [
plgo.Heatmap(x=nodes,
y=chains,
z=data_r,
visible=True,
colorbar=dict(
title=plot.get("z-axis", ""),
titleside="right",
titlefont=dict(
size=16
),
tickfont=dict(
size=16,
),
tickformat=".1f",
yanchor="bottom",
y=-0.02,
len=0.925,
),
showscale=True,
colorscale=my_green,
reversescale=False,
text=hovertext,
hoverinfo="text"),
plgo.Heatmap(x=nodes,
y=chains,
z=data_c,
visible=False,
colorbar=dict(
title=plot.get("z-axis", ""),
titleside="right",
titlefont=dict(
size=16
),
tickfont=dict(
size=16,
),
tickformat=".1f",
yanchor="bottom",
y=-0.02,
len=0.925,
),
showscale=True,
colorscale=my_blue,
reversescale=False,
text=hovertext,
hoverinfo="text"),
plgo.Heatmap(x=nodes,
y=chains,
z=diff,
name="Diff",
visible=False,
colorbar=dict(
title="Relative Difference {name_c} vs. {name_r} [%]".
format(name_c=plot["compare"]["name"],
name_r=plot["reference"]["name"]),
titleside="right",
titlefont=dict(
size=16
),
tickfont=dict(
size=16,
),
tickformat=".1f",
yanchor="bottom",
y=-0.02,
len=0.925,
),
showscale=True,
colorscale=my_grey,
reversescale=False,
text=hovertext,
hoverinfo="text")
]
for idx, item in enumerate(txt_nodes):
# X-axis, numbers:
annotations.append(dict(
x=idx+1,
y=0.05,
xref="x",
yref="y",
xanchor="center",
yanchor="top",
text=item,
font=dict(
size=16,
),
align="center",
showarrow=False
))
for idx, item in enumerate(txt_chains):
# Y-axis, numbers:
annotations.append(dict(
x=0.35,
y=idx+1,
xref="x",
yref="y",
xanchor="right",
yanchor="middle",
text=item,
font=dict(
size=16,
),
align="center",
showarrow=False
))
# X-axis, title:
annotations.append(dict(
x=0.55,
y=-0.15,
xref="paper",
yref="y",
xanchor="center",
yanchor="bottom",
text=plot.get("x-axis", ""),
font=dict(
size=16,
),
align="center",
showarrow=False
))
# Y-axis, title:
annotations.append(dict(
x=-0.1,
y=0.5,
xref="x",
yref="paper",
xanchor="center",
yanchor="middle",
text=plot.get("y-axis", ""),
font=dict(
size=16,
),
align="center",
textangle=270,
showarrow=False
))
updatemenus = list([
dict(
active=0,
x=1.0,
y=0.0,
xanchor='right',
yanchor='bottom',
direction='up',
buttons=list([
dict(
label=plot["reference"]["name"],
method="update",
args=[
{
"visible": [True, False, False]
},
{
"colorscale": [my_green, ],
"reversescale": False,
"annotations": annotations + annotations_r,
},
]
),
dict(
label=plot["compare"]["name"],
method="update",
args=[
{
"visible": [False, True, False]
},
{
"colorscale": [my_blue, ],
"reversescale": False,
"annotations": annotations + annotations_c,
},
]
),
dict(
label="Diff",
method="update",
args=[
{
"visible": [False, False, True]
},
{
"colorscale": [my_grey, ],
"reversescale": False,
"annotations": annotations + annotations_diff,
},
]
),
])
)
])
try:
layout = deepcopy(plot["layout"])
except KeyError as err:
logging.error("Finished with error: No layout defined")
logging.error(repr(err))
return
layout["annotations"] = annotations + annotations_r
layout['updatemenus'] = updatemenus
try:
# Create plot
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"]))
except PlotlyError as err:
logging.error(" Finished with error: {}".
format(str(err).replace("\n", " ")))
return