+++ /dev/null
-# Copyright (c) 2020 Cisco and/or its affiliates.
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at:
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Algorithms to generate plots.
-"""
-
-
-import re
-import logging
-
-import hdrh.histogram
-import hdrh.codec
-import pandas as pd
-import plotly.offline as ploff
-import plotly.graph_objs as plgo
-
-from collections import OrderedDict
-from copy import deepcopy
-from math import log
-
-from plotly.exceptions import PlotlyError
-
-from pal_utils import mean, stdev
-
-
-COLORS = (
- u"#1A1110",
- u"#DA2647",
- u"#214FC6",
- u"#01786F",
- u"#BD8260",
- u"#FFD12A",
- u"#A6E7FF",
- u"#738276",
- u"#C95A49",
- u"#FC5A8D",
- u"#CEC8EF",
- u"#391285",
- u"#6F2DA8",
- u"#FF878D",
- u"#45A27D",
- u"#FFD0B9",
- u"#FD5240",
- u"#DB91EF",
- u"#44D7A8",
- u"#4F86F7",
- u"#84DE02",
- u"#FFCFF1",
- u"#614051"
-)
-
-REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)-')
-
-
-def generate_plots(spec, data):
- """Generate all plots specified in the specification file.
-
- :param spec: Specification read from the specification file.
- :param data: Data to process.
- :type spec: Specification
- :type data: InputData
- """
-
- generator = {
- u"plot_nf_reconf_box_name": plot_nf_reconf_box_name,
- u"plot_perf_box_name": plot_perf_box_name,
- u"plot_tsa_name": plot_tsa_name,
- u"plot_http_server_perf_box": plot_http_server_perf_box,
- u"plot_nf_heatmap": plot_nf_heatmap,
- u"plot_hdrh_lat_by_percentile": plot_hdrh_lat_by_percentile
- }
-
- logging.info(u"Generating the plots ...")
- for index, plot in enumerate(spec.plots):
- try:
- logging.info(f" Plot nr {index + 1}: {plot.get(u'title', u'')}")
- plot[u"limits"] = spec.configuration[u"limits"]
- generator[plot[u"algorithm"]](plot, data)
- logging.info(u" Done.")
- except NameError as err:
- logging.error(
- f"Probably algorithm {plot[u'algorithm']} is not defined: "
- f"{repr(err)}"
- )
- logging.info(u"Done.")
-
-
-def plot_hdrh_lat_by_percentile(plot, input_data):
- """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- # Transform the data
- logging.info(
- f" Creating the data set for the {plot.get(u'type', u'')} "
- f"{plot.get(u'title', u'')}."
- )
- if plot.get(u"include", None):
- data = input_data.filter_tests_by_name(
- plot,
- params=[u"name", u"latency", u"parent", u"tags", u"type"]
- )[0][0]
- elif plot.get(u"filter", None):
- data = input_data.filter_data(
- plot,
- params=[u"name", u"latency", u"parent", u"tags", u"type"],
- continue_on_error=True
- )[0][0]
- else:
- job = list(plot[u"data"].keys())[0]
- build = str(plot[u"data"][job][0])
- data = input_data.tests(job, build)
-
- if data is None or len(data) == 0:
- logging.error(u"No data.")
- return
-
- desc = {
- u"LAT0": u"No-load.",
- u"PDR10": u"Low-load, 10% PDR.",
- u"PDR50": u"Mid-load, 50% PDR.",
- u"PDR90": u"High-load, 90% PDR.",
- u"PDR": u"Full-load, 100% PDR.",
- u"NDR10": u"Low-load, 10% NDR.",
- u"NDR50": u"Mid-load, 50% NDR.",
- u"NDR90": u"High-load, 90% NDR.",
- u"NDR": u"Full-load, 100% NDR."
- }
-
- graphs = [
- u"LAT0",
- u"PDR10",
- u"PDR50",
- u"PDR90"
- ]
-
- file_links = plot.get(u"output-file-links", None)
- target_links = plot.get(u"target-links", None)
-
- for test in data:
- try:
- if test[u"type"] not in (u"NDRPDR",):
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- name = re.sub(REGEX_NIC, u"", test[u"parent"].
- replace(u'-ndrpdr', u'').replace(u'2n1l-', u''))
- try:
- nic = re.search(REGEX_NIC, test[u"parent"]).group(1)
- except (IndexError, AttributeError, KeyError, ValueError):
- nic = u""
- name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'')
-
- logging.info(f" Generating the graph: {name_link}")
-
- fig = plgo.Figure()
- layout = deepcopy(plot[u"layout"])
-
- for color, graph in enumerate(graphs):
- for idx, direction in enumerate((u"direction1", u"direction2")):
- xaxis = [0.0, ]
- yaxis = [0.0, ]
- hovertext = [
- f"<b>{desc[graph]}</b><br>"
- f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
- f"Percentile: 0.0%<br>"
- f"Latency: 0.0uSec"
- ]
- try:
- decoded = hdrh.histogram.HdrHistogram.decode(
- test[u"latency"][graph][direction][u"hdrh"]
- )
- except hdrh.codec.HdrLengthException:
- logging.warning(
- f"No data for direction {(u'W-E', u'E-W')[idx % 2]}"
- )
- continue
-
- for item in decoded.get_recorded_iterator():
- percentile = item.percentile_level_iterated_to
- if percentile > 99.9:
- continue
- xaxis.append(percentile)
- yaxis.append(item.value_iterated_to)
- hovertext.append(
- f"<b>{desc[graph]}</b><br>"
- f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
- f"Percentile: {percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- fig.add_trace(
- plgo.Scatter(
- x=xaxis,
- y=yaxis,
- name=desc[graph],
- mode=u"lines",
- legendgroup=desc[graph],
- showlegend=bool(idx),
- line=dict(
- color=COLORS[color],
- dash=u"dash" if idx % 2 else u"solid"
- ),
- hovertext=hovertext,
- hoverinfo=u"text"
- )
- )
-
- layout[u"title"][u"text"] = f"<b>Latency:</b> {name}"
- fig.update_layout(layout)
-
- # Create plot
- file_name = f"{plot[u'output-file']}-{name_link}.html"
- logging.info(f" Writing file {file_name}")
-
- try:
- # Export Plot
- ploff.plot(fig, show_link=False, auto_open=False,
- filename=file_name)
- # Add link to the file:
- if file_links and target_links:
- with open(file_links, u"a") as file_handler:
- file_handler.write(
- f"- `{name_link} "
- f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n"
- )
- except FileNotFoundError as err:
- logging.error(
- f"Not possible to write the link to the file "
- f"{file_links}\n{err}"
- )
- except PlotlyError as err:
- logging.error(f" Finished with error: {repr(err)}")
-
- except hdrh.codec.HdrLengthException as err:
- logging.warning(repr(err))
- continue
-
- except (ValueError, KeyError) as err:
- logging.warning(repr(err))
- continue
-
-
-def plot_hdrh_lat_by_percentile_x_log(plot, input_data):
- """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile_x_log
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- # Transform the data
- logging.info(
- f" Creating the data set for the {plot.get(u'type', u'')} "
- f"{plot.get(u'title', u'')}."
- )
- if plot.get(u"include", None):
- data = input_data.filter_tests_by_name(
- plot,
- params=[u"name", u"latency", u"parent", u"tags", u"type"]
- )[0][0]
- elif plot.get(u"filter", None):
- data = input_data.filter_data(
- plot,
- params=[u"name", u"latency", u"parent", u"tags", u"type"],
- continue_on_error=True
- )[0][0]
- else:
- job = list(plot[u"data"].keys())[0]
- build = str(plot[u"data"][job][0])
- data = input_data.tests(job, build)
-
- if data is None or len(data) == 0:
- logging.error(u"No data.")
- return
-
- desc = {
- u"LAT0": u"No-load.",
- u"PDR10": u"Low-load, 10% PDR.",
- u"PDR50": u"Mid-load, 50% PDR.",
- u"PDR90": u"High-load, 90% PDR.",
- u"PDR": u"Full-load, 100% PDR.",
- u"NDR10": u"Low-load, 10% NDR.",
- u"NDR50": u"Mid-load, 50% NDR.",
- u"NDR90": u"High-load, 90% NDR.",
- u"NDR": u"Full-load, 100% NDR."
- }
-
- graphs = [
- u"LAT0",
- u"PDR10",
- u"PDR50",
- u"PDR90"
- ]
-
- file_links = plot.get(u"output-file-links", None)
- target_links = plot.get(u"target-links", None)
-
- for test in data:
- try:
- if test[u"type"] not in (u"NDRPDR",):
- logging.warning(f"Invalid test type: {test[u'type']}")
- continue
- name = re.sub(REGEX_NIC, u"", test[u"parent"].
- replace(u'-ndrpdr', u'').replace(u'2n1l-', u''))
- try:
- nic = re.search(REGEX_NIC, test[u"parent"]).group(1)
- except (IndexError, AttributeError, KeyError, ValueError):
- nic = u""
- name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'')
-
- logging.info(f" Generating the graph: {name_link}")
-
- fig = plgo.Figure()
- layout = deepcopy(plot[u"layout"])
- xaxis_max = 0
-
- for color, graph in enumerate(graphs):
- for idx, direction in enumerate((u"direction1", u"direction2")):
- xaxis = list()
- yaxis = list()
- hovertext = list()
- try:
- decoded = hdrh.histogram.HdrHistogram.decode(
- test[u"latency"][graph][direction][u"hdrh"]
- )
- except hdrh.codec.HdrLengthException:
- logging.warning(
- f"No data for direction {(u'W-E', u'E-W')[idx % 2]}"
- )
- continue
-
- for item in decoded.get_recorded_iterator():
- percentile = item.percentile_level_iterated_to
- if percentile > 99.9999999:
- continue
- xaxis.append(100.0 / (100.0 - percentile))
- yaxis.append(item.value_iterated_to)
- hovertext.append(
- f"<b>{desc[graph]}</b><br>"
- f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
- f"Percentile: {percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- fig.add_trace(
- plgo.Scatter(
- x=xaxis,
- y=yaxis,
- name=desc[graph],
- mode=u"lines",
- legendgroup=desc[graph],
- showlegend=not(bool(idx)),
- line=dict(
- color=COLORS[color],
- dash=u"dash" if idx % 2 else u"solid"
- ),
- hovertext=hovertext,
- hoverinfo=u"text"
- )
- )
- xaxis_max = max(xaxis) if xaxis_max < max(
- xaxis) else xaxis_max
-
- layout[u"title"][u"text"] = f"<b>Latency:</b> {name}"
- layout[u"xaxis"][u"range"] = [0, int(log(xaxis_max, 10)) + 1]
- fig.update_layout(layout)
-
- # Create plot
- file_name = f"{plot[u'output-file']}-{name_link}.html"
- logging.info(f" Writing file {file_name}")
-
- try:
- # Export Plot
- ploff.plot(fig, show_link=False, auto_open=False,
- filename=file_name)
- # Add link to the file:
- if file_links and target_links:
- with open(file_links, u"a") as file_handler:
- file_handler.write(
- f"- `{name_link} "
- f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n"
- )
- except FileNotFoundError as err:
- logging.error(
- f"Not possible to write the link to the file "
- f"{file_links}\n{err}"
- )
- except PlotlyError as err:
- logging.error(f" Finished with error: {repr(err)}")
-
- except hdrh.codec.HdrLengthException as err:
- logging.warning(repr(err))
- continue
-
- except (ValueError, KeyError) as err:
- logging.warning(repr(err))
- continue
-
-
-def plot_nf_reconf_box_name(plot, input_data):
- """Generate the plot(s) with algorithm: plot_nf_reconf_box_name
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- # Transform the data
- logging.info(
- f" Creating the data set for the {plot.get(u'type', u'')} "
- f"{plot.get(u'title', u'')}."
- )
- data = input_data.filter_tests_by_name(
- plot, params=[u"result", u"parent", u"tags", u"type"]
- )
- if data is None:
- logging.error(u"No data.")
- return
-
- # Prepare the data for the plot
- y_vals = OrderedDict()
- loss = dict()
- for job in data:
- for build in job:
- for test in build:
- if y_vals.get(test[u"parent"], None) is None:
- y_vals[test[u"parent"]] = list()
- loss[test[u"parent"]] = list()
- try:
- y_vals[test[u"parent"]].append(test[u"result"][u"time"])
- loss[test[u"parent"]].append(test[u"result"][u"loss"])
- except (KeyError, TypeError):
- y_vals[test[u"parent"]].append(None)
-
- # Add None to the lists with missing data
- max_len = 0
- nr_of_samples = list()
- for val in y_vals.values():
- if len(val) > max_len:
- max_len = len(val)
- nr_of_samples.append(len(val))
- for val in y_vals.values():
- if len(val) < max_len:
- val.extend([None for _ in range(max_len - len(val))])
-
- # Add plot traces
- traces = list()
- df_y = pd.DataFrame(y_vals)
- df_y.head()
- for i, col in enumerate(df_y.columns):
- tst_name = re.sub(REGEX_NIC, u"",
- col.lower().replace(u'-ndrpdr', u'').
- replace(u'2n1l-', u''))
-
- traces.append(plgo.Box(
- x=[str(i + 1) + u'.'] * len(df_y[col]),
- y=[y if y else None for y in df_y[col]],
- name=(
- f"{i + 1}. "
- f"({nr_of_samples[i]:02d} "
- f"run{u's' if nr_of_samples[i] > 1 else u''}, "
- f"packets lost average: {mean(loss[col]):.1f}) "
- f"{u'-'.join(tst_name.split(u'-')[3:-2])}"
- ),
- hoverinfo=u"y+name"
- ))
- try:
- # Create plot
- layout = deepcopy(plot[u"layout"])
- layout[u"title"] = f"<b>Time Lost:</b> {layout[u'title']}"
- layout[u"yaxis"][u"title"] = u"<b>Effective Blocked Time [s]</b>"
- layout[u"legend"][u"font"][u"size"] = 14
- layout[u"yaxis"].pop(u"range")
- plpl = plgo.Figure(data=traces, layout=layout)
-
- # Export Plot
- file_type = plot.get(u"output-file-type", u".html")
- logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
- ploff.plot(
- plpl,
- show_link=False,
- auto_open=False,
- filename=f"{plot[u'output-file']}{file_type}"
- )
- except PlotlyError as err:
- logging.error(
- f" Finished with error: {repr(err)}".replace(u"\n", u" ")
- )
- return
-
-
-def plot_perf_box_name(plot, input_data):
- """Generate the plot(s) with algorithm: plot_perf_box_name
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- # Transform the data
- logging.info(
- f" Creating data set for the {plot.get(u'type', u'')} "
- f"{plot.get(u'title', u'')}."
- )
- data = input_data.filter_tests_by_name(
- plot,
- params=[u"throughput", u"gbps", u"result", u"parent", u"tags", u"type"])
- if data is None:
- logging.error(u"No data.")
- return
-
- # Prepare the data for the plot
- plot_title = plot.get(u"title", u"").lower()
-
- if u"-gbps" in plot_title:
- value = u"gbps"
- multiplier = 1e6
- else:
- value = u"throughput"
- multiplier = 1.0
- y_vals = OrderedDict()
- test_type = u""
-
- for item in plot.get(u"include", tuple()):
- reg_ex = re.compile(str(item).lower())
- for job in data:
- for build in job:
- for test_id, test in build.iteritems():
- if not re.match(reg_ex, str(test_id).lower()):
- continue
- if y_vals.get(test[u"parent"], None) is None:
- y_vals[test[u"parent"]] = list()
- try:
- if test[u"type"] in (u"NDRPDR", u"CPS"):
- test_type = test[u"type"]
-
- if u"-pdr" in plot_title:
- ttype = u"PDR"
- elif u"-ndr" in plot_title:
- ttype = u"NDR"
- else:
- raise RuntimeError(
- u"Wrong title. No information about test "
- u"type. Add '-ndr' or '-pdr' to the test "
- u"title."
- )
-
- y_vals[test[u"parent"]].append(
- test[value][ttype][u"LOWER"] * multiplier
- )
-
- elif test[u"type"] in (u"SOAK",):
- y_vals[test[u"parent"]]. \
- append(test[u"throughput"][u"LOWER"])
- test_type = u"SOAK"
-
- elif test[u"type"] in (u"HOSTSTACK",):
- if u"LDPRELOAD" in test[u"tags"]:
- y_vals[test[u"parent"]].append(
- float(
- test[u"result"][u"bits_per_second"]
- ) / 1e3
- )
- elif u"VPPECHO" in test[u"tags"]:
- y_vals[test[u"parent"]].append(
- (float(
- test[u"result"][u"client"][u"tx_data"]
- ) * 8 / 1e3) /
- ((float(
- test[u"result"][u"client"][u"time"]
- ) +
- float(
- test[u"result"][u"server"][u"time"])
- ) / 2)
- )
- test_type = u"HOSTSTACK"
-
- else:
- continue
-
- except (KeyError, TypeError):
- y_vals[test[u"parent"]].append(None)
-
- # Add None to the lists with missing data
- max_len = 0
- nr_of_samples = list()
- for val in y_vals.values():
- if len(val) > max_len:
- max_len = len(val)
- nr_of_samples.append(len(val))
- for val in y_vals.values():
- if len(val) < max_len:
- val.extend([None for _ in range(max_len - len(val))])
-
- # Add plot traces
- traces = list()
- df_y = pd.DataFrame(y_vals)
- df_y.head()
- y_max = list()
- for i, col in enumerate(df_y.columns):
- tst_name = re.sub(REGEX_NIC, u"",
- col.lower().replace(u'-ndrpdr', u'').
- replace(u'2n1l-', u''))
- kwargs = dict(
- x=[str(i + 1) + u'.'] * len(df_y[col]),
- y=[y / 1e6 if y else None for y in df_y[col]],
- name=(
- f"{i + 1}. "
- f"({nr_of_samples[i]:02d} "
- f"run{u's' if nr_of_samples[i] > 1 else u''}) "
- f"{tst_name}"
- ),
- hoverinfo=u"y+name"
- )
- if test_type in (u"SOAK", ):
- kwargs[u"boxpoints"] = u"all"
-
- traces.append(plgo.Box(**kwargs))
-
- try:
- val_max = max(df_y[col])
- if val_max:
- y_max.append(int(val_max / 1e6) + 2)
- except (ValueError, TypeError) as err:
- logging.error(repr(err))
- continue
-
- try:
- # Create plot
- layout = deepcopy(plot[u"layout"])
- if layout.get(u"title", None):
- if test_type in (u"HOSTSTACK", ):
- layout[u"title"] = f"<b>Bandwidth:</b> {layout[u'title']}"
- elif test_type in (u"CPS", ):
- layout[u"title"] = f"<b>CPS:</b> {layout[u'title']}"
- else:
- layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
- if y_max:
- layout[u"yaxis"][u"range"] = [0, max(y_max)]
- plpl = plgo.Figure(data=traces, layout=layout)
-
- # Export Plot
- logging.info(f" Writing file {plot[u'output-file']}.html.")
- ploff.plot(
- plpl,
- show_link=False,
- auto_open=False,
- filename=f"{plot[u'output-file']}.html"
- )
- except PlotlyError as err:
- logging.error(
- f" Finished with error: {repr(err)}".replace(u"\n", u" ")
- )
- return
-
-
-def plot_tsa_name(plot, input_data):
- """Generate the plot(s) with algorithm:
- plot_tsa_name
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- # Transform the data
- plot_title = plot.get(u"title", u"")
- logging.info(
- f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
- )
- data = input_data.filter_tests_by_name(
- plot,
- params=[u"throughput", u"gbps", u"parent", u"tags", u"type"]
- )
- if data is None:
- logging.error(u"No data.")
- return
-
- plot_title = plot_title.lower()
-
- if u"-gbps" in plot_title:
- value = u"gbps"
- h_unit = u"Gbps"
- multiplier = 1e6
- else:
- value = u"throughput"
- h_unit = u"Mpps"
- multiplier = 1.0
-
- y_vals = OrderedDict()
- for item in plot.get(u"include", tuple()):
- reg_ex = re.compile(str(item).lower())
- for job in data:
- for build in job:
- for test_id, test in build.iteritems():
- if re.match(reg_ex, str(test_id).lower()):
- if y_vals.get(test[u"parent"], None) is None:
- y_vals[test[u"parent"]] = {
- u"1": list(),
- u"2": list(),
- u"4": list()
- }
- try:
- if test[u"type"] not in (u"NDRPDR", u"CPS"):
- continue
-
- if u"-pdr" in plot_title:
- ttype = u"PDR"
- elif u"-ndr" in plot_title:
- ttype = u"NDR"
- else:
- continue
-
- if u"1C" in test[u"tags"]:
- y_vals[test[u"parent"]][u"1"].append(
- test[value][ttype][u"LOWER"] * multiplier
- )
- elif u"2C" in test[u"tags"]:
- y_vals[test[u"parent"]][u"2"].append(
- test[value][ttype][u"LOWER"] * multiplier
- )
- elif u"4C" in test[u"tags"]:
- y_vals[test[u"parent"]][u"4"].append(
- test[value][ttype][u"LOWER"] * multiplier
- )
- except (KeyError, TypeError):
- pass
-
- if not y_vals:
- logging.warning(f"No data for the plot {plot.get(u'title', u'')}")
- return
-
- y_1c_max = dict()
- for test_name, test_vals in y_vals.items():
- for key, test_val in test_vals.items():
- if test_val:
- avg_val = sum(test_val) / len(test_val)
- y_vals[test_name][key] = [avg_val, len(test_val)]
- ideal = avg_val / (int(key) * 1e6)
- if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
- y_1c_max[test_name] = ideal
-
- vals = OrderedDict()
- y_max = list()
- nic_limit = 0
- lnk_limit = 0
- pci_limit = 0
- for test_name, test_vals in y_vals.items():
- try:
- if test_vals[u"1"][1]:
- name = re.sub(
- REGEX_NIC,
- u"",
- test_name.replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')
- )
- vals[name] = OrderedDict()
- y_val_1 = test_vals[u"1"][0] / 1e6
- y_val_2 = test_vals[u"2"][0] / 1e6 if test_vals[u"2"][0] \
- else None
- y_val_4 = test_vals[u"4"][0] / 1e6 if test_vals[u"4"][0] \
- else None
-
- vals[name][u"val"] = [y_val_1, y_val_2, y_val_4]
- vals[name][u"rel"] = [1.0, None, None]
- vals[name][u"ideal"] = [
- y_1c_max[test_name],
- y_1c_max[test_name] * 2,
- y_1c_max[test_name] * 4
- ]
- vals[name][u"diff"] = [
- (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None
- ]
- vals[name][u"count"] = [
- test_vals[u"1"][1],
- test_vals[u"2"][1],
- test_vals[u"4"][1]
- ]
-
- try:
- val_max = max(vals[name][u"val"])
- except ValueError as err:
- logging.error(repr(err))
- continue
- if val_max:
- y_max.append(val_max)
-
- if y_val_2:
- vals[name][u"rel"][1] = round(y_val_2 / y_val_1, 2)
- vals[name][u"diff"][1] = \
- (y_val_2 - vals[name][u"ideal"][1]) * 100 / y_val_2
- if y_val_4:
- vals[name][u"rel"][2] = round(y_val_4 / y_val_1, 2)
- vals[name][u"diff"][2] = \
- (y_val_4 - vals[name][u"ideal"][2]) * 100 / y_val_4
- except IndexError as err:
- logging.warning(f"No data for {test_name}")
- logging.warning(repr(err))
-
- # Limits:
- if u"x520" in test_name:
- limit = plot[u"limits"][u"nic"][u"x520"]
- elif u"x710" in test_name:
- limit = plot[u"limits"][u"nic"][u"x710"]
- elif u"xxv710" in test_name:
- limit = plot[u"limits"][u"nic"][u"xxv710"]
- elif u"xl710" in test_name:
- limit = plot[u"limits"][u"nic"][u"xl710"]
- elif u"x553" in test_name:
- limit = plot[u"limits"][u"nic"][u"x553"]
- elif u"cx556a" in test_name:
- limit = plot[u"limits"][u"nic"][u"cx556a"]
- else:
- limit = 0
- if limit > nic_limit:
- nic_limit = limit
-
- mul = 2 if u"ge2p" in test_name else 1
- if u"10ge" in test_name:
- limit = plot[u"limits"][u"link"][u"10ge"] * mul
- elif u"25ge" in test_name:
- limit = plot[u"limits"][u"link"][u"25ge"] * mul
- elif u"40ge" in test_name:
- limit = plot[u"limits"][u"link"][u"40ge"] * mul
- elif u"100ge" in test_name:
- limit = plot[u"limits"][u"link"][u"100ge"] * mul
- else:
- limit = 0
- if limit > lnk_limit:
- lnk_limit = limit
-
- if u"cx556a" in test_name:
- limit = plot[u"limits"][u"pci"][u"pci-g3-x8"]
- else:
- limit = plot[u"limits"][u"pci"][u"pci-g3-x16"]
- if limit > pci_limit:
- pci_limit = limit
-
- traces = list()
- annotations = list()
- x_vals = [1, 2, 4]
-
- # Limits:
- if u"-gbps" not in plot_title and u"-cps-" not in plot_title:
- nic_limit /= 1e6
- lnk_limit /= 1e6
- pci_limit /= 1e6
- min_limit = min((nic_limit, lnk_limit, pci_limit))
- if nic_limit == min_limit:
- traces.append(plgo.Scatter(
- x=x_vals,
- y=[nic_limit, ] * len(x_vals),
- name=f"NIC: {nic_limit:.2f}Mpps",
- showlegend=False,
- mode=u"lines",
- line=dict(
- dash=u"dot",
- color=COLORS[-1],
- width=1),
- hoverinfo=u"none"
- ))
- annotations.append(dict(
- x=1,
- y=nic_limit,
- xref=u"x",
- yref=u"y",
- xanchor=u"left",
- yanchor=u"bottom",
- text=f"NIC: {nic_limit:.2f}Mpps",
- font=dict(
- size=14,
- color=COLORS[-1],
- ),
- align=u"left",
- showarrow=False
- ))
- y_max.append(nic_limit)
- elif lnk_limit == min_limit:
- traces.append(plgo.Scatter(
- x=x_vals,
- y=[lnk_limit, ] * len(x_vals),
- name=f"Link: {lnk_limit:.2f}Mpps",
- showlegend=False,
- mode=u"lines",
- line=dict(
- dash=u"dot",
- color=COLORS[-1],
- width=1),
- hoverinfo=u"none"
- ))
- annotations.append(dict(
- x=1,
- y=lnk_limit,
- xref=u"x",
- yref=u"y",
- xanchor=u"left",
- yanchor=u"bottom",
- text=f"Link: {lnk_limit:.2f}Mpps",
- font=dict(
- size=14,
- color=COLORS[-1],
- ),
- align=u"left",
- showarrow=False
- ))
- y_max.append(lnk_limit)
- elif pci_limit == min_limit:
- traces.append(plgo.Scatter(
- x=x_vals,
- y=[pci_limit, ] * len(x_vals),
- name=f"PCIe: {pci_limit:.2f}Mpps",
- showlegend=False,
- mode=u"lines",
- line=dict(
- dash=u"dot",
- color=COLORS[-1],
- width=1),
- hoverinfo=u"none"
- ))
- annotations.append(dict(
- x=1,
- y=pci_limit,
- xref=u"x",
- yref=u"y",
- xanchor=u"left",
- yanchor=u"bottom",
- text=f"PCIe: {pci_limit:.2f}Mpps",
- font=dict(
- size=14,
- color=COLORS[-1],
- ),
- align=u"left",
- showarrow=False
- ))
- y_max.append(pci_limit)
-
- # Perfect and measured:
- cidx = 0
- for name, val in vals.items():
- hovertext = list()
- try:
- for idx in range(len(val[u"val"])):
- htext = ""
- if isinstance(val[u"val"][idx], float):
- htext += (
- f"No. of Runs: {val[u'count'][idx]}<br>"
- f"Mean: {val[u'val'][idx]:.2f}{h_unit}<br>"
- )
- if isinstance(val[u"diff"][idx], float):
- htext += f"Diff: {round(val[u'diff'][idx]):.0f}%<br>"
- if isinstance(val[u"rel"][idx], float):
- htext += f"Speedup: {val[u'rel'][idx]:.2f}"
- hovertext.append(htext)
- traces.append(
- plgo.Scatter(
- x=x_vals,
- y=val[u"val"],
- name=name,
- legendgroup=name,
- mode=u"lines+markers",
- line=dict(
- color=COLORS[cidx],
- width=2),
- marker=dict(
- symbol=u"circle",
- size=10
- ),
- text=hovertext,
- hoverinfo=u"text+name"
- )
- )
- traces.append(
- plgo.Scatter(
- x=x_vals,
- y=val[u"ideal"],
- name=f"{name} perfect",
- legendgroup=name,
- showlegend=False,
- mode=u"lines",
- line=dict(
- color=COLORS[cidx],
- width=2,
- dash=u"dash"),
- text=[f"Perfect: {y:.2f}Mpps" for y in val[u"ideal"]],
- hoverinfo=u"text"
- )
- )
- cidx += 1
- except (IndexError, ValueError, KeyError) as err:
- logging.warning(f"No data for {name}\n{repr(err)}")
-
- try:
- # Create plot
- file_type = plot.get(u"output-file-type", u".html")
- logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
- layout = deepcopy(plot[u"layout"])
- if layout.get(u"title", None):
- layout[u"title"] = f"<b>Speedup Multi-core:</b> {layout[u'title']}"
- layout[u"yaxis"][u"range"] = [0, int(max(y_max) * 1.1)]
- layout[u"annotations"].extend(annotations)
- plpl = plgo.Figure(data=traces, layout=layout)
-
- # Export Plot
- ploff.plot(
- plpl,
- show_link=False,
- auto_open=False,
- filename=f"{plot[u'output-file']}{file_type}"
- )
- except PlotlyError as err:
- logging.error(
- f" Finished with error: {repr(err)}".replace(u"\n", u" ")
- )
- return
-
-
-def plot_http_server_perf_box(plot, input_data):
- """Generate the plot(s) with algorithm: plot_http_server_perf_box
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- # Transform the data
- logging.info(
- f" Creating the data set for the {plot.get(u'type', u'')} "
- f"{plot.get(u'title', u'')}."
- )
- data = input_data.filter_data(plot)
- if data is None:
- logging.error(u"No data.")
- return
-
- # Prepare the data for the plot
- y_vals = dict()
- for job in data:
- for build in job:
- for test in build:
- if y_vals.get(test[u"name"], None) is None:
- y_vals[test[u"name"]] = list()
- try:
- y_vals[test[u"name"]].append(test[u"result"])
- except (KeyError, TypeError):
- y_vals[test[u"name"]].append(None)
-
- # Add None to the lists with missing data
- max_len = 0
- nr_of_samples = list()
- for val in y_vals.values():
- if len(val) > max_len:
- max_len = len(val)
- nr_of_samples.append(len(val))
- for val in y_vals.values():
- if len(val) < max_len:
- val.extend([None for _ in range(max_len - len(val))])
-
- # Add plot traces
- traces = list()
- df_y = pd.DataFrame(y_vals)
- df_y.head()
- for i, col in enumerate(df_y.columns):
- name = \
- f"{i + 1}. " \
- f"({nr_of_samples[i]:02d} " \
- f"run{u's' if nr_of_samples[i] > 1 else u''}) " \
- f"{col.lower().replace(u'-ndrpdr', u'')}"
- if len(name) > 50:
- name_lst = name.split(u'-')
- name = u""
- split_name = True
- for segment in name_lst:
- if (len(name) + len(segment) + 1) > 50 and split_name:
- name += u"<br> "
- split_name = False
- name += segment + u'-'
- name = name[:-1]
-
- traces.append(plgo.Box(x=[str(i + 1) + u'.'] * len(df_y[col]),
- y=df_y[col],
- name=name,
- **plot[u"traces"]))
- try:
- # Create plot
- plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
-
- # Export Plot
- logging.info(
- f" Writing file {plot[u'output-file']}"
- f"{plot[u'output-file-type']}."
- )
- ploff.plot(
- plpl,
- show_link=False,
- auto_open=False,
- filename=f"{plot[u'output-file']}{plot[u'output-file-type']}"
- )
- except PlotlyError as err:
- logging.error(
- f" Finished with error: {repr(err)}".replace(u"\n", u" ")
- )
- return
-
-
-def plot_nf_heatmap(plot, input_data):
- """Generate the plot(s) with algorithm: plot_nf_heatmap
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- regex_cn = re.compile(r'^(\d*)R(\d*)C$')
- regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
- r'(\d+mif|\d+vh)-'
- r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$')
- vals = dict()
-
- # Transform the data
- logging.info(
- f" Creating the data set for the {plot.get(u'type', u'')} "
- f"{plot.get(u'title', u'')}."
- )
- data = input_data.filter_data(plot, continue_on_error=True)
- if data is None or data.empty:
- logging.error(u"No data.")
- return
-
- for job in data:
- for build in job:
- for test in build:
- for tag in test[u"tags"]:
- groups = re.search(regex_cn, tag)
- if groups:
- chain = str(groups.group(1))
- node = str(groups.group(2))
- break
- else:
- continue
- groups = re.search(regex_test_name, test[u"name"])
- if groups and len(groups.groups()) == 3:
- hover_name = (
- f"{str(groups.group(1))}-"
- f"{str(groups.group(2))}-"
- f"{str(groups.group(3))}"
- )
- else:
- hover_name = u""
- if vals.get(chain, None) is None:
- vals[chain] = dict()
- if vals[chain].get(node, None) is None:
- vals[chain][node] = dict(
- name=hover_name,
- vals=list(),
- nr=None,
- mean=None,
- stdev=None
- )
- try:
- if plot[u"include-tests"] == u"MRR":
- result = test[u"result"][u"receive-rate"]
- elif plot[u"include-tests"] == u"PDR":
- result = test[u"throughput"][u"PDR"][u"LOWER"]
- elif plot[u"include-tests"] == u"NDR":
- result = test[u"throughput"][u"NDR"][u"LOWER"]
- else:
- result = None
- except TypeError:
- result = None
-
- if result:
- vals[chain][node][u"vals"].append(result)
-
- if not vals:
- logging.error(u"No data.")
- return
-
- txt_chains = list()
- txt_nodes = list()
- for key_c in vals:
- txt_chains.append(key_c)
- for key_n in vals[key_c].keys():
- txt_nodes.append(key_n)
- if vals[key_c][key_n][u"vals"]:
- vals[key_c][key_n][u"nr"] = len(vals[key_c][key_n][u"vals"])
- vals[key_c][key_n][u"mean"] = \
- round(mean(vals[key_c][key_n][u"vals"]) / 1000000, 1)
- vals[key_c][key_n][u"stdev"] = \
- round(stdev(vals[key_c][key_n][u"vals"]) / 1000000, 1)
- txt_nodes = list(set(txt_nodes))
-
- def sort_by_int(value):
- """Makes possible to sort a list of strings which represent integers.
-
- :param value: Integer as a string.
- :type value: str
- :returns: Integer representation of input parameter 'value'.
- :rtype: int
- """
- return int(value)
-
- txt_chains = sorted(txt_chains, key=sort_by_int)
- txt_nodes = sorted(txt_nodes, key=sort_by_int)
-
- chains = [i + 1 for i in range(len(txt_chains))]
- nodes = [i + 1 for i in range(len(txt_nodes))]
-
- data = [list() for _ in range(len(chains))]
- for chain in chains:
- for node in nodes:
- try:
- val = vals[txt_chains[chain - 1]][txt_nodes[node - 1]][u"mean"]
- except (KeyError, IndexError):
- val = None
- data[chain - 1].append(val)
-
- # Color scales:
- my_green = [[0.0, u"rgb(235, 249, 242)"],
- [1.0, u"rgb(45, 134, 89)"]]
-
- my_blue = [[0.0, u"rgb(236, 242, 248)"],
- [1.0, u"rgb(57, 115, 172)"]]
-
- my_grey = [[0.0, u"rgb(230, 230, 230)"],
- [1.0, u"rgb(102, 102, 102)"]]
-
- hovertext = list()
- annotations = list()
-
- text = (u"Test: {name}<br>"
- u"Runs: {nr}<br>"
- u"Thput: {val}<br>"
- u"StDev: {stdev}")
-
- for chain, _ in enumerate(txt_chains):
- hover_line = list()
- for node, _ in enumerate(txt_nodes):
- if data[chain][node] is not None:
- annotations.append(
- dict(
- x=node+1,
- y=chain+1,
- xref=u"x",
- yref=u"y",
- xanchor=u"center",
- yanchor=u"middle",
- text=str(data[chain][node]),
- font=dict(
- size=14,
- ),
- align=u"center",
- showarrow=False
- )
- )
- hover_line.append(text.format(
- name=vals[txt_chains[chain]][txt_nodes[node]][u"name"],
- nr=vals[txt_chains[chain]][txt_nodes[node]][u"nr"],
- val=data[chain][node],
- stdev=vals[txt_chains[chain]][txt_nodes[node]][u"stdev"]))
- hovertext.append(hover_line)
-
- traces = [
- plgo.Heatmap(
- x=nodes,
- y=chains,
- z=data,
- colorbar=dict(
- title=plot.get(u"z-axis", u""),
- titleside=u"right",
- titlefont=dict(
- size=16
- ),
- tickfont=dict(
- size=16,
- ),
- tickformat=u".1f",
- yanchor=u"bottom",
- y=-0.02,
- len=0.925,
- ),
- showscale=True,
- colorscale=my_green,
- text=hovertext,
- hoverinfo=u"text"
- )
- ]
-
- for idx, item in enumerate(txt_nodes):
- # X-axis, numbers:
- annotations.append(
- dict(
- x=idx+1,
- y=0.05,
- xref=u"x",
- yref=u"y",
- xanchor=u"center",
- yanchor=u"top",
- text=item,
- font=dict(
- size=16,
- ),
- align=u"center",
- showarrow=False
- )
- )
- for idx, item in enumerate(txt_chains):
- # Y-axis, numbers:
- annotations.append(
- dict(
- x=0.35,
- y=idx+1,
- xref=u"x",
- yref=u"y",
- xanchor=u"right",
- yanchor=u"middle",
- text=item,
- font=dict(
- size=16,
- ),
- align=u"center",
- showarrow=False
- )
- )
- # X-axis, title:
- annotations.append(
- dict(
- x=0.55,
- y=-0.15,
- xref=u"paper",
- yref=u"y",
- xanchor=u"center",
- yanchor=u"bottom",
- text=plot.get(u"x-axis", u""),
- font=dict(
- size=16,
- ),
- align=u"center",
- showarrow=False
- )
- )
- # Y-axis, title:
- annotations.append(
- dict(
- x=-0.1,
- y=0.5,
- xref=u"x",
- yref=u"paper",
- xanchor=u"center",
- yanchor=u"middle",
- text=plot.get(u"y-axis", u""),
- font=dict(
- size=16,
- ),
- align=u"center",
- textangle=270,
- showarrow=False
- )
- )
- updatemenus = list([
- dict(
- x=1.0,
- y=0.0,
- xanchor=u"right",
- yanchor=u"bottom",
- direction=u"up",
- buttons=list([
- dict(
- args=[
- {
- u"colorscale": [my_green, ],
- u"reversescale": False
- }
- ],
- label=u"Green",
- method=u"update"
- ),
- dict(
- args=[
- {
- u"colorscale": [my_blue, ],
- u"reversescale": False
- }
- ],
- label=u"Blue",
- method=u"update"
- ),
- dict(
- args=[
- {
- u"colorscale": [my_grey, ],
- u"reversescale": False
- }
- ],
- label=u"Grey",
- method=u"update"
- )
- ])
- )
- ])
-
- try:
- layout = deepcopy(plot[u"layout"])
- except KeyError as err:
- logging.error(f"Finished with error: No layout defined\n{repr(err)}")
- return
-
- layout[u"annotations"] = annotations
- layout[u'updatemenus'] = updatemenus
-
- try:
- # Create plot
- plpl = plgo.Figure(data=traces, layout=layout)
-
- # Export Plot
- logging.info(f" Writing file {plot[u'output-file']}.html")
- ploff.plot(
- plpl,
- show_link=False,
- auto_open=False,
- filename=f"{plot[u'output-file']}.html"
- )
- except PlotlyError as err:
- logging.error(
- f" Finished with error: {repr(err)}".replace(u"\n", u" ")
- )
- return
Code Review / csit.git / blobdiff