X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_plots.py;h=8af9a945d12d92d06dbd6085d4361e7830368175;hp=890de208e35a5b964d37f4f0065c1b8765853d70;hb=af940b4664f4778ac4857cbdea6eed16ab2722f7;hpb=8243ea78854683f2f80da53d8f197f10316e4801
diff --git a/resources/tools/presentation/generator_plots.py b/resources/tools/presentation/generator_plots.py
index 890de208e3..8af9a945d1 100644
--- a/resources/tools/presentation/generator_plots.py
+++ b/resources/tools/presentation/generator_plots.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2018 Cisco and/or its affiliates.
+# Copyright (c) 2019 Cisco and/or its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
@@ -15,24 +15,32 @@
"""
+import re
import logging
+
+from collections import OrderedDict
+from copy import deepcopy
+
+import hdrh.histogram
+import hdrh.codec
import pandas as pd
import plotly.offline as ploff
import plotly.graph_objs as plgo
+from plotly.subplots import make_subplots
from plotly.exceptions import PlotlyError
-from collections import OrderedDict
-from copy import deepcopy
-from utils import mean
+from pal_utils import mean, stdev
+
+COLORS = [u"SkyBlue", u"Olive", u"Purple", u"Coral", u"Indigo", u"Pink",
+ u"Chocolate", u"Brown", u"Magenta", u"Cyan", u"Orange", u"Black",
+ u"Violet", u"Blue", u"Yellow", u"BurlyWood", u"CadetBlue", u"Crimson",
+ u"DarkBlue", u"DarkCyan", u"DarkGreen", u"Green", u"GoldenRod",
+ u"LightGreen", u"LightSeaGreen", u"LightSkyBlue", u"Maroon",
+ u"MediumSeaGreen", u"SeaGreen", u"LightSlateGrey"]
-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):
@@ -44,22 +52,34 @@ def generate_plots(spec, data):
:type data: InputData
"""
- logging.info("Generating the plots ...")
+ generator = {
+ u"plot_nf_reconf_box_name": plot_nf_reconf_box_name,
+ u"plot_perf_box_name": plot_perf_box_name,
+ u"plot_lat_err_bars_name": plot_lat_err_bars_name,
+ u"plot_tsa_name": plot_tsa_name,
+ u"plot_http_server_perf_box": plot_http_server_perf_box,
+ u"plot_nf_heatmap": plot_nf_heatmap,
+ u"plot_lat_hdrh_bar_name": plot_lat_hdrh_bar_name,
+ u"plot_lat_hdrh_percentile": plot_lat_hdrh_percentile
+ }
+
+ logging.info(u"Generating the plots ...")
for index, plot in enumerate(spec.plots):
try:
- logging.info(" Plot nr {0}: {1}".format(index + 1,
- plot.get("title", "")))
- plot["limits"] = spec.configuration["limits"]
- eval(plot["algorithm"])(plot, data)
- logging.info(" Done.")
+ logging.info(f" Plot nr {index + 1}: {plot.get(u'title', u'')}")
+ plot[u"limits"] = spec.configuration[u"limits"]
+ generator[plot[u"algorithm"]](plot, data)
+ logging.info(u" Done.")
except NameError as err:
- logging.error("Probably algorithm '{alg}' is not defined: {err}".
- format(alg=plot["algorithm"], err=repr(err)))
- logging.info("Done.")
+ logging.error(
+ f"Probably algorithm {plot[u'algorithm']} is not defined: "
+ f"{repr(err)}"
+ )
+ logging.info(u"Done.")
-def plot_performance_box(plot, input_data):
- """Generate the plot(s) with algorithm: plot_performance_box
+def plot_lat_hdrh_percentile(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_lat_hdrh_percentile
specified in the specification file.
:param plot: Plot to generate.
@@ -69,114 +89,478 @@ def plot_performance_box(plot, input_data):
"""
# 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)
+ plot_title = plot.get(u"title", u"")
+ logging.info(
+ f" Creating the data set for the {plot.get(u'type', u'')} "
+ f"{plot_title}."
+ )
+ data = input_data.filter_tests_by_name(
+ plot, params=[u"latency", u"parent", u"tags", u"type"])
+ if data is None or len(data[0][0]) == 0:
+ logging.error(u"No data.")
+ return
+
+ fig = plgo.Figure()
+
+ # Prepare the data for the plot
+ directions = [u"W-E", u"E-W"]
+ for test in data[0][0]:
+ try:
+ if test[u"type"] in (u"NDRPDR",):
+ if u"-pdr" in plot_title.lower():
+ ttype = u"PDR"
+ elif u"-ndr" in plot_title.lower():
+ ttype = u"NDR"
+ else:
+ logging.warning(f"Invalid test type: {test[u'type']}")
+ continue
+ name = re.sub(REGEX_NIC, u"", test[u"parent"].
+ replace(u'-ndrpdr', u'').
+ replace(u'2n1l-', u'').
+ replace(u'avf-', u''))
+ for idx, direction in enumerate(
+ (u"direction1", u"direction2", )):
+ try:
+ hdr_lat = test[u"latency"][ttype][direction][u"hdrh"]
+ # TODO: Workaround, HDRH data must be aligned to 4
+ # bytes, remove when not needed.
+ hdr_lat += u"=" * (len(hdr_lat) % 4)
+ xaxis = list()
+ yaxis = list()
+ hovertext = list()
+ decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat)
+ for item in decoded.get_recorded_iterator():
+ percentile = item.percentile_level_iterated_to
+ if percentile != 100.0:
+ xaxis.append(100.0 / (100.0 - percentile))
+ yaxis.append(item.value_iterated_to)
+ hovertext.append(
+ f"Test: {name}
"
+ f"Direction: {directions[idx]}
"
+ f"Percentile: {percentile:.5f}
%"
+ f"Latency: {item.value_iterated_to}uSec"
+ )
+ fig.add_trace(
+ plgo.Scatter(
+ x=xaxis,
+ y=yaxis,
+ name=name,
+ mode=u"lines",
+ hovertext=hovertext,
+ hoverinfo=u"text"
+ )
+ )
+ except hdrh.codec.HdrLengthException as err:
+ logging.warning(
+ f"No or invalid data for HDRHistogram for the test "
+ f"{name}\n{err}"
+ )
+ continue
+ else:
+ logging.warning(f"Invalid test type: {test[u'type']}")
+ continue
+ except (ValueError, KeyError) as err:
+ logging.warning(repr(err))
+
+ layout = deepcopy(plot[u"layout"])
+
+ layout[u"title"][u"text"] = \
+ f"Latency: {plot.get(u'graph-title', u'')}"
+ fig[u"layout"].update(layout)
+
+ # Create plot
+ file_type = plot.get(u"output-file-type", u".html")
+ logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
+ try:
+ # Export Plot
+ ploff.plot(fig, show_link=False, auto_open=False,
+ filename=f"{plot[u'output-file']}{file_type}")
+ except PlotlyError as err:
+ logging.error(f" Finished with error: {repr(err)}")
+
+
+def plot_lat_hdrh_bar_name(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_lat_hdrh_bar_name
+ specified in the specification file.
+
+ :param plot: Plot to generate.
+ :param input_data: Data to process.
+ :type plot: pandas.Series
+ :type input_data: InputData
+ """
+
+ # Transform the data
+ plot_title = plot.get(u"title", u"")
+ logging.info(
+ f" Creating the data set for the {plot.get(u'type', u'')} "
+ f"{plot_title}."
+ )
+ data = input_data.filter_tests_by_name(
+ plot, params=[u"latency", u"parent", u"tags", u"type"])
+ if data is None or len(data[0][0]) == 0:
+ logging.error(u"No data.")
+ return
+
+ # Prepare the data for the plot
+ directions = [u"W-E", u"E-W"]
+ tests = list()
+ traces = list()
+ for idx_row, test in enumerate(data[0][0]):
+ try:
+ if test[u"type"] in (u"NDRPDR",):
+ if u"-pdr" in plot_title.lower():
+ ttype = u"PDR"
+ elif u"-ndr" in plot_title.lower():
+ ttype = u"NDR"
+ else:
+ logging.warning(f"Invalid test type: {test[u'type']}")
+ continue
+ name = re.sub(REGEX_NIC, u"", test[u"parent"].
+ replace(u'-ndrpdr', u'').
+ replace(u'2n1l-', u''))
+ histograms = list()
+ for idx_col, direction in enumerate(
+ (u"direction1", u"direction2", )):
+ try:
+ hdr_lat = test[u"latency"][ttype][direction][u"hdrh"]
+ # TODO: Workaround, HDRH data must be aligned to 4
+ # bytes, remove when not needed.
+ hdr_lat += u"=" * (len(hdr_lat) % 4)
+ xaxis = list()
+ yaxis = list()
+ hovertext = list()
+ decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat)
+ total_count = decoded.get_total_count()
+ for item in decoded.get_recorded_iterator():
+ xaxis.append(item.value_iterated_to)
+ prob = float(item.count_added_in_this_iter_step) / \
+ total_count * 100
+ yaxis.append(prob)
+ hovertext.append(
+ f"Test: {name}
"
+ f"Direction: {directions[idx_col]}
"
+ f"Latency: {item.value_iterated_to}uSec
"
+ f"Probability: {prob:.2f}%
"
+ f"Percentile: "
+ f"{item.percentile_level_iterated_to:.2f}"
+ )
+ marker_color = [COLORS[idx_row], ] * len(yaxis)
+ marker_color[xaxis.index(
+ decoded.get_value_at_percentile(50.0))] = u"red"
+ marker_color[xaxis.index(
+ decoded.get_value_at_percentile(90.0))] = u"red"
+ marker_color[xaxis.index(
+ decoded.get_value_at_percentile(95.0))] = u"red"
+ histograms.append(
+ plgo.Bar(
+ x=xaxis,
+ y=yaxis,
+ showlegend=False,
+ name=name,
+ marker={u"color": marker_color},
+ hovertext=hovertext,
+ hoverinfo=u"text"
+ )
+ )
+ except hdrh.codec.HdrLengthException as err:
+ logging.warning(
+ f"No or invalid data for HDRHistogram for the test "
+ f"{name}\n{err}"
+ )
+ continue
+ if len(histograms) == 2:
+ traces.append(histograms)
+ tests.append(name)
+ else:
+ logging.warning(f"Invalid test type: {test[u'type']}")
+ continue
+ except (ValueError, KeyError) as err:
+ logging.warning(repr(err))
+
+ if not tests:
+ logging.warning(f"No data for {plot_title}.")
+ return
+
+ fig = make_subplots(
+ rows=len(tests),
+ cols=2,
+ specs=[
+ [{u"type": u"bar"}, {u"type": u"bar"}] for _ in range(len(tests))
+ ]
+ )
+
+ layout_axes = dict(
+ gridcolor=u"rgb(220, 220, 220)",
+ linecolor=u"rgb(220, 220, 220)",
+ linewidth=1,
+ showgrid=True,
+ showline=True,
+ showticklabels=True,
+ tickcolor=u"rgb(220, 220, 220)",
+ )
+
+ for idx_row, test in enumerate(tests):
+ for idx_col in range(2):
+ fig.add_trace(
+ traces[idx_row][idx_col],
+ row=idx_row + 1,
+ col=idx_col + 1
+ )
+ fig.update_xaxes(
+ row=idx_row + 1,
+ col=idx_col + 1,
+ **layout_axes
+ )
+ fig.update_yaxes(
+ row=idx_row + 1,
+ col=idx_col + 1,
+ **layout_axes
+ )
+
+ layout = deepcopy(plot[u"layout"])
+
+ layout[u"title"][u"text"] = \
+ f"Latency: {plot.get(u'graph-title', u'')}"
+ layout[u"height"] = 250 * len(tests) + 130
+
+ layout[u"annotations"][2][u"y"] = 1.06 - 0.008 * len(tests)
+ layout[u"annotations"][3][u"y"] = 1.06 - 0.008 * len(tests)
+
+ for idx, test in enumerate(tests):
+ layout[u"annotations"].append({
+ u"font": {
+ u"size": 14
+ },
+ u"showarrow": False,
+ u"text": f"{test}",
+ u"textangle": 0,
+ u"x": 0.5,
+ u"xanchor": u"center",
+ u"xref": u"paper",
+ u"y": 1.0 - float(idx) * 1.06 / len(tests),
+ u"yanchor": u"bottom",
+ u"yref": u"paper"
+ })
+
+ fig[u"layout"].update(layout)
+
+ # Create plot
+ file_type = plot.get(u"output-file-type", u".html")
+ logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
+ try:
+ # Export Plot
+ ploff.plot(fig, show_link=False, auto_open=False,
+ filename=f"{plot[u'output-file']}{file_type}")
+ except PlotlyError as err:
+ logging.error(f" Finished with error: {repr(err)}")
+
+
+def plot_nf_reconf_box_name(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_nf_reconf_box_name
+ specified in the specification file.
+
+ :param plot: Plot to generate.
+ :param input_data: Data to process.
+ :type plot: pandas.Series
+ :type input_data: InputData
+ """
+
+ # Transform the data
+ logging.info(
+ f" Creating the data set for the {plot.get(u'type', u'')} "
+ f"{plot.get(u'title', u'')}."
+ )
+ data = input_data.filter_tests_by_name(
+ plot, params=[u"result", u"parent", u"tags", u"type"]
+ )
if data is None:
- logging.error("No data.")
+ logging.error(u"No data.")
return
# Prepare the data for the plot
- y_vals = dict()
- y_tags = dict()
+ 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"Time Lost: {layout[u'title']}"
+ layout[u"yaxis"][u"title"] = u"Implied Time Lost [s]"
+ layout[u"legend"][u"font"][u"size"] = 14
+ layout[u"yaxis"].pop(u"range")
+ plpl = plgo.Figure(data=traces, layout=layout)
+
+ # Export Plot
+ file_type = plot.get(u"output-file-type", u".html")
+ logging.info(f" Writing file {plot[u'output-file']}{file_type}.")
+ ploff.plot(
+ plpl,
+ show_link=False,
+ auto_open=False,
+ filename=f"{plot[u'output-file']}{file_type}"
+ )
+ except PlotlyError as err:
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
+ return
+
+
+def plot_perf_box_name(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_perf_box_name
+ specified in the specification file.
+
+ :param plot: Plot to generate.
+ :param input_data: Data to process.
+ :type plot: pandas.Series
+ :type input_data: InputData
+ """
+
+ # Transform the data
+ logging.info(
+ f" Creating data set for the {plot.get(u'type', u'')} "
+ f"{plot.get(u'title', u'')}."
+ )
+ data = input_data.filter_tests_by_name(
+ plot, params=[u"throughput", u"parent", u"tags", u"type"])
+ if data is None:
+ logging.error(u"No data.")
+ return
+
+ # Prepare the data for the plot
+ y_vals = OrderedDict()
for job in data:
for build in job:
for test in build:
- if y_vals.get(test["parent"], None) is None:
- y_vals[test["parent"]] = list()
- y_tags[test["parent"]] = test.get("tags", None)
+ if y_vals.get(test[u"parent"], None) is None:
+ y_vals[test[u"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
+ if (test[u"type"] in (u"NDRPDR", ) and
+ u"-pdr" in plot.get(u"title", u"").lower()):
+ y_vals[test[u"parent"]].\
+ append(test[u"throughput"][u"PDR"][u"LOWER"])
+ elif (test[u"type"] in (u"NDRPDR", ) and
+ u"-ndr" in plot.get(u"title", u"").lower()):
+ y_vals[test[u"parent"]]. \
+ append(test[u"throughput"][u"NDR"][u"LOWER"])
+ elif test[u"type"] in (u"SOAK", ):
+ y_vals[test[u"parent"]].\
+ append(test[u"throughput"][u"LOWER"])
else:
continue
except (KeyError, TypeError):
- y_vals[test["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.info(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.info(suite)
- except KeyError as err:
- logging.error("Not found: {0}".format(err))
- finally:
- break
- else:
- y_sorted = y_vals
+ y_vals[test[u"parent"]].append(None)
# Add None to the lists with missing data
max_len = 0
- for val in y_sorted.values():
+ nr_of_samples = list()
+ for val in y_vals.values():
if len(val) > max_len:
max_len = len(val)
- for key, val in y_sorted.items():
+ nr_of_samples.append(len(val))
+ for val in y_vals.values():
if len(val) < max_len:
val.extend([None for _ in range(max_len - len(val))])
# Add plot traces
traces = list()
- df = pd.DataFrame(y_sorted)
- df.head()
+ df_y = pd.DataFrame(y_vals)
+ df_y.head()
y_max = list()
- for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-ndrpdrdisc', '').
- replace('-ndrpdr', ''))
- logging.info(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"]))
- val_max = max(df[col])
- if val_max:
- y_max.append(int(val_max / 1000000) + 1)
+ for i, col in enumerate(df_y.columns):
+ tst_name = re.sub(REGEX_NIC, u"",
+ col.lower().replace(u'-ndrpdr', u'').
+ replace(u'2n1l-', u''))
+ traces.append(
+ plgo.Box(
+ x=[str(i + 1) + u'.'] * len(df_y[col]),
+ y=[y / 1000000 if y else None for y in df_y[col]],
+ name=(
+ f"{i + 1}. "
+ f"({nr_of_samples[i]:02d} "
+ f"run{u's' if nr_of_samples[i] > 1 else u''}) "
+ f"{tst_name}"
+ ),
+ hoverinfo=u"y+name"
+ )
+ )
+ try:
+ val_max = max(df_y[col])
+ if val_max:
+ y_max.append(int(val_max / 1000000) + 2)
+ except (ValueError, TypeError) as err:
+ logging.error(repr(err))
+ continue
try:
# Create plot
- layout = deepcopy(plot["layout"])
- if layout.get("title", None):
- layout["title"] = "Packet Throughput: {0}". \
- format(layout["title"])
+ layout = deepcopy(plot[u"layout"])
+ if layout.get(u"title", None):
+ layout[u"title"] = f"Throughput: {layout[u'title']}"
if y_max:
- layout["yaxis"]["range"] = [0, max(y_max)]
+ layout[u"yaxis"][u"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"]))
+ 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(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
return
-def plot_latency_error_bars(plot, input_data):
- """Generate the plot(s) with algorithm: plot_latency_error_bars
+def plot_lat_err_bars_name(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_lat_err_bars_name
specified in the specification file.
:param plot: Plot to generate.
@@ -186,22 +570,27 @@ def plot_latency_error_bars(plot, input_data):
"""
# 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)
+ plot_title = plot.get(u"title", u"")
+ logging.info(
+ f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
+ )
+ data = input_data.filter_tests_by_name(
+ plot, params=[u"latency", u"parent", u"tags", u"type"])
if data is None:
- logging.error("No data.")
+ logging.error(u"No data.")
return
# Prepare the data for the plot
- y_tmp_vals = dict()
- y_tags = dict()
+ y_tmp_vals = OrderedDict()
for job in data:
for build in job:
for test in build:
- if y_tmp_vals.get(test["parent"], None) is None:
- y_tmp_vals[test["parent"]] = [
+ try:
+ logging.debug(f"test[u'latency']: {test[u'latency']}\n")
+ except ValueError as err:
+ logging.warning(repr(err))
+ if y_tmp_vals.get(test[u"parent"], None) is None:
+ y_tmp_vals[test[u"parent"]] = [
list(), # direction1, min
list(), # direction1, avg
list(), # direction1, max
@@ -209,82 +598,72 @@ def plot_latency_error_bars(plot, input_data):
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:
- 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"])
+ if test[u"type"] not in (u"NDRPDR", ):
+ logging.warning(f"Invalid test type: {test[u'type']}")
+ continue
+ if u"-pdr" in plot_title.lower():
+ ttype = u"PDR"
+ elif u"-ndr" in plot_title.lower():
+ ttype = u"NDR"
else:
+ logging.warning(
+ f"Invalid test type: {test[u'type']}"
+ )
continue
- except (KeyError, TypeError):
- pass
-
- # 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 suite, tags in y_tags_l.items():
- if tag.lower() in tags:
- try:
- y_sorted[suite] = y_tmp_vals.pop(suite)
- y_tags_l.pop(suite)
- except KeyError as err:
- logging.error("Not found: {0}".format(err))
- finally:
- break
- else:
- y_sorted = y_tmp_vals
+ y_tmp_vals[test[u"parent"]][0].append(
+ test[u"latency"][ttype][u"direction1"][u"min"])
+ y_tmp_vals[test[u"parent"]][1].append(
+ test[u"latency"][ttype][u"direction1"][u"avg"])
+ y_tmp_vals[test[u"parent"]][2].append(
+ test[u"latency"][ttype][u"direction1"][u"max"])
+ y_tmp_vals[test[u"parent"]][3].append(
+ test[u"latency"][ttype][u"direction2"][u"min"])
+ y_tmp_vals[test[u"parent"]][4].append(
+ test[u"latency"][ttype][u"direction2"][u"avg"])
+ y_tmp_vals[test[u"parent"]][5].append(
+ test[u"latency"][ttype][u"direction2"][u"max"])
+ except (KeyError, TypeError) as err:
+ logging.warning(repr(err))
x_vals = list()
y_vals = list()
y_mins = list()
y_maxs = list()
- for key, val in y_sorted.items():
- key = "-".join(key.split("-")[1:-1])
- x_vals.append(key) # dir 1
+ nr_of_samples = list()
+ for key, val in y_tmp_vals.items():
+ name = re.sub(REGEX_NIC, u"", key.replace(u'-ndrpdr', u'').
+ replace(u'2n1l-', u''))
+ x_vals.append(name) # dir 1
y_vals.append(mean(val[1]) if val[1] else None)
y_mins.append(mean(val[0]) if val[0] else None)
y_maxs.append(mean(val[2]) if val[2] else None)
- x_vals.append(key) # dir 2
+ 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)):
+ for idx, _ in enumerate(x_vals):
if not bool(int(idx % 2)):
- direction = "West - East"
+ direction = u"West-East"
else:
- direction = "East - West"
- hovertext = ("Test: {test}
"
- "Direction: {dir}
".format(test=x_vals[idx],
- dir=direction))
+ direction = u"East-West"
+ hovertext = (
+ f"No. of Runs: {nr_of_samples[idx]}
"
+ f"Test: {x_vals[idx]}
"
+ f"Direction: {direction}
"
+ )
if isinstance(y_maxs[idx], float):
- hovertext += "Max: {max:.2f}uSec
".format(max=y_maxs[idx])
+ hovertext += f"Max: {y_maxs[idx]:.2f}uSec
"
if isinstance(y_vals[idx], float):
- hovertext += "Avg: {avg:.2f}uSec
".format(avg=y_vals[idx])
+ hovertext += f"Mean: {y_vals[idx]:.2f}uSec
"
if isinstance(y_mins[idx], float):
- hovertext += "Min: {min:.2f}uSec".format(min=y_mins[idx])
+ hovertext += f"Min: {y_mins[idx]:.2f}uSec"
if isinstance(y_maxs[idx], float) and isinstance(y_vals[idx], float):
array = [y_maxs[idx] - y_vals[idx], ]
@@ -300,9 +679,9 @@ def plot_latency_error_bars(plot, input_data):
name=x_vals[idx],
legendgroup=x_vals[idx],
showlegend=bool(int(idx % 2)),
- mode="markers",
+ mode=u"markers",
error_y=dict(
- type='data',
+ type=u"data",
symmetric=False,
array=array,
arrayminus=arrayminus,
@@ -313,48 +692,49 @@ def plot_latency_error_bars(plot, input_data):
color=COLORS[int(idx / 2)],
),
text=hovertext,
- hoverinfo="text",
+ hoverinfo=u"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",
+ xref=u"x",
+ yref=u"y",
+ xanchor=u"center",
+ yanchor=u"top",
+ text=u"E-W" if bool(int(idx % 2)) else u"W-E",
font=dict(
size=16,
),
- align="center",
+ align=u"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"] = "Packet Latency: {0}".\
- format(layout["title"])
- layout["annotations"] = annotations
+ 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"Latency: {layout[u'title']}"
+ layout[u"annotations"] = annotations
plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
- ploff.plot(plpl,
- show_link=False, auto_open=False,
- filename='{0}{1}'.format(plot["output-file"],
- plot["output-file-type"]))
+ ploff.plot(
+ plpl,
+ show_link=False, auto_open=False,
+ filename=f"{plot[u'output-file']}{file_type}"
+ )
except PlotlyError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
return
-def plot_throughput_speedup_analysis(plot, input_data):
+def plot_tsa_name(plot, input_data):
"""Generate the plot(s) with algorithm:
- plot_throughput_speedup_analysis
+ plot_tsa_name
specified in the specification file.
:param plot: Plot to generate.
@@ -364,311 +744,334 @@ def plot_throughput_speedup_analysis(plot, input_data):
"""
# 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)
+ plot_title = plot.get(u"title", u"")
+ logging.info(
+ f" Creating data set for the {plot.get(u'type', u'')} {plot_title}."
+ )
+ data = input_data.filter_tests_by_name(
+ plot, params=[u"throughput", u"parent", u"tags", u"type"])
if data is None:
- logging.error("No data.")
+ logging.error(u"No data.")
return
- y_vals = dict()
- y_tags = dict()
+ 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()}
- y_tags[test["parent"]] = test.get("tags", None)
+ 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["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"])
+ if test[u"type"] not in (u"NDRPDR",):
+ continue
+
+ if u"-pdr" in plot_title.lower():
+ ttype = u"PDR"
+ elif u"-ndr" in plot_title.lower():
+ ttype = u"NDR"
+ else:
+ continue
+
+ if u"1C" in test[u"tags"]:
+ y_vals[test[u"parent"]][u"1"]. \
+ append(test[u"throughput"][ttype][u"LOWER"])
+ elif u"2C" in test[u"tags"]:
+ y_vals[test[u"parent"]][u"2"]. \
+ append(test[u"throughput"][ttype][u"LOWER"])
+ elif u"4C" in test[u"tags"]:
+ y_vals[test[u"parent"]][u"4"]. \
+ append(test[u"throughput"][ttype][u"LOWER"])
except (KeyError, TypeError):
pass
if not y_vals:
- logging.warning("No data for the plot '{}'".
- format(plot.get("title", "")))
+ 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:
- y_vals[test_name][key] = sum(test_val) / len(test_val)
- if key == "1":
- y_1c_max[test_name] = max(test_val) / 1000000.0
+ 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()
+ vals = OrderedDict()
y_max = list()
nic_limit = 0
lnk_limit = 0
- pci_limit = plot["limits"]["pci"]["pci-g3-x8"]
+ pci_limit = plot[u"limits"][u"pci"][u"pci-g3-x8"]
for test_name, test_vals in y_vals.items():
- if test_vals["1"]:
- name = "-".join(test_name.split('-')[1:-1])
-
- vals[name] = dict()
- y_val_1 = test_vals["1"] / 1000000.0
- y_val_2 = test_vals["2"] / 1000000.0 if test_vals["2"] else None
- y_val_4 = test_vals["4"] / 1000000.0 if test_vals["4"] 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]
-
- val_max = max(max(vals[name]["val"], vals[name]["ideal"]))
- if val_max:
- y_max.append(int((val_max / 10) + 1) * 10)
+ try:
+ if test_vals[u"1"][1]:
+ name = re.sub(
+ REGEX_NIC,
+ u"",
+ test_name.replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')
+ )
+ vals[name] = OrderedDict()
+ y_val_1 = test_vals[u"1"][0] / 1000000.0
+ y_val_2 = test_vals[u"2"][0] / 1000000.0 if test_vals[u"2"][0] \
+ else None
+ y_val_4 = test_vals[u"4"][0] / 1000000.0 if test_vals[u"4"][0] \
+ else None
+
+ vals[name][u"val"] = [y_val_1, y_val_2, y_val_4]
+ vals[name][u"rel"] = [1.0, None, None]
+ vals[name][u"ideal"] = [
+ y_1c_max[test_name],
+ y_1c_max[test_name] * 2,
+ y_1c_max[test_name] * 4
+ ]
+ vals[name][u"diff"] = [
+ (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None
+ ]
+ vals[name][u"count"] = [
+ test_vals[u"1"][1],
+ test_vals[u"2"][1],
+ test_vals[u"4"][1]
+ ]
- 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
+ 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 "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"]
+ if u"x520" in test_name:
+ limit = plot[u"limits"][u"nic"][u"x520"]
+ elif u"x710" in test_name:
+ limit = plot[u"limits"][u"nic"][u"x710"]
+ elif u"xxv710" in test_name:
+ limit = plot[u"limits"][u"nic"][u"xxv710"]
+ elif u"xl710" in test_name:
+ limit = plot[u"limits"][u"nic"][u"xl710"]
+ elif u"x553" in test_name:
+ limit = plot[u"limits"][u"nic"][u"x553"]
else:
limit = 0
if limit > nic_limit:
nic_limit = limit
- mul = 2 if "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
+ 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
- # 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 = "-".join(test.split('-')[1:-1])
- 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:
- threshold = 1.1 * max(y_max) # 10%
-
+ 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)
+ traces.append(plgo.Scatter(
+ x=x_vals,
+ y=[nic_limit, ] * len(x_vals),
+ name=f"NIC: {nic_limit:.2f}Mpps",
+ showlegend=False,
+ mode=u"lines",
+ line=dict(
+ dash=u"dot",
+ color=COLORS[-1],
+ width=1),
+ hoverinfo=u"none"
+ ))
+ annotations.append(dict(
+ x=1,
+ y=nic_limit,
+ xref=u"x",
+ yref=u"y",
+ xanchor=u"left",
+ yanchor=u"bottom",
+ text=f"NIC: {nic_limit:.2f}Mpps",
+ font=dict(
+ size=14,
+ color=COLORS[-1],
+ ),
+ align=u"left",
+ showarrow=False
+ ))
+ y_max.append(nic_limit)
lnk_limit /= 1000000.0
if lnk_limit < threshold:
traces.append(plgo.Scatter(
x=x_vals,
y=[lnk_limit, ] * len(x_vals),
- name="Link: {0:.2f}Mpps".format(lnk_limit),
+ name=f"Link: {lnk_limit:.2f}Mpps",
showlegend=False,
- mode="lines",
+ mode=u"lines",
line=dict(
- dash="dot",
+ dash=u"dot",
color=COLORS[-2],
width=1),
- hoverinfo="none"
+ hoverinfo=u"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),
+ xref=u"x",
+ yref=u"y",
+ xanchor=u"left",
+ yanchor=u"bottom",
+ text=f"Link: {lnk_limit:.2f}Mpps",
font=dict(
size=14,
color=COLORS[-2],
),
- align="left",
+ align=u"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:
+ 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),
+ name=f"PCIe: {pci_limit:.2f}Mpps",
showlegend=False,
- mode="lines",
+ mode=u"lines",
line=dict(
- dash="dot",
+ dash=u"dot",
color=COLORS[-3],
width=1),
- hoverinfo="none"
+ hoverinfo=u"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),
+ xref=u"x",
+ yref=u"y",
+ xanchor=u"left",
+ yanchor=u"bottom",
+ text=f"PCIe: {pci_limit:.2f}Mpps",
font=dict(
size=14,
color=COLORS[-3],
),
- align="left",
+ align=u"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():
+ for name, val in vals.items():
hovertext = list()
- for idx in range(len(val["val"])):
- htext = ""
- if isinstance(val["val"][idx], float):
- htext += "value: {0:.2f}Mpps
".format(val["val"][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+markers",
- line=dict(
- color=COLORS[cidx],
- width=2,
- dash="dash"),
- marker=dict(
- symbol="circle",
- size=10
- ),
- text=["perfect: {0:.2f}Mpps".format(y)
- for y in val["ideal"]],
- hoverinfo="text"
- ))
- cidx += 1
+ 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]}
"
+ f"Mean: {val[u'val'][idx]:.2f}Mpps
"
+ )
+ if isinstance(val[u"diff"][idx], float):
+ htext += f"Diff: {round(val[u'diff'][idx]):.0f}%
"
+ 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
- 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["annotations"].extend(annotations)
+ 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"Speedup Multi-core: {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='{0}{1}'.format(plot["output-file"],
- plot["output-file-type"]))
+ ploff.plot(
+ plpl,
+ show_link=False,
+ auto_open=False,
+ filename=f"{plot[u'output-file']}{file_type}"
+ )
except PlotlyError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
return
-def plot_http_server_performance_box(plot, input_data):
- """Generate the plot(s) with algorithm: plot_http_server_performance_box
+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.
@@ -678,11 +1081,13 @@ def plot_http_server_performance_box(plot, input_data):
"""
# Transform the data
- logging.info(" Creating the data set for the {0} '{1}'.".
- format(plot.get("type", ""), plot.get("title", "")))
+ 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("No data.")
+ logging.error(u"No data.")
return
# Prepare the data for the plot
@@ -690,44 +1095,395 @@ def plot_http_server_performance_box(plot, input_data):
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()
+ if y_vals.get(test[u"name"], None) is None:
+ y_vals[test[u"name"]] = list()
try:
- y_vals[test["name"]].append(test["result"])
+ y_vals[test[u"name"]].append(test[u"result"])
except (KeyError, TypeError):
- y_vals[test["name"]].append(None)
+ 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)
- for key, val in y_vals.items():
+ nr_of_samples.append(len(val))
+ for val in y_vals.values():
if len(val) < max_len:
val.extend([None for _ in range(max_len - len(val))])
# Add plot traces
traces = list()
- df = pd.DataFrame(y_vals)
- df.head()
- for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-cps', '').
- replace('-rps', ''))
- traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
- y=df[col],
+ 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"
"
+ 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["traces"]))
+ **plot[u"traces"]))
try:
# Create plot
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
+ plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
+
+ # Export Plot
+ logging.info(
+ f" Writing file {plot[u'output-file']}"
+ f"{plot[u'output-file-type']}."
+ )
+ ploff.plot(
+ plpl,
+ show_link=False,
+ auto_open=False,
+ filename=f"{plot[u'output-file']}{plot[u'output-file-type']}"
+ )
+ except PlotlyError as err:
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
+ return
+
+
+def plot_nf_heatmap(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_nf_heatmap
+ specified in the specification file.
+
+ :param plot: Plot to generate.
+ :param input_data: Data to process.
+ :type plot: pandas.Series
+ :type input_data: InputData
+ """
+
+ regex_cn = re.compile(r'^(\d*)R(\d*)C$')
+ regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
+ r'(\d+mif|\d+vh)-'
+ r'(\d+vm\d+t|\d+dcr\d+t).*$')
+ vals = dict()
+
+ # Transform the data
+ logging.info(
+ f" Creating the data set for the {plot.get(u'type', u'')} "
+ f"{plot.get(u'title', u'')}."
+ )
+ data = input_data.filter_data(plot, continue_on_error=True)
+ if data is None or data.empty:
+ logging.error(u"No data.")
+ return
+
+ for job in data:
+ for build in job:
+ for test in build:
+ for tag in test[u"tags"]:
+ groups = re.search(regex_cn, tag)
+ if groups:
+ chain = str(groups.group(1))
+ node = str(groups.group(2))
+ break
+ else:
+ continue
+ groups = re.search(regex_test_name, test[u"name"])
+ if groups and len(groups.groups()) == 3:
+ hover_name = (
+ f"{str(groups.group(1))}-"
+ f"{str(groups.group(2))}-"
+ f"{str(groups.group(3))}"
+ )
+ else:
+ hover_name = u""
+ if vals.get(chain, None) is None:
+ vals[chain] = dict()
+ if vals[chain].get(node, None) is None:
+ vals[chain][node] = dict(
+ name=hover_name,
+ vals=list(),
+ nr=None,
+ mean=None,
+ stdev=None
+ )
+ try:
+ if plot[u"include-tests"] == u"MRR":
+ result = test[u"result"][u"receive-rate"]
+ elif plot[u"include-tests"] == u"PDR":
+ result = test[u"throughput"][u"PDR"][u"LOWER"]
+ elif plot[u"include-tests"] == u"NDR":
+ result = test[u"throughput"][u"NDR"][u"LOWER"]
+ else:
+ result = None
+ except TypeError:
+ result = None
+
+ if result:
+ vals[chain][node][u"vals"].append(result)
+
+ if not vals:
+ logging.error(u"No data.")
+ return
+
+ txt_chains = list()
+ txt_nodes = list()
+ for key_c in vals:
+ txt_chains.append(key_c)
+ for key_n in vals[key_c].keys():
+ txt_nodes.append(key_n)
+ if vals[key_c][key_n][u"vals"]:
+ vals[key_c][key_n][u"nr"] = len(vals[key_c][key_n][u"vals"])
+ vals[key_c][key_n][u"mean"] = \
+ round(mean(vals[key_c][key_n][u"vals"]) / 1000000, 1)
+ vals[key_c][key_n][u"stdev"] = \
+ round(stdev(vals[key_c][key_n][u"vals"]) / 1000000, 1)
+ txt_nodes = list(set(txt_nodes))
+
+ def sort_by_int(value):
+ """Makes possible to sort a list of strings which represent integers.
+
+ :param value: Integer as a string.
+ :type value: str
+ :returns: Integer representation of input parameter 'value'.
+ :rtype: int
+ """
+ return int(value)
+
+ txt_chains = sorted(txt_chains, key=sort_by_int)
+ txt_nodes = sorted(txt_nodes, key=sort_by_int)
+
+ chains = [i + 1 for i in range(len(txt_chains))]
+ nodes = [i + 1 for i in range(len(txt_nodes))]
+
+ data = [list() for _ in range(len(chains))]
+ for chain in chains:
+ for node in nodes:
+ try:
+ val = vals[txt_chains[chain - 1]][txt_nodes[node - 1]][u"mean"]
+ except (KeyError, IndexError):
+ val = None
+ data[chain - 1].append(val)
+
+ # Color scales:
+ my_green = [[0.0, u"rgb(235, 249, 242)"],
+ [1.0, u"rgb(45, 134, 89)"]]
+
+ my_blue = [[0.0, u"rgb(236, 242, 248)"],
+ [1.0, u"rgb(57, 115, 172)"]]
+
+ my_grey = [[0.0, u"rgb(230, 230, 230)"],
+ [1.0, u"rgb(102, 102, 102)"]]
+
+ hovertext = list()
+ annotations = list()
+
+ text = (u"Test: {name}
"
+ u"Runs: {nr}
"
+ u"Thput: {val}
"
+ 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(" 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"]))
+ logging.info(
+ f" Writing file {plot[u'output-file']}"
+ f"{plot[u'output-file-type']}."
+ )
+ ploff.plot(
+ plpl,
+ show_link=False,
+ auto_open=False,
+ filename=f"{plot[u'output-file']}{plot[u'output-file-type']}"
+ )
except PlotlyError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
return