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plot.py
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plot.py
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#!/usr/bin/env python3
import math
import matplotlib.cbook as cbook
import matplotlib.colors as col
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import re
import string
# local import
import utils
#####
##### Global vars are for lazy people
#####
BAR_FORMAT = {
### Eval plots
"CC-BFT-SMaRt": {"hatch": "/", "facecolor": "white", "edgecolor": "tab:blue"},
"CC-HotStuff": {"hatch": "x", "facecolor": "white", "edgecolor": "tab:red"},
"NW-Bullshark-sig": {"hatch": "O", "facecolor": "white", "edgecolor": "tab:green"},
### Motivation plot
"motivation-service": {"hatch": "", "facecolor": "white", "edgecolor": "black"},
}
LINE_FORMAT = {
### Comma
"CC-BFT-SMaRt": {"marker": "|", "linestyle": "solid", "color": "tab:blue"},
"CC-HotStuff": {"marker": "x", "linestyle": "dashed", "color": "tab:red"},
"NW-Bullshark": {"marker": "^", "linestyle": "dashdot", "color": "tab:orange"}, # tab:purple
"NW-Bullshark-sig": {"marker": "v", "linestyle": "dotted", "color": "tab:green"},
"BFT-SMaRt": {"marker": "D", "linestyle": "solid", "color": "darkblue"},
"HotStuff": {"marker": "s", "linestyle": "dashed", "color": "darkred"},
### Linerate
"Input rate": {"marker": "", "linestyle": "dotted", "color": "black"},
"Network rate": {"marker": "^", "linestyle": "dashed", "color": "tab:blue"},
"Output rate": {"marker": "v", "linestyle": "dashdot", "color": "tab:red"},
}
##########
########## Chopchop utils
##########
def barplot(ax, data, dataErr):
xTicksLabels = list(list(data.values())[0].keys())
### Bar locations and width
xTicks = np.arange(len(xTicksLabels)) # label locations
nbBars = len(data.keys())
width = 1.0 / (nbBars + 1) # 2 sets: 0.33 width, 3 sets: 0.25, 4: 0.2, ...
location = lambda i: xTicks + (i-nbBars/2+0.5)*width
bars = {}
for i, approach in enumerate(data.keys()):
bars[approach] = ax.bar(location(i), data[approach].values(), width,
yerr=dataErr[approach].values(), label=approach, fill=True, rasterized=True, **BAR_FORMAT[approach])
### Ticks, limits
ax.set_xticks(xTicks)
ax.set_xticklabels(xTicksLabels)
### Grid y only
ax.grid(which='both', axis='x', linestyle='')
# ax.grid(which='both', axis='y', linestyle='--')
ax.set_axisbelow(True)
return ax
def plotCurve(ax, x, y, yerr, label, withShadedArea=True):
ax.errorbar(x, y,
label=label,
# errorevery=markPeriod,
# markevery=markPeriod,
# rasterized=True,
markerfacecolor="none",
**LINE_FORMAT[label]
)
# Shaded areas for deviations
# https://github.com/mwhittaker/frankenpaxos/blob/master/benchmarks/vldb20_matchmaker/leader_failure/plot.py
if withShadedArea:
ax.fill_between(x, y,
y-yerr,
# csv["lat 5th"],
color=LINE_FORMAT[label]["color"], alpha=0.20)
ax.fill_between(x, y,
y+yerr,
# csv["lat 95th"],
color=LINE_FORMAT[label]["color"], alpha=0.20)
### Populate subplots
def plotCurvesInFiles(fileList, labelList, ax, xColumn, yColumn, yColumnError):
for i, file in enumerate(fileList):
csv = pd.read_csv(utils.DIR_STATS + "/" + file, index_col=0)
# print(csv)
x = csv[xColumn]
y = csv[yColumn]
yerr = csv[yColumnError]
plotCurve(ax, x, y, yerr, labelList[i])
### Add values at the top of bars
# valueList = []
# for fault in ["0", "1", "threshold"]:
# valueList.append(data[labels[0]][fault])
# valueList.append(data[labels[1]][fault])
# print(valueList)
# for i in range(len(valueList)):
# plt.text(i, valueList[i], valueList[i], ha = 'center')
def decorateBarPlotLog(ax):
### Ticks
ax.set_yscale("log")
ax.set_ylim(10**5, 1.4*10**8)
ax.yaxis.set_major_locator(ticker.LogLocator(base=10, numticks=4))
ax.yaxis.set_minor_locator(ticker.LogLocator(base=10, numticks=10, subs=[x/10 for x in range(1,10)]))
ax.set_yticks([10**v for v in [5, 6, 7, 8]])
ax.set_yticklabels(["100k", "1M", "10M", "100M"])
##########
########## Chopchop figs
##########
def plotMotivation(fileCCHotstuff, fileCCBftsmart):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL_MOTIV)
utils.commonFigFormat(ax)
ax.grid(which='both', axis='y', linestyle='') # Only show y grid
### One line for chopchop+bftsmart
data = {}
csv = pd.read_csv(utils.DIR_STATS + "/" + fileCCBftsmart, index_col=0)
data["Chop Chop"] = csv["op avg"].max()
keys = [k for k in data]
values = [v for v in data.values()]
ax.barh(keys, values, label='',
align='center', height=0.8,
**BAR_FORMAT["CC-BFT-SMaRt"])
### One line for chopchop+hotstuff
# data = {}
# csv = pd.read_csv(utils.DIR_STATS + "/" + fileCCHotstuff, index_col=0)
# data["Chop Chop + HotStuff"] = csv["op avg"].max()
# keys = [k for k in data]
# values = [v for v in data.values()]
# ax.barh(keys, values, label='',
# align='center', height=0.8,
# **BAR_FORMAT["CC-HotStuff"])
### Throughput / second
data = {}
data["WhatsApp messages"] = 1157000 # 100 billion / day
data["Google searches"] = 108507
data["Credit card payments"] = 18423 # 581*10**3 / day
data["Youtube video watches"] = 11570 # 1 billion / day
data["Tweets"] = 10311
keys = [k for k in data]
values = [v for v in data.values()]
ax.barh(keys, values, label='',
align='center', height=0.8,
**BAR_FORMAT["motivation-service"])
ax.set_xlabel("Throughput [event/s, log scale]")
# ax.set_xscale("log")
ax.set_xscale("symlog")
ax.set_xlim(1)
utils.saveFig("motivation-throughput-services")
### Value of a bar inside it
# https://matplotlib.org/3.1.1/gallery/lines_bars_and_markers/horizontal_barchart_distribution.html#sphx-glr-gallery-lines-bars-and-markers-horizontal-barchart-distribution-py
# r, g, b, _ = color
# text_color = 'white' if r * g * b < 0.5 else 'darkgrey'
# for y, (x, c) in enumerate(zip(xcenters, widths)):
# ax.text(x, y, str(int(c)), ha='center', va='center',
# color=text_color)
# ax.legend(ncol=len(category_names), bbox_to_anchor=(0, 1),
# loc='lower left', fontsize='small')
# To invert the x axis and have labels on the right side
# ax.spines['left'].set_visible(False)
# ax.spines['right'].set_visible(True)
# ax.yaxis.set_label_position("right")
# ax.yaxis.tick_right()
# ax.set_xlim(135*1000, 0)
def _plotCommaSingle(labelList, fileList):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL)
utils.commonFigFormat(ax)
### Plot data points
plotCurvesInFiles(fileList, labelList, ax, "op avg", "lat avg", "lat std")
### Labels
ax.set_xlabel("Throughput [op/s]")
ax.set_ylabel("Latency [s]")
### Throughput op/s -> M op/s
# ax.set_xlim(0, 50)
ax.xaxis.set_minor_locator(ticker.MultipleLocator(5 * 10**6))
ax.xaxis.set_major_locator(ticker.MultipleLocator(10 * 10**6))
ax.set_xticks([0*10**6, 10*10**6, 20*10**6, 30*10**6, 40*10**6, 50*10**6])
ax.set_xticklabels(["0", "10M", "20M", "30M", "40M", "50M"])
### Latency ms -> s
ax.set_ylim(0)
ax.yaxis.set_major_locator(ticker.MultipleLocator(2*10**3))
ax.yaxis.set_minor_locator(ticker.MultipleLocator(1*10**3))
ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: "%.0f" % (x/10**3)))
ax.legend(**utils.FORMAT_LEGEND, ncol=3, columnspacing=1)
utils.saveFig("comma-single")
# ax.fill_between(before.index[::sample_every],
# median_before[::sample_every],
# p95_before[::sample_every],
# color=line.get_color(), alpha=0.25)
# ax.set_xscale('log')
# ax.set_yscale('log')
# # ax.xaxis.set_major_formatter(lambda x, pos: round(x/(1000)))
# # ax.xaxis.set_major_locator(ticker.MultipleLocator(200*1000))
# # ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: "%.0fk" % (x/1000)))
# # ax.xaxis.set_minor_formatter(ticker.FuncFormatter(lambda x, pos: "%.0fk" % (x/1000)))
# ax.xaxis.set_minor_locator(ticker.LogLocator(base=10, numticks=10)) #, subs=[x/10 for x in range(1,10)]))
# ax.xaxis.set_major_locator(ticker.LogLocator(base=10, numticks=10))
def plotCommaSplit(labelListA, labelListB, labelListC, labelListD, fileListA, fileListB, fileListC, fileListD):
fig, ax = plt.subplots(1, 4, **utils.FIG_SIZE_TWO_COL, sharey=True, squeeze=True)
nbRows, nbCols = len(ax), len(ax)
utils.commonFigFormat(ax[0])
utils.commonFigFormat(ax[1])
utils.commonFigFormat(ax[2])
utils.commonFigFormat(ax[3])
### Remove left border except on leftmost plot
ax[1].spines['left'].set_visible(False)
ax[2].spines['left'].set_visible(False)
ax[3].spines['left'].set_visible(False)
### Add break clips on bottom axis
# Source: http://matplotlib.org/examples/pylab_examples/broken_axis.html
utils.addBreakClipsX(ax[0], ax[1])
utils.addBreakClipsX(ax[1], ax[2])
utils.addBreakClipsX(ax[2], ax[3])
### Plot data points
plotCurvesInFiles(fileListA, labelListA, ax[0], "op avg", "lat avg", "lat std")
plotCurvesInFiles(fileListB, labelListB, ax[1], "op avg", "lat avg", "lat std")
plotCurvesInFiles(fileListC, labelListC, ax[2], "op avg", "lat avg", "lat std")
plotCurvesInFiles(fileListD, labelListD, ax[3], "op avg", "lat avg", "lat std")
### Labels
ax[0].set_xlabel("Throughput [op/s]")
ax[1].set_xlabel("")
ax[2].set_xlabel("")
ax[3].set_xlabel("")
ax[0].set_ylabel("Latency [s]")
### Throughput op/s
ax[0].set_xlim(0.7*10**3, 1.7*10**3)
ax[0].xaxis.set_minor_locator(ticker.MultipleLocator(2*10**2))
ax[0].xaxis.set_major_locator(ticker.MultipleLocator(4*10**2))
# ax[0].set_xticks([8*10**2, 12*10**2, 16*10**2])
# ax[0].set_xticklabels(["1k", "2k", "3k", "4k"])
ax[1].set_xlim(0.7*10**5, 4.2*10**5)
ax[1].xaxis.set_minor_locator(ticker.MultipleLocator(5*10**4))
ax[1].xaxis.set_major_locator(ticker.MultipleLocator(10*10**4))
ax[1].set_xticks([1*10**5, 2*10**5, 3*10**5, 4*10**5])
ax[1].set_xticklabels(["100k", "200k", "300k", "400k"])
ax[2].set_xlim(0.8*10**6, 4.2*10**6)
ax[2].xaxis.set_minor_locator(ticker.MultipleLocator(5*10**5))
ax[2].xaxis.set_major_locator(ticker.MultipleLocator(10*10**5))
ax[2].set_xticks([1*10**6, 2*10**6, 3*10**6, 4*10**6])
ax[2].set_xticklabels(["1M", "2M", "3M", "4M"])
### Plot 3 show all points
# ax[3].set_xlim(-2*10**6,50*10**6)
# ax[3].xaxis.set_minor_locator(ticker.MultipleLocator(5*10**6))
# ax[3].xaxis.set_major_locator(ticker.MultipleLocator(10*10**6))
# ax[3].set_xticks([0*10**6, 10*10**6, 20*10**6, 30*10**6, 40*10**6, 50*10**6])
# ax[3].set_xticklabels(["0", "10M", "20M", "30M", "40M", "50M"])
### Plot 3 show points starting at 10M
ax[3].set_xlim(8*10**6,52*10**6)
ax[3].xaxis.set_minor_locator(ticker.MultipleLocator(5*10**6))
ax[3].xaxis.set_major_locator(ticker.MultipleLocator(10*10**6))
ax[3].set_xticks([10*10**6, 20*10**6, 30*10**6, 40*10**6, 50*10**6])
ax[3].set_xticklabels(["10M", "20M", "30M", "40M", "50M"])
### Latency ms -> s
for a in ax:
a.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: "%.0f" % (x/10**3)))
### Separate yscales per subplot
# ax[0].set_ylim(0, 13.2*10**3)
# ax[0].yaxis.set_major_locator(ticker.MultipleLocator(3*10**3))
# ax[1].set_ylim(0, 88*10**3)
# ax[1].yaxis.set_major_locator(ticker.MultipleLocator(20*10**3))
# ax[2].set_ylim(0, 44*10**3)
# ax[2].yaxis.set_major_locator(ticker.MultipleLocator(10*10**3))
# ax[3].set_ylim(0, 8.8*10**3)
# ax[3].yaxis.set_major_locator(ticker.MultipleLocator(2*10**3))
### Same yscales across subplots + mirrors ticks
ax[0].set_ylim(0, 11.1*10**3)
ax[0].yaxis.set_major_locator(ticker.MultipleLocator(2*10**3))
ax[1].yaxis.set_ticks_position('none')
ax[2].yaxis.set_ticks_position('none')
ax[3].yaxis.set_ticks_position('none')
fig.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(1.02, 0.33))
utils.saveFig("comma-split")
def _plotMergeMostBars(
payloadLabels, payloadFilesA, payloadFilesB, payloadFilesC, payloadFilesD,
systemLabels, systemFilesA, systemFilesB, systemFilesC, systemFilesD,
distillationLabelsA, distillationFilesNoFaults, distillationFilesFaults, distillationLabelsB, distillationFilesB,
appLabels, appFilesA, appFilesB, appFilesC
):
fig, ax = plt.subplots(1, 4, **utils.FIG_SIZE_TWO_COL, sharey=True, squeeze=True)
nbRows, nbCols = len(ax), len(ax)
utils.commonFigFormat(ax[0])
utils.commonFigFormat(ax[1])
utils.commonFigFormat(ax[2])
utils.commonFigFormat(ax[3])
### Remove left border except on leftmost plot
ax[1].spines['left'].set_visible(False)
ax[2].spines['left'].set_visible(False)
ax[3].spines['left'].set_visible(False)
plotPayloadSizesAx(ax[0], payloadLabels, payloadFilesA, payloadFilesB, payloadFilesC, payloadFilesD)
plotSystemSizesAx(ax[1], systemLabels, systemFilesA, systemFilesB, systemFilesC, systemFilesD)
plotDistillationAx(ax[2], distillationLabelsA, distillationFilesNoFaults, distillationFilesFaults, distillationLabelsB, distillationFilesB)
plotAppsAx(ax[3], appLabels, appFilesA, appFilesB, appFilesC)
# plotServerFaultsAx(ax[1], labels, filesNoFaults, filesFaults)
### Labels
ax[0].set_ylabel("Throughput\n[op/s, log]") #, loc="top")
# ax[0].set_xlabel("Payload size")
# ax[1].set_xlabel("System size")
# ax[2].set_xlabel("Distillation ratio")
# ax[3].set_xlabel("Application")
### Same yscales across subplots + mirrors ticks
decorateBarPlotLog(ax[0])
ax[1].yaxis.set_ticks_position('none')
ax[2].yaxis.set_ticks_position('none')
ax[3].yaxis.set_ticks_position('none')
### Smaller font size for some ticks
# ax[0].tick_params(axis='x', which='major', labelsize=utils.FONT_SIZE_S)
ax[3].tick_params(axis='x', which='major', labelsize=utils.FONT_SIZE_XS)
ax[0].legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(5.3, 0.5))
utils.saveFig("merged-bars")
def plotMergeDistillationAndPayloadSizes(
distillationLabelsA, distillationFilesNoFaults, distillationFilesFaults, distillationLabelsB, distillationFilesB,
payloadLabels, payloadFilesA, payloadFilesB, payloadFilesC, payloadFilesD,
):
fig, ax = plt.subplots(1, 2, **utils.FIG_SIZE_ONE_COL_LINERATE, sharey=True, squeeze=True) # FIG_SIZE_ONE_COL_LINERATE
nbRows, nbCols = len(ax), len(ax)
utils.commonFigFormat(ax[0])
utils.commonFigFormat(ax[1])
### Remove left border except on leftmost plot
ax[1].spines['left'].set_visible(False)
plotDistillationAx(ax[0], distillationLabelsA, distillationFilesNoFaults, distillationFilesFaults, distillationLabelsB, distillationFilesB)
plotPayloadSizesAx(ax[1], payloadLabels, payloadFilesA, payloadFilesB, payloadFilesC, payloadFilesD)
### Labels
ax[0].set_ylabel("Throughput\n[op/s, log]") #, loc="top")
### Same yscales across subplots + mirrors ticks
decorateBarPlotLog(ax[0])
ax[1].yaxis.set_ticks_position('none')
ax[0].legend(**utils.FORMAT_LEGEND, ncol=3, columnspacing=1, loc='center', bbox_to_anchor=(0.87, 1.2))
utils.saveFig("merged-distillation-payloed-sizes")
def plotMergeSystemSizesAndMatchingResources(
systemLabels, systemFilesA, systemFilesB, systemFilesC, systemFilesD,
matchingLabelsAB, matchingFilesA, matchingFilesB, matchingLabelCD, matchingFileC, matchingFileD
):
fig, ax = plt.subplots(1, 2, **utils.FIG_SIZE_ONE_COL_LINERATE, sharey=True, squeeze=True) # FIG_SIZE_ONE_COL_LINERATE
nbRows, nbCols = len(ax), len(ax)
utils.commonFigFormat(ax[0])
utils.commonFigFormat(ax[1])
### Remove left border except on leftmost plot
ax[1].spines['left'].set_visible(False)
plotSystemSizesAx(ax[0], systemLabels, systemFilesA, systemFilesB, systemFilesC, systemFilesD)
plotMatchingTrustedResourcesAx(ax[1], matchingLabelsAB, matchingFilesA, matchingFilesB, matchingLabelCD, matchingFileC, matchingFileD)
### Labels
ax[0].set_ylabel("Throughput\n[op/s, log]") #, loc="top")
### Same yscales across subplots + mirrors ticks
decorateBarPlotLog(ax[0])
ax[1].yaxis.set_ticks_position('none')
ax[0].legend(**utils.FORMAT_LEGEND, ncol=3, columnspacing=1, loc='center', bbox_to_anchor=(0.87, 1.2))
utils.saveFig("merged-system-sizes-matching-trusted-resources")
def plotMergeServerFaultsAndApps(
faultsLabels, filesNoFaults, filesFaults,
appLabels, appFilesA, appFilesB, appFilesC
):
fig, ax = plt.subplots(1, 2, **utils.FIG_SIZE_ONE_COL_LINERATE, sharey=True, squeeze=True) # FIG_SIZE_ONE_COL_LINERATE
nbRows, nbCols = len(ax), len(ax)
utils.commonFigFormat(ax[0])
utils.commonFigFormat(ax[1])
### Remove left border except on leftmost plot
ax[1].spines['left'].set_visible(False)
plotServerFaultsAx(ax[0], faultsLabels, filesNoFaults, filesFaults)
plotAppsAx(ax[1], appLabels, appFilesA, appFilesB, appFilesC)
### Labels
ax[0].set_ylabel("Throughput\n[op/s, log]") #, loc="top")
### Same yscales across subplots + mirrors ticks
decorateBarPlotLog(ax[0])
ax[1].yaxis.set_ticks_position('none')
### Smaller font size for the application ticks
ax[1].tick_params(axis='x', which='major', labelsize=utils.FONT_SIZE_XS)
ax[0].legend(**utils.FORMAT_LEGEND, ncol=3, columnspacing=1, loc='center', bbox_to_anchor=(0.87, 1.2))
utils.saveFig("merged-servers-faults-applications")
def plotPayloadSizesAx(ax, labels, filesComma, filesPayloadA, filesPayloadB, filesPayloadC):
### Kind of manually reconstruct the data
data = {}
dataErr = {}
for i, label in enumerate(labels):
data[label] = {}
dataErr[label] = {}
### Payload syze = 8 is comma data
csv = pd.read_csv(utils.DIR_STATS + "/" + filesComma[i], index_col=0)
p = "8"
data[label][p] = csv["op avg"].max()
if label.startswith("CC"):
### Can only show statistically significant std for chopchop, not bftsmart or bullshark
dataErr[label][p] = csv.loc[csv["op avg"] == data[label][p]]["op std"].values[0]
else:
dataErr[label][p] = 0
#### Other payload sizes are in their own files
for p, files in [("32", filesPayloadA[i]),
("128", filesPayloadB[i]),
("512", filesPayloadC[i])]:
csv = pd.read_csv(utils.DIR_STATS + "/" + files, index_col=0)
data[label][p] = csv["op avg"].max()
if label.startswith("CC"):
### Can only show statistically significant std for chopchop, not bftsmart or bullshark
dataErr[label][p] = csv.loc[csv["op avg"] == data[label][p]]["op std"].values[0]
else:
dataErr[label][p] = 0
# print(data)
ax = barplot(ax, data, dataErr)
def _plotPayloadSizes(labels, filesComma, filesPayloadA, filesPayloadB, filesPayloadC):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL_SMALL)
utils.commonFigFormat(ax)
plotPayloadSizesAx(ax, labels, filesComma, filesPayloadA, filesPayloadB, filesPayloadC)
decorateBarPlotLog(ax)
ax.set_xlabel("Message size [B]")
ax.set_ylabel("Throughput [op/s]", loc="top")
ax.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(1.53, 0.5))
utils.saveFig("payload-sizes")
def plotSystemSizesAx(ax, labels, filesSystemA, filesSystemB, filesSystemC, filesComma):
### Kind of manually reconstruct the data
data = {}
dataErr = {}
for i, label in enumerate(labels):
data[label] = {}
dataErr[label] = {}
#### Other system sizes are in their own files
for p, files in [("8", filesSystemA[i]),
("16", filesSystemB[i]),
("32", filesSystemC[i])]:
csv = pd.read_csv(utils.DIR_STATS + "/" + files, index_col=0)
data[label][p] = csv["op avg"].max()
if label.startswith("CC"):
### Can only show statistically significant std for chopchop, not bftsmart or bullshark
dataErr[label][p] = csv.loc[csv["op avg"] == data[label][p]]["op std"].values[0]
else:
dataErr[label][p] = 0
### System size = 64 is comma data
csv = pd.read_csv(utils.DIR_STATS + "/" + filesComma[i], index_col=0)
p = "64"
data[label][p] = csv["op avg"].max()
if label.startswith("CC"):
## Can only show statistically significant std for chopchop, not bftsmart or bullshark
dataErr[label][p] = csv.loc[csv["op avg"] == data[label][p]]["op std"].values[0]
else:
dataErr[label][p] = 0
ax = barplot(ax, data, dataErr)
def _plotSystemSizes(labels, filesSystemA, filesSystemB, filesSystemC, filesComma):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL_SMALL)
utils.commonFigFormat(ax)
plotSystemSizesAx(ax, labels, filesSystemA, filesSystemB, filesSystemC, filesComma)
decorateBarPlotLog(ax)
ax.set_xlabel("System size")
ax.set_ylabel("Throughput [op/s]", loc="top")
ax.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(1.53, 0.5))
utils.saveFig("system-sizes")
def plotDistillationAx(ax, labelsA, filesNoFaults, filesFaults, labelB, fileB):
### Kind of manually reconstruct the data
data = {}
dataErr = {}
for i, label in enumerate(labelsA):
data[label] = {}
dataErr[label] = {}
### 0% distillation
csv = pd.read_csv(utils.DIR_STATS + "/" + filesFaults[i], index_col=0)
for p in ["0%"]:
data[label][p] = csv["op avg"].values[0]
dataErr[label][p] = csv["op std"].values[0]
### 100% distillation is comma data
csv = pd.read_csv(utils.DIR_STATS + "/" + filesNoFaults[i], index_col=0)
p = "100%"
data[label][p] = csv["op avg"].max()
dataErr[label][p] = csv.loc[csv["op avg"] == data[label][p]]["op std"].values[0]
### Bullshark
label = labelB
data[label] = {}
dataErr[label] = {}
csv = pd.read_csv(utils.DIR_STATS + "/" + fileB, index_col=0)
for p in ["0%", "100%"]:
data[label][p] = csv["op avg"].max()
dataErr[label][p] = 0
# print(data)
ax = barplot(ax, data, dataErr)
def _plotDistillation(labelsA, filesNoFaults, filesFaults, distillationLabelsB, distillationFilesB):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL_SMALL)
utils.commonFigFormat(ax)
plotDistillationAx(ax, labelsA, filesNoFaults, filesFaults, distillationLabelsB, distillationFilesB)
decorateBarPlotLog(ax)
ax.set_xlabel("Distillation ratio")
ax.set_ylabel("Throughput [op/s]", loc="top")
fig.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(1.25, 0.5))
utils.saveFig("distillation")
def plotAppsAx(ax, labels, filesAuction, filesPayments, filesPixelwar):
### Kind of manually reconstruct the data
data = {}
dataErr = {}
for i, label in enumerate(labels):
data[label] = {}
dataErr[label] = {}
for app, files in [("Auction", filesAuction[i]),
("Payment", filesPayments[i]),
("Pixel war", filesPixelwar[i])]:
csv = pd.read_csv(utils.DIR_STATS + "/" + files, index_col=0)
data[label][app] = csv["op avg"].max()
dataErr[label][app] = csv.loc[csv["op avg"] == data[label][app]]["op std"].values[0]
# print(data)
ax = barplot(ax, data, dataErr)
def _plotApps(labels, filesAuction, filesPayments, filesPixelwar):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL_SMALL)
utils.commonFigFormat(ax)
plotAppsAx(ax, labels, filesAuction, filesPayments, filesPixelwar)
decorateBarPlotLog(ax)
### Labels
ax.set_xlabel("Application")
ax.set_ylabel("Throughput [op/s]", loc="top")
ax.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(1.5, 0.5))
utils.saveFig("applications")
def plotServerFaultsAx(ax, labels, filesNoFaults, filesFaults):
### Kind of manually reconstruct the data
data = {}
dataErr = {}
for i, label in enumerate(labels):
data[label] = {}
dataErr[label] = {}
### f = 0 is comma data
csv = pd.read_csv(utils.DIR_STATS + "/" + filesNoFaults[i], index_col=0)
p = "0"
data[label][p] = csv["op avg"].max()
dataErr[label][p] = csv.loc[ csv["op avg"] == data[label][p] ]["op std"].values[0]
### f = 1 and f = t
csv = pd.read_csv(utils.DIR_STATS + "/" + filesFaults[i], index_col=0)
for p in ["1", "threshold"]:
data[label][p] = csv.loc[csv["nb-faults"] == p]["op avg"].values[0]
dataErr[label][p] = csv.loc[csv["nb-faults"] == p]["op std"].values[0]
# print(data)
ax = barplot(ax, data, dataErr)
def _plotServerFaults(labels, filesNoFaults, filesFaults):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL_SMALL)
utils.commonFigFormat(ax)
plotServerFaultsAx(ax, labels, filesNoFaults, filesFaults)
# decorateBarPlot(ax)
### Labels
ax.set_xlabel("Number of faults")
# ax.set_ylabel("Throughput [Mop/s]", loc="top")
ax.set_ylabel("Throughput\n[M op/s]", loc="center")
### Ticks
ax.set_ylim(0*10**6, 55*10**6)
# ax.yaxis.set_minor_locator(ticker.MultipleLocator(5*10**6))
ax.yaxis.set_major_locator(ticker.MultipleLocator(10*10**6))
ax.set_yticks([v*10**7 for v in [0, 1, 2, 3, 4, 5]])
ax.set_yticklabels(range(0, 60, 10))
### Ticks log
# ax.set_yscale("log")
# ax.set_ylim(10**5, 1.4*10**8)
# ax.yaxis.set_major_locator(ticker.LogLocator(base=10, numticks=4))
# ax.yaxis.set_minor_locator(ticker.LogLocator(base=10, numticks=10, subs=[x/10 for x in range(1,10)]))
# ax.set_yticks([10**v for v in [5, 6, 7, 8]])
# ax.set_yticklabels(["100k", "1M", "10M", "100M"])
ax.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(1.47, 0.5))
utils.saveFig("server-faults")
def plotMatchingTrustedResourcesAx(ax, labelsAB, filesA, filesB, labelCD, fileC, fileD):
### Kind of manually reconstruct the data
data = {}
dataErr = {}
### Chopchop
for i, label in enumerate(labelsAB):
data[label] = {}
dataErr[label] = {}
### First cluster of bars is chopchop comma data
p = 1
csv = pd.read_csv(utils.DIR_STATS + "/" + filesA[i], index_col=0)
data[label][p] = csv["op avg"].max()
dataErr[label][p] = csv.loc[ csv["op avg"] == data[label][p] ]["op std"].values[0]
### Second cluster of bars is chopchop with only honest brokers (no load brokers)
p = 2
csv = pd.read_csv(utils.DIR_STATS + "/" + filesB[i], index_col=0)
data[label][p] = csv["op avg"].max()
dataErr[label][p] = csv.loc[ csv["op avg"] == data[label][p] ]["op std"].values[0]
### Fill third cluster with empty values for bullshark
data[label][3] = 0
dataErr[label][3] = 0
### Bullshark
label = labelCD
data[label] = {}
dataErr[label] = {}
### Fill first cluster with empty values for bullshark
data[label][1] = 0
### No error bars on bullshark
for p in [1, 2, 3]:
dataErr[label][p] = 0
### Second cluster of bars is bullshark with 2x workers
csv = pd.read_csv(utils.DIR_STATS + "/" + fileC, index_col=0)
data[label][2] = csv["op avg"].max()
### Third cluster of bars is bullshark comma data
csv = pd.read_csv(utils.DIR_STATS + "/" + fileD, index_col=0)
data[label][3] = csv["op avg"].max()
# print(data)
ax = barplot(ax, data, dataErr)
### Ticks
ax.set_xticklabels(["64 s\n$\\infty$ m", "64 s\n128 m", "64 s\n 64 m"])
def _plotMatchingTrustedResources(labelsAB, filesA, filesB, labelCD, fileC, fileD):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL_SMALL)
utils.commonFigFormat(ax)
plotMatchingTrustedResourcesAx(ax, labelsAB, filesA, filesB, labelCD, fileC, fileD)
decorateBarPlotLog(ax)
### Labels
# ax.set_xlabel("")
# ax.set_ylabel("Throughput [Mop/s]", loc="top")
ax.set_ylabel("Throughput\n[op/s, log]", loc="center")
ax.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(1.55, 0.5))
utils.saveFig("matching-trusted-resources")
def _plotLinerateRatio(labels, files):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL)
utils.commonFigFormat(ax)
### Optimal line
label = "Optimal"
# x = range(0, 70*10**6, 10**5) # (start, end, steps)
# ax.errorbar(x, [1.0],
# label=label,
# markevery=100,
# # rasterized=True,
# markerfacecolor="none",
# **LINE_FORMAT[label]
# )
ax.axhline(y=1.0,
label=label,
markevery=10,
markerfacecolor="none",
# zorder=-1,
**LINE_FORMAT[label])
### Plot data points
plotCurvesInFiles(files, labels, ax, "input", "goodput ratio avg", "goodput ratio std")
### Labels
ax.set_xlabel("Input rate [Mop/s]")
ax.set_ylabel("Goodput ratio", loc="top")
### Ticks
ax.set_xlim(0*10**6, 72*10**6)
# ax.xaxis.set_minor_locator(ticker.MultipleLocator(5*10**6))
ax.xaxis.set_major_locator(ticker.MultipleLocator(10*10**6))
ax.set_xticks([0, 10*10**6, 20*10**6, 30*10**6, 40*10**6, 50*10**6, 60*10**6, 70*10**6])
ax.set_xticklabels(["0", "10", "20", "30", "40", "50", "60", "70"])
ax.set_ylim(0, 1.05)
# ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.2))
# ax.set_yticks([0, 10*10**6, 20*10**6, 30*10**6, 40*10**6, 50*10**6, 60*10**6, 70*10**6])
# ax.set_yticklabels(["0", "10", "20", "30", "40", "50", "60", "70"])
fig.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(0.35, 0.43))
utils.saveFig("linerate-ratio")
def _plotLinerateThroughputV1(labels, files):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL_SQUARE, squeeze=True)
utils.commonFigFormat(ax)
### Optimal line
label = "Optimal"
x = range(0, 70*10**6, 10**5) # (start, end, steps)
ax.errorbar(x, x,
label=label,
markevery=100,
# rasterized=True,
markerfacecolor="none",
**LINE_FORMAT[label]
)
### Plot data points
plotCurvesInFiles(files, labels, ax, "input", "output avg", "output std")
### Labels
ax.set_xlabel("Input rate [Mop/s]")
ax.set_ylabel("Output rate [Mop/s]")
### Ticks
ax.set_xlim(0*10**6, 72*10**6)
# ax.xaxis.set_minor_locator(ticker.MultipleLocator(5*10**6))
ax.xaxis.set_major_locator(ticker.MultipleLocator(10*10**6))
ax.set_xticks([0, 10*10**6, 20*10**6, 30*10**6, 40*10**6, 50*10**6, 60*10**6, 70*10**6])
ax.set_xticklabels(["0", "10", "20", "30", "40", "50", "60", "70"])
ax.set_ylim(0*10**6, 72*10**6)
# ax.xaxis.set_minor_locator(ticker.MultipleLocator(5*10**6))
ax.yaxis.set_major_locator(ticker.MultipleLocator(10*10**6))
ax.set_yticks([0, 10*10**6, 20*10**6, 30*10**6, 40*10**6, 50*10**6, 60*10**6, 70*10**6])
ax.set_yticklabels(["0", "10", "20", "30", "40", "50", "60", "70"])
fig.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(0.35, 0.75))
utils.saveFig("linerate-throughput-v1")
def _plotLinerateThroughputV2(labels, files):
fig, ax = plt.subplots(1, 1, **utils.FIG_SIZE_ONE_COL_SQUARE)
utils.commonFigFormat(ax)
### Plot data points
# plotCurve(files, labels, ax, "input", "output avg", "output std")
plotCurvesInFiles(files, labels, ax, "input", "goodput bandwidth avg", "goodput bandwidth std")
### Optimal line
label = "Optimal"
minPayloadSize = 11.5 # 8 B of payload + 3.5 B of id
x = range(0, 60*10**6, 10**5) # (start, end, steps)
y = range(0, int(minPayloadSize*60*10**6), int(minPayloadSize*10**5)) # (start, end, steps)
ax.errorbar(x, y,
label=label,
markevery=100,
# rasterized=True,
markerfacecolor="none",
**LINE_FORMAT[label]
)
# ax.fill_between(x, y,
# y+yerr,
# color=LINE_FORMAT[labelList[i]]["color"], alpha=0.20)
### Labels
ax.set_xlabel("Input rate [Mop/s]")
ax.set_ylabel("Output bandwidth [MB/s]")
### Ticks
ax.set_xlim(0*10**6, 62*10**6)
# ax.xaxis.set_minor_locator(ticker.MultipleLocator(5*10**6))
ax.xaxis.set_major_locator(ticker.MultipleLocator(10*10**6))
ax.set_xticks([v*10**7 for v in [0, 1, 2, 3, 4, 5, 6]])
# ax.set_xticklabels(["0", "10", "20", "30", "40", "50", "60", "70"])
ax.set_xticklabels(range(0, 61, 10))
ax.set_ylim(0*10**6, 72*10**7)
# # ax.xaxis.set_minor_locator(ticker.MultipleLocator(5*10**6))
# ax.yaxis.set_major_locator(ticker.MultipleLocator(10*10**6))
ax.set_yticks([v*10**8 for v in [0, 1, 2, 3, 4, 5, 6, 7]])
ax.set_yticklabels(["0", "100", "200", "300", "400", "500", "600", "700"])
fig.legend(**utils.FORMAT_LEGEND, ncol=1, columnspacing=1, loc='center', bbox_to_anchor=(0.35, 0.75))
utils.saveFig("linerate-throughput-v2")
def plotLinerateThroughput(labelA, labelB, fileA, fileB):
fig, ax = plt.subplots(1, 2, **utils.FIG_SIZE_ONE_COL_LINERATE) #, squeeze=True)
nbRows, nbCols = len(ax), len(ax)
utils.commonFigFormat(ax[0])
utils.commonFigFormat(ax[1])
### Input rate baseline
label = "Input rate"
minPayloadSize = 11.5 # 8 B of payload + 3.5 B of id
x = range(0, 80*10**4, 10**3)
y = range(0, int(minPayloadSize*80*10**4), int(minPayloadSize*10**3))
ax[0].errorbar(x, y,
label=label,
markevery=100,
# rasterized=True,
markerfacecolor="none",
**LINE_FORMAT[label]
)
x = range(0, 60*10**6, 10**5)
y = range(0, int(minPayloadSize*60*10**6), int(minPayloadSize*10**5))
ax[1].errorbar(x, y,
label=label,
markevery=100,
# rasterized=True,
markerfacecolor="none",
**LINE_FORMAT[label]
)
### Left plot bullshark
plotConfig = [
("Network rate", "workload", "output rate B avg"),
("Output rate", "workload", "goodput rate B avg"),
]
csv = pd.read_csv(utils.DIR_STATS + "/" + fileA, index_col=0)
for label, xColumn, yColumn in plotConfig:
# print(csv)
x = csv[xColumn]
y = csv[yColumn]
yerr = []
plotCurve(ax[0], x, y, yerr, label, withShadedArea=False)
### Shaded gray area for the linerate lost bandwidth
x = csv[xColumn]
y1 = csv["output rate B avg"]
y2 = csv["goodput rate B avg"]
ax[0].fill_between(x, y1, y2, color="black", alpha=0.15)
### Right plot chopchop
plotConfig = [
("Network rate", "input", "output rate B avg", "output rate B std"),
("Output rate", "input", "goodput rate B avg", "goodput rate B std"),
]
csv = pd.read_csv(utils.DIR_STATS + "/" + fileB, index_col=0)
for label, xColumn, yColumn, yErrColumn in plotConfig:
x = csv[xColumn]
y = csv[yColumn]
yerr = csv[yErrColumn]
plotCurve(ax[1], x, y, yerr, label, withShadedArea=True)
### Shaded gray area for the linerate lost bandwidth
x = csv[xColumn]
y1 = csv["output rate B avg"]
y2 = csv["goodput rate B avg"]
ax[1].fill_between(x, y1, y2, color="black", alpha=0.15)
### Labels
ax[0].set_title(labelA) #, color=LINE_FORMAT[labelA]["color"])
ax[1].set_title(labelB) #, color=LINE_FORMAT[labelB]["color"])
ax[0].set_xlabel("Input rate [op/s]")
ax[0].set_ylabel("Rate [MB/s]") #, loc="top")
### Ticks left plot
ax[0].set_xlim(0, 82*10**4)
ax[0].xaxis.set_major_locator(ticker.MultipleLocator(20*10**4))
ax[0].xaxis.set_minor_locator(ticker.MultipleLocator(10*10**4))
ax[0].set_xticks([v*10**4 for v in [0, 20, 40, 60, 80]])
ax[0].set_xticklabels(["0", "200k", "400k", "600k", "800k"])
## Linear yscale
# ax[0].set_ylim(0, 8.2*10**6)
# ax[0].yaxis.set_major_locator(ticker.MultipleLocator(20*10**6))
# ax[0].yaxis.set_minor_locator(ticker.MultipleLocator(10*10**6))
# ax[0].set_yticks([v*10**6 for v in [0, 20, 40, 60, 80]])
# ax[0].set_yticklabels(["0", "20", "40", "60", "80"])
## Log yscale