def Over(over_1, over_2, over_3):
mplot.plotPrepareLogLog()
o3 = mplot.plotMMA(
over_3, 'round_wall', color3_light, 0,
dict(label='32 Machines',
linestyle='-',
marker='s',
color=color3_dark,
zorder=3))
o2 = mplot.plotMMA(
over_2, 'round_wall', color2_light, 0,
dict(label='16 Machines',
linestyle='-',
marker='^',
color=color2_dark,
zorder=3))
o1 = mplot.plotMMA(
over_1, 'round_wall', color1_light, 0,
dict(label='8 Machines',
linestyle='-',
marker='o',
color=color1_dark,
zorder=3))
xmin, xmax, ymin, ymax = CSVStats.get_min_max(o1, o2, o3)
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax * 1.2)
plt.ylabel('Seconds per round')
plt.legend(loc=u'lower right')
mplot.plotEnd()
def PlotMultiBF(*values_bf):
mplot.plotPrepareLogLog(2, 0)
plotbf = []
pparams = [[color1_light, color1_dark, 'o'],
[color2_light, color2_dark, 's'],
[color3_light, color3_dark, '^'],
[color4_light, color4_dark, '.']]
for i, value in enumerate(values_bf):
label = str(int(value.columns['Peers'][0])) + " Peers"
c1, c2, m = pparams[i]
plotbf.append(
mplot.plotMMA(
value, 'round_wall', c1, 0,
dict(label=label, linestyle='-', marker=m, color=c2,
zorder=3)))
xmin, xmax, ymin, ymax = CSVStats.get_min_max(*plotbf)
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax * 1.2)
plt.ylabel('Seconds per round')
plt.xlabel('Branching factor')
plt.legend(loc=u'upper right')
mplot.plotEnd()
def plotAvgMM(co, jvss, naive, nt):
mplot.plotPrepareLogLog()
nt = mplot.plotMMA(ntree, 'round_wall', color4_light, 0,
dict(label='Ntree', linestyle='-', marker='v', color=color4_dark, zorder=3))
# mplot.arrow("{:.1f} sec ".format(mplot.avg[-2]), x[-2], 4, color3_dark)
# mplot.arrow(" {:.0f} sec".format(mplot.avg[-1]), x[-1], 4, color3_dark)
j = mplot.plotMMA(jvss, 'round_wall', color2_light, 0,
dict(label='JVSS', linestyle='-', marker='^', color=color2_dark, zorder=3))
#j_p = jvss.get_values('round_wall')
#plt.plot(j_p.x, j_p.avg, label="JVSS", color=color2_dark, marker='^')
#mplot.arrow("{:.1f} sec ".format(j_p.avg[-2]), j_p.x[-2], 4, color2_dark)
#mplot.arrow(" {:.0f} sec".format(j_p.avg[-1]), j_p.x[-1], 8, color2_dark)
na = mplot.plotMMA(naive, 'round_wall', color3_light, 0,
dict(label='Naive', linestyle='-', marker='s', color=color3_dark, zorder=3))
na_p = naive.get_values('round_wall')
#mplot.arrow("{:.1f} sec ".format(na_p.avg[8]), na_p.x[8], 4, color3_dark)
mplot.arrow(" {:.0f} sec".format(na_p.avg[9]), na_p.x[9], 8, color3_dark)
co = mplot.plotMMA(cothority, 'round_wall', color1_light, 4,
dict(label='Cothority', linestyle='-', marker='o', color=color1_dark, zorder=5))
# Make horizontal lines and add arrows for JVSS
xmin, xmax, ymin, ymax = CSVStats.get_min_max(na, co)
plt.ylim(ymin, 8)
plt.xlim(xmin, xmax * 1.2)
plt.ylabel('Seconds per round')
plt.legend(loc=u'lower right')
mplot.plotEnd()
def plotAvg(co, jvss, naive, nt):
mplot.plotPrepareLogLog()
j_p = jvss.get_values('round_wall')
plt.plot(j_p.x, j_p.avg, label="JVSS", color=color2_dark, marker='^')
na_p = naive.get_values('round_wall')
plt.plot(na_p.x, na_p.avg, label="Naive", color=color3_dark, marker='s')
#mplot.arrow(" {:.0f} sec".format(na_p.avg[9]), na_p.x[9], 8, 1, color3_dark)
nt_p = nt.get_values('round_wall')
plt.plot(nt_p.x, nt_p.avg, label="Ntree", color=color4_dark, marker='v')
co_p = cothority.get_values('round_wall')
plt.plot(co_p.x,
co_p.avg,
label="Cothority",
color=color1_dark,
marker='o')
# Make horizontal lines and add arrows for JVSS
xmin, xmax, ymin, ymax = CSVStats.get_min_max(j_p, na_p, nt_p, co_p)
plt.ylim(ymin, 8)
plt.xlim(xmin, 1024 * 1.2)
plt.ylabel('Seconds per round')
plt.legend(loc=u'lower right')
mplot.plotEnd()
def plotData(data,
name,
xlabel="Number of witnesses",
ylabel="Signing round latency in seconds",
xticks=[],
loglog=[2, 2],
xname="hosts",
legend_pos="lower right",
yminu=0,
ymaxu=0,
xminu=0,
xmaxu=0,
title="",
read_plots=True):
mplot.plotPrepareLogLog(loglog[0], loglog[1])
if read_plots:
plots = read_csvs_xname(xname, *data[0])
else:
plots = data[0]
ranges = []
data_label = []
plot_show(name)
for index, label in enumerate(data[1]):
data_label.append([plots[index], label])
ranges.append(
mplot.plotMMA(
plots[index], 'round_wall', colors[index][0], 4,
dict(label=label,
linestyle='-',
marker='o',
color=colors[index][1],
zorder=5)))
# Make horizontal lines and add arrows for JVSS
xmin, xmax, ymin, ymax = CSVStats.get_min_max(*ranges)
if yminu != 0:
ymin = yminu
if ymaxu != 0:
ymax = ymaxu
if xminu != 0:
xmin = xminu
if xmaxu != 0:
xmax = xmaxu
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.legend(loc=legend_pos)
plt.axes().xaxis.grid(color='gray', linestyle='dashed', zorder=0)
if len(xticks) > 0:
ax = plt.axes()
ax.set_xticks(xticks)
if title != "":
plt.title(title)
mplot.plotEnd()
return data_label
def plotData(data, name,
xlabel="Time since last update", ylabel="Bandwidth",
xticks=[], loglog=[0, 0], xname="time",
legend_pos="lower right",
yname="bandwidth",
yminu=0, ymaxu=0,
xminu=0, xmaxu=0,
title="", read_plots=True,
restart_x=False):
mplot.plotPrepareLogLog(loglog[0], loglog[1])
if read_plots:
plots = read_csvs_xname(xname, *data[0])
else:
plots = data[0]
ranges = []
data_label = []
plot_show(name)
subx = 0
if restart_x:
subx = plots[0].columns[xname][0]
for index, label in enumerate(data[1]):
data_label.append([plots[index], label])
# plots[index].print_short()
if restart_x:
print subx
plots[index].column_add(xname, -subx)
plots[index].get_values("time")
ranges.append(
mplot.plotMMA(plots[index], yname, colors[index][0], 4,
dict(label=label, linestyle='-', marker='.',
color=colors[index][1], zorder=5)))
# Make horizontal lines and add arrows for JVSS
xmin, xmax, ymin, ymax = CSVStats.get_min_max(*ranges)
if yminu != 0:
ymin = yminu
if ymaxu != 0:
ymax = ymaxu
if xminu != 0:
xmin = xminu
if xmaxu != 0:
xmax = xmaxu
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.legend(loc=legend_pos)
plt.axes().xaxis.grid(color='gray', linestyle='dashed', zorder=0)
if len(xticks) > 0:
ax = plt.axes()
ax.set_xticks(xticks)
if title != "":
plt.title(title)
mplot.plotEnd()
return data_label
def PlotBF(values_bf):
mplot.plotPrepareLogLog(2, 0)
plotbf = mplot.plotMMA(values_bf, 'round_wall', color1_light, 0,
dict(label='4096 Peers', linestyle='-', marker='o', color=color2_dark, zorder=3))
xmin, xmax, ymin, ymax = CSVStats.get_min_max(plotbf)
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax * 1.2)
plt.ylabel('Seconds per round')
plt.xlabel('Branching factor')
plt.legend(loc=u'upper right')
mplot.plotEnd()
def PlotStamp(stamp):
mplot.plotPrepareLogLog(10, 0)
plotbf = mplot.plotMMA(stamp, 'round_wall', color1_light, 0,
dict(label='4096 Peers', linestyle='-', marker='o', color=color2_dark, zorder=3))
xmin, xmax, ymin, ymax = CSVStats.get_min_max(plotbf)
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax * 1.2)
plt.ylabel('Seconds per round')
plt.xlabel('Stamping rate [1/s]')
plt.legend(loc=u'upper right')
mplot.plotEnd()
def Over(over_1, over_2, over_3):
mplot.plotPrepareLogLog()
o3 = mplot.plotMMA(over_3, 'round_wall', color3_light, 0,
dict(label='32 Machines', linestyle='-', marker='s', color=color3_dark, zorder=3))
o2 = mplot.plotMMA(over_2, 'round_wall', color2_light, 0,
dict(label='16 Machines', linestyle='-', marker='^', color=color2_dark, zorder=3))
o1 = mplot.plotMMA(over_1, 'round_wall', color1_light, 0,
dict(label='8 Machines', linestyle='-', marker='o', color=color1_dark, zorder=3))
xmin, xmax, ymin, ymax = CSVStats.get_min_max(o1, o2, o3)
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax * 1.2)
plt.ylabel('Seconds per round')
plt.legend(loc=u'lower right')
mplot.plotEnd()
def plotScatter():
mplot.plotPrepareLogLog(0, 0)
plot_show('scatter')
plt.ylabel('Bandwidth [bytes]')
plt.xlabel('Time since start [s]')
data = read_csvs_xname("time", *sc_scatter)
styles = ["^"] * 4 + ["s"] * 4
ranges = []
for index, label in enumerate(sc_titles):
if index == 0:
continue
plot = data[index]
y = plot.get_values("bandwidth")
ranges.append(y)
plt.scatter(plot.x, y.y, label=label,
color=colors[index][1], marker=styles[index])
plt.legend(loc="upper left")
xmin, xmax, ymin, ymax = CSVStats.get_min_max(*ranges)
plt.xlim(0, xmax)
plt.ylim(0, ymax)
mplot.plotEnd()
def PlotMultiBF(*values_bf):
mplot.plotPrepareLogLog(2, 0)
plotbf = []
pparams = [[color1_light, color1_dark, 'o'],
[color2_light, color2_dark, 's'],
[color3_light, color3_dark, '^'],
[color4_light, color4_dark, '.']]
for i, value in enumerate(values_bf):
label = str(int(value.columns['Peers'][0])) + " Peers"
c1, c2, m = pparams[i]
plotbf.append( mplot.plotMMA(value, 'round_wall', c1, 0,
dict(label=label, linestyle='-', marker=m, color=c2, zorder=3)) )
xmin, xmax, ymin, ymax = CSVStats.get_min_max(*plotbf)
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax * 1.2)
plt.ylabel('Seconds per round')
plt.xlabel('Branching factor')
plt.legend(loc=u'upper right')
mplot.plotEnd()
def plotAvg(co, jvss, naive, nt):
mplot.plotPrepareLogLog()
j_p = jvss.get_values('round_wall')
plt.plot(j_p.x, j_p.avg, label="JVSS", color=color2_dark, marker='^')
na_p = naive.get_values('round_wall')
plt.plot(na_p.x, na_p.avg, label="Naive", color=color3_dark, marker='s')
#mplot.arrow(" {:.0f} sec".format(na_p.avg[9]), na_p.x[9], 8, color3_dark)
nt_p = nt.get_values('round_wall')
plt.plot(nt_p.x, nt_p.avg, label="Ntree", color=color4_dark, marker='v')
co_p = cothority.get_values('round_wall')
plt.plot(co_p.x, co_p.avg, label="Cothority", color=color1_dark, marker='o')
# Make horizontal lines and add arrows for JVSS
xmin, xmax, ymin, ymax = CSVStats.get_min_max(j_p, na_p, nt_p, co_p)
plt.ylim(ymin, 8)
plt.xlim(xmin, 1024 * 1.2)
plt.ylabel('Seconds per round')
plt.legend(loc=u'lower right')
mplot.plotEnd()
def plotScatter():
mplot.plotPrepareLogLog(0, 0)
plot_show('scatter')
plt.ylabel('Bandwidth [bytes]')
plt.xlabel('Time since start [s]')
data = read_csvs_xname("time", *sc_scatter)
styles = ["^"] * 4 + ["s"] * 4
ranges = []
for index, label in enumerate(sc_titles):
if index == 0:
continue
plot = data[index]
y = plot.get_values("bandwidth")
ranges.append(y)
plt.scatter(plot.x,
y.y,
label=label,
color=colors[index][1],
marker=styles[index])
plt.legend(loc="upper left")
xmin, xmax, ymin, ymax = CSVStats.get_min_max(*ranges)
plt.xlim(0, xmax)
plt.ylim(0, ymax)
mplot.plotEnd()
def args_to_csv(argn):
stats = []
for a in sys.argv[2:argn + 2]:
stats.append(CSVStats(a))
plot_show(argn)
return stats
from mplot import MPlot
from stats import CSVStats
color1_light = 'lightgreen'
color1_dark = 'green'
color2_light = 'lightblue'
color2_dark = 'blue'
color3_light = 'yellow'
color3_dark = 'brown'
color4_light = 'pink'
color4_dark = 'red'
mplot = MPlot()
mplot.plotPrepareLogLog()
mplot.show_fig = True
jvss = CSVStats('test_naive_multi.csv').get_values('round_wall')
plt.plot(jvss.x,
jvss.avg,
label='JVSS',
linestyle='-',
marker='^',
color=color2_dark,
zorder=3)
mplot.plotFilledLegend(jvss.x,
jvss.min,
jvss.max,
"min-max",
color2_light,
z=0)
mplot.plotEnd()
for a in sys.argv[2:argn + 2]:
stats.append(CSVStats(a, xname))
plot_show(argn)
return stats
option = sys.argv[1]
if option == "0":
cothority, jvss, naive, naive_sc, ntree = args_to_csv(5)
plotAvgMM(cothority, jvss, naive, ntree)
elif option == "1":
cothority, jvss, naive, naive_sc, ntree = args_to_csv(5)
CoJVTimeBars(cothority, jvss, naive)
elif option == "2":
cothority, jvss, naive, naive_sc, ntree = args_to_csv(5)
CoJVTimeArea(cothority, jvss)
elif option == "3":
cothority, jvss, naive, naive_sc, ntree = args_to_csv(5)
SigCheck(naive, naive_sc)
elif option == "4":
Over(*args_to_csv(3))
elif option == "5":
PlotMultiBF(*args_to_csv(1, "bf"))
elif option == "6":
plot_show(1)
stamp = CSVStats(sys.argv[2], "rate")
PlotStamp(stamp)
elif option == "7":
PlotMultiBF(*args_to_csv(4, "bf"))
def args_to_csv(argn, xname="Peers"):
stats = []
for a in sys.argv[2:argn + 2]:
stats.append(CSVStats(a, xname))
plot_show(argn)
return stats
def read_csvs_xname(xname, *values):
stats = []
for a in values:
file = a + '.csv'
stats.append(CSVStats(file, xname))
return stats
stats = []
for a in sys.argv[2:argn + 2]:
stats.append(CSVStats(a))
plot_show(argn)
return stats
option = sys.argv[1]
if option == "0":
cothority, jvss, naive, naive_sc, ntree = args_to_csv(5)
plotAvgMM(cothority, jvss, naive, ntree)
elif option == "1":
cothority, jvss, naive, naive_sc, ntree = args_to_csv(5)
CoJVTimeBars(cothority, jvss, naive)
elif option == "2":
cothority, jvss, naive, naive_sc, ntree = args_to_csv(5)
CoJVTimeArea(cothority, jvss)
elif option == "3":
cothority, jvss, naive, naive_sc, ntree = args_to_csv(5)
SigCheck(naive, naive_sc)
elif option == "4":
Over(*args_to_csv(3))
elif option == "5":
plot_show(1)
PlotBF(CSVStats(sys.argv[2], "bf"))
elif option == "6":
plot_show(1)
stamp = CSVStats(sys.argv[2], "rate")
PlotStamp(stamp)
color1_dark = 'green'
color2_light = 'lightblue'
color2_dark = 'blue'
color3_light = 'yellow'
color3_dark = 'brown'
color4_light = 'pink'
color4_dark = 'red'
mplot = MPlot()
def plot_save(file):
mplot.pngname = file
mplot.show_fig = False
stats = "round_wall"
argn = len(sys.argv)
if argn < 2:
print "Syntax is:"
print "plot.py file.csv [stat]"
print "where stat is one of {round_wall, round_system, round_user}"
sys.exit(1)
if argn > 2:
stats = sys.argv[2]
data = CSVStats(sys.argv[1], "hosts")
mplot.plotPrepareLogLog()
na = mplot.plotMMA(data, stats, color1_light, 0,
dict(label=sys.argv[1], linestyle='-', marker='s', color=color2_dark, zorder=3))
plt.legend(loc='upper left')
plt.ylabel('Time for verification')
mplot.plotEnd()
def plotData(
data,
name,
xlabel="Number of witnesses",
ylabel="Signing round latency in seconds",
xticks=[],
loglog=[2, 2],
xname="hosts",
legend_pos="lower right",
yminu=0,
ymaxu=0,
xminu=0,
xmaxu=0,
title="",
read_plots=True,
):
mplot.plotPrepareLogLog(loglog[0], loglog[1])
if read_plots:
plots = read_csvs_xname(xname, *data[0])
else:
plots = data[0]
ranges = []
data_label = []
plot_show(name)
for index, label in enumerate(data[1]):
data_label.append([plots[index], label])
ranges.append(
mplot.plotMMA(
plots[index],
"round_wall",
colors[index][0],
4,
dict(label=label, linestyle="-", marker="o", color=colors[index][1], zorder=5),
)
)
# Make horizontal lines and add arrows for JVSS
xmin, xmax, ymin, ymax = CSVStats.get_min_max(*ranges)
if yminu != 0:
ymin = yminu
if ymaxu != 0:
ymax = ymaxu
if xminu != 0:
xmin = xminu
if xmaxu != 0:
xmax = xmaxu
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.legend(loc=legend_pos)
plt.axes().xaxis.grid(color="gray", linestyle="dashed", zorder=0)
if len(xticks) > 0:
ax = plt.axes()
ax.set_xticks(xticks)
if title != "":
plt.title(title)
mplot.plotEnd()
return data_label
def plotData(data,
name,
xlabel="Time since last update",
ylabel="Bandwidth",
xticks=[],
loglog=[0, 0],
xname="time",
legend_pos="lower right",
yname="bandwidth",
yminu=0,
ymaxu=0,
xminu=0,
xmaxu=0,
title="",
read_plots=True,
restart_x=False):
mplot.plotPrepareLogLog(loglog[0], loglog[1])
if read_plots:
plots = read_csvs_xname(xname, *data[0])
else:
plots = data[0]
ranges = []
data_label = []
plot_show(name)
subx = 0
if restart_x:
subx = plots[0].columns[xname][0]
for index, label in enumerate(data[1]):
data_label.append([plots[index], label])
# plots[index].print_short()
if restart_x:
print subx
plots[index].column_add(xname, -subx)
plots[index].get_values("time")
ranges.append(
mplot.plotMMA(
plots[index], yname, colors[index][0], 4,
dict(label=label,
linestyle='-',
marker='.',
color=colors[index][1],
zorder=5)))
# Make horizontal lines and add arrows for JVSS
xmin, xmax, ymin, ymax = CSVStats.get_min_max(*ranges)
if yminu != 0:
ymin = yminu
if ymaxu != 0:
ymax = ymaxu
if xminu != 0:
xmin = xminu
if xmaxu != 0:
xmax = xmaxu
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.legend(loc=legend_pos)
plt.axes().xaxis.grid(color='gray', linestyle='dashed', zorder=0)
if len(xticks) > 0:
ax = plt.axes()
ax.set_xticks(xticks)
if title != "":
plt.title(title)
mplot.plotEnd()
return data_label