tor_multipath_voip/r/lightning_begin.R

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library(ggplot2)
library(sqldf)
library(plyr)
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lib
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library(cowplot)
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link_info <- read.csv("../res/tmp_graph/u.txt")
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ggplot(data=link_info, aes(x=timestamp, y=link, color=speed)) +
#geom_line() +
geom_point() +
theme_classic()
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xx <- read.csv("../../donar-res/tmp_light/v2.csv")
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xx2 <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way from xx group by packet_id,way")
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ggplot(data=xx2, aes(x=packet_id, y=lat, color=way)) +
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geom_line() +
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geom_hline(yintercept=400) +
geom_hline(yintercept=200) +
coord_cartesian(ylim=c(0,1000)) +
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#geom_point() +
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theme_classic()
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xx4 <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way from xx where flag = 0 group by packet_id,way")
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ggplot(data=xx4, aes(x=packet_id, y=lat, color=way)) +
geom_line() +
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geom_hline(yintercept=400) +
geom_hline(yintercept=200) +
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coord_cartesian(ylim=c(0,1000)) +
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#geom_point() +
theme_classic()
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xx5 <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way from xx where flag = 1 group by packet_id,way")
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ggplot(data=xx5, aes(x=packet_id, y=lat, color=way)) +
geom_line() +
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geom_hline(yintercept=400) +
geom_hline(yintercept=200) +
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coord_cartesian(ylim=c(0,1000)) +
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#geom_point() +
theme_classic()
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prepros <- sqldf(
"select
r.packet_id,
r.way,
r.lat,
s.flag
from
(select
packet_id,
way,
min(latency) as lat
from xx
group by packet_id,way) as r,
xx as s
where
s.packet_id = r.packet_id and
s.way = r.way and
r.lat = s.latency")
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xx3 <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,flag,way from xx group by packet_id,way,flag")
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xx3$flag <- factor(xx3$flag)
ggplot(data=xx3, aes(x=lat, group=flag, color=flag)) +
stat_ecdf(pad = FALSE) +
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geom_vline(xintercept = 200) +
geom_vline(xintercept = 400) +
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coord_cartesian(xlim=c(0,1200)) +
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theme_classic()
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xy <- read.csv("../../donar-res/tmp_light/light.csv")
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xz <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way,conf,run from xy where packet_id > 50 and packet_id < 7400 group by packet_id,way,conf,run")
xz$conf <- factor(xz$conf)
ggplot(data=xz, aes(x=lat, group=conf, color=conf)) +
stat_ecdf(pad = FALSE) +
geom_vline(xintercept = 200) +
geom_vline(xintercept = 400) +
coord_cartesian(xlim=c(0,600)) +
theme_classic()
ggplot(data=xz, aes(y=lat, x=conf)) +
geom_violin(scale='width') +
geom_boxplot(width=0.1, outlier.shape=NA) +
theme_classic()
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xa <- sqldf("select packet_id,1.0 * MIN(latency) / 1000.0 as lat,way,conf,run from xy group by packet_id,way,conf,run")
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ggplot(data=sqldf("select * from xa where run='out/I0mj7t5OJu9DGMq1-6'"), aes(x=packet_id, y=lat, color=way)) +
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geom_line() +
geom_hline(yintercept=400) +
geom_hline(yintercept=200) +
coord_cartesian(ylim=c(0,1000)) +
#geom_point(aes(shape=conf)) +
theme_classic()
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xb <- read.csv("../../donar-res/tmp_light/v1.csv")
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xb$flag <- factor(xb$flag)
xb$link_id <- factor(xb$link_id)
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xc <- sqldf("select *, 1.0 * latency / 1000.0 as lat from xb where vanilla = 1 and link_id = 5")
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ggplot(data=xc, aes(x=packet_id, y=lat, color=link_id:way)) +
coord_cartesian(ylim=c(100,600)) +
geom_line() +
#geom_point() +
theme_classic()
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ggplot(data=sqldf("select
packet_id,way,latency,1.0 * MIN(latency) / 1000 as lat
from xb
group by packet_id,way"), aes(x=packet_id, y=lat, color=way)) +
coord_cartesian(ylim=c(100,600)) +
geom_line() +
#geom_point() +
theme_classic()
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xd <- sqldf("
select
lat,
xb.latency,
vanilla,
xb.packet_id,
xb.way,
link_id,
flag
from
(select
packet_id,way,latency,1.0 * MIN(latency) / 1000 as lat
from xb
group by packet_id,way) nn,
xb
where
xb.latency = nn.latency and
xb.packet_id = nn.packet_id and
xb.way = nn.way
")
ggplot(data=xd, aes(x=packet_id, y=lat, color=link_id)) +
#coord_cartesian(ylim=c(0,1000),xlim=c(3200,3500)) +
geom_line() +
theme_classic()
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ggplot(data=sqldf("select * from xb where vanilla = 1 and way= 'client'"), aes(x=packet_id, y=link_id, color=flag)) +
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geom_point() +
theme_classic()
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torbw <- read.csv("../../donar-res/tmp_light/tor_bw.csv")
torbw <- sqldf("select *, 1.0 * latency / 1000.0 as lat from torbw")
torbw2 <- torbw %>%
dplyr::group_by(rate) %>%
dplyr::summarise(
third = quantile(lat,0.75),
median = median(lat)
)
coefs <- coef(lm(median ~ rate, data = torbw2))
coefs2 <- coef(lm(third ~ rate, data = torbw2))
lats <- ggplot(torbw, aes(x=rate,y=lat, group=rate)) +
coord_cartesian(ylim=c(0,650)) +
geom_hline(yintercept=400, linetype="dashed") +
geom_hline(yintercept=200, linetype="dashed") +
ylab("latency (ms)") +
xlab("packets/sec") +
geom_abline(intercept = coefs[1], slope = coefs[2]) +
geom_abline(intercept = coefs2[1], slope = coefs2[2]) +
geom_boxplot(outlier.shape = NA) +
theme_classic()
lats + ggsave("tor_bw.png", dpi=300, dev='png', height=8, width=15, units="cm")