173 lines
4.9 KiB
R
173 lines
4.9 KiB
R
library(ggplot2)
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library(sqldf)
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library(plyr)
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library(dplyr)
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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)) +
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#geom_line() +
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geom_point() +
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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) +
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geom_hline(yintercept=200) +
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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)) +
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geom_line() +
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geom_hline(yintercept=400) +
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geom_hline(yintercept=200) +
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coord_cartesian(ylim=c(0,1000)) +
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#geom_point() +
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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)) +
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geom_line() +
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geom_hline(yintercept=400) +
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geom_hline(yintercept=200) +
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coord_cartesian(ylim=c(0,1000)) +
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#geom_point() +
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theme_classic()
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prepros <- sqldf(
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"select
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r.packet_id,
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r.way,
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r.lat,
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s.flag
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from
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(select
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packet_id,
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way,
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min(latency) as lat
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from xx
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group by packet_id,way) as r,
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xx as s
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where
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s.packet_id = r.packet_id and
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s.way = r.way and
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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)
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ggplot(data=xx3, aes(x=lat, group=flag, color=flag)) +
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stat_ecdf(pad = FALSE) +
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geom_vline(xintercept = 200) +
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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")
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xz$conf <- factor(xz$conf)
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ggplot(data=xz, aes(x=lat, group=conf, color=conf)) +
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stat_ecdf(pad = FALSE) +
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geom_vline(xintercept = 200) +
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geom_vline(xintercept = 400) +
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coord_cartesian(xlim=c(0,600)) +
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theme_classic()
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ggplot(data=xz, aes(y=lat, x=conf)) +
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geom_violin(scale='width') +
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geom_boxplot(width=0.1, outlier.shape=NA) +
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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() +
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geom_hline(yintercept=400) +
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geom_hline(yintercept=200) +
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coord_cartesian(ylim=c(0,1000)) +
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#geom_point(aes(shape=conf)) +
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theme_classic()
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xb <- read.csv("../../donar-res/tmp_light/v1.csv")
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xb$flag <- factor(xb$flag)
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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)) +
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coord_cartesian(ylim=c(100,600)) +
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geom_line() +
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#geom_point() +
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theme_classic()
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ggplot(data=sqldf("select
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packet_id,way,latency,1.0 * MIN(latency) / 1000 as lat
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from xb
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group by packet_id,way"), aes(x=packet_id, y=lat, color=way)) +
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coord_cartesian(ylim=c(100,600)) +
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geom_line() +
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#geom_point() +
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theme_classic()
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xd <- sqldf("
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select
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lat,
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xb.latency,
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vanilla,
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xb.packet_id,
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xb.way,
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link_id,
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flag
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from
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(select
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packet_id,way,latency,1.0 * MIN(latency) / 1000 as lat
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from xb
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group by packet_id,way) nn,
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xb
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where
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xb.latency = nn.latency and
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xb.packet_id = nn.packet_id and
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xb.way = nn.way
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")
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ggplot(data=xd, aes(x=packet_id, y=lat, color=link_id)) +
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#coord_cartesian(ylim=c(0,1000),xlim=c(3200,3500)) +
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geom_line() +
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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() +
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theme_classic()
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torbw <- read.csv("../../donar-res/tmp_light/tor_bw.csv")
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torbw <- sqldf("select *, 1.0 * latency / 1000.0 as lat from torbw")
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torbw2 <- torbw %>%
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dplyr::group_by(rate) %>%
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dplyr::summarise(
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third = quantile(lat,0.75),
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median = median(lat)
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)
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coefs <- coef(lm(median ~ rate, data = torbw2))
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coefs2 <- coef(lm(third ~ rate, data = torbw2))
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lats <- ggplot(torbw, aes(x=rate,y=lat, group=rate)) +
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coord_cartesian(ylim=c(0,650)) +
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geom_hline(yintercept=400, linetype="dashed") +
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geom_hline(yintercept=200, linetype="dashed") +
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ylab("latency (ms)") +
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xlab("packets/sec") +
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geom_abline(intercept = coefs[1], slope = coefs[2]) +
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geom_abline(intercept = coefs2[1], slope = coefs2[2]) +
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geom_boxplot(outlier.shape = NA) +
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theme_classic()
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lats + ggsave("tor_bw.png", dpi=300, dev='png', height=8, width=15, units="cm")
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lightlinks <- read.csv("../../donar-res/tmp_light/lightning-links.csv")
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