4 # system("parse_rt_data.py > rt_data.csv");
5 # ./bmevents.py events.1-18-10 BootUpdateNode > bm_reboot_2010-01-18.csv
6 # ./bmevents.py events.10-08-09 BootUpdateNode > bm_reboot_2009-10-08.csv
7 # ./bmevents.py events.29.12.08.dump BootUpdateNode > bm_reboot_2008-12-29.csv
8 # ./bmevents.py events.8-25-09.dump BootUpdateNode > bm_reboot_2009-08-25.csv
10 t <- read.csv('bm_reboot.csv', sep=',', header=TRUE)
14 tstamp_78 <-unclass(as.POSIXct("2008-01-01", origin="1960-01-01"))[1]
15 tstamp_89 <-unclass(as.POSIXct("2009-01-01", origin="1960-01-01"))[1]
17 t_7 <- t2[which( t2$start < tstamp_78 ),]
18 t_8 <- t2[which( t2$start >= tstamp_78 & t2$start < tstamp_89 ),]
19 t_9 <- t2[which( t2$start >= tstamp_89 ),]
21 tstamp <-unclass(as.POSIXct("2008-01-01", origin="1960-01-01"))
22 t_67 <- t2[which( t2$start < tstamp[1] ),]
23 t_89 <- t2[which( t2$start >= tstamp[1] ),]
26 #start_image("bm_reboot.png")
29 par(mai=c(.5,.4,.5,.4))
30 year_hist(t_9, "2009", "2009/06/21", "2010/2/10", 500, 'day', "Daily Reboot Rates")
31 rows <- year_hist_unique(t_9, "2009", "2009/06/21", "2010/2/10", 100, 'day', "Unique Daily Reboots")
35 start_image("reboot_distributions.png")
37 par(mai=c(.5,.5,.5,.5))
39 m<-mean(rows$reboots[which(rows$reboots>0&rows$reboots<50)])
40 s<-sd(rows$reboots[which(rows$reboots>0&rows$reboots<50)])
42 qqnorm(rows$reboots[which(rows$reboots>0&rows$reboots<50)])
43 qqline(rows$reboots[which(rows$reboots>0&rows$reboots<50)])
45 h<-hist(rows$reboots[which(rows$reboots>0&rows$reboots<50)], breaks=20)
47 y<- dnorm(x, mean=m, sd=s)
48 lines(x,y*max(h$counts)/max(y))
52 par(mai=c(.7,.7,.7,.7))