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113 lines
4.3 KiB
R
113 lines
4.3 KiB
R
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# Download ODS file from here:
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# https://docs.google.com/spreadsheets/d/1zR5XsKws_bKFFVGbf5FMaw0vcjLwFpUTveBCz4__bJg
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# MUST add filepath to downloaded .ods file here:
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P2P.Results.filepath <- ""
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# MUST Install these packages below once by un-commenting these lines:
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# install.packages("data.table")
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# install.packages("readODS")
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# install.packages("boot")
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# install.packages("presize")
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# install.packages("knitr")
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library(data.table)
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library(readODS)
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library(boot)
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library(presize)
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library(knitr)
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bchn.90p <- read_ods(P2P.Results.filepath, sheet = "bchn-90p")
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bchn.90p.start <- read_ods(P2P.Results.filepath, sheet = "bchn_start-90p")
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bchn.0p <- read_ods(P2P.Results.filepath, sheet = "bchn-0p")
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bchn.0p.start <- read_ods(P2P.Results.filepath, sheet = "bchn-start-0p")
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fulcrum.90p <- read_ods(P2P.Results.filepath, sheet = "fulcrum-90p")
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fulcrum.0p <- read_ods(P2P.Results.filepath, sheet = "fulcrum-0p")
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for (i in c("bchn.90p", "bchn.90p.start", "bchn.0p", "bchn.0p.start", "fulcrum.90p", "fulcrum.0p")) {
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setDT(get(i))
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setnames(get(i), colnames(get(i)), gsub("[^0-9a-zA-Z]", "", colnames(get(i))) )
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}
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block.phases <- data.table(BlockHeight = 0:max(bchn.90p$BlockHeight), phase = "warmup", stringsAsFactors = FALSE)
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block.phases[BlockHeight %in% 145:244, phase := "empty"]
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block.phases[BlockHeight %in% 245:254, phase := "fan-out"]
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block.phases[BlockHeight %in% 255:259, phase := "steady state 1"]
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block.phases[BlockHeight %in% 260:261, phase := "fan-in"]
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block.phases[BlockHeight %in% 262:266, phase := "steady state 2"]
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bchn.90p <- merge(bchn.90p, bchn.90p.start)
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bchn.90p[, bchn.90p.elasped.time := as.numeric(as.POSIXlt(CompletedTimestamp) - as.POSIXlt(StartTimestamp))]
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bchn.0p <- merge(bchn.0p, bchn.0p.start)
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bchn.0p[, bchn.0p.elasped.time := as.numeric(as.POSIXlt(CompletedTimestamp) - as.POSIXlt(StartTimestamp))]
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fulcrum.90p[, fulcrum.90p.elasped.time := ProcessingTimemsec]
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fulcrum.0p[, fulcrum.0p.elasped.time := ProcessingTimemsec]
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timing.data <- merge(block.phases, bchn.0p[, .(BlockHeight, bchn.0p.elasped.time)])
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timing.data <- merge(timing.data, bchn.90p[, .(BlockHeight, bchn.90p.elasped.time)], all.x = TRUE)
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timing.data <- merge(timing.data, fulcrum.0p[, .(BlockHeight, fulcrum.0p.elasped.time)], all.x = TRUE)
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timing.data <- merge(timing.data, fulcrum.90p[, .(BlockHeight, fulcrum.90p.elasped.time)], all.x = TRUE)
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timing.data <- timing.data[ ! phase %in% c("warmup", "empty", "fan-in"), ]
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mean.statistic <- function(x, w) {mean(x[w])}
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ci.table <- list()
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power.table <- list()
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set.seed(314)
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# bootstrapping involves some randomization
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for (i in setdiff(names(timing.data), c("BlockHeight", "phase"))) {
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by.output <- by(unlist(timing.data[, ..i]), timing.data$phase, FUN = function(x) {
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if ( any(is.na(x))) {
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return(list(boot.ci = c(NA, NA), prec_mean.10 = NA, prec_mean.25 = NA, prec_mean.50 = NA))
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}
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list(
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boot.ci = round(boot.ci(boot(x, mean.statistic, R = 10000, stype = "i"), conf = 0.90, type = "perc")$percent[4:5]),
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prec_mean.10 = ceiling(prec_mean(mean(x), sd(x), conf.width = 0.10 * mean(x), conf.level = 0.90)$n),
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prec_mean.25 = ceiling(prec_mean(mean(x), sd(x), conf.width = 0.25 * mean(x), conf.level = 0.90)$n),
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prec_mean.50 = ceiling(prec_mean(mean(x), sd(x), conf.width = 0.50 * mean(x), conf.level = 0.90)$n)
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)
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})
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ci.lower <- sapply(by.output, FUN = function(x) {x$boot.ci[1]})
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ci.upper <- sapply(by.output, FUN = function(x) {x$boot.ci[2]})
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ci.table[[i]] <- data.table(processing.type = gsub(".elasped.time", "", i),
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block.type = names(by.output), ci.lower, ci.upper)
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prec_mean.10 <- sapply(by.output, FUN = function(x) {x$prec_mean.10})
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prec_mean.25 <- sapply(by.output, FUN = function(x) {x$prec_mean.25})
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prec_mean.50 <- sapply(by.output, FUN = function(x) {x$prec_mean.50})
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power.table[[i]] <- data.table(processing.type = gsub(".elasped.time", "", i),
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block.type = names(by.output), prec_mean.10, prec_mean.25, prec_mean.50)
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}
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ci.table <- data.table::rbindlist(ci.table)
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power.table <- data.table::rbindlist(power.table)
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colnames(ci.table) <- c("Processing Type", "Block Type",
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"Lower 90% Confidence Interval", "Upper 90% Confidence Interval")
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colnames(power.table) <- c("Processing Type", "Block Type",
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"N for C.I. width < 10% of mean", "25%", "50%")
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knitr::kable(ci.table, format = "pipe")
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knitr::kable(power.table, format = "pipe")
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