--- /dev/null
+#-------------------------------------------------------------------------------
+# Copyright (c) 2012 University of Illinois, NCSA.
+# All rights reserved. This program and the accompanying materials
+# are made available under the terms of the
+# University of Illinois/NCSA Open Source License
+# which accompanies this distribution, and is available at
+# http://opensource.ncsa.illinois.edu/license.html
+#-------------------------------------------------------------------------------
+##--------------------------------------------------------------------------------------------------#
+##' Variable-width (dagonally cut) histogram
+##'
+##'
+##' When constructing a histogram, it is common to make all bars the same width.
+##' One could also choose to make them all have the same area.
+##' These two options have complementary strengths and weaknesses; the equal-width histogram oversmooths in regions of high density, and is poor at identifying sharp peaks; the equal-area histogram oversmooths in regions of low density, and so does not identify outliers.
+##' We describe a compromise approach which avoids both of these defects. We regard the histogram as an exploratory device, rather than as an estimate of a density.
+##' @name dhist
+##' @title Diagonally Cut Histogram
+##' @param x is a numeric vector (the data)
+##' @param a is the scaling factor, default is 5 * IQR
+##' @param nbins is the number of bins, default is assigned by the Stuges method
+##' @param rx is the range used for the left of the left-most bin to the right of the right-most bin
+##' @param eps used to set artificial bound on min width / max height of bins as described in Denby and Mallows (2009) on page 24.
+##' @param xlab is label for the x axis
+##' @param plot = TRUE produces the plot, FALSE returns the heights, breaks and counts
+##' @param lab.spikes = TRUE labels the \% of data in the spikes
+##' @return list with two elements, heights of length n and breaks of length n+1 indicating the heights and break points of the histogram bars.
+##' @author Lorraine Denby, Colin Mallows
+##' @references Lorraine Denby, Colin Mallows. Journal of Computational and Graphical Statistics. March 1, 2009, 18(1): 21-31. doi:10.1198/jcgs.2009.0002.
+dhist <- function(x, a=5*iqr(x),
+ nbins=nclass.Sturges(x), rx = range(x,na.rm = TRUE),
+ eps=.15, xlab = "x", plot = TRUE,lab.spikes = TRUE)
+{
+
+ if(is.character(nbins))
+ nbins <- switch(casefold(nbins),
+ sturges = nclass.Sturges(x),
+ fd = nclass.FD(x),
+ scott = nclass.scott(x),
+ stop("Nclass method not recognized"))
+ else if(is.function(nbins))
+ nbins <- nbins(x)
+
+ x <- sort(x[!is.na(x)])
+ if(a == 0)
+ a <- diff(range(x))/100000000
+ if(a != 0 & a != Inf) {
+ n <- length(x)
+ h <- (rx[2] + a - rx[1])/nbins
+ ybr <- rx[1] + h * (0:nbins)
+ yupper <- x + (a * (1:n))/n
+ # upper and lower corners in the ecdf
+ ylower <- yupper - a/n
+ #
+ cmtx <- cbind(cut(yupper, breaks = ybr), cut(yupper, breaks =
+ ybr, left.include = TRUE), cut(ylower, breaks = ybr),
+ cut(ylower, breaks = ybr, left.include = TRUE))
+ cmtx[1, 3] <- cmtx[1, 4] <- 1
+ # to replace NAs when default r is used
+ cmtx[n, 1] <- cmtx[n, 2] <- nbins
+ #
+ #checksum <- apply(cmtx, 1, sum) %% 4
+ checksum <- (cmtx[, 1] + cmtx[, 2] + cmtx[, 3] + cmtx[, 4]) %%
+ 4
+ # will be 2 for obs. that straddle two bins
+ straddlers <- (1:n)[checksum == 2]
+ # to allow for zero counts
+ if(length(straddlers) > 0) {
+ counts <- table(c(1:nbins, cmtx[ - straddlers, 1]))
+ } else {
+ counts <- table(c(1:nbins, cmtx[, 1]))
+ }
+ counts <- counts - 1
+ #
+ if(length(straddlers) > 0) {
+ for(i in straddlers) {
+ binno <- cmtx[i, 1]
+ theta <- ((yupper[i] - ybr[binno]) * n)/a
+ counts[binno - 1] <- counts[binno - 1] + (
+ 1 - theta)
+ counts[binno] <- counts[binno] + theta
+ }
+ }
+ xbr <- ybr
+ xbr[-1] <- ybr[-1] - (a * cumsum(counts))/n
+ spike<-eps*diff(rx)/nbins
+ flag.vec<-c(diff(xbr)<spike,F)
+ if ( sum(abs(diff(xbr))<=spike) >1) {
+ xbr.new<-xbr
+ counts.new<-counts
+ diff.xbr<-abs(diff(xbr))
+ amt.spike<-diff.xbr[length(diff.xbr)]
+ for (i in rev(2:length(diff.xbr))) {
+ if (diff.xbr[i-1] <= spike&diff.xbr[i] <= spike &
+ !is.na(diff.xbr[i])) {
+ amt.spike <- amt.spike+diff.xbr[i-1]
+ counts.new[i-1] <- counts.new[i-1]+counts.new[i]
+ xbr.new[i] <- NA
+ counts.new[i] <- NA
+ flag.vec[i-1] <- T
+ }
+ else amt.spike<-diff.xbr[i-1]
+ }
+ flag.vec<-flag.vec[!is.na(xbr.new)]
+ flag.vec<-flag.vec[-length(flag.vec)]
+ counts<-counts.new[!is.na(counts.new)]
+ xbr<-xbr.new[!is.na(xbr.new)]
+
+ }
+ else flag.vec<-flag.vec[-length(flag.vec)]
+ widths <- abs(diff(xbr))
+ ## N.B. argument "widths" in barplot must be xbr
+ heights <- counts/widths
+ }
+ bin.size <- length(x)/nbins
+ cut.pt <- unique(c(min(x) - abs(min(x))/1000,
+ approx(seq(length(x)), x, (1:(nbins - 1)) * bin.size, rule = 2)$y, max(x)))
+ aa <- hist(x, breaks = cut.pt, plot = FALSE, probability = TRUE)
+ if(a == Inf) {
+ heights <- aa$counts
+ xbr <- aa$breaks
+ }
+ amt.height<-3
+ q75<-quantile(heights,.75)
+ if (sum(flag.vec)!=0) {
+ amt<-max(heights[!flag.vec])
+ ylim.height<-amt*amt.height
+ ind.h<-flag.vec&heights> ylim.height
+ flag.vec[heights<ylim.height*(amt.height-1)/amt.height]<-F
+ heights[ind.h] <- ylim.height
+ }
+ amt.txt<-0
+ end.y<-(-10000)
+ if(plot) {
+ barplot(heights, abs(diff(xbr)), space = 0, density = -1, xlab =
+ xlab, plot = TRUE, xaxt = "n",yaxt='n')
+ at <- pretty(xbr)
+ axis(1, at = at - xbr[1], labels = as.character(at))
+ if (lab.spikes) {
+ if (sum(flag.vec)>=1) {
+ usr<-par('usr')
+ for ( i in seq(length(xbr)-1)) {
+ if (!flag.vec[i]) {
+ amt.txt<-0
+ if (xbr[i]-xbr[1]<end.y) amt.txt<-1
+ }
+ else {
+ amt.txt<-amt.txt+1
+ end.y<-xbr[i]-xbr[1]+3*par('cxy')[1]
+ }
+ if (flag.vec[i]) {
+ txt<-paste(' ',format(round(counts[i]/
+ sum(counts)*100)),'%',sep='')
+ par(xpd = TRUE)
+ text(xbr[i+1]-xbr[1],ylim.height-par('cxy')[2]*(amt.txt-1),txt, adj=0)
+ }}
+ }
+ else print('no spikes or more than one spike')
+ }
+ invisible(list(heights = heights, xbr = xbr))
+ }
+ else {
+ return(list(heights = heights, xbr = xbr,counts=counts))
+ }
+}
+#==================================================================================================#
+
+
+#--------------------------------------------------------------------------------------------------#
+##' Calculate interquartile range
+##'
+##' Calculates the 25th and 75th quantiles given a vector x; used in function \link{dhist}.
+##' @name iqr
+##' @title Interquartile range
+##' @param x vector
+##' @return numeric vector of length 2, with the 25th and 75th quantiles of input vector x.
+iqr <- function(x){
+ return(diff(quantile(x, c(0.25, 0.75), na.rm = TRUE)))
+}
+##==================================================================================================#
+
+##--------------------------------------------------------------------------------------------------#
+##' Creates empty ggplot object
+##'
+##' An empty base plot to which layers created by other functions
+##' (\code{\link{plot.data}}, \code{\link{plot.prior.density}},
+##' \code{\link{plot.posterior.density}}) can be added.
+##' @name create.base.plot
+##' @title Create Base Plot
+##' @return empty ggplot object
+##' @export
+##' @author David LeBauer
+create.base.plot <- function() {
+ base.plot <- ggplot()
+ return(base.plot)
+}
+#==================================================================================================#
+
+
+
+
+##--------------------------------------------------------------------------------------------------#
+##' Add data to an existing plot or create a new one from \code{\link{create.base.plot}}
+##'
+##' Used to add raw data or summary statistics to the plot of a distribution.
+##' The height of Y is arbitrary, and can be set to optimize visualization.
+##' If SE estimates are available, tehse wil be plotted
+##' @name plot.data
+##' @title Add data to plot
+##' @param trait.data data to be plotted
+##' @param base.plot a ggplot object (grob),
+##' created by \code{\link{create.base.plot}} if none provided
+##' @param ymax maximum height of y
+##' @seealso \code{\link{create.base.plot}}
+##' @return updated plot object
+##' @author David LeBauer
+##' @export
+##' @examples
+##' \dontrun{plot.data(data.frame(Y = c(1, 2), se = c(1,2)), base.plot = NULL, ymax = 10)}
+plot.data <- function(trait.data, base.plot = NULL, ymax, color = 'black') {
+ if(is.null(base.plot)) base.plot <- create.base.plot()
+ n.pts <- nrow(trait.data)
+ if(n.pts == 1){
+ ymax <- ymax/8
+ } else if (n.pts < 5) {
+ ymax <- ymax / 4
+ } else {
+ ymax <- ymax / 2
+ }
+ y.pts <- seq(0, ymax, length.out = 1 + n.pts)[-1]
+ plot.data <- data.frame(x = trait.data$Y,
+ y = y.pts,
+ se = trait.data$se,
+ control = !trait.data$trt == 1 & trait.data$ghs == 1)
+ new.plot <- base.plot +
+ geom_point(data = plot.data,
+ aes(x = x, y = y,
+ color = control)) +
+ geom_segment(data = plot.data,
+ aes(x = x - se, y = y, xend = x + se, yend = y,
+ color = control)) +
+ scale_color_manual(values = c('black', 'grey')) +
+ opts(legend_position = "none")
+ return(new.plot)
+}
+##==================================================================================================#
+
+
+#--------------------------------------------------------------------------------------------------#
+##' Add borders to .. content for \description{} (no empty lines) ..
+##'
+##' Has ggplot2 display only specified borders, e.g. ("L"-shaped) borders, rather than a rectangle or no border. Note that the order can be significant; for example, if you specify the L border option and then a theme, the theme settings will override the border option, so you need to specify the theme (if any) before the border option, as above.
+##' @name theme_border
+##' @title Theme border for plot
+##' @param type
+##' @param colour
+##' @param size
+##' @param linetype
+##' @return adds borders to ggplot as a side effect
+##' @author Rudolf Cardinal
+##' @author \url{ggplot2 google group}{https://groups.google.com/forum/?fromgroups#!topic/ggplot2/-ZjRE2OL8lE}
+##' @examples
+##' \dontrun{
+##' df = data.frame( x=c(1,2,3), y=c(4,5,6) )
+##' ggplot(data=df, aes(x=x, y=y)) + geom_point() + theme_bw() +
+##' opts(panel.border = theme_border(c("bottom","left")) )
+##' ggplot(data=df, aes(x=x, y=y)) + geom_point() + theme_bw() +
+##' opts(panel.border = theme_border(c("b","l")) )
+##' }
+theme_border <- function(type = c("left", "right", "bottom", "top",
+ "none"), colour = "black", size = 1, linetype = 1) {
+ type <- match.arg(type, several.ok=TRUE)
+ structure(
+ function(x = 0, y = 0, width = 1, height = 1, ...) {
+ xlist <- c()
+ ylist <- c()
+ idlist <- c()
+ if ("bottom" %in% type) { # bottom
+ xlist <- append(xlist, c(x, x+width))
+ ylist <- append(ylist, c(y, y))
+ idlist <- append(idlist, c(1,1))
+ }
+ if ("top" %in% type) { # top
+ xlist <- append(xlist, c(x, x+width))
+ ylist <- append(ylist, c(y+height, y+height))
+ idlist <- append(idlist, c(2,2))
+ }
+ if ("left" %in% type) { # left
+ xlist <- append(xlist, c(x, x))
+ ylist <- append(ylist, c(y, y+height))
+ idlist <- append(idlist, c(3,3))
+ }
+ if ("right" %in% type) { # right
+ xlist <- append(xlist, c(x+width, x+width))
+ ylist <- append(ylist, c(y, y+height))
+ idlist <- append(idlist, c(4,4))
+ }
+ polylineGrob(
+ x=xlist, y=ylist, id=idlist, ..., default.units = "npc",
+ gp=gpar(lwd=size, col=colour, lty=linetype),
+ )
+ },
+ class = "theme",
+ type = "box",
+ call = match.call()
+ )
+}
+#==================================================================================================#
+
+
+####################################################################################################
+### EOF. End of R script file.
+####################################################################################################
# Can be called from the bash script with the following code:
# export R_INPUT=$inputfile
# export R_OUTPUT=$outputfile
+# export R_TYPE=$hist_type
+
# R CMD BATCH $this_script.R
+#or
+# Rscript $this_script.R
# Use functions from bench.h to benchmark execution time of the desired block,
# then Rhist.R script to read all timings and produce histograms
# and finally inject.h to inject values instead of executing block
+# This is a small function to help merging empty nbins for dhist histograms
+merge_empty_bins <- function (h)
+{
+ i<-1
+ j<-1
+ counts2<--1
+ breaks2<-h$breaks[1]
+
+ if (length(h$counts)>1)
+ for(i in 1:(length(h$counts)-1))
+ {
+ if(h$counts[i]!=0 || h$counts[i+1]!=0)
+ {
+ counts2[j]<-h$counts[i]
+ breaks2[j+1]<-h$breaks[i+1];
+ j<-j+1
+ }
+ }
+ counts2[j]<-h$counts[length(h$counts)]
+ breaks2[j+1]<-h$breaks[length(h$breaks)]
+
+ h$counts<-counts2
+ h$breaks<-breaks2
+
+ return (h)
+}
+
+# Main
+source("analysis/hist_script/Rdhist.R")
+
inputfile<-Sys.getenv("R_INPUT")
outputfile<-Sys.getenv("R_OUTPUT")
+type<-Sys.getenv("R_TYPE")
+
+if (!(type %in% c("mean","default","sturges","scott")))
+ {
+ stop("Wrong histogram type")
+ }
df<-read.table(inputfile,header=F)
df<-df[,c(1,4)]
for(i in unique(NAME))
{
vector1<-df[NAME==i,2]
- h<-hist(vector1)
-
+
+ if (length(vector1)==1)
+ {
+ #If there is only one element
+ h$breaks<-c(vector1,vector1)
+ h$counts<-1
+ }
+ else
+ {
+ if (type=="mean")
+ #Mean value only
+ h$breaks<-c(mean(vector1),mean(vector1))
+ h$counts<-length(vector1)
+ else
+ if (type=="default")
+ #Standard HISTOGRAM:
+ h<-hist(vector1)
+ else
+ {
+ #Dhist:
+ h<-dhist(vector1,nbins=type, plot = FALSE, lab.spikes = FALSE, a=5*iqr(vector1), eps=0.15)
+ h$breaks<-h$xbr
+ h$count<-as.vector(h$counts)
+ h$counts<-h$count
+ h<-merge_empty_bins(h)
+ }
+ }
+
cat(i, file = outputfile, sep = "\t", append = TRUE)
- cat(" ", file = outputfile, sep = "\t", append = TRUE)
- cat(sprintf("%.8f", mean(vector1)), file =outputfile, sep = "\t ", append = TRUE)
+ cat("\t", file = outputfile, append = TRUE)
+ cat(sum(h$counts), file =outputfile, sep = "\t", append = TRUE)
+ cat("\t", file = outputfile, append = TRUE)
+ cat(sprintf("%.8f", mean(vector1)), file =outputfile, sep = "\t", append = TRUE)
cat("\t", file = outputfile, append = TRUE)
cat(length(h$breaks), file = outputfile, append = TRUE)
cat("\t", file = outputfile, append = TRUE)
- cat(sprintf("%.8f", h$breaks), file = outputfile, sep = " \t", append = TRUE)
+ cat(sprintf("%.8f", h$breaks), file = outputfile, sep = "\t", append = TRUE)
cat("\t", file = outputfile, append = TRUE)
h$density = h$counts/sum(h$counts)
- cat(sprintf("%.14f", h$density), file = outputfile, sep = " \t", append = TRUE)
+ cat(sprintf("%.8f", h$density), file = outputfile, sep = "\t", append = TRUE)
cat("\n", file = outputfile, append = TRUE)
}
--- /dev/null
+# R script showing .pdf file with plots of all injection histograms for a certain file
+
+# Can be called from the command line with:
+# Rscript $this_script.R inputfile
+
+# Necessary libraries
+library(plyr)
+library(ggplot2)
+library(data.table)
+library(grid)
+
+# Functions for arranging multiple plots
+vp.layout <- function(x, y) viewport(layout.pos.row=x, layout.pos.col=y)
+arrange_ggplot2 <- function(list, nrow=NULL, ncol=NULL, as.table=FALSE) {
+n <- length(list)
+if(is.null(nrow) & is.null(ncol)) { nrow = floor(n/2) ; ncol = ceiling(n/nrow)}
+if(is.null(nrow)) { nrow = ceiling(n/ncol)}
+if(is.null(ncol)) { ncol = ceiling(n/nrow)}
+## NOTE see n2mfrow in grDevices for possible alternative
+grid.newpage()
+pushViewport(viewport(layout=grid.layout(nrow,ncol) ) )
+ii.p <- 1
+for(ii.row in seq(1, nrow)){
+ii.table.row <- ii.row
+if(as.table) {ii.table.row <- nrow - ii.table.row + 1}
+for(ii.col in seq(1, ncol)){
+ii.table <- ii.p
+if(ii.p > n) break
+print(list[[ii.table]], vp=vp.layout(ii.table.row, ii.col))
+ii.p <- ii.p + 1
+}
+}
+}
+
+### Main
+
+# Reading command line argument with the input file path
+args <- commandArgs(trailingOnly = TRUE)
+fp <- file(args[1], open = "r")
+
+plots<-list()
+i<-1
+
+# Reading histograms one by one, line by line
+while (length(oneLine <- readLines(fp, n = 1, warn = FALSE)) > 0)
+{ myVector <- (strsplit(oneLine, "\t"))
+
+ dfl <- ldply (myVector, data.frame)
+
+ name<-as.character(dfl[1,])
+ nbins<-as.numeric(as.character(dfl[4,]))
+ allbreaks<-as.numeric(as.character(dfl[5:(5+nbins-1),]))
+
+ dh<-data.frame(Name=as.character(dfl[1,]), Total=as.numeric(as.character(dfl[2,])), Mean=as.numeric(as.character(dfl[3,])), Nbins=as.numeric(as.character(dfl[4,])))
+ dh<-cbind(dh,Bstart=allbreaks[-length(allbreaks)])
+ dh<-cbind(dh,Bend=allbreaks[-1])
+ dh<-cbind(dh,Density=as.numeric(as.character(dfl[(5+nbins):(5+nbins*2-2),])))
+
+ # Plotting single histogram, if it only has one value then use geom_bar
+ if (nbins > 2)
+ plots[[i]]<-ggplot(data=data.frame(dh), aes(xmin=Bstart, xmax=Bend, ymin=0, ymax=Density)) + geom_rect(aes(fill=Density)) + theme_bw() + scale_x_continuous("Time [s]", allbreaks) + labs(title=name, y=element_text("Density %"))
+ else
+ plots[[i]]<-ggplot(data=data.frame(dh), aes(factor(Bstart))) + geom_bar(aes(fill=Density)) + theme_bw() + labs(title=name, y=element_text("Density %"), x=element_text("Time [s]"))
+
+ i<-i+1
+}
+
+# Printing all plots together in a table
+arrange_ggplot2(plots, as.table=TRUE)
+
+# End
+write("Done producing a histogram plot. Open Rplots.pdf located in this folder to see the results", stdout())
#include "xbt/dict.h"
#include "xbt/sysdep.h"
-#define MAX_LINE_INJ 1000
+#define MAX_LINE_INJ 10000
/*
* Histogram entry for each measured block
* Each entry is guarded inside xbt dictionary which is read from the file */
typedef struct xbt_hist {
int n;
+ int counts;
double mean;
double *breaks;
double *percentage;
printf("Error input file is empty!");//Skipping first row
while (fgets(line, 200, fpInput) != NULL)
{
- key = strtok(line, " \t");
+ key = strtok(line, "\t");
data = xbt_dict_get_or_null(mydict, key);
if (data)
data = (xbt_hist_t *) xbt_new(xbt_hist_t, 1);
data->block_id = key;
- data->mean = atof(strtok(NULL, " \t"));
- data->n = atoi(strtok(NULL, " \t"));
+ data->counts = atoi(strtok(NULL, "\t"));
+ data->mean = atof(strtok(NULL, "\t"));
+ data->n = atoi(strtok(NULL, "\t"));
data->breaks = (double*) malloc(sizeof(double) * data->n);
data->percentage = (double*) malloc(sizeof(double) * (data->n - 1));
for (i = 0; i < data->n; i++)
- data->breaks[i] = atof(strtok(NULL, " \t"));
+ data->breaks[i] = atof(strtok(NULL, "\t"));
for (i = 0; i < (data->n - 1); i++)
- data->percentage[i] = atof(strtok(NULL, " \t"));
+ data->percentage[i] = atof(strtok(NULL, "\t"));
xbt_dict_set(mydict, key, data, NULL);
}
mydict = *dict;
FILE* fpInput = fopen(inputfile, "r");
if (fpInput == NULL)
+ {
printf("Error while opening the inputfile");
+ return;
+ }
fseek(fpInput, 0, 0);
xbt_hist_t* data;
if (fgets(line, MAX_LINE_INJ, fpInput) == NULL)
+ {
printf("Error input file is empty!");//Skipping first row
+ return;
+ }
while (fgets(line, MAX_LINE_INJ, fpInput) != NULL)
{
- key = strtok(line, " \t");
+ key = strtok(line, "\t");
data = xbt_dict_get_or_null(mydict, key);
if (data)
data = (xbt_hist_t *) xbt_new(xbt_hist_t, 1);
data->block_id = key;
- data->mean = atof(strtok(NULL, " \t"));
- data->n = atoi(strtok(NULL, " \t"));
+ data->counts = atoi(strtok(NULL, "\t"));
+ data->mean = atof(strtok(NULL, "\t"));
+ data->n = atoi(strtok(NULL, "\t"));
data->breaks = (double*) malloc(sizeof(double) * data->n);
data->percentage = (double*) malloc(sizeof(double) * (data->n - 1));
for (i = 0; i < data->n; i++)
- data->breaks[i] = atof(strtok(NULL, " \t"));
+ data->breaks[i] = atof(strtok(NULL, "\t"));
for (i = 0; i < (data->n - 1); i++)
{
- data->percentage[i] = atof(strtok(NULL, " \t"));
+ data->percentage[i] = atof(strtok(NULL, "\t"));
}
xbt_dict_set(mydict, key, data, NULL);
