#############################################################
# these colors were shamefully stolen from the guide at
# http://jfly.iam.u-tokyo.ac.jp/color/
# entitled "How to make figures and presentations that are
# friendly to color blind people"
#############################################################
cb_colors <- c(rgb(0,   0  , 0  , maxColorValue=255),
	       rgb(230, 159, 0  , maxColorValue=255),
	       rgb( 86, 180, 233, maxColorValue=255),
	       rgb(  0, 158, 115, maxColorValue=255),
	       rgb(240, 228,  66, maxColorValue=255),
	       rgb(  0, 114, 178, maxColorValue=255),
	       rgb(213,  94,   0, maxColorValue=255),
	       rgb(204, 121, 167, maxColorValue=255))


###############################################
# read.rdb(filename)
#   reads in an RDB file to an R data frame
###############################################
read.rdb <- function(file)
{   rdb.header.lines <- function(file)
    {   fh <- file(file)
	header.lines <- 0
	while(substr(readLines(fh, n=header.lines+1)[header.lines+1],1,1)=="#")
	    header.lines <- header.lines + 1
	close(fh)
	header.lines
    }

    format.rdb.to.r.element <- function(elem)
    {   if(substr(elem,nchar(elem),nchar(elem)) == "N")
	    return("numeric")
	else
	    return("character")
    }

    header.lines <- rdb.header.lines(file)

    data <- read.delim(file, skip=header.lines, nrows=1, colClasses="character")

    col.names <- colnames(data)
    col.classes <- sapply(as.vector(data[1,], mode="character"),
			  format.rdb.to.r.element)

    read.delim(file, skip=header.lines+2, header=FALSE,
	             col.names=col.names, colClasses=col.classes)
}


#############################################################
# pmulthist
#   is just like multhist from the plotrix library except
#   this does a density histogram rather than using counts
#   (code from Ben Bolker at the University of Flordia;
#   slightly modified for rescaling y-axis to make room for
#   legends)
#############################################################
pmulthist <- function (x, breaks = "Sturges", yscale = 1, ...)
{   allhist <- hist(unlist(x), breaks = breaks, plot = FALSE)
    combhist <- sapply(x, function(z) hist(z, breaks = allhist$breaks,
						     plot = FALSE)$density)

    ylim = c(0, max(unlist(combhist)) * yscale) 

    barplot(t(combhist), beside = TRUE, names = signif(allhist$mids, 2),
	    ylim=ylim, ...)
}


###############################################
# denplot(data, ...)
#   computes a kernel density estimate for each
#   column of the input and plots it;
#   ... is passed as parameters to matplot
###############################################
denplot <- function (a, ...)
{   denlist <- lapply(a, density, na.rm=TRUE)
    xmat <- sapply(1:length(denlist), function(i) denlist[[i]]$x)
    ymat <- sapply(1:length(denlist), function(i) denlist[[i]]$y)
    matplot(xmat, ymat, type="l", lty="solid", ...)
}


################################################
# eps_output(filename)
#   open filename for eps output using graphics
#   parameters that I like
################################################
eps_output <- function(file)
{   postscript(file, family="Times", height=5, width=6,
	       horizontal=FALSE, onefile=FALSE, paper= "special")
}