#' Computation and visualization of the empirical attainment function (EAF) for
#' the analysis of random sets in multi-criterion optimization.
#'
#'  The empirical attainment function (EAF) describes the probabilistic
#'  distribution of the outcomes obtained by a stochastic algorithm in the
#'  objective space. This package implements plots of summary
#'  attainment surfaces and differences between the first-order
#'  EAFs. These plots may be used for exploring the performance of
#'  stochastic local search algorithms for biobjective optimization
#'  problems and help in identifying certain algorithmic behaviors in a
#'  graphical way. 
#'
#' @section Functions:
#'
#'\tabular{rl}{
#'[eafdiffplot()] \tab  Empirical attainment function differences\cr
#'[eafplot()] \tab  Plot the Empirical Attainment Function for two objectives\cr
#'[read_datasets()] \tab  Read several data.frame sets
#'}
#'
#' @section Data:
#'
#'\describe{
#'\item{[`gcp2x2`]}{  Metaheuristics for solving the Graph Vertex Coloring Problem}
#'\item{[`HybridGA`]}{  Results of Hybrid GA on vanzyl and Richmond
#'water networks}
#'\item{[`SPEA2minstoptimeRichmond`]}{ Results of SPEA2 when minimising electrical cost and maximising the
#'minimum idle time of pumps on Richmond water network}
#'}
#' 
#' Extras are available at `system.file(package="eaf")`:
#'
#'\tabular{rl}{
#' `extdata`        \tab  External data sets (see [`read_datasets`]) \cr
#' `scripts/eaf`    \tab  EAF command-line program \cr
#' `scripts/eafplot`\tab  Perl script to generate plots of attainment surfaces\cr
#' `scripts/eafdiff`\tab  Perl script to generate plots of EAF differences
#'}
#'
#' @import graphics
#' @importFrom grDevices pdf colorRampPalette embedFonts gray
#' @importFrom Rdpack reprompt
#' @importFrom utils modifyList write.table tail
#'
#' @useDynLib eaf, .registration = TRUE
#'
#' @references
#' 
#' \insertRef{Grunert01}{eaf}
#'
#' \insertRef{GruFon2009:emaa}{eaf}
#'  
#' \insertRef{LopPaqStu09emaa}{eaf}
#'
#' \insertRef{FonGueLopPaq2011emo}{eaf}
#'  
#'
#'@examples
#' data(gcp2x2)
#' tabucol<-subset(gcp2x2, alg!="TSinN1")
#' tabucol$alg<-tabucol$alg[drop=TRUE]
#' eafplot(time+best~run,data=tabucol,subset=tabucol$inst=="DSJC500.5")
#' 
#' eafplot(time+best~run|inst,groups=alg,data=gcp2x2)
#' eafplot(time+best~run|inst,groups=alg,data=gcp2x2,
#' 	percentiles = c(0,50,100), cex = 1.4, lty = c(2,1,2),lwd = c(2,2,2),
#'         col = c("black","blue","grey50"))
#'  
#' extdata_path <- system.file(package="eaf","extdata")
#' A1 <- read_datasets(file.path(extdata_path, "wrots_l100w10_dat"))
#' A2 <- read_datasets(file.path(extdata_path, "wrots_l10w100_dat"))
#' eafplot(A1, percentiles=c(50))
#' eafplot(list(A1=A1, A2=A2), percentiles=c(50))
#' eafdiffplot(A1, A2)
#' ## Save to a PDF file
#' # dev.copy2pdf(file="eaf.pdf", onefile=TRUE, width=5, height=4)
#' @keywords internal
#' @concept multivariate
#' @concept optimize
#' @concept time-quality algorithm profile
#' @concept empirical attainment function
"_PACKAGE"


#' Results of Hybrid GA on vanzyl and Richmond water networks
#'
#' The data has the only goal of providing an example of use of eafplot.
#'
#'@format
#'  A list with two data frames, each of them with three columns, as
#'  produced by [read_datasets()].
#'  \describe{
#'    \item{`$vanzyl`}{data frame of results on vanzyl network}
#'    \item{`$richmond`}{data frame of results on Richmond
#'      network. The second column is filled with `NA`}
#'  }
#' 
#'@source \insertRef{LopezIbanezPhD}{eaf}.
#'
#' @examples
#'data(HybridGA)
#'print(HybridGA$vanzyl)
#'print(HybridGA$richmond)
#' @keywords datasets
"HybridGA"

#'Results of SPEA2 when minimising electrical cost and maximising the
#'minimum idle time of pumps on Richmond water network.
#'
#'The data has the only goal of providing an example of use of eafplot.
#'
#'@format 
#'  A data frame as produced by [read_datasets()]. The second
#'  column measures time in seconds and corresponds to a maximisation problem.
#'
#' @source \insertRef{LopezIbanezPhD}{eaf}
#'
#'@examples 
#' data(HybridGA)
#' data(SPEA2minstoptimeRichmond)
#' SPEA2minstoptimeRichmond[,2] <- SPEA2minstoptimeRichmond[,2] / 60
#' eafplot (SPEA2minstoptimeRichmond, xlab = expression(C[E]),
#'          ylab = "Minimum idle time (minutes)", maximise = c(FALSE, TRUE),
#'          las = 1, log = "y", legend.pos = "bottomright")
#' @keywords datasets
"SPEA2minstoptimeRichmond"

#' Results of SPEA2 with relative time-controlled triggers on Richmond water
#' network.
#'
#' The data has the only goal of providing an example of use of eafplot.
#'
#'@format 
#'  A data frame as produced by [read_datasets()].
#'
#' @source \insertRef{LopezIbanezPhD}{eaf}
#'
#'@examples 
#'data(HybridGA)
#'data(SPEA2relativeRichmond)
#'eafplot (SPEA2relativeRichmond, percentiles = c(25, 50, 75),
#'         xlab = expression(C[E]), ylab = "Total switches",
#'         xlim = c(90, 140), ylim = c(0, 25),
#'         extra.points = HybridGA$richmond, extra.lty = "dashed",
#'         extra.legend = "Hybrid GA")
#' @keywords datasets
"SPEA2relativeRichmond"

#'Results of SPEA2 with relative time-controlled triggers on Vanzyl's
#'water network.
#'
#'The data has the only goal of providing an example of use of eafplot.
#'
#'@format 
#'  A data frame as produced by [read_datasets()].
#'
#'@source \insertRef{LopezIbanezPhD}{eaf}
#'
#'@examples 
#'data(HybridGA)
#'data(SPEA2relativeVanzyl)
#'eafplot(SPEA2relativeVanzyl, percentiles = c(25, 50, 75), 
#'        xlab = expression(C[E]), ylab = "Total switches", xlim = c(320, 400),
#'        extra.points = HybridGA$vanzyl, extra.legend = "Hybrid GA")
#' @keywords datasets
"SPEA2relativeVanzyl"

#' Metaheuristics for solving the Graph Vertex Coloring Problem
#'
#'  Two metaheuristic algorithms, TabuCol (Hertz et al., 1987) and
#'  simulated annealing \citep{JohAraMcGSch1991}, to find a good
#'  approximation of the chromatic number of two random graphs. The data
#'  here has the only goal of providing an example of use of eafplot for
#'  comparing algorithm performance with respect to both time and quality
#'  when modelled as two objectives in trade off.
#'
#' @format 
#'  A data frame with 3133 observations on the following 6 variables.
#'  \describe{
#'    \item{`alg`}{a factor with levels `SAKempeFI` and `TSinN1`}
#'    \item{`inst`}{a factor with levels `DSJC500.5` and
#'      `DSJC500.9`. Instances are taken from the DIMACS repository.}
#'    \item{`run`}{a numeric vector indicating the run to
#'  which the observation belong. }
#'    \item{`best`}{a numeric vector indicating the best solution in
#'  number of colors found in the corresponding run up to that time.}
#'    \item{`time`}{a numeric vector indicating the time since the
#'  beginning of the run for each observation. A rescaling is applied.}
#'    \item{`titer`}{a numeric vector indicating iteration number
#'  corresponding to the observations.}
#'  }
#'
#'@details
#'  Each algorithm was run 10 times per graph registering the time and
#'  iteration number at which a new best solution was found. A time limit
#'  corresponding to 500*10^5 total iterations of TabuCol was imposed. The
#'  time was then normalized on a scale from 0 to 1 to make it instance
#'  independent.
#'
#'@source \insertRef{ChiarandiniPhD}{eaf} (page 138)
#'
#'@references 
#'  A. Hertz and D. de Werra. Using Tabu Search Techniques for Graph
#'  Coloring. Computing, 1987, 39(4), 345-351.
#'
#' \insertAllCited{}
#'
#'@examples 
#' data(gcp2x2)
#' @keywords datasets
"gcp2x2"

#' Conditional Pareto fronts obtained from Gaussian processes simulations.
#'
#' The data has the only goal of providing an example of use of [vorobT()] and
#' [vorobDev()]. It has been obtained by fitting two Gaussian processes on 20
#' observations of a bi-objective problem, before generating conditional
#' simulation of both GPs at different locations and extracting non-dominated
#' values of coupled simulations.
#'
#' @format 
#'  A data frame with 2967 observations on the following 3 variables.
#'  \describe{
#'    \item{`f1`}{first objective values.}
#'    \item{`f2`}{second objective values.}
#'    \item{`set`}{indices of corresponding conditional Pareto fronts.}
#'  }
#'
#'@source
#'  
#' \insertRef{BinGinRou2015gaupar}{eaf}
#'
#'@examples 
#' data(CPFs)
#' 
#' res <- vorobT(CPFs, reference = c(2, 200))
#' eafplot(CPFs[,1:2], sets = CPFs[,3], percentiles = c(0, 20, 40, 60, 80, 100),
#'        col = gray(seq(0.8, 0.1, length.out = 6)^2), type = "area",
#'        legend.pos = "bottomleft", extra.points = res$VE, extra.col = "cyan")
#'@keywords datasets
"CPFs"