#' Sequence-Order-Coupling Numbers
#'
#' Sequence-Order-Coupling Numbers
#'
#' This function calculates the Sequence-Order-Coupling Numbers
#' (Dim: \code{nlag * 2}, default is 60).
#'
#' @param x A character vector, as the input protein sequence.
#'
#' @param nlag The maximum lag, defualt is 30.
#'
#' @return A length \code{nlag * 2} named vector
#'
#' @keywords extract SOCN extractProtSOCN Sequence Order Coupling Number
#'
#' @aliases extractProtSOCN
#'
#' @author Nan Xiao <\url{https://nanx.me}>
#'
#' @seealso See \code{\link{extractProtQSO}} for
#' quasi-sequence-order descriptors.
#'
#' @export extractProtSOCN
#'
#' @references
#' Kuo-Chen Chou. Prediction of Protein Subcellar Locations by
#' Incorporating Quasi-Sequence-Order Effect.
#' \emph{Biochemical and Biophysical Research Communications},
#' 2000, 278, 477-483.
#'
#' Kuo-Chen Chou and Yu-Dong Cai. Prediction of Protein Sucellular Locations by
#' GO-FunD-PseAA Predictor.
#' \emph{Biochemical and Biophysical Research Communications},
#' 2004, 320, 1236-1239.
#'
#' Gisbert Schneider and Paul Wrede. The Rational Design of
#' Amino Acid Sequences by Artifical Neural Networks and Simulated
#' Molecular Evolution: Do Novo Design of an Idealized Leader Cleavge Site.
#' \emph{Biophys Journal}, 1994, 66, 335-344.
#'
#' @examples
#' x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
#' extractProtSOCN(x)
#'

extractProtSOCN = function (x, nlag = 30) {

    if (checkProt(x) == FALSE) stop('x has unrecognized amino acid type')

    N = nchar(x)
    if (N <= nlag) stop('Length of the protein sequence must be greater than "nlag"')

    DistMat1 = read.csv(system.file('sysdata/Schneider-Wrede.csv', package = 'Rcpi'), header = TRUE)
    DistMat2 = read.csv(system.file('sysdata/Grantham.csv', package = 'Rcpi'), header = TRUE)
    row.names(DistMat1) = as.character(DistMat1[, 1])
    DistMat1 = DistMat1[, -1]
    row.names(DistMat2) = as.character(DistMat2[, 1])
    DistMat2 = DistMat2[, -1]

    xSplitted = strsplit(x, split = '')[[1]]

    # Compute Schneider.tau_d

    tau1 = vector('list', nlag)

    for (d in 1:nlag) {
        for (i in 1:(N - d)) {
            tau1[[d]][i] = (DistMat1[xSplitted[i], xSplitted[i + d]])^2
        }
    }

    tau1 = sapply(tau1, sum)

    names(tau1) = paste('Schneider.lag', 1:nlag, sep = '')

    # Compute Grantham.tau_d

    tau2 = vector('list', nlag)

    for (d in 1:nlag) {
        for (i in 1:(N - d)) {
            tau2[[d]][i] = (DistMat2[xSplitted[i], xSplitted[i + d]])^2
        }
    }

    tau2 = sapply(tau2, sum)

    names(tau2) = paste('Grantham.lag', 1:nlag, sep = '')

    tau = c(tau1, tau2)

    return(tau)

}
