#' Simulate a sequence assuming node substitutions are not shared amongst
#' offspring, given two substitution matrices: one for substitutions occuring
#' on the nodes, and one for substitutions occuring along the branches.
#' @param phy tree for which to simulate sequences
#' @param Q1 substitution matrix along the branches, default = JC
#' @param Q2 substitution matrix on the nodes, default = JC
#' @param rate1 mutation rate along the branch, default = 0.1
#' @param rate2 mutation rate on the node, default = 0.1
#' @param l number of base pairs to simulate
#' @param bf base frequencies, default = c(0.25, 0.25, 0.25, 0.25)
#' @param rootseq sequence at the root, simulated by default
#' @param node_time amount of time spent at the nodes
#' @return list with four items \enumerate{
#' \item{alignment} Phydat object with the resulting alignment
#' \item{rootseq} the rootsequence used
#' \item{total_branch_substitutions} total number of substitutions accumulated
#' on the branches
#' \item{total_node_substitutions} total number of substitutions accumulated at
#' the nodes}
#' @export
sim_unlinked <- function(phy,
                         Q1 = rep(1, 6),   # nolint
                         Q2 = rep(1, 6),   # nolint
                         rate1 = 0.1,
                         rate2 = 0.1,
                         l = 1000,
                         bf = rep(0.25, 4),
                         rootseq = NULL,
                         node_time = 1e-3) {
  levels <- c("a", "c", "g", "t")
  if (is.null(rootseq)) {
    rootseq <- sample(levels, l, replace = TRUE, prob = bf)
  }
  if (length(rootseq) != l) {
    stop(
      "'rootseq' must have the same length as 'l'. \n",
      "length 'rootseq': ", length(rootseq), " \n",
      "value of 'l': ", l, " \n"
    )
  }
  # only extract the 6 important rates.
  if (is.matrix(Q1)) Q1 <- Q1[lower.tri(Q1)]  # nolint
  if (is.matrix(Q2)) Q2 <- Q2[lower.tri(Q2)]  # nolint
  # capital Q is retained to conform to mathematical notation on wikipedia
  # and in the literature
  eig_q1 <- phangorn::edQt(Q1, bf) # eigen values
  eig_q2 <- phangorn::edQt(Q2, bf) # eigen values
  m <- length(levels) # always 4 (bases)
  phy <- stats::reorder(phy)
  edge <- phy$edge
  num_nodes <- max(edge)
  parent <- as.integer(edge[, 1])
  child <- as.integer(edge[, 2])
  root <- as.integer(parent[!match(parent, child, 0)][1])
  res <- matrix(NA, l, num_nodes)
  res[, root] <- rootseq
  tl <- phy$edge.length
  branch_subs_all <- rep(0, length(parent))
  node_subs_all   <- rep(0, length(parent))
  for (i in seq_along(tl)) {
    from <- parent[i]
    to <- child[i]
    # first we do substitutions due to the node model:
    P <- get_p_matrix(node_time, eig_q2, rate2)  # nolint
    # capital P is retained to conform to mathematical notation on wikipedia
    # and in the literature
    for (j in 1:m) {
      ind <- res[, from] == levels[j]
      res[ind, to] <- sample(levels, sum(ind), replace = TRUE, prob = P[, j])
    }
    node_subs <- sum(res[, to] != res[, from])
    # and then we add extra substitutions
    from <- to # the parent is now the individual again
    P <- get_p_matrix(tl[i], eig_q1, rate1)  # nolint
    # capital P is retained to conform to mathematical notation on wikipedia
    # and in the literature
    before_mut_seq <- res[, from]
    after_mut_seq <- before_mut_seq
    for (j in 1:m) {
      ind <- before_mut_seq == levels[j]
      after_mut_seq[ind] <- sample(levels, sum(ind), replace = TRUE,
                                   prob = P[, j])
    }
    branch_subs <- sum(after_mut_seq != before_mut_seq)
    res[, to] <- after_mut_seq
    branch_subs_all[i] <- branch_subs_all[i] + branch_subs
    node_subs_all[i]   <- node_subs_all[i] + node_subs
  }
  updated_subs <- calc_accumulated_substitutions(phy, branch_subs_all,
                                                 node_subs_all)
  phy_no_extinct <- geiger::drop.extinct(phy)
  k <- length(phy$tip.label)
  label <- c(phy$tip.label, as.character((k + 1):num_nodes))
  colnames(res) <- label
  res <- res[, phy_no_extinct$tip.label, drop = FALSE]
  alignment_phydat <- phyDat.DNA(as.data.frame(res, stringsAsFactors = FALSE))
  output <- list("alignment" = alignment_phydat,
                 "root_seq" = rootseq,
                 "total_branch_substitutions" = updated_subs$total_branch_subs,
                 "total_node_substitutions" = updated_subs$total_node_subs,
                 "total_accumulated_substitutions" =
                    updated_subs$total_accumulated_substitutions)
  return(output)
}
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