##' @importFrom ape reorder.phylo

 layout.unrooted <- function(model, branch.length="branch.length", layout.method="equal_angle", MAX_COUNT=5, ...) {

     df <- switch(layout.method,

                  equal_angle = layoutEqualAngle(model, branch.length),

                  daylight = layoutDaylight(model, branch.length, MAX_COUNT),
     			 ape = layoutApe(model, branch.length)

                  )

     return(df)
 }

 set_branch_length_cladogram <- function(tree) {
     phylo <- as.phylo(tree)
     edge <- phylo$edge
     xpos <- getXcoord_no_length(phylo)
     phylo$edge.length <- xpos[edge[,2]] - xpos[edge[,1]]
 
     if (is(tree, "phylo")) {
         return(phylo)
     } else if (is(tree, "treedata")) {
         tree@phylo <- phylo
         return(tree)
     }
     message("unknown tree object, fail to set branch length for cladogram...")
     return(tree)
 }

 ##' 'Equal-angle layout algorithm for unrooted trees'
 ##'

 ##' @references

 ##' "Inferring Phylogenies" by Joseph Felsenstein.

 ##'

 ##' @title layoutEqualAngle

 ##' @param model tree object, e.g. phylo or treedata

 ##' @param branch.length set to 'none' for edge length of 1. Otherwise the phylogenetic tree edge length is used.
 ##' @return tree as data.frame with equal angle layout.

 layoutEqualAngle <- function(model, branch.length = "branch.length"){

 	tree <- as.phylo(model)

   if (! is.null(tree$edge.length)) {
       if (anyNA(tree$edge.length)) {
           warning("'edge.length' contains NA values...\n## setting 'edge.length' to NULL automatically when plotting the tree...")
           tree$edge.length <- NULL
       }
   }

   if (is.null(tree$edge.length) || branch.length == "none") {

       tree <- set_branch_length_cladogram(tree)
   }

   N <- treeio::Nnode2(tree)
   brlen <- numeric(N)

   brlen[tree$edge[,2]] <- tree$edge.length
 

   root <- tidytree::rootnode(tree)

   ## Convert Phylo tree to data.frame.
   ## df <- as.data.frame.phylo_(tree)

   df <- as_tibble(model) %>%

       mutate(isTip = ! .data$node %in% .data$parent)

 
     ## NOTE: Angles (start, end, angle) are in half-rotation units (radians/pi or degrees/180)
 
     ## create and assign NA to the following fields.

     df$x <- 0
     df$y <- 0
     df$start <- 0 # Start angle of segment of subtree.
     df$end   <- 0 # End angle of segment of subtree
     df$angle <- 0 # Orthogonal angle to beta for tip labels.

     ## Initialize root node position and angles.
     df[root, "x"] <- 0
     df[root, "y"] <- 0
     df[root, "start"] <- 0 # 0-degrees
     df[root, "end"]   <- 2 # 360-degrees
     df[root, "angle"] <- 0 # Angle label.
 

     df$branch.length <- brlen[df$node] # for cladogram
 
 

     ## Get number of tips for each node in tree.

   ## nb.sp <- sapply(1:N, function(i) length(get.offspring.tip(tree, i)))

   ## self_include = TRUE to return itself if the input node is a tip

   nb.sp <- vapply(1:N, function(i) length(offspring(tree, i, tiponly = TRUE, self_include = TRUE)), numeric(1))

     ## Get list of node id's.
     nodes <- getNodes_by_postorder(tree)
 
     for(curNode in nodes) {
         ## Get number of tips for current node.
         curNtip <- nb.sp[curNode]
         ## Get array of child node indexes of current node.

         ## children <- getChild(tree, curNode)
         children <- treeio::child(tree, curNode)

 
         ## Get "start" and "end" angles of a segment for current node in the data.frame.
         start <- df[curNode, "start"]
         end <- df[curNode, "end"]

         cur_x = df[curNode, "x"]
         cur_y = df[curNode, "y"]
         for (child in children) {

             ## Get the number of tips for child node.
             ntip.child <- nb.sp[child]
 
             ## Calculated in half radians.
             ## alpha: angle of segment for i-th child with ntips_ij tips.
             ## alpha = (left_angle - right_angle) * (ntips_ij)/(ntips_current)
             alpha <- (end - start) * ntip.child / curNtip
             ## beta = angle of line from parent node to i-th child.
             beta <- start + alpha / 2
 

             length.child <- df[child, "branch.length"]

 
             ## update geometry of data.frame.
             ## Calculate (x,y) position of the i-th child node from current node.

             df[child, "x"] <- cur_x + cospi(beta) * length.child
             df[child, "y"] <- cur_y + sinpi(beta) * length.child

             ## Calculate orthogonal angle to beta for tip label.

             df[child, "angle"] <- -90 - 180 * beta * sign(beta - 1)
             ## Update the start and end angles of the childs segment.
             df[child, "start"] <- start
             df[child, "end"] <- start + alpha
             start <- start + alpha
         }

     }

   tree_df <- as_tibble(df)
   class(tree_df) <- c("tbl_tree", class(tree_df))
   return(tree_df)

 }

 ##' Equal daylight layout method for unrooted trees.

 ##'

 ##' #' @title

 ##' @param model tree object, e.g. phylo or treedata

 ##' @param branch.length set to 'none' for edge length of 1. Otherwise the phylogenetic tree edge length is used.

 ##' @param MAX_COUNT the maximum number of iterations to run (default 5)

 ##' @return tree as data.frame with equal angle layout.

 ##' @references

 ##' The following aglorithm aims to implement the vague description of the "Equal-daylight Algorithm"
 ##' in "Inferring Phylogenies" pp 582-584 by Joseph Felsenstein.

 ##'

 ##' ```
 ##' Leafs are subtrees with no children
 ##' Initialise tree using equal angle algorithm
 ##' tree_df = equal_angle(tree)
 ##'
 ##' nodes = get list of nodes in tree_df breadth-first
 ##' nodes = remove tip nodes.

 ##'

 ##' ```

 layoutDaylight <- function(model, branch.length, MAX_COUNT=5 ){

 	tree <- as.phylo(model)

     ## How to set optimal

     MINIMUM_AVERAGE_ANGLE_CHANGE <- 0.05

     ## Initialize tree.

     tree_df <- layoutEqualAngle(model, branch.length)

     ## nodes = get list of nodes in tree_df
     ## Get list of node id's.
     ## nodes <- getNodes_by_postorder(tree)

     ## nodes <- getSubtree.df(tree_df, root)

     ## Get list of internal nodes
     ## nodes <- tree_df[tree_df$IsTip != TRUE]$nodes

     nodes <- getNodesBreadthFirst.df(tree_df)
     ## select only internal nodes
     internal_nodes <- tree_df[!tree_df$isTip,]$node
     ## Remove tips from nodes list, but keeping order.
     nodes <- intersect(nodes, internal_nodes)

     ave_change <- 1.0

     for (i in seq_len(MAX_COUNT)) {

         ## Reset max_change after iterating over tree.
         total_max <- 0.0

         for(currentNode_id in nodes){

             result <- applyLayoutDaylight(tree_df, currentNode_id)
             tree_df <- result$tree
             total_max <- total_max + result$max_change
         }

         # Calculate the running average of angle changes.

         ave_change <- total_max / length(nodes)

         message('Average angle change [',i,'] ', ave_change)

         if (ave_change <= MINIMUM_AVERAGE_ANGLE_CHANGE) break

     }

   tree_df <- as_tibble(tree_df)
   class(tree_df) <- c("tbl_tree", class(tree_df))
   return(tree_df)

 }

 ##' Apply the daylight alorithm to adjust the spacing between the subtrees and tips of the
 ##' specified node.

 ##'

 ##' @title applyLayoutDaylight
 ##' @param df tree data.frame
 ##' @param node_id is id of the node from which daylight is measured to the other subtrees.

 ##' @return list with tree data.frame with updated layout using daylight algorithm and max_change angle.

 ##' @importFrom rlang .data

 ##
 ##
 ## ```
 ## for node in nodes {
 ##   if node is a leaf {
 ##     next
 ##   }
 ##
 ##   subtrees = get subtrees of node
 ##
 ##   for i-th subtree in subtrees {
 ##     [end, start] = get left and right angles of tree from node id.
 ##     angle_list[i, 'left'] = end
 ##     angle_list[i, 'beta'] = start - end  # subtree arc angle
 ##     angle_list[i, 'index'] = i-th # index of subtree/leaf
 ##   }
 ##
 ##   sort angle_list by 'left' column in ascending order.
 ##
 ##   D = 360 - sum( angle_list['beta'] ) # total daylight angle
 ##   d = D / |subtrees| # equal daylight angle.
 ##
 ##   new_L = left angle of first subtree.
 ##
 ##   for n-th row in angle_list{
 ##     # Calculate angle to rotate subtree/leaf to create correct daylight angle.
 ##     new_left_angle = new_left_angle + d + angle_list[n, 'beta']
 ##     Calculate the difference between the old and new left angles.
 ##     adjust_angle = new_left_angle - angle_list[n, 'left']
 ##
 ##     index = angle_list['index']
 ##     rotate subtree[index] wrt n-th node by adjust_angle
 ##     }
 ##   }
 ## }
 ## ```

 applyLayoutDaylight <- function(df, node_id){

   # Get lists of node ids for each subtree, including  rest of unrooted tree.
   subtrees <- getSubtreeUnrooted.df(df, node_id)
 
   # Return tree if only 2 or less subtrees to adjust.
   if(length(subtrees) <= 2){

     return( list(tree = df, max_change = 0.0) )

   }

   # Find start and end angles for each subtree.
   #   subtrees = get subtrees of node
   #   for i-th subtree in subtrees {

   angle_list = purrr::map_dfr(subtrees, ~{
     getTreeArcAngles(df, node_id, .x) %>% dplyr::bind_rows()
   }) %>% dplyr::transmute(

     left = .data$left,

     beta = .data$left - .data$right,
     beta = ifelse(.data$beta < 0, .data$beta + 2, .data$beta),
     subtree_id = seq_len(nrow(.))
   ) %>% dplyr::arrange(.data$left)

   #   sort angle_list by 'left angle' column in ascending order.
   #   D = 360 - sum( angle_list['beta'] ) # total day
   #   d = D / |subtrees| # equal daylight angle.

   total_daylight <- 2 - sum(angle_list[['beta']])

   d <- total_daylight / length(subtrees)

   # Initialise new left-angle as first subtree left-angle.

   new_left_angle <- angle_list$left[1]

 
   # Adjust angles of subtrees and tips connected to current node.
   # for n-th row in angle_list{
   # Skip the first subtree as it is not adjusted.

   max_change <- 0.0

   for (i in 2:nrow(angle_list) ) {
     # Calculate angle to rotate subtree/leaf to create correct daylight angle.

     new_left_angle <- new_left_angle + d + angle_list$beta[i]

     # Calculate the difference between the old and new left angles.

     adjust_angle <- new_left_angle - angle_list$left[i]

     max_change <- max(max_change, abs(adjust_angle))
     #cat('Adjust angle:', abs(adjust_angle), ' Max change:', max_change ,'\n')

     # rotate subtree[index] wrt current node

     subtree_id <- angle_list$subtree_id[i]

     subtree_nodes <- subtrees[[subtree_id]]$subtree
     # update tree_df for all subtrees with rotated points.
     df <- rotateTreePoints.df(df, node_id, subtree_nodes, adjust_angle)
   }

 
   return( list(tree = df, max_change = max_change) )
 

 }
 
 

 ##' Find the right (clockwise rotation, angle from +ve x-axis to furthest subtree nodes) and

 ##' left (anti-clockwise angle from +ve x-axis to subtree) Returning arc angle in `[0, 2]` (0 to 360) domain.

 ##'

 ##' @title getTreeArcAngles
 ##' @param df tree data.frame
 ##' @param origin_id node id from which to calculate left and right hand angles of subtree.

 ##' @param subtree named list of root id of subtree (node) and list of node ids for given subtree (subtree).

 ##' @return named list with right and left angles in range `[0,2]` i.e 1 = 180 degrees, 1.5 = 270 degrees.

 getTreeArcAngles <- function(df, origin_id, subtree) {

     df_x = df$x
     df_y = df$y
     x_origin = df_x[origin_id]
     y_origin = df_y[origin_id]

     ## Initialise variables
     theta_child <- 0.0
     subtree_root_id <- subtree$node
     subtree_node_ids <- subtree$subtree
     ## Initialise angle from origin node to parent node.
     ## If subtree_root_id is child of origin_id

     ## if (subtree_root_id %in% getChild.df(df, origin_id)) {

     if (subtree_root_id %in% tidytree:::child.tbl_tree(df, origin_id)$node) {

         ## get angle from original node to parent of subtree.

         theta_left <- getNodeAngle.vector(x_origin, y_origin, df_x[subtree_root_id], df_y[subtree_root_id])

         theta_right <- theta_left
     } else if( subtree_root_id == origin_id ){
         ## Special case.
         ## get angle from parent of subtree to children

         ## children_ids <- getChild.df(df, subtree_root_id)

         children_ids <- tidytree:::child.tbl_tree(df, subtree_root_id)$node

         if(length(children_ids) == 2){
             ## get angles from parent to it's two children.

             theta1 <- getNodeAngle.vector(x_origin, y_origin, df_x[children_ids[1]], df_y[children_ids[1]])
             theta2 <- getNodeAngle.vector(x_origin, y_origin, df_x[children_ids[2]], df_y[children_ids[2]])

             delta <- theta1 - theta2
             ## correct delta for points crossing 180/-180 quadrant.
             if(delta > 1){
                 delta_adj = delta - 2
             } else if(delta < -1){
                 delta_adj = delta + 2
             } else{
                 delta_adj <- delta
             }
             if(delta_adj >= 0){
                 theta_left = theta1
                 theta_right = theta2
             } else if(delta_adj < 0){
                 theta_left = theta2
                 theta_right = theta1
             }
         }else{
             ## subtree only has one child node.

             theta_left <- getNodeAngle.vector(x_origin, y_origin, df_x[children_ids[1]], df_y[children_ids[1]])

             theta_right <- theta_left
         }
     } else {
         ## get the real root of df tree to initialise left and right angles.
         tree_root <- getRoot.df(df)
         if( !is.na(tree_root) & is.numeric(tree_root) ){

             theta_left <- getNodeAngle.vector(x_origin, y_origin, df_x[tree_root], df_y[tree_root])

             theta_right <- theta_left
         } else{

       print('ERROR: no root found!')
       theta_left <- NA
     }

   }

   # no parent angle found.

   # Subtree has to have 1 or more nodes to compare.
   if (is.na(theta_left) || (length(subtree_node_ids) == 0)){

       return(c('left' = 0, 'right' = 0))

   }

   # create vector with named columns
   # left-hand and right-hand angles between origin node and the extremities of the tree nodes.

     arc <- c('left' = theta_left, 'right' = theta_right)

   # Calculate the angle from the origin node to each child node.
   # Moving from parent to children in depth-first traversal.

   # Skip if parent_id is a tip or parent and child node are the same.
   subtree_node_ids = subtree_node_ids[subtree_node_ids %in% df$parent]
   subtree_node_ids = subtree_node_ids[subtree_node_ids != origin_id]
   for(parent_id in subtree_node_ids){

     # Get angle from origin node to parent node.

     theta_parent <- getNodeAngle.vector(x_origin, y_origin, df_x[parent_id], df_y[parent_id])

       ## children_ids <- getChild.df(df, parent_id)

       children_ids <- tidytree:::child.tbl_tree(df, parent_id)$node

     # Skip if child is parent node of subtree.
     children_ids = children_ids[children_ids != origin_id]
     for(child_id in children_ids){
       theta_child <- getNodeAngle.vector(x_origin, y_origin, df_x[child_id], df_y[child_id])

       # Skip if child node is already inside arc.
       # if left < right angle (arc crosses 180/-180 quadrant) and child node is not inside arc of tree.
       # OR if left > right angle (arc crosses 0/360 quadrant) and child node is inside gap

       if ((arc['left'] < arc['right'] & !(theta_child > arc['left'] & theta_child < arc['right'])) |
           (arc['left'] > arc['right'] &  (theta_child < arc['left'] & theta_child > arc['right'])) ){

         # child node inside arc.
         next
       }
       delta <- theta_child - theta_parent
       delta_adj <- delta
       # Correct the delta if parent and child angles cross the 180/-180 half of circle.
       # If delta > 180
       if( delta > 1){ # Edge between parent and child cross upper and lower quadrants of cirlce on 180/-180 side.
         delta_adj <- delta - 2 # delta' = delta - 360
       # If delta < -180
       }else if( delta < -1){ # Edge between parent and child cross upper and lower quadrants of cirlce
         delta_adj <- delta + 2 # delta' = delta - 360
       }
       theta_child_adj <- theta_child
       # If angle change from parent to node is positive (anti-clockwise), check left angle
       if(delta_adj > 0){

         # If child/parent edges cross the -180/180 quadrant (angle between them is > 180),

         # check if right angle and child angle are different signs and adjust if needed.
         if( abs(delta) > 1){
           if( arc['left'] > 0 & theta_child < 0){
             theta_child_adj <- theta_child + 2
           }else if (arc['left'] < 0 & theta_child > 0){
             theta_child_adj <- theta_child - 2
           }
         }

         # check if left angle of arc is less than angle of child. Update if true.

         if( arc['left'] < theta_child_adj ){
           arc['left'] <- theta_child

         }

       # If angle change from parent to node is negative (clockwise), check right angle

       }else if(delta_adj < 0){

         # If child/parent edges cross the -180/180 quadrant (angle between them is > 180),

         # check if right angle and child angle are different signs and adjust if needed.
         if( abs(delta) > 1){
           # Else change in angle from parent to child is negative, then adjust child angle if right angle is a different sign.
           if( arc['right'] > 0 & theta_child < 0){
             theta_child_adj <- theta_child + 2
           }else if (arc['right'] < 0 & theta_child > 0){
             theta_child_adj <- theta_child - 2
           }
         }
         # check if right angle of arc is greater than angle of child. Update if true.
         if( arc['right'] > theta_child_adj  ){
           arc['right'] <- theta_child

         }

       }
     }

   }

   # Convert arc angles of [1, -1] to [2,0] domain.
   arc[arc<0] <- arc[arc<0] + 2

   arc

 }

 ##' Rotate the points in a tree data.frame around a pivot node by the angle specified.

 ##'

 ##' @title rotateTreePoints.data.frame
 ##' @rdname rotateTreePoints

 ##' @param df tree data.frame
 ##' @param pivot_node is the id of the pivot node.
 ##' @param nodes list of node numbers that are to be rotated by angle around the pivot_node

 ##' @param angle in range `[0,2]`, ie degrees/180, radians/pi

 ##' @return updated tree data.frame with points rotated by angle
 rotateTreePoints.df <- function(df, pivot_node, nodes, angle){
   # Rotate nodes around pivot_node.
   # x' = cos(angle)*delta_x - sin(angle)*delta_y + delta_x
   # y' = sin(angle)*delta_x + cos(angle)*delta_y + delta_y
   cospitheta <- cospi(angle)
   sinpitheta <- sinpi(angle)

   pivot_x = df$x[pivot_node]
   pivot_y = df$y[pivot_node]

   delta_x = df$x - pivot_x
   delta_y = df$y - pivot_y

   df = mutate(df,

     x = ifelse(.data$node %in% nodes, cospitheta * delta_x - sinpitheta * delta_y + pivot_x, .data$x),
     y = ifelse(.data$node %in% nodes, sinpitheta * delta_x + cospitheta * delta_y + pivot_y, .data$y)

   )

   x_parent = df$x[df$parent]
   y_parent = df$y[df$parent]

   # Now update tip labels of rotated tree.
   # angle is in range [0, 360]

   # Update label angle of tipnode if not root node.
   nodes = nodes[! nodes %in% df$parent]

   df %>% mutate(

     angle = ifelse(.data$node %in% nodes,
        getNodeAngle.vector(x_parent, y_parent, .data$x, .data$y) %>%
          {180 * ifelse(. < 0, 2 + ., .)},
        .data$angle)
   )

 }

 ##' Get the angle between the two nodes specified.

 ##'

 ##' @title getNodeAngle.df
 ##' @param df tree data.frame
 ##' @param origin_node_id origin node id number
 ##' @param node_id end node id number

 ##' @return angle in range `[-1, 1]`, i.e. degrees/180, radians/pi

 getNodeAngle.df <- function(df, origin_node_id, node_id){

   if (origin_node_id != node_id) {

     df_x = df$x
     df_y = df$y
     atan2(df_y[node_id] - df_y[origin_node_id], df_x[node_id] - df_x[origin_node_id]) / pi

   }else{

     NA

   }
 }

 getNodeAngle.vector <- function(x_origin, y_origin, x, y) {
   atan2(y - y_origin, x - x_origin) / pi
 }
 

 euc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2))
 

 ## Get the distances from the node to all other nodes in data.frame (including itself if in df)

 getNodeEuclDistances <- function(df, node){
   # https://stackoverflow.com/questions/24746892/how-to-calculate-euclidian-distance-between-two-points-defined-by-matrix-contain#24747155
   dist <- NULL
   for(i in 1:nrow(df)) dist[i] <- euc.dist(df[df$node==node, c('x', 'y')], df[i, c('x', 'y')])
   return(dist)
 }

 ##' Get all children of node from tree, including start_node.

 ##'

 ##' @title getSubtree
 ##' @param tree ape phylo tree object
 ##' @param node is the tree node id from which the tree is derived.
 ##' @return list of all child node id's from starting node.
 getSubtree <- function(tree, node){

   ## subtree <- c(node)
   ## i <- 1
   ## while( i <= length(subtree)){
   ##   subtree <- c(subtree, treeio::child(tree, subtree[i]))
   ##   # remove any '0' root nodes
   ##   subtree <- subtree[subtree != 0]
   ##   i <- i + 1
   ## }
     ## return(subtree)
     tidytree::offspring(tree, node, self_include = TRUE)

 }
 

 ##' Get all children of node from df tree using breath-first.
 ##'

 ##' @title getSubtree.df

 ##' @param df tree data.frame
 ##' @param node id of starting node.
 ##' @return list of all child node id's from starting node.

 getSubtree.df <- function(df, node){

   ## subtree <- node[node != 0]
   ## i <- 1
   ## while( i <= length(subtree)){
   ##     ## subtree <- c(subtree, getChild.df(df, subtree[i]))
   ##     subtree <- c(subtree, tidytree::child(df, subtree[i])$node)
   ##   i <- i + 1
   ## }
     ## subtree

     #tidytree:::offspring.tbl_tree(df, node, self_include = TRUE)$node
     offspring.tbl_tree(df, node, self_include = TRUE)$node

 }
 

 ##' Get all subtrees of specified node. This includes all ancestors and relatives of node and

 ##' return named list of subtrees.

 ##'

 ##' @title getSubtreeUnrooted

 ##' @param tree ape phylo tree object
 ##' @param node is the tree node id from which the subtrees are derived.
 ##' @return named list of subtrees with the root id of subtree and list of node id's making up subtree.

 getSubtreeUnrooted <- function(tree, node){

   # if node leaf, return nothing.

   if( treeio::isTip(tree, node) ){

     # return NA
     return(NA)
   }

   subtrees <- list()

   # get subtree for each child node.

     ## children_ids <- getChild(tree, node)
     children_ids <- treeio::child(tree, node)

   remaining_nodes <- getNodes_by_postorder(tree)
   # Remove current node from remaining_nodes list.
   remaining_nodes <- setdiff(remaining_nodes, node)

   for( child in children_ids ){
     # Append subtree nodes to list if not 0 (root).
     subtree <- getSubtree(tree, child)
     subtrees[[length(subtrees)+1]] <- list( node = child, subtree = subtree)
     # remove subtree nodes from remaining nodes.
     remaining_nodes <- setdiff(remaining_nodes, as.integer(unlist(subtrees[[length(subtrees)]]['subtree']) ))
   }

   # The remaining nodes that are not found in the child subtrees are the remaining subtree nodes.
   # ie, parent node and all other nodes. We don't care how they are connect, just their ids.

   parent_id <- parent(tree, node)

   # If node is not root, add remainder of tree nodes as subtree.
   if( parent_id != 0 & length(remaining_nodes) >= 1){
     subtrees[[length(subtrees)+1]] <- list( node = parent_id, subtree = remaining_nodes)
   }

   return(subtrees)
 }
 
 
 ##' Get all subtrees of node, as well as remaining branches of parent (ie, rest of tree structure as subtree)
 ##' return named list of subtrees with list name as starting node id.

 ##' @title getSubtreeUnrooted

 ##' @param df tree data.frame
 ##' @param node is the tree node id from which the subtrees are derived.

 ##' @importFrom tidytree parent

 ##' @return named list of subtrees with the root id of subtree and list of node id's making up subtree.
 getSubtreeUnrooted.df <- function(df, node){
   # get subtree for each child node.

                                         # children_ids <- getChild.df(df, node)

     children_ids <- child.tbl_tree(df, node)$node

   if (length(children_ids) == 0L) return(NULL)
   # if node leaf, return nothing.

   subtrees = tibble::tibble(
     node = children_ids,
     subtree = purrr::map(.data$node, ~getSubtree.df(df, .x))
   )
   remaining_nodes = setdiff(df$node, purrr::flatten_int(subtrees$subtree))

   # The remaining nodes that are not found in the child subtrees are the remaining subtree nodes.
   # ie, parent node and all other nodes. We don't care how they are connected, just their id.

   parent_id <- parent.tbl_tree(df, node)$node

   # If node is not root.

   if ((length(parent_id) > 0) & (length(remaining_nodes) > 0)) {
     subtrees = tibble::add_row(subtrees, node = parent_id, subtree = list(remaining_nodes))

   }

   purrr::transpose(subtrees)

 }
 
 
 getRoot.df <- function(df, node){

   root <- which(is.na(df$parent))

   # Check if root was found.
   if(length(root) == 0){

       ## Alternatively, root can self reference, eg node = 10, parent = 10
       root <- df$node[df$parent == df$node]
       ## root <- unlist(apply(df, 1, function(x){ if(x['node'] == x['parent']){ x['node'] } }))

   }

   return(root)
 }
 
 ##' Get the nodes of tree from root in breadth-first order.

 ##'

 ##' @title getNodesBreadthFirst.df
 ##' @param df tree data.frame
 ##' @return list of node id's in breadth-first order.
 getNodesBreadthFirst.df <- function(df){
 
   root <- getRoot.df(df)

   if(treeio::isTip(df, root)){

     return(root)
   }
 
   tree_size <- nrow(df)
   # initialise list of nodes
   res <- root

   i <- 1
   while(length(res) < tree_size){
     parent <- res[i]
     i <- i + 1

     # Skip if parent is a tip.

     if(treeio::isTip(df, parent)){

       next
     }

     # get children of current parent.

     children <- tidytree::child(df,parent)$node

 
     # add children to result
     res <- c(res, children)

   }

   return(res)

 }
 
 

 isRoot <- function(tr, node) {
     getRoot(tr) == node
 }
 
 getNodeName <- function(tr) {
     if (is.null(tr$node.label)) {
         n <- length(tr$tip.label)
         nl <- (n + 1):(2 * n - 2)
         nl <- as.character(nl)
     }
     else {
         nl <- tr$node.label
     }
     nodeName <- c(tr$tip.label, nl)
     return(nodeName)
 }
 
 
 
 get.trunk <- function(tr) {
     root <- getRoot(tr)
     path_length <- sapply(1:(root-1), function(x) get.path_length(tr, root, x))
     i <- which.max(path_length)
     return(get.path(tr, root, i))
 }
 
 ##' path from start node to end node
 ##'
 ##'
 ##' @title get.path
 ##' @param phylo phylo object
 ##' @param from start node
 ##' @param to end node
 ##' @return node vectot

 ##' @importFrom tidytree ancestor

 ##' @export
 ##' @author Guangchuang Yu
 get.path <- function(phylo, from, to) {

     anc_from <- ancestor(phylo, from)

     anc_from <- c(from, anc_from)

     anc_to <- ancestor(phylo, to)

     anc_to <- c(to, anc_to)
     mrca <- intersect(anc_from, anc_to)[1]
 
     i <- which(anc_from == mrca)
     j <- which(anc_to == mrca)
 
     path <- c(anc_from[1:i], rev(anc_to[1:(j-1)]))
     return(path)
 }
 
 
 get.path_length <- function(phylo, from, to, weight=NULL) {
     path <- get.path(phylo, from, to)
     if (is.null(weight)) {
         return(length(path)-1)
     }
 
     df <- fortify(phylo)
     if ( ! (weight %in% colnames(df))) {
         stop("weight should be one of numerical attributes of the tree...")
     }
 
     res <- 0
 
     get_edge_index <- function(df, from, to) {
         which((df[,1] == from | df[,2] == from) &
                   (df[,1] == to | df[,2] == to))
     }
 
     for(i in 1:(length(path)-1)) {
         ee <- get_edge_index(df, path[i], path[i+1])
         res <- res + df[ee, weight]
     }
 
     return(res)
 }
 
 ##' @importFrom ape reorder.phylo
 getNodes_by_postorder <- function(tree) {
   tree <- reorder.phylo(tree, "postorder")
     unique(rev(as.vector(t(tree$edge[,c(2,1)]))))
 }
 
 getXcoord2 <- function(x, root, parent, child, len, start=0, rev=FALSE) {
     x[root] <- start
     x[-root] <- NA  ## only root is set to start, by default 0
 
     currentNode <- root
     direction <- 1
     if (rev == TRUE) {
         direction <- -1
     }
 

     ignore_negative_edge <- getOption("ignore.negative.edge", default=FALSE)
 
     if (any(len < 0) && !ignore_negative_edge) {

         warning_wrap("The tree contained negative ", 

                      ifelse(sum(len < 0)>1, "edge lengths", "edge length"),
                      ". If you want to ignore the ", 
                      ifelse(sum(len<0) > 1, "edges", "edge"),
                      ", you can set 'options(ignore.negative.edge=TRUE)', then re-run ggtree."
                      )

     }

     while(anyNA(x)) {
         idx <- which(parent %in% currentNode)
         newNode <- child[idx]
         if (ignore_negative_edge){

             x[newNode] <- x[parent[idx]]+len[idx] * direction * sign(len[idx])

         } else {

             x[newNode] <- x[parent[idx]]+len[idx] * direction

         }
         currentNode <- newNode

     }
     

     return(x)
 }
 
 
 
 
 
 
 getXcoord_no_length <- function(tr) {
     edge <- tr$edge
     parent <- edge[,1]
     child <- edge[,2]
     root <- getRoot(tr)
 
     len <- tr$edge.length
 
     N <- getNodeNum(tr)
     x <- numeric(N)
     ntip <- Ntip(tr)
     currentNode <- 1:ntip
     x[-currentNode] <- NA
 
     cl <- split(child, parent)
     child_list <- list()
     child_list[as.numeric(names(cl))] <- cl
 
     while(anyNA(x)) {
         idx <- match(currentNode, child)
         pNode <- parent[idx]
         ## child number table
         p1 <- table(parent[parent %in% pNode])
         p2 <- table(pNode)
         np <- names(p2)
         i <- p1[np] == p2
         newNode <- as.numeric(np[i])
 
         exclude <- rep(NA, max(child))
         for (j in newNode) {
             x[j] <- min(x[child_list[[j]]]) - 1
             exclude[child_list[[j]]] <- child_list[[j]]
         }
         exclude <- exclude[!is.na(exclude)]
 
         ## currentNode %<>% `[`(!(. %in% exclude))
         ## currentNode %<>% c(., newNode) %>% unique
         currentNode <- currentNode[!currentNode %in% exclude]
         currentNode <- unique(c(currentNode, newNode))
 
     }
     x <- x - min(x)
     return(x)
 }
 
 
 
 
 getXcoord <- function(tr) {
     edge <- tr$edge
     parent <- edge[,1]
     child <- edge[,2]
     root <- getRoot(tr)
 
     len <- tr$edge.length
 
     N <- getNodeNum(tr)
     x <- numeric(N)
     x <- getXcoord2(x, root, parent, child, len)
     return(x)
 }
 
 
 
 
 ## scale the branch (the line plotted) to the actual value of edge length
 ## but it seems not the good idea as if we want to add x-axis (e.g. time-scaled tree)
 ## then the x-value is not corresponding to edge length as in rectangular layout
 ## getXYcoord_slanted <- function(tr) {
 ##     edge <- tr$edge
 ##     parent <- edge[,1]
 ##     child <- edge[,2]
 ##     root <- getRoot(tr)
 
 ##     N <- getNodeNum(tr)
 ##     len <- tr$edge.length
 ##     y <- getYcoord(tr, step=min(len)/2)
 ##     len <- sqrt(len^2 - (y[parent]-y[child])^2)
 ##     x <- numeric(N)
 ##     x <- getXcoord2(x, root, parent, child, len)
 ##     res <- data.frame(x=x, y=y)
 ##     return(res)
 ## }
 
 
 
 ## @importFrom magrittr %>%
 ##' @importFrom magrittr equals

 getYcoord <- function(tr, step=1, tip.order = NULL) {

     Ntip <- length(tr[["tip.label"]])
     N <- getNodeNum(tr)
 
     edge <- tr[["edge"]]
     parent <- edge[,1]
     child <- edge[,2]
 
     cl <- split(child, parent)
     child_list <- list()
     child_list[as.numeric(names(cl))] <- cl
 
     y <- numeric(N)

     if (is.null(tip.order)) {
         tip.idx <- child[child <= Ntip]
         y[tip.idx] <- 1:Ntip * step
     } else {
         tip.idx <- 1:Ntip
         y[tip.idx] <- match(tr$tip.label, tip.order) * step
     }

     y[-tip.idx] <- NA

 
     ## use lookup table
     pvec <- integer(max(tr$edge))
     pvec[child] = parent
 
     currentNode <- 1:Ntip
     while(anyNA(y)) {
         ## pNode <- unique(parent[child %in% currentNode])
         pNode <- unique(pvec[currentNode])
 
         ## piping of magrittr is slower than nested function call.
         ## pipeR is fastest, may consider to use pipeR
         ##
         ## child %in% currentNode %>% which %>% parent[.] %>% unique
         ## idx <- sapply(pNode, function(i) all(child[parent == i] %in% currentNode))
         idx <- sapply(pNode, function(i) all(child_list[[i]] %in% currentNode))
         newNode <- pNode[idx]
 
         y[newNode] <- sapply(newNode, function(i) {
             mean(y[child_list[[i]]], na.rm=TRUE)
             ##child[parent == i] %>% y[.] %>% mean(na.rm=TRUE)
         })
 
         currentNode <- c(currentNode[!currentNode %in% unlist(child_list[newNode])], newNode)
         ## currentNode <- c(currentNode[!currentNode %in% child[parent %in% newNode]], newNode)
         ## parent %in% newNode %>% child[.] %>%
         ##     `%in%`(currentNode, .) %>% `!` %>%
         ##         currentNode[.] %>% c(., newNode)
     }
 
     return(y)
 }
 
 
 getYcoord_scale <- function(tr, df, yscale) {
 
     N <- getNodeNum(tr)
     y <- numeric(N)
 
     root <- getRoot(tr)
     y[root] <- 0
     y[-root] <- NA
 
     edge <- tr$edge
     parent <- edge[,1]
     child <- edge[,2]
 
     currentNodes <- root
     while(anyNA(y)) {
         newNodes <- c()
         for (currentNode in currentNodes) {
             idx <- which(parent %in% currentNode)
             newNode <- child[idx]
             direction <- -1
             for (i in seq_along(newNode)) {
                 y[newNode[i]] <- y[currentNode] + df[newNode[i], yscale] * direction
                 direction <- -1 * direction
             }
             newNodes <- c(newNodes, newNode)
         }
         currentNodes <- unique(newNodes)
     }
     if (min(y) < 0) {
         y <- y + abs(min(y))
     }
     return(y)
 }
 
 
 getYcoord_scale2 <- function(tr, df, yscale) {
     root <- getRoot(tr)
 
     pathLength <- sapply(1:length(tr$tip.label), function(i) {
         get.path_length(tr, i, root, yscale)
     })
 
     ordered_tip <- order(pathLength, decreasing = TRUE)
     ii <- 1
     ntip <- length(ordered_tip)
     while(ii < ntip) {

         sib <- tidytree::sibling(tr, ordered_tip[ii])

         if (length(sib) == 0) {
             ii <- ii + 1
             next
         }
         jj <- which(ordered_tip %in% sib)
         if (length(jj) == 0) {
             ii <- ii + 1
             next
         }
         sib <- ordered_tip[jj]
         ordered_tip <- ordered_tip[-jj]
         nn <- length(sib)
         if (ii < length(ordered_tip)) {
             ordered_tip <- c(ordered_tip[1:ii],sib, ordered_tip[(ii+1):length(ordered_tip)])
         } else {
             ordered_tip <- c(ordered_tip[1:ii],sib)
         }
 
         ii <- ii + nn + 1
     }
 
 

     long_branch <- ancestor(tr, ordered_tip[1]) %>% rev

     long_branch <- c(long_branch, ordered_tip[1])
 
     N <- getNodeNum(tr)
     y <- numeric(N)
 
     y[root] <- 0
     y[-root] <- NA
 
     ## yy <- df[, yscale]
     ## yy[is.na(yy)] <- 0
 
     for (i in 2:length(long_branch)) {
         y[long_branch[i]] <- y[long_branch[i-1]] + df[long_branch[i], yscale]
     }
 
     parent <- df[, "parent"]
     child <- df[, "node"]
 
     currentNodes <- root
     while(anyNA(y)) {
         newNodes <- c()
         for (currentNode in currentNodes) {
             idx <- which(parent %in% currentNode)
             newNode <- child[idx]
             newNode <- c(newNode[! newNode %in% ordered_tip],
                          rev(ordered_tip[ordered_tip %in% newNode]))
             direction <- -1
             for (i in seq_along(newNode)) {
                 if (is.na(y[newNode[i]])) {
                     y[newNode[i]] <- y[currentNode] + df[newNode[i], yscale] * direction
                     direction <- -1 * direction
                 }
             }
             newNodes <- c(newNodes, newNode)
         }
         currentNodes <- unique(newNodes)
     }
     if (min(y) < 0) {
         y <- y + abs(min(y))
     }
     return(y)
 }
 
 
 
 getYcoord_scale_numeric <- function(tr, df, yscale, ...) {
     df <- .assign_parent_status(tr, df, yscale)
     df <- .assign_child_status(tr, df, yscale)
 
     y <- df[, yscale]
 
     if (anyNA(y)) {
         warning("NA found in y scale mapping, all were setting to 0")
         y[is.na(y)] <- 0
     }
 
     return(y)
 }
 
 
 .assign_parent_status <- function(tr, df, variable) {
     yy <- df[[variable]]
     na.idx <- which(is.na(yy))
     if (length(na.idx) > 0) {
         tree <- get.tree(tr)
         nodes <- getNodes_by_postorder(tree)
         for (curNode in nodes) {

             children <- treeio::child(tree, curNode)

             if (length(children) == 0) {
                 next
             }
             idx <- which(is.na(yy[children]))
             if (length(idx) > 0) {
                 yy[children[idx]] <- yy[curNode]
             }
         }
     }
     df[, variable] <- yy
     return(df)
 }
 
 
 .assign_child_status <- function(tr, df, variable, yscale_mapping=NULL) {
     yy <- df[[variable]]
     if (!is.null(yscale_mapping)) {
         yy <- yscale_mapping[yy]
     }
 
     na.idx <- which(is.na(yy))
     if (length(na.idx) > 0) {
         tree <- get.tree(tr)
         nodes <- rev(getNodes_by_postorder(tree))
         for (curNode in nodes) {

             parent <- parent(tree, curNode)

             if (parent == 0) { ## already reach root
                 next
             }
             idx <- which(is.na(yy[parent]))
             if (length(idx) > 0) {

                 child <- treeio::child(tree, parent)

                 yy[parent[idx]] <- mean(yy[child], na.rm=TRUE)
             }
         }
     }
     df[, variable] <- yy
     return(df)
 }
 
 
 getYcoord_scale_category <- function(tr, df, yscale, yscale_mapping=NULL, ...) {
     if (is.null(yscale_mapping)) {
         stop("yscale is category variable, user should provide yscale_mapping,
              which is a named vector, to convert yscale to numberical values...")
     }
     if (! is(yscale_mapping, "numeric") ||
         is.null(names(yscale_mapping))) {
         stop("yscale_mapping should be a named numeric vector...")
     }
 
     if (yscale == "label") {
         yy <- df[[yscale]]
         ii <- which(is.na(yy))
         if (length(ii)) {

             ## df[ii, yscale] <- df[ii, "node"]
             df[[yscale]][ii] <- as.character(df[['node']][ii])

         }
     }
 
     ## assign to parent status is more prefer...
     df <- .assign_parent_status(tr, df, yscale)
     df <- .assign_child_status(tr, df, yscale, yscale_mapping)
 
     y <- df[[yscale]]
 
     if (anyNA(y)) {
         warning("NA found in y scale mapping, all were setting to 0")
         y[is.na(y)] <- 0
     }
     return(y)
 }
 
 
 add_angle_slanted <- function(res) {
     x <- res[["x"]]
     y <- res[["y"]]
     dy <- (y - y[match(res$parent, res$node)]) / diff(range(y))
     dx <- (x - x[match(res$parent, res$node)]) / diff(range(x))
     theta <- atan(dy/dx)
     theta[is.na(theta)] <- 0 ## root node
     res$angle <- theta/pi * 180
 
     branch.y <- (y[match(res$parent, res$node)] + y)/2
     idx <- is.na(branch.y)
     branch.y[idx] <- y[idx]
     res[, "branch.y"] <- branch.y
     return(res)
 }
 
 

 calculate_branch_mid <- function(res, layout) {
     if (layout %in% c("equal_angle", "daylight", "ape")){
         res$branch.y <- with(res, (y[match(parent, node)] + y)/2)
         res$branch.y[is.na(res$branch.y)] <- 0
     }

     res$branch <- with(res, (x[match(parent, node)] + x)/2)

     if (!is.null(res[['branch.length']])) {

         res$branch.length[is.na(res$branch.length)] <- 0
     }
     res$branch[is.na(res$branch)] <- 0

     if (layout %in% c("equal_angle", "daylight", "ape")){
         res$branch.x <- res$branch
     }

     return(res)
 }
 
 
 re_assign_ycoord_df <- function(df, currentNode) {
     while(anyNA(df$y)) {
         pNode <- with(df, parent[match(currentNode, node)]) %>% unique
         idx <- sapply(pNode, function(i) with(df, all(node[parent == i & parent != node] %in% currentNode)))
         newNode <- pNode[idx]
         ## newNode <- newNode[is.na(df[match(newNode, df$node), "y"])]

         if (length(newNode) == 0)
             break

         df[match(newNode, df$node), "y"] <- sapply(newNode, function(i) {
             with(df, mean(y[parent == i], na.rm = TRUE))
         })
         traced_node <- as.vector(sapply(newNode, function(i) with(df, node[parent == i])))
         currentNode <- c(currentNode[! currentNode %in% traced_node], newNode)
     }
     return(df)
 }
 

 layoutApe <- function(model, branch.length="branch.length") {

 	tree <- as.phylo(model) %>% stats::reorder("postorder")

 	if (! is.null(tree$edge.length)) {
 		if (anyNA(tree$edge.length)) {
 			warning("'edge.length' contains NA values...\n## setting 'edge.length' to NULL automatically when plotting the tree...")
 			tree$edge.length <- NULL
 		}
 	}

 	if (is.null(tree$edge.length) || branch.length == "none") {
 		tree <- set_branch_length_cladogram(tree)
 	}

 	edge <- tree$edge
 	edge.length <- tree$edge.length
 	nb.sp <- ape::node.depth(tree)

 	df <- as_tibble(model) %>%

 		mutate(isTip = ! .data$node %in% .data$parent)

 	#df$branch.length <- edge.length[df$node] # for cladogram

 	# unrooted layout from cran/ape
 	M <- ape::unrooted.xy(Ntip(tree),
 						  Nnode(tree),
 						  tree$edge,
 						  tree$edge.length,
 						  nb.sp,
 						  0)$M
 	xx <- M[, 1]
 	yy <- M[, 2]

 	M <- tibble::tibble(

 		node = 1:(Ntip(tree) + Nnode(tree)),
 		x = xx - min(xx),

 		y = yy - min(yy)

 	)

 	tree_df <- dplyr::full_join(df, M, by = "node") %>%
 		as_tibble()
 	class(tree_df) <- c("tbl_tree", class(tree_df))
 	tree_df
 }