rename all deprecated igraph functions

* there are many functions deprecated in igraph v2.0.0
* unfortunately, I don't know what is the minimum igraph version for the new function names
  currently we only require igraph (>= 0.7.1)

get.vertex.attribute->vertex_attr
add.edges->add_edges
decompose.graph->decompose
graph.bfs->bfs
graph.dfs->dfs
is.connected->is_connected
is.directed->is_directed
get.vertex.attribute->vertex_attr
shortest.paths->distances
get.shortest.paths->shortest_paths

induced.subgraph -> induced_subgraph
no.clusters -> count_components
clusters -> components
minimum.spanning.tree -> mst
delete.vertices -> delete_vertices
set.vertex.attribute -> set_vertex_attr
get.edgelist -> as_edgelist
delete.edges -> delete_edges

NAMESPACE

@@ -389,27 +389,26 @@ importFrom(digest,digest)
 importFrom(igraph,"V<-")
 importFrom(igraph,E)
 importFrom(igraph,V)
-importFrom(igraph,add.edges)
+importFrom(igraph,add_edges)
 importFrom(igraph,as.directed)
 importFrom(igraph,as.undirected)
 importFrom(igraph,as_edgelist)
-importFrom(igraph,decompose.graph)
+importFrom(igraph,bfs)
+importFrom(igraph,decompose)
 importFrom(igraph,degree)
 importFrom(igraph,delete.vertices)
+importFrom(igraph,dfs)
 importFrom(igraph,diameter)
 importFrom(igraph,distances)
 importFrom(igraph,get.diameter)
-importFrom(igraph,get.shortest.paths)
-importFrom(igraph,get.vertex.attribute)
-importFrom(igraph,graph.bfs)
-importFrom(igraph,graph.dfs)
-importFrom(igraph,is.connected)
-importFrom(igraph,is.directed)
+importFrom(igraph,is_connected)
+importFrom(igraph,is_directed)
 importFrom(igraph,make_empty_graph)
 importFrom(igraph,neighborhood)
 importFrom(igraph,set_edge_attr)
-importFrom(igraph,shortest.paths)
+importFrom(igraph,shortest_paths)
 importFrom(igraph,vcount)
+importFrom(igraph,vertex_attr)
 importFrom(methods,is)
 importFrom(nabor,knn)
 importFrom(nat.utils,RunCmdForNewerInput)

R/graph-nodes.R

@@ -13,7 +13,7 @@
 #'   available). Set to FALSE when this is not desired.
 #' @param exclude.isolated Do not count isolated vertices as root/end points
 #'   (default)
-#' @importFrom igraph V degree get.vertex.attribute
+#' @importFrom igraph V degree vertex_attr
 #' @export
 #' @seealso \code{\link{rootpoints}}, \code{\link{ngraph}}
 #' @examples
@@ -27,7 +27,7 @@
 graph.nodes<-function(x, type=c('root','end','branch'), original.ids='name',
                       exclude.isolated=TRUE){
   type=match.arg(type)
-  if(type=='root' && !is.directed(x))
+  if(type=='root' && !is_directed(x))
     stop("Cannot establish root points for undirected graph")
 
   # root points are those without incoming edges
@@ -42,7 +42,7 @@ graph.nodes<-function(x, type=c('root','end','branch'), original.ids='name',
   }
 
   if(is.character(original.ids))
-    vertex_names=get.vertex.attribute(x, original.ids, index=vertex_idxs)
+    vertex_names=vertex_attr(x, original.ids, index=vertex_idxs)
   if(!is.character(original.ids) || is.null(vertex_names))
     as.integer(vertex_idxs)
   else

R/neuron.R

@@ -67,7 +67,7 @@
 #' 
 #' # converting back and forth between neurons and graphs
 #' g=as.ngraph(Cell07PNs[[1]])
-#' gstem=igraph::induced.subgraph(g, 1:10)
+#' gstem=igraph::induced_subgraph(g, 1:10)
 #' # this is fine
 #' plot(gstem)
 #' plot(as.neuron(gstem))
@@ -206,9 +206,9 @@ normalise_swc<-function(x, requiredColumns=c('PointNo','Label','X','Y','Z','W','
 #'   (NumPoints,StartPoint,BranchPoints,EndPoints,nTrees,NumSegs,SegList, 
 #'   [SubTrees]) NB SubTrees will only be present when nTrees>1.
 #' @export
-#' @importFrom igraph V V<- vcount decompose.graph
+#' @importFrom igraph V V<- vcount decompose
 #' @rdname neuron
-#' @seealso \code{\link{graph.dfs}, \link{as.seglist}}
+#' @seealso \code{\link{dfs}, \link{as.seglist}}
 as.neuron.ngraph<-function(x, vertexData=NULL, origin=NULL, Verbose=FALSE, ...){
   # translate origin into raw vertex id if necessary 
   if(length(origin)){
@@ -221,27 +221,27 @@ as.neuron.ngraph<-function(x, vertexData=NULL, origin=NULL, Verbose=FALSE, ...){
   # save original vertex ids
   igraph::V(x)$vid=seq.int(igraph::vcount(x))
   # check if we have multiple subgraphs
-  if(igraph::no.clusters(x)>1){
+  if(igraph::count_components(x)>1){
     if(!length(origin)){
       # no origin specified, will pick the biggest subtree
       # decompose into list of subgraphs
-      gg=igraph::decompose.graph(x)
+      gg=igraph::decompose(x)
       # reorder by descending number of vertices
       gg=gg[order(sapply(gg,igraph::vcount), decreasing=TRUE)]
       subtrees=lapply(gg, as.seglist, Verbose=Verbose)
       sl=subtrees[[1]]
       masterg=gg[[1]]
     } else {
       # origin specified, subtree containing origin will be the master
-      cg=igraph::clusters(x)
+      cg=igraph::components(x)
       master_tree_num=cg$membership[origin]
       # make a master graph with the vertices from subgraph including origin
-      masterg=igraph::induced.subgraph(x, which(cg$membership==master_tree_num))
+      masterg=igraph::induced_subgraph(x, which(cg$membership==master_tree_num))
       # ... and then corresponding seglist
       sl=as.seglist(masterg, origin=origin)
       # now deal with remaining vertices
-      remainderg=igraph::induced.subgraph(x, which(cg$membership!=master_tree_num))
-      gg=igraph::decompose.graph(remainderg)
+      remainderg=igraph::induced_subgraph(x, which(cg$membership!=master_tree_num))
+      gg=igraph::decompose(remainderg)
       # reorder by descending number of vertices
       gg=gg[order(sapply(gg,igraph::vcount), decreasing=TRUE)]
       subtrees=c(list(sl),lapply(gg, as.seglist, Verbose=Verbose))
@@ -262,7 +262,7 @@ as.neuron.ngraph<-function(x, vertexData=NULL, origin=NULL, Verbose=FALSE, ...){
   # sort out the vertex information
   # TODO refactor this into a separate function e.g. normalise.swc since
   # we need to do something similar in as.neuron.dataframe and seglist2swc etc
-  d=data.frame(PointNo=get.vertex.attribute(x,'name'))
+  d=data.frame(PointNo=vertex_attr(x,'name'))
   if(is.null(vertexData)){
     # get vertex information from graph object
     xyz=xyzmatrix(x)
@@ -776,7 +776,7 @@ smooth_segment_spline <- function(xyz, ...) {
 #' # now find the points downstream (distal) of that with respect to the root
 #' ng=as.ngraph(n)
 #' # use a depth first search
-#' distal_points=igraph::graph.dfs(ng, root=n$AxonLHEP, unreachable=FALSE,
+#' distal_points=igraph::dfs(ng, root=n$AxonLHEP, unreachable=FALSE,
 #'   mode='out')$order
 #' distal_tree=subset(n, distal_points)
 #' plot(distal_tree, add=TRUE, col='red', lwd=2)
@@ -945,7 +945,7 @@ simplify_neuron <- function(x, n=1, invert=FALSE, ...) {
       # there will be warnings because most branch points will not be reachable
       # from the selected leaf
       res=suppressWarnings(
-        igraph::get.shortest.paths(ng, from = farthest_leaf, 
+        igraph::shortest_paths(ng, from = farthest_leaf, 
                                    to = bps[bps_available], 
                                    mode = "in", weights = NA))
       pathlengths=sapply(res$vpath, length)
@@ -973,7 +973,7 @@ simplify_neuron <- function(x, n=1, invert=FALSE, ...) {
 }
 
 leafpath <- function(ng, from, to) {
-  res=igraph::get.shortest.paths(ng,from = from,to = to,mode = "out", weights=NA)
+  res=igraph::shortest_paths(ng,from = from,to = to,mode = "out", weights=NA)
   as.integer(res$vpath[[1]])
 }
 
@@ -1169,7 +1169,7 @@ stitch_neurons_mst <- function(x, threshold = Inf, k=10L) {
   mod_graph <- igraph::add_edges(ng,edge_list,"weight"= weights)
 
   #Step 7: Find the minimum spanning tree of the new graph..
-  mst <- igraph::minimum.spanning.tree(mod_graph)
+  mst <- igraph::mst(mod_graph)
 
   #Step 8: Find the new edges added by the mst..
   new_edges <- igraph::difference(igraph::E(mst),igraph::E(masterng))
@@ -1208,7 +1208,7 @@ stitch_neurons_mst <- function(x, threshold = Inf, k=10L) {
     warning("The root node doesn't belong to the largest fragment")
   }
 
-  stitchedng=igraph::delete.vertices(stitchedng, verticestoprune)
+  stitchedng=igraph::delete_vertices(stitchedng, verticestoprune)
 
   #Step 11: Set the root of the stitched graph now..
   stitchedneuron <- as.neuron(stitchedng, origin = master_root)

R/ngraph.R

@@ -50,12 +50,12 @@
 #' @param vertex.attributes,graph.attributes List of named attributes to be
 #'   added to the graph. The elements of \code{vertex.attributes} must be
 #'   vectors whose length is compatible with the number of elements in the
-#'   graph. See \code{\link[igraph]{set.vertex.attribute}} for details.
+#'   graph. See \code{\link[igraph]{set_vertex_attr}} for details.
 #' @return an \code{igraph} object with additional class \code{ngraph}, having a
 #'   vertex for each entry in vertexnames, each vertex having a \code{label}
 #'   attribute. All vertices are included whether connected or not.
 #' @family neuron
-#' @seealso \code{\link{igraph}}, \code{\link[igraph]{set.vertex.attribute}},
+#' @seealso \code{\link{igraph}}, \code{\link[igraph]{set_vertex_attr}},
 #'   \code{\link{subset.neuron}} for example of graph-based manipulation of a
 #'   neuron, \code{\link{plot3d.ngraph}}
 #' @export
@@ -72,9 +72,9 @@
 #' # find longest path across graph
 #' d=get.diameter(gw)
 #' # make a new neuron using the longest path
-#' gw_spine=as.neuron(induced.subgraph(gw, d))
+#' gw_spine=as.neuron(induced_subgraph(gw, d))
 #' # make a new neuron containing all nodes except those in longest path
-#' gw_antispine=as.neuron(delete.vertices(gw, d))
+#' gw_antispine=as.neuron(delete_vertices(gw, d))
 #'
 #' # note use of bounding box of original neuron to set plot axes
 #' plot(gw_spine, col='red', boundingbox=boundingbox(n))
@@ -110,10 +110,10 @@ ngraph<-function(el, vertexnames, xyz=NULL, diam=NULL, directed=TRUE,
   }
   if(!is.null(diam)) igraph::V(g)$diam=diam
   for(n in names(vertex.attributes)){
-    g=igraph::set.vertex.attribute(g, name=n,value=vertex.attributes[[n]])
+    g=igraph::set_vertex_attr(g, name=n,value=vertex.attributes[[n]])
   }
   for(n in names(graph.attributes)){
-    g=igraph::set.graph.attribute(g, name=n,value=graph.attributes[[n]])
+    g=igraph::set_graph_attr(g, name=n,value=graph.attributes[[n]])
   }
   class(g)=c("ngraph", class(g))
   g
@@ -159,10 +159,10 @@ as.ngraph.neuron<-function(x, directed=TRUE, method=c('swc','seglist'), ...){
 #' @export
 as.ngraph.igraph<-function(x, directed=TRUE, root, mode=c('out','in'), ...){
   if(inherits(x,'ngraph'))
-    if(igraph::is.directed(x)==directed) return(x)
+    if(igraph::is_directed(x)==directed) return(x)
 
-  if(igraph::is.directed(x) && !directed) x=as.undirected(x, ...)
-  else if(!igraph::is.directed(x) && directed) x=as.directed.usingroot(x, root, mode=mode, ...)
+  if(igraph::is_directed(x) && !directed) x=as.undirected(x, ...)
+  else if(!igraph::is_directed(x) && directed) x=as.directed.usingroot(x, root, mode=mode, ...)
 
   if(!inherits(x,'ngraph')){
     class(x)=c("ngraph",class(x))
@@ -173,11 +173,11 @@ as.ngraph.igraph<-function(x, directed=TRUE, root, mode=c('out','in'), ...){
 as.directed.usingroot<-function(g, root, mode=c('out','in')){
   mode=match.arg(mode)
   # make a directed graph _keeping any attributes_
-  if(!igraph::is.directed(g))
+  if(!igraph::is_directed(g))
     dg=igraph::as.directed(g, mode='arbitrary')
   else dg=g
-  dfs=igraph::graph.dfs(dg, root, unreachable=FALSE, dist=TRUE, mode='all')
-  el=igraph::get.edgelist(dg)
+  dfs=igraph::dfs(dg, root, unreachable=FALSE, dist=TRUE, mode='all')
+  el=igraph::as_edgelist(dg)
 
   connected_vertices=which(is.finite(dfs$order))
   edges_to_check=which(el[,1]%in%connected_vertices)
@@ -193,8 +193,8 @@ as.directed.usingroot<-function(g, root, mode=c('out','in')){
   if(any(same_dist)) warning(sum(same_dist)," edges connect vertices that are the same distance from the root => cycles.")
   edges_to_flip <- edges_to_check[if(mode=='out') parent_further else parent_closer]
 
-  dg=igraph::delete.edges(dg,edges_to_flip)
-  dg=igraph::add.edges(dg,t(el[edges_to_flip,2:1]))
+  dg=igraph::delete_edges(dg,edges_to_flip)
+  dg=igraph::add_edges(dg,t(el[edges_to_flip,2:1]))
   dg
 }
 
@@ -225,7 +225,7 @@ as.directed.usingroot<-function(g, root, mode=c('out','in')){
 #'
 #'   }
 #' @seealso \code{\link[igraph]{diameter}},
-#'   \code{\link[igraph]{shortest.paths}}, \code{\link{prune_strahler}} for
+#'   \code{\link[igraph]{distances}}, \code{\link{prune_strahler}} for
 #'   removing lower order branches from a neuron, \code{\link{prune}} for
 #'   removing parts of a neuron by spatial criteria.
 #' @export
@@ -247,7 +247,7 @@ as.directed.usingroot<-function(g, root, mode=c('out','in')){
 #' plot3d(antispine, lwd=4, col='red')
 #' }
 #'
-#' @importFrom igraph shortest.paths get.shortest.paths diameter get.diameter
+#' @importFrom igraph distances shortest_paths diameter get.diameter
 #'   delete.vertices
 #' @family neuron
 spine <- function(n, UseStartPoint=FALSE, SpatialWeights=TRUE, invert=FALSE,
@@ -261,7 +261,7 @@ spine <- function(n, UseStartPoint=FALSE, SpatialWeights=TRUE, invert=FALSE,
   start <- if(UseStartPoint) n$StartPoint else n$EndPoints
   # Find distances for longest shortest paths from given start point(s) to 
   # all end points
-  lps=shortest.paths(graph = ng, start, to = n$EndPoints, mode = 'all')
+  lps=distances(graph = ng, start, to = n$EndPoints, mode = 'all')
   if(rval=='length') return(max(lps))
   if(!UseStartPoint && length(start)>1 && length(n$EndPoints)>1) {
     # we have a square distance matrix which is symmetric across
@@ -273,7 +273,7 @@ spine <- function(n, UseStartPoint=FALSE, SpatialWeights=TRUE, invert=FALSE,
   wmi=arrayInd(which.max(lps), dim(lps))
   from=start[wmi[1]]
   to=n$EndPoints[wmi[2]]
-  longestpath=get.shortest.paths(ng, from = from, to = to, mode = 'all')$vpath[[1]]
+  longestpath=shortest_paths(ng, from = from, to = to, mode = 'all')$vpath[[1]]
 
   if(rval=='ids') {
     if(invert) {
@@ -308,7 +308,7 @@ spine <- function(n, UseStartPoint=FALSE, SpatialWeights=TRUE, invert=FALSE,
 #' @return \code{igraph} object containing only nodes of neuron keeping original
 #'   labels (\code{x$d$PointNo} => \code{V(g)$label}) and vertex indices 
 #'   (\code{1:nrow(x$d)} => \code{V(g)$vid)}.
-#' @importFrom igraph make_empty_graph add.edges E
+#' @importFrom igraph make_empty_graph add_edges E
 #' @export
 #' @examples 
 #' sg=segmentgraph(Cell07PNs[[1]])
@@ -344,9 +344,9 @@ segmentgraph<-function(x, weights=TRUE, segids=FALSE, exclude.isolated=FALSE,
   }
   if(weights){
     weights=seglengths(x, all=TRUE)
-    g=add.edges(g, elred, weight=weights, segid=segids)
+    g=add_edges(g, elred, weight=weights, segid=segids)
   } else {
-    g=add.edges(g, elred, segid=segids)
+    g=add_edges(g, elred, segid=segids)
   }
 
   if(include.xyz){
@@ -357,7 +357,7 @@ segmentgraph<-function(x, weights=TRUE, segids=FALSE, exclude.isolated=FALSE,
   if(exclude.isolated){
     # remove any points with no neighbours
     isolated_vertices=igraph::V(g)[igraph::degree(g)==0]
-    g=igraph::delete.vertices(graph=g,isolated_vertices)
+    g=igraph::delete_vertices(graph=g,isolated_vertices)
   }
   g
 }
@@ -381,7 +381,7 @@ segmentgraph<-function(x, weights=TRUE, segids=FALSE, exclude.isolated=FALSE,
 #' @seealso \code{\link{prune_strahler}}, a \code{\link{segmentgraph}} (a form
 #'   of \code{\link{ngraph}}) representation is used to calculate the Strahler 
 #'   order.
-#' @importFrom igraph graph.bfs neighborhood V
+#' @importFrom igraph bfs neighborhood V
 #' @return A list containing \itemize{
 #'   
 #'   \item points Vector of integer Strahler orders for each point in the neuron
@@ -400,7 +400,7 @@ strahler_order<-function(x){
     return(list(points=rep(1L, nrow(x$d)),
                 segments=rep(1L, length(x$SegList))))
 
-  b=graph.bfs(s, root=roots, mode = 'out', unreachable=F, father=T)
+  b=bfs(s, root=roots, mode = 'out', unreachable=F, father=T)
 
   # find neighbours for each node
   n=neighborhood(s, 1, mode='out')
@@ -540,8 +540,8 @@ prune_vertices<-function(x, verticestoprune, invert=FALSE, ...) {
     nvertices=nrow(xyzmatrix(x))
     verticestoprune=setdiff(seq_len(nvertices), verticestoprune)
   }
-  dg=igraph::delete.vertices(g, verticestoprune)
-  # delete.vertices will return an igraph
+  dg=igraph::delete_vertices(g, verticestoprune)
+  # delete_vertices will return an igraph
   as.neuron(as.ngraph(dg), ...)
 }
 
@@ -583,10 +583,10 @@ prune_edges_ng <- function(g, edges, invert=FALSE) {
 
   if(invert) edges=setdiff(igraph::E(g), edges)
 
-  dg=igraph::delete.edges(g, edges = edges)
+  dg=igraph::delete_edges(g, edges = edges)
 
   # remove unreferenced vertices
-  dg=igraph::delete.vertices(dg, which(igraph::degree(dg, mode='all')==0))
+  dg=igraph::delete_vertices(dg, which(igraph::degree(dg, mode='all')==0))
 }
 
 # Construct EdgeList matrix with start and end points from SegList

R/seglist.R

@@ -92,16 +92,16 @@ as.seglist.default<-function(x, ...) stop("Not yet implemented!")
 #' @return a \code{list} with one entry for each unbranched segment.
 #' @seealso \code{\link{ngraph},\link{igraph}}
 #' @export
-#' @importFrom igraph is.directed is.connected graph.dfs degree
+#' @importFrom igraph is_directed is_connected dfs degree
 #' @rdname seglist
 as.seglist.igraph<-function(x, origin=NULL, Verbose=FALSE, ...){
   # Handle degenerate cases
   if(vcount(x)==0) {
     if(Verbose) warning("Empty graph! Seglist not defined")
     return(NULL)
   }
-  if(!is.connected(x)) stop("Graph is not fully connected!")
-  if(is.directed(x) && !igraph::is.dag(x)){
+  if(!is_connected(x)) stop("Graph is not fully connected!")
+  if(is_directed(x) && !igraph::is_dag(x)){
     stop("Graph has cycles!")
   }
 
@@ -123,7 +123,7 @@ as.seglist.igraph<-function(x, origin=NULL, Verbose=FALSE, ...){
   # Handle Origin
   if(is.null(origin)){
     # no explicit origin specified, use raw vertex id of graph root
-    if(is.directed(x))
+    if(is_directed(x))
       origin=rootpoints(x, original.ids=FALSE)
   } else {
     # we've been given an origin but it may not be a raw vertex id for this
@@ -140,7 +140,7 @@ as.seglist.igraph<-function(x, origin=NULL, Verbose=FALSE, ...){
   }
 
   # Now do a depth first search to ensure that ordering is correct
-  dfs=graph.dfs(x, root=origin, father=TRUE, mode='all')
+  dfs=dfs(x, root=origin, father=TRUE, mode='all')
   # cache orders for speed: dfs$order[i] is slooooow in igraph>=1.0
   orders=as.integer(dfs$order)
   ncount=degree(x)