Update to latest base master

.gitignore

@@ -2,3 +2,4 @@
 *.jl.mem
 *.svg
 Manifest.toml
+test/data/*-comparison.png

.travis.yml

@@ -3,7 +3,6 @@ os:
   - linux
   - osx
 julia:
-  - 0.7
   - 1.0
   - nightly
 

Project.toml

@@ -1,7 +1,7 @@
 name = "GraphPlot"
 uuid = "a2cc645c-3eea-5389-862e-a155d0052231"
 authors = ["JuliaGraphs"]
-version = "0.4.0"
+version = "0.4.2"
 
 [deps]
 ArnoldiMethod = "ec485272-7323-5ecc-a04f-4719b315124d"
@@ -14,6 +14,15 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
+[compat]
+ArnoldiMethod = "0.0.4"
+ColorTypes = "0.9, 0.10"
+Colors = "0.11, 0.12"
+Compose = "0.7, 0.8"
+LightGraphs = "1.1"
+VisualRegressionTests = "0.2, 0.3"
+julia = "1"
+
 [extras]
 Cairo = "159f3aea-2a34-519c-b102-8c37f9878175"
 ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
@@ -22,8 +31,3 @@ VisualRegressionTests = "34922c18-7c2a-561c-bac1-01e79b2c4c92"
 
 [targets]
 test = ["Test", "Cairo", "ImageMagick", "VisualRegressionTests"]
-
-[compat]
-"Compose" = "0.7"
-"LightGraphs" = "1.1"
-"VisualRegressionTests" = "0.2"

README.md

@@ -25,15 +25,16 @@ using GraphPlot
 # Usage
 ## karate network
 ```julia
-g = graphfamous("karate")
+using LightGraphs: smallgraph
+g = smallgraph(:karate)
 gplot(g)
 
 ```
 
 ## Add node label
 ```julia
-using Graphs
-nodelabel = [1:num_vertices(g)]
+using LightGraphs
+nodelabel = [1:nv(g)]
 gplot(g, nodelabel=nodelabel)
 
 ```
@@ -46,7 +47,7 @@ gplot(g, nodelabel=nodelabel, nodelabeldist=1.5, nodelabelangleoffset=π/4)
 ## Control the node size
 ```julia
 # nodes size proportional to their degree
-nodesize = [Graphs.out_degree(v, g) for v in Graphs.vertices(g)]
+nodesize = [LightGraphs.out_degree(g, v) for v in LightGraphs.vertices(g)]
 gplot(g, nodesize=nodesize)
 ```
 
@@ -56,7 +57,7 @@ Feed the keyword argument `nodefillc` a color array, ensure each node has a colo
 using Colors
 
 # Generate n maximally distinguishable colors in LCHab space.
-nodefillc = distinguishable_colors(num_vertices(g), colorant"blue")
+nodefillc = distinguishable_colors(nv(g), colorant"blue")
 gplot(g, nodefillc=nodefillc, nodelabel=nodelabel, nodelabeldist=1.8, nodelabelangleoffset=π/4)
 ```
 
@@ -75,13 +76,13 @@ gplot(g, nodelabelsize=nodelabelsize, nodesize=nodesize, nodelabel=nodelabel)
 
 ## Draw edge labels
 ```julia
-edgelabel = [1:Graphs.num_edges(g)]
+edgelabel = [1:LightGraphs.ne(g)]
 gplot(g, edgelabel=edgelabel, nodelabel=nodelabel)
 ```
 
 ## Adjust edge labels
 ```julia
-edgelabel = [1:Graphs.num_edges(g)]
+edgelabel = [1:LightGraphs.ne(g)]
 gplot(g, edgelabel=edgelabel, nodelabel=nodelabel, edgelabeldistx=0.5, edgelabeldisty=0.5)
 ```
 
@@ -119,9 +120,9 @@ gplot(g, layout=spectral_layout)
 ```
 ### shell layout
 ```julia
-nlist = Array(Vector{Int}, 2) # two shells
+nlist = Vector{Vector{Int}}(undef, 2) # two shells
 nlist[1] = [1:5] # first shell
-nlist[2] = [6:num_vertices(g)] # second shell
+nlist[2] = [6:nv(g)] # second shell
 locs_x, locs_y = shell_layout(g, nlist)
 gplot(g, locs_x, locs_y, nodelabel=nodelabel)
 ```
@@ -133,7 +134,7 @@ gplot(g, linetype="curve")
 
 ## Save to figure
 ```{execute="false"}
-using Compose
+using Cairo, Compose
 # save to pdf
 draw(PDF("karate.pdf", 16cm, 16cm), gplot(g))
 # save to png

src/collapse_plot.jl

@@ -1,6 +1,6 @@
 using GraphPlot
 
-function collapse_graph(g::AbstractGraph{T}, membership::Vector{Int}) where {T<:Integer}
+function collapse_graph(g::AbstractGraph, membership::Vector{Int})
     nb_comm = maximum(membership)
 
     collapsed_edge_weights = Vector{Dict{Int,Float64}}(undef, nb_comm)
@@ -36,7 +36,7 @@ function collapse_graph(g::AbstractGraph{T}, membership::Vector{Int}) where {T<:
         end
     end
 
-    collapsed_graph, collapsed_weights
+    return collapsed_graph, collapsed_weights
 end
 
 function community_layout(g::AbstractGraph, membership::Vector{Int})
@@ -60,10 +60,10 @@ function community_layout(g::AbstractGraph, membership::Vector{Int})
             ly[node] = 1.8*length(nodes)/N*sin(θ[idx]) + cly[lbl]
         end
     end
-    lx, ly
+    return lx, ly
 end
 
-function collapse_layout(g::AbstractGraph{T}, membership::Vector{Int}) where {T<:Integer}
+function collapse_layout(g::AbstractGraph, membership::Vector{Int})
     lightg = LightGraphs.SimpleGraph(nv(g))
     for e in edges(g)
         u = src(e)
@@ -92,5 +92,5 @@ function collapse_layout(g::AbstractGraph{T}, membership::Vector{Int}) where {T<
             ly[node] = 1.8*length(nodes)/N*subly[idx] + cly[lbl]
         end
     end
-    lx, ly
+    return lx, ly
 end

src/layout.jl

@@ -1,5 +1,6 @@
 using SparseArrays: SparseMatrixCSC, sparse
-using ArnoldiMethod: LR
+using ArnoldiMethod: SR
+using Base: OneTo
 using LinearAlgebra: eigen
 
 """
@@ -39,7 +40,7 @@ Position nodes on a circle.
 
 **Parameters**
 
-*G*
+*g*
 a graph
 
 **Returns**
@@ -51,7 +52,7 @@ but will be normalized and centered anyway
 **Examples**
 
 ```
-julia> g = simple_house_graph()
+julia> g = smallgraph(:house)
 julia> locs_x, locs_y = circular_layout(g)
 ```
 """
@@ -60,18 +61,18 @@ function circular_layout(g)
         return [0.0], [0.0]
     else
         # Discard the extra angle since it matches 0 radians.
-        θ = range(0, stop=2pi, length=_nv(G)+1)[1:end-1]
+        θ = range(0, stop=2pi, length=nv(g)+1)[1:end-1]
         return cos.(θ), sin.(θ)
     end
 end
 
 """
 This function is copy from [IainNZ](https://github.com/IainNZ)'s [GraphLayout.jl](https://github.com/IainNZ/GraphLayout.jl)
 
-Use the spring/repulsion model of Fruchterman and Reingold (1991):
+Use a modified version of the spring/repulsion model of Fruchterman and Reingold (1991):
 
-+ Attractive force:  f_a(d) =  d^2 / k
-+ Repulsive force:  f_r(d) = -k^2 / d
++ Attractive force:  f_a(d) =  d / k
++ Repulsive force:  f_r(d) = -k^2 / d^2
 
 where d is distance between two vertices and the optimal distance
 between vertices k is defined as C * sqrt( area / num_vertices )
@@ -96,63 +97,66 @@ Integer seed for pseudorandom generation of locations (default = 0).
 
 **Examples**
 ```
-julia> g = graphfamous("karate")
+julia> g = smallgraph(:karate)
 julia> locs_x, locs_y = spring_layout(g)
 ```
 """
-function spring_layout(g::AbstractGraph{T}, locs_x, locs_y; C=2.0, MAXITER=100, INITTEMP=2.0) where {T<:Integer}
-
-    #size(adj_matrix, 1) != size(adj_matrix, 2) && error("Adj. matrix must be square.")
-    N = nv(g)
+function spring_layout(g::AbstractGraph,
+                       locs_x=2*rand(nv(g)).-1.0,
+                       locs_y=2*rand(nv(g)).-1.0;
+                       C=2.0,
+                       MAXITER=100,
+                       INITTEMP=2.0)
+
+    nvg = nv(g)
     adj_matrix = adjacency_matrix(g)
 
     # The optimal distance bewteen vertices
-    K = C * sqrt(4.0 / N)
+    k = C * sqrt(4.0 / nvg)
+    k² = k * k
 
     # Store forces and apply at end of iteration all at once
-    force_x = zeros(N)
-    force_y = zeros(N)
+    force_x = zeros(nvg)
+    force_y = zeros(nvg)
 
     # Iterate MAXITER times
     @inbounds for iter = 1:MAXITER
         # Calculate forces
-        for i = 1:N
+        for i = 1:nvg
             force_vec_x = 0.0
             force_vec_y = 0.0
-            for j = 1:N
+            for j = 1:nvg
                 i == j && continue
                 d_x = locs_x[j] - locs_x[i]
                 d_y = locs_y[j] - locs_y[i]
-                d   = sqrt(d_x^2 + d_y^2)
-                if adj_matrix[i,j] != zero(eltype(adj_matrix)) || adj_matrix[j,i] != zero(eltype(adj_matrix))
-                    # F = d^2 / K - K^2 / d
-                    F_d = d / K - K^2 / d^2
+                dist²  = (d_x * d_x) + (d_y * d_y)
+                dist = sqrt(dist²)
+
+                if !( iszero(adj_matrix[i,j]) && iszero(adj_matrix[j,i]) )
+                    # Attractive + repulsive force
+                    # F_d = dist² / k - k² / dist # original FR algorithm
+                    F_d = dist / k - k² / dist²
                 else
                     # Just repulsive
-                    # F = -K^2 / d^
-                    F_d = -K^2 / d^2
+                    # F_d = -k² / dist  # original FR algorithm
+                    F_d = -k² / dist²
                 end
-                # d  /          sin θ = d_y/d = fy/F
-                # F /| dy fy    -> fy = F*d_y/d
-                #  / |          cos θ = d_x/d = fx/F
-                # /---          -> fx = F*d_x/d
-                # dx fx
                 force_vec_x += F_d*d_x
                 force_vec_y += F_d*d_y
             end
             force_x[i] = force_vec_x
             force_y[i] = force_vec_y
         end
         # Cool down
-        TEMP = INITTEMP / iter
+        temp = INITTEMP / iter
         # Now apply them, but limit to temperature
-        for i = 1:N
-            force_mag  = sqrt(force_x[i]^2 + force_y[i]^2)
-            scale      = min(force_mag, TEMP)/force_mag
+        for i = 1:nvg
+            fx = force_x[i]
+            fy = force_y[i]
+            force_mag  = sqrt((fx * fx) + (fy * fy))
+            scale      = min(force_mag, temp) / force_mag
             locs_x[i] += force_x[i] * scale
-            #locs_x[i]  = max(-1.0, min(locs_x[i], +1.0))
             locs_y[i] += force_y[i] * scale
-            #locs_y[i]  = max(-1.0, min(locs_y[i], +1.0))
         end
     end
 
@@ -165,11 +169,12 @@ function spring_layout(g::AbstractGraph{T}, locs_x, locs_y; C=2.0, MAXITER=100,
     map!(z -> scaler(z, min_x, max_x), locs_x, locs_x)
     map!(z -> scaler(z, min_y, max_y), locs_y, locs_y)
 
-    return locs_x,locs_y
+    return locs_x, locs_y
 end
 
 using Random: MersenneTwister
-function spring_layout(g::AbstractGraph; seed::Integer=0, kws...)
+
+function spring_layout(g::AbstractGraph, seed::Integer, kws...)
     rng = MersenneTwister(seed)
     spring_layout(g, 2 .* rand(rng, nv(g)) .- 1.0, 2 .* rand(rng,nv(g)) .- 1.0; kws...)
 end
@@ -181,28 +186,27 @@ Position nodes in concentric circles.
 
 **Parameters**
 
-*G*
+*g*
 a graph
 
 *nlist*
 Vector of Vector, Vector of node Vector for each shell.
 
 **Examples**
 ```
-julia> g = graphfamous("karate")
+julia> g = smallgraph(:karate)
 julia> nlist = Array{Vector{Int}}(2)
 julia> nlist[1] = [1:5]
 julia> nlist[2] = [6:num_vertiecs(g)]
 julia> locs_x, locs_y = shell_layout(g, nlist)
 ```
 """
-function shell_layout(G, nlist::Union{Nothing, Vector{Vector{Int}}} = nothing)
-    if _nv(G) == 1
+function shell_layout(g, nlist::Union{Nothing, Vector{Vector{Int}}} = nothing)
+    if nv(g) == 1
         return [0.0], [0.0]
     end
     if nlist == nothing
-        nlist = Array{Vector{Int}}(1)
-        nlist[1] = collect(1:_nv(G))
+        nlist = [collect(1:nv(g))]
     end
     radius = 0.0
     if length(nlist[1]) > 1
@@ -217,7 +221,7 @@ function shell_layout(G, nlist::Union{Nothing, Vector{Vector{Int}}} = nothing)
         append!(locs_y, radius*sin.(θ))
         radius += 1.0
     end
-    locs_x, locs_y
+    return locs_x, locs_y
 end
 
 """
@@ -237,20 +241,20 @@ the edge weight.  If None, then all edge weights are 1.
 
 **Examples**
 ```
-julia> g = graphfamous("karate")
+julia> g = smallgraph(:karate)
 julia> weight = rand(num_edges(g))
 julia> locs_x, locs_y = spectral_layout(g, weight)
 ```
 """
-function spectral_layout(g::AbstractGraph{T}, weight=nothing) where {T<:Integer}
+function spectral_layout(g::AbstractGraph, weight=nothing)
     if nv(g) == 1
         return [0.0], [0.0]
     elseif nv(g) == 2
         return [0.0, 1.0], [0.0, 0.0]
     end
 
     if weight == nothing
-        weight = ones(length(edges(g)))
+        weight = ones(ne(g))
     end
     if nv(g) > 500
         A = sparse(Int[src(e) for e in edges(g)],
@@ -276,7 +280,7 @@ function _spectral(A::SparseMatrixCSC)
     data = vec(sum(A, dims=1))
     D = sparse(Base.OneTo(length(data)), Base.OneTo(length(data)), data)
     L = D - A
-    eigenvalues, eigenvectors = LightGraphs.LinAlg.eigs(L, nev=3, which=LR())
+    eigenvalues, eigenvectors = LightGraphs.LinAlg.eigs(L, nev=3, which=SR())
     index = sortperm(real(eigenvalues))[2:3]
     return real(eigenvectors[:, index[1]]), real(eigenvectors[:, index[2]])
 end