Some fixes for v1.0 (#66)

* fix for function gplot (#63)

* upgraded .travis.yml to v0.7

* added a minimum version for VisualRegressionTests for the tests

* using builtin function LightGraphs.LinAlg.eigs instead uf Arpack.eigs

* further fixes for problems that are not covered by tests

* added min requirement for Compose.jl

* added VisualRegresionTests to the requirements

* some changes to deal with an error on travis

Author: simonschoelly

.travis.yml

@@ -3,7 +3,8 @@ os:
   - linux
   - osx
 julia:
-  - 0.6
+  - 0.7
+  - 1.0
   - nightly
 
 matrix:
@@ -16,3 +17,6 @@ notifications:
 #script:
 #  - if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
 #  - julia --check-bounds=yes -e 'Pkg.clone(pwd()); Pkg.build("GraphPlot"); Pkg.test("GraphPlot"; coverage=true)'
+after_success:
+  # push coverage results to Codecov
+  - julia -e 'cd(using Pkg; Pkg.dir("GraphPlot")); Pkg.add("Coverage"); using Coverage; Codecov.submit(Codecov.process_folder())'

REQUIRE

@@ -1,5 +1,7 @@
-julia 0.6
+julia 0.7
+ArnoldiMethod
 Colors
 ColorTypes
-Compose
-LightGraphs
+Compose 0.7.0
+LightGraphs 1.1.0
+VisualRegressionTests 0.2.2 # this shouldn't be necessary here as it is only used by the tests

src/GraphPlot.jl

@@ -1,5 +1,3 @@
-__precompile__(true)
-
 module GraphPlot
 
 using Compose  # for plotting features
@@ -12,6 +10,8 @@ export
     gplothtml,
     random_layout,
     circular_layout,
+    collapse_layout,
+    community_layout,
     spring_layout,
     spectral_layout,
     shell_layout,
@@ -27,14 +27,11 @@ include("stress.jl")
 include("shape.jl")
 include("lines.jl")
 include("plot.jl")
+include("collapse_plot.jl")
 
 # read graph
 include("graphio.jl")
 
-function test()
-    include(joinpath(dirname(@__DIR__), "test", "runtests.jl"))
-end
-
 
 # These functions are mappings to various graph packages.
 # Currently only LightGraphs is supported.

src/collapse_plot.jl

@@ -1,24 +1,21 @@
-using Graphs
 using GraphPlot
 
-function collapse_graph{V}(g::AbstractGraph{V}, membership::Vector{Int})
+function collapse_graph(g::AbstractGraph, membership::Vector{Int})
     nb_comm = maximum(membership)
 
-    collapsed_edge_weights = Array(Dict{Int,Float64}, nb_comm)
+    collapsed_edge_weights = Vector{Dict{Int,Float64}}(undef, nb_comm)
     for i=1:nb_comm
         collapsed_edge_weights[i] = Dict{Int,Float64}()
     end
 
-    for e in Graphs.edges(g)
-        u = source(e,g)
-        v = target(e,g)
-        u_idx = vertex_index(u,g)
-        v_idx = vertex_index(v,g)
-        u_comm = membership[u_idx]
-        v_comm = membership[v_idx]
+    for e in _edges(g)
+        u = _src_index(e,g)
+        v = _dst_index(e,g)
+        u_comm = membership[u]
+        v_comm = membership[v]
 
         # for special case of undirected network
-        if !Graphs.is_directed(g)
+        if !_is_directed(g)
             u_comm, v_comm = minmax(u_comm, v_comm)
         end
 
@@ -29,20 +26,20 @@ function collapse_graph{V}(g::AbstractGraph{V}, membership::Vector{Int})
         end
     end
 
-    collapsed_graph = simple_graph(nb_comm, is_directed=false)
+    collapsed_graph = SimpleGraph(nb_comm)
     collapsed_weights = Float64[]
 
     for u=1:nb_comm
         for (v,w) in collapsed_edge_weights[u]
-            Graphs.add_edge!(collapsed_graph, u, v)
+            add_edge!(collapsed_graph, u, v)
             push!(collapsed_weights, w)
         end
     end
 
     collapsed_graph, collapsed_weights
 end
 
-function community_layout{V}(g::AbstractGraph{V}, membership::Vector{Int})
+function community_layout(g::AbstractGraph, membership::Vector{Int})
     N = length(membership)
     lx = zeros(N)
     ly = zeros(N)
@@ -51,13 +48,13 @@ function community_layout{V}(g::AbstractGraph{V}, membership::Vector{Int})
         if haskey(comms, lbl)
             push!(comms[lbl], idx)
         else
-            comms[lbl] = collect(idx)
+            comms[lbl] = Int[idx]
         end
     end
     h, w = collapse_graph(g, membership)
     clx, cly = spring_layout(h)
     for (lbl, nodes) in comms
-        θ = linspace(0, 2pi, length(nodes) + 1)[1:end-1]
+        θ = range(0, stop=2pi, length=(length(nodes) + 1))[1:end-1]
         for (idx, node) in enumerate(nodes)
             lx[node] = 1.8*length(nodes)/N*cos(θ[idx]) + clx[lbl]
             ly[node] = 1.8*length(nodes)/N*sin(θ[idx]) + cly[lbl]
@@ -66,11 +63,11 @@ function community_layout{V}(g::AbstractGraph{V}, membership::Vector{Int})
     lx, ly
 end
 
-function collapse_layout{V}(g::AbstractGraph{V}, membership::Vector{Int})
-    lightg = LightGraphs.Graph(num_vertices(g))
-    for e in Graphs.edges(g)
-        u = vertex_index(source(e,g), g)
-        v = vertex_index(target(e,g), g)
+function collapse_layout(g::AbstractGraph, membership::Vector{Int})
+    lightg = LightGraphs.SimpleGraph(_nv(g))
+    for e in _edges(g)
+        u = _src_index(e, g)
+        v = _dst_index(e, g)
         LightGraphs.add_edge!(lightg, u, v)
     end
     N = length(membership)
@@ -81,15 +78,15 @@ function collapse_layout{V}(g::AbstractGraph{V}, membership::Vector{Int})
         if haskey(comms, lbl)
             push!(comms[lbl], idx)
         else
-            comms[lbl] = collect(idx)
+            comms[lbl] = Int[idx]
         end
     end
     h, w = collapse_graph(g, membership)
     clx, cly = spring_layout(h)
     for (lbl, nodes) in comms
         subg = lightg[nodes]
         sublx, subly = spring_layout(subg)
-        θ = linspace(0, 2pi, length(nodes) + 1)[1:end-1]
+        θ = range(0, stop=2pi, length=(length(nodes) + 1))[1:end-1]
         for (idx, node) in enumerate(nodes)
             lx[node] = 1.8*length(nodes)/N*sublx[idx] + clx[lbl]
             ly[node] = 1.8*length(nodes)/N*subly[idx] + cly[lbl]

src/graphio.jl

@@ -1,3 +1,5 @@
+using DelimitedFiles: readdlm
+
 """
 read some famous graphs
 
@@ -21,9 +23,9 @@ end
 """read graph from in edgelist format"""
 function readedgelist(filename; is_directed::Bool=false, start_index::Int=0, delim::Char=' ')
     es = readdlm(filename, delim, Int)
-    es = unique(es, 1)
+    es = unique(es, dims=1)
     if start_index == 0
-        es = es + 1
+        es = es .+ 1
     end
     N = maximum(es)
     if is_directed
@@ -38,7 +40,7 @@ function readedgelist(filename; is_directed::Bool=false, start_index::Int=0, del
                 es[i,1], es[i,2] = es[i,2], es[i,1]
             end
         end
-        es = unique(es, 1)
+        es = unique(es, dims=1)
         g = Graph(N)
         for i=1:size(es,1)
             add_edge!(g, es[i,1], es[i,2])

src/layout.jl

@@ -1,3 +1,7 @@
+using SparseArrays: SparseMatrixCSC, sparse
+using ArnoldiMethod: LR
+using LinearAlgebra: eigen
+
 """
 Position nodes uniformly at random in the unit square.
 For every node, a position is generated by choosing each of dim
@@ -56,7 +60,7 @@ function circular_layout(G)
         return [0.0], [0.0]
     else
         # Discard the extra angle since it matches 0 radians.
-        θ = linspace(0, 2pi, _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
@@ -96,11 +100,11 @@ julia> locs_x, locs_y = spring_layout(g)
 function spring_layout(G, locs_x=2*rand(_nv(G)).-1.0, locs_y=2*rand(_nv(G)).-1.0; C=2.0, MAXITER=100, INITTEMP=2.0)
 
     #size(adj_matrix, 1) != size(adj_matrix, 2) && error("Adj. matrix must be square.")
-    const N = _nv(G)
+    N = _nv(G)
     adj_matrix = _adjacency_matrix(G)
 
     # The optimal distance bewteen vertices
-    const K = C * sqrt(4.0 / N)
+    K = C * sqrt(4.0 / N)
 
     # Store forces and apply at end of iteration all at once
     force_x = zeros(N)
@@ -183,7 +187,7 @@ julia> nlist[2] = [6:num_vertiecs(g)]
 julia> locs_x, locs_y = shell_layout(g, nlist)
 ```
 """
-function shell_layout(G, nlist::Union{Void, Vector{Vector{Int}}} = nothing)
+function shell_layout(G, nlist::Union{Nothing, Vector{Vector{Int}}} = nothing)
     if _nv(G) == 1
         return [0.0], [0.0]
     end
@@ -199,7 +203,7 @@ function shell_layout(G, nlist::Union{Void, Vector{Vector{Int}}} = nothing)
     locs_y = Float64[]
     for nodes in nlist
         # Discard the extra angle since it matches 0 radians.
-        θ = linspace(0, 2pi, length(nodes) + 1)[1:end-1]
+        θ = range(0, stop=2pi, length=length(nodes)+1)[1:end-1]
         append!(locs_x, radius*cos.(θ))
         append!(locs_y, radius*sin.(θ))
         radius += 1.0
@@ -229,37 +233,41 @@ julia> weight = rand(num_edges(g))
 julia> locs_x, locs_y = spectral_layout(g, weight)
 ```
 """
-function spectral_layout(G)
+function spectral_layout(G, 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)))
+    end
     if _nv(G) > 500
-        A = sparse(Int[_src_index(e) for e in _edges(G)],
-                   Int[_dst_index(e) for e in _edges(G)],
+        A = sparse(Int[_src_index(e, G) for e in _edges(G)],
+                   Int[_dst_index(e, G) for e in _edges(G)],
                    weight, _nv(G), _nv(G))
         if _is_directed(G)
             A = A + A'
         end
         return _spectral(A)
     else
         L = _laplacian_matrix(G)
-        return _spectral(full(L))
+        return _spectral(Matrix(L))
     end
 end
 
 function _spectral(L::Matrix)
-    eigenvalues, eigenvectors = eig(L)
+    eigenvalues, eigenvectors = eigen(L)
     index = sortperm(eigenvalues)[2:3]
     eigenvectors[:, index[1]], eigenvectors[:, index[2]]
 end
 
 function _spectral(A::SparseMatrixCSC)
-    data = collect(sum(A, 1))
-    D = sparse([1:length(data)], [1:length(data)], data)
+    data = vec(sum(A, dims=1))
+    D = sparse(Base.OneTo(length(data)), Base.OneTo(length(data)), data)
     L = D - A
-    ncv = max(7, int(sqrt(length(data))))
-    eigenvalues, eigenvectors = eigs(L, nev=3, ncv=ncv)
+    eigenvalues, eigenvectors = LightGraphs.LinAlg.eigs(L, nev=3, which=LR())
     index = sortperm(real(eigenvalues))[2:3]
     real(eigenvectors[:, index[1]]), real(eigenvectors[:, index[2]])
 end