fixes doc deprecation warnings (#861)

* fixes doc deprecation warnings

* adding Base64 to runtests

docs/make.jl

@@ -1,5 +1,5 @@
 using Documenter
-include("../src/LightGraphs.jl")
+#include("../src/LightGraphs.jl")
 using LightGraphs
 
 # same for contributing and license

src/SimpleGraphs/SimpleGraphs.jl

@@ -73,7 +73,7 @@ function rem_edge!(g::AbstractSimpleGraph{T}, u::Integer, v::Integer) where T
     rem_edge!(g, edgetype(g)(T(u), T(v)))
 end
 
-@doc_str """
+"""
     rem_vertex!(g, v)
 
 Remove the vertex `v` from graph `g`. Return false if removal fails

src/biconnectivity/biconnect.jl

@@ -53,7 +53,7 @@ function visit!(g::AbstractGraph, state::Biconnections, u::Integer, v::Integer)
     end
 end
 
-@doc_str """
+"""
     biconnected_components(g)
 
 Compute the [biconnected components](https://en.wikipedia.org/wiki/Biconnected_component)

src/centrality/betweenness.jl

@@ -2,7 +2,7 @@
 # TODO - weighted, separate unweighted, edge betweenness
 
 
-@doc_str """
+"""
     betweenness_centrality(g[, vs])
     betweenness_centrality(g, k)
     parallel_betweenness_centrality(g[, vs])

src/centrality/closeness.jl

@@ -1,4 +1,4 @@
-@doc_str """
+"""
     closeness_centrality(g)
 
 Calculate the [closeness centrality](https://en.wikipedia.org/wiki/Centrality#Closeness_centrality)

src/community/clustering.jl

@@ -46,7 +46,7 @@ function local_clustering!(storage::AbstractVector{Bool},
     return ntriang, nalltriang
 end
 
-@doc_str """
+"""
     local_clustering(g, v)
     local_clustering(g, vs)
 

src/core.jl

@@ -183,7 +183,7 @@ Return the number of self loops in `g`.
 """
 num_self_loops(g::AbstractGraph) = nv(g) == 0 ? 0 : sum(v -> has_edge(g, v, v), vertices(g))
 
-@doc_str """
+"""
     density(g)
 Return the density of `g`.
 Density is defined as the ratio of the number of actual edges to the

src/digraph/cycles/johnson.jl

@@ -22,7 +22,7 @@ The formula is coming from [Johnson, 1973](Johnson).
 maxsimplecycles(n::Integer) = sum(x -> ncycles_n_i(n, x), 1:(n - 1))
 
 
-@doc_str """
+"""
     maxsimplecycles(dg::::IsDirected, byscc::Bool = true)
 
 Compute the theoretical maximum number of cycles in the directed graph `dg`.
@@ -102,7 +102,7 @@ function unblock!(v::T, blocked::BitArray, B::Vector{Set{T}}) where T<:Integer
     end
 end
 
-@doc_str """
+"""
     circuit{T<:Integer}(v::T, dg::::IsDirected, vis::JohnsonVisitor{T}, 
     allcycles::Vector{Vector{T}}, vmap::Vector{T}, startnode::T = v)
 
@@ -154,7 +154,7 @@ allcycles::Vector{Vector{T}}, vmap::Vector{T}, startnode::T = v) where T<:Intege
 end
 
 
-@doc_str """
+"""
     simplecycles(dg::::IsDirected)
 
 Compute all cycles of the given directed graph, using
@@ -186,7 +186,7 @@ end
 
 ##########################################################
 #### Iterative version, using Tasks, of the previous algorithms.
-@doc_str """
+"""
     circuit_iter{T<:Integer}(v::T, dg::::IsDirected, vis::JohnsonVisitor{T}, 
     vmap::Vector{T}, cycle::Channel, startnode::T = v)
 
@@ -267,7 +267,7 @@ function itercycles end
     end
 end
 
-@doc_str """
+"""
     simplecyclescount(dg::DiGraph, ceiling = 10^6)
 
 Count the number of cycles in a directed graph, using
@@ -292,7 +292,7 @@ function simplecyclescount end
     return len
 end
 
-@doc_str """
+"""
     simplecycles_iter(dg::DiGraph, ceiling = 10^6)
 
 Search all cycles of the given directed graph, using
@@ -314,7 +314,7 @@ function simplecycles_iter end
 @traitfn simplecycles_iter(dg::::IsDirected, ceiling = 10^6) =
     collect(Iterators.take(Channel(c -> itercycles(dg, c)), ceiling))
 
-@doc_str """
+"""
     simplecycleslength(dg::DiGraph, ceiling = 10^6)
 
 Search all cycles of the given directed graph, using

src/digraph/transitivity.jl

@@ -1,4 +1,4 @@
-@doc_str """
+"""
     transitiveclosure!(g, selflooped=false)
 
 Compute the transitive closure of a directed graph, using the Floyd-Warshall

src/edit_distance.jl

@@ -1,4 +1,4 @@
-@doc_str """
+"""
     edit_distance(G₁::AbstractGraph, G₂::AbstractGraph)
 
 Compute the edit distance between graphs `G₁` and `G₂`. Return the minimum

src/generators/euclideangraphs.jl

@@ -1,4 +1,4 @@
-@doc_str """
+"""
     euclidean_graph(N, d; seed=-1, L=1., p=2., cutoff=-1., bc=:open)
 
 Generate `N` uniformly distributed points in the box ``[0,L]^{d}``

src/generators/randgraphs.jl

@@ -357,7 +357,7 @@ function barabasi_albert!(g::AbstractGraph, n::Integer, k::Integer; seed::Int=-1
 end
 
 
-@doc_str """
+"""
     static_fitness_model(m, fitness)
 
 Generate a random graph with ``|fitness|`` vertices and `m` edges,
@@ -393,7 +393,7 @@ function static_fitness_model(m::Integer, fitness::Vector{T}; seed::Int=-1) wher
     return g
 end
 
-@doc_str """
+"""
     static_fitness_model(m, fitness_out, fitness_in)
 
 Generate a random graph with ``|fitness\\_out + fitness\\_in|`` vertices and `m` edges,
@@ -449,7 +449,7 @@ function _create_static_fitness_graph!(g::AbstractGraph, m::Integer, cum_fitness
     end
 end
 
-@doc_str """
+"""
     static_scale_free(n, m, α)
 
 Generate a random graph with `n` vertices, `m` edges and expected power-law
@@ -475,7 +475,7 @@ function static_scale_free(n::Integer, m::Integer, α::Real; seed::Int=-1, finit
     static_fitness_model(m, fitness, seed=seed)
 end
 
-@doc_str """
+"""
     static_scale_free(n, m, α_out, α_in)
 
 Generate a random graph with `n` vertices, `m` edges and expected power-law
@@ -523,7 +523,7 @@ function _construct_fitness(n::Integer, α::Real, finite_size_correction::Bool)
     return fitness
 end
 
-@doc_str """
+"""
     random_regular_graph(n, k)
 
 Create a random undirected
@@ -565,7 +565,7 @@ function random_regular_graph(n::Integer, k::Integer; seed::Int=-1)
     return g
 end
 
-@doc_str """
+"""
     random_configuration_model(n, ks)
 
 Create a random undirected graph according to the [configuration model]
@@ -605,7 +605,7 @@ function random_configuration_model(n::Integer, k::Array{T}; seed::Int=-1, check
     return g
 end
 
-@doc_str """
+"""
     random_regular_digraph(n, k)
 
 Create a random directed [regular graph](https://en.wikipedia.org/wiki/Regular_graph)
@@ -649,7 +649,7 @@ function random_regular_digraph(n::Integer, k::Integer; dir::Symbol=:out, seed::
     end
 end
 
-@doc_str """
+"""
     random_tournament_digraph(n)
 
 Create a random directed [tournament graph]
@@ -671,7 +671,7 @@ function random_tournament_digraph(n::Integer; seed::Int=-1)
     return g
 end
 
-@doc_str """
+"""
     stochastic_block_model(c, n)
 
 Return a Graph generated according to the Stochastic Block Model (SBM).
@@ -722,7 +722,7 @@ function stochastic_block_model(c::Matrix{T}, n::Vector{U}; seed::Int = -1) wher
     return g
 end
 
-@doc_str """
+"""
     stochastic_block_model(cint, cext, n)
 
 Return a Graph generated according to the Stochastic Block Model (SBM), sampling
@@ -813,7 +813,7 @@ end
 const biclique = ones(2, 2) - eye(2)
 
 #TODO: this documentation needs work. sbromberger 20170326
-@doc_str """
+"""
     nearbipartiteaffinity(sizes, between, intra)
 
 Construct the affinity matrix for a near bipartite SBM.

src/generators/smallgraphs.jl

@@ -18,7 +18,7 @@ function _make_simple_directed_graph(n::T, edgelist::Vector{Tuple{T,T}}) where T
     return g
 end
 
-doc"""
+"""
     smallgraph(s)
     smallgraph(s)
 

src/generators/staticgraphs.jl

@@ -18,7 +18,7 @@ function CompleteGraph(n::Integer)
 end
 
 
-@doc_str """
+"""
     CompleteBipartiteGraph(n1, n2)
 
 Create an undirected [complete bipartite graph](https://en.wikipedia.org/wiki/Complete_bipartite_graph)
@@ -169,7 +169,7 @@ function WheelDiGraph(n::Integer)
     return g
 end
 
-@doc_str """
+"""
     Grid(dims; periodic=false)
 
 Create a ``|dims|``-dimensional cubic lattice, with length `dims[i]`

src/linalg/graphmatrices.jl

@@ -40,7 +40,7 @@ function CombinatorialAdjacency(A::SparseMatrix{T}) where T
 end
 
 
-@doc_str """
+"""
 	NormalizedAdjacency{T}
 
 The normalized adjacency matrix is ``\\hat{A} = D^{-1/2} A D^{-1/2}``.
@@ -133,7 +133,7 @@ struct CombinatorialLaplacian{T} <: Laplacian{T}
 	A::CombinatorialAdjacency{T}
 end
 
-@doc_str """
+"""
 	NormalizedLaplacian{T}
 
 The normalized Laplacian is ``\\hat{L} = I - D^{-1/2} A D^{-1/2}``.

src/linalg/nonbacktracking.jl

@@ -1,5 +1,5 @@
 
-@doc_str """
+"""
     non_backtracking_matrix(g)
 
 Return a non-backtracking matrix `B` and an edgemap storing the oriented
@@ -40,7 +40,7 @@ function non_backtracking_matrix(g::AbstractGraph)
     return B, edgeidmap
 end
 
-@doc_str """
+"""
     Nonbacktracking{G}
 
 A compact representation of the nonbacktracking operator.

src/linalg/spectral.jl

@@ -84,7 +84,7 @@ function laplacian_matrix(g::AbstractGraph{U}, T::DataType=Int; dir::Symbol=:uns
     return D - A
 end
 
-@doc_str """
+"""
     laplacian_spectrum(g[, T=Int; dir=:unspec])
 
 Return the eigenvalues of the Laplacian matrix for a graph `g`, indexed
@@ -103,7 +103,7 @@ eigenvalues/eigenvectors.
 """
 laplacian_spectrum(g::AbstractGraph, T::DataType=Int; dir::Symbol=:unspec) = eigvals(Matrix(laplacian_matrix(g, T; dir=dir)))
 
-@doc_str """
+"""
 Return the eigenvalues of the adjacency matrix for a graph `g`, indexed
 by vertex. Default values for `T` are the same as those in
 [`adjacency_matrix`](@ref).
@@ -166,7 +166,7 @@ function incidence_matrix(g::AbstractGraph, T::DataType=Int; oriented=false)
     return spmx
 end
 
-@doc_str """
+"""
     spectral_distance(G₁, G₂ [, k])
 
 Compute the spectral distance between undirected n-vertex

src/operators.jl

@@ -62,7 +62,7 @@ function reverse! end
     return g
 end
 
-doc"""
+"""
     blkdiag(g, h)
 
 Return a graph with ``|V(g)| + |V(h)|`` vertices and ``|E(g)| + |E(h)|``

src/shortestpaths/dijkstra.jl

@@ -119,7 +119,7 @@ struct MultipleDijkstraState{T<:Real,U<:Integer} <: AbstractPathState
     parents::Matrix{U}
 end
 
-@doc_str """
+"""
     parallel_multisource_dijkstra_shortest_paths(g, sources=vertices(g), distmx=weights(g))
 
 Compute the shortest paths between all pairs of vertices in graph `g` by running

src/shortestpaths/floyd-warshall.jl

@@ -12,7 +12,7 @@ struct FloydWarshallState{T,U<:Integer} <: AbstractPathState
     parents::Matrix{U}
 end
 
-@doc_str """
+"""
 floyd_warshall_shortest_paths(g, distmx=weights(g))
 Use the [Floyd-Warshall algorithm](http://en.wikipedia.org/wiki/Floyd–Warshall_algorithm)
 to compute the shortest paths between all pairs of vertices in graph `g` using an

src/traversals/greedy_color.jl

@@ -108,7 +108,7 @@ If parallel is true then the colorings are executed in parallel.
 random_greedy_color(g::AbstractGraph{T}, reps::Integer = 1, parallel::Bool = false) where {T<:Integer} =
 parallel ? parallel_random_greedy_color(g, reps) : seq_random_greedy_color(g, reps)
 
-@doc_str """
+"""
     greedy_color(g; sort_degree=false, parallel=false, reps = 1)
 
 Color graph `g` based on [Greedy Coloring Heuristics](https://en.wikipedia.org/wiki/Greedy_coloring)

test/runtests.jl

@@ -4,6 +4,7 @@ using Test
 using SparseArrays
 using LinearAlgebra
 using DelimitedFiles
+using Base64
 
 const testdir = dirname(@__FILE__)