use svg

Author: GiggleLiu

examples/Coloring.jl

@@ -16,7 +16,7 @@ rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*
 
 locations = [[rot15(0.0, 2.0, i) for i=0:4]..., [rot15(0.0, 1.0, i) for i=0:4]...]
 
-show_graph(graph; locs=locations)
+show_graph(graph; locs=locations, format=:svg)
 
 # ## Generic tensor network representation
 #
@@ -55,14 +55,14 @@ is_vertex_coloring(graph, single_solution.c.data)
 
 vertex_color_map = Dict(0=>"red", 1=>"green", 2=>"blue")
 
-show_graph(graph; locs=locations, vertex_colors=[vertex_color_map[Int(c)]
+show_graph(graph; locs=locations, format=:svg, vertex_colors=[vertex_color_map[Int(c)]
      for c in single_solution.c.data])
 
 # Let us try to solve the same issue on its line graph, a graph that generated by mapping an edge to a vertex and two edges sharing a common vertex will be connected.
 linegraph = line_graph(graph)
 
 show_graph(linegraph; locs=[0.5 .* (locations[e.src] .+ locations[e.dst])
-     for e in edges(graph)])
+     for e in edges(graph)], format=:svg)
 
 # Let us construct the tensor network and see if there are solutions.
 lineproblem = Coloring{3}(linegraph);

examples/DominatingSet.jl

@@ -19,7 +19,7 @@ rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*
 
 locations = [[rot15(0.0, 2.0, i) for i=0:4]..., [rot15(0.0, 1.0, i) for i=0:4]...]
 
-show_graph(graph; locs=locations)
+show_graph(graph; locs=locations, format=:svg)
 
 # ## Generic tensor network representation
 # We can use [`DominatingSet`](@ref) to construct the tensor network for solving the dominating set problem as
@@ -72,7 +72,7 @@ all(c->is_dominating_set(graph, c), min_configs)
 
 #
 
-show_gallery(graph, (2, 5); locs=locations, vertex_configs=min_configs)
+show_gallery(graph, (2, 5); locs=locations, vertex_configs=min_configs, format=:svg)
 
 # Similarly, if one is only interested in computing one of the minimum dominating sets,
 # one can use the graph property [`SingleConfigMin`](@ref).

examples/IndependentSet.jl

@@ -15,7 +15,7 @@ rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*
 
 locations = [[rot15(0.0, 2.0, i) for i=0:4]..., [rot15(0.0, 1.0, i) for i=0:4]...]
 
-show_graph(graph; locs=locations)
+show_graph(graph; locs=locations, format=:svg)
 
 # The graphical display is available in the following editors
 # * a [VSCode](https://github.com/julia-vscode/julia-vscode) editor,
@@ -118,7 +118,7 @@ single_solution = max_config.c.data
 
 # This bit string should be read from left to right, with the i-th bit being 1 (0) to indicate the i-th vertex is present (absent) in the set.
 # We can visualize this MIS with the following function.
-show_graph(graph; locs=locations, vertex_colors=
+show_graph(graph; locs=locations, format=:svg, vertex_colors=
     [iszero(single_solution[i]) ? "white" : "red" for i=1:nv(graph)])
 
 # ##### Enumerate all solutions and best several solutions
@@ -131,7 +131,7 @@ all_max_configs = solve(problem, ConfigsMax(; bounded=true))[]
 all_max_configs.c.data
 
 # These solutions can be visualized with the [`show_gallery`](@ref) function.
-show_gallery(graph, (1, length(all_max_configs.c)); locs=locations, vertex_configs=all_max_configs.c);
+show_gallery(graph, (1, length(all_max_configs.c)); locs=locations, vertex_configs=all_max_configs.c, format=:svg)
 
 # We can use [`ConfigsAll`](@ref) to enumerate all sets satisfying the independence constraint.
 all_independent_sets = solve(problem, ConfigsAll())[]

examples/Matching.jl

@@ -17,7 +17,7 @@ rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*
 
 locations = [[rot15(0.0, 2.0, i) for i=0:4]..., [rot15(0.0, 1.0, i) for i=0:4]...]
 
-show_graph(graph; locs=locations)
+show_graph(graph; locs=locations, format=:svg)
 
 # ## Generic tensor network representation
 # We construct the tensor network for the matching problem by typing
@@ -66,7 +66,7 @@ matching_poly = solve(problem, GraphPolynomial())[]
 match_config = solve(problem, SingleConfigMax())[]
 
 # Let us show the result by coloring the matched edges to red
-show_graph(graph; locs=locations, edge_colors=
+show_graph(graph; locs=locations, format=:svg, edge_colors=
     [isone(match_config.c.data[i]) ? "red" : "black" for i=1:ne(graph)])
 
 # where we use edges with red color to related pairs of matched vertices.

examples/MaxCut.jl

@@ -20,7 +20,7 @@ rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*
 
 locations = [[rot15(0.0, 2.0, i) for i=0:4]..., [rot15(0.0, 1.0, i) for i=0:4]...]
 
-show_graph(graph; locs=locations)
+show_graph(graph; locs=locations, format=:svg)
 
 # ## Generic tensor network representation
 # We define the cutting problem as
@@ -69,6 +69,6 @@ max_cut_size_verify = cut_size(graph, max_vertex_config)
 # You should see a consistent result as above `max_cut_size`.
 
 show_graph(graph; locs=locations, vertex_colors=[
-        iszero(max_vertex_config[i]) ? "white" : "red" for i=1:nv(graph)])
+        iszero(max_vertex_config[i]) ? "white" : "red" for i=1:nv(graph)], format=:svg)
 
 # where red vertices and white vertices are separated by the cut.

examples/MaximalIS.jl

@@ -18,7 +18,7 @@ rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*
 
 locations = [[rot15(0.0, 2.0, i) for i=0:4]..., [rot15(0.0, 1.0, i) for i=0:4]...]
 
-show_graph(graph; locs=locations)
+show_graph(graph; locs=locations, format=:svg)
 
 # ## Generic tensor network representation
 # We can use [`MaximalIS`](@ref) to construct the tensor network for solving the maximal independent set problem as
@@ -73,7 +73,7 @@ all(c->is_maximal_independent_set(graph, c), maximal_configs)
 
 #
 
-show_gallery(graph, (3, 5); locs=locations, vertex_configs=maximal_configs)
+show_gallery(graph, (3, 5); locs=locations, vertex_configs=maximal_configs, format=:svg)
 
 # This result should be consistent with that given by the [Bron Kerbosch algorithm](https://en.wikipedia.org/wiki/Bron%E2%80%93Kerbosch_algorithm) on the complement of Petersen graph.
 cliques = maximal_cliques(complement(graph))
@@ -84,7 +84,7 @@ cliques = maximal_cliques(complement(graph))
 # It is the [`ConfigsMin`](@ref) property in the program.
 minimum_maximal_configs = solve(problem, ConfigsMin())[].c
 
-show_gallery(graph, (2, 5); locs=locations, vertex_configs=minimum_maximal_configs)
+show_gallery(graph, (2, 5); locs=locations, vertex_configs=minimum_maximal_configs, format=:svg)
 
 # Similarly, if one is only interested in computing one of the minimum sets,
 # one can use the graph property [`SingleConfigMin`](@ref).

examples/PaintShop.jl

@@ -27,7 +27,7 @@ for i=1:length(sequence)
     i != j && add_edge!(graph, i, j)
 end
 
-show_graph(graph; locs=locations, texts=string.(sequence), edge_colors=
+show_graph(graph; locs=locations, texts=string.(sequence), format=:svg, edge_colors=
     [sequence[e.src] == sequence[e.dst] ? "blue" : "black" for e in edges(graph)])
 
 # Vertices connected by blue edges must have different colors,
@@ -90,7 +90,7 @@ best_configs = solve(problem, ConfigsMin())[]
 
 painting1 = paint_shop_coloring_from_config(problem, best_configs.c.data[1])
 
-show_graph(graph; locs=locations, texts=string.(sequence),
+show_graph(graph; locs=locations, format=:svg, texts=string.(sequence),
     edge_colors=[sequence[e.src] == sequence[e.dst] ? "blue" : "black" for e in edges(graph)],
     vertex_colors=[isone(c) ? "red" : "black" for c in painting1], vertex_text_color="white")
 

examples/weighted.jl

@@ -19,7 +19,7 @@ rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*
 
 locations = [[rot15(0.0, 2.0, i) for i=0:4]..., [rot15(0.0, 1.0, i) for i=0:4]...]
 
-show_graph(graph; locs=locations, vertex_colors=
+show_graph(graph; locs=locations, format=:svg, vertex_colors=
           [iszero(max_config_weighted.c.data[i]) ? "white" : "red" for i=1:nv(graph)])
 
 # The only solution space property that can not be defined for general real-weighted (not including integer-weighted) graphs is the [`GraphPolynomial`](@ref).
@@ -39,5 +39,5 @@ max5_configs = solve(problem, SingleConfigMax(5))[]
 max5_configs.orders
 
 # Let us visually check these configurations
-show_gallery(graph, (1, 5); locs=locations, vertex_configs=[max5_configs.orders[k].c.data for k=1:5])
+show_gallery(graph, (1, 5); locs=locations, format=:svg, vertex_configs=[max5_configs.orders[k].c.data for k=1:5])