update

Author: GiggleLiu

Project.toml

@@ -37,7 +37,7 @@ DocStringExtensions = "0.8, 0.9"
 FFTW = "1.4"
 Graphs = "1.7"
 LinearAlgebra = "1"
-LuxorGraphPlot = "0.3"
+LuxorGraphPlot = "0.4"
 OMEinsum = "0.8"
 Polynomials = "4"
 Primes = "0.5"

examples/Coloring.jl

@@ -13,10 +13,8 @@ graph = Graphs.smallgraph(:petersen)
 
 # We can visualize this graph using the following function
 rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
-
-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, format=:svg)
+locations = [[rot15(0.0, 60.0, i) for i=0:4]..., [rot15(0.0, 30, i) for i=0:4]...]
+show_graph(graph, locations; format=:svg)
 
 # ## Generic tensor network representation
 # We can define the 3-coloring problem with the [`Coloring`](@ref) type as

examples/DominatingSet.jl

@@ -16,10 +16,8 @@ graph = Graphs.smallgraph(:petersen)
 
 # We can visualize this graph using the following function
 rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
-
-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, format=:svg)
+locations = [[rot15(0.0, 60.0, i) for i=0:4]..., [rot15(0.0, 30, i) for i=0:4]...]
+show_graph(graph, locations; format=:svg)
 
 # ## Generic tensor network representation
 # We can use [`DominatingSet`](@ref) to construct the tensor network for solving the dominating set problem as

examples/IndependentSet.jl

@@ -12,10 +12,8 @@ graph = Graphs.smallgraph(:petersen)
 # We can visualize this graph using the [`show_graph`](@ref) function
 ## set the vertex locations manually instead of using the default spring layout
 rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
-
-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, format=:svg)
+locations = [[rot15(0.0, 60.0, i) for i=0:4]..., [rot15(0.0, 30, i) for i=0:4]...]
+show_graph(graph, locations; format=:svg)
 
 # The graphical display is available in the following editors
 # * a [VSCode](https://github.com/julia-vscode/julia-vscode) editor,
@@ -122,7 +120,7 @@ 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, format=:svg, vertex_colors=
+show_graph(graph, locations; format=:svg, vertex_colors=
     [iszero(single_solution[i]) ? "white" : "red" for i=1:nv(graph)])
 
 # ##### Enumerate all solutions and best several solutions

examples/Matching.jl

@@ -14,8 +14,8 @@ graph = Graphs.smallgraph(:petersen)
 
 # We can visualize this graph using the following function
 rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
-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, format=:svg)
+locations = [[rot15(0.0, 60.0, i) for i=0:4]..., [rot15(0.0, 30, i) for i=0:4]...]
+show_graph(graph, locations; format=:svg)
 
 # ## Generic tensor network representation
 # We can define the matching problem with the [`Matching`](@ref) type as

examples/MaxCut.jl

@@ -17,8 +17,8 @@ graph = Graphs.smallgraph(:petersen)
 
 # We can visualize this graph using the following function
 rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
-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, format=:svg)
+locations = [[rot15(0.0, 60.0, i) for i=0:4]..., [rot15(0.0, 30, i) for i=0:4]...]
+show_graph(graph, locations; format=:svg)
 
 # ## Generic tensor network representation
 # We can define the cutting problem with the [`MaxCut`](@ref) type as

examples/MaximalIS.jl

@@ -15,8 +15,8 @@ graph = Graphs.smallgraph(:petersen)
 
 # We can visualize this graph using the following function
 rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
-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, format=:svg)
+locations = [[rot15(0.0, 60.0, i) for i=0:4]..., [rot15(0.0, 30, i) for i=0:4]...]
+show_graph(graph, 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

examples/PaintShop.jl

@@ -18,16 +18,13 @@ sequence = collect("iadgbeadfcchghebif")
 
 # We can visualize this paint shop problem as a graph
 rot(a, b, θ) = cos(θ)*a + sin(θ)*b, cos(θ)*b - sin(θ)*a
-
-locations = [rot(0.0, 2.0, -0.25π - 1.5*π*(i-0.5)/length(sequence)) for i=1:length(sequence)]
-
+locations = [rot(0.0, 100.0, -0.25π - 1.5*π*(i-0.5)/length(sequence)) for i=1:length(sequence)]
 graph = path_graph(length(sequence))
 for i=1:length(sequence) 
     j = findlast(==(sequence[i]), sequence)
     i != j && add_edge!(graph, i, j)
 end
-
-show_graph(graph; locs=locations, texts=string.(sequence), format=:svg, edge_colors=
+show_graph(graph, 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,

examples/SpinGlass.jl

@@ -21,8 +21,8 @@ graph = Graphs.smallgraph(:petersen)
 
 # We can visualize this graph using the following function
 rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
-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, format=:svg)
+locations = [[rot15(0.0, 60.0, i) for i=0:4]..., [rot15(0.0, 30, i) for i=0:4]...]
+show_graph(graph, locations; format=:svg)
 
 # ## Generic tensor network representation
 # An anti-ferromagnetic spin glass problem can be defined with the [`SpinGlass`](@ref) type as
@@ -86,7 +86,7 @@ Emin_verify = spinglass_energy(spinglass, ground_state)
 
 # You should see a consistent result as above `Emin`.
 
-show_graph(graph; locs=locations, vertex_colors=[
+show_graph(graph, locations; vertex_colors=[
         iszero(ground_state[i]) ? "white" : "red" for i=1:nv(graph)], format=:svg)
 
 # In the plot, the red vertices are the ones with spin value `-1` (or `1` in the boolean representation).

examples/weighted.jl

@@ -20,8 +20,8 @@ max_config_weighted = solve(problem, SingleConfigMax())[]
 
 # Let us visualize the solution.
 rot15(a, b, i::Int) = cos(2i*π/5)*a + sin(2i*π/5)*b, cos(2i*π/5)*b - sin(2i*π/5)*a
-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, format=:svg, vertex_colors=
+locations = [[rot15(0.0, 60.0, i) for i=0:4]..., [rot15(0.0, 30, i) for i=0:4]...]
+show_graph(graph, 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).