Mark GraphMakie and Graphs as weak-dependency

Project.toml

@@ -7,8 +7,6 @@ version = "0.4.1"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 DeltaArrays = "10b0fc19-5ccc-4427-889b-d75dd6306188"
 EinExprs = "b1794770-133b-4de1-afb4-526377e9f4c5"
-GraphMakie = "1ecd5474-83a3-4783-bb4f-06765db800d2"
-Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Muscle = "21fe5c4b-a943-414d-bf3e-516f24900631"
 OMEinsum = "ebe7aa44-baf0-506c-a96f-8464559b3922"
@@ -21,19 +19,22 @@ ValSplit = "0625e100-946b-11ec-09cd-6328dd093154"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+GraphMakie = "1ecd5474-83a3-4783-bb4f-06765db800d2"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 
 [extensions]
 TenetChainRulesCoreExt = "ChainRulesCore"
 TenetChainRulesTestUtilsExt = ["ChainRulesCore", "ChainRulesTestUtils"]
 TenetFiniteDifferencesExt = "FiniteDifferences"
+TenetGraphMakieExt = ["GraphMakie", "Makie"]
 TenetMakieExt = "Makie"
 
 [compat]
 ChainRulesCore = "1.0"
 Combinatorics = "1.0"
 DeltaArrays = "0.1.1"
-EinExprs = "0.5.5"
+EinExprs = "0.5, 0.6"
 GraphMakie = "0.4,0.5"
 Graphs = "1.7"
 LinearAlgebra = "1.9"

ext/TenetGraphMakieExt.jl

@@ -0,0 +1,144 @@
+module TenetGraphMakieExt
+
+function __init__()
+    try
+        Base.require(Main, :Graphs)
+    catch
+        @warn """Package Graphs or Graphs not found in current path. It is needed to plot `Tenet`s with `GraphMakie`.
+        - Run `import Pkg; Pkg.add(\"Graphs\")` or `]add Graphs` to install the Graphs package, then restart julia.
+        """
+    end
+end
+
+using Tenet
+using Tenet: AbstractTensorNetwork
+using Combinatorics: combinations
+using Graphs
+using Makie
+
+using GraphMakie
+
+"""
+    plot(tn::TensorNetwork; kwargs...)
+    plot!(f::Union{Figure,GridPosition}, tn::TensorNetwork; kwargs...)
+    plot!(ax::Union{Axis,Axis3}, tn::TensorNetwork; kwargs...)
+
+Plot a [`TensorNetwork`](@ref) as a graph.
+
+# Keyword Arguments
+
+  - `labels` If `true`, show the labels of the tensor indices. Defaults to `false`.
+  - The rest of `kwargs` are passed to `GraphMakie.graphplot`.
+"""
+function Makie.plot(@nospecialize tn::AbstractTensorNetwork; kwargs...)
+    f = Figure()
+    ax, p = plot!(f[1, 1], tn; kwargs...)
+    return Makie.FigureAxisPlot(f, ax, p)
+end
+
+# NOTE this is a hack! we did it in order not to depend on NetworkLayout but can be unstable
+__networklayout_dim(x) = typeof(x).super.parameters |> first
+
+function Makie.plot!(f::Union{Figure,GridPosition}, @nospecialize tn::AbstractTensorNetwork; kwargs...)
+    ax = if haskey(kwargs, :layout) && __networklayout_dim(kwargs[:layout]) == 3
+        Axis3(f[1, 1])
+    else
+        ax = Axis(f[1, 1])
+        ax.aspect = DataAspect()
+        ax
+    end
+
+    hidedecorations!(ax)
+    hidespines!(ax)
+
+    p = plot!(ax, tn; kwargs...)
+
+    return Makie.AxisPlot(ax, p)
+end
+
+function Makie.plot!(ax::Union{Axis,Axis3}, @nospecialize tn::AbstractTensorNetwork; labels = false, kwargs...)
+    hypermap = Tenet.hyperflatten(tn)
+    tn = transform(tn, Tenet.HyperindConverter)
+
+    tensormap = IdDict(tensor => i for (i, tensor) in enumerate(tensors(tn)))
+
+    # TODO how to mark multiedges? (i.e. parallel edges)
+    graph = SimpleGraph([
+        Edge(map(Base.Fix1(getindex, tensormap), tensors)...) for (_, tensors) in tn.indexmap if length(tensors) > 1
+    ])
+
+    # TODO recognise `copytensors` by using `DeltaArray` or `Diagonal` representations
+    copytensors = findall(tensor -> any(flatinds -> issetequal(inds(tensor), flatinds), keys(hypermap)), tensors(tn))
+    ghostnodes = map(inds(tn, :open)) do index
+        # create new ghost node
+        add_vertex!(graph)
+        node = nv(graph)
+
+        # connect ghost node
+        tensor = only(tn.indexmap[index])
+        add_edge!(graph, node, tensormap[tensor])
+
+        return node
+    end
+
+    # configure graphics
+    # TODO refactor hardcoded values into constants
+    kwargs = Dict{Symbol,Any}(kwargs)
+
+    if haskey(kwargs, :node_size)
+        append!(kwargs[:node_size], zero(ghostnodes))
+    else
+        kwargs[:node_size] = map(1:nv(graph)) do i
+            i ∈ ghostnodes ? 0 : max(15, log2(length(tensors(tn)[i])))
+        end
+    end
+
+    if haskey(kwargs, :node_marker)
+        append!(kwargs[:node_marker], fill(:circle, length(ghostnodes)))
+    else
+        kwargs[:node_marker] = map(i -> i ∈ copytensors ? :diamond : :circle, 1:nv(graph))
+    end
+
+    if haskey(kwargs, :node_color)
+        kwargs[:node_color] = vcat(kwargs[:node_color], fill(:black, length(ghostnodes)))
+    else
+        kwargs[:node_color] = map(1:nv(graph)) do v
+            v ∈ copytensors ? Makie.to_color(:black) : Makie.RGBf(240 // 256, 180 // 256, 100 // 256)
+        end
+    end
+
+    get!(kwargs, :node_attr, (colormap = :viridis, strokewidth = 2.0, strokecolor = :black))
+
+    # configure labels
+    labels == true && get!(kwargs, :elabels) do
+        opentensors = findall(t -> !isdisjoint(inds(t), inds(tn, :open)), tensors(tn))
+        opencounter = IdDict(tensor => 0 for tensor in opentensors)
+
+        map(edges(graph)) do edge
+            # case: open edge
+            if any(∈(ghostnodes), [src(edge), dst(edge)])
+                notghost = src(edge) ∈ ghostnodes ? dst(edge) : src(edge)
+                inds = Tenet.inds(tn, :open) ∩ Tenet.inds(tensors(tn)[notghost])
+                opencounter[notghost] += 1
+                return inds[opencounter[notghost]] |> string
+            end
+
+            # case: hyperedge
+            if any(∈(copytensors), [src(edge), dst(edge)])
+                i = src(edge) ∈ copytensors ? src(edge) : dst(edge)
+                # hyperindex = filter(p -> isdisjoint(inds(tensors)[i], p[2]), hypermap) |> only |> first
+                hyperindex = hypermap[Tenet.inds(tensors(tn)[i])]
+                return hyperindex |> string
+            end
+
+            return join(Tenet.inds(tensors(tn)[src(edge)]) ∩ Tenet.inds(tensors(tn)[dst(edge)]), ',')
+        end
+    end
+    get!(() -> repeat([:black], ne(graph)), kwargs, :elabels_color)
+    get!(() -> repeat([17], ne(graph)), kwargs, :elabels_textsize)
+
+    # plot graph
+    graphplot!(ax, graph; kwargs...)
+end
+
+end

ext/TenetMakieExt.jl

@@ -1,134 +1,13 @@
 module TenetMakieExt
 
-using Tenet
-using Tenet: AbstractTensorNetwork
-using Combinatorics: combinations
-using Graphs
-using Makie
-
-using GraphMakie
-
-"""
-    plot(tn::TensorNetwork; kwargs...)
-    plot!(f::Union{Figure,GridPosition}, tn::TensorNetwork; kwargs...)
-    plot!(ax::Union{Axis,Axis3}, tn::TensorNetwork; kwargs...)
-
-Plot a [`TensorNetwork`](@ref) as a graph.
-
-# Keyword Arguments
-
-  - `labels` If `true`, show the labels of the tensor indices. Defaults to `false`.
-  - The rest of `kwargs` are passed to `GraphMakie.graphplot`.
-"""
-function Makie.plot(@nospecialize tn::AbstractTensorNetwork; kwargs...)
-    f = Figure()
-    ax, p = plot!(f[1, 1], tn; kwargs...)
-    return Makie.FigureAxisPlot(f, ax, p)
-end
-
-# NOTE this is a hack! we did it in order not to depend on NetworkLayout but can be unstable
-__networklayout_dim(x) = typeof(x).super.parameters |> first
-
-function Makie.plot!(f::Union{Figure,GridPosition}, @nospecialize tn::AbstractTensorNetwork; kwargs...)
-    ax = if haskey(kwargs, :layout) && __networklayout_dim(kwargs[:layout]) == 3
-        Axis3(f[1, 1])
-    else
-        ax = Axis(f[1, 1])
-        ax.aspect = DataAspect()
-        ax
+function __init__()
+    try
+        Base.require(Main, :GraphMakie)
+    catch
+        @warn """Package GraphMakie not found in current path. It is needed to plot `Tenet`s with `Makie`.
+        - Run `import Pkg; Pkg.add(\"GraphMakie\")` or `]add GraphMakie` to install the GraphMakie package, then restart julia.
+        """
     end
-
-    hidedecorations!(ax)
-    hidespines!(ax)
-
-    p = plot!(ax, tn; kwargs...)
-
-    return Makie.AxisPlot(ax, p)
-end
-
-function Makie.plot!(ax::Union{Axis,Axis3}, @nospecialize tn::AbstractTensorNetwork; labels = false, kwargs...)
-    hypermap = Tenet.hyperflatten(tn)
-    tn = transform(tn, Tenet.HyperindConverter)
-
-    tensormap = IdDict(tensor => i for (i, tensor) in enumerate(tensors(tn)))
-
-    # TODO how to mark multiedges? (i.e. parallel edges)
-    graph = SimpleGraph([
-        Edge(map(Base.Fix1(getindex, tensormap), tensors)...) for (_, tensors) in tn.indexmap if length(tensors) > 1
-    ])
-
-    # TODO recognise `copytensors` by using `DeltaArray` or `Diagonal` representations
-    copytensors = findall(tensor -> any(flatinds -> issetequal(inds(tensor), flatinds), keys(hypermap)), tensors(tn))
-    ghostnodes = map(inds(tn, :open)) do index
-        # create new ghost node
-        add_vertex!(graph)
-        node = nv(graph)
-
-        # connect ghost node
-        tensor = only(tn.indexmap[index])
-        add_edge!(graph, node, tensormap[tensor])
-
-        return node
-    end
-
-    # configure graphics
-    # TODO refactor hardcoded values into constants
-    kwargs = Dict{Symbol,Any}(kwargs)
-
-    if haskey(kwargs, :node_size)
-        append!(kwargs[:node_size], zero(ghostnodes))
-    else
-        kwargs[:node_size] = map(1:nv(graph)) do i
-            i ∈ ghostnodes ? 0 : max(15, log2(length(tensors(tn)[i])))
-        end
-    end
-
-    if haskey(kwargs, :node_marker)
-        append!(kwargs[:node_marker], fill(:circle, length(ghostnodes)))
-    else
-        kwargs[:node_marker] = map(i -> i ∈ copytensors ? :diamond : :circle, 1:nv(graph))
-    end
-
-    if haskey(kwargs, :node_color)
-        kwargs[:node_color] = vcat(kwargs[:node_color], fill(:black, length(ghostnodes)))
-    else
-        kwargs[:node_color] = map(1:nv(graph)) do v
-            v ∈ copytensors ? Makie.to_color(:black) : Makie.RGBf(240 // 256, 180 // 256, 100 // 256)
-        end
-    end
-
-    get!(kwargs, :node_attr, (colormap = :viridis, strokewidth = 2.0, strokecolor = :black))
-
-    # configure labels
-    labels == true && get!(kwargs, :elabels) do
-        opentensors = findall(t -> !isdisjoint(inds(t), inds(tn, :open)), tensors(tn))
-        opencounter = IdDict(tensor => 0 for tensor in opentensors)
-
-        map(edges(graph)) do edge
-            # case: open edge
-            if any(∈(ghostnodes), [src(edge), dst(edge)])
-                notghost = src(edge) ∈ ghostnodes ? dst(edge) : src(edge)
-                inds = Tenet.inds(tn, :open) ∩ Tenet.inds(tensors(tn)[notghost])
-                opencounter[notghost] += 1
-                return inds[opencounter[notghost]] |> string
-            end
-
-            # case: hyperedge
-            if any(∈(copytensors), [src(edge), dst(edge)])
-                i = src(edge) ∈ copytensors ? src(edge) : dst(edge)
-                # hyperindex = filter(p -> isdisjoint(inds(tensors)[i], p[2]), hypermap) |> only |> first
-                hyperindex = hypermap[Tenet.inds(tensors(tn)[i])]
-                return hyperindex |> string
-            end
-
-            return join(Tenet.inds(tensors(tn)[src(edge)]) ∩ Tenet.inds(tensors(tn)[dst(edge)]), ',')
-        end
-    end
-    get!(() -> repeat([:black], ne(graph)), kwargs, :elabels_color)
-    get!(() -> repeat([17], ne(graph)), kwargs, :elabels_textsize)
-
-    # plot graph
-    graphplot!(ax, graph; kwargs...)
 end
 
 end