fix GraphRecipes and StatsPlots

GraphRecipes/Project.toml

@@ -1,6 +1,6 @@
 name = "GraphRecipes"
 uuid = "bd48cda9-67a9-57be-86fa-5b3c104eda73"
-version = "1.0"
+version = "1.0.0"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@@ -12,6 +12,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 NetworkLayout = "46757867-2c16-5918-afeb-47bfcb05e46a"
 PlotUtils = "995b91a9-d308-5afd-9ec6-746e21dbc043"
+PlotsBase = "c52230a3-c5da-43a3-9e85-260fcdfdc737"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -29,17 +30,18 @@ Statistics = "1"
 julia = "1.10"
 
 [extras]
+GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
 Gtk = "4c0ca9eb-093a-5379-98c5-f87ac0bbbf44"
 ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+PlotsBase = "c52230a3-c5da-43a3-9e85-260fcdfdc737"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 VisualRegressionTests = "34922c18-7c2a-561c-bac1-01e79b2c4c92"
 
 [targets]
-test = ["Gtk", "ImageMagick", "LinearAlgebra", "Logging", "Markdown", "Plots", "Random", "SparseArrays", "StableRNGs", "Test", "VisualRegressionTests"]
+test = ["Gtk", "GR", "ImageMagick", "LinearAlgebra", "Logging", "Markdown", "PlotsBase", "Random", "SparseArrays", "StableRNGs", "Test", "VisualRegressionTests"]

GraphRecipes/README.md

@@ -21,7 +21,9 @@ For a given graph, there are many legitimate ways to display and visualize the g
 ## An example
 ```julia
 using GraphRecipes
-using Plots
+using PlotsBase
+
+import GR; gr()
 
 g = [0 1 1;
      1 0 1;

GraphRecipes/src/graphs.jl

@@ -961,7 +961,7 @@ more details.
     end
 
     @series begin
-        @debug num_edges_nodes := (length(edges_list[1]), length(node_vec_vec_xy))  # for debugging / tests
+        # @debug num_edges_nodes := (length(edges_list[1]), length(node_vec_vec_xy))  # for debugging / tests
 
         seriestype := if method in (:tree, :buchheim, :chorddiagram)
             :curves

GraphRecipes/src/trees.jl

@@ -12,10 +12,10 @@ Wrap a tree-like object for plotting. Uses `AbstractTrees.children()` to recursi
 
 ```julia
 using AbstractTrees, GraphRecipes
-AbstractTrees.children(d::Dict) = [p for p in d]
-AbstractTrees.children(p::Pair) = AbstractTrees.children(p[2])
-function AbstractTrees.printnode(io::IO, p::Pair)
-    str = isempty(AbstractTrees.children(p[2])) ? string(p[1], ": ", p[2]) : string(p[1], ": ")
+@eval AbstractTrees children(d::AnstractDict) = [p for p in d]
+@eval AbstractTrees children(p::Pair) = AbstractTrees.children(p[2])
+@eval AbstractTrees  function printnode(io::IO, p::Pair)
+    str = isempty(children(p[2])) ? string(p[1], ": ", p[2]) : string(p[1], ": ")
     print(io, str)
 end
 

GraphRecipes/test/functions.jl

@@ -1,8 +1,4 @@
-using Plots
-using StableRNGs
-using GraphRecipes
-using GraphRecipes.Colors
-using GraphRecipes.AbstractTrees
+
 function random_labelled_graph()
     n = 15
     rng = StableRNG(1)
@@ -172,11 +168,11 @@ julia_type_tree() = plot(
     rng = StableRNG(1),
 )
 
-AbstractTrees.children(d::Dict) = [p for p ∈ d]
-AbstractTrees.children(p::Pair) = AbstractTrees.children(p[2])
-function AbstractTrees.printnode(io::IO, p::Pair)
+@eval AbstractTrees children(d::AbstractDict) = [p for p ∈ d]
+@eval AbstractTrees children(p::Pair) = AbstractTrees.children(p[2])
+@eval AbstractTrees function printnode(io::IO, p::Pair)
     str =
-        isempty(AbstractTrees.children(p[2])) ? string(p[1], ": ", p[2]) :
+        isempty(children(p[2])) ? string(p[1], ": ", p[2]) :
         string(p[1], ": ")
     print(io, str)
 end

GraphRecipes/test/parse_readme.jl

@@ -1,6 +1,5 @@
-using Markdown
 using GraphRecipes
-using Plots
+using Markdown
 
 cd(@__DIR__)
 

GraphRecipes/test/pkg_deps.jl

@@ -1,4 +1,3 @@
-
 module PkgDeps
 
 using GraphRecipes

GraphRecipes/test/runtests.jl

@@ -1,17 +1,19 @@
 using VisualRegressionTests
 using AbstractTrees
 using LinearAlgebra
-using Logging
+using GraphRecipes.AbstractTrees
+using GraphRecipes.Colors
 using GraphRecipes
 using SparseArrays
 using ImageMagick
 using StableRNGs
+using PlotsBase
+using Logging
 using Graphs
-using Plots
 using Test
 using Gtk  # for popup
 
-import Plots: PlotsBase
+import GR; gr()
 
 isci() = get(ENV, "CI", "false") == "true"
 itol(tol = nothing) = something(tol, isci() ? 1e-3 : 1e-5)

StatsPlots/Project.toml

@@ -1,6 +1,6 @@
 name = "StatsPlots"
 uuid = "f3b207a7-027a-5e70-b257-86293d7955fd"
-version = "1.0"
+version = "1.0.0"
 
 [deps]
 AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c"
@@ -13,7 +13,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 Observables = "510215fc-4207-5dde-b226-833fc4488ee2"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+PlotsBase = "c52230a3-c5da-43a3-9e85-260fcdfdc737"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 RecipesPipeline = "01d81517-befc-4cb6-b9ec-a95719d0359c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
@@ -32,7 +32,6 @@ KernelDensity = "0.6"
 MultivariateStats = "0.10"
 NaNMath = "1"
 Observables = "0.5"
-Plots = "2"
 RecipesBase = "1"
 RecipesPipeline = "1"
 Reexport = "1"
@@ -43,9 +42,11 @@ Widgets = "0.6"
 julia = "1.10"
 
 [extras]
+GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
+PlotsBase = "c52230a3-c5da-43a3-9e85-260fcdfdc737"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "NaNMath", "StableRNGs"]
+test = ["NaNMath", "GR", "PlotsBase", "StableRNGs", "Test"]

StatsPlots/src/StatsPlots.jl

@@ -5,8 +5,8 @@ import RecipesBase: recipetype
 import Tables
 import TableOperations
 using RecipesPipeline
-@reexport using Plots
-import Plots.PlotsBase.Commons: _cycle
+@reexport using PlotsBase
+import PlotsBase.Commons: _cycle
 using StatsBase
 using Distributions
 using LinearAlgebra: eigen, diagm

StatsPlots/src/boxplot.jl

@@ -24,7 +24,7 @@ notch_width(q2, q4, N) = 1.58 * (q4 - q2) / sqrt(N)
             [getindex(x, plotattributes[:series_plotindex])]
         end
     end
-    xsegs, ysegs = Plots.PlotsBase.Segments(), Plots.PlotsBase.Segments()
+    xsegs, ysegs = PlotsBase.Segments(), PlotsBase.Segments()
     texts = String[]
     glabels = sort(collect(unique(x)))
     warning = false

StatsPlots/src/violin.jl

@@ -46,9 +46,9 @@ get_quantiles(n::Int) = range(0, 1, length = n + 2)[2:(end - 1)]
             [getindex(x, plotattributes[:series_plotindex])]
         end
     end
-    xsegs, ysegs = Plots.PlotsBase.Segments(), Plots.PlotsBase.Segments()
-    qxsegs, qysegs = Plots.PlotsBase.Segments(), Plots.PlotsBase.Segments()
-    mxsegs, mysegs = Plots.PlotsBase.Segments(), Plots.PlotsBase.Segments()
+    xsegs, ysegs = PlotsBase.Segments(), PlotsBase.Segments()
+    qxsegs, qysegs = PlotsBase.Segments(), PlotsBase.Segments()
+    mxsegs, mysegs = PlotsBase.Segments(), PlotsBase.Segments()
     glabels = sort(collect(unique(x)))
     bw = plotattributes[:bar_width]
     bw == nothing && (bw = 0.8)

StatsPlots/test/runtests.jl

@@ -3,11 +3,11 @@ using Distributions
 using StatsPlots
 using StableRNGs
 using Clustering
+using PlotsBase
 using NaNMath
-using Plots
 using Test
 
-import Plots: PlotsBase
+import GR; gr()
 
 @testset "Grouped histogram" begin
     rng = StableRNG(1337)
@@ -17,7 +17,7 @@ import Plots: PlotsBase
         ylims = (1e-2, 1e4),
         bar_position = :stack,
     )
-    @test NaNMath.minimum(gpl[1][1][:y]) <= 1e-2
+    @test NaNMath.minimum(gpl[1][1][:y]) ≤ 1e-2
     @test NaNMath.minimum(gpl[1][1][:y]) > 0
     rng = StableRNG(1337)
     gpl = groupedhist(
@@ -26,7 +26,7 @@ import Plots: PlotsBase
         ylims = (1e-2, 1e4),
         bar_position = :dodge,
     )
-    @test NaNMath.minimum(gpl[1][1][:y]) <= 1e-2
+    @test NaNMath.minimum(gpl[1][1][:y]) ≤ 1e-2
     @test NaNMath.minimum(gpl[1][1][:y]) > 0
 
     data = [1, 1, 1, 1, 2, 1]