CompatHelper: bump compat for FillArrays to 1, (keep existing compat) (#64)

* CompatHelper: bump compat for FillArrays to 1, (keep existing compat)

* Bump LazyArrays compat

* Bump BlockBandedMatrices compat

* Bump version

* Added Dependabot

* Transition documentation plots to Python{Call,Plot}

---------

Co-authored-by: CompatHelper Julia <[email protected]>
Co-authored-by: Stefanos Carlström <[email protected]>

Author: 3 people

.github/dependabot.yml

@@ -0,0 +1,7 @@
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

.github/workflows/CI.yml

@@ -57,10 +57,7 @@ jobs:
           julia --project=docs -e '
             using Pkg
             Pkg.instantiate()
-            pkg"add https://github.com/jagot/Jagot.jl.git https://github.com/jagot/PyPlotRecipes.jl.git"
-            pkg"build PyPlot"'
-        env:
-          PYTHON: ""
+            pkg"add https://github.com/jagot/Jagot.jl.git"'
       - uses: julia-actions/julia-docdeploy@v1
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -10,3 +10,4 @@ ltximg
 /docs/src/figures
 /docs/src/assets/logo.svg
 Manifest.toml
+.CondaPkg/

Project.toml

@@ -1,7 +1,7 @@
 name = "CompactBases"
 uuid = "2c0377a8-7469-4ebd-be0f-82e501f20078"
 authors = ["Stefanos Carlström <[email protected]>"]
-version = "0.3.13"
+version = "0.3.14"
 
 [deps]
 BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
@@ -21,13 +21,13 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 [compat]
 ArnoldiMethod = "0.2"
 BandedMatrices = "0.16,0.17"
-BlockBandedMatrices = "0.11"
+BlockBandedMatrices = "0.11, 0.12"
 ContinuumArrays = "0.10, 0.11, 0.12"
 FastGaussQuadrature = "0.4, 0.5"
-FillArrays = "0.12,0.13"
+FillArrays = "0.12,0.13, 1"
 Formatting = "0.4"
 IntervalSets = "0.5.1,0.6,0.7"
-LazyArrays = "0.22"
+LazyArrays = "0.22, 1"
 OffsetArrays = "1.1"
 PrettyTables = "2"
 ProgressMeter = "1.5"

docs/Project.toml

@@ -4,15 +4,15 @@ Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
-PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
+PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
+PythonPlot = "274fc56d-3b97-40fa-a1cd-1b4a50311bf9"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
-Documenter = "0.27"
 ArnoldiMethod = "0.2"
 Colors = "0.12"
+Documenter = "0.27"
 IntervalSets = "0.5.1,0.6,0.7"
-PyCall = "1.94"
-PyPlot = "2.11"
+PythonCall = "0.9"
+PythonPlot = "1"

docs/bspline_plots.jl

@@ -23,7 +23,7 @@ function bsplines_cardinal_splines()
             csubplot(5,1,k,nox=k<5) do
                 for j = 1:size(χ,2)
                     ϕ = view(χ, :, j)
-                    l=plot(x, ϕ)[1]
+                    l=first(plot(x, ϕ))
                     Bspline_text(x, ϕ, j, k, l.get_color())
                 end
                 ylabel(latexstring("k = $k"))
@@ -68,7 +68,7 @@ function bsplines_full_multiplicity_splines()
             csubplot(5,1,k,nox=k<5) do
                 for j = 1:size(χ,2)
                     ϕ = view(χ, :, j)
-                    l=plot(x, ϕ)[1]
+                    l=first(plot(x, ϕ))
                     Bspline_text(x, ϕ, j, k, l.get_color())
                 end
                 ylabel(latexstring("k = $k"))
@@ -101,7 +101,7 @@ function bsplines_spline1d()
         csubplot(312, nox=true) do
             for j = 1:size(χ,2)
                 ϕ = view(χ, :, j)
-                l=plot(x, ϕ)[1]
+                l=first(plot(x, ϕ))
                 Bspline_text(x, ϕ, j, k, l.get_color())
             end
             margins(0.1,0.1)
@@ -223,15 +223,15 @@ function bsplines_restricted_basis_interpolation()
     yl = nothing
     cfigure("restricted basis interpolation", figsize=(7,9)) do
         csubplot(321, nox=true) do
-            l=plot(x, χ*c1)[1]
+            l=first(plot(x, χ*c1))
             plot(x_avg, c1, "s:", color=l.get_color())
-            l=plot(x, χ*c2)[1]
+            l=first(plot(x, χ*c2))
             plot(x_avg, c2, "s:", color=l.get_color())
         end
         csubplot(322, nox=true) do
-            l=plot(x, χ̃*c̃1)[1]
+            l=first(plot(x, χ̃*c̃1))
             plot(x̃_avg, c̃1, "s:", color=l.get_color())
-            l=plot(x, χ̃*c̃2)[1]
+            l=first(plot(x, χ̃*c̃2))
             plot(x̃_avg, c̃2, "s:", color=l.get_color())
             axes_labels_opposite(:y)
         end
@@ -377,12 +377,12 @@ function bsplines_sine_derivative()
 
     cfigure("derivatives", figsize=(7,9)) do
         csubplot(411,nox=true) do
-            l=plot(x, χ*cf, label=L"\tilde{f}(x)")[1]
+            l=first(plot(x, χ*cf, label=L"\tilde{f}(x)"))
             plot(x, f.(x), ":", label=L"f(x)")
             legend(framealpha=0.75)
         end
         csubplot(412,nox=true) do
-            l=plot(x, (χ*cg), label=L"\tilde{g}(x)")[1]
+            l=first(plot(x, (χ*cg), label=L"\tilde{g}(x)"))
             plot(x, g.(x), ":", label=L"g(x)")
             legend(framealpha=0.75)
         end
@@ -426,28 +426,28 @@ function bsplines_ode_hookes_law(xₘₐₓ, kspring, k, N)
 
     cfigure("Hooke's law",figsize=(7,9)) do
         csubplot(411,nox=true) do
-            l=plot(x, χ*cF, label=L"\tilde{F}(x)")[1]
+            l=first(plot(x, χ*cF, label=L"\tilde{F}(x)"))
             plot(-x, -χ*cF, "--", color=l.get_color())
             plot(x_avg, cF, ".:", color=l.get_color(), label=L"c_F")
             plot(x, F.(x), ":", label=L"F(x)")
             legend(framealpha=0.75)
         end
         csubplot(412,nox=true) do
-            l=plot(x, χ*cV, label=L"\tilde{V}(x)")[1]
+            l=first(plot(x, χ*cV, label=L"\tilde{V}(x)"))
             plot(-x, χ*cV, "--", color=l.get_color())
             plot(x_avg, cV, ".:", color=l.get_color(), label=L"c_V")
             plot(x, V.(x), ":", label=L"V(x)")
             legend(framealpha=0.75)
         end
         csubplot(413,nox=true) do
             plot(x, χ*cV - V.(x))
-            xl = xlim()
+            xl = pyconvert(Tuple, xlim())
             xlim(-xl[2],xl[2])
             ylabel("Error")
         end
         csubplot(414) do
             plot(x, χ)
-            xl = xlim()
+            xl = pyconvert(Tuple, xlim())
             xlim(-xl[2],xl[2])
             xlabel(L"x")
         end

docs/fd_plots.jl

@@ -64,9 +64,9 @@ function fd_compare_staggered_non_uniform()
 
     cfigure("staggered non_uniform reconstruction") do
         csubplot(211,nox=true) do
-            l = plot(x1, c1, ".:", label="Uniform coeffs")[1]
+            l = first(plot(x1, c1, ".:", label="Uniform coeffs"))
             plot(ξ, f1, color=l.get_color(), label="Uniform reconstruction")
-            l = plot(x2, c2, ".:", label="Non-uniform coeffs")[1]
+            l = first(plot(x2, c2, ".:", label="Non-uniform coeffs"))
             plot(ξ, f2, "--", color=l.get_color(), label="Non-uniform reconstruction")
             plot(ξ, fe, ":", label="Exact")
             xscale("log")

docs/plots.jl

@@ -1,8 +1,7 @@
-using PyPlot
+using PythonPlot
 using Jagot.plotting
 plot_style("ggplot")
-using PyPlotRecipes
-using PyCall
+using PythonCall
 PathEffects = pyimport("matplotlib.patheffects")
 using Statistics
 using Random
@@ -20,6 +19,8 @@ function mean_color(color::String)
     mean([c.r,c.g,c.b])
 end
 
+mean_color(color::Py) = mean_color(pyconvert(String, color))
+
 lerp(a,b,t) = (1-t)*a + t*b
 
 mean_position(x, ϕ) = ϕ'*Diagonal(x)*ϕ/(ϕ'ϕ)
@@ -30,7 +31,7 @@ function savedocfig(name,dir="figures")
     savefig(filename,
             transparent=true,
             facecolor=fig.get_facecolor())
-    close(fig)
+    PythonPlot.close("all")
     if isfile(filename)
         println("Saved $(name) to $(filename)")
     else
@@ -49,7 +50,7 @@ function logo()
         margins(0.1, 0.1)
         axis("off")
     end
-    savefig("docs/src/assets/logo.svg", transparent=true)
+    savedocfig("logo", "assets")
 end
 
 function simple_example()
@@ -163,7 +164,7 @@ function densities()
             for ϕ in ϕ
                 loglog(rr, abs.(ϕ-h.(rr)))
             end
-            yl = ylim()
+            yl = pyconvert(Tuple, ylim())
             ylim(max(yl[1],1e-15), yl[2])
             legend(["Finite-differences",
                     "Log–linear finite-differences",