Refactor time integrator 2N and 3Star more similar to OrdinaryDiffEq.jl integrators

docs/literate/make.jl

@@ -3,22 +3,22 @@ using Test: @testset
 import Pkg
 
 # Create markdown and notebook files for `file`
-function create_files(title, file, repo_src, pages_dir, notebooks_dir; folder="")
+function create_files(title, file, repo_src, pages_dir, notebooks_dir; folder = "")
     notebook_filename = first(splitext(file)) * ".ipynb"
     if !isempty(folder)
         notebook_filename = joinpath(folder, notebook_filename)
     end
 
-    binder_logo   = "https://mybinder.org/badge_logo.svg"
+    binder_logo = "https://mybinder.org/badge_logo.svg"
     nbviewer_logo = "https://img.shields.io/badge/render-nbviewer-f37726"
     raw_notebook_logo = "https://img.shields.io/badge/raw-notebook-4cc61e"
 
     notebook_path = "tutorials/notebooks/$notebook_filename"
-    binder_url   = "https://mybinder.org/v2/gh/trixi-framework/Trixi.jl/tutorial_notebooks?filepath=$notebook_path"
+    binder_url = "https://mybinder.org/v2/gh/trixi-framework/Trixi.jl/tutorial_notebooks?filepath=$notebook_path"
     nbviewer_url = "https://nbviewer.jupyter.org/github/trixi-framework/Trixi.jl/blob/tutorial_notebooks/$notebook_path"
     raw_notebook_url = "https://raw.githubusercontent.com/trixi-framework/Trixi.jl/tutorial_notebooks/$notebook_path"
 
-    binder_badge   = "# [![]($binder_logo)]($binder_url)"
+    binder_badge = "# [![]($binder_logo)]($binder_url)"
     nbviewer_badge = "# [![]($nbviewer_logo)]($nbviewer_url)"
     raw_notebook_badge = "# [![]($raw_notebook_logo)]($raw_notebook_url)"
 
@@ -28,25 +28,28 @@ function create_files(title, file, repo_src, pages_dir, notebooks_dir; folder=""
         # available for the latest stable release of Trixi.jl at the time of caching.\n\n"
         return string("# # $title\n\n", warning, content)
     end
-    Literate.notebook(joinpath(repo_src, folder, file), joinpath(notebooks_dir, folder); execute=false, preprocess=preprocess_notebook, credit=false)
+    Literate.notebook(joinpath(repo_src, folder, file), joinpath(notebooks_dir, folder);
+                      execute = false, preprocess = preprocess_notebook, credit = false)
 
     # Generate markdown file
     function preprocess_docs(content)
-        return string("# # [$title](@id $(splitext(file)[1]))\n $binder_badge\n $nbviewer_badge\n $raw_notebook_badge\n\n", content)
+        return string("# # [$title](@id $(splitext(file)[1]))\n $binder_badge\n $nbviewer_badge\n $raw_notebook_badge\n\n",
+                      content)
     end
-    Literate.markdown(joinpath(repo_src, folder, file), joinpath(pages_dir, folder); preprocess=preprocess_docs,)
+    Literate.markdown(joinpath(repo_src, folder, file), joinpath(pages_dir, folder);
+                      preprocess = preprocess_docs,)
 end
 
 # Create tutorials with Literate.jl
 function create_tutorials(files)
-    repo_src        = joinpath(@__DIR__, "src", "files")
+    repo_src = joinpath(@__DIR__, "src", "files")
 
-    pages_dir       = joinpath(@__DIR__, "..", "src", "tutorials")
-    notebooks_dir   = joinpath(pages_dir, "notebooks")
+    pages_dir = joinpath(@__DIR__, "..", "src", "tutorials")
+    notebooks_dir = joinpath(pages_dir, "notebooks")
 
-    Sys.rm(pages_dir;       recursive=true, force=true)
+    Sys.rm(pages_dir; recursive = true, force = true)
 
-    Sys.rm("out"; recursive=true, force=true)
+    Sys.rm("out"; recursive = true, force = true)
 
     # Run tests on all tutorial files
     @testset "TrixiTutorials" begin
@@ -59,15 +62,16 @@ function create_tutorials(files)
                     mod = gensym(filename[j][2][2])
                     @testset "$(filename[j][2][2])" begin
                         @eval module $mod
-                            include(joinpath($repo_src, $(filename[j][2][1]), $(filename[j][2][2])))
+                        include(joinpath($repo_src, $(filename[j][2][1]),
+                                         $(filename[j][2][2])))
                         end
                     end
                 end
             else # Single files
                 mod = gensym(title)
                 @testset "$title" begin
                     @eval module $mod
-                        include(joinpath($repo_src, $filename))
+                    include(joinpath($repo_src, $filename))
                     end
                 end
             end
@@ -85,18 +89,20 @@ function create_tutorials(files)
         end
         return content
     end
-    Literate.markdown(joinpath(repo_src, "index.jl"), pages_dir; name="introduction", preprocess=preprocess_introduction)
+    Literate.markdown(joinpath(repo_src, "index.jl"), pages_dir; name = "introduction",
+                      preprocess = preprocess_introduction)
     # Navigation system for makedocs
-    pages = Any["Introduction" => "tutorials/introduction.md",]
+    pages = Any["Introduction" => "tutorials/introduction.md"]
 
     # Create markdown and notebook files for tutorials
     for (i, (title, filename)) in enumerate(files)
         # Several files of one topic are created separately and pushed to `pages` together.
         if filename isa Vector
             vector = []
             for j in eachindex(filename)
-                create_files("$i.$j: $title: $(filename[j][1])", filename[j][2][2], repo_src,
-                             pages_dir, notebooks_dir; folder=filename[j][2][1])
+                create_files("$i.$j: $title: $(filename[j][1])", filename[j][2][2],
+                             repo_src,
+                             pages_dir, notebooks_dir; folder = filename[j][2][1])
 
                 path = "$(filename[j][2][1])/$(splitext(filename[j][2][2])[1]).md"
                 push!(vector, "$i.$j $(filename[j][1])" => "tutorials/$path")

docs/literate/src/files/DGMulti_1.jl

@@ -30,22 +30,22 @@ dg = DGMulti(polydeg = 3,
 cells_per_dimension = (32, 32)
 mesh = DGMultiMesh(dg,
                    cells_per_dimension, # initial_refinement_level = 5
-                   coordinates_min=(-2.0, -2.0),
-                   coordinates_max=( 2.0,  2.0),
-                   periodicity=true)
+                   coordinates_min = (-2.0, -2.0),
+                   coordinates_max = (2.0, 2.0),
+                   periodicity = true)
 
 semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition, dg,
-                                    boundary_conditions=boundary_condition_periodic)
+                                    boundary_conditions = boundary_condition_periodic)
 tspan = (0.0, 0.4)
 ode = semidiscretize(semi, tspan)
 
-alive_callback = AliveCallback(alive_interval=10)
-analysis_callback = AnalysisCallback(semi, interval=100, uEltype=real(dg))
+alive_callback = AliveCallback(alive_interval = 10)
+analysis_callback = AnalysisCallback(semi, interval = 100, uEltype = real(dg))
 callbacks = CallbackSet(analysis_callback, alive_callback);
 
 # Run the simulation with the same time integration algorithm as before.
-sol = solve(ode, RDPK3SpFSAL49(), abstol=1.0e-6, reltol=1.0e-6,
-            callback=callbacks, save_everystep=false);
+sol = solve(ode, RDPK3SpFSAL49(), abstol = 1.0e-6, reltol = 1.0e-6,
+            callback = callbacks, save_everystep = false);
 #-
 using Plots
 pd = PlotData2D(sol)
@@ -60,7 +60,6 @@ plot!(getmesh(pd))
 # (2021) provides a nice runtime comparison between the different mesh types. On the other hand,
 # the functions are more general and thus we have more option we can choose from.
 
-
 # ## Simulation with Gauss nodes
 # For instance, we can change the approximation type of our simulation.
 using Trixi, OrdinaryDiffEq
@@ -78,28 +77,27 @@ dg = DGMulti(polydeg = 3,
 
 cells_per_dimension = (32, 32)
 mesh = DGMultiMesh(dg,
-             cells_per_dimension, # initial_refinement_level = 5
-             coordinates_min=(-2.0, -2.0),
-             coordinates_max=( 2.0,  2.0),
-             periodicity=true)
+                   cells_per_dimension, # initial_refinement_level = 5
+                   coordinates_min = (-2.0, -2.0),
+                   coordinates_max = (2.0, 2.0),
+                   periodicity = true)
 
 semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition, dg,
-                              boundary_conditions=boundary_condition_periodic)
+                                    boundary_conditions = boundary_condition_periodic)
 tspan = (0.0, 0.4)
 ode = semidiscretize(semi, tspan)
 
-alive_callback = AliveCallback(alive_interval=10)
-analysis_callback = AnalysisCallback(semi, interval=100, uEltype=real(dg))
+alive_callback = AliveCallback(alive_interval = 10)
+analysis_callback = AnalysisCallback(semi, interval = 100, uEltype = real(dg))
 callbacks = CallbackSet(analysis_callback, alive_callback);
 
-sol = solve(ode, RDPK3SpFSAL49(); abstol=1.0e-6, reltol=1.0e-6,
-            ode_default_options()..., callback=callbacks);
+sol = solve(ode, RDPK3SpFSAL49(); abstol = 1.0e-6, reltol = 1.0e-6,
+            ode_default_options()..., callback = callbacks);
 #-
 using Plots
 pd = PlotData2D(sol)
 plot(pd)
 
-
 # ## Simulation with triangular elements
 # Also, we can set another element type. We want to use triangles now.
 using Trixi, OrdinaryDiffEq
@@ -119,21 +117,21 @@ dg = DGMulti(polydeg = 3,
 cells_per_dimension = (32, 32)
 mesh = DGMultiMesh(dg,
                    cells_per_dimension, # initial_refinement_level = 5
-                   coordinates_min=(-2.0, -2.0),
-                   coordinates_max=( 2.0,  2.0),
-                   periodicity=true)
+                   coordinates_min = (-2.0, -2.0),
+                   coordinates_max = (2.0, 2.0),
+                   periodicity = true)
 
 semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition, dg,
-                                    boundary_conditions=boundary_condition_periodic)
+                                    boundary_conditions = boundary_condition_periodic)
 tspan = (0.0, 0.4)
 ode = semidiscretize(semi, tspan)
 
-alive_callback = AliveCallback(alive_interval=10)
-analysis_callback = AnalysisCallback(semi, interval=100, uEltype=real(dg))
+alive_callback = AliveCallback(alive_interval = 10)
+analysis_callback = AnalysisCallback(semi, interval = 100, uEltype = real(dg))
 callbacks = CallbackSet(analysis_callback, alive_callback);
 
-sol = solve(ode, RDPK3SpFSAL49(); abstol=1.0e-6, reltol=1.0e-6,
-            ode_default_options()..., callback=callbacks);
+sol = solve(ode, RDPK3SpFSAL49(); abstol = 1.0e-6, reltol = 1.0e-6,
+            ode_default_options()..., callback = callbacks);
 #-
 using Plots
 pd = PlotData2D(sol)
@@ -142,7 +140,6 @@ plot(pd)
 plot(pd["rho"])
 plot!(getmesh(pd))
 
-
 # ## Triangular meshes on non-Cartesian domains
 # To use triangular meshes on a non-Cartesian domain, Trixi.jl uses the package [StartUpDG.jl](https://github.com/jlchan/StartUpDG.jl).
 # The following example is based on [`elixir_euler_triangulate_pkg_mesh.jl`](https://github.com/trixi-framework/Trixi.jl/blob/main/examples/dgmulti_2d/elixir_euler_triangulate_pkg_mesh.jl)
@@ -157,7 +154,7 @@ source_terms = source_terms_convergence_test
 
 # We create the solver `DGMulti` with triangular elements (`Tri()`) as before.
 dg = DGMulti(polydeg = 3, element_type = Tri(),
-             approximation_type=Polynomial(),
+             approximation_type = Polynomial(),
              surface_flux = flux_lax_friedrichs,
              volume_integral = VolumeIntegralFluxDifferencing(flux_ranocha))
 
@@ -168,11 +165,11 @@ meshIO = StartUpDG.triangulate_domain(StartUpDG.RectangularDomainWithHole());
 
 # The pre-defined Triangulate geometry in StartUpDG has integer boundary tags. With [`DGMultiMesh`](@ref)
 # we assign boundary faces based on these integer boundary tags and create a mesh compatible with Trixi.jl.
-mesh = DGMultiMesh(dg, meshIO, Dict(:outer_boundary=>1, :inner_boundary=>2))
+mesh = DGMultiMesh(dg, meshIO, Dict(:outer_boundary => 1, :inner_boundary => 2))
 #-
 boundary_condition_convergence_test = BoundaryConditionDirichlet(initial_condition)
 boundary_conditions = (; :outer_boundary => boundary_condition_convergence_test,
-                         :inner_boundary => boundary_condition_convergence_test)
+                       :inner_boundary => boundary_condition_convergence_test)
 
 semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition, dg,
                                     source_terms = source_terms,
@@ -181,12 +178,12 @@ semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition, dg,
 tspan = (0.0, 0.2)
 ode = semidiscretize(semi, tspan)
 
-alive_callback = AliveCallback(alive_interval=20)
-analysis_callback = AnalysisCallback(semi, interval=200, uEltype=real(dg))
+alive_callback = AliveCallback(alive_interval = 20)
+analysis_callback = AnalysisCallback(semi, interval = 200, uEltype = real(dg))
 callbacks = CallbackSet(alive_callback, analysis_callback);
 
-sol = solve(ode, CarpenterKennedy2N54(williamson_condition=false),
-            dt = 0.5 * estimate_dt(mesh, dg), save_everystep=false, callback=callbacks);
+sol = solve(ode, CarpenterKennedy2N54(williamson_condition = false),
+            dt = 0.5 * estimate_dt(mesh, dg), save_everystep = false, callback = callbacks);
 #-
 using Plots
 pd = PlotData2D(sol)
@@ -195,7 +192,6 @@ plot!(getmesh(pd))
 
 # For more information, please have a look in the [StartUpDG.jl documentation](https://jlchan.github.io/StartUpDG.jl/stable/).
 
-
 # ## Package versions
 
 # These results were obtained using the following versions.
@@ -205,4 +201,4 @@ versioninfo()
 
 using Pkg
 Pkg.status(["Trixi", "StartUpDG", "OrdinaryDiffEq", "Plots"],
-           mode=PKGMODE_MANIFEST)
+           mode = PKGMODE_MANIFEST)

docs/literate/src/files/DGMulti_2.jl

@@ -8,8 +8,8 @@
 # to the `DGMulti` constructor. For example, the classical second-order FD SBP operator
 # can be created as
 using Trixi.SummationByPartsOperators # or add SummationByPartsOperators to your project and use it directly
-D = derivative_operator(MattssonNordström2004(), derivative_order=1, accuracy_order=2,
-                        xmin=0.0, xmax=1.0, N=11)
+D = derivative_operator(MattssonNordström2004(), derivative_order = 1, accuracy_order = 2,
+                        xmin = 0.0, xmax = 1.0, N = 11)
 # Here, the arguments `xmin` and `xmax` do not matter beyond setting the real type
 # used for the operator - they just set a reference element and are rescaled on the
 # physical elements. The parameter `N` determines the number of finite difference nodes.
@@ -20,8 +20,8 @@ D = derivative_operator(MattssonNordström2004(), derivative_order=1, accuracy_o
 #
 # You can also use fully periodic single-block FD methods by creating a periodic SBP
 # operator. For example, a fully periodic FD operator can be constructed as
-D = periodic_derivative_operator(derivative_order=1, accuracy_order=2,
-                                 xmin=0.0, xmax=1.0, N=11)
+D = periodic_derivative_operator(derivative_order = 1, accuracy_order = 2,
+                                 xmin = 0.0, xmax = 1.0, N = 11)
 # An example using such an FD method is implemented in
 # [`elixir_euler_fdsbp_periodic.jl`](https://github.com/trixi-framework/Trixi.jl/blob/main/examples/dgmulti_2d/elixir_euler_fdsbp_periodic.jl).
 # For all parameters and other calling options, please have a look in the
@@ -31,15 +31,14 @@ D = periodic_derivative_operator(derivative_order=1, accuracy_order=2,
 # method with polynomial degree of `3` (`N=4` Legendre Lobatto nodes on `[0, 1]`) coupled continuously
 # on a uniform mesh with `Nx=10` elements by setting `approximation_type` to
 using Trixi.SummationByPartsOperators # or add SummationByPartsOperators to your project and use it directly
-D = couple_continuously(legendre_derivative_operator(xmin=0.0, xmax=1.0, N=4),
-                        UniformPeriodicMesh1D(xmin=-1.0, xmax=1.0, Nx=10))
+D = couple_continuously(legendre_derivative_operator(xmin = 0.0, xmax = 1.0, N = 4),
+                        UniformPeriodicMesh1D(xmin = -1.0, xmax = 1.0, Nx = 10))
 
 # To choose a discontinuous coupling (DGSEM), use `couple_discontinuously()` instead of `couple_continuously()`.
 
 # For more information and other SBP operators, see the documentations of [StartUpDG.jl](https://jlchan.github.io/StartUpDG.jl/dev/)
 # and [SummationByPartsOperators.jl](https://ranocha.de/SummationByPartsOperators.jl/stable/).
 
-
 # ## Package versions
 
 # These results were obtained using the following versions.
@@ -49,4 +48,4 @@ versioninfo()
 
 using Pkg
 Pkg.status(["Trixi", "StartUpDG", "SummationByPartsOperators"],
-           mode=PKGMODE_MANIFEST)
+           mode = PKGMODE_MANIFEST)