Merge branch 'main' into sc/polytropic_2d_wave_speed

.github/workflows/ci.yml

@@ -101,6 +101,10 @@ jobs:
             os: windows-latest
             arch: x64
             trixi_test: threaded
+          - version: '1.9'
+            os: macos-14
+            arch: arm64
+            trixi_test: threaded
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v1
@@ -134,7 +138,8 @@ jobs:
           file: ./lcov.info
           flags: unittests
           name: codecov-umbrella
-          fail_ci_if_error: false
+          fail_ci_if_error: true
+          verbose: true
           token: ${{ secrets.CODECOV_TOKEN }}
       # The standard setup of Coveralls is just annoying for parallel builds, see, e.g.,
       # https://github.com/trixi-framework/Trixi.jl/issues/691
@@ -195,7 +200,7 @@ jobs:
           coverage = merge_coverage_counts(coverage)
           @show covered_lines, total_lines = get_summary(coverage)
           LCOV.writefile("./lcov.info", coverage)
-      - uses: coverallsapp/github-action@master
+      - uses: coverallsapp/github-action@v2
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           path-to-lcov: ./lcov.info

Project.toml

@@ -68,7 +68,7 @@ LinearAlgebra = "1"
 LinearMaps = "2.7, 3.0"
 LoopVectorization = "0.12.118"
 MPI = "0.20"
-Makie = "0.19"
+Makie = "0.19, 0.20"
 MuladdMacro = "0.2.2"
 Octavian = "0.3.5"
 OffsetArrays = "1.3"

benchmark/benchmarks.jl

@@ -55,5 +55,5 @@ let
   SUITE["latency"]["euler_2d"] = @benchmarkable run(
     `$(Base.julia_cmd()) -e 'using Trixi; trixi_include(joinpath(examples_dir(), "tree_2d_dgsem", "elixir_euler_kelvin_helmholtz_instability.jl"), tspan=(0.0, 1.0e-10), save_restart=TrivialCallback(), save_solution=TrivialCallback())'`) seconds=60
   SUITE["latency"]["mhd_2d"] = @benchmarkable run(
-    `$(Base.julia_cmd()) -e 'using Trixi; trixi_include(joinpath(examples_dir(), "tree_2d_dgsem", "elixir_mhd_blast_wave.jl"), tspan=(0.0, 1.0e-10), save_restart=TrivialCallback(), save_solution=TrivialCallback())'`) seconds=60
+    `$(Base.julia_cmd()) -e 'using Trixi; trixi_include(joinpath(examples_dir(), "tree_2d_dgsem", "elixir_mhd_blast_wave.jl"), tspan=(0.0, 1.0e-10), save_solution=TrivialCallback())'`) seconds=60
 end

docs/Project.toml

@@ -12,7 +12,7 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Trixi2Vtk = "bc1476a1-1ca6-4cc3-950b-c312b255ff95"
 
 [compat]
-CairoMakie = "0.6, 0.7, 0.8, 0.9, 0.10"
+CairoMakie = "0.6, 0.7, 0.8, 0.9, 0.10, 0.11"
 Documenter = "1"
 ForwardDiff = "0.10"
 HOHQMesh = "0.1, 0.2"

docs/literate/src/files/first_steps/changing_trixi.jl

@@ -0,0 +1,77 @@
+#src # Changing Trixi.jl itself
+
+# If you plan on editing Trixi.jl itself, you can download Trixi.jl locally and run it from
+# the cloned directory.
+
+
+# ## Cloning Trixi.jl
+
+
+# ### Windows
+
+# If you are using Windows, you can clone Trixi.jl by using the GitHub Desktop tool:
+# - If you do not have a GitHub account yet, create it on
+#   the [GitHub website](https://github.com/join).
+# - Download and install [GitHub Desktop](https://desktop.github.com/) and then log in to
+#   your account.
+# - Open GitHub Desktop, press `Ctrl+Shift+O`.
+# - In the opened window, paste `trixi-framework/Trixi.jl` and choose the path to the folder where
+#   you want to save Trixi.jl. Then click `Clone` and Trixi.jl will be cloned to your computer. 
+
+# Now you cloned Trixi.jl and only need to tell Julia to use the local clone as the package sources:
+# - Open a terminal using `Win+r` and `cmd`. Navigate to the folder with the cloned Trixi.jl using `cd`.
+# - Create a new directory `run`, enter it, and start Julia with the `--project=.` flag:
+#   ```shell
+#   mkdir run 
+#   cd run
+#   julia --project=.
+#   ```
+# - Now run the following commands to install all relevant packages:
+#   ```julia
+#   using Pkg; Pkg.develop(PackageSpec(path="..")) # Tell Julia to use the local Trixi.jl clone
+#   Pkg.add(["OrdinaryDiffEq", "Plots"])  # Install additional packages
+#   ```
+
+# Now you already installed Trixi.jl from your local clone. Note that if you installed Trixi.jl
+# this way, you always have to start Julia with the `--project` flag set to your `run` directory,
+# e.g.,
+# ```shell
+# julia --project=.
+# ```
+# if already inside the `run` directory.
+
+
+# ### Linux
+
+# You can clone Trixi.jl to your computer by executing the following commands:
+# ```shell
+# git clone [email protected]:trixi-framework/Trixi.jl.git 
+# # If an error occurs, try the following:
+# # git clone https://github.com/trixi-framework/Trixi.jl
+# cd Trixi.jl
+# mkdir run 
+# cd run
+# julia --project=. -e 'using Pkg; Pkg.develop(PackageSpec(path=".."))' # Tell Julia to use the local Trixi.jl clone
+# julia --project=. -e 'using Pkg; Pkg.add(["OrdinaryDiffEq", "Plots"])' # Install additional packages
+# ```
+# Note that if you installed Trixi.jl this way,
+# you always have to start Julia with the `--project` flag set to your `run` directory, e.g.,
+# ```shell
+# julia --project=.
+# ```
+# if already inside the `run` directory.
+
+
+# ## Additional reading
+
+# To further delve into Trixi.jl, you may have a look at the following introductory tutorials.
+# - [Introduction to DG methods](@ref scalar_linear_advection_1d) will teach you how to set up a
+#   simple way to approximate the solution of a hyperbolic partial differential equation. It will
+#   be especially useful to learn about the 
+#   [Discontinuous Galerkin method](https://en.wikipedia.org/wiki/Discontinuous_Galerkin_method)
+#   and the way it is implemented in Trixi.jl.
+# - [Adding a new scalar conservation law](@ref adding_new_scalar_equations) and
+#   [Adding a non-conservative equation](@ref adding_nonconservative_equation)
+#   describe how to add new physics models that are not yet included in Trixi.jl.
+# - [Callbacks](@ref callbacks-id) gives an overview of how to regularly execute specific actions
+#   during a simulation, e.g., to store the solution or adapt the mesh.