Separate code for master thesis from package (#69)

* separate code for master thesis from package

* remove visualization folder from format path

.github/workflows/Format-check.yml

@@ -25,7 +25,7 @@ jobs:
       - name: Install JuliaFormatter and format
         run: |
           julia -e 'using Pkg; Pkg.add(PackageSpec(name = "JuliaFormatter"))'
-          julia -e 'using JuliaFormatter; format(["src", "test", "examples", "visualization"], verbose = true)'
+          julia -e 'using JuliaFormatter; format(["src", "test", "examples"], verbose = true)'
       - name: Format check
         run: |
           julia -e '

README.md

@@ -58,7 +58,7 @@ e.g., `include(joinpath(examples_dir(), "svaerd_kalisch_1d", "svaerd_kalisch_1d_
 
 ## Authors
 
-The package is developed and maintained by Joshua Lampert and was initiated as part of the master thesis "Structure-Preserving Numerical Methods for Dispersive Shallow Water Models" (2023).
+The package is developed and maintained by Joshua Lampert (University of Hamburg).
 Some parts of this repository are based on parts of [Dispersive-wave-schemes-notebooks. A Broad Class of Conservative Numerical Methods for Dispersive Wave Equations](https://github.com/ranocha/Dispersive-wave-schemes-notebooks)
 by Hendrik Ranocha, Dimitrios Mitsotakis and David Ketcheson. The code structure is inspired by [Trixi.jl](https://github.com/trixi-framework/Trixi.jl/).
 

docs/make.jl

@@ -18,7 +18,6 @@ makedocs(;
          pages = [
              "Home" => "index.md",
              "Overview" => "overview.md",
-             "Reproduce figures" => "reproduce.md",
              "Reference" => "ref.md",
              "License" => "license.md",
          ])

docs/src/index.md

@@ -57,7 +57,7 @@ e.g., `include(joinpath(examples_dir(), "svaerd_kalisch_1d", "svaerd_kalisch_1d_
 
 # Authors
 
-The package is developed and maintained by Joshua Lampert and was initiated as part of the master thesis "Structure-Preserving Numerical Methods for Dispersive Shallow Water Models" (2023).
+The package is developed and maintained by Joshua Lampert (University of Hamburg).
 Some parts of this repository are based on parts of [Dispersive-wave-schemes-notebooks. A Broad Class of Conservative Numerical Methods for Dispersive Wave Equations](https://github.com/ranocha/Dispersive-wave-schemes-notebooks)
 by Hendrik Ranocha, Dimitrios Mitsotakis and David Ketcheson. The code structure is inspired by [Trixi.jl](https://github.com/trixi-framework/Trixi.jl/).
 

docs/src/overview.md

@@ -117,7 +117,8 @@ end
 gif(anim, "shoaling_solution.gif", fps = 25)
 ```
 
-It is also possible to plot the solution variables at a fixed spatial point over time by calling `plot(semi => sol, x)` for some `x`-value, see [plot_examples.jl](https://github.com/JoshuaLampert/DispersiveShallowWater.jl/blob/main/visualization/plot_examples.jl) for some examples.
+It is also possible to plot the solution variables at a fixed spatial point over time by calling `plot(semi => sol, x)` for some `x`-value, see [plot_examples.jl](https://github.com/JoshuaLampert/2023-master-thesis/blob/main/code/plot_examples.jl) from
+the reproducibility repository of the master thesis of Joshua Lampert for some examples.
 
 Often, it is interesting to have a look at how the quantities that are recorded by the `AnalysisCallback` evolve in time. To this end, you can `plot` the `AnalysisCallback` by
 
@@ -224,7 +225,7 @@ For more details see also the [documentation of SummationByPartsOperators.jl](ht
 
 Some more examples sorted by the simulated equations can be found in the [examples/](https://github.com/JoshuaLampert/DispersiveShallowWater.jl/tree/main/examples) subdirectory. Especially, in [examples/svaerd\_kalisch\_1d/](https://github.com/JoshuaLampert/DispersiveShallowWater.jl/tree/main/examples/svaerd_kalisch_1d) you can find Julia scripts that solve the [`SvaerdKalischEquations1D`](@ref) that were not covered in this tutorial. The same steps as described above, however, apply in the same way to these equations. Attention must be paid for these equations because they do not conserve the classical total entropy ``\mathcal E``, but a modified entropy ``\hat{\mathcal E}``, available as [`entropy_modified`](@ref).
 
-More examples, especially focussing on plotting, can be found in the scripts [create_figures.jl](https://github.com/JoshuaLampert/DispersiveShallowWater.jl/blob/main/visualization/create_figures.jl) and [plot_examples.jl](https://github.com/JoshuaLampert/DispersiveShallowWater.jl/blob/main/visualization/plot_examples.jl).
+More examples, especially focussing on plotting, can be found in the scripts [create_figures.jl](https://github.com/JoshuaLampert/2023-master-thesis/blob/main/code/create_figures.jl) and [plot_examples.jl](https://github.com/JoshuaLampert/2023-master-thesis/blob/main/code/plot_examples.jl) from the reproducibility repository of the master thesis of Joshua Lampert.
 
 ## References
 

src/util.jl

@@ -48,22 +48,6 @@ function default_example()
              "bbm_bbm_variable_bathymetry_1d_basic.jl")
 end
 
-"""
-    path_create_figures()
-
-Return the path to the file that creates all figures used in the master thesis "Structure-preserving
-Numerical Methods for Dispersive Shallow Water Model" (2023). Executing this julia script may take a
-while.
-
-# Examples
-```@example
-include(DispersiveShallowWater.path_create_figures())
-```
-"""
-function path_create_figures()
-    pkgdir(DispersiveShallowWater, "visualization", "create_figures.jl")
-end
-
 # Note: We can't call the method below `DispersiveShallowWater.include` since that is created automatically
 # inside `module DispersiveShallowWater` to `include` source files and evaluate them within the global scope
 # of `DispersiveShallowWater`. However, users will want to evaluate in the global scope of `Main` or something

test/test_unit.jl

@@ -216,7 +216,6 @@ using SparseArrays: sparse, SparseMatrixCSC
 
     @testset "util" begin
         @test_nowarn get_examples()
-        @test_nowarn DispersiveShallowWater.path_create_figures()
         @test_nowarn trixi_include(default_example(), tspan = (0.0, 0.1))
 
         accuracy_orders = [2, 4, 6]