Add preprocessing step to automate numbering in tutorial introduction

docs/literate/make.jl

@@ -75,7 +75,16 @@ function create_tutorials(files)
     end
 
     # Generate markdown file for introduction page
-    Literate.markdown(joinpath(repo_src, "index.jl"), pages_dir; name="introduction")
+    # Preprocessing introduction file: Generate tutorial numbers
+    function preprocess_introduction(content)
+        counter = 1
+        while occursin("\$index", content)
+            content = replace(content, "\$index" => "$counter", count = 1)
+            counter += 1
+        end
+        return content
+    end
+    Literate.markdown(joinpath(repo_src, "index.jl"), pages_dir; name="introduction", preprocess=preprocess_introduction)
     # Navigation system for makedocs
     pages = Any["Introduction" => "tutorials/introduction.md",]
 

docs/literate/src/files/index.jl

@@ -14,14 +14,14 @@
 
 # There are tutorials for the following topics:
 
-# ### [1 First steps in Trixi.jl](@ref getting_started)
+# ### [$index: First steps in Trixi.jl](@ref getting_started)
 #-
 # This tutorial provides guidance for getting started with Trixi.jl, and Julia as well. It outlines
 # the installation procedures for both Julia and Trixi.jl, the execution of Trixi.jl elixirs, the
 # fundamental structure of a Trixi.jl setup, the visualization of results, and the development
 # process for Trixi.jl.
 
-# ### [2 Behind the scenes of a simulation setup](@ref behind_the_scenes_simulation_setup)
+# ### [$index: Behind the scenes of a simulation setup](@ref behind_the_scenes_simulation_setup)
 #-
 # This tutorial will guide you through a simple Trixi.jl setup ("elixir"), giving an overview of
 # what happens in the background during the initialization of a simulation. While the setup
@@ -30,92 +30,92 @@
 # the more fundamental, *technical* concepts that are applicable to a variety of
 # (also more complex) configurations.s
 
-# ### [3 Introduction to DG methods](@ref scalar_linear_advection_1d)
+# ### [$index: Introduction to DG methods](@ref scalar_linear_advection_1d)
 #-
 # This tutorial gives an introduction to discontinuous Galerkin (DG) methods with the example of the
 # scalar linear advection equation in 1D. Starting with some theoretical explanations, we first implement
 # a raw version of a discontinuous Galerkin spectral element method (DGSEM). Then, we will show how
 # to use features of Trixi.jl to achieve the same result.
 
-# ### [4 DGSEM with flux differencing](@ref DGSEM_FluxDiff)
+# ### [$index: DGSEM with flux differencing](@ref DGSEM_FluxDiff)
 #-
 # To improve stability often the flux differencing formulation of the DGSEM (split form) is used.
 # We want to present the idea and formulation on a basic 1D level. Then, we show how this formulation
 # can be implemented in Trixi.jl and analyse entropy conservation for two different flux combinations.
 
-# ### [5 Shock capturing with flux differencing and stage limiter](@ref shock_capturing)
+# ### [$index: Shock capturing with flux differencing and stage limiter](@ref shock_capturing)
 #-
 # Using the flux differencing formulation, a simple procedure to capture shocks is a hybrid blending
 # of a high-order DG method and a low-order subcell finite volume (FV) method. We present the idea on a
 # very basic level and show the implementation in Trixi.jl. Then, a positivity preserving limiter is
 # explained and added to an exemplary simulation of the Sedov blast wave with the 2D compressible Euler
 # equations.
 
-# ### [6 Non-periodic boundary conditions](@ref non_periodic_boundaries)
+# ### [$index: Non-periodic boundary conditions](@ref non_periodic_boundaries)
 #-
 # Thus far, all examples used periodic boundaries. In Trixi.jl, you can also set up a simulation with
 # non-periodic boundaries. This tutorial presents the implementation of the classical Dirichlet
 # boundary condition with a following example. Then, other non-periodic boundaries are mentioned.
 
-# ### [7 DG schemes via `DGMulti` solver](@ref DGMulti_1)
+# ### [$index: DG schemes via `DGMulti` solver](@ref DGMulti_1)
 #-
 # This tutorial is about the more general DG solver [`DGMulti`](@ref), introduced [here](@ref DGMulti).
 # We are showing some examples for this solver, for instance with discretization nodes by Gauss or
 # triangular elements. Moreover, we present a simple way to include pre-defined triangulate meshes for
 # non-Cartesian domains using the package [StartUpDG.jl](https://github.com/jlchan/StartUpDG.jl).
 
-# ### [8 Other SBP schemes (FD, CGSEM) via `DGMulti` solver](@ref DGMulti_2)
+# ### [$index: Other SBP schemes (FD, CGSEM) via `DGMulti` solver](@ref DGMulti_2)
 #-
 # Supplementary to the previous tutorial about DG schemes via the `DGMulti` solver we now present
 # the possibility for `DGMulti` to use other SBP schemes via the package
 # [SummationByPartsOperators.jl](https://github.com/ranocha/SummationByPartsOperators.jl).
 # For instance, we show how to set up a finite differences (FD) scheme and a continuous Galerkin
 # (CGSEM) method.
 
-# ### [9 Upwind FD SBP schemes](@ref upwind_fdsbp)
+# ### [$index: Upwind FD SBP schemes](@ref upwind_fdsbp)
 #-
 # General SBP schemes can not only be used via the [`DGMulti`](@ref) solver but
 # also with a general `DG` solver. In particular, upwind finite difference SBP
 # methods can be used together with the `TreeMesh`. Similar to general SBP
 # schemes in the `DGMulti` framework, the interface is based on the package
 # [SummationByPartsOperators.jl](https://github.com/ranocha/SummationByPartsOperators.jl).
 
-# ### [10 Adding a new scalar conservation law](@ref adding_new_scalar_equations)
+# ### [$index: Adding a new scalar conservation law](@ref adding_new_scalar_equations)
 #-
 # This tutorial explains how to add a new physics model using the example of the cubic conservation
 # law. First, we define the equation using a `struct` `CubicEquation` and the physical flux. Then,
 # the corresponding standard setup in Trixi.jl (`mesh`, `solver`, `semi` and `ode`) is implemented
 # and the ODE problem is solved by OrdinaryDiffEq's `solve` method.
 
-# ### [11 Adding a non-conservative equation](@ref adding_nonconservative_equation)
+# ### [$index: Adding a non-conservative equation](@ref adding_nonconservative_equation)
 #-
 # In this part, another physics model is implemented, the nonconservative linear advection equation.
 # We run two different simulations with different levels of refinement and compare the resulting errors.
 
-# ### [12 Parabolic terms](@ref parabolic_terms)
+# ### [$index: Parabolic terms](@ref parabolic_terms)
 #-
 # This tutorial describes how parabolic terms are implemented in Trixi.jl, e.g.,
 # to solve the advection-diffusion equation.
 
-# ### [13 Adding new parabolic terms](@ref adding_new_parabolic_terms)
+# ### [$index: Adding new parabolic terms](@ref adding_new_parabolic_terms)
 #-
 # This tutorial describes how new parabolic terms can be implemented using Trixi.jl.
 
-# ### [14 Adaptive mesh refinement](@ref adaptive_mesh_refinement)
+# ### [$index: Adaptive mesh refinement](@ref adaptive_mesh_refinement)
 #-
 # Adaptive mesh refinement (AMR) helps to increase the accuracy in sensitive or turbolent regions while
 # not wasting resources for less interesting parts of the domain. This leads to much more efficient
 # simulations. This tutorial presents the implementation strategy of AMR in Trixi.jl, including the use of
 # different indicators and controllers.
 
-# ### [15 Structured mesh with curvilinear mapping](@ref structured_mesh_mapping)
+# ### [$index: Structured mesh with curvilinear mapping](@ref structured_mesh_mapping)
 #-
 # In this tutorial, the use of Trixi.jl's structured curved mesh type [`StructuredMesh`](@ref) is explained.
 # We present the two basic option to initialize such a mesh. First, the curved domain boundaries
 # of a circular cylinder are set by explicit boundary functions. Then, a fully curved mesh is
 # defined by passing the transformation mapping.
 
-# ### [16 Unstructured meshes with HOHQMesh.jl](@ref hohqmesh_tutorial)
+# ### [$index: Unstructured meshes with HOHQMesh.jl](@ref hohqmesh_tutorial)
 #-
 # The purpose of this tutorial is to demonstrate how to use the [`UnstructuredMesh2D`](@ref)
 # functionality of Trixi.jl. This begins by running and visualizing an available unstructured
@@ -124,26 +124,26 @@
 # software in the Trixi.jl ecosystem, and then run a simulation using Trixi.jl on said mesh.
 # In the end, the tutorial briefly explains how to simulate an example using AMR via `P4estMesh`.
 
-# ### [17 P4est mesh from gmsh](@ref p4est_from_gmsh)
+# ### [$index: P4est mesh from gmsh](@ref p4est_from_gmsh)
 #-
 # This tutorial describes how to obtain a [`P4estMesh`](@ref) from an existing mesh generated
 # by [`gmsh`](https://gmsh.info/) or any other meshing software that can export to the Abaqus
 # input `.inp` format. The tutorial demonstrates how edges/faces can be associated with boundary conditions based on the physical nodesets.
 
-# ### [18 Explicit time stepping](@ref time_stepping)
+# ### [$index: Explicit time stepping](@ref time_stepping)
 #-
 # This tutorial is about time integration using [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl).
 # It explains how to use their algorithms and presents two types of time step choices - with error-based
 # and CFL-based adaptive step size control.
 
-# ### [19 Differentiable programming](@ref differentiable_programming)
+# ### [$index: Differentiable programming](@ref differentiable_programming)
 #-
 # This part deals with some basic differentiable programming topics. For example, a Jacobian, its
 # eigenvalues and a curve of total energy (through the simulation) are calculated and plotted for
 # a few semidiscretizations. Moreover, we calculate an example for propagating errors with Measurement.jl
 # at the end.
 
-# ### [20 Custom semidiscretization](@ref custom_semidiscretization)
+# ### [$index: Custom semidiscretization](@ref custom_semidiscretization)
 #-
 # This tutorial describes the [semidiscretiations](@ref overview-semidiscretizations) of Trixi.jl
 # and explains how to extend them for custom tasks.