getting_started_with_julia_and_trixi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Getting started with Julia and Trixi.jl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Trixi.jl](https://github.com/trixi-framework/Trixi.jl) is a numerical simulation framework for hyperbolic conservation laws written in [Julia](https://julialang.org). A key objective for the framework is to be useful to both scientists and students. Therefore, next to having an extensible design with a fast implementation, Trixi is focused on being easy to use for new or inexperienced users, including the installation and postprocessing procedures.\n",
    "\n",
    "The primary goal of this notebook is to provide a live demonstration of Julia and Trixi during a talk given at the Numerical Analysis Seminar at Lund University in January 2021. Here, we will show how to use Trixi for setting up and running simulations, how to visualize the results, and how to extend Trixi with new functionality. The notebook and a description of how to run it with Jupyter are available at https://github.com/trixi-framework/talk-2021-julia-adaptive-multi-physics-simulations. For more information about Trixi and how to use it, please visit Trixi on [GitHub](https://github.com/trixi-framework/Trixi.jl) or refer to the [official documentation](https://trixi-framework.github.io/Trixi.jl/stable/). This notebook was set up and tested with Julia 1.5.3 but may also work with other versions.\n",
    "\n",
    "*Note:* If you change a variable in a later cell and then re-execute an earlier cell, the results might change unexpectedly. Thus if in doubt, re-run the entire notebook *in order*. The reason is that all cells in a Jupyter notebooks share a common variable space. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Table of contents**\n",
    "1. [Quickstart](#Quickstart)\n",
    "2. [Mixing an elixir: creating a Trixi simulation from scratch](#Mixing-an-elixir:-creating-a-Trixi-simulation-from-scratch)\n",
    "   1. [The basics](#The-basics)\n",
    "   2. [Using a different initial condition](#Using-a-different-initial-condition)\n",
    "   3. [Adaptive mesh refinement](#Adaptive-mesh-refinement)\n",
    "   4. [In-situ visualization](#In-situ-visualization)\n",
    "3. [Advanced usage](#Advanced-usage)\n",
    "   1. [Analyzing the solution](#Analyzing-the-solution)\n",
    "   2. [Running convergence tests](#Running-convergence-tests)\n",
    "   3. [Visualizing the spectrum](#Visualizing-the-spectrum)\n",
    "   4. [Postprocessing with Trixi2Vtk and ParaView](#Postprocessing-with-Trixi2Vtk-and-ParaView)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quickstart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To quickly get up and running with Trixi, load the package (only required once) and start an example 2D simulation with the following commands (to execute a Jupyter cell, select it and hit `Ctrl-Enter`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "using Trixi\n",
    "\n",
    "trixi_include(default_example())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`trixi_include(...)` is a function that loads a Julia file and executes its contents. We call such files that contain a valid Trixi setup **elixirs**. `default_example()` is part of the Trixi package and returns the path to an example elixir for running a 2D linear advection simulation.\n",
    "\n",
    "**Please note that during the first invocation this may take a minute or two, since Julia has to compile all functions at first usage.**\n",
    "\n",
    "The results of a Trixi simulation can easily be visualized with the [Plots](https://github.com/JuliaPlots/Plots.jl) package. Simply load `Plots` and call the `plot(...)` command with the argument `sol`. By convention, in all our elixirs `sol` (short for *solution*) is the variable name that contains the result of a simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Plots\n",
    "\n",
    "plot(sol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Mixing an elixir: creating a Trixi simulation from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In this section, we will see how one can combine the individual components that make up Trixi into a full simulation setup that can be executed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's have a look at what an elixir looks like. Generally speaking, the following components are required:\n",
    "* an **equation** object that contains all physics-specific data and functionality\n",
    "* a **solver** that represents the numerical method and its algorithms\n",
    "* a **mesh** that holds the grid data\n",
    "* a **semidiscretization** that encapsulates equation, solver, and mesh, together with initial and boundary conditions\n",
    "\n",
    "The semidiscretization object is then used to create an `ODEProblem` that can be solved with one of the solvers for ordinary differential equations (ODEs) from the [OrdinaryDiffEq](https://github.com/SciML/OrdinaryDiffEq.jl) package."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    "  <img src=\"attachment:ec01b418-74d1-4b51-83f0-01624de0f652.png\" width=\"80%\" />\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In the following are the contents of a *minimum* elixir that produces the same result as the `trixi_include(...)` command above (the elixir can be found in [examples/elixir_advection_simple.jl](examples/elixir_advection_simple.jl)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load necessary packages\n",
    "using OrdinaryDiffEq\n",
    "using Trixi\n",
    "\n",
    "# Create equations\n",
    "advectionvelocity = (1.0, 1.0)\n",
    "equations = LinearScalarAdvectionEquation2D(advectionvelocity)\n",
    "\n",
    "# Create DGSEM solver for polynomial degree = 3\n",
    "solver = DGSEM(3, flux_lax_friedrichs)\n",
    "\n",
    "# Create a uniformely refined mesh with periodic boundaries\n",
    "coordinates_min = (-1, -1) # minimum coordinates\n",
    "coordinates_max = ( 1,  1) # maximum coordinates\n",
    "mesh_static = TreeMesh(coordinates_min, coordinates_max,\n",
    "                       initial_refinement_level=4,\n",
    "                       n_cells_max=30_000)\n",
    "\n",
    "# Create semidiscretization with all spatial discretization-related components\n",
    "semi = SemidiscretizationHyperbolic(mesh_static, equations,\n",
    "                                    initial_condition_convergence_test,\n",
    "                                    solver)\n",
    "\n",
    "# Create ODE problem from semidiscretization with time span from 0.0 to 1.0\n",
    "ode = semidiscretize(semi, (0.0, 1.0));\n",
    "\n",
    "# Evolve ODE problem in time using OrdinaryDiffEq's `solve` method\n",
    "sol = solve(ode, CarpenterKennedy2N54(williamson_condition=false), dt=2.5e-2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that's it. In this minimum example, there is no output to the terminal/notebook, thus only the lack of errors tells us that everything went smoothly. However, as before, we can visualize the solution by plotting it with the `plot` function of the `Plots` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Plots\n",
    "\n",
    "plot(sol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from the snippet above, the entirety of a simulation setup is defined in pure Julia: There are no \"special\" parameter files, and changing a simulation setup means to modify its elixir. For example, we can change the advection speeds by passing a different velocity vector to `LinearScalarAdvectionEquation2D`,\n",
    "\n",
    "```julia\n",
    "# Create equations with different advection velocity\n",
    "advectionvelocity = (1.0, 0.1)\n",
    "equations = LinearScalarAdvectionEquation2D(advectionvelocity);\n",
    "```\n",
    "\n",
    "and re-run the simulation by re-creating the semidiscretization and the `ODEProblem`, and then calling the `solve(...)` function again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using a different initial condition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we have only used functionality that is provided by Trixi. With its modular architecture, however, it is easy to extend Trixi with your own features. Let's define a new initial condition with a cosine-shaped pulse at its center:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "function cosine_pulse(x, t, equations::LinearScalarAdvectionEquation2D)\n",
    "  halfwidth = 0.5\n",
    "  radius = sqrt(x[1]^2 + x[2]^2)\n",
    "\n",
    "  if radius > halfwidth\n",
    "    u = 0.0\n",
    "  else\n",
    "    u = 0.1 + 0.1 * cos(pi * radius / halfwidth)\n",
    "  end\n",
    "\n",
    "  return Trixi.@SVector [u]\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use this function for the initial condition parameter in the semidiscretization, re-run the simulation, and plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recreate semidiscretization with the new initial condition\n",
    "semi = SemidiscretizationHyperbolic(mesh_static, equations,\n",
    "                                    cosine_pulse, # <-- here is the new initialization function\n",
    "                                    solver)\n",
    "\n",
    "# Create and solve ODE problem\n",
    "ode = semidiscretize(semi, (0.0, 1.7));\n",
    "sol = solve(ode, CarpenterKennedy2N54(williamson_condition=false), dt=2.5e-2);\n",
    "\n",
    "# Plot result\n",
    "plot(sol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaptive mesh refinement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous examples, we have always used a static, uniform mesh. This means we did not fully exploit one of Trixi's fundamental building blocks: the underlying hierarchical Cartesian mesh. With its ability to locally refine the mesh in a solution-adaptive way, it can be used to greatly speed up simulations with no or minimal loss in overall accuracy. The following image series of the grid for an expanding blast wave simulation ([elixir](https://github.com/trixi-framework/Trixi.jl/blob/v0.3.7/examples/2d/elixir_euler_blast_wave_amr.jl) available with Trixi) at $t = [0.2, 0.6, 1.0]$ (left to right) illustrates how the mesh resolution is increased only where needed."
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    "  <img src=\"attachment:1f20be7c-e949-410f-b11a-ea4e37a3b2b5.png\" width=\"50%\" />\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With Trixi, you can statically refine the mesh upfront, adaptively refine it during a simulation, or use a combination of both. Here, we will focus on how to do adaptive mesh refinement (AMR), which is implemented in Trixi as a **step callback**. A callback is an algorithmic entity that is registered with the ODE solver and then *called* in regular intervals to perform certain tasks: *step* callbacks are run after each time step, while *stage* callbacks are run after each stage of the ODE solver."
   ]
  },
  {
   "attachments": {
    "ec907c55-bf3b-4826-be48-e95bf4a0bf10.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    "  <img src=\"attachment:ec907c55-bf3b-4826-be48-e95bf4a0bf10.png\" width=\"25%\" />\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using callbacks instead of hard-coding all features in the main loop has the advantage that it allows to extend Trixi with new functionality without having to modify its code directly. Beyond AMR, in Trixi we make use of callbacks also for tasks such as solution analysis, file I/O, in-situ visualization, or time step size calculation.\n",
    "\n",
    "Building upon the previous example, we will now introduce the `AMRCallback` to adaptively refine the mesh around the cosine pulse every 5 steps. The callback also requires the definition of an *AMR controller*, i.e., an object that tells the AMR algorithms which cells to refine/coarsen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a simple AMR controller that to refine cells between level 4 and level 6, based on the maximum value of the solution in an element\n",
    "amr_controller = ControllerThreeLevel(semi,\n",
    "                                      IndicatorMax(semi, variable=first),\n",
    "                                      base_level=4,\n",
    "                                      med_level=5, med_threshold=0.05,\n",
    "                                      max_level=6, max_threshold=0.15);\n",
    "\n",
    "# Create the AMR callback that will be called every 5 time steps\n",
    "amr_callback = AMRCallback(semi, amr_controller,\n",
    "                           interval=5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `AMRCallback` can now be passed to the ODE solver and will be invoked (i.e., performs adaptive mesh refinement) every 5 time steps. Note that we also create a new mesh with a lower initial refinement, since by default the `AMRCallback` will iteratively refine the initial mesh based on the AMR controller until it does not change anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new mesh and semidiscretization with lower initial refinement level\n",
    "mesh_amr = TreeMesh(coordinates_min, coordinates_max,\n",
    "                    initial_refinement_level=2,\n",
    "                    n_cells_max=30_000)\n",
    "semi = SemidiscretizationHyperbolic(mesh_amr, equations,\n",
    "                                    cosine_pulse,\n",
    "                                    solver)\n",
    "\n",
    "# Create and solve ODE problem, using the previously constructed AMR callback\n",
    "ode = semidiscretize(semi, (0.0, 1.7));\n",
    "sol = solve(ode, CarpenterKennedy2N54(williamson_condition=false), callback=amr_callback, dt=6.25e-3);\n",
    "\n",
    "# Plot result\n",
    "pd = PlotData2D(sol)\n",
    "plot(pd, seriescolor=:heat)\n",
    "plot!(getmesh(pd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we also changed the color scheme and added the mesh lines to the plot to demonstrate how the mesh is locally refined around the cosine pulse. To find out more about how to visualize solutions with the `Plots` package, please refer to the [Trixi documentation](https://trixi-framework.github.io/Trixi.jl/stable/visualization/#Plots.jl)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In-situ visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another feature that is implemented in Trixi via callbacks is the ability to plot the solution during a simulation, also known as *in-situ visualization*. This can be useful to quickly check how a solution is developing over time, and is great for live demonstrations of a simulation. In its simplest form, you can create a `VisualizationCallback` only with the information in which frequency the results should be plotted, but it also allows to set formatting options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualization_callback = VisualizationCallback(interval=50, seriescolor=:heat);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualization callback and the AMR callback are combined into a `CallbackSet` and then passed to the `solve` method as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "callback_set = CallbackSet(visualization_callback, amr_callback)\n",
    "\n",
    "# Create and solve ODE problem, using the previously constructed callback set\n",
    "ode = semidiscretize(semi, (0.0, 1.7));\n",
    "sol = solve(ode, CarpenterKennedy2N54(williamson_condition=false), callback=callback_set, dt=6.25e-3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a simulation started from the REPL, the `VisualizationCallback` will not create seven individual images but open a single window that gets updated with each new plot as it is generated (see, e.g., this [YouTube video](https://www.youtube.com/watch?v=UZtrqeDY1Fs)). The full elixir including adaptive mesh refinement and in-situ visualization is available at [examples/elixir_advection_amr_visualization.jl](examples/elixir_advection_amr_visualization.jl)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we will introduce some of the more advanced features in Trixi (advanced in the sense that they go beyond basic functionality, not that they are particularly complicated to use)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oftentimes it is desireable to analyze a running simulation quantitatively by calculating integral quantities on the fly, which can be achieved by creating an `AnalysisCallback`. By default, it computes the $L^2$ and $L^\\infty$ errors and prints it to the terminal (we have seen an example of this in the [Quickstart](#Quickstart) section above). This can be extended to other built-in or user-provided integral quantities, such as the entropy time derivative $\\partial S/\\partial u \\cdot \\partial u/\\partial t$, the conservation error, or the total kinetic energy.\n",
    "\n",
    "In the following, we will create and `AnalysisCallback` that performs a solution analysis every 20 time steps, additionally computes the conservation error, and saves everything to a file (in addition to showing it on the terminal):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_callback = AnalysisCallback(semi,\n",
    "                                     interval=20,\n",
    "                                     extra_analysis_errors=(:conservation_error,),\n",
    "                                     save_analysis=true);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the $L^2$ and $L^\\infty$ errors are computed with respect to the (time-resolved) initial condition function, we will again use the  fully periodic`initial_condition_convergence_test` to initialize the solution. We will also use a static mesh again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create semidiscretization with a fully periodic initial condition\n",
    "semi = SemidiscretizationHyperbolic(mesh_static, equations, initial_condition_convergence_test, solver)\n",
    "\n",
    "# Create and solve ODE problem\n",
    "ode = semidiscretize(semi, (0.0, 1.0));\n",
    "sol = solve(ode, CarpenterKennedy2N54(williamson_condition=false), callback=analysis_callback, dt=2.5e-2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides the errors and analysis quantities, the `AnalysisCallback` also prints out additional information on the running simulation such as the current simulation time and some performance metrics. The requested analysis data was also written to `out/analysis.dat`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(read(joinpath(\"out\", \"analysis.dat\"), String))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running convergence tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When developing new numerical methods, a common part of the workflow is the check if the implemented scheme still exhibits the expected order of convergence (EOC) for mesh refinement. Trixi has a helper function `convergence_test(...)`, which re-runs a given elixir multiple times, each time increasing the mesh resolution by one. For this, we will again use the default example elixir provided by Trixi.\n",
    "\n",
    "*Note:* Since changing the mesh resolution also affects the allowed time step through the CFL condition, `convergence_test(...)` requires the elixir to use a [StepsizeCallback](https://trixi-framework.github.io/Trixi.jl/stable/reference-trixi/#Trixi.StepsizeCallback) to automatically compute the explicit time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Run convergence test with default example and 3 different mesh refinement levels\n",
    "convergence_test(default_example(), 3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trixi contains a method `linear_structure` that wraps the right-hand side operator of a semidiscretization as a linear operator. This can then be used to plot the spectrum for, e.g., the discretization of the scalar advection equation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Include an elixir to quickly obtain an appropriate semi discretization\n",
    "trixi_include(joinpath(\"examples\", \"elixir_advection_simple.jl\"), initial_refinement_level=2)\n",
    "\n",
    "# Get linear operator\n",
    "A, b = linear_structure(semi)\n",
    "\n",
    "# Compute eigenvalues\n",
    "using LinearAlgebra # Contains `eigvals` to compute the eigenvalues (part of Julia)\n",
    "λ = eigvals(Matrix(A))\n",
    "\n",
    "# Plot eigenvalues in complex plane\n",
    "plot(real(λ), imag(λ), seriestype=:scatter, xlims=(-40,5), ylims=(-30,30), legend=nothing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, to analyse the spectrum of non-linear operators we can use `jacobian_fd`, which uses second-order finite differences to compute the Jacobian `J` of the operator. Here we use the elixir [examples/elixir_euler_simple.jl](examples/elixir_euler_simple.jl), which is a simplified version of an elixir for the compressible Euler equations that also comes packaged with Trixi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Include an elixir to quickly obtain an appropriate semi discretization\n",
    "trixi_include(joinpath(\"examples\", \"elixir_euler_simple.jl\"))\n",
    "\n",
    "# Determine Jacobian\n",
    "J = jacobian_fd(semi)\n",
    "\n",
    "# Compute eigenvalues\n",
    "λ = eigvals(J)\n",
    "\n",
    "# Plot eigenvalues in complex plane\n",
    "plot(real(λ), imag(λ), seriestype=:scatter, xlims=(-2,2), ylims=(-1000,1000), legend=nothing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Postprocessing with Trixi2Vtk and ParaView"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we have only visualized the solutions ad-hoc using the `plots(...)` function of the `Plots` package. However, for a more in-depth solution analysis especially of 3D data, it is often helpful to rely on a proper visualization program. Trixi supports converting its solution files, which are created as HDF5 files by the [SaveSolutionCallback](https://trixi-framework.github.io/Trixi.jl/stable/reference-trixi/#Trixi.SaveSolutionCallback)), to VTK files that can be opened and visualized with [ParaView](https://www.paraview.org) or [VisIt](http://visit.llnl.gov). This functionality is available in the [Trixi2Vtk](https://github.com/trixi-framework/Trixi2Vtk.jl) package, which provides a `trixi2vtk(...)` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Trixi2Vtk\n",
    "\n",
    "trixi2vtk(joinpath(\"out\", \"solution_000000.h5\"), output_directory=\"out\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will create two files in the `out` directory, `solution_000000.vtu` and `solution_000000_celldata.vtu`. More information about how to use `trixi2vtk(...)` can be found in the [Trixi documentation](https://trixi-framework.github.io/Trixi.jl/stable/visualization/#Trixi2Vtk)."
   ]
  }
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