Add support for plotting 1D function (#2250)

* add support for plotting 1D function * allow more than one variable * add a test * format * format * simplify * add comment * function -> method * add support for 1D StructuredMesh and 1D DGMulti * specialize method for 1D meshes * format * Apply suggestions from code review Co-authored-by: Hendrik Ranocha <[email protected]> * always use solution_variables = cons2cons * don't pass variable_names to recipe (title can be changed by `title`) * fix plotting scalar function for DGMulti * fix for StructuredMesh * Update src/visualization/types.jl Co-authored-by: Hendrik Ranocha <[email protected]> * fix comment * clarify output of function --------- Co-authored-by: Hendrik Ranocha <[email protected]>
trixi-framework · Feb 5, 2025 · c24ce91 · c24ce91
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diff --git a/src/visualization/recipes_plots.jl b/src/visualization/recipes_plots.jl
@@ -171,6 +171,19 @@ RecipesBase.@recipe function f(u, semi::AbstractSemidiscretization;
     end
 end
 
+# Also allow plotting a function with signature `func(x, equations)`, e.g., for initial conditions.
+# We need this recipe in addition to the one above to avoid method ambiguities.
+RecipesBase.@recipe function f(func::Function, semi::AbstractSemidiscretization;
+                               solution_variables = nothing)
+    n_variables = length(func(0.0, semi.equations))
+    variable_names = SVector(["func[$i]" for i in 1:n_variables]...)
+    if ndims(semi) == 1
+        return PlotData1D(func, semi; solution_variables = cons2cons, variable_names)
+    else
+        throw(ArgumentError("Plotting of functions is only supported in 1D."))
+    end
+end
+
 # Recipe specifically for TreeMesh-type solutions
 # Note: If you change the defaults values here, you need to also change them in the PlotData1D or PlotData2D
 #       constructor.
@@ -189,6 +202,22 @@ RecipesBase.@recipe function f(u, semi::SemidiscretizationHyperbolic{<:TreeMesh}
     end
 end
 
+# Also allow plotting a function with signature `func(x, equations)`, e.g., for initial conditions.
+RecipesBase.@recipe function f(func::Function,
+                               semi::SemidiscretizationHyperbolic{<:TreeMesh};
+                               solution_variables = nothing,
+                               nvisnodes = nothing, slice = :xy,
+                               point = (0.0, 0.0, 0.0), curve = nothing)
+    n_variables = length(func(0.0, semi.equations))
+    variable_names = SVector(["func[$i]" for i in 1:n_variables]...)
+    if ndims(semi) == 1
+        return PlotData1D(func, semi; solution_variables = cons2cons, nvisnodes, slice,
+                          point, curve, variable_names)
+    else
+        throw(ArgumentError("Plotting of functions is only supported in 1D."))
+    end
+end
+
 # Series recipe for PlotData2DTriangulated
 RecipesBase.@recipe function f(pds::PlotDataSeries{<:PlotData2DTriangulated})
     pd = pds.plot_data

diff --git a/src/visualization/types.jl b/src/visualization/types.jl
@@ -519,10 +519,15 @@ end
                solution_variables=nothing, nvisnodes=nothing)
 
 Create a new `PlotData1D` object that can be used for visualizing 1D DGSEM solution data array
-`u` with `Plots.jl`. All relevant geometrical information is extracted from the semidiscretization
-`semi`. By default, the primitive variables (if existent) or the conservative variables (otherwise)
-from the solution are used for plotting. This can be changed by passing an appropriate conversion
-function to `solution_variables`.
+`u` with `Plots.jl`. All relevant geometrical information is extracted from the
+semidiscretization `semi`. By default, the primitive variables (if existent)
+or the conservative variables (otherwise) from the solution are used for
+plotting. This can be changed by passing an appropriate conversion function to
+`solution_variables`, e.g., [`cons2cons`](@ref) or [`cons2prim`](@ref).
+
+Alternatively, you can also pass a function `u` with signature `u(x, equations)`
+returning a vector. In this case, the `solution_variables` are ignored. This is useful,
+e.g., to visualize an analytical solution.
 
 `nvisnodes` specifies the number of visualization nodes to be used. If it is `nothing`,
 twice the number of solution DG nodes are used for visualization, and if set to `0`,
@@ -547,11 +552,19 @@ function PlotData1D(u_ode, semi; kwargs...)
                kwargs...)
 end
 
+function PlotData1D(func::Function, semi; kwargs...)
+    PlotData1D(func,
+               mesh_equations_solver_cache(semi)...;
+               kwargs...)
+end
+
 function PlotData1D(u, mesh::TreeMesh, equations, solver, cache;
                     solution_variables = nothing, nvisnodes = nothing,
-                    slice = :x, point = (0.0, 0.0, 0.0), curve = nothing)
+                    slice = :x, point = (0.0, 0.0, 0.0), curve = nothing,
+                    variable_names = nothing)
     solution_variables_ = digest_solution_variables(equations, solution_variables)
-    variable_names = SVector(varnames(solution_variables_, equations))
+    variable_names_ = digest_variable_names(solution_variables_, equations,
+                                            variable_names)
 
     original_nodes = cache.elements.node_coordinates
     unstructured_data = get_unstructured_data(u, solution_variables_, mesh, equations,
@@ -610,15 +623,17 @@ function PlotData1D(u, mesh::TreeMesh, equations, solver, cache;
         end
     end
 
-    return PlotData1D(x, data, variable_names, mesh_vertices_x,
+    return PlotData1D(x, data, variable_names_, mesh_vertices_x,
                       orientation_x)
 end
 
 function PlotData1D(u, mesh, equations, solver, cache;
                     solution_variables = nothing, nvisnodes = nothing,
-                    slice = :x, point = (0.0, 0.0, 0.0), curve = nothing)
+                    slice = :x, point = (0.0, 0.0, 0.0), curve = nothing,
+                    variable_names = nothing)
     solution_variables_ = digest_solution_variables(equations, solution_variables)
-    variable_names = SVector(varnames(solution_variables_, equations))
+    variable_names_ = digest_variable_names(solution_variables_, equations,
+                                            variable_names)
 
     original_nodes = cache.elements.node_coordinates
     unstructured_data = get_unstructured_data(u, solution_variables_, mesh, equations,
@@ -642,15 +657,25 @@ function PlotData1D(u, mesh, equations, solver, cache;
                                                                slice, point, nvisnodes)
     end
 
-    return PlotData1D(x, data, variable_names, mesh_vertices_x,
+    return PlotData1D(x, data, variable_names_, mesh_vertices_x,
                       orientation_x)
 end
 
+function PlotData1D(func::Function, mesh, equations, dg::DGMulti{1}, cache;
+                    solution_variables = nothing, variable_names = nothing)
+    x = mesh.md.x
+    u = func.(x, equations)
+
+    return PlotData1D(u, mesh, equations, dg, cache;
+                      solution_variables, variable_names)
+end
+
 # Specializes the `PlotData1D` constructor for one-dimensional `DGMulti` solvers.
 function PlotData1D(u, mesh, equations, dg::DGMulti{1}, cache;
-                    solution_variables = nothing)
+                    solution_variables = nothing, variable_names = nothing)
     solution_variables_ = digest_solution_variables(equations, solution_variables)
-    variable_names = SVector(varnames(solution_variables_, equations))
+    variable_names_ = digest_variable_names(solution_variables_, equations,
+                                            variable_names)
 
     orientation_x = 0 # Set 'orientation' to zero on default.
 
@@ -679,11 +704,16 @@ function PlotData1D(u, mesh, equations, dg::DGMulti{1}, cache;
         # Same as above - we create `data_plot` as array of size `num_plotting_points`
         # by "number of plotting variables".
         x_plot = vec(x)
-        data_plot = permutedims(reinterpret(reshape, eltype(eltype(data)), vec(data)),
-                                (2, 1))
+        data_ = reinterpret(reshape, eltype(eltype(data)), vec(data))
+        # If there is only one solution variable, we need to add a singleton dimension
+        if ndims(data_) == 1
+            data_plot = reshape(data_, :, 1)
+        else
+            data_plot = permutedims(data_, (2, 1))
+        end
     end
 
-    return PlotData1D(x_plot, data_plot, variable_names, mesh.md.VX, orientation_x)
+    return PlotData1D(x_plot, data_plot, variable_names_, mesh.md.VX, orientation_x)
 end
 
 """

diff --git a/src/visualization/utilities.jl b/src/visualization/utilities.jl
@@ -256,6 +256,10 @@ function digest_solution_variables(equations, solution_variables::Nothing)
     end
 end
 
+digest_variable_names(solution_variables_, equations, variable_names) = variable_names
+digest_variable_names(solution_variables_, equations, ::Nothing) = SVector(varnames(solution_variables_,
+                                                                                    equations))
+
 """
     adapt_to_mesh_level!(u_ode, semi, level)
     adapt_to_mesh_level!(sol::Trixi.TrixiODESolution, level)
@@ -481,6 +485,18 @@ function get_unstructured_data(u, solution_variables, mesh, equations, solver, c
     return unstructured_data
 end
 
+# This method is only for plotting 1D functions
+function get_unstructured_data(func::Function, solution_variables,
+                               mesh::AbstractMesh{1}, equations, solver, cache)
+    original_nodes = cache.elements.node_coordinates
+    # original_nodes has size (1, nnodes, nelements)
+    # we want u to have size (nvars, nnodes, nelements)
+    # func.(original_nodes, equations) has size (1, nnodes, nelements), where each component has length n_vars
+    # Therefore, we drop the first (singleton) dimension and then stack the components
+    u = stack(func.(SVector.(dropdims(original_nodes; dims = 1)), equations))
+    return get_unstructured_data(u, solution_variables, mesh, equations, solver, cache)
+end
+
 # Convert cell-centered values to node-centered values by averaging over all
 # four neighbors and making use of the periodicity of the solution
 #

diff --git a/test/test_visualization.jl b/test/test_visualization.jl
@@ -188,6 +188,10 @@ end
         @test_nowarn_mod Plots.plot(pd)
         @test_nowarn_mod Plots.plot(pd["p"])
         @test_nowarn_mod Plots.plot(getmesh(pd))
+        initial_condition_t_end(x, equations) = initial_condition(x, last(tspan),
+                                                                  equations)
+        @test_nowarn_mod Plots.plot(initial_condition_t_end, semi)
+        @test_nowarn_mod Plots.plot((x, equations) -> x, semi)
     end
 
     # Fake a PlotDataXD objects to test code for plotting multiple variables on at least two rows
@@ -220,13 +224,34 @@ end
                                    tspan = (0.0, 0.0),
                                    approximation_type = Polynomial())
     @test PlotData1D(sol) isa PlotData1D
+    initial_condition_t_end(x, equations) = initial_condition(x, last(tspan), equations)
+    @test_nowarn_mod Plots.plot(initial_condition_t_end, semi)
+    @test_nowarn_mod Plots.plot((x, equations) -> x, semi)
 
     @test_nowarn_mod trixi_include(@__MODULE__,
                                    joinpath(examples_dir(), "dgmulti_1d",
                                             "elixir_euler_flux_diff.jl"),
                                    tspan = (0.0, 0.0),
                                    approximation_type = SBP())
     @test PlotData1D(sol) isa PlotData1D
+    @test_nowarn_mod Plots.plot(initial_condition_t_end, semi)
+    @test_nowarn_mod Plots.plot((x, equations) -> x, semi)
+end
+
+@timed_testset "1D plot recipes (StructuredMesh)" begin
+    @test_nowarn_mod trixi_include(@__MODULE__,
+                                   joinpath(examples_dir(), "structured_1d_dgsem",
+                                            "elixir_euler_source_terms.jl"),
+                                   tspan = (0.0, 0.0))
+
+    pd = PlotData1D(sol)
+    initial_condition_t_end(x, equations) = initial_condition(x, last(tspan), equations)
+    @test_nowarn_mod Plots.plot(sol)
+    @test_nowarn_mod Plots.plot(pd)
+    @test_nowarn_mod Plots.plot(pd["p"])
+    @test_nowarn_mod Plots.plot(sol.u[end], semi)
+    @test_nowarn_mod Plots.plot(initial_condition_t_end, semi)
+    @test_nowarn_mod Plots.plot((x, equations) -> x, semi)
 end
 
 @timed_testset "plot time series" begin