21638916092

Committed 03 Feb 2026 04:42PM UTC coverage: 25.939% (-71.1%) from 97.034%

Build # 21638916092

Build Type

Pull #2754

github

Committed by

web-flow

Commit Message

Merge ee9b4b1c0 into ead0db32a

Pull Request Pull Request #2754: `VolumeIntegralAdaptive` with `IndicatorEntropyChange`

Run Details

0 of 83 new or added lines in 6 files covered. (0.0%)

31545 existing lines in 536 files now uncovered.

11563 of 44578 relevant lines covered (25.94%)

469675.74 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

0.0

/src/visualization/types.jl

# Convenience type to allow dispatch on solution objects that were created by Trixi.jl
#
# This is a union of a Trixi.jl-specific SciMLBase.ODESolution and of Trixi.jl's own
# TimeIntegratorSolution.
#
#! format: off
const TrixiODESolution = Union{ODESolution{T, N, uType, uType2, DType, tType, rateType, discType, P} where
    {T, N, uType, uType2, DType, tType, rateType, discType, P<:ODEProblem{uType_, tType_, isinplace, P_, F_} where
     {uType_, tType_, isinplace, P_<:AbstractSemidiscretization, F_}}, TimeIntegratorSolution}
#! format: on

# By default, Julia/LLVM does not use fused multiply-add operations (FMAs).
# Since these FMAs can increase the performance of many numerical algorithms,
# we need to opt-in explicitly.
# See https://ranocha.de/blog/Optimizing_EC_Trixi for further details.
@muladd begin
#! format: noindent

# This file holds plotting types which can be used for both Plots.jl and Makie.jl.

# This abstract type is used to derive PlotData types of different dimensions; but still allows to share some functions for them.
abstract type AbstractPlotData{NDIMS} end

# Define additional methods for convenience.
# These are defined for AbstractPlotData, so they can be used for all types of plot data.
Base.firstindex(pd::AbstractPlotData) = first(pd.variable_names)
Base.lastindex(pd::AbstractPlotData) = last(pd.variable_names)
Base.length(pd::AbstractPlotData) = length(pd.variable_names)
Base.size(pd::AbstractPlotData) = (length(pd),)
Base.keys(pd::AbstractPlotData) = tuple(pd.variable_names...)

function Base.iterate(pd::AbstractPlotData, state = 1)
    if state > length(pd)
        return nothing
    else
        return (pd.variable_names[state] => pd[pd.variable_names[state]], state + 1)
    end
end

"""
    Base.getindex(pd::AbstractPlotData, variable_name)

Extract a single variable `variable_name` from `pd` for plotting with `Plots.plot`.
"""
function Base.getindex(pd::AbstractPlotData, variable_name)
    variable_id = findfirst(isequal(variable_name), pd.variable_names)

    if isnothing(variable_id)
        throw(KeyError(variable_name))
    end

    return PlotDataSeries(pd, variable_id)
end

Base.eltype(pd::AbstractPlotData) = Pair{String, PlotDataSeries{typeof(pd)}}

"""
    PlotData2D

Holds all relevant data for creating 2D plots of multiple solution variables and to visualize the
mesh.
"""
struct PlotData2DCartesian{Coordinates, Data, VariableNames, Vertices} <:
       AbstractPlotData{2}
    x::Coordinates
    y::Coordinates
    data::Data
    variable_names::VariableNames
    mesh_vertices_x::Vertices
    mesh_vertices_y::Vertices
    orientation_x::Int
    orientation_y::Int
end

# Show only a truncated output for convenience (the full data does not make sense)
function Base.show(io::IO, pd::PlotData2DCartesian)
    @nospecialize pd # reduce precompilation time

    print(io, "PlotData2DCartesian{",
          typeof(pd.x), ",",
          typeof(pd.data), ",",
          typeof(pd.variable_names), ",",
          typeof(pd.mesh_vertices_x),
          "}(<x>, <y>, <data>, <variable_names>, <mesh_vertices_x>, <mesh_vertices_y>)")
    return nothing
end

# holds plotting information for UnstructuredMesh2D and DGMulti-compatible meshes
struct PlotData2DTriangulated{DataType, NodeType, FaceNodeType, FaceDataType,
                              VariableNames, PlottingTriangulation} <:
       AbstractPlotData{2}
    x::NodeType # physical nodal coordinates, size (num_plotting_nodes x num_elements)
    y::NodeType
    data::DataType
    t::PlottingTriangulation
    x_face::FaceNodeType
    y_face::FaceNodeType
    face_data::FaceDataType
    variable_names::VariableNames
end

# Show only a truncated output for convenience (the full data does not make sense)
function Base.show(io::IO, pd::PlotData2DTriangulated)
    @nospecialize pd # reduce precompilation time

    print(io, "PlotData2DTriangulated{",
          typeof(pd.x), ", ",
          typeof(pd.data), ", ",
          typeof(pd.x_face), ", ",
          typeof(pd.face_data), ", ",
          typeof(pd.variable_names),
          "}(<x>, <y>, <data>, <plot_triangulation>, <x_face>, <y_face>, <face_data>, <variable_names>)")
    return nothing
end

"""
    PlotData1D

Holds all relevant data for creating 1D plots of multiple solution variables and to visualize the
mesh.
"""
struct PlotData1D{Coordinates, Data, VariableNames, Vertices} <: AbstractPlotData{1}
    x::Coordinates
    data::Data
    variable_names::VariableNames
    mesh_vertices_x::Vertices
    orientation_x::Integer
end

# Show only a truncated output for convenience (the full data does not make sense)
function Base.show(io::IO, pd::PlotData1D)
    print(io, "PlotData1D{",
          typeof(pd.x), ",",
          typeof(pd.data), ",",
          typeof(pd.variable_names), ",",
          typeof(pd.mesh_vertices_x),
          "}(<x>, <data>, <variable_names>, <mesh_vertices_x>)")
    return nothing
end

# Auxiliary data structure for visualizing a single variable
struct PlotDataSeries{PD <: AbstractPlotData}
    plot_data::PD
    variable_id::Int
end

# Show only a truncated output for convenience (the full data does not make sense)
function Base.show(io::IO, pds::PlotDataSeries)
    @nospecialize pds # reduce precompilation time

    print(io, "PlotDataSeries{", typeof(pds.plot_data), "}(<plot_data>, ",
          pds.variable_id, ")")
    return nothing
end

# Generic PlotMesh wrapper type.
struct PlotMesh{PD <: AbstractPlotData}
    plot_data::PD
end

# Show only a truncated output for convenience (the full data does not make sense)
function Base.show(io::IO, pm::PlotMesh)
    @nospecialize pm # reduce precompilation time

    print(io, "PlotMesh{", typeof(pm.plot_data), "}(<plot_data>)")
    return nothing
end

"""
    getmesh(pd::AbstractPlotData)

Extract grid lines from `pd` for plotting with `Plots.plot`.
"""
getmesh(pd::AbstractPlotData) = PlotMesh(pd)

"""
    PlotData2D(u, semi [or mesh, equations, solver, cache];
               solution_variables=nothing,
               grid_lines=true, max_supported_level=11, nvisnodes=nothing,
               slice=:xy, point=(0.0, 0.0, 0.0))

Create a new `PlotData2D` object that can be used for visualizing 2D/3D 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`.

For coupled semidiscretizations, i.e., `semi isa` [`SemidiscretizationCoupled`](@ref) a vector of
`PlotData2D` objects is returned, one for each semidiscretization which is part of the
coupled semidiscretization.

If `grid_lines` is `true`, also extract grid vertices for visualizing the mesh. The output
resolution is indirectly set via `max_supported_level`: all data is interpolated to
`2^max_supported_level` uniformly distributed points in each spatial direction, also setting the
maximum allowed refinement level in the 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`, exactly the number of nodes in the DG elements are used.

When visualizing data from a three-dimensional simulation, a 2D slice is extracted for plotting.
`slice` specifies the plane that is being sliced and may be `:xy`, `:xz`, or `:yz`.
The slice position is specified by a `point` that lies on it, which defaults to `(0.0, 0.0, 0.0)`.
Both of these values are ignored when visualizing 2D data.

# Examples
```julia
julia> using Trixi, Plots

julia> trixi_include(default_example())
[...]

julia> pd = PlotData2D(sol)
PlotData2D(...)

julia> plot(pd) # To plot all available variables

julia> plot(pd["scalar"]) # To plot only a single variable

julia> plot!(getmesh(pd)) # To add grid lines to the plot
```
"""
function PlotData2D(u_ode, semi; kwargs...)
    return PlotData2D(wrap_array_native(u_ode, semi),
                      mesh_equations_solver_cache(semi)...;
                      kwargs...)
end

function PlotData2D(u_ode, semi::SemidiscretizationCoupled; kwargs...)
    plot_data_array = []
    @unpack semis = semi

    foreach_enumerate(semis) do (i, semi_)
        u_loc = get_system_u_ode(u_ode, i, semi)
        u_loc_wrapped = wrap_array_native(u_loc, semi_)

        return push!(plot_data_array,
                     PlotData2D(u_loc_wrapped,
                                mesh_equations_solver_cache(semi_)...;
                                kwargs...))
    end

    return plot_data_array
end

# Redirect `PlotDataTriangulated2D` constructor.
function PlotData2DTriangulated(u_ode, semi; kwargs...)
    return PlotData2DTriangulated(wrap_array_native(u_ode, semi),
                                  mesh_equations_solver_cache(semi)...;
                                  kwargs...)
end

# Create a PlotData2DCartesian object for TreeMeshes on default.
function PlotData2D(u, mesh::TreeMesh, equations, solver, cache; kwargs...)
    return PlotData2DCartesian(u, mesh::TreeMesh, equations, solver, cache; kwargs...)
end

# Create a PlotData2DTriangulated object for any type of mesh other than the TreeMesh.
function PlotData2D(u, mesh, equations, solver, cache; kwargs...)
    return PlotData2DTriangulated(u, mesh, equations, solver, cache; kwargs...)
end

# Create a PlotData2DCartesian for a TreeMesh.
function PlotData2DCartesian(u, mesh::TreeMesh, equations, solver, cache;
                             solution_variables = nothing,
                             grid_lines = true, max_supported_level = 11,
                             nvisnodes = nothing,
                             slice = :xy, point = (0.0, 0.0, 0.0))
    @assert ndims(mesh) in (2, 3) "unsupported number of dimensions $ndims (must be 2 or 3)"
    solution_variables_ = digest_solution_variables(equations, solution_variables)

    # Extract mesh info
    center_level_0 = mesh.tree.center_level_0
    length_level_0 = mesh.tree.length_level_0
    leaf_cell_ids = leaf_cells(mesh.tree)
    coordinates = mesh.tree.coordinates[:, leaf_cell_ids]
    levels = mesh.tree.levels[leaf_cell_ids]

    unstructured_data = get_unstructured_data(u, solution_variables_, mesh, equations,
                                              solver, cache)
    x, y, data, mesh_vertices_x, mesh_vertices_y = get_data_2d(center_level_0,
                                                               length_level_0,
                                                               leaf_cell_ids,
                                                               coordinates, levels,
                                                               ndims(mesh),
                                                               unstructured_data,
                                                               nnodes(solver),
                                                               grid_lines,
                                                               max_supported_level,
                                                               nvisnodes,
                                                               slice, point)
    variable_names = SVector(varnames(solution_variables_, equations))

    orientation_x, orientation_y = _get_orientations(mesh, slice)

    return PlotData2DCartesian(x, y, data, variable_names, mesh_vertices_x,
                               mesh_vertices_y,
                               orientation_x, orientation_y)
end

"""
    PlotData2D(sol; kwargs...)

Create a `PlotData2D` object from a solution object created by either `OrdinaryDiffEq.solve!` (which
returns a `SciMLBase.ODESolution`) or Trixi.jl's own `solve!` (which returns a
`TimeIntegratorSolution`).
"""
function PlotData2D(sol::TrixiODESolution; kwargs...)
    return PlotData2D(sol.u[end], sol.prob.p; kwargs...)
end

# Also redirect when using PlotData2DTriangulate.
function PlotData2DTriangulated(sol::TrixiODESolution; kwargs...)
    return PlotData2DTriangulated(sol.u[end], sol.prob.p; kwargs...)
end

# If `u` is an `Array{<:SVectors}` and not a `StructArray`, convert it to a `StructArray` first.
function PlotData2D(u::Array{<:SVector, 2}, mesh, equations, dg::DGMulti, cache;
                    solution_variables = nothing, nvisnodes = 2 * nnodes(dg))
    nvars = length(first(u))
    u_structarray = StructArray{eltype(u)}(ntuple(_ -> zeros(eltype(first(u)), size(u)),
                                                  nvars))
    for (i, u_i) in enumerate(u)
        u_structarray[i] = u_i
    end

    # re-dispatch to PlotData2D with mesh, equations, dg, cache arguments
    return PlotData2D(u_structarray, mesh, equations, dg, cache;
                      solution_variables = solution_variables, nvisnodes = nvisnodes)
end

# constructor which returns an `PlotData2DTriangulated` object.
function PlotData2D(u::StructArray, mesh, equations, dg::DGMulti, cache;
                    solution_variables = nothing, nvisnodes = 2 * nnodes(dg))
    rd = dg.basis
    md = mesh.md

    # Vp = the interpolation matrix from nodal points to plotting points
    @unpack Vp = rd
    interpolate_to_plotting_points!(out, x) = mul!(out, Vp, x)

    solution_variables_ = digest_solution_variables(equations, solution_variables)
    variable_names = SVector(varnames(solution_variables_, equations))

    if Vp isa UniformScaling
        num_plotting_points = size(u, 1)
    else
        num_plotting_points = size(Vp, 1)
    end
    nvars = nvariables(equations)
    uEltype = eltype(first(u))
    u_plot = StructArray{SVector{nvars, uEltype}}(ntuple(_ -> zeros(uEltype,
                                                                    num_plotting_points,
                                                                    md.num_elements),
                                                         nvars))

    for e in eachelement(mesh, dg, cache)
        # interpolate solution to plotting nodes element-by-element
        StructArrays.foreachfield(interpolate_to_plotting_points!, view(u_plot, :, e),
                                  view(u, :, e))

        # transform nodal values of the solution according to `solution_variables`
        transform_to_solution_variables!(view(u_plot, :, e), solution_variables_,
                                         equations)
    end

    # interpolate nodal coordinates to plotting points
    x_plot, y_plot = map(x -> Vp * x, md.xyz) # md.xyz is a tuple of arrays containing nodal coordinates

    # construct a triangulation of the reference plotting nodes
    t = reference_plotting_triangulation(rd.rstp) # rd.rstp = reference coordinates of plotting points

    x_face, y_face, face_data = mesh_plotting_wireframe(u, mesh, equations, dg, cache;
                                                        nvisnodes = nvisnodes)

    return PlotData2DTriangulated(x_plot, y_plot, u_plot, t, x_face, y_face, face_data,
                                  variable_names)
end

# specializes the PlotData2D constructor to return an PlotData2DTriangulated for any type of mesh.
function PlotData2DTriangulated(u, mesh, equations, dg::DGSEM, cache;
                                solution_variables = nothing,
                                nvisnodes = 2 * polydeg(dg))
    @assert ndims(mesh)==2 "Input must be two-dimensional."

    n_nodes_2d = nnodes(dg)^ndims(mesh)
    n_elements = nelements(dg, cache)

    # build nodes on reference element (seems to be the right ordering)
    r, s = reference_node_coordinates_2d(dg)

    # reference plotting nodes
    if nvisnodes == 0 || nvisnodes === nothing
        nvisnodes = polydeg(dg) + 1
    end
    plotting_interp_matrix = plotting_interpolation_matrix(dg; nvisnodes = nvisnodes)

    # create triangulation for plotting nodes
    r_plot, s_plot = (x -> plotting_interp_matrix * x).((r, s)) # interpolate dg nodes to plotting nodes

    # construct a triangulation of the plotting nodes
    t = reference_plotting_triangulation((r_plot, s_plot))

    # extract x,y coordinates and solutions on each element
    uEltype = eltype(u)
    nvars = nvariables(equations)
    x = reshape(view(cache.elements.node_coordinates, 1, :, :, :), n_nodes_2d,
                n_elements)
    y = reshape(view(cache.elements.node_coordinates, 2, :, :, :), n_nodes_2d,
                n_elements)
    u_extracted = StructArray{SVector{nvars, uEltype}}(ntuple(_ -> similar(x,
                                                                           (n_nodes_2d,
                                                                            n_elements)),
                                                              nvars))
    for element in eachelement(dg, cache)
        sk = 1
        for j in eachnode(dg), i in eachnode(dg)
            u_node = get_node_vars(u, equations, dg, i, j, element)
            u_extracted[sk, element] = u_node
            sk += 1
        end
    end

    # interpolate to volume plotting points
    xplot, yplot = plotting_interp_matrix * x, plotting_interp_matrix * y
    uplot = StructArray{SVector{nvars, uEltype}}(map(x -> plotting_interp_matrix * x,
                                                     StructArrays.components(u_extracted)))

    xfp, yfp, ufp = mesh_plotting_wireframe(u_extracted, mesh, equations, dg, cache;
                                            nvisnodes = nvisnodes)

    # convert variables based on solution_variables mapping
    solution_variables_ = digest_solution_variables(equations, solution_variables)
    variable_names = SVector(varnames(solution_variables_, equations))

    transform_to_solution_variables!(uplot, solution_variables_, equations)
    transform_to_solution_variables!(ufp, solution_variables_, equations)

    return PlotData2DTriangulated(xplot, yplot, uplot, t, xfp, yfp, ufp, variable_names)
end

# Wrapper struct to indicate that an array represents a scalar data field. Used only for dispatch.
struct ScalarData{T}
    data::T
end

"""
    ScalarPlotData2D(u, semi::AbstractSemidiscretization; kwargs...)

Returns an `PlotData2DTriangulated` object which is used to visualize a single scalar field.
`u` should be an array whose entries correspond to values of the scalar field at nodal points.
"""
function ScalarPlotData2D(u, semi::AbstractSemidiscretization; kwargs...)
    return ScalarPlotData2D(u, mesh_equations_solver_cache(semi)...; kwargs...)
end

# Returns an `PlotData2DTriangulated` which is used to visualize a single scalar field
function ScalarPlotData2D(u, mesh, equations, dg::DGMulti, cache;
                          variable_name = nothing, nvisnodes = 2 * nnodes(dg))
    rd = dg.basis
    md = mesh.md

    # Vp = the interpolation matrix from nodal points to plotting points
    @unpack Vp = rd

    # interpolate nodal coordinates and solution field to plotting points
    x_plot, y_plot = map(x -> Vp * x, md.xyz) # md.xyz is a tuple of arrays containing nodal coordinates
    u_plot = Vp * u

    # construct a triangulation of the reference plotting nodes
    t = reference_plotting_triangulation(rd.rstp) # rd.rstp = reference coordinates of plotting points

    # Ignore face data when plotting `ScalarPlotData2D`, since mesh lines can be plotted using
    # existing functionality based on `PlotData2D(sol)`.
    x_face, y_face, face_data = mesh_plotting_wireframe(ScalarData(u), mesh, equations,
                                                        dg, cache;
                                                        nvisnodes = 2 * nnodes(dg))

    # wrap solution in ScalarData struct for recipe dispatch
    return PlotData2DTriangulated(x_plot, y_plot, ScalarData(u_plot), t,
                                  x_face, y_face, face_data, variable_name)
end

function ScalarPlotData2D(u, mesh, equations, dg::DGSEM, cache; variable_name = nothing,
                          nvisnodes = 2 * nnodes(dg))
    n_nodes_2d = nnodes(dg)^ndims(mesh)
    n_elements = nelements(dg, cache)

    # build nodes on reference element (seems to be the right ordering)
    r, s = reference_node_coordinates_2d(dg)

    # reference plotting nodes
    if nvisnodes == 0 || nvisnodes === nothing
        nvisnodes = polydeg(dg) + 1
    end
    plotting_interp_matrix = plotting_interpolation_matrix(dg; nvisnodes = nvisnodes)

    # create triangulation for plotting nodes
    r_plot, s_plot = (x -> plotting_interp_matrix * x).((r, s)) # interpolate dg nodes to plotting nodes

    # construct a triangulation of the plotting nodes
    t = reference_plotting_triangulation((r_plot, s_plot))

    # extract x,y coordinates and reshape them into matrices of size (n_nodes_2d, n_elements)
    x = view(cache.elements.node_coordinates, 1, :, :, :)
    y = view(cache.elements.node_coordinates, 2, :, :, :)
    x, y = reshape.((x, y), n_nodes_2d, n_elements)

    # interpolate to volume plotting points by multiplying each column by `plotting_interp_matrix`
    x_plot, y_plot = plotting_interp_matrix * x, plotting_interp_matrix * y
    u_plot = plotting_interp_matrix * reshape(u, size(x))

    # Ignore face data when plotting `ScalarPlotData2D`, since mesh lines can be plotted using
    # existing functionality based on `PlotData2D(sol)`.
    x_face, y_face, face_data = mesh_plotting_wireframe(ScalarData(u), mesh, equations,
                                                        dg, cache;
                                                        nvisnodes = 2 * nnodes(dg))

    # wrap solution in ScalarData struct for recipe dispatch
    return PlotData2DTriangulated(x_plot, y_plot, ScalarData(u_plot), t,
                                  x_face, y_face, face_data, variable_name)
end

"""
    PlotData1D(u, semi [or mesh, equations, solver, cache];
               solution_variables=nothing, nvisnodes=nothing,
               reinterpolate=Trixi.default_reinterpolate(solver),
               slice=:x, point=(0.0, 0.0, 0.0), curve=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`, 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. The spatial variable `x` is a vector with one
element.

`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`,
exactly the number of nodes as in the DG elements are used. The solution is interpolated to these
nodes for plotting. If `reinterpolate` is `false`, the solution is not interpolated to the `visnodes`,
i.e., the solution is plotted at the solution nodes. In this case `nvisnodes` is ignored. By default,
`reinterpolate` is set to `false` for `FDSBP` approximations and to `true` otherwise.

When visualizing data from a two-dimensional simulation, a 1D slice is extracted for plotting.
`slice` specifies the axis along which the slice is extracted and may be `:x`, or `:y`.
The slice position is specified by a `point` that lies on it, which defaults to `(0.0, 0.0)`.
Both of these values are ignored when visualizing 1D data.
This applies analogously to three-dimensional simulations, where `slice` may be `:xy`, `:xz`, or `:yz`.

Another way to visualize 2D/3D data is by creating a plot along a given curve.
This is done with the keyword argument `curve`. It can be set to a list of 2D/3D points
which define the curve. When using `curve` any other input from `slice` or `point` will be ignored.
"""
function PlotData1D(u_ode, semi; kwargs...)
    return PlotData1D(wrap_array_native(u_ode, semi),
                      mesh_equations_solver_cache(semi)...;
                      kwargs...)
end

function PlotData1D(func::Function, semi; kwargs...)
    return PlotData1D(func,
                      mesh_equations_solver_cache(semi)...;
                      kwargs...)
end

function PlotData1D(u, mesh::TreeMesh, equations, solver, cache;
                    solution_variables = nothing, nvisnodes = nothing,
                    reinterpolate = default_reinterpolate(solver),
                    slice = :x, point = (0.0, 0.0, 0.0), curve = nothing,
                    variable_names = nothing)
    solution_variables_ = digest_solution_variables(equations, solution_variables)
    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,
                                              solver, cache)

    orientation_x = 0 # Set 'orientation' to zero on default.

    if ndims(mesh) == 1
        x, data, mesh_vertices_x = get_data_1d(original_nodes, unstructured_data,
                                               nvisnodes, reinterpolate)
        orientation_x = 1

        # Special care is required for first-order FV approximations since the nodes are the
        # cell centers and do not contain the boundaries
        n_nodes = size(unstructured_data, 1)
        if n_nodes == 1
            n_visnodes = length(x) ÷ nelements(solver, cache)
            if n_visnodes != 2
                throw(ArgumentError("This number of visualization nodes is currently not supported for finite volume approximations."))
            end
            left_boundary = mesh.tree.center_level_0[1] - mesh.tree.length_level_0 / 2
            dx_2 = zero(left_boundary)
            for i in 1:div(length(x), 2)
                # Adjust plot nodes so that they are at the boundaries of each element
                dx_2 = x[2 * i - 1] - left_boundary
                x[2 * i - 1] -= dx_2
                x[2 * i] += dx_2
                left_boundary = left_boundary + 2 * dx_2

                # Adjust mesh plot nodes
                mesh_vertices_x[i] -= dx_2
            end
            mesh_vertices_x[end] += dx_2
        end
    elseif ndims(mesh) == 2
        if curve !== nothing
            x, data, mesh_vertices_x = unstructured_2d_to_1d_curve(original_nodes,
                                                                   unstructured_data,
                                                                   nvisnodes, curve,
                                                                   mesh, solver, cache)
        else
            x, data, mesh_vertices_x = unstructured_2d_to_1d(original_nodes,
                                                             unstructured_data,
                                                             nvisnodes, reinterpolate,
                                                             slice, point)
        end
    else # ndims(mesh) == 3
        if curve !== nothing
            x, data, mesh_vertices_x = unstructured_3d_to_1d_curve(original_nodes,
                                                                   unstructured_data,
                                                                   nvisnodes, curve,
                                                                   mesh, solver, cache)
        else
            x, data, mesh_vertices_x = unstructured_3d_to_1d(original_nodes,
                                                             unstructured_data,
                                                             nvisnodes, reinterpolate,
                                                             slice, point)
        end
    end

    return PlotData1D(x, data, variable_names_, mesh_vertices_x,
                      orientation_x)
end

# unwrap u if it is VectorOfArray
PlotData1D(u::VectorOfArray, mesh, equations, dg::DGMulti{1}, cache; kwargs...) = PlotData1D(parent(u),
                                                                                             mesh,
                                                                                             equations,
                                                                                             dg,
                                                                                             cache;
                                                                                             kwargs...)
PlotData2D(u::VectorOfArray, mesh, equations, dg::DGMulti{2}, cache; kwargs...) = PlotData2D(parent(u),
                                                                                             mesh,
                                                                                             equations,
                                                                                             dg,
                                                                                             cache;
                                                                                             kwargs...)

function PlotData1D(u, mesh, equations, solver, cache;
                    solution_variables = nothing, nvisnodes = nothing,
                    reinterpolate = default_reinterpolate(solver),
                    slice = :x, point = (0.0, 0.0, 0.0), curve = nothing,
                    variable_names = nothing)
    solution_variables_ = digest_solution_variables(equations, solution_variables)
    variable_names_ = digest_variable_names(solution_variables_, equations,
                                            variable_names)

    original_nodes = cache.elements.node_coordinates

    orientation_x = 0 # Set 'orientation' to zero on default.

    if ndims(mesh) == 1
        unstructured_data = get_unstructured_data(u, solution_variables_,
                                                  mesh, equations, solver, cache)
        x, data, mesh_vertices_x = get_data_1d(original_nodes, unstructured_data,
                                               nvisnodes, reinterpolate)
        orientation_x = 1
    elseif ndims(mesh) == 2
        x, data, mesh_vertices_x = unstructured_2d_to_1d_curve(u, mesh, equations,
                                                               solver, cache,
                                                               curve, slice,
                                                               point, nvisnodes,
                                                               solution_variables_)
    else # ndims(mesh) == 3
        # Extract the information required to create a PlotData1D object.
        # If no curve is defined, create an axis curve.
        if curve === nothing
            curve = axis_curve(view(original_nodes, 1, :, :, :, :),
                               view(original_nodes, 2, :, :, :, :),
                               view(original_nodes, 3, :, :, :, :),
                               slice, point, nvisnodes)
        end

        # We need to loop through all the points and check in which element they are
        # located. A general implementation working for all mesh types has to perform
        # a naive loop through all nodes. However, the P4estMesh and the T8codeMesh
        # can make use of the efficient search functionality of p4est/t8code
        # to speed up the process. Thus, we pass the mesh, too.
        x, data, mesh_vertices_x = unstructured_3d_to_1d_curve(u, mesh, equations,
                                                               solver, cache,
                                                               curve,
                                                               solution_variables_)
    end

    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, variable_names = nothing)
    solution_variables_ = digest_solution_variables(equations, solution_variables)
    variable_names_ = digest_variable_names(solution_variables_, equations,
                                            variable_names)

    orientation_x = 0 # Set 'orientation' to zero on default.

    if u isa StructArray
        # Convert conserved variables to the given `solution_variables` and set up
        # plotting coordinates
        # This uses a "structure of arrays"
        data = map(x -> vcat(dg.basis.Vp * x, fill(NaN, 1, size(u, 2))),
                   StructArrays.components(solution_variables_.(u, equations)))
        x = vcat(dg.basis.Vp * mesh.md.x, fill(NaN, 1, size(u, 2)))

        # Here, we ensure that `DGMulti` visualization uses the same data layout and format
        # as `TreeMesh`. This enables us to reuse existing plot recipes. In particular,
        # `hcat(data...)` creates a matrix of size `num_plotting_points` by `nvariables(equations)`,
        # with data on different elements separated by `NaNs`.
        x_plot = vec(x)
        data_plot = hcat(vec.(data)...)
    else
        # Convert conserved variables to the given `solution_variables` and set up
        # plotting coordinates
        # This uses an "array of structures"
        data_tmp = dg.basis.Vp * solution_variables_.(u, equations)
        data = vcat(data_tmp, fill(NaN * zero(eltype(data_tmp)), 1, size(u, 2)))
        x = vcat(dg.basis.Vp * mesh.md.x, fill(NaN, 1, size(u, 2)))

        # Same as above - we create `data_plot` as array of size `num_plotting_points`
        # by "number of plotting variables".
        x_plot = vec(x)
        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)
end

"""
    PlotData1D(sol; kwargs...)

Create a `PlotData1D` object from a solution object created by either `OrdinaryDiffEq.solve!`
(which returns a `SciMLBase.ODESolution`) or Trixi.jl's own `solve!` (which returns a
`TimeIntegratorSolution`).
"""
function PlotData1D(sol::TrixiODESolution; kwargs...)
    return PlotData1D(sol.u[end], sol.prob.p; kwargs...)
end

function PlotData1D(time_series_callback::TimeSeriesCallback, point_id::Integer)
    @unpack time, variable_names, point_data = time_series_callback

    n_solution_variables = length(variable_names)
    data = Matrix{Float64}(undef, length(time), n_solution_variables)
    reshaped = reshape(point_data[point_id], n_solution_variables, length(time))
    for v in 1:n_solution_variables
        @views data[:, v] = reshaped[v, :]
    end

    mesh_vertices_x = Vector{Float64}(undef, 0)

    return PlotData1D(time, data, SVector(variable_names), mesh_vertices_x, 0)
end

function PlotData1D(cb::DiscreteCallback{<:Any, <:TimeSeriesCallback},
                    point_id::Integer)
    return PlotData1D(cb.affect!, point_id)
end
end # @muladd

trixi-framework / Trixi.jl / 21638916092

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous