time_series.jl

# 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

"""
    TimeSeriesCallback(semi, point_coordinates;
                       interval=1, solution_variables=cons2cons,
                       output_directory="out", filename="time_series.h5",
                       RealT=real(solver), uEltype=eltype(cache.elements))

Create a callback that records point-wise data at points given in `point_coordinates` every
`interval` time steps. The point coordinates are to be specified either as a vector of coordinate
tuples or as a two-dimensional array where the first dimension is the point number and the second
dimension is the coordinate dimension. By default, the conservative variables are recorded, but this
can be controlled by passing a different conversion function to `solution_variables`.

After the last time step, the results are stored in an HDF5 file `filename` in directory
`output_directory`.

The real data type `RealT` and data type for solution variables `uEltype` default to the respective
types used in the solver and the cache.

Currently this callback is only implemented for [`TreeMesh`](@ref) and [`UnstructuredMesh2D`](@ref).
"""
mutable struct TimeSeriesCallback{RealT <: Real, uEltype <: Real, SolutionVariables,
                                  VariableNames, Cache}
    const interval::Int
    const solution_variables::SolutionVariables
    const variable_names::VariableNames
    const output_directory::String
    const filename::String
    const point_coordinates::Array{RealT, 2}
    # Point data is stored as a vector of vectors of the solution data type:
    # * the "outer" `Vector` contains one vector for each point at which a time_series is recorded
    # * the "inner" `Vector` contains the actual time series for a single point,
    #   with each record  adding "n_vars" entries
    # The reason for using this data structure is that the length of the inner vectors needs to be
    # increased for each record, which can only be realized in Julia using ordinary `Vector`s.
    point_data::Vector{Vector{uEltype}}
    time::Vector{RealT}
    step::Vector{Int}
    const time_series_cache::Cache
end

function Base.show(io::IO, cb::DiscreteCallback{<:Any, <:TimeSeriesCallback})
    @nospecialize cb # reduce precompilation time

    time_series_callback = cb.affect!
    @unpack interval, solution_variables, output_directory, filename = time_series_callback
    print(io, "TimeSeriesCallback(",
          "interval=", interval, ", ",
          "solution_variables=", interval, ", ",
          "output_directory=", "\"output_directory\"", ", ",
          "filename=", "\"filename\"",
          ")")
    return nothing
end

function Base.show(io::IO, ::MIME"text/plain",
                   cb::DiscreteCallback{<:Any, <:TimeSeriesCallback})
    @nospecialize cb # reduce precompilation time

    if get(io, :compact, false)
        show(io, cb)
    else
        time_series_callback = cb.affect!

        setup = [
            "#points" => size(time_series_callback.point_coordinates, 2),
            "interval" => time_series_callback.interval,
            "solution_variables" => time_series_callback.solution_variables,
            "output_directory" => time_series_callback.output_directory,
            "filename" => time_series_callback.filename
        ]
        summary_box(io, "TimeSeriesCallback", setup)
    end
end

# Main constructor
function TimeSeriesCallback(mesh, equations, solver, cache, point_coordinates;
                            interval::Integer = 1,
                            solution_variables = cons2cons,
                            output_directory = "out",
                            filename = "time_series.h5",
                            RealT = real(solver),
                            uEltype = eltype(cache.elements))
    # check arguments
    if !(interval isa Integer && interval >= 0)
        throw(ArgumentError("`interval` must be a non-negative integer (provided `interval = $interval`)"))
    end

    if ndims(point_coordinates) != 2 || size(point_coordinates, 2) != ndims(mesh)
        throw(ArgumentError("`point_coordinates` must be a matrix of size n_points × ndims"))
    end

    # create the output folder if it does not exist already
    if mpi_isroot() && !isdir(output_directory)
        mkpath(output_directory)
    end

    # Transpose point_coordinates to our usual format [ndims, n_points]
    # Note: They are accepted in a different format to allow direct input from `readdlm`
    point_coordinates_ = permutedims(point_coordinates)

    # Invoke callback every `interval` time steps or after final step (for storing the data on disk)
    if interval > 0
        # With error-based step size control, some steps can be rejected. Thus,
        #   `integrator.iter >= integrator.stats.naccept`
        #    (total #steps)       (#accepted steps)
        # We need to check the number of accepted steps since callbacks are not
        # activated after a rejected step.
        condition = (u, t, integrator) -> ((integrator.stats.naccept % interval == 0 &&
                                            !(integrator.stats.naccept == 0 &&
                                              integrator.iter > 0)) ||
                                           isfinished(integrator))
    else # disable the callback for interval == 0
        condition = (u, t, integrator) -> false
    end

    # Create data structures that are to be filled by the callback
    variable_names = varnames(solution_variables, equations)
    n_points = size(point_coordinates_, 2)
    point_data = Vector{uEltype}[Vector{uEltype}() for _ in 1:n_points]
    time = Vector{RealT}()
    step = Vector{Int}()
    time_series_cache = create_cache_time_series(point_coordinates_, mesh, solver,
                                                 cache)

    time_series_callback = TimeSeriesCallback(interval,
                                              solution_variables,
                                              variable_names,
                                              output_directory,
                                              filename,
                                              point_coordinates_,
                                              point_data,
                                              time,
                                              step,
                                              time_series_cache)

    return DiscreteCallback(condition, time_series_callback,
                            save_positions = (false, false))
end

# Convenience constructor that unpacks the semidiscretization into mesh, equations, solver, cache
function TimeSeriesCallback(semi, point_coordinates; kwargs...)
    mesh, equations, solver, cache = mesh_equations_solver_cache(semi)

    return TimeSeriesCallback(mesh, equations, solver, cache, point_coordinates;
                              kwargs...)
end

# Convenience constructor that converts a vector of points into a Trixi.jl-style coordinate array
function TimeSeriesCallback(mesh, equations, solver, cache,
                            point_coordinates::AbstractVector;
                            kwargs...)
    # Coordinates are usually stored in [ndims, n_points], but here as [n_points, ndims]
    n_points = length(point_coordinates)
    point_coordinates_ = Matrix{eltype(eltype(point_coordinates))}(undef, n_points,
                                                                   ndims(mesh))

    for p in 1:n_points
        for d in 1:ndims(mesh)
            point_coordinates_[p, d] = point_coordinates[p][d]
        end
    end

    return TimeSeriesCallback(mesh, equations, solver, cache, point_coordinates_;
                              kwargs...)
end

# This method is called as callback during the time integration.
function (time_series_callback::TimeSeriesCallback)(integrator)
    # Ensure this is not accidentally used with AMR enabled
    if uses_amr(integrator.opts.callback)
        error("the TimeSeriesCallback does not work with AMR enabled")
    end

    @unpack interval = time_series_callback

    # Create record if in correct interval (needs to be checked since the callback is also called
    # after the final step for storing the data on disk, independent of the current interval)
    if integrator.stats.naccept % interval == 0
        @trixi_timeit timer() "time series" begin
            # Store time and step
            push!(time_series_callback.time, integrator.t)
            push!(time_series_callback.step, integrator.stats.naccept)

            # Unpack data
            u_ode = integrator.u
            semi = integrator.p
            mesh, equations, solver, cache = mesh_equations_solver_cache(semi)
            u = wrap_array(u_ode, mesh, equations, solver, cache)

            @unpack (point_data, solution_variables,
            variable_names, time_series_cache) = time_series_callback

            # Record state at points (solver/mesh-dependent implementation)
            record_state_at_points!(point_data, u, solution_variables,
                                    length(variable_names), mesh,
                                    equations, solver, time_series_cache)
        end
    end

    # Store time_series if this is the last time step
    if isfinished(integrator)
        semi = integrator.p
        mesh, equations, solver, _ = mesh_equations_solver_cache(semi)
        save_time_series_file(time_series_callback, mesh, equations, solver)
    end

    # avoid re-evaluating possible FSAL stages
    u_modified!(integrator, false)

    return nothing
end

include("time_series_dg.jl")
include("time_series_dg_tree.jl")
include("time_series_dg_unstructured.jl")
end # @muladd