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Replace deprecated destats by stats #2994

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ranocha merged 1 commit into from
May 5, 2026
Merged

Replace deprecated destats by stats #2994

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10 changes: 5 additions & 5 deletions docs/literate/src/files/adding_new_parabolic_terms.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -170,7 +170,7 @@ callbacks = CallbackSet(SummaryCallback())
time_int_tol = 1.0e-6
sol = solve(ode, RDPK3SpFSAL49(); abstol = time_int_tol, reltol = time_int_tol,
ode_default_options()..., callback = callbacks);
println("Number of timesteps: ", sol.destats.naccept)
println("Number of timesteps: ", sol.stats.naccept)

using Plots
plot(sol)
Expand All @@ -179,9 +179,9 @@ plot(sol)

# In the example above, we used an adaptive timestep based on truncation error estimates.
# Alternatively, we can also use a CFL-based timestep control, cf. [`StepsizeCallback`](@ref).
# To be able to do so, we need to define [`max_diffusivity`](@ref) and
# To be able to do so, we need to define [`max_diffusivity`](@ref) and
# [`have_constant_diffusivity`](@ref) for the new parabolic terms.
# In Trixi.jl, currently only the standard Laplace Diffusion and Compressible Navier-Stokes-Fourier
# In Trixi.jl, currently only the standard Laplace Diffusion and Compressible Navier-Stokes-Fourier
# parabolic terms are implemented.
# Since these equations have **isotropic** diffusivity, i.e., direction-independent coefficients,
# [`max_diffusivity`](@ref) is expected to return a scalar value.
Expand All @@ -198,7 +198,7 @@ lambda_max() = maximum(abs.(eigvals(diffusivity)))
return Trixi.True()
end

# Return the estimated maximum diffusivity for CFL calculations based on
# Return the estimated maximum diffusivity for CFL calculations based on
# the spectral radius of the diffusivity matrix computed above
@inline function Trixi.max_diffusivity(equations_parabolic::ConstantAnisotropicDiffusion2D)
return lambda_max()
Expand All @@ -216,7 +216,7 @@ callbacks = CallbackSet(SummaryCallback(), stepsize_callback);
sol = solve(ode, RDPK3SpFSAL49();
adaptive = false, dt = stepsize_callback(ode),
ode_default_options()..., callback = callbacks);
println("Number of timesteps: ", sol.destats.naccept)
println("Number of timesteps: ", sol.stats.naccept)

plot(sol)

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