Add a better footer and update benchmarks/runall.jl

benchmarks/lotkavolterra.jmd

@@ -4,7 +4,7 @@ Adapted from
 [SciMLBenchmarks.jl](https://docs.sciml.ai/SciMLBenchmarksOutput/stable/NonStiffODE/LotkaVolterra_wpd/).
 
 ```julia
-using LinearAlgebra, Statistics, InteractiveUtils
+using LinearAlgebra, Statistics
 using DiffEqDevTools, ParameterizedFunctions, SciMLBase, OrdinaryDiffEq, Plots
 using ProbNumDiffEq
 
@@ -215,6 +215,7 @@ wp = WorkPrecisionSet(
 plot(wp, color=[2 2 2 4 4 4 5 5 5], xticks = 10.0 .^ (-16:1:5))
 ```
 
+
 ## Conclusion
 
 - **Use the EK1!** It seems to be strictly better than the EK0 here.
@@ -224,11 +225,21 @@ plot(wp, color=[2 2 2 4 4 4 5 5 5], xticks = 10.0 .^ (-16:1:5))
 - Most likely, the default choice of `diffusionmodel=DynamicDiffusion` and `initialization=TaylorModeInit` are fine.
 
 
-
 ## Appendix
 
 Computer information:
-
 ```julia
+using InteractiveUtils
 InteractiveUtils.versioninfo()
 ```
+
+Package Information:
+```julia
+using Pkg
+Pkg.status()
+```
+
+And the full manifest:
+```julia
+Pkg.status(mode=Pkg.PKGMODE_MANIFEST)
+```

benchmarks/multi-language-wrappers.jmd

@@ -5,7 +5,7 @@ Adapted from
 
 ```julia
 # Imports
-using LinearAlgebra, Statistics, InteractiveUtils
+using LinearAlgebra, Statistics
 using StaticArrays, DiffEqDevTools, ParameterizedFunctions, Plots, SciMLBase, OrdinaryDiffEq
 using ODEInterface, ODEInterfaceDiffEq, Sundials, SciPyDiffEq, deSolveDiffEq, MATLABDiffEq, LSODA
 using LoggingExtras
@@ -330,7 +330,18 @@ plot(
 ## Appendix
 
 Computer information:
-
 ```julia
+using InteractiveUtils
 InteractiveUtils.versioninfo()
 ```
+
+Package Information:
+```julia
+using Pkg
+Pkg.status()
+```
+
+And the full manifest:
+```julia
+Pkg.status(mode=Pkg.PKGMODE_MANIFEST)
+```

benchmarks/pleiades.jmd

@@ -1,7 +1,7 @@
 # Pleiades benchmark
 
 ```julia
-using LinearAlgebra, Statistics, InteractiveUtils
+using LinearAlgebra, Statistics
 using DiffEqDevTools, ParameterizedFunctions, SciMLBase, OrdinaryDiffEq, Sundials, Plots
 using ModelingToolkit
 using ProbNumDiffEq
@@ -119,13 +119,23 @@ plot(wp, color=[1 1 2 2 3 3 4 4],
 ## Conclusion
 
 - If the problem is a second-order ODE, _implement it as a second-order ODE_!
-  Just use `SecondOrderODEProblem`.
 
 
 ## Appendix
 
 Computer information:
-
 ```julia
+using InteractiveUtils
 InteractiveUtils.versioninfo()
 ```
+
+Package Information:
+```julia
+using Pkg
+Pkg.status()
+```
+
+And the full manifest:
+```julia
+Pkg.status(mode=Pkg.PKGMODE_MANIFEST)
+```

benchmarks/rober.jmd

@@ -4,7 +4,7 @@ Adapted from
 [SciMLBenchmarks.jl](https://docs.sciml.ai/SciMLBenchmarksOutput/stable/DAE/ROBERDAE/).
 
 ```julia
-using LinearAlgebra, Statistics, InteractiveUtils
+using LinearAlgebra, Statistics
 using DiffEqDevTools, ParameterizedFunctions, SciMLBase, OrdinaryDiffEq, Sundials, Plots
 using ModelingToolkit
 using ProbNumDiffEq
@@ -35,7 +35,7 @@ daeprob = DAEProblem(sys,[D(y₁)=>-0.04, D(y₂)=>0.04, D(y₃)=>0.0],[],(0.0,1
 odaeprob = ODAEProblem(structural_simplify(sys),[],(0.0,1e5)) # can't handle this yet
 
 ref_sol = solve(daeprob,IDA(),abstol=1/10^14,reltol=1/10^14,dense=false)
-plot(ref_sol, vars=[y₁,y₂,y₃], title="ROBER Solution", legend=false, ylims=(0, 1))
+plot(ref_sol, idxs=[y₁,y₂,y₃], title="ROBER Solution", legend=false, ylims=(0, 1))
 ```
 
 ## EK1 accross orders
@@ -82,10 +82,22 @@ plot(wp, palette=Plots.palette([:blue, :red], length(_setups)), xticks = 10.0 .^
 - The `EK1` can solve mass-matrix DAEs! But it only really works well for low errors.
 - Order 3 seems to work well here. But the order-to-error-tolerance heuristic should in principle still hold: lower tolerance level ``\rightarrow`` higher order.
 
+
 ## Appendix
 
 Computer information:
-
 ```julia
+using InteractiveUtils
 InteractiveUtils.versioninfo()
 ```
+
+Package Information:
+```julia
+using Pkg
+Pkg.status()
+```
+
+And the full manifest:
+```julia
+Pkg.status(mode=Pkg.PKGMODE_MANIFEST)
+```

benchmarks/runall.jl

@@ -8,12 +8,13 @@ FILES = [
     "multi-language-wrappers.jmd",
 ]
 
+filedir = @__DIR__
 for file in FILES
     @info "Weave file" file
     weave(
         file;
         doctype="github",
-        out_path="../docs/src/benchmarks/",
+        out_path=joinpath(filedir, "../docs/src/benchmarks/"),
         fig_ext=".svg",
     )
 end

benchmarks/vanderpol.jmd

@@ -2,7 +2,7 @@
 
 
 ```julia
-using LinearAlgebra, Statistics, InteractiveUtils
+using LinearAlgebra, Statistics
 using DiffEqDevTools, ParameterizedFunctions, SciMLBase, OrdinaryDiffEq, Plots
 using ProbNumDiffEq
 
@@ -117,15 +117,28 @@ wp = WorkPrecisionSet(
 plot(wp, color=[1 1 1 1 2 2 2 2], xticks = 10.0 .^ (-16:1:5))
 ```
 
+
 ## Conclusion
 
 - Use the `EK1` to solve stiff problems, with orders $\leq 6$ depending on the error tolerance.
 - When the problem is actually a second-order ODE, as is the case for the Van der Pol system here, _solve it as a second-order ODE_.
 
+
 ## Appendix
 
 Computer information:
-
 ```julia
+using InteractiveUtils
 InteractiveUtils.versioninfo()
 ```
+
+Package Information:
+```julia
+using Pkg
+Pkg.status()
+```
+
+And the full manifest:
+```julia
+Pkg.status(mode=Pkg.PKGMODE_MANIFEST)
+```