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4 changes: 2 additions & 2 deletions .github/workflows/Documenter.yml
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ name: Documentation
on:
push:
branches:
- main
- 'main'
tags: '*'
pull_request:

Expand All @@ -21,4 +21,4 @@ jobs:
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # For authentication with SSH deploy key
run: julia --project=docs/ docs/make.jl
run: julia --project=docs --color=yes docs/make.jl
3 changes: 2 additions & 1 deletion docs/make.jl
Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,8 @@ makedocs(
"Authors" => "authors.md",
"Contributing" => "contributing.md",
"License" => "license.md"
]
],
strict = true # to make the GitHub action fail when doctests fail, see https://github.com/neuropsychology/Psycho.jl/issues/34
)

deploydocs(
Expand Down
34 changes: 18 additions & 16 deletions docs/src/differentiable_programming.md
Original file line number Diff line number Diff line change
Expand Up @@ -78,11 +78,11 @@ julia> λ = eigvals(J);

julia> scatter(real.(λ), imag.(λ));

julia> round(maximum(real, λ) / maximum(abs, λ), sigdigits=2)
6.7e-10
julia> 3.0e-10 < maximum(real, λ) / maximum(abs, λ) < 8.0e-10
true

julia> round(maximum(real, λ), sigdigits=2)
2.1e-7
julia> 1.0e-7 < maximum(real, λ) < 5.0e-7
true
```

Interestingly, if we add dissipation by switching to the `flux_lax_friedrichs` at the interfaces,
Expand Down Expand Up @@ -133,16 +133,16 @@ julia> λ = eigvals(J);

julia> scatter(real.(λ), imag.(λ));

julia> round(maximum(real, λ) / maximum(abs, λ), sigdigits=2)
3.2e-16
julia> 1.0e-16 < maximum(real, λ) / maximum(abs, λ) < 6.0e-16
true

julia> round(maximum(real, λ), sigdigits=2)
3.2e-12
julia> 1.0e-12 < round(maximum(real, λ) < 6.0e-12
true

julia> λ, V = eigen(J);

julia> round(cond(V), sigdigits=2)
250.0
julia> 200 < cond(V) < 300
true
```

If we add dissipation, the maximal real part is still approximately zero.
Expand All @@ -158,16 +158,18 @@ julia> λ = eigvals(J);

julia> scatter!(real.(λ), imag.(λ));

julia> λ = eigvals(J); round(maximum(real, λ) / maximum(abs, λ), sigdigits=2)
5.3e-18
julia> λ = eigvals(J);

julia> round(maximum(real, λ), sigdigits=2)
7.7e-14
julia> 1.0e-18 < maximum(real, λ) / maximum(abs, λ) < 1.0e-16
true

julia> 5.0e-14 < maximum(real, λ) < 5.0e-13
true

julia> λ, V = eigen(J);

julia> round(cond(V), sigdigits=2)
93000.0
julia> 90_000 < cond(V) < 100_000
true
```
Note that the condition number of the eigenvector matrix increases but is still smaller than for the
example in 2D.
Expand Down