From 277c3a0c98850ca61f7818ae6fa30e3e5ed8e91b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lars=20G=C3=B6ttgens?= Date: Tue, 14 Jan 2025 09:48:30 +0100 Subject: [PATCH] Fix some typos around backticks in docstrings --- experimental/AlgebraicStatistics/src/CI.jl | 8 +++---- .../src/GaussianGraphicalModels.jl | 24 +++++++++---------- .../src/GraphicalModels.jl | 2 +- .../AlgebraicStatistics/src/Markov.jl | 16 ++++++------- .../FTheoryTools/src/TateModels/attributes.jl | 2 +- experimental/ModStd/src/ModStdQt.jl | 2 +- .../Schemes/src/DerivedPushforward.jl | 2 +- .../EllipticSurface/EllipticSurface.jl | 2 +- 8 files changed, 29 insertions(+), 29 deletions(-) diff --git a/experimental/AlgebraicStatistics/src/CI.jl b/experimental/AlgebraicStatistics/src/CI.jl index 093434e7cbb3..1539c1a7f8fc 100644 --- a/experimental/AlgebraicStatistics/src/CI.jl +++ b/experimental/AlgebraicStatistics/src/CI.jl @@ -30,7 +30,7 @@ to ensure consistent comparison and hashing. ## Examples -``` jldoctest +```jldoctest julia> ci_stmt(["A"], ["B"], ["X"]) [A _||_ B | X] @@ -78,7 +78,7 @@ are extracted, `ci_stmt` is called. ## Examples -``` jldoctest +```jldoctest julia> CI"AB|X" [A _||_ B | X] @@ -124,7 +124,7 @@ distribution. ## Examples -``` jldoctest +```jldoctest julia> ci_statements(["A", "B", "X", "Y"]) 24-element Vector{CIStmt}: [A _||_ Y | {}] @@ -183,7 +183,7 @@ above `K` but is always fixed to `K`. Semigaussoids are also known as ## Examples -``` jldoctest +```jldoctest julia> make_elementary(CI"12,34|56") 16-element Vector{CIStmt}: [1 _||_ 3 | {5, 6}] diff --git a/experimental/AlgebraicStatistics/src/GaussianGraphicalModels.jl b/experimental/AlgebraicStatistics/src/GaussianGraphicalModels.jl index 404284a1805a..0e2218c7becb 100644 --- a/experimental/AlgebraicStatistics/src/GaussianGraphicalModels.jl +++ b/experimental/AlgebraicStatistics/src/GaussianGraphicalModels.jl @@ -26,7 +26,7 @@ If `cached` is `true`, the internally generated polynomial ring will be cached. ## Examples -``` jldoctest +```jldoctest julia> R = gaussian_ring(3) Gaussian ring over Rational field in 6 variables s[1, 1], s[1, 2], s[1, 3], s[2, 2], s[2, 3], s[3, 3] @@ -56,7 +56,7 @@ Return the multivariate polynomial ring inside `R`. ## Examples -``` jldoctest +```jldoctest julia> R = gaussian_ring(3) Gaussian ring over Rational field in 6 variables s[1, 1], s[1, 2], s[1, 3], s[2, 2], s[2, 3], s[3, 3] @@ -77,7 +77,7 @@ Return the generators of the multivariate polynomial ring inside the GaussianRin ## Examples -``` jldoctest +```jldoctest julia> R = gaussian_ring(3) Gaussian ring over Rational field in 6 variables s[1, 1], s[1, 2], s[1, 3], s[2, 2], s[2, 3], s[3, 3] @@ -103,7 +103,7 @@ Return the covariance matrix associated to `R` as a matrix over the underlying p ## Examples -``` jldoctest +```jldoctest julia> R = gaussian_ring(3) Gaussian ring over Rational field in 6 variables s[1, 1], s[1, 2], s[1, 3], s[2, 2], s[2, 3], s[3, 3] @@ -135,7 +135,7 @@ If `cached` is `true`, the internally generated polynomial ring will be cached. ## Examples -``` jldoctest directed_ggm +```jldoctest julia> M = graphical_model(graph_from_edges(Directed, [[1,2], [2,3]]), gaussian_ring(3)) Gaussian graphical model on a directed graph with edges: (1, 2), (2, 3) @@ -169,7 +169,7 @@ Creates the weighted adjacency matrix $\Lambda$ of a directed graph `G` whose en ## Examples -``` jldoctest +```jldoctest julia> M = graphical_model(graph_from_edges(Directed, [[1,2], [2,3]]), gaussian_ring(3)) Gaussian graphical model on a directed graph with edges: (1, 2), (2, 3) @@ -193,7 +193,7 @@ Creates the covariance matrix $ \Omega $ of the independent error terms in a dir ## Examples -``` jldoctest +```jldoctest julia> M = graphical_model(graph_from_edges(Directed, [[1,2], [2,3]]), gaussian_ring(3)) Gaussian graphical model on a directed graph with edges: (1, 2), (2, 3) @@ -219,7 +219,7 @@ $(Id - \Lambda)^{-T} \Omega (Id - \Lambda)^{T} \mapsto \Sigma$ where $\Lambda =$ ## Examples -``` jldoctest +```jldoctest julia> M = graphical_model(graph_from_edges(Directed, [[1,2], [2,3]]), gaussian_ring(3)) Gaussian graphical model on a directed graph with edges: (1, 2), (2, 3) @@ -265,7 +265,7 @@ If `cached` is `true`, the internally generated polynomial ring will be cached. ## Examples -``` jldoctest undirected_ggm +```jldoctest julia> M = graphical_model(graph_from_edges([[1,2], [2,3]]), gaussian_ring(3)) Gaussian graphical model on an undirected graph with edges: (1, 2), (2, 3) @@ -300,7 +300,7 @@ whose nonzero entries correspond to the edges of the associated graph. ## Examples -``` jldoctest +```jldoctest julia> M = graphical_model(graph_from_edges([[1,2], [2,3]]), gaussian_ring(3)) Gaussian graphical model on an undirected graph with edges: (1, 2), (2, 3) @@ -337,7 +337,7 @@ $ K \mapsto K^{-1}$ where $ K = $ `concentration_matrix(M)` and the entries of ## Examples -``` jldoctest +```jldoctest julia> M = graphical_model(graph_from_edges([[1,2], [2,3]]), gaussian_ring(3)) Gaussian graphical model on an undirected graph with edges: (1, 2), (2, 3) @@ -373,7 +373,7 @@ and then eliminating all variables `k[i,j]` where $ K =$ `concentration_matrix(M ## Examples -``` jldoctest undirected_ggm +```jldoctest julia> M = graphical_model(graph_from_edges([[1,2], [2,3]]), gaussian_ring(3)) Gaussian graphical model on an undirected graph with edges: (1, 2), (2, 3) diff --git a/experimental/AlgebraicStatistics/src/GraphicalModels.jl b/experimental/AlgebraicStatistics/src/GraphicalModels.jl index ed779a57bbc1..f09b92efbc66 100644 --- a/experimental/AlgebraicStatistics/src/GraphicalModels.jl +++ b/experimental/AlgebraicStatistics/src/GraphicalModels.jl @@ -82,7 +82,7 @@ This is done by computing the kernel of the parametrization. ## Examples -``` jldoctest +```jldoctest julia> M = graphical_model(graph_from_edges(Directed, [[1,2], [2,3]]), gaussian_ring(3)) Gaussian graphical model on a directed graph with edges: (1, 2), (2, 3) diff --git a/experimental/AlgebraicStatistics/src/Markov.jl b/experimental/AlgebraicStatistics/src/Markov.jl index 3fb204f42e1e..3b11a944aaa6 100644 --- a/experimental/AlgebraicStatistics/src/Markov.jl +++ b/experimental/AlgebraicStatistics/src/Markov.jl @@ -30,7 +30,7 @@ with the Macaulay2 package `GraphicalModels`. ## Examples -``` jldoctest +```jldoctest julia> R = markov_ring("A" => 1:2, "B" => 1:2, "X" => 1:2, "Y" => 1:2) MarkovRing for random variables A -> {1, 2}, B -> {1, 2}, X -> {1, 2}, Y -> {1, 2} in 16 variables over Rational field ``` @@ -67,7 +67,7 @@ Return the multivariate polynomial ring inside `R`. ## Examples -``` jldoctest +```jldoctest julia> R = markov_ring("A" => 1:2, "B" => 1:2, "X" => 1:2, "Y" => 1:2) MarkovRing for random variables A -> {1, 2}, B -> {1, 2}, X -> {1, 2}, Y -> {1, 2} in 16 variables over Rational field @@ -87,7 +87,7 @@ Return the list of random variables used to create the MarkovRing. ## Examples -``` jldoctest +```jldoctest julia> R = markov_ring("A" => 1:2, "B" => 1:2, "X" => 1:2, "Y" => 1:2) MarkovRing for random variables A -> {1, 2}, B -> {1, 2}, X -> {1, 2}, Y -> {1, 2} in 16 variables over Rational field @@ -108,7 +108,7 @@ end Returns all the `CIStmt` objects which can be formed on the `random_variables(R)`. -``` jldoctest +```jldoctest julia> R = markov_ring("A" => 1:2, "B" => 1:2, "X" => 1:2, "Y" => 1:2) MarkovRing for random variables A -> {1, 2}, B -> {1, 2}, X -> {1, 2}, Y -> {1, 2} in 16 variables over Rational field @@ -145,7 +145,7 @@ Return the generators of the polynomial ring. ## Examples -``` jldoctest +```jldoctest julia> R = markov_ring("A" => 1:2, "B" => 1:2, "X" => 1:2, "Y" => 1:2) MarkovRing for random variables A -> {1, 2}, B -> {1, 2}, X -> {1, 2}, Y -> {1, 2} in 16 variables over Rational field @@ -221,7 +221,7 @@ in the ring `R`. The result is an `Iterators.product` iterator unless ## Examples -``` jldoctest +```jldoctest julia> R = markov_ring("A" => 1:2, "B" => 1:2, "X" => 1:2, "Y" => 1:2) MarkovRing for random variables A -> {1, 2}, B -> {1, 2}, X -> {1, 2}, Y -> {1, 2} in 16 variables over Rational field @@ -263,7 +263,7 @@ variables in `R` are summed over their respective state spaces. ## Examples -``` jldoctest +```jldoctest julia> R = markov_ring("A" => 1:2, "B" => 1:2, "X" => 1:2, "Y" => 1:2) MarkovRing for random variables A -> {1, 2}, B -> {1, 2}, X -> {1, 2}, Y -> {1, 2} in 16 variables over Rational field @@ -294,7 +294,7 @@ given by `stmts`. ## Examples -``` jldoctest +```jldoctest julia> R = markov_ring("A" => 1:2, "B" => 1:2, "X" => 1:2) MarkovRing for random variables A -> {1, 2}, B -> {1, 2}, X -> {1, 2} in 8 variables over Rational field diff --git a/experimental/FTheoryTools/src/TateModels/attributes.jl b/experimental/FTheoryTools/src/TateModels/attributes.jl index c641e12bbe36..dd981dbf97fe 100644 --- a/experimental/FTheoryTools/src/TateModels/attributes.jl +++ b/experimental/FTheoryTools/src/TateModels/attributes.jl @@ -366,7 +366,7 @@ end singular_loci(t::GlobalTateModel) Return the singular loci of the global Tate model, along with the order of -vanishing of ``(f, g, \Delta)``` at each locus and the refined Tate fiber type. +vanishing of ``(f, g, \Delta)`` at each locus and the refined Tate fiber type. For the time being, we either explicitly or implicitly focus on toric varieties as base spaces. Explicitly, in case the user provides such a variety as base space, diff --git a/experimental/ModStd/src/ModStdQt.jl b/experimental/ModStd/src/ModStdQt.jl index 9fb3642f9434..67c9b1b0d864 100644 --- a/experimental/ModStd/src/ModStdQt.jl +++ b/experimental/ModStd/src/ModStdQt.jl @@ -596,7 +596,7 @@ Multivariate polynomial ring in 2 variables X[1], X[2] julia> parent(f[2][1]) Multivariate polynomial ring in 2 variables X[1], X[2] over residue field of univariate polynomial ring modulo t^2 + a[1] -``` +``` """ function Oscar.factor_absolute(f::MPolyRingElem{Generic.FracFieldElem{QQMPolyRingElem}}) Qtx = parent(f) # Q[t1,t2][x1,x2] diff --git a/experimental/Schemes/src/DerivedPushforward.jl b/experimental/Schemes/src/DerivedPushforward.jl index 5547ac3c5449..a83b06b0fe26 100644 --- a/experimental/Schemes/src/DerivedPushforward.jl +++ b/experimental/Schemes/src/DerivedPushforward.jl @@ -44,7 +44,7 @@ end We consider a graded module `M` over a standard graded polynomial ring ``S = A[x₀,…,xₙ]`` as a representative of a coherent sheaf ``ℱ`` on -relative projective space ``ℙ ⁿ_A``. Then we compute ``Rπ_* ℱ``` as a +relative projective space ``ℙ ⁿ_A``. Then we compute ``Rπ_* ℱ`` as a complex of ``A``-modules where ``π : ℙ ⁿ_A → Spec(A)`` is the projection to the base. """ diff --git a/src/AlgebraicGeometry/Surfaces/EllipticSurface/EllipticSurface.jl b/src/AlgebraicGeometry/Surfaces/EllipticSurface/EllipticSurface.jl index 1dd46a72d4de..3ccbb2ee1f8a 100644 --- a/src/AlgebraicGeometry/Surfaces/EllipticSurface/EllipticSurface.jl +++ b/src/AlgebraicGeometry/Surfaces/EllipticSurface/EllipticSurface.jl @@ -250,7 +250,7 @@ Return sections ``P_1,\dots P_n`` of the generic fiber, such that together with the generators of the algebraic lattice ``A``, they generate ```math \frac{1}{p} A \cap N -``` +``` where ``N`` is the numerical lattice of ``X``. The algorithm proceeds by computing division points in the Mordell-Weil subgroup of `X`