Fix doc example, and remove LaTeXStrings (#94)

* Fix doc example, and remove LaTeXStrings * vers
DanielVandH · Dec 5, 2023 · 2268df9 · 2268df9
1 parent 42d6403
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diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "ProfileLikelihood"
 uuid = "3fca794e-44fa-49a6-b6a0-8cd45572ba0a"
 authors = ["Daniel VandenHeuvel <danj.vandenheuvel@gmail.com>"]
-version = "0.3.3"
+version = "0.3.4"
 
 [deps]
 ChunkSplitters = "ae650224-84b6-46f8-82ea-d812ca08434e"

diff --git a/docs/src/exponential.md b/docs/src/exponential.md
@@ -7,7 +7,6 @@ using OrdinaryDiffEq
 using ProfileLikelihood
 using Optimization 
 using CairoMakie 
-using LaTeXStrings 
 using Random
 using Distributions
 using MuladdMacro

diff --git a/docs/src/heat.md b/docs/src/heat.md
@@ -12,7 +12,6 @@ using Random
 using LinearSolve 
 using OrdinaryDiffEq
 using CairoMakie 
-using LaTeXStrings
 using StaticArraysCore
 using Optimization 
 using OptimizationNLopt
@@ -240,7 +239,7 @@ Confidence intervals:
 See that all the true parameter intervals are inside these confidence intervals except for $k$, although $c$'s upper bound is right at the bounds we gave it in the problem. Let's now view the profile curves.
 
 ```julia 
-using CairoMakie, LaTeXStrings
+using CairoMakie
 fig = plot_profiles(prof; nrow=1, ncol=3,
     latex_names=[L"k", L"c", L"u_0"],
     true_vals=[k[1], c, u₀],
@@ -615,7 +614,7 @@ prof = profile(likprob, mle_sol; alg=NLopt.LN_BOBYQA,
     resolution=60)
 
 ## Step 8: Visualise 
-using CairoMakie, LaTeXStrings
+using CairoMakie
 fig = plot_profiles(prof; nrow=1, ncol=3,
     latex_names=[L"k", L"c", L"u_0"],
     true_vals=[k[1], c, u₀],

diff --git a/docs/src/logistic.md b/docs/src/logistic.md
@@ -8,7 +8,6 @@ using ProfileLikelihood
 using Optimization 
 using OrdinaryDiffEq
 using CairoMakie 
-using LaTeXStrings
 using OptimizationNLopt
 using Test
 using StableRNGs
@@ -110,7 +109,7 @@ Confidence intervals:
 We can visualise as we did before:
 
 ```julia
-using CairoMakie, LaTeXStrings
+using CairoMakie
 fig = plot_profiles(prof;
     latex_names=[L"\lambda", L"K", L"u_0"],
     show_mles=true,

diff --git a/docs/src/regression.md b/docs/src/regression.md
@@ -8,7 +8,6 @@ using Random
 using PreallocationTools 
 using Distributions 
 using CairoMakie 
-using LaTeXStrings 
 using LinearAlgebra
 using Optimization 
 using OptimizationOptimJL
@@ -178,7 +177,7 @@ prof(0.50, 4)
 We can now also visualise the results. In the plot below, the red line is at the threshold for the confidence region, so that the parameters between these values define the confidence interval. The red lines are at the MLEs, and the black lines are at the true values. 
 
 ```julia 
-using CairoMakie, LaTeXStrings
+using CairoMakie
 fig = plot_profiles(prof;
     latex_names=[L"\sigma", L"\beta_0", L"\beta_1", L"\beta_2", L"\beta_3"], # default names would be of the form θᵢ
     show_mles=true,
@@ -189,7 +188,7 @@ fig = plot_profiles(prof;
 xlims!(fig.content[1], 0.045, 0.055) # fix the ranges
 xlims!(fig.content[2], -1.025, -0.975)
 xlims!(fig.content[4], 0.475, 0.525)
-resize_to_layout!9fig)
+resize_to_layout!(fig)
 ```
 
 ```@raw html

diff --git a/src/bivariate.jl b/src/bivariate.jl
@@ -17,8 +17,7 @@ Computes bivariates profile likelihoods for the parameters from a likelihood pro
 this function using `Symbols`, e.g. if `get_syms(prob) = [:λ, :K, :u₀]`, then calling `bivariate_profile(prob, sol, ((:λ, :K), (:K, u₀)))`
 is the same as calling `bivariate_profile(prob, sol, ((1, 2), (2, 3)))` (the integer coordinate representation is still used in the solution, though).
        
-For plotting, see the `plot_profiles` function (requires that you have loaded CairoMakie.jl and 
-LaTeXStrings.jl to access the function).
+For plotting, see the `plot_profiles` function (requires that you have loaded a backend of Makie.jl).
 
 # Arguments 
 - `prob::LikelihoodProblem`: The [`LikelihoodProblem`](@ref).

diff --git a/src/profile_likelihood.jl b/src/profile_likelihood.jl
@@ -19,8 +19,7 @@ const dict_lock = ReentrantLock()
 Computes profile likelihoods for the parameters from a likelihood problem `prob` with MLEs `sol`.
 
 See also [`replace_profile!`](@ref) which allows you to re-profile a parameter in case you are not satisfied with 
-the results. For plotting, see the `plot_profiles` function (requires that you have loaded CairoMakie.jl and 
-LaTeXStrings.jl to access the function).
+the results. For plotting, see the `plot_profiles` function (requires that you have loaded a backend of Makie.jl).
 
 # Arguments 
 - `prob::LikelihoodProblem`: The [`LikelihoodProblem`](@ref).

diff --git a/test/Manifest.toml b/test/Manifest.toml
@@ -2,7 +2,7 @@
 
 julia_version = "1.10.0-rc2"
 manifest_format = "2.0"
-project_hash = "40117e3c9c4592f6a041cc79b3e1d123b5f6edfb"
+project_hash = "fb8efbffffc3f376dc902b6951a77b5075734dd5"
 
 [[deps.ADTypes]]
 git-tree-sha1 = "332e5d7baeff8497b923b730b994fa480601efc7"

diff --git a/test/Project.toml b/test/Project.toml
@@ -13,7 +13,6 @@ FunctionWrappers = "069b7b12-0de2-55c6-9aab-29f3d0a68a2e"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
 InvertedIndices = "41ab1584-1d38-5bbf-9106-f11c6c58b48f"
-LaTeXStrings = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
 LatinHypercubeSampling = "a5e1c1ea-c99a-51d3-a14d-a9a37257b02d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"

diff --git a/test/bivariate_profile.jl b/test/bivariate_profile.jl
@@ -3,7 +3,6 @@ using ..ProfileLikelihood
 using Optimization
 using OrdinaryDiffEq
 using CairoMakie
-using LaTeXStrings
 using OptimizationNLopt
 using Distributions
 using PreallocationTools

diff --git a/test/linear_exponential_example.jl b/test/linear_exponential_example.jl
@@ -2,7 +2,6 @@ using OrdinaryDiffEq
 using ..ProfileLikelihood
 using Optimization 
 using CairoMakie 
-using LaTeXStrings 
 using Random
 using Distributions
 using MuladdMacro
@@ -154,7 +153,7 @@ prof2 = profile(prob, sol; alg=NLopt.LN_NELDERMEAD, parallel=false)
 prof = prof1
 
 ## Step 5: Visualise 
-using CairoMakie, LaTeXStrings
+using CairoMakie
 fig = plot_profiles(prof; nrow=1, ncol=3,
     latex_names=[L"\lambda", L"\sigma", L"y_0"],
     true_vals=[λ, σ, y₀],

diff --git a/test/logistic.jl b/test/logistic.jl
@@ -3,7 +3,6 @@ using ..ProfileLikelihood
 using Optimization
 using OrdinaryDiffEq
 using CairoMakie
-using LaTeXStrings
 using OptimizationOptimJL
 using OptimizationNLopt
 using StableRNGs
@@ -106,7 +105,7 @@ prof2 = profile(prob, sol; alg=NLopt.LN_NELDERMEAD, parallel=true)
 
 prof = prof1
 
-using CairoMakie, LaTeXStrings
+using CairoMakie
 fig = plot_profiles(prof;
     latex_names=[L"\lambda", L"K", L"u_0"],
     show_mles=true,

diff --git a/test/lotka_volterra_example.jl b/test/lotka_volterra_example.jl
@@ -3,7 +3,6 @@ using ..ProfileLikelihood
 using Optimization
 using OrdinaryDiffEq
 using CairoMakie
-using LaTeXStrings
 using OptimizationNLopt
 using LoopVectorization
 using PolygonOps

diff --git a/test/profile_likelihood.jl b/test/profile_likelihood.jl
@@ -13,7 +13,6 @@ using InteractiveUtils
 using DelaunayTriangulation
 using Interpolations
 using CairoMakie
-using LaTeXStrings
 include("templates.jl")
 
 @testset "Test that we can correctly construct the parameter ranges" begin

diff --git a/test/regression_example.jl b/test/regression_example.jl
@@ -3,7 +3,6 @@ using Random
 using PreallocationTools
 using Distributions
 using CairoMakie
-using LaTeXStrings
 using LinearAlgebra
 using Optimization
 using OptimizationOptimJL
@@ -131,7 +130,7 @@ prof(0.50, 4)
 @test prof[:β₂](0.50) == prof(0.50, :β₂) == prof(0.50, 4)
 
 ## Step 5: Visualise 
-using CairoMakie, LaTeXStrings
+using CairoMakie
 fig = plot_profiles(prof;
     latex_names=[L"\sigma", L"\beta_0", L"\beta_1", L"\beta_2", L"\beta_3"], # default names would be of the form θᵢ
     show_mles=true,