Update spacing, line-wrapping, and syntax highlighting

Author: abhro

docs/src/tutorials/curve-fit.md

@@ -68,15 +68,12 @@ Before using any packages, let's perform a linear fit from scratch using some li
 
 The equation of a line can be written in matrix form as
 ```math
-\quad
 \begin{pmatrix}
-N & \sum y_i \\
-\sum y_i & \sum y_{i}^2
+  N        & \sum y_i \\
+  \sum y_i & \sum y_i^2
 \end{pmatrix}
-\begin{pmatrix}
-c_1 \\
-c_2 \\
-\end{pmatrix}=
+\begin{pmatrix} c_1 \\ c_2 \end{pmatrix}
+=
 \begin{pmatrix}
 \sum y_i \\
 \sum y_i x_i
@@ -88,27 +85,20 @@ where $c_1$ and $c_2$ are the intercept and slope.
 Multiplying both sides by the inverse of the first matrix gives
 
 ```math
-\quad
-\begin{pmatrix}
-c_1 \\
-c_2 \\
-\end{pmatrix}=
-\begin{pmatrix}
-N & \sum y_i \\
-\sum y_i & \sum y_{i}^2
+\begin{pmatrix} c_1 \\ c_2 \end{pmatrix}
+= \begin{pmatrix}
+  N & \sum y_i \\
+  \sum y_i & \sum y_i^2
 \end{pmatrix}^{-1}
-\begin{pmatrix}
-\sum y_i \\
-\sum y_i x_i
-\end{pmatrix}
+\begin{pmatrix} \sum y_i \\ \sum y_i x_i \end{pmatrix}
 ```
 
 We can write the right-hand side matrix and vector (let's call them `A` and `b`) in Julia notation like so:
-```julia
+```julia-repl
 julia> A = [
           length(x) sum(x)
           sum(x)    sum(x.^2)
-      ]
+       ]
 2×2 Matrix{Int64}:
    21   1050
  1050  71750
@@ -181,22 +171,20 @@ Now, we'll use SciML's problem-algorithm-solve workflow to solve our optimizatio
 # Define the initial parameter values for slope and intercept
 u0 = [1.0, 1.0]
 # Pass through the data we want to fit
-data = [x,y]
+data = [x, y]
 
 # Create an OptimizationProblem object to hold the function, initial
 # values, and data.
 using Optimization
-prob = OptimizationProblem(objective,u0,data)
+prob = OptimizationProblem(objective, u0, data)
 
 # Import the optimization backend we want to use
 using OptimizationOptimJL
 
-# Minimize the function. Optimization.jl uses the SciML common solver
-# interface. Pass the problem you want to solve (optimization problem
-# here) and a solver to use.
-# NelderMead() is a derivative-free method for finding a function's
-# local minimum.
-sol = solve(prob,NelderMead())
+# Minimize the function. Optimization.jl uses the SciML common solver interface.
+# Pass the problem you want to solve (optimization problem here) and a solver to use.
+# NelderMead() is a derivative-free method for finding a function's local minimum.
+sol = solve(prob, NelderMead())
 
 # Exctract the best-fitting parameters
 slope, intercept = sol.u
@@ -231,10 +219,10 @@ end
 
 ```julia
 u0 = [1.0, 1.0, 1.0]
-data = [x,y]
-prob = OptimizationProblem(objective,u0,data)
+data = [x, y]
+prob = OptimizationProblem(objective, u0, data)
 using OptimizationOptimJL
-sol = solve(prob,NelderMead())
+sol = solve(prob, NelderMead())
 u = sol.u
 
 yfit = u[1] .* x.^2 .+ u[2] .* x .+ u[3]
@@ -251,8 +239,8 @@ First, you can use automatic differentiation and a higher order optimization alg
 ```julia
 using ForwardDiff
 optf = OptimizationFunction(objective, Optimization.AutoForwardDiff())
-prob = OptimizationProblem(optf,u0,data)
-@time sol = solve(prob,BFGS())  # another good algorithm is Newton()
+prob = OptimizationProblem(optf, u0, data)
+@time sol = solve(prob, BFGS())  # another good algorithm is Newton()
 ```
 You can also write an "in-place" version of `objective` that doesn't allocate new arrays with each iteration.
 
@@ -325,30 +313,26 @@ Quantiles
 ```
 
 
-```
+```julia
 intercept = chain["intercept"]
 slope = chain["slope"]
 σ₂ = chain["σ₂"]
 
-plot(x, x .* slope' .+ intercept';
-    label="",
-    color=:gray,
-    alpha=0.05
-)
+plot(x, x .* slope' .+ intercept'; label="", color=:gray, alpha=0.05)
 scatter!(x, y, xlabel="x", ylabel="y", label="data", color=1)
 ```
 ![](../assets/tutorials/curve-fit/bayesian-lin-regression.svg)
 
 Each gray curve is a sample from the posterior distribution of this model. To examine the model parameters and their covariance in greater detail, we can make a corner plot using the PairPlots.jl package. We'll need a few more samples for a nice plot, so re-run the NUTS sampler with more iterations first.
-```
+```julia
 Random.seed!(1234)
 chain = sample(model, NUTS(0.65), 25_000)
 
 using PairPlots
 table = (;
-    intercept= chain["intercept"],
-    slope= chain["slope"],
-    σ= sqrt.(chain["σ₂"])
+    intercept = chain["intercept"],
+    slope = chain["slope"],
+    σ = sqrt.(chain["σ₂"])
 )
 PairPlots.corner(table)
 ```
@@ -414,18 +398,13 @@ Quantiles
           u3    0.9635    1.7155    2.1211    2.5172    3.3960
 ```
 
-
-```
+```julia
 u1 = chain["u1"]
 u2 = chain["u2"]
 u3 = chain["u3"]
 posterior = u1' .* x.^2 .+ u2' .* x .+ u3'
 
-plot(x, posterior;
-    label="",
-    color=:gray,
-    alpha=0.1
-)
+plot(x, posterior; label="", color=:gray, alpha=0.1)
 scatter!(x, y, xlabel="x", ylabel="y", label="data", color=1)
 ```
 ![](../assets/tutorials/curve-fit/bayesian-quad-regression.svg)
@@ -440,7 +419,7 @@ table = (;
     u_1 = chain["u1"],
     u_2 = chain["u2"],
     u_3 = chain["u3"],
-    σ= sqrt.(chain["σ₂"])
+    σ = sqrt.(chain["σ₂"])
 )
 PairPlots.corner(table)
 ```

docs/src/tutorials/tabular-data.md

@@ -30,7 +30,6 @@ If you will be using these tools as part of a larger project, it's strongly reco
 If you're using [Pluto notebooks](https://plutojl.org), installing and recording package versions in a project are handled for you automatically.
 
 
-
 ## Downloading the data
 
 The table in question is hosted alongside the [article](https://iopscience.iop.org/article/10.3847/1538-4365/abf93c). Go to Table 4 and click the link at the bottom to download it in FITS format. You'll need to uncompress the archive to see the `HGCA_vEDR3.fits` file.
@@ -135,7 +134,6 @@ julia> describe(df)
   34 │ nonlinear_dpmdec        0.000311498  -4.1194        1.92019e-7   16.0394                     0  Float32
   35 │ chisq                   566.555      3.11559e-5     3.35103      3.67633e5                   0  Float32
                                                                                                   6 rows omitted
-
 ```
 
 
@@ -180,14 +178,14 @@ Let's now visualize these stars as they appear in the plane of the sky. We'll co
 ```julia-repl
 julia> using Plots
 julia> scatter(
-    nearby.gaia_ra,
-    nearby.gaia_dec;
-    marker_z = log10.(nearby.chisq),
-    colorbartitle="log10 χ²", # typed as \chi <tab> \^2 <tab>
-    label = "",
-    xlabel = "right ascension (°)", # typed as \degree <tab>
-    ylabel = "declination (°)",
-)
+           nearby.gaia_ra,
+           nearby.gaia_dec;
+           marker_z = log10.(nearby.chisq),
+           colorbartitle="log10 χ²", # typed as \chi <tab> \^2 <tab>
+           label = "",
+           xlabel = "right ascension (°)", # typed as \degree <tab>
+           ylabel = "declination (°)",
+       )
 ```
 
 Let's improve this plot by using a different map projection. We can make this conversion using [AstroLib.jl](https://juliaastro.org/AstroLib/stable/).
@@ -218,20 +216,20 @@ Finally, we'll make the plot and tweak some formatting options:
 
 ```julia-repl
 julia> scatter(
-    newx,
-    newy;
-    marker_z = log10.(nearby.chisq),
-    color = :turbo,
-    colorbartitle="log10 χ²", # typed as \chi <tab> \^2 <tab>
-    label = "",
-    xlabel = "right ascension (°)", # typed as \degree <tab>
-    ylabel = "declination (°)",
-    background=:transparent,
-    foreground=:gray,
-    framestyle=:box,
-    markerstrokewidth=0,
-    grid=:none
-)
+           newx,
+           newy;
+           marker_z = log10.(nearby.chisq),
+           color = :turbo,
+           colorbartitle="log10 χ²", # typed as \chi <tab> \^2 <tab>
+           label = "",
+           xlabel = "right ascension (°)", # typed as \degree <tab>
+           ylabel = "declination (°)",
+           background = :transparent,
+           foreground = :gray,
+           framestyle = :box,
+           markerstrokewidth = 0,
+           grid = :none,
+       )
 ```
 ![Plot of nearby stars with significant acceleration](../assets/tutorials/tabular-data/starplot-1.svg)