MOLE.jl: Apply JuliaFormatter changes

julia/MOLE.jl/README.md

@@ -85,8 +85,38 @@ Once you have built the documentation (either from the REPL or the command line)
 
 The MOLE library contains examples demonstrating how to use the operators, in a broad range of partial differential equations (PDEs). More information on the mathematical content can be found in the [main MOLE documentation](https://mole-docs.readthedocs.io/en/main/examples/index.html).
 
-Currently, the following examples are available in the MOLE Julia package.
+The examples are organized by PDE type and dimension in the base MOLE documentation:
 
-- Elliptic Problems
-    - 1D Examples
-        - `elliptic1D`: A script that solves the 1D Poisson's equation with Robin boundary conditions using mimetic operators.
+- **Base Examples Documentation:** https://csrc-sdsu.github.io/mole/stable/examples/
+
+Currently, not all of them are available in the MOLE Julia package. They will be added in the near future.
+
+## Formatter
+
+To test the JuliaFormatter locally, please make sure to have a working julia installation and add the `JuliaFormatter` package to your either global or local environment. At a global, system level, this would be:
+
+```
+julia -e 'using Pkg; Pkg.add("JuliaFormatter")'
+```
+
+Now that your environment has the JuliaFormatter package, do the following to apply format style changes to the MOLE.jl directory:
+
+1. From the `mole/julia/MOLE.jl` directory, run 
+
+```
+julia -e 'using JuliaFormatter; format(".", overwrite=true, verbose=true) || error("Formatting issues detected")'
+```
+
+2. Review the generated style changes applied by the formatter with 
+
+```
+git status
+``` 
+
+and 
+
+```
+git diff
+```
+
+Stage the modified files and commit the necessary changes.

julia/MOLE.jl/docs/make.jl

@@ -1,4 +1,4 @@
-push!(LOAD_PATH,"../src/")
+push!(LOAD_PATH, "../src/")
 using Documenter, MOLE, SparseArrays
 
 makedocs(
@@ -12,15 +12,12 @@ makedocs(
                 "1D Elliptic Problems" => [
                     "Overview" => "examples/Elliptic/1D/index.md",
                     "Elliptic 1D" => "examples/Elliptic/1D/Elliptic1D.md",
-                    "Elliptic 1D Add Scalar Boundary Conditions" =>
-                        "examples/Elliptic/1D/Elliptic1D-add-Scalar-BC.md",
+                    "Elliptic 1D Add Scalar Boundary Conditions" => "examples/Elliptic/1D/Elliptic1D-add-Scalar-BC.md",
                 ],
                 "2D Elliptic Problems" => [
                     "Overview" => "examples/Elliptic/2D/index.md",
-                    "X Dirichlet Y Dirichlet" =>
-                        "examples/Elliptic/2D/Elliptic2D-X-Dirichlet-Y-Dirichlet.md",
-                    "X Periodic Y Dirichlet" =>
-                        "examples/Elliptic/2D/Elliptic2D-X-Periodic-Y-Dirichlet.md",
+                    "X Dirichlet Y Dirichlet" => "examples/Elliptic/2D/Elliptic2D-X-Dirichlet-Y-Dirichlet.md",
+                    "X Periodic Y Dirichlet" => "examples/Elliptic/2D/Elliptic2D-X-Periodic-Y-Dirichlet.md",
                 ],
             ],
             "Hyperbolic Problems" => [
@@ -40,4 +37,4 @@ makedocs(
             ],
         ],
     ],
-)
+)

julia/MOLE.jl/examples/elliptic/elliptic1D.jl

@@ -28,15 +28,15 @@ dx = (east-west)/m # step length
 path = joinpath(@__DIR__, "output") # Output path to store generated plots
 mkpath(path)
 
-L = Operators.lap(k,m,dx)
+L = Operators.lap(k, m, dx)
 
 # Impose Robin boundary condition on laplacian operator
 a = 1.0
 b = 1.0
-L = L + BCs.robinBC(k,m,dx,a,b)
+L = L + BCs.robinBC(k, m, dx, a, b)
 
 # 1D staggered grid
-grid = [west; (west+(dx/2)):dx:(east-(dx/2)); east]
+grid = [west; (west + (dx / 2)):dx:(east - (dx / 2)); east]
 
 # RHS
 U = exp.(grid)
@@ -46,13 +46,13 @@ U[end] = 2*exp(1)
 U = L\U
 
 # Plot result
-p = Plots.scatter(grid, U, label="Approximated", show = false)
-plot!(p, grid, exp.(grid), label="Analytical", show = false)
+p = Plots.scatter(grid, U, label = "Approximated", show = false)
+plot!(p, grid, exp.(grid), label = "Analytical", show = false)
 plot!(
     p,
-    xlabel="x",
-    ylabel="u(x)",
-    title="Poisson's equation with Robin BC",
-    show = false
+    xlabel = "x",
+    ylabel = "u(x)",
+    title = "Poisson's equation with Robin BC",
+    show = false,
 )
 Plots.png(p, joinpath(path, "elliptic1D_output.png"))

julia/MOLE.jl/examples/elliptic/elliptic1DaddScalarBC.jl

@@ -24,23 +24,23 @@ import MOLE: Operators, BCs
 west = 0.0
 east = 1.0
 
-k  = 6 # operator order of accuracy
-m  = 2k + 1 # minimum number of cells to attain desired accuracy
+k = 6 # operator order of accuracy
+m = 2k + 1 # minimum number of cells to attain desired accuracy
 dx = (east - west) / m # step length
 
 path = joinpath(@__DIR__, "output") # Output path to store generated plots
 mkpath(path)
 
 # 1D staggered grid
-grid = [west; (west + dx/2):dx:(east - dx/2); east]
+grid = [west; (west + dx / 2):dx:(east - dx / 2); east]
 
 # Mimetic Laplacian operator
 L = Operators.lap(k, m, dx)
 
 # Robin boundary conditions:  dc*u + nc*(du/dn) = v
 dc = (1.0, 1.0)
 nc = (1.0, 1.0)
-v  = (0.0, 2 * exp(1.0))
+v = (0.0, 2 * exp(1.0))
 
 bc = BCs.ScalarBC1D(dc, nc, v)
 
@@ -54,14 +54,14 @@ L0, rhs0 = BCs.addScalarBC!(sparse(L), rhs, bc, k, m, dx)
 u = L0 \ rhs0
 
 # Plot
-p = scatter(grid, u; label="Approximated", marker=:circle, show = false)
-plot!(p, grid, exp.(grid); label="Analytical", show = false)
+p = scatter(grid, u; label = "Approximated", marker = :circle, show = false)
+plot!(p, grid, exp.(grid); label = "Analytical", show = false)
 plot!(
-    p, 
-    title="Poisson's equation with Robin BC using addScalarBC",
-    xlabel="x",
-    ylabel="u(x)",
-    legend=:topleft,
-    show = false
+    p,
+    title = "Poisson's equation with Robin BC using addScalarBC",
+    xlabel = "x",
+    ylabel = "u(x)",
+    legend = :topleft,
+    show = false,
 )
-Plots.png(p, joinpath(path, "elliptic1DaddScalarBC_output.png"))
+Plots.png(p, joinpath(path, "elliptic1DaddScalarBC_output.png"))

julia/MOLE.jl/examples/elliptic/elliptic2DXDirichletYDirichlet.jl

@@ -34,8 +34,8 @@ path = joinpath(@__DIR__, "output") # Output path to store generated plots
 mkpath(path)
 
 # Grid
-xc = [0; (dx / 2.0) : dx : (pi - dx / 2.0); pi]
-yc = [0; (dy / 2.0) : dy : (pi - dy / 2.0); pi]
+xc = [0; (dx / 2.0):dx:(pi - dx / 2.0); pi]
+yc = [0; (dy / 2.0):dy:(pi - dy / 2.0); pi]
 X = ones(1, n + 2) .* xc
 Y = yc' .* ones(m + 2, 1)
 
@@ -45,11 +45,11 @@ ue = exp.(X) .* cos.(Y)
 # Boundary Conditions
 dc = (1.0, 1.0, 1.0, 1.0)
 nc = (0.0, 0.0, 0.0, 0.0)
-v = (vec(ue[1,2:end-1]'), vec(ue[end,2:end-1]'), vec(ue[:,1]), vec(ue[:, end]))
+v = (vec(ue[1, 2:(end - 1)]'), vec(ue[end, 2:(end - 1)]'), vec(ue[:, 1]), vec(ue[:, end]))
 bc = BCs.ScalarBC2D(dc, nc, v)
 
 # Operator
-A = - Operators.lap(k, m, dx, n, dy, dc=dc, nc=nc)
+A = - Operators.lap(k, m, dx, n, dy, dc = dc, nc = nc)
 
 # RHS
 b = zeros(m + 2, n + 2)
@@ -64,37 +64,37 @@ ua = Matrix(reshape(ua, m + 2, n + 2))
 
 Plots.png(
     Plots.heatmap(
-        xc, 
-        yc, 
-        ua, 
-        title = "Approximate Solution", 
-        xlabel = "X", 
-        ylabel = "Y", 
-        colorbar_title = "u(x,y)", 
+        xc,
+        yc,
+        ua,
+        title = "Approximate Solution",
+        xlabel = "X",
+        ylabel = "Y",
+        colorbar_title = "u(x,y)",
         aspect_ratio = :equal,
         colormap = :jet1,
-        show = false
+        show = false,
     ),
-    joinpath(path, "elliptic2DXDYD_approximate.png")
+    joinpath(path, "elliptic2DXDYD_approximate.png"),
 )
 
 Plots.png(
     Plots.heatmap(
-        xc, 
-        yc, 
-        ue, 
-        title = "Exact Solution", 
-        xlabel = "X", 
-        ylabel = "Y", 
-        colorbar_title = "u(x,y)", 
+        xc,
+        yc,
+        ue,
+        title = "Exact Solution",
+        xlabel = "X",
+        ylabel = "Y",
+        colorbar_title = "u(x,y)",
         aspect_ratio = :equal,
         colormap = :jet1,
-        show = false
+        show = false,
     ),
-    joinpath(path, "elliptic2DXDYD_exact.png")
+    joinpath(path, "elliptic2DXDYD_exact.png"),
 )
 
 max_err = maximum(abs, ue - ua)
 println("Maximum error: $max_err")
 rel_err = 100 * max_err ./ (maximum(ue) - minimum(ue))
-println("Relative error: $rel_err")
+println("Relative error: $rel_err")

julia/MOLE.jl/examples/elliptic/elliptic2DXPerYDirichlet.jl

@@ -34,8 +34,8 @@ path = joinpath(@__DIR__, "output") # Output path to store generated plots
 mkpath(path)
 
 # Grid
-xc = (dx / 2.0) : dx : (1.0 - dx / 2.0)
-yc = [0; (dy / 2.0) : dy : (1.0 - dy / 2.0); 1.0]
+xc = (dx / 2.0):dx:(1.0 - dx / 2.0)
+yc = [0; (dy / 2.0):dy:(1.0 - dy / 2.0); 1.0]
 X = ones(1, n + 2) .* xc
 Y = yc' .* ones(m, 1)
 
@@ -49,7 +49,7 @@ v = ([0.0], [0.0], vec(zeros(m, 1)), vec(zeros(m, 1)))
 bc = BCs.ScalarBC2D(dc, nc, v)
 
 # Operator
-A = - Operators.lap(k, m, dx, n, dy, dc=dc, nc=nc)
+A = - Operators.lap(k, m, dx, n, dy, dc = dc, nc = nc)
 
 # RHS
 b = 2.0 * sin.(2.0 * pi .* X) .* (1.0 .+ 2.0 * pi^2 .* Y .* (1.0 .- Y))
@@ -64,37 +64,37 @@ ua = Matrix(reshape(ua, m, n + 2))
 
 Plots.png(
     Plots.heatmap(
-        xc, 
-        yc, 
-        ua, 
-        title = "Approximate Solution", 
-        xlabel = "X", 
-        ylabel = "Y", 
-        colorbar_title = "u(x,y)", 
+        xc,
+        yc,
+        ua,
+        title = "Approximate Solution",
+        xlabel = "X",
+        ylabel = "Y",
+        colorbar_title = "u(x,y)",
         aspect_ratio = :equal,
         colormap = :jet1,
-        show = false
+        show = false,
     ),
-    joinpath(path, "elliptic2DXPYD_approximate.png")
+    joinpath(path, "elliptic2DXPYD_approximate.png"),
 )
 
 Plots.png(
     Plots.heatmap(
-        xc, 
-        yc, 
-        ue, 
-        title = "Exact Solution", 
-        xlabel = "X", 
-        ylabel = "Y", 
-        colorbar_title = "u(x,y)", 
+        xc,
+        yc,
+        ue,
+        title = "Exact Solution",
+        xlabel = "X",
+        ylabel = "Y",
+        colorbar_title = "u(x,y)",
         aspect_ratio = :equal,
         colormap = :jet1,
-        show = false
+        show = false,
     ),
-    joinpath(path, "elliptic2DXPYD_exact.png")
+    joinpath(path, "elliptic2DXPYD_exact.png"),
 )
 
 max_err = maximum(abs, ue - ua)
 println("Maximum error: $max_err")
 rel_err = 100 * max_err ./ (maximum(ue) - minimum(ue))
-println("Realative error: $rel_err")
+println("Realative error: $rel_err")