Lint code and add JuliaFormatter GH action

docs/make.jl

@@ -1,6 +1,6 @@
 using Pkg
 
-checkpointingspec = PackageSpec(path=joinpath(dirname(@__FILE__), ".."))
+checkpointingspec = PackageSpec(path = joinpath(dirname(@__FILE__), ".."))
 Pkg.develop(checkpointingspec)
 
 # when first running instantiate
@@ -13,7 +13,7 @@ makedocs(
     sitename = "Checkpointing.jl",
     format = Documenter.HTML(
         prettyurls = Base.get(ENV, "CI", nothing) == "true",
-        mathengine = Documenter.KaTeX()
+        mathengine = Documenter.KaTeX(),
     ),
     modules = [Checkpointing],
     repo = "https://github.com/Argonne-National-Laboratory/Checkpointing.jl/blob/{commit}{path}#{line}",
@@ -25,7 +25,7 @@ makedocs(
         "Rules" => "rules.md",
         "Storage" => "storage.md",
         "API" => "lib/checkpointing.md",
-    ]
+    ],
 )
 
 deploydocs(
@@ -34,4 +34,4 @@ deploydocs(
     devbranch = "main",
     devurl = "main",
     push_preview = true,
-)
+)

examples/box_model.jl

@@ -2,40 +2,44 @@ const blength = [5000.0e5; 1000.0e5; 5000.0e5]   ## north-south size of boxes, c
 
 const bdepth = [1.0e5; 5.0e5; 4.0e5]   ## depth of boxes, centimeters
 
-const delta = bdepth[1]/(bdepth[1] + bdepth[3])  ## constant ratio of two depths
+const delta = bdepth[1] / (bdepth[1] + bdepth[3])  ## constant ratio of two depths
 
-const bwidth = 4000.0*1e5  ## box width, centimeters
+const bwidth = 4000.0 * 1e5  ## box width, centimeters
 
 # box areas
-const barea = [blength[1]*bwidth;
-         blength[2]*bwidth;
-         blength[3]*bwidth]
+const barea = [
+    blength[1] * bwidth
+    blength[2] * bwidth
+    blength[3] * bwidth
+]
 
 # box volumes
-const bvol = [barea[1]*bdepth[1];
-        barea[2]*bdepth[2];
-        barea[3]*bdepth[3]]
+const bvol = [
+    barea[1] * bdepth[1]
+    barea[2] * bdepth[2]
+    barea[3] * bdepth[3]
+]
 
 # parameters that are used to ensure units are in CGS (cent-gram-sec)
 
 const hundred = 100.0
 const thousand = 1000.0
-const day = 3600.0*24.0
-const year = day*365.0
+const day = 3600.0 * 24.0
+const year = day * 365.0
 const Sv = 1e12     ## one Sverdrup (a unit of ocean transport), 1e6 meters^3/second
 
 # parameters that appear in box model equations
-const u0 = 16.0*Sv/0.0004
+const u0 = 16.0 * Sv / 0.0004
 const alpha = 1668e-7
 const beta = 0.7811e-3
 
-const gamma = 1/(300*day)
+const gamma = 1 / (300 * day)
 
 # robert filter coefficient for the smoother part of the timestep
 const robert_filter_coeff = 0.25
 
 # freshwater forcing
-const FW = [(hundred/year) * 35.0 * barea[1]; -(hundred/year) * 35.0 * barea[1]]
+const FW = [(hundred / year) * 35.0 * barea[1]; -(hundred / year) * 35.0 * barea[1]]
 
 # restoring atmospheric temperatures
 const Tstar = [22.0; 0.0]
@@ -47,7 +51,7 @@ const Sstar = [36.0; 34.0];
 
 function U_func(dens)
 
-    U = u0*(dens[2] - (delta * dens[1] + (1 - delta)*dens[3]))
+    U = u0 * (dens[2] - (delta * dens[1] + (1 - delta) * dens[3]))
     return U
 
 end
@@ -123,9 +127,10 @@ end
 function forward_func_4_AD(in_now, in_old, out_old, out_now)
     rho_now = rho_func(in_now)                             ## compute density
     u_now = U_func(rho_now)                                ## compute transport
-    in_new = timestep_func(in_now, in_old, u_now, 10*day)  ## compute new state values
+    in_new = timestep_func(in_now, in_old, u_now, 10 * day)  ## compute new state values
     for j = 1:6
-        in_now[j] = in_now[j] + robert_filter_coeff * (in_new[j] - 2.0 * in_now[j] + in_old[j])
+        in_now[j] =
+            in_now[j] + robert_filter_coeff * (in_new[j] - 2.0 * in_now[j] + in_old[j])
     end
     out_old[:] = in_now
     out_now[:] = in_new
@@ -138,7 +143,7 @@ function advance(box::Box)
 end
 
 function timestepper_for(box::Box, scheme::Scheme, tsteps::Int)
-    @checkpoint_struct scheme box for i in 1:tsteps
+    @checkpoint_struct scheme box for i = 1:tsteps
         advance(box)
         box.in_now[:] = box.out_old
         box.in_old[:] = box.out_now
@@ -157,7 +162,13 @@ function box_for(scheme::Scheme, tsteps::Int, ::EnzymeTool)
     dbox = Box(zeros(6), zeros(6), zeros(6), zeros(6), 0)
 
     # Compute gradient
-    autodiff(Enzyme.ReverseWithPrimal, timestepper_for, Duplicated(box, dbox), Const(scheme), Const(tsteps))
+    autodiff(
+        Enzyme.ReverseWithPrimal,
+        timestepper_for,
+        Duplicated(box, dbox),
+        Const(scheme),
+        Const(tsteps),
+    )
     return box.out_now[1], dbox.in_old
 end
 
@@ -175,12 +186,12 @@ end
 
 
 function timestepper_while(box::Box, scheme::Scheme, tsteps::Int)
-    box.i=1
+    box.i = 1
     @checkpoint_struct scheme box while box.i <= tsteps
         advance(box)
         box.in_now[:] = box.out_old
         box.in_old[:] = box.out_now
-        box.i = box.i+1
+        box.i = box.i + 1
         nothing
     end
     return box.out_now[1]
@@ -196,7 +207,13 @@ function box_while(scheme::Scheme, tsteps::Int, ::EnzymeTool)
     dbox = Box(zeros(6), zeros(6), zeros(6), zeros(6), 0)
 
     # Compute gradient
-    autodiff(Enzyme.ReverseWithPrimal, timestepper_while, Duplicated(box, dbox), Const(scheme), Const(tsteps))
+    autodiff(
+        Enzyme.ReverseWithPrimal,
+        timestepper_while,
+        Duplicated(box, dbox),
+        Const(scheme),
+        Const(tsteps),
+    )
     return box.out_now[1], dbox.in_old
 end
 

examples/heat.jl

@@ -15,17 +15,17 @@ function advance(heat::Heat)
     last = heat.Tlast
     λ = heat.λ
     n = heat.n
-    for i in 2:(n-1)
-        next[i] = last[i] + λ*(last[i-1]-2*last[i]+last[i+1])
+    for i = 2:(n-1)
+        next[i] = last[i] + λ * (last[i-1] - 2 * last[i] + last[i+1])
     end
     return nothing
 end
 
 
 function sumheat_for(heat::Heat, chkpscheme::Scheme, tsteps::Int64)
     # AD: Create shadow copy for derivatives
-    @checkpoint_struct chkpscheme heat for i in 1:tsteps
-    # checkpoint_struct_for(advance, heat)
+    @checkpoint_struct chkpscheme heat for i = 1:tsteps
+        # checkpoint_struct_for(advance, heat)
         heat.Tlast .= heat.Tnext
         advance(heat)
     end
@@ -45,8 +45,8 @@ end
 
 function heat_for(scheme::Scheme, tsteps::Int, ::EnzymeTool)
     n = 100
-    Δx=0.1
-    Δt=0.001
+    Δx = 0.1
+    Δt = 0.001
     # Select μ such that λ ≤ 0.5 for stability with μ = (λ*Δt)/Δx^2
     λ = 0.5
 
@@ -56,27 +56,33 @@ function heat_for(scheme::Scheme, tsteps::Int, ::EnzymeTool)
     dheat = Heat(zeros(n), zeros(n), n, λ, tsteps)
 
     # Boundary conditions
-    heat.Tnext[1]   = 20.0
+    heat.Tnext[1] = 20.0
     heat.Tnext[end] = 0
 
     # Compute gradient
-    autodiff(Enzyme.ReverseWithPrimal, sumheat_for, Duplicated(heat, dheat), Const(scheme), Const(tsteps))
+    autodiff(
+        Enzyme.ReverseWithPrimal,
+        sumheat_for,
+        Duplicated(heat, dheat),
+        Const(scheme),
+        Const(tsteps),
+    )
 
     return heat.Tnext, dheat.Tnext[2:end-1]
 end
 
 function heat_for(scheme::Scheme, tsteps::Int, ::ZygoteTool)
     n = 100
-    Δx=0.1
-    Δt=0.001
+    Δx = 0.1
+    Δt = 0.001
     # Select μ such that λ ≤ 0.5 for stability with μ = (λ*Δt)/Δx^2
     λ = 0.5
 
     # Create object from struct. tsteps is not needed for a for-loop
     heat = Heat(zeros(n), zeros(n), n, λ, tsteps)
 
     # Boundary conditions
-    heat.Tnext[1]   = 20.0
+    heat.Tnext[1] = 20.0
     heat.Tnext[end] = 0
 
     # Compute gradient
@@ -87,8 +93,8 @@ end
 
 function heat_while(scheme::Scheme, tsteps::Int, ::EnzymeTool)
     n = 100
-    Δx=0.1
-    Δt=0.001
+    Δx = 0.1
+    Δt = 0.001
     # Select μ such that λ ≤ 0.5 for stability with μ = (λ*Δt)/Δx^2
     λ = 0.5
 
@@ -98,27 +104,33 @@ function heat_while(scheme::Scheme, tsteps::Int, ::EnzymeTool)
     dheat = Heat(zeros(n), zeros(n), n, λ, tsteps)
 
     # Boundary conditions
-    heat.Tnext[1]   = 20.0
+    heat.Tnext[1] = 20.0
     heat.Tnext[end] = 0
 
     # Compute gradient
-    autodiff(Enzyme.ReverseWithPrimal, sumheat_while, Duplicated(heat, dheat), Const(scheme), Const(tsteps))
+    autodiff(
+        Enzyme.ReverseWithPrimal,
+        sumheat_while,
+        Duplicated(heat, dheat),
+        Const(scheme),
+        Const(tsteps),
+    )
 
     return heat.Tnext, dheat.Tnext[2:end-1]
 end
 
 function heat_while(scheme::Scheme, tsteps::Int, ::ZygoteTool)
     n = 100
-    Δx=0.1
-    Δt=0.001
+    Δx = 0.1
+    Δt = 0.001
     # Select μ such that λ ≤ 0.5 for stability with μ = (λ*Δt)/Δx^2
     λ = 0.5
 
     # Create object from struct
     heat = Heat(zeros(n), zeros(n), n, λ, 1)
 
     # Boundary conditions
-    heat.Tnext[1]   = 20.0
+    heat.Tnext[1] = 20.0
     heat.Tnext[end] = 0
 
     # Compute gradient