format

Author: ChrisRackauckas

test/sparse.jl

@@ -1,48 +1,53 @@
 using NonlinearSolve, LinearAlgebra, SparseArrays, Symbolics
 
 const N = 32
-const xyd_brusselator = range(0,stop=1,length=N)
-brusselator_f(x, y) = (((x-0.3)^2 + (y-0.6)^2) <= 0.1^2) * 5.
-limit(a, N) = a == N+1 ? 1 : a == 0 ? N : a
+const xyd_brusselator = range(0, stop = 1, length = N)
+brusselator_f(x, y) = (((x - 0.3)^2 + (y - 0.6)^2) <= 0.1^2) * 5.0
+limit(a, N) = a == N + 1 ? 1 : a == 0 ? N : a
 function brusselator_2d_loop(du, u, p)
-  A, B, alpha, dx = p
-  alpha = alpha/dx^2
-  @inbounds for I in CartesianIndices((N, N))
-    i, j = Tuple(I)
-    x, y = xyd_brusselator[I[1]], xyd_brusselator[I[2]]
-    ip1, im1, jp1, jm1 = limit(i+1, N), limit(i-1, N), limit(j+1, N), limit(j-1, N)
-    du[i,j,1] = alpha*(u[im1,j,1] + u[ip1,j,1] + u[i,jp1,1] + u[i,jm1,1] - 4u[i,j,1]) +
-                B + u[i,j,1]^2*u[i,j,2] - (A + 1)*u[i,j,1] + brusselator_f(x, y)
-    du[i,j,2] = alpha*(u[im1,j,2] + u[ip1,j,2] + u[i,jp1,2] + u[i,jm1,2] - 4u[i,j,2]) +
-                A*u[i,j,1] - u[i,j,1]^2*u[i,j,2]
+    A, B, alpha, dx = p
+    alpha = alpha / dx^2
+    @inbounds for I in CartesianIndices((N, N))
+        i, j = Tuple(I)
+        x, y = xyd_brusselator[I[1]], xyd_brusselator[I[2]]
+        ip1, im1, jp1, jm1 = limit(i + 1, N), limit(i - 1, N), limit(j + 1, N),
+                             limit(j - 1, N)
+        du[i, j, 1] = alpha * (u[im1, j, 1] + u[ip1, j, 1] + u[i, jp1, 1] + u[i, jm1, 1] -
+                       4u[i, j, 1]) +
+                      B + u[i, j, 1]^2 * u[i, j, 2] - (A + 1) * u[i, j, 1] +
+                      brusselator_f(x, y)
+        du[i, j, 2] = alpha * (u[im1, j, 2] + u[ip1, j, 2] + u[i, jp1, 2] + u[i, jm1, 2] -
+                       4u[i, j, 2]) +
+                      A * u[i, j, 1] - u[i, j, 1]^2 * u[i, j, 2]
     end
 end
-p = (3.4, 1., 10., step(xyd_brusselator))
+p = (3.4, 1.0, 10.0, step(xyd_brusselator))
 
 function init_brusselator_2d(xyd)
-  N = length(xyd)
-  u = zeros(N, N, 2)
-  for I in CartesianIndices((N, N))
-    x = xyd[I[1]]
-    y = xyd[I[2]]
-    u[I,1] = 22*(y*(1-y))^(3/2)
-    u[I,2] = 27*(x*(1-x))^(3/2)
-  end
-  u
+    N = length(xyd)
+    u = zeros(N, N, 2)
+    for I in CartesianIndices((N, N))
+        x = xyd[I[1]]
+        y = xyd[I[2]]
+        u[I, 1] = 22 * (y * (1 - y))^(3 / 2)
+        u[I, 2] = 27 * (x * (1 - x))^(3 / 2)
+    end
+    u
 end
 u0 = init_brusselator_2d(xyd_brusselator)
-prob_brusselator_2d = NonlinearProblem(brusselator_2d_loop,u0,p)
+prob_brusselator_2d = NonlinearProblem(brusselator_2d_loop, u0, p)
 sol = solve(prob_brusselator_2d, NewtonRaphson())
 
 du0 = copy(u0)
-jac_sparsity = Symbolics.jacobian_sparsity((du,u)->brusselator_2d_loop(du,u,p),du0,u0)
+jac_sparsity = Symbolics.jacobian_sparsity((du, u) -> brusselator_2d_loop(du, u, p), du0,
+                                           u0)
 
-ff = NonlinearFunction(brusselator_2d_loop;jac_prototype=float.(jac_sparsity))
-prob_brusselator_2d = NonlinearProblem(ff,u0,p)
+ff = NonlinearFunction(brusselator_2d_loop; jac_prototype = float.(jac_sparsity))
+prob_brusselator_2d = NonlinearProblem(ff, u0, p)
 sol = solve(prob_brusselator_2d, NewtonRaphson())
 @test norm(sol.resid) < 1e-8
 
-sol = solve(prob_brusselator_2d, NewtonRaphson(autodiff=false))
+sol = solve(prob_brusselator_2d, NewtonRaphson(autodiff = false))
 @test norm(sol.resid) < 1e-6
 
 cache = init(prob_brusselator_2d, NewtonRaphson())