Revert #190 and anticipate deprecating implicit scalar broadcasting in setindex!

src/array/subarray.jl

@@ -13,13 +13,13 @@ function perf_lucompletepivCopy!(A)
         μ, λ = _maxind(As)
         μ += k-1; λ += k-1
         rowpiv[k] = μ
-        A[[k,μ], 1:n] .= A[[μ,k], 1:n]
+        A[[k,μ], 1:n] = A[[μ,k], 1:n]
         colpiv[k] = λ
-        A[1:n, [k,λ]] .= A[1:n, [λ,k]]
+        A[1:n, [k,λ]] = A[1:n, [λ,k]]
         if A[k,k] ≠ 0
             ρ = k+1:n
-            A[ρ, k] ./= A[k, k]
-            A[ρ, ρ] .-= A[ρ, k:k] .* A[k:k, ρ]
+            A[ρ, k] = A[ρ, k]/A[k, k]
+            A[ρ, ρ] = A[ρ, ρ] - A[ρ, k:k] * A[k:k, ρ]
         end
     end
     return (A, rowpiv, colpiv)
@@ -34,13 +34,13 @@ function perf_lucompletepivSub!(A)
         μ, λ = _maxind(As)
         μ += k-1; λ += k-1
         rowpiv[k] = μ
-        A[[k,μ], 1:n] .= view(A, [μ,k], 1:n)
+        A[[k,μ], 1:n] = view(A, [μ,k], 1:n)
         colpiv[k] = λ
-        A[1:n, [k,λ]] .= view(A, 1:n, [λ,k])
+        A[1:n, [k,λ]] = view(A, 1:n, [λ,k])
         if A[k,k] ≠ 0
             ρ = k+1:n
-            A[ρ, k] ./= A[k, k]
-            A[ρ, ρ] .-= view(A, ρ, k:k) .* view(A, k:k, ρ)
+            A[ρ, k] = view(A, ρ, k)/A[k, k]
+            A[ρ, ρ] = view(A, ρ, ρ) - view(A, ρ, k:k) * view(A, k:k, ρ)
         end
     end
     return (A, rowpiv, colpiv)

src/array/sumindex.jl

@@ -75,7 +75,7 @@ function perf_sumlogical(A)
     nrows = size(A, 1)
     ncols = size(A, 2)
     r = falses(nrows)
-    r[1:4:end] = true
+    r[1:4:end] .= true
     @simd for i = 1:ncols
         val = Base.unsafe_getindex(A, r, i)
         s += first(val)
@@ -151,7 +151,7 @@ function perf_sumlogical_view(A)
     nrows = size(A, 1)
     ncols = size(A, 2)
     r = falses(nrows)
-    r[1:4:end] = true
+    r[1:4:end] .= true
     @inbounds for i = 1:ncols
         val = view(A, r, i)
         s += first(val)
@@ -287,7 +287,7 @@ end
 function makearrays(::Type{T}, r::Integer, c::Integer) where T
     A = samerand(T, r, c)
     B = similar(A, r+1, c+1)
-    B[1:r, 1:c] .= A
+    B[1:r, 1:c] = A
     AS = ArrayLS(B)
     ASS = ArrayLSLS(B)
     AF = ArrayLF(A)

src/problem/GoGame.jl

@@ -297,7 +297,7 @@ end
 #          that will not happen.
 #
 function compute_final_status!(board::Board)
-    board.final_status[:] = UNKNOWN
+    fill!(board.final_status, UNKNOWN)
     for i = 1:board.size, j = 1:board.size
         if board[i, j] == EMPTY
             for k = 1:4
@@ -351,15 +351,15 @@ function generate_move(board::Board, color::Int)
             # Further require the move not to be suicide for the opponent...
             if !suicide(board, ai, aj, other_color(color))
                 num_moves += 1
-                moves[:,num_moves] .= (ai, aj)
+                moves[:,num_moves] = [ai, aj]
             else
                 # ...however, if the move captures at least one stone,
                 # consider it anyway.
                 for k = 1:4
                     (bi, bj) = neighbor(ai, aj, k)
                     if on_board(board, bi, bj) && board[bi, bj] == other_color(color)
                         num_moves += 1
-                        moves[:,num_moves] .= (ai, aj)
+                        moves[:,num_moves] = [ai, aj]
                         break
                     end
                 end
@@ -415,7 +415,7 @@ function perf_go_game(num_games_per_point::Int)
             passes = 0
             num_moves = 1
             color = WHITE
-            board.board[:] = EMPTY # clear board
+            fill!(board.board, EMPTY) # clear board
             play_move!(board, i, j, BLACK)
             while passes < 3 && num_moves < 600
                 (movei, movej) = generate_move(board, color)

src/problem/Laplacian.jl

@@ -33,7 +33,7 @@ function ddx_spdiags(m)
     # Append new d[k]-th diagonal to compact form
     for k = 1:p
         i = max(1,1-d[k]):min(m,n-d[k])
-        a[(len[k]+1):len[k+1],:] .= [i i.+d[k] B[i.+(m>=n)*d[k],k]]
+        a[(len[k]+1):len[k+1],:] = [i i.+d[k] B[i.+(m>=n)*d[k],k]]
     end
 
     return sparse(a[:,1], a[:,2], a[:,3], m, n)
@@ -62,7 +62,7 @@ end
 
 function perf_laplace_iter_devec(N)
     u = zeros(N, N)
-    u[1, :] = 1
+    u[1, :] .= 1
     Niter = 2^10
     dx2 = 0.1*0.1
     dy2 = dx2
@@ -80,7 +80,7 @@ end
 
 function perf_laplace_iter_vec(N)
     u = zeros(N, N)
-    u[1,:] = 1
+    u[1,:] .= 1
     Niter = 2^10
     dx2 = dy2 = 0.1*0.1
     for i = 1:Niter
@@ -91,7 +91,7 @@ end
 
 function perf_laplace_iter_sub(N)
     u = zeros(N, N)
-    u[1,:] = 1
+    u[1,:] .= 1
     Niter = 2^10
     dx2 = dy2 = 0.1*0.1
     u0 = view(u, 2:N-1, 2:N-1)

src/problem/SparseFEM.jl

@@ -21,7 +21,7 @@ end
 # get the list of boundary dof-indices
 function get_free(N)
     L = zeros(Int, N, N)
-    L[2:N-1, 2:N-1] = 1
+    L[2:N-1, 2:N-1] .= 1
     return findall(!iszero, L)
 end
 

src/problem/StockCorr.jl

@@ -20,9 +20,9 @@ function perf_stockcorr()
 
     ## Define storages
     SimulPriceA = zeros(T,n) # Simulated Price of Asset A
-    SimulPriceA[1,:] = CurrentPrice[1]
+    SimulPriceA[1,:] .= CurrentPrice[1]
     SimulPriceB = zeros(T,n) # Simulated Price of Asset B
-    SimulPriceB[1,:] = CurrentPrice[2]
+    SimulPriceB[1,:] .= CurrentPrice[2]
 
     ## Generating the paths of stock prices by Geometric Brownian Motion
     UpperTriangle=chol(Corr) # UpperTriangle Matrix by Cholesky decomposition