deprecate non-sharing forms of reshape. fixes #4211

NEWS.md

@@ -158,8 +158,12 @@ Deprecated or removed
 
   * `scale` is deprecated in favor of either `α*A`, `Diagonal(x)*A`, or `A*Diagonal(x)`. ([#15258])
 
+  * `reshape` is now defined to always share data with the original array.
+    Uses like `reshape(1:10, (2,5))` should be replaced with `collect(1:10, (2,5))` ([#4211]).
+
 [PkgDev]: https://github.com/JuliaLang/PkgDev.jl
 <!--- generated by NEWS-update.jl: -->
+[#4211]: https://github.com/JuliaLang/julia/issues/4211
 [#8036]: https://github.com/JuliaLang/julia/issues/8036
 [#8846]: https://github.com/JuliaLang/julia/issues/8846
 [#9503]: https://github.com/JuliaLang/julia/issues/9503
@@ -190,6 +194,7 @@ Deprecated or removed
 [#15192]: https://github.com/JuliaLang/julia/issues/15192
 [#15242]: https://github.com/JuliaLang/julia/issues/15242
 [#15258]: https://github.com/JuliaLang/julia/issues/15258
+[#15409]: https://github.com/JuliaLang/julia/issues/15409
 [#15550]: https://github.com/JuliaLang/julia/issues/15550
 [#15609]: https://github.com/JuliaLang/julia/issues/15609
 [#15763]: https://github.com/JuliaLang/julia/issues/15763

base/abstractarray.jl

@@ -198,12 +198,6 @@ similar(   a::AbstractArray, T::Type, dims::Integer...)  = similar(a, T, dims)
 similar(   a::AbstractArray, T::Type, dims::DimsInteger) = Array(T, dims...)
 similar(   a::AbstractArray, T::Type, dims::Dims)        = Array(T, dims)
 
-function reshape(a::AbstractArray, dims::Dims)
-    if prod(dims) != length(a)
-        throw(ArgumentError("dimensions must be consistent with array size (expected $(length(a)), got $(prod(dims)))"))
-    end
-    copy!(similar(a, dims), a)
-end
 reshape(a::AbstractArray, dims::Int...) = reshape(a, dims)
 
 ## from general iterable to any array

base/array.jl

@@ -208,16 +208,25 @@ _similar_for(c::AbstractArray, T, itr, ::HasShape) = similar(c, T, convert(Dims,
 _similar_for(c, T, itr, isz) = similar(c, T)
 
 """
-    collect(element_type, collection)
+    collect(element_type, iterator)
 
-Return an array of type `Array{element_type,1}` of all items in a collection.
+Return an array of type `Array{element_type,1}` of all items from an iterator.
 """
 collect{T}(::Type{T}, itr) = collect(Generator(T, itr))
 
+collect{T}(::Type{T}, itr::Dims) = collect(Generator(T, itr))
+
+"""
+    collect(iterator, dims)
+
+Return an array with the given dimensions of all elements from an iterator.
+"""
+collect(itr, dims::Dims) = collect(IteratorND(itr, dims))
+
 """
-    collect(collection)
+    collect(iterator)
 
-Return an array of all items in a collection. For associative collections, returns Pair{KeyType, ValType}.
+Return an array of all items from an iterator. For associative collections, returns Pair{KeyType, ValType}.
 """
 collect(itr) = _collect(1:1 #= Array =#, itr, iteratoreltype(itr), iteratorsize(itr))
 

base/deprecated.jl

@@ -1037,3 +1037,8 @@ function pmap(f, c...; err_retry=nothing, err_stop=nothing, pids=nothing)
 
     return pmap(p, f, c...)
 end
+
+# 4211
+@deprecate reshape(a::AbstractArray, dims::Dims)                          copy!(similar(a, dims), a)
+@deprecate reshape{Tv,Ti}(a::SparseMatrixCSC{Tv,Ti}, dims::NTuple{2,Int}) copy!(similar(a, dims), a)
+@deprecate reshape(r::Range, dims::Dims)                                  collect(r, dims)

base/docs/helpdb/Base.jl

@@ -1572,9 +1572,7 @@ ind2chr
 """
     reshape(A, dims)
 
-Create an array with the same data as the given array, but with different dimensions. An
-implementation for a particular type of array may choose whether the data is copied or
-shared.
+Create an array with the same data as the given array, but with different dimensions.
 """
 reshape
 

base/sparse/sparsematrix.jl

@@ -172,22 +172,6 @@ function reinterpret{T,Tv,Ti,N}(::Type{T}, a::SparseMatrixCSC{Tv,Ti}, dims::NTup
     return SparseMatrixCSC(mS, nS, colptr, rowval, nzval)
 end
 
-function reshape{Tv,Ti}(a::SparseMatrixCSC{Tv,Ti}, dims::NTuple{2,Int})
-    if prod(dims) != length(a)
-        throw(DimensionMismatch("new dimensions $(dims) must be consistent with array size $(length(a))"))
-    end
-    mS,nS = dims
-    mA,nA = size(a)
-    numnz = nnz(a)
-    colptr = Array(Ti, nS+1)
-    rowval = similar(a.rowval)
-    nzval = copy(a.nzval)
-
-    sparse_compute_reshaped_colptr_and_rowval(colptr, rowval, mS, nS, a.colptr, a.rowval, mA, nA)
-
-    return SparseMatrixCSC(mS, nS, colptr, rowval, nzval)
-end
-
 ## Constructors
 
 copy(S::SparseMatrixCSC) =

test/abstractarray.jl

@@ -78,7 +78,7 @@ import Base: trailingsize
 const can_inline = Base.JLOptions().can_inline != 0
 function test_scalar_indexing{T}(::Type{T}, shape, ::Type{TestAbstractArray})
     N = prod(shape)
-    A = reshape(1:N, shape)
+    A = collect(1:N, shape)
     B = T(A)
     @test A == B
     # Test indexing up to 5 dimensions
@@ -186,23 +186,23 @@ end
 
 function test_vector_indexing{T}(::Type{T}, shape, ::Type{TestAbstractArray})
     N = prod(shape)
-    A = reshape(1:N, shape)
+    A = collect(1:N, shape)
     B = T(A)
     idxs = rand(1:N, 3, 3, 3)
     @test B[idxs] == A[idxs] == idxs
     @test B[vec(idxs)] == A[vec(idxs)] == vec(idxs)
     @test B[:] == A[:] == collect(1:N)
     @test B[1:end] == A[1:end] == collect(1:N)
-    @test B[:,:] == A[:,:] == reshape(1:N, shape[1], prod(shape[2:end]))
-    @test B[1:end,1:end] == A[1:end,1:end] == reshape(1:N, shape[1], prod(shape[2:end]))
+    @test B[:,:] == A[:,:] == collect(1:N, (shape[1], prod(shape[2:end])))
+    @test B[1:end,1:end] == A[1:end,1:end] == collect(1:N, (shape[1], prod(shape[2:end])))
     # Test with containers that aren't Int[]
     @test B[[]] == A[[]] == []
     @test B[convert(Array{Any}, idxs)] == A[convert(Array{Any}, idxs)] == idxs
 end
 
 function test_primitives{T}(::Type{T}, shape, ::Type{TestAbstractArray})
     N = prod(shape)
-    A = reshape(1:N, shape)
+    A = collect(1:N, shape)
     B = T(A)
 
     # last(a)
@@ -221,9 +221,6 @@ function test_primitives{T}(::Type{T}, shape, ::Type{TestAbstractArray})
         @test isassigned(B, length(B) + 1) == false
     end
 
-    # reshape(a::AbstractArray, dims::Dims)
-    @test_throws ArgumentError reshape(B, (0, 1))
-
     # copy!(dest::AbstractArray, src::AbstractArray)
     @test_throws BoundsError copy!(Array(Int, 10), [1:11...])
 
@@ -252,7 +249,7 @@ type UnimplementedArray{T, N} <: AbstractArray{T, N} end
 
 function test_getindex_internals{T}(::Type{T}, shape, ::Type{TestAbstractArray})
     N = prod(shape)
-    A = reshape(1:N, shape)
+    A = collect(1:N, shape)
     B = T(A)
 
     @test getindex(A) == 1
@@ -272,7 +269,7 @@ end
 
 function test_setindex!_internals{T}(::Type{T}, shape, ::Type{TestAbstractArray})
     N = prod(shape)
-    A = reshape(1:N, shape)
+    A = collect(1:N, shape)
     B = T(A)
 
     Base.unsafe_setindex!(B, 1)
@@ -362,7 +359,7 @@ function test_ind2sub(::Type{TestAbstractArray})
     n = rand(2:5)
     dims = tuple(rand(1:5, n)...)
     len = prod(dims)
-    A = reshape(1:len, dims...)
+    A = collect(1:len, dims)
     I = ind2sub(dims, [1:len...])
     for i in 1:len
         idx = [ I[j][i] for j in 1:n ]

test/arrayops.jl

@@ -100,11 +100,11 @@ a = rand(1, 1, 8, 8, 1)
 @test_throws ArgumentError squeeze(a, 6)
 
 sz = (5,8,7)
-A = reshape(1:prod(sz),sz...)
+A = collect(1:prod(sz), sz)
 @test A[2:6] == [2:6;]
 @test A[1:3,2,2:4] == cat(2,46:48,86:88,126:128)
 @test A[:,7:-3:1,5] == [191 176 161; 192 177 162; 193 178 163; 194 179 164; 195 180 165]
-@test A[:,3:9] == reshape(11:45,5,7)
+@test A[:,3:9] == collect(11:45, (5,7))
 rng = (2,2:3,2:2:5)
 tmp = zeros(Int,map(maximum,rng)...)
 tmp[rng...] = A[rng...]
@@ -138,7 +138,7 @@ B[[3,1],[2,4]] = [21 22; 23 24]
 B[4,[2,3]] = 7
 @test B == [0 23 1 24 0; 11 12 13 14 15; 0 21 3 22 0; 0 7 7 0 0]
 
-@test isequal(reshape(1:27, 3, 3, 3)[1,:], [1,  4,  7,  10,  13,  16,  19,  22,  25])
+@test isequal(collect(1:27, (3, 3, 3))[1,:], [1,  4,  7,  10,  13,  16,  19,  22,  25])
 
 a = [3, 5, -7, 6]
 b = [4, 6, 2, -7, 1]
@@ -169,7 +169,7 @@ rt = Base.return_types(setindex!, Tuple{Array{Int32, 3}, UInt8, Vector{Int}, Int
 
 # get
 let
-    A = reshape(1:24, 3, 8)
+    A = collect(1:24, (3, 8))
     x = get(A, 32, -12)
     @test x == -12
     x = get(A, 14, -12)
@@ -368,7 +368,7 @@ p = permutedims(s, [2,1])
 @test p[2,1]==a[2,3] && p[2,2]==a[3,3]
 
 # of a non-strided subarray
-a = reshape(1:60, 3, 4, 5)
+a = collect(1:60, (3, 4, 5))
 s = sub(a,:,[1,2,4],[1,5])
 c = convert(Array, s)
 for p in ([1,2,3], [1,3,2], [2,1,3], [2,3,1], [3,1,2], [3,2,1])
@@ -553,7 +553,7 @@ let
                   3 3 4 4 3 3 4 4;
                   3 3 4 4 3 3 4 4]
 
-    A = reshape(1:8, 2, 2, 2)
+    A = collect(1:8, (2, 2, 2))
     R = repeat(A, inner = [1, 1, 2], outer = [1, 1, 1])
     T = reshape([1:4; 1:4; 5:8; 5:8], 2, 2, 4)
     @test R == T
@@ -623,7 +623,7 @@ a[[true,false,true,false,true]] = 6
 a[[true,false,true,false,true]] = [7,8,9]
 @test a == [7,2,8,4,9]
 @test_throws DimensionMismatch (a[[true,false,true,false,true]] = [7,8,9,10])
-A = reshape(1:15, 3, 5)
+A = collect(1:15, (3, 5))
 @test A[[true, false, true], [false, false, true, true, false]] == [7 10; 9 12]
 @test_throws BoundsError A[[true, false], [false, false, true, true, false]]
 @test_throws BoundsError A[[true, false, true], [false, true, true, false]]
@@ -1092,7 +1092,7 @@ a = ones(5,0)
 b = sub(a, :, :)
 @test mdsum(b) == 0
 
-a = reshape(1:60, 3, 4, 5)
+a = collect(1:60, (3, 4, 5))
 @test a[CartesianIndex{3}(2,3,4)] == 44
 a[CartesianIndex{3}(2,3,3)] = -1
 @test a[CartesianIndex{3}(2,3,3)] == -1
@@ -1353,7 +1353,6 @@ C = copy(B)
 @test A == C
 @test B == C
 
-@test vec(A) == vec(B) == vec(S)
 @test minimum(A) == minimum(B) == minimum(S)
 @test maximum(A) == maximum(B) == maximum(S)
 

test/dict.jl

@@ -262,7 +262,7 @@ end
 # show
 for d in (Dict("\n" => "\n", "1" => "\n", "\n" => "2"),
           [string(i) => i for i = 1:30],
-          [reshape(1:i^2,i,i) => reshape(1:i^2,i,i) for i = 1:24],
+          [collect(1:i^2,(i,i)) => collect(1:i^2,(i,i)) for i = 1:24],
           [utf8(Char['α':'α'+i;]) => utf8(Char['α':'α'+i;]) for i = (1:10)*10],
           Dict("key" => zeros(0, 0)))
     for cols in (12, 40, 80), rows in (2, 10, 24)

test/examples.jl

@@ -22,10 +22,10 @@ y = inv(x)
 
 include(joinpath(dir, "ndgrid.jl"))
 r = repmat(1:10,1,10)
-r1, r2 = ndgrid(1:10, 1:10)
+r1, r2 = ndgrid(collect(1:10), collect(1:10))
 @test r1 == r
 @test r2 == r'
-r3, r4 = meshgrid(1:10,1:10)
+r3, r4 = meshgrid(collect(1:10),collect(1:10))
 @test r3 == r'
 @test r4 == r
 

test/fft.jl

@@ -31,7 +31,7 @@ b = rand(17,14)
 b[3:6,9:12] = m4
 sm4 = slice(b,3:6,9:12)
 
-m3d = map(Float32,reshape(1:5*3*2, 5, 3, 2))
+m3d = map(Float32,collect(1:5*3*2, (5, 3, 2)))
 true_fftd3_m3d = Array(Float32, 5, 3, 2)
 true_fftd3_m3d[:,:,1] = 17:2:45
 true_fftd3_m3d[:,:,2] = -15

test/functional.jl

@@ -220,7 +220,7 @@ end
 @test collect((i+10j for i=1:2,j=3:4,k=1:1)) == reshape([31 41; 32 42], (2,2,1))
 
 let I = Base.IteratorND(1:27,(3,3,3))
-    @test collect(I) == reshape(1:27,(3,3,3))
+    @test collect(I) == collect(1:27,(3,3,3))
     @test size(I) == (3,3,3)
     @test length(I) == 27
     @test eltype(I) === Int

test/linalg/matmul.jl

@@ -114,7 +114,7 @@ C = zeros(Float64,6,6)
 @test Base.LinAlg.Ac_mul_B!(C,A,B) == A.'*B
 
 # matrix algebra with subarrays of floats (stride != 1)
-A = reshape(map(Float64,1:20),5,4)
+A = collect(1.0:20.0, (5,4))
 Aref = A[1:2:end,1:2:end]
 Asub = sub(A, 1:2:5, 1:2:4)
 b = [1.2,-2.5]
@@ -127,23 +127,23 @@ Asub = sub(Ai, 1:2:5, 1:2:4)
 @test Ac_mul_B(Asub, Asub) == Ac_mul_B(Aref, Aref)
 @test A_mul_Bc(Asub, Asub) == A_mul_Bc(Aref, Aref)
 # issue #15286
-let C = zeros(8, 8), sC = sub(C, 1:2:8, 1:2:8), B = reshape(map(Float64,-9:10),5,4)
+let C = zeros(8, 8), sC = sub(C, 1:2:8, 1:2:8), B = collect(-9.0:10.0, (5,4))
     @test At_mul_B!(sC, A, A) == A'*A
     @test At_mul_B!(sC, A, B) == A'*B
 end
-let Aim = A .- im, C = zeros(Complex128,8,8), sC = sub(C, 1:2:8, 1:2:8), B = reshape(map(Float64,-9:10),5,4) .+ im
+let Aim = A .- im, C = zeros(Complex128,8,8), sC = sub(C, 1:2:8, 1:2:8), B = collect(-9.0:10.0, (5,4)) .+ im
     @test Ac_mul_B!(sC, Aim, Aim) == Aim'*Aim
     @test Ac_mul_B!(sC, Aim, B) == Aim'*B
 end
 
 
 # syrk & herk
-A = reshape(1:1503, 501, 3).-750.0
+A = collect(1:1503, (501, 3)).-750.0
 res = Float64[135228751 9979252 -115270247; 9979252 10481254 10983256; -115270247 10983256 137236759]
 @test At_mul_B(A, A) == res
 @test A_mul_Bt(A',A') == res
 cutoff = 501
-A = reshape(1:6*cutoff,2*cutoff,3).-(6*cutoff)/2
+A = collect(1:6*cutoff, (2*cutoff,3)).-(6*cutoff)/2
 Asub = sub(A, 1:2:2*cutoff, 1:3)
 Aref = A[1:2:2*cutoff, 1:3]
 @test At_mul_B(Asub, Asub) == At_mul_B(Aref, Aref)

test/parallel_exec.jl

@@ -311,7 +311,7 @@ d = SharedArray(Float64, (2,3))
 fn = tempname()
 write(fn, 1:30)
 sz = (6,5)
-Atrue = reshape(1:30, sz)
+Atrue = collect(1:30, sz)
 
 S = SharedArray(fn, Int, sz)
 @test S == Atrue

test/reduce.jl

@@ -45,7 +45,7 @@
 @test sum([3]) === 3
 @test sum([3.0]) === 3.0
 
-z = reshape(1:16, (2,2,2,2))
+z = collect(1:16, (2,2,2,2))
 fz = float(z)
 @test sum(z) === 136
 @test sum(fz) === 136.0
@@ -167,9 +167,9 @@ prod2(itr) = invoke(prod, Tuple{Any}, itr)
 @test maximum(collect(Int16(1):Int16(100))) === Int16(100)
 @test maximum(Int32[1,2]) === Int32(2)
 
-@test extrema(reshape(1:24,2,3,4),1) == reshape([(1,2),(3,4),(5,6),(7,8),(9,10),(11,12),(13,14),(15,16),(17,18),(19,20),(21,22),(23,24)],1,3,4)
-@test extrema(reshape(1:24,2,3,4),2) == reshape([(1,5),(2,6),(7,11),(8,12),(13,17),(14,18),(19,23),(20,24)],2,1,4)
-@test extrema(reshape(1:24,2,3,4),3) == reshape([(1,19),(2,20),(3,21),(4,22),(5,23),(6,24)],2,3,1)
+@test extrema(collect(1:24,(2,3,4)),1) == reshape([(1,2),(3,4),(5,6),(7,8),(9,10),(11,12),(13,14),(15,16),(17,18),(19,20),(21,22),(23,24)],1,3,4)
+@test extrema(collect(1:24,(2,3,4)),2) == reshape([(1,5),(2,6),(7,11),(8,12),(13,17),(14,18),(19,23),(20,24)],2,1,4)
+@test extrema(collect(1:24,(2,3,4)),3) == reshape([(1,19),(2,20),(3,21),(4,22),(5,23),(6,24)],2,3,1)
 
 # any & all
 
@@ -275,11 +275,11 @@ end
 @test sum(collect(map(UInt8,0:255))) == 32640
 @test sum(collect(map(UInt8,254:255))) == 509
 
-A = reshape(map(UInt8, 101:109), (3,3))
+A = collect(UInt8(101):UInt8(109), (3,3))
 @test @inferred(sum(A)) == 945
 @test @inferred(sum(sub(A, 1:3, 1:3))) == 945
 
-A = reshape(map(UInt8, 1:100), (10,10))
+A = collect(UInt8(1):UInt8(100), (10,10))
 @test @inferred(sum(A)) == 5050
 @test @inferred(sum(sub(A, 1:10, 1:10))) == 5050
 

test/reducedim.jl

@@ -78,13 +78,13 @@ fill!(r, -1.1)
 @test_approx_eq sumabs2!(r, Breduc, init=false) safe_sumabs2(Breduc, 1)-1.1
 
 # Small arrays with init=false
-A = reshape(1:15, 3, 5)
+A = collect(1:15, (3, 5))
 R = ones(Int, 3)
 @test sum!(R, A, init=false) == [36,41,46]
 R = ones(Int, 1, 5)
 @test sum!(R, A, init=false) == [7 16 25 34 43]
 R = [2]
-A = reshape(1:6, 3, 2)
+A = collect(1:6, (3, 2))
 @test prod!(R, A, init=false) == [1440]
 
 # Small integers