# This file is a part of Julia. License is MIT: https://julialang.org/license Dims{N} = NTuple{N,Int} DimsInteger{N} = NTuple{N,Integer} Indices{N} = NTuple{N,AbstractUnitRange} # array shape rules promote_shape(::Tuple{}, ::Tuple{}) = () function promote_shape(a::Tuple{Int,}, b::Tuple{Int,}) if a[1] != b[1] throw(DimensionMismatch("dimensions must match")) end return a end function promote_shape(a::Tuple{Int,Int}, b::Tuple{Int,}) if a[1] != b[1] || a[2] != 1 throw(DimensionMismatch("dimensions must match")) end return a end promote_shape(a::Tuple{Int,}, b::Tuple{Int,Int}) = promote_shape(b, a) function promote_shape(a::Tuple{Int, Int}, b::Tuple{Int, Int}) if a[1] != b[1] || a[2] != b[2] throw(DimensionMismatch("dimensions must match")) end return a end """ promote_shape(s1, s2) Check two array shapes for compatibility, allowing trailing singleton dimensions, and return whichever shape has more dimensions. ```jldoctest julia> a = ones(3,4,1,1,1); julia> b = ones(3,4); julia> promote_shape(a,b) (Base.OneTo(3), Base.OneTo(4), Base.OneTo(1), Base.OneTo(1), Base.OneTo(1)) julia> promote_shape((2,3,1,4), (2, 3, 1, 4, 1)) (2, 3, 1, 4, 1) ``` """ function promote_shape(a::Dims, b::Dims) if length(a) < length(b) return promote_shape(b, a) end for i=1:length(b) if a[i] != b[i] throw(DimensionMismatch("dimensions must match")) end end for i=length(b)+1:length(a) if a[i] != 1 throw(DimensionMismatch("dimensions must match")) end end return a end function promote_shape(a::AbstractArray, b::AbstractArray) promote_shape(axes(a), axes(b)) end function promote_shape(a::Indices, b::Indices) if length(a) < length(b) return promote_shape(b, a) end for i=1:length(b) if a[i] != b[i] throw(DimensionMismatch("dimensions must match")) end end for i=length(b)+1:length(a) if a[i] != 1:1 throw(DimensionMismatch("dimensions must match")) end end return a end function throw_setindex_mismatch(X, I) if length(I) == 1 throw(DimensionMismatch("tried to assign $(length(X)) elements to $(I[1]) destinations")) else throw(DimensionMismatch("tried to assign $(dims2string(size(X))) array to $(dims2string(I)) destination")) end end # check for valid sizes in A[I...] = X where X <: AbstractArray # we want to allow dimensions that are equal up to permutation, but only # for permutations that leave array elements in the same linear order. # those are the permutations that preserve the order of the non-singleton # dimensions. function setindex_shape_check(X::AbstractArray, I::Integer...) li = ndims(X) lj = length(I) i = j = 1 while true ii = length(axes(X,i)) jj = I[j] if i == li || j == lj while i < li i += 1 ii *= length(axes(X,i)) end while j < lj j += 1 jj *= I[j] end if ii != jj throw_setindex_mismatch(X, I) end return end if ii == jj i += 1 j += 1 elseif ii == 1 i += 1 elseif jj == 1 j += 1 else throw_setindex_mismatch(X, I) end end end setindex_shape_check(X::AbstractArray) = (_length(X)==1 || throw_setindex_mismatch(X,())) setindex_shape_check(X::AbstractArray, i::Integer) = (_length(X)==i || throw_setindex_mismatch(X, (i,))) setindex_shape_check(X::AbstractArray{<:Any,1}, i::Integer) = (_length(X)==i || throw_setindex_mismatch(X, (i,))) setindex_shape_check(X::AbstractArray{<:Any,1}, i::Integer, j::Integer) = (_length(X)==i*j || throw_setindex_mismatch(X, (i,j))) function setindex_shape_check(X::AbstractArray{<:Any,2}, i::Integer, j::Integer) if length(X) != i*j throw_setindex_mismatch(X, (i,j)) end sx1 = length(axes(X,1)) if !(i == 1 || i == sx1 || sx1 == 1) throw_setindex_mismatch(X, (i,j)) end end setindex_shape_check(X, I...) = nothing # Non-arrays broadcast to all idxs # convert to a supported index type (array or Int) """ to_index(A, i) Convert index `i` to an `Int` or array of indices to be used as an index into array `A`. Custom array types may specialize `to_index(::CustomArray, i)` to provide special indexing behaviors. Note that some index types (like `Colon`) require more context in order to transform them into an array of indices; those get converted in the more complicated `to_indices` function. By default, this simply calls the generic `to_index(i)`. This must return either an `Int` or an `AbstractArray` of scalar indices that are supported by `A`. """ to_index(A, i) = to_index(i) """ to_index(i) Convert index `i` to an `Int` or array of `Int`s to be used as an index for all arrays. Custom index types may specialize `to_index(::CustomIndex)` to provide special indexing behaviors. This must return either an `Int` or an `AbstractArray` of `Int`s. """ to_index(i::Integer) = convert(Int,i)::Int # TODO: Enable this new definition after the deprecations introduced in 0.7 are removed # to_index(i::Bool) = throw(ArgumentError("invalid index: $i")) to_index(I::AbstractArray{Bool}) = LogicalIndex(I) to_index(I::AbstractArray) = I to_index(I::AbstractArray{<:Union{AbstractArray, Colon}}) = throw(ArgumentError("invalid index: $I")) to_index(::Colon) = throw(ArgumentError("colons must be converted by to_indices(...)")) to_index(i) = throw(ArgumentError("invalid index: $i")) # The general to_indices is mostly defined in multidimensional.jl, but this # definition is required for bootstrap: """ to_indices(A, I::Tuple) Convert the tuple `I` to a tuple of indices for use in indexing into array `A`. The returned tuple must only contain either `Int`s or `AbstractArray`s of scalar indices that are supported by array `A`. It will error upon encountering a novel index type that it does not know how to process. For simple index types, it defers to the unexported `Base.to_index(A, i)` to process each index `i`. While this internal function is not intended to be called directly, `Base.to_index` may be extended by custom array or index types to provide custom indexing behaviors. More complicated index types may require more context about the dimension into which they index. To support those cases, `to_indices(A, I)` calls `to_indices(A, axes(A), I)`, which then recursively walks through both the given tuple of indices and the dimensional indices of `A` in tandem. As such, not all index types are guaranteed to propagate to `Base.to_index`. """ to_indices(A, I::Tuple) = (@_inline_meta; to_indices(A, axes(A), I)) to_indices(A, I::Tuple{Any}) = (@_inline_meta; to_indices(A, (linearindices(A),), I)) to_indices(A, inds, ::Tuple{}) = () to_indices(A, inds, I::Tuple{Any, Vararg{Any}}) = (@_inline_meta; (to_index(A, I[1]), to_indices(A, _maybetail(inds), tail(I))...)) _maybetail(::Tuple{}) = () _maybetail(t::Tuple) = tail(t) """ Slice(indices) Represent an AbstractUnitRange of indices as a vector of the indices themselves. Upon calling `to_indices`, Colons are converted to Slice objects to represent the indices over which the Colon spans. Slice objects are themselves unit ranges with the same indices as those they wrap. This means that indexing into Slice objects with an integer always returns that exact integer, and they iterate over all the wrapped indices, even supporting offset indices. """ struct Slice{T<:AbstractUnitRange} <: AbstractUnitRange{Int} indices::T end axes(S::Slice) = (S.indices,) unsafe_indices(S::Slice) = (S.indices,) indices1(S::Slice) = S.indices first(S::Slice) = first(S.indices) last(S::Slice) = last(S.indices) errmsg(A) = error("size not supported for arrays with indices $(axes(A)); see https://docs.julialang.org/en/latest/devdocs/offset-arrays/") size(S::Slice) = first(S.indices) == 1 ? (length(S.indices),) : errmsg(S) length(S::Slice) = first(S.indices) == 1 ? length(S.indices) : errmsg(S) unsafe_length(S::Slice) = first(S.indices) == 1 ? unsafe_length(S.indices) : errmsg(S) getindex(S::Slice, i::Int) = (@_inline_meta; @boundscheck checkbounds(S, i); i) show(io::IO, r::Slice) = print(io, "Base.Slice(", r.indices, ")") start(S::Slice) = start(S.indices) next(S::Slice, s) = next(S.indices, s) done(S::Slice, s) = done(S.indices, s)