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abstractarray.jl
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abstractarray.jl
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# This file is a part of Julia. License is MIT: https://julialang.org/license
## Basic functions ##
"""
AbstractArray{T,N}
Supertype for `N`-dimensional arrays (or array-like types) with elements of type `T`.
[`Array`](@ref) and other types are subtypes of this. See the manual section on the
[`AbstractArray` interface](@ref man-interface-array).
See also: [`AbstractVector`](@ref), [`AbstractMatrix`](@ref), [`eltype`](@ref), [`ndims`](@ref).
"""
AbstractArray
convert(::Type{T}, a::T) where {T<:AbstractArray} = a
convert(::Type{AbstractArray{T}}, a::AbstractArray) where {T} = AbstractArray{T}(a)
convert(::Type{AbstractArray{T,N}}, a::AbstractArray{<:Any,N}) where {T,N} = AbstractArray{T,N}(a)
"""
size(A::AbstractArray, [dim])
Return a tuple containing the dimensions of `A`. Optionally you can specify a
dimension to just get the length of that dimension.
Note that `size` may not be defined for arrays with non-standard indices, in which case [`axes`](@ref)
may be useful. See the manual chapter on [arrays with custom indices](@ref man-custom-indices).
See also: [`length`](@ref), [`ndims`](@ref), [`eachindex`](@ref), [`sizeof`](@ref).
# Examples
```jldoctest
julia> A = fill(1, (2,3,4));
julia> size(A)
(2, 3, 4)
julia> size(A, 2)
3
```
"""
size(t::AbstractArray{T,N}, d) where {T,N} = d::Integer <= N ? size(t)[d] : 1
"""
axes(A, d)
Return the valid range of indices for array `A` along dimension `d`.
See also [`size`](@ref), and the manual chapter on [arrays with custom indices](@ref man-custom-indices).
# Examples
```jldoctest
julia> A = fill(1, (5,6,7));
julia> axes(A, 2)
Base.OneTo(6)
```
# Usage note
Each of the indices has to be an `AbstractUnitRange{<:Integer}`, but at the same time can be
a type that uses custom indices. So, for example, if you need a subset, use generalized
indexing constructs like `begin`/`end` or [`firstindex`](@ref)/[`lastindex`](@ref):
```julia
ix = axes(v, 1)
ix[2:end] # will work for eg Vector, but may fail in general
ix[(begin+1):end] # works for generalized indexes
```
"""
function axes(A::AbstractArray{T,N}, d) where {T,N}
@_inline_meta
d::Integer <= N ? axes(A)[d] : OneTo(1)
end
"""
axes(A)
Return the tuple of valid indices for array `A`.
See also: [`size`](@ref), [`keys`](@ref), [`eachindex`](@ref).
# Examples
```jldoctest
julia> A = fill(1, (5,6,7));
julia> axes(A)
(Base.OneTo(5), Base.OneTo(6), Base.OneTo(7))
```
"""
function axes(A)
@_inline_meta
map(oneto, size(A))
end
"""
has_offset_axes(A)
has_offset_axes(A, B, ...)
Return `true` if the indices of `A` start with something other than 1 along any axis.
If multiple arguments are passed, equivalent to `has_offset_axes(A) | has_offset_axes(B) | ...`.
"""
has_offset_axes(A) = _tuple_any(x->Int(first(x))::Int != 1, axes(A))
has_offset_axes(A::AbstractVector) = Int(firstindex(A))::Int != 1 # improve performance of a common case (ranges)
has_offset_axes(A...) = _tuple_any(has_offset_axes, A)
has_offset_axes(::Colon) = false
require_one_based_indexing(A...) = !has_offset_axes(A...) || throw(ArgumentError("offset arrays are not supported but got an array with index other than 1"))
# Performance optimization: get rid of a branch on `d` in `axes(A, d)`
# for d=1. 1d arrays are heavily used, and the first dimension comes up
# in other applications.
axes1(A::AbstractArray{<:Any,0}) = OneTo(1)
axes1(A::AbstractArray) = (@_inline_meta; axes(A)[1])
axes1(iter) = oneto(length(iter))
unsafe_indices(A) = axes(A)
unsafe_indices(r::AbstractRange) = (oneto(unsafe_length(r)),) # Ranges use checked_sub for size
"""
keys(a::AbstractArray)
Return an efficient array describing all valid indices for `a` arranged in the shape of `a` itself.
They keys of 1-dimensional arrays (vectors) are integers, whereas all other N-dimensional
arrays use [`CartesianIndex`](@ref) to describe their locations. Often the special array
types [`LinearIndices`](@ref) and [`CartesianIndices`](@ref) are used to efficiently
represent these arrays of integers and `CartesianIndex`es, respectively.
Note that the `keys` of an array might not be the most efficient index type; for maximum
performance use [`eachindex`](@ref) instead.
"""
keys(a::AbstractArray) = CartesianIndices(axes(a))
keys(a::AbstractVector) = LinearIndices(a)
"""
keytype(T::Type{<:AbstractArray})
keytype(A::AbstractArray)
Return the key type of an array. This is equal to the
`eltype` of the result of `keys(...)`, and is provided
mainly for compatibility with the dictionary interface.
# Examples
```jldoctest
julia> keytype([1, 2, 3]) == Int
true
julia> keytype([1 2; 3 4])
CartesianIndex{2}
```
!!! compat "Julia 1.2"
For arrays, this function requires at least Julia 1.2.
"""
keytype(a::AbstractArray) = keytype(typeof(a))
keytype(A::Type{<:AbstractArray}) = CartesianIndex{ndims(A)}
keytype(A::Type{<:AbstractVector}) = Int
valtype(a::AbstractArray) = valtype(typeof(a))
"""
valtype(T::Type{<:AbstractArray})
valtype(A::AbstractArray)
Return the value type of an array. This is identical to `eltype` and is
provided mainly for compatibility with the dictionary interface.
# Examples
```jldoctest
julia> valtype(["one", "two", "three"])
String
```
!!! compat "Julia 1.2"
For arrays, this function requires at least Julia 1.2.
"""
valtype(A::Type{<:AbstractArray}) = eltype(A)
prevind(::AbstractArray, i::Integer) = Int(i)-1
nextind(::AbstractArray, i::Integer) = Int(i)+1
"""
eltype(type)
Determine the type of the elements generated by iterating a collection of the given `type`.
For dictionary types, this will be a `Pair{KeyType,ValType}`. The definition
`eltype(x) = eltype(typeof(x))` is provided for convenience so that instances can be passed
instead of types. However the form that accepts a type argument should be defined for new
types.
See also: [`keytype`](@ref), [`typeof`](@ref).
# Examples
```jldoctest
julia> eltype(fill(1f0, (2,2)))
Float32
julia> eltype(fill(0x1, (2,2)))
UInt8
```
"""
eltype(::Type) = Any
eltype(::Type{Bottom}) = throw(ArgumentError("Union{} does not have elements"))
eltype(x) = eltype(typeof(x))
eltype(::Type{<:AbstractArray{E}}) where {E} = @isdefined(E) ? E : Any
"""
elsize(type)
Compute the memory stride in bytes between consecutive elements of `eltype`
stored inside the given `type`, if the array elements are stored densely with a
uniform linear stride.
"""
elsize(A::AbstractArray) = elsize(typeof(A))
"""
ndims(A::AbstractArray) -> Integer
Return the number of dimensions of `A`.
See also: [`size`](@ref), [`axes`](@ref).
# Examples
```jldoctest
julia> A = fill(1, (3,4,5));
julia> ndims(A)
3
```
"""
ndims(::AbstractArray{T,N}) where {T,N} = N
ndims(::Type{<:AbstractArray{<:Any,N}}) where {N} = N
"""
length(collection) -> Integer
Return the number of elements in the collection.
Use [`lastindex`](@ref) to get the last valid index of an indexable collection.
See also: [`size`](@ref), [`ndims`](@ref), [`eachindex`](@ref).
# Examples
```jldoctest
julia> length(1:5)
5
julia> length([1, 2, 3, 4])
4
julia> length([1 2; 3 4])
4
```
"""
length
"""
length(A::AbstractArray)
Return the number of elements in the array, defaults to `prod(size(A))`.
# Examples
```jldoctest
julia> length([1, 2, 3, 4])
4
julia> length([1 2; 3 4])
4
```
"""
length(t::AbstractArray) = (@_inline_meta; prod(size(t)))
# `eachindex` is mostly an optimization of `keys`
eachindex(itrs...) = keys(itrs...)
# eachindex iterates over all indices. IndexCartesian definitions are later.
eachindex(A::AbstractVector) = (@_inline_meta(); axes1(A))
@noinline function throw_eachindex_mismatch_indices(::IndexLinear, inds...)
throw(DimensionMismatch("all inputs to eachindex must have the same indices, got $(join(inds, ", ", " and "))"))
end
@noinline function throw_eachindex_mismatch_indices(::IndexCartesian, inds...)
throw(DimensionMismatch("all inputs to eachindex must have the same axes, got $(join(inds, ", ", " and "))"))
end
"""
eachindex(A...)
Create an iterable object for visiting each index of an `AbstractArray` `A` in an efficient
manner. For array types that have opted into fast linear indexing (like `Array`), this is
simply the range `1:length(A)`. For other array types, return a specialized Cartesian
range to efficiently index into the array with indices specified for every dimension. For
other iterables, including strings and dictionaries, return an iterator object
supporting arbitrary index types (e.g. unevenly spaced or non-integer indices).
If you supply more than one `AbstractArray` argument, `eachindex` will create an
iterable object that is fast for all arguments (a [`UnitRange`](@ref)
if all inputs have fast linear indexing, a [`CartesianIndices`](@ref)
otherwise).
If the arrays have different sizes and/or dimensionalities, a DimensionMismatch exception
will be thrown.
# Examples
```jldoctest
julia> A = [1 2; 3 4];
julia> for i in eachindex(A) # linear indexing
println(i)
end
1
2
3
4
julia> for i in eachindex(view(A, 1:2, 1:1)) # Cartesian indexing
println(i)
end
CartesianIndex(1, 1)
CartesianIndex(2, 1)
```
"""
eachindex(A::AbstractArray) = (@_inline_meta(); eachindex(IndexStyle(A), A))
function eachindex(A::AbstractArray, B::AbstractArray)
@_inline_meta
eachindex(IndexStyle(A,B), A, B)
end
function eachindex(A::AbstractArray, B::AbstractArray...)
@_inline_meta
eachindex(IndexStyle(A,B...), A, B...)
end
eachindex(::IndexLinear, A::AbstractArray) = (@_inline_meta; oneto(length(A)))
eachindex(::IndexLinear, A::AbstractVector) = (@_inline_meta; axes1(A))
function eachindex(::IndexLinear, A::AbstractArray, B::AbstractArray...)
@_inline_meta
indsA = eachindex(IndexLinear(), A)
_all_match_first(X->eachindex(IndexLinear(), X), indsA, B...) ||
throw_eachindex_mismatch_indices(IndexLinear(), eachindex(A), eachindex.(B)...)
indsA
end
function _all_match_first(f::F, inds, A, B...) where F<:Function
@_inline_meta
(inds == f(A)) & _all_match_first(f, inds, B...)
end
_all_match_first(f::F, inds) where F<:Function = true
# keys with an IndexStyle
keys(s::IndexStyle, A::AbstractArray, B::AbstractArray...) = eachindex(s, A, B...)
"""
lastindex(collection) -> Integer
lastindex(collection, d) -> Integer
Return the last index of `collection`. If `d` is given, return the last index of `collection` along dimension `d`.
The syntaxes `A[end]` and `A[end, end]` lower to `A[lastindex(A)]` and
`A[lastindex(A, 1), lastindex(A, 2)]`, respectively.
See also: [`axes`](@ref), [`firstindex`](@ref), [`eachindex`](@ref), [`prevind`](@ref).
# Examples
```jldoctest
julia> lastindex([1,2,4])
3
julia> lastindex(rand(3,4,5), 2)
4
```
"""
lastindex(a::AbstractArray) = (@_inline_meta; last(eachindex(IndexLinear(), a)))
lastindex(a, d) = (@_inline_meta; last(axes(a, d)))
"""
firstindex(collection) -> Integer
firstindex(collection, d) -> Integer
Return the first index of `collection`. If `d` is given, return the first index of `collection` along dimension `d`.
The syntaxes `A[begin]` and `A[1, begin]` lower to `A[firstindex(A)]` and
`A[1, firstindex(A, 2)]`, respectively.
See also: [`first`](@ref), [`axes`](@ref), [`lastindex`](@ref), [`nextind`](@ref).
# Examples
```jldoctest
julia> firstindex([1,2,4])
1
julia> firstindex(rand(3,4,5), 2)
1
```
"""
firstindex(a::AbstractArray) = (@_inline_meta; first(eachindex(IndexLinear(), a)))
firstindex(a, d) = (@_inline_meta; first(axes(a, d)))
first(a::AbstractArray) = a[first(eachindex(a))]
"""
first(coll)
Get the first element of an iterable collection. Return the start point of an
[`AbstractRange`](@ref) even if it is empty.
See also: [`only`](@ref), [`firstindex`](@ref), [`last`](@ref).
# Examples
```jldoctest
julia> first(2:2:10)
2
julia> first([1; 2; 3; 4])
1
```
"""
function first(itr)
x = iterate(itr)
x === nothing && throw(ArgumentError("collection must be non-empty"))
x[1]
end
"""
first(itr, n::Integer)
Get the first `n` elements of the iterable collection `itr`, or fewer elements if `itr` is not
long enough.
See also: [`startswith`](@ref), [`Iterators.take`](@ref).
!!! compat "Julia 1.6"
This method requires at least Julia 1.6.
# Examples
```jldoctest
julia> first(["foo", "bar", "qux"], 2)
2-element Vector{String}:
"foo"
"bar"
julia> first(1:6, 10)
1:6
julia> first(Bool[], 1)
Bool[]
```
"""
first(itr, n::Integer) = collect(Iterators.take(itr, n))
# Faster method for vectors
function first(v::AbstractVector, n::Integer)
n < 0 && throw(ArgumentError("Number of elements must be nonnegative"))
@inbounds v[begin:min(begin + n - 1, end)]
end
"""
last(coll)
Get the last element of an ordered collection, if it can be computed in O(1) time. This is
accomplished by calling [`lastindex`](@ref) to get the last index. Return the end
point of an [`AbstractRange`](@ref) even if it is empty.
See also [`first`](@ref), [`endswith`](@ref).
# Examples
```jldoctest
julia> last(1:2:10)
9
julia> last([1; 2; 3; 4])
4
```
"""
last(a) = a[end]
"""
last(itr, n::Integer)
Get the last `n` elements of the iterable collection `itr`, or fewer elements if `itr` is not
long enough.
!!! compat "Julia 1.6"
This method requires at least Julia 1.6.
# Examples
```jldoctest
julia> last(["foo", "bar", "qux"], 2)
2-element Vector{String}:
"bar"
"qux"
julia> last(1:6, 10)
1:6
julia> last(Float64[], 1)
Float64[]
```
"""
last(itr, n::Integer) = reverse!(collect(Iterators.take(Iterators.reverse(itr), n)))
# Faster method for arrays
function last(v::AbstractVector, n::Integer)
n < 0 && throw(ArgumentError("Number of elements must be nonnegative"))
@inbounds v[max(begin, end - n + 1):end]
end
"""
strides(A)
Return a tuple of the memory strides in each dimension.
See also: [`stride`](@ref).
# Examples
```jldoctest
julia> A = fill(1, (3,4,5));
julia> strides(A)
(1, 3, 12)
```
"""
function strides end
"""
stride(A, k::Integer)
Return the distance in memory (in number of elements) between adjacent elements in dimension `k`.
See also: [`strides`](@ref).
# Examples
```jldoctest
julia> A = fill(1, (3,4,5));
julia> stride(A,2)
3
julia> stride(A,3)
12
```
"""
function stride(A::AbstractArray, k::Integer)
st = strides(A)
k ≤ ndims(A) && return st[k]
return sum(st .* size(A))
end
@inline size_to_strides(s, d, sz...) = (s, size_to_strides(s * d, sz...)...)
size_to_strides(s, d) = (s,)
size_to_strides(s) = ()
function isassigned(a::AbstractArray, i::Integer...)
try
a[i...]
true
catch e
if isa(e, BoundsError) || isa(e, UndefRefError)
return false
else
rethrow()
end
end
end
function isstored(A::AbstractArray{<:Any,N}, I::Vararg{Integer,N}) where {N}
@boundscheck checkbounds(A, I...)
return true
end
# used to compute "end" for last index
function trailingsize(A, n)
s = 1
for i=n:ndims(A)
s *= size(A,i)
end
return s
end
function trailingsize(inds::Indices, n)
s = 1
for i=n:length(inds)
s *= unsafe_length(inds[i])
end
return s
end
# This version is type-stable even if inds is heterogeneous
function trailingsize(inds::Indices)
@_inline_meta
prod(map(unsafe_length, inds))
end
## Bounds checking ##
# The overall hierarchy is
# `checkbounds(A, I...)` ->
# `checkbounds(Bool, A, I...)` ->
# `checkbounds_indices(Bool, IA, I)`, which recursively calls
# `checkindex` for each dimension
#
# See the "boundscheck" devdocs for more information.
#
# Note this hierarchy has been designed to reduce the likelihood of
# method ambiguities. We try to make `checkbounds` the place to
# specialize on array type, and try to avoid specializations on index
# types; conversely, `checkindex` is intended to be specialized only
# on index type (especially, its last argument).
"""
checkbounds(Bool, A, I...)
Return `true` if the specified indices `I` are in bounds for the given
array `A`. Subtypes of `AbstractArray` should specialize this method
if they need to provide custom bounds checking behaviors; however, in
many cases one can rely on `A`'s indices and [`checkindex`](@ref).
See also [`checkindex`](@ref).
# Examples
```jldoctest
julia> A = rand(3, 3);
julia> checkbounds(Bool, A, 2)
true
julia> checkbounds(Bool, A, 3, 4)
false
julia> checkbounds(Bool, A, 1:3)
true
julia> checkbounds(Bool, A, 1:3, 2:4)
false
```
"""
function checkbounds(::Type{Bool}, A::AbstractArray, I...)
@_inline_meta
checkbounds_indices(Bool, axes(A), I)
end
# Linear indexing is explicitly allowed when there is only one (non-cartesian) index
function checkbounds(::Type{Bool}, A::AbstractArray, i)
@_inline_meta
checkindex(Bool, eachindex(IndexLinear(), A), i)
end
# As a special extension, allow using logical arrays that match the source array exactly
function checkbounds(::Type{Bool}, A::AbstractArray{<:Any,N}, I::AbstractArray{Bool,N}) where N
@_inline_meta
axes(A) == axes(I)
end
"""
checkbounds(A, I...)
Throw an error if the specified indices `I` are not in bounds for the given array `A`.
"""
function checkbounds(A::AbstractArray, I...)
@_inline_meta
checkbounds(Bool, A, I...) || throw_boundserror(A, I)
nothing
end
"""
checkbounds_indices(Bool, IA, I)
Return `true` if the "requested" indices in the tuple `I` fall within
the bounds of the "permitted" indices specified by the tuple
`IA`. This function recursively consumes elements of these tuples,
usually in a 1-for-1 fashion,
checkbounds_indices(Bool, (IA1, IA...), (I1, I...)) = checkindex(Bool, IA1, I1) &
checkbounds_indices(Bool, IA, I)
Note that [`checkindex`](@ref) is being used to perform the actual
bounds-check for a single dimension of the array.
There are two important exceptions to the 1-1 rule: linear indexing and
CartesianIndex{N}, both of which may "consume" more than one element
of `IA`.
See also [`checkbounds`](@ref).
"""
function checkbounds_indices(::Type{Bool}, IA::Tuple, I::Tuple)
@_inline_meta
checkindex(Bool, IA[1], I[1])::Bool & checkbounds_indices(Bool, tail(IA), tail(I))
end
function checkbounds_indices(::Type{Bool}, ::Tuple{}, I::Tuple)
@_inline_meta
checkindex(Bool, OneTo(1), I[1])::Bool & checkbounds_indices(Bool, (), tail(I))
end
checkbounds_indices(::Type{Bool}, IA::Tuple, ::Tuple{}) = (@_inline_meta; all(x->unsafe_length(x)==1, IA))
checkbounds_indices(::Type{Bool}, ::Tuple{}, ::Tuple{}) = true
throw_boundserror(A, I) = (@_noinline_meta; throw(BoundsError(A, I)))
# check along a single dimension
"""
checkindex(Bool, inds::AbstractUnitRange, index)
Return `true` if the given `index` is within the bounds of
`inds`. Custom types that would like to behave as indices for all
arrays can extend this method in order to provide a specialized bounds
checking implementation.
See also [`checkbounds`](@ref).
# Examples
```jldoctest
julia> checkindex(Bool, 1:20, 8)
true
julia> checkindex(Bool, 1:20, 21)
false
```
"""
checkindex(::Type{Bool}, inds::AbstractUnitRange, i) =
throw(ArgumentError("unable to check bounds for indices of type $(typeof(i))"))
checkindex(::Type{Bool}, inds::AbstractUnitRange, i::Real) = (first(inds) <= i) & (i <= last(inds))
checkindex(::Type{Bool}, inds::AbstractUnitRange, ::Colon) = true
checkindex(::Type{Bool}, inds::AbstractUnitRange, ::Slice) = true
function checkindex(::Type{Bool}, inds::AbstractUnitRange, r::AbstractRange)
@_propagate_inbounds_meta
isempty(r) | (checkindex(Bool, inds, first(r)) & checkindex(Bool, inds, last(r)))
end
checkindex(::Type{Bool}, indx::AbstractUnitRange, I::AbstractVector{Bool}) = indx == axes1(I)
checkindex(::Type{Bool}, indx::AbstractUnitRange, I::AbstractArray{Bool}) = false
function checkindex(::Type{Bool}, inds::AbstractUnitRange, I::AbstractArray)
@_inline_meta
b = true
for i in I
b &= checkindex(Bool, inds, i)
end
b
end
# See also specializations in multidimensional
## Constructors ##
# default arguments to similar()
"""
similar(array, [element_type=eltype(array)], [dims=size(array)])
Create an uninitialized mutable array with the given element type and size, based upon the
given source array. The second and third arguments are both optional, defaulting to the
given array's `eltype` and `size`. The dimensions may be specified either as a single tuple
argument or as a series of integer arguments.
Custom AbstractArray subtypes may choose which specific array type is best-suited to return
for the given element type and dimensionality. If they do not specialize this method, the
default is an `Array{element_type}(undef, dims...)`.
For example, `similar(1:10, 1, 4)` returns an uninitialized `Array{Int,2}` since ranges are
neither mutable nor support 2 dimensions:
```julia-repl
julia> similar(1:10, 1, 4)
1×4 Array{Int64,2}:
4419743872 4374413872 4419743888 0
```
Conversely, `similar(trues(10,10), 2)` returns an uninitialized `BitVector` with two
elements since `BitArray`s are both mutable and can support 1-dimensional arrays:
```julia-repl
julia> similar(trues(10,10), 2)
2-element BitVector:
0
0
```
Since `BitArray`s can only store elements of type [`Bool`](@ref), however, if you request a
different element type it will create a regular `Array` instead:
```julia-repl
julia> similar(falses(10), Float64, 2, 4)
2×4 Array{Float64,2}:
2.18425e-314 2.18425e-314 2.18425e-314 2.18425e-314
2.18425e-314 2.18425e-314 2.18425e-314 2.18425e-314
```
See also: [`undef`](@ref), [`isassigned`](@ref).
"""
similar(a::AbstractArray{T}) where {T} = similar(a, T)
similar(a::AbstractArray, ::Type{T}) where {T} = similar(a, T, to_shape(axes(a)))
similar(a::AbstractArray{T}, dims::Tuple) where {T} = similar(a, T, to_shape(dims))
similar(a::AbstractArray{T}, dims::DimOrInd...) where {T} = similar(a, T, to_shape(dims))
similar(a::AbstractArray, ::Type{T}, dims::DimOrInd...) where {T} = similar(a, T, to_shape(dims))
# Similar supports specifying dims as either Integers or AbstractUnitRanges or any mixed combination
# thereof. Ideally, we'd just convert Integers to OneTos and then call a canonical method with the axes,
# but we don't want to require all AbstractArray subtypes to dispatch on Base.OneTo. So instead we
# define this method to convert supported axes to Ints, with the expectation that an offset array
# package will define a method with dims::Tuple{Union{Integer, UnitRange}, Vararg{Union{Integer, UnitRange}}}
similar(a::AbstractArray, ::Type{T}, dims::Tuple{Union{Integer, OneTo}, Vararg{Union{Integer, OneTo}}}) where {T} = similar(a, T, to_shape(dims))
similar(a::AbstractArray, ::Type{T}, dims::Tuple{Integer, Vararg{Integer}}) where {T} = similar(a, T, to_shape(dims))
# similar creates an Array by default
similar(a::AbstractArray, ::Type{T}, dims::Dims{N}) where {T,N} = Array{T,N}(undef, dims)
to_shape(::Tuple{}) = ()
to_shape(dims::Dims) = dims
to_shape(dims::DimsOrInds) = map(to_shape, dims)::DimsOrInds
# each dimension
to_shape(i::Int) = i
to_shape(i::Integer) = Int(i)
to_shape(r::OneTo) = Int(last(r))
to_shape(r::AbstractUnitRange) = r
"""
similar(storagetype, axes)
Create an uninitialized mutable array analogous to that specified by
`storagetype`, but with `axes` specified by the last
argument.
**Examples**:
similar(Array{Int}, axes(A))
creates an array that "acts like" an `Array{Int}` (and might indeed be
backed by one), but which is indexed identically to `A`. If `A` has
conventional indexing, this will be identical to
`Array{Int}(undef, size(A))`, but if `A` has unconventional indexing then the
indices of the result will match `A`.
similar(BitArray, (axes(A, 2),))
would create a 1-dimensional logical array whose indices match those
of the columns of `A`.
"""
similar(::Type{T}, dims::DimOrInd...) where {T<:AbstractArray} = similar(T, dims)
similar(::Type{T}, shape::Tuple{Union{Integer, OneTo}, Vararg{Union{Integer, OneTo}}}) where {T<:AbstractArray} = similar(T, to_shape(shape))
similar(::Type{T}, dims::Dims) where {T<:AbstractArray} = T(undef, dims)
"""
empty(v::AbstractVector, [eltype])
Create an empty vector similar to `v`, optionally changing the `eltype`.
See also: [`empty!`](@ref), [`isempty`](@ref), [`isassigned`](@ref).
# Examples
```jldoctest
julia> empty([1.0, 2.0, 3.0])
Float64[]
julia> empty([1.0, 2.0, 3.0], String)
String[]
```
"""
empty(a::AbstractVector{T}, ::Type{U}=T) where {T,U} = Vector{U}()
# like empty, but should return a mutable collection, a Vector by default
emptymutable(a::AbstractVector{T}, ::Type{U}=T) where {T,U} = Vector{U}()
emptymutable(itr, ::Type{U}) where {U} = Vector{U}()
"""
copy!(dst, src) -> dst
In-place [`copy`](@ref) of `src` into `dst`, discarding any pre-existing
elements in `dst`.
If `dst` and `src` are of the same type, `dst == src` should hold after
the call. If `dst` and `src` are multidimensional arrays, they must have
equal [`axes`](@ref).
See also [`copyto!`](@ref).
!!! compat "Julia 1.1"
This method requires at least Julia 1.1. In Julia 1.0 this method
is available from the `Future` standard library as `Future.copy!`.
"""
function copy!(dst::AbstractVector, src::AbstractVector)
if length(dst) != length(src)
resize!(dst, length(src))
end
for i in eachindex(dst, src)
@inbounds dst[i] = src[i]
end
dst
end
function copy!(dst::AbstractArray, src::AbstractArray)
axes(dst) == axes(src) || throw(ArgumentError(
"arrays must have the same axes for copy! (consider using `copyto!`)"))
copyto!(dst, src)
end
## from general iterable to any array
function copyto!(dest::AbstractArray, src)
destiter = eachindex(dest)
y = iterate(destiter)
for x in src
y === nothing &&
throw(ArgumentError("destination has fewer elements than required"))
dest[y[1]] = x
y = iterate(destiter, y[2])
end
return dest
end
function copyto!(dest::AbstractArray, dstart::Integer, src)
i = Int(dstart)
for x in src
dest[i] = x
i += 1
end
return dest
end
# copy from an some iterable object into an AbstractArray
function copyto!(dest::AbstractArray, dstart::Integer, src, sstart::Integer)
if (sstart < 1)
throw(ArgumentError(string("source start offset (",sstart,") is < 1")))
end
y = iterate(src)
for j = 1:(sstart-1)
if y === nothing
throw(ArgumentError(string("source has fewer elements than required, ",
"expected at least ",sstart,", got ",j-1)))
end
y = iterate(src, y[2])
end
if y === nothing
throw(ArgumentError(string("source has fewer elements than required, ",
"expected at least ",sstart,", got ",sstart-1)))
end
i = Int(dstart)
while y !== nothing
val, st = y
dest[i] = val
i += 1
y = iterate(src, st)
end
return dest
end
# this method must be separate from the above since src might not have a length
function copyto!(dest::AbstractArray, dstart::Integer, src, sstart::Integer, n::Integer)
n < 0 && throw(ArgumentError(string("tried to copy n=", n, " elements, but n should be nonnegative")))
n == 0 && return dest
dmax = dstart + n - 1
inds = LinearIndices(dest)
if (dstart ∉ inds || dmax ∉ inds) | (sstart < 1)
sstart < 1 && throw(ArgumentError(string("source start offset (",sstart,") is < 1")))
throw(BoundsError(dest, dstart:dmax))
end
y = iterate(src)
for j = 1:(sstart-1)
if y === nothing
throw(ArgumentError(string("source has fewer elements than required, ",
"expected at least ",sstart,", got ",j-1)))
end
y = iterate(src, y[2])
end
i = Int(dstart)
while i <= dmax && y !== nothing
val, st = y
@inbounds dest[i] = val
y = iterate(src, st)
i += 1
end
i <= dmax && throw(BoundsError(dest, i))
return dest
end
## copy between abstract arrays - generally more efficient
## since a single index variable can be used.
"""
copyto!(dest::AbstractArray, src) -> dest
Copy all elements from collection `src` to array `dest`, whose length must be greater than
or equal to the length `n` of `src`. The first `n` elements of `dest` are overwritten,
the other elements are left untouched.
See also [`copy!`](@ref Base.copy!), [`copy`](@ref).
# Examples
```jldoctest
julia> x = [1., 0., 3., 0., 5.];
julia> y = zeros(7);
julia> copyto!(y, x);
julia> y
7-element Vector{Float64}:
1.0
0.0
3.0
0.0
5.0
0.0
0.0
```
"""
function copyto!(dest::AbstractArray, src::AbstractArray)
isempty(src) && return dest
src′ = unalias(dest, src)