https://github.com/JuliaLang/julia
Revision 06a49a392db85e1f55a1810669559389e8ecf37c authored by Simeon David Schaub on 25 April 2022, 12:30:31 UTC, committed by Simeon David Schaub on 25 April 2022, 20:15:55 UTC
Not sure whether putting hints into the lowering pass like this is a great solution. I thought about maybe using Julia-side error hints for this instead, but using some kind of pattern-matching doesn't seem like a great solution either. fixes #45031
1 parent 9d14cb1
Tip revision: 06a49a392db85e1f55a1810669559389e8ecf37c authored by Simeon David Schaub on 25 April 2022, 12:30:31 UTC
improve error message for invalid function args
improve error message for invalid function args
Tip revision: 06a49a3
abstractarraymath.jl
# This file is a part of Julia. License is MIT: https://julialang.org/license
## Basic functions ##
isreal(x::AbstractArray) = all(isreal,x)
iszero(x::AbstractArray) = all(iszero,x)
isreal(x::AbstractArray{<:Real}) = true
## Constructors ##
"""
vec(a::AbstractArray) -> AbstractVector
Reshape the array `a` as a one-dimensional column vector. Return `a` if it is
already an `AbstractVector`. The resulting array
shares the same underlying data as `a`, so it will only be mutable if `a` is
mutable, in which case modifying one will also modify the other.
# Examples
```jldoctest
julia> a = [1 2 3; 4 5 6]
2×3 Matrix{Int64}:
1 2 3
4 5 6
julia> vec(a)
6-element Vector{Int64}:
1
4
2
5
3
6
julia> vec(1:3)
1:3
```
See also [`reshape`](@ref), [`dropdims`](@ref).
"""
vec(a::AbstractArray) = reshape(a,length(a))
vec(a::AbstractVector) = a
_sub(::Tuple{}, ::Tuple{}) = ()
_sub(t::Tuple, ::Tuple{}) = t
_sub(t::Tuple, s::Tuple) = _sub(tail(t), tail(s))
"""
dropdims(A; dims)
Return an array with the same data as `A`, but with the dimensions specified by
`dims` removed. `size(A,d)` must equal 1 for every `d` in `dims`,
and repeated dimensions or numbers outside `1:ndims(A)` are forbidden.
The result shares the same underlying data as `A`, such that the
result is mutable if and only if `A` is mutable, and setting elements of one
alters the values of the other.
See also: [`reshape`](@ref), [`vec`](@ref).
# Examples
```jldoctest
julia> a = reshape(Vector(1:4),(2,2,1,1))
2×2×1×1 Array{Int64, 4}:
[:, :, 1, 1] =
1 3
2 4
julia> b = dropdims(a; dims=3)
2×2×1 Array{Int64, 3}:
[:, :, 1] =
1 3
2 4
julia> b[1,1,1] = 5; a
2×2×1×1 Array{Int64, 4}:
[:, :, 1, 1] =
5 3
2 4
```
"""
dropdims(A; dims) = _dropdims(A, dims)
function _dropdims(A::AbstractArray, dims::Dims)
for i in eachindex(dims)
1 <= dims[i] <= ndims(A) || throw(ArgumentError("dropped dims must be in range 1:ndims(A)"))
length(axes(A, dims[i])) == 1 || throw(ArgumentError("dropped dims must all be size 1"))
for j = 1:i-1
dims[j] == dims[i] && throw(ArgumentError("dropped dims must be unique"))
end
end
ax = _foldoneto((ds, d) -> d in dims ? ds : (ds..., axes(A,d)), (), Val(ndims(A)))
reshape(A, ax::typeof(_sub(axes(A), dims)))
end
_dropdims(A::AbstractArray, dim::Integer) = _dropdims(A, (Int(dim),))
## Unary operators ##
"""
conj!(A)
Transform an array to its complex conjugate in-place.
See also [`conj`](@ref).
# Examples
```jldoctest
julia> A = [1+im 2-im; 2+2im 3+im]
2×2 Matrix{Complex{Int64}}:
1+1im 2-1im
2+2im 3+1im
julia> conj!(A);
julia> A
2×2 Matrix{Complex{Int64}}:
1-1im 2+1im
2-2im 3-1im
```
"""
conj!(A::AbstractArray{<:Number}) = (@inbounds broadcast!(conj, A, A); A)
conj!(x::AbstractArray{<:Real}) = x
"""
conj(A::AbstractArray)
Return an array containing the complex conjugate of each entry in array `A`.
Equivalent to `conj.(A)`, except that when `eltype(A) <: Real`
`A` is returned without copying, and that when `A` has zero dimensions,
a 0-dimensional array is returned (rather than a scalar).
# Examples
```jldoctest
julia> conj([1, 2im, 3 + 4im])
3-element Vector{Complex{Int64}}:
1 + 0im
0 - 2im
3 - 4im
julia> conj(fill(2 - im))
0-dimensional Array{Complex{Int64}, 0}:
2 + 1im
```
"""
conj(A::AbstractArray) = broadcast_preserving_zero_d(conj, A)
conj(A::AbstractArray{<:Real}) = A
"""
real(A::AbstractArray)
Return an array containing the real part of each entry in array `A`.
Equivalent to `real.(A)`, except that when `eltype(A) <: Real`
`A` is returned without copying, and that when `A` has zero dimensions,
a 0-dimensional array is returned (rather than a scalar).
# Examples
```jldoctest
julia> real([1, 2im, 3 + 4im])
3-element Vector{Int64}:
1
0
3
julia> real(fill(2 - im))
0-dimensional Array{Int64, 0}:
2
```
"""
real(A::AbstractArray) = broadcast_preserving_zero_d(real, A)
real(A::AbstractArray{<:Real}) = A
"""
imag(A::AbstractArray)
Return an array containing the imaginary part of each entry in array `A`.
Equivalent to `imag.(A)`, except that when `A` has zero dimensions,
a 0-dimensional array is returned (rather than a scalar).
# Examples
```jldoctest
julia> imag([1, 2im, 3 + 4im])
3-element Vector{Int64}:
0
2
4
julia> imag(fill(2 - im))
0-dimensional Array{Int64, 0}:
-1
```
"""
imag(A::AbstractArray) = broadcast_preserving_zero_d(imag, A)
imag(A::AbstractArray{<:Real}) = zero(A)
"""
reim(A::AbstractArray)
Return a tuple of two arrays containing respectively the real and the imaginary
part of each entry in `A`.
Equivalent to `(real.(A), imag.(A))`, except that when `eltype(A) <: Real`
`A` is returned without copying to represent the real part, and that when `A` has
zero dimensions, a 0-dimensional array is returned (rather than a scalar).
# Examples
```jldoctest
julia> reim([1, 2im, 3 + 4im])
([1, 0, 3], [0, 2, 4])
julia> reim(fill(2 - im))
(fill(2), fill(-1))
```
"""
reim(A::AbstractArray)
-(A::AbstractArray) = broadcast_preserving_zero_d(-, A)
+(x::AbstractArray{<:Number}) = x
*(x::AbstractArray{<:Number,2}) = x
# index A[:,:,...,i,:,:,...] where "i" is in dimension "d"
"""
selectdim(A, d::Integer, i)
Return a view of all the data of `A` where the index for dimension `d` equals `i`.
Equivalent to `view(A,:,:,...,i,:,:,...)` where `i` is in position `d`.
See also: [`eachslice`](@ref).
# Examples
```jldoctest
julia> A = [1 2 3 4; 5 6 7 8]
2×4 Matrix{Int64}:
1 2 3 4
5 6 7 8
julia> selectdim(A, 2, 3)
2-element view(::Matrix{Int64}, :, 3) with eltype Int64:
3
7
julia> selectdim(A, 2, 3:4)
2×2 view(::Matrix{Int64}, :, 3:4) with eltype Int64:
3 4
7 8
```
"""
@inline selectdim(A::AbstractArray, d::Integer, i) = _selectdim(A, d, i, _setindex(i, d, map(Slice, axes(A))...))
@noinline function _selectdim(A, d, i, idxs)
d >= 1 || throw(ArgumentError("dimension must be ≥ 1, got $d"))
nd = ndims(A)
d > nd && (i == 1 || throw(BoundsError(A, (ntuple(Returns(Colon()),d-1)..., i))))
return view(A, idxs...)
end
function circshift(a::AbstractArray, shiftamt::Real)
circshift!(similar(a), a, (Integer(shiftamt),))
end
circshift(a::AbstractArray, shiftamt::DimsInteger) = circshift!(similar(a), a, shiftamt)
"""
circshift(A, shifts)
Circularly shift, i.e. rotate, the data in an array. The second argument is a tuple or
vector giving the amount to shift in each dimension, or an integer to shift only in the
first dimension.
See also: [`circshift!`](@ref), [`circcopy!`](@ref), [`bitrotate`](@ref), [`<<`](@ref).
# Examples
```jldoctest
julia> b = reshape(Vector(1:16), (4,4))
4×4 Matrix{Int64}:
1 5 9 13
2 6 10 14
3 7 11 15
4 8 12 16
julia> circshift(b, (0,2))
4×4 Matrix{Int64}:
9 13 1 5
10 14 2 6
11 15 3 7
12 16 4 8
julia> circshift(b, (-1,0))
4×4 Matrix{Int64}:
2 6 10 14
3 7 11 15
4 8 12 16
1 5 9 13
julia> a = BitArray([true, true, false, false, true])
5-element BitVector:
1
1
0
0
1
julia> circshift(a, 1)
5-element BitVector:
1
1
1
0
0
julia> circshift(a, -1)
5-element BitVector:
1
0
0
1
1
```
"""
function circshift(a::AbstractArray, shiftamt)
circshift!(similar(a), a, map(Integer, (shiftamt...,)))
end
## Other array functions ##
"""
repeat(A::AbstractArray, counts::Integer...)
Construct an array by repeating array `A` a given number of times in each dimension, specified by `counts`.
See also: [`fill`](@ref), [`Iterators.repeated`](@ref), [`Iterators.cycle`](@ref).
# Examples
```jldoctest
julia> repeat([1, 2, 3], 2)
6-element Vector{Int64}:
1
2
3
1
2
3
julia> repeat([1, 2, 3], 2, 3)
6×3 Matrix{Int64}:
1 1 1
2 2 2
3 3 3
1 1 1
2 2 2
3 3 3
```
"""
function repeat(A::AbstractArray, counts...)
return _RepeatInnerOuter.repeat(A, outer=counts)
end
"""
repeat(A::AbstractArray; inner=ntuple(Returns(1), ndims(A)), outer=ntuple(Returns(1), ndims(A)))
Construct an array by repeating the entries of `A`. The i-th element of `inner` specifies
the number of times that the individual entries of the i-th dimension of `A` should be
repeated. The i-th element of `outer` specifies the number of times that a slice along the
i-th dimension of `A` should be repeated. If `inner` or `outer` are omitted, no repetition
is performed.
# Examples
```jldoctest
julia> repeat(1:2, inner=2)
4-element Vector{Int64}:
1
1
2
2
julia> repeat(1:2, outer=2)
4-element Vector{Int64}:
1
2
1
2
julia> repeat([1 2; 3 4], inner=(2, 1), outer=(1, 3))
4×6 Matrix{Int64}:
1 2 1 2 1 2
1 2 1 2 1 2
3 4 3 4 3 4
3 4 3 4 3 4
```
"""
function repeat(A::AbstractArray; inner = nothing, outer = nothing)
return _RepeatInnerOuter.repeat(A, inner=inner, outer=outer)
end
module _RepeatInnerOuter
function repeat(arr; inner=nothing, outer=nothing)
check(arr, inner, outer)
arr, inner, outer = resolve(arr, inner, outer)
repeat_inner_outer(arr, inner, outer)
end
to_tuple(t::Tuple) = t
to_tuple(x::Integer) = (x,)
to_tuple(itr) = tuple(itr...)
function pad(a, b)
N = max(length(a), length(b))
Base.fill_to_length(a, 1, Val(N)), Base.fill_to_length(b, 1, Val(N))
end
function pad(a, b, c)
N = max(max(length(a), length(b)), length(c))
Base.fill_to_length(a, 1, Val(N)), Base.fill_to_length(b, 1, Val(N)), Base.fill_to_length(c, 1, Val(N))
end
function resolve(arr::AbstractArray{<:Any, N}, inner::NTuple{N, Any}, outer::NTuple{N,Any}) where {N}
arr, inner, outer
end
function resolve(arr, inner, outer)
dims, inner, outer = pad(size(arr), to_tuple(inner), to_tuple(outer))
reshape(arr, dims), inner, outer
end
function resolve(arr, inner::Nothing, outer::Nothing)
return arr, inner, outer
end
function resolve(arr, inner::Nothing, outer)
dims, outer = pad(size(arr), to_tuple(outer))
reshape(arr, dims), inner, outer
end
function resolve(arr, inner, outer::Nothing)
dims, inner = pad(size(arr), to_tuple(inner))
reshape(arr, dims), inner, outer
end
function check(arr, inner, outer)
if inner !== nothing
# TODO: Currently one based indexing is demanded for inner !== nothing,
# but not for outer !== nothing. Decide for something consistent.
Base.require_one_based_indexing(arr)
if any(<(0), inner)
throw(ArgumentError("no inner repetition count may be negative; got $inner"))
end
if length(inner) < ndims(arr)
throw(ArgumentError("number of inner repetitions ($(length(inner))) cannot be less than number of dimensions of input array ($(ndims(arr)))"))
end
end
if outer !== nothing
if any(<(0), outer)
throw(ArgumentError("no outer repetition count may be negative; got $outer"))
end
if (length(outer) < ndims(arr)) && (inner !== nothing)
throw(ArgumentError("number of outer repetitions ($(length(outer))) cannot be less than number of dimensions of input array ($(ndims(arr)))"))
end
end
end
repeat_inner_outer(arr, inner::Nothing, outer::Nothing) = arr
repeat_inner_outer(arr, ::Nothing, outer) = repeat_outer(arr, outer)
repeat_inner_outer(arr, inner, ::Nothing) = repeat_inner(arr, inner)
repeat_inner_outer(arr, inner, outer) = repeat_outer(repeat_inner(arr, inner), outer)
function repeat_outer(a::AbstractMatrix, (m,n)::NTuple{2, Any})
o, p = size(a,1), size(a,2)
b = similar(a, o*m, p*n)
for j=1:n
d = (j-1)*p+1
R = d:d+p-1
for i=1:m
c = (i-1)*o+1
@inbounds b[c:c+o-1, R] = a
end
end
return b
end
function repeat_outer(a::AbstractVector, (m,)::Tuple{Any})
o = length(a)
b = similar(a, o*m)
for i=1:m
c = (i-1)*o+1
@inbounds b[c:c+o-1] = a
end
return b
end
function repeat_outer(arr::AbstractArray{<:Any,N}, dims::NTuple{N,Any}) where {N}
insize = size(arr)
outsize = map(*, insize, dims)
out = similar(arr, outsize)
for I in CartesianIndices(arr)
for J in CartesianIndices(dims)
TIJ = map(Tuple(I), Tuple(J), insize) do i, j, d
i + d * (j-1)
end
IJ = CartesianIndex(TIJ)
@inbounds out[IJ] = arr[I]
end
end
return out
end
function repeat_inner(arr, inner)
outsize = map(*, size(arr), inner)
out = similar(arr, outsize)
for I in CartesianIndices(arr)
for J in CartesianIndices(inner)
TIJ = map(Tuple(I), Tuple(J), inner) do i, j, d
(i-1) * d + j
end
IJ = CartesianIndex(TIJ)
@inbounds out[IJ] = arr[I]
end
end
return out
end
end#module
"""
eachrow(A::AbstractVecOrMat)
Create a generator that iterates over the first dimension of vector or matrix `A`,
returning the rows as `AbstractVector` views.
See also [`eachcol`](@ref), [`eachslice`](@ref), [`mapslices`](@ref).
!!! compat "Julia 1.1"
This function requires at least Julia 1.1.
# Example
```jldoctest
julia> a = [1 2; 3 4]
2×2 Matrix{Int64}:
1 2
3 4
julia> first(eachrow(a))
2-element view(::Matrix{Int64}, 1, :) with eltype Int64:
1
2
julia> collect(eachrow(a))
2-element Vector{SubArray{Int64, 1, Matrix{Int64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}:
[1, 2]
[3, 4]
```
"""
eachrow(A::AbstractVecOrMat) = (view(A, i, :) for i in axes(A, 1))
"""
eachcol(A::AbstractVecOrMat)
Create a generator that iterates over the second dimension of matrix `A`, returning the
columns as `AbstractVector` views.
See also [`eachrow`](@ref) and [`eachslice`](@ref).
!!! compat "Julia 1.1"
This function requires at least Julia 1.1.
# Example
```jldoctest
julia> a = [1 2; 3 4]
2×2 Matrix{Int64}:
1 2
3 4
julia> first(eachcol(a))
2-element view(::Matrix{Int64}, :, 1) with eltype Int64:
1
3
julia> collect(eachcol(a))
2-element Vector{SubArray{Int64, 1, Matrix{Int64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}:
[1, 3]
[2, 4]
```
"""
eachcol(A::AbstractVecOrMat) = (view(A, :, i) for i in axes(A, 2))
"""
eachslice(A::AbstractArray; dims)
Create a generator that iterates over dimensions `dims` of `A`, returning views that select all
the data from the other dimensions in `A`.
Only a single dimension in `dims` is currently supported. Equivalent to `(view(A,:,:,...,i,:,:
...)) for i in axes(A, dims))`, where `i` is in position `dims`.
See also [`eachrow`](@ref), [`eachcol`](@ref), [`mapslices`](@ref), and [`selectdim`](@ref).
!!! compat "Julia 1.1"
This function requires at least Julia 1.1.
# Example
```jldoctest
julia> M = [1 2 3; 4 5 6; 7 8 9]
3×3 Matrix{Int64}:
1 2 3
4 5 6
7 8 9
julia> first(eachslice(M, dims=1))
3-element view(::Matrix{Int64}, 1, :) with eltype Int64:
1
2
3
julia> collect(eachslice(M, dims=2))
3-element Vector{SubArray{Int64, 1, Matrix{Int64}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}}:
[1, 4, 7]
[2, 5, 8]
[3, 6, 9]
```
"""
@inline function eachslice(A::AbstractArray; dims)
length(dims) == 1 || throw(ArgumentError("only single dimensions are supported"))
dim = first(dims)
dim <= ndims(A) || throw(DimensionMismatch("A doesn't have $dim dimensions"))
inds_before = ntuple(Returns(:), dim-1)
inds_after = ntuple(Returns(:), ndims(A)-dim)
return (view(A, inds_before..., i, inds_after...) for i in axes(A, dim))
end
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