StackViews
StackViews provides only one array type: StackView. There are multiple ways to understand StackView:
- inverse of
eachslice catvariant- view object
- lazy version of
repeatspecial case
StackView as the inverse of eachslice
StackView can be seen as the inverse of eachslice:
julia> using StackViews
julia> X = rand(100, 100);
julia> StackView(collect(eachslice(X, dims=1)), 1) == X
trueStackView as a variant of cat
StackView works very similar to cat and its vcat/hcat/hvcat variants in Base.
julia> using StackViews
julia> A = reshape(collect(1:6), 2, 3)
2×3 Matrix{Int64}:
1 3 5
2 4 6
julia> B = reshape(collect(7:12), 2, 3)
2×3 Matrix{Int64}:
7 9 11
8 10 12
julia> StackView(A, B; dims=3) # mostly equivalent to `cat(A, B, dims=3)`
2×3×2 StackView{Int64, 3, 3, ...}:
[:, :, 1] =
1 3 5
2 4 6
[:, :, 2] =
7 9 11
8 10 12Unlike cats, StackView always creats new dimension:
julia> StackView(A, B; dims=1) # `cat(A, B, dims=1)` outputs 4×3 Matrix
2×2×3 StackView{Int64, 3, 1, ...}:
[:, :, 1] =
1 2
7 8
[:, :, 2] =
3 4
9 10
[:, :, 3] =
5 6
11 12
julia> StackView(A, B; dims=2) # `cat(A, B, dims=2)` outputs 2×6 Matrix
2×2×3 StackView{Int64, 3, 2, ...}:
[:, :, 1] =
1 7
2 8
[:, :, 2] =
3 9
4 10
[:, :, 3] =
5 11
6 12StackView as a view object
Without StackView, people use reshape + cat for the previous examples:
julia> StackView(A, B, dims=1) == cat(map(x->reshape(x, 1, axes(x)...), (A, B))...; dims=1)
truebut StackView is only a view and thus don't create any memory:
frames = [rand(1000, 1000) for _ in 1:100];
@btime StackView($frames, Val(1)); # 143.905 ns (0 allocations: 0 bytes)
@btime cat(map(x->reshape(x, 1, axes(x)...), $frames)...; dims=1); # 1.127 s (1119 allocations: 763.06 MiB)Of course, since it is a view, if you modify it, original arrays get modified, too:
A = [1, 2, 3, 4]
B = [5, 6, 7, 8]
sv = StackView(A, B)
fill!(sv, -1) # A and B are modified
A == B == fill(-1, 4) # trueIf indexed in contiguous memeory order, it has almost zero overhead for getindex and setindex!:
function arrsum_cart(A::AbstractArray)
rst = zero(eltype(A))
@inbounds for I in CartesianIndices(A)
rst += A[I]
end
return rst
end
As = StackView(frames);
Ac = cat(map(x->reshape(x, axes(x)..., 1), frames)...; dims=3);
As == Ac # true
@btime arrsum_cart($As); # 122.703 ms (0 allocations: 0 bytes)
@btime arrsum_cart($Ac); # 123.813 ms (0 allocations: 0 bytes)StackView as a lazy version of repeat special case
StackView allows you to stack the same array object multiple times, which makes a special
version of repeat when there's only one none-1 repeat count:
A = rand(1000, 1000);
n = 100;
StackView([A for _ in 1:n]) == repeat(A, ntuple(_->1, ndims(A))..., n) # true
@btime StackView([$A for _ in 1:$n]); # 403.156 ns (2 allocations: 1.75 KiB)
@btime repeat($A, ntuple(_->1, ndims($A))..., $n) # 590.043 ms (4 allocations: 762.94 MiB)More examples
When arrays are of different types and sizes, StackView just kills cats:
julia> using StackViews, PaddedViews
julia> A = collect(reshape(1:8, 2, 4));
julia> B = collect(reshape(9:16, 4, 2));
julia> StackView(paddedviews(-1, A, B), 3)
4×4×2 StackView{Int64, 3, 3, ...}:
[:, :, 1] =
1 3 5 7
2 4 6 8
-1 -1 -1 -1
-1 -1 -1 -1
[:, :, 2] =
9 13 -1 -1
10 14 -1 -1
11 15 -1 -1
12 16 -1 -1
julia> StackView(sym_paddedviews(-1, A, B), 3)
4×4×2 StackView{Int64, 3, 3, ...):
[:, :, 1] =
-1 -1 -1 -1
1 3 5 7
2 4 6 8
-1 -1 -1 -1
[:, :, 2] =
-1 9 13 -1
-1 10 14 -1
-1 11 15 -1
-1 12 16 -1There is some mind work here but by chaining more views you can get some interesting result:
using PaddedViews, StackViews
using ImageCore, ImageShow, TestImages, ColorVectorSpace
toucan = testimage("toucan") # 150×162 RGBA image
moon = testimage("moon") # 256×256 Gray image
# equivalently, you can just use `mosaic(toucan, moon; nrow=1)`
mosaicview(StackView(sym_paddedviews(zero(RGB), toucan, moon)); nrow=1)
Other similar packages
- There's a lot of overlap between this and LazyStack.jl; I didn't notice its existance when I wrote this.