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why is Julia 1.3 incompatible?

I've tested it locally with Julia 1.3 and the test passes, is that because you disabled Julia 1.3 test in travis? In this case, we could just switch to Github Actions.

My understanding is that "drop CI test for a version" doesn't necessarily mean "being incompatible with a version".

Said that, it's not a big issue to not bring 1.3-compatibility back since Julia 1.3 isn't an LTS version.

method ambiguities

Tuple{Method, Method}[(-(A::LinearMapAX, B::AbstractMatrix) @ LinearMapsAA ~/.julia/dev/LinearMapsAA/src/lm-aa.jl:126, -(a::AbstractArray, b::StaticArraysCore.StaticArray) @ StaticArrays ~/.julia/packages/StaticArrays/9KYrc/src/linalg.jl:17), (+(A::LinearMapAX, B::AbstractMatrix) @ LinearMapsAA ~/.julia/dev/LinearMapsAA/src/lm-aa.jl:108, +(a::AbstractArray, b::StaticArraysCore.StaticArray) @ StaticArrays ~/.julia/packages/StaticArrays/9KYrc/src/linalg.jl:13), (+(A::AbstractMatrix, B::LinearMapAX) @ LinearMapsAA ~/.julia/dev/LinearMapsAA/src/lm-aa.jl:123, +(a::StaticArraysCore.StaticArray, b::AbstractArray) @ StaticArrays ~/.julia/packages/StaticArrays/9KYrc/src/linalg.jl:14), (-(A::AbstractMatrix, B::LinearMapAX) @ LinearMapsAA ~/.julia/dev/LinearMapsAA/src/lm-aa.jl:127, -(a::StaticArraysCore.StaticArray, b::AbstractArray) @ StaticArrays ~/.julia/packages/StaticArrays/9KYrc/src/linalg.jl:18)]

register 0.5.2

@JuliaRegistrator register()

register 0.5.1

@JuliaRegistrator register()

return GPU arrays

When running the following block of code

using LinearMapsAA
nx = 8
ny = 8
nz = 6
idim = (nx,ny,nz)
odim = (nx,ny,nz)
T = Float32
A = LinearMapAA(x -> 2 * x, y -> 2 * y, (prod(odim), prod(idim)); T, odim, idim)
A * CuArray(ones(T, nx, ny, nz))

it returns an array of type Array{Float32, 3} not CuArray{Float32, 3}.
So I am thinking that we should make the type of output and input to be consistent.
I am using Julia v1.6.3 on Ubuntu 20.04 and LinearMapsAA@v0.10.0.

support lazy sizes

see
JuliaLinearAlgebra/LinearMaps.jl#118

like how I works in LinearAlgebra

register 0.5.0

@JuliaRegistrator register()

remove ambiguity code

Note to self: remove most (if not all?) the code in ambiguity.jl
after JuliaLang/julia#41188 is merged.

Probably not until Julia 1.8 ?

Register 0.1.0

@JuliaRegistrator register()

register 0.6.1

@JuliaRegistrator register()

LinearMapsAA is not CUDA compatible?

I found that LinearMapsAA built on CuSPECTrecon does not support CUDA.
Here is my test code:

using CuSPECTrecon
using LinearMapsAA
using CUDA
CUDA.allowscalar(false)

T = Float32
nx = 8; ny = nx
nz = 6
nview = 7

mumap = rand(T, nx, ny, nz)

px = 5
pz = 3
psfs = rand(T, px, pz, ny, nview)
psfs = psfs .+ mapslices(reverse, psfs, dims = [1, 2]) # symmetrize
psfs = psfs ./ mapslices(sum, psfs, dims = [1, 2])

dy = T(4.7952)

Cuimage = CuArray(zeros(T, nx, ny, nz))
Cuviews = CuArray(rand(T, nx, nz, nview))

Cumumap = CuArray(mumap)
Cupsfs = CuArray(psfs)
Cuplan = CuSPECTplan(Cumumap, Cupsfs, dy; T)

idim = (nx, ny, nz)
odim = (nx, nz, nview)

A = LinearMapAA(x -> Cuproject(x, Cuplan),
                y -> Cubackproject(y, Cuplan),
                (prod(odim), prod(idim));
                idim = idim,
                odim = odim,
                T = Float32)

I can do both

mul!(Cuviews, A, Cuimage)

and

Cuviews = Cuproject(Cuimage, Cuplan)

but when I run

Cuviews = A * Cuimage

Julia throws me the following error:

Scalar indexing is disallowed.
Invocation of getindex resulted in scalar indexing of a GPU array.
This is typically caused by calling an iterating implementation of a method.
Such implementations *do not* execute on the GPU, but very slowly on the CPU,
and therefore are only permitted from the REPL for prototyping purposes.
If you did intend to index this array, annotate the caller with @allowscalar.
error(s::String) at error.jl:33
assertscalar(op::String) at indexing.jl:53
getindex(xs::CuArray{Float32, 3, CUDA.Mem.DeviceBuffer}, I::Int64) at indexing.jl:86
copyto_unaliased!(deststyle::IndexLinear, dest::SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}, srcstyle::IndexLinear, src::CuArray{Float32, 3, CUDA.Mem.DeviceBuffer}) at abstractarray.jl:1018
copyto!(dest::SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}, src::CuArray{Float32, 3, CUDA.Mem.DeviceBuffer}) at abstractarray.jl:998
_unsafe_mul!(y::SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}, A::LinearMaps.FunctionMap{Float32, LinearMapsAA.var"#6#10"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#9#11"}, LinearMapsAA.var"#8#12"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#10#12"}}, x::CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}) at functionmap.jl:114
mul! at LinearMaps.jl:163 [inlined]
_generic_mapvec_mul!(y::SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}, A::LinearMaps.FunctionMap{Float32, LinearMapsAA.var"#6#10"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#9#11"}, LinearMapsAA.var"#8#12"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#10#12"}}, x::CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, α::Int64, β::Int64) at LinearMaps.jl:204
_unsafe_mul! at LinearMaps.jl:249 [inlined]
mul! at LinearMaps.jl:198 [inlined]
lm_mul! at multiply.jl:88 [inlined]
lmao_mul!(Y::Array{Float32, 3}, Lm::LinearMaps.FunctionMap{Float32, LinearMapsAA.var"#6#10"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#9#11"}, LinearMapsAA.var"#8#12"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#10#12"}}, X::CuArray{Float32, 3, CUDA.Mem.DeviceBuffer}, α::Int64, β::Int64; idim::Tuple{Int64, Int64, Int64}, odim::Tuple{Int64, Int64, Int64}) at multiply.jl:153
(::LinearMapsAA.var"#lmao_mul!##kw")(::NamedTuple{(:idim, :odim), Tuple{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}}}, ::typeof(LinearMapsAA.lmao_mul!), Y::Array{Float32, 3}, Lm::LinearMaps.FunctionMap{Float32, LinearMapsAA.var"#6#10"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#9#11"}, LinearMapsAA.var"#8#12"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#10#12"}}, X::CuArray{Float32, 3, CUDA.Mem.DeviceBuffer}, α::Int64, β::Int64) at multiply.jl:136
lmax_mul(A::LinearMapAO{Float32, 3, 3, LinearMaps.FunctionMap{Float32, LinearMapsAA.var"#6#10"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#9#11"}, LinearMapsAA.var"#8#12"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#10#12"}}, NamedTuple{(), Tuple{}}}, X::CuArray{Float32, 3, CUDA.Mem.DeviceBuffer}) at multiply.jl:212
*(A::LinearMapAO{Float32, 3, 3, LinearMaps.FunctionMap{Float32, LinearMapsAA.var"#6#10"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#9#11"}, LinearMapsAA.var"#8#12"{Tuple{Int64, Int64, Int64}, Tuple{Int64, Int64, Int64}, var"#10#12"}}, NamedTuple{(), Tuple{}}}, X::CuArray{Float32, 3, CUDA.Mem.DeviceBuffer}) at multiply.jl:191
top-level scope at Cuautodiff.jl:36
eval at boot.jl:373 [inlined]

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register 0.4.0

@JuliaRegistrator register()

Use _cat

Consider using _cat parroting
https://github.com/Jutho/LinearMaps.jl/pull/114/files

register 0.6.4

@JuliaRegistrator register()

Example for LinearMapAO?

I was trying to explore functionality mentioned in this comment JuliaLinearAlgebra/LinearMaps.jl#44 (comment), but couldn't figure out how to create a LinearMapAO (or LinearMapAX). Is there an example available somewhere?

Using `getproperty` is not type stable

See JuliaLang/julia#48004
May need to revise to improve stability and performance.

register 0.6.3

@JuliaRegistrator register()

register 0.6.5

@JuliaRegistrator register()

Support arrays of arrays?

A very general linear mapping could map between vectors spaces defined by arrays of arrays.
This could be useful, e.g., for dynamic MRI where it might be more natural to have the time sequence of 3D images be stored as a vector of 3D arrays, rather than as a 4D array, and the corresponding k-space data could also be a vector of arrays, especially if the number of k-space samples varies between frames.

This extension would require something more general than idim and odim to describe the input and output "dimensions." Tuples of Dims?

register 0.6.0

@JuliaRegistrator register()

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