IndexFunArrays.jl

Fun with indices (and functions on them)
Author bionanoimaging
Popularity
2 Stars
Updated Last
1 Year Ago
Started In
March 2021

IndexFunArrays.jl

This package allows to generate complex array expressions based on the indices. The IndexFunArray does not allocate memory but instead is entry-wise generated once it is accessed. IndexFunArray <: AbstractArray and behaves almost like a normal array.

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Installation

Type ]in the REPL to get to the package manager and install it:

julia> ] add IndexFunArrays

Quick Examples

We ship a lot of convenient wrappers but you can also use an IndexFunArray directly. See below:

julia> using IndexFunArrays

julia> rr2((4,4), offset=CtrMid)  # IndexFunArray containing the square of the radius to the mid position
  4×4 IndexFunArray{Float64, 2, IndexFunArrays.var"#4#5"{Float64, Tuple{Float64, Float64}, Tuple{Int64, Int64}}}:
   4.5  2.5  2.5  4.5
   2.5  0.5  0.5  2.5
   2.5  0.5  0.5  2.5
   4.5  2.5  2.5  4.5

julia> rr2((3, 3), offset=(1, 1)) # square distance to the top left pixel
  3×3 IndexFunArray{Float64, 2, IndexFunArrays.var"#4#5"{Float64, Tuple{Int64, Int64}, Tuple{Int64, Int64}}}:
   0.0  1.0  4.0
   1.0  2.0  5.0
   4.0  5.0  8.0

julia> rr((4,4), scale=ScaUnit)  # distance (not square) to the Fourier-center with unity pixel scaling
  4×4 IndexFunArray{Float64, 2, IndexFunArrays.var"#9#10"{Float64, Tuple{Float64, Float64}, Tuple{Int64, Int64}}}:
   2.82843  2.23607  2.0  2.23607
   2.23607  1.41421  1.0  1.41421
   2.0      1.0      0.0  1.0
   2.23607  1.41421  1.0  1.41421

julia> z = IndexFunArray(x -> sum(abs2.(x)), (3, 3))   # directly using the constructor and supplying a function to store in the array
  3×3 IndexFunArray{Int64, 2, var"#184#185"}:
    2   5  10
    5   8  13
   10  13  18

julia> z[3,3] # use it like a normal array
18

julia> @view z[1:1, 1:3] # for slices you need @view because we cannot assign to a IndexFunArray
1×3 view(::IndexFunArray{Int64, 2, var"#1#2"}, 1:1, 1:3) with eltype Int64:
 2  5  10

julia> z .^ 2 # once you apply operations on it, it returns a allocated array
3×3 Matrix{Int64}:
   4   25  100
  25   64  169
 100  169  324

More complex examples

Assign to allocated memory

If we assume the following situation where we have a large array created. We can assign the IndexFunArray (in this case rr being the distance to a reference position) to this array without allocation of a significant amount of new memory:

julia> @time x = randn((10000, 10000));
  0.278858 seconds (25 allocations: 762.959 MiB, 1.31% gc time)

julia> @time x = randn((10000, 10000));
  0.261548 seconds (2 allocations: 762.940 MiB, 0.96% gc time)

julia> using IndexFunArrays

julia> @time x .= rr(size(x)); # first run contains some compilation
  0.361640 seconds (1.18 M allocations: 70.353 MiB, 46.29% compilation time)

julia> @time x .= rr(size(x));
  0.194074 seconds (11 allocations: 496 bytes)

Apply further operations without allocations

For that use, one can use LazyArrays.jl which implements that efficiently.

Below an example (where we removed the lines with compilation calls). Thanks to LazyArrays.jl, y is not evaluated but at the end z is:

julia> using IndexFunArrays, LazyArrays

julia> @time x = rr((1000, 1000));
  0.000014 seconds (6 allocations: 240 bytes)

julia> @time y = @~ exp.(x)
  0.000022 seconds (5 allocations: 336 bytes)
Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{2}}(exp, ([707.1067811865476 706.4000283125703705.6939846704093 706.4000283125703; 706.4000283125703 705.6925676241744704.985815460141 705.6925676241744; … ; 705.6939846704093 704.985815460141704.2783540618013 704.985815460141; 706.4000283125703 705.6925676241744704.985815460141 705.6925676241744],))

julia> @time z = materialize(y);
  0.018635 seconds (2 allocations: 7.629 MiB)

julia> typeof(z)
Matrix{Float64} (alias for Array{Float64, 2})

Benchmark

We can see that there is only a small number of 496 bytes allocated and not the full memory. The following benchmark shows that the performance is almost as good as with CartesianIndices:

julia> include("examples/benchmark.jl")
compare_to_CartesianIndices (generic function with 1 method)

julia> compare_to_CartesianIndices()
[ Info: rr2 based
  1.981 ms (18 allocations: 720 bytes)
  1.979 ms (18 allocations: 720 bytes)
[ Info: CartesianIndices based
  1.938 ms (0 allocations: 0 bytes)
  1.940 ms (0 allocations: 0 bytes)
[ Info: CartesianIndices based with initialized function
  1.941 ms (0 allocations: 0 bytes)
  1.942 ms (0 allocations: 0 bytes)

Why this package?

In image processing and other applications you often encounter position-dependent functions some of which can be a bit of work to code. It helps the thinking to picture such functions as arrays, which contain the index-dependent values. A good examples are windowing functions. Another more complicated example is a complex-valued free-space (optical) propagator. Yet storing such arrays can be memory intensive and slow and one would ideally perform such calculations "on-the-fly", e.g. only when applying the filter to the Fourier-transformation. Julia has a great mechanism for this: syntactic loop fusion and broadcasting (e.g. using ".*").

Using CartesianIndices it is possible to write such index-expressions yet they do not "feel" like arrays. IndexFunArrays allow index-based calculations to look like arrays and to take part in loop fusion. This eases the writing of more complicated expressions without loss in speed due to Julia's syntactic loop fusion mechanism. You can think of a IndexFunArray of being an array that stores an expression calculating with indices inside. This also means you cannot assing to such arrays which also precludes using range indices. However views are possible and range indices can be applied to such views. Of course such arrays can generate any datatype. See ?IndexFunArray for more detail.

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