Support for NaNStatistics

[yuvalwas]

It would be very handy if there was support for NaNStatistics. I couldn't find an easy way to rewrap the output array so I did this ugly workaround:

NaNStatistics.nansum(A::KeyedArray; dim=:, dims=:) = rewrap_keyedarray(nansum(A.data.data; dim, dims), A, dims isa Colon ? dim : dims)

function rewrap_keyedarray(new, original, dims)
    axs = NamedTuple{propertynames(original)}(original.keys) |> pairs
    if ndims(new) < ndims(original)
        axs = collect(axs)[1:end .!= dims]
    else
        axs = Dict(axs)
        for dim in dims
            axs[propertynames(original)[dim]] = axes(new, dim)
        end
        axs = [k => axs[k] for k in propertynames(original)]
    end
    KeyedArray(new; axs...)
end

Thanks

[mcabbott]

This code is not so far from everything else in this package, and now that it has extensions (thanks to #151) it's fine to have one more for this package. Probably mostly copied from here:

https://github.com/mcabbott/AxisKeys.jl/blob/master/ext/StatisticsExt.jl

[aplavin]

Of course, this support can explicitly be added as an extension here, you may want to make a PR for that.

But note that Julia already has a fully general way to apply reductions (like sum, nansum, ...) along array axes:

julia> A = KeyedArray(rand(2, 3), x=[:a,:b], y=10:10:30)
2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   x ∈ 2-element Vector{Symbol}
→   y ∈ 3-element StepRange{Int64,...}
And data, 2×3 Matrix{Float64}:
        (10)         (20)          (30)
  (:a)     0.990372     0.0498909     0.869283
  (:b)     0.994879     0.541963      0.279493

julia> nansum(A)
3.7258801629137928

julia> map(nansum, eachslice(A, dims=:x))
1-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   x ∈ 2-element Vector{Symbol}
And data, 2-element Vector{Float64}:
 (:a)  1.9095453258981778
 (:b)  1.8163348370156154

This way, there's no need for explicit support of each individual reduction function, everything just works.