robertdj / sparsegrids.jl Goto Github PK

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Hi,
I've just installed the package in a new 1.9 environment and tried using the function tensorgrid but it returns a MethodError. A minimal working example is

using SparseGrids, FastGaussQuadrature

Nt, Wt = tensorgrid(2, 4, gausslegendre)

which produces the following error

ERROR: MethodError: objects of type Vector{Vector{Float64}} are not callable
Use square brackets [] for indexing an Array.

If I instead use the sparsegrid method with the same syntax, everything's fine:

n,w = sparsegrid(2,4,gausslegendre)

produces the correct output.

I've dived a bit into the code and discovered that the code line where tensorW is defined raises the Error. I don't know what the correct output should be, so I couldn't discover much at the moment.

This issue also arises if using julia 1.7 or 1.5 in new environments. Note that I haven't tested every Julia version, I just chose those two.

Best regards,
pmc4

Ps.: Could you please add the bug label to the issue? The option is disabled for me.

Something seems buggy with the internal package management "using" and "include" (at least on my El Capitan Mac with Julia 0.4.6)

SparseGrids does not seem to locate the "kpn" function when called. For example:
sparsegrid(3,5,f=kpn). However, when kpn is copied explicitly from your source code and defined in the Julia kernel, the example works.

Additionally, SparseGrids does not recognize gausshermite unless an explicit statement "using FastGuassQuadrature" is run in advance of "using SparseGrids".

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The code below produces

1 7 (7, 7)
1 11 (11, 11)
2 7 (49, 137)
2 11 (121, 501)
3 7 (343, 681)
3 11 (1331, 5297)

I had expected the number of sparse nodes to be less than the number of tensor nodes. What am I doing wrong?

using SparseGrids, FastGaussQuadrature


function hmmm( d, o )
    nt, wt = tensorgrid( d, o )
    ns, ws = sparsegrid( d, o )
    length( wt ), length( ws )
end


for d ∈ 1:3, o ∈ [ 7, 11 ]
    println( "$d $o ", hmmm( d,o) )
end

I'd be interested to see if this package could compose with the QuadGK.jl package for Gauss-Kronrod points. This type of points has Gauss points mixed in with Kronrod points, together with weights. The Kronrod points work to augment the accuracy of the overall scheme as well as to provide an estimate of the error.

julia> FastGaussQuadrature.gausslegendre(2)
([-0.5773502691896258, 0.5773502691896258], [1.0, 1.0])

julia> QuadGK.kronrod(2)
([-0.9258200997725514, -0.5773502691896257, 0.0], [0.19797979797979798, 0.4909090909090911, 0.6222222222222223], [1.0000000000000002])

How easy would it be to accommodate this functionality?

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