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Note that chebyshevtransform(::Matrix)
is a 2D transform:
julia> using FastTransforms, ClassicalOrthogonalPolynomials
julia> n,m = 3,4; f = (x,y) -> chebyshevt(n, x)chebyshevt(m,y)
#13 (generic function with 1 method)
julia> x = ChebyshevGrid{1}(10);
julia> chebyshevtransform(f.(x', x))
10×10 Matrix{Float64}:
0.0 1.79736e-18 0.0 1.62102e-17 0.0 9.42055e-18 0.0 1.70874e-18 0.0 -9.27717e-18
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 1.11341e-18 0.0 7.41045e-17 0.0 -4.09803e-19 0.0 1.07597e-17 0.0 5.84104e-19
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 5.90641e-17 0.0 1.0 0.0 1.50729e-16 0.0 2.84217e-16 0.0 8.12948e-17
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 -2.19853e-17 0.0 3.47114e-16 0.0 -1.88411e-17 0.0 -1.22156e-17 0.0 -1.4348e-17
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 6.63185e-18 0.0 2.18604e-16 0.0 1.14231e-17 0.0 -4.61608e-19 0.0 5.85226e-18
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
On the other hand, the following applies 1D transform to each column:
julia> n,m = 3,4; f = (x,y) -> legendrep(n, x)legendrep(m,y)
#15 (generic function with 1 method)
julia> cheb2leg(chebyshevtransform(f.(x', x)))
10×10 Matrix{Float64}:
0.0 1.83296e-17 0.0 6.46249e-17 0.0 7.18753e-18 0.0 1.30004e-18 0.0 -1.21711e-18
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 1.19456e-17 0.0 -8.58383e-17 0.0 9.06258e-19 0.0 2.95814e-17 0.0 4.36288e-19
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.375 0.0 0.625 0.0 6.39255e-17 0.0 1.35064e-16 0.0 1.36998e-16
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 5.4873e-17 0.0 1.12785e-16 0.0 -4.54724e-17 0.0 -1.78334e-17 0.0 -1.84471e-17
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 1.45648e-16 0.0 2.70896e-16 0.0 7.99515e-18 0.0 9.33654e-18 0.0 3.97946e-18
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
The following gives a quick work-around that I think should be the default behaviour:
julia> function cheb2leg2d(A::AbstractMatrix)
B = cheb2leg(A); cheb2leg(B')'
end
cheb2leg2d (generic function with 1 method)
julia> cheb2leg2d(chebyshevtransform(f.(x',x)))
10×10 adjoint(::Matrix{Float64}) with eltype Float64:
0.0 -2.15114e-17 0.0 9.6853e-17 0.0 1.35234e-17 0.0 4.64116e-18 0.0 -3.28115e-18
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 6.13301e-17 0.0 -1.44889e-16 0.0 -3.0644e-17 0.0 7.0058e-17 0.0 1.17616e-18
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 -2.34741e-17 0.0 1.0 0.0 -5.64224e-17 0.0 1.4927e-16 0.0 3.69325e-16
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 -4.39458e-18 0.0 2.26976e-16 0.0 -6.76889e-17 0.0 -1.92562e-17 0.0 -4.97304e-17
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 -1.88093e-17 0.0 4.23769e-16 0.0 4.91028e-18 0.0 1.72571e-17 0.0 1.0728e-17
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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