Comments (7)
Can you isolate the problem without TaylorIntegration? I mean, create such a vector, fill it partially, and transpose it. Perhaps this should be posted directly to Julia.
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I think the problem can be solved replacing the lines which look like:
return view(tv,1:nsteps), view(transpose(xv),1:nsteps,:)
by
return view(tv,1:nsteps), transpose(view(xv,:,1:nsteps))
Maybe it impacts in performance, but my tests make it difficult to decide...
from taylorintegration.jl.
Can you isolate the problem without TaylorIntegration? I mean, create such a vector, fill it partially, and transpose it. Perhaps this should be posted directly to Julia.
Following your recommendation, I think an example of this would be:
julia> A = Array{Float64}(2,3)
2×3 Array{Float64,2}:
2.34463e-314 2.34463e-314 2.34463e-314
2.34463e-314 2.34463e-314 2.34463e-314
julia> A[1,:] .= rand(3)
3-element SubArray{Float64,1,Array{Float64,2},Tuple{Int64,Base.Slice{Base.OneTo{Int64}}},true}:
0.171995
0.35663
0.263829
julia> transpose(A)
3×2 Array{Float64,2}:
0.171995 2.34463e-314
0.35663 2.34463e-314
0.263829 2.34463e-314
julia> B = Array{BigFloat}(2,3)
2×3 Array{BigFloat,2}:
#undef #undef #undef
#undef #undef #undef
julia> B[1,:] .= BigFloat.(rand(3))
3-element SubArray{BigFloat,1,Array{BigFloat,2},Tuple{Int64,Base.Slice{Base.OneTo{Int64}}},true}:
5.221675004832033728519036230864003300666809082031250000000000000000000000000000e-01
9.018393963488640086012537722126580774784088134765625000000000000000000000000000e-01
3.842859182000140094714879523962736129760742187500000000000000000000000000000000e-01
julia> transpose(B)
ERROR: UndefRefError: access to undefined reference
Stacktrace:
[1] transpose_f!(::Base.#transpose, ::Array{BigFloat,2}, ::Array{BigFloat,2}) at ./linalg/transpose.jl:54
[2] transpose(::Array{BigFloat,2}) at ./linalg/transpose.jl:121
Do you think it would be worthwhile to report this issue to the corresponding Julia org?
from taylorintegration.jl.
I agree that #29 could be a workaround!
from taylorintegration.jl.
Maybe it impacts in performance, but my tests make it difficult to decide...
Do you want me to check some performance benchmarks?
from taylorintegration.jl.
Yes, I think it is a good idea to perform some benchmarks.
Regarding the issue with the transpose
, I do think this is an issue for Julia directly. Yet, as you can see in your example, when you allocate the matrix of Float64
, it is filled with whatever Float64
s, which is not the case for BigFloat
s. That is the reason that the error does not occur with the Float64
matrix.
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Closed by #33
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