Fix interpolation #30
Conversation
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@lbenet @LuEdRaMo these changes fix most of the problems with Another thing to note is that there are some on TaylorInterpolant methods defined in NEOs.jl, like zero and some serializations. I think we should bring those to the same place (i.e., here), but we can leave that for another PR. Lastly, I'm just thinking out loud here, but the functionality of the Since tests are passing, I'll bump the minor version and go ahead and merge this. |
(I meant the patch version) |
Regarding this, I'll investigate further and report an issue in TaylorSeries if needed. |
Can you provide some details? Maybe I can help 😄 . One more question: have you tested this PR using |
Of course, many thanks! On current julia> using TaylorSeries
julia> x = Taylor1.([rand(3) for _ in 1:5, __ in 1:2])
5×2 Matrix{Taylor1{Float64}}:
0.8440031850115604 + 0.27396498091154997 t + 0.8593268973294502 t² + 𝒪(t³) … 0.304833144771666 + 0.8015270517578177 t + 0.6663648827023969 t² + 𝒪(t³)
0.4769667257928858 + 0.18328727716869164 t + 0.7720343081783517 t² + 𝒪(t³) 0.25023114334238694 + 0.5608979582062102 t + 0.65675485312088 t² + 𝒪(t³)
0.3558175964225522 + 0.34934157610451766 t + 0.8965424895085355 t² + 𝒪(t³) 0.06192817828628172 + 0.03617621509228641 t + 0.27532384700131196 t² + 𝒪(t³)
0.3761780646204895 + 0.006473829252253704 t + 0.6103337009204509 t² + 𝒪(t³) 0.28422146538051607 + 0.47690611728020404 t + 0.6515769217433143 t² + 𝒪(t³)
0.9324978508223983 + 0.4249540247058746 t + 0.6168014064890693 t² + 𝒪(t³) 0.9948579177745833 + 0.4566885347215339 t + 0.6809588553613743 t² + 𝒪(t³)
julia> dq = TaylorSeries.set_variables("dq", order=2, numvars=2)
2-element Vector{TaylorN{Float64}}:
1.0 dq₁ + 𝒪(‖x‖³)
1.0 dq₂ + 𝒪(‖x‖³)
julia> x(3.04 + dq[1])
5×2 Matrix{TaylorN{Float64}}:
9.61841218134252 + 5.498672516674608 dq₁ + 0.8593268973294502 dq₁² + 𝒪(‖x‖³) … 8.899753082097902 + 4.853025538588391 dq₁ + 0.6663648827023969 dq₁² + 𝒪(‖x‖³)
8.168992310846765 + 4.87725587089307 dq₁ + 0.7720343081783517 dq₁² + 𝒪(‖x‖³) 8.02482658689119 + 4.553967465181161 dq₁ + 0.65675485312088 dq₁² + 𝒪(‖x‖³)
9.703303058822367 + 5.8003199123164135 dq₁ + 0.8965424895085355 dq₁² + 𝒪(‖x‖³) 2.7163367366141573 + 1.7101452048602632 dq₁ + 0.27532384700131196 dq₁² + 𝒪(‖x‖³)
6.03631843597378 + 3.7173027308485955 dq₁ + 0.6103337009204509 dq₁² + 𝒪(‖x‖³) 7.755629341895349 + 4.438493801479555 dq₁ + 0.6515769217433143 dq₁² + 𝒪(‖x‖³)
7.92458996413764 + 4.175106576159417 dq₁ + 0.6168014064890693 dq₁² + 𝒪(‖x‖³) 8.676340421035723 + 4.59691837531869 dq₁ + 0.6809588553613743 dq₁² + 𝒪(‖x‖³)
julia> @code_warntype x(3.04 + dq[1])
MethodInstance for (::Matrix{Taylor1{Float64}})(::TaylorN{Float64})
from (p::AbstractArray{Taylor1{T}})(x) where T<:Number @ TaylorSeries ~/.julia/dev/TaylorSeries/src/evaluate.jl:106
Static Parameters
T = Float64
Arguments
p::Matrix{Taylor1{Float64}}
x::TaylorN{Float64}
Body::Any
1 ─ %1 = Base.broadcasted(TaylorSeries.evaluate, p, x)::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{2}, Nothing, typeof(evaluate), Tuple{Matrix{Taylor1{Float64}}, TaylorN{Float64}}}
│ %2 = Base.materialize(%1)::Any
└── return %2(note the |
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Current master refers to TaylorSeries, I guess... |
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In the branch |
Indeed, I meant TaylorSeries |
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