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ext/SciMLBaseForwardDiffExt.jl

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@@ -25,17 +25,6 @@ function prob2dtmin(tspan, ::ForwardDiff.Dual, use_end_time)
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end
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end
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# function hasdualpromote(u0, t::Number)
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# hasmethod(ArrayInterface.promote_eltype,
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# Tuple{Type{typeof(u0)}, Type{dualgen(eltype(u0))}}) &&
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# hasmethod(promote_rule,
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# Tuple{Type{eltype(u0)}, Type{dualgen(eltype(u0))}}) &&
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# hasmethod(promote_rule,
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# Tuple{Type{eltype(u0)}, Type{typeof(t)}})
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# end
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promote_dual(::Type{T}, ::Type{T2}) where {T <: ForwardDiff.Dual, T2} = T
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function promote_dual(::Type{T},
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::Type{T2}) where {T <: ForwardDiff.Dual, T2 <: ForwardDiff.Dual}

src/solve.jl

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@@ -313,36 +313,6 @@ function get_updated_symbolic_problem(indp, prob; kw...)
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return prob
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end
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# promote_tspan(u0, p, tspan, prob, kwargs) = _promote_tspan(tspan, kwargs)
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# function _promote_tspan(tspan, kwargs)
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# if (dt = get(kwargs, :dt, nothing)) !== nothing
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# tspan1, tspan2, _ = promote(tspan..., dt)
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# return (tspan1, tspan2)
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# else
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# return tspan
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# end
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# end
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# prepare_alg(alg, u0, p, f) = alg
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# function get_concrete_tspan(prob, isadapt, kwargs, p)
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# if prob.tspan isa Function
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# tspan = prob.tspan(p)
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# elseif haskey(kwargs, :tspan)
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# tspan = kwargs[:tspan]
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# elseif prob.tspan === (nothing, nothing)
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# throw(NoTspanError())
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# else
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# tspan = prob.tspan
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# end
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# isadapt && eltype(tspan) <: Integer && (tspan = float.(tspan))
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# any(isnan, tspan) && throw(NaNTspanError())
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# tspan
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# end
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function isconcreteu0(prob, t0, kwargs)
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!eval_u0(prob.u0) && prob.u0 !== nothing && !isdistribution(prob.u0)
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end

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