Floating point valued outputs are required: potential for automatic conversion

[YUJINGl3]

Here is my code:

using Distributions, Distances, Random, Plots
using Catalyst
using DifferentialEquations, GlobalSensitivity, QuasiMonteCarlo

stem_model = @reaction_network begin
(k0O(c0 + c1N^2 + k0O + c250))/(1 + (k0O*(c1N^2 + k0O + c250 + c3F^2)) + c4OG^2), ∅ --> N
(e0 + e1O)/(1 + e1O + e2G^2), ∅ --> O
(a0 + a1O)/(1 + a1O), ∅ --> F
(b0 + b1G^2 + b3O)/(1 + b1G^2 + b2N^2 + b3O), ∅ --> G
γ, N -->∅
γ, O -->∅
γ, F -->∅
γ, G -->∅
end k0 c0 c1 c2 c3 c4 e0 e1 e2 a0 a1 b0 b1 b2 b3 γ

p = [0.005, 0.01, 0.4, 1, 0.1, 0.00135, 0.01, 1, 1, 0.01, 1.0, 0.005, 0.005, 1.0, 1, 0.01]
T = 3000.0
u₀ = [105,110,100,5]
tspan = (0.0,T)
dprob = DiscreteProblem(stem_model, u₀, tspan, p)
jprob = JumpProblem(stem_model, dprob, Direct(),save_positions=(false,false))
function f(param)
prob1 = remake(jprob;p=param)
sol = solve(prob1, SSAStepper(), saveat=1)
[sol[1,3000], sol[2,3000], sol[3,3000], sol[4,3000]]
end

samples = 1000
lb = [0.00050, 0.00100, 0.04000, 0.10000, 0.10000, 0.00035, 0.00100, 0.10000, 0.10000, 0.00100, 0.10000, 0.00050, 0.00050, 0.10000, 0.10000, 0.00100]
ub = [0.05000, 0.02500, 1.0000, 2.0000, 0.5000, 0.00200, 0.02500, 2.00000, 2.00000, 0.02500, 2.00000, 0.05000, 0.05000, 2.00000, 2.00000, 0.02500]
sampler = SobolSample()
A,B = QuasiMonteCarlo.generate_design_matrices(samples,lb,ub,sampler)
sobol_result = gsa(f,Sobol(),A,B)

and got the error

InexactError: Int64(17.849)

Stacktrace:
[1] Int64
@ ./float.jl:812 [inlined]
[2] convert
@ ./number.jl:7 [inlined]
[3] setindex!
@ ./array.jl:903 [inlined]
[4] _unsafe_copyto!(dest::Matrix{Int64}, doffs::Int64, src::Matrix{Float64}, soffs::Int64, n::Int64)
@ Base ./array.jl:253
[5] unsafe_copyto!
@ ./array.jl:307 [inlined]
[6] _copyto_impl!
@ ./array.jl:331 [inlined]
[7] copyto!
@ ./array.jl:317 [inlined]
[8] copyto!
@ ./array.jl:343 [inlined]
[9] copyto_axcheck!(dest::Matrix{Int64}, src::Matrix{Float64})
@ Base ./abstractarray.jl:1104
[10] Array
@ ./array.jl:563 [inlined]
[11] convert
@ ./array.jl:554 [inlined]
[12] push!
@ ./array.jl:994 [inlined]
[13] gsa_sobol_all_y_analysis(method::Sobol, all_y::Matrix{Int64}, d::Int64, n::Int64, Ei_estimator::Symbol, y_size::Nothing, #unused#::Val{true})
@ GlobalSensitivity ~/.julia/packages/GlobalSensitivity/t3hIm/src/sobol_sensitivity.jl:99
[14] gsa(f::typeof(f), method::Sobol, A::Matrix{Float64}, B::Matrix{Float64}; batch::Bool, Ei_estimator::Symbol, distributed::Val{false}, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ GlobalSensitivity ~/.julia/packages/GlobalSensitivity/t3hIm/src/sobol_sensitivity.jl:63
[15] gsa(f::Function, method::Sobol, A::Matrix{Float64}, B::Matrix{Float64})
@ GlobalSensitivity ~/.julia/packages/GlobalSensitivity/t3hIm/src/sobol_sensitivity.jl:28
[16] top-level scope
@ In[44]:29
[17] eval
@ ./boot.jl:373 [inlined]
[18] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
@ Base ./loading.jl:1196

[ChrisRackauckas]

GSA assumes the output of f is a floating point number. See if changing the last line of your f to Float64[sol[1,3000], sol[2,3000], sol[3,3000], sol[4,3000]] fixes it. If that does it, we can put some automated conversions in the package to make that simpler.

[YUJINGl3]

GSA assumes the output of f is a floating point number. See if changing the last line of your f to Float64[sol[1,3000], sol[2,3000], sol[3,3000], sol[4,3000]] fixes it. If that does it, we can put some automated conversions in the package to make that simpler.

Yes that solved the problem. Thanks !! :)